AN EMPIRICAL STUDY OF BEHAVIOURAL INTENTIONS IN THE TAIWAN HOTEL INDUSTRY _______________________________________________________ A thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy at Lincoln University by Hung-Che Wu _______________________________________________________ Lincoln University 2009 ii Abstract of a thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy AN EMPIRICAL STUDY OF BEHAVIOURAL INTENTIONS IN THE TAIWAN HOTEL INDUSTRY by Hung-Che Wu The issue of behavioural intentions has attracted the attention of hotel marketers and academics because favourable behavioural intentions help hotels to retain customers. The marketing literature has identified that service quality, perceived value, image, customer satisfaction and demographic variables are significant determinants of behavioural intentions. This suggests that behavioural intentions are a multi-dimensional concept. Despite the importance of behavioural intentions, there is limited research on this construct in the hotel industry. The aim of this research was to gain an empirical understanding of behavioural intentions in the Taiwan hotel sector. A multi-level model was used as a framework for the analysis. The dimensions of service quality as perceived by hotel customers were identified through the literature review and focus group discussions. Hypotheses were formulated and tested to examine the interrelationships between behavioural intentions, service quality, customer satisfaction, perceived value and image, and to determine if perceived value plays a moderating role between service quality and customer satisfaction. Finally, customer perceptions of these constructs were compared based on demographic factors such as age, gender and income. The findings of this study were based on the analysis of a sample of 580 customers who had stayed at a five-star hotel in Kaohsiung City of Taiwan. Support was found for the use of a multi-level model and the primary dimensions: Interaction Quality, Physical Environment Quality and Outcome Quality, as broad dimensions of service quality. The 12 sub- dimensions of service quality, as perceived by hotel customers, were identified. These were: iii Employees? Conduct, Employees? Expertise, Employees? Problem-Solving, Customer-to- Customer Interaction, Décor & Ambience, Room Quality, Availability of Facility, Design, Location, Valence, Waiting Time and Sociability. The results indicated that each of the primary dimensions varied in terms of their importance to overall perceived service quality, as did the sub-dimensions of the primary dimensions. In addition, the statistical results supported a relationship between perceived value and service quality, image and service quality, customer satisfaction, perceived value, image and service quality, and behavioural intentions, image and customer satisfaction. The results also revealed that customer perceptions of the constructs were primarily affected by their purpose of travel and occupation. The results contribute to the services marketing theory by providing an empirically based insight into the service quality, perceived value, image, customer satisfaction and behavioural intentions constructs in the Taiwan hotel industry. This research also provides an analytical framework for understanding the effects of the three primary dimensions on service quality and the effect of service quality on constructs, such as, perceived value, image, customer satisfaction and behavioural intentions. This study will assist the management of the hotel industry to develop and implement a market-oriented service strategy in order to achieve a high quality of service, upgrade customers? levels of satisfaction, and create favourable future behavioural intentions. Key Words: Hotel Industry, Multi-level Model, Service Quality, Service Quality Dimensions, Perceived Value, Image, Customer Satisfaction, Behavioural Intentions. iv ACKNOWLEDGEMENTS At the risk of abusing a tired cliché, I must admit that the number of people who have contributed to this dissertation are too numerous to mention. However, given that this indisputable reality has never stopped my PhD predecessors from acknowledging worthy parties, I too will attempt to condense my gratitude to but a few short sentences. First and foremost, I would like to gracefully acknowledge the input, support and guidance from my main supervisor Dr. Baiding Hu. His patience, flexibility, energy and insightful discussions were invaluable. Especially thank you to my associate supervisor, Mr. Michael D. Clemes, who provided valuable perspectives and guidance on this research at key times. Both of my supervisors have been like my friends, mentors, and part-time teammates over the past three and a half years and for that, I am forever grateful. I will surely miss their guidance in the years to come. In addition, I also appreciated the assistance of my advisor, Associate Professor Christopher Gan, who continued encouraging and providing me with timely assistance to complete this research. I also would like to appreciate the examiners for their diligent examination of this thesis. Their thorough scrutiny and insightful comments play determinant roles in improving this study. Sincere thanks goes to Chris Odell for providing me with a venue for the focus group research and the incentives for the participants. Without her help, my task would have been much more difficult. In addition, I sincerely appreciated, Karen Johnson and Caitriona Cameron, who provided me with timely help for research writing. Furthermore, I would also like to thank all of the front-desk staff at the surveyed five-star hotel in Taiwan for helping me to hand questionnaires to the customers. Again, I could not have completed this research without their help. I also would like to extend my special acknowledgement to Dr. Eric Scott, Geraldine Murphy, Penny Thai Yoong Mok, Gillian Kabwe and Belinda Kemp for their kind support in the English editing of this study. v My research would not have been possible without the assistance of my Taiwanese friends, Endless Wang and Hector Chang. I show my sincere gratitude for their assistance in translating my English survey into the Chinese language. To my single mother for her moral and financial support and her constant encouragement throughout the hard times when I was staying away from home, I am much more than appreciative. To my family members including my older brother and younger sister, a sincere thank you for all of their support and encouragement during my rigorous doctoral studies. Much appreciation to my good Taiwanese and Chinese friends, Wen-Feng Hung, Tse-Shuen Shih, Yi-Ju Hou, Yuan-Chih Lin, Yuan Zhang, Min Ren, and Dan Zhu for their emotional support and continuing encouragement. In particular, an enormous appreciation goes to Fu- Sung Hsu for continuing to be patient in instructing me how to analyse the data through the SPSS software. My special thanks go to all the individuals and postgraduate fellows in the Commerce Division. Their willingness to help and give advice on this research is much appreciated. In addition, I sincerely appreciate the postgraduate administrators and receptionist of the Commerce Division, Annette Brixton, Eileen Seymour and Marian Pearson, for providing me with timely assistance related to research materials. Without their help, this research may not have gone so smoothly. Finally, I would like to thank the Goddess of Mercy for her unconditional love and for providing me with talented supervisors, a loving and supporting family, and wonderful friends who have helped me to achieve these remarkable steps. Furthermore, she always inspires me when I struggle with the research. Without her spiritual support, my research could not be finalised smoothly. vi TABLE OF CONTENTS Page ABSTRACT ii ACKNOWLEDGEMENTS iv TABLE OF CONTENTS vi LIST OF FIGURES xiii LIST OF TABLES xiv LIST OF APPENDICES xvii CHAPTER 1 INTRODUCTION 1 1.1 Introduction 1 1.2 An Overview of the Tourism and Hotel Industry in Taiwan 3 1.2.1 The Tourism Industry in Taiwan 3 1.2.2 The Hotel Industry in Taiwan 4 1.3 Justification for the Research 6 1.3.1 The Relationship between Customer Satisfaction and Behavioural Intentions and the Importance of Service Quality and Perceived Value 6 1.3.2 The Relationship between Perceived Value, Service Quality and Customer Satisfaction 8 1.3.3 The Relationship between Image, Service Quality and Customer Satisfaction 9 1.3.4 The Relationship between Image and Behavioural Intentions 10 1.3.5 Measurement of Hotel Service Quality 11 1.3.6 The Least and Most Important Dimensions of Service Quality 13 1.3.7 The Relationship between Demographic Factors, Behavioural Intentions, Customer Satisfaction, Service Quality, Perceived Value and Image 13 1.3.8 Summary 14 1.4 Objectives of the Research 15 1.5 Contribution of Research 17 1.6 Thesis Plan 17 vii CHAPTER 2 LITERATURE REVIEW 19 2.1 Chapter Introduction 19 2.2 Behavioural Intentions 20 2.3 Customer Satisfaction 21 2.4 Service Quality 23 2.5 Perceived Value 25 2.6 Image 27 2.7 Relationship between Constructs Related to Behavioural Intentions 28 2.7.1 The Relationship between Behavioural Intentions, Customer Satisfaction, Service Quality and Perceived Value 29 2.7.2 The Relationship between Service Quality and Customer Satisfaction 30 2.7.3 The Relationship between Perceived Value, Service Quality and Customer Satisfaction 32 2.7.4 The Relationship between Image, Service Quality and Customer Satisfaction 33 2.7.5 The Relationship between Image and Behavioural Intentions 33 2.7.6 Demographic Characteristics and their Relationship with Behavioural Intentions, Customer Satisfaction, Service Quality, Perceived Value and Image 34 2.8 An Overview of Criticism of the Measurement of Hotel Service Quality 36 2.8.1 SERVQUAL 36 2.8.2 SERVPERF 39 2.8.3 LODGQUAL 40 2.8.4 HOLSERV 40 2.8.5 LODGSERV 41 2.9 Multi-Dimensional Measurement of Service Quality 42 2.10 Hierarchical Models of Service Quality 44 2.10.1 The Service Environment Hierarchical Model 45 2.10.2 A Hierarchical Model of Service Quality for the Recreational Sports Industry 47 2.10.3 A Hierarchical Model of Service Quality for the Telecommunication Industry 48 2.10.4 A Hierarchical Model of Service Quality for the Sports Tourism Industry 50 2.10.5 A Hierarchical Model for the Quality of Electronic Services 51 viii 2.10.6 A Hierarchical Model of Service Quality for the Travel and Tourism Industry 53 2.10.7 A Hierarchical Model of Service Quality for the Urgent Transport Industry 55 2.10.8 A Hierarchical Model of Health Service Quality 56 2.10.9 A Hierarchical Model of Higher Education Service Quality 58 2.11 An Overview of Dimensions of Hotel Service Quality 60 2.11.1 Interaction Quality 60 2.11.1.1 Sub-dimensions of Interaction Quality 61 2.11.2 Physical Environment Quality 65 2.11.2.1 Sub-dimensions of Physical Environment Quality 65 2.11.3 Outcome Quality 70 2.11.3.1 Sub-dimensions of Outcome Quality 71 2.12 Chapter Summary 73 CHAPTER 3 CONCEPTUAL GAPS AND HYPOTHESES 75 3.1 Introduction 75 3.2 Conceptual Gaps in the Literature 75 3.3 Hypotheses Development 80 3.3.1 Hypotheses Relating to Research Objective One 82 3.3.2 Hypothesis Relating to Research Objective Two 89 3.3.3 Hypotheses Relating to Research Objective Three 90 3.3.4 Hypotheses Relating to Research Objective Four 97 3.3.5 Hypotheses Relating to Research Objective Five 97 3.4 Chapter Summary 101 CHAPTER 4 RESEARCH METHODOLOGY 102 4.1 Introduction 102 4.2 Research Design 102 4.3 Sample Derivation 103 4.4 Sample Size 103 4.5 Sampling Method 104 ix 4.6 Data Collection 105 4.7 Questionnaire Design 106 4.7.1 Construct Operationalisation 106 4.7.1.1 Summary of Construct Operationlisation 109 4.7.2 Questionnaire Format 110 4.7.3 Variable Measurement 112 4.7.4 Pre-testing Procedures 113 4.8 Data Analysis Techniques 114 4.8.1 Factor Analysis 114 4.8.1.1 Modes of Factor Analysis 115 4.8.1.2 Types of Factor Analysis 116 4.8.1.3 Assumption Testing for Factor Analysis 118 4.8.1.4 Tests for Determining the Appropriateness of Factor Analysis 119 4.8.1.5 Factor Extraction in Principal Components Analysis 121 4.8.1.6 Factor Rotation 122 4.8.1.7 Interpretation of Factors 124 4.8.2 Summated Scale 126 4.8.2.1 Content Validity 127 4.8.2.2 Dimensionality 128 4.8.2.3 Reliability 128 4.8.3 Regression Analysis 129 4.8.3.1 Moderated Multiple Regression (MMR) Analysis 130 4.8.3.2 Simple Regression Analysis 132 4.8.3.3 Multiple Regression Analysis 133 4.8.4 Analysis of Variance (ANOVA) 135 4.8.5 Assumption for Regression Analysis and Analysis of Variance 136 4.8.5.1 Assumption Testing for Regression Analysis 136 4.8.5.2 Error Term Assumptions 140 4.9 Chapter Summary 143 x CHAPTER 5 RESULTS 145 5.1 Introduction 145 5.2 Sample and Response Rates 145 5.2.1 Non-response Bias 145 5.2.1.1 Early and Late Responses 145 5.2.1.2 Missing Data 146 5.3 Descriptive Statistics 147 5.4 Assessment for Factor Analysis 150 5.4.1 Statistical Assumptions for Factor Analysis 150 5.4.1.1 Examination of the Correlation Matrix 151 5.4.1.2 Inspection of the Anti-Image Correlation Matrix 151 5.4.1.3 Barlett?s Test of Sphericity 151 5.4.1.4 Kaiser-Meyer-Olkin measure of sampling adequacy, MSA 151 5.4.2 Factor Analysis Results 152 5.4.2.1 Latent Root Criterion 152 5.4.2.2 Scree Test Criterion 152 5.4.2.3 Factor Rotation 152 5.4.2.4 Interpretation of Factors 154 5.4.3 Summated Scale 154 5.4.3.1 Content Validity 154 5.4.3.2 Dimensionality 155 5.4.3.3 Reliability 155 5.5 Assessment of the Regression Models and ANOVA 158 5.5.1 Assumptions for Regression Analysis and ANOVA 158 5.5.1.1 Outliers 158 5.5.1.2 Multi-collinearity 159 5.5.1.3 Linearity 160 5.5.1.4 Normality of the Error Term Distribution 160 5.5.1.5 Independence of the Error Terms (No Autocorrelation) 160 5.5.1.6 Homoscedasticity of the Error Terms 161 5.5.2 Results Pertaining to Research Objective One 161 xi 5.5.2.1 Hypothesis One 161 5.5.2.2 Hypothesis Two 162 5.5.2.3 Hypothesis Three 163 5.5.2.4 Hypotheses Four, Five and Six 164 5.5.2.5 Discussion Regarding Research Objective One 165 5.5.3 Results Pertaining to Research Objective Two 166 5.5.3.1 Hypothesis Seven 166 5.5.3.2 Discussion Regarding Research Objective Two 167 5.5.4 Results Pertaining to Research Objective Three 167 5.5.4.1 Hypothesis Eight 167 5.5.4.2 Hypothesis 10 168 5.5.4.3 Hypotheses 9, 11 and 13 169 5.5.4.4 Hypotheses 12 and 14 169 5.5.4.5 Discussion Regarding Research Objective Three 170 5.5.5 Results Pertaining to Research Objective Four 171 5.5.5.1 Hypothesis 15 171 5.5.5.2 Discussion Regarding Research Objective Four 171 5.5.6 Results Pertaining to Research Objective Five 173 5.5.6.1 Hypothesis 16a 173 5.5.6.2 Hypothesis 16b 174 5.5.6.3 Hypothesis 16c 175 5.5.6.4 Discussion Regarding Research Objective Five 176 5.6 Chapter Summary 177 CHAPTER 6 DISCUSSIONS AND IMPLICATIONS 179 6.1 Introduction 179 6.2 Summary of this Study 179 6.3 Conclusions Pertaining to Research Objective One 181 6.4 Conclusions Pertaining to Research Objective Two 187 6.5 Conclusions Pertaining to Research Objective Three 187 6.6 Conclusions Pertaining to Research Objective Four 191 xii 6.7 Conclusions Pertaining to Research Objective Five 192 6.8 Contributions 194 6.8.1 Theoretical Implications 195 6.8.2 Managerial Implications 199 6.9 Limitations of the Research 202 6.10 Directions for Future Research 204 REFERENCES 207 APPENDICES 267 xiii LIST OF FIGURES Figure Page 2.1 The Nordic Model of Perceived Service Quality 43 2.2 The Three-Component Model of Service Quality 43 2.3 The Multi-level Model of Retail Service Quality 44 2.4 Service Environment Hierarchical Model 46 2.5 Hierarchical Model of Service Quality for the Recreational Sports Industry 48 2.6 Hierarchical Model of Service Quality for the Telecommunication Industry 50 2.7 Hierarchical Model of Service Quality for the Sports Tourism Industry 51 2.8 Hierarchical Model for the Quality of Electronic Services 53 2.9 Hierarchical Model of Service Quality for the Travel and Tourism Industry 54 2.10 Hierarchical Model of Service Quality for the Urgent Transport Industry 56 2.11 Hierarchical Model of Health Service Quality 57 2.12 Hierarchical Model of Higher Education Service Quality 59 3.1 A Conceptual Hierarchical Model of Service Quality 81 5.1 The Scree Plot 153 5.2 Behavioural Intentions of Surveyed Customers in the Hotel Industry: Path Model 172 16A Residual Scatter Plots 317 17A Normal P-P Plot of Regression Standardised Residual 319 18A Residual Scatter Plots 321 xiv LIST OF TABLES Table Page 1.1 The Development of the Taiwan Hotel Rating System 5 2.1 Literature Related to Hotel Service Quality 24 4.1 Construct Operationalisation 109 4.2 Modes of Factor Analysis 116 4.3 Guidelines for Identifying Significance Factor Loadings Based on Sample Size 125 5.1 Independent Sample Test for Non-Response Bias 146 5.2 Gender of Questionnaire Respondents 147 5.3 Marital Status of Questionnaire Respondents 148 5.4 Age of Questionnaire Respondents 148 5.5 Level of Education of Questionnaire Respondents 148 5.6 Average Annual Income of Questionnaire Respondents 149 5.7 Main Purpose of Trip for Questionnaire Respondents 149 5.8 Ethnicity of Questionnaire Respondents 150 5.9 Occupation of Questionnaire Respondents 150 5.10 Reliability of Scaled Items for Interaction Quality 155 5.11 Reliability of Scaled Items for Physical Environment Quality 156 5.12 Reliability of Scaled Items for Outcome Quality 156 5.13 Reliability of Scaled Items for Behavioural Intentions and Related Constructs 158 5.14 Durbin-Watson Test Statistics 160 5.15 Model One: Multiple Regression Results Relating to Hypothesis One 162 5.16 Model Two: Multiple Regression Results Relating to Hypothesis Two 162 5.17 Model Three: Multiple Regression Results Relating to Hypothesis Three 163 5.18 Model Four: Multiple Regression Results Relating to Hypotheses Four, Five and Six 164 5.19 Model Five: Moderated Multiple Regression Results Relating to Hypothesis Seven 166 5.20 Model Six: Simple Regression Results Relating to Hypothesis Eight 168 xv 5.21 Model Seven: Simple Regression Results Relating to Hypothesis 10 168 5.22 Model Eight: Multiple Regression Results Relating to Hypotheses 9, 11 and 13 169 5.23 Model Nine: Multiple Regression Results Relating to Hypotheses 12 and 14 170 5.24 ANOVA Results Relating to Hypothesis 16a 174 5.25 ANOVA Results Relating to Hypothesis 16b 174 5.26 ANOVA Results Relating to Hypothesis 16c 176 32A International Tourist Hotels in Taiwan, 2003-2007 267 33A Ordinary Tourist Hotels in Taiwan, 2003-2007 267 34A Summary Statistics of Missing Data for Original Sample (N=580) 294 35A Estimated Means Results 295 36A Correlation Matrix 296 37A Anti-Image Correlation Matrix 301 38A Eigenvalues and the Explained Percentage of Variance by the Factors 306 39A Rotated Component Matrices with VARIMAX Rotation 307 40A Pattern Matrix with OBLIMIN Rotation 308 41A VARIMAX Rotated Component Matrix with Variables 309 42A Split Sample One 311 43A Split Sample Two 312 44A Pearson Correlation Matrix, Model 1 313 45A Pearson Correlation Matrix, Model 2 313 46A Pearson Correlation Matrix, Model 3 314 47A Pearson Correlation Matrix, Model 4 314 48A Pearson Correlation Matrix, Model 5A 314 49A Pearson Correlation Matrix, Model 5B 314 50A Pearson Correlation Matrix, Model 6 315 51A Pearson Correlation Matrix, Model 7 315 52A Pearson Correlation Matrix, Model 8 315 53A Pearson Correlation Matrix, Model 9 315 54A Multi-collinearity Statistics 316 55A Customer Perceptions of Behavioural Intentions and Pertaining Constructs 323 xvi 56A Customer Perceptions of the Primary Dimensions of Service Quality 326 57A Customer Perceptions of the Sub-dimensions of Service Quality 328 xvii LIST OF APPENDICES Appendix Page 1 An Overview of International and Ordinary Tourist Hotels in Taiwan, 2003-2007 267 2 A List of Taiwan?s Hotel Rating 268 3 Sample Size Calculation 269 4 Focus Group for Dimensions of Hotel Service Quality 270 5 Letter of Invitation to the Focus Group 273 6 Dimensions of Hotel Service Quality Focus Group Discussion Guide 274 7 Responses to the Questions of the Focus Group Interview 276 8 Constructs / Items / Description / References 281 9 Cover Letter and Questionnaire 284 10 Chinese Cover Letter and Questionnaire 289 11 Data Imputation 294 12 Correlation Matrix 296 13 Anti-Image Correlation Matrix 301 14 Factor Extraction Table 306 15 Rotated Factor Tables 307 16 Questionnaire Items with Orthogonal Rotation (VARIMAX) 309 17 Validation of Component Factor Analysis by Split-Sample Estimation with VARIMAX Rotation 311 18 Multi-collinearity Statistics 313 19 Scatter Plots 317 20 Normality Plots 319 21 Analysis of Variance Results 323 1 CHAPTER 1 INTRODUCTION 1.1 Introduction The service sector has played an important role in most economies (Tam, 2000). This sector comprises a number of industries, of which accommodation is one of the largest (Yang, 2005). Hotels are an important part of the accommodation industry and have become one of the most competitive businesses in the world in recent years (Harrison & Enz, 2005). For example, lodging in the United States was a $108 billion industry, with over 536,500 hotels and 4.1 million guestrooms in 2006 (The American Hotel & Lodging Association, 2006). Recently, there has been an increased focus on the management and marketing of hotels (Reisinger, 2001). Hotels provide services that are different from tangible goods because hotel services are immediately consumed and require a people-intensive creation process (Harrison & Enz, 2005). Further, Alexandris, Dimitriadis and Markata (2002) stated that the issue of customer behavioural intentions could not be neglected in the hotel industry if hotels were going to maintain repeat-customers. Intentions to perform a behaviour, such as a purchase or consumption behaviour, have been widely investigated in the marketing literature (Gabler & Jones, 2000). Customer behavioural intentions 1 involve significant decision-making, particularly in repurchase decisions (White & Yu, 2005). According to Kang, Okamoto and Donovan (2004), customer behavioural intentions were related to customer satisfaction in the hotel industry. Customer satisfaction affected behavioural intentions towards the service provider, and satisfaction with the service then influenced behavioural intentions towards the services that hotels offered (Kang et al., 2004). ______________________________ 1 ?Behavioural intentions? were defined as ?the degree to which a person has formulated conscious plans to perform or not perform some specified future behaviour? (Warshaw & Davis, 1985, p. 214). That is, the intention to perform a behaviour is the proximal cause of such a behaviour (Shim, Eastlick, Lotz, & Warrington, 2001). 2 Kang et al. (2004) and Anderson, Fornell and Lehman (1994) indicated that hotel organisations that have increased customer satisfaction have also raised existing customers? positive behavioural intentions, prevented customer defection, lowered marketing costs, and cut customer cultivation costs. Kang et al. (2004) argued that the issue of customer behavioural intentions in the satisfaction-behavioural mix was often ignored, regardless of whether the impact of customer satisfaction on behavioural intentions was significant in the hotel industry. Brady, Robertson and Cronin (2001) indicated that service quality, perceived value and customer satisfaction have been directly associated with behavioural intentions in the fast- food sector. However, Cronin, Brady and Hult (2000) found that the effects of service quality and perceived value indirectly influenced behavioural intentions through customer satisfaction in the education, fast-food, recreational sports, and health care sectors. Several researchers found that service quality had a significant positive impact on image, and a favourable image in turn positively influenced customer satisfaction in the airline, restaurant, retailing, tourism, and telecommunication sectors (Chi & Qu, 2008; Ryu, Han, & Kim, 2008; Aydin & Ozer, 2005; Park, Robertson, & Wu, 2005; Andreassen & Lindestad, 1998; Schlosser, 1998). A favourable image has also been found to contribute to customers? recommendations of the organisation to other customers in the airline, manufacturing, telecommunication, retailing, education, tourism, and restaurant sectors (Ryu et al., 2008; Castro, Armario, & Ruiz, 2007; Chang, 2006; Cheng, 2006; Park et al., 2004; Nguyen & LeBlanc, 2001; da Costa, Deliza, Rosenthal, Hedderley, & Frewer, 2000). Furthermore, customer satisfaction has been suggested as having a direct impact on behavioural intentions in the airline, restaurant, technology, and tourism sectors (Bosque & Martín, 2008; Chen, 2008; Ladhari, Brun, & Morales, 2008; Chen & Tsai, 2007; Namkung & Jang, 2007; Birgelen, Jong, & Ruyter, 2006). However, several researchers proposed that the interrelationships among service quality, perceived value, image, customer satisfaction, and behavioural intentions have not attracted a lot of attention in the hotel industry (Hu, Kandampully, & Juwaheer, 2009; Kandampully & Hu, 2007; Claver, Tari, & Pereira, 2006; Kang et al., 2004; Kandampully & Suhartanto, 2003, 2000; Oh, 1999; Suhartanto, 1998). 3 1.2 An Overview of the Tourism and Hotel Industry in Taiwan 1.2.1 The Tourism Industry in Taiwan With the service industry developing across the world in the 21st century, Taiwan has been transformed from a manufacturing economy to a service-oriented one (Hsieh, Lin, &, Lin, 2008). Because of rapid economic prosperity and continued improvement in living standards, tourism in Taiwan has become an important industry. In order to satisfy tourists? needs and wants, the Taiwanese government has been engaged in tourism development. Such development could not only provide more variety for leisure life, but could also enrich the content of development and help to expand people?s standard of living (Taiwan Agriculture Information Centre, 1996). In order to promote the tourism industry in Taiwan, the Provincial Government attempted to develop new scenic areas, such as the west coast highway travel and leisure system, the Central Taiwan north-south highway travel and leisure system, and hot spring scenic areas. In addition, the Provincial Government continued to strengthen measures to ensure travel safety. The Provincial Government also encouraged counties and cities to establish tourism associations to strengthen tourism industry management and employee training and also to actively promote the healthy development of the domestic travel industry (Taiwan Agriculture Information Centre, 1996). In general, the high season of travelling in Taiwan is July, the peak of the summer vacation period (Lang, O?Leary, & Morrison, 1997). According to the Taiwan Tourism Bureau (2007), 285,075 visitors arrived in Taiwan in July, 2007, up 5.25 percent from the 270,850 in July, 2006. The arrivals included 222,187 foreign visitors and 62,888 overseas Chinese. Compared with July 2006, the number of foreign visitors increased by 9,059 or 4.25 percent, and the number of overseas Chinese visitors increased by 5,166 or 8.95 percent. Daily arrivals in July, 2007 averaged 9,196. The main purposes of visitor arrivals to Taiwan are categorised as pleasure, business, relative visits, conference attendance, and study (Taiwan Tourism Bureau, 2007). According 4 to the high season comparison between 2006 and 2007, the percentage of pleasure visitors increased from 39.06 percent to 39.55 percent (Taiwan Tourism Bureau, 2007, 2006b). Similarly, for conference attendance purposes, from 1.38 percent to 1.43 percent, for other purposes from 6.55 percent to 6.76 percent, and from 11.00 percent to 11.09 percent for unstated purposes. Conversely, the percentage of travel for business decreased from 27.73 percent to 27.18 percent, from 13.08 percent to 12.91 percent for relative visit purposes, and from 1.21 percent to 1.07 percent for study purposes. Therefore, the pleasure visitors are the largest group. In order to satisfy the pleasure visitors? demands for accommodation in Taiwan, the issue of increasing levels of service quality need to be greatly focused in the hotel industry (Hsieh et al., 2008; Su & Sun, 2007). 1.2.2 The Hotel Industry in Taiwan In essence, a tourist hotel is a service organisation offering individual service for tourists from different countries (Tsaur, Lin, & Wu, 2005). In order to provide tourists with a wide choice of accommodation, Pine, Zhang and Qi (2000) recommended that Taiwan and international hotel investors should actively seek investment opportunities to increase the room supply by building new five-star hotels, especially in the gateway cities or top tourism destinations in Taiwan. Taiwan?s hotel rating system has been evolving over the past 30 years, as shown in Table 1.1. In response to rapid growth in the hotel industry, the Taiwan Tourism Bureau announced a revised hotel rating system in December 2002 to provide customers with a reference for Taiwan hotel selection (Su & Sun, 2007). According to the Taiwan Tourism Bureau, the hotel classification system consisted of two groups: international tourist hotels and ordinary tourist hotels (Chen, 2007a). Four- and five- star hotels were classified as international tourist classes whereas one-, two- and three- star hotels were categorised as ordinary tourist classes (Taipei Times, 2004). According to Su and Sun (2007), the Taiwan Hotel Rating System was updated every three years. International and ordinary tourist hotels were evaluated by different supervising organisations. The Tourism Bureau administered 5 international tourist hotels, and local county or municipal governments administered ordinary tourist hotels. Table 1.1: The Development of the Taiwan Hotel Rating System Year Process 1977 An international tourist hotel association began evaluating international tourist hotels. 1979 The Taiwan Tourism Bureau commissioned ?The Study of Minimum Facility Standard and Classification Method to International Tourist Hotels in Taiwan? by the Architectural Institute of Taiwan. 1980 The Tourism Bureau drafted ?The Criteria of International Tourist Hotel Grades.? 1983 The Tourism Bureau completed the ?Plum Blossom Evaluation System? and started evaluating international tourist hotels rated with four or five plum blossoms. 1984 The Tourism Bureau started evaluating international tourist hotels rated with two or three plum blossoms for the first time. Tourist hotels participated voluntarily. 1986 The Tourism Bureau evaluated international tourist hotels rated with four or five plum blossoms for the second time and announced subsequent evaluations will be conducted every three years. 1989 The Tourism Bureau discontinued the evaluation system for international tourist hotels and included fire prevention and building management in the evaluation system. 1992 The Tourism Bureau re-evaluated all international tourist hotels in Taiwan and considered replacing the plum blossom rating system with a star rating system. 2002 The Tourism Bureau drafted the evaluation system for international tourist hotels by establishing ?The Draft Plan of Hotel Building Equipment and Service Quality Evaluation Standard.? 2003 The Tourism Bureau adopted and began testing the star evaluation system. All hotels were evaluated on the facilities. 2005 The Tourism Bureau formally adopted the ?Star Hotel Rating System.? Source: Su and Sun (2007, p. 396). From 2003 to 2007, the number of international tourist hotels remained at 60, while the number of rooms decreased from 214,843 to 211,465 (see Appendix 1, Table 33A). During the same period, the number of ordinary tourist hotels was 30 but the number of rooms increased from 33,182 to 41,335 (see Appendix 1, Table 34A). In addition, Kuan (1996) found that the foreign travellers who particularly originated from North America, Japan and 6 China, preferred to stay in international tourist hotels rather than in ordinary tourist hotels when travelling in Taiwan. Thus, this fact should encourage investors to establish more international tourist hotels rather than ordinary tourist hotels throughout Taiwan in order to satisfy the foreign travellers? needs and wants (Taiwan Tourism Bureau, 2006a). As price competition in the Taiwan hotel industry has been increasing in recent years, customer behavioural intentions are likely to play an important role in determining hotels? profits (Kang et al., 2004; Chou, 2003; Yang, 2001). In general, customers would be satisfied if they received good service quality from hotels and their behavioural intentions would be favourable (Kang et al., 2004). Yet, very little empirical research has focused on the issue of customer behavioural intentions in the hotel industry, particularly in Taiwan?s international tourist hotel industry (Kang et al., 2004; Chou, 2003; Lai, Ping, & Yeh, 1999). Therefore, Chou (2003) recommended that hotel management should not neglect the important issues of behavioural intentions, customer satisfaction and service quality. 1.3 Justification for the Research 1.3.1 The Relationship between Customer Satisfaction and Behavioural Intentions and the Importance of Service Quality and Perceived Value Increasing favourable behavioural intentions, or lowering the rate of customer defection, would help service providers to generate profits (Zeithaml, Berry, & Parasuraman, 1996). However, the relationship between service quality, customer satisfaction and behavioural intentions has not been clearly identified in the hotel industry (Kang et al., 2004). In terms of the relationship between service quality, customer satisfaction and behavioural intentions, Zeithaml et al. (1996), Gale (1992), and Berry and Parasuraman (1991) found that service quality directly influenced behavioural intentions, which could be viewed as signals enabling customers to remain with or defect from an organisation. However, several studies confirmed that service quality indirectly influenced behavioural intentions through customer satisfaction (He & Song, 2009; Kuo, Wu, & Deng, 2009; Chen, Chen, & Hsieh, 2007; Chen, 2007b; Tian-Cole, Crompton, & Willson, 2002; Brady et al., 2001; Tam, 2000; Gotlieb, Grewal, & Brown, 1994; Anderson & Sullivan, 1993; Bagozzi, 1992; Woodside, 7 Frey, & Daly, 1989). Even though service quality has been found to have an indirect influence on behavioural intentions, Brady et al. (2001) stressed that service quality was not suggested to be an unimportant factor in the formation of customer behavioural intentions. Alternatively, with regard to the relationship between perceived value, customer satisfaction and behavioural intention, researchers found that perceived value had an indirect effect on behavioural intentions through customer satisfaction (Chen, 2007b; Chen & Tsai, 2007; Mugabi & Njiru, 2005; Chen, 2002; Cronin et al., 2000). Instead, researchers have found that customer satisfaction played an important role in predicting behavioural intentions (Bigné, Andreu, & Gnoth, 2005; Yavas, Benkenstein, & Stuhldreier, 2004; Zeithaml et al., 1996; Parasuraman, Zeithaml, & Berry, 1991). Therefore, a large number of researchers have noted that customer satisfaction was a better determinant of behavioural intentions than service quality and perceived value in the automobile, banking, education, health care, pest control, dry cleaning, fast-food, retailing, and tourism sectors (He & Song, 2009; Chen, 2007b; Chen & Tsai, 2007; Clemes, Gan, & Kao, 2007; Dagger, Sweeney, & Johnson, 2007; González, Comesaña, & Brea, 2007; Kao, 2007; Babin & Babin, 2001; Brady et al., 2001; Cronin et al., 2000; Tam, 2000; Anderson & Sullivan, 1993; Cronin & Taylor, 1992; Bearden & Teel, 1983). High levels of customer satisfaction were likely to reinforce customer intentions of using the service and to engage in positive customer recommendations to family and friends (Tian- Cole et al., 2002). Furthermore, in the marketing literature, several researchers have found that the relationship between customer satisfaction and behavioural intentions was not straightforward (Hu et al., 2009; White & Yu, 2005; Mittal, Ross, & Baldasare, 1998; Gotleib et al., 1994). In addition, the existing literature on lodging services seldom addressed a particular customer?s favourable or unfavourable behaviour because of customer satisfaction (González et al., 2007). As a result, the lack of empirical research has prompted academic interest in the role of customer satisfaction in influencing and predicting behavioural intentions in the hotel industry (Kang et al., 2004). 8 1.3.2 The Relationship between Perceived Value, Service Quality and Customer Satisfaction The importance of perceived value, service quality and customer satisfaction was paramount in the service industries (Brady et al., 2001). However, little empirical research on services has examined the perceived value, service quality and customer satisfaction constructs concurrently (Cronin et al., 2000; Ostrom & Iacobucci, 1995). Caruana, Money and Berthon (2000) indicated that the perceived value, service quality and customer satisfaction constructs played an important role in determining customers? choices, their decisions to deepen or terminate a relationship and therefore customer retention and long-term profitability. In general, the value as perceived by customers was based on the price (Zeithaml, 1988). Hartline and Jones (1996) suggested that perceived value was not only relative to service quality but also a direct consequence of perceived service quality in the hotel industry. Perceived value has been considered an important factor in determining customers? overall satisfaction levels and their likelihood of returning to the same hotels (Choi & Chu, 2001). In contrast, Oh (1999) found that high pricing in isolation adversely affected customer perceptions of value, which also weakened customer satisfaction and intentions to repurchase and to recommend the hotel to their family, friends or relatives. However, Oh (1999) noted that the relationship between perceived value, service quality and customer satisfaction in the hotel industry should be further investigated. The effect of service quality on customer satisfaction has been suggested to be not only direct, but also moderated by perceived value in the auditing, banking, insurance, telecommunication, technology and tourism sectors (Gil, Berenguer, & Cervera, 2008; Lin, 2007; Gallarza & Saura, 2006; Wang, Lo, & Yang, 2004; Hellier, Geursen, Carr, & Rickard, 2003; Caruana et al., 2000). The perceived value construct was a rather neglected aspect in the discussion of customer evaluations of services (Caruana et al., 2000). Oh (1999) noted that little empirical research has focused on the perceived value construct as a moderating variable between service quality and customer satisfaction in the hotel industry. 9 1.3.3 The Relationship between Image, Service Quality and Customer Satisfaction There was a close link between image, service quality and customer satisfaction in the tourism sector (Chi & Qu, 2008). Image was considered to influence customer perceptions because the combined effects of advertising, public relations, physical image, word-of- mouth, and actual experiences with goods and services influenced image (Normann, 1991). In terms of the relationship between image and service quality, image originated from all of customers? consumption experiences, and service quality was a function of these experiences (Kayaman & Arasli, 2007). However, customer perceptions of service quality have been identified as having a direct effect on the perception of image in the airline, retailing, and telecommunication sectors (Chebat, Sirgy, & St-James, 2006; Park et al., 2006; Aydin & Ozer, 2005; Gummesson & Grönroos, 1988). Bloemer and de Ruyter (1998) determined that image was a predictor of customer satisfaction. Previous studies focused on the impact of store image on customer satisfaction in the retailing sector (Bloemer & Oderkerken-Schroder, 2002). Several researchers have claimed that image was a function of the cumulative effect of customer satisfaction (Fornell, 1992; Bolton & Drew, 1991a; Johnson & Fornell, 1991; Oliver & Linda, 1981). When services were difficult to evaluate, image was believed to become an important factor in influencing the perception of quality and customer evaluations of satisfaction with the service (Andreassen & Lindestad, 1998). Image was also believed to create a halo effect on the judgment of customer satisfaction. When customers were satisfied with the services rendered, their attitudes towards the organisation might be improved (Andreassen & Lindestad, 1998). Kandampully and Suhartanto (2000) claimed that the linkage between image and customer satisfaction has attracted little attention in the hotel literature. In the hotel industry, customers with a favourable and desirable service image might positively perceive service quality, which contributed to greater levels of customer satisfaction (Kandampully & Suhartanto, 2000). Therefore, image was not only related to service quality, but also to customer satisfaction in the tourism and retailing sectors (Chi & Qu, 2008; Koo, 2003; Andreassen & Lindestad, 1998). 10 The direct relationship between service quality and customer satisfaction has attracted the attention of researchers in the airline, tourism, hospitality, retailing, and health care sectors (Clemes, Gan, Kao, & Choong, 2008; Chi & Qu, 2008; Chow, Lau, Lo, Sha, & Yun, 2007; Clemes, Ozanne, & Laurensen, 2001; Qu, Li, & Chu, 2000; Hung, Hsu, & Lee, 1997; Barsky, 1992; Parasuraman et al., 1988). Kayaman and Arasli (2007) and Mazanec (1995) found that service quality had a positive and significant impact on image, and that image positively influenced customer satisfaction with the hotel industry. The relationship between image, service quality and customer satisfaction has been studied in the banking, engineering, health care, manufacturing, restaurant, retailing, and tourism sectors (Chen & Tsai, 2007; Lee, Chang, & Chao, 2007; Ryu et al., 2008; Cretu & Brodie, 2007; Bloemer & Oderkerken-Schroder, 2002; Andreassen & Lindestad, 1998; Bloemer, de Ruyter, & Peeters, 1998; Lapierre, 1998). However, few studies have paid attention to the relationship between image, service quality and customer satisfaction in the hotel industry (Ryu et al., 2008; Claver et al., 2006; Kandampully & Suhartanto, 2000). 1.3.4 The Relationship between Image and Behavioural Intentions Perceived value, service quality and customer satisfaction have been the subjects of much research (Nguyen & LeBlanc, 1998). However, Nguyen and LeBlanc (1998) recommended that marketing researchers needed to assist management in ensuring the competitive performance of the service organisation with a better understanding of the effect on the overall image left on the minds of customers in the form of attitudes and behavioural intentions. Customers? images were functionally related to behavioural intentions, which predicted customers? behaviour (Fishbein & Ajzen, 1975). Consequently, several researchers indicated that image affected behavioural intentions such as loyalty (Johnson, Gustafsson, Andreassen, Lervik, & Cha, 2001; Andreassen & Lindestad, 1998; Dick & Basu, 1994). Research on the concept of image relating positively to behavioural intentions has been conducted in the airline, education, manufacturing, telecommunication, retailing, tourism and restaurant sectors (Ryu et al., 2008; Castro et al., 2007; Chang, 2006; Cheng, 2006; Park et al., 2004; Nguyen & LeBlanc, 2001; da Costa et al., 2000). However, few studies have 11 studied the effect of image on behavioural intentions in the hotel industry (Hu et al., 2009; Kandampully & Suhartanto, 2000). 1.3.5 Measurement of Hotel Service Quality The measurement of hotel service quality, using SERVQUAL (a disconfirmation-based measure of service quality), SERVPERF (a performance-based measure of service quality), LODGQUAL (a performance-based measure of quality for the lodging industry), HOLSERV (an instrument adapted from SERVQUAL to measure service quality in the hotel industry), and LODGSERV (a measuring scale for service quality in lodging properties) has been seriously criticised. These methods have been criticised as inappropriate measures of hotel service quality (Albacete-Sáez, Fuentes-Fuentes, & Lloréns-Montes, 2007; Akbaba, 2006; Wilkins, 2005; Luk & Layton, 2004; Olorunniwo, Hsu, & Udo, 2003; Ekinci, 1999; Mei, Dean, & White, 1999; Oh, 1999; Ekinci & Riley, 1998; Saleh & Ryan, 1991). Several researchers proposed that the effective measurement of service quality should be divided into various primary dimensions with a pertaining structure, and then the primary dimensions should be further divided into a number of sub-dimensions using hierarchical models in the education, health care, retailing, tourism, telecommunication, technology, transport, and recreational sports sectors (Pollack, 2009; Caro & García, 2008, 2007; Su, 2008; Clemes et al., 2007; Dagger et al., 2007; Caro & Roemer, 2006; Fassnacht & Koese, 2006; Kang, 2006; Shonk, 2006; Collins, 2005; Jones, 2005; Ko & Pastore, 2005, 2001; Kim, 2003; Brady & Cronin, 2001; Dabholkar, Thorpe, & Rentz, 1996; Cronin & Taylor, 1992; Carman, 1990; Grönroos, 1990, 1982). Many researchers have suggested that service quality should be more appropriately conceptualised as a formative construct rather than a reflective construct when the direction of causality was from the dimensions to the construct (Parasuraman, Zeithaml, & Malhotra, 2005; Jarvis, MacKenzie, & Podsakoff, 2003; Rossiter, 2002; Diamantopoulos & Winklhofer, 2001; Dabholkar, Shepherd, & Thorpe, 2000; Donovan & Rossiter, 1982). In addition, Parasuraman et al. (2005), Jarvis et al. (2003) and Rossiter (2002) suggested that modelling service quality as a formative construct insofar as the dimensions was able to drive service quality perceptions. Under the formative measurement, Diamantopoulos (2008) 12 and Dagger et al. (2007) indicated that changes in the dimensions were assumed to cause variation in the service quality construct rather than the other way round. In other words, the dimensions formed or determined the service quality construct (Dagger et al., 2007; Bollen & Lennox, 1991; Bollen, 1989). Jarvis et al. (2003) indicated that the dimensions of the construct give rise to, or cause, the overall construct through the formative measurement. In the reflective measurement, dimensions were seen as reflective indicators of their higher order construct. According to Coltman, Devinney, Midgley and Venaik (2008), most researchers in the management sciences assumed that the correct measurement model was a reflective one, whereas there were many instances in which it may be hard to justify the assumption from either theory or practice. In terms of reflective indicators, the latent variable caused the observed variables (Bollen, 2002, 1989). In contrast, formative indicators can be viewed ?as causing rather than being caused by the latent variable measured by the indicators? (MacCallum & Browne, 1993, p. 533). Siegel and Doner (1998) claimed that the formative measurement was more commonly used than the reflective measurement in the services marketing literature. Under the formative measurement, Dagger et al. (2007) indicated that the service quality construct was determined by its dimensions rather than vice versa. According to Evanschitzky, Iyer, Plassmann, Niessing and Meffert (2006), the formative indicators of continuance commitment were the major dimensions that customers identified as being important in their use of particular service providers. However, some researchers showed that the failure to consider all facets of service quality will result in the exclusion of relevant dimensions (Dagger et al., 2007; Diamantopoulos & Winklhofer, 2001). Dagger et al. (2007) proposed that modelling service quality as a formative construct through a multi-level model rather than in the more traditional reflective way highlighted the influences of dimensions on the service quality construct. In addition, Diamantopoulos (2006) found that modelling the service quality construct through the formative measurement resulted in a better specification for the construct. According to Jarvis et al. (2003), the existing literature suggested that few studies used formative indicator measurement models, even though they should. In addition, a multi-level model of service quality as a formative construct has not been developed in an applied framework to identify the primary and sub dimensions of hotel 13 service quality, and the relationship of the primary and sub dimensions with service quality (Wilkins, Merrilees, & Herington, 2007). 1.3.6 The Least and Most Important Dimensions of Service Quality The perceptions of the dimensions of service quality have been claimed to affect customer perceptions of service quality in the fast-food, photograph developing, amusement parks, dry cleaning, tourism, technology, transport, and recreational sports sectors (Caro & García, 2008, 2007; Caro & Roemer, 2006; Fassnacht & Koese, 2006; Ko & Pastore, 2005, 2001; Brady & Cronin, 2001). Although a few studies have measured customers? experiences in the hotel industry (Shi & Su, 2007; Choi & Chu, 2001), the comparative importance of the service quality dimensions identified in these studies has not been adequately assessed. Marketing researchers have been asked to pay more attention to identifying the least and most important attributes of hotel service quality (Callan & Bowman, 2000). 1.3.7 The Relationship between Demographic Factors, Behavioural Intentions, Customer Satisfaction, Service Quality, Perceived Value and Image Demographic factors have been found to influence customer perceptions of behavioural intentions, satisfaction, service quality, value and image in the airline, banking, education, technology, retailing, health care and tourism sectors (Surovitskikh & Lubbe, 2008; Clemes et al., 2008, 2007, 2001; Kao, 2007; Chao, 2006; Beerli & Marti?n, 2004; Kwong, Yau, Lee, Sin, & Tse, 2003; Robinson & Smith, 2002; Stafford, 1996; Snepenger & Milner, 1990). However, limited research has been directed at determining the effects of demographic characteristics on customer perceptions of behavioural intentions, satisfaction, service quality, value and image in the hotel industry (Al-Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Kung & Tseng, 1994). Therefore, hotel managers should pay more attention to demographic factors because demographic characteristics provide a biographical sketch that suggests how age, gender, and income are likely to have an impact on behavioural intentions and their related constructs (Al-Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Min, Min, & Emam, 2002; Kung & Tseng, 1994). 14 1.3.8 Summary Based on the review of the existing literature, the following issues have been identified and will be addressed in this study. First, the primary and sub dimensions of service quality have been identified for a variety of industries such as the education, health care, retailing, tourism, telecommunication, technology, transport, and recreational sports sectors using a hierarchical model as a framework (Caro & García, 2008, 2007; Clemes et al., 2007; Dagger et al., 2007; Kao, 2007; Caro & Roemer, 2006; Fassnacht & Koese, 2006; Kang, 2006; Collins, 2005; Jones, 2005; Ko & Pastore, 2005, 2001; Kim, 2003; Brady & Cronin, 2001; Dabholkar et al., 1996; Cronin & Taylor, 1992). However, no research has attempted to identify the primary and sub dimensions of hotel service quality using a multi-level model (Wilkins et al., 2007). Second, customer satisfaction has been regarded not only as dependent on service quality, but also is moderated by perceived value in the auditing, banking, technology, telecommunication, and tourism sectors (Gil et al., 2008; Lin, 2007; Gallarza & Saura, 2006; Caruana et al., 2000; Andreassen & Lindestad, 1998). However, Oh (1999) suggested that, in the hotel industry, more studies should pay attention to the perceived value construct as a moderating variable between service quality and customer satisfaction. Third, some of the existing studies focused on the effect of customer satisfaction on behavioural intentions in the fast-food, banking, pest control, dry cleaning, technology, restaurant, and medical sectors (Lin & Hsieh, 2007; Choi, Cho, Lee, Lee, & Kim, 2004; Brady et al., 2001; Oh, 2000; Cronin & Taylor, 1992; Dubé-Rioux, 1990). However, little empirical research has paid attention to the direct effect of customer satisfaction on behavioural intentions in the hotel industry (Kang et al., 2004). Fourth, based on the existing literature, some researchers proposed that there has been a lack of studies in the hotel industry focusing on the effect of service quality on perceived value and image, and the influences of perceived value and image on customer satisfaction (Claver et al., 2006; Kandampully & Suhartanto, 2000; Oh, 1999). 15 Fifth, Hu et al. (2009) and Kandampully and Suhartanto (2000) showed that few empirical studies have been conducted on the effect of image on behavioural intentions in the hotel industry. Sixth, because of the fact that the comparative importance of the service quality dimensions has not been appropriately assessed in the existing hotel literature, Callan and Bowman (2000) suggested that more studies should focus on the least and most important hotel dimensions of service quality as perceived by customers. Finally, Snepenger and Milner (1990) indicated that demographic characteristics were correlated with length of stay, but not with the planning horizon or the evaluation of a travel experience. However, limited studies in the hotel industry have been conducted on the effects of demographic characteristics on behavioural intentions, customer satisfaction, service quality, perceived value, image, and the primary and sub dimensions of service quality (Al-Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Kung & Tseng, 1994). 1.4 Objectives of the Research Kang et al. (2004) proposed that understanding customer behavioural intentions may play an important role in determining hotels? profits. In general, service quality has been seen as an antecedent of customer satisfaction (Ekinci, 2004; Caruana, 2002; Parasuraman et al., 1994; Teas, 1994; Anderson & Sullivan, 1993; Cronin & Taylor, 1992; Woodside et al., 1989), and customer satisfaction as an antecedent of behavioural intentions (Babin & Babin, 2001; Brady et al., 2001; Tam, 2000; Anderson & Sullivan, 1993; Cronin & Taylor, 1992). Several researchers have focused on the relationship between service quality and customer satisfaction in the hotel industry (Briggs, Sutherland, & Drummond, 2007; Akbaba, 2006; Juwaheer, 2004; Ekinci, Prokopaki, & Cobanoglu, 2003; Callan & Kyndt, 2001). Kang et al. (2004) indicated that customer satisfaction was a powerful influence on behavioural intentions in the hotel industry. However, little empirical research has focused on the relationship between customer satisfaction and behavioural intentions in the hotel industry (Kang et al., 2004). In addition, despite the importance of behavioural intentions, the 16 theoretical and conceptual basis for understanding the behavioural intentions construct is still relatively immature in the Taiwan hotel industry (Lai et al., 1999). The overall purpose of this research is to gain an empirical understanding of behavioural intentions in the Taiwan hotel sector. In particular, this research will identify the dimensions of service quality as perceived by hotel customers. In addition, the research will determine if perceived value plays a moderating role between service quality and customer satisfaction. The interrelationships between customers? overall behavioural intentions and influential factors, which include service quality, customer satisfaction, perceived value and image, are also examined. The least and most important service quality dimensions as perceived by customers will also be identified. Finally, customers? overall behavioural intentions and related constructs will be compared on a demographic base using factors such as age, gender and income. In order to have an understanding of the interrelationship between behavioural intentions, customer satisfaction, service quality, perceived value and image, this study initially developed a multi-level model structure by using Brady and Cronin?s (2001) and Dabholkar et al.?s (1996) models as a foundation. Therefore, the main objectives of this research are: (1) To identify the dimensions of service quality as perceived by customers in the Taiwan hotel industry. (2) To determine if perceived value plays a moderating role between service quality and customer satisfaction as perceived by customers in the Taiwan hotel industry. (3) To examine the interrelationships between behavioural intentions and the other constructs related to behavioural intentions as perceived by customers in the Taiwan hotel industry. (4) To identify the least and most important service quality dimensions as perceived by customers in the Taiwan hotel industry. (5) To examine the effects of demographic factors on behavioural intentions and related constructs as perceived by customers in the Taiwan hotel industry. 17 1.5 Contribution of Research By achieving the objectives stated in Section 1.4, this study will contribute to the marketing literature from both an academic and practical perspective. First, this study will contribute to the marketing literature by providing an examination of several services marketing constructs. This is an important contribution as it provides a better understanding of customer perceptions of service quality, value, image, satisfaction, and favourable future behavioural intentions. Second, this study conceptualises and measures customer perceptions of hotel service quality by adopting a multi-dimensional approach. This approach helps to overcome some of the weaknesses of traditional measurement methods (SERVQUAL, SERVPERF, LODGQUAL, HOLSERV, and LODGSERV) and thus provides a more accurate method for assessing service quality in the hotel sector. Third, this study will benefit practitioners (e.g., hotel owners, mangers and marketers) in the lodging sector. For example, the research findings will provide practical information about what customers with different travel purposes or occupations consider important in their evaluations of service quality, and the other important constructs related to behavioural intentions. The findings are important as they may assist lodging practitioners in developing and implementing services marketing strategies to ensure a high quality of service and upgrade levels of customer satisfaction. Higher levels of customer satisfaction achieved through applying the correct marketing strategies should increase favourable behavioural intentions. 1.6 Thesis Plan This study consists of six chapters in order to meet the research objectives outlined in Section 1.4. Chapter Two reviews each construct related to behavioural intentions, the literature on the traditional measurements of hotel service quality, and the literature related to the 18 hierarchical models. Chapter Three presents the conceptual model based on the findings of the literature review undertaken in Chapter Two, and develops several hypotheses to satisfy Research Objectives One, Two, Three, Four and Five. Chapter Four details the methodology used to test the hypotheses, whereas Chapter Five presents and discusses the results of the analysis undertaken in this study. Finally, Chapter Six offers a summary of the conclusions of this study, the implications, limitations, and directions for future research based on the results presented in Chapter Five. 19 CHAPTER 2 LITERATURE REVIEW 2.1 Chapter Introduction This chapter begins with a review of the relevant literature on behavioural intentions and the other constructs related to behavioural intentions. Section 2.2 focuses on behavioural intentions whereas Section 2.3 reviews customer satisfaction. Section 2.4 concentrates on service quality. Section 2.5 discusses perceived value, and Section 2.6 discusses image. Next, Section 2.7 discusses the relationship of behavioural intentions to related constructs. Section 2.7.1 describes the relationship between behavioural intentions, customer satisfaction, service quality and perceived value whereas Section 2.7.2 focuses on the relationship between service quality and customer satisfaction. Section 2.7.3 reports the relationship between perceived value, service quality and customer satisfaction whereas Section 2.7.4 presents the relationship between image, service quality and customer satisfaction. Section 2.7.5 identifies the relationship between image and behavioural intentions. Finally, Section 2.7.6 focuses on demographic characteristics and their relationship to service quality, customer satisfaction and behavioural intentions Section 2.8 reviews the criticism of the measurement of hotel service quality. The criticism focuses mainly on the SERVQUAL, SERVPERF, LODGQUAL, HOLSERV, and LODGSERV measures from Sections 2.8.1 to 2.8.5. Section 2.9 discusses the multi- dimensional measurement of service quality. Section 2.10 reviews the existing hierarchical models of service quality. Section 2.10.1 focuses on the service environment hierarchical model and Section 2.10.2 concentrates on a hierarchical model of service quality for the recreational sports industry. Section 2.10.3 reviews a hierarchical model of service quality for the telecommunication industry. Section 2.10.4 discusses a hierarchical model of service quality for the sports tourism industry. Section 2.10.5 describes a hierarchical model for the quality of electronic services. Section 20 2.10.6 emphasises a hierarchical model of service quality for the travel and tourism industry whereas Section 2.10.7 shows a hierarchical model of service quality for the urgent transport industry. Section 2.10.8 presents a hierarchical model of health service quality. Section 2.10.9 focuses on a hierarchical model of higher education service quality. Finally, Section 2.11 focuses on three primary dimensions of hotel service quality: interaction quality, physical environment quality and outcome quality. Therefore, Section 2.11.1 focuses on interaction quality whereas Section 2.11.2 concentrates on physical environment quality. Section 2.11.3 presents outcome quality. 2.2 Behavioural Intentions Several researchers suggested that behavioural intentions were indications whether hotel customers would remain with or defect from an organisation (Alexandris et al., 2002; Zeithaml et al., 1996). In general, behavioural intentions were associated with customer retention and customer loyalty (Alexandris et al., 2002). Zeithaml et al. (1996) noted that increasing customer retention, or lowering the rate of customer defection, was a major key to the ability of service providers to produce profits. James (2007) indicated that behavioural intentions were verbal indications based on an individual?s intention. Fishbein and Ajzen (1975) defined behavioural intentions as ?a measure of the strength of one?s intention to perform a specific behaviour? (p. 288). Jaccard and King (1977) defined behavioural intentions as ?a perceived relation between oneself and some behaviour? (p. 328). Alternatively, the concept of behavioural intentions was referred to as people?s beliefs about what they intended to do in a certain situation (Ajzen & Fishbein, 1980). Specifically, Zeithaml et al. (1996) recommended that favourable behavioural intentions were associated with service providers? ability to make its customers: (1) say positive things about them (Boulding et al., 1993), (2) recommend them to other customers (Parasuraman et al., 1991, 1988), (3) remain loyal to them (Rust & Zahorik, 1993), (4) spend more with the organisation (Lin & Hsieh, 2007), and (5) pay price premiums (Lin & Hsieh, 2007). Conversely, Lobo, Maritz and Mehta (2007) indicated that unfavourable behavioural intentions included customer switching behaviour and complaint behaviour. Compared with Zeithaml et al.?s (1996) study, unfavourable behavioural intentions included customer 21 complaints and a multi-faceted concept, which included voice responses, private responses, and third-party responses. Ajzen and Fishbein (1980) suggested that behavioural intentions could largely predict the actual customer behaviour when behavioural intentions were appropriately measured. Several studies have focused on the assessment and measurement of behavioural intentions in the tourism industry (Chen & Tsai, 2007; González et al., 2007; Lee, Graefe, & Burns, 2004; Baker & Crompton, 2000). Alexandris et al. (2002) suggested that an understanding of the reasons why customers stay in hotels and identifying the factors that influenced their behavioural intentions of choosing a hotel were beneficial to hospitality planning and marketing. However, some researchers indicated that few empirical studies have paid attention to the issue of behavioural intentions in the hotel industry (Kandampully & Suhartanto, 2000; Suhartanto, 1998). Therefore, Alexandris et al. (2002) recommended that the issue of behavioural intentions in the hotel industry should be further investigated. 2.3 Customer Satisfaction Customer satisfaction has been an interesting area in academic research for a long time (Choi & Chu, 2001). Anderson et al. (1994) referred to customer satisfaction as an overall evaluation of the service provider?s performance based on all of their prior experiences with an organisation. Hu et al. (2009) defined customer satisfaction as ?a cognitive or affective reaction that emerges in response to a single or prolonged set of service encounters? (p. 115). Many studies have supported the view that customer satisfaction was important to the success of organisations, and that this construct was associated with the profits (Hocutt, Chakraborty, & Mowen, 1997; Brown, Fisk, & Bitner, 1994; Heskett, Jones, Loveman, Sasser, & Schlesinger, 1994; Bitner, 1990; Bell & Zemke, 1988). Gilbert and Horsnell (1998) showed that the task of increasing or maintaining the level of customer satisfaction has become an important issue of contemporary challenge for hotel management. Several researchers have identified customer satisfaction as an affective condition that was customers? emotional reactions to the experience of a product or service (Gunderson, Heide, & Olsson, 1996; Spreng & Mackoy, 1996; Cadotte, Woodruff, & Jenkins, 1987; Oliver, 1981, 1980). Kondou (1999) viewed customer satisfaction as a positive emotional response 22 resulting from customers? subjective evaluations of their experience. However, Fujimura (1992) argued that customer satisfaction was a core concept in contemporary marketing theory and practice, and that the foundation of services marketing was obtaining a satisfying profit in return for achieving customers? needs and wants. Bitner and Hubbert (1994) referred to customer satisfaction as a function of satisfaction with multiple experiences or encounters with the organisation. In general, customer satisfaction has been conceptualised as whether a product or service satisfied customers? demands and expectations (Zeithaml & Bitner, 2000). Although the concept of customer satisfaction has been reviewed in various ways, the underlying conceptualisation was that satisfaction was a post-purchase evaluative judgment that resulted in an overall feeling about a specific transaction (Fornell, 1992). Therefore, Wang, Chen and Zhao (2007) recommended that service organisations should pay more attention to the issue of customer satisfaction, and strive to achieve higher levels of customer satisfaction. Achieving customer satisfaction has been identified as a primary target and impending task in most organisations (Jones & Sasser, 1995); for example, customer satisfaction has been explicitly associated with the success of organisations in the hotel, catering and tourism sectors (Pizam & Ellis, 1999; Legoherel, 1998; Barsky & Labagh, 1992; LeBlanc, 1992). Lam and Zhang (1999) demonstrated that most studies focusing on customer satisfaction in the hospitality literature have attempted to identify service attributes treated as customers? needs and wants. From a marketing perspective, customer satisfaction has been achieved when customers? demands were satisfied (Lam & Zhang, 1999). However, the multi- functional nature of hospitality services has resulted in the development of multi-attribute scales, as reflected in many studies measuring customer satisfaction (Callan, 1994; Knutson, 1988). Oh and Parks (1997) suggested that expectancy-disconfirmation has been generally accepted and conceptually applied in the hospitality study of customer satisfaction. Oh and Parks (1997) claimed that a positive disconfirmation would occur and further contribute to customer satisfaction if the product or service were beyond customer expectations. Conversely, a negative disconfirmation might happen when a service provider?s performance became worse than customer expectations (Oh & Parks, 1997). 23 Su (2004) indicated that the biggest contemporary challenge for hotel management was to increase or maintain customer satisfaction. Some researchers have indicated that customer satisfaction has been secured through high-quality products and services in the airline, fast- food restaurant, hotel, and telecommunication sectors (Getty & Getty, 2003; Tsang & Qu, 2000; Gupta & Chen, 1995). According to Juwaheer (2004), customer satisfaction may be a good predictor of customers? willingness to return and to recommend the hotel to other people. However, Juwaheer (2004) argued that many studies have attempted to resolve the difficulties in measuring customer satisfaction in the hotel industry. 2.4 Service Quality In the past, service quality has attracted much attention from both academics and practitioners because of its impact on costs, financial performance, customer satisfaction and customer retention (Caro & Roemer, 2006; Tam, 2000). Bitner and Hubbert (1994) defined service quality as ?the customer?s overall impression of the relative inferiority and superiority of the organisation and its services? (p. 77). Several studies revealed that service quality has been a prerequisite for success and survival in today?s competitive environment, and interest in service quality has obviously increased (Gr?ini?, 2007; Ghobadian, Speller, & Jones, 1994). Service quality has attracted the interest of many hospitality researchers (Kitapci, 2007; Akbaba, 2006; Mey, Akbar, & Fie, 2006; Kim & Oh, 2004; Su, 2004; Ekinci et al., 2003; Carneiro & Costa, 2001; Ekinci & Riley, 2001; Oh, 1999). Akbaba (2006) recommended that the role of service quality in the success of hotel organisations should not be neglected. Grönroos (1984) maintained that customer perceptions of service quality should be measured based on a comparison between expected and perceived service, and be therefore the outcome of a comparative evaluation process. Based on customers? points of view, Holbrook and Corfman (1985) showed that service quality was a highly subjective and relativistic phenomenon. Recently, a number of researchers in the hotel industry have identified and emphasised the importance of service quality from a variety of aspects (see Table 2.1). Although the 24 importance of hotel service quality has been recognised (Min, Min, & Chung, 2002; Callan & Kyndt, 2001; Callan & Bowman, 2000; Danaher & Mattsson, 1994; Saleh & Ryan, 1991), limited research has addressed the structure and antecedents of the concept of service quality (Wilkins et al., 2006a). In addition, there is considerable debate in the literature on how best to conceptualise service quality as this construct is intrinsically an elusive concept in the hotel industry (Akbaba, 2006). Though service quality has received more attention recently, few studies have focused on how to establish a reasonable framework of assessing service quality for hotels, specifically for five-star hotels (Hsieh et al., 2008; Carneiro & Costa, 2001). Therefore, Akbaba (2006) recommended that the service quality construct in the hotel industry should be further examined. Table 2.1: Literature Related to Hotel Service Quality Authors Objectives Authors Objectives Lewis (1987) To present the results of hotel service quality using the model developed by Parasuraman et al. (1985). Saleh and Ryan (1991) To report an application in the hospitality industry of the SERVQUAL model developed by Parasuraman et al. (1985). Ghobadian et al. (1994) To examine the underlying concepts of service quality and review some of the improvement models of service quality. Akan (1995) To examine whether the quality dimensions included into the SERVQUAL model applied in Turkey?s hotel industry. Harrington and Akehurst (1996) To examine the performance implications of institutionalising service quality initiatives by focusing on the nature of service quality in the UK hotel industry. Johns, Lee-Ross and Ingram (1997) To indicate the best and worst aspects of the service that customers have experienced. Armstrong, Mok, Go and Chan (1997) To examine the impact of customer expectations and perceptions of service quality in the Hong Kong hotel industry involving cross- cultural samples. Heung and Wang (1997) To measure travellers? expectations of service quality in Hong Kong?s hotel industry. Mei et al. (1999) To examine the different dimensions of service quality and determine which dimensions best predicted the overall quality of service in the hospitality industry. Tsang and Qu (2000) To assess the overall perceptions of service quality in China?s hotel industry from the perspectives of managers and customers. Carneiro and Costa (2001) To look at the way in which service quality has had an effect on the positioning of five-star hotels. Wang and Pearson (2002) To examine personal service quality in international tourism hotels. Kim and Cha (2002) To investigate the antecedents and consequences of the relationship between service quality and constructs related to service quality. Juwaheer and Ross (2003) To assess customer expectations and perceptions of service provided by the Mauritius hotels. Keating and Harrington (2003) To review the literature on the implementation of quality programmes in the Irish hotel industry. Costa, Glinia, Goudas, and Antoniou (2004) To review the nature of recreational services, as a component of the hotel product, in order to appropriately assess and standardise quality towards customer satisfaction through SERVQUAL and its modification. 25 Table 2.1 (Continued) Authors Objectives Authors Objectives Juwaheer (2004) To investigate international tourists? perceptions of service quality in Mauritius hotels. Mohsin and Ryan (2005) To undertake the assessment of hotel service quality as perceived by customers in Darwin, Northern Territory (NT), Australia. Akbaba (2006) To investigate customer expectations of service quality in business hotels. Mey et al. (2006) To assess customer expectations and perceptions of service quality in Malaysia?s hotels. Wilkins et al. (2006a) To demonstrate the antecedents and structure of service quality in the context of the luxury and first class hotels. Briggs et al. (2007) To establish customers? current perceptions of service quality performance and an effective management. Gr?ini? (2007) To show the importance of service quality in the hotel industry from both the conceptual standpoint and that of service quality measurement. Kitapci (2007) To measure the perceptions of service quality in Turkey?s hotel industry from the perspective of international tourists. Ramsaran-Fowdar (2007) To examine the attributes that tourists use to evaluate the quality of hotel services Hsieh et al. (2008) To explore customer expectations of service quality in hot spring hotels. 2.5 Perceived Value Gounaris, Tzempelikos and Chatzipanagiotou (2007) showed that the concept of perceived value has become a matter of increasing concern in the marketing literature. Zeithaml (1988) defined perceived value as ?the customer?s overall assessment of the utility of a product based on perceptions of what is received and what is given? (p. 14). In addition, that author recommended that perceived value should be assessed through the perceived utility or worth resulting from the trade-off of ?get? versus ?give-up.? Parasuraman (1997) identified perceived value as one of the most important measures for an organisation seeking to gain a competitive edge. Accordingly, perceived value has been identified as having an important role in increasing the competitiveness of the service organisation. A number of researchers have paid attention to the issue of perceived value in consumption contexts. For example, Zeithaml (1988) provided evidence that supported the influential effect of perceived value on customer purchase decision-making. Based on the means-end model proposed by Zeithaml (1988), perceived value has been identified as a direct antecedent of a purchase decision and a direct consequence of perceived service quality. Customer perceptions of value have been conceptualised as a trade-off between perceived quality and perceived psychology, as well as monetary, sacrifice (Dodds, Monroe, & Grewal, 26 1991). Parasuraman (1997) and Slater (1997) provided their support for the role of perceived value in understanding customer behaviour. Based on aspects of economic value and customer behaviour theories, Jayanti and Ghosh (1996) identified perceived value as a direct consequence of perceived service quality except for the price-based transaction and acquisition utilities. Jayanti and Ghosh (1996) proposed three hypotheses in their study. The first hypothesis was described as ?perceptions of price-based utility as well as quality are determinants of perceived value? (p. 13). The second hypothesis was presented as ?transaction utility and perceived quality are the primary determinants of perceived value for services? (p. 13). The final hypothesis was illustrated as ?perceived quality is a stronger predictor of perceived value than transaction utility? (p. 17). Therefore, Jayanti and Ghosh?s (1996) hypotheses have supported that there should be an understanding of the role of perceived value among hospitality customers. Nasution and Mavondo (2008) demonstrated that perceived value was a process of interpreting what customers were concerned about the product or service consumed, regarding the sacrifice, which was generally price or time. In this regard, sacrifice represented a broad construct comprising monetary and non-monetary costs, such as effort and risk perceptions (Kleijnen, de Ruyter, & Wetzels, 2007). However, studies in retail service settings have provided mixed results with regard to perceived value as a compensatory trade-off (Kleijnen et al., 2007). Cronin et al. (2000) found that the relationship between sacrifice and perceived value was not significant. In addition, Woodall (2003) saw perceived value as the outcome of a personal comparison of sacrifices and benefits. In terms of the marketing literature, Patterson and Spreng (1997) pointed out that the definition of perceived value was generally based on customers? perspectives. In the tourism sector, Sanchez, Callarisa, Rodriguez and Moliner (2005) investigated value as perceived by customers in general, and tourists in particular. However, many researchers recognised a lack of interest in understanding and measuring perceived value, which has been considered as an old and endemic concept of customer behaviour (Holbrook, 1999; Jensen, 1996; Dodds et al., 1991; Zeithaml, 1988). In addition, based on the hospitality literature, the perceived value construct has not attracted sufficient conceptual and empirical studies (Oh 27 & Parks, 1997). Thus, Oh (1999) suggested that more attention should be paid to the perceived value construct in the hotel industry. 2.6 Image Image has been studied extensively in the last three decades (Nguyen & LeBlanc, 1998). Suhartanto (1998) showed that image was an important variable in influencing marketing activities. In the retailing sector, a store image might be applied to increase communication efficiency, particularly when the new market was clearly beyond the current market scope of the organisation (Keller & Aaker, 1997). Keller (1993) referred to image as perceptions of an organisation reflected in the associations held in customers? memories. Barich and Kotler (1991) identified image as the overall impression left in the minds of the public associated with an organisation. Alternatively, Dowling (1993) identified the concept of image as ?the total impression an entity makes on the minds of people? (p. 104). However, Dichter (1985) argued that image has a powerful effect on the way customers perceive and react to things. The literature on the concept of image has focused on the formation process of image (Carroll, 1995; Kennedy, 1977), the role of image in the formulation of organisational strategy (Gray & Smeltzer, 1985), the influence of image on customer behaviour (Abratt, 1989), particularly on customer behavioural intentions (Andreassen & Lindestad, 1998), and how to manage image properly in order to help organisations deal with changes in their environments (Barich & Kotler, 1991; Abratt, 1989). However, Nguyen and LeBlanc (1998) noted that existing studies in hotel management on the concept of image remained scarce. According to Kandampully and Suhartanto (2000), the image of a service organisation has played an important role in positively or negatively affecting marketing activities. Many conceptualisations of image have been advanced in the literature (James, Durand, & Dreves, 1976; Doyle & Fenwick, 1974-1975) and image has been treated as a ?gestalt,? reflecting customers? overall impression (Bloemer et al., 1998). Bloemer and de Ruyter (1998) expressed the view that image was a function of the salient attributes of a service organisation that were evaluated and weighed against each other. Andreassen and Lindestad (1998) considered image as ?a function of the accumulation of purchasing or consumption experience over time? (p. 84). In this regard, Kennedy (1977) proposed two principal 28 components of image: functional and emotional. The functional component was associated with easily measured tangible characteristics. In contrast, the emotional component was related to an individual?s psychological dimensions manifested by feelings and attitudes towards the product or service of an organisation. Korgaonkar, Lund and Price (1985) and Granbois (1981) viewed a favourable image as a critical aspect of the ability of an organisation to maintain its market position, because image was a key success factor for the business organisation in customer patronage. Zeithaml and Bitner (1996) identified image as the ability to influence customer perceptions of the services offered by an organisation. Thus, image has been believed to have an impact on customers? purchasing behaviour. In order to establish a good image, managers should be aware that customers? impressions of the product or service are important to an organisation, particularly a service business (Barich & Kotler, 1991). As a result, service organisations should attempt to determine what service or product could be held in customers? impressions (Barich & Kotler, 1991). Though image is important to a service organisation, few empirical hotel studies have focused on the role image played in the behavioural intentions of customers (Hu et al., 2009; Kandampully & Hu, 2007; Kandampully & Suhartanto, 2003, 2000; Suhartanto, 1998). 2.7 Relationship between Constructs Related to Behavioural Intentions Behavioural intentions have been identified as a multi-dimensional construct that has been conceptualised in the marketing literature (Skogland & Siguaw, 2004; Alexandris et al., 2002; Oliver, 1999). Based on the existing literature, customer satisfaction, service quality, perceived value, image, and demographic characteristics have been identified as determinants of behavioural intentions (Hu et al., 2009; Alexandris et al., 2002; Tan, 2002; Brady et al., 2001; Cronin et al., 2000; Kandampully & Suhartanto, 2000; Oh, 2000; Anderson & Sullivan, 1993). Therefore, the following paragraphs will explain the relationship between behavioural intentions and the other constructs related to behavioural intentions. 29 2.7.1 The Relationship between Behavioural Intentions, Customer Satisfaction, Service Quality and Perceived Value There is a great amount of research focusing on the interrelationship between behavioural intentions, customer satisfaction, service quality, and perceived value (Kuo et al., 2009; Baker & Crompton, 2000; Cronin et al., 2000; Backman & Veldkamp, 1995). Mittal, Kumar and Tsiros (1999) found a link between product or service satisfaction and behavioural intentions. Severt, Wang, Chen and Breiter (2007) indicated that behavioural intentions should be examined through two variables: word-of-mouth behaviour and intentions to return or revisit. Swan and Combs (1976) identified that customer satisfaction was associated with customers? prospective decision-making. Swan and Combs (1976) also viewed customer satisfaction as a post-purchase attitude that affected the cognitive and affective aspects in pre-purchase, purchase, and post-purchase phases of purchasing goods or receiving services. According to Hallowell (1996), behavioural intentions were the result of customer satisfaction with received products or services where perceived value was equivalent to perceived service quality relative to price. McAlexander, Kaldenberg and Koenig (1994) found that patient satisfaction and service quality had an effect on future purchase intentions in the health care sector. However, Cronin et al. (2000) indicated that service quality and perceived value have an indirect effect on behavioural intentions. In contrast, Choi et al. (2004) argued that both service quality and perceived value should have a direct impact on behavioural intentions. In addition, several researchers claimed that customer satisfaction was an antecedent of behavioural intentions instead of service quality and perceived value (Caruana, 2002; McDougall & Levesque, 2000; Tam, 2000; Buttle, 1996; Bloemer & Kasper, 1995). In this regard, therefore, Hu et al. (2009) and Tam (2000) suggested that the relationship between behavioural intentions, customer satisfaction, service quality and perceived value should be further investigated. Several hotel studies have noted that customer satisfaction with the price played an important role in determining customer intentions to repurchase or revisit, to recommend positive things to others, and to show their loyalty (Benítez, Martín, & Román, 2007; Ekinci et al., 2003; Bowen & Chen, 2001; Kandampully & Suhartanto, 2000; Suhartanto, 1998). Kang et al. (2004) proposed that customer satisfaction and behavioural intentions were 30 associated with each other in the hotel industry. Behavioural intentions were positive reactions from satisfied customers, which appeared as outcome dimensions (Kang et al., 2004). In spite of a considerable amount of research into behavioural intentions (Lee et al., 2004), few empirical hotel studies have focused on the relationship between customer satisfaction and behavioural intentions (Kang et al., 2004). Therefore, Kang et al. (2004) recommended that the relationship between behavioural intentions and customer satisfaction should be further investigated in the hotel industry. 2.7.2 The Relationship between Service Quality and Customer Satisfaction The relationship between service quality and customer satisfaction has attracted much attention in the marketing literature (Chen et al., 2007; Shi & Su, 2007; Johnston, 2004, 1995; Getty & Getty, 2003; Juwaheer & Ross, 2003; Qu et al., 2000; Tsang & Qu, 2000; Oh, 1999; Qu & Tsang, 1998; Callan, 1994; Anderson & Sullivan, 1993; Oliver, 1993; Barsky, 1992; Cronin & Taylor, 1992; Parasuraman et al., 1988). Gilbert and Horsnell (1998) noted that service quality has become a key development in the measurement of customer satisfaction. Due to the effect of customer service on customer satisfaction, many organisations have been concerned about the provision of service quality (Berry & Parasuraman, 1991). The importance of service quality achieved by the service provider?s performance has been established in the hospitality industry (Pizam & Ellis, 1999; Bowen & Shoemaker, 1998) and in a broader business context (Bloemer et al., 1998; Zeithaml et al., 1996). Some studies demonstrated that offering a high quality of service and an increasing level of customer satisfaction have been important factors in the success of hospitality organisations (Barsky & Labagh, 1992; LeBlanc, 1992). Su (2004) showed that providing the service that customers preferred has become a starting point for enhancing the level of customer satisfaction. Some studies found a significant relationship between customer satisfaction and service quality (Oh & Parks, 1997). In essence, the concept of customer satisfaction was different from service quality (Oh & Parks, 1997). Several studies identified that service quality was customers? attitudes or global judgments of service superiority over time, but customer satisfaction was associated with a specific transaction (Bolton & Drew, 1991a; Bitner, 1990; 31 Parasuraman et al., 1988). Since customer satisfaction decayed to form the overall assessment of perceived service quality, customer satisfaction preceded perceived service quality (Oliver, 1981). Bolton and Drew (1991a) and Bitner (1990) suggested that customer satisfaction or dissatisfaction with specific transactions was superior to customers? overall evaluations of service quality. Bitner, Booms and Tetreault (1990) found that service quality was derived from the individual service encounter between customers and service providers, during which customers assessed the quality and expressed the level of satisfaction or dissatisfaction. Lam and Zhang (1999) conducted a study to assess customer expectations and perceptions of service quality, and identified a gap between customer expectations and perceptions. In addition, those authors explored the influences of service quality factors on overall customer satisfaction. Their findings indicated that ?reliability,? ?responsiveness? and ?assurance? played the most significant roles in predicting customer satisfaction. In addition, customer expectations and perceptions had the largest differential scores indicating that customer perceptions fell well short of their expectations (Lam & Zhang, 1999). Accordingly, Su (2004) showed that it was necessary to measure the level of customer satisfaction in order to assess the quality of the existing management practices and identify directions for improving service quality as perceived by hotel customers. Several researchers have agreed that service quality was generally an antecedent of customer satisfaction (Caruana, 2002; Cronin et al., 2000; Anderson et al., 1994; Parasuraman et al., 1994; Teas, 1994; Anderson & Sullivan, 1993; Cronin & Taylor, 1992) rather than customer satisfaction being an antecedent of service quality (Bolton & Drew, 1991a, b; Bitner, 1990). In the hotel literature, service quality has been identified as a determinant of achieving customer satisfaction with service providers (Gilbert & Horsnell, 1998). Chen (1998) explained that a service was intrinsically intangible and was not amenable to testing before purchase. Therefore, customers easily tended to judge the quality of hotel service through their experience in achieving the level of satisfaction (Chen, 1998). Oh and Parks (1997) proposed that, in the hotel industry, it was necessary to further test the effect of service quality on customer satisfaction. 32 2.7.3 The Relationship between Perceived Value, Service Quality and Customer Satisfaction In the service sector, Zeithaml (1988) argued that perceived value would involve a trade-off between customers? evaluations of the benefits of using a service and paying the cost if the perceived service value were similar to the concept of perceived product value. Perceived value was inextricably associated with the major customer behavioural constructs such as service quality and customer satisfaction (Gallarza & Saura, 2006). Several studies have focused on the concept of service quality recognising that perceived value has played a key role in customers? overall assessments of service quality (Cronin & Taylor, 1992; Bolton & Drew, 1991a). Giese and Cote (2000) claimed that customer satisfaction was typically conceptualised as either an emotional or cognitive response to service quality. Previous studies in the concept of service quality confirmed that service quality should be determined by customer satisfaction (Giese & Cote, 2000; Oliver, 1997). Therefore, the evaluation of customer satisfaction has been a primary and important target for most service organisations to attempt to develop good customer service (Ramsaran-Fowdar, 2007). However, a consensus between perceived value and customer satisfaction is hard to find and moreover, the debate has remained open (Gallarza & Saura, 2006). Oh (1999) emphasised that customers perceived greater value for money when experiencing a high level of service quality in the hotel industry. Increased value perceptions then resulted in customer satisfaction (Oh, 1999). However, Oh (1999) commented that the hotel literature on the relationship between perceived value, service quality and customer satisfaction has remained scarce. When viewed from a managerial perspective of building customer satisfaction, perceived value played a moderating role between service quality and customer satisfaction in the insurance and auditing sectors (Hellier et al., 2003; Caruana et al., 2000). Caruana et al. (2000) indicated that the impact of service quality on customer satisfaction was not only direct but also moderated by perceived value. Although perceived value was an important construct considered in studies on service quality and customer satisfaction, Oh (1999) claimed that few hotel studies identified the perceived value construct as a moderating variable between service quality and customer satisfaction. 33 2.7.4 The Relationship between Image, Service Quality and Customer Satisfaction Zeithaml and Bitner (1996) suggested that image was important for the organisation, because of its ability to influence customer perceptions of the goods and services offered. Normann (1991) contended that customers? experiences with the products and services were the most important factors in the development of image. Andreassen and Lindestad (1998) expected that the image of a service organisation could function as a filter in the perception of service quality, satisfaction and as a simplification of the decision process when customers determined to purchase services. Bolton and Drew (1991a) found that image was a function of the cumulative effect of customer satisfaction or dissatisfaction. However, Wilkins et al. (2006b) indicated that few studies in the hotel literature focused on the relationship between image and customer satisfaction. Grönroos (1983) found that service quality was the single most important determinant of image. In Grönroos?s (1983) model, image was formed by service quality, traditional marketing activities (such as advertising, public relations and pricing), and external influences (such as tradition and word-of-mouth). Though services were difficult to evaluate, the perception of quality was identified as an important factor that affected image and customer evaluations of satisfaction with the service (Andreassen & Lindestad, 1998). Gummesson and Grönroos (1988) identified image as an important factor in the overall evaluation of the service offered by an organisation. However, several researchers showed that the link between image, service quality and customer satisfaction in the hotel industry was not clear (Claver et al., 2006; Kandampully & Suhartanto, 2000). 2.7.5 The Relationship between Image and Behavioural Intentions Johnson et al. (2001) noted that image had a positive effect on behavioural intentions such as customer loyalty. According to Suhartanto (1998), a positive image facilitated the organisation?s effective communication with customers and rendered other customers more favourably disposed towards more positive behavioural intentions. In contrast, a negative image may not enable customers to recommend the organisation to other people. In an effort to minimise risk, customers preferred to purchase from providers with a good service image 34 (Heung, Mok, & Kwan, 1996). Naumann and Giel (1995) and Callan (1994) indicated that customers adopted the image of the organisation as a surrogate cue and guide in their behavioural intentions. Concisely, image was thus important for every organisation because it constituted the foundation for behavioural intentions (Suhartanto, 1998). Kandampully and Hu (2007) found that image in the hotel industry was a significant predictor of behavioural intentions. Behavioural intentions were identified as key determinants in fostering a favourable image of the hotel organisation created by satisfying customers? needs and wants (Kandampully & Hu, 2007). According to Chang (2006), the image of a service organisation influenced customers? selection behaviour. For example, if customers never visit hotels, image may be their first impression of the service organisation and it is likely to have a great impact on their intentions to revisit or return to the hotel (Nguyen, 2006). Heung et al. (1996) found that hotel image was an important factor in hotel choice among loyal customers. Though image has been believed to be an antecedent of behavioural intentions, few studies in the hotel industry have been conducted on the effect of image on behavioural intentions (Hu et al., 2009; Kandampully & Suhartanto, 2003, 2000; Suhartanto, 1998). 2.7.6 Demographic Characteristics and their Relationship with Behavioural Intentions, Customer Satisfaction, Service Quality, Perceived Value and Image Belch and Belch (1993) and Kotler and Armstrong (1991) noted that demographic characteristics were one of the most popular and well-accepted bases for segmenting markets and customers. By specifically identifying the key demographic characteristics of one?s target market, a basic profile of the targeted customers emerged (Stafford, 1996). Despite other types of segmentation variables being used (e.g., behavioural, psychographic), marketers must know and understand demographic characteristics to assess the size, reach and efficiency of the market (Kotler & Armstrong, 1991). Demographic characteristics, such as gender, age, or income, directly affected customer perceptions of behavioural intentions, satisfaction, service quality, value and image in the airline, banking, education, tourism, technology, telecommunication, recreational sports, and 35 health care sectors (Seo, Ranganathan, & Babad, 2008; Clemes et al., 2008, 2007, 2001; Ho & Lee, 2007; Kao, 2007; Snipes & Ingram, 2007; Wong & Chung, 2007; Chao, 2006; Beerli & Marti?n, 2004; Chong, 2004; Tan, 2002; Stafford, 1996). Choi and Chu (2001) and Engel, Blackwell and Miniard (1990) discussed the theory of customer behaviour, and indicated that customer purchase behaviour and levels of customer satisfaction were greatly influenced by customers? backgrounds, demographic characteristics and some external stimuli. Keaveney and Parthasarathy (2001) indicated that customers with higher incomes and levels of education may develop their own sophisticated and accurate estimates of what to expect from a service. For example, customers with higher incomes may more frequently use services or a greater variety of services. In contrast, customers with lower incomes and less education had ambiguous expectations and their ability to learn from experience was limited (Hoch & Deighton, 1989). In addition, Keaveney and Parthasarathy (2001) found that lower income and less educated customers? assessments remained uncertain and their evaluations of the service more vulnerable to instances of dissatisfaction. Studies in the retailing sector indicated that demographic characteristics were related to customer expectations of service quality (Gagliano & Hathcote, 1994; Thompson & Kaminski, 1993; Webster, 1989). Chao (2006) indicated that the difference in customer perceptions of value in an on-line travel store resulted from demographic factors. In addition, Snipes and Ingram (2007) showed that differences in demographic characteristics existed in the perceived value of certain marketing motivators. Perceptions have been identified as the process through which an individual selected, organised and interpreted incoming information in order to develop an image through specific stimuli, as well as the stimuli which were generally associated with the environment and the individual?s demographic characteristics and circumstances (Beerli & Marti?n, 2004). Several researchers identified that tourists? images differed according to different demographic characteristics (Baloglu, 1997; MacKay & Fesenmaier, 1997; Walmsley & Jenkins, 1993). Skogland and Siguaw (2004) proposed that demographic variables positively influenced customer satisfaction. Robinson and Smith (2002) found that, in the retailing sector, demographic characteristics were associated with customer behavioural intentions to purchase sustainably produced 36 foods. In spite of few empirical studies focused on the influences of demographic factors on service quality, perceived value, image, customer satisfaction and behavioural intentions in the hotel industry, the literature suggests that hotel managers should not overlook the importance of the effect of demographic factors on customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the dimensions of service quality (Al-Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Min et al., 2002; Kung & Tseng, 1994). 2.8 An Overview of Criticism of the Measurement of Hotel Service Quality The issue of service quality has attracted much attention in the lodging industry (Hsieh et al., 2008; Benítez et al., 2007; Briggs et al., 2007; Akbaba, 2006; Wang, Shang, & Hung, 2006; Wilkins et al., 2006a; Su, 2004; Kim & Cha, 2002). However, the measurement of hotel service quality through the SERVQUAL, SERVPERF, LODGQUAL, HOLSERV, and LODGSERV measures has been argued to be insufficiently comprehensive to capture the hotel service quality construct (Albacete-Sáez et al., 2007; Nadiri & Hussain, 2005; Wilkins, 2005; Ekinci, 1999; Mei et al., 1999; Buttle, 1996). In addition, the SERVQUAL, SERVPERF and LODGQUAL measures have also been criticised because these measures focused only on the process quality attributes, not on the outcome quality attributes (Wilkins, 2005; Baker & Lamb, 1993; Richard & Allaway, 1993). In the following sections, the SERVQUAL, SERVPERF, LODGQUAL, HOLSERV, and LODGSERV measures are further explained. 2.8.1 SERVQUAL Ramsaran-Fowdar (2007) identified SERVQUAL as a framework of service quality. This measure has been widely used by both academics and practising managers across industries in different countries (Ramsaran-Fowdar, 2007). Since 1985, the disconfirmation-based SERVQUAL has measured the service quality of the technology sector (Ramsaran-Fowdar, 2007). When the research into the technology sector was first conducted, the innovators of SERVQUAL, Parasuraman, Zeithaml and Berry, further developed, promulgated and 37 promoted the service quality of technology through a series of publications (Parasuraman et al., 1994, 1991, 1988, 1985; Zeithaml et al., 1990). Parasuraman et al. (1985) proposed 10 dimensions of service quality that included tangibles, reliability, responsiveness, understanding the customers, access, communication, credibility, security, competence and courtesy. Later, Parasuraman et al. (1988) reduced the original 10 dimensions to five (tangibles, reliability, responsiveness, assurance, and empathy), resulting in a widely used instrument known as SERVQUAL. A large number of hotel studies have applied the five- dimension SERVQUAL instrument to assess service quality (Gr?ini?, 2007; Murphy, Schegg, & Olaru, 2007; Ramsaran-Fowdar, 2007; Mey et al., 2006; Wilkins et al., 2006a; Fernandez, Concepcion, Bedia, & Ma, 2005; Costa et al., 2004; Juwaheer, 2004; Ekinci et al., 2003; Wang & Pearson, 2002; Ekinci, Riley, & Fife-Schaw, 1998; Armstrong et al., 1997; Gabbie & O?Neill, 1997; Buttle, 1996; Bojanic & Rosen, 1994; Webster & Hung, 1994; Saleh & Ryan, 1991; Oberoi & Hales, 1990). According to Cronin and Taylor (1994), SERVQUAL could be applied to measure service quality and customer satisfaction. The SERVQUAL instrument was originally designed to assess the difference between quality expectations and perceived service along the five dimensions: tangibles, reliability, responsiveness, assurance and empathy (Curry & Sinclair, 2002). Based on Parasuraman, Zeithaml and Berry?s (1988, 1985) studies, several marketing researchers have criticised their methodology and the psychometric setting (Ko & Pastore, 2005; Buttle, 1996; Carman, 1990). Fernie, Fernie and Moore (2003) emphasised two aspects of criticism of SERVQUAL. First, SERVQUAL generalised the relationship between customers and service providers. Second, this measure disregarded the crucial relationship between customers and service providers. These criticisms triggered the development of alternative models to measure customer perceptions of service quality (Caro & García, 2008; Caro & Roemer, 2006; Fernie et al., 2003). In spite of its growing popularity and widespread application, SERVQUAL has been subjected to a number of theoretical and operational criticisms that are detailed below (Buttle, 1996, p. 10-11). 38 Theoretical: ? Paradigmatic objections: SERVQUAL is based on a disconfirmation paradigm rather than an attitudinal paradigm; it fails to draw on established economic, statistical and psychological theory. ? Gaps model: There is little evidence that customers assess service quality in terms of P - E gaps. ? Process orientation: SERVQUAL focuses only on the process of service delivery, not the outcomes of the service encounter. ? Dimensionality: The five dimensions of SERVQUAL are not universal; the number of dimensions comprising service quality is contextualised; items do not always load on to the factors that one would a priori expect; and there is a high degree of inter- correlation between the five RATER dimensions. Operational: Expectations: The term expectation is poly-semic; customers use standards other than expectations to evaluate service quality; and SERVQUAL fails to measure absolute service quality expectations. ? Item composition: Four or five items cannot capture the variability within each service quality dimension. ? Moments of truth (MOT): Customers? assessments of service quality may vary from MOT to MOT. ? Polarity: The reversed polarity of items in the scale causes respondent error. ? Scale points: The seven-point Likert-type scale is flawed. ? Two administrations: Two administrations of the instrument cause boredom and confusion. ? Variance extracted: The SERVQUAL score accounts for a disappointing proportion of item variances. 39 Babakus and Boller (1992) and Carman (1990) criticised SERVQUAL as being inappropriately applied to measure service quality. The reasons for the criticism were as follows: ? The five dimensions of the SERVQUAL measure may not be applied in all service settings. ? Items on some dimensions had been suggested in earlier research of Parasuraman et al. (1985) until factor analysis revealed that they were not distinct during scale purifications. ? SERVQUAL focused on the comparison of expectations with perceptions of actual service delivery. ? SERVQUAL could not adequately cover the complexity of customer perceptions. Based on the existing literature on the SERVQUAL measure, Saleh and Ryan (1991) found that the dimensions of SERVQUAL could not be used to accurately measure hotel service quality. In addition, Buttle (1996) proposed three doubts about the face validity of the hotel service quality construct when measured using SERVQUAL. These three doubts were whether: (1) customers actually evaluate service quality in terms of their expectations and perceptions; (2) the five dimensions incorporate the full range of service quality; and (3) customers incorporate outcome evaluations into their assessments of service quality. Based on a review of the literature, there has been much debate over hotel service quality when the construct is measured using SERVQUAL. 2.8.2 SERVPERF A performance-based model of service quality (SERVPERF) was developed by Cronin and Taylor (1992). SERVPERF measures service quality based only on customer perceptions of the performance of a service provider?s attitude-based (Cronin & Taylor, 1994). Service quality, together with the performance-based model as a foundation, was analysed from the adequacy-importance perspective of the attitude literature proposed by Mazis, Olli and Klippel (1975). According to this perspective, an individual?s attitude towards an object depended on the importance-weighted evaluation of various attributes in an object (Mazis et 40 al., 1975). Following the adequacy-importance perspectives, Cronin and Taylor (1992) identified service quality as an attitude and termed this view as ?the performance-based model.? Theoretically, SERVPERF was superior to SERVQUAL (Torres-Moraga, Jara- Sarrua, & Moneva, 2008; Brochado & Marques, 2007; Asubonteng, McCleary, & Swan, 1996; Cronin & Taylor, 1994, 1992; McAlexander et al., 1994). However, Cronin and Taylor (1994) argued that the SERVPERF measure should explain more of the variance in an overall measure of service quality than SERVQUAL instrument. Conversely, Nadiri and Hussain (2005) found that the SERVPERF instrument failed to form its five assumed dimensions: tangibles, reliability, responsiveness, assurance and empathy in the hotel industry. In terms of validity and reliability of SERVPERF, Robledo (2001) indicated that SERVPERF was not an efficient measurement scale, 2.8.3 LODGQUAL LODGQUAL has been regarded as a specific application for the hotel industry (Getty & Thompson, 1994). Getty and Thompson (1994) indicated that LODGQUAL was developed as a derivative of SERVQUAL and has applied dimensions similar to SERVQUAL. LODGQUAL was a measure used to assess service quality based on customer perceptions of a service provider?s performance in the lodging industry (Getty & Thompson, 1994). Getty and Thompson (1994) designed the LODGQUAL instrument from customer perceptions of the SERVQUAL measure, but also considered the dimensions of tangibles, reliability and ?contact,? which included attributes associated with response capacity, safety and empathy. However, Wilkins (2005) found that the dimensions of the LODGQUAL measure left many aspects of hotel performance unanswered despite this measure having been linked with the research on customer satisfaction. 2.8.4 HOLSERV Mei et al. (1999) used the SERVQUAL instrument as a foundation and then developed a new measure termed HOLSERV, which was an instrument used to measure service quality in the hotel industry. HOLSERV applied hospitality service and, in the related literature, it was a grading measure created for the measurement and the assessment of the hotels? 41 service (Holserv Pvt Ltd, 2006). Mei et al. (1999) showed that HOLSERV was a shorter, more user-friendly instrument than SERVQUAL. Mei et al. (1999) found that service quality was represented by three dimensions. These three dimensions were referred to as employees, tangibles and reliability, and the best predictor of overall service quality was the dimension referred to as ?employees? (Mei et al., 1999). Mei et al. (1999) recommended that hotel managers should supplement the HOLSERV measure with additional qualitative research. Though HOLSERV was a useful starting point for identifying current levels of quality, it was not the ultimate solution for understanding and enhancing service quality in the hotel industry (Mei et al., 1999). 2.8.5 LODGSERV Because of a lot of criticism associated with the SERVQUAL measurement, Knutson, Stevens, Wullaert, Patton and Yokoyama (1991) developed another instrument, LODGSERV, which was designed to measure customer expectations of service quality in the hotel industry through the application of SERVQUAL as a foundation. Knutson et al. (1991) made an effort to apply LODGSERV to improve what a generic instrument could do when service quality was defined and measured for lodging properties. In Knutson et al.?s (1991) study, five service quality dimensions emerged. Among these five dimensions, ?reliability? was ranked as the first order in the hierarchy of importance for the evaluation of service quality, followed by ?assurance? ?responsiveness? ?tangibles? and ?empathy? (Knutson et al., 1991). Alternatively, Patton, Stevens and Knutson (1994) found support for LODGSERV, an adaptation of SERVQUAL in the context of hotels, consisting of 26 items. Patton et al. (1994) attempted to validate the LODGSERV measure in the United States, Japan, Taiwan, Hong Kong, Australia and the United Kingdom. However, the superiority of LODGSERV over SERVQUAL remained questionable when LODGSERV was applied to the measurement of hotel service quality (Ekinci, 1999). As mentioned above, therefore, the traditional measures of service quality through SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV could not be used to appropriately measure service quality in a hotel setting (Albacete-Sáez et al., 2007; Nadiri & Hussain, 2005; Wilkins, 2005; Mei et al., 1999; Ekinci, 1999; Buttle, 1996). 42 2.9 Multi-Dimensional Measurement of Service Quality After the review of the literature related to the measurement of service quality, the traditional measures of hotel service quality using SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV were deemed to be inappropriate for use in the lodging industry (Albacete-Sáez et al., 2007; Nadiri & Hussain, 2005; Wilkins, 2005; Mei et al., 1999; Ekinci, 1999; Buttle, 1996). Yong (2000) noted that there were two major problems with the traditional measures of service quality: customers? needs were not always easy to identify, and inappropriately identified needs resulted in measuring conformance to a specification that was inappropriate. Knutson et al. (1991) demonstrated that validating the measurement of service quality would assist hotel management to identify and reward employees, properties, districts or regions fulfilling or exceeding customer expectations. Therefore, service quality has been suggested to be a multi-dimensional concept (Caro & García, 2008; Torres-Moraga et al., 2008; Fodness & Murray, 2007; Kang, 2006; Shonk, 2006; Liu, 2005; Mills & Morrison, 2003; Brady & Cronin, 2001; Dabholkar et al., 1996; Lapierre, 1996; Spreng & Mackoy, 1996; Feinburg, de Ruyter, Trappey, & Lee, 1995; Grönroos, 1993, 1990, 1982; Gummesson, 1991; Parasuraman et al., 1988, 1985; Berry, Zeithaml, & Parasuraman, 1985). Early conceptualisations of the disconfirmation paradigm of service quality have been employed in the literature of physical goods (Parasuraman et al., 1985; Grönroos, 1984, 1982; Churchill & Surprenant, 1982; Cardozo, 1965). This concept suggested that quality resulted from the comparison between perceived and expected performance, as reflected in Grönroos?s (1984, 1982) seminal conceptualisation of service quality that ?puts the perceived service against the expected service? (Grönroos, 1984, p. 37). In addition to the adaptation of the disconfirmation paradigm to the measurement of service quality, Grönroos (1984) developed a two-dimensional model to measure service quality: technical quality and functional quality (see Figure 2.1). The first dimension, technical quality, referred to the outcome of the service performance. The second dimension, functional quality, was referred to as the subjective perception of the way the service was delivered. In contrast, functional 43 quality was measured based on the reflection of customer perceptions of the interactions between customers and service providers. Figure 2.1: The Nordic Model of Perceived Service Quality (Grönroos, 1984, p. 40). Rust and Oliver (1994) proposed another conceptualisation of the dimensions of service quality. They attempted to develop a three-component model. In this model, the overall perception of service quality was based on customer evaluations of three dimensions of the service encounter: the customer-employee interaction (e.g., functional or process quality), the service environment and the outcome (e.g., technical quality) (see Figure 2.2). Though Rust and Oliver have not tested their conceptualisation, support has been found for similar models applied in the health care and retail banking sectors (McAlexander et al., 1994; McDougall & Levesque, 1994). Figure 2.2: The Three-Component Model of Service Quality (Rust & Oliver, 1994, p. 11). 44 Because of studies related to the inconsistent factor structure of SERVQUAL, Dabholkar et al. (1996) identified and tested a hierarchical conceptualisation of retail service quality that proposed three levels: (1) customers? overall perceptions of service quality, (2) primary dimensions, and (3) sub-dimensions (see Figure 2.3). Brady and Cronin (2001) found that this multi-level model recognised many facets and dimensions of service quality perceptions. Retail service quality, in other words, has been viewed as a higher-order factor identified by two additional levels of attributes (Brady & Cronin, 2001). Getty and Thompson (1994) noted that the development of procedures and scales, which accurately assessed the level of customer perceptions of service quality, remained in its infancy. Therefore, the measurement of hotel service quality should be conducted through a multi-dimensional structure, because of the weaknesses of traditional SERVQUAL, SERVPERF, LODGQUAL, HOLSERV, and LODGSERV measures. Figure 2.3: The Multi-level Model of Retail Service Quality (Dabholkar et al., 1996). 2.10 Hierarchical Models of Service Quality Clemes et al. (2008) claimed that the debate on service quality dimensions remained ambiguous. In the literature, however, several researchers proposed that service quality was a multi-dimensional or multi-attribute construct (Clemes et al., 2008; Becker et al., 2007; Sastry, 2006; Shonk, 2006; Liu, 2005; Mills & Morrison, 2003; Wang & Pearson, 2002; Brady & Cronin, 2001; Dabholkar et al., 1996; Lapierre, 1996; Spreng, MacKenzie, & Olshavsky, 1996; Kim & Kim, 1995; Cronin & Taylor, 1992; Grönroos, 1990, 1982; Parasuraman et al., 1988, 1985), and that the existing measurement systems, such as 45 SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV, failed to capture customers? overall evaluations of hotel service quality as a separate and multi-item construct (Albacete-Sáez et al., 2007; Nadiri & Hussain, 2005; Wilkins, 2005; Mei et al., 1999; Ekinci, 1999; Buttle, 1996). Some researchers, therefore, measured the service quality construct through the primary and sub dimensions using a hierarchical model in the education, retailing, fast-food, photograph developing, amusement parks, dry-cleaning, tourism, telecommunication, technology, transport, health care, and recreational sports sectors (Pollack, 2009; Caro & García, 2008, 2007; Su, 2008; Clemes et al., 2007; Dagger et al., 2007; Kao, 2007; Caro & Roemer, 2006; Fassnacht & Koese, 2006; Kang, 2006; Shonk, 2006; Collins, 2005; Jones, 2005; Ko & Pastore, 2005, 2001; Kim, 2003; Brady & Cronin, 2001; Dabholkar et al., 1996; Carman, 1990). Using a multi-level model based on the hierarchical structure may overcome some of the weaknesses of the traditional SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV measures (Cronin & Taylor, 1992), and provide a more accurate method for assessing service quality in the hotel industry. The following sections will present an overview of the existing hierarchical models of service quality in a variety of industries. 2.10.1 The Service Environment Hierarchical Model Although customer perceptions of service quality were apparently measured based on multiple dimensions, there was no general agreement on the nature or content of the dimensions (Brady & Cronin, 2001). Indeed, Carman (1990) noted that customer evaluations of service quality were a highly complex process that might be operated at several levels of abstraction. Brady and Cronin (2001) indicated that the missing link appeared to be a unifying theory or conceptualisation, which reflected the complexity and hierarchical nature of the service quality construct. In an attempt to integrate the differing conceptualisations of service quality and unify the abundance of theory on service quality, Brady and Cronin (2001) combined Rust and Oliver?s (1994) and Dabholkar et al.?s (1996) hierarchical approaches to develop a hierarchical model of perceived service quality. Through qualitative and empirical studies, 46 Brady and Cronin (2001) found that the service quality construct conformed to the structure of a third-order factor model that tied service quality perceptions to distinct and noticeable primary dimensions: interaction quality, environmental quality, and outcome quality (see Figure 2.4). Each of the primary dimensions consisted of three corresponding sub- dimensions: (a) interaction quality: attitude, behaviour, and expertise; (b) physical environment quality: ambient conditions, design, and social factors; and (c) outcome quality: waiting time, tangibles, and valence. Brady and Cronin (2001) indicated that customers aggregated their evaluations of the sub-dimensions to form their perceptions of an organisation?s service performance based on each of the three primary dimensions. Overall, service quality resulted from customer perceptions. Concisely, customers formed their perceptions of service quality based on their overall evaluations of service performance in a service organisation at multiple levels and ultimately arrived at an overall perception of service quality (Brady & Cronin, 2001). Brady and Cronin (2001) tested and supported this conceptualisation of service quality using a hierarchical model across four service industries: fast-food, photograph developing, amusement parks, and dry-cleaning. Note: R = a reliability item, SP = a responsiveness item, E = an empathy item. The broken line indicates that the path was added as part of model respecification. Figure 2.4: Service Environment Hierarchical Model (Brady & Cronin, 2001, p. 37). Some researchers believed that the hierarchical and multi-dimensional approach offered an improved explanation of the complexity of human perceptions rather than the 47 conceptualisations currently existing in the literature (Brady & Cronin, 2001; Dabholkar et al., 1996). The empirical test of Brady and Cronin?s hierarchical model indicated that this model was psychometrically sound (Chang, Chen, & Hsu, 2002). 2.10.2 A Hierarchical Model of Service Quality for the Recreational Sports Industry Recently, many sports organisations have been competing for spectators and attempting to satisfy their needs and wants through good service quality (Ko & Pastore, 2005). Ko and Pastore (2005) demonstrated that providing spectators with good service quality was a key determinant of success of a sports organisation. Although previous studies on service quality paid attention to identifying the dimensions of service quality in the recreational sports industry, no studies focused on a re-examination of the dimensions of service quality for this industry (Ko & Pastore, 2005). In order to fill the gap in the literature, Ko and Pastore (2005) further developed a hierarchical model by adapting Brady and Cronin?s (2001) and Dabholkar et al.?s (1996) models to use in their study of service quality in the recreational sports industry. In Ko and Pastore?s (2005) hierarchical model, service quality for the recreational sports industry consisted of four primary dimensions and several corresponding sub-dimensions: (a) programme quality: range of activity programmes, operating time, and information; (b) interaction quality: client-employee interaction and inter-client interaction; (c) outcome quality: physical change, valence, and sociability; and (d) environment quality: ambient condition, design, and equipment (see Figure 2.5). Ko and Pastore (2005) tested the model using the two-step approach of structural equation modelling. Ko and Pastore?s (2005) findings supported the multi-dimensional conceptualisation of service quality perception in the recreational sports industry. Finally, Ko and Pastore (2005) suggested that it was necessary to investigate if this hierarchical model could also be applied to other industries that were similar to the recreational sports industry. 48 Figure 2.5: Hierarchical Model of Service Quality for the Recreational Sports Industry (Ko & Pastore, 2005, p. 91). 2.10.3 A Hierarchical Model of Service Quality for the Telecommunication Industry Grönroos (1990, 1984, 1982) noted that customer perceptions of service quality were based on two dimensions: a functional dimension and a technical dimension. According to Grönroos (1983), functional quality referred to the quality of how was provided whereas technical quality referred to the quality of what was provided. Since functional quality referred to the delivery of the service, the quality was more easily judged (Grönroos, 1983). Conversely, technical quality involved the actual competence of the provider and the technical outcome of the product or service (Grönroos, 1983). However, Grönroos (1983) 49 noted that technical quality was difficult to evaluate because of a general lack of knowledge on the part of customers. Kang (2006) suggested that a large number of previous studies on service quality have concentrated on the SERVQUAL instrument, and stressed the dimensions of functional quality. However, Kang (2006) found that there have been limited studies into testing a two- component model of service quality that includes both functional quality and technical quality. In order to increase the understanding of service quality in the telecommunication industry, Kang (2006) attempted to propose a modelling framework for service quality on the basis that service quality was a multi-dimensional construct. In addition, that author developed a hierarchical model involving identification of the dimensions of service quality (both technical and functional), and the components thought to make up each dimension (see Figure 2.6). In Kang?s (2006) hierarchical model of service quality, there were two primary dimensions: process quality and outcome quality. Kang (2006) referred to process quality as the evaluation that occurred while the service was being performed. Outcome quality was the evaluation occurring after service performance, and focused on ?what? service has been delivered. Therefore, based on Grönroos?s theory of functional quality and technical quality, process quality denoted functional quality whereas outcome quality implied technical quality (Kang, 2006). In Kang?s (2006) hierarchical model, process quality was divided into five dimensions: reliability, assurance, tangibles, empathy and responsiveness on the basis of the five SERVQUAL dimensions. However, Kang (2006) and Parasuraman et al. (1985) noted that it was difficult to find a reasonable explanation to address outcome quality in the SERVQUAL instrument, even though quality evaluations were not made solely on the outcome of service but also involved evaluations of the service delivery process. Kang (2006) and Kang and James (2004) proposed that outcome quality was disregarded in the measurement of service quality through SERVQUAL. 50 Figure 2.6: Hierarchical Model of Service Quality for the Telecommunication Industry (Kang, 2006, p. 41). Kang (2006) presented that it was difficult to analyse a third-order factor model and technical quality because of a lack of support in the literature. Therefore, Kang (2006) did not attempt to fully analyse the third-order factor model. Instead, that author focused on the relationship between service quality perceptions and dimensions of process and outcome quality. Kang (2006) found that the relative influences of process quality and outcome quality on customer perceptions of service quality were not clearly addressed. Accordingly, that author suggested that more studies were needed to clarify the influences of process quality and outcome quality on service quality perceptions. In general, outcome quality has been relatively ignored because it was believed that customers would not be able to discern the technical quality of services with accuracy, and they thus would rely on other measures of quality attributes, specifically for those associated with the process of service delivery (Kang, 2006). 2.10.4 A Hierarchical Model of Service Quality for the Sports Tourism Industry Ritchie and Adair (2004) proposed that sport and tourism were among the world?s most popular leisure experiences. However, Shonk (2006) showed that few studies have paid attention to the service quality dimensions as perceived by travelling spectators. 51 To measure service quality in the sports tourism industry, Shonk (2006) proposed a hierarchical model composed of four primary dimensions and 11 sub-dimensions (see Figure 2.7). Shonk?s (2006) model suggested that tourists attending a sporting event were satisfied when they perceived a high quality service within the context of: (a) access to the destination where the event occurred; (b) the accommodation during the stay; (c) the venue for the event; and (d) the sport contest. These four primary dimensions and their pertaining sub-dimensions accounted for the overall quality of sports tourism that, if positive, resulted in satisfaction with the visit to the event. Satisfaction with the event, in turn, influenced the tourists? intentions to return to the event (Shonk, 2006). Figure 2.7: Hierarchical Model of Service Quality for the Sports Tourism Industry (Shonk, 2006, p. 21). 2.10.5 A Hierarchical Model for the Quality of Electronic Services For the providers of electronic services, quality has been identified as a major driving force on the route to long-term success (Fassnacht & Koese, 2006). Fassnacht and Koese (2006) found that the comprehensive measurement of quality has been considered the key determinant of effective quality management. Although several studies on electronic 52 services have been conducted (Parasuraman et al., 2005; Yang, Cai, Zhou, & Zhou, 2005; Gounaris & Dimitriadis, 2003; Aladwani & Prashant, 2002), important research gaps have been identified pertaining to the scope of the dimensions of electronic service quality (Bressolles, Durrieu, & Giraud, 2007; Fassnacht & Koese, 2006). Based on the literature, QES (quality of electronic services) has been found to be a multi-dimensional construct (Fassnacht & Koese, 2006). However, Fassnacht and Koese (2006) remarked that no consensus on the relevant dimensions of QES had been reached and that the various proposed dimensions should be investigated more systematically. Initially, Fassnacht and Koese (2006) briefly outlined the service quality framework developed by Rust and Oliver (1994), which served as a theoretical reference for the conceptualisation of QES. According to Rust and Kannan (2002), the service environment, service delivery and service product should be applied to electronic services. The appearance of the user interface through the web site represented the service environment, the service delivery was characterised by the interaction between customers and user interfaces during service usage. Finally, the service product, as an outcome dimension, was applied to electronic services (Fassnacht & Koese, 2006). Fassnacht and Koese (2006) applied Rust and Kannan?s (2002) theory, and then developed a hierarchical quality model for electronic services, which included three dimensions and nine sub-dimensions (see Figure 2.8). Fassnacht and Koese?s (2006) hierarchical model was tested by a large aggregated sample drawn from customers based on three different electronic services: a service for the creation and maintenance of personal homepages, a sports coverage service, and an online shop. In Fassnacht and Koese?s (2006) hierarchical model, the sub-dimensions were treated as first-order factors and the dimensions as second- order factors of the service quality construct. Fassnacht and Koese (2006) adopted three primary dimensions: environment quality, delivery quality and outcome quality, as a basis for the conceptualisation of QES. The first primary dimension, environment quality, consisted of two sub-dimensions: graphic quality and clarity of layout. The second primary dimension, delivery quality, was composed of four sub-dimensions: attractiveness of selection, information quality, ease of use, and technical quality. Finally, the third primary 53 dimension, outcome quality, comprised three sub-dimensions: reliability, functional benefit, and emotional benefit. Figure 2.8: Hierarchical Model for the Quality of Electronic Services (Fassnacht & Koese, 2006, p. 27). Fassnacht and Koese (2006) believed that their hierarchical model could assist telecommunication managers to improve QES, because the need to consider the environment, delivery and outcome dimensions of quality were fully stressed. The hierarchical nature of the electronic service quality construct demonstrated that there were several levels of abstraction that should be taken into account (Fassnacht & Koese, 2006). The environment, delivery and outcome qualities were reflected in nine sub-dimensions that have consistently recurred in the literature as well as in the authors? qualitative study (Fassnacht & Koese, 2006). 2.10.6 A Hierarchical Model of Service Quality for the Travel and Tourism Industry Caro and Roemer (2006) attempted to fill a gap in the literature on service quality by developing an integrated model of service quality for the tourism industry. In addition, those authors demonstrated that a number of empirical studies focused on service quality in the tourism industry. Most studies measured service quality by replicating or adapting the SERVQUAL model (Fick & Ritchie, 1991; Saleh & Ryan, 1991; Lewis, 1987). Furthermore, several marketing researchers admitted that the use of generic models such as SERVQUAL 54 or SERVPERF to measure service quality across different industries was not feasible (Caro & Roemer, 2006; Karatepe, Yavas, & Babakus, 2005; Mattila, 1999). Researchers have agreed that travel agents? perceptions of service quality were multi-dimensional (Caro & Roemer, 2006). However, the number and content of these dimensions remained open for debate. Therefore, Caro and Roemer (2006) proposed a multi-level and multi-dimensional model of service quality in accordance with the hierarchy of perceptions as proposed by Brady and Cronin (2001). Based on the findings from the qualitative research and the service quality literature, Caro and Roemer (2006) proposed the following model: a hierarchical and multi-dimensional model in which quality was a higher order factor identified by three primary dimensions and seven sub-dimensions (see Figure 2.9). The three primary dimensions were personal interaction, physical environment and outcome. These three primary dimensions were also divided into seven sub-dimensions, namely, conduct, expertise, problem-solving, equipment, ambient conditions, waiting time and value. Figure 2.9: Hierarchical Model of Service Quality for the Travel and Tourism Industry (Caro & Roemer, 2006, p. 7). Caro and Roemer (2006) argued that models of service quality needed to be conceptualised based on the specific characteristics of the travel and tourism industry. In order to conceptualise service quality in the travel and tourism industry, Caro and Roemer (2006) developed a multi-dimensional and hierarchical model of service quality that reflected these characteristics. Through an extensive literature review and qualitative research, those authors found that service quality in the travel and tourism industry should be divided into three primary dimensions and seven sub-dimensions. 55 2.10.7 A Hierarchical Model of Service Quality for the Urgent Transport Industry The proportion of ISO (International Organisation for Standardisation) certifications has been greatly increased because of the economic importance of the urgent transport industry in Spain (Caro & García, 2007). The urgent transport industry has grown by eight percent in recent years and, unlike other countries, the business was dominated by national organisations (Caro & García, 2007). Therefore, service quality has become an essential competitive element in the urgent transport industry (Caro & García, 2007). Many business organisations have realised that there was a critical need to deploy a tool as a measurement of service quality in order to appropriately assess and improve their service performance (Caro & García, 2007). After reviewing the literature related to service quality, Caro and García (2007) found that no research focused on the measurement of service quality in the urgent transport industry. Therefore, in order to develop a reliable and valid instrument, it was necessary to consider which aspects should be used to measure service quality in the urgent transport industry. Caro and García (2007) proposed an instrument of service quality incorporating performance-based measures using scales which were similar to the ones developed by Brady and Cronin (2001) and Dabholkar et al. (1996). Based on the results of qualitative research, as well as the review of the qualitative literature, Caro and García (2007) proposed the following model: a hierarchical and multi-dimensional model where quality was treated as a higher-order factor composed of four primary dimensions and 10 sub-dimensions (see Figure 2.10). In Caro and García?s (2007) hierarchical model, there were four primary dimensions of service quality. The first primary dimension was personal interaction. This dimension consisted of four sub-dimensions: attitude, behaviour, expertise, and problem-solving. The second primary dimension of service quality was termed design. This design dimension included all aspects associated with the configuration of the service and was identified by two sub-dimensions: range of service and operating time. The third primary dimension of service quality was physical environment. This dimension comprised two identified sub- 56 dimensions: tangibles and information. The last dimension of service quality was outcome. This dimension contained two sub-dimensions: punctuality and valence. Figure 2.10: Hierarchical Model of Service Quality for the Urgent Transport Industry (Caro & García, 2007, p. 62). Concisely, this hierarchical model, which incorporated the performance-based measures proposed by Brady and Cronin (2001) and Dabholkar et al. (1996), assisted prospective researchers to be aware of the factors that comprised customer perceptions of service quality and the way service quality was formed on the basis of customer perceptions in the urgent transport industry (Caro & García, 2007). 2.10.8 A Hierarchical Model of Health Service Quality Andaleeb (2001) indicated that health care has been one of the fastest growing industries in the service economy. Quality in the health care industry was in the forefront of professional, political and managerial attention, and was regarded as an approach to achieving patronage increase, competitive advantage and long-term profitability (Brown & Swartz, 1989). In addition, several researchers claimed that quality in the health care industry has been identified as a means of achieving better health outcomes for customers (Dagger & Sweeney, 2006; Marshall, Hays, & Mazel, 1996; O?Connor, Shewchuk, & Carney, 1994). Therefore, Dagger et al. (2007) indicated that service quality has become an important corporate strategy for the health care organisations. Although a considerable amount of research has been conducted on service quality perceptions, some studies have not directly examined the 57 way how customers assess health service quality overall (Dagger et al., 2007; Clemes et al., 2001). In order to measure health service quality in depth, Dagger et al. (2007) developed a hierarchical model to reflect service quality perceptions in the health care industry. In Dagger et al.?s (2007) hierarchical model of health service quality, four primary dimensions were identified: interpersonal quality, technical quality, environment quality and administrative quality (see Figure 2.11). Each primary dimension was composed of at least two sub-dimensions. Though the development of the sub-dimensions was based on the themes identified in the qualitative study, the literature was consulted to support Dagger et al.?s findings (Brady & Cronin 2001; Parasuraman et al., 1985). The first primary dimension, interpersonal quality, was composed of two sub-dimensions: interaction and relationship. Technical quality, as the second primary dimension, was made up of two sub-dimensions: outcome and expertise. The third primary dimension, environment quality, also comprised two sub-dimensions: atmosphere and tangibles. Finally, the fourth primary dimension, administrative quality, included three sub-dimensions: timeliness, operation, and support. Figure 2.11: Hierarchical Model of Health Service Quality (Dagger et al., 2007, p. 131). 58 Dagger et al. (2007) found that health service quality has been identified as an important determinant of patient satisfaction and behavioural intentions, thus emphasising that the importance of health quality could be viewed as a decision-making variable. In general, customer satisfaction has been more closely aligned with behavioural intentions, in that satisfaction was typically modelled as mediating the link between service quality and behavioural intentions, so the strong relationship between service quality and behavioural intentions becomes noteworthy (Brady et al., 2001; Dabholkar et al., 2000; Gotlieb et al., 1994; Anderson & Sullivan, 1993; Cronin & Taylor, 1992). Dagger et al.?s (2007) study supported the mediating role of service quality in the service attributes-behavioural intentions relationship. This mediation mechanism implied that the service attributes were more strongly related to overall service quality than behavioural intentions and that customers? overall perceptions of service quality continued to play an important role in generating customer outcomes (Dagger et al., 2007). 2.10.9 A Hierarchical Model of Higher Education Service Quality Since restructuring of the national economy in the mid 1980s, higher education in New Zealand has suffered from four major issues: political reforms, social changes, economic changes and globalisation (Clemes et al., 2001). Clemes et al. (2007) demonstrated that these issues have prompted the New Zealand higher education industry to become more internationally competitive. In order to seek marketing strategies to assist New Zealand?s higher education industry to succeed in a competitive marketplace, Clemes et al. (2007) identified the important dimensions of service quality as perceived by university students. In the education service, Curran and Rosen (2006) suggested that a hierarchical factor structure may be appropriate because the students? perceptions of college or university quality were multi-dimensional. When assessing students? perceptions of service quality, the measured factors were highly similar to the factors identified in Brady and Cronin?s (2001) hierarchical model. Several studies on student satisfaction have focused on the evaluation of the broad aspects of teacher-student relationships (interaction quality), physical facilities 59 (physical environment quality), and student learning outcomes (outcome quality) (Tam, 2006; DeShields, Kara, & Kaynak, 2005; Clemes et al., 2001). To measure higher education service quality, Clemes et al. (2007) developed a hierarchical model to reflect service quality perceptions in the higher education industry. In Clemes et al.?s (2007) hierarchical model of higher education service quality, three primary dimensions were identified: interaction quality, physical environment quality and outcome quality (see Figure 2.12). Each primary dimension was composed of at least three sub-dimensions. The first primary dimension, interaction quality, comprised four sub-dimensions: academic staff, administration staff, academic staff availability, and course content. Physical environment quality, as the second primary dimension, was composed of three sub-dimensions: library atmosphere, physical appeal, and social factors. The last primary dimension, outcome quality, was made up of another three sub-dimensions: personal development, academic development, and career opportunity. Note: AC = Academic Staff, AD = Administration Staff, AA = Academic Staff Availability, CC = Course Content, LIB = Library Atmosphere, PA = Physically Appealing, SF = Social Factors, PD = Personal Development, ACD = Academic Development, CO = Career Opportunity. Figure 2.12: Hierarchical Model of Higher Education Service Quality (Clemes et al., 2007, p. 310). The results of Clemes et al.?s (2007) empirical study supported the use of a hierarchical factor structure, such as those developed by Brady and Cronin (2001) and Dabholkar et al. (1996) to conceptualise and measure service quality. However, the three primary dimensions 60 may not be generic for all service industries outside of the education sector, and may be subject to cultural differences (Clemes et al., 2007). Indeed, Clemes et al. (2007) noted that these three primary dimensions should be identified through the application of an appropriate qualitative and quantitative analysis. The sub- dimensions should also be identified using an appropriate qualitative and quantitative analysis, because they may also vary across industries and cultures (Clemes et al., 2007). Clemes et al. (2007) noted that it would be prudent to compare the derived importance of any primary and sub dimensions of service quality that were identified in future higher education research. 2.11 An Overview of Dimensions of Hotel Service Quality After the review of the literature related to applying a hierarchical modelling approach to conceptualising service quality in a variety of different areas (Pollack, 2009; Caro & García, 2008, 2007; Clemes et al., 2007; Dagger et al., 2007; Kao, 2007; Caro & Roemer, 2006; Fassnacht & Koese, 2006; Shonk, 2006; Jones, 2005; Ko & Pastore, 2005, 2001; Liu, 2005; Brady & Cronin, 2001; Spreng et al., 1996; Rust & Oliver, 1994), the researcher of this study determined that overall perceptions of hotel service quality should be based on customer evaluations of three dimensions of the service encounter: interaction quality, physical environment quality and outcome quality. As a result, the following sections will address the primary and sub dimensions of service quality that were applied to the hotel industry based on the literature review. 2.11.1 Interaction Quality The first primary dimension of service quality, interaction quality, mainly focused on the way the service was delivered (Brady & Cronin, 2001; Czepiel, Solomon, & Suprenant, 1985; Grönroos, 1984). Several studies have indicated the importance of the interaction quality dimension in the delivery of services and have identified this dimension as the one that has the most significant effect on service quality perceptions (Bigné, Martínez, Miquel, & Belloch, 1996; LeBlanc, 1992; Grönroos, 1982). Several researchers reported that 61 services were inherently intangible and characterised by inseparability (Parasuraman et al., 1985; Lovelock, 1983, 1981; Berry, 1980; Shostack, 1977). Parasuraman et al. (1985) demonstrated that most services of an organisation could not be counted, measured, examined, confirmed and inventoried in advance of sale to ensure quality. In Lehtinen and Lehtinen?s (1985) study, their preliminary assumption was that service quality derived from the interaction between contact personnel and customers, as well as between some customers and other customers. Hartline and Ferrell (1996) and Surprenant and Solomon (1987) showed that the interpersonal interactions occurring during service delivery often had the greatest effect on customer perceptions of service quality. 2.11.1.1 Sub-dimensions of Interaction Quality In this study, five sub-dimensions are proposed as constituting the interaction quality dimension: attitude, behaviour, expertise, problem-solving, and customer interaction (Dagger et al., 2007; Wu, 2007a; Gouthier & Schmid, 2003; Kim & Cha, 2002; Kim & Jin, 2002; Connolly, 2000; Wong & Keung, 2000; Martin, 1996; Martin & Pranter, 1989; Geller, 1985). The first sub-dimension, attitude, is an individual?s feeling of the favourableness or unfavourableness through their behavioural performance (Lam, Cho, & Qu, 2007). Fishbein and Ajzen (1975) referred to attitude as the function of behavioural beliefs and evaluation of outcomes. Ajzen (1988) defined attitude as ?an individual?s disposition to respond favourably or unfavourably to an object, person, institution, or event? (p. 4). Czepiel et al. (1985) referred to attitude as an employee?s traits (e.g., friendliness, warmth, politeness, conduct, concern, openness, helpfulness and so on). Williams (2005) showed that certain employee attitude towards aspects of their job and working environment has been known to be predictive of future behaviour. In Lam et al.?s hotel study (2007), a multi-level model including cognition (beliefs), affect (feelings) and conation (intentions) as the first-order factors, and attitude as a single second- order factor, was used as a starting point for many attitude-behaviour theories. Kuo (2007) indicated that attitude has played a vital role in customer satisfaction because there is a close 62 interaction between customers and employees in the hotel industry. Geller (1985) demonstrated that employee service attitude was a key factor in successfully running hotels. However, Sharpley and Forster (2003) showed that little attention has been paid to the hotel employee?s service attitude. The second sub-dimension, behaviour, has been referred to as the manifest function that influenced customer perceptions of interaction quality (Czepiel et al., 1985). Tsaur and Lin (2004) defined employee service behaviour as ?extra-role? and ?role-prescribed.? This definition has been consistent with that of pro-social service behaviour in the organisational behaviour or marketing literature (Bettencourt & Brown, 1997). Chelladurai and Chang (2000) indicated their support for the importance of service employees? behaviour (e.g., service failure and recovery). Bitner et al. (1990) and Parasuraman et al. (1988) emphasised the critical role of customer-contact employees in that their behaviour had a major influence on customer perceptions of service quality. Therefore, an understanding of customer perceptions of service providers? behaviour has been identified as added-value information for hotel owners or managers, and the information should assist them to design suitable policies and procedures for their customers and employees (Wong & Keung, 2000). The third sub-dimension, expertise, has been identified as the degree to which the interaction was affected by the employee?s task-oriented skills (Czepiel et al., 1985). According to Kim and Cha (2002, p. 326), expertise exists when (1) a hotel employee has professional training and education about service; (2) a hotel employee demonstrates adequate knowledge about the hotel?s products and services; (3) a hotel employee shows interest in self-development to provide better service; and (4) a hotel employee is competent in providing service. Crosby, Evans and Cowles (1990) found that expertise had an effect on customers? assessments of service quality. Several researchers indicated that expertise has been identified as one of the important components of interaction quality (Caro & García, 2008, 2007; Caro & Roemer, 2006; Ko & Pastore, 2005; Brady & Cronin, 2001). However, Solnet 63 (2006) and King (1995) suggested that more hotel studies should pay attention to the combination of employees? expertise in the interaction quality dimension. The fourth sub-dimension, problem-solving, was a dimension identified by Dabholkar et al. (1996). Kim and Jin (2002) and Dabholkar et al. (1996) applied the problem-solving sub- dimension to measure a store?s employee ability to handle returns and exchanges, customers? problems and complaints. However, this sub-dimension has been viewed as separability from the personal interaction dimension because ?service recovery was being identified as a critical part of good service? (Dabholkar et al., 1996, p. 7). Ross (1997) showed that most managers expected that their employees were qualified to be problem-solvers, who were also able to adapt to rapidly changing circumstances. However, Saibang and Schwindt (1998) viewed the problem-solving skill as essentially in short supply. Heinemann (1996) noted that anything that employees clarify has been considered as the most basic of all skills. In addition, that author explained that today more employers not only desired to hire employees who could independently perform their jobs but also realised that teamwork could occasionally involve doing the other person?s work as well. Several researchers demonstrated that the problem-solving sub-dimension should be combined with the personal interaction dimension (Caro & García, 2008, 2007; Caro & Roemer, 2006; Ko & Pastore, 2005; Dabholkar et al., 1996). However, Breiter, Tyink and Corey-Tuckwell (1995) proposed that limited hotel studies have considered the issue of employees? problem-solving abilities. The last sub-dimension, customer interaction, has been defined as customers? subjective perceptions of how the service were delivered during the service encounter in which the attitudes and behaviours of other customers were evaluated (Ko & Pastore, 2005). Venkat (2008) defined customer interaction as ?a direct or indirect, face-to-face or technology- mediated, active or passive interaction between two or more customers occurring inside or outside the service setting, which may or may not involve verbal communication? (p. 2). Inman (2008) presented that customer interaction represented the degree to which customers could intervene in the service process. Several researchers indicated that customer 64 perceptions of the quality of a service could be influenced by other customers? attitudes and behaviours (Brady & Cronin, 2001; Lovelock, 1991; Baker, 1987). Ko and Pastore (2005) illustrated that displaying an appropriate behaviour and attitude towards other customers may not only give an individual comfort but also an optimal learning experience. However, few empirical studies have been conducted on the impact of customer interaction on customers in the service industry (Harris & Reynolds, 2003; Martin, 1996). Lehtinen and Lehtinen (1985) indicated that interaction quality also involved the interaction between some customers and other customers in addition to the interaction between customers and service employees. However, several researchers argued that the customer interaction sub-dimension may greatly affect customers? holistic evaluations of the business, their propensity to affect other people?s future patronage via word-of-mouth communication, and their willingness to acclimatise others or to return in the future (Wu, 2007a; Martin, 1996; Martin & Pranter, 1989; Zeithaml, 1981). In addition, several researchers proposed that limited research has considered the interaction among customers, in spite of the customer interaction sub-dimension playing an important role in the service delivery (Moore, Moore, & Capella, 2005; Genzi & Pelloni, 2004; Gouthier & Schmid, 2003; Parker & Ward, 2000; Grove & Fisk, 1997; Clark & Martin, 1994; Harris, Baron, & Radcliffe, 1994). Furthermore, Connolly (2000) found that the issue of customer interaction has attracted little attention in hotel studies. In general, an employee?s attitude, behaviour, expertise and problem-solving have been identified as the quality of the delivered service and they may ultimately ?affect what clients evaluate as a satisfactory encounter? (Czepiel et al., 1985, p. 9). Bitner et al. (1990) divided the employee-customer interaction into three distinct aspects that included demeanour, actions, and skills of employees in resolving failed service incidents. Caro and Roemer (2006) and Grönroos (1990) suggested that the attitude, behaviour, expertise and problem- solving of employees were important factors of interaction quality when customers assessed the overall quality of an organisation?s service. In sum, the service attitude, behaviour, expertise, problem-solving, and customer interaction sub-dimensions may change customers? assessments of the services (Caro & García, 2008, 65 2007; Wu, 2007a; Caro & Roemer, 2006; Ko & Pastore, 2005, 2001). Therefore, these five sub-dimensions may play an important role in forming customer perceptions of interaction quality in the hotel industry. 2.11.2 Physical Environment Quality The second primary dimension of service quality, physical environment quality, has been specifically investigated for its environmental influences on customer behaviour since the beginning of the 1970s (Kotler, 1973). In a traditional service setting, the service environment was associated with the physical ambience of the service encounter, or what was treated as the servicescape (Fassnacht & Koese, 2006). Bitner (1992) indicated that the servicescape was a possible determinant of the quality of the physical environment. In addition, that author found that the physical environment was a constructed facility in which service delivery took place, as opposed to the natural or social environment. Elliott, Hall and Stiles (1992) referred to physical environment quality as the physical features of the service production process. Rys, Fredericks and Luery (1987) found that customers inferred physical environment quality based on their perceptions of the physical facilities. A large number of researchers showed that physical environment quality has been considered as one of the most important aspects in customer evaluations of service quality (Howat, Absher, Crilley, & Miline, 1996; Wakefield, Blodgett, & Sloan, 1996; McDougall & Levesque, 1994; Rust & Oliver, 1994; Wright, Duray, & Goodale, 1992; Bitner, 1990; Baker, 1987; Lehtinen & Lehtinen, 1985). However, Nguyen and Leblanc (2002) found that previous studies on the combined effect of multiple elements comprising the physical environment quality have not been identified in the hotel industry. 2.11.2.1 Sub-dimensions of Physical Environment Quality Six sub-dimensions are proposed as constituting the physical environment quality dimension in this study: décor, ambience, location, cleanliness, security and safety, and design (Hilliard & Baloglu, 2008; McGoey, 2008; Dagger et al., 2007; Heide, Laerdal, & Gronhauh, 2007; Lockyer, 2003; Ekinci & Riley, 2001; Ransley & Ingram, 2001; Niblo & Jackson, 1999; Spector, 1999). 66 The first sub-dimension, décor, has been referred to as the art of decorating a room so that it was attractive, easy to use, and functioned well with the existing architecture (Fornes, 2007). Several researchers maintained that customer preferences in the use of a hotel have been determined by a number of factors and interior décor was important in customer selection of hotels (Wu & Weber, 2005; Lockyer, 2002; Min & Min, 1997; Saleh & Ryan, 1992). Susskind and Chan (2000) found that décor was a key determinant in increasing the accuracy of customers? assessments of the quality of a restaurant?s service. However, Ekinci and Riley (2001) indicated that the décor sub-dimension has received little attention in the hotel industry. The second sub-dimension, ambience, has been referred to as the conscious design of space to create certain effects in customers to increase their purchase likelihood (Kotler, 1973). This sub-dimension may include attributes such as lighting, music, noise, temperature, signage, and wall colour (Bonn & Joseph-Mathews, 2007). Heide et al. (2007) explained that a large number of hospitality managers worldwide have become concerned about the issue of ambience. In order to improve the quality of ambience, Heide et al. (2007) suggested that different groups of practitioners, hospitality managers and outside experts (e.g., designers, architects, etc.) should be involved in conducting this task. Based on the services marketing literature, Heide et al. (2007) found that ambience had an association with customers and was seen as a tool for changing customers? attitudes and behaviours. According to Bitner (1992), the role of ambience was important for service organisations, rather than for producers of tangible goods. Regardless of different geographical areas, nationality of customers and types of hotel, ambience has been identified as an essential determinant in explaining customer satisfaction among hotel customers (Troye & Heide, 1987). Based on the literature, Heide et al. (2007) indicated that ambience was a key success factor in financial results. In order to increase the level of service quality, hospitality managers attempted to improve the ambience of an organisation (Heide et al., 2007). Because of the perceived need for assistance in this task, managers often became involved with external design experts, such as designers, architects, and so on (Heide et al., 2007). Since ambience was an intrinsically 67 complex phenomenon and an ambiguous concept, as well as the fact that training and experience differed among hospitality managers and design experts, their perceptions and knowledge of the role of ambience might be different (Heide et al., 2007). In the literature, several studies in engineering and design have focused on human physiological responses to ambient conditions (Heide et al., 2007; Oborne, 1987; Sanders & McCormick, 1987; Bennett, 1977). However, Kirk, Wakefield and Blodgett (1996) and Bitner (1992) proposed that a very limited number of empirical studies in customer research have identified how ambient factors affected customer responses. In addition, Heide et al. (2007) found that an apparent lack of empirical research has addressed ambience and its role in the hospitality setting. The third sub-dimension, location, involved the provision of an overall distribution blueprint for the region (Coltman, 1989). Coltman (1989) proposed that traffic and transportation conditions were important factors when customers considered the location of their accommodation. In addition, Pan (2002) reported that the base station suitability, traffic convenience and fine visual perception, public facilities and other services, application of certain regulations, and flexible space were important factors when customers selected their accommodation. Chou, Hsu and Chen (2008) indicated that the basis of these discussions has focused on the overall facilities surrounding the hotel, traffic conditions and future considerations for expansion. Lee, Lee and Hsu (2000) indicated that the base station area was a major factor in location selection; the operating area was positively related to sales. Meanwhile, Chou et al. (2008) demonstrated that accessibility or convenience of the traffic to the other base stations has been considered one of customers? primary concerns in selecting a tourist hotel location. Several researchers noted that parking conditions should also be considered as location selection factors, because additional numbers of parking spaces would attract more customers to revisit or return to an organisation (Tzeng, Teng, Chen, & Opricovic, 2002; Teng, 2000). Park (2004) said that the issue of the utilitarian value of dining-out greatly affected customer evaluation and selection when customers considered the issues of convenience and prices for a dining-out place. Pan (2005) found that location played an 68 important role in determining hotel success and location required more attention in planning private and public investment in and policies towards the hotel industry. Furthermore, Danziger, Israeli and Bekerman (2004) recommended that hotel star rating and location were the most important attributes when assessing hotel service quality. Ekinci and Riley (2001) demonstrated that location was a key determinant when customers selected their own accommodation. When markets were developed, location decisions were inherently different between hotels and restaurants (Jones, 1999). Jones (1999) showed that hotels were generally developed in major urban or resort areas whereas restaurants tended to be developed in a region before they were moved on to the next. This was because hotel chains preferred to conduct national and international advertisements but restaurant chains, particularly the quick service chains, often utilised television promotion regionally and nationally (Jones, 1999). Although location was a highly important determinant of the success of a hotel (Lundberg, Krishnamoorthy, & Stavenga, 1995), the issue of location has received little attention (Urtasun & Gutiérrez, 2006). The fourth sub-dimension, cleanliness, has been identified as one of the most important factor and features a hotel could offer its customers (Callan, 1996). Min et al. (2002) demonstrated that cleanliness was the most important feature of hotel rooms. Many hotel studies indicated that cleanliness was a highly important factor in customers? selection of accommodation (Ryan & Qu, 2007; Nash, Thyne, & Davies, 2006; Lockyer, 2002, 2000; Callan, 1996; Weaver & Oh, 1993; McCleary & Weaver, 1992; Saleh & Ryan, 1992; Knutson, 1988). In general, before the potential customers stayed in a particular hotel, they had little or no idea about its level of cleanliness (Lockyer, 2005). Several studies proposed that cleanliness was a factor in influencing whether customers returned to a hotel and thus the level of repeat business (Lockyer, 2005; Weaver & Oh, 1993). Taninecz (1990) reported that room cleanliness, particularly, was one of the most important attributes for business customers in their hotel selection. Weaver and McCleary (1991) indicated that over 90 percent of hotel business customers ranked cleanliness as the most important aspect when selecting hotels 69 for their accommodation. However, Lockyer (2003) indicated that the issue of cleanliness has not attracted a lot of attention in the hotel industry. The fifth sub-dimension, security and safety, emerged as a major force that drove change in the multi-national hotel industry (Punpugdee, 2005). Punpugdee (2005) noted that security and safety did not belong particularly to one hotel, one organisation, or even one country. Instead, this issue belonged to the hospitality industry regionally and internationally (Punpugdee, 2005). Murphy (1988) demonstrated that the issue of security at the hotel was unobtrusive and preventive because liability was a major problem. In order to confirm if the hotel was a safe place, Murphy (1988) suggested that managers should conduct a training programme to increase employees? awareness of safety and to decrease the frequency and severity of accidents occurring at the hotel. In general, safety considerations involved protecting people, but security factors embraced protecting the hotel property and customers? possessions, in addition to ensuring employees? and customers? individual safety (Enz & Taylor, 2002). Enz and Taylor (2002) illustrated that security features included electronic locks and security cameras whereas safety facilities included items such as sprinklers and smoke detectors. McGoey (2008) noted that security and safety have become pivotal concerns among travellers throughout the world. Alternatively, Hilliard and Baloglu (2008) proposed that safety and security has been identified as an important part of the hotel physical environment quality dimension. The last sub-dimension, design, represented the layout or architecture of the service facility of an organisation, including aesthetic (visually pleasing) and functional (practical) components of the physical environment in the hotel industry (Heide et al., 2007; Moye, 2000; Aubert-Gamet, 1997). Although the aesthetic and functional components were closely related, the former promoted sensory pleasure in the service experience whereas the latter facilitated customers? behaviour (Aubert-Gamet, 1997). Bitner (1992) and Baker (1987) showed that design indeed existed at the forefront of customer awareness. Veronique (1997) and Bitner (1992) demonstrated that design has a comparatively greater potential for producing positive customer perceptions of service 70 quality of an organisation. Brady and Cronin (2001) identified design as one of the important sub-dimensions in the physical environment quality dimension. In Aubert- Gamet?s (1997) hotel study, design was a visual stimulus that was far more likely to be apparent to customers than ambience. However, Ransley and Ingram (2001) illustrated that the design sub-dimension has not attracted a lot of attention in studies of physical environment quality in the hotel industry. Therefore, décor, ambience, location, cleanliness, security and safety, and design may play an important role in determining customer perceptions of the quality of a hotel?s physical environment. 2.11.3 Outcome Quality The third primary dimension of service quality, outcome quality, referred to what customers were left with after service delivery (Fassnacht & Koese, 2006; Grönroos, 1984). Grönroos (1990, 1982) indicated that outcome quality was the result of the service transaction. Powpaka (1996) showed that outcome quality was associated with what customers actually received from the service transaction or, conversely, what was delivered by the service provider. The outcome quality dimension focused on the outcome of the service act and indicated what customers gained from the service; in other words, whether the outcome dimension satisfied customers? needs and wants (McDougall & Levesque, 1994; Rust & Oliver, 1994). However, outcome quality, in fact, was not equivalent to overall quality (Ekinci & Riley, 2001). The outcome quality dimension has been labelled ?technical quality? by Grönroos (1984), who defined outcome quality as ?what the customer is left with when the production process is finished? (p. 37). Several studies proposed that the outcome quality dimension of service quality has been a significant determinant of customers? overall assessments of service quality, and that the addition of the outcome quality dimension factored into the model or measurement scale significantly improved the explanatory power and predictive validity (Powpaka, 1996; Baker 71 & Lamb, 1993; Richard & Allaway, 1993; Mangold & Babakus, 1991; Grönroos, 1990, 1982; Lehtinen & Lehtinen, 1985). Powpaka (1996) doubted whether the outcome quality dimension should be required to exist in every service industry even though recent studies in service quality have supported the addition of the outcome quality dimension in assessing the overall service quality. For example, service providers in some industries perceived the outcome dimension of service quality as a more important dimension than interaction quality and physical environment quality. However, Swan and Combs (1976) found that customers became satisfied with a service when they perceived the outcome to be unsatisfactory but the process to be satisfactory. This finding supported one theory, namely, the outcome quality dimension may not be significant but is required in every service industry, and whether customers used this dimension in their overall assessment of the overall service quality of a service depends on their ability to assess outcome quality of the service accurately and efficiently (Powpaka, 1996). Although some studies have recognised the importance of outcome attributes to customer evaluations of service quality, few hotel studies have paid attention to the outcome quality dimension (Luk & Layton, 2004). 2.11.3.1 Sub-dimensions of Outcome Quality There are three sub-dimensions proposed in this study as components of the outcome quality dimension: sociability, valence and waiting time (Dung, 2003; Brady & Cronin, 2001; Jones & Dent, 1994). The first sub-dimension, sociability, represented the number and type of people evident in the service setting as well as their behaviour (Aubert-Gamet & Cova, 1999). Milne and McDonald (1999) referred to sociability as positive social experiences that resulted from the social gratification of being with others who also enjoyed the same activity. Ko and Pastore (2005) indicated that the social experience focused on the overall after-consumption outcome instead of the inter-client interaction that occurred during the service delivery. Therefore, family members, friends and other people could be considered as important 72 social factors for hotel participants (Baldacchino, 1995). However, Dung (2003) suggested that little hotel research has paid attention to the sociability sub-dimension. The second sub-dimension, valence, implied customers? post-consumption assessments of whether the service outcome were acceptable or unacceptable (Ko & Pastore, 2005). Regardless of customer evaluations of any other aspects of the experience, valence mainly focused on the attributes dominating whether customers could or could not accept the service outcome (Brady & Cronin, 2001). An example is when customers may have a positive perception of the service quality, yet the negative valence of an outcome ultimately enabled them to form an unfavourable service experience (Brady & Cronin, 2001). Concisely, several researchers found that valence was a key determinant of service outcome (Martinez & Martinez, 2007; Ko & Pastore, 2005; Brady & Cronin, 2001). In terms of this sub-dimension, valence, therefore, Ko and Pastore (2005) illustrated that customers? post- consumptions of intangible evidence could be totalled and analysed. However, valence has not been studied in the hotel literature (Marmorstein, Sarel, & Lassar, 2001). The last sub-dimension, waiting time, referred to the amount of time that customers spend waiting in line for service (Katz, Larson, & Larson, 1991; Hornik, 1982). When customers entered a service system, they have, to some extent, expectations regarding an acceptable waiting time that contributed to satisfaction (Taylor, 1994). Therefore, the manager?s primary goal in a service organisation was to offer an acceptable level of customer satisfaction, namely, to provide customers with an acceptable period of waiting time (Hwang & Lambert, 2008). However, Hwang and Lambert (2008) found that there was no absolute level of acceptable customer satisfaction. In general, customer satisfaction mainly depended on the context of the service operation (Hwang & Lambert, 2008). Therefore, in order to achieve an acceptable level of customer satisfaction, Davis (1991) identified the proportion of customers who were highly satisfied, moderately satisfied, and dissatisfied with a given waiting time. In the service industry, waiting for service has generally been a frustrating experience for many customers (McDougall & Levesque, 1999). Several researchers indicated that longer waiting periods resulted in customers? negative perceptions of service quality (Hui & Tse 73 1996; Taylor, 1994; Katz et al., 1991). Thus, Katz et al. (1991) presented that speed of service has increasingly become a highly important service attribute. Geist (1984) explained that, in fact, some customers did not like waiting so much that they were willing to employ other people to wait for them. For these reasons, service managers should continually search for various ways to speed up their service, and must realise that longer waiting time would affect service evaluation negatively (Taylor, 1994). Houston, Bettencourt and Wenger (1998) incorporated waiting time into their analysis of service encounter quality, and found that waiting time was an important predictor of outcome quality. Several studies have provided empirical verification of the effect of waiting time on bank and airline customers (Taylor & Claxton, 1994; Katz et al., 1991). However, Hwang and Lambert (2008) stressed that the issue of waiting time has received little attention in the hospitality industry. Accordingly, Jones and Dent (1994) suggested that the issue of waiting time should be further investigated. Therefore, sociability, valence and waiting time as proposed by several researchers (Caro & García, 2008, 2007; Caro & Roemer, 2006; Ko & Pastore, 2005; Brady & Cronin, 2001; Jones & Dent, 1994) may be considered in forming customer perceptions of outcome quality in the hotel industry. 2.12 Chapter Summary This chapter presented the relevant literature regarding the conceptualisation of behavioural intentions, and the relationship of behavioural intentions to related constructs, including customer satisfaction, service quality, perceived value and image. The major changes in the conceptualisation and measurement of hotel service quality that primarily occurred as a result of the large amount of discussion and debate surrounding the SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV measures (Albacete-Sáez et al., 2007; Nadiri & Husssain, 2005; Wilkins, 2005; Ekinci, 1999; Mei et al., 1999; Cronin & Taylor, 1994; Saleh & Ryan, 1991) were outlined. The existing hierarchical models of service quality were also reviewed. Finally, this chapter ended with a 74 discussion of the primary dimensions of hotel service quality: interaction quality, physical environment quality and outcome quality and their relevant sub-dimensions. Although the issue of behavioural intentions has received considerable attention in different areas, research has not been able to combine identifiable variables into a model. There are several gaps in the literature that are outlined and discussed in detail in the following chapter. In addition, the next chapter also details the underlying theory that provides the foundation of the model for this study. The chapter also reviews relevant literature to build analytical support for the development of the hypotheses. 75 CHAPTER 3 CONCEPTUAL GAPS AND HYPOTHESES 3.1 Introduction This chapter discusses the conceptual gaps identified in the literature review discussed in Chapter Two. A conceptual model of customer behavioural intentions is presented, and 16 hypotheses proposed in this study are discussed. The proposed hypotheses address the following five research objectives: (1) To identify the dimensions of service quality as perceived by customers in the Taiwan hotel industry. (2) To determine if perceived value plays a moderating role between service quality and customer satisfaction as perceived by customers in the Taiwan hotel industry. (3) To examine the interrelationships between behavioural intentions and the other constructs related to behavioural intentions as perceived by customers in the Taiwan hotel industry. (4) To identify the least and most important service quality dimensions as perceived by customers in the Taiwan hotel industry. (5) To examine the effects of demographic factors on behavioural intentions and related constructs as perceived by customers in the Taiwan hotel industry. 3.2 Conceptual Gaps in the Literature A review of the literature on service quality and the constructs related to behavioural intentions in the hotel industry has identified five conceptual gaps. Each gap will be explicitly explained in the following paragraphs. The first gap relates to a lack of research in the Taiwan hotel industry with regard to customer perceptions of service quality. Several studies have measured hotel service quality 76 using SERVQUAL, SERVPERF, LODGQUAL, HOLSERV and LODGSERV (Wilkins et al., 2006a; Fernandez et al., 2005; Juwaheer, 2004; Lai et al., 1999; Gabbie & O?Neill, 1996; Getty & Thompson, 1994; Mei et al., 1999; Akan, 1995; Knutson et al., 1991). However, some researchers have criticised the measurement of service quality using these scales, which have not appropriately measured hotel service quality (Albacete-Sáez et al., 2007; Nadiri & Hussain, 2005; Wilkins, 2005; Keating & Harrington, 2002; Ekinci, 1999; Mei et al., 1999; Buttle, 1996). Chen (1998) indicated that customers tended to judge their perceptions of hotel service quality through their experiences instead of expectations because a service was inherently intangible and was not amenable to testing before purchase. In order to thoroughly measure customer perceptions of service quality, several researchers suggested that the service quality dimensions should be divided into various sub-dimensions using a hierarchical model (Brady & Cronin, 2001; Dabholkar et al., 1996; Cronin & Taylor, 1992; Carman, 1990; Grönroos, 1990, 1982; Parasuraman et al., 1988, 1985). However, Wilkins et al. (2007) indicated that the existing studies have not made an effort to identify the attributes or factors that were considered the primary and sub dimensions for the measurement of hotel service quality as a formative construct through a multi-level model. The second gap relates to a lack of studies pertaining to the moderating effect of perceived value on service quality and customer satisfaction in the Taiwan hotel industry. The effect of service quality on customer satisfaction was not only direct, but was also moderated by perceived value in the auditing, banking, insurance, technology and tourism sectors (Gil et al., 2008; Lin, 2007; Gallarza & Saura, 2006; Hellier et al., 2003; Caruana et al., 2000; Oh, 1999). Although perceived value was an important construct in service quality and customer satisfaction studies, Caruana et al. (2000) considered perceived value a rather neglected aspect in discussions about customer evaluations of services. Therefore, Oh (1999) recommended that researchers should put more effort into focusing on perceived value as a moderating variable between service quality and customer satisfaction in the hotel industry. The third gap relates to a lack of research with regard to the impact of influential factors in the Taiwan hotel industry. First, in terms of the relationship between service quality and 77 customer satisfaction, many hotel studies revealed that service quality positively affected customer satisfaction (Matzler, Renzl, & Rothenberger, 2006; Mey et al., 2006; Wilkins et al., 2006a; Su, 2004; Choi & Chu, 2001; Knutson, 1988). The literature noted that service quality was an antecedent of satisfaction (Caruana, 2002; Parasuraman et al., 1994; Teas, 1994; Anderson & Sullivan, 1993; Cronin & Taylor, 1992), and that service quality had a positive impact on satisfaction (Yuan & Jang, 2008; Yu, Wu, Chiao, & Tai, 2005; Lai, 2004; Wen, 2003). In general, Chen (1998) indicated that customers tended to judge their perceptions of hotel service quality through their experiences. Therefore, Jin (2005) suggested that it was necessary to examine the effects of service quality performances on customer satisfaction in the hotel industry. Secondly, the third gap also concentrates on the effect of service quality on perceived value and image, and the effects of perceived value and image on customer satisfaction. Caruana et al. (2000) emphasised that the perceived value construct has received little attention in the services marketing literature. Fornell, Johnson, Anderson, Cha and Bryant (1996) found that perceived value was influenced by service quality, and perceived value would then influence customer satisfaction. Alternatively, Grönroos (1983) found that service quality was the single most important determinant of image. Fornell (1992) and Bolton and Drew (1991a) identified that image was claimed to be an important factor in influencing customer satisfaction. In hotel studies, Hartline and Jones (1996) explained that perceived value was not only relative to service quality but also a direct consequence of perceived service quality. In addition, Choi and Chu (2001) said that perceived value was an influential factor in determining customers? overall satisfaction levels and their likelihood of returning to the same hotels. Kayaman and Arasli (2007) showed that hotel image stemmed from all of customers? consumption experiences, and service quality was a function of these consumption experiences. In addition, Kandampully & Suhartanto (2000) indicated that the inclusion of image and customer satisfaction in one model not only highlighted the importance of image, but also provided a more comprehensive understanding of how image influenced customer satisfaction in the hotel industry. However, few hotel studies have investigated whether perceived value and image played important roles between service 78 quality and customer satisfaction (Ryu et al., 2008; Claver et al., 2006; Kandampully & Suhartanto, 2000; Oh, 1999). Finally, the third gap also focuses on the effects of customer satisfaction and image on behavioural intentions in the Taiwan hotel industry. Although many studies have investigated the relationship between service quality, perceived value, image, customer satisfaction and behavioural intentions (Chen & Tsai, 2006; Tian-Cole et al., 2002; Chen, 2001; Kashyap & Bojanic, 2000; Woodside et al., 1989), Cronin et al. (2000) found that customer satisfaction significantly influenced behavioural intentions more than service quality and perceived value. However, Kang et al. (2004) noted that a small amount of research has focused on the effect of customer satisfaction on behavioural intentions in the hotel industry. Alternatively, in terms of image, this construct has been identified as having a direct effect on behavioural intentions in the manufacturing, technology, telecommunication, retailing, education, tourism, airline and restaurant sectors (Ryu et al., 2008; Castro et al., 2007; Chang, 2006; Cheng, 2006; Liu, 2007; Park et al., 2004; Nguyen & LeBlanc, 2001; da Costa et al., 2000). However, Hu et al. (2009) and Kandampully and Suhartanto (2000) indicated that the relationship between image and customer behaviour consequences has remained a matter of debate in the hotel industry. The fourth gap is a lack of research about the dimensions of hotel service quality that customers perceive to be more or less important in Taiwan. Brady and Cronin (2001) suggested that little empirical research attempted to identify the attributes or factors considered as sub-dimensions. This gap is important because hotel management can be more confident that it is measuring the aspects of hotels as perceived by customers. However, Callan and Bowman (2000) noted that marketing researchers have not identified the important and unimportant attributes or factors of service quality as perceived by hotel customers. The fifth gap relates to the effect of demographic characteristics on customer perceptions of service quality, satisfaction, value, image, behavioural intentions, and the dimensions of 79 service quality in the Taiwan hotel industry. Shergill and Sun (2004) found that different perceptions of hotel service quality existed between business customers and leisure customers in terms of different demographic characteristics, such as gender, age and ethnic background. The differences in gender, age and ethnic background suggested that hotel managers needed to become aware of customer perceptions of service quality, which always remained unstable across demographic characteristics (Shergill & Sun, 2004). However, Shergill and Sun (2004) recommended that more research should pay attention to the influences of different demographic characteristics on hotel service quality. In addition, those authors commented that few researchers focused on the influence of different demographic characteristics on customer perceptions of the dimensions of hotel service quality. Alternatively, Skogland and Siguaw (2004) revealed that little empirical research has been conducted on the influences of demographic characteristics on customer satisfaction in the hotel industry despite some research showing that demographic characteristics, such as gender, age, education, income and purpose of travel, could influence customer satisfaction with service quality. In addition, several researchers such as Al-Sabbahy and Ekinci (2004) and Kung and Tseng (1994) pointed out that there should be more research into the effects of demographic characteristics on perceived value and image in the hotel industry. Wang (2004) found that demographic characteristics affected customers? intentions and attitudes during the process of decision-making by the purchaser. Tan (2002) reported that behavioural intentions were influenced by demographic factors. However, Skogland and Siguaw (2004) emphasised that only a few studies have focused on the relationship between demographic characteristics and behavioural intentions in the hotel industry. In sum, several researchers suggested that hotel managers should pay more attention to demographic factors because demographic characteristics provided a biographical sketch, suggesting how gender, marital status, age, level of education, income, purpose of travel, ethnic background and occupation were likely to have an impact on behavioural intentions and the related constructs, service quality, customer satisfaction, perceived value and image 80 (Al Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Kung & Tseng, 1994; Snepenger & Milner, 1990). 3.3 Hypotheses Development A proposed multi-level model has been developed for this study based on an adaptation of Brady and Cronin?s (2001) service environment hierarchical model, and Dabholkar et al.?s (1996) multi-level model of retail service quality (see Figure 3.1). Information obtained from the literature review presented in Chapter Two and from information gained in the focus group interviews (see Section 4.7.1) has also been used to develop a conceptual multi- level model of service quality. According to Jarvis et al. (2003), the construct should be modelled as having formative dimensions if the dimensions are not expected to have the same antecedents and consequences, the same or similar content, and the necessary covariance with one another. This conceptual multi-level model of service quality as a formative construct (see Figure 3.1) suggests that hotel customers are expected to form their perceptions of each of three primary dimensions: interaction quality, physical environment quality and outcome quality, in order to form an overall service quality perception. The first primary dimension, interaction quality, comprises five sub-dimensions: attitude, behaviour, expertise, problem-solving, and customer interaction. The second primary dimension, physical environment quality, consists of eight sub-dimensions: décor, ambience, location, cleanliness, room quality, design, food and beverage, and security and safety. The last primary dimension, outcome quality, is composed of three sub-dimensions: sociability, valence, and waiting time. In this conceptual model, the first-order sub-dimensions were treated as formative indicators of the second- order primary dimensions whereas the second-order primary dimensions were regarded as formative indicators of the higher order service quality construct. Likewise, in this conceptual model, customer perceptions of service quality are assumed to influence value and image respectively. Furthermore, customer perceptions of service quality are also assumed to influence their overall satisfaction through the moderating variable of perceived value. Finally, customer perceptions of service quality, value and 81 image are presumed to influence customer satisfaction; moreover, customer satisfaction and image are then assumed to affect behavioural intentions. There are 16 formulated hypotheses; the first 14 hypotheses are formulated to test each path in the model. The 15th hypothesis tests the relative importance of the service quality dimensions and the 16th hypothesis is formulated to examine the differences in customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the primary and sub dimensions of service quality based on demographic factors. 82 3.3.1 Hypotheses Relating to Research Objective One Several researchers recommended that dimensional structures needed to be investigated for each research setting because customer satisfaction and service quality are culturally sensitive (Ueltschy & Krampf, 2001; Cronin & Taylor, 1994). Therefore, the proposed set of sub-dimensions in Figure 3.1 will be specifically identified for the Taiwan hotel sector through a review of the literature, focus group interviews and exploratory factor analysis. This approach adopted the recommendations proposed by Dabholkar et al. (1996), Powpaka (1996), and Rust and Oliver (1994). Grönroos (1992) and LeBlanc (1992) indicated the importance of interaction quality in the delivery of services and identified interaction as having the most significant effect on service quality perceptions. The quality of personal interactions with employees in a service organisation was seen as a critical component of service quality evaluation and has been viewed as an important factor that affected customers? selection of overnight accommodation (Knutson, 1988). Parasuraman et al. (1988, 1985) identified that customers? comprehensive assessments of service quality in a service organisation were made based on how they interacted with service providers. According to Ko and Pastore (2005), researchers realised that interaction quality was important in the production and consumption of a service. Therefore, interaction quality, which involved employee-customer relationships and customer-constructs related to employees (e.g., attitude, behaviour, expertise and problem- solving), has been identified as an important factor when customers assessed the service quality of a service organisation (Caro & García, 2008, 2007; Caro & Roemer, 2006; Brady & Cronin, 2001; Dabholkar et al., 1996; Parasuraman et al., 1991, 1988; Bitner et al., 1990). Several researchers noted that customer perceptions of service quality could be influenced by the attitudes and behaviours of other customers (Brady & Cronin, 2001; Lovelock, 1991; Baker, 1987). Ko and Pastore (2005) stressed that demonstrating an appropriate behaviour and attitude towards other customers may not only give an individual comfort but also an optimal learning experience. Moreover, Ojasalo (2003) showed that the interactions among customers during the service production process may affect customer perceptions of service 83 quality. However, Connolly (2000) proposed that the issue of customer interaction has received little attention in the hotel industry. Based on the existing literature, therefore, the proposed set of sub-dimensions that customers evaluated as components of interaction quality are stated as follows: (a) Attitude (Lu, Zhang, & Wang, 2009; Caro & García, 2008, 2007; Lam et al., 2007; Ko & Pastore, 2005; Sharpley & Forster, 2003; Brady & Cronin, 2001; Clemes et al., 2001; Ekinci & Riley, 2001; Ajzen, 1989, 1988; Fishbein & Ajzen, 1975); (b) Behaviour (Caro & García, 2008, 2007; Ko & Pastore, 2005; Tsaur & Lin, 2004; Brady & Cronin, 2001; Clemes et al., 2001; Ekinci & Riley, 2001; Chelladurai & Chang, 2000; Wong & Keung, 2000; Czepiel et al., 1985); (c) Expertise (Lu et al., 2009; Caro & García, 2008, 2007; Dagger et al., 2007; Caro & Roemer, 2006; Ko & Pastore, 2005; Kim & Cha, 2002; Brady & Cronin, 2001; Crosby et al., 1990; Czepiel et al., 1985); (d) Problem-Solving (Lu et al., 2009; Caro & García, 2008, 2007; Caro & Roemer, 2006; Dabholkar et al., 1996; Heinemann, 1996); and (e) Customer Interaction (Venkat, 2008; Wu, 2007a; Ko & Pastore, 2005; Gouthier & Schmid, 2003; Harris & Reynolds, 2003; Grönroos, 2000; Parker & Ward, 2000; Martin, 1996). These five sub-dimensions are expected to positively influence interaction quality. Accordingly, the first hypothesis is: H1: Higher perceptions of each interaction quality sub-dimension (H1a, H1b, H1c, H1d, and H1e) positively affect interaction quality. Several studies illustrated that physical environment quality has been identified as one of the important aspects during the service assessment (Caro & García, 2008, 2007; Clemes et al., 2007; Dagger et al., 2007; Caro & Roemer, 2006; Ko & Pastore, 2005; Brady & Cronin, 2001; Brady, 1997; Howat el al., 1996; Wakefield et al., 1996; McDonald, Sutton, & Miline, 1995; McDougall & Levesque, 1994; Rust & Oliver, 1994; Bitner, 1992, 1990; Wright et al., 1992; Baker, 1987). Ko and Pastore (2005) indicated that the quality of the service 84 environment was a constructed facility in which service delivery occurred, as opposed to the natural or social environment. Researchers have considered the influence of the physical or ?built? environment on customer service evaluations (Spangenberg, Crowley, & Henderson 1996; Wakefield et al., 1996; Baker, Grewal, & Parasuraman 1994; Bitner, 1992, 1990; Baker, 1987). Several studies pointed out that services were intrinsically intangible and often required customers to be present during the process (Bitner, 1992; Lovelock, 1981; Berry, 1980; Bateson, 1977). Bitner (1992) found that the surrounding environment had a significant influence on the perceptions of the overall quality of the service encounter. Tyra and Hilliard (2008) proposed that customers may evaluate services through tangible physical surroundings (e.g., décor, ambience and location) in the hotel industry. The existing literature revealed that the proposed sub-dimensions as components of physical environment quality were: (a) Décor (Wu & Weber, 2005; Lockyer, 2002; Brady & Cronin, 2001; Ekinci & Riley, 2001; Min & Min, 1997; Saleh & Ryan, 1992; Bitner, 1992, 1990); (b) Ambience (Kim & Moon, 2009; Tripathi & Siddiqui, 2008; Bonn & Joseph-Mathews, 2007; Dagger et al., 2007; Heide et al., 2007; Wall & Berry, 2007; Ko & Pastore, 2005; Brady & Cronin, 2001; Spector, 1999; Baker et al., 1994; Bitner, 1992; Baker, 1987); (c) Location (Chou et al., 2008; Urtasun & Gutiérrez, 2006; Pan, 2005, 2002; Ekinci & Riley, 2001; Chu & Choi, 2000; Lee et al., 2000; Lundberg et al., 1995; Pyo, Chang, & Chon, 1995); (d) Cleanliness (Gu & Ryan, 2008; Ryan & Qu, 2007; Lockyer, 2003, 2002; Ekinci & Riley, 2001; Callan & Bowman, 2000; Pettijohn, Pettijohn, & Luke, 1997; Weaver & Oh, 1993; Weaver & McCleary, 1991; Knutson, 1988); (e) Room Quality (Choi & Chu, 2001; Chu & Choi, 2000; Min & Min, 1997); (f) Design (Tripathi & Siddiqui, 2008; Bonn & Joseph-Mathews, 2007; Ko & Pastore, 2005; Brady & Cronin, 2001; Ransley & Ingram, 2001; Dabholkar et al., 1996; Bitner, 1992; West & Purvis, 1992; Baker, 1987); (g) Food & Beverage (Lee, 2007; Weng & Wang, 2006; Pan, 2005; Walsh, Enz, & Canina, 2004; Li, 2003; Cho & Wong, 1998; Gunderson et al., 1996; Nebel, Braunlich, & Zhang, 1994; Axler & Litrides, 1990); and 85 (h) Security & Safety (Clemes et al., 2008; Hilliard & Baloglu, 2008; McGoey, 2008; Enz & Taylor, 2002; Choi & Chu, 2001; Niblo & Jackson, 1999; Weimair & Fuchs, 1999; Murphy, 1988; Sheldon, 1983). Higher perceptions of these sub-dimensions are expected to positively influence physical environment quality. Therefore, the second hypothesis is: H2: Higher perceptions of each physical environment quality sub-dimension (H2a, H2b, H2c, H2d, H2e, H2f, H2g, and H2h) positively affect physical environment quality. Marketing researchers have agreed that the outcome of the service encounter affected customer perceptions of service quality (Carman, 2000; McDougall & Levesque, 1994; Rust & Oliver 1994; Grönroos, 1990, 1984). Powpaka (1996) noted that outcome quality was a determinant of customers? overall assessments of service quality. There was consensus in the literature that the technical outcome quality of service encounters influenced customer perceptions of service quality (Carman, 2000; Rust & Oliver, 1994; Grönroos, 1990, 1984, 1983, 1982). Several researchers indicated that the outcome quality component of service quality has been a determinant of the overall service quality assessed by customers, and the addition of outcome quality factored into the model or measurement scale could improve the predictive validity and explanatory power (Powpaka, 1996; Richard & Allaway, 1993; Mangold & Babakus, 1991). Based on the existing literature, therefore, the proposed sub- dimensions of outcome quality are as follows: (a) Sociability (Bonn & Joseph-Mathews, 2007; Clemes et al., 2007; Ko & Pastore, 2005; Dung, 2003; Brady & Cronin, 2001; Aubert-Gamet & Cova, 1999; Grove & Fisk, 1997; Baker et al., 1994); (b) Valence (Lu et al., 2009; Caro & García, 2008, 2007; Brady, Voorhees, Cronin, & Bourdeau, 2006; Ko & Pastore, 2005; Brady & Cronin, 2001; Parker & Dyer, 1976); and (c) Waiting time (Caro & García, 2008, 2007; Dagger et al., 2007; Caro & Roemer, 2006; Brady & Cronin, 2001; McDougall & Levesque, 1999; Baker & Cameron, 1996; Taylor, 1994; Jones & Dent, 1994; Katz et al., 1991; Clemmer & Schneider, 1989; Hornik, 1982). 86 These sub-dimensions are expected to positively affect outcome quality. Accordingly, the third hypothesis is: H3: Higher perceptions of each outcome quality sub-dimension (H3a, H3b, and H3c) positively affect outcome quality. Since services are inherently intangible and characterised by inseparability (Parasuraman et al., 1985; Lovelock, 1981; Berry, 1980; Shostack, 1977), interpersonal interactions occurring during service delivery often have the greatest influences on service quality perceptions (Hartline & Ferrell, 1996; Surprenant & Solomon, 1987; Grönroos, 1982). These interactions have been identified as the employee-customer interface (Hartline & Ferrell, 1996) and the key element in a service exchange (Czepiel, 1990). Surprenant and Solomon (1987) indicated that service quality was a more cumulative result of processes than outcomes. Several researchers indicated the importance of service interaction in the delivery of services and identified interaction as having the most significant effect on service quality perceptions (Bigné et al., 1996; LeBlanc, 1992; Grönroos, 1982). Based on the work of Brady & Cronin (2001), there was strong support for including an interaction dimension in the conceptualisation of perceived service quality. Mattsson (1994) indicated that very few models or theories have been developed for customer perceptions of interaction quality even though the core of most services was a person-to-person encounter. In the hotel industry, Lynch (1989) recommended that customers involved in the provision of services needed to be open to new and innovative ideas if service delivery was to improve. This relationship suggested that hotels should seek to create an organisational environment which both supported quality and enhanced communication between employees and customers (Garavan, 1997). However, Garavan (1997) and Hartline and Jones (1996) indicated that little hotel research has paid attention to the relationship between interaction quality and service quality. Therefore, the fourth hypothesis is: H4: Higher perceptions of the quality of service interactions positively affect overall service quality perceptions. 87 In a traditional service setting, the service environment associated with the physical ambience of the service encounter was termed as the servicescape (Fassnacht & Koese, 2006). Kotler (1973) claimed that a servicescape was an important tangible component of the service product that provided cues for customers. In addition, Kotler (1973) believed that the service encounter could create an immediate perceptual image in the minds of customers. According to Levitt (1981), customers always relied to some extent on both appearance and external impression; servicescapes, in this context, encompassed the appearance and impression of the service organisation?s overall products and services when they assessed the overall aspects of intangible products (e.g., services). In addition, based on Levitt?s explanation, Lin (2004) indicated that customers might use intangible aspects like appearances to make overall judgments and assessments since the hotel industry offered a high degree of intangible product levels like services. Therefore, Lin (2004) proposed that servicescapes have not only been identified as an important component of a customer?s impression formation, but also as an important source of evidence in the overall evaluation of the servicescape and the service organisation. Since services were inherently intangible and often required customers to be present during the service process, the surrounding environment had a significant influence on customers? overall perceptions of the quality of service encounter (Brady & Cronin, 2001; Bitner, 1992, 1990; Parasuraman et al., 1985; Lovelock, 1981; Shostack, 1977). In contrast to the tangible dimension of SERVQUAL, physical environment quality had a broader meaning (Parasuraman et al., 1988). In addition, several researchers found that the physical environment quality had been identified as one of the most important aspects in a service quality evaluation (Brady, 1997; Wakefield et al., 1996; McDougall & Levesque, 1994; Bitner, 1992, 1990). Parasuraman et al. (1985) indicated that the physical and security features should be included into the environmental considerations. Kim and Moon (2009) and Rys et al. (1987) later found that customers formed quality perceptions on the basis of their perceptions of the physical facilities in the restaurant industry. In a cross-sectional qualitative study, Crane and Clarke (1988) indicated that the service environment influenced customer perceptions of 88 service quality because customers in the fast-food, photograph developing, amusement parks and dry cleaning sectors have considered service to be an important factor in their service quality evaluations. In view of the prominence of the environment during service delivery, upgrading the hotel physical environment quality appeared to play an integral role in the formation of customer perceptions of service quality (de Burgos-Jiménez, Cano-Guillén, & Céspedes-Lorente, 2002; Nankervis, 1995). However, Reimer and Kuehn (2005) and Bitner (1990, 1986) proposed that only a little research focused on customer perceptions of physical environment quality in service settings. In addition, Faulk (2000) stressed that little hotel literature paid attention to the direct influence of customer perceptions of physical environment on service quality. Nadiri and Hussain (2005) suggested that managers should pay attention to the physical facilities of the hotel if they attempted to improve quality of services. As a result, the fifth hypothesis is: H5: Higher perceptions of the quality of the physical environment positively affect overall service quality perceptions. Many providers in the service sector perceived the outcome dimension of service quality to be more important than interaction quality and physical environment quality (Powpaka, 1996). However, Powpaka (1996) indicated that customers in the service sector could not frequently assess technical quality, namely, outcome quality. Baker and Lamb (1993) explained that customers must depend on the process dimension as an indicator of the quality of service that they have received. Swan and Combs (1976) found that customers became satisfied with the service when they realised that the outcome was unsatisfactory, if the process was satisfactory. Powpaka (1996) contended that the outcome dimension of service quality was required in every service industry. In addition, that author claimed that customers using outcome quality as their overall assessment of service quality relied on their ability to assess outcome quality of the service accurately and efficiently. Rust and Oliver (1994) referred to the service outcome as the ?service product,? and suggested that outcome quality was the relevant feature that customers assessed after service 89 delivery. McAlexander et al. (1994) identified the service outcome of the health care industry as ?technical care? and found that outcome quality was identified as a primary determinant of patients? perceptions of service quality. Similarly, de Ruyter and Wetzels (1998) included the service outcome in their health care investigation and found a direct link with service quality. Several marketing researchers proposed that the outcome quality dimension has been a significant determinant of customers? overall assessments of service quality (Fullerton, 2005; Powpaka, 1996; Baker & Lamb, 1993; Richard & Allaway, 1993; Mangold & Babakus, 1991; Grönroos, 1990, 1984, 1982; Lehtinen & Lehtinen, 1985). However, Chan, Wan and Sin (2007) and Powpaka (1996) proposed that an important question was whether the outcome quality dimension was significant in the service industry. In addition, several researchers proposed that little research has examined the impact of outcome on perceptions of service quality, despite the outcome being an important driver of service quality perceptions (Richard & Allaway, 1993; Mangold & Babakus, 1991; Grönroos, 1984). Therefore, in order to have a thorough understanding of whether outcome quality is expected to influence perceived hotel service quality, the sixth hypothesis is: H6: Higher perceptions of the quality of service outcomes positively affect overall service quality perceptions. 3.3.2 Hypothesis Relating to Research Objective Two Several researchers have paid attention to the link between service quality and customer satisfaction in different areas (Ladhari et al., 2008; Shi & Su, 2007; Johnston, 2004, 1995; Getty & Getty, 2003; Qu et al., 2000; Tsang & Qu, 2000; Oh, 1999; Fornell et al., 1996; Spreng & Mackoy, 1996; Parasuraman et al., 1994; Rust & Oliver, 1994). Caruana et al. (2000) believed that the influence of service quality on customer satisfaction was not only direct but also moderated by perceived value. Caruana et al. (2000) noted that understanding the relationship between the perceived value, service quality and customer satisfaction constructs could help service organisations to develop more effective management. Although the perceived value, service quality and customer satisfaction constructs were 90 usually subject to an individual?s subjectivity, they have played important roles in determining customer choices, their decisions to deepen or terminate a relationship and therefore customer retention and long-term profitability (Caruana et al., 2000). However, Caruana et al. (2000) found that perceived value might moderate the relationship between service quality and customer satisfaction but had received relatively little attention in the services marketing literature. In addition, a review of the literature provided support for a strong direct link between service quality and customer satisfaction but did not substantiate a direct causal link between perceived value and customer satisfaction (Caruana et al., 2000). Therefore, Caruana et al. (2000) proposed that there was a moderating influence of perceived value on the link between service quality and customer satisfaction. The hospitality literature has not provided many conceptual and empirical studies on the simultaneous relationship among perceived value, service quality and customer satisfaction (Oh & Parks, 1997). Oh (1999) proposed perceived value, together with service quality, may completely moderate the effects of perceptions of customer satisfaction in the hotel industry. In order to provide good service quality to achieve higher levels of satisfaction through customer perceptions of value, Oh (1999) suggested that hotel managers should pay more attention to the perceived value, service quality and customer satisfaction constructs. However, Oh (1999) demonstrated that the moderating role of perceived value between service quality and customer satisfaction has received little attention in hotel studies. As a result, hypothesis seven tests if perceived value plays a moderating role between service quality and customer satisfaction: H7: Perceived value moderates the relationship between service quality and customer satisfaction. 3.3.3 Hypotheses Relating to Research Objective Three Many researchers have provided some support for a link between service quality and customer satisfaction (Clemes et al., 2008, 2007; Gallarza & Saura, 2006; Mey et al., 2006; Gilbert & Horsnell, 1998; Bitner & Hubbert, 1994; Cronin & Taylor, 1994, 1992; Rust & Oliver, 1994; Oliver, 1993), but perceived value has been a rather neglected aspect in the 91 discussion of customer evaluations of service quality (Caruana et al., 2000). Gallarza and Saura (2006) demonstrated that the link between service quality and perceived value has generated a wide consensus in the literature, and that service quality was an input to perceived value. Rust and Oliver (1994) identified that perceived value, like service quality, had an encounter-specific input to customer satisfaction. Several researchers have demonstrated that perceived value has been viewed as the best and most complete antecedent of customer satisfaction (Chen, 2007b; Park, 2007; Cronin et al., 2000; McDougall & Levesque, 2000; Oliver, 1997; Parasuraman, 1997; Dodds et al., 1991). According to Shonk (2006), an extensive body of research recommended that customer satisfaction with a service was influenced in part by value. Gallarza and Saura (2006) showed that there appeared to be a natural chain between service quality, perceived value and customer satisfaction. Zeithaml (1988) indicated that a business organisation could increase the overall perceived value of its service by increasing customers? overall perceptions of service quality. Several researchers have found that service quality had a direct influence on perceived value (Chen, 2007b; Wei, 2004; Petrick & Backman, 2002; Cronin et al., 2000; Tam, 2000; Zeithaml, 1988). Hellier et al. (2003) proposed that the influence of perceived value on customer satisfaction was supported by a value disconfirmation experience. Customer perceptions of value would be altered after their purchase if there was an unexpected increase or decrease in the cost incurred or benefit received (Hellier et al., 2003). Several researchers noted that customer satisfaction or dissatisfaction affected their subsequent expectations of value, purchase behaviour and overall levels of satisfaction (Voss, Parasuraman, & Grewal, 1998; Woodruff, 1997). Hellier et al. (2003) indicated that customers? overall perceptions of service value positively influenced overall service satisfaction. However, a lot of debate regarding the relationship between perceived value and customer satisfaction has remained open in the services marketing literature (Hu et al., 2009). In the hotel industry, Oh (1999) noted that customers might perceive greater value for money when experiencing a high service quality. Increased value perceptions then resulted in increased customer satisfaction (Oh, 1999). Wang and Lo (2002) claimed that perceived 92 value, service quality and customer satisfaction have become the most important factors of business success for service providers. However, Oh (1999) claimed that the relationship between perceived value, service quality and customer satisfaction in hotel studies should be further investigated. Hypotheses Eight and Nine are proposed for perceived value?s relationship with service quality and customer satisfaction: H8: Higher perceptions of overall service quality have a positive impact on perceived value. H9: Higher perceptions of value have a positive impact on customer satisfaction. The marketing literature revealed that research on the concept of image has mostly been conducted in goods-producing organisations and retail stores (Donovan & Rossiter, 1982). Grönroos (1984) argued that image was very important to service organisations, and was determined largely by customers? overall assessments of the services that they received. Nguyen and LeBlanc (1998) showed that understanding image helped management to improve the competitive performance of the organisation. Image management has been used to develop a deeper understanding of the process by which image was formed and customers? beliefs and attitudes with regard to the product or service offering in an organisation (Nguyen & LeBlanc, 1998). Park et al. (2006) and Lapierre (1998) found that there was a strong relationship between service quality and image. Baker et al. (1994) said that service quality was posited to be an antecedent of image. Several services marketing studies have identified image as an important factor in customers? overall evaluations of the service and the organisation (Park et al., 2006; Gummesson & Grönroos, 1988). Aydin and Ozer (2005) and Schlosser (1998) indicated that customer perceptions of service quality directly affected image. Normann (1991) showed that customers? experiences with services were the most important factors in influencing the development of image. Nguyen and LeBlanc (1998) found that service quality influenced the overall image of the service organisation. In general, customer perceptions of low quality of merchandise and service may be transferred into the image of organisation (Chebat et al., 2006). 93 Grönroos (1983) found that service quality was the single most important determinant of customer satisfaction and image. Kandampully and Suhartanto (2000) explained that customers? experiences with the products and services were the most important factors in influencing customers with regard to image. Alternatively, Bolton and Drew (1991a) proposed that image was a function of the cumulative effect of customer satisfaction or dissatisfaction. In addition, Urquhart (1996) indicated that image had an effect on customer perceptions, customer preferences, buying patterns and satisfaction levels. Research by Cronin and Taylor (1992) validated prior studies indicating that service quality was an antecedent of customer satisfaction (Woodside et al., 1989; Parasuraman et al., 1988, 1985). If customer evaluations of past service quality are high, they will tend to evaluate the most recent service encounter as satisfactory. If the level of service quality increases, there should be a corresponding impact on the image of an organisation (Grönroos, 1990). Therefore, service quality affected image and image then had a direct influence on customer satisfaction (Lucio, Magdalena, Angal, & Javier, 2006; Back, 2005; Yu, Sun, & Wang, 2005; Koo, 2003; Baker et al., 1994). In hotel studies, Hu et al. (2009) and Kandampully and Hu (2007) found that service quality positively affected image. Mazanec (1995) found image to be positively associated with customer satisfaction and customer preference (a dimension of customer loyalty). Mazanec (1995) illustrated that a desirable image contributed to customer satisfaction and customer preference whereas an undesirable image might result in customer dissatisfaction. In addition, some studies identified image as an important factor in the overall evaluation of the service and the business organisation (Gummesson & Grönroos, 1988), but it remained unclear whether this relationship was mediated by customers? overall satisfaction and perceived service quality (Bloemer et al., 1998). However, several researchers indicated that limited research in the hotel industry has focused on the relationship between image, service quality and customer satisfaction (Ryu et al., 2008; Claver et al., 2006). Therefore, Hypotheses 10 and 11, proposed for image?s relationship with service quality and customer satisfaction, are: 94 H10: Higher perceptions of service quality have a positive influence on image. H11: Higher perceptions of image have a positive influence on the customer?s overall satisfaction. According to several researchers (Faullant, Matzler, & Fuller, 2008; Ryu et al., 2008), image was a significant predictor of behavioural intentions. In the tourism industry, the destination image affected tourists? behavioural intentions and then tourists? behaviour was partly conditioned by the destination image (Chi & Qu, 2008). Chi and Qu (2008) said that image influenced tourists in the process of choosing a destination, the subsequent evaluation of the trip, and in their future intentions. Therefore, Chi and Qu (2008) contended that a more favourable image might enable tourists to have a higher likelihood of revisiting or returning to the same area. In the airline industry, Park et al. (2004) found that there was a strong relationship between image and behavioural intentions. Moreover, Clemes et al. (2008) and Park et al. (2004) also found that passenger satisfaction had a positive influence on behavioural intentions, suggesting that satisfied passengers would form favourable images of the airline. This favourable image then resulted in passengers travelling on the airline again and recommending the airline to others (Park et al., 2004). Moore and Benbasat (1991) depicted image as referring to the degree to which an innovation was perceived to increase an individual?s status in a social system. Moore and Benbasat (1991) showed that people often responded to social normative influences to establish or to maintain a favourable image within a reference group. Thus, Liu (2007) and Moore and Benbasat (1991) proposed that a positive perceived image, as a result of using technology, generally contributed to a favourable behavioural intention. Image in the service sector has been considered as customers? first impression of the organisation and image may have a great influence on their purchase intentions when they have never visited a service organisation, an attraction, a particular place, or even a country (Berry, 2000; Bitner, 1992). Several studies found that image positively affected behavioural intentions in different service industries (Kaplanidou & Vogt, 2007; Chang, 2006; Park et al., 95 2006; Johnson et al., 2001; Nguyen & LeBlanc, 1998; Heung et al., 1996; Osman, 1993). However, other researchers claimed that only a few hotel studies have paid attention to the effect of image on behavioural intentions (Hu et al., 2009; Kim & Kim, 2005; Kandampully & Suhartanto, 2003, 2000; Suhartanto, 1998). Based on the literature, therefore, Hypothesis 12 is proposed for image?s relationship with behavioural intentions: H12: Higher perceptions of the hotel?s image positively affect the intentions to visit the hotel in the future. Anderson and Fornell (1994) suggested that customer satisfaction was a post-consumption experience which compared perceived quality with expected quality, whereas service quality referred to an overall evaluation of an organisation?s service delivery system. Dagger et al. (2007), Zeithaml et al. (1996) and Parasuraman et al. (1988, 1985) explained that, in general, customer satisfaction depended on how customers perceived service quality, namely, higher levels of perceived service quality contributed to increased levels of customer satisfaction. However, the exact relationship between service quality and customer satisfaction has been considered a complicated issue, characterised by debate regarding the distinction between the two constructs and the causal direction of their relationship (Brady, Cronin, & Brand, 2002). In terms of the relationship among service quality, customer satisfaction and behavioural intentions, Gotlieb et al. (1994) found that service quality contributed to customer satisfaction and satisfaction then resulted in behavioural intentions. Brady et al. (2001) explained that a cognitively oriented service quality evaluation contributed to the primary emotive assessment of customer satisfaction, which then drove behavioural intentions. Several researchers have reported an empirical association between customer satisfaction and such service outcomes as loyalty, positive word-of-mouth and purchase intentions (Anderson & Sullivan, 1993; Oliver & Swan, 1989). Previous research on customer satisfaction-behavioural consequences maintained that customer satisfaction directly influenced behavioural intentions (Seiders, Voss, Grewal, & Godfrey, 2005; Chang, 2003; 96 Cronin et al., 2000; Jones, Mothersbaugh, & Beatty, 2000; Anderson & Sullivan, 1993; Bitner, 1990; Woodside et al., 1989). Service-related research based on interpersonal interaction showed that customer satisfaction positively influenced behavioural intentions (Clemes et al., 2008; Chen, 2007b; Lin & Hsieh, 2007; Wang, Chang, & Hsiu, 2006; Shonk, 2006; González & Brea, 2005; Athanassopoulos, Gounaris, & Stathakopoulos, 2001; Cronin et al., 2000; McDougall & Levesque, 2000; Tam, 2000; Dabholkar & Thorpe, 1994; Anderson & Sullivan, 1993; Fornell, 1992; Halstead & Page, 1992; Bolton & Drew, 1991a; Bitner, 1990; Zeithaml et al., 1996; Woodside et al., 1989). However, Magnus and Niclas (2003) and Bigné, Sánchez and Sánchez (2001) argued that the exact influence of customer satisfaction on behavioural intentions has not been clearly identified. Several researchers found that service quality was an antecedent of customer satisfaction, and customer satisfaction exerted a stronger influence on favourable behavioural intentions to stay at a hotel than service quality (Kang et al., 2004; Choi & Chu, 2001). Getty and Thompson (1994) studied the relationships between the quality of lodging and customer satisfaction, and the resulting effect on customers? intentions to recommend the lodging to prospective customers. In addition, those authors found that customers? intentions to recommend were identified as a function of the perception of both customer satisfaction and service quality through their lodging experiences. Hence, Kandampully and Suhartanto (2003, 2000) and Suhartanto (1998) concluded that there was a positive link between customer satisfaction and behavioural intentions in the hotel industry. In addition, Chou (2004) and Kang et al. (2004) proposed that customer satisfaction was a powerful factor that influenced behavioural intentions in the hotel industry. However, Kang et al. (2004) indicated that only limited hotel studies focused on the exact relationship between customer satisfaction and behavioural intentions. In addition, Jin (2005) and Chen (1998) suggested that it was necessary to further test the effect of service quality performances on customer satisfaction because services were not amenable to testing before purchase in the hospitality industry. As a consequence, Hypotheses 13 and 14 are proposed for the interrelationship among service quality, customer satisfaction and behavioural intentions: 97 H13: Higher perceptions of overall hotel service quality positively affect customers? overall satisfaction. H14: Higher perceptions of customer satisfaction positively affect the intentions to visit the hotel in the future. 3.3.4 Hypotheses Relating to Research Objective Four The interaction quality, physical environment quality and outcome quality dimensions have been found to affect customer perceptions of service quality in the education, fast-food, photograph developing, amusement parks, dry cleaning, tourism, technology, transport and recreational sports sectors (Caro & García, 2008, 2007; Clemes et al., 2007; Caro & Roemer, 2006; Kao, 2007; Fassnacht & Koese, 2006; Collins, 2005; Ko & Pastore, 2005; Brady & Cronin, 2001). However, Ganesan-Lim and Bennett (2005) emphasised that many marketing researchers have not identified the important dimensions of service quality. Although several studies measured customers? experiences in the hotel industry (Shi & Su, 2007; Choi & Chu, 2001), the comparative importance of the service quality dimensions identified in these studies was ambiguous. Clemes et al. (2008) recommended that more studies should focus on the most and least important dimensions of service quality. Therefore, in order to gain an understanding of customer perceptions of the important and unimportant dimensions of hotel service quality, the following hypothesis is proposed: H15: Hotel customers vary in their perceptions of the importance of (a) each of the primary dimensions, and (b) each of the sub-dimensions. 3.3.5 Hypotheses Relating to Research Objective Five Peterson and Wilson (1992) indicated that understanding what determined customer satisfaction and knowing what variables or factors related to customer satisfaction were a prerequisite to effectively interpret and utilise customer satisfaction ratings. In addition, those authors demonstrated that customers? demographic characteristics were likely to affect their level of satisfaction towards the services they received. Several studies demonstrated that demographic factors, such as gender, income, age, education, ethnic background, and 98 purpose of travel, influenced customer satisfaction in the airline, education, tourism, and health care sectors (Clemes et al., 2008, 2007; Jose & Alfons, 2007; Kao, 2007; Chong, 2004; Oyewole, 2001; Young, Meterko, & Desai, 2000; Snepenger & Milner, 1990). Skogland and Siguaw (2004) recommended that hotel managers should not neglect demographic factors despite little research having been conducted on the influences of demographic characteristics on customer satisfaction. Some studies identified the effects of demographic characteristics on service quality in the airline, banking, education and retailing sectors (Clemes et al., 2008, 2007; Surovitskikh & Lubbe, 2008; Kao, 2007; Siu & Cheung, 2001; Stafford, 1996). However, Shergill and Sun (2004) emphasised that only a few hotel studies have paid attention to the effects of demographic characteristics on service quality. Several researchers have indicated that demographic differences existed in the relationship between the perceived value and image constructs in the retailing, recreational sports, telecommunication, transport, tourism, and technology sectors (Hung, Chen, & Lin, 2008; Lin, 2008a; Snipes & Ingram, 2007; Chao, 2006; Beerli & Marti?n, 2004; Wu, 2004). However, several researchers noted that little attention has been paid to the influences of demographic characteristics on perceived value and image in the hotel industry (Al-Sabbahy & Ekinci, 2004; Kung & Tseng, 1994). Lewis (1981) noted that demographic factors have been used in the marketing as a basis for understanding customer characteristics and behaviour. Wu (2003) showed that personal, social, cultural, and psychological characteristics strongly influenced customer purchasing intentions. Wu (2003) found that behavioural processes included the motivational, perceptual, learning, attitude formation, and decision-making tools that customers used to complete the activities satisfying their needs and wants. Unlike background characteristics, Wu (2003) found behavioural processes could be influenced by individuals? different environments since they were applied in specific occasions. In addition, that author indicated that the background characteristics of customers were the influential elements of the behavioural processes. 99 Wang (2004) found that demographic characteristics affected customers? intentions and attitudes during the decision-making process of purchase. Tan (2002) reported that behavioural intentions were greatly influenced by demographic characteristics. Wu (2003) proposed that external influences on behavioural intentions included demographic, economic, social, situational and technological factors. However, Skogland and Siguaw (2004) claimed that there has been limited research in the hotel industry into the relationship between demographic characteristics and behavioural intentions. Several researchers proposed that hotel managers should pay more attention to demographic factors because demographic characteristics provided a biographical sketch suggesting how age, gender and income were likely to influence behavioural intentions and related constructs (Al-Sabbahy & Ekinci, 2004; Kim & Kim, 2004; Shergill & Sun, 2004; Skogland & Siguaw, 2004; Kung & Tseng, 1994). In order to identify whether demographic characteristics influence behavioural intentions and related constructs, customer satisfaction, service quality, perceived value, and image in the hotel industry, Hypothesis 16a is proposed as: H16a: Favourable future behavioural intentions and related constructs differ based on customer demographic characteristics (gender, marital status, age, level of education, income, purpose of travel, ethnic background, and occupation). Several researchers proposed that customer demographic characteristics provided significant differences among customer perceptions of the dimensions of service quality (Clemes et al., 2008, 2007, 2001; Ganesan-Lim & Bennett, 2005; Kelley & Turley, 2001; Oyewole, 2001; Gagliano & Hathcote, 1994). Stafford (1996) maintained that it was critical to determine which dimensions of service quality were more important to different customers. Webster (1989) studied demographic characteristics and their relationship with service quality perceptions and found customer demographic characteristics to be highly associated with service quality in professional services. In contrast, Gagliano and Hathcote (1994) found that demographic characteristics played an important role in determining perceived service quality for non-professional services. 100 In terms of the hotel service dimensions, such as the employees? courtesy and friendliness, housekeeping and maintenance, baggage handling, design, atmosphere, and room cleanliness, Shergill and Sun (2004) found that different perceptions existed between business and leisure customers towards a hotel service according to different demographic characteristics. However, Shergill and Sun (2004) indicated that not all of the customer demographic characteristics had a significant effect on these dimensions since the dimensions perceived by leisure and business customers were different. Therefore, Shergill and Sun (2004) explained that determining the underlying reasons for the different customer perceptions and investigating the implications for management were useful in the hotel industry. Shergill and Sun (2004) suggested that hotel managers needed to be aware of customer perceptions of the dimensions of service quality because they were not stable across demographic characteristics. Min et al. (2002) emphasised that hotel management should consider a multitude of attributes composed of customer demographic profiles and how different demographic characteristics affected customer perceptions of the dimensions of service quality in order to enable customers to maintain favourable behavioural intentions. However, Shergill and Sun (2004) found that few researchers have focused on the influence of different demographic characteristics on customer perceptions of the dimensions of hotel service quality. Shergill and Sun (2004) suggested that it was necessary to know how demographic characteristics influenced hotel customer perceptions of the dimensions of service quality. As a result, two hypotheses are proposed: H16b: Hotel customer perceptions of the primary dimensions of service quality differ based on customer demographic characteristics (gender, marital status, age, level of education, income, purpose of travel, ethnic background, and occupation). H16c: Hotel customer perceptions of the sub-dimensions of service quality differ based on customer demographic characteristics (gender, marital status, age, level of education, income, purpose of travel, ethnic background, and occupation). 101 3.4 Chapter Summary This chapter has identified five gaps in the literature: (1) a lack of research on customer perceptions of service quality in the Taiwan hotel industry; (2) a lack of research on the moderating effect of perceived value on service quality and customer satisfaction in the Taiwan hotel industry; (3) very little research on the impact of influential factors in the Taiwan hotel industry; (4) very few studies paying attention to the dimensions of service quality that customers perceive to be more or less important in the Taiwan hotel industry; and (5) very little research focusing on the effect of demographic characteristics on customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the primary and sub dimensions of service quality in the Taiwan hotel industry. A conceptual multi-level model has been developed based on Brady and Cronin?s (2001) service environment hierarchical model, and Dabholkar et al.?s (1996) multi-level model of retail service quality. 16 hypotheses were proposed to address the five research objectives stated in Chapter One. Chapter Four discusses the research methods to test the 16 hypotheses developed in Chapter Three. 102 CHAPTER 4 RESEARCH METHODOLOGY 4.1 Introduction This chapter outlines the research framework and methodology used to collect the data to test the 16 hypotheses developed in Section 3.3 and satisfy the five research objectives stated in Section 3.1. The research plan includes sample derivation, sample size, sampling method, data collection, questionnaire design, and the data analysis techniques used in this study. 4.2 Research Design Frazer and Lawley (2000) referred to the research design as a blueprint or plan of the way the information satisfying the research objective. Cooper and Schindler (2006) indicated that selecting a design may be difficult because of the availability of a large variety of methods, techniques, procedures, protocols and sampling plans. William (2006) demonstrated that a research design provided the glue that united the research project. According to William (2006), a design was not only used to structure the research but also to show how all of the major parts of the research project (e.g., the samples or groups, measures, treatments or programmes, and methods of assignment) worked together to address the central research questions or objectives. In order to ensure that the research design was consistent with the research objectives, the first step was taken selecting a five-star hotel in Taiwan as a sample to examine the factors affecting customer behavioural intentions. Secondly, focus group interviews were used to develop a suitable questionnaire. Thirdly, a self-administered questionnaire was considered an appropriate approach to collecting the data for this research. Finally, pre-testing of the questionnaire was conducted before the questionnaire was distributed to the sample respondents. 103 4.3 Sample Derivation The lack of research relating to hotel customer behavioural intentions in Taiwan made it necessary to collect primary data to test the 16 hypotheses and to meet the research objectives of this study. In this study, therefore, hotel customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the primary and sub dimensions of service quality were specifically examined. The data were collected using the convenience sampling method at a five-star hotel in Kaohsiung City of Taiwan, from 15 February to 15 April, 2008. According to Vine (1981), a five-star hotel is synonymous with luxury and any ?starred? establishment is generally highly regarded by hotel customers. In addition, several studies have shown that hotels categorised as five-star provided excellent and extensive facilities, a high quality of service, as well as being able to satisfy customer demands (Su & Sun, 2007; Wilkins et al., 2007; Wu, 2007b; AAA, 2003-2004; Callan, 1995; Akan, 1995; Clavey, 1992; Howe, 1986). Su and Sun (2007) demonstrated that the criteria for evaluating four- and five- star hotels were based on the total of the scores of service quality and the hotel facilities in Taiwan. A four- star international tourist hotel would score between 601 and 750 points, and a hotel could be rated as a five-star international tourist hotel if the score was over 750 points (see Appendix 2). Customers aged 18 and over were invited to be surveyed at the five-star hotel in Taiwan. Hotel customers under 18 years were excluded from the sample because it was assumed that they would not have sufficient hotel experiences to respond to all the questions in the questionnaire. 4.4 Sample Size Kumar (2005) stressed that the sample size was important to the hypothesis test or the association being established. Therefore, in order to understand the interrelationships between behavioural intentions, service quality, customer satisfaction, perceived value, 104 image and demographic factors, a sample of customers at a five-star hotel in Kaohsiung City of Taiwan was used in this study. Sekaran (2003) defined the sample size as the actual number of subjects chosen as a sample to represent the population. Alternatively, Kumar (1996) referred to the sample size as the number of students, families or electors from whom researchers obtain the required information. Though most researchers generally accepted that larger samples would be more representative than smaller ones, the advantages of larger samples could be outweighed by their increased cost (Ruane, 2005). Ruane (2005) claimed that researchers needed to employ sampling ratios that established acceptable sample sizes for a variety of population sizes. Generally, the larger the population size, the smaller the sampling ratio needed to obtain a representative sample (Ruane, 2005). Alternatively, Saunders, Lewis and Thornhill (2007) proposed the larger the sample size, the lower the likely error in generalising to the population. Hair, Anderson, Tathan and Black (1998) noted that a minimum sample size of 200 was required by statistical analysis and Schumacker and Lomax (1996) found that many researchers used a sample size from 250 to 500 respondents. In this study, the surveyed hotel had 298 rooms and an average occupancy rate of 80% on a daily basis. Consequently, the total population at this hotel during 2007 was approximately 87,016 (0.8 × 298 rooms × 365 days). The sample size was determined by adopting the Mendenhall, Beaver and Beaver (1993) formula. Applying Mendenhall et al.?s (1993) formula gave 382 as the minimum acceptable number of completed questionnaires from hotel customers (see Appendix 3). 4.5 Sampling Method According to Cooper and Schindler (2006), a sample has been identified as a part of the target population and researchers should carefully select the sample to represent the population of the study. In order to achieve representativeness, sampling procedures should follow certain standards and methodological principles (Sarantakos, 2005). In general, sampling procedures vary considerably. Sarantakos (2005) indicated that a sample could be constructed through self-selection or, as was common, could be determined by researchers. 105 Sekaran (2003) claimed that, theoretically, the sampling procedures conducted were mainly based on probability standards (random or probability samples) and non-probability standards (non-probability samples). Convenience sampling inherently is a non-probability sample method. Zikmund (2003) demonstrated that convenience sampling was referred to as sampling by obtaining units or people who were most conveniently available. Cooper and Schindler (2006) noted that convenience sampling was element selection based on accessibility. Zikmund (2003) illustrated that researchers generally adopted convenience sampling to obtain a large number of completed questionnaires quickly and economically. Kumar (2005) showed that convenience sampling was common with market researchers and newspaper reporters. Starmass (2007) and Cooper and Emory (1995) indicated that the obvious advantages of adopting convenience sampling were low cost and saved time. Although useful applications of the convenience sampling technique were somewhat limited, the sample could deliver accurate results when the population was homogeneous (Starmass, 2007). Lunsford and Lunsford (1995) indicated that subjects were selected because of their convenient accessibility to researchers through the convenience sampling. These subjects were simply chosen because they were the easiest to obtain for the study. In general, a convenience sample was obtained by simply stopping people in the street who were willing to respond to the questions of the survey (Starmass, 2007). Lunsford and Lunsford (1995) demonstrated that the convenience sampling was a easy, fast and usually the least expensive and troublesome method. In order to have a convenient access to the subjects, the convenience sampling method was used in this study. Hotel customers who were willing to fill out the questionnaire distributed by the hotel front-desk employees were invited to participate in this study. 4.6 Data Collection This research used data from a self-administered questionnaire using structured questions. The researcher required hotel front-desk employees at the surveyed five-star hotel to distribute the questionnaires with a personalised cover letter to the customers. The cover letter explained the purpose of the study, the approximate length of time it would take to 106 complete the questionnaire, an assurance about the confidentiality of the response, age eligibility (18 years or older), and the method of contacting the researcher and his supervisors. After pre-testing the procedures and making some minor adjustments to the questionnaire, instructions and cover letter, the completed questionnaires were collected between 15 February and 15 April, 2008, at the surveyed five-star hotel in Kaohsiung City of Taiwan. The major responsibilities of the hotel front-desk employees can be summarised as follows: copies of the questionnaire were left at the front-desk reception and customers were invited to take a copy either after they had completed their check-in process or before they had finished their check-out. A clear statement was made to each participant that this was an entirely voluntary and anonymous survey for people aged 18 years and over. Customers were encouraged to participate by suggesting that their responses to the survey would assist hotel operators to understand customers? real perceptions of service quality, and provide new and better services. Participants were told that they could decline to participate for any reasons. In order to ensure confidentiality and anonymity, each respondent was required to return the completed questionnaire to the drop box at the hotel front-desk reception. In order that foreign and Taiwanese customers could understand the content of the survey, the researcher provided them with questionnaire versions in both English and Chinese. Therefore, the questionnaire was written originally in English and later translated into Chinese by the researcher. 4.7 Questionnaire Design To analyse customer perceptions of hotel service quality in Taiwan, a specific questionnaire was designed. The construct operationalisation, questionnaire format, variable measurement and pre-testing procedures are discussed in the following sections. 4.7.1 Construct Operationalisation The extensive review of the literature presented in Chapter Two identified the proposed primary and sub dimensions of service quality, as well as the important factors related to 107 customer perceptions of behavioural intentions, satisfaction, service quality, value and image in the hotel industry. Focus group interviews were conducted to provide additional insights into the proposed dimensions and develop a questionnaire specifically for customers at the surveyed five-star hotel in Kaohsiung City of Taiwan. Krueger (1998) reported that a focus group study was frequently used to design a questionnaire for a quantitative survey. A focus group is a carefully planned discussion designed to obtain perceptions of a specific area of interest in a permissive and nonthreatening environment (Krueger, 1998). Participants can share their ideas and perceptions based on the researcher?s questions if the focus group discussion is conducted in a relaxed, comfortable and enjoyable way (Krueger, 1998). In addition, participants should not influence each other when they respond to ideas and comments in the discussion during the focus group interview. Finally, the moderator should not influence the participants? responses. Otherwise, these participants? responses may contribute to a serious bias (Krueger, 1998). According to Edmunds (1999), the focus group discussions include general issues on a topic, and respondents? comments should aid researchers to identify relevant issues that may otherwise be left out of a survey. In addition, quantitative methods used in the statistical analysis frequently resulted from the hypotheses generated in focus group discussions (Edmunds, 1999). Lu et al. (2009) and Cox, Higginbotham and Burton (1976) proposed that the focus group was an effective qualitative technique for use in the marketing and management research. Calder (1977) demonstrated that focus group techniques were widely applied in the qualitative marketing research. Focus groups are commonly composed of people guided by group moderators using an open and in-depth discussion (Calder, 1977). In general, the moderator?s objective is to focus the discussion on relevant subject areas in a nondirective manner. Cox et al. (1976) noted that focus groups could be used to develop hypotheses in the planning or qualitative stage of marketing research. In addition, focus groups provide an in-depth basis for the development of additional research, and they may be seen as an 108 approach to generating new and fresh ideas for such things as new products and services, advertising themes, and packaging evaluations (Cox et al., 1976). Several researchers noted that a full focus group typically comprised eight to ten participants who were unfamiliar with one another (Greenbaum, 1998; Calder, 1977; Cox et al., 1976). Greenbaum (1998) proposed that a mini focus group could be composed of four to six participants. In addition, that author indicated that some researchers preferred to conduct mini groups instead of full groups because they could gain in-depth information from a smaller group. In order to obtain in-depth information, the researcher conducted three mini focus groups in this study. Edmunds (1999) noted that it was generally rare to use a single focus group for a study. Each group comprised six participants who had stayed in Taiwanese five-star hotels. Before conducting the focus group interviews, the researcher telephoned people to confirm whether they were aged 18 or over in order that each group member was mature enough to understand the content of the interview questions. Moreover, the researcher further inquired whether people had previously stayed in Taiwanese five-star hotels. If they had, the researcher would further explain the topic of focus group interviews and then ask if they were willing to participate in focus group discussions (see Appendix 4). Before conducting the focus groups, the researcher mailed or e-mailed invitation letters to group members who agreed to participate in the interviews (see Appendix 5). In the invitation letter, a clear statement was made to each member that the focus group interviews would last for two hours. This letter also explained that each member?s response to the questions would be anonymous. During the interview process, the group members were encouraged to list all of the factors that might comprise their perceptions of the interaction quality, physical environment quality and outcome quality dimensions (see Appendix 6). Finally, the researcher summarised the discussion, drew inferences and then categorised what was said during the focus group discussions (see Appendix 7). After the focus group interviews were completed, the researcher identified two sub-dimensions of physical environment quality that were not identified in the literature review (see Section 2.11): room quality and food and beverage. 109 4.7.1.1 Summary of Construct Operationlisation Before constructing the questionnaire, a set of general service quality dimensions specific to the hotel industry was identified based on the literature review and the focus group interviews. Table 4.1 provides a summary of constructs and a synopsis of the items used in each construct operationalisation. The completely worded individual items (statements) used to measure each construct are presented in Appendix 8. Table 4.1: Construct Operationalisation Construct No. of Items Description of Items Physical Environment Quality 1 Customer perceptions of the hotel physical environment quality Décor 3 Customer perceptions of the level of the hotel décor Ambience 3 Hotel atmosphere Location 3 Convenience of the retail store, dining-out facilities and parking Cleanliness 4 Cleanliness of the hotel bathroom and toilet, room, reception area and employees Room Quality 4 Comfortable bed/mattress/pillow, quiet room, room size and in- room temperature Design 3 Hotel layout Food & Beverage 3 Quality, hygiene, adequacy, sufficiency and facilities Security & Safety 3 Accessible fire exit, noticeable sprinkler system and available room safe Interaction Quality 1 Interaction with the employees Attitude 3 Employees? willingness, friendliness and understandability Behaviour 4 Employees? service behaviour Expertise 3 Employees? knowledge Problem-Solving 3 Employees? problem-solving skills Customer Interaction 3 Interaction with the other hotel customers Outcome Quality 1 Customers? feelings about the hotel Sociability 4 Social opportunity, a sense of belonging, social contact and enjoyment of the social interaction Valence 3 Customers? overall perceptions of the hotel experience Waiting time 4 Employees? service speed Service Quality 3 Overall evaluation of the hotel service quality Perceived Value 3 Overall evaluation of the hotel experience based on the customers? paid price Image 3 Image of the hotel Customer Satisfaction 4 Right thing to use the hotel, and customers? overall satisfaction with staying at the hotel Behavioural Intentions 4 Word-of-mouth, future intention to visit and consider the hotel, and the hotel recommendation 110 4.7.2 Questionnaire Format Kumar (2005) and Sekaran (2003) demonstrated that a questionnaire was a pre-formulated written set of questions to which respondents recorded their answers, usually within rather closely defined alternatives. According to Frazer and Lawley (2000), a well-designed and administered questionnaire could provide useful ways to address research questions or to satisfy research objectives. Sekaran (2003) explained that questionnaires were an efficient data collection mechanism when researchers were aware of what information was required and realised how to measure the variables of interest. In addition, questionnaires may be administered by individuals, mailed to the respondents or sent electronically. According to Sekaran (2003), a good approach to data collection was to adopt a self-administered questionnaire when the survey was limited to a local area, and the organisation was willing and able to assemble groups of people to respond to the questionnaires at one specific place. Sekaran (2003) noted that the main advantage of conducting a self-administered questionnaire was that researchers could collect all of the completed responses within a short time. Through the questionnaire conducted in this way, any doubts that respondents may have on any question can be immediately clarified (Sekaran, 2003). In addition, researchers could be provided with an opportunity to introduce the research topic and motivate the respondents to offer their true responses (Sekaran, 2003). Self-administering questionnaires to large numbers of individuals at the same time could be less expensive and save more time compared with an interview. Moreover, a self-administered questionnaire did not require as much skill to administer (Sekaran, 2003). In consideration of these advantages, therefore, the researcher adopted the approach of a self-administered questionnaire for this study. The self-administered questionnaire was developed based on the constructs described in Figure 3.1. The questionnaire items were created based on the literature review, the theoretical framework, and the focus group interviews. In this study, the questionnaire comprised 81 items and was divided into five sections (A to E) (see Appendix 9). Physical Environment Quality in Section A comprised 27 items; Interaction Quality in Section B, 17 items; and Outcome Quality in Section C, 12 items. In addition, 17 items were used to measure respondents? perceptions of Behavioural Intentions, Satisfaction, Value, Image and 111 Service Quality which were included into Section D. Eight items that related to respondents? demographic information were included into Section E. Zikmund (2003) defined a Likert-type scale as ?a measure of attitudes designed to allow respondents to indicate how strongly respondents agree or disagree with carefully constructed statements ranging from highly positive to highly negative toward an attitudinal object? (p. 738). Schall (2003) proposed that the term ?scale? had two meanings. First, the scale was the ruler used to measure a response, as when a question used a seven-point Likert-type scale that ranged from ?very little agreement? to ?very much agreement.? This ruler was generally termed a response scale. Second, the scale referred to the questions used to measure something specific, as in a 10-question scale that measured extroversion. Schall (2003) explained that a seven-point Likert-type scale was the optimum size compared with five- and 10-point Likert-type scales. Empirical studies on interaction quality, physical environment quality, outcome quality, behavioural intentions, customer satisfaction, service quality, perceived value and image constructs adopted Likert-type scales (Caro & García, 2008, 2007; Clemes et al., 2008, 2007, 2001; Ladhari et al., 2008; González et al., 2007; Caro & Roemer, 2006; Dagger et al., 2007; Kao, 2007; Fassnacht & Koese, 2006; Gallarza & Saura, 2006; Shonk, 2006; Collins, 2005; Ko & Pastore, 2005; Park et al., 2005; Brady & Cronin, 2001; Parasuraman et al., 1988). All items in Sections A, B, C and D of the questionnaire used a seven-point Likert-type scale ranging from Strongly Disagree (1) to Strongly Agree (7). Respondents were required to circle the number that most accurately reflected their overall hotel experience. The questions included into Sections A, B, C and D were used to test Hypotheses 1 to 15 developed in Section 3.3. According to Cooper and Schindler (2006), the multiple-choice format with a single response scale implied that only one answer was sought in the questionnaire when there were multiple options for the respondents. Therefore, the multiple-choice format with a single response scale was considered for the study. In this questionnaire, Section E was designed to gain descriptive information associated with the respondent?s demographic factors. In this section, the researcher adopted a multiple-choice, single response format for 112 the questions using a nominal scale. In this section, respondents were required to tick an appropriate box for each question related to gender, marital status, age, level of education, income, purpose of travel, ethnic background and occupation. The information in Section E was used to test Hypothesis 16 developed in Section 3.3. Most of the respondents at the surveyed five-star hotel were Taiwanese. In order to consider the cultural and linguistic background of Taiwanese customers, as already mentioned, the researcher provided questionnaires in Chinese (see Appendix 10). 4.7.3 Variable Measurement Kerlinger (1986) defined a variable as ?a symbol to which numerals or values are assigned? (p. 27). According to Sekaran (2003), a variable implied anything being able to take on differing or varying values. Sarantakos (2005) identified that a measurement was made to facilitate adequacy, uniformity, comparison, consistency, accuracy and precision during the process of description and assessment of the concepts. Whenever researchers measured a variable, it could be a measurement (quantitative) difference or a categorical (qualitative) difference (Wallace, 2004). In general, the measurement variables were those that researchers could measure, whereas categorical variables were measures of differences in type rather than amount. In addition, these measurement variables were considered qualitative variables because there was some quality that distinguished these objects. According to Churchill (1979), the first step in the procedure for developing better measures involved specifying the domain of the construct. In this step, the researcher was required to delineate accurately what was included into the definition and what should be excluded (Churchill, 1979). It was imperative that researchers referred to the literature when conceptualising the constructs and specifying the domains (Churchill, 1979). In this study, therefore, the researcher adopted a questionnaire to measure variables based on Figure 3.1. In addition, the researcher completed a thorough review of the literature before conceptualising each construct in Figure 3.1. Churchill (1979) reported that a Likert-type scale could help researchers to improve the content validity of a measure because the various parts should complement each other in 113 representing the construct. In addition, Sarantakos (1993) showed that a Likert-type scale was useful for measuring attitudes, perceptions and other complicated issues. In this study, therefore, the researcher employed a seven-point Likert-type scale to measure hotel customer perceptions of behavioural intentions, satisfaction, service quality, value, image, and the primary and sub dimensions of service quality. In order to minimise response bias, reverse-code questions were also used in the questionnaire. In addition, demographic items with a multi-choice single response format were measured by asking respondents to tick the box which best described themselves. 4.7.4 Pre-testing Procedures Dane (1990) defined a pre-test as ?administering research measures under special conditions, usually before full-scale administration to participants? (p. 127). Saunders et al. (2007) indicated that the primary purpose of the pre-test was to refine the questionnaire so that respondents would have no problems in responding to the questions and, importantly, researchers would have no problems in recording the data. Ruane (2005) proposed that researchers should administer the questionnaire to a small group of people who closely resembled their research population in order to conduct a pre-test. In general, a pre-test was not only used to confirm the reliability of the attributes, but also to ensure that the wording of the questionnaire was clear (Saunders et al., 2007). Ruane (2005) proposed that researchers should conduct a pre-test after a good solid questionnaire was developed. Saunders et al. (2007) recommended that researchers should undertake a pre-test before the data collection was conducted through the questionnaire. Sarantakos (2005) noted that a pre- test was a small test of single elements of a research instrument that were predominantly used to check its ?mechanical? structure. In a pre-test, a small sample would be selected, and the respondents would be required to respond to the whole or part of the questionnaire (Sarantakos, 1993). Therefore, a questionnaire should be pre-tested, and the responses would demonstrate whether there was a need to re-arrange the response categories to a particular question (Sarantakos, 2005). Dane (1990) reported that a pre-test allowed researchers to adjust the instrument in the same way that a bench check allowed technicians to evaluate a part before installing it. In addition, Ruane (2005) claimed that a pre-test allowed 114 researchers to assess the impact of word selection, question sequencing, as well as various formatting and layout issues. In order to conduct a pre-test in this study, the researcher administered English and Chinese questionnaires to a small group of people resembling the research population. In terms of the English questionnaire, 35 randomly selected English-speaking people who had previously stayed in the surveyed five-star hotels of Taiwan were asked to respond to the English survey. The translated version was also pre-tested to ensure that the Chinese version conveyed the same meaning and that the translation would not affect or distort the correct understanding of the subject. Therefore, 35 randomly chosen Taiwanese, who had previously stayed in the surveyed five-star hotels of Taiwan, were required to respond to the Chinese survey. Once the pre-test was completed, the researcher worked on the text editing, spelling, legibility, instructions, layout space for responses, pre-coding, scaling issues, and the general presentation of the questionnaire. Finally, the questionnaires were distributed to the customers through the front-desk employees at the surveyed five-star hotel in Kaohsiung City of Taiwan. 4.8 Data Analysis Techniques In order to meet the research objectives of this study, all valid responses were assessed using a variety of statistical techniques: factor analysis, regression analysis and analysis of variance. Factor analysis was used to determine the underlying factor structure that made up the sub-dimensions, regression analysis was used to test the conceptual model, and analysis of variance (ANOVA) was applied to compare the results based on the demographic factors. 4.8.1 Factor Analysis According to Spearman (1904), factor analysis theory has influenced perspectives on measurement in most of the social sciences. Lawley and Maxwell (1971) indicate that factor analysis is a branch of multivariate analysis that mainly focuses on the internal relationships of a set of variables. If an underlying combination of the original variables (factors) 115 summarises the original set, factor analysis is a technique used to find patterns among the original variables (Cooper & Schindler, 2006). Crawford and Lomas (1980) explain that the factor analysis technique is a data reduction method using a number of different variables, which attempt to identify the underlying relationships that may be present. The application of factor analysis in the marketing literature can be divided into two primary categories (Crawford & Lomas, 1980, p. 414): (1) to attempt to understand behavioural processes by trying to identify and give descriptive definitions to underlying factors, and (2) to reduce large groups of descriptive variables into a smaller but more manageable representative subset. Stewart (1981) demonstrates that factor analysis is one of the more widely used procedures in the marketing researcher?s arsenal of analytical tools. In addition, Stewart (1981, p. 51) proposes that three general functions may be served by factor analysis: 1. The number of variables for further research can be minimised while the amount of information in the analysis is maximised. The original set of variables can be reduced to a small set that accounts for most of the variance of the initial set. 2. When the amount of data is so large as to be beyond comprehension, factor analysis can be used to search data for qualitative and quantitative distinctions. 3. If a domain is hypothesised to have certain qualitative and quantitative distinctions, factor analysis can test this hypothesis. Thus, if a researcher has an a priori hypothesis about the number of factors underlying a set of data, this hypothesis can be submitted to a statistical test. The following sections review modes of factor analysis, the different types of factor analysis, the assumptions of factor analysis, factor rotation and interpretation of the resulting factors. 4.8.1.1 Modes of Factor Analysis There are several modes of factor analysis (see Table 4.2), all of which provide information about the dimensional structure of data (Stewart, 1981). The appropriate mode of factor analysis depends on whether the research objective is to identify the relationships among variables, respondents or occasions (Hair et al., 1998). In this study, the first objective was 116 to identify the relationships among variables from the data set collected from a number of individuals on one occasion. Therefore, the R mode of factor analysis was appropriate for use in this study to identify groups of variables forming latent dimensions (factors). Table 4.2: Modes of Factor Analysis Technique Factors are loaded by Indices of association are computed across Data are collected on R Variables Persons One occasion Q Persons Variables One occasion S Persons Occasions One variable T Occasions Persons One variable P Variables Occasions One person O Occasions Variables One person Source: Stewart (1981, p. 53). 4.8.1.2 Types of Factor Analysis Stewart (1981) proposes that two general types of factor analysis exist: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The first type, EFA, is used to uncover the underlying structure of a relatively large set of variables (Coetzee, 2005). Stewart (1981) shows that EFA has been the most common type of analysis used in the marketing research. The second type of factor analysis, CFA, is applied to determine if the number of factors and the loadings of measured (indicator) variables on them conforms to what is expected on the basis of pre-established theory (Sudhahar, Israel, Britto, & Selvam, 2006). Because EFA is a statistical technique used for uncovering the underlying structure (dimensions) of a large set of variables, this technique can explore the data and provide researchers with information on how many factors were needed to best represent the data (Grafarend, 2007; Hair, Black, Babin, Anderson, & Tatham, 2006). In simple terms, EFA can be performed in a situation where researchers have not realised how many factors really exist or which variables belong with which constructs (Hair et al., 2006). Through the EFA technique, all measured variables are related to every factor by a factor loading 2 estimate. ________________________________ 2 The ?factor loading? was defined as ?correlation between the original variables and the factors, and the key to understanding the nature of a particular factor? (Hair et al., 2006, p. 102). 117 Simple structure results occur when each measured variable loads highly on only one factor and has smaller loadings on other factors (Hair et al., 2006). In addition, the distinctive feature of EFA is that the factors originate from statistical results instead of theory (Hair et al., 2006). After the factor analysis, all new variables loading on each factor should be renamed (Hair et al., 2006). In this study, the researcher adopted EFA. In general, EFA can obtain a solution through two methods: component analysis and common factor analysis (Hair et al., 2006). Hair et al. (2006) propose that the selection of one method over the other is based on two criteria: (1) the objectives of the factor analysis, and (2) the amount of prior knowledge about the variance in the variables (p. 117). Component analysis is used when the objective is to summarise most of the original information (variance) in a minimum number of factors for prediction purposes (Hair et al., 2006). In contrast, common factor analysis is primarily used to identify underlying factors or dimensions reflecting what the variables share in common (Hair et al., 2006). Though the two methods look functionally similar and are used for the same purpose (data reduction), they are quite different in their underlying assumptions (ACITS, 1995). In common factor analysis, the factor model on which the factors are based is a reduced correlation matrix. Communalities are inserted in the diagonal of the correlation matrix, and the extracted factors are based only on the common variance with specific and error variance excluded (Hair et al., 2006). The term ?common? in common factor analysis describes the variance that is analysed. It is assumed that the variance of a single variable can be divided into a common variance that is shared by the inclusion of other variables in the model, and unique variance that is unique to a particular variable and includes the error component (ACITS, 1995). Therefore, Hair et al. (2006) illustrate that common factor analysis analyses only the common variance related to a set of variables. According to Hair et al. (1998), component analysis, which is known as principal components analysis, considers the total variance and derived factors including small proportions of unique variance. Principal components analysis reduces data dimensionality 118 by performing a covariance analysis between factors (Agilent Technologies, 2005). This method frequently involves a mathematical procedure that switches a (larger) number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components (Boersma & Weenink, 1999). According to Boersma and Weenink (1999), there are two objectives of principal components analysis: (1) to discover or to reduce the dimensionality of the data set, and (2) to identify new meaningful underlying variables. In summary, principal components analysis considers mainly the total variance and makes no distinction between common and unique variance. Since common factor analysis has been considered more problematic and complicated, the application of principal components analysis has become much more widespread (Hair et al., 1998). If researchers are concerned with the assumptions of principal components analysis, common factor analysis should also be applied to assess its representation of the structure (Hair et al., 1998). In this study, the researcher adopted principal components analysis for the data analysis because of the drawbacks with common factor analysis. 4.8.1.3 Assumption Testing for Factor Analysis Regardless of the type of factor analysis being used, several assumptions and practical considerations underlie factor analysis (Coakes, Steed, & Dzidic, 2006). These assumptions are: (i) No selection bias/proper specification (ii) Linearity (iii) Normality (iv) Homoscedasticity No selection bias/proper specification: The relevant variables excluded from and the irrelevant variables included in the correlation matrix frequently substantially influence the uncovered factors (Garson, 2007). Therefore, researchers must ensure that the observed patterns are conceptually valid and appropriate to conduct factor analysis (Hair et al., 1998). 119 Linearity: Linearity is used to express the concept that the model has the properties of additivity and homogeneity (Hair et al., 2006). Linear models are used to predict values falling in a straight line that have a constant unit (slope) of the dependent variable for a constant unit change of the independent variable (Hair et al., 2006). In general, the solution may be degraded if linearity is not present (Coakes et al., 2006). Normality: Hair et al. (2006) notes that normality is the most basic assumption in multivariate analysis. This assumption refers to the shape of the data distribution for an individual metric variable and its correspondence to the normal distribution, the benchmark for statistical methods (Hair et al., 2006). Factor analysis is robust to assumptions of normality (Coakes et al., 2006). However, the solution may be improved if variables are normally distributed (Coakes et al., 2006). Homoscedasticity: Hair et al. (2006) notes that data are termed as homoscedastic when the variance of the error terms )(e appears constant over a range of predictor variables. Homoscedasticity of the relationship is assumed because factors are linear functions of measured variables (Garson, 2007). However, Garson (2007) indicates that homoscedasticity has not been considered as a critical assumption in factor analysis. However, normality and homoscedasticity would not be necessarily satisfied if the data matrix has sufficient correlations to justify the application of factor analysis and the statistical assumptions of linearity (Hair et al., 1998). The methods to justify sufficient correlations for factor analysis are discussed in Section 4.8.1.4. 4.8.1.4 Tests for Determining the Appropriateness of Factor Analysis Hair et al. (2006) suggest that there are several methods to determine whether the correlations in the data matrix are sufficient for factor analysis. The methods are: (i) Examination of the correlation matrix (ii) Inspection of the anti-image correlation matrix (iii) Bartlett?s test of sphericity (iv) Kaiser-Meyer-Olkin measure of sampling adequacy 120 Examination of the correlation matrix: This is the simplest method for determining the appropriateness of factor analysis (Hair et al., 2006). If the objective of the research is to summarise the characteristics, factor analysis can be utilised on a correlation matrix of the variables (Hair et al., 2006). Hair et al. (2006) propose that factor analysis is appropriate if visual inspection reveals most of a substantial number of correlations to be greater than 0.30. However, factoring may be inappropriate if the correlation coefficients are small throughout the matrix (Stewart, 1981). Inspection of the anti-image correlation matrix: Stewart (1981) proposes that the approach to determining the appropriateness of a correlation matrix for factor analysis is to adopt an inspection of the off-diagonal elements of the anti-image covariance or correlation matrix. The anti-image correlation matrix includes the negatives of the partial correlation coefficients whereas the anti-image covariance matrix includes the negatives of the partial covariances (SPSS, 2005). In terms of the anti-image correlation matrix, most of the off- diagonal elements should be small in a good factor model (SPSS, 2005). The measures of sampling adequacy for a variable can be shown on the diagonal of the anti-image correlation matrix (SPSS, 2005). According to Stewart (1981), the correlation matrix is not appropriate for factor analysis if the anti-image matrix has a number of non-zero off diagonal entries. According to psychometric theory, the matrix is appropriate for factor analysis if the inverse of the correlation matrix, 1?R , is near diagonal (Dziuban & Shirkey, 1974). Bartlett?s test of sphericity: Hair et al. (2006) demonstrate that this statistical test is to identify if correlations are present among the variables. This assumption provides the statistical significance that the correlation matrix has significant correlations among at least some of the variables (Hair et al., 2006). According to Stewart (1981), Barlett?s test of sphericity is computed by the following formula (p. 57): RLogPN e? ? ? ?? ? ? ? ?? ? ? +??? 6 52)1( Equation 4.1: Barlett?s test of sphericity where N is the sample size, P is the number of variables, and R is the determinant of the correlation matrix 121 The hypothesis tested was that the correlation matrix was derived from a population of dependent variables. Stewart (1981) indicated that rejecting the hypothesis was an indication that the data were appropriate for factor analysis. Kaiser-Meyer-Olkin measure of sampling adequacy (MSA): Dixon (1975) indicates that this measure appears to have a large amount of utility and also has been included into some statistical software packages. Although MSA involves additional computation, it has become the standard test procedure for the factor analysis. Stewart (1981) said that MSA could be obtained for both the correlation matrix as a whole and for each variable separately. According to Stewart (1981), the overall MSA is computed as follows (p. 57): ???? ?? += = = jkqjkr jkr MSA kj kj 22 2 Equation 4.2: Measure of sampling adequacy where: 2jkq is the square of the off-diagonal elements of the anti-image correlation matrix and 2jkr is the square of the off-diagonal elements of the original correlations. The index ranges from zero to one, reaching one when each variable is perfectly predicted without error by other variables (Hair et al., 1998). Kaiser and Rice (1974) give the following calibration of the MSA: 0.90+ (marvellous); 0.80+ (meritorious); 0.70+ (middling); 0.60+ (mediocre); 0.50+ (miserable); below 0.50 (unacceptable). 4.8.1.5 Factor Extraction in Principal Components Analysis Stewart (1981) considered that there was a well established body of literature associated with the role of factor analysis in determining how many factors should be extracted, and the criteria for ceasing extraction. Common criteria are as follows: (i) Latent root criterion (ii) Scree test criterion 122 Latent root criterion is the most commonly used technique for selecting the number of factors for further analysis (Hair et al., 2006). With component analysis, each variable contributes a value of one to the total eigenvalues 3 (Hair et al., 2006). Only the factors with latent roots or eigenvalues greater than one are considered significant, otherwise they should be disregarded (Hair et al., 2006). Hair et al. (2006) indicate that the latent root criterion can be considered as the most reliable method when the number of variables is between 20 and 50 (Hair et al., 2006). Scree test criterion is a criterion that is derived by plotting the latent roots against the number of factors in their order of extraction, and the shape of the resulting curve is used to assess the cut-off point (Hair et al., 2006). The procedure is explained by Stewart (1981, p. 58) as follows: ?A straight edge is laid across the bottom portion of the roots to see where they form an approximate straight line. The point where the factors curve above the straight line gives the number of factors, the last factor being the one whose eigenvalue immediately precedes the straight line.? 4.8.1.6 Factor Rotation Hair et al. (2006) indicate that factor rotation has been identified as the most important approach to factor interpretation. In addition, those authors comment that factor rotation is the process that handles or adjusts the factor axes to achieve a simpler and more pragmatically meaningful factor solution. Dutter (1996) proposes that factor rotation should be used to correspond to a transformation of the loadings matrix. According to MathWorks (2007), the primary goal of factor rotation is to seek a solution for each variable with only a small number of large loadings. Orthogonal and oblique factor rotation methods, therefore, are commonly used in factor rotation. Orthogonal factor rotation methods are the most widely used rotational methods (Hair et al., 2006). Orthogonal factor rotation is a factor rotation where the factors are extracted so that ______________________________ 3 ?Eigenvalue? was defined as ?column sum of squared loadings for a factor; also referred to as the latent root? (Hair et al., 2006, p. 102). 123 their axes are maintained at 90 degress (Hair et al., 2006). Each factor is independent of, or orthogonal to, all other factors. The correlation between the factors is determined to reach zero (Hair et al., 2006). Hair et al. (2006) state that three major orthogonal approaches have been developed: VARIMAX, QUARTIMAX and EQUIMAX. VARIMAX is the most popular orthogonal factor rotation method focusing on simplifying the columns in a factor matrix (Hair et al., 2006). Pohlmann (2007) notes that the VARIMAX rotational approach maximises the variance of the squared elements in the columns of a factor matrix. Through VARIMAX, the maximum possible simplification will be reached if there are only ones and zeros in a column (Hair et al., 2006). When the correlation is close to positive or negative one, this approach can be interpreted as a highly positive or negative association between the variable and the factor (Hair et al., 2006). VARIMAX indicates a lack of association when the correlation is close to zero (Hair et al., 2006). QUARTIMAX is a form of orthogonal rotation used to rotate the original principal component or factor vectors into a new set intended to approximate simple structure used either to simplify the interpretation of the principal components or factors or to cluster the original variables (Jackson, 2005). The primary goal of using this method is to simplify the rows of a factor matrix (Hair et al., 2006). QUARTIMAX mainly focuses on rotating the initial factor so that a variable loads highly on one factor and as low as possible on all other factors (Hair et al., 2006). Through QUARTIMAX, many variables can load highly or nearly highly on the same factor because the technique centres on simplifying the rows (Hair et al., 2006). The EQUIMAX approach is a compromise between the VARIMAX and QUARTIMAX approaches (Hair et al., 2006). Rather than concentrating either on simplification of the rows or on simplification of the columns, EQUIMAX attempts to accomplish some of each (Hair et al., 2006). EQUIMAX, however, has not had widespread acceptance and is not suggested as a common approach to data analysis (Hair et al., 2006). In addition to the orthogonal approaches, oblique factor rotation derives factor loadings based on the assumption that the factors are correlated and this is probably most likely true 124 for most measures (Newsom, 2005). This rotation gives the correlation between the factors in addition to the loadings. Newsom (2005) proposes two common methods of oblique factor rotation: OBLIMIN and PROMAX. OBLIMIN, also known as simple structure, is referred to as the rotated factor loadings matrix (Garson, 2007). This approach is the standard method when researchers desire a non- orthogonal (oblique) solution, namely, one in which the factors are allowed to be correlated (Garson, 2007). This method contributes to higher eigenvalues; however, it will also diminish the interpretability of the factors (Garson, 2007). PROMAX is an alternative non-orthogonal (oblique) rotation method that is computationally faster than the OBLIMIN method (Garson, 2007). In addition, this rotation is applied, at times, to large datasets (Garson, 2007). The factor loadings for the PROMAX oblique rotation represent the way each of the variables is weighted for each factor (UCLA Academic Technology Services, 2007a). PROMAX rotation allows the factors to be correlated when researchers attempt to produce an approximate, simple structure to have a better performance (UCLA Academic Technology Services, 2007a). Although a large number of factor analytic studies have been conducted, the marketing literature provides few examples of oblique factor rotation (Stewart, 1981). The orthogonal rotation dominates in spite of a strong likelihood that correlated factors and hierarchical factors are intuitively attractive and theoretically justified in many marketing applications (Stewart, 1981). Stewart (1981) demonstrates that oblique factor rotation has been useful in building the theory of other disciplines (e.g., psychology, sociology, regional science, biology). Oblique factor rotation may play a significant role in developing the theory of customer behaviour (Stewart, 1981). In order to perform the data analysis in this study, a VARIMAX orthogonal factor rotation and an OBLIMIN oblique factor rotation were applied. 4.8.1.7 Interpretation of Factors Hair et al. (2006) claim that a decision must be made regarding the factor loadings seen as worth consideration and attention when factors are interpreted. The significance of factor loadings generally depends on the sample size (see Table 4.3). 125 Table 4.3: Guidelines for Identifying Significance Factor Loadings Based on Sample Size Factor Loading Sample Size Needed for Significance * 0.30 350 0.35 250 0.40 200 0.45 150 0.50 120 0.55 100 0.60 85 0.65 70 0.70 60 0.75 50 * Based on a 0.05 significance level and power level of 80 percent; the standard error assumed to be twice those of conventional correlation coefficients. Source: Hair et al. (2006, p. 128). According to Hair et al. (2006), ?the larger the absolute size of the factor loading, the more important the loading in interpreting the factor matrix? (p. 127-128). Hair et al. (2006, p. 129) summarise the criteria for the practical or statistical significance of factor loadings as follows: ? Although factor loadings of ± 0.30 to ± 0.40 are minimally acceptable, values greater than ± 0.50 are generally considered necessary for practical significance. ? To be considered significant: ? A smaller loading is needed given either a larger sample size or a larger number of variables being analysed. ? A larger loading is needed given a factor solution with a larger number of factors, especially in evaluating the loadings on later factors. ? Statistical tests of significance for factor loadings are generally conservative and should be considered only as starting points needed for including a variable for further consideration. Hair et al. (2006) demonstrate that identifying whether the structure among the variables appears overwhelming is the ultimate goal of interpreting a factor loading matrix. Furthermore, interpreting the complex interrelationships represented in a factor matrix requires a combination of applying objective criteria with managerial judgments (Hair et al., 126 2006). Therefore, in order to interpret the factors, Hair et al. (2006, p. 133) propose some general principles as follows: ? An optimal structure exists when all variables have high loadings solely on a single factor. ? Variables that cross-load (load highly on two or more factors) are usually deleted unless theoretically justified or the objective is strictly data reduction. ? Variables should generally have communalities of greater than 0.50 to be retained in the analysis. ? Re-specification of a factor analysis can include such options as the following: ? Deleting (a) variable(s). ? Changing rotation methods. ? Increasing or decreasing the number of factors. 4.8.2 Summated Scale In order to reduce measurement error 4 by improving individual variables, Hair et al. (2006) recommend using multivariate measurements, which are known as summated scales, as identified as replacement variables. Hair et al. (2006) define a summated scale as ?a method of combining several variables that measure the same concept into a single variable in an attempt to increase the reliability of the measurement through multivariate measurement? (p. 3). The ultimate goal of adopting summated scales is to avoid the use of only a single variable to represent a concept and, instead, to use several variables as indicators, all representing differing facets of the concept to obtain a more well-rounded perspective (Hair et al., 2006). The use of multiple indicators enables researchers to specify more accurately the desired responses (Hair et al., 2006). Hair et al. (2006) show that a summated scale can be formed through the combination of several individual variables into a single composite measure. In simple terms, all of the variables loading highly on a factor are combined, and the total or, more commonly, the ______________________________ 4 ?Measurement error? was defined as ?inaccuracies in measuring the ?true? variable values owing to the fallibility of the measurement instrument (e.g., inappropriate response scales), data entry errors, or respondent errors? (Hair et al., 2006, p. 103). 127 average score of the variable is used as a replacement variable. Hair et al. (2006, p. 136) indicate that a summated scale provides two specific benefits: ? A means of overcoming, to some extent, the measurement error inherent in all measured variables. ? It represents the multiple aspects of a concept in a single measure. However, the content validity, dimensionality and reliability of the measure must be assessed before the creation of a summated scale. Sections 4.8.2.1 to 4.8.2.3 will review the content validity, dimensionality and reliability of the measure. 4.8.2.1 Content Validity Content validity, also known as face validity, is the assessment of the correspondence of the variables to be included into a summated scale and its conceptual definition (Hair et al., 2006). Carmines and Zeller (1991) indicate that content validity is based on ?the extent to which a measurement reflects the specific intended domain of content? (p. 20). According to Anastasi and Urbina (1997), content validity is a non-statistical type of validity involving ?the systematic examination of test content to determine whether it includes a representative sample of the behaviour domain to be measured? (p. 114). Sekaran (2003) demonstrates that content validity is used to ensure that the measure includes an adequate and representative set of items that can tap the concept. Ruane (2005) suggests that content validity is an important consideration whenever researchers are investigating complicated and multi- dimensional concepts. Multiple items must be applied to document the concept if concepts are identified as having more than one dimension (Ruane, 2005). In essence, content validity is a subjective validity test (Ruane, 2005). Therefore, the judgments are essentially made whether the chosen empirical indicators can truly represent the full content or facet of a concept (Ruane, 2005). Anastasi and Urbina (1997) note that a test has content validity established through the careful selection of the items to be included. Once selected, the items should follow the test specification that is drawn up through a means of a comprehensive examination of the subject domain (Anastasi & Urbina, 1997). Through content validity, a test can be improved 128 if researchers employ a panel of experts to review the test specifications and to select the items (Anastasi, 1988). In addition, the experts can review the selected items and give comments on whether the items should include a representative sample of the behaviour domain (Anastasi, 1988). Content validity deals with the ?relationship between the content of a test and some well- defined domain of knowledge or behaviour? (Hogan, 2003, p. 177). There are two primary applications of content validity: educational achievement tests and employment tests. In each of these areas, there is a well-defined body of content. Therefore, researchers are required to determine the extent to which the test content matches the content of the relevant educational area or job (Hogan, 2003). 4.8.2.2 Dimensionality Dane (1990) refers to dimensionality as the number of different qualities inherited in a theoretical concept. According to Hattie (1985) and McDonald (1981), an underlying assumption and essential requirement for creating a summated scale is that the items are uni- dimensional, implying that they are strongly associated with each other and represent a single concept. Hair et al. (2006) indicate that factor analysis plays an important role in making an empirical assessment of the dimensionality of a set of items by determining the number of factors and the loadings of each variable on the factor(s). Through the test of uni- dimensionality, each summated scale should be composed of items loading highly on a single factor. According to Hair et al. (2006), each dimension can be reflected by a separate factor if a summated scale is proposed to have multiple dimensions. Researchers can assess uni-dimensionality with either exploratory factor analysis or confirmatory factor analysis (Hair et al., 2006). 4.8.2.3 Reliability Reliability is referred to as ?the stability of test scores? (Hogan, 2003, p. 17). Hair et al. (2006) refer to reliability as an approach to assessing the degree of consistency between multiple measurements of a variable. If a method of collecting evidence is reliable, anyone using this method, or the same person using it at another time, can derive the same results 129 (McNeill & Chapman, 2005). In that way, the research method can be repeated and the same results can be obtained (McNeill & Chapman, 2005). In general, reliability is used to test the internal consistency among the variables or items through a summated scale (Hair et al., 2006). The most widely used test for internal consistency reliability is Cronbach?s Coefficient Alpha (Cronbach, 1946), which is used for multipoint-scaled items. Cronbach?s Alpha is used to measure how well a set of items (or variables) measure a single uni-dimensional latent construct (UCLA Academic Technology Services, 2007b). Cronbach?s Alpha is low when data have a multi-dimensional structure. Technically speaking, Cronbach?s Alpha is a coefficient of reliability (or consistency) although it is not a statistical test (UCLA Academic Technology Services, 2007b). Churchill (1979) suggests that an alpha of 0.60 or greater should be considered adequate to develop a new questionnaire. Therefore, a low coefficient alpha indicates the sample of items perform poorly in capturing the construct motivating the measure (Churchill, 1979). Conversely, a large coefficient alpha implies that the k-items test correlates with the true scores closely (Churchill, 1979). 4.8.3 Regression Analysis Kometa (2007) notes that regression is a technique used to predict the value of a dependent variable using one or more independent variables. Sykes (1993) shows that regression analysis is a statistical tool for the investigation of relationships between variables. In order to ascertain the causal influence of one variable upon another, researchers assemble data on the underlying variables of the causal variables upon the variable that they influence (Sykes, 1993). Researchers typically evaluate the ?statistical significance? of the estimated relationships, namely, the degree of confidence that the true relationship is close to the estimated relationship (Sykes, 1993). In terms of comparison of equations, the explanatory variable is latent and the dependent variables are observed in the reflective specification whereas the explanatory variables are observed and the dependent variable is latent in the formative specification (Diamantopoulos, 1999). Diamantopoulos and Winklhofer (2001) point out that a formative approach to 130 measurement is essentially based on a multiple regression with the construct representing the dependent variable and the indicators as the predictors. Therefore, several researchers propose that the multi-level model of service quality as a formative construct should be analysed through the multiple regression (Dagger et al., 2007; Höck & Ringle, 2006; Diamantopoulos & Winklhofer, 2001). Gelman (2006) suggests that multi-level modeling is a generalisation of linear and generalised linear modeling in which regression coefficients are themselves given a model. Fornell, Rhee and Yi (1991) claim that a formative formulation can account for more variance in the latent variable of the regression model compared with the reflective specification. In this study, the interrelationships among the behavioural intentions, customer satisfaction, service quality, image and perceived value constructs, and the primary and sub dimensions of service quality based on the conceptual model were analysed using the moderated multiple, simple and multiple regression analyses. 4.8.3.1 Moderated Multiple Regression (MMR) Analysis Greenley (1999) and Aguinis (1995) claim that moderated multiple regression (MMR) analysis is a statistical procedure frequently used in management research to examine the presence of moderating effects. Overton (2001) demonstrates that MMR is a technique that is widely used to investigate the regression slope differences (interactions) across groups. In general, MMR uses a hierarchical entry of predictor variables to determine if the relationship between one predictor variable (X) and one criterion variable (Y) is affected by a third (moderating) variable (Z) (Nunnally & Bernstein, 1994; Nunnally, 1978). Generally, moderating variables can be identified as a subset of a category of variables (Linn, Casey, Johnson, & Ellis, 2001). Sharma, Durand and Gur-Arie (1981) define a moderating variable as ?one which systematically modifies either the form and/or strength of the relationship between a predictor and a criterion variable? (p. 291). Therefore, using MMR to assess the influences of categorical moderator variables (e.g., slope differences across groups) involves a regression equation examining the relationship between a predictor X (e.g., service quality) and categorical moderator Z (e.g., perceived value) with a criterion Y (e.g., customer satisfaction) (Aguinis, Beaty, Boik, & Pierce, 2005). 131 MMR is a statistical procedure frequently used in management research to detect hypothesised moderating effects (Aguinis, 1995) and consists of comparing two least- squares regression equations (Cohen & Cohen, 1983). Given a criterion or dependent variable Y, a predictor X and a second predictor Z hypothesised to interact with X in affecting Y, the first regression equation (i.e., Step I) tests the additive model of the main effects for predicting Y from X and Z (Aguinis et al., 1996). The second equation (i.e., Step II) adds a third term, which carries information regarding the X by Z interaction, which is obtained by multiplying the predictors (i.e., X x Z). The interaction term can be computed for each subject by multiplying the two predictors so that the resulting regression equation is in the form below (Cohen & Cohen, 1983): ?+++= ? XbZbXbaY 321 Z Equation 4.3: Moderated multiple regression equation where ? Y is the predicted value for Y , a is the least squares intercept, 1b is the least squares estimate of the population regression coefficient for X (predictor), 2b is the least squares estimate of the population regression coefficient for Z , and 3b is the least squares estimate of the population regression coefficient about the interaction between X and Z (Cohen & Cohen, 1983). Rejecting the null hypothesis that 3b = 0 indicates the presence of an interaction or moderating effect (Cohen & Cohen, 1983). The ?hierarchical? form of regression reveals that predictors are not entered into the regression equation heuristic simultaneously, but in a logical order (Aiken & West, 1991). Typically, the continuous predictor (e.g., service quality, X ) and the polychotomous predictor (e.g., perceived value, Z , dummy coded) are entered in the first step, and the interaction term ( ZX ? ) is entered in the second step (Aiken & West, 1991). However, research concludes that the only unacceptable sequence of entering variables is when the interaction term ( ZX ? ) is entered into the regression as the first step by itself. Entering the predictors and interaction term simultaneously in a single step is acceptable and generates the same results as entering non-interaction terms first (Stone & Hollenbeck, 1984). 132 Aiken and West (1991) present a comprehensive discussion related to appropriate approaches to applying the interactions via MMR. One such recommendation involves ?centring? predictor variables that are entered into the regression. Although this procedure minimises problems associated with predictor multi-collinearity and eases interpretation of the non-product terms in the final regression model, it has no influence on the slope of the interaction term. 4.8.3.2 Simple Regression Analysis According to Hair et al. (2006), simple regression, also known as bivariate regression, is a regression model with a single independent variable. Hair et al. (2006) demonstrate that the ultimate goal for adopting regression analysis is to predict a single dependent variable from the knowledge of one or more independent variables. This statistical technique is termed as a simple regression when the problem involves a single independent variable (Hair et al., 2006). Hair et al. (2006) indicate ?the researcher?s objective for simple regression is to find an independent variable that will improve on the baseline prediction? (p. 178). In general, simple regression analysis allows researchers to determine how one variable changes in relation to the change in another variable (Botswana Distance Learning Project, 2004). Optionetics (2007) explains that simple regression is a mathematical approach to stating the statistical linear relationship between one independent and one dependent variable. According to MoneyChimp (2007), the most typical type of simple regression is simple linear regression, implying that researchers use the equation for a straight line instead of some other type of curve. According to Keller, Warrack and Bartel (1994, p. 624), the simple linear regression model is given below: ??? ++= xy 10 Equation 4.4: Simple linear regression model where: =y Dependent variable =x Independent variable =0? Intercept =1? Slope of the line (defined as the ratio Rise/Run) 133 =? Error variable Simple linear regression analysis is intrinsically concerned with describing the linear relationship between a dependent (outcome) variable, y, and single explanatory (independent or predictor) variable, x (Petrie, Bulman, & Osborn, 2002). Generally, simple linear regression accounts for only one predictor in modelling a response variable (Singh, 2008). Therefore, in order to achieve Research Objective Three of this study, simple regression analysis was used to analyse the effect of Service Quality on Perceived Value and Image respectively. For example, in terms of the relationship between service quality and perceived value on the basis of the conceptual model (see Figure 3.1), perceived value can be seen as a single dependent variable whereas service quality is regarded as an independent variable in a simple regression model. 4.8.3.3 Multiple Regression Analysis According to Coakes et al. (2006), multiple regression is intrinsically an extension of bivariate correlation. In general, multiple regression frequently involves two or more independent variables (Hair et al., 2006). In terms of multiple regression analysis, researchers? tasks are to expand on the simple regression models by adding more than one independent variable with the greatest additional predictive power (Hair et al., 2006). According to the Botswana Distance Learning Project (2004), multiple regression analysis allows researchers to explain how one variable changes in response to a change in another variable, keeping all other relevant variables constant. Sykes (1993) indicates that multiple regression analysis is a technique that allows additional factors to enter the analysis separately so that the effect of each can be estimated. This analysis is beneficial to quantify the influence of various simultaneous effects on a single dependent variable (Sykes, 1993). Further, multiple regression analysis is often essential even when researchers are only interested in the influence of one of the independent variables because of the omitted variables bias using simple regression (Sykes, 1993). The multiple regression equation takes the form ....2211 cXbXbXby nn ++++= The sb' are the regression coefficients, which represent the amount the dependent variable 134 y changes when the corresponding independent variable changes by one unit (Garson, 2007). The c is the constant, where the regression line intercepts the y axis, representing the amount the dependent variable y will be when all the independent variables are zero (Garson, 2007). The standardised version of the b coefficient is the beta weight, and the ratio of the beta coefficients is the ratio of the relative predictive power of the independent variables (Garson, 2007). Chu (2002) indicates that the b coefficients of the independent variables can be used to determine their derived importance to the dependent variable compared with other independent variables in the same model. In general, the relationship of the independent variable with the dependent variable will be positive if the b coefficient is positive. In contrast, if the b coefficient is negative, the relationship between the independent and dependent variables will become negative. Of course, the b coefficient equalling zero implies that there is no relationship between both of the independent and dependent variables (StatSoft, 2008). The multiple correlation 2R , which is associated with multiple regression, is the percentage of variance in the dependent variable explained collectively by all of the independent variables (Garson, 2007). Hair et al. (2006) demonstrate that 2R , which is known as the correlation coefficient square, is referred to as the coefficient of determination in the regression model (Hair et al., 2006). In general, 2R is used to indicate the percentage of total variation of Y explained by the regression model comprising iX (Hair et al., 2006). In the regression model, 2R ranges from zero to one, where a value closer to one implies the better the fit of the regression model, namely, almost all of the variability with the variables specified in the regression model has been accounted (Liu, Kuang, Gong, & Hou, 2003). Conversely, values of 2R closer to zero imply that the regression model is a bad fit. As 2R is affected by the number of independent variables in the model and the sample size, the adjusted 2R should be adopted when the goodness of fit is compared between different regression models (Liu et al., 2003). In general, the adjusted 2R is used to compensate for the optimistic bias of 2R (Liu et al., 2003). The statistical F test is used to determine how well the regression equation fits the data. According to several researchers, the F test is computed as MSR/MSE, where MSR is the 135 mean square of regression obtained by dividing the sum of squares of the regression by the degrees of freedom; and MSE is the error sum of squares divided by its degrees of freedom (Dielman, 2001; Lay, Lee, & Noike, 1999). If the calculated value of F is greater than that in the F table at a specified probability level (e.g., F ( 1?P , v , a?1 )), a ?statistically significant? regression model is obtained, where v is the degrees of freedom of error, and P is the number of parameters (Lay et al., 1999). In addition, a is a significance level related to the statistical test of the differences between two or more groups (Hair et al., 2006). F ( 1?P , v , a?1 ) is the F value at the a probability level (Lay et al., 1999). 4.8.4 Analysis of Variance (ANOVA) In this study, analysis of variance (ANOVA) was used to analyse the difference in the constructs, and dimensions of service quality within the demographic factors. ANOVA is a statistical test to determine whether samples from two or more groups originate from populations with equal means (Hair et al., 2006). Saunders et al. (2007) propose that the main goal of adopting ANOVA is to assess the likelihood of any difference between these groups occurring by chance. In this study, therefore, ANOVA is used to test for customers? perceptual differences of the higher order constructs, and the primary and sub dimensions of service quality based on their demographic characteristics. ANOVA examines the variance (i.e., the spread of data values within and between groups of data) by comparing means (Saunders et al., 2007). The F ratio or F statistic represents these differences (Saunders et al., 2007). If the likelihood of any difference between groups occurring by chance alone is low, this is represented by a large F ratio with a probability of less than 0.05, which is termed as statistically significant (Saunders et al., 2007). According to Hair et al. (2006), the logic of an ANOVA statistical test is straightforward. As the name analysis of variance implies, two independent estimates of the variance for the dependent variable are compared (Hair et al., 2006). The first reflects the general variability of respondents within the groups ( WMS ) and the second represents the differences between groups attributable to the treatment effects BMS (Hair et al., 2006). 136 The ratio of BMS to WMS is a measure of how much variance is attributable to the different treatments versus the variance expected from random sampling (Hair et al., 2006). The key statistic used to conduct the test is the F statistic of difference of group means (Hair et al., 2006, p. 392): F statistic = W B MS MS Equation 4.5: F statistic for ANOVA In this equation, BMS is the mean square within groups whereas WMS is the mean square between groups (Hair et al., 2006). Because differences between the groups inflate BMS , large values of the F statistic contribute to rejection of the null hypothesis of no difference in means across groups (Hair et al., 2006). If the analysis has several different treatments (independent variables), then estimates of BMS are calculated for each treatment and F statistics are calculated for each treatment. This approach allows the separate assessment of each treatment (Hair et al., 2006). 4.8.5 Assumptions for Regression Analysis and Analysis of Variance The following assumptions have been met before reporting the results of the regression analysis and analysis of variance: 4.8.5.1 Assumption Testing for Regression Analysis Meeting the assumptions of regression analysis is necessary to confirm that the obtained data truly represented the sample and that researcher has obtained the best results (Hair et al., 2006). In general, research to achieve the basic assumptions of regression analysis involves two tests: (1) the individual dependent and independent variables, and (2) the overall relationship after model estimation (Hair et al., 2006, p. 236). Three assumptions for regression analysis used in this study will be discussed for the individual variables: outlier, multi-collinearity and linearity. In the following paragraphs, each assumption is explained. 137 Outlier The outlier is an observation with a unique combination of characteristics identified as distinctly different from the other observations (Hair et al., 2006). An outlier observation is usually produced by some unusual factors (Maddala, 2001). However, a single outlier observation can generate substantial changes when the least squares method is used through the estimated regression equation. In simple regression, the outlier can be detected by plotting the data. In multiple regression, however, such plotting will not be possible and the residuals should be analysed (Maddala, 2001). Outliers imply that the data points lie outside the general linear pattern of which the midline is the regression line (Garson, 2007). A general rule of thumb is that the outlier is a point whose standardised residual should be greater than three (Maddala, 2001). However, the outlier can occasionally dramatically affect the performance of a regression model when it is removed from the data set under analysis (Garson, 2007). Outliers should be removed if there is a reasonable reason to believe that other variables which are not in the regression model explain why the outlier cases are unusual, namely, these cases need a separate model. Alternatively, outliers may recommend that additional explanatory variables need to be brought into the regression model, that is, the model needs respecification (Garson, 2007). Multi-collinearity Hill, Griffiths and Judge (1997) explain that economic variables may move together in systematic ways when the data are the result of an uncontrolled experiment. Such variables are believed to have problems with collinearity or multi-collinearity when several variables are involved (Hill et al., 1997). According to Hair et al. (2006), multi-collinearity represents ?the extent to which any variable?s effect can be predicted or accounted for by the other variables in the analysis? (p. 24). Alternatively, Neter, Wasserman and Kutner (1985) indicate that multi-collinearity exists where the independent variables are correlated among themselves. Generally, as multi-collinearity rises, it will complicate the interpretation of the variables because it is more difficult to confirm the effect of any single variable, owing to their interrelationship (Hair et al., 2006). 138 In multiple regression analysis, multi-collinearity arises when there are approximate linear relationships between two or more independent variables (Lin, 2008b). This may make estimation of regression coefficients impossible (Dorak, 2007). Multi-collinearity can also generate unexpectedly large estimated standard errors for the coefficients of the X variables involved. This is the reason why an exploratory analysis of the data should be conducted to see whether any multi-collinearity is present among explanatory variables (Dorak, 2007). In addition, multi-collinearity has been suggested by non-significant results in individual tests of the regression coefficients for important explanatory (predictor) variables (Dorak, 2007). Multi-collinearity may make determining the main predictor variable, which has an influence on the outcome, difficult (Dorak, 2007). According to Neter et al. (1989), multi- collinearity is not a violation of the assumptions of regression but it may cause serious difficulties. Lin (2008b) proposes that these serious difficulties include: (1) variances of parameter estimates may be unreasonably large; (2) parameter estimates may not be significant; and (3) a parameter estimate may have a sign different from what is expected (p. 417). Maddala (2001) states that detecting the influences of multi-collinearity can be achieved through analyses of the 2R , F-ratio and t-ratios of individual regression equations. If 2R is very high, and the F-ratio highly significant, but the individual t-ratios are all not significant, multi-collinearity has been identified as a significant influence on the regression equations. In order to measure the strength of the relationship between one explanatory and other explanatory variables in the regression model, variance inflation factors (VIF) need to be used. According to Maddala (2001, p. 272), the VIF is computed as follows: 21 1 j j RVIF ?= Equation 4.6: Variance inflation factor If there is no relationship, then 2jR =0.00 and jVIF increases as 2jR increases. If the individual jVIF values are large (e.g., greater than 10), or the average of the jVIF is greater than 10, then multi-collinearity may be affecting the least-squares estimates of the regression coefficient (Dielman, 2001). Conversely, VIF values below 10 indicate that multi- 139 collinearity is not a problem (Myers, 1990). Moreover, the VIF values should also be evaluated relative to the overall fit of the model, namely, when the VIF values are less than 1/(1- 2R ) where 2R is the coefficient of the determination for the model with all explanatory variables included, it reveals that the explanatory variables are more strongly related to the dependent variables than they are to each other. In this case, multi-collinearity is not a serious problem (Dielman, 2001). In order to assess multi-collinearity, researchers should adopt a tolerance or VIF that has been established in the regression of each independent on all the others (Garson, 2007). Even when multi-collinearity is present, the estimates of the importance of other variables in the equation (variables which are not collinear with others) are not influenced (Garson, 2007). According to Drazin and Rao (1999), the rule of thumb is that tolerance values greater than 0.20 do not have problems with interpretability, whereas tolerance values between 0.20 and 0.10 suggest that researchers should view the results with caution. Tolerance values less than 0.10 indicate a serious multi-collinearity problem, suggesting that researchers should reconsider the independent variables (Lin, 2008b; Thompson & O?Hair, 2008; Drazin & Rao, 1999; Menard, 1995). Alternatively, the most common rule of thumb for a VIF is 10, which is regarded by many researchers as a sign of severe or serious multi- collinearity problems (O?Brien, 2007). Marquardt (1970) uses a VIF greater than 10 as a guideline for serious multi-collinearity problems. In contrast, Hair et al. (1995) comment that a VIF less than 10 can be seen as an indication of inconsequential multi-collinearity. Values of the VIF of 10, 20, 40, or higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require the combining of independent variables into a single index (O?Brien, 2007). The condition index in SPSS is an alternative approach to assessing excessive multi- collinearity in data (Garson, 2007). This method uses square roots of the ratio of the largest eigenvalues to each other eigenvalue (Garson, 2007). According to several researchers, a rule of thumb is that condition index values greater than 15 indicate a possible multi- collinearity problem, and condition index values over 30 suggest harmful multi-collinearity 140 among regression variables (Amiama, Bueno, & Álvarez, 2008; Joshua, 2008; Garson, 2007; Tremblay, 2007; van Vuuren, de Jong, & Seydel, 2007; Momartin, Steel, Coello, Aroche, Silove, & Brooks, 2006; Novack & Guenther, 2005; Cohen et al., 2003; Liu et al., 2003; Montgomery, Peck, & Vining, 2001; Allison, 1999; Draper & Smith, 1998; Kennedy, 1998; Neter, Kutner, Nachtsheim, & Wasserman, 1996; Stevens, 1996; Myers, 1990; Montgomery & Peck, 1982). When high values of the condition index are detected, some independent variables are gradually dropped from the model by using the variance inflation factor (Amiama et al., 2008). However, Belsley, Kuh and Welsch (1980) argue that condition index values greater than 30 do not necessarily indicate problematic multi- collinearity. William (2008) notes that the aforementioned rule of thumb for the condition index values is informal. Therefore, the rule of thumb may be considered as a reference. Linearity The linearity of the relationship between the dependent and independent variables represented the degree to which the change in the dependent variable is associated with the independent variable (Hair et al., 2006). In a simple sense, linear models predict values falling in a straight line by having a constant unit change (slope) of the dependent variable for a constant unit change of the independent variable (Hair et al., 2006). Regression analysis is a linear procedure (Garson, 2007). Conventional regression analysis will underestimate the relationship when nonlinear relationships are present, i.e., 2R underestimates the variance explained overall and the betas underestimate the importance of the variables involved in the non-linear relationship (Garson, 2007). Substantial violation of linearity implies that regression results may be more or less unusable (Garson, 2007). 4.8.5.2 Error Term Assumptions In terms of the error term assumption, researchers should examine the individual variables for satisfying the assumptions required for regression analysis (Hair et al., 2006). However, researchers must also assess that the variable satisfies these assumptions (Hair et al., 2006). In this study, three error term assumptions were tested: normality, independence and homoscedasticity. Each assumption is explained below. 141 Normality of the Error Term Distribution In terms of this assumption, a check for normality of the error term is conducted by a visual examination of the normal probability plots of the residuals (Ndubisi & Koo, 2006). Ryan and Joiner (1976) propose that normal probability plots are often conducted as an informal means of assessing the non-normality of a set of data. According to Hair et al. (2006), the plots are different from residuals plots in that the standardised residuals are compared with the normal distribution. In general, the normal distribution makes a straight diagonal line, and the plotted residuals are compared with the diagonal (Hair et al., 2006). If a distribution is normal, the residual line will closely follow the diagonal (Hair et al., 2006). Ryan and Joiner (1976) explain that the ?correlation coefficient? will be near unity if the data fall nearly on a straight line. The ?correlation coefficient? will become smaller if the plot is curved. However, doubt will be cast on the null hypothesis of normality if the data do not fall beyond an appropriate critical value (Ryan & Joiner, 1976). Independence of the Error Terms (No Autocorrelation) Ndubisi and Koo (2006) refer to this assumption as the effects of carryover from one observation to another, thus making the residual dependent. In addition, Verbeek (2008) and Ndubisi and Koo (2006) indicate that there are no problems with auto-correlation which is often viewed as a sign of misspecification when the error terms in the independent variables are not correlated. However, this should be confirmed by referring to the Durbin-Watson test to ensure that the Durbin-Watson values fall within the acceptable region of 1.5 and 2.5, because any value outside this range indicates that auto-correlation is present in the regression (Ndubisi & Koo, 2006). According to Garson (2007), the Durbin-Watson test is used to confirm whether the residuals from a simple linear regression or multiple regression are independent. Because most regression problems involving time series data demonstrate positive autocorrelation, the hypotheses usually considered in the Durbin-Watson test are given below (Montgomery et al., 2001): 142 0:0 =?H implies that auto-correlation is not present. 0:1 >?H implies that auto-correlation is present. The test statistic is ? ? = ? = ? ? ? ? ?= n i n i ii e ee d 1 2 1 2 2 1 )( where ? ie = ? ? yiyi and yi and ? yi are, respectively, the observed and predicted values of the response variable for individual i ; d becomes smaller as serial correlations increase. Upper and lower critical values, Ud and Ld have been tabulated for different values of k (number of explanatory variables) and n (number of observations or cases) (Montgomery et al., 2001). The value of d ranges from zero to four; a value close to zero indicates extreme positive autocorrelation, a value close to four reveals extreme negative autocorrelation, and a value close to two shows that there is no serial autocorrelation (Garson, 2007). The decision rule for the Durbin-Watson test is to: (1) reject the null hypothesis if d < Ld ; (2) accept the null hypothesis if d> Ud ; and (3) become inconclusive if Ld < d < Ud (Dielman, 2001). Homoscedasticity of the Error Terms Hair et al. (2006) identify homoscedasticity as homogeneity of variance. This assumption is referred to as the description of data in which the variance of the error terms )(e appears constant over the range of values of an independent variable. The assumption of equal variance of the population ? (where ? is estimated from the sample value, e ) is critical to the proper application of linear regression. When the error terms have increasing or modulating variance, the data are considered as heteroscedastic (Hair et al., 2006). In contrast, Maddala (2001) explains two basic consequences of heteroscedasticity: (1) the least squares estimators remain unbiased, but inefficient, and (2) the estimates of the variances are biased. This contributes to underestimation of the true variance of the ordinary 143 least squares estimator, influences the confidence intervals, and invalidates the tests of significance of the independent variables. Hair et al. (2006) show that heteroscedastic variables can be remedied through data transformations similar to those used to reach normality. In addition, Hair et al. (2006, p. 87) indicate that data transformations provide an approach to modifying variables for one of two reasons: (1) to correct violations of the statistical assumptions underlying the multivariate techniques, or (2) to improve the relationship (correlation) between variables. In general, heteroscedasticity is the result of non-normality of one of the variables, and the correction of the non-normality remedies the unequal dispersion of variance (Hair et al., 2006). A homoscedasticity plot is a graphical data analysis technique used to assess the assumption of constant variance across subsets of the data (DATAPLOT Reference Manual, 1997). The first variable is a response variable and the second variable identifies subsets of the data. The mean and standard deviation are calculated for each of these subsets. The following plot is produced: Vertical axis = subset standard deviations; and Horizontal axis = subset means. This plot can be interpreted as the greater the spread on the vertical axis, the less valid the assumption of constant variance (DATAPLOT Reference Manual, 1997). In geneal, a common pattern occurs in one situation, where the spread (e.g., the standard deviation) also increases when the location (e.g., the mean) increases. This shows the need for some sort of transformation such as a square root or log (DATAPLOT Reference Manual, 1997). 4.9 Chapter Summary This chapter has outlined the research framework and methodology used to test Hypotheses 1 to 16, as stated in Chapter Three, and to satisfy the five research objectives, as stated in Section 1.4. 144 In order to address the five research objectives of this study, a questionnaire consisting of 81 items relating to hotel customer perceptions of behavioural intentions, satisfaction, value, image, service quality, and the primary and sub dimensions of service quality was developed. In addition, several demographic questions were also included into the questionnaire. Hotel customer perceptions were collected between 15 February and 15 April, 2008, at one surveyed five-star hotel in Kaohsiung City of Taiwan. The sample excluded customers who were less than 18 years of age. In addition, the statistical methodology used in this study including factor analysis, regression analysis, and analysis of variance and their assumptions, was explained. 145 CHAPTER 5 RESULTS 5.1 Introduction This chapter presents the results of the analysis according to the research methodology discussed in Chapter Four. The data set is examined to ensure its appropriateness for factor analysis. The statistical assumptions of factor analysis, regression analysis, and ANOVA are tested to ensure the validity of the results. The results of factor analysis, moderated, simple and multiple regression analyses, and ANOVA are presented and the 16 hypotheses are tested. The results are discussed in terms of their relation to each of the pertaining research objectives. 5.2 Sample and Response Rates Of the 730 questionnaires randomly handed out by front desk employees to the customers staying at one surveyed five-star hotel in Kaohsiung City of Taiwan, 613 (83.97%) were returned within two months. Thirty-three of the questionnaires were incomplete or were unsuitable for use in this study. This resulted in a total of 580 usable responses, or an 85.86% usable response rate. The 580 usable responses were greater than the sample size of 382 considered adequate to provide a 95% confidence level as suggested by Mendenhall et al. (1993). 5.2.1 Non-response Bias 5.2.1.1 Early and Late Responses Churchill (1979) considered that the generalisability of results could be affected by non- response bias. Armstrong and Overton (1977) suggested an extrapolation method for 146 estimating non-response bias. The extrapolation method is based on the assumption that a subject who has responded less readily 5 may answer similarly to non-respondents. In this study, the researcher received 300 questionnaires between 15 February and 15 March, 2008. 280 questionnaires were received between 16 March and 15 April, 2008. First, the mean scores for the sum of sub-dimensions, the Service Quality items, the Perceived Value items, the Image items, the Customer Satisfaction items, and the Behavioural Intentions items of the two groups were computed. Secondly, independent t-tests (see Table 5.1) were conducted to determine if the group means were statistically significant. The equal variance significance values for all constructs were greater than the 0.05 significance level of Levene?s test, indicating that the two groups had equal variances, as suggested by Howell and Habron (2004). The equal variances implied significance values were also greater than the 0.05 significance of t-test, indicating that the two groups had equal means. The researcher, therefore, concluded that there was no evidence of non-response bias in this research. Table 5.1: Independent Sample Test for Non-Response Bias Equal Variance Assumed Construct Levene?s Test for Equality of Variances T-test for Equality of Means Significant at 5% Level F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference Interaction Quality 0.059 0.808 0.331 578 0.741 0.017 0.051 Physical Environment Quality 1.467 0.226 -1.131 578 0.259 -0.058 0.052 Outcome Quality 0.156 0.693 0.139 578 0.889 0.008 0.054 Service Quality 0.600 0.439 -1.780 578 0.076 -0.137 0.077 Perceived Value 0.606 0.436 -0.509 578 0.611 -0.040 0.078 Image 0.227 0.634 -1.214 578 0.225 -0.094 0.077 Customer Satisfaction 0.488 0.485 -0.954 578 0.340 -0.076 0.080 Behavioural Intentions 0.300 0.584 -0.892 578 0.373 -0.077 0.086 5.2.1.2 Missing Data Missing data implies that information is not available for a subject (or case) for which other information is available (Hair et al., 2006). Missing data frequently occurs in a situation in which a respondent cannot respond to one or more questions of a survey (Hair et al., 2006). _____________________________ 5 ?Less readily? was defined as ?answering later, or as requiring more prodding to answer? (Armstrong & Overton, 1977, p. 397). 147 In this study, most of the missing items were under 1%, and only six items (B2, B5, B10, B11, B12, and B14) had greater than 1% of missing data (see Appendix 11, Table 34A). In addition, the p-value (0.000) for missing items was less than the 5% level of significance, indicating that these missing values were missing at random (MAR) 6 rather than missing completely at random (MCAR) 7 as suggested by Garson (2007) and SPSS (2007), and imputation for these missing values was undertaken. The missing values have been imputed with the Estimated means (see Appendix 11, Table 35A) based on the Maximum Likelihood Estimation (MLE) 8 method under the normality assumption, as suggested by Garson (2007). Because of low percentages of missing values, the primary procedure used in this study was to replace missing values with mean substitution. According to Hair et al. (2006), mean substitution is a widely used method for replacing missing data, whereby missing values for a variable are replaced with the mean value based on all valid responses. 5.3 Descriptive Statistics In the questionnaire, Section E was designed to capture some basic demographic details of the respondents involved in the study. Results of the demographic characteristics of respondents are presented in Tables 5.2 to 5.9. There were slightly more female respondents (51.4%) than male (48.6%) respondents (see Table 5.2). Table 5.2: Gender of Questionnaire Respondents Gender Frequency Percentage Male 282 48.6 Female 298 51.4 Total 580 100.0 In terms of marital status, single respondents were the largest group (53.8%), followed by married respondents (40.7%), living with a partner (3.3%), divorced or separated (1.7%), and widowed (0.5%) (see Table 5.3). ______________________________ 6 Missing at random (MAR) is a condition which exists when missing values are not randomly distributed across all observations but are randomly distributed within one or more sub-samples (Garson, 2007). 7 Missing completely at random (MCAR) exists when missing values are randomly distributed across all observations (Garson, 2007). 8 Estimation method commonly employed in structural equation models. An alternative to ordinary least squares used in multiple regression, MLE is a procedure that iteratively improves a parameter estimate to minimise a specified fit function (Hair et al., 2007, p. 708). 148 Table 5.3: Marital Status of Questionnaire Respondents Marital Status Frequency Percentage Single 312 53.8 Married 236 40.7 Divorced/Separated 10 1.7 Living with a Partner 19 3.3 Widowed 3 0.5 Total 580 100.0 Respondents aged between 26 and 35 accounted for 45.7% of the sample, followed by respondents aged between 36 and 45 (23.8%) (see Table 5.4). Table 5.4: Age of Questionnaire Respondents Age (in years) Frequency Percentage 18-25 80 13.8 26-35 265 45.7 36-45 138 23.8 46-55 66 11.4 56-65 30 5.2 66+ 1 0.2 Total 580 100.0 In terms of level of education, 57.9% of people had graduated from college or university, followed by 17.2% from graduate school or above, and 14.0% from junior college (see Table 5.5). Table 5.5: Level of Education of Questionnaire Respondents Level of Education Frequency Percentage Secondary School or Below 5 0.9 High School 58 10.0 Junior College 81 14.0 College or University 336 57.9 Graduate School or Above 100 17.2 Total 580 100.0 As shown in Table 5.6, respondents? average annual income was mainly concentrated between TW $500,001 and TW $600,000 (20.2%), followed by TW $400,001 and TW $500,000 (14.8%), and over TW $800,001 (14.3%). 149 Table 5.6: Average Annual Income of Questionnaire Respondents Average Annual Income Frequency Percentage TW$0-TW$200,000 53 9.1 TW$200,001-TW$300,000 42 7.2 TW$300,001-TW$400,000 66 11.4 TW$400,001-TW$500,000 86 14.8 TW$500,001-TW$600,000 117 20.2 TW$600,001-TW$700,000 79 13.6 TW$700,001-TW$800,000 54 9.3 TW$800,001+ 83 14.3 Total 580 100.0 From Table 5.7, the main purpose for which most of the respondents went on a trip was for pleasure (79.3%), followed by business (8.8%), and for visiting relatives (4.0%). The main purpose of the trip for 4.0% of respondents was recorded as other. Table 5.7: Main Purpose of Trip for Questionnaire Respondents Main Purpose of Trip Frequency Percentage Pleasure 460 79.3 Business 51 8.8 Visiting Relatives 23 4.0 Conference 5 0.9 Study 18 3.1 Other 23 4.0 Total 580 100.0 In Table 5.8, Asians were the largest ethnic group (84.1%) followed by North Americans (7.1%) and Europeans (4.3%). Only 1.6%, 1.0%, 0.9%, 0.5%, and 0.3% of respondents were from Central America, Australia, South America, Africa, and New Zealand, respectively. Table 5.9 shows that most respondents were working as employees in a company (33.4%). Additionally, 16.6% and 14.1% of respondents were working as professionals and managers, respectively. Other jobs not stated in the questionnaire accounted for 11.6% of respondents. 150 Table 5.8: Ethnicity of Questionnaire Respondents Ethnicity Frequency Percentage Asian 488 84.1 North American 41 7.1 Central American 9 1.6 South American 5 0.9 European 25 4.3 African 3 0.5 Australian 6 1.0 New Zealand 2 0.3 Other 1 0.2 Total 580 100.0 Table 5.9: Occupation of Questionnaire Respondents Occupation Frequency Percentage Student 12 2.1 Professional 96 16.6 Manager 82 14.1 Government Employee 63 10.9 Employee of a Company 194 33.4 Housewife 22 3.8 Soldier 7 1.2 Labour 2 0.3 Farmer 4 0.7 Self-Employed 9 1.6 Retired 12 2.1 Unemployed 10 1.7 Other 67 11.6 Total 580 100.0 5.4 Assessment for Factor Analysis After the data were collected and tabulated, a series of statistical assumptions were tested to ensure the appropriateness of the data for factor analysis. 5.4.1 Statistical Assumptions for Factor Analysis As discussed in Section 4.8.1.4, any observed correlations between variables may be diminished if the statistical assumptions of linearity, normality, and homoscedasticity for factor analysis are not met. When the data matrix has sufficient correlations, the potential 151 influence of violations of these assumptions is minimised, and the use of factor analysis is justified (Hair et al., 2006). The data matrix was examined for sufficient correlations by computing the correlation matrix, inspecting the anti-image correlation matrix, conducting Barlett?s test of sphericity, and assessing the Kaiser-Meyer-Olkin measure of sampling adequacy. 5.4.1.1 Examination of the Correlation Matrix The correlation matrix (see Appendix 12, Table 36A) revealed that there were many substantial correlations above 0.30, as suggested by Hair et al. (1998). Thus, this indicated that the items shared common factors and were therefore suitable for factor analysis. 5.4.1.2 Inspection of the Anti-Image Correlation Matrix The anti-image correlation matrix (see Appendix 13, Table 37A), which represented the negative values of the partial correlations, showed that the majority of the off diagonal values were low. This indicated that the correlation matrix was appropriate for factor analysis. 5.4.1.3 Barlett?s Test of Sphericity Barlett?s test of sphericity is a statistical test for the presence of correlations among the variables (Hair et al., 2006). The main purpose of conducting this test was to examine whether the correlation matrix was different from an identity matrix 9 (Lai, Lo, & Shieh, 2007; Fischman, Shinholser, & Powers, 1987). In the correlation matrix for this study, the test value was large (23796.36) and p-value low (0.000), which implied that the data set was appropriate for factor analysis. 5.4.1.4 Kaiser-Meyer-Olkin measure of sampling adequacy, MSA The MSA index ranges from zero to one, reaching one when each variable was perfectly predicted without error by the other variables (Hair et al., 2006). The measure can be ______________________________ 9 An ?identity matrix? was defined as a correlation matrix with 1.0 on the principal diagonal and zeros in all other correlations (Coughlin & Knight, 2008, p. 6). 152 interpreted with the following guidelines: 0.90 or above, marvellous; 0.80 or above, meritorious; 0.70 or above, middling; 0.60 or above, mediocre; 0.50 or above, miserable; and below 0.50, unacceptable (Kaiser & Rice, 1974). In this study, the MSA value was 0.878. According to Kaiser and Rice (1974), the value is ?meritorious,? which implies that the variables belong together and are appropriate for factor analysis. 5.4.2 Factor Analysis Results The tests for the statistical assumptions revealed that the data set was appropriate for factor analysis and therefore principal components and factor analysis was conducted on all of the items that were compiled from the information gathered in the focus group interviews and from the literature review. The following sections summarise the key results. 5.4.2.1 Latent Root Criterion In the latent root criterion, all factors with an eigenvalue (latent root criterion) greater than one are considered significant (Hair et al., 2006). The results of the latent root criterion (see Appendix 14, Table 38A) demonstrated that the 53 variables submitted for factor analysis should be extracted to form 12 dimensions. These 12 dimensions explained 72.02% of the variation in the data. 5.4.2.2 Scree Test Criterion By placing a straight edge across the bottom portion of the roots, there were 12 factors before the curve became approximately a straight line (see Figure 5.1). This indicated that the extraction of 12 dimensions was appropriate for this analysis. 5.4.2.3 Factor Rotation The selection of the final factors involved interpreting the computed factor matrix (Hair et al., 1998). In this study, the initial inspection of the unrotated factor matrix revealed that 36 variables highly loaded on a single factor, only one variable (A26) loaded on two factors, and 16 variables (A3, A4, A7, A12, A19, A23, B3, B5, B10, B12, B16, C1, C4, C7, C8, and 153 Component Number 5351494745434139373533312927252321191715131197531 Eig env alu e 12.5 10.0 7.5 5.0 2.5 0.0 Figure 5.1: The Scree Plot C9) were less than the significance of factor loadings for the sample. However, this matrix did not have any meaningful patterns. In order to reduce ambiguity, an orthogonal rotation (VARIMAX) and an oblique rotation (OBLIMIN) were conducted. After factor rotation, both the VARIMAX and OBLIMIN rotations (see Appendix 15, Tables 39A and 40A) demonstrated similar factor loadings on most of the variables. The only exception was that the OBLIMIN rotation determined five variables (A19, A23, B11, B15, and C9) as insignificant. The VARIMAX rotation also determined that these four variables (A19, A23, B15, and C9) were not significant and did not load on any factors. However, in the VARIMAX rotation, variable B11 was significant and loaded on factor five. Although the significance of the variables? loadings was slightly different and the significance of the loadings changed slightly between rotations, most of the variables consistently loaded on the same factors for both the VARIMAX and OBLIMIN rotations. Accordingly, the final factorial structure was based on the VARIMAX rotation method because VARIMAX considered the factors as independent (Hair et al., 1998). 154 5.4.2.4 Interpretation of Factors Hair et al. (2006) recommended that a sample size of approximately 350 and factor loadings greater than ± 0.30 should be considered as significant. The square loading is the amount of the variable?s total variance explained by the factor because a factor loading is the correlation of the variable and the factor (Hair et al., 2006). In addition, a 0.50 loading implies that 25% of the variance is explained by the factor (Hair et al., 2006). In this study, therefore, VARIMAX considered the factor loadings of ± 0.50 for 49 variables as significant but four variables (A23, A19, B15, and C9) were less than 0.50 loading. The remaining 49 variables had one loading on one factor (see Appendix 16, Table 41A for details of the variable loadings). Each factor was subsequently renamed in accordance with the construct that they represented. The 12 factors were respectively renamed: (1) Employees? Conduct; (2) Employees? Expertise; (3) Employees? Problem-Solving; (4) Customer-to-Customer Interaction; (5) Décor & Ambience; (6) Room Quality; (7) Availability of Facility; (8) Design; (9) Location; (10) Valence; (11) Waiting Time; and (12) Sociability. 5.4.3 Summated Scale In order to sum the items, the content validity, dimensionality, and reliability of the measurement scales were assessed. 5.4.3.1 Content Validity All variables (items) were inspected by the researcher and three marketing experts to ensure that they were an adequate and a thorough representation of the construct under investigation. In the final rotation, some of the items did not load exactly on the 16 sub-dimensions that were originally proposed to represent the primary dimensions. However, these items did load on the primary dimensions that were originally proposed. The only exception was the item (B13) under Behaviour that was originally proposed as a sub-dimension of Interaction Quality. After the rotation, this item loaded on one of the sub-dimensions of Outcome Quality, Waiting Time (see Table 5.12). It was therefore concluded that the items exhibited adequate content validity. 155 5.4.3.2 Dimensionality Through the VARIMAX rotation, no variables loaded on the different factors in the component matrix. The only exception was that items A23, A19, B15 and C9 were not greater than 0.50 loading. Therefore, the outcome of this process resulted in 49 variables representing 12 factors. 5.4.3.3 Reliability The remaining 49 variables were subjected to reliability tests. Cronbach?s Coefficient Alpha was used to measure reliability. All of the factors had a Cronbach?s Coefficient Alpha greater than 0.60 as suggested by De Vellis (2003, 1991) and Churchill (1979) for exploratory research. As for the variables representing the summated scales and their Cronbach?s Coefficient Alpha scores, see Tables 5.10, 5.11 and 5.12. Table 5.10: Reliability of Scaled Items for Interaction Quality Sub-Dimension Cronbach?s Alpha Item No. Items Rotation Loading Employees? Conduct 0.915 B7 B6 B2 B8 B11 Employees? service provision Employees? willingness to help customers Employees allow customers to trust their services Dependability of friendly employees Employees? understanding of customer needs 0.659 0.645 0.594 0.578 0.547 Employees? Problem-Solving 0.878 B4 B14 B9 Employees showing a sincere interest in solving problems Employees being able to handle customer complaints Employees? understanding of resolving customer complaints 0.865 0.852 0.833 Employees? Expertise 0.910 B3 B16 B1 Dependability of employees knowing their jobs/responsibilities Competent employees Employees? professional knowledge to meet customer needs 0.908 0.879 0.742 Customer-to- Customer Interaction 0.748 B5 B12 B10 Impressions of the other customers? behaviour The rules and regulations followed by customers The positive impact of interaction with other customers 0.803 0.787 0.786 156 Table 5.11: Reliability of Scaled Items for Physical Environment Quality Sub-Dimension Cronbach?s Alpha Item No. Items Rotation Loading Décor & Ambience 0.900 A10 A26 A24 A11 A18 A3 The style of décor is to the customers? liking Excellent ambience Stylish and attractive décor The enjoyment of atmosphere Décor showing a great deal of thought and style The atmosphere is what customers expect 0.846 0.793 0.786 0.782 0.723 0.682 Room Quality 0.938 A5 A9 A14 A2 A6 A20 Clean bathroom and toilet Clean room Quiet room Adequate room size Comfortable bed/mattress/pillow High quality of in-room temperature control 0.830 0.823 0.816 0.815 0.800 0.788 Availability of Facility 0.896 A17 A25 A13 A8 A16 A21 Availability of noticeable sprinkler systems Availability of secure safes Accessibility of fire exits Availability of high quality food & beverage Sanitary, adequate and sufficient food & beverage served Availability of a variety of food & beverage facilities 0.799 0.765 0.761 0.741 0.722 0.669 Design 0.828 A7 A15 A1 The layout makes it easy for customers to move around The layout serves customer purposes/needs Aesthetical attractiveness 0.784 0.756 0.743 Location 0.773 A4 A12 A22 Convenient location for retail stores Convenient location for dining-out facilities Convenient parking spaces availability 0.827 0.820 0.636 Table 5.12: Reliability of Scaled Items for Outcome Quality Sub-Dimension Cronbach?s Alpha Item No. Items Rotation Loading Valence 0.902 C5 C10 C2 When leaving, customers had got what they wanted Favourable evaluation of the outcome of services Customers have had good experiences at the end of their stay 0.870 0.856 0.640 Waiting Time 0.888 C8 C11 C6 C3 B13 Employees? understanding of the importance of waiting time Employees? punctual provision of service Employees try to minimise customer waiting time Reasonable waiting time for service Employees? ability to answer customer questions quickly 0.853 0.766 0.736 0.700 0.552 Sociability 0.793 C1 C4 C7 Provision of opportunities for social interaction A sense of belonging with other customers Social contacts 0.845 0.790 0.773 The researcher split the sample into two halves in order to confirm if the extracted sub- dimensions could be used in the regression analysis and avoid potential estimation problems (e.g., multi-collinearity) (see Appendix 17, Tables 42A and 43A). These two split-half samples revealed similar factor loading, communalities, eigenvalues, the variance explained by the factor solution and Cronbach?s Coefficient Alpha. According to Hair et al. (2006), researchers can ensure that the variables reach acceptable levels of explanation if all variables with communalities, which represent the amount of variance accounted for by the 157 factor solution for each variable, are greater than 0.50. Based on the results of the split-half samples, variable C9 from sample one, and variables A23, C4 and C9 from sample two, did not have a significant value greater than 0.50. Both of the split samples revealed that the communality for each variable was greater than 0.50, except for item C9 in sample one. According to Hair et al. (2006), the researcher can ensure that the variables reach acceptable levels of explanation if all variables with communalities are greater than 0.50. In addition, both sets of the results consistently indicated a single factor structure with eigenvalues of 38.56 and 38.24, respectively, and the explained amount of variance equalling 72.75% and 74.03%, respectively. Furthermore, the results of the randomly split-half sub-samples also generated similar and consistent findings for scale reliability, the Cronbach?s Coefficient Alpha scores were all higher than 0.60 as suggested by Churchill (1979). The Cronbach?s Coefficient Alpha scores were also fairly constant across these two sub-sample, and according to De Vellis (2003), it can be assumed that these values were not distorted accidentally. Therefore, it was concluded that the measurement instrument for the sub- dimensions of service quality was a valid and reliable measurement, comprising the 12 sub- dimensions derived from the exploratory factor analysis (see Appendix 15, Table 40A). In addition, the 12 sub-dimensions of service quality were suitable for use in the regression analysis. In addition to the reliability tests conducted on the summated scales of the 12 sub- dimensions, reliability tests were also performed on the summated scales for the Service Quality, Perceived Value, Image, Customer Satisfaction, and Behavioural Intentions constructs. The items used in the summated scales are shown in Table 5.13. Since the Cronbach?s Coefficient Alpha scores for each of these constructs were all above 0.60, as suggested by Churchill (1979), hence, it was concluded that these measures were also reliable. All of the summated scales were judged to demonstrate sufficient validity, uni- dimensionality, and reliability for a newly developed questionnaire. The mean of each of the scales was then used to represent each one of the dimensions identified in Tables 5.10, 5.11, 5.12, and 5.13 for further analysis. 158 Table 5.13: Reliability of Scaled Items for Behavioural Intentions and Related Constructs Construct Cronbach?s Alpha Item No. Items D1 The overall quality of services Service Quality 0.946 D6 Provision of high quality services D13 Comparison of service quality D3 The value of hotel experience Perceived Value 0.848 D12 The minimum of waiting time D14 The high value for its price D2 Good impression Image 0.914 D7 A better image than that of competitors D17 A good image in the minds of customers D5 To make a right choice by staying at the hotel Customer 0.949 D8 To satisfy customer needs and wants Satisfaction D10 Satisfaction with hotel stay D16 Pleasant experience D4 Customers always say positive things about the hotel to other people Behavioural 0.942 D9 Likelihood of coming back to the hotel again Intentions D11 To consider the hotel as the first one on the list when searching for accommodations D15 To recommend the hotel to other people 5.5 Assessment of the Regression Models and ANOVA All of the nine regression models were tested for the presence of outliers, multi-collinearity, linearity, normality, independence, and homoscedasticity of the error term. 5.5.1 Assumptions for Regression Analysis and ANOVA A series of statistical assumption tests were applied to each of the nine regression models to ensure a robust result. 5.5.1.1 Outliers Each of the nine regression models was examined for the presence of outliers. Outliers were identified as the outlying observations whose standardised residual was greater than three. As recommended by Maddala (2001), outliers were removed from the analysis in order to reduce their influence on the performance of the nine regression models in this study. 159 5.5.1.2 Multi-collinearity Multi-collinearity was assessed for each regression equation. The initial inspection of the Pearson Correlation Matrix (see Appendix 18, Tables 44A-53A) for each of the regression models revealed that the correlations between the independent variables did not exceed 0.80. Moreover, the 2R values for each regression model were not exceedingly large. The F- values for all regression models were highly significant, individual t-values were also significant except for two variables (Customer-to-Customer Interaction and Sociability) in Models One and Three (see Tables 5.15 and 5.17). Collinearity statistics (see Appendix 18, Table 54A) were also evaluated for all of the regression models. Values of tolerance for all regression models were greater than 0.20. According to Drazin and Rao?s (1999) rule of thumb, tolerance values greater than 0.20 do not indicate problems with interpretability. In addition, according to O?Brien (2007), values of the VIF of 10, 20, 40, or higher, call for the elimination of one or more independent variables from the analysis. The results of this study revealed that the VIF values for all independent variables in each regression model were less than 3.0. Therefore, none of the independent variables in each of the nine regression models should be eliminated. Furthermore, the VIF values for the nine regression models were less than 1/(1- 2R ), indicating that the independent variables were related to the dependent variables more than to each other. Multi-collinearity was therefore deemed not to be a serious problem. In addition, all tolerance values were above 0.20 for each model. However, the condition indices for some models exceeded 15, indicating that there was a possible problem with multi-collinearity, but none of the condition indices exceeded 30, indicating that multi- collinearity was not a very serious concern. A further examination of the results of the Pearson Correlation Matrix and the multiple regression results showed that no large unexpected changes occurred in the direction and magnitude of the coefficients. It was concluded that there was a degree of multi-collinearity in each of the models excluding Models Six and Seven (as evidenced by the conditional indices); however, it did not seriously impact on any of the regression models. 160 5.5.1.3 Linearity The scatter plot of standardised residuals versus the fitted values (see Appendix 19, Figure 16A) for all regression models were visually inspected. The plots did not reveal any systematic pattern, thus providing support for the specified linear relationship, as suggested by Garson (2007). 5.5.1.4 Normality of the Error Term Distribution The histogram residuals and the normality probability plots were plotted to assess normality (see Appendix 20, Figures 17A and 18A). The histogram plots revealed that the distribution approximated the normal distribution, and that the P-P plots were approximately a straight line instead of a curve. Accordingly, the residuals were deemed to have a reasonably normal distribution, as suggested by Ndubisi and Koo (2006). 5.5.1.5 Independence of the Error Terms (No Autocorrelation) The Durbin-Watson test was computed to diagnose the independence of the error terms. The test values and corresponding critical values are summarised in Table 5.14. Table 5.14 Durbin-Watson Test Statistics Model Dependent Variable Durbin-Watson Critical Value (at 1% level) DL DU 1 Interaction Quality 1.930 1.633 1.715 2 Physical Environment Quality 1.979 1.623 1.725 3 Outcome Quality 1.934 1.643 1.704 4 Service Quality 1.871 1.643 1.704 Step 1: 1.867 1.653 1.693 5 Customer Satisfaction Step 2: 1.854 1.664 1.684 6 Perceived Value 1.830 1.664 1.684 7 Image 2.047 1.664 1.684 8 Customer Satisfaction 1.883 1.643 1.704 9 Behavioural Intentions 1.842 1.653 1.693 Table 5.14 shows that the Durbin-Watson values for each of the nine regression models were greater than the DU and fell within the acceptable region between 1.5 and 2.5, indicating that there was no autocorrelation in the residuals, as recommended by Ndubisi and Koo (2006). Thus, the assumption of independence of the error terms was satisfied. 161 5.5.1.6 Homoscedasticity of the Error Terms The error terms were expected to have equal variances. In the scattered residual plots (see Appendix 19, Figure 16A), the residuals scattered randomly about the zero line and did not exhibit a triangular-shaped pattern, thus providing sufficient evidence to satisfy the assumption for homoscedasticity of the error terms. 5.5.2 Results Pertaining to Research Objective One This section presents the results relating to Hypotheses One to Six that were formulated in order to achieve Research Objective One. Hypotheses One, Two, and Three were proposed to test the second-order of the multi-level model. The summated scaled sub-dimensions were regressed against their pertaining primary dimensions as derived from the literature review, perceived by focus group respondents, determined by the researcher, and confirmed by the exploratory factor analysis. Hypotheses Four, Five, and Six were proposed to test the first- order of the multi-level model, therefore, the primary dimensions were regressed against Total Service Quality. 5.5.2.1 Hypothesis One The regression model for Hypothesis One has Interaction Quality as the dependent variable and four relevant sub-dimensions as the independent variables. Four sub-dimensions relating to Interaction Quality were identified: Employees? Conduct, Employees? Expertise, Employees? Problem-Solving, and Customer-to-Customer Interaction. The results relating to Hypothesis One are presented in Table 5.15. The F statistic of 127.892 was significant at the 1% level of significance, revealing that the model helped to explain some of the variation in Interaction Quality. Further, the adjusted coefficient of determination revealed that 46.7% of the variance in Interaction Quality was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Employees? Conduct and Problem-Solving, and less than the 5% level of significance for Employees? Expertise, indicating that the beta coefficients of these three sub-dimensions were significant, and explained some of the variation in Interaction Quality. 162 However, the p-value of the t-test was greater than the 10% level of significance for Customer-to-Customer Interaction, showing that when the other sub-dimensions were included into the model, the beta coefficient of the Customer-to-Customer Interaction sub- dimension did not help explain the additional variation in Interaction Quality. Accordingly, the results partially supported Hypothesis One. Table 5.15: Model One: Multiple Regression Results Relating to Hypothesis One Unstandardised Model One Coefficient B Std. Error Standardised Coefficient Beta t Sig. Interaction Quality (Constant) 0.312 0.296 1.053 0.293 Employees? Conduct 0.705 0.040 0.618 17.759 0.000 *** Employees? Expertise 0.073 0.037 0.067 1.992 0.047 ** Employees? Problem-Solving 0.120 0.035 0.110 3.422 0.001 *** Customer-to-Customer Interaction 0.030 0.043 0.022 0.702 0.483 Adjusted R2=0.467 *** Significant at 1% level F=127.892*** ** Significant at 5% level * Significant at 10% level 5.5.2.2 Hypothesis Two The regression model for Hypothesis Two has Physical Environment Quality as the dependent variable and five relevant sub-dimensions as the independent variables. The five sub-dimensions associated with Physical Environment Quality were Décor & Ambience, Room Quality, Availability of Facility, Design, and Location. The results associated with Hypothesis Two are presented in Table 5.16. Table 5.16: Model Two: Multiple Regression Results Relating to Hypothesis Two Unstandardised Model Two Coefficient B Std. Error Standardised Coefficient Beta t Sig. Physical Environment Quality (Constant) -2.358 0.417 -5.650 0.000 Décor & Ambience 0.385 0.064 0.244 6.054 0.000 *** Room Quality 0.272 0.054 0.183 5.062 0.000 *** Availability of Facility 0.296 0.061 0.200 4.842 0.000 *** Design 0.232 0.057 0.166 4.098 0.000 *** Location 0.106 0.052 0.077 2.028 0.043 ** Adjusted R2=0.340 *** Significant at 1% level F=60.663*** ** Significant at 5% level * Significant at 10% level 163 The F statistic of 60.663 was significant at the 1% level of significance, indicating that at least one of the independent variables helped to explain some of the variation in Physical Environment Quality. Further, the adjusted coefficient of determination revealed that 34.0% of the variance in Physical Environment Quality was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Décor & Ambience, Room Quality, Availability of Facility and Design, and less than the 5% level of significance for Location, indicating that the beta coefficients of these five sub-dimensions were significant, and explained some of the variation in Physical Environment Quality. Therefore, the results fully supported Hypothesis Two. 5.5.2.3 Hypothesis Three The regression model for Hypothesis Three has Outcome Quality as the dependent variable and the pertaining sub-dimensions as the independent variables. The three sub-dimensions pertaining to Outcome Quality were Valence, Waiting Time, and Sociability. The results relating to Hypothesis Three are presented in Table 5.17. Table 5.17: Model Three: Multiple Regression Results Relating to Hypothesis Three Unstandardised Model Three Coefficient B Std. Error Standardised Coefficient Beta t Sig. Outcome Quality (Constant) 2.082 0.256 8.132 0.000 Valence 0.419 0.040 0.429 10.578 0.000 *** Waiting Time 0.167 0.044 0.147 3.763 0.000 *** Sociability 0.062 0.038 0.060 1.607 0.109 Adjusted R2=0.278 *** Significant at 1% level F=75.328*** ** Significant at 5% level * Significant at 10% level The F statistic of 75.328 was significant at the 1% level of significance, indicating that at least one of the independent variables helped to explain some of the variation in Outcome Quality. Further, the adjusted coefficient of determination revealed that 27.8% of the variance in Outcome Quality was explained by the regression model. The p-values of the t- tests were less than the 1% level of significance for Valence and Waiting Time, revealing that the beta coefficient of these two sub-dimensions were significant, and explained some of the variation in Outcome Quality. However, the p-value of the t-test was greater than 10% 164 level of significance for Sociability, showing that when the other sub-dimensions were included into the model, the beta coefficient of the Sociability sub-dimension did not help explain the additional variation in Outcome Quality. Thus, Hypothesis Three is only partially supported. 5.5.2.4 Hypotheses Four, Five and Six Model Four has three independent variables, Interaction Quality, Physical Environment Quality and Outcome Quality, and they were regressed against Service Quality. The Interaction Quality, Physical Environment Quality, and Outcome Quality dimensions are included as independent variables to test their effects on Service Quality. The results relating to Hypotheses Four, Five and Six are presented in Table 5.18. Table 5.18: Model Four: Multiple Regression Results Relating to Hypotheses Four, Five and Six Unstandardised Model Four Coefficient B Std. Error Standardised Coefficient Beta t Sig. Service Quality (Constant) 1.692 0.167 10.156 0.000 Interaction Quality 0.254 0.034 0.287 7.369 0.000 *** Physical Environment Quality 0.088 0.022 0.136 3.986 0.000 *** Outcome Quality 0.368 0.034 0.405 10.858 0.000 *** Adjusted R2=0.469 *** Significant at 1% level F=169.665*** ** Significant at 5% level * Significant at 10% level The F statistic of 169.665 was significant at the 1% level of significance. Therefore, the independent variable helped to explain some of the variation in Service Quality. Further, the adjusted coefficient of determination revealed that 46.9% of the variance in Service Quality was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Interaction Quality, Physical Environment Quality, and Outcome Quality. Since all primary dimensions were significant, each of these variables helped to explain some of the variation in Service Quality. Accordingly, Hypotheses Four, Five, and Six were all supported by the results of the statistical analysis. 165 5.5.2.5 Discussion Regarding Research Objective One There were 10 significant sub-dimensions of service quality as perceived by customers at the surveyed five-star hotel. These were Employees? Conduct, Employees? Expertise, Employees? Problem-Solving, Décor & Ambience, Room Quality, Availability of Facility, Design, Location, Valence, and Waiting Time. The beta coefficients suggested that an increase in these sub-dimensions would positively affect their relevant primary dimensions. The influence of Customer-to-Customer Interaction on Interaction Quality and effect of Sociability on Outcome Quality were not significant, suggesting that increasing the performance on these two sub-dimensions might not positively affect the performances of Interaction Quality and Outcome Quality respectively. It should be noted, however, that the adjusted 2R values for Models Two and Three were relatively low. The two explanatory variables explained only 34.0% and 27.8% respectively, leaving much of the variation in the dependent variables unexplained by the regression models. The support found for Hypotheses Four, Five, and Six provides further evidence for the use of Interaction Quality, Physical Environment Quality, and Outcome Quality as primary dimensions of service quality in the context of the hotel industry. Further, the results of Hypotheses One to Six suggested that there was support for a multi-dimensional factor structure of service quality for the hotel industry. Furthermore, Service Quality was positively influenced by perceptions of the three primary dimensions. The standardised coefficients of Interaction Quality, Physical Environment Quality, and Outcome Quality explained Service Quality numerically, and identified that Outcome Quality ( ? =0.405) had the most influential effect on Service Quality, followed by Interaction Quality ( ? =0.287) and Physical Environment Quality ( ? =0.136) had the least influence. 166 5.5.3 Results Pertaining to Research Objective Two This section presents the results for Hypothesis Seven in order to achieve Research Objective Two. Research Objective Two examines whether Perceived Value plays a moderating role between Service Quality and Customer Satisfaction. 5.5.3.1 Hypothesis Seven For Hypothesis Seven, the relationship between Service Quality and Customer Satisfaction moderated by Perceived Value was examined. The results relating to Hypothesis Seven are presented in Table 5.19. Table 5.19: Model Five: Moderated Multiple Regression Results Relating to Hypothesis Seven Unstandardised Model Five Coefficient B Std. Error Standardised Coefficient Beta t Sig. Step 1 Customer Satisfaction (Constant) 0.827 0.161 5.147 0.000 Service Quality 0.351 0.040 0.335 8.747 0.000 *** Perceived Value 0.509 0.040 0.491 12.818 0.000 *** Step 2 Customer Satisfaction (Constant) 2.960 0.089 33.245 0.000 (Interactions) Service Quality × Perceived Value 0.084 0.003 0.761 28.218 0.000 *** Step 1 Adjusted R2=0.590 F=417.363*** *** Significant at 1% level ** Significant at 5% level * Significant at 10% level Step 2 Adjusted R2=0.579 F=796.252*** In step one, the F statistic of 417.363 was significant at the 1% level of significance. Thus, the independent variables helped to explain some of the variation in Customer Satisfaction. Further, the adjusted coefficient of determination revealed that 59.0% of the variance in Customer Satisfaction was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Service Quality and Perceived Value, indicating that the beta coefficients of these two independent variables were significant, and explained some of the variation in Customer Satisfaction. 167 In terms of step two, the F statistic of 796.252 was significant at the 1% level of significance. Thus, the moderating and independent variables helped to explain some of the variation in Customer Satisfaction. Further, the adjusted coefficient of determination revealed that 57.9% of the variance in Customer Satisfaction was explained by the regression model. The p-value of the t-test was less than the 1% level of significance for Service Quality x Perceived Value, indicating that the beta coefficients of both of the independent (Service Quality) and moderating (Perceived Value) variables were significant, and explained some of the variation in Customer Satisfaction. Accordingly, the interaction term (Service Quality x Perceived Value) revealed that the hypothesised moderating influence of Perceived Value on the relation between Service Quality and Customer Satisfaction was supported by the results of the statistical analysis. 5.5.3.2 Discussion Regarding Research Objective Two Hypothesis Seven postulated that Perceived Value would moderate the relationship between Service Quality and Customer Satisfaction. As shown in Table 5.19, Perceived Value played an important role as a moderator between Service Quality and Customer Satisfaction in the hotel industry ( ? =0.761). 5.5.4 Results Pertaining to Research Objective Three This section presents the results for Hypotheses 8, 9, 10, 11, 12, 13 and 14 in order to achieve Research Objective Three. Research Objective Three examined the interrelationships between Behavioural Intentions and its related constructs, Service Quality, Perceived Value, Image, and Customer Satisfaction. 5.5.4.1 Hypothesis Eight For Hypothesis Eight, the relationship between Perceived Value and Service Quality was examined. The results associated with Hypothesis Eight are presented in Table 5.20. 168 Table 5.20: Model Six: Simple Regression Results Relating to Hypothesis Eight Unstandardised Model Six Coefficient B Std. Error Standardised Coefficient Beta t Sig. Perceived Value (Constant) 1.254 0.160 7.834 0.000 Service Quality 0.728 0.029 0.720 24.930 0.000 *** Adjusted R2=0.517 *** Significant at 1% level F=621.529*** ** Significant at 5% level * Significant at 10% level The F statistic of 621.529 was significant at the 1% level of significance. Consequently, the independent variable helped to explain some of the variation in Perceived Value. Further, the adjusted coefficient of determination revealed that 51.7% of the variance in Perceived Value was explained by the regression model. The p-value of the t-test was less than the 1% level of significance for Service Quality, indicating that the beta coefficient of this independent variable was significant, and explained some of the variation in Perceived Value. Accordingly, Hypothesis Eight was supported by the results of the statistical analysis. 5.5.4.2 Hypothesis 10 Hypothesis 10 examined the relationship between Image and Service Quality. The results relating to Hypothesis 10 are presented in Table 5.21. Table 5.21: Model Seven: Simple Regression Results Relating to Hypothesis 10 Unstandardised Model Seven Coefficient B Std. Error Standardised Coefficient Beta t Sig. Image (Constant) 1.575 0.166 9.478 0.000 Service Quality 0.708 0.030 0.697 23.339 0.000 *** Adjusted R2=0.484 *** Significant at 1% level F=544.724*** ** Significant at 5% level * Significant at 10% level The F statistic of 544.724 was significant at the 1% level of significance. Accordingly, the independent variable helped to explain some of the variation in Image. Further, the adjusted coefficient of determination revealed that 48.4% of the variance in Image was explained by the regression model. The p-value of the t-test was less than the 1% level of significance for Service Quality, showing that the beta coefficient of this independent variable was 169 significant, and explained some of the variation in Image. Thus, the results supported Hypothesis 10. 5.5.4.3 Hypotheses 9, 11 and 13 Model Eight examined the constructs that may affect Customer Satisfaction. Accordingly, three hypotheses related to Perceived Value, Image, and Service Quality were tested. The results associated with Hypotheses 9, 11 and 13 are summarised in Table 5.22. Table 5.22: Model Eight: Multiple Regression Results Relating to Hypotheses 9, 11 and 13 Unstandardised Model Eight Coefficient B Std. Error Standardised Coefficient Beta t Sig. Customer Satisfaction (Constant) 0.618 0.164 3.774 0.000 Perceived Value 0.435 0.042 0.420 10.354 0.000 *** Image 0.191 0.040 0.186 4.730 0.000 *** Service Quality 0.270 0.043 0.257 6.266 0.000 *** Adjusted R2=0.605 *** Significant at 1% level F=296.002*** ** Significant at 5% level * Significant at 10% level The F statistic of 296.002 was significant at the 1% level of significance. Therefore, the independent variable helped to explain some of the variation in Customer Satisfaction. Further, the adjusted coefficient of determination revealed that 60.5% of the variance in Customer Satisfaction was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Perceived Value, Image, and Service Quality, revealing that the beta coefficients of these three independent variables were significant, and explained some of the variation in Customer Satisfaction. Thus, Hypotheses 9, 11 and 13 were supported by the results of the statistical analysis. 5.5.4.4 Hypotheses 12 and 14 Model Nine examined the constructs that may affect Behavioural Intentions. Accordingly, two hypotheses related to Image and Customer Satisfaction were tested. The results relating to Hypotheses 12 and 14 are presented in Table 5.23. 170 Table 5.23: Model Nine: Multiple Regression Results Relating to Hypotheses 12 and 14 Unstandardised Model Nine Coefficient B Std. Error Standardised Coefficient Beta t Sig. Behavioural Intentions (Constant) -0.028 0.160 -0.176 0.860 Image 0.390 0.036 0.351 10.895 0.000 *** Customer Satisfaction 0.579 0.035 0.536 16.654 0.000 *** Adjusted R2=0.656 *** Significant at 1% level F=552.498*** ** Significant at 5% level * Significant at 10% level The F statistic of 552.498 was significant at the 1% level of significance. Therefore, the independent variables helped to explain some of the variation in Behavioural Intentions. Further, the adjusted coefficient of determination revealed that 65.6% of the variance in Behavioural Intentions was explained by the regression model. The p-values of the t-tests were less than the 1% level of significance for Image and Customer Satisfaction, showing that the beta coefficients of these two independent variables were significant, and explained some of the variation in Behavioural Intentions. Accordingly, the results supported Hypotheses 12 and 14. 5.5.4.5 Discussion Regarding Research Objective Three Increased favourable perceptions of Service Quality had a positive effect on the perceptions of Value ( ? =0.720) and Image ( ? =0.697) respectively. Furthermore, Customer Satisfaction was positively affected by increased perceptions of Value, Image, and Service Quality. Comparing the standardised coefficients of Service Quality, Perceived Value, and Image, Customer Satisfaction was most influenced by perceptions of Value ( ? =0.420), followed by Service Quality ( ? =0.257) and Image ( ? =0.186). Behavioural Intentions were positively affected by increases in Customer Satisfaction ( ? =0.536) as well as Image ( ? =0.351). However, the results indicated that the intentions to visit the hotel were influenced more strongly by Customer Satisfaction than by Image. 171 5.5.5 Results Pertaining to Research Objective Four Multiple Regression Models One, Two, Three and Four were used in order to identify the least and the most important Service Quality dimensions as perceived by hotel customers. The results are presented in Tables 5.15, 5.16, 5.17 and 5.18. 5.5.5.1 Hypothesis 15 Hypothesis 15a postulated that customers perceived each of the three primary dimensions to be more or less important, and this was supported by the results. The most important primary dimension as perceived by customers was Outcome Quality ( ? =0.405), followed by Interaction Quality ( ? =0.287) and Physical Environment Quality ( ? =0.136) that was perceived as the least important dimension of the three primary dimensions. Hypothesis 15b postulated that the sub-dimensions relevant to the three primary dimensions would vary in importance. This information is summarised in Figure 5.2, which lists all the standardised beta coefficients of the nine regression models. 5.5.5.2 Discussion Regarding Research Objective Four The three primary dimensions, Interaction Quality, Physical Environment Quality, and Outcome Quality varied in terms of their importance to overall perceived Service Quality. In addition, each of the relevant sub-dimensions also varied in importance to each of the primary dimensions. The results of nine regression models are illustrated in Figure 5.2, with the standardised coefficients (rounded-up) listed next to all the significant paths. Outcome Quality was perceived as the most important primary dimension and this primary dimension had two significant and one insignificant sub-dimensions. Valence ( ? =0.429) was perceived as the most important sub-dimension, followed by Waiting Time ( ? =0.147). The sub- dimension, Sociability ( ? =0.060) was not significant, but it did have a small impact on the perceptions of Outcome Quality. 172 0.54 0.42 0.19 0.35 0.76 0.26 0.72 0.70 H7(+) 0.29 0.14 0.41 Primary Dimension 0.62 0.07 0.11 0.02 0.24 0.18 0.20 0.17 0.08 0.43 0.15 0.06 Pertaining Sub-dimensions (5 items)(3 items)(3 items)(3 items) (6 items)(6 items)(6 items)(3 items)(3 items) (3 items)(5 items)(3 items) Note: EC = Employees? Conduct, EE = Employees? Expertise, EPS = Employees? Problem-Solving, CCI = Customer-to-Customer Interaction, DA = Décor & Ambience, RQ = Room Quality, AF = Availability of Facility, DES = Design, LO = Location, VA = Valence, WT = Waiting Time, SO = Sociability. Figure 5.2: Behavioural Intentions of Surveyed Customers in the Hotel Industry: Path Model Interaction Quality was perceived as the second most important primary dimension of Service Quality and it had three significant and one insignificant sub-dimensions. Employees? Conduct ( ? =0.618) was perceived as the greatest sub-dimension, followed by Employees? Problem-Solving ( ? =0.110) and Employees? Expertise ( ? =0.067). The sub- dimension, Customer-to-Customer Interaction ( ? =0.022) was not significant, but it did have a small impact on the perceptions of Interaction Quality. Perceived Value Service Quality Interaction Quality Image Physical Environment Quality Outcome Quality EC EE EPS CCI RQ AF DES LO WT SO DA VA Customer Satisfaction Behavioural Intentions 173 Finally, Physical Environment Quality was perceived as the least important dimension of the three Service Quality dimensions. Physical Environment Quality had five significant sub- dimensions. Décor & Ambience ( ? =0.244) were perceived as the most important sub- dimensions, followed by Availability of Facility ( ? =0.200), Room Quality ( ? =0.183), Design ( ? =0.166), and Location ( ? =0.077). 5.5.6 Results Pertaining to Research Objective Five In order to achieve Research Objective Five, Hypothesis 16 was formulated to test if there were differences between groups based on the demographic characteristics of the respondents. One crucial assumption for an analysis of variance to be effective is that the groups being compared must be of a similar sample size. In this study, four groups: Gender, Marital Status, Purpose of Travel and Occupation fulfilled this criterion. However, the other four groups: Age, Level of Education, Annual Income, and Ethnic Background have disproportionate sample sizes. Therefore, to obtain a reliable statistical result, the age groups were combined into four groups, 18 to 25 years, 26 to 35 years, 36 to 45 years, and 46 years and over. The educational level groups were combined into three groups, junior college and under, college or university, and graduate school and over. The annual income groups were combined into two groups, TW$500,000 and under, and TW$500,001 and over. Finally, the ethnic background groups were also combined into two groups, Asian and Western. 5.5.6.1 Hypothesis 16a Hypothesis 16a postulated that there were perceptual differences between Behavioural Intentions and related constructs, Service Quality, Perceived Value, Image and Customer Satisfaction within the Gender, Marital Status, Age, Level of Education, Annual Income, Purpose of Travel, Ethnic Background, and Occupation Groups. The F statistics (see Appendix 21, Table 55A) revealed that there were no perceptual differences in the Perceived Value, Image and Behavioural Intentions constructs within the eight demographic groups. The F statistics showed that the mean of the Service Quality construct (10% level) was significantly different within the Gender Group. There was a mean difference in the Service Quality (10% level) and Customer Satisfaction (5% level) constructs within the Purpose of 174 Travel Group. Finally, Service Quality (5% level) was perceived differently within the Occupation Group. Table 5.24 summarises the ANOVA results related to Hypothesis 16a, the significant perceptual differences are indicated. Table 5.24: ANOVA Results Relating to Hypothesis 16a Construct Gender Marital Status Age Level of Education Annual Income Purpose of Travel Ethnic Background Occupation Service Quality * * ** Perceived Value Image Customer Satisfaction ** Behavioural Intentions *** Significant at 1% level ** Significant at 5% level * Significant at 10% level 5.5.6.2 Hypothesis 16b Hypothesis 16b postulated that there were perceptual differences in the Primary Dimensions, Interaction Quality, Physical Environment Quality and Outcome Quality within the Gender, Marital Status, Age, Level of Education, Annual Income, Purpose of Travel, Ethnic Background, and Occupation Groups. Of the eight demographic groups, only the Outcome Quality dimension was perceived differently within the Age, Level of Education, and Ethnic Background Groups at the 1%, 10%, and 10% levels of significance respectively (see Appendix 21, 56A). However, there were no perceptual differences in the Interaction Quality and Physical Environment Quality dimensions within the eight demographic groups. Table 5.25 summarises the ANOVA results associated with Hypothesis 16b, the significant perceptual differences are indicated. Table 5.25: ANOVA Results Relating to Hypothesis 16b Primary Dimension Gender Marital Status Age Level of Education Annual Income Purpose of Travel Ethnic Background Occupation Interaction Quality Physical Environment Quality Outcome Quality *** * * *** Significant at 1% level ** Significant at 5% level * Significant at 10% level 175 5.5.6.3 Hypothesis 16c Hypothesis 16c postulated that there were perceptual differences in the sub-dimensions that pertained to the primary dimensions within the Gender, Marital Status, Age, Level of Education, Annual Income, Purpose of Travel, Ethnic Background, and Occupation Groups. The results indicated that there was a mean perceptual difference in each sub-dimension within the eight demographic groups (see Appendix 21, Table 57A). The F-statistics of the sub-dimensions indicated that there were perceptual differences in Décor & Ambience and Room Quality between males and females at the 10% and 1% levels of significance respectively. There was a perceptual difference in the Room Quality sub- dimension within the Marital Status Group at the 5% level of significance. The Age Group had perceptual differences on four sub-dimensions: Employees? Conduct (10% level), Employees? Expertise (10% level), Room Quality (5% level), and Valence (10% level). The Design and Sociability sub-dimensions were perceived differently within the Level of Education Group at the 5% and 10% level of significance respectively. The Annual Income Group had perceptual differences on five sub-dimensions: Employees? Expertise (1% level), Décor & Ambience (1% level), Room Quality (10% level), Availability of Facility (10% level), and Valence (10% level). The Purpose of Travel Group had perceptual differences on four sub-dimensions: Décor & Ambience (1% level), Availability of Facility (5% level), Valence (10% level), and Sociability (5% level). As for the Ethnic Background Group, there were perceptual differences in the Customer-to-Customer Interaction (10% level), Décor & Ambience (1% level), Valence (10% level), and Sociability (5% level) sub-dimensions. The Occupation Group had perceptual differences on eight sub-dimensions: Employees? Problem-Solving (10% level), Customer-to-to-Customer Interaction (1% level), Room Quality (1% level), Design (5% level), Location (5% level), Valence (1% level), Waiting Time (1% level), and Sociability (1% level). Table 5.26 presents a summary of ANOVA results relating to Hypothesis 16c, the significance perceptual differences are indicated. 176 Table 5.26: ANOVA Results Relating to Hypothesis 16c Sub-dimension Gender Marital Status Age Level of Education Annual Income Purpose of Travel Ethnic Background Occupation Employees? Conduct * Employees? Expertise * *** Employees? Problem-Solving * Customer-to-Customer Interaction * *** Décor & Ambience * *** *** *** Room Quality *** ** ** * *** Availability of Facility * ** Design ** ** Location ** Valence * * * * *** Waiting Time *** Sociability * ** ** *** *** Significant at 1% level ** Significant at 5% level * Significant at 10% level 5.5.6.4 Discussion Regarding Research Objective Five The eight demographic groups, Gender, Marital Status, Age, Level of Education, Annual Income, Purpose of Travel, Ethnic Background, and Occupation perceived differences on Service Quality, Perceived Value, Image, Customer Satisfaction, Behavioural Intentions, and the primary and sub dimensions of Service Quality were perceived. In terms of the five constructs, Service Quality was perceived differently within the Gender, Purpose of Travel, and Occupation Groups. In addition, the Customer Satisfaction construct was perceived differently within the Purpose of Travel Group. However, the remaining three constructs, Perceived Value, Image and Behavioural Intentions, had no perceptual differences within the eight demographic groups. Of the three primary dimensions, only Outcome Quality was perceived differently within the Age, Level of Education, and Ethnic Background Groups. In terms of the Employees? Conduct, Employees? Expertise, Employees? Problem-Solving, Customer-to-Customer Interaction, Décor & Ambience, Room Quality, Availability of Facility, Design, Location, Valence, Waiting Time, and Sociability sub-dimensions, there was a mean perceptual difference in each sub-dimension within the eight demographic groups, Gender, Marital Status, Age, Level of Education, Annual Income, Purpose of Travel, Ethnic Background, and Occupation. First, in terms of the first sub-dimension of Interaction Quality, the Age Group had mean differences on the Employees? Conduct sub-dimension. 177 For the second sub-dimension of Interaction Quality, the Age and Annual Income Groups had mean differences on the Employees? Expertise sub-dimension. With the third sub- dimension, the Occupation Group had perceptual differences on the Employees? Problem- Solving sub-dimension. Lastly, with regard to the fourth sub-dimension of Interaction Quality, the Customer-to-Customer Interaction sub-dimension was perceived differently within the Ethnic Background and Occupation Groups. The first sub-dimension of Physical Environment Quality, Décor & Ambience was perceived differently within the Gender, Annual Income, Purpose of Travel, Ethnic Background, and Occupation Groups. The second sub-dimension of Physical Environment Quality, the Gender, Marital Status, Age, Annual Income, and Occupation Groups had perceptual differences on the Room Quality sub-dimension. The third sub-dimension, Availability of Facility, was perceived differently within the Annual Income and Purpose of Travel Groups. For the fourth sub-dimension, the Level of Education and Occupation Groups had perceptual differences on the Design sub-dimension. Finally, the fifth sub- dimension, Location, was perceived differently within the Occupation Group. Finally, Valence as the first sub-dimension of Outcome Quality was perceived differently within the Age, Annual Income, Purpose of Travel, Ethnic Background, and Occupation Groups. In addition, the second sub-dimension of Outcome Quality, Waiting Time was perceived differently within the Occupation Group. Finally, the third sub-dimension, Level of Education, Purpose of Travel, Ethnic Background, and Occupation Groups had perceptual differences on the Sociability sub-dimension. 5.6 Chapter Summary This chapter presented the empirical results based on the research plan and the methodology outlined in Chapter Four. A preliminary examination of the data set indicated that the questionnaire was reliable and valid. Examination of the data set indicated that the statistical assumptions required for performing factor analysis, regression analysis and analysis of variance were met. 178 After using principal components and factor analysis, the originally proposed 16 sub- dimensions representing service quality were reduced to 12 sub-dimensions (see Appendix 15, Tables 39A and 40A). Each path in the conceptual model presented in Section 3.3 was subsequently tested using nine regression models. While Hypotheses One and Three were partially supported, the remaining 13 hypotheses were fully supported by the results. Hypothesis 16, relating to the different perceptions that may exist between demographic groups, demonstrated that of all the groups, the Purpose of Travel and Occupation Groups had the most perceptual differences within their groups. The remaining demographic groups had minimal perceptual differences within their groups. 179 CHAPTER 6 DISCUSSIONS AND IMPLICATIONS 6.1 Introduction This chapter provides a summary of the research, reviews the results, and reports several conclusions based on the results and discussions presented in Chapter Five. The theoretical and managerial contributions, limitations, and directions for future research are also discussed. 6.2 Summary of this Study The literature review presented in Chapter Two recommended that the multi-dimensional structure used as a framework to conceptualise and measure service quality in other service industries is also appropriate for use in the hotel industry. The literature review, the focus group interviews and the statistical analyses provided support for the presence of a multi- dimensional structure and for three primary dimensions underlying service quality in the hotel industry, namely, Interaction Quality, Physical Environment Quality and Outcome Quality. The three primary dimensions of service quality may be appropriate for use across industries and cultures. However, several researchers suggested that the sub-dimensions of service quality should be developed specifically for each industry under investigation because of the inability to identify a common set of sub-dimensions (Kang, 2006; Wang & Pearson, 2002; Brady & Cronin, 2001; Dabholkar et al., 1996; Cronin & Taylor, 1992; Grönroos, 1990, 1982; Parasuraman et al., 1988, 1985). In agreement with these researchers, this study has identified the sub-dimensions of service quality as perceived by customers at one five-star hotel in Taiwan. 180 Several constructs related to service quality were identified in the literature. Service quality has been related to perceived value (Hu et al., 2009; Choi et al., 2004; Petrick & Backman, 2002; Caruana et al., 2000; Bolton & Drew, 1991a; Zeithaml, 1988) and image (Hu et al., 2009; Kayaman & Arasli, 2007; Aydin & Ozer, 2005; Andreassen & Lindestad, 1998; Lapierre, 1998) respectively. In addition, customer satisfaction has been associated with perceived value (Hu et al., 2009; Gallarza & Saura, 2006; Hellier et al., 2003; Choi & Chu, 2001; Tam, 2000; Fornell et al., 1996), image (Hu et al., 2009; Chun, 2005; Koo, 2003; Bloemer & Oderkerken-Schroder, 2002; Bloemer & de Ruyter, 1998) and service quality (Hu et al., 2009; Clemes et al., 2008, 2007; Lu & Ling, 2008; Kao, 2007; Gallarza & Saura, 2006; Zeithaml et al., 1996; Cronin & Taylor, 1994, 1992; Parasuraman et al., 1994; Rust & Oliver, 1994). Behavioural intentions were related to customer satisfaction (Pollack, 2009; Clemes et al., 2008; Lin & Hsieh, 2007; Kang et al., 2004; Cronin et al., 2000; Tam, 2000; Dabholkar & Thorpe, 1994) and image (Hu et al., 2009; Ryu et al., 2008; Castro et al., 2007; Park et al., 2004; Nguyen & LeBlanc, 2001, 1998). Therefore, this study analysed each of these constructs and the relationships among them in a hotel setting. In addition, several researchers suggested that the perceptions of service quality, value, image, customer satisfaction, and behavioural intentions not only varied across industries but also differed according to customer demographics (Clemes et al., 2007, 2001; Ho & Lee, 2007; Kao, 2007; Tan, 2002). Accordingly, the constructs have also been examined based on the respondents? gender, marital status, age, level of education, annual income, purpose of travel, ethnic background, and occupation. In order to achieve a better understanding of hotel customer perceptions of service quality and the effects of these perceptions on the related constructs such as behavioural intentions, customer satisfaction, perceived value, and image, five research objectives were established: (1) To identify the dimensions of service quality as perceived by customers in the Taiwan hotel industry. (2) To determine if perceived value plays a moderating role between service quality and customer satisfaction as perceived by customers in the Taiwan hotel industry. 181 (3) To examine the interrelationships between behavioural intentions and the other constructs related to behavioural intentions as perceived by customers in the Taiwan hotel industry. (4) To identify the least and most important service quality dimensions as perceived by customers in the Taiwan hotel industry. (5) To examine the effects of demographic factors on behavioural intentions and related constructs as perceived by customers in the Taiwan hotel industry. These five research objectives were addressed by testing 16 hypotheses, developed in Chapter Three. Hypotheses 1 to 6 relate to Research Objective One, Hypothesis 7 relates to Research Objective Two, Hypotheses 8 to 14 relate to Objective Three, Hypothesis 15 relates to Research Objective Four, and Hypothesis 16 relates to Research Objective Five. 6.3 Conclusions Pertaining to Research Objective One Research Objective One was achieved. The dimensions of service quality, as perceived by customers at a five-star hotel in Taiwan, were identified. The primary dimensions of service quality were Interaction Quality, Physical Environment Quality and Outcome Quality, as identified in the literature review, supported by the focus group research, and confirmed by the statistical analyses. The findings specifically support the presence of a multi-dimensional structure of service quality (Brady & Cronin, 2001) for the hotel industry. The factor analysis reduced the 16 sub-dimensions originally proposed to 12. The 12 sub- dimensions were: Employees? Conduct, Employees? Expertise, Employees? Problem- Solving, Customer-to-Customer Interaction, Décor & Ambience, Room Quality, Availability of Facility, Design, Location, Valence, Waiting Time and Sociability. The number of sub-dimensions is not the same as the number of sub-dimensions identified by Clemes et al. (2007), Fassnacht and Koese (2006) and Collins (2005) for other service industries. This difference supports the contention of earlier studies (van Dyke, Kappelman, & Prybutok, 1997) that identified different factor structures across the service industries. However, some of the 12 sub-dimensions are similar in content to the dimensions factored by other researchers in the airline, communication, education, health care, recreational sport, 182 retailing, travel and tourism, and urgent transport sectors (Caro & García, 2008, 2007; Clemes et al., 2008, 2007; Dagger et al., 2007; Caro & Roemer, 2006; Shonk, 2006; Jones, 2005; Ko & Pastore, 2005, 2001; Brady & Cronin, 2001). Some of the 12 sub-dimensions identified in this research are also similar in content to those factored by other researchers who have focused on hotel studies (Sa´nchez-Herna´ndeza, Mart?´nez-Tura, Peiro, & Ramosa, 2009; Heide et al., 2007; Pan, 2005, 2002; Sharpley & Forster, 2003; Choi & Chu, 2001; Ekinci & Riley, 2001; Chu & Choi, 2000; Min & Min, 1997; Saleh & Ryan, 1992; West & Purvis, 1992; Lennon & Wood, 1989). However, the 12 sub-dimensions differ in number from other hotel studies (Gu & Ryan, 2008; Wilkins et al., 2006a; Choi & Chu, 2001; Chu & Choi, 2000; Callan, 1996; Saleh & Ryan, 1992). The different sub-dimensional factor structure supports the view that the dimensionality of the service quality construct depends on the service industry under investigation and adds support to the claims that industry- and cultural-specific measures of service quality need to be developed to identify different dimensional structures (Clemes et al., 2007, 2001; Kang, 2006; Brady & Cronin, 2001; Dabholkar et al., 1996; Powpaka, 1996; Saleh & Ryan, 1992). Three primary dimensions were identified in this study: Interaction Quality, Physical Environment Quality, and Outcome Quality. Several previous studies on hotel service quality have focused on customer and employee interaction, physical environment and outcome quality (Antony, Antony, & Ghosh, 2004; Luk & Layton, 2004; LeBlanc, 2002). This research adds empirical support to this vein of literature and has identified Interaction Quality, Physical Environment Quality and Outcome Quality as important dimensions when customers assess hotel service quality. In this research, the statistical analyses indicated that Outcome Quality ( ? =0.405) had a stronger effect on Service Quality than Interaction Quality ( ? =0.287) and Physical Environment Quality ( ? =0.136). This finding coincides with some literature proposing that the outcome of the service encounter significantly affected customer perceptions of service quality (Carman, 2000; McDougall & Levesque, 1994; Rust & Oliver, 1994; Grönroos, 1990, 1984). This research also supports Powpaka?s (1996) result in which outcome quality, 183 as one of the components of service quality, could be a significant determinant of the overall service quality as judged by customers in a service industry. In addition, this result supports Powpaka?s (1996) contention that the ?inclusion of the outcome quality component into the model and measurement scale significantly improves its explanatory power and predictive validity? (p. 5). Furthermore, these findings support Caro and García?s (2008) result in which that outcome was the key manifestation of service quality. In addition, this result also coincides with the finding of Caro and García (2008) that the outcome quality dimension was unexpectedly close to the meaning of the higher order construct as it perfectly reflected customer evaluations of service quality. Specifically, this finding agrees with Luk and Layton?s (2004) result in which managing outcome quality was identified as substantial when managing front-line service providers? manner and behaviour in the hotel industry. Surprisingly, the reviewed literature on service quality in the hotel industry does not define the outcome quality construct in its measurement instruments. Therefore, these results support Luk and Layton?s (2004) recommendation that more research should recognise the importance of outcome attributes to customers in their evaluations of hotel service quality. The results of this research showed only the Valence and Waiting Time sub-dimensions positively influenced Outcome Quality. The Valence sub-dimension result supports some researchers? (Martinez & Martinez, 2007; Ko & Pastore, 2005; Brady & Cronin, 2001) contentions that valence was a key determinant of outcome quality in service industries. In addition, this result is consistent with Brady and Cronin?s (2001) recommendation that the positive valence of an outcome ultimately led customers to favourable service experiences when customers had a positive perception of service quality. The Waiting Time sub- dimension result supports Hightower, Brady and Baker?s (2002) finding that perceived waiting time affected overall service quality. In addition, this result empirically supports Houston et al. (1998) who incorporated waiting time into their analysis of outcome quality and found that waiting time was an important component of outcome quality. The analysis of this research indicated that the Sociability sub-dimension negatively affected Outcome Quality. However, the statistical result revealed that the Sociability sub-dimension had only a small effect on Outcome Quality ( ? =0.060). This differs from Heide et al.?s (2007), Collins? (2005), and Brady and Cronin?s (2001) findings that sociability played an important 184 role in composing the physical environment quality instead of outcome quality. Conversely, this result is consistent with the contention of another study (Ko & Pastore, 2005) that the social experience focused on the overall after-consumption outcome. In this study, the statistical analyses showed that Interaction Quality had less effect on Service Quality than Outcome Quality. However, Interaction Quality positively affected overall service quality perceptions. This result agrees with the findings of several studies (Sa´nchez-Herna´ndeza et al., 2009; Martinez & Martinez, 2007; Ko & Pastore, 2005; Brady & Cronin, 2001) that interaction quality still played an important role in customer evaluations of service quality even though outcome quality was found to be a key manifestation of perceived quality. Furthermore, this finding supports Caro and García?s (2008) and Collins?s (2005) results that interaction quality had less impact on service quality than outcome quality. In addition, this result agrees with the findings of some earlier research (Bigné et al., 1996; LeBlanc, 1992; Bitner et al., 1990; Grönroos, 1982) who indicated that interaction quality was important in the service delivery process, and that interaction quality had a significant effect on service quality perceptions. Brady and Cronin (2001) also demonstrated that there was strong support in the literature for including an interaction dimension in the conceptualisation of perceived service quality. Interaction Quality had less impact on Service Quality than Outcome Quality in this research as most of the respondents were composed of Asian customers ( N =488). According to Hofstede and Hofstede (2004), Asian cultures are collectivist whereas Western cultures are individualist. Donthu and Yoo (1998) demonstrated that collectivistic customers did not expect service providers to respect and care about them and show empathy and attention compared with individualistic customers. In addition, Donthu and Yoo (1998) hypothesised that individualistic customers had higher expectations of outcome quality from service providers than collectivistic customers because individualistic customers would expect service providers ?to give them confidence about the service they are receiving? (p. 181). According to Miyahara (2004), Asian people?s social behaviours have been largely left to speculations, and often labeled ?mysterious? and ?deviant.? In addition, previous cross- cultural research indicated that customers from different cultural backgrounds had different perceptions of service quality (Heo, Jogaratnam, & Buchanan, 2004; Kim & Jin, 2002; 185 Witkowski & Wolfinbarger, 2002; Kandampully, Mok, & Sparks, 2001; Furrer, Liu, & Sudharshanan, 2000; Mattila, 2000, 1999). Espinoza (1999) proposed that cultural differences would account for differences in service quality assessment. Therefore, the relative importance of service quality dimensions differed based on different cultural contexts. Individualism or collectivism related to humans? relationships with one another. Individualistic societies referred to those which valued the individual relative to the group. Alternatively, collectivist societies placed an emphasis on the group rather than the individual. In this study, therefore, the difference of cultural background may be a key determinant to explain that Interaction Quality had less effect on Service Quality than Outcome Quality. This study indicated that three sub-dimensions, Employees? Conduct, Employees? Expertise and Employees? Problem-Solving, positively affected the Interaction Quality primary dimension. This result supports Caro and García?s (2008) result in which employees? conduct was the best manifestation of the Interaction Quality primary dimension. Also, these findings agree with Czepiel et al. (1985) who suggested that the attitude, behaviour and skill of service employees defined the quality of the delivered service and ultimately ?affect what clients evaluate as a satisfactory encounter? (p. 9). In addition, this result supports the findings of Bitner (1990) and Grönroos (1990) that the attitudes, behaviour and skills of employees were key determinants that largely influenced customer overall perceptions of interaction quality. However, this research indicates that the Customer-to-Customer Interaction sub-dimension had only a small influence on Interaction Quality ( ? =0.022). This result supports the finding of Ko and Pastore (2005) that customer-to-customer interaction existed during a service delivery in which each customer had a level of interaction with other customers, to some extent. Physical Environment Quality, while important, was identified as the least influential effect on Service Quality in this study. This finding supports Nankervis?s (1995) study that physical environment was a major contact arena for customers and service providers in the hotel industry. In addition, this finding agrees with Nguyen and LeBlanc?s (2002) result that, for services management, a hotel?s physical environment was one crucial element that determined the success of the service delivery process. Furthermore, this result is consistent 186 with the finding of Ou (2002) that physical environment played an important role in raising the level of customer satisfaction with hotel service quality and this dimension should not be ignored in hotel studies. The statistical analyses showed that Décor & Ambience, Room Quality, Availability of Facility, Design, and Location had a positive influence on Physical Environment Quality. The Décor & Ambience result concurs with previous hotel studies (Heide et al., 2007; Wu & Weber, 2005; Lockyer, 2002; Min & Min, 1997; Saleh & Ryan, 1992) that décor and ambience are the key themes underlying customer perceptions of service environment quality. The Room Quality finding is consistent with Choi and Chu?s (2001) result that room quality was seen as a key determinant of customer evaluations of hotel service quality and selection of accommodation. The Availability of Facility finding concurs with some earlier hotel studies (Hilliard & Baloglu, 2008; Akbaba, 2006; Chu & Choi, 2000; Murphy, 1988) that availability of facility was an important part of constituting the hotel physical environment quality dimension. The Design result coincides with the findings of Ko and Pastore (2005) and Brady and Cronin (2001) that customer perceptions of the facility design directly influenced the quality of the physical environment. Finally, in terms of Location, the result agrees with Chou et al.?s (2008) and Coltman?s (1989) studies that location was one of customers? primary concerns in selecting a hotel as their accommodation. This research, however, indicated that five sub-dimensions and three sub-dimensions accounted for only a small amount of variation in Physical Environment Quality ( 2R =34.0%) and Outcome Quality ( 2R =27.8%), respectively (see Tables 5.16 and 5.17). This implies that there were other important sub-dimensions of Physical Environment Quality and Outcome Quality that have not been identified in this study. According to Nakajima (2007), a low 2R implied that there might be the possibility of under-fitting the regression model. However, Bruhn, Georgi and Hadwich (2008) proposed that 2R values of at least 26% represented large effect sizes in a multiple regression. In this research, all of the 2R values in the regression models were greater than 26%. Therefore, the finding of this study supports Bruhn et al. (2008), who proposed that the first-order and second-order dimensions appeared well described by the third-order service quality construct. 187 6.4 Conclusions Pertaining to Research Objective Two Hypothesis Seven proposed that Perceived Value had a moderating effect on the relationship between the Service Quality and Customer Satisfaction constructs. Research Objective Two was achieved as Hypothesis Seven was confirmed by the significant positive moderating influence of Perceived Value on the relationship between the Service Quality and Customer Satisfaction constructs. This finding concurs with the results of several researchers (Gil et al., 2008; Lin, 2007; Gallarza & Saura, 2006; Hellier et al., 2003; Caruana et al., 2000; Oh, 1999) that the influence of Service Quality on Customer Satisfaction was not just direct but was also moderated by Perceived Value. In addition, the beta coefficient ( ? =0.761) indicated that the moderating effect of Perceived Value on Service Quality and Customer Satisfaction was important in the hotel industry. This finding supports the finding of Cronin and Taylor (1992) that marketers needed to focus on perceived value as an important determinant of enhancing the predictive power of service quality. This result also concurs with Bagozzi (1980), who indicated that ignoring or omitting the perceived value construct from the conceptual model may cause problems of model misspecification. In addition, this study supports Oh?s (1999) result that perceived value was an important variable or construct in service quality and customer satisfaction studies and vice versa. Furthermore, Oh (1999) showed that service quality and perceived value in combination may completely moderate the perceptions of customer satisfaction in the hotel industry. Therefore, this research has identified that perceived value is an important variable or construct that moderates the relationship between service quality and customer satisfaction in the hotel industry. 6.5 Conclusions Pertaining to Research Objective Three Research Objective Three was achieved as Hypotheses 8, 9, 10, 11, 12, 13 and 14 were supported by the significant positive effects on their related constructs. Hypothesis 8 proposed that Perceived Value was positively influenced by Service Quality. This result supports the trade-off relationship identified by Oh (1999), Bolton and Drew (1991a) and Zeithaml (1988) between service quality and price. In addition, this finding supports Hartline and Jones (1996), who determined that the service performance of front- 188 line employees had a significant influence on the overall perceptions of value. In addition, this result also agrees with the contentions of earlier studies (Chen, 2007b; Sweeney, Soutar, & Johnson, 1997) that identified service quality as an important indicator of perceived value. Hypothesis 10 proposed that Service Quality positively influenced Image. The results of this research support Andreassen and Lindestad?s (1998) contention that the perception of service quality was an important factor in influencing image because services were difficult to evaluate. In addition, this result supports Zeithaml?s (1988) proposition that service quality was customers? judgment about the overall excellence or superiority of a service or, in other words, the image. Furthermore, this result supports Kayaman and Arasli?s (2007) study in which image resulted from all of customers? service experiences. Also, this finding is consistent with Hu et al.?s (2009) study that ?customers who received high service quality during service delivery would form a favourable image of the hotel? (p. 120-121). Finally, this finding supports the contentions of early studies (Bitner, 1991; Gummesson & Grönroos, 1988; Grönroos, 1984, 1982) in which a corporate image in the services marketing literature has been identified as an important factor in the overall evaluation of the service and the business organisation. Hypothesis 9 proposed that Perceived Value had a positive influence on Customer Satisfaction. The strongest positive effect was between Perceived Value and Customer Satisfaction ( ? =0.420). This statistical result coincides with Choi and Chu?s (2001) finding that perceived value appeared to be a top factor in determining the overall level of customer satisfaction in the hotel industry. Likewise, this study supports other studies (Hu et al., 2009; Caruana et al., 2000; McDougall & Lesvesque, 2000) in which customer perceptions of value had a strong impact on satisfaction. In addition, the result agrees with Nasution and Mavondo?s (2008) finding that there was more satisfaction with the value received if customers perceived significantly more value for what they were paying for in the hotel industry. Therefore, this result, based on Oh?s (1999) suggestion, confirmed that customers perceived greater value for money when they experienced a high level of service quality in the hotel industry. Increased value perceptions then contributed to customer satisfaction (Oh, 1999). 189 Hypothesis 13 proposed that Service Quality positively affected Customer Satisfaction. This result supports several researchers? points of view that service quality was an antecedent of customer satisfaction (Hu et al., 2009; Chen et al., 2007; Tsiotsou & Vasioti, 2006; Wilkins et al., 2006a; Lee & Hwan, 2005; Caruana, 2002; Brady et al., 2001; Brady, 1997; de Ruyter, Bloemer, & Peeters, 1997; Parasuraman et al., 1994, 1985; Taylor & Baker, 1994; Teas, 1994; Anderson & Sullivan, 1993; Cronin & Taylor, 1992; Woodside et al., 1989) rather than customer satisfaction being an antecedent of service quality (Bitner & Hubbert, 1994; Bolton & Drew, 1991a, b; Bitner, 1990; Parasuraman et al., 1988, 1985). The result of this research is consistent with Su?s (2004) contention that providing services that customers prefer was obviously a starting point for providing customer satisfaction in the hotel industry. In addition, this finding supports Su?s (2004) study on customer satisfaction that focused on identifying service attributes, that is, customers? needs and wants. Hypothesis 11 proposed that Image had an influence on Customer Satisfaction ( ? =0.186). This result supports the contention of an early study (Andreassen & Lindestad, 1998) in which image was believed to influence the judgment of customer satisfaction. When customers were satisfied with the services rendered, their attitudes towards the organisation would be improved (Andreassen & Lindestad, 1998). This attitude then affected customer satisfaction with the business organisation (Andreassen & Lindestad, 1998). However, the beta coefficient indicated that the importance of Image on Customer Satisfaction was less than the importance of Perceived Value and Service Quality on Customer Satisfaction. This finding is consistent with the contentions of early research (Kim & Kim, 2005; Kandampully & Suhartanto, 2003; Suhartanto, 1998) in which image has been identified as a key determinant upgrading the levels of customer satisfaction in the hotel industry. Hypotheses 12 and 14 proposed that Customer Satisfaction and Image positively affected Behavioural Intentions, respectively. This result agrees with Hu et al. (2009), Kandampully and Suhartanto (2003, 2000) and Suhartanto (1998) who showed that customer satisfaction and image were two important aspects that largely influenced behavioural intentions in the hotel industry. This result supports many researchers? propositions that customer satisfaction influenced behavioural intentions, which, in turn, resulted in long or short term profitability (Rust, Zahorik, & Keiningham, 1995; Schneider & Bowen, 1995; Anderson & Fornell, 1994; 190 Heskett et al., 1994; Storbacka, Strandvik, & Grönroos, 1994; Reicheld & Sasser, 1990; Zeithaml et al., 1990). In addition, this result supports Gilbert and Horsnell?s (1998) contention that the aim of managing customer satisfaction was to obtain a higher rate of customers? favourable behavioural intentions and to improve the market share and profits of an organisation. Although the beta coefficient indicated that Image ( ? =0.351) had less impact on Behavioural Intentions than Customer Satisfaction ( ? =0.536), Image also had a positive effect on Behavioural Intentions. This result supports Hu et al.?s (2009), Bigné et al.?s (2001) and Bhote?s (1996) findings that image had a positive influence on behavioural variables as well as on the evaluation variables. When the overall image of an organisation held by customers was improved, their favourable behavioural intentions may be to return to the organisation, or recommend the organisation to others (Bigné et al., 2001). Besides, the result supports Hung?s (2006) finding that customers? behavioural intentions to choose the hotel in their future trips were only affected by their image formed after making a stay this time. Namely, the more positive the customers? image was for the hotel, the higher their behavioural intentions were to accommodate in the hotel in the future (Hung, 2006). In addition, this result is also consistent with Chen and Tsai?s (2007) finding that image not only affected customers? decision-making processes but also conditioned their after- decision-making behaviours. In other words, the effect of image was not limited only to the stage of the organisation selection, but also affected the behaviour of customers in general (Bigne et al., 2001). Furthermore, this result supports Hu et al.?s (2009), Kandampully and Suhartanto?s (2003, 2000), and Suhartanto?s (1998) findings that image was an antecedent of behavioural intentions in the hotel industry. Overall, Research Objective Three of this study has been satisfied. Chen et al.?s (2007) finding recommended that if hotels plan to increase their profits and maintain their growth via enhancement of customer behavioural intentions, they must focus on improving customer satisfaction for the purpose of increasing customer behavioural intentions or improving service quality and take advantage of its indirect positive effect on customer behavioural intentions. 191 Also, Hu et al.?s (2009) study suggested that satisfying customers may not be sufficient in today?s world of intense competition. Hotel management should not only focus on improving customer satisfaction but also target on improving customer perceptions of overall service quality and increasing customer perceived value and image. In addition, greater competitiveness is associated with higher levels of service quality, greater perceived value and image that are key determinants of upgrading customer satisfaction and increasing favourable behavioural intentions. 6.6 Conclusions Pertaining to Research Objective Four Research Objective Four was achieved as Hypothesis 15 proposed that the least and most important service quality dimensions were identified as perceived by customers in the Taiwan hotel industry. The primary dimension, Outcome Quality ( ? =0.405), was perceived by hotel customers as the most important followed by Interaction Quality ( ? =0.287) and Physical Environment Quality ( ? =0.136). This finding agrees with Powpaka?s (1996) contention that the outcome service quality dimension was required in every service industry. In addition, this result supports Powpaka?s (1996) proposition that customers using the outcome quality dimension as their overall assessment of service quality of an organisation generally depended on their ability to evaluate outcome quality of the service accurately and efficiently. The beta coefficient of this research revealed that Interaction Quality exerted a stronger impact on Service Quality than Physical Environment Quality, implying that customers perceived the interaction with service providers as more important than the hotel?s facilities and accommodation environment. The result supports Bieger and Laesser?s (2004) study in which interaction quality between service providers and customers was a more important contributor to service quality experience than physical environment quality in the hospitality industry. This study showed that Physical Environment Quality was perceived by hotel customers as the least important primary dimension. However, Physical Environment Quality had a small 192 effect on Service Quality. Therefore, the result of this study provides empirical support for the importance of the physical environment in the consumption of hotel services as called for by Ryu and Jang (2007). Furthermore, the finding of this research supports Ryu and Jang?s (2007) recommendation that physical environment quality was an important component of hotel service quality. The result also agrees with several researchers (Baker, 1987; Booms & Bitner, 1981; Shostack, 1977) who determined that physical environment quality influenced customer perceptions of service quality. Each of the sub-dimensions varied considerably in terms of their importance to the three primary dimensions (see Figure 5.2). 6.7 Conclusions Pertaining to Research Objective Five Research Objective Five was partially fulfilled because not each of the constructs and the service quality dimensions was perceived differently within the eight demographic groups. In the following paragraphs, the conclusions from the inter-relationships between the eight demographic groups, the constructs, and the primary and sub dimensions of service quality, are presented based on the discussion in Section 5.5.6. The conclusions about five constructs (Service Quality, Perceived Value, Image, Customer Satisfaction and Behavioural Intentions) were based on the results of the relationship with the eight demographic groups and themselves. The result supports Ekinci et al.?s (2003) hotel study, as in this research the Service Quality construct was perceived differently within the Gender Group. However, the result does not agree with Choi and Chu?s (2001) hotel finding, as in this study the Purpose of Travel Group exhibited no perceptual differences on the Perceived Value construct. In addition, the result does not coincide with Skogland and Siguaw?s (2004) hotel study because there were no perceptual differences on the Image construct within the Age and Level of Education Groups in this research. The result also differs from Solnet?s (2007), Mey et al.?s (2006), Tsiotsou and Vasioti?s (2006), and Skogland and Siguaw?s (2004) hotel findings because there were no perceptual differences in the Customer Satisfaction construct within the Gender, Age, Level of Education and Ethnic Background groups in this study. Finally, the result differs from Skogland and 193 Siguaw?s (2004) and Wong and Keung?s (2000) hotel findings, as in this research the Age, Purpose of Travel, and Ethnic Background Groups exhibited no perceptual differences on the Behavioural Intentions construct. The conclusions concerning the three primary dimensions (Interaction Quality, Physical Environment Quality and Outcome Quality) were based on the results of the inter- relationship between demographic groups and themselves. The result is inconsistent with Chow et al.?s (2007) and Mattila?s (2000) hospitality studies, as in this study there were no perceptual differences in the Interaction Quality dimension within the Age Group. In addition, the result does not agree with Chow et al.?s (2007) and Mattila?s (2000) hospitality findings because there were no perceptual differences in the Physical Environment Quality dimension within the Gender and Ethnic Background Groups in this research. However, the finding supports Chan et al.?s (2007) hotel study that there were perceptual differences in the Outcome Quality dimension within the Ethnic Background Group. Finally, the conclusions regarding the 12 sub-dimensions (Employees? Conduct, Employees? Expertise, Employees? Problem-Solving, Customer-to-Customer Interaction, Décor & Ambience, Room Quality, Availability of Facility, Design, Location, Valence, Waiting Time, and Sociability) were based on the results of the inter-relationship between demographic groups and themselves. First, the results do not agree with Ramsaran-Fowdar?s (2007) and Knutson?s (1988) hotel findings because the Purpose of Travel Group exhibited no perceptual differences on the Employees? Conduct sub-dimension in this study. Secondly, this finding does not coincide with Akbaba?s (2006) hotel study, as in this research the Purpose of Travel Group showed no perceptual differences on the Employees? Expertise sub-dimension. Thirdly, the results are, however, consistent with McCleary, Weaver and Lan?s (1994) hotel study that the Purpose of Travel Group exhibited no perceptual differences on the Employees? Problem-Solving sub-dimension. Fourthly, the result supports Ramsaran-Fowdar?s (2007) hotel finding that there were perceptual differences in the Décor and Ambience sub-dimension within the Purpose of the Travel Group. Fifthly, the finding is not consistent with Choi and Chu?s (2001), McCleary et al.?s (1994), and Lewis? (1985) hotel studies, as in this study there were no perceptual differences in the Room Quality sub- dimension within the Purpose of Travel Group. Sixthly, the result is consistent with 194 Knutson?s (1988) and Lewis? (1985) hotel findings that there were perceptual differences in the Availability of Facility sub-dimension within the Purpose of Travel Group. Seventhly, the result does not support Siguaw and Enz?s (1999) hotel finding because the Purpose of Travel Group showed no perceptual differences on the Design sub-dimension in this research. Eighthly, the result does not agree with Akbaba?s (2006) and Knutson?s (1988) hotel studies, as in this study the Purpose of Travel Group exhibited no perceptual differences on the Location sub-dimension. However, the result agrees with Chen?s (2001) study that there were perceptual differences in the Location sub-dimension within the Occupation Group in the hotel industry. Ninthly, the finding agrees with Wilkins et al.?s (2006a) hotel finding that the Purpose of Travel Group exhibited perceptual differences in the Valence sub-dimension. Tenthly, the result does not coincide with Ramsaran-Fowdar?s (2007), and Min and Min?s (1997) hotel findings because there were no perceptual differences in the Waiting Time sub-dimension within the Purpose of Travel Group in this research. Finally, the result of this study revealed that there were perceptual differences in the Customer-to-Customer Interaction and Sociability sub-dimensions within the Level of Education, Purpose of Travel, Ethnic Background, and Occupation Groups. The differences in both of these two sub-dimensions have not previously been identified in hotel studies. 6.8 Contributions Achieving the five research objectives of this research makes several contributions to improving the theoretical understanding of the hotel industry. First, achieving Research Objective One supports Luk and Layton?s (2004) call for further research to revisit the dimensions of service quality in the hotel industry. This research provides a more detailed analysis of customer perceptions of the hotel industry and adds additional information to the existing hotel literature. Secondly, achieving Research Objective Two meets Oh?s (1999) recommendation to examine whether perceived value moderates the relationship between service quality and customer satisfaction in the hotel industry. Consequently, this study identifies that perceived value plays a moderating role between service quality and customer satisfaction in the hotel industry. Thirdly, achieving Research Objective Three reinforces a number of hotel researchers? (Kandampully & Hu, 2007; Solnet, 2007; Juwaheer, 2004; 195 Shergill & Sun, 2004; Skogland & Siguaw, 2004; Ekinci et al., 2003; Alexandris et al., 2002; Choi & Chu, 2001; Kandampully & Suhartanto, 2000; Wong & Keung, 2000; Oh, 2000; Tsang & Qu, 2000; Suhartanto, 1998; Snepenger & Milner, 1990) recommendations to investigate the interrelationships between behavioural intentions and the influential constructs: service quality, perceived value, image, customer satisfaction and demographic characteristics as perceived by customers. Accordingly, this research provides a more comprehensive understanding of the interrelationships between behavioural intentions and the other constructs related to behavioural intentions in the hotel industry. Fourthly, achieving Research Objective Four supports several researchers? (Ryu & Jang, 2007; Bieger & Laesser, 2004; Chu, 2002; Powpaka, 1996) contentions that the derived importance of a construct is more important than the relative importance of a construct. Finally, Research Objective Five supports several researchers? (Kim & Kim, 2004, Shergill & Sun, 2004, Skogland & Siguaw, 2004, Snepenger & Milner, 1990) calls to analyse the demographic characteristics of hotel customers such as gender, marital status, age, level of education, income, purpose of travel, ethnic background and occupation in relation to the primary and sub dimensions of service quality, as well as the behavioural intentions, customer satisfaction, service quality, perceived value and image constructs. In addition, the research model that was developed for the hotel industry in a Taiwan setting provides a valuable framework for hotel management to aid in identifying the variables that are important to customers when they evaluate their accommodation experience. 6.8.1 Theoretical Implications The result of this study increases support for the use of a multi-level structure, such as those developed by Brady and Cronin (2001) and Dabholkar et al. (1996), to conceptualise and measure service quality. However, the three primary dimensions identified in this research may not be general for all service industries outside the accommodation sector, or for different cultures. The primary dimensions identified in this study should be confirmed for other service industries through the use of an appropriate qualitative and quantitative analysis. In addition, the sub-dimensions also need to be confirmed using an appropriate qualitative and quantitative analysis because they also may vary across industries and 196 cultures. It is also valuable to compare the derived importance of the three primary dimensions and 12 sub-dimensions of the hotel service quality construct identified in this research with the derived importance of these dimensions identified in additional studies. Customer-to-Customer Interaction and Sociability were two sub-dimensions of service quality identified in the factor analysis. However, it should be noted that these two sub- dimensions were not significant in Regression Models One and Three (as discussed in Sections 5.5.2.1 and 5.5.2.3). This result may be attributed to the idea that physical contact between people in interpersonal relationships is minimised in Asian cultures (Argyle, 1975). In contrast, Western people tend to have more interpersonal interaction than Asian people (Reisinger & Turner, 1998). According to Asian culture, people do not like to have a verbal, eye, physical, or even emotional contact with someone that they do not know (McGee, 2003; Friesen, 1972). This research provided a framework for understanding the moderating effect of Perceived Value on the relationship between Service Quality and Customer Satisfaction. Oh (1999) suggested that perceived value together with service quality may completely moderate the effect of perceptions on customer satisfaction in the hotel industry. Therefore, the researcher investigated the interactions (Service Quality x Perceived Value) in Regression Model Five (as discussed in Section 5.5.3.1) to identify if Perceived Value moderated the relationship between Service Quality and Customer Satisfaction. The statistical analyses showed that Perceived Value had the most influential moderating effect on the relationship between Service Quality and Customer Satisfaction ( ? =0.761). The positive relationship that was identified between Perceived Value, Service Quality, and Customer Satisfaction may be interpreted as customer satisfaction being increased as a result of experiencing a high quality of service when customers have high perceptions of value. However, customer satisfaction may not always change in response to different levels of hotel service quality. In addition, this research also provided a framework for understanding the interrelationships between Behavioural Intentions and the other constructs related to Behavioural Intentions. In terms of the relationship between Service Quality and Perceived Value, the result of this study suggested that Service Quality had a direct impact on customer perceptions of Value. 197 The positive relationship that was identified between Service Quality and Perceived Value may be interpreted as the higher the service quality as perceived by hotel customers, the more willing customers are to pay a higher price for their accommodation. In addition, the results indicated that Service Quality also had a direct influence on Image. The positive relationship identified between Service Quality and Image may be interpreted as the higher the service quality as perceived by hotel customers, the better impressions of the hotel that the customers have in their minds. Therefore, a good image is a result of high levels of service quality provided by an organisation (Grönroos, 1982). In this research, the Image construct was the second most powerful indicator of hotel service quality. The results of this study indicated that Perceived Value, Service Quality, and Image directly influenced Customer Satisfaction. According to the regression result (see Section 5.5.4.3), Perceived Value had a stronger effect on Customer Satisfaction ( ? =0.420) than Service Quality ( ? =0.257) and Image ( ? =0.186). This may be interpreted as increased perceived value contributing to satisfaction after customers experience a good quality of hotel service at a higher price. In addition, the analyses indicated that Service Quality also had an impact on Customer Satisfaction. This may be interpreted as service quality being an antecedent of customer satisfaction because service quality is the driver of the hotel performance (Wilkins et al., 2006a). Finally, the beta coefficient of Image showed that Image had less influence on Customer Satisfaction when compared with the influence of Perceived Value on Customer Satisfaction. However, the result revealed that Image also had a direct influence on Customer Satisfaction. The positive relationship identified between Image and Customer Satisfaction may be interpreted as the better impression of the hotel service quality customers have in their minds, the more satisfied they feel. Although the Image construct had less influence on Customer Satisfaction, this construct should not be neglected since it plays an important role in enhancing the level of Customer Satisfaction in the hotel industry. The results indicated that Customer Satisfaction ( ? =0.536) and Image ( ? =0.351) directly influenced Behavioural Intentions. The positive relationship identified between Customer Satisfaction and Behavioural Intentions may be interpreted as satisfied customers having favourable behavioural intentions to revisit or return to the same hotel after paying a high 198 price to experience high levels of hotel service quality that produces a good image in their minds. According to Brady et al. (2001), researchers and practitioners should identify customer satisfaction as a means of driving behavioural intentions. The positive relationship identified between Image and Behavioural Intentions may also be interpreted as it being likely that customers will have favourable behavioural intentions to revisit or return to the same hotel after leaving with a good impression of the quality of hotel service in their mind. Although Customer Satisfaction has a stronger influence on Behavioural Intentions than Image in this study, Kandampully and Suhartanto (2000) and Suhartanto (1998) indicated that hotel image and customer satisfaction are both important factors in determining behavioural intentions. This research also reveals that the inclusion of Image and Customer Satisfaction in Regression Model Nine (see Table 5.23) not only highlights the importance of Image and Customer Satisfaction, but also provides a more comprehensive understanding of how both Image and Customer Satisfaction influence Behavioural Intentions. Image and Customer Satisfaction in this research are important drivers of Behavioural Intentions. Therefore, as suggested in this study, both Image and Customer Satisfaction should be included when assessing Behavioural Intentions in the hotel industry. The constructs in this research were also assessed based on the perceptions of the demographic groups. The Purpose of Travel Group had the most perceptual differences on several constructs; in particular, the sub-dimensions pertaining to Physical Environment Quality and Outcome Quality. First, the result shows that customers with a different purpose for travel may have different perceptual levels of hotel service quality. However, if the hotel service satisfies different travel types of customers? demands, their satisfaction level will relatively increase. The results reveal that customers with different travel purposes may demand different levels of Décor & Ambience and Availability of Facility. Leisure customers may expect that hotels can provide them with more relaxing occasions and entertainment facilities. Business customers may be keen that the hotel can offer the internet, facsimile machines, and office space, which facilitate their business activities. Different travel types of customers place much emphasis on the intangible environment, in particular valence and sociability. For the Valence sub-dimension, customers may have a post- 199 consumption assessment of whether the hotel service outcome is acceptable or unacceptable. In the Sociability sub-dimension, leisure customers may need to socialise with other customers. Through the social occasion interaction where leisure customers interact with other customers, they can relax themselves; in addition, they may be able to make new friends to exchange and obtain more travel information. Alternatively, business customers may meet new clients to extend their business at the hotel?s social occasions. In addition, the Occupation Group had also the most perceptual differences on several constructs; in particular the sub-dimensions pertaining to Interaction Quality, Physical Environment Quality, and Outcome Quality. First, different occupational types of customers may demand different levels of hotel service quality. Secondly, customers with different occupations may have different perceptions of employees? ability to solve problems. Thirdly, different occupations also give customers different impressions of the other customers? behaviour. In addition, if different occupational groups of customers interact with other customers, this may have a positive or negative impact on their perceptions of the hotel services. Fourthly, different occupations also affect customers? perceptual levels of hotel room quality. Fifthly, customers working in different occupations also have different perceptions of hotel layouts that serve their purposes and needs. Sixthly, the Occupation Group also had perceptual differences on the Location sub-dimension. For example, business customers, such as professionals, managers and organisational employees, may consider location as one of the important factors, because they may choose hotels that facilitate their business trips (Lewis & Chambers, 1989). Seventhly, customers with different occupations may have different post-consumption assessment of whether the hotel service outcome is acceptable or unacceptable. Eighthly, different occupations also affect customers? levels of patience in waiting for hotel service. Finally, different occupational types of customers may socialise with other customers and make friends with them in the hotel. 6.8.2 Managerial Implications In relation to Research Objective One, the results of this study identify three primary dimensions of hotel service quality and 12 sub-dimensions pertaining to primary dimensions. 200 Hotel management can use the multi-level model developed in this research for strategic planning because the model provides a framework for evaluating customer perceptions of service quality. However, because the dimensions of service quality vary across industries and cultures, hotel managers should note that the primary and sub dimensional structures must be determined for their own specific organisation and cultural setting to measure accurately customer perceptions of their hotel experiences. In relation to Research Objective Two, the results of this research indicate that Perceived Value plays a moderating role between Service Quality and Customer Satisfaction. The results of this research may account for the hotel studies in which Perceived Value moderated the relationship between Service Quality and Customer Satisfaction. Caruana et al. (2000) demonstrated that the service quality, perceived value and customer satisfaction constructs were increasingly playing a key role in services marketing and that these three constructs had a significant influence on behavioural intentions and ultimately long-term profitability. In this research, the implications for management of the results concern the important effect of price, namely, perceived value. The results indicated that Perceived Value ( ? =0.491) and Service Quality ( ? =0.335) had an independent influence on Customer Satisfaction (as discussed in Section 5.5.3.1). The positive regression coefficient ( ? =0.761) for the interaction between Service Quality and Perceived Value implied that the moderating variable (Service Quality x Perceived Value) had a positive impact on Customer Satisfaction. This result indicates that customers may believe that customer satisfaction will be high when hotels provide high levels of service quality. If the hotel service quality is high, customers will be willing to pay more. Moreover, if the cost that customers paid was perceived to be high, this might contribute to a positive influence on customer satisfaction. Customer satisfaction might not only depend on service quality, but also on high levels of quality, if customers believed that perceived value was being enhanced (Caruana et al., 2000). Therefore, this result can be attributed to one fact, that hotel customers may be more satisfied with a higher level of service quality at a higher price rather than with a lower level of service quality at a lower price (Nasution & Mavondo, 2008). In relation to Research Objective Three, the results provide hotel management with an improved understanding of the influence of Service Quality on Perceived Value and Image, 201 the influences of Perceived Value, Image and Service Quality on Customer Satisfaction, and the effects of Image and Customer Satisfaction on Behavioural Intentions. The results suggest that improved service quality can increase customer perceptions of value and customer impressions of the hotel, respectively. In addition, a higher level of value, image, and improved service quality can help the hotel to improve and upgrade the level of customer satisfaction. Furthermore, a higher level of image and satisfaction should then ultimately increase customers? favourable intentions to revisit or return to the same hotel and may further increase positive word-of-mouth recommendations about their good experience at the hotel. Finally, with respect to the results relevant to the Service Quality, Customer Satisfaction, and Behavioural Intentions constructs, Dagger et al. (2007) suggested that managers should consider both the service quality and customer satisfaction constructs as determinants of behavioural intentions because these two constructs can help managers to ensure positive behavioural intentions in their cohort. Based on this research, therefore, customers are expected to have a high involvement and high contact with service quality, because service quality may have a significant impact on long-term behavioural intentions through high levels of customer satisfaction. In relation to Research Objective Four, the results of this study indicate that Outcome Quality is the most important primary dimension of Service Quality in a hotel context, followed by Interaction Quality and then Physical Environment Quality. When designing a measurement to evaluate customer perceptions of service quality, management should recognise that the order of importance of the primary dimensions of service quality may vary across different hotels on service organisations. Hotel management that participated in the survey or in general should concentrate on the sub-dimensions under Outcome Quality and improve the hotel?s performance on the sub-dimensions. Resources can be allocated to the sub-dimensions based on the empirical findings. However, the sub-dimensions of Interaction Quality and Physical Environment Quality should also be resourced, as customers? overall perceptions of the service quality of accommodation experiences do not only depend on the employee and customer relationships, but also on the relationship between the service environment and customers. 202 In relation to Research Objective Five, the results (as discussed in Section 5.5.6) indicate that there were cultural differences in the perceptions of Asian and Western customers, and perceptual differences between leisure and business customers. Hotel management should be aware of the presence of perceptual differences between Asian and Western customers, and leisure and business customers. Hotel management should consider whether to adjust service strategies to cater more for Western and business customers, or to retain the current strategy that offers primary Asian and leisure styles of accommodation, and encourage Western and business customers to adjust to the Asian and leisure styles of accommodation environment. For example, hotel management may consider offering special food and beverage, an architectural design, and in-room equipment that specifically focuses on Western and business customers. 6.9 Limitations of the Research Although this research provides a number of important contributions to the marketing theory and for hotel management, organisations and individuals wishing to use the results in relation to specific strategic decisions should note several characteristics of the study that may limit its applicability. First, this research is limited to the effects of image and customer satisfaction on customer behavioural intentions since several researchers (Kang et al., 2004; Kandampully & Suhartanto, 2003, 2000; Suhartanto, 1998) claimed that both the image and customer satisfaction constructs should attract more attention in the hotel literature. However, Jeong and Lambert (2001) suggested that there were undoubtedly other constructs which also drove customer behavioural intentions (e.g., the person?s attitude, social pressure or subjective norm). It is likely that not all of the factors that influence customer behavioural intentions have been included into the conceptual model of this study. Secondly, this research used only a single-item scale to measure the three primary dimensions of service quality. Additional studies may desire to use multiple items to measure the primary dimensions of service quality. 203 Thirdly, the standardised coefficients were compared with those in the same multiple regression models but not with other regression models. Comparisons could be made within the independent variables from different multiple regression models based on the data collected at other hotels. Fourthly, this research developed a conceptual model based on service quality as a formative construct rather than a reflective one. Brady and Cronin (2001) and Brady (1997) suggested adopting a reflective measurement based on confirmatory factor analysis using a structural equation model (SEM) if researchers intend to examine the influence of the service quality construct on its relevant dimensions. In contrast, a formative measurement based on multiple regression analysis, as suggested by Diamantopoulos and Winklhofer (2001) and Diamantopoulos (1999), examines only how dimensions of service quality influence the service quality construct. Fifthly, in spite of a lot of literature on service quality, it has been difficult to offer a full description of the nature of the hotel service quality construct. Despite this difficulty, this research conducted in-depth focus group interviews to identify and examine all of the dimensions of the service quality construct for hotels, because focus group interviews are believed to be more useful than relying only on a literature review. Still, there may be some other dimensions of service quality that have not been identified in the conceptual framework of this study. Sixthly, there was approximately an even number of males and females who responded to the survey, but 45.7% of respondents were aged between 26 and 35. The age demographic characteristics may limit how applicable the results are to other age groups. Seventhly, this research focused only on the perceptions of customers and did not measure the perceptions of employee and manager regarding customer behavioural intentions and the relevant constructs. Eighthly, the data were collected from only the customers staying at a five-star hotel in Kaohsiung City of Taiwan. This may limit the ability to generalise the results to five-star hotels in other countries. 204 Ninthly, the most obvious limitation is the type of questionnaire used. Ary, Jacobs and Razavieh (2002) suggested that survey research, as employed in this research, may be problematic in the sense that: a) respondents may misinterpret various items on the questionnaire; b) some subjects in the study may simply forget to complete and return questionnaires to the drop box at the hotel front-desk reception; and c) it is possible that segments of the population may not be able to read and respond to the questionnaire (p. 384). In addition, the researcher must be aware that respondents may not provide socially acceptable answers (Miller, 2004). Finally, when the questionnaire was translated from English into Chinese, translation distortion may arise from differences in the meanings of words, syntactical contexts, and the cultural context of the readers or hearers as explained by Ervin and Bower (1952). 6.10 Directions for Future Research This study represented an important step in understanding the issues involved in the operationalisation of hotel customer behavioural intentions. However, several additional research areas of interest have surfaced. First, this research was limited to a five-star hotel in Taiwan. Future studies should attempt to examine service quality across different hotel ratings in other regions. This may provide an opportunity to compare the quality of service based on different hotel ratings (e.g., three- or four- star hotels) in other regions. In addition, the conceptual model identified in this study can be used in other classes of accommodation, such as caravan parks, backpacker hostels, bed and breakfast motels, inns, resorts and lodges. Second, future researchers can analyse changes in the importance of the dimensions. For example, a longitudinal study focusing on hotel customers from check-in to check-out may provide more information about their levels of satisfaction and the importance of the relevant constructs over time. 205 Third, this research conducted convenience sampling, a non-probability sampling method. Future research can use probability sampling methods in order to make the sample more representative of the population. Fourth, this study identified that the customer-to-customer interaction and sociability sub- dimensions using exploratory factor analysis. Future researchers conducting research with customers at other hotels need to develop their own multi-level model of service quality. For example, future researchers may combine the customer-to-customer interaction and sociability sub-dimensions into one sub-dimension, depending on the results of their own exploratory factor analysis. Fifth, this research measured the perceptual differences between Asian and Western customers, and leisure and business customers, based on demographic characteristics; however, perceptual differences between Western and Asian customers, and leisure and business customers, based on psychographic characteristics (e.g., different preferences for hotel food provision between Western and Asian customers, and different preferences about bringing their family, relatives, colleagues or friends between leisure and business customers) were not identified. Future researchers may determine to concentrate more fully on psychographic differences between Asian and Western customers, and leisure and business customers. Moreover, the impact of these differences on the perceptions of satisfaction, service quality, image, value, and favourable future behavioural intentions may also vary. Sixth, few studies have examined the impact of the outcome dimension on the perceptions of the service quality construct, despite suggestions that outcome is an important driver of service quality perceptions (Caro & García, 2008, 2007; Caro & Roemer, 2006; Collins, 2005; Dabholkar & Overby, 2005; Brady & Cronin, 2001). Therefore, further research may be required to clarify the relationship between the outcome dimension and its relevant components and to examine how the outcome dimension influences the service quality construct in different service industries. 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An Overview of International and Ordinary Tourist Hotels in Taiwan, 2003-2007 Table 32A: International Tourist Hotels in Taiwan, 2003-2007 Year Number of Hotels Number of Rooms 2003 60 214,843 2004 61 211,901 2005 60 212,224 2006 60 215,394 2007 60 211,465 Sources: Tourism Statistics, Bureau of Tourism, Taiwan (2007). Table 33A: Ordinary Tourist Hotels in Taiwan, 2003-2007 Year Number of Hotels Number of Rooms 2003 30 33,182 2004 26 34,701 2005 27 35,412 2006 29 38,267 2007 30 41,335 Sources: Tourism Statistics, Bureau of Tourism, Taiwan (2007). 268 Appendix 2. A List of Taiwan?s Hotel Rating Hotel Star Rating Score One-Star Tourist Hotel 61-180 Two-Star Tourist Hotel 181-300 Three-Star Tourist Hotel 301-600 Four-Star International Tourist Hotel 601-750 Five-Star International Tourist Hotel 750+ Source: Su and Sun (2007). 269 Appendix 3. Sample Size Calculation The sample size used in this research was determined using the following formula (Mendenhall et al., 1993): pqZeN pqNZn 2 2/ 2 2 2/ )1( ? ? +?= Where: n = the sample size 2 2/?Z = confidence interval estimate (expressed in standard normal variable form set at 95%) e = the tolerable error level for estimation (5%) N = population pq = component of sample proportion variance estimate (maximize 0.5) As the variance of the population is unknown, this research assigned p = 0.5 and q = 0.5 to the equation above. Accordingly, pq equals 0.25. The purpose is to allow the maximum possible variation contained in the data set. Applying the Mendenhall et al. (1993) formula, my number of respondents required will be: =n ( ) ( )( ) ( ) ( )25.096.105.0015,87 25.096.1016,87 22 2 + ×× 9604.0218 17.83570+=n 9604.218 17.83570=n 6680.381=n Therefore, 382 respondents are considered adequate as the formula provides a 95% of confidence level. 270 Appendix 4. Focus Group for Dimensions of Hotel Service Quality Focus Group Screener Date: __ __ / __ __ / __ __ Call Start: __ __: __ __ Call End: __ __ : __ __ Interviewer ID#: __ __ __ __ Phone: (__ __ __) __ __ __ - __ __ __ [Ask to speak with person listed. If not available, arrange callback.] Hello, my name is Hung-Che Wu. I am undertaking a Focus Group meeting as part of my doctoral research. The aim of the Focus Group meeting is to evaluate how you judge hotel service quality and I would really appreciate your input. I am not selling anything and the initial questions will only take a few minutes of your time. All of your responses will maintain confidentiality. Q1. In order to satisfy the qualification of Focus Group meeting, are you 18 years old or over? 1. Yes [continue to Q2] 2. No [thank respondent and terminate] 3. Refused [thank respondent and terminate] Q2. Have you ever stayed in any Taiwanese five-star hotels before? 1. Yes [continue to Q3] 2. No [thank respondent and terminate] 3. Refused [thank respondent and terminate] Q3. Have you participated in a focus group regarding a study of hotel service quality within the past 6 months? 1. Yes [thank respondent and terminate] 2. No [continue to Q4] 271 3. Don?t know [clarify, re-ask; otherwise thank respondent and terminate] Q4. The discussion of this Focus Group mainly centres on your perceptions of the importance of the dimensions of hotel service quality. The purpose of the groups is solely to obtain your opinions; no sales will be involved. The session will last approximately two hours, and refreshments will be served. Would you be interested in attending this group? 1. Yes [continue to Q5] 2. No [thank respondent and terminate] 3. Don?t know [if interested, go to Q5; otherwise thank respondent and terminate] 4. Refused [thank respondent and terminate] Q5. [When six are recruited for one group, recruit only for the open group. Do not accept over six for either group.] The first group is scheduled between 2:00 P.M. and 4:00 P.M. on Monday, November 5, 2007. The second group is scheduled between 2:00 P.M. and 4:00 P.M. on Wednesday, November 7, 2007. The third group is scheduled between 2:00 P.M. and 4:00 P.M. on Monday, November 12, 2007. Will you be able to attend any of these groups? 1. Yes [continue to information section] 2. No [thank respondent and terminate] 3. Don?t know [schedule callback] 4. Refused [thank and terminate] Participant Information: [If yes, provide respondent with general location of facility. Explain that detailed directions will be sent by mail or e-mail soon.] Note exact group attending time: 1. Monday on November 5: _____ 2. Wednesday on November 7: ____ 3. Monday on November 12: ____ 272 I will be either sending or e-mailing you a letter in a few days to confirm this meeting. If you need help with directions or if you need to cancel, please call my office at (03) 325- 3838 extension 8366 or e-mail me at Hung-Che.Wu2@lincolnuni.ac.nz. Now, I would like you to provide your full name and position /title I also need your mailing and e-mail addresses. Name: ___________________________________________________________________ Title: ____________________________________________________________________ Address: _________________________________________________________________ City/State: __________________________________________ Zip: _________________ E-mail Address: ___________________________________________________________ In addition, I would like to confirm that the phone number at which I reach you is: [Read number from sample and record or correct it below. Add extension if applicable.] (__ __) __ __ __ - __ __ __ __ Extension: __ __ __ __ Thank you for your time! I look forward to seeing you at 2:00 P.M. on Monday, November 5, [2:00 P.M. on Wednesday, November 7, or 2:00 P.M. on Monday, November 12, depending on quota group filled]. 273 Appendix 5. Letter of Invitation to the Focus Group Dear Participants, You are invited to take part in a Focus Group to discuss service quality in the hotel industry. The aim of the Focus Group is to enable the researchers to design a questionnaire to collect data on an empirical study of behavioural intentions in the Taiwan hotel industry. The Focus Group will comprise members who have stayed in Taiwanese five-star hotels before. It is anticipated that the Focus Group discussion time will be approximately two hours. In order to be eligible to answer the questions, you must be 18 years or older, and understand the contents of the questions. Any information that you contribute in the Focus Group will not lead to your being identified in any subsequent components of the study. At the same time, it is also important that you respect the confidentiality of fellow Focus Group members. ? Participation in this Focus Group is voluntary. ? You may choose to withdraw at any time, or decline to be involved in any part of the discussion. ? You may ask to view any notes compiled by the researcher during the Focus Group discussion. Any such notes will be destroyed after the questionnaire has been finalised. ? Your comments will be recorded. I am undertaking this Focus Group as part of my doctoral research. My supervisors for this research are Dr. Baiding Hu and Mr. Michael D. Clemes. My supervisors and I will address any questions you might have regarding this research. Our contact details are given below. Dr. Baiding Hu Mr. Michael D. Clemes Senior Lecturer Senior Lecturer (03) 325-3838 ext 8069 (03) 325-3838 ext 8292 hub3@lincoln.ac.nz clemes@lincoln.ac.nz Thank you for your valued assistance. Kind regards, Hung-Che Wu PhD Candidate of the Commerce Division (03) 325-3838 ext 8366 Hung-Che.Wu2@lincolnuni.ac.nz 274 Appendix 6. Dimensions of Hotel Service Quality Focus Group Discussion Guide (15 min.) Introduction ? Greeting ? Purpose of focus group ? Opportunity to discuss each participant?s experience with the hotel stay ? Ground rules ? Roles of moderator ? Recording groups ? Confidentiality of comments ? Individual opinions (no right or wrong answers) ? Speak one at a time and as clearly as possible (30 min.) Concept One ? Presentation of the first primary dimension of service quality: Interaction quality ? Which factor (s) do you think can comprise interaction quality? For what reasons? (30 min.) Concept Two (put concept one aside for now) ? Presentation of the second primary dimension of service quality: Physical environment quality ? Which factor (s) do you think can comprise physical environment quality? For what reason? 275 (30 min.) Concept Three (put concept two aside for now) ? Presentation of the third primary dimension of service quality: Outcome quality ? Which factor (s) do you think can comprise outcome quality? For what reason? (15 min.) Closing Comments ? Any additional comments or suggestions? ? Thank participants. 276 Appendix 7. Responses to the Questions of the Focus Group Interview Components of Interaction Quality Attitude 1. If I go to a hotel for the room check-in and realise the front-desk employee is rude to me, I will not come back to this hotel or its related chains again. 2. Basically, I seldom encounter or get in touch with the housekeeper. Therefore, the housekeeper?s service attitude or behaviour will not influence me to evaluate the service quality of this hotel. 3. Employees? attitudes have a direct influence on me. 4. The employees should be friendly to me. Behaviour 1. I like employees to smile at me. 2. Employees? service behaviour will have a direct influence on my intention to come back to the hotel again. 3. Most of the employees? greetings are too mechanical in the five-star hotels. 4. The employees keep smiling at me even if they are busy. 5. When the employees use the speaking standard to talk to me, I feel that they are unfriendly. Expertise 1. I realised that the employees? knowledge at a five-star hotel is professional. 2. When employees are dealing with many customers to check in, they should ask more employees to serve customers. 3. I really care about the employees? expertise. 4. I am satisfied with the employees? expertise. 5. The employee can correctly lead me to my room. 6. When I need medicine to take, the front-desk employees can tell me how to get to the pharmacy. 7. The employees can help me to book tickets for outdoor activities. Problem-Solving 1. When I have problems, the employees always deal with them efficiently. 2. When I need help, the employees attempt to provide me with assistance properly. 3. When I complain about something, the employees do not take action immediately. 277 Customer Interaction 1. Basically, I sometimes care if the other customers greet me with stony silence. 2. At the end of my stay in this hotel, I will not come back again if I hear that many customers do not like the service quality of this hotel. 3. For me, inter-client interaction is just a reference. 4. The customer quality will influence me to evaluate the service quality of the hotel. 5. When I found that customers chewed betel nuts in the hotel, I did not feel uncomfortable. 6. I do not frequently stay in the hotel room, so I seldom interact with other customers in the hotel. 7. When the customers make a noise, I feel uncomfortable. 8. I almost cannot stand it when the customers smoke or chew betel nuts in the hotel. 9. I saw other customers in bathrobes walk around the resort hotel. Components of Physical Environment Quality Décor 1. Décor is one of the important factors when I select a hotel to stay. 2. I do care about the decoration of the room or even the hotel. 3. Good décor makes me feel comfortable staying in the hotel. 4. The décor of the hotel should be as natural as possible. 5. The overall décor of the hotel is good. 6. If the hotel desires to attract more customers to come, it should make an effort with the overall décor of the hotel. Ambience 1. If the room is very dark, I may feel as if I am staying in a haunted house or a brothel. Therefore, I will feel uncomfortable. 2. The atmosphere should be good for me because I always go to the hotel with my girlfriend. 3. I really care about the atmosphere when I have a meal in the hotel restaurant. 4. The musical effects influence my feeling of having a meal in the hotel restaurant. 5. If I went to the hotel as a couple, I would really care about the hotel atmosphere. 6. I really enjoy the atmosphere in the hotel restaurant. 7. No matter what kind of trip I am taking, the atmosphere in the hotel is very important. 8. When I am on a business trip, I do not like either strong or soft light. Room Quality 1. I frequently use the coffee maker in the room. 2. I really care about the internet connection because I need to check e-mail letters very often. 3. I need to watch TV in the room to kill time because I seldom stay out at night. 278 4. For me, the hotel room is just used for sleeping. Therefore, room quality is not so important for me. 5. I prefer to go to the hotels providing entertainment facilities for children in the room. 6. I always care if the hotel provides me with comfortable rooms because I need to go on business trips often. 7. If I planned to stay in the hotel for one week, I would care if the hotel provided the customers with room service. 8. The hotel should provide me with a hair dryer after taking a bath or shower. 9. I found that no facilities are provided for the disabled in most of the hotel rooms, such as beds, toilets and so on. 10. The hotel should provide me with better lighting effects. 11. I prefer hotels that provide facilities for customers to have a party in the room. 12. I need to do business through the internet connection in the room. 13. If the hotel provided me with video games in the room, that would be great. 14. The overall facility in the room of the hotel should be of high quality. 15. Even if I am on a leisure trip, the availability of the internet in the room is still important. 16. I still need to get the latest news by watching TV in the room even if I am on a trip. 17. The room settings influence my sleeping quality based on Chinese Feng-Sui. Cleanliness 1. I do care about personal hygiene. 2. Hair litter and an unclean toilet in the room will affect my health. 3. If the sheeting were not regularly replaced, I will feel disgusted. 4. I usually use the interior quilt to cover my body. 5. If the button of the elevator is dirty, I won?t touch that one or take the elevator again. 6. Sometimes, I found that there was some dust in the carpets of the room and the hallway. 7. I found that the previous beans in the coffee maker were not removed when I was ready to make coffee. 8. If I found that the hotel were not clean, I would not come back again. 9. The bathing accessories of most of the hotels are clean. 10. Cleanliness is one of the most important factors when I choose accommodation. 11. No cleanliness, no comfort. 12. If I am choosing a hotel where I am going to stay, I will hear from my friends if the hotel is clean. 13. For the hotel industry, cleanliness is a preliminary requirement for. Location 1. I do not like to stay in a hotel on the outskirts. 2. I do not like to stay in a hotel which is located in the main street. It?s too noisy. 3. I like to stay in a hotel which is easily accessible to public transportation and the nearby tourist attraction sites. 4. The location should be convenient. 279 5. The hotel should be located in the place where I can have access to food and famous attraction sites. 6. If I go out for leisure, I hope that the hotel is located in the natural environment. 7. If I need to spend a long time getting to this hotel, I will not make it my first choice on my accommodation list. 8. Before reserving a room, I will check if there are parking lots around the hotel. 9. If the hotel does not offer parking areas, I won?t go. 10. If the hotel does not provide parking lots, I won?t drive there. 11. I am afraid that my car would be towed away or stolen if I parked my car on the main road. 12. Even if the hotel does not provide a parking area for the customers, it still has to provide an alternative which is the shuttle bus service. 13. Most of the hotels provide parking lots for their customers. Design 1. The architectural design of the hotel sometimes attracts me to go. 2. I prefer to stay in a room facing the ocean and mountain views. 3. Before booking a room, I will check the architectural design through the hotel brochure. 4. I prefer to stay in a beautiful hotel because everything will be comfortable. 5. The hotels always do a good job on their design. Food and Beverage 1. I cannot stand salty food or sweet beverages. 2. I do not like to have meals provided by the hotel. Therefore, I eat out very often. 3. There are not so many options for food or beverage in the hotel. 4. The price of food is not only costly but also distasteful. 5. The food in a five-star hotel is always good. 6. If the hotel does not provide me with the food that I expect, I will not come back again. Security and Safety 1. Security is one of the factors when I choose a hotel. 2. I prefer to stay in a hotel that provides customers with a card to open the room door. When the card is lost, the hotel just needs to change the password. However, if the key is lost, the hotel may need to change the whole lock. 3. I sometimes check out the emergency exit map. 4. The sign for the emergency exit is not clear in the hotel. 5. I always check if there are hydrants in the hotel. 6. I do not like to stay in a place where the light is dim. 7. I can place something valuable in the hotel safety box. 8. When I walk into my room, I always check the location of the fire escape. 9. When drunk customers make a noise, the security man should take action immediately. 280 Components of Outcome Quality Sociability 1. I really care about the type of customers. For example, someone has tattoos all over his/ /her body. 2. If I have the opportunity, I prefer to socialise with other customers. 3. I never make any friends in the hotel. 4. The front-desk reception may be a good place where I can talk to other customers. 5. I never have a sense of family when interacting with other customers. 6. I just have a short talk with the customers in the swimming pool. 7. Based on the Taiwanese culture, to socialise with the customers is not common in the hotel. Valence 1. I went to a five-star hotel. However, I realised that the overall service quality in this hotel just looked like a three-star hotel before I checked out my room. Therefore, there was a big gap based on my initial expectation of this hotel. 2. If I pay a high price, I should get higher service quality from the hotel. 3. I seldom evaluate the outcome of hotel service quality. 4. I know the price is always positively proportionate to the service. 5. I always believe ?high price, high service.? 6. If I get a good quality of service in the hotel, I do not mind paying a high price. 7. Sometimes I pay a high price. However, I do not use some facilities because I am not informed of the open hours for the facilities when I check into the room. 8. In general, I always have a good experience when I stay in the hotel. Waiting time 1. When the toilet did not work and I then called the front-desk reception for help, it really took a long time for a plumber to have a look in my room. 2. When I found that the internet could not be connected I then asked for help. To my surprise, the service provider spent roughly three hours making the internet connect. 3. One day, I found that the room was so dirty after I checked in. I went to ask the front- desk for help. However, no housekeepers came to clean my room for five hours. 4. If I need to spend a long time waiting for services, I will not go to that hotel again. 5. I do not spend too much time on the check-in and check-out. 6. Waiting time does not often occur in the five-star hotels because of many competitors. 7. I waited for more than 30 minutes to get my milk in my room. 8. Although waiting time is important for me, I seldom complain about my waiting time in the hotel. 9. When I need to spend a long time waiting for check-out, the employees should inform me first. 281 Appendix 8. Constructs / Items / Description / References Construct Item Description Reference Physical Environment Quality A27 ? The physical environment of this hotel is the best I have experienced. ? Chow et al., 2007 ? Brady and Cronin, 2001 Décor A10 A18 A24 ? The style of décor is to my liking at this hotel. ? The décor of this hotel exhibits a great deal of thought and style. ? The décor of this hotel is stylish and attractive. ? Ekinci and Riley, 2001 Ambience A3 A11 A26 ? The atmosphere is what I expect in a hotel ? I really enjoy the atmosphere of this hotel. ? The ambience of this hotel is excellent. ? Ko and Pastore, 2005 Location A4 A12 A22 ? The retail stores around this hotel are conveniently located. ? The dining-out facilities around this hotel are conveniently located. ? There are convenient parking spaces available at this hotel. ? Shonk, 2006 ? Park, 2004 ? Tzeng et al., 2002 ? Teng, 2000 Cleanliness A5 A9 A19 A23 ? This hotel?s bathroom and toilet are clean. ? This hotel?s room is clean. ? This hotel?s reception area is clean. ? The employees of this hotel look clean and neat. ? Lockyer, 2003 Room Quality A2 A6 A14 A20 ? This hotel?s room size is adequate. ? This hotel?s bed/mattress/pillow are comfortable. ? This hotel?s room is quiet. ? In-room temperature control is of high quality at this hotel. ? Choi and Chu, 2001 ? Min and Min, 1997 Design A1 A7 A15 ? This hotel is aesthetically attractive. ? The layout of this hotel makes it easy for customers to move around. ? The layout of this hotel serves my purposes/needs. ? Ko and Pastore, 2005 ? Brady and Cronin, 2001 ? Dabholkar et al., 1996 Food & Beverage A8 A16 A21 ? This hotel?s food & beverage are of high quality. ? This hotel?s food & beverage served are sanitary, adequate, and sufficient. ? There are a variety of food & beverage facilities at this hotel. ? Akbaba, 2006 ? Chu and Choi, 2000 Security & Safety A13 A17 A25 ? There are accessible fire exits at this hotel. ? There are noticeable sprinkler systems at this hotel. ? A secure safe is available in the room of this hotel. ? Choi and Chu, 2001 Interaction Quality B17 ? Overall, I would say that the quality of my interaction with the employees of this hotel is excellent. ? Dagger et al., 2007 ? Ko and Pastore, 2005 Attitude B6 B8 B11 ? The attitude of employees of this hotel demonstrates their willingness to help me. ? I can depend on the employees at this hotel being friendly. ? The attitude of employees of this hotels shows me that they understand my needs. ? Caro and García, 2008, 2007 ? Caro and Roemer, 2006 ? Brady and Cronin, 2001 282 Construct Item Description Reference Behaviour B2 B7 B13 B15 ? The behaviour of the employees of this hotel allows me to trust their services. ? The employees of this hotel always provide the best service for me. ? The employees of this hotel are able to answer my questions quickly. ? I can rely on the employees at this hotel taking actions to address my needs. ? Caro and García, 2008, 2007 ? Caro and Roemer, 2006 ? Brady and Cronin, 2001 Expertise B1 B3 B16 ? The employees of this hotel understand that I rely on their professional knowledge to meet my needs. ? I can count on the employees of this hotel knowing their jobs/responsibilities. ? The employees of this hotel are competent. ? Caro and Roemer, 2006 ? Brady and Cronin, 2001 Problem-Solving B4 B9 B14 ? When a customer has a problem, the employees of this hotel show a sincere interest in solving it. ? The employees of this hotel understand the importance of resolving my complaints. ? The employees of this hotel are able to handle my complaints directly and immediately. ? Caro and García, 2008, 2007 ? Caro and Roemer, 2006 ? Dabholkar et al., 1996 Customer Interaction B5 B10 B12 ? I am generally impressed with the behaviour of the other customers of this hotel. ? My interaction with the other customers has a positive impact on my perception of this hotel?s services. ? The customers follow this hotel?s rules and regulations. ? Ko and Pastore, 2005 Outcome Quality C12 ? I feel good about this hotel in general. ? Brady and Cronin, 2001 Sociability C1 C4 C7 C9 ? This hotel provides me with opportunities for social interaction. ? I feel a sense of belonging with other customers at this hotel. ? I have made social contacts at this hotel. ? The other customers at this hotel do not affect the hotel?s ability to provide me with good service. ? Ko and Pastore, 2005 ? Brady and Cronin, 2001 Valence C2 C5 C10 ? At the end of my stay at this hotel, I feel that I have had a good experience. ? When I leave this hotel, I feel that I?ve got what I wanted. ? I would evaluate the outcome of this hotel?s services favourably. ? Caro and García, 2008 ? Caro and Roemer, 2006 ? Brady and Cronin, 2001 Waiting Time C3 C6 C8 C11 ? The waiting time for service is reasonable at this hotel. ? The employees of this hotel try to minimise my waiting time. ? The employees of this hotel understand that waiting time is important to me. ? The employees of this hotel provide service for me punctually. ? Caro and García, 2008 ? Dagger et al., 2007 ? Caro and Roemer, 2006 ? Brady and Cronin, 2001 Service Quality D1 D6 D13 ? The overall quality of this hotel?s services is good. ? This hotel provides high quality services. ? The quality of this hotel could be considered superior when compared to other hotels. ? Clemes et al., 2007 ? Kao, 2007 ? Oh, 2000 Perceived Value D3 D12 D14 ? Overall, the value of this hotel experience is good. ? Overall, I am satisfied with the value I received, for the price that I paid at this hotel. ? The value that this hotel offers for its price is high. ? Ladhari et al., 2008 ? Gallarza and Saura, 2006 ? Oh, 2000, 1999 283 Construct Item Description Reference Image D2 D7 D17 ? I have always had a good impression of this hotel. ? I believe that this hotel has a better image than its competitors. ? In my opinion, this hotel has a good image in the minds of its customers. ? Clemes et al., 2007 ? Kao, 2007 ? Kayaman and Arasli, 2007 ? Park et al., 2005, 2004 Customer Satisfaction D5 D8 D10 D16 ? I believe that I made the right choice by staying at this hotel. ? This hotel experience has satisfied my needs and wants. ? I am satisfied with my hotel stay. ? Overall, my hotel stay was a pleasant experience. ? Gallarza and Saura, 2006 ? Shonk, 2006 ? Kang et al., 2004 ? Park et al., 2004 ? Skogland and Siguaw, 2004 ? Brady et al., 2001 Behavioural Intentions D4 D9 D11 D15 ? I always say positive things about this hotel to other people. ? If I could, I would stay at this hotel again. ? I always consider this hotel to be the first one on my list when searching for accommodations. ? I would recommend this hotel to other people. ? Dagger et al., 2007 ? González et al., 2007 ? Park et al., 2005, 2004 ? Kang et al., 2004 ? Baker and Crompton, 2000 Demographics E1 E2 E3 E4 E5 E6 E7 E8 ? Gender ? Marital status ? Age ? Level of education ? Annual income ? Purpose of travel ? Ethnic background ? Occupation ? Clemes et al., 2008, 2007 ? Dagger et al., 2007 ? Park et al., 2005 ? Shergill and Sun, 2004 ? Skogland and Siguaw, 2004 ? Zane, 1997 284 Appendix 9. Cover Letter and Questionnaire Dear Sir/Madam, I am a PhD candidate in the Commerce Division at Lincoln University, Canterbury, New Zealand. My PhD research project involves asking customers about their perceptions of hotel experiences in Taiwan. Would you please complete the attached questionnaire? The questionnaire will take approximately 10 to 15 minutes to complete. Your answers will be anonymous and confidential. Once you have completed the questionnaire, please return it to the drop box at the hotel front-desk reception. I deeply appreciate your valuable participation. In order to be eligible to answer the questions, you must be 18 years or older, and understand the contents of the questions. If you choose to complete the survey, it will be understood that you have consented to participate in the research project and to publication of the results of the research project. This research project has been reviewed and approved by the Lincoln University Human Ethics Committee. If you have any questions or concerns, please e-mail me at Hung- Che.Wu2@lincolnuni.ac.nz, or telephone me at 002-64-3-325-3838* ext 8366. Alternatively, you may contact my research supervisors, Dr. Baiding Hu, at 002-64-3-325-3838 ext 8069 (hub3@lincoln.ac.nz), or Mr. Michael D. Clemes at 002-64-3-325-3838 ext 8292 (clemes@lincoln.ac.nz). Once again, thank you very much for your co-operation and assistance. Best regards, Hung-Che Wu PhD Candidate Commerce Division Lincoln University *Note: 002 International Dialling Code 64 Country Code for New Zealand 285 A SURVEY OF CUSTOMERS? ACCOMMODATION EXPERIENCES IN TAIWAN HOTEL INDUSTRY Only those 18 years or older are asked to complete the questionnaire QUESTIONNAIRE FOR POSTGRADUATE RESEARCH This questionnaire is for postgraduate research only, and your consent to participate in this research project is deemed to be given by the completion of the questionnaire. This questionnaire comprises sections A, B, C, D and E. Please respond to all of the statements in the relevant sections. The listed statements below are related to your overall experience at this hotel. From Section A through Section D, please CIRCLE to indicate how strongly you agree or disagree with each of the following statements on a scale of 1 to 7. 1 = you strongly disagree, 7 = you strongly agree, 4 = neutral. If you are unable to answer a question, use the neutral value of 4 on the scale. Section A Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree 1 2 3 4 5 6 7 1. This hotel is aesthetically attractive. 1 2 3 4 5 6 7 2. This hotel?s room size is adequate. 1 2 3 4 5 6 7 3. The atmosphere is what I expect in a hotel. 1 2 3 4 5 6 7 4. The retail stores around this hotel are conveniently located. 1 2 3 4 5 6 7 5. This hotel?s bathroom and toilet are clean. 1 2 3 4 5 6 7 6. This hotel?s bed/mattress/pillow are comfortable. 1 2 3 4 5 6 7 7. The layout of this hotel makes it easy for me to move around. 1 2 3 4 5 6 7 8. This hotel?s food & beverage are of high quality. 1 2 3 4 5 6 7 9. This hotel?s room is clean. 1 2 3 4 5 6 7 10. The style of décor is to my liking at this hotel. 1 2 3 4 5 6 7 11. I really enjoy the atmosphere of this hotel. 1 2 3 4 5 6 7 12. The dining-out facilities around this hotel are conveniently located. 1 2 3 4 5 6 7 13. There are accessible fire exits at this hotel. 1 2 3 4 5 6 7 14. This hotel?s room is quiet. 1 2 3 4 5 6 7 15. The layout of this hotel serves my purposes/needs. 1 2 3 4 5 6 7 16. This hotel?s food & beverage served are sanitary, adequate, and sufficient. 1 2 3 4 5 6 7 17. There are noticeable sprinkler systems at this hotel. 1 2 3 4 5 6 7 18. The décor of this hotel exhibits a great deal of thought and style. 1 2 3 4 5 6 7 19. This hotel?s reception area is clean. 1 2 3 4 5 6 7 20. In-room temperature control is of high quality at this hotel. 1 2 3 4 5 6 7 21. There are a variety of food & beverage facilities at this hotel. 1 2 3 4 5 6 7 22. There are convenient parking spaces available at this hotel. 1 2 3 4 5 6 7 23. The employees of this hotel look clean and neat. 1 2 3 4 5 6 7 24. The décor of this hotel is stylish and attractive. 1 2 3 4 5 6 7 25. A secure safe is available in the room of this hotel. 1 2 3 4 5 6 7 26. The ambience of this hotel is excellent. 1 2 3 4 5 6 7 27. The physical environment of this hotel is the best I have experienced. 1 2 3 4 5 6 7 Please turn the page and continue to complete Sections B and C 286 Section B Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree 1 2 3 4 5 6 7 1. The employees of this hotel understand that I rely on their professional knowledge to meet my needs. 1 2 3 4 5 6 7 2. The behaviour of the employees of this hotel allows me to trust their services. 1 2 3 4 5 6 7 3. I can count on the employees at this hotel knowing their jobs/ responsibilities. 1 2 3 4 5 6 7 4. When I have a problem, the employees of this hotel show a sincere interest in solving it. 1 2 3 4 5 6 7 5. I am generally impressed with the behaviour of the other customers of this hotel. 1 2 3 4 5 6 7 6. The attitude of the employees of this hotel demonstrates their willingness to help me. 1 2 3 4 5 6 7 7. The employees of this hotel always provide the best service for me. 1 2 3 4 5 6 7 8. I can depend on the employees at this hotel being friendly. 1 2 3 4 5 6 7 9. The employees of this hotel understand the importance of resolving my complaints. 1 2 3 4 5 6 7 10. My interaction with the other customers has a positive impact on my perception of this hotel?s services. 1 2 3 4 5 6 7 11. The attitude of the employees of this hotel shows me that they understand my needs. 1 2 3 4 5 6 7 12. The other customers follow this hotel?s rules and regulations. 1 2 3 4 5 6 7 13. The employees of this hotel are able to answer my questions quickly. 1 2 3 4 5 6 7 14. The employees of this hotel are able to handle my complaints directly and immediately. 1 2 3 4 5 6 7 15. I can rely on the employees at this hotel taking actions to address my needs. 1 2 3 4 5 6 7 16. The employees of this hotel are competent. 1 2 3 4 5 6 7 17. Overall, I would say that the quality of my interaction with the employees of this hotel is excellent. 1 2 3 4 5 6 7 Section C Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree 1 2 3 4 5 6 7 1. This hotel provides me with opportunities for social interaction. 1 2 3 4 5 6 7 2. At the end of my stay at this hotel, I feel that I have had a good experience. 1 2 3 4 5 6 7 3. The waiting time for service is reasonable at this hotel. 1 2 3 4 5 6 7 4. I feel a sense of belonging with other customers at this hotel. 1 2 3 4 5 6 7 5. When I leave this hotel, I feel that I?ve got what I wanted. 1 2 3 4 5 6 7 6. The employees of this hotel try to minimise my waiting time. 1 2 3 4 5 6 7 7. I have made social contacts at this hotel. 1 2 3 4 5 6 7 8. The employees of this hotel understand that waiting time is important to me. 1 2 3 4 5 6 7 9. The other customers at this hotel do not affect the hotel?s ability to provide me with good service. 1 2 3 4 5 6 7 10. I would evaluate the outcome of this hotel?s services favourably. 1 2 3 4 5 6 7 11. The employees of this hotel provide service for me punctually. 1 2 3 4 5 6 7 12. I feel good about this hotel in general. 1 2 3 4 5 6 7 Please turn the page and continue to complete Sections D and E 287 Section D Strongly Disagree Disagree Slightly Disagree Neutral Slightly Agree Agree Strongly Agree 1 2 3 4 5 6 7 1. The overall quality of this hotel?s services is good. 1 2 3 4 5 6 7 2. I have always had a good impression of this hotel. 1 2 3 4 5 6 7 3. Overall, the value of this hotel experience is good. 1 2 3 4 5 6 7 4. I always say positive things about this hotel to other people. 1 2 3 4 5 6 7 5. I believe that I made the right choice by staying at this hotel. 1 2 3 4 5 6 7 6. This hotel provides high quality services. 1 2 3 4 5 6 7 7. I believe that this hotel has a better image than its competitors. 1 2 3 4 5 6 7 8. This hotel experience has satisfied my needs and wants. 1 2 3 4 5 6 7 9. If I could, I would stay at this hotel again. 1 2 3 4 5 6 7 10. I am satisfied with my hotel stay. 1 2 3 4 5 6 7 11. I always consider this hotel to be the first one on my list when searching for accommodations. 1 2 3 4 5 6 7 12. Overall, I am satisfied with the value I received, for the price that I paid at this hotel. 1 2 3 4 5 6 7 13. The quality of this hotel could be considered superior when compared to other hotels. 1 2 3 4 5 6 7 14. The value that this hotel offers for its price is high. 1 2 3 4 5 6 7 15. I would recommend this hotel to other people. 1 2 3 4 5 6 7 16. Overall, my hotel stay was a pleasant experience. 1 2 3 4 5 6 7 17. In my opinion, this hotel has a good image in the minds of its customers. 1 2 3 4 5 6 7 Section E The questions below relate to personal data. Please TICK (check) one box which is best applicable to you. 1. What is your gender? g1005 Male g1005 Female 2. What is your marital status? g1005 Single g1005 Married g1005 Divorced/Separated g1005g691Living with a Partner g1005 Widowed g691g691 3. What is your age? g1005 18-25 g1005 26-35 g1005 36-45 g1005 46-55 g1005 56-65 g1005g69166+ 4. What is your highest level of education? g1005g691Secondary School or Below g1005g691High School g1005g691Junior Collegeg691g691 g1005g691College or University g1005g691Graduate School or Above Please turn the page and continue to complete Sections E 288 Thank you very much for your time. Wishing you a very good day. 5. What is your average annual income? g1005g691TW$200,000- g1005g691TW$300,001-TW$400,000 g1005g691TW$200,001-TW$300,000g691 g1005g691TW$400,001-TW$500,000 g1005g691TW$500,001-TW$600,000 g1005 TW$600,001-TW$700,000 g1005g691TW$700,001-TW$800,000 g1005 TW$800,001+ 6. What is the main purpose of your trip? g1005g691Pleasure g1005g691Business (Please choose one only) g1005g691Visiting Relatives g1005g691Conference g1005g691Study g1005 Other (please specify) ______________________ 7. What is your ethnic background? g1005g691Asian g1005 North American g1005g691Central American g1005g691South American g1005g691European g1005 African g1005 Australian g1005g691New Zealandg691g691 g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691 g1005 Other (please specify) ______________________ 8. What is your occupation? g1005 Student g1005 Professional (Please choose one only) g1005g691Manager g1005 Government Employee g1005g691Employee of a Company g1005 Housewife g1005g691Soldier g1005g691Labour g1005g691Farmer g1005 Self-Employed g1005g691Retired g1005g691Unemployed g1005g691Other (please specify) ______________________ 289 Appendix 10. Chinese Cover Letter and Questionnaire ?????/??: ??????????????????????????????????? ??????????? ???????????????????????????? 10 ? 15 ??? ??????????????????????????????????? ?????????????????????????? ???????????????????? 18 ???????????? ??????????????????????????????????? ??????????????????????????????g2889?g5129g2615? g3009g2865g3727g4632? ???g1718g1626g1782??g2216g5532g5153?g4585?g5358g4193g4669????? Hung- Che.Wu2@lincolnuni.ac.nz g2216g2371?g4669? 002-64-3-325-3838*?g5554 8366?g2216g2371g4950?? ??g2576g5508g3474g2947g5924g6272??g1521?g4669 002-64-3-325-3838 ?g5554 8069?g2216g2371g4193g4669 hub3@lincoln.ac.nzg4950Baiding Hu??g5924g6272?g3272?g1392g1542??g1352g1521?g4669002-64-3-325- 3838?g55548292?g2216g2371g4193g4669clemes@lincoln.ac.nzg4950Michael D. Clemes??g5924g6272? g1638g1723g2426g3396????g1661g1794g4950g2096g1815? ?g2752? g1830g1883g2883 g4355? ???????????? g691 g701g4143g6441g717g691g707g707g709g691g3343g5054g2455g5272g691g713g711g691g3147g1771g6494g3343g5272g691g691 g691 g691 290 ????????????????? g4251g2811g2253g708g715g4387g699g699g699g699g1846g700g700g700g700g1488g1346g2353g1331g1385g3176g1881g1707g1724g1629g3330g2100g5363g2615g691g691g691g691 ???????? ????g4251g2811???????????g4761????????g3318g4950?g4142????? ???g5363g2615???????A, B, C, D, Eg1398g3741?g3636g1661g1750??g5358g3262g4761???g1718g3741? ???g3755g2794g1794????g1344?g1640g1504???g3755g2794?g1718g6338???????????? ??g3413Ag3741?g2089Dg3741??g5358?1?7?g2085g2539??????g1457g1700??g2260?g2849?g1718g1677 g5078??g2216g1718g1677g5078g1388??g1344g2815???g3755g2794?1 ?g1602?g2426g3396g1388???7 ?g1602?g2426g3396? ??4 ?g1602g1970?g2016????g4004g2308g3262g4761g2604??g3755g2794g1794g1504???g4585?g5358?g2085g2539??? g3342?4??g2260?g2849??g1970?g2016?? A?? g2426g3396g1388 ?? g1388?? g1718g6042g1388? ? g1970?g2016 g1718g6042?? ?? g2426g3396? ? g708 g709 g710 g711 g712 g713 g714 1. g4576g2995g22626g2080g1718g2733g4322g1482g1838g1447g1331g2353g918 1 2 3 4 5 6 7 2.g691g691g691g4576g2995g22626g2218g4212g2353g2364g4212g1365g1371g2594g5406g2165g2353g918g691 1 2 3 4 5 6 7 3.g691g691g691g4576g2995g22626g2353g3035g2295g1573g2594g1914g1671g2995g22626g4368g2220g3930g3496g2353g918g691 1 2 3 4 5 6 7 4.g691g691g691g4576g2995g22626g2124g3822g1542g2353g4671g3335g3320g2187g1670g3680g2253g2433g1811g2353g1775g4508g918g691 1 2 3 4 5 6 7 5.g691g691g691g4576g2995g22626g3054g2523g1431g3277g3503g2594g3283g3555g2353g918g691 1 2 3 4 5 6 7 6.g691g691g691g4576g2995g22626g1897g706g1897g4734g706g2270g5744g2594g4100g5406g2353g918g691 1 2 3 4 5 6 7 7. g691g4576g2995g22626g2353g3025g1885g3707g2784g6659g6525g2524g2930g2256g1671g22626g1411g2024g3311g918g691g691g691g691g691g691g691g691g691g691 1 2 3 4 5 6 7 8.g691g691g691g4576g2995g22626g22602g22572g2594g3279g2484g5382g2353g918g691g691 1 2 3 4 5 6 7 9.g691g691g691g4576g2995g22626g2218g4212g2594g3283g3555g2353g918g691 1 2 3 4 5 6 7 10. g4576g2995g22626g4568g22584g2821g3025g2594g1914g3803g6559g2353g918 1 2 3 4 5 6 7 11. g1914g3103g2353g2544g3803g4324g4576g2995g22626g2353g3035g2295g918 1 2 3 4 5 6 7 12. g4576g2995g22626g2124g3822g1542g2353g22602g6682g1670g3680g2253g2433g1811g2353g1775g4508g918 1 2 3 4 5 6 7 13. g4576g2995g22626g2080g1718g2930g2256g2242g4626g2353g1470g6430g1692g1636g1504g1361g918 1 2 3 4 5 6 7 14. g4576g2995g22626g2218g4212g2594g1692g5737g2353g918 1 2 3 4 5 6 7 15. g4576g2995g22626g2353g3025g1885g3707g2784g3620g1661g1914g2353g1598g2353/g5057g1955g918 1 2 3 4 5 6 7 16. g4576g2995g22626g2062g5800g2353g22602g22572g2589g5345g1587g1342g1498g2025g918 1 2 3 4 5 6 7 17.g691g4576g2995g22626g2080g1718g5201g1602g2260g6633g2353g2353g3043g2043g2003g3629g918g691 1 2 3 4 5 6 7 18. g4576g2995g22626g4568g22584g2934g3580g1504g1677g4930g2552g4323g4950g2821g3025g918 1 2 3 4 5 6 7 19. g4576g2995g22626g3447g2545g3316g2594g3283g3555g2353g918 1 2 3 4 5 6 7 20. g4576g2995g22626g2218g1411g2080g1718g3279g2484g5382g2353g4405g3443g3707g2588g918 1 2 3 4 5 6 7 21.g691g4576g2995g22626g2080g1718g1658g1700g1658g5196g2353g22602g22572g3707g2588g918 1 2 3 4 5 6 7 22. g4576g2995g22626g3900g2062g1329g2433g1811g2353g3286g2027g3825g918 1 2 3 4 5 6 7 23. g4576g2995g22626g2889g1376g5082g2930g2702g3238g2064g2589g3283g3555g1342g5534g5221g918 1 2 3 4 5 6 7 24. g4576g2995g22626g4568g22584g2080g1718g2821g3025g1482g1838g1447g1331g918 1 2 3 4 5 6 7 25. g4576g2995g22626g2218g4212g3900g2062g1329g1692g1636g2353g2437g5728g5283g918 1 2 3 4 5 6 7 26. g4576g2995g22626g2353g3035g2295g2594g4373g1683g2353g918 1 2 3 4 5 6 7 27. g4576g2995g22626g2353g4756g5382g5865g4732g699g1846g3724g4047g6639g3707g3778g700g2594g1914g6639g6637g4632g3784g1683g2353g918g691g691g691g691 1 2 3 4 5 6 7 ??????????? B?C?? 291 B?? g2426g3396g1388 ?? g1388?? g1718g6042g1388? ? g1970?g2016 g1718g6042?? ?? g2426g3396? ? g708 g709 g710 g711 g712 g713 g714 1. g4576g2995g22626g2889g1376g5877g4574g1914g5057g2782g1492g2842g2353g3377g4368g2357g6301g2064g4840g2025g1914g2353g5057g1955g918 1 2 3 4 5 6 7 2.g691g691g691g4576g2995g22626g2889g1376g2353g1769g2657g6659g1914g4761g1492g2842g3900g2062g2353g2266g3309g3586g1587g2430g1626g918g691 1 2 3 4 5 6 7 3.g691g691g691g1914g1521g1488g1627g5688g4576g2995g22626g2039g2052g5237g3420g1492g2842g1376g1794g706g6125g3714g2353g2889g1376g918g691 1 2 3 4 5 6 7 4. g691g4445g1914g1718g3330g6194g2996g916g4576g2995g22626g2889g1376g4366g1488g3103g4584g2353g4778g2539g2064g4574g1964g918g691 1 2 3 4 5 6 7 5. g691g691g1365g2752g1346g916g1914g4761g4576g2995g22626g2081g1492g6525g2524g2353g5936g1463g1769g2657g1718g2012g1683g1649g4157g918g691g691g691 1 2 3 4 5 6 7 6.g691g691g691g4576g2995g22626g2889g1376g2353g4778g2539g2404g3580g1504g1492g2842g2594g5207g4320g5797g1815g1914g2353g918g691 1 2 3 4 5 6 7 7. g4576g2995g22626g2889g1376g5908g4366g2657g1914g3900g2062g3784g1683g2353g2266g3309g918 1 2 3 4 5 6 7 8.g691g691g691g1914g1521g1488g1627g5688g4576g2995g22626g1430g4083g2353g2889g1376g918g691 1 2 3 4 5 6 7 9.g691g691g691g4576g2995g22626g2889g1376g3743g2357g4624g4574g1964g1914g2220g2244g2556g3330g6194g2353g2809g2782g2212g918g691 1 2 3 4 5 6 7 10. g2121g2081g1492g6525g2524g1397g3311g4366g2356g3447g5145g6524g2089g1914g4761g4576g2995g22626g2266g3309g2353g4322g6426g918 1 2 3 4 5 6 7 11. g4576g2995g22626g2889g1376g2353g4778g2539g2404g3580g916g6659g1914g6426g3411g1492g2842g2594g5877g4574g1914g2353g5057g1955g2353g918g691 1 2 3 4 5 6 7 12. g2081g1492g6525g2524g4366g5701g1690g4576g2995g22626g2353g1776g3374g3701g2462g4950g4901g3579g3514g3955g918 1 2 3 4 5 6 7 13. g4576g2995g22626g2889g1376g1521g1488g1819g2085g1667g4064g1914g2353g3330g6194g918 1 2 3 4 5 6 7 14. g4576g2995g22626g2889g1376g1521g1488g2356g3447g1342g1910g3730g1670g3680g3579g1914g2353g2244g2556g918 1 2 3 4 5 6 7 15. g1914g1521g1488g2430g5688g4576g2995g22626g2889g1376g3736g4632g2266g3309g2353g1457g1700g2064g4840g2025g1914g2849g1331g5057g1955g918 1 2 3 4 5 6 7 16. g4576g2995g22626g2889g1376g2594g4896g6125g2353g918 1 2 3 4 5 6 7 17. g1365g6639g1346g916g1914g4366g4993g2657g1914g4950g4576g2995g22626g2889g1376g1397g3311g2353g2484g5382g2594g4373g1683g2353g918 1 2 3 4 5 6 7 C?? g2426g3396g1388 ?? g1388?? g1718g6042g1388? ? g1970?g2016 g1718g6042?? ?? g2426g3396? ? g708 g709 g710 g711 g712 g713 g714 1. g4576g2995g22626g3900g2062g1914g1331g5054g1397g3311g2353g5554g4366g918 1 2 3 4 5 6 7 2. g1671g3784g2549g3372g3374g2253g4576g2995g22626g1392g2996g916g1914g6426g3411g1914g1378g3411g2089g1329g1323g2849g2012g1683g2353g1776g6637g918g691 1 2 3 4 5 6 7 3.g691g691g691g1671g4576g2995g22626g2353g4059g2545g2266g3309g2996g4212g2594g1661g3579g2353g918g691 1 2 3 4 5 6 7 4. g691g691g4576g2995g22626g6659g1914g6426g3411g2121g2081g1492g6525g2524g1671g1323g3238g1718g4895g6076g6470g4322g918g691g691g691g691g691 1 2 3 4 5 6 7 5. g691g691g1671g6182g4210g4576g2995g22626g1392g2996g916g1914g6426g3411g1378g3411g2089g1914g4323g2782g2353g1323g1418g918g691g691g691g691g691g691 1 2 3 4 5 6 7 6.g691g691g691g4576g2995g22626g2889g1376g4578g4729g1922g1914g1671g2995g22626g1411g2353g4059g2545g2996g4212g2814g2089g3784g1797g918g691g691g691g691g691 1 2 3 4 5 6 7 7. g1914g1671g4576g2995g22626g1718g1329g2359g1612g3447g6427g918g691g691g691 1 2 3 4 5 6 7 8.g691g691g691g4576g2995g22626g2889g1376g691g5877g4574g2995g22626g1411g4059g2545g2996g4212g2353g2415g4045g4761g1914g2594g2809g2782g2353g918g691 1 2 3 4 5 6 7 9.g691g691g691g4576g2995g22626g2353g2081g1492g6525g2524g2048g1388g5145g6524g2995g22626g4761g1914g3900g2062g2012g1683g2266g3309g2484g5382g2353g3176g1338g918g691 1 2 3 4 5 6 7 10. g1914g4761g2253g4576g2995g22626g2266g3309g2353g1707g2991g2594g2404g1602g6700g3706g2353g918 1 2 3 4 5 6 7 11. g4576g2995g22626g2889g1376g4366g4407g2996g2657g1914g3900g2062g2266g3309g918 1 2 3 4 5 6 7 12. g1365g6639g1346g916g1914g4761g4576g2995g22626g4322g6426g2594g2012g1683g2353g918 1 2 3 4 5 6 7 ??????????? D?E?? 292 D?? g2426g3396g1388 ?? g1388?? g1718g6042g1388? ? g1970?g2016 g1718g6042?? ?? g2426g3396? ? g708 g709 g710 g711 g712 g713 g714 1. g1365g2752g1346g916g4576g2995g22626g2353g2266g3309g2484g5382g2594g2012g1683g2353g918 1 2 3 4 5 6 7 2.g691g691g691g1914g4761g4576g2995g22626g5908g2594g3086g1344g2012g1683g2353g1649g4157g918g691 1 2 3 4 5 6 7 3.g691g691g691g1365g6639g1346g916g1671g4576g2995g22626g1776g3374g6639g6637g2353g2838g4322g2594g2012g1683g2353g918g691 1 2 3 4 5 6 7 4. g691g691g1914g3378g4366g1835g4153g1809g1331g4576g2995g22626g2353g5772g6042g918g691g691g691g691g691 1 2 3 4 5 6 7 5. g691g691g1914g2700g2430g1914g3372g3374g2253g4576g2995g22626g2594g1573g5268g2353g5703g5527g918g691g691g691g691g691 1 2 3 4 5 6 7 6.g691g691g691g4576g2995g22626g3900g2062g3279g2484g5382g2353g2266g3309g918g691g691g691g691g691 1 2 3 4 5 6 7 7. g1914g2700g2430g4576g2995g22626g1466g2081g1492g1653g4368g6401g2334g2371g2080g1718g1939g1683g2353g1902g4157g918g691g691g691g691 1 2 3 4 5 6 7 8.g691g691g691g3372g3374g2253g4576g2995g22626g1378g4840g2025g1914g2849g1331g2353g5057g1955g4950g4323g3496g918g691 1 2 3 4 5 6 7 9.g691g691g691g3287g1685g1521g1769g2353g4585g916g1914g4366g1638g1723g1667g2089g4576g2995g22626g1776g3374g918g691 1 2 3 4 5 6 7 10. g1914g4761g2253g4576g2995g22626g1776g3374g2594g4840g4320g2353g918 1 2 3 4 5 6 7 11. g1671g1570g2064g3848g1924g1776g3374g1392g2996g916g1914g3743g4366g1922g4576g2995g22626g1640g2657g3619g1323g1749g4198g918g691g691 1 2 3 4 5 6 7 12. g1365g6639g1346g916g1914g1671g4576g2995g22626g2220g1453g1489g2353g4164g1588g4950g4756g5054g3411g2089g2353g5085g2838g2594g1495g1331g4840g4320g691 g691g691g691g691g691g691g2353g918 1 2 3 4 5 6 7 13. g4607g2081g1492g2995g22626g2700g1466g916g4576g2995g22626g2353g2484g5382g1521g4142g2657g5772g2012g2353g918 1 2 3 4 5 6 7 14. g4576g2995g22626g3176g3900g2062g4950g1548g5085g1775g2700g5800g2353g3279g2838g4322g6639g6637g918 1 2 3 4 5 6 7 15. g1914g4366g1922g4576g2995g22626g3458g6138g4079g2081g1492g1331g918 1 2 3 4 5 6 7 16. g1365g6639g1346g916g3372g3374g2253g4576g2995g22626g4079g1329g1914g3885g1910g2353g6639g6637g918 1 2 3 4 5 6 7 17. g2057g1914g2702g916g4576g2995g22626g1671g6525g2524g2353g1448g1598g1389g1378g5547g1605g1323g2849g2012g1683g2353g1902g4157g918g691g691 1 2 3 4 5 6 7 E ?? ?g1344???g1718g6338?g2849???g2993?g5358?g3784g5406g4445?g2364g3025??? (check)? 1.g691g691g691g3422g2353g2212g1809g722g691g691 g1005g691g1994g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691 g1005g691g1366g691 g691 g691 g691 2.g691g691g691g3422g2353g3366g2519g2339g2313g722g691g691 g1005g691g3810g2026g691g691g691g691 g1005g691g1378g3366g691 g691 g1005g691g6182g3366g706g1417g2170g691 g1005g691g4950g1780g2439g1653g2170g691 g691 g1005g691g2706g4754g691g691g691g691g691g691 g691 g691 g691 3.g691g691g691g3422g2353g1699g6463g722g691g691g691 g1005g691g708g715g1759g709g712g4387g691 g1005g691g709g713g1759g710g712g4387g691g691g691 g691 g1005g691g710g713g1759g711g712g4387g691 g1005g691g711g713g1759g712g712g4387g691g691g691g691 g691 g1005g691g712g713g1759g713g712g4387g691 g1005g691g713g713g4387g691g699g1846g1488g1346g700g691 g691 g691 g691 4.g691g691g691g3422g2353g3784g3279g3474g2011g4051g2539g722g691 g1005g691g3343g1389g699g1846g1488g1344g700g691 g1005g691g3279g1389g6125g691g691g691 g691 g1005g691g3377g2717g691g691g691g691g691 g1005g691g5506g3267g2216g1365g5506g691 g691 g1005g691g2708g2002g2220g691g699g1846g1488g1346g700g691 g691 ???????????E?? 293 ?????????????????????? 5. g691g691g3422g2353g1555g1857g1699g1711g1333g722g691 g1005g691g1536g4766g695g691g709g707g707g703g707g707g707g691g699g1846g1488g1344g700g691 g1005g691g1536g4766g695g691g709g707g707g703g707g707g708g1759g695g691g710g707g707g703g707g707g707g691 g691 g1005g691g1536g4766g695g691g710g707g707g703g707g707g708g1759g695g691g711g707g707g703g707g707g707g691 g1005g691g1536g4766g695g691g711g707g707g703g707g707g708g1759g695g691g712g707g707g703g707g707g707g691 g691 g1005g691g1536g4766g695g691g712g707g707g703g707g707g708g1759g695g691g713g707g707g703g707g707g707g691 g1005g691g1536g4766g695g691g713g707g707g703g707g707g708g1759g695g691g714g707g707g703g707g707g707g691 g691 g1005g691g1536g4766g695g691g714g707g707g703g707g707g708g1759g695g691g715g707g707g703g707g707g707g691 g1005g691g1536g4766g695g691g715g707g707g703g707g707g708g691g699g1846g1488g1346g700g691 g691 g691 6. g691g691g3422g2995g4623g2353g1484g2782g1598g2353g722g691g699g5358g4251g1533g5703g2103g1323g4232g700g691 g1005g691g6688g1632g691g691g691g691g691g691 g1005g691g1504g2945g691 g691g691g691g691 g1005g691g3446g3702g5667g1430g691 g1005g691g4366g6428g691 g691 g1005g691g5506g3646g691 g1005g691g2081g1492g691g699g5358g3892g2794g700g691 g691g691g691g691g691g691 _____________________ g691 g691 g691 7. g691g691g3422g2353g1575g3484g2745g3923g722g691g691 g1005g691g2053g2634g1331g691 g1005g691g1511g2733g1331g691 g691 g1005g691g1389g2733g2634g1331g691 g1005g691g2468g2733g1331g691 g691 g1005g691g5211g2634g1331g691 g1005g691g2426g2634g1331g691 g691 g1005g691g5566g2634g1331g691 g1005g691g3147g1771g6494g1331g691 g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691g691 g1005g691g2081g1492g691g699g5358g3892g2794g700g691 g691 g691g691g691g691g691_______________________ g691 g691 g691 g691 8. g691g691g3422g2353g6125g4368g722g691g699g5358g4251g1533g5703g2103g1323g4232g700g691 g1005g691g5506g1587g691g691 g1005g691g3377g4368g1918g3692g1331g2889g691 g691 g1005g691g4901g3579g1331g2889g691 g1005g691g1414g3309g1331g2889g691 g691 g1005g691g1414g1529g6125g2889g691 g1005g691g2926g4901g691 g691 g1005g691g2792g1331 g1005g691g1376g1331g691 g691 g1005g691g4621g1435g691 g1005g691g1758g4703g1331g2889g691 g691 g1005g691g3247g1622g1331g2889 g1005g691g2545g4368g1389g691 g691 g1005g691g2081g1492g691g699g5358g3892g2794g700g691 g691g691g691g691g691g691g691 g691g691g691g691g691_______________________ g691 294 Appendix 11. Data Imputation Table 34A: Summary Statistics of Missing Data for Original Sample (N=580) Missing Data Missing Data Item Number of Cases Mean Standard Deviation Number Percent Item Number of Cases Mean Standard Deviation Number Percent A1 578 5.27 1.194 2 0.3 C3 576 5.52 1.125 4 0.7 A2 580 5.68 1.032 0 0.0 C4 578 4.02 1.164 2 0.3 A3 579 5.47 1.082 1 0.2 C5 577 5.10 1.114 3 0.5 A4 578 5.20 1.211 2 0.3 C6 578 5.61 1.031 2 0.3 A5 576 5.59 1.090 4 0.7 C7 576 3.41 1.207 4 0.7 A6 577 5.61 1.018 3 0.5 C8 577 5.58 1.090 3 0.5 A7 578 5.33 1.166 2 0.3 C9 579 4.93 1.285 1 0.2 A8 576 5.33 1.185 4 0.7 C10 579 5.07 1.114 1 0.2 A9 580 5.56 1.055 0 0.0 C11 579 5.56 0.994 1 0.2 A10 578 5.41 1.023 2 0.3 C12 580 5.39 0.997 0 0.0 A11 579 5.33 1.145 1 0.2 D1 578 5.39 0.954 2 0.3 A12 579 5.09 1.282 1 0.2 D2 579 5.43 0.965 1 0.2 A13 579 5.48 1.156 1 0.2 D3 579 5.31 0.999 1 0.2 A14 578 5.51 1.130 2 0.3 D4 576 5.24 1.072 4 0.7 A15 579 5.27 1.127 1 0.2 D5 575 5.38 1.004 5 0.9 A16 578 5.41 1.119 2 0.3 D6 575 5.39 0.965 5 0.9 A17 579 5.52 1.091 1 0.2 D7 580 5.37 1.004 0 0.0 A18 578 5.26 1.128 2 0.3 D8 580 5.24 1.073 0 0.0 A19 577 5.56 1.248 3 0.5 D9 576 5.20 1.114 4 0.7 A20 577 5.49 1.180 3 0.5 D10 577 5.40 1.009 3 0.5 A21 580 5.08 1.279 0 0.0 D11 579 5.14 1.151 1 0.2 A22 576 5.21 1.229 4 0.7 D12 578 5.12 1.069 2 0.3 A23 580 5.67 1.090 0 0.0 D13 576 5.43 0.968 4 0.7 A24 575 5.31 1.090 5 0.9 D14 577 5.15 1.094 3 0.5 A25 580 5.33 1.246 0 0.0 D15 579 5.15 1.143 1 0.2 A26 579 5.42 1.089 1 0.2 D16 579 5.42 1.028 1 0.2 A27 578 4.58 1.402 2 0.3 D17 577 5.47 0.968 3 0.5 B1 580 5.56 0.986 0 0.0 B2 571 5.08 1.152 9 1.6 B3 579 5.38 1.045 1 0.2 B4 576 5.93 1.036 4 0.7 B5 573 4.14 0.964 7 1.2 B6 578 5.31 1.037 2 0.3 B7 575 5.20 1.037 5 0.9 B8 577 5.28 0.985 3 0.5 B9 578 5.86 1.131 2 0.3 B10 574 4.12 0.926 6 1.0 B11 573 5.12 1.071 7 1.2 B12 573 3.97 0.920 7 1.2 B13 577 5.26 1.063 3 0.5 B14 574 5.78 1.005 6 1.0 B15 577 5.17 0.997 3 0.5 B16 578 5.37 1.035 2 0.3 B17 580 5.20 1.021 0 0.0 C1 578 3.78 1.085 2 0.3 C2 577 5.25 1.135 3 0.5 295 Table 35A: Estimated Means Results Summary of Estimated Means Item A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 All Values EM 5.27 5.28 5.68 5.68 5.47 5.47 5.20 5.20 5.59 5.60 5.61 5.61 5.33 5.32 5.33 5.32 5.56 5.56 5.41 5.41 5.33 5.33 5.09 5.09 Physical Item A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 Environment Quality All Values EM 5.48 5.48 5.51 5.51 5.27 5.27 5.41 5.41 5.52 5.52 5.26 5.26 5.56 5.56 5.49 5.49 5.08 5.08 5.21 5.20 5.67 5.67 5.31 5.31 Item A25 A26 A27 All Values EM 5.33 5.33 5.42 5.42 4.58 4.58 Item B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 Interaction All Values EM 5.56 5.56 5.08 5.07 5.38 5.38 5.93 5.93 4.14 4.14 5.31 5.31 5.20 5.20 5.28 5.27 5.86 5.86 4.12 4.12 5.12 5.12 3.97 3.97 Quality Item B13 B14 B15 B16 B17 All Values EM 5.26 5.26 5.78 5.78 5.17 5.17 5.37 5.36 5.20 5.20 Outcome Item C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 Quality All Values EM 3.78 3.77 5.25 5.24 5.52 5.52 4.02 4.01 5.10 5.10 5.61 5.61 3.41 3.41 5.58 5.59 4.93 4.93 5.07 5.07 5.56 5.56 5.39 5.39 Item D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 SQ, PV, All Values EM 5.39 5.39 5.43 5.43 5.31 5.31 5.24 5.24 5.38 5.38 5.39 5.39 5.39 5.39 5.24 5.24 5.20 5.20 5.40 5.40 5.14 5.14 5.12 5.13 Image, CS, Item D13 D14 D15 D16 D17 BI All Values EM 5.43 5.43 5.15 5.15 5.15 5.15 5.42 5.42 5.47 5.47 296 Appendix 12. Correlation Matrix Table 36A: Correlation Matrix A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 1.000 0.107 0.420 0.247 0.098 0.145 0.549 0.292 0.081 0.313 0.317 0.200 0.292 0.080 0.592 0.279 0.300 0.305 0.065 0.135 0.269 0.292 0.097 0.380 0.161 0.378 0.192 0.342 0.139 0.060 0.007 0.282 0.323 0.324 0.086 0.082 0.276 -0.006 0.164 0.090 0.312 0.179 0.201 0.260 0.123 0.163 0.150 0.129 0.142 0.104 0.176 0.178 0.135 0.107 1.000 0.035 0.130 0.794 0.719 0.201 0.190 0.698 0.035 0.012 0.181 0.268 0.696 0.148 0.243 0.282 -0.059 0.278 0.754 0.233 0.231 0.317 0.068 0.215 0.091 0.240 0.403 0.189 -0.055 -0.017 0.588 0.471 0.409 -0.135 -0.006 0.371 -0.018 0.286 -0.126 0.363 0.197 0.187 0.379 0.453 0.265 0.318 0.475 0.172 0.368 -0.015 0.335 0.362 0.420 0.035 1.000 0.187 0.003 0.038 0.275 0.304 0.032 0.565 0.549 0.162 0.269 -0.007 0.363 0.322 0.255 0.461 0.021 0.079 0.288 0.197 0.043 0.492 0.209 0.549 0.259 0.290 0.227 0.058 -0.002 0.212 0.224 0.292 0.015 0.044 0.210 0.016 0.139 0.044 0.293 0.221 0.128 0.191 0.013 0.084 0.118 -0.043 0.054 0.051 0.005 0.115 0.035 0.247 0.130 0.187 1.000 0.146 0.151 0.202 0.223 0.132 0.175 0.153 0.657 0.225 0.113 0.227 0.279 0.271 0.193 0.136 0.144 0.256 0.456 0.198 0.281 0.208 0.273 0.171 0.262 0.170 0.042 -0.068 0.244 0.273 0.276 -0.001 -0.039 0.242 -0.083 0.168 0.013 0.269 0.177 0.183 0.186 0.104 0.180 0.134 0.085 0.148 0.091 0.014 0.116 0.092 0.098 0.794 0.003 0.146 1.000 0.732 0.176 0.179 0.717 0.003 -0.034 0.200 0.257 0.738 0.113 0.201 0.248 -0.108 0.217 0.767 0.198 0.286 0.318 0.027 0.209 0.044 0.191 0.363 0.140 -0.074 -0.017 0.399 0.582 0.367 -0.146 -0.020 0.343 -0.055 0.301 -0.147 0.308 0.168 0.154 0.398 0.423 0.242 0.304 0.571 0.113 0.377 -0.025 0.322 0.362 0.145 0.719 0.038 0.151 0.732 1.000 0.149 0.157 0.680 0.091 0.067 0.193 0.243 0.666 0.143 0.184 0.263 -0.046 0.235 0.683 0.170 0.259 0.303 0.099 0.155 0.120 0.233 0.332 0.190 -0.092 -0.045 0.383 0.407 0.611 -0.134 -0.009 0.326 -0.017 0.246 -0.124 0.341 0.207 0.218 0.407 0.361 0.301 0.340 0.404 0.157 0.320 -0.023 0.366 0.336 0.549 0.201 0.275 0.202 0.176 0.149 1.000 0.319 0.133 0.289 0.244 0.176 0.327 0.144 0.704 0.396 0.335 0.224 -0.005 0.151 0.263 0.259 0.036 0.286 0.210 0.289 0.192 0.304 0.171 0.166 0.110 0.333 0.360 0.322 0.115 0.142 0.296 0.041 0.196 0.122 0.338 0.169 0.167 0.265 0.071 0.177 0.237 0.117 0.186 0.055 0.139 0.255 0.109 0.292 0.190 0.304 0.223 0.179 0.157 0.319 1.000 0.129 0.352 0.340 0.212 0.570 0.106 0.395 0.694 0.590 0.257 0.114 0.175 0.631 0.265 0.056 0.299 0.527 0.339 0.228 0.400 0.193 0.187 0.078 0.395 0.399 0.350 0.212 0.140 0.330 0.102 0.337 0.159 0.359 0.203 0.097 0.253 0.230 0.098 0.171 0.213 0.114 0.196 0.177 0.184 0.200 0.081 0.698 0.032 0.132 0.717 0.680 0.133 0.129 1.000 -0.004 -0.004 0.195 0.231 0.713 0.098 0.170 0.227 -0.080 0.227 0.690 0.161 0.204 0.432 0.005 0.200 0.052 0.198 0.269 0.212 -0.061 0.016 0.278 0.308 0.294 -0.156 -0.012 0.298 0.011 0.294 -0.144 0.520 0.224 0.178 0.383 0.316 0.246 0.333 0.333 0.122 0.341 -0.059 0.357 0.487 0.313 0.035 0.565 0.175 0.003 0.091 0.289 0.352 -0.004 1.000 0.722 0.177 0.312 -0.005 0.367 0.398 0.309 0.603 -0.011 0.057 0.289 0.197 -0.023 0.706 0.221 0.688 0.300 0.337 0.243 0.089 -0.042 0.275 0.294 0.377 0.105 0.021 0.276 0.046 0.186 0.132 0.326 0.220 0.104 0.213 0.021 0.145 0.185 -0.029 0.069 0.037 0.027 0.188 0.036 0.317 0.012 0.549 0.153 -0.034 0.067 0.244 0.340 -0.004 0.722 1.000 0.188 0.285 -0.033 0.362 0.347 0.287 0.502 0.003 0.025 0.304 0.208 0.067 0.582 0.191 0.620 0.273 0.316 0.269 0.026 -0.048 0.241 0.241 0.322 0.051 0.029 0.226 0.049 0.114 0.039 0.310 0.250 0.112 0.167 -0.035 0.164 0.163 -0.103 0.071 -0.062 -0.023 0.168 -0.058 0.200 0.181 0.162 0.657 0.200 0.193 0.176 0.212 0.195 0.177 0.188 1.000 0.253 0.124 0.196 0.238 0.278 0.144 0.112 0.192 0.202 0.469 0.201 0.248 0.179 0.222 0.152 0.239 0.195 0.048 -0.065 0.248 0.261 0.262 0-.009 -0.019 0.186 -0.072 0.202 0.025 0.288 0.202 0.198 0.123 0.136 0.185 0.136 0.153 0.188 0.148 -0.001 0.130 0.135 297 Appendix 12. Correlation Matrix Table 36A: Correlation Matrix (Continued) A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.292 0.268 0.269 0.225 0.257 0.243 0.327 0.570 0.231 0.312 0.285 0.253 1.000 0.209 0.367 0.588 0.780 0.237 0.151 0.276 0.498 0.310 0.156 0.275 0.585 0.259 0.273 0.410 0.236 0.150 0.048 0.410 0.356 0.357 0.110 0.055 0.329 0.026 0.313 0.100 0.372 0.259 0.112 0.324 0.309 0.155 0.231 0.287 0.086 0.251 0.144 0.225 0.249 0.080 0.696 -0.007 0.113 0.738 0.666 0.144 0.106 0.713 -0.005 -0.033 0.124 0.209 1.000 0.063 0.168 0.198 -0.100 0.240 0.694 0.158 0.218 0.327 0.013 0.189 0.033 0.169 0.333 0.175 -0.035 0.017 0.333 0.392 0.335 -0.071 -0.013 0.571 0.008 0.310 -0.098 0.367 0.188 0.162 0.371 0.373 0.286 0.317 0.400 0.117 0.357 0.010 0.333 0.368 0.592 0.148 0.363 0.227 0.113 0.143 0.704 0.395 0.098 0.367 0.362 0.196 0.367 0.063 1.000 0.453 0.350 0.327 0.026 0.130 0.354 0.299 0.057 0.368 0.248 0.428 0.206 0.382 0.193 0.157 0.090 0.347 0.368 0.377 0.167 0.153 0.293 0.035 0.239 0.173 0.379 0.205 0.103 0.298 0.097 0.135 0.237 0.111 0.139 0.079 0.203 0.270 0.117 0.279 0.243 0.322 0.279 0.201 0.184 0.396 0.694 0.170 0.398 0.347 0.238 0.588 0.168 0.453 1.000 0.649 0.324 0.138 0.233 0.606 0.292 0.088 0.342 0.512 0.350 0.292 0.456 0.258 0.199 0.052 0.441 0.425 0.385 0.180 0.128 0.397 0.100 0.367 0.173 0.422 0.275 0.111 0.295 0.279 0.110 0.236 0.251 0.146 0.223 0.211 0.218 0.216 0.300 0.282 0.255 0.271 0.248 0.263 0.335 0.590 0.227 0.309 0.287 0.278 0.780 0.198 0.350 0.649 1.000 0.250 0.132 0.273 0.503 0.358 0.141 0.300 0.636 0.242 0.238 0.379 0.207 0.132 0.054 0.420 0.346 0.364 0.080 0.113 0.324 0.064 0.280 0.096 0.357 0.234 0.155 0.311 0.261 0.192 0.250 0.256 0.142 0.200 0.116 0.253 0.185 0.305 -0.059 0.461 0.193 -0.108 -0.046 0.224 0.257 -0.080 0.603 0.502 0.144 0.237 -0.100 0.327 0.324 0.250 1.000 0.050 -0.057 0.264 0.205 0.077 0.652 0.168 0.594 0.278 0.238 0.290 0.061 -0.010 0.245 0.222 0.277 0.052 0.002 0.218 0.030 0.214 0.124 0.289 0.266 0.148 0.139 -0.039 0.143 0.174 -0.054 0.144 -0.003 0.039 0.169 -0.040 0.065 0.278 0.021 0.136 0.217 0.235 -0.005 0.114 0.227 -0.011 0.003 0.112 0.151 0.240 0.026 0.138 0.132 0.050 1.000 0.203 0.174 0.181 0.471 0.087 0.112 0.078 0.283 0.171 0.284 -0.092 -0.060 0.212 0.187 0.185 -0.079 -0.075 0.214 0.052 0.259 -0.099 0.178 0.268 0.156 0.116 0.280 0.125 0.033 0.272 0.056 0.341 -0.082 0.047 0.237 0.135 0.754 0.079 0.144 0.767 0.683 0.151 0.175 0.690 0.057 0.025 0.192 0.276 0.694 0.130 0.233 0.273 -0.057 0.203 1.000 0.243 0.231 0.307 0.057 0.221 0.085 0.212 0.572 0.179 -0.069 -0.003 0.371 0.384 0.320 -0.121 0.009 0.312 -0.028 0.235 -0.150 0.290 0.202 0.193 0.386 0.574 0.246 0.325 0.428 0.134 0.326 0.013 0.348 0.324 0.269 0.233 0.288 0.256 0.198 0.170 0.263 0.631 0.161 0.289 0.304 0.202 0.498 0.158 0.354 0.606 0.503 0.264 0.174 0.243 1.000 0.339 0.146 0.317 0.508 0.357 0.272 0.449 0.248 0.100 0.064 0.362 0.377 0.337 0.099 0.078 0.341 0.111 0.282 0.105 0.354 0.251 0.152 0.206 0.282 0.128 0.148 0.238 0.141 0.184 0.168 0.161 0.186 0.292 0.231 0.197 0.456 0.286 0.259 0.259 0.265 0.204 0.197 0.208 0.469 0.310 0.218 0.299 0.292 0.358 0.205 0.181 0.231 0.339 1.000 0.250 0.337 0.294 0.315 0.181 0.341 0.151 0.063 0.021 0.330 0.384 0.373 0.041 0.050 0.316 -0.006 0.246 0.027 0.338 0.171 0.223 0.280 0.220 0.222 0.205 0.242 0.122 0.199 0.096 0.215 0.202 0.097 0.317 0.043 0.198 0.318 0.303 0.036 0.056 0.432 -0.023 0.067 0.201 0.156 0.327 0.057 0.088 0.141 0.077 0.471 0.307 0.146 0.250 1.000 0.060 0.156 0.060 0.251 0.218 0.291 -0.011 -0.012 0.219 0.211 0.197 -0.115 -0.040 0.251 0.000 0.144 -0.072 0.343 0.267 0.164 0.188 0.299 0.198 0.148 0.300 0.086 0.279 -0.143 0.176 0.367 0.380 0.068 0.492 0.281 0.027 0.099 0.286 0.299 0.005 0.706 0.582 0.248 0.275 0.013 0.368 0.342 0.300 0.652 0.087 0.057 0.317 0.337 0.060 1.000 0.226 0.713 0.282 0.356 0.263 0.042 -0.049 0.331 0.330 0.382 0.059 0.017 0.298 0.034 0.154 0.059 0.331 0.243 0.151 0.184 0.019 0.140 0.150 -0.017 0.129 0.016 0.054 0.150 -0.016 298 Appendix 12. Correlation Matrix Table 36A: Correlation Matrix (Continued) A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.161 0.215 0.209 0.208 0.209 0.155 0.210 0.527 0.200 0.221 0.191 0.179 0.585 0.189 0.248 0.512 0.636 0.168 0.112 0.221 0.508 0.294 0.156 0.226 1.000 0.215 0.208 0.327 0.166 0.105 0.069 0.338 0.280 0.245 0.043 0.038 0.302 0.035 0.252 0.045 0.299 0.174 0.174 0.256 0.214 0.213 0.180 0.199 0.135 0.170 0.143 0.190 0.190 0.378 0.091 0.549 0.273 0.044 0.120 0.289 0.339 0.052 0.688 0.620 0.222 0.259 0.033 0.428 0.350 0.242 0.594 0.078 0.085 0.357 0.315 0.060 0.713 0.215 1.000 0.329 0.403 0.303 0.057 0.002 0.357 0.378 0.416 0.120 0.018 0.348 0.028 0.228 0.115 0.391 0.294 0.137 0.250 0.032 0.162 0.203 -0.020 0.075 0.039 0.057 0.206 0.039 0.192 0.240 0.259 0.171 0.191 0.233 0.192 0.228 0.198 0.300 0.273 0.152 0.273 0.169 0.206 0.292 0.238 0.278 0.283 0.212 0.272 0.181 0.251 0.282 0.208 0.329 1.000 0.387 0.706 0.039 -0.017 0.388 0.375 0.392 0.009 -0.016 0.326 0.001 0.241 0.005 0.375 0.666 0.159 0.374 0.184 0.163 0.170 0.126 0.121 0.106 0.047 0.177 0.119 0.342 0.403 0.290 0.262 0.363 0.332 0.304 0.400 0.269 0.337 0.316 0.239 0.410 0.333 0.382 0.456 0.379 0.238 0.171 0.572 0.449 0.341 0.218 0.356 0.327 0.403 0.387 1.000 0.337 0.193 0.103 0.694 0.697 0.628 0.198 0.101 0.639 0.071 0.440 0.135 0.602 0.380 0.200 0.414 0.549 0.209 0.320 0.378 0.194 0.287 0.207 0.343 0.350 0.139 0.189 0.227 0.170 0.140 0.190 0.171 0.193 0.212 0.243 0.269 0.195 0.236 0.175 0.193 0.258 0.207 0.290 0.284 0.179 0.248 0.151 0.291 0.263 0.166 0.303 0.706 0.337 1.000 0.056 0.017 0.293 0.315 0.313 -0.015 0.004 0.315 -0.009 0.215 0.018 0.379 0.930 0.197 0.125 0.137 0.219 0.281 0.086 0.154 0.056 0.057 0.308 0.109 0.060 -0.055 0.058 0.042 -0.074 -0.092 0.166 0.187 -0.061 0.089 0.026 0.048 0.150 -0.035 0.157 0.199 0.132 0.061 -0.092 -0.069 0.100 0.063 -0.011 0.042 0.105 0.057 0.039 0.193 0.056 1.000 0.157 0.211 0.210 0.121 0.699 0.205 0.197 0.131 0.294 0.695 0.244 0.067 -0.082 0.101 0.111 -0.038 0.106 0.095 -0.013 0.115 0.210 0.122 0.138 0.007 -0.017 -0.002 -0.068 -0.017 -0.045 0.110 0.078 0.016 -0.042 -0.048 -0.065 0.048 0.017 0.090 0.052 0.054 -0.010 -0.060 -0.003 0.064 0.021 -0.012 -0.049 0.069 0.002 -0.017 0.103 0.017 0.157 1.000 0.086 0.081 0.062 0.169 0.533 0.100 0.475 0.128 0.220 0.102 0.030 -0.001 0.114 0.041 -0.046 0.130 0.035 -0.014 0.049 0.057 0.146 0.111 0.282 0.588 0.212 0.244 0.399 0.383 0.333 0.395 0.278 0.275 0.241 0.248 0.410 0.333 0.347 0.441 0.420 0.245 0.212 0.371 0.362 0.330 0.219 0.331 0.338 0.357 0.388 0.694 0.293 0.211 0.086 1.000 0.752 0.700 0.157 0.083 0.672 0.072 0.482 0.186 0.646 0.310 0.174 0.422 0.382 0.229 0.309 0.389 0.173 0.290 0.165 0.329 0.315 0.323 0.471 0.224 0.273 0.582 0.407 0.360 0.399 0.308 0.294 0.241 0.261 0.356 0.392 0.368 0.425 0.346 0.222 0.187 0.384 0.377 0.384 0.211 0.330 0.280 0.378 0.375 0.697 0.315 0.210 0.081 0.752 1.000 0.705 0.186 0.054 0.687 0.032 0.559 0.189 0.653 0.349 0.168 0.457 0.393 0.218 0.346 0.560 0.179 0.364 0.194 0.363 0.388 0.324 0.409 0.292 0.276 0.367 0.611 0.322 0.350 0.294 0.377 0.322 0.262 0.357 0.335 0.377 0.385 0.364 0.277 0.185 0.320 0.337 0.373 0.197 0.382 0.245 0.416 0.392 0.628 0.313 0.121 0.062 0.700 0.705 1.000 0.135 0.087 0.622 0.086 0.443 0.141 0.653 0.348 0.202 0.443 0.294 0.257 0.337 0.348 0.160 0.274 0.137 0.355 0.327 0.086 -0.135 0.015 -0.001 -0.146 -0.134 0.115 0.212 -0.156 0.105 0.051 -0.009 0.110 -0.071 0.167 0.180 0.080 0.052 -0.079 -0.121 0.099 0.041 -0.115 0.059 0.043 0.120 0.009 0.198 -0.015 0.699 0.169 0.157 0.186 0.135 1.000 0.227 0.211 0.184 0.311 0.726 0.212 -0.019 -0.184 0.049 0.132 -0.159 -0.010 0.090 -0.132 0.112 0.244 0.002 0.122 0.082 -0.006 0.044 -0.039 -0.020 -0.009 0.142 0.140 -0.012 0.021 0.029 -0.019 0.055 -0.013 0.153 0.128 0.113 0.002 -0.075 0.009 0.078 0.050 -0.040 0.017 0.038 0.018 -0.016 0.101 0.004 0.205 0.533 0.083 0.054 0.087 0.227 1.000 0.077 0.462 0.069 0.257 0.077 0.001 -0.057 0.113 0.017 -0.077 0.122 -0.007 -0.013 0.012 0.005 0.132 0.017 299 Appendix 12. Correlation Matrix Table 36A: Correlation Matrix (Continued) B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.276 0.371 0.210 0.242 0.343 0.326 0.296 0.330 0.298 0.276 0.226 0.186 0.329 0.571 0.293 0.397 0.324 0.218 0.214 0.312 0.341 0.316 0.251 0.298 0.302 0.348 0.326 0.639 0.315 0.197 0.100 0.672 0.687 0.622 0.211 0.077 1.000 0.094 0.496 0.235 0.690 0.343 0.207 0.411 0.340 0.261 0.332 0.345 0.155 0.312 0.168 0.340 0.402 -0.006 -0.018 0.016 -0.083 -0.055 -0.017 0.041 0.102 0.011 0.046 0.049 -0.072 0.026 0.008 0.035 0.100 0.064 0.030 0.052 -0.028 0.111 -0.006 0.000 0.034 0.035 0.028 0.001 0.071 -0.009 0.131 0.475 0.072 0.032 0.086 0.184 0.462 0.094 1.000 0.155 0.199 0.104 -0.009 -0.058 0.097 0.026 -0.094 0.058 -0.018 -0.002 0.073 0.062 0.075 0.071 0.164 0.286 0.139 0.168 0.301 0.246 0.196 0.337 0.294 0.186 0.114 0.202 0.313 0.310 0.239 0.367 0.280 0.214 0.259 0.235 0.282 0.246 0.144 0.154 0.252 0.228 0.241 0.440 0.215 0.294 0.128 0.482 0.559 0.443 0.311 0.069 0.496 0.155 1.000 0.346 0.553 0.247 0.145 0.423 0.393 0.132 0.387 0.443 0.155 0.700 0.222 0.407 0.495 0.090 -0.126 0.044 0.013 -0.147 -0.124 0.122 0.159 -0.144 0.132 0.039 0.025 0.100 -0.098 0.173 0.173 0.096 0.124 -0.099 -0.150 0.105 0.027 -0.072 0.059 0.045 0.115 0.005 0.135 0.018 0.695 0.220 0.186 0.189 0.141 0.726 0.257 0.235 0.199 0.346 1.000 0.263 0.024 -0.078 0.076 0.087 -0.072 0.086 0.064 -0.021 0.133 0.231 0.108 0.128 0.312 0.363 0.293 0.269 0.308 0.341 0.338 0.359 0.520 0.326 0.310 0.288 0.372 0.367 0.379 0.422 0.357 0.289 0.178 0.290 0.354 0.338 0.343 0.331 0.299 0.391 0.375 0.602 0.379 0.244 0.102 0.646 0.653 0.653 0.212 0.077 0.690 0.104 0.553 0.263 1.000 0.406 0.156 0.444 0.300 0.218 0.357 0.295 0.145 0.317 0.163 0.364 0.517 0.179 0.197 0.221 0.177 0.168 0.207 0.169 0.203 0.224 0.220 0.250 0.202 0.259 0.188 0.205 0.275 0.234 0.266 0.268 0.202 0.251 0.171 0.267 0.243 0.174 0.294 0.666 0.380 0.930 0.067 0.030 0.310 0.349 0.348 -0.019 0.001 0.343 -0.009 0.247 0.024 0.406 1.000 0.206 0.161 0.172 0.243 0.299 0.117 0.191 0.097 0.076 0.329 0.148 0.201 0.187 0.128 0.183 0.154 0.218 0.167 0.097 0.178 0.104 0.112 0.198 0.112 0.162 0.103 0.111 0.155 0.148 0.156 0.193 0.152 0.223 0.164 0.151 0.174 0.137 0.159 0.200 0.197 -0.082 -0.001 0.174 0.168 0.202 -0.184 -0.057 0.207 -0.058 0.145 -0.078 0.156 0.206 1.000 0.224 0.086 0.664 0.232 0.040 0.541 0.123 -0.058 0.258 0.071 0.260 0.379 0.191 0.186 0.398 0.407 0.265 0.253 0.383 0.213 0.167 0.123 0.324 0.371 0.298 0.295 0.311 0.139 0.116 0.386 0.206 0.280 0.188 0.184 0.256 0.250 0.374 0.414 0.125 0.101 0.114 0.422 0.457 0.443 0.049 0.113 0.411 0.097 0.423 0.076 0.444 0.161 0.224 1.000 0.327 0.248 0.658 0.317 0.123 0.369 0.171 0.667 0.366 0.123 0.453 0.013 0.104 0.423 0.361 0.071 0.230 0.316 0.021 -0.035 0.136 0.309 0.373 0.097 0.279 0.261 -0.039 0.280 0.574 0.282 0.220 0.299 0.019 0.214 0.032 0.184 0.549 0.137 0.111 0.041 0.382 0.393 0.294 0.132 0.017 0.340 0.026 0.393 0.087 0.300 0.172 0.086 0.327 1.000 0.069 0.233 0.758 0.118 0.622 0.151 0.263 0.636 0.163 0.265 0.084 0.180 0.242 0.301 0.177 0.098 0.246 0.145 0.164 0.185 0.155 0.286 0.135 0.110 0.192 0.143 0.125 0.246 0.128 0.222 0.198 0.140 0.213 0.162 0.163 0.209 0.219 -0.038 -0.046 0.229 0.218 0.257 -0.159 -0.077 0.261 -0.094 0.132 -0.072 0.218 0.243 0.664 0.248 0.069 1.000 0.309 0.072 0.484 0.094 -0.035 0.325 0.101 0.150 0.318 0.118 0.134 0.304 0.340 0.237 0.171 0.333 0.185 0.163 0.136 0.231 0.317 0.237 0.236 0.250 0.174 0.033 0.325 0.148 0.205 0.148 0.150 0.180 0.203 0.170 0.320 0.281 0.106 0.130 0.309 0.346 0.337 -0.010 0.122 0.332 0.058 0.387 0.086 0.357 0.299 0.232 0.658 0.233 0.309 1.000 0.234 0.206 0.289 0.126 0.940 0.275 0.129 0.475 -0.043 0.085 0.571 0.404 0.117 0.213 0.333 -0.029 -0.103 0.153 0.287 0.400 0.111 0.251 0.256 -0.054 0.272 0.428 0.238 0.242 0.300 -0.017 0.199 -0.020 0.126 0.378 0.086 0.095 0.035 0.389 0.560 0.348 0.090 -0.007 0.345 -0.018 0.443 0.064 0.295 0.117 0.040 0.317 0.758 0.072 0.234 1.000 0.084 0.701 0.144 0.261 0.665 300 Appendix 12. Correlation Matrix Table 36A: Correlation Matrix (Continued) C7 C8 C9 C10 C11 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.142 0.172 0.054 0.148 0.113 0.157 0.186 0.114 0.122 0.069 0.071 0.188 0.086 0.117 0.139 0.146 0.142 0.144 0.056 0.134 0.141 0.122 0.086 0.129 0.135 0.075 0.121 0.194 0.154 -0.013 -0.014 0.173 0.179 0.160 -0.132 -0.013 0.155 -0.002 0.155 -0.021 0.145 0.191 0.541 0.123 0.118 0.484 0.206 0.084 1.000 0.111 0.109 0.229 0.068 0.104 0.368 0.051 0.091 0.377 0.320 0.055 0.196 0.341 0.037 -0.062 0.148 0.251 0.357 0.079 0.223 0.200 -0.003 0.341 0.326 0.184 0.199 0.279 0.016 0.170 0.039 0.106 0.287 0.056 0.115 0.049 0.290 0.364 0.274 0.112 0.012 0.312 0.073 0.700 0.133 0.317 0.097 0.123 0.369 0.622 0.094 0.289 0.701 0.111 1.000 0.099 0.317 0.704 0.176 -0.015 0.005 0.014 -0.025 -0.023 0.139 0.177 -0.059 0.027 -0.023 -0.001 0.144 0.010 0.203 0.211 0.116 0.039 -0.082 0.013 0.168 0.096 -0.143 0.054 0.143 0.057 0.047 0.207 0.057 0.210 0.057 0.165 0.194 0.137 0.244 0.005 0.168 0.062 0.222 0.231 0.163 0.076 -0.058 0.171 0.151 -0.035 0.126 0.144 0.109 0.099 1.000 0.148 0.110 0.178 0.335 0.115 0.116 0.322 0.366 0.255 0.184 0.357 0.188 0.168 0.130 0.225 0.333 0.270 0.218 0.253 0.169 0.047 0.348 0.161 0.215 0.176 0.150 0.190 0.206 0.177 0.343 0.308 0.122 .0146 0.329 0.363 0.355 0.002 0.132 0.340 0.075 0.407 0.108 0.364 0.329 0.258 0.667 0.263 0.325 0.940 0.261 0.229 0.317 0.148 1.000 0.299 0.135 0.362 0.035 0.092 0.362 0.336 0.109 0.200 0.487 0.036 -0.058 0.135 0.249 0.368 0.117 0.216 0.185 -0.040 0.237 0.324 0.186 0.202 0.367 -0.016 0.190 0.039 0.119 0.350 0.109 0.138 0.111 0.315 0.388 0.327 0.122 0.017 0.402 0.071 0.495 0.128 0.517 0.148 0.071 0.366 0.636 0.101 0.275 0.665 0.068 0.704 0.110 0.299 1.000 301 Appendix 13. Anti-Image Correlation Matrix Table 37A: Anti-Image Correlation Matrix A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.895 0.056 -0.226 -0.061 0.075 -0.093 -0.257 -0.051 -0.002 0.076 -0.027 0.008 -0.002 -0.026 -0.243 0.148 -0.084 -0.046 -0.021 -0.011 -0.007 -0.008 -0.037 -0.086 0.087 -0.002 -0.027 -0.040 0.141 0.093 0.075 -0.019 -0.056 0.089 -0.034 -0.060 0.004 0.014 0.058 -0.035 0.009 -0.128 -0.111 -0.024 0.027 0.006 0.029 -0.048 0.051 -0.014 -0.130 -0.001 -0.039 0.056 0.890 -0.022 -0.002 -0.233 -0.125 -0.050 0.033 -0.120 -0.003 -0.032 0.035 0.064 -0.148 -0.051 -0.005 -0.003 0.048 -0.098 -0.201 -0.105 0.041 0.076 0.005 0.051 -0.034 -0.008 0.168 -0.012 0.023 0.028 -0.664 0.120 0.092 -0.026 -0.005 0.117 0.000 0.100 0.016 0.037 0.001 -0.009 0.049 -0.014 0.020 -0.074 -0.014 -0.092 -0.073 0.016 0.048 -0.038 -0.226 -0.022 0.921 -0.004 -0.030 0.141 0.035 -0.019 0.034 -0.112 -0.176 0.015 -0.010 0.003 -0.010 -0.025 0.018 -0.073 0.025 -0.102 -0.014 0.015 0.034 0.024 -0.048 -0.143 0.018 0.045 -0.057 -0.116 -0.023 0.057 0.024 -0.133 0.104 -0.010 0.012 0.029 0.060 0.021 -0.049 0.025 -0.049 -0.055 0.032 0.092 -0.009 0.067 0.003 -0.118 0.054 0.051 -0.012 -0.061 -0.002 -0.004 0.847 0.018 0.010 -0.010 0.019 -0.023 0.026 0.114 -0.555 0.056 0.012 0.008 -0.083 -0.029 -0.021 -0.017 0.031 -0.056 -0.125 -0.068 -0.029 -0.016 -0.050 0.047 -0.020 -0.033 -0.016 0.010 0.046 -0.055 -0.037 0.014 0.039 -0.046 0.050 0.004 0.011 0.057 0.023 0.042 -0.105 -0.049 -0.032 -0.035 0.099 -0.003 0.001 0.044 0.064 0.016 0.075 -0.233 -0.030 0.018 0.867 -0.217 -0.032 -0.042 -0.232 -0.035 0.035 -0.022 -0.078 -0.180 0.001 0.019 0.067 0.033 0.071 -0.262 0.023 -0.115 -0.061 -0.016 -0.061 0.057 0.013 0.139 0.094 0.051 0.029 0.122 -0.612 0.178 0.064 -0.042 0.095 0.002 -0.041 -0.045 0.187 -0.097 0.018 -0.066 0.106 -0.020 0.004 -0.201 0.075 0.057 0.075 0.043 0.075 -0.093 -0.125 0.141 0.010 -0.217 0.862 0.090 -0.025 -0.153 0.024 -0.071 0.022 0.014 -0.100 -0.015 -0.011 -0.058 0.088 -0.032 -0.180 0.033 0.021 0.001 -0.030 0.055 -0.054 0.010 0.177 -0.076 -0.018 0.044 0.098 0.158 -0.742 0.019 0.021 0.067 0.015 0.038 -0.058 0.136 0.067 -0.020 -0.022 -0.023 -0.012 0.007 0.020 -0.068 -0.006 -0.002 -0.025 -0.032 -0.257 -0.050 0.035 -0.010 -0.032 0.090 0.882 0.026 0.055 -0.060 0.057 0.020 -0.041 -0.045 -0.498 -0.105 -0.029 0.065 0.040 -0.045 0.045 -0.020 0.046 -0.013 0.045 0.043 -0.058 0.094 -0.064 -0.108 -0.066 0.005 -0.032 -0.038 0.008 0.005 -0.003 -0.013 0.004 0.074 -0.056 0.097 -0.052 0.058 0.032 0.008 -0.010 0.006 -0.075 0.046 0.057 -0.031 -0.032 -0.051 0.033 -0.019 0.019 -0.042 -0.025 0.026 0.940 -0.002 -0.024 -0.062 -0.039 -0.088 0.057 -0.015 -0.313 -0.066 0.019 -0.034 0.008 -0.296 0.061 0.049 0.050 -0.123 -0.022 0.031 0.052 -0.017 0.000 -0.004 -0.041 -0.065 0.012 -0.125 -0.061 -0.008 0.020 -0.033 0.088 0.034 0.025 0.013 -0.004 -0.019 0.011 0.064 0.077 -0.029 0.003 0.003 -0.054 -0.064 -0.002 -0.120 0.034 -0.023 -0.232 -0.153 0.055 -0.002 0.848 0.057 0.017 0.013 -0.020 -0.227 -0.007 -0.016 -0.040 0.051 0.000 -0.225 0.002 0.055 -0.093 -0.008 -0.006 -0.082 -0.055 0.099 0.000 -0.056 -0.014 0.085 0.162 0.135 0.007 -0.006 0.221 -0.029 -0.057 0.127 -0.616 0.015 -0.081 0.035 0.185 0.086 0.072 0.061 -0.038 0.056 0.045 -0.108 -0.296 0.076 -0.003 -0.112 0.026 -0.035 0.024 -0.060 -0.024 0.057 0.900 -0.406 -0.019 -0.039 0.006 0.030 -0.095 -0.015 -0.147 0.033 -0.055 0.095 0.096 0.134 -0.302 -0.007 -0.177 -0.115 0.007 -0.012 -0.020 0.043 0.084 0.004 -0.082 0.039 0.030 -0.016 -0.026 0.045 -0.086 0.001 0.078 0.022 0.047 0.019 -0.048 0.037 0.040 0.041 -0.043 0.031 -0.064 -0.123 -0.027 -0.032 -0.176 0.114 0.035 -0.071 0.057 -0.062 0.017 -0.406 0.909 -0.079 -0.027 -0.005 -0.049 -0.020 -0.024 0.028 0.067 0.100 -0.033 -0.020 -0.137 -0.009 0.060 -0.115 0.023 -0.094 -0.017 0.045 0.049 0.039 -0.017 0.061 -0.035 0.001 0.026 -0.056 -0.033 0.074 -0.067 0.005 0.043 -0.001 -0.034 -0.085 0.013 0.020 0.022 0.052 0.081 -0.035 0.115 0.008 0.035 0.015 -0.555 -0.022 0.022 0.020 -0.039 0.013 -0.019 -0.079 0.835 -0.059 0.025 -0.002 0.022 -0.014 0.091 0.045 -0.092 0.070 -0.221 -0.007 -0.031 0.052 0.008 0.007 0.049 -0.026 -0.006 0.028 -0.059 0.057 -0.008 0.021 -0.014 0.054 0.010 -0.049 -0.028 -0.087 -0.006 -0.046 0.091 0.064 0.033 -0.018 -0.086 -0.092 -0.008 0.029 0.012 0.005 302 Appendix 13. Anti-Image Correlation Matrix Table 37A: Anti-Image Correlation Matrix (Continued) A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 -0.002 0.064 -0.010 0.056 -0.078 0.014 -0.041 -0.088 -0.020 -0.039 -0.027 -0.059 0.927 -0.034 -0.058 0.020 -0.529 -0.023 -0.018 0.053 -0.064 0.035 0.007 0.029 -0.113 0.021 0.030 -0.066 -0.028 -0.026 -0.033 -0.064 0.081 -0.015 -0.004 0.064 0.038 0.049 0.006 -0.001 0.020 -0.025 0.025 -0.084 -0.043 -0.007 -0.076 0.004 0.056 -0.039 -0.027 0.112 -0.017 -0.026 -0.148 0.003 0.012 -0.180 -0.100 -0.045 0.057 -0.227 0.006 -0.005 0.025 -0.034 0.857 0.060 -0.005 0.047 0.019 0.005 -0.164 -0.005 -0.031 0.002 -0.025 -0.001 0.058 0.039 0.127 -0.058 -0.047 -0.063 0.185 0.119 0.026 -0.084 0.041 -0.720 0.001 -0.060 0.124 0.109 0.046 0.146 -0.028 -0.006 -0.120 0.031 0.003 -0.025 -0.057 -0.042 -0.013 0.126 -0.243 -0.051 -0.010 0.008 0.001 -0.015 -0.498 -0.015 -0.007 0.030 -0.049 -0.002 -0.058 0.060 0.907 -0.120 0.076 -0.036 -0.019 0.033 -0.029 -0.042 -0.015 0.034 -0.015 -0.105 0.065 -0.087 -0.016 0.046 -0.013 0.047 0.036 -0.031 -0.020 -0.048 0.025 0.067 -0.017 -0.047 -0.021 0.003 0.104 -0.065 0.047 -0.023 0.088 -0.024 -0.021 0.015 -0.051 -0.088 0.025 0.148 -0.005 -0.025 -0.083 0.019 -0.011 -0.105 -0.313 -0.016 -0.095 -0.020 0.022 0.020 -0.005 -0.120 0.943 -0.241 -0.073 -0.031 -0.004 -0.177 0.044 0.037 0.037 -0.023 0.041 -0.002 -0.017 0.027 -0.025 0.064 -0.016 0.002 0.041 0.008 -0.050 -0.036 -0.023 -0.022 -0.003 -0.010 -0.058 -0.022 -0.015 -0.021 0.067 -0.137 -0.031 -0.015 -0.009 -0.091 0.152 0.017 -0.084 -0.003 0.018 -0.029 0.067 -0.058 -0.029 -0.066 -0.040 -0.015 -0.024 -0.014 -0.529 0.047 0.076 -0.241 0.909 -0.022 0.003 -0.048 0.025 -0.104 0.023 -0.042 -0.274 0.107 0.019 0.057 0.049 0.019 0.018 -0.056 0.013 0.009 0.011 -0.056 -0.023 -0.028 0.018 -0.031 -0.001 -0.053 0.021 0.001 0.022 -0.018 0.028 -0.079 -0.017 0.016 0.069 -0.052 0.088 -0.046 0.048 -0.073 -0.021 0.033 0.088 0.065 0.019 0.051 -0.147 0.028 0.091 -0.023 0.019 -0.036 -0.073 -0.022 0.907 0.047 -0.092 -0.009 -0.006 -0.136 -0.310 0.071 -0.133 -0.025 0.102 -0.038 0.021 -0.022 -0.052 0.072 -0.043 0.063 0.036 0.014 0.008 -0.203 -0.039 -0.048 0.019 -0.006 0.029 0.050 -0.024 -0.030 -0.117 -0.080 0.121 0.046 -0.002 0.041 -0.021 -0.098 0.025 -0.017 0.071 -0.032 0.040 -0.034 0.000 0.033 0.067 0.045 -0.018 0.005 -0.019 -0.031 0.003 0.047 0.873 0.032 -0.017 -0.044 -0.353 -0.069 0.040 -0.019 -0.026 0.020 -0.062 0.067 0.051 0.015 -0.012 0.005 -0.010 0.039 -0.045 -0.102 -0.131 0.049 0.071 -0.014 -0.048 -0.022 -0.051 -0.018 0.014 -0.004 0.061 -0.090 0.063 0.056 0.072 -0.011 -0.201 -0.102 0.031 -0.262 -0.180 -0.045 0.008 -0.225 -0.055 0.100 -0.092 0.053 -0.164 0.033 -0.004 -0.048 -0.092 0.032 0.856 0.023 0.041 0.001 0.062 -0.002 0.024 0.021 -0.637 -0.005 0.049 0.005 0.111 0.132 0.144 0.046 -0.007 0.121 0.013 0.021 -0.070 0.110 0.021 -0.007 -0.020 -0.325 -0.049 -0.025 0.118 0.118 -0.001 -0.034 0.007 0.180 -0.007 -0.105 -0.014 -0.056 0.023 0.033 0.045 -0.296 0.002 0.095 -0.033 0.070 -0.064 -0.005 -0.029 -0.177 0.025 -0.009 -0.017 0.023 0.940 -0.109 -0.004 -0.015 -0.157 -0.094 -0.060 -0.110 -0.018 0.024 0.003 0.129 -0.002 -0.020 0.080 0.031 -0.002 -0.093 0.009 -0.079 -0.027 0.040 -0.047 0.089 -0.018 0.024 0.028 -0.058 0.006 0.017 -0.031 -0.031 0.044 -0.008 0.041 0.015 -0.125 -0.115 0.021 -0.020 0.061 0.055 0.096 -0.020 -0.221 0.035 -0.031 -0.042 0.044 -0.104 -0.006 -0.044 0.041 -0.109 0.946 -0.079 -0.117 -0.040 -0.075 -0.008 0.006 0.047 -0.022 -0.012 0.013 0.006 -0.060 -0.033 -0.051 0.017 0.023 0.008 0.060 -0.027 -0.007 -0.080 -0.007 -0.048 -0.028 0.006 0.028 0.082 0.001 -0.080 -0.019 -0.025 -0.037 0.076 0.034 -0.068 -0.061 0.001 0.046 0.049 -0.093 0.134 -0.137 -0.007 0.007 0.002 -0.015 0.037 0.023 -0.136 -0.353 0.001 -0.004 -0.079 0.869 0.036 -0.065 0.034 -0.034 -0.022 -0.106 -0.095 -0.009 -0.039 0.055 0.031 0.072 0.018 -0.021 -0.015 0.198 -0.028 -0.112 0.090 0.019 0.012 -0.007 -0.028 0.048 -0.041 -0.010 -0.081 0.133 -0.057 -0.075 -0.086 0.005 0.024 -0.029 -0.016 -0.030 -0.013 0.050 -0.008 -0.302 -0.009 -0.031 0.029 -0.025 0.034 0.037 -0.042 -0.310 -0.069 0.062 -0.015 -0.117 0.036 0.919 -0.047 -0.278 0.053 -0.051 -0.043 -0.023 0.043 -0.037 -0.035 0.013 -0.019 -0.021 0.012 -0.037 0.134 0.061 -0.020 0.032 -0.017 -0.001 -0.017 0.084 -0.008 0.046 -0.058 -0.093 -0.034 0.023 0.077 303 Appendix 13. Anti-Image Correlation Matrix Table 37A: Anti-Image Correlation Matrix (Continued) A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.087 0.051 -0.048 -0.016 -0.061 0.055 0.045 -0.123 -0.006 -0.007 0.060 0.052 -0.113 -0.001 -0.015 -0.023 -0.274 0.071 0.040 -0.002 -0.157 -0.040 -0.065 -0.047 0.940 -0.027 -0.037 -0.006 -0.009 -0.036 -0.056 -0.061 0.083 0.026 0.028 0.040 -0.042 0.028 -0.059 0.048 0.005 0.041 -0.023 -0.006 0.030 -0.091 0.032 -0.021 -0.003 0.049 -0.066 -0.028 -0.036 -0.002 -0.034 -0.143 -0.050 0.057 -0.054 0.043 -0.022 -0.082 -0.177 -0.115 0.008 0.021 0.058 -0.105 0.041 0.107 -0.133 -0.019 0.024 -0.094 -0.075 0.034 -0.278 -0.027 0.942 0.008 -0.027 0.009 0.107 -0.051 -0.008 -0.095 0.043 -0.098 0.042 -0.056 0.035 0.006 -0.026 0.058 -0.041 0.034 -0.042 -0.010 -0.045 -0.020 0.074 0.068 -0.023 0.018 0.020 0.013 -0.027 -0.008 0.018 0.047 0.013 0.010 -0.058 0.031 -0.055 -0.115 0.023 0.007 0.030 0.039 0.065 -0.002 0.019 -0.025 -0.026 0.021 -0.060 -0.008 -0.034 0.053 -0.037 0.008 0.855 -0.024 -0.414 0.016 0.017 -0.073 -0.005 -0.062 -0.007 0.032 0.020 0.029 -0.031 -0.009 0.090 -0.019 0.070 -0.561 -0.035 0.021 0.032 0.023 -0.081 -0.002 0.070 0.221 0.069 -0.040 0.168 0.045 -0.020 0.139 0.177 0.094 0.052 0.099 0.007 -0.094 0.049 -0.066 0.127 -0.087 -0.017 0.057 0.102 0.020 -0.637 -0.110 0.006 -0.022 -0.051 -0.006 -0.027 -0.024 0.892 0.047 -0.069 -0.041 -0.184 -0.253 -0.187 -0.110 -0.032 -0.166 0.012 -0.016 0.189 -0.067 -0.068 -0.023 0.041 -0.193 0.034 0.022 0.135 -0.092 0.031 -0.015 -0.029 -0.065 0.141 -0.012 -0.057 -0.033 0.094 -0.076 -0.064 -0.017 0.000 -0.012 -0.017 -0.026 -0.028 -0.058 -0.016 0.027 0.049 -0.038 -0.062 -0.005 -0.018 0.047 -0.106 -0.043 -0.009 0.009 -0.414 0.047 0.772 0.039 0.025 -0.015 -0.056 0.112 -0.054 -0.066 0.020 0.000 -0.012 0.019 -0.030 -0.812 -0.109 0.302 0.010 0.034 -0.031 -0.048 0.126 0.085 -0.075 -0.113 0.000 0.093 0.023 -0.116 -0.016 0.051 -0.018 -0.108 0.000 -0.056 -0.020 0.045 -0.006 -0.026 -0.047 0.046 -0.025 0.019 0.021 0.067 0.049 0.024 -0.022 -0.095 -0.023 -0.036 0.107 0.016 -0.069 0.039 0.814 0.037 -0.091 -0.056 0.081 -0.393 -0.024 0.094 0.022 0.006 -0.355 0.040 -0.072 0.028 -0.009 0.029 -0.047 -0.015 -0.010 -0.011 0.013 -0.011 -0.011 -0.010 0.075 0.028 -0.023 0.010 0.029 0.044 -0.066 -0.004 -0.014 0.043 0.049 0.028 -0.033 -0.063 -0.013 0.064 0.018 -0.022 0.051 0.005 0.003 -0.012 -0.009 0.043 -0.056 -0.051 0.017 -0.041 0.025 0.037 0.721 -0.030 -0.034 0.000 0.027 -0.376 0.054 -0.303 -0.026 -0.074 0.039 -0.047 -0.082 0.001 0.031 0.032 -0.006 -0.025 0.054 0.058 -0.003 -0.015 -0.093 -0.019 -0.664 0.057 0.046 0.122 0.098 0.005 -0.041 0.085 0.084 0.039 -0.059 -0.064 0.185 0.047 -0.016 -0.056 -0.052 0.015 0.111 0.129 0.013 -0.039 -0.037 -0.061 -0.008 -0.073 -0.184 -0.015 -0.091 -0.030 0.896 -0.205 -0.194 0.104 0.008 -0.216 -0.002 -0.073 -0.019 -0.106 0.090 0.047 -0.007 -0.057 -0.054 0.087 0.045 0.063 0.032 -0.003 -0.076 0.104 -0.056 0.120 0.024 -0.055 -0.612 0.158 -0.032 -0.065 0.162 0.004 -0.017 0.057 0.081 0.119 0.036 0.002 0.013 0.072 -0.012 0.132 -0.002 0.006 0.055 -0.035 0.083 -0.095 -0.005 -0.253 -0.056 -0.056 -0.034 -0.205 0.892 -0.194 -0.004 0.057 -0.148 0.032 -0.168 0.009 -0.191 0.025 -0.006 -0.036 0.189 0.026 -0.004 -0.337 -0.088 0.122 -0.016 -0.002 0.024 0.089 0.092 -0.133 -0.037 0.178 -0.742 -0.038 0.012 0.135 -0.082 0.061 -0.008 -0.015 0.026 -0.031 0.041 0.009 -0.043 0.005 0.144 -0.020 -0.060 0.031 0.013 0.026 0.043 -0.062 -0.187 0.112 0.081 0.000 -0.194 -0.194 0.886 -0.044 -0.045 -0.039 -0.051 -0.008 0.047 -0.206 -0.097 -0.014 0.014 0.077 -0.020 0.002 -0.070 0.071 0.008 0.005 -0.007 0.013 -0.034 -0.026 0.104 0.014 0.064 0.019 0.008 -0.125 0.007 0.039 -0.035 0.021 -0.004 -0.084 -0.020 0.008 0.011 0.063 -0.010 0.046 0.080 -0.033 0.072 -0.019 0.028 -0.098 -0.007 -0.110 -0.054 -0.393 0.027 0.104 -0.004 -0.044 0.803 -0.037 0.020 -0.034 -0.113 -0.409 0.010 0.082 0.052 -0.021 -0.059 0.009 0.015 -0.038 0.109 0.062 -0.031 0.049 0.010 -0.060 -0.005 -0.010 0.039 -0.042 0.021 0.005 -0.061 -0.006 0.030 0.001 -0.014 0.064 0.041 -0.048 -0.050 -0.056 0.036 0.039 -0.007 0.031 -0.051 0.018 -0.021 0.040 0.042 0.032 -0.032 -0.066 -0.024 -0.376 0.008 0.057 -0.045 -0.037 0.744 -0.030 -0.260 0.077 -0.079 0.013 0.049 0.058 -0.042 0.014 0.023 -0.020 0.004 -0.040 -0.049 0.120 -0.001 0.059 304 Appendix 13. Anti-Image Correlation Matrix Table 37A: Anti-Image Correlation Matrix (Continued) B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.004 0.117 0.012 -0.046 0.095 0.067 -0.003 -0.008 0.221 -0.016 0.026 0.054 0.038 -0.720 0.025 -0.036 -0.023 0.014 -0.045 0.121 -0.002 0.017 -0.021 0.012 -0.042 -0.056 0.020 -0.166 0.020 0.094 0.054 -0.216 -0.148 -0.039 0.020 -0.030 0.884 -0.015 0.037 -0.136 -0.226 -0.038 -0.126 -0.001 0.025 0.035 -0.043 0.029 0.037 0.035 0.030 0.027 -0.145 0.014 0.000 0.029 0.050 0.002 0.015 -0.013 0.020 -0.029 -0.026 -0.056 0.010 0.049 0.001 0.067 -0.023 -0.028 0.008 -0.102 0.013 -0.093 0.023 -0.015 -0.037 0.028 0.035 0.029 0.012 0.000 0.022 -0.303 -0.002 0.032 -0.051 -0.034 -0.260 -0.015 0.755 -0.065 -0.010 0.015 0.010 0.044 -0.059 -0.013 0.045 0.036 0.059 -0.061 -0.004 -0.039 -0.010 -0.010 0.058 0.100 0.060 0.004 -0.041 0.038 0.004 -0.033 -0.057 0.045 -0.033 -0.049 0.006 -0.060 -0.017 -0.022 0.018 -0.203 -0.131 0.021 0.009 0.008 0.198 0.134 -0.059 0.006 -0.031 -0.016 -0.012 0.006 -0.026 -0.073 -0.168 -0.008 -0.113 0.077 0.037 -0.065 0.881 -0.093 -0.131 0.017 -0.028 0.037 0.024 0.050 -0.036 0.189 -0.026 -0.645 -0.067 -0.040 00.087 -0.035 0.016 0.021 0.011 -0.045 -0.058 0.074 0.088 0.127 -0.086 0.074 -0.028 -0.001 0.124 -0.047 -0.003 -0.031 -0.039 0.049 -0.070 -0.079 0.060 -0.028 0.061 0.048 -0.026 -0.009 0.189 0.019 -0.355 -0.074 -0.019 0.009 0.047 -0.409 -0.079 -0.136 -0.010 -0.093 0.793 -0.149 0.007 -0.032 0.064 -0.051 0.009 0.053 0.055 -0.032 -0.007 -0.044 -0.086 0.012 0.009 0.037 -0.049 0.057 0.187 0.136 -0.056 0.034 -0.616 0.001 -0.067 -0.087 0.020 0.109 -0.021 -0.010 -0.001 -0.048 0.071 0.110 -0.027 -0.027 -0.112 -0.020 0.005 0.058 0.090 -0.067 -0.030 0.040 0.039 -0.106 -0.191 -0.206 0.010 0.013 -0.226 0.015 -0.131 -0.149 0.900 -0.049 0.089 -0.110 -0.055 -0.045 -0.075 0.087 0.021 0.096 -0.034 0.134 -0.158 -0.128 0.001 0.025 0.023 -0.097 0.067 0.097 0.025 0.015 0.078 0.005 -0.006 -0.025 0.046 0.003 -0.058 -0.053 0.019 -0.014 0.021 0.040 -0.007 0.090 0.032 0.041 -0.041 -0.019 -0.068 -0.812 -0.072 -0.047 0.090 0.025 -0.097 0.082 0.049 -0.038 0.010 0.017 0.007 -0.049 0.828 0.076 0.017 -0.031 -0.064 0.038 0.064 -0.094 -0.051 0.020 -0.056 -0.009 -0.111 -0.009 -0.049 0.042 0.018 -0.020 -0.052 0.013 -0.081 0.022 0.043 -0.046 0.025 0.146 0.104 -0.022 0.021 -0.006 -0.048 -0.007 -0.047 -0.080 0.019 -0.017 -0.023 0.034 0.070 -0.023 -0.109 0.028 -0.082 0.047 -0.006 -0.014 0.052 0.058 -0.126 0.044 -0.028 -0.032 0.089 0.076 0.787 -0.124 -0.039 -0.494 0.065 0.076 -0.316 -0.063 0.117 -0.026 0.061 -0.024 0.049 -0.055 -0.105 -0.066 -0.022 0.058 -0.004 0.035 0.047 -0.001 0.091 -0.084 -0.028 -0.065 -0.015 0.001 0.029 -0.022 -0.020 0.089 -0.007 0.012 -0.001 -0.006 -0.042 -0.561 0.041 0.302 -0.009 0.001 -0.007 -0.036 0.014 -0.021 -0.042 -0.001 -0.059 0.037 0.064 -0.110 0.017 -0.124 0.889 -0.037 0.011 -0.120 0.064 0.122 -0.050 -0.118 -0.251 -0.057 0.027 -0.014 0.032 -0.049 0.106 -0.023 0.032 -0.019 0.185 0.019 -0.034 0.064 -0.043 -0.006 0.047 -0.021 0.022 0.050 -0.051 -0.325 -0.018 -0.048 -0.007 -0.017 0.030 -0.010 -0.035 -0.193 0.010 0.029 0.031 -0.057 0.189 0.077 -0.059 0.014 0.025 -0.013 0.024 -0.051 -0.055 -0.031 -0.039 -0.037 0.881 0.096 0.024 -0.513 -0.091 -0.041 0.004 -0.018 -0.248 0.006 0.020 0.092 -0.032 -0.020 -0.012 0.008 0.011 0.086 -0.048 -0.085 0.033 -0.007 -0.120 -0.023 0.067 -0.018 -0.024 -0.018 -0.049 0.024 -0.028 -0.028 0.084 -0.091 -0.045 0.021 0.034 0.034 -0.047 0.032 -0.054 0.026 -0.020 0.009 0.023 0.035 0.045 0.050 0.009 -0.045 -0.064 -0.494 0.011 0.096 0.860 -0.047 -0.011 -0.198 0.017 0.025 -0.005 -0.066 0.029 -0.074 -0.009 -0.035 0.004 0.007 -0.010 0.064 0.072 0.037 0.013 -0.018 -0.076 0.031 0.088 -0.137 0.028 -0.030 0.014 -0.025 0.028 0.006 0.048 -0.008 0.032 -0.020 0.032 0.022 -0.031 -0.015 -0.006 0.087 -0.004 0.002 0.015 -0.020 -0.043 0.036 -0.036 0.053 -0.075 0.038 0.065 -0.120 0.024 -0.047 0.834 0.006 -0.002 0.032 0.053 -0.845 -0.008 -0.048 -0.014 0.067 0.099 -0.201 0.020 0.006 0.077 0.061 0.040 0.020 -0.086 0.004 0.003 -0.024 -0.031 -0.079 -0.117 -0.004 0.118 -0.058 0.028 -0.041 0.046 -0.021 0.074 0.023 0.135 -0.048 -0.010 -0.025 0.045 -0.337 -0.070 -0.038 0.004 0.029 0.059 0.189 0.055 0.087 0.064 0.076 0.064 -0.513 -0.011 0.006 0.864 0.023 -0.338 -0.065 -0.025 -0.187 305 Appendix 13. Anti-Image Correlation Matrix Table 37A: Anti-Image Correlation Matrix (Continued) C7 C8 C9 C10 C11 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 0.051 -0.092 0.003 -0.003 0.075 -0.068 -0.075 -0.029 -0.038 0.041 0.022 -0.092 0.056 -0.025 -0.021 -0.015 -0.017 -0.080 0.061 0.118 0.006 0.082 -0.010 -0.058 -0.003 0.068 -0.081 -0.092 0.126 -0.011 0.054 0.063 -0.088 0.071 0.109 -0.040 0.037 -0.061 -0.026 -0.032 0.021 -0.094 -0.316 0.122 -0.091 -0.198 -0.002 0.023 0.799 -0.008 -0.150 -0.041 0.044 -0.014 -0.073 -0.118 0.001 0.057 -0.006 0.046 0.003 0.056 -0.043 0.052 -0.008 -0.039 -0.057 0.015 -0.009 0.016 0.121 -0.090 -0.001 0.017 0.001 -0.081 -0.093 0.049 -0.023 -0.002 0.031 0.085 0.013 0.058 0.032 0.122 0.008 0.062 -0.049 0.035 -0.004 -0.645 -0.007 0.096 -0.051 -0.063 -0.050 -0.041 0.017 0.032 -0.338 -0.008 0.844 0.057 -0.031 -0.314 -0.130 0.016 0.054 0.044 0.075 -0.002 0.057 0.003 0.045 0.031 0.081 0.029 -0.027 -0.042 -0.051 -0.091 0.069 0.046 0.063 -0.034 -0.031 -0.080 0.133 -0.034 -0.066 0.018 0.070 -0.015 -0.075 -0.011 -0.003 -0.003 -0.016 0.005 -0.031 0.120 0.030 -0.039 -0.067 -0.044 -0.034 0.020 0.117 -0.118 0.004 0.025 0.053 -0.065 -0.150 0.057 0.813 -0.044 0.003 -0.001 0.048 0.051 0.064 0.043 -0.025 -0.031 -0.054 -0.108 -0.064 -0.035 0.012 0.112 -0.013 -0.088 0.152 -0.052 -0.002 0.056 0.007 -0.031 -0.019 -0.057 0.023 -0.028 0.020 0.221 -0.029 -0.113 -0.011 -0.015 -0.076 -0.002 -0.007 0.049 -0.001 0.027 -0.010 -0.040 -0.086 0.134 -0.056 -0.026 -0.251 -0.018 -0.005 -0.845 -0.025 -0.041 -0.031 -0.044 0.823 0.023 -0.039 -0.038 -0.012 0.016 0.075 -0.032 -0.032 -0.064 -0.296 -0.123 0.115 0.005 -0.017 0.126 0.025 0.017 0.088 0.041 0.072 0.180 0.044 -0.025 -0.075 0.077 -0.036 0.013 0.069 -0.065 0.000 -0.010 -0.093 0.104 0.024 0.013 0.010 0.059 -0.145 -0.010 0.087 0.012 -0.158 -0.009 0.061 -0.057 -0.248 -0.066 -0.008 -0.187 0.044 -0.314 0.003 0.023 0.896 306 Appendix 14. Factor Extraction Table Table 38A: Eigenvalues and the Explained Percentage of Variance by the Factors Initial Eigenvalues Extraction Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % 13.670 5.497 3.667 2.382 2.159 1.934 1.866 1.613 1.554 1.347 1.329 1.152 25.793 10.371 6.919 4.494 4.073 3.649 3.520 3.044 2.932 2.541 2.507 2.174 25.793 36.164 43.084 47.578 51.651 55.300 58.821 61.864 64.796 67.337 69.844 72.018 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 13.670 5.497 3.667 2.382 2.159 1.934 1.866 1.613 1.554 1.347 1.329 1.152 0.996 0.841 0.810 0.796 0.684 0.674 0.645 0.606 0.568 0.524 0.496 .0488 0.471 0.457 0.420 0.411 0.394 0.379 0.353 0.322 0.319 0.299 0.292 0.290 0.254 0.248 0.237 0.221 0.196 0.180 0.156 0.142 0.122 0.087 0.082 0.080 0.070 0.062 0.057 0.050 0.049 25.793 10.371 6.919 4.494 4.073 3.649 3.520 3.044 2.932 2.541 2.507 2.174 1.879 1.586 1.529 1.503 1.291 1.272 1.216 1.144 1.071 0.989 0.936 0.920 0.889 0.862 0.793 0.775 0.744 0.714 0.666 0.607 0.602 0.564 0.552 0.546 0.478 0.467 0.447 0.418 0.370 0.340 0.295 0.268 0.231 0.165 0.155 0.151 0.132 0.117 0.108 0.094 0.092 25.793 36.164 43.084 47.578 51.651 55.300 58.821 61.864 64.796 67.337 69.844 72.018 73.897 75.483 77.012 78.514 79.805 81.077 82.293 83.437 84.508 85.498 86.434 87.354 88.244 89.106 89.899 90.675 91.418 92.133 92.799 93.406 94.008 94.572 95.124 95.670 96.149 96.616 97.063 97.481 97.851 98.191 98.486 98.754 98.985 99.150 99.305 99.457 99.588 99.706 99.813 99.908 100.000 307 Appendix 15. Rotated Factor Tables Table 39A: Rotated Component Matrices with VARIMAX Rotation Component 1 2 3 4 5 6 7 8 9 10 11 12 A5 A9 A14 A2 A6 A20 A10 A26 A24 A11 A18 A3 A17 A25 A13 A8 A16 A21 C8 C11 C6 C3 B13 A19 A23 B7 B6 B2 B8 B11 C9 B15 B3 B16 B1 B4 B14 B9 C5 C10 C2 C1 C4 C7 A4 A12 A22 A7 A15 A1 B5 B12 B10 0.830 0.823 0.816 0.815 0.800 0.788 0.846 0.793 0.786 0.782 0.723 0.682 0.799 0.765 0.761 0.741 0.722 0.669 0.853 0.766 0.736 0.700 0.552 0.659 0.645 0.594 0.578 0.547 0.908 0.879 0.742 0.865 0.852 0.833 0.870 0.856 0.640 0.845 0.790 0.773 0.827 0.820 0.636 0.784 0.756 0.743 0.803 0.787 0.786 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 10 iterations. 308 Table 40A: Pattern Matrix with OBLIMIN Rotation Component 1 2 3 4 5 6 7 8 9 10 11 12 B6 B7 B2 B8 B11 C9 A10 A11 A26 A24 A18 A3 B4 B14 B9 B15 A25 A17 A13 A8 A16 A21 B3 B16 B1 C1 C7 C4 A4 A12 A22 C8 C11 C6 C3 B13 A19 A23 B5 B12 B10 C5 C10 C2 A7 A1 A15 A14 A9 A5 A2 A6 A20 0.572 0.571 0.510 0.507 0.856 0.769 0.762 0.758 0.701 0.650 0.901 0.868 0.848 -0.824 -0.822 -0.776 -0.748 -0.705 -0.665 -0.966 -0.935 -0.785 0.870 0.810 0.806 -0.898 -0.884 -0.670 -0.899 -0.779 -0.747 -0.704 -0.526 0.812 0.797 0.784 -0.883 -0.860 -0.628 0.828 0.794 0.786 0.818 0.812 0.809 0.796 0.788 0.766 Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalisation. a. Rotation converged in 15 iterations. 309 Appendix 16. Questionnaire Items with Orthogonal Rotation (VARIMAX) Table 41A: VARIMAX Rotated Component Matrix with Variables Component Item No. Item Name 1 2 3 4 5 6 7 8 A5 A9 A14 A2 A6 A20 Clean bathroom and toilet Clean room Quiet room Adequate room size Comfortable bed/mattress/pillow High quality of in-room temperature control 0.83 0.82 0.82 0.82 0.80 0.79 A10 A26 A24 A11 A18 A3 The style of décor is to the customers? liking Excellent ambience Stylish and attractive décor The enjoyment of atmosphere Décor showing a great deal of thought and style The atmosphere is what customers expect 0.85 0.79 0.79 0.78 0.72 0.68 A17 A25 A13 A8 A16 A21 Availability of noticeable sprinkler systems Availability of secure safes Accessibility of fire exits Availability of high quality food & beverage Sanitary, adequate and sufficient food & beverage served Availability of a variety of food & beverage facilities 0.80 0.77 0.76 0.74 0.72 0.67 C8 C11 C6 C3 B13 A19 A23 Employees? understanding of the importance of waiting time Employees? punctual provision of service Employees try to minimise customer waiting time Reasonable waiting time for service Employees? ability to answer customer questions quickly Clean reception area Clean and neat employees 0.85 0.77 0.73 0.70 0.55 B7 B6 B2 B8 B11 C9 B15 Employees? service provision Employees? willingness to help customers Employees allow customers to trust their services Dependability of friendly employees Employees? understanding of customer needs The other customers? influence on provisions of good service Reliability of employees taking actions to address customer needs 0.66 0.64 0.59 0.58 0.55 B3 B16 B1 Dependability of employees knowing their jobs/responsibilities Competent employees Employees? professional knowledge to meet customer needs 0.91 0.88 0.74 B4 B14 B9 Employees showing a sincere interest in solving problems Employees being able to handle customer complaints Employees? understanding of resolving customer complaints 0.87 0.85 0.83 C5 C10 C2 When leaving, customers had got what they wanted Favourable evaluation of the outcome of services Customers have had good experiences at the end of their stay 0.87 0.86 0.64 C1 C4 C7 Provision of opportunities for social interaction A sense of belonging with other customers Social contacts A4 A12 A22 Convenient location for retail stores Convenient location for dining-out facilities Convenient parking spaces availability A7 A15 A1 The layout makes it easy for customers to move around The layout serves customer purposes/needs Aesthetical attractiveness B5 B12 B10 Impressions of the other customers? behaviour The rules and regulations followed by customers The positive impact of interaction with other customers Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 10 iterations. 310 Table 41A: VARIMAX Rotated Component Matrix with Variables (Continued) Component Item No. Item Name 9 10 11 12 A5 A9 A14 A2 A6 A20 Clean bathroom and toilet Clean room Quiet room Adequate room size Comfortable bed/mattress/pillow High quality of in-room temperature control A10 A26 A24 A11 A18 A3 The style of décor is to the customers? liking Excellent ambience Stylish and attractive décor The enjoyment of atmosphere Décor showing a great deal of thought and style The atmosphere is what customers expect A17 A25 A13 A8 A16 A21 Availability of noticeable sprinkler systems Availability of secure safes Accessibility of fire exits Availability of high quality food & beverage Sanitary, adequate and sufficient food & beverage served Availability of a variety of food & beverage facilities C8 C11 C6 C3 B13 A19 A23 Employees? understanding of the importance of waiting time Employees? punctual provision of service Employees try to minimise customer waiting time Reasonable waiting time for service Employees? ability to answer customer questions quickly Clean reception area Clean and neat employees B7 B6 B2 B8 B11 C9 B15 Employees? service provision Employees? willingness to help customers Employees allow customers to trust their services Dependability of friendly employees Employees? understanding of customer needs The other customers? influence on provisions of good service Reliability of employees taking actions to address customer needs B3 B16 B1 Dependability of employees knowing their jobs/responsibilities Competent employees Employees? professional knowledge to meet customer needs B4 B14 B9 Employees showing a sincere interest in solving problems Employees being able to handle customer complaints Employees? understanding of resolving customer complaints C5 C10 C2 When leaving, customers had got what they wanted Favourable evaluation of the outcome of services Customers have had good experiences at the end of their stay C1 C4 C7 Provision of opportunities for social interaction A sense of belonging with other customers Social contacts 0.85 0.79 0.77 A4 A12 A22 Convenient location for retail stores Convenient location for dining-out facilities Convenient parking spaces availability 0.83 0.82 0.64 A7 A15 A1 The layout makes it easy for customers to move around The layout serves customer purposes/needs Aesthetical attractiveness 0.78 0.76 0.74 B5 B12 B10 Impressions of the other customers? behaviour The rules and regulations followed by customers The positive impact of interaction with other customers 0.80 0.79 0.79 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 10 iterations. 311 Appendix 17. Validation of Component Factor Analysis by Split-Sample Estimation with VARIMAX Rotation Table 42A: Split Sample One Split-Sample 1 Rotated Component Matrices with VARIMAX Rotation (N=290) 1 2 3 4 5 6 7 8 9 10 11 12 Communality A6 A5 A2 A20 A9 A14 A10 A24 A26 A11 A18 A3 A17 A13 A25 A8 A16 A21 B7 B11 B6 B8 B15 B2 B13 C9 C8 C11 C6 A23 C3 A19 C5 C10 C2 B14 B4 B9 B3 B16 B1 C1 C7 C4 A4 A12 A22 B10 B5 B12 A7 A15 A1 0.870 0.864 0.845 0.827 0.759 0.734 0.809 0.798 0.744 0.703 0.683 0.614 0.830 0.756 0.733 0.715 0.686 0.581 0.730 0.730 0.710 0.681 0.566 0.550 0.507 0.830 0.773 0.675 0.673 0.671 0.618 0.872 0.859 0.740 0.837 0.836 0.800 0.855 0.853 0.627 0.826 0.769 0.758 0.839 0.826 0.718 0.777 0.771 0.699 0.682 0.630 0.590 0.835 0.860 0.853 0.812 0.768 0.713 0.774 0.763 0.720 0.681 0.622 0.568 0.767 0.703 0.617 0.660 0.701 0.592 0.807 0.751 0.770 0.749 0.744 0.687 0.717 0.471 0.813 0.713 0.741 0.664 0.735 0.554 0.899 0.895 0.739 0.780 0.750 0.734 0.918 0.925 0.658 0.775 0.633 0.713 0.809 0.776 0.721 0.664 0.633 0.575 0.682 0.725 0.631 Notes: Only factors loading 0.500 are shown Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 8 iterations. Total Sum of Squares (eigenvalue) 14.312 6.259 2.862 2.438 2.246 1.846 1.830 1.528 1.485 1.372 1.290 1.092 38.560 Percentage of Trace 27.00 11.81 5.40 4.60 4.24 3.49 3.45 2.88 2.80 2.59 2.43 2.06 72.75 Cronbach?s Alpha 0.945 0.891 0.884 0.926 0.874 0.908 0.828 0.906 0.796 0.847 0.677 0.764 312 Table 43A: Split Sample Two Split-Sample 2 Rotated Component Matrices with VARIMAX Rotation (N=290) 1 2 3 4 5 6 7 8 9 10 11 12 Communality A2 A5 A18 A22 A6 B6 A14 B7 B11 B2 B8 B15 A15 A26 A16 A25 A20 A3 A12 A11 A13 A8 A9 A10 C8 B13 C7 C11 C5 B9 B14 B4 C9 B3 B16 B1 A7 A19 A1 C1 C3 C2 B10 B12 B5 C6 C10 C4 A17 A4 A23 A24 A21 0.820 0.812 0.805 0.793 0.730 0.683 0.682 0.664 0.648 0.646 0.606 0.526 0.863 0.829 0.807 0.781 0.755 0.676 0.812 0.787 0.787 0.762 0.734 0.690 0.890 0.779 0.730 0.713 0.651 0.845 0.826 0.815 0.919 0.897 0.772 0.828 0.791 0.771 0.798 0.769 0.751 0.818 0.815 0.808 0.717 0.698 0.838 0.803 0.767 0.755 0.756 0.790 0.734 0.750 0.674 0.736 0.799 0.772 0.683 0.776 0.685 0.702 0.778 0.771 0.707 0.702 0.630 0.595 0.794 0.755 0.693 0.709 0.704 0.614 0.859 0.784 0.819 0.739 0.728 0.854 0.802 0.802 0.548 0.920 0.871 0.710 0.819 0.815 0.780 0.727 0.664 0.721 0.740 0.735 0.724 0.821 0.864 0.632 0.781 0.716 0.506 0.712 0.735 Notes: Only factors loading 0.500 are shown Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 8 iterations. Total Sum of Squares (eigenvalue) 13.729 5.490 4.374 2.649 2.360 2.110 1.872 1.603 1.540 1.319 1.156 1.036 39.238 Percentage of Trace 25.90 10.36 8.25 5.00 4.45 3.98 3.53 3.02 2.91 2.49 2.18 1.95 74.03 Cronbach?s Alpha 0.916 0.903 0.907 0.902 0.931 0.908 0.880 0.792 0.801 0.956 0.746 0.617 313 Appendix 18. Multi-collinearity Statistics Table 44A: Pearson Correlation Matrix, Model 1 B17 IT1 IT2 IT3 IT4 B17: Interaction Pearson Correlation 1 0.673** 0.350** 0.258** 0.106* Quality Sig. (2-tailed) 0.000 0.000 0.000 0.014 N 561 535 558 549 540 IT1: Employees? Pearson Correlation 0.673** 1 0.448** 0.229** 0.087* Conduct Sig. (2-tailed) 0.000 0.000 0.000 0.048 N 535 535 533 524 520 IT2: Employees? Pearson Correlation 0.350** 0.448** 1 0.020 -0.019 Expertise Sig. (2-tailed) 0.000 0.000 0.640 0.655 N 558 533 558 546 537 IT3: Employees? Pearson Correlation 0.258** 0.229** 0.020 1 0.273** Problem-Solving Sig. (2-tailed) 0.000 0.000 0.640 0.000 N 549 524 546 549 528 IT4: Customer to Pearson Correlation 0.106* 0.087* -0.019 0.273** 1 Customer Sig. (2-tailed) 0.014 0.048 0.655 0.000 Interaction N 540 520 537 528 540 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Table 45A: Pearson Correlation Matrix, Model 2 A27 PE1 PE2 PE3 PE4 PE5 A27: Physical Pearson Correlation 1 0.438** 0.294** 0.463** 0.424** 0.330** Environment Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 Quality N 578 572 569 571 573 571 PE1: Pearson Correlation 0.438** 1 0.023 0.423** 0.466** 0.315** Décor & Sig. (2-tailed) 0.000 0.588 0.000 0.000 0.000 Ambience N 572 574 567 569 571 567 PE2: Room Pearson Correlation 0.294** 0.023 1 0.292** 0.167** 0.256** Quality Sig. (2-tailed) 0.000 0.588 0.000 0.000 0.000 N 569 567 571 565 567 567 PE3: Pearson Correlation 0.463** 0.423** 0.292** 1 0.440** 0.391** Availability of Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 Facility N 571 569 565 573 571 568 PE4: Design Pearson Correlation 0.424** 0.466** 0.167** 0.440** 1 0.319** Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 N 573 571 567 571 575 569 PE5: Location Pearson Correlation 0.330** 0.315** 0.256** 0.391** 0.319** 1 Sig. (2-tailed) 0.000 0.000 0.000 0.000 0.000 N 571 567 567 568 569 573 ** Correlation is significant at the 0.01 level (2-tailed). 314 Table 46A: Pearson Correlation Matrix, Model 3 C12 OC1 OC2 OC3 C12: Outcome Pearson Correlation 1 0.511** 0.341** 0.216** Quality Sig. (2-tailed) 0.000 0.000 0.000 N 580 574 568 572 OC1: Valence Pearson Correlation 0.511** 1 0.431** 0.313** Sig. (2-tailed) 0.000 0.000 0.000 N 574 574 563 566 OC2: Waiting Pearson Correlation 0.341** 0.431** 1 0.150** Time Sig. (2-tailed) 0.000 0.000 0.000 N 568 563 568 561 OC3: Sociability Pearson Correlation 0.216** 0.313** 0.150** 1 Sig. (2-tailed) 0.000 0.000 0.000 N 572 566 561 572 ** Correlation is significant at the 0.01 level (2-tailed). Table 47A: Pearson Correlation Matrix, Model 4 Service Quality B17 A27 C12 Service Pearson Correlation 1 0.581** 0.406** 0.621** Quality Sig. (2-tailed) 0.000 0.000 0.000 N 569 569 567 569 B17: Interaction Pearson Correlation 0.581** 1 0.435** 0.571** Quality Sig. (2-tailed) 0.000 0.000 0.000 N 569 580 578 580 A27: Physical Pearson Correlation 0.406** 0.435** 1 0.346** Environment Sig. (2-tailed) 0.000 0.000 0.000 Quality N 567 578 578 578 C12: Outcome Pearson Correlation 0.621** 0.571** 0.346** 1 Quality Sig. (2-tailed) 0.000 0.000 0.000 N 569 580 578 580 ** Correlation is significant at the 0.01 level (2-tailed). Table 48A: Pearson Correlation Matrix, Model 5A Customer Satisfaction Service Quality Perceived Value Customer Pearson Correlation 1 0.698** 0.738** Satisfaction Sig. (2-tailed) 0.000 0.000 N 571 560 566 Service Quality Pearson Correlation 0.698** 1 0.724** Sig. (2-tailed) 0.000 0.000 N 560 569 564 Perceived Value Pearson Correlation 0.738** 0.724** 1 Sig. (2-tailed) 0.000 0.000 N 566 564 574 ** Correlation is significant at the 0.01 level (2-tailed). Table 49A: Pearson Correlation Matrix, Model 5B Customer Satisfaction Service Quality × Perceived Value Customer Pearson Correlation 1 0.771** Satisfaction Sig. (2-tailed) 0.000 N 571 556 Service Quality × Pearson Correlation 0.771** 1 Perceived Value Sig. (2-tailed) 0.000 N 556 564 ** Correlation is significant at the 0.01 level (2-tailed). 315 Table 50A: Pearson Correlation Matrix, Model 6 Perceived Value Service Quality Perceived Pearson Correlation 1 0.724** Value Sig. (2-tailed) 0.000 N 574 564 Service Pearson Correlation 0.724** 1 Quality Sig. (2-tailed) 0.000 N 564 569 ** Correlation is significant at the 0.01 level (2-tailed). Table 51A: Pearson Correlation Matrix, Model 7 Image Service Quality Image Pearson Correlation 1 0.705** Sig. (2-tailed) 0.000 N 576 565 Service Pearson Correlation 0.705** 1 Quality Sig. (2-tailed) 0.000 N 565 569 ** Correlation is significant at the 0.01 level (2-tailed). Table 52A: Pearson Correlation Matrix, Model 8 Customer Satisfaction Perceived Value Image Service Quality Customer Pearson Correlation 1 0.738** 0.656** 0.698** Satisfaction Sig. (2-tailed) 0.000 0.000 0.000 N 571 566 568 560 Perceived Pearson Correlation 0.738** 1 0.688** 0.724** Value Sig. (2-tailed) 0.000 0.000 0.000 N 566 574 570 564 Image Pearson Correlation 0.656** 0.688** 1 0.705** Sig. (2-tailed) 0.000 0.000 0.000 N 568 570 576 565 Service Pearson Correlation 0.698** 0.724** 0.705** 1 Quality Sig. (2-tailed) 0.000 0.000 0.000 N 560 564 565 569 ** Correlation is significant at the 0.01 level (2-tailed). Table 53A: Pearson Correlation Matrix, Model 9 Behavioural Intention Image Customer Satisfaction Behavioural Pearson Correlation 1 0.705** 0.771** Intentions Sig. (2-tailed) 0.000 0.000 N 570 567 562 Image Pearson Correlation 0.705** 1 0.656** Sig. (2-tailed) 0.000 0.000 N 567 576 568 Customer Pearson Correlation 0.771** 0.656** 1 Satisfaction Sig. (2-tailed) 0.000 0.000 N 562 568 571 ** Correlation is significant at the 0.01 level (2-tailed). 316 Table 54A: Multi-collinearity Statistics Collinearity Statistics Model Dependent Variables Independent Variables 1/(1- 2R ) Tolerance VIF Condition Index 1 B17: Interaction Quality Employees? Conduct Employees? Expertise Employees? Problem-Solving Customer-to-Customer Interaction 1.890 0.761 0.804 0.884 0.927 1.314 1.244 1.131 1.078 11.752 15.361 17.762 24.103 2 A27: Physical Environment Quality Décor & Ambience Room Quality Availability of Facility Design Location 1.529 0.700 0.872 0.667 0.699 0.786 1.429 1.147 1.499 1.432 1.273 13.933 15.511 18.477 18.787 25.154 3 C12: Outcome Quality Valence Waiting Time Sociability 1.393 0.756 0.818 0.904 1.322 1.222 1.106 9.397 13.719 18.201 4 Service Quality Interaction Quality Physical Environment Quality Outcome Quality 1.883 0.610 0.797 0.662 1.640 1.255 1.511 8.618 14.498 16.379 Step One Service Quality Perceived Value 2.433 0.482 0.482 2.075 2.075 13.303 19.219 5 Customer Satisfaction Step Two Service Quality × Perceived Value 2.375 1.000 1.000 6.790 6 Perceived Value Service Quality 2.075 1.000 1.000 11.980 7 Image Service Quality 1.942 1.000 1.000 11.980 8 Customer Satisfaction Perceived Value Image Service Quality 2.545 0.415 0.443 0.404 2.409 2.255 2.472 15.053 20.751 22.349 9 Behavioural Intentions Image Customer Satisfaction 2.915 0.573 0.573 1.745 1.745 13.452 17.110 317 Appendix 19. Scatter Plots Figure 16A: Residual Scatter Plots Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 2 0 -2 -4 Dependent Variable: Interaction Quality Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 2 0 -2 -4 Dependent Variable: Physical Environment Quality Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 4 2 0 -2 -4 -6 Dependent Variable: Outcome Quality Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 4 2 0 -2 -4 -6 Dependent Variable: Service Quality Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 6 4 2 0 -2 -4 Dependent Variable:Customer Satisfaction (Step One) Regression Standardised Predicted Value 3210-1-2-3 Re gr ess ion St an da rd ise d R esi du al 6 4 2 0 -2 -4 Dependent Variable: Customer Satisfaction (Step Two) Regression Standardised Predicted Value 210-1-2-3-4 Re gr ess ion St an da rd ise d R es idu al 4 2 0 -2 -4 Dependent Variable: Perceived Value Regression Standardised Predicted Value 210-1-2-3-4 Re gr ess ion St an da rd ise d R esi du al 4 2 0 -2 -4 -6 Dependent Variable: Image 318 Regression Standardised Predicted Value 20-2-4 Re gr ess ion St an da rd ise d R esi du al 6 4 2 0 -2 -4 Dependent Variable: Customer Satisfaction Regression Standardised Predicted Value 210-1-2-3-4 Re gr ess ion St an da rd ise d R esi du al 4 2 0 -2 -4 -6 Dependent Variable: Behavioural Intentions 319 Appendix 20. Normality Plots Figure 17A: Residual Scatter Plots Regression Standardised Residual 20-2-4 Fr eq ue nc y 80 60 40 20 0 Dependent Variable: Interaction Quality Mean =2.72E-15 Std. Dev. =0.997 N =580 Regression Standardised Residual 20-2-4 Fr eq ue nc y 60 40 20 0 Dependent Variable: Physical Environment Quality Mean =1.19E-15 Std. Dev. =0.996 N =580 Regression Standardised Residual 420-2-4-6 Fr eq ue nc y 100 80 60 40 20 0 Dependent Variable: Outcome Quality Mean =-8.35E-16 Std. Dev. =0.997 N =580 Regression Standardised Residual 420-2-4-6 Fr eq ue nc y 120 100 80 60 40 20 0 Dependent Variable: Service Quality Mean =1.17E-15 Std. Dev. =0.997 N =580 Regression Standardised Residual 6420-2-4 Fr eq ue nc y 120 100 80 60 40 20 0 Dependent Variable: Customer Satisfaction (Step One) Mean =-5.69E-16 Std. Dev. =0.998 N =580 Regression Standardised Residual 6420-2-4 Fr eq ue nc y 100 80 60 40 20 0 Dependent Variable: Customer Satisfaction (Step Two) Mean =1.35E-15 Std. Dev. =0.999 N =580 Regression Standardised Residual 420-2-4 Fr eq ue nc y 100 80 60 40 20 0 Dependent Variable: Perceived Value Mean =-1.23E-15 Std. Dev. =0.999N =580 Regression Standardised Residual 420-2-4-6 Fr eq ue nc y 120 100 80 60 40 20 0 Dependent Variable: Image Mean =1.13E-15 Std. Dev. =0.999N =580 320 Regression Standardised Residual 6420-2-4 Fr eq ue nc y 100 80 60 40 20 0 Dependent Variable: Customer Satisfaction Mean =-1.14E-15 Std. Dev. =0.997N =580 Regression Standardised Residual 420-2-4-6 Fr eq ue nc y 120 100 80 60 40 20 0 Dependent Variable: Behavioural Intentions Mean =-1.08E-15 Std. Dev. =0.998N =580 321 Figure 18A: Normal P-P Plot of Regression Standardised Residual Observed Cum Prob 1.00.80.60.40.20.0 Ex pe cte d C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Interaction Quality Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Physical Environment Quality Observed Cum Prob 1.00.80.60.40.20.0 Ex pe cte d C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Outcome Quality Observed Cum Prob 1.00.80.60.40.20.0 Ex pe cte d C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Service Quality Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Customer Satisfaction (Step One) Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Customer Satisfaction (Step Two) Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Perceived Value Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Image 322 Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Customer Satisfaction Observed Cum Prob 1.00.80.60.40.20.0 Ex pe ct ed C um P ro b 1.0 0.8 0.6 0.4 0.2 0.0 Dependent Variable: Behavioural Intentions 323 Appendix 21. Analysis of Variance Results Table 55A: Customer Perceptions of Behavioural Intentions and Pertaining Constructs Gender Marital Status Age Level of Education Variable Gender Frequency Mean F Sig. Service Quality Male Female Total 282 298 580 5.36 5.49 5.43 2.847 0.092* Perceived Value Male Female Total 282 298 580 5.17 5.24 5.21 0.956 0.329 Image Male Female Total 282 298 580 5.37 5.44 5.41 1.013 0.315 Customer Satisfaction Male Female Total 282 298 580 5.32 5.43 5.38 1.930 0.165 Behavioural Intentions Male Female Total 282 298 580 5.14 5.26 5.20 2.097 0.148 Variable Marital Status Frequency Mean F Sig. Service Quality Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.43 5.40 5.23 5.63 5.89 5.43 0.575 0.681 Perceived Value Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.23 5.17 4.97 5.25 6.22 5.21 1.240 0.293 Image Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.42 5.38 5.33 5.42 6.00 5.41 0.391 0.815 Customer Satisfaction Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.38 5.37 5.08 5.57 5.83 5.38 0.610 0.656 Behavioural Intentions Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.18 5.18 5.38 5.58 5.92 5.20 1.125 0.344 Variable Age Frequency Mean F Sig. Service Quality 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.27 5.51 5.34 5.62 5.43 1.442 0.207 Perceived Value 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.04 5.30 5.11 5.35 5.21 1.515 0.183 Image 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.34 5.48 5.32 5.54 5.41 1.259 0.280 Customer Satisfaction 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.19 5.44 5.34 5.59 5.38 0.990 0.423 Behavioural Intentions 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.06 5.23 5.15 5.44 5.20 0.687 0.633 Variable Level of Education Frequency Mean F Sig. Service Quality Junior College- College or University Graduate School + Total 144 336 100 580 5.55 5.43 5.46 5.43 0.585 0.673 Perceived Value Junior College- College or University Graduate School + Total 144 336 100 580 5.10 5.26 5.20 5.21 1.006 0.404 Image Junior College- College or University Graduate School + Total 144 336 100 580 5.38 5.44 5.38 5.41 0.358 0.838 Customer Satisfaction Junior College- College or University Graduate School + Total 144 336 100 580 5.28 5.44 5.33 5.38 1.144 0.335 Behavioural Intentions Junior College- College or University Graduate School + Total 144 336 100 580 5.34 5.26 4.98 5.20 1.682 0.153 324 Annual Income Purpose of Travel Ethnic Background Variable Annual Income Frequency Mean F Sig. Service Quality TW$500,000- TW$500,001+ Total 247 333 580 5.45 5.39 5.43 0.995 0.434 Perceived Value TW$500,000- TW$500,001+ Total 247 333 580 5.19 5.23 5.21 0.646 0.718 Image TW$500,000- TW$500,001+ Total 247 333 580 5.46 5.37 5.41 0.824 0.568 Customer Satisfaction TW$500,000- TW$500,001+ Total 247 333 580 5.37 5.37 5.38 0.683 0.687 Behavioural Intentions TW$500,000- TW$500,001+ Total 247 333 580 5.20 5.18 5.20 1.118 0.350 Variable Purpose of Travel Frequency Mean F Sig. Service Quality Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.47 5.31 5.06 5.80 4.98 5.51 5.43 2.140 0.059* Perceived Value Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.22 5.20 4.93 4.87 4.94 5.45 5.21 1.164 0.326 Image Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.44 5.29 5.16 5.73 5.06 5.46 5.41 1.265 0.277 Customer Satisfaction Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.42 5.22 5.03 5.65 4.85 5.46 5.38 2.341 0.040** Behavioural Intentions Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.24 5.11 4.75 5.50 4.78 5.35 5.20 1.852 0.101 Variable Ethnic Background Frequency Mean F Sig. Service Quality Asian Western Total 488 92 580 5.44 5.56 5.43 0.714 0.680 Perceived Value Asian Western Total 488 92 580 5.23 5.38 5.21 1.067 0.385 Image Asian Western Total 488 92 580 5.41 5.60 5.41 0.769 0.630 Customer Satisfaction Asian Western Total 488 92 580 5.37 5.73 5.38 1.031 0.411 Behavioural Intentions Asian Western Total 488 92 580 5.22 5.43 5.20 1.119 0.348 325 Occupation Variable Occupation Frequency Mean F Sig. Service Quality Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.86 5.36 5.28 5.41 5.54 5.68 5.81 5.50 4.83 4.93 5.03 5.83 5.49 5.43 1.835 0.040** Perceived Value Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.11 5.12 5.13 5.23 5.31 5.24 5.57 5.50 5.33 4.89 4.89 5.23 5.21 5.21 0.787 0.664 Image Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.11 5.29 5.35 5.31 5.48 5.85 5.62 5.67 5.50 4.85 5.19 5.37 5.50 5.41 1.269 0.233 Customer Satisfaction Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.83 5.32 5.27 5.38 5.44 5.53 5.57 5.63 5.31 4.58 5.48 5.33 5.51 5.38 1.235 0.255 Behavioural Intentions Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.65 5.05 5.02 5.26 5.33 5.10 5.71 5.63 5.13 4.83 5.15 5.30 5.32 5.20 1.298 0.215 326 Table 56A: Customer Perceptions of the Primary Dimensions of Service Quality Gender Marital Status Age Level of Education Annual Income Purpose of Travel Ethnic Background Variable Gender Frequency Mean F Sig. Interaction Quality Male Female Total 282 298 580 5.15 5.24 5.20 1.023 0.312 Physical Environment Quality Male Female Total 282 298 580 4.55 4.64 4.60 0.593 0.442 Outcome Quality Male Female Total 282 298 580 5.36 5.41 5.39 0.379 0.538 Variable Marital Status Frequency Mean F Sig. Interaction Quality Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.17 5.22 5.50 5.11 5.67 5.20 0.501 0.735 Physical Environment Quality Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 4.50 4.76 4.40 4.26 5.67 4.60 1.889 0.111 Outcome Quality Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.36 5.40 5.50 5.63 6.00 5.39 0.684 0.603 Variable Age Frequency Mean F Sig. Interaction Quality 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.04 5.25 5.09 5.17 5.20 1.551 0.172 Physical Environment Quality 18-25 26-35 36-45 46+ Total 80 265 138 97 580 4.46 4.57 4.60 4.83 4.60 0.549 0.739 Outcome Quality 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.19 5.54 5.23 5.51 5.39 3.839 0.002*** Variable Level of Education Frequency Mean F Sig. Interaction Quality Junior College- College or University Graduate School + Total 144 336 100 580 5.39 5.22 5.09 5.20 1.033 0.389 Physical Environment Quality Junior College- College or University Graduate School + Total 144 336 100 580 4.87 4.57 4.62 4.60 0.804 0.523 Outcome Quality Junior College- College or University Graduate School + Total 144 336 100 580 5.56 5.45 5.32 5.39 2.008 0.092* Variable Purpose of Travel Frequency Mean F Sig. Interaction Quality Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.24 5.12 4.74 5.20 5.06 5.13 5.20 1.219 0.299 Physical Environment Quality Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 4.59 4.73 4.17 4.80 4.33 5.09 4.60 1.181 0.317 Outcome Quality Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.42 5.16 5.26 5.60 5.06 5.52 5.39 1.273 0.274 Variable Annual Income Frequency Mean F Sig. Interaction Quality TW$500,000- TW$500,001+ Total 247 333 580 5.21 5.19 5.20 1.211 0.295 Physical Environment Quality TW$500,000- TW$500,001+ Total 247 333 580 4.60 4.63 4.60 0.898 0.508 Outcome Quality TW$500,000- TW$500,001+ Total l 247 333 580 5.42 5.36 5.39 0.436 0.879 Variable Ethnic Background Frequency Mean F Sig. Interaction Quality Asian Western Total 488 92 580 5.22 5.46 5.20 1.219 0.285 Physical Environment Quality Asian Western Total 488 92 580 4.57 4.95 4.60 0.837 0.570 Outcome Quality Asian Western Total 488 92 580 5.42 5.63 5.39 1.900 0.058* 327 Occupation Variable Occupation Frequency Mean F Sig. Interaction Quality Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.08 5.05 5.00 5.32 5.29 5.23 5.57 5.00 5.75 4.89 4.50 5.20 5.37 5.20 1.533 0.108 Physical Environment Quality Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.58 4.25 4.55 4.89 4.64 4.77 5.71 4.50 5.50 4.22 4.58 3.90 4.72 4.60 1.549 0.103 Outcome Quality Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.92 5.16 5.32 5.44 5.51 5.55 5.43 6.00 5.75 5.11 5.08 5.50 5.46 5.39 1.305 0.211 328 Table 57A: Customer Perceptions of the Sub-dimensions of Service Quality Gender Marital Status Variable Gender Frequency Mean F Sig. Employees? Conduct Male Female Total 282 298 580 5.23 5.23 5.23 0.011 0.918 Employees? Expertise Male Female Total 282 298 580 5.38 5.50 5.44 2.160 0.142 Employees? Problem-Solving Male Female Total 282 298 580 5.88 5.88 5.88 0.010 0.922 Customer-to- Customer Interaction Male Female Total 282 298 580 4.15 4.12 4.13 0.234 0.629 Décor & Ambience Male Female Total 282 298 580 5.31 5.45 5.38 3.365 0.067* Room Quality Male Female Total 282 298 580 5.49 5.67 5.58 5.423 0.020** Availability of Facility Male Female Total 282 298 580 5.32 5.41 5.37 1.520 0.218 Design Male Female Total 282 298 580 5.24 5.35 5.30 1.710 0.191 Location Male Female Total 282 298 580 5.13 5.23 5.18 1.374 0.242 Valence Male Female Total 282 298 580 5.12 5.19 5.16 0.839 0.360 Waiting Time Male Female Total 282 298 580 5.52 5.52 5.52 0.008 0.928 Sociability Male Female Total 282 298 580 3.75 3.76 3.76 0.011 0.915 Variable Marital Status Frequency Mean F Sig. Employees? Conduct Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.24 5.20 5.18 5.49 5.67 5.23 0.624 0.645 Employees? Expertise Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.42 5.46 5.87 5.25 6.22 5.44 1.320 0.261 Employees? Problem- Solving Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.81 5.97 5.60 6.18 5.33 5.88 1.886 0.111 Customer-to- Customer Interaction Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 4.10 4.18 3.83 4.19 4.11 4.13 0.729 0.572 Décor & Ambience Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.40 5.42 4.92 4.95 5.17 5.38 1.918 0.106 Room Quality Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.63 5.48 6.22 5.70 6.67 5.58 3.077 0.016** Availability of Facility Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.29 5.48 5.37 5.12 5.83 5.37 1.772 0.133 Design Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.30 5.31 4.97 5.35 5.11 5.30 0.318 0.866 Location Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.17 5.22 5.03 4.74 6.00 5.18 1.556 0.185 Valence Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.19 5.11 4.93 5.26 5.33 5.16 0.375 0.826 Waiting Time Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 5.49 5.53 5.76 5.65 5.93 5.52 0.545 0.703 Sociability Single Married Divorced/Separate Living with a Partner Widowed Total 312 236 10 19 3 580 3.75 3.76 3.97 3.74 3.67 3.76 0.122 0.975 329 Age Level of Education Variable Age Frequency Mean F Sig. Employees? Conduct 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.06 5.34 5.15 5.28 5.23 2.093 0.065* Employees? Expertise 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.25 5.43 5.41 5.63 5.44 2.193 0.054* Employees? Problem-Solving 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.74 5.90 5.89 6.12 5.88 1.577 0.165 Customer-to- Customer Interaction 18-25 26-35 36-45 46+ Total 80 265 138 97 580 4.13 4.11 4.12 4.18 4.13 0.821 0.535 Décor & Ambience 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.25 5.38 5.48 5.54 5.38 1.080 0.370 Room Quality 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.52 5.72 5.41 5.56 5.58 2.269 0.046** Availability of Facility 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.19 5.32 5.50 5.44 5.37 1.565 0.168 Design 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.27 5.30 5.36 5.43 5.30 0.895 0.484 Location 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.12 5.21 5.16 5.39 5.18 1.113 0.352 Valence 18-25 26-35 36-45 46+ Total 80 265 138 97 580 4.93 5.25 5.03 5.15 5.16 2.225 0.050* Waiting Time 18-25 26-35 36-45 46+ Total 80 265 138 97 580 5.44 5.57 5.49 5.53 5.52 0.331 0.894 Sociability 18-25 26-35 36-45 46+ Total 80 265 138 97 580 3.75 3.78 3.63 4.37 3.76 1.315 0.256 Variable Level of Education Frequency Mean F Sig. Employees? Conduct Junior College- College or University Graduate School + Total 144 336 100 580 5.20 5.27 5.23 5.23 0.802 0.524 Employees? Expertise Junior College- College or University Graduate School + Total 144 336 100 580 5.59 5.45 5.54 5.44 1.598 0.173 Employees? Problem- Solving Junior College- College or University Graduate School + Total 144 336 100 580 5.80 5.90 5.80 5.88 0.379 0.824 Customer- to-Customer Interaction Junior College- College or University Graduate School + Total 144 336 100 580 4.10 4.12 4.19 4.13 1.014 0.400 Décor & Ambience Junior College- College or University Graduate School + Total 144 336 100 580 5.52 5.37 5.43 5.38 0.431 0.786 Room Quality Junior College- College or University Graduate School + Total 144 336 100 580 5.46 5.62 5.61 5.58 1.236 0.294 Availability of Facility Junior College- College or University Graduate School + Total 144 336 100 580 5.47 5.43 5.31 5.37 1.751 0.137 Design Junior College- College or University Graduate School + Total 144 336 100 580 5.34 5.38 5.15 5.30 2.410 0.048** Location Junior College- College or University Graduate School + Total 144 336 100 580 5.38 5.14 5.23 5.18 1.027 0.392 Valence Junior College- College or University Graduate School + Total 144 336 100 580 5.49 5.17 5.01 5.16 1.544 0.188 Waiting Time Junior College- College or University Graduate School + Total 144 336 100 580 5.68 5.52 5.49 5.52 0.361 0.837 Sociability Junior College- College or University Graduate School + Total 144 336 100 580 4.14 3.76 3.66 3.76 2.006 0.092* 330 Annual Income Purpose of Travel Ethnic Background Variable Annual Income Frequency Mean F Sig. Employees? Conduct TW$500,000- TW$500,001+ Total 247 333 580 5.22 5.24 5.23 1.100 0.362 Employees? Expertise TW$500,000- TW$500,001+ Total 247 333 580 5.36 5.53 5.44 2.722 0.009*** Employees? Problem- Solving TW$500,000- TW$500,001+ Total 247 333 580 5.89 5.86 5.88 0.671 0.697 Customer-to- Customer Interaction TW$500,000- TW$500,001+ Total 247 333 580 4.13 4.13 4.13 1.051 0.394 Décor & Ambience TW$500,000- TW$500,001+ Total 247 333 580 5.37 5.42 5.38 4.184 0.000*** Room Quality TW$500,000- TW$500,001+ Total 247 333 580 5.72 5.51 5.58 1.968 0.057* Availability of Facility TW$500,000- TW$500,001+ Total 247 333 580 5.32 5.43 5.37 1.762 0.092* Design TW$500,000- TW$500,001+ Total 247 333 580 5.30 5.32 5.30 0.844 0.551 Location TW$500,000- TW$500,001+ Total 247 333 580 5.18 5.17 5.18 0.905 0.502 Valence TW$500,000- TW$500,001+ Total 247 333 580 5.22 5.09 5.16 1.904 0.067* Waiting Time TW$500,000- TW$500,001+ Total 247 333 580 5.60 5.48 5.52 1.611 0.129 Sociability TW$500,000- TW$500,001+ Total 247 333 580 3.80 3.73 3.76 0.734 0.643 Variable Purpose of Travel Frequency Mean F Sig. Employees? Conduct Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.25 5.28 5.08 5.12 4.96 5.17 5.23 0.537 0.748 Employees? Expertise Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.43 5.46 5.49 5.53 5.31 5.65 5.44 0.339 0.890 Employees? Problem- Solving Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.88 5.90 5.74 5.67 6.11 5.80 5.88 0.396 0.852 Customer- to-Customer Interaction Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 4.14 4.22 3.96 4.13 4.04 4.01 4.13 0.495 0.780 Décor & Ambience Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.41 5.52 5.22 5.77 4.56 5.28 5.38 3.795 0.002*** Room Quality Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.60 5.48 5.22 6.07 5.85 5.61 5.58 1.327 0.251 Availability of Facility Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.38 5.50 5.11 5.77 4.71 5.45 5.37 2.498 0.030** Design Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.34 5.18 4.80 5.27 5.19 5.39 5.30 1.520 0.182 Location Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.22 5.06 5.12 5.20 5.06 4.83 5.18 0.924 0.465 Valence Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.19 5.02 4.58 5.73 5.28 5.07 5.16 2.134 0.060* Waiting Time Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 5.52 5.55 5.37 5.76 5.68 5.54 5.52 0.342 0.887 Sociability Pleasure Business Visiting Relatives Conference Study Other Total 460 51 23 5 18 23 580 3.77 3.59 3.46 3.20 3.91 4.29 3.76 2.491 0.030** Variable Ethnic Background Frequency Mean F Sig. Employees? Conduct Asian Western Total 488 92 580 5.24 5.42 5.23 1.396 0.195 Employees? Expertise Asian Western Total 488 92 580 5.42 5.57 5.44 1.099 0.362 Employees? Problem- Solving Asian Western Total 488 92 580 5.89 6.11 5.88 0.862 0.548 Customer-to- Customer Interaction Asian Western Total 488 92 580 4.14 3.91 4.13 1.932 0.053* Décor & Ambience Asian Western Total 488 92 580 5.37 5.46 5.38 0.968 0.460** Room Quality Asian Western Total 488 92 580 5.60 5.89 5.58 1.487 0.159 Availability of Facility Asian Western Total 488 92 580 5.37 6.34 5.37 0.554 0.816 Design Asian Western Total 488 92 580 5.30 5.54 5.30 1.004 0.432 Location Asian Western Total 488 92 580 5.19 5.38 5.18 0.682 0.707 Valence Asian Western Total 488 92 580 5.19 5.22 5.16 1.732 0.088* Waiting Time Asian Western Total 488 92 580 5.52 5.77 5.52 0.496 0.860 Sociability Asian Western Total 488 92 580 3.78 3.78 3.76 2.877 0.004*** 331 Occupation Variable Occupation Frequency Mean F Sig. Employees? Conduct Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.73 5.17 5.19 5.35 5.31 5.25 5.66 5.40 5.35 4.73 4.72 5.46 5.17 5.23 1.285 0.223 Employees? Expertise Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.00 5.42 5.65 5.40 5.37 5.64 5.10 5.50 5.83 5.11 5.83 5.47 5.43 5.44 1.150 0.317 Employees? Problem- Solving Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 6.44 6.05 5.66 6.11 5.81 5.79 5.71 4.83 6.00 5.70 5.58 6.07 5.90 5.88 1.745 0.054* Customer- to-Customer Interaction Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.44 4.01 3.94 4.43 4.28 3.91 3.86 4.00 3.58 4.04 4.06 4.27 3.93 4.13 2.797 0.001*** Décor & Ambience Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.15 5.16 5.35 5.37 5.45 5.58 5.83 5.42 5.79 5.26 5.11 5.55 5.49 5.38 1.127 0.335 Room Quality Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.71 5.47 5.59 5.36 5.68 5.83 6.55 6.33 5.67 5.57 5.38 5.70 5.63 5.58 2.381 0.005*** 332 Occupation (Continued) Variable Occupation Frequency Mean F Sig. Availability of Facility Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.90 5.27 5.34 5.42 5.43 5.56 5.71 6.00 5.83 5.06 5.00 5.12 5.40 5.37 1.011 0.436 Design Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 5.83 5.11 5.12 5.33 5.45 5.53 5.43 5.33 5.58 5.22 4.61 4.97 5.32 5.30 1.861 0.036** Location Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.72 5.09 5.15 5.19 5.17 5.42 5.76 5.50 5.92 4.59 4.92 4.53 5.48 5.18 1.931 0.028** Valence Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.72 4.81 4.99 5.16 5.40 5.15 5.95 5.50 5.67 4.74 4.72 5.37 5.19 5.16 3.058 0.000*** Waiting Time Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 4.78 5.55 5.43 5.46 5.52 5.91 6.17 5.20 6.30 5.56 4.93 5.68 5.63 5.52 2.336 0.006*** Sociability Student Professional Manager Government Employee Employee of a Company Housewife Soldier Labour Farmer Self-Employed Retired Unemployed Other Total 12 96 82 63 194 22 7 2 4 9 12 10 67 580 3.11 3.50 3.69 3.95 3.87 3.86 4.24 5.83 4.75 3.04 3.94 3.27 3.77 3.76 3.486 0.000***