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Antecedents of Destination Loyalty

Permanent Link: http://ufdc.ufl.edu/UFE0041512/00001

Material Information

Title: Antecedents of Destination Loyalty
Physical Description: 1 online resource (117 p.)
Language: english
Creator: Kim, Soon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: image, loyalty, quality, satisfaction, value
Tourism, Recreation, and Sport Management -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Antecedents of Destination Loyalty Abstract: The primary objectives of this study were to investigate and develop a theoretical relationship among destination image, service quality, and perceived value, and to empirically test the constructs that are likely to affect tourist satisfaction, which in turn influence revisit intentions and Word-of-Mouth (WOM). To achieve these purposes, measurement scales for destination image, service quality, perceived value, loyalty were developed relying on previous studies across various contexts. Then, the measurement scales were tested and validated through multiple CFAs. Next, the structural nature of the relationship of destination loyalty, service quality, perceived value, satisfaction, and destination loyalty constructs were explored. The results of empirical study indicated that destination image influences on service quality, perceived value and destination loyalty. These findings were not evident in previous studies. Also, service quality was found to exert a positive influence on perceived value, satisfaction, and loyalty, respectively. In addition, the findings revealed that perceived value has a significant effect on satisfaction and loyalty. It is worth noting that destination image and perceived value were included into the quality-satisfaction-loyalty paradigm.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Soon Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Holland, Stephen.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041512:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041512/00001

Material Information

Title: Antecedents of Destination Loyalty
Physical Description: 1 online resource (117 p.)
Language: english
Creator: Kim, Soon
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: image, loyalty, quality, satisfaction, value
Tourism, Recreation, and Sport Management -- Dissertations, Academic -- UF
Genre: Health and Human Performance thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Antecedents of Destination Loyalty Abstract: The primary objectives of this study were to investigate and develop a theoretical relationship among destination image, service quality, and perceived value, and to empirically test the constructs that are likely to affect tourist satisfaction, which in turn influence revisit intentions and Word-of-Mouth (WOM). To achieve these purposes, measurement scales for destination image, service quality, perceived value, loyalty were developed relying on previous studies across various contexts. Then, the measurement scales were tested and validated through multiple CFAs. Next, the structural nature of the relationship of destination loyalty, service quality, perceived value, satisfaction, and destination loyalty constructs were explored. The results of empirical study indicated that destination image influences on service quality, perceived value and destination loyalty. These findings were not evident in previous studies. Also, service quality was found to exert a positive influence on perceived value, satisfaction, and loyalty, respectively. In addition, the findings revealed that perceived value has a significant effect on satisfaction and loyalty. It is worth noting that destination image and perceived value were included into the quality-satisfaction-loyalty paradigm.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Soon Kim.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Holland, Stephen.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-04-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041512:00001


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1 ANTECEDENTS OF DESTINATION LOYALTY By SOON HO KIM A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Soon Ho Kim

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3 To my family

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4 ACKNOWLEDGMENTS It is my pleasure to express my thank s to those who helped me completing this dissertation. I would like to give many thank s my mentor, Dr. Stephen Holland, who has consiste ntly helped me with patience. He has encouraged and tremendously supported me during my doctoral program. He also provided me many opportunities to actively involve in several funded research projects. Without his fully support, I would not be able to com plete the doctoral program. My gratitude is also extended to my committee, Drs Yong Jae Ko, Kiki Kaplanidou and Richard Lutz. These committee members guided my understanding of the literature, assisted my analysis and offered valuable assistance in proofr eading and recommending useful changes to the document. I also would like to specially thank Michael J. Caires (Marketing/Public Relations Manager) at the Sanford International Airport. Most importantly, I would like to thank my families whose unconditiona l love enabled me to complete this work. This dissertation could not have been finished without the encouragement and support from my wife, HaeYoung Chung. None of this would have been possible without their belief in me.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ............... 4 LIST OF TABLES ................................ ................................ ................................ ........................... 7 LIST OF FIGURES ................................ ................................ ................................ ......................... 9 ABSTRACT ................................ ................................ ................................ ................................ ... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 11 2 REVIEW OF LITERATURE ................................ ................................ ................................ 15 Destination Image ................................ ................................ ................................ ................... 15 Definition of Destination I mage ................................ ................................ ...................... 15 Why Study Tourist Destination Image? ................................ ................................ .......... 16 Image Formation ................................ ................................ ................................ ............. 17 Measurement of Image ................................ ................................ ................................ .... 18 Service Quality ................................ ................................ ................................ ....................... 19 Measurement of Service Quality ................................ ................................ ............................ 20 Perceived Value ................................ ................................ ................................ ...................... 22 Definition of Perceived Value ................................ ................................ ......................... 23 Relationship among Perceived Value, Satisfaction and Behavioral Intentions .............. 26 Satisfaction ................................ ................................ ................................ ............................. 30 Definition of Satisfaction ................................ ................................ ................................ 30 Relationship between Satisfaction and Destination Loyalty ................................ ........... 32 Dest ination Loyalty ................................ ................................ ................................ ................ 34 Definition of Destination Loyalty ................................ ................................ ................... 34 Loyalty Versus Satisfaction ................................ ................................ ............................. 36 Operationalization of Loyalty ................................ ................................ .......................... 37 Intention to Revisit ................................ ................................ ................................ .......... 37 Word of Mouth ................................ ................................ ................................ ............... 38 Conceptual Model ................................ ................................ ................................ ................... 39 Conceptual background and Hypotheses ................................ ................................ ................ 40 3 METHODOLOGY ................................ ................................ ................................ ................. 42 Construct Measurement ................................ ................................ ................................ .......... 42 (1) Destination Image. ................................ ................................ ................................ ..... 42 (2) Service Quality ................................ ................................ ................................ .......... 43 (3) Perceived Value ................................ ................................ ................................ ......... 43 (4) Overall Satisfaction ................................ ................................ ................................ .... 43 (5) Destination Loyal ty ................................ ................................ ................................ .... 44

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6 (6) Demographic Information ................................ ................................ .......................... 44 Study Site and Sample ................................ ................................ ................................ ............ 44 Pr ocedures ................................ ................................ ................................ ............................... 45 Data Analyses ................................ ................................ ................................ ......................... 46 4 RESULTS ................................ ................................ ................................ ............................... 49 Descriptive Statistics ................................ ................................ ................................ .............. 49 Demographics ................................ ................................ ................................ .................. 49 Destination Image ................................ ................................ ................................ ............ 49 Service Quality ................................ ................................ ................................ ................ 50 Perceived Value ................................ ................................ ................................ ............... 50 Satisfaction ................................ ................................ ................................ ...................... 50 Destination Loyalty ................................ ................................ ................................ ......... 51 Data Screening and Test of Assumptions for Structural Equation Modeling (SEM) ............. 51 Exploratory Factor Analyses ................................ ................................ ................................ .. 51 Destination Image ................................ ................................ ................................ ............ 51 Service Quality ................................ ................................ ................................ ................ 53 Perceived Value ................................ ................................ ................................ ............... 53 Loyalty ................................ ................................ ................................ ............................. 54 Measurement Models: Confirmatory Factor Analyses ................................ ......................... 55 5 Destination Image ................................ ................................ ................................ ............ 55 Service Quality ................................ ................................ ................................ ................ 57 Perceived Value ................................ ................................ ................................ ............... 58 Loyalty ................................ ................................ ................................ ........................... 60 0 Structural Model ................................ ................................ ................................ ................... 61 1 5 DISCUSSION ................................ ................................ ................................ ......................... 95 Hypotheses Testing ................................ ................................ ................................ ............... 9 95 Implications ................................ ................................ ................................ ............................ 99 Theoretical/Conceptual Implications ................................ ................................ ............... 99 Managerial Implications ................................ ................................ ................................ 100 Limitations and Recommendations for Future Research ................................ ...................... 101 LIST OF REFERENCES ................................ ................................ ................................ ......... 10 105 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ....... 117

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7 LIST OF TABLES Table page 4 1 Frequency distri butions for the sociodemographic variables ( N = 581) ............................ 70 4 2 Descriptive statistics for the Destination Image Variables ( N = 581) ............................... 72 4 3 Descriptive statistics for the Service Quality Variables ( N = 581) ................................ .... 73 4 4 Descriptive statistics for the Perceived Value Variables ( N = 581) ................................ ... 74 4 5 Descriptive statistics for the Satisfaction Variables ( N = 581) ................................ .......... 75 4 6 Descriptive statistics for the Loyalty Variables ( N = 581) ................................ ................ 76 4 7 EFA for Destination Image variables: varimax rotation using first half data ( n = 290) .... 77 4 8 EFA for Service Quality variables: varimax rotation using firs t half data ( n = 290) ........ 78 4 9 EFA for Perceived Value variables: varimax rotation using first half data ( n = 290) ....... 79 4 10 EFA for Loyalty variables: varimax rotation using first half data ( n = 290) ..................... 80 4 11 Model Fit comparison between the six factor model and five factor model of destination image using second half data ( n = 291) ................................ ........................... 81 4 12 Model Fit of service quality using second half data ( n = 291) ................................ .......... 82 4 13 Model Fit comparison between the eleven item model and seven item model of perceived value using second half data ( n = 291) ................................ .............................. 83 4 14 Model Fit of loyalty using second half data ( n = 291) ................................ ....................... 84 4 15 Overall model fit of hypothesized structural model using second half data ( n = 291) ...... 85 4 16 ity, average variance extracted for the destination image ( n = 291) ................................ ...................... 86 4 17 Correlations among destination image constructs ( n = 291) ................................ ........... 84 7 4 18 variance extracted for the service quality ( n = 291) ................................ ........................ 87 8 4 19 Correlations between service qu ality constructs ( n = 291) ................................ .............. 84 9

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8 4 20 variance extracted for the perceived value ( n = 291) ................................ ......................... 90 4 21 Correlations between service quality constructs ( n = 291) ................................ ................ 91 4 22 variance extracted for the loyalty ( n = 291) ................................ ................................ ..... 91 2 4 21 Correlations between destination loyalty constructs ( n = 291) ................................ .......... 93 4 24 Maximum likelihood standardi (SE), and t values for the hypothesized structural model using second half data ( n = 291) ................................ ................................ ................................ ................................ .... 93

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9 LIST OF FIGURES Figure page 2 1 Antecedents of Destination Loyalty ................................ ................................ ................... 40 4 1 First order confirmatory factor analysis for Destination Image ................................ ........ 65 4 2 First order confirmatory factor analysis for Service Quality ................................ ............. 66 4 3 First order confirmatory factor analysis for Perceived Values ................................ .......... 67 4 4 First order confirmatory factor analysis for Loyalty ................................ ......................... 68 4 5 Tested Structured Model ................................ ................................ ................................ .... 69

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10 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ANTECEDENTS OF DESTINATION LOYALTY By Soon Ho Kim May 2010 Chair: Stephen Holland Major: Health and Human Per formance The primary objectives of this study were to investigate and develop a theoretical relationship among destination image, service quality, and perceived value, and to empirically test the constructs that are likely to affect tourist satisfaction, which in turn influence revisit intentions and Word of Mouth (WOM). To achieve these purposes, measurement scales for destination image, service quality, perceived value, loyalty were developed relying on previous studies across various contexts. Then, th e measurement scales were tested and validated through multiple CFAs. Next, the structural nature of the relationship of destination loyalty, service quality, perceived value, satisfaction, and destination loyalty constructs were explored. The results of e mpirical study indicated that destination image influences on service quality perceived value and destination loyalty These finding s were not ev ident in previous studies. Also, service quality was found to exert a positive influence on perceived value, satisfaction, and loyalty, respectively. In addition, the findings revealed that perceived value has a significant effect on satisfaction and loyalty. It is worth noting that destination image and satisfactio n

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11 CHAPTER 1 INTRODUCTION In an increasingly saturated marketplace, competitive destinations should redesign their marketing strategies to increase customer loyalty and build long term relationships with their customers (Baloglu, 2001; Yoon & Uysal, 2005 ). A review of the literature on loyalty reveals that repeat purchase s and/or visits have often been regarded as desirable ( Alegre & Juaneda, 2006; Oppermann, 2000) because it is believed that the marketing costs needed to attract repeat er s are lower than those required for first time tourists (Alegre & Juaneda 2006) Fornell and Wernerfelt (1987) have found that maintaining existing customers generally has a much low er associated cost than recruiting new ones. Reichheld (1996) has docum ented that a 5% increase in customer retention can generate profit growth of 25 95% across a range of industries. Thus, a large r proportion of gross profit counts towards the bottom line (Chi & Qu, 2008). In addition, loyal customers are more likely to act as free word of mouth (WOM) advertising agents that informally bring networks of friends, relatives and other potential consumers to a product/service (Shoemaker & Lewis, 1999). In fact, WOM referrals account for up to 60% of sales to new customers (Reich held & Sasser, 1990). With such exceptional returns, loyalty becomes a fundamental strategic component for o rganizations. The implications of loyalty in consumer behavior have been examined in numerous studies. I n the context of travel and tourism, a plet hora of stu dies on tourist satisfaction are available; but tourist loyalty has received less attention in the destination literature (Baloglu, 2001; Baloglu & Erickson, 1998; Beaman, Huan & K ozak, 2002; Bign, Snchez & S nchez, 2001; Darnell & Johnson, 20 01; Fyall, Callod & Edwards, 2003; Kozak 2001; Oppermann, 1999, 2000; Yoon et al., 2005). Therefore, the time is ripe for academics and practiti oners to conduct additional studies on loyalty in order to facilitate greater understanding of this concept, to better

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12 comprehend the role of satisfaction in developing loyalty, the impact of antecedents of satisfaction determinants on customer loyalty, and their interrelationships. In the tourism context, u nderstand ing the determinants of tourist loyalty has been accepted as an important phenomenon at the management level as a whole and for individual attraction s (Darnell & Johnson, 2001) Given the potential role of tourist loyalty, one should not be surprised that a number of studies shed light on the major influ encing factors that lead to customer retention (Chi & Qu, 2008) though most do not focus on destinations. A great deal of research has been devoted to investigating the antecedents of repeat purchase intentions including satisfaction (Baker & Crompton, 20 00; Kozak, 2001; Petrick, Morais, & Norman, 2001), quality constructs (Baker & Crompton, 2000; Chen & Gursory, 2001; Yuksel, 2001), perceived value (McDougall & Levesque, 2000; Lee, Yoon, & Lee, 2007; Parasuraman & Grewal, 2000; Petrick & Backman, 2002) a nd destination image (Baloglu & McCleary, 1999; Court & Lupton, 1997; Chon, 1992; Chi & Qu, 2008). Past studies (Baloglu & McCleary, 1999; Chi & Qu, 2008) have suggested that destination image s influence travelers in the process of selecting a destination, the subsequent evaluation of the trip and in their future intentions. Destination image facilitates a positive influence on perceived quality, satisfaction and intentions to return to a destination (Bigne et al., 2001; Chi & Qu, 2008; Court & Lupton, 1997 ). A positive image deriving from positive t ravel experiences result s in a positive evaluation of a destination. Tour ist loyalty would i mprove if destination image has a direct effect on behavioral intentions through quality, perceived value and satisfacti on, which in turn affects behavioral intentions. In other words, more favorable image s will lead to a higher likelihood of return ing to the same destination.

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13 Many researchers have suggested that destination image has played a pivotal role in tourist behavi ors (Bigne et al, 2001; Fakeye & Crompton, 1991; Lee, Lee, & Lee, 2005). In general, tourist behavior s are composed of three stages from the choi ce of a destination to visit, resultant evaluation and to subsequent behavioral intentions. The resultant evalu ations include the travel experience or perceived service quality during the stay, perceive d value and overall satisfaction while the subsequent behavioral intentions include intention s to revisit and the willingness to recommend to others. The interrelati onship between quality, satisfact ion and behavioral intentions has been studied in the field of hospitality and tourism for the last two decades (Backman & Veldkamp, 1995; Baker & Crompton, 2000; Cronin, Brady, & Hult, 2000; Oh, 1999). However, perceive d v alue has only recently been studied by tourism researchers (Murphy, Pritchard, & Smith, 2000; Oh, 1999, 2000; Petrick, 2004; Petric & Backman, 2002; Petrick, Morais, & Norman, 2001; Tam, 2000). Some researchers even suggested that perceived value measureme nts should b e associated with measure s of satisfaction (Oh, 2000; Woodruff, 1997) and that perceived value plays a moderating role between service quality and satisfaction (Caruana, Money, & Berthon, 2000). Furthermore, perceived value associates the bene fits recei ved with the price paid (Zeithaml, 19 88) and is distinguished from service quality and sa tisfaction. Empirical research has also found that perceived value has positively influenced both future behavioral intentions and actual behaviors. Therefor e, service evaluation variables (e.g., service quality, perceived value, and satisfaction) have been found to be good predictors of destination loyalty (Baker & Crompton, 2000; Bojanic, 1996; Cronin et al., 2000; Petric, 2004 ; Tam, 2000). Although theory based research efforts have developed the understanding of key service oriented constructs in hospitality and tourism context s there cont inues to be a need to refine

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14 theories and methodologies by introducing new variables and/or modified frameworks to enh ance the predictive power of these models (Hutchison, Lai, & Wang, 2009; Oh & Parks, 1997). For example, service quality has been exclusively investigated as the single antecedent to customer satisfaction in most hospitality and tourism service evaluation research, and other constructs have been examined to improve the accuracy of prediction s For instance, perceived value has been empirically examined as a second proposed antecedent variable to satisfaction in recent studies (Oh, 1999; Petrick, 2004; Tam, 2000). By understanding the relationships between destination loyalty and its determinants with in an integrated approach, destination tourism managers would better know how to build an attractive image and improve their marketing efforts to maximize their use of resources. Therefore, the objective of this study is to empirically investigate a proposed model of tourist consumption process es s atisfaction loyalty The second purpos e of this study is to examine the relationships between destination image and evaluative factors (e.g., service quality, perceived value, and satisfaction) in their p rediction of intentions to repurchase and positive word of mouth publicity To accomplish this aim, this study provides a thorough review of the literature on destination image, service quality, perceived value, satisfaction, and loyalty including empirically tested relationships among these constructs, and seeks to test the most promising mode ls.

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15 CHAPTER 2 REVIEW OF LITERATURE The fundamental objective of this study was to develop and test a n integrative model, which represents the elements contributin g to the building of destination loyalty: destination image, perceived quality, perceiv ed value, and overall satisfaction. Previous studies reveal that customer and satisfaction is affected by destination image (Chen & Tsai, 2007; Gallarza & Sau ra, 2006), perceived value (Petrick, 2004; Petrick & Backman, 2002 ), and perceived quality (Bign et al., 2001 ). The hypothesized causal relationships between satisfaction and destination loyalty is referred to as tourism destination loyalty theory (Yoon & Uysal, 2005). Below is a comprehensive overview of those constructs and of the interrelati onships of the constructs in a model proposed later Destination I mage Definition of D estination I mage Many authors agree that tourism destination image research e 1975 seminal publication (Echtner & Ritchie 1991, Fakeye & Crompton 1991 ; Gallarza, Saura, & Garca, 2002; Reilly, 1990 tourism des tination image have been published in touri sm journals or major conference proceedings since 1973. This history and abundance of articles verifies there is agreement on the significance and worth of the study of destination image. Within social psychology, the term image has referred to a ref l ecti on or representation of sensory or conceptual information that build s on past experience and governs an individual s actions (Stringer, 1984). Tyagi (1989) also connoted that image is not a static or objective phenomenon because it changes as unexpected co nditions emerge. Thus, image evolution has

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16 been critical topic of study (Chon, 1991; Galla r za et al., 2002; Gunn, 1972; Kim & Morrison, 2005). The definition of tourist destination image most commonly cited is that by Crompton definition relates to the individual, whereas other definitions acknowledge that images can be shared by groups of people (Jenkins, 1999). His definition has led to many researchers payi ng attention to image as a salient concept in understanding the destination selection process of tourists (Baloglu & McCleary, 1999; Beerli & Martn, 2004; Pike, 2002). Why Study Tourist Destination Image? Tourist destination image is important because i making (Chon, 1990; Echtner & Ritchie 1991; Gunn, 1972; Hunt, 1975; P earce, 1982) and behavior at a particular destination (Crompton, 1979; Jenkins, 1999). Also understanding current destination image s and creating app ropriate image s in the mind of potential visitors is an immensely important part of successful po sitioning and marketing strategies (Echtner & Ritchie non visit ors have of a destination is invaluable, enabling the salient attributes of the nave image and the re Marketers can also utilize destination image s to boost satisfaction and to e ncourage re visits of destination s National tourist organizations, such as the China National Tourism Administration (CNTA), track image s held by potential visitors in the international marketplace (Lee et al ., 2005). Market segmentation through these an alyses is utilized in market promotion s (Jenkins, 1999). Growth in international visitors after the 2002 World Cup indicates that Korea has a positive image compared with other Asian destinations (Lee et al ., 2005). However, other factors including motivat ion s distance

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17 decision making process (Ahmed, 1991; Jenkins, 1999). The curren t study does not cover this topic since these variables are beyond the scope of this study. Image Formation A consid erable amount of research has discussed the management and formation of destination image s and many authors have attempted to conceptualize the components of destination image s (Um & Crompton, 1990; Echtner & Ritchie, 1991; Baloglu & McCleary, 1999). In ad dition, many researchers have investigated the image formation process, beginning with s (1972) seven stage theory on induced and organic image. He elaborated that travelers experience development on the basis of seven stages of imagery change. This includes the accumulation of mental image s about a vacation experience (1 st stage), change in those images through additional information (2 nd stage), choosing to take a vacation trip (3 rd stage), travel to the destination (4 th stage), participation at the destination (5 th stage), return travel (6 th stage) and new accumulation of images based on the travel experience (7 th stage). As a consequence, Gunn (1972) recommended that destination image s could be tailored or altered over the seven stages. He sugges ted that destination images evolve at two levels organic image s and induced images That is, the organic image an individual holds of a destination arises from a long history of non tourism specific information, such as history and geography books, newspap er reports, magazine articles, and television reports that were not intended as tourism specific. Thus, individuals who have never visited a destination nor have sought out any tourism specific information will likely have some kind of information stored i n their memory. This might be an incomplete image, to which the traveler adds other bits and pieces. An induced image is derived from a conscious effort of tourism promotion directed by tourism organizations. While the organic image is usually beyond the control of the destination marketers, induced image s are directed by the destination

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18 upon colorful brochures distributed at Visitor Information C enters, information available at travel agencies, travel articles in maga zines, TV adver tisements; the Internet and other activities a tourism organization might choose to promote the destination (Gunn, 1972). The role of information sources in this formation is also emphasized in Fakeye and model described tourists developing organic images of a set of alternative destinations from various non t ourism information sources. When the y desire to travel, they may get involved in active infor mati on search and consult specific information sources (Baloglu & McCleary 1999). As a result, they add another level of information to their destination image, which of the complex image to conceptualize the formation of destination images. A complex im a ge is formed when a tourist visit s a destination. In addition, Fakeye and Crompton investigated differences among prospective, first time visitors and repeat visitors in images of the Lower Rio Grande Valley in Texas. Major differences among the three gro ups were found on five extracted image factors. For instance, repeat visitors perc eived the highest image level for social opportunities and attractions factor s followed by first time and prospective visitors Gartner (1993) also noted that the type and a mount of external stimuli (information sources) received influence s the formation of image. Measurement of I mage The measurement of destination image s has been of great interest to tourism researchers and practitioners (Echtner and Ritchie, 1993). An accu rate assessment of destination im age is key to designing an effective marketing and positioning strategy (Echtner and Ritchie, 1993). Echtner and Ritchie (1991) identified two basic approaches to the measurement of image: structured and unstructured. The s tructured methodology involves va rious image attributes distinct and integrated into a standardized instrument, usually with a set of semantic differential

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19 or Likert scales. Most studies adopting a structured measurement tech nique have employed both dimen s ions that consist of cognitive and affective image s ( Baloglu & McCleary, 1999; Beerli & Mart n, 2004; Milman & Pizam, 1995). T he unstructured approach employs an alternative form of measurement using free form descriptions to measure image. These studies a rgue that measuring image by ity image. Some researches used an unstructured approach to measure destination image (Dann, 1996; Reilly, 1990). Echtner and Ritchie (1993) manifested th at a combination of structured and unstructured approach es are necessary to accurately measure destination image. They indicated that responses to open ended image questions provided more holistic characteristics of the destination image and allowed unique images of each destination under study to emerge. Murphy (1999) and Hsu, Wolfe, and Kang (2004) also employed a combination of structured and unstructured methods of destination image measurement and concluded that the dual approach provided insight into destination image s Ser vice Q uality Research concerning the nature and measurement of service quality and customer satisfaction is prevalent in the marketing literature (Cronin & Taylor, 1994; Oliver, 1980; Parasuraman, Zeithaml, & Berry, 1988, 1994). I n the tourism industry, customer perceptions of satisfaction service quality are important to successful destination marketing because of their influence on the selection of destination s (Ahmed, 1991), the consumpti on of goods and services at destination s an d the decision to return to a destination (Stevens, 1992). As a result, researchers have attempted to adapt service quality and customer satisfaction theories to hospitality and tourism industry settings. For example, researchers have tested the SERVQUA L

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20 framework in restaurant s (e.g., Bojanic & Rosen, 1994), lodging (e.g., Saleh & Ryan, 1992), and destination s (e.g., Pizam, Neumann, & R ichel, 1978). Quality has been considered to be one of the critical antecedents of both satisfaction (Baker & Crompto n, 2000; Caruana, Mondy, & Berthon, 2000 ; Cronin & Taylor, 1992, 1994 ) perceived value (B aker et al, 2002; Fornell et al., 1996; Petrick & Backman 2002) and to be a good predictor of repurchase intentions (Baker & Crompton; Getty & Thompson, 1994) The t heoretical justification for the linkages between quality, value, and satisfaction is derived from leads to an emotional reaction tha t, in turn, drives behav ior (Go tlieb, Grewal, & Brown, 1994). cognitively oriented service quality and value appraisals precede satisfaction ( Anderson, Fornell & Lehmann, 1994; Cronin & Taylo r, 1992 Gottlieb et al., 1994; Woodruff, 1997). By the same token, s ervice qual ity has been referred to a s a specific point in time, whereas satisfaction has involved both end state an d process judgments an d reflected emotional state s of mind created by exposure to service experience s (Baker & Crompton, 2000; Cronin & Taylor, 1994). Prior research has discussed that there is a relationship between quality and perceived value (Baker & Crompton, 2000; Cronin et al., 2000; Grewal, Monroe, & Krishnan, 1998; Parasuraman & Grewal, 2000 ; Zeithaml, 1988). This study proposes that quality is not embedded in perceived value, but it is a direct antecedent and is generally the best predictor of perceived value. Measur ement of Service Quality One of the most extensively used measures of service quality is SERVQUAL (Cronin and Taylor, 1992; Oh, 1999). The SERVQUAL questionnaire was developed by Parasuraman,

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21 Zeithaml, and Berry (1988) and conceptualizes service quality as the difference between if service performance meets expectations, the expectation is confirmed. Even though the SERVQUAL questionnaire has been extensively used to measure servi ce quality, many researchers have criticized its applicability (Oh, 1999). Research has shown that the SERVQUAL conceptualization of service quality (Cronin & Taylor, 1992) and the relevance of the disconfirmation of expectations as the basis for measuring service quality are inadequate (Carman, 1990). In the marketing literature, it has been suggested that a simple performance based measure of service quality is superior and that the current conceptualization confounds satisfaction and attitude (Cronin & T aylor, 1992). Cronin and Taylor (1992) proposed that service quality should be conceptualized and measured as an attitude. They further suggested that the SERVQUAL questionnaire is a better measure of service quality if used as a performance based measur e, without a comparison of expectations. They empirically examined the performance only model (SERVPERF) to SERVQUAL in four industries (banking, pest control, dry cleaning, and fast food) (Cronin & Taylor, 1992). By reducing the number of items from 44 to 22 (50%), t hey found that the structural model of SERVPERF was superior in all four industries,. Crompton and Love (1995) and Petrick and Backman (2002) also found that performance only measures are superior to contrast measures utilizing expectations. Recent conceptualizations suggest alternative measures of service quality are more appropriate than utilizing SERVQUAL (Baker & Crompton, 2000; Oh, 1999). Petrick and PERVAL scale measures quality based on Zeithaml (1988) definition

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22 excellence or superiority. Utilizing this definition, the resultant items that measure quality are related to the reliability of service. Given that reliability has consis tently been found to be the most important dimension of quality for recreation and tourism managers (Asubontegn, McCleary, & Swan, 1996; Backman & Veldkamp, 1995; Howat, Crilley, & Milne, 1995; 1994) and that performance only measures have been found to be superior to expectation disconfirmation measures (Cronin & Love, 1995; Petrick & Backma n, 2002), the current study operationalize s quality with the use of a quality dimension adapted from Cro nin et al., (2000) and Gallarza and Saura (2006) which are consistent with the SERVQUAL dimensions. Perceived V alue In recent years perceived value has been the object of attention by marketing managers and researchers as one of the most influential meas ures of customer satisfaction and loyalty (Cronin, Brady, & Hult, 2000; Eggert & Ulaga, 2002; Parasuraman, 1997; Parasuraman & Grewal, 2000; Patterson & Spreng, 1997; Sweeney, Soutar, & Johnson, 1996). Perceived value not only affects customer choice behav ior at the pre purchase stage, but also influences satisfaction and intentions to recommend and repurchase at the postpurchase stage (Parasuraman & Grewal, 2000). While some marketing researchers have examined the relationships among perceived value, custo mer satisfaction and/or behavioral intentions (Cronin et al., 2000; Patterson & Spreng, 1997; Woodruff, 1997), research on perceived value as related to customer satisfaction and/or behavioral intention s has not been given much attention in the tourism lit erature (Petrick & Backman, 2002). In previous literature, perceived value has been operationalized with a single item scale, such as value for money (Gallarza & Saura, 2006 ; Sweeney et al., 1996). The use of a single item scale does not address this conc ept in its entirety, as perceived value is constructed with

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23 multiple dimensions (Al Sabbahy, Ekinci, & Riley, 2004). Therefore, measuring multiple components of perceived value has been recommended by many researc hers (Gallarza & Saura, 2006 ; Snchez, Ca llarisa, Rodr guez, & Moliner, 2006 ; Sweeney & Soutar, 2001; Sweeney et al., 1996). The purpose of this study is to identify the multiple dimensions of perceived value for tion and recommendations to others, using a structural equation model. Definition of Perceived Value While perceived value has received increased attention, no clear and widely accepted definition of the concept exists (McDougall & Levesque, 2000; Zeithaml 1988). Perceived value has been conceptualized as: customer utility, perceived benefits relative to sacrifice, psychological price, worth and quality (Woodruff, 1997). This variability impedes consensus on its definition. Furthermore, perceived value var ies depending on the types of products or services offered (e.g., manufactured products or tourism products) and the personal characteristics of customers (Zeithaml, 1988). The role of perceived value in consumer behavior has received far less attention t han quality and customer satisfaction (Tam, 2000), perhaps due to the lack of adequate concept measures (Petrick, 2004). Perceived value is operationalized in some hospitality and marketing literature with a single item scale which attempts to measure over all customer value in terms of however, note that perceived value should not be viewed as the outcome of a trade off between a single overall quality and sacrifice, b ecause perceived value is more complex. Al Sabbahy et al. (2004) also insist that the single item scale does not fully address the concept of perceived value. Thus, many researchers have recommended that perceived value be measured with a multiple

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24 item sca le (Gallarza & Saura, 2006; S nchez et al., 2006; Sweeney & Soutar, 2001; Sweeney et al., 1996). One of the most frequently used definitions of perceived value is that of Zeithaml (1988). Zeithaml (1988, p. 13) identifies four aspects of value in an explo ratory study: (1) low price, (2) whatever I want in a product, (3) the quality I get for the price I pay, and (4) what I get for what I give. Then, Zeithaml (1988, p. 14) incorporates these four aspects of consumer value into one evaluation of what is received and what is given. Grewal, Monroe, and Krishnan (1998) separate perceived value into two components acquisition and transaction value s. They define perceived acquisition value as the perceived net gains from the products or services customers acquire. Perceived transaction value is defined as the perceived psychological satisfaction gained from getting a good deal. These definitions of perceived value are adopted by Al Sabbahy et al. (2004), who measured (with some modificat ion) the hospitality industry. Woodruff (1997) suggests that customers may perceive value differently at the stage of purchasing a product or service than during or after its use. With this notion, Woodruff developed a customer value hierarchy model. He a rgued that consumers may first desire a certain value (desired value) and subsequently evaluate the product or service as experienced (received value). Parasuraman and Grewal (2000) propose four types of perceived value: acquisition, transaction, in use a nd redemption value. They define the perceived acquisition and transaction

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25 values similarly to those of Grewal et al. (1998). The perceived in use value as defined as the utility gained from the use of the product and services, and the perceived redemption value as residual gain at the end of the life or the termination of services. They note that acquisition and transaction values occur during and immediately following the purchase stage, whereas in use and redemption values take place at a later stage. Thus, they imply that these types of perceived values are a dynamic construct which may change longitudinally. called PERVAL scale). They developed questionnaire it ems intended to measure four items of emotional value, (3) four items of price (functional value), and (4) four items of social value. The r esults of this study indicate that these multiple value dimensions performed better s Following Sweeney and Soutar (2001) study S nchez et al. (2006 ) developed perceived value more extensively in terms of functional, emotional and social values in a tourism package product. An initial 40 items of perceived value were reduced to 24 items and grouped into six dimensions of values: (1) four relating to functional value s of the travel agency (insta llations), (2) four relating to functional value s of contact personnel of the travel agency (professionalism), (3) four relating to functional value s of the tourism package purchased (quality), (4) three relating to the functional value of prices, (5) five relating to emotional value s and (6) four relating to social value s In sum, a review of the literatur e a ssures us that multiple items of perceived value explain tourist satisfaction and choice of a destination better than single item s of perceived valu e. I tems of perceived value have been identified as forms of emotional, functional and overall value

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26 which can be applied to the value attached to specific destinations. For example, the functional value for a destination can be measured by the following i tems functionally affect the perceived value of visiting that destination. If visiting the place value. Additionally, tourists can evaluate their overall value of visiting tou rism destinations by The following section reviews previous research on how perceived value is related to customer satisfaction and behavioral intention s in marketing and tourism literature. Relationship among Perceived Value, Satisfaction and Behavioral Intentions Perceived value has been found to be a s ignificant predictor of customer satisfaction and behavioral intention s (Cronin et al., 2000). Ravald and Gronroos (1996) suggest that value is regarded as an important construct of relationship marketing, and one of the most successful competitive strateg ies. A s the most important measure for gaining a competitive edge, perceived value is considered to be an important predictor and the key determinant of customer satisfaction and loyalty (McDougall & Levesque, 2000; Parasuraman & Grewal, 2000; Petrick & Ba ckman, 2002). Woodruff (1997) contends that measures of received (attribute) value are antecedents to overall customer satisfaction, and these measures are proven to correlate well with such customer behaviors as word of mouth and intention s to purchase. D odds (1991) also conceptualized a model where perceived value is the link between perceived quality, perceived sacrifice, and behavioral intention s

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27 Cronin et al. (2000) exa mined the relationship between service quality, service value, satisfaction and be havioral intention s in six industries including spectator sports, participant sports, entertainment, fast food, healthcare and long distance ca rriers. The results show that service value influences customer satisfaction and behavioral intention s (in all in dustries except health care). Service value is also found to be indirectly related to behavioral intention s through customer satisfaction. Patterson and Spreng (1997) develop ed a conceptual model to test the relationship s among performance, value, satisfac tion and behavioral intention s in the consulting industry based on functional value s They define d functional value as performance (quality) and price (sacrifice). The results of the study indicate d that value had a strong and significant effect on satisfa ction, which indirectly affected repurcha se intentions. However, value was not found to directly affect repurchase intentions. McDougall and Levesque (2000) investigate d the relationship among three elements of value (core quality, relational quality and service value), customer satisfaction and future intentions across four services (dentist, hairstylist, auto repair and restaurant). The results reveal ed that all three variables of core quality, relational quality and service value significantly affect ed customer satisfaction which subsequently affected future intentions. The findings indicate d that perceived value had the largest impact on potential demand for restaurants, followed by auto repair, dentist and hairstylist. This implies that restaurant mana gers should be concern customers. Thus, they recommend researchers incorporate perceived value into con ceptual models to understand key determinant s of customer satisfaction and loyalty.

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28 Eggert and Ulaga (2002) prop ose d two types of conceptual models. The first model is related to the mediated impa ct model, which aims to test relationships among customer perceived value, satisfaction and repurchase and wo rd of mouth. The second model was related to the direct model which aimed to test the direct relationship s between perceived value, repurchase, and word of mouth without satisfaction. The researchers conceptualize d and measure d perceived value as a cognitive variable, satisfaction as an affective construct and repurc hase and word of mouth as cognitive constructs. The test results of the mediated impact model indicate that customer perceived value had a significant positive impact on satisf action, which in turn influenced repurchase and willingness to initiate word of mouth recommendations The test results of the direct impact model also indicate that customer perceived value has a significant positive impact on repurchase and word of mouth. The findings indicate that all substantive relationships in both models are statistically significant, but the mediated impact model performs better than the direct impact model. Petrick, Morais, and Norman (2001) examine d the relationship s between past visits, perc eived value and satisfaction to intention s to revisit a destinati on. The results show that all three variables have an effect on revisit intentions to the destination, but these variables have no effect on intention to revisit for a show or to book a package. The findings suggest that perceived value along with th e othe r two variables are good predictor s of revisit intentions to the destination. Based on the definition of perceived value suggested by Grewal et al. (1998), Petrick and Backman (2002) investigate d the relationship between the construct of perceived value ( study show that the relationship between transaction va lue and intentions to revisit was

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29 statistically significant. However, the relationship between acquisiti on va lue and intentions to revisit was not significant. The researchers also examine d the effect of demographic variables on only age had an effect on perceiv ed value, with older participants reporting lower value s These results appear to be somewhat different from those in the study of Grewal et al. (1998), which found that both acquisition and transaction value had a significant effect on willingness to buy in two data sets (student group and employee group), but transaction value had no effect on willingness to buy in employee group data. Based on the test results of the relationship between value and willingness to buy, the study by Sweeney et al. (1996) al so demon strated that perceived value had the greatest influence on willingness to buy. Al Sabbahy et al. (2004) test ed the validity and reliability of the perceived value dimensions (acquisition and transaction values) in the evaluation of the hospitality industry proposed by Grewal et al. (1998). The results of the study show that the correlation between overall value, perceived acquisition value and transaction value was extremely high, indicating that both scales measured the same construct. Also, only one factor was extracted when the discriminant validity of both scales was tested using principal component analysis. The findings cast doubt on the discriminant validity of the scale and the multidimensionality of perceived value (acquisition and transact ion value). As justification for the lack of discrimination between the two types of values, the researchers suggest ed that transaction value might be confused with acquisition value. Gallarza and Saura (2006 ) explore d the relationship among perceived valu e, satisfaction and loyalty for university students. The results of the study indicate d that perceived value was action, which in turn influenced their loyalty.

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30 To this point the literature review suggests that per ceived value has a significant effect on customer satisfaction, which in turn influences behavioral intentions such as word of mouth intentions and intention s to purchase. Based on the past research findings, this study propose d a conceptual model (see f ig ure 1). Satisfaction Definition of Satisfaction Satisfaction is another critical concept that has received much attention in general consumer behavior research as well as tourism research because it influences the choice of destination and the understandin g of satisfaction provides managerial guidance in the industry and th other hand, Oliver (1997) defined satisfaction as customer judgment s about products or service fulfillment. Exis ting literature has indicated wide variability in the definition s of satisfaction. The lack of agreement among these definitions impedes research into consumer satisfaction. After making a thorough literature review of conceptual and operational definitions, Giese and Cote (2000) conclude d three general compo nents were shared by the definitions: (1) consumer satisfaction is an emotional response; (2) the response refers to a specific focus; (3) the response is determined by limited time. With these in mind, the authors identify that specific definition s of con sumer satisfaction should be used based on the context, taking into account the above dimensions. Various frameworks and theories have been developed over the years to explain customer satisfaction. Most of the studies conducted to evaluate consumer satis faction have utilized models of expectation/disconfirmation, which postulates that satisfaction is a result of the

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31 discrepancy between expectations and perceived performance, so that the consumer will feel satisfied whenever performance exceeds expectation s According to the expectation/disconfirmation model contributed by Oliver (1980), consumers develop expectations about a product before purchas ing and subsequently, they compare their actual experience with expectations. A positive disconfirmation occurs if the actual performance/experience is higher than their expectation ; that is the consumer is highly satisfied and wil ling to purchase the product again. However, if the actual performance is below his/her expectations, this leads to negative disconfirma tion, which means that the consumer is unsatisfied and will most likely look for other alternatives for the next purchase. Research by Chon (1989) also contended that tourist satisfaction is a function of the goodness of fit between tourist expectation s ab out the destination and the perceived evaluative outcome of the experience at the destination area, which is simply the result of a comparison between their previous images of the destina tion and what they actually see, feel, and achieve at the destination With reference to the measurement of consumer satisfaction, there is wide acceptance of multiple item measures. Still, many studies have operationalized overall satisfaction, using a single item scale ranging from very unsatisfied to very satisfied ( Cronin and Taylor, 1992; Howat, Murray, and Crilley, 1999; Parasuraman et al., 1994). Bigne et al. (2001) argued that tourist satisfa ction can be measured either using specific service elements or a single global item. n justified by empirical studies that used a single item measuring overall satisfaction in the tourism literature (Castro et al., 2007; Chen and Tsai, 2007). However, based on the notion that satisfaction is a multi dimensional construct, multi item scales have been frequently used in marketing (Oliver and Swan, 1989; Parasuraman et al., 1994) and tourism (Lee et al., 2007; Yoon & Uysal., 2005). Even though understanding satisfaction has

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32 generally been based on subjective disconfirmation theory (Oliver, 19 80), measuring satisfaction has remained complex and indeterminate (Lee et al., 2005). Relationship between Satisfaction and Destination Loyalty High levels of satisfaction at the destination results in increased loyalty and future revisit s and an enhan ced reputation, and then ultimately enhanced profitability and political support (Baker & Crompton., 2000). In tourism and leisure research, destination loyalty ha s been measured as a making. Generally, thr ee forms of measuring destination loyalty have been identified: 1) intention to re/visit, 2) recommend to others, and 3) word of mouth. The relationship of satisfaction to destination loyalty has been well researched in the tourism and leisure literature. Overall, satisfaction has been found to have a substantial impact on destination loyalty such as intention s to visit/revisit and recommend to others (Bigne, Sanchez and Sanchez, 2001; Castro, Armario, and Ruiz, 2007; Chen and Tsai, 2007; Crompton and Love 1995; Lee, Graefe, and Burns, 2004; Parasuraman, Zeithaml, and Berry, 1994; Yoon and Uysal, 2005). Although it was found that satisfaction affected destination loyalty directly (Crompton et al., 1995; Lee et al., 2005), a majority of studies found that s atisfaction played a mediating role in the relationship between destination image and revisit intentions (Bigne et al., 2001), motivation and destination loyalty (Yoon & Uysal, 2005), perceived value and recommending to other s (Lee et al., 2007), overal l trip quality and willingness to recommend (Chen & Tsai, 2007), service quality and intention to visit (Castro et al., 2007), and destination image and destination loyalty (Chi & Qu, 2008). Bigne et al. (2001) examined the relationships among destin ation image, perceived quality, satisfaction, and behavioral intentions using tourists who visited two famous resorts. The results of the study showed th at satisfaction mediated the relationships between destination

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33 image, service quality and behavioral in tentions. Yoon and Uysal (2005) developed a conceptual model to test the relationships between motivation, travel satisfaction, and destination loyalty in the context of vacation traveling. Based on a motivation theory suggested by Dann (1981), the authors divided motivation into push and pull motivations. The results of the study indicated that both motivations had effects on travel satisfaction, which in turn affects destination loyalty. Lee et al. (2007) investigated the relationship between perceive d value, satisfaction, and word of mouth in the context of traveling to an unexplored and historical site (i.e., demilitarized zone). The results revealed that perceived values (functional, overall, and emotional) were found to be related to tour satisfact ion, which in turn influenced word of mouth. This study confirmed previous studies that indicated that satisfaction was an important construct when considering the ; Cronin et al., 2000). Chen and Tsai (2007) examined how destination image and evaluative factors such as trip quality and satisfaction affect behavioral intentions for vacationers. The results showed that destination image had a direct effect on beha vioral intentions through trip quality and satisfaction, which in turn affected behavioral intentions. Utilizing a market heterogeneity concept Castro et al. (2007) tested a conceptual model that examined the relationship among destination image, service quality, satisfaction, and future behaviors. On the basis of levels of need for variety (Chen & Paliwoda, 2004), the authors classified the conceptual model into four nested models. The results showed that in all classifications, except for high need for v ariety but separate in time, destination image had indirect effects on behavioral intentions through tourist satisfaction levels. Chi and Qu (2008) explored the theoretical relationship among destination image, attribute satisfaction, overall satisfaction, and behavioral loyalty for visitors to a famous spring. The

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34 results indicated that overall satisfaction had been found to have partial mediation in the relationship among destination image, attribute satisfaction, and behavioral loyalty. In sum, a review of previous research suggests that satisfaction has not onl y a direct affect on consumer loyalty, but also plays a mediating role in the relationship s between various psychological tendencies (e.g., perceived value, destination image, and service quality) and futur e tourism behavior s (e.g., intent ion to revisit, recommend to others or word of mouth). Based on past research findings, this study propose d a conceptual model (see f ig ure 1). Destination L oyalty Definition of Destination Loyalty Although the loyalty concept has been extensively investigated in the marketing literature, destination loyal ty has received relatively little attention Smith (1998) suggests that loyalty occurs when the cus tomer feels so strongly that s/he can best meet his or her r elevant needs that the competition is virtually excluded from consideration and customer s buy almost exclusively from their favorite restaurant or hotel. Shoemaker and Lewis (1999) claim that loyalty is the likelihood of customer s returning to a place and those customer s ness to behave as a partner with the organiz ation (e.g., spend more while in that area and tell management when problems occur). repatronize a pr eferred product or service consistently in the future, despite situational influences manifests the two different aspects of brand loyalty: behavioral and attitudinal loyalty Behavioral brand loyalty is made up of repeated patronage of the brand loyalty includes a degree of dispositional commitment in terms of some unique value associated titudinal brand loyalty is investigated

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35 by intention of word of mouth communications (Mangold & Miller, 1999), repurchase intention s (Cronin & Taylor, 1992), and willingness to pay premium price s (Zeithmal, Berry, & Parasuraman 1996). More specifically, loyalty has generally been measured in on e of the following ways: (1) a behavioral approach, (2) an attitudinal approach, or (3) a combination approach. The characte rized as a sequence purchase, proportion of patronage, or probability of purchase. It has been debated that the measurement of this approach lacks a conceptual standpoint, and produces only the static outcome of a dynamic process (Yoon & Uysal, 2005). This loyalty measurement does not attempt to explain the factors that affect customer loyalty (Yoon & Uysal, 2005). That is, tourist loyalty to products or destinations may not be enough to explain why and how they are willing to revisit or recommend these to other potential tourists. On the other hand, in the attitudinal approach, consumer loyalty is an attempt on the part of consumers to go beyond overt behavior and express their loyalty in terms of psychological commitment or statement of preference. Touris ts may have favorable attitude s toward s a particular product or destination, and express their intention to purchase the product or visit the or product, as well as explain ing an additional portion of unexplained variance that behavioral approaches do not address (Backman & Crompton, 1991; Yoon & Uysal, 2005) Specifically repurchase intentions or recommendations to other people are usually considered to be two most importa nt consumer loyalty indicators in the marketing literature. Flavian, Martinez and Polo (2001), examining the different characteristics associated with store loyalty in the grocery sector,

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36 suggested that degree of loyalty has been one of the crucial baromet ers used to measure t he success of marketing strategies Similarly tourism destination s can be considered as products, and tourists m ay revisit or recommend destinations to other potential tourists such as friends and relatives (Yoon & Uysal, 2005) In t he last decade, tourism or leisure researchers have incorporated the concept of consumer loyalty into tourism products, destinations, or leisure/recreation activities (Backman & Crompton, 1991; Baloglu, 2001; Iwasaki & Havitz, 1998; Pritchard & Howard, 199 7). However, evaluating the usefulness of the concept of loyalty and its applications to tourism products or services has been limited, even though loyalty has been thought of as one of the major driving forces in competitive market s (Yoon & Uysal, 2005) Loyalty Versus Satisfaction Numerous researchers have investigated the relationship between customer satisfaction and brand loyalty (Back & Parks, 2003). It is generally believed that satisfaction leads to repeat purchase s and positive word of mouth recom mendation s, which are primary indicators of loyalty. However, customer loyalty is not the same as customer satisfaction. Customer loyalty is often recognized as being a strategic objective for companies (Oliver, 1999). According to Petrick and Sirakaya (20 04), customer loyalty is clearly a critical aspect for companies because it is more desirable, and less expensive, to retain existing customers than to seek new ones. On the other hand, Oliver (1997) proposes that the real value of measuring customer satis faction is the consumption responses. That is, customer customer loyalty measures how likely a customer is to re purchase and engage in partnership activities. Therefore, it is necessa ry to understand satisfaction as a necessary but not a sufficient

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37 condition for loyalty. In other words, customers can have satisfaction without loyalty, but it is hard to have loyalty without satisfaction. Operationalization of L oyalty The theoretical model presented in f ig ure 1 includes two frequently used behavioral intention variables: intentions to revisit and word of mouth communication s The following classification is an elabor ation of the two proposed behavioral intention constructs included in the study and their relationships with the evaluation variables. Intention to R evisit Cronin, Brady, and Hult (2000) have argued that quality not only has a direct relationship to beha vioral intentions, but also is mediated by perceived value and satisfaction in the prediction of consumer behavioral intentions. In addition, some empirical studies have concluded that service quality has a direct effect on behavioral intentions (Cronin et al., 2000; Zeithaml et al., 1996), while others have reported that quality has an indirect effect on behavioral intentions through satisfaction (Anderson et al., 1994; Brady et al., 2001; Cronin & Taylor, 1992). Also some empirical studies have reported that perceived value and satisfaction are direct antecedents of behavioral intentions (Cronin et al., 2000; Tam, 2000) Babin et al. (2005) in their study of restaurant patrons in Korea with structural eq uation analyses, demonstrated that both satisfactio n and perceived value had positive and significant effects on positive word of mouth communication s. Other research has suggested that perceived value may be a better predict or of repurchase intentions than either quality or satisfaction (Cronin et al., 20 00; Oh, 2000). In a study of fine s perceptions of value seem to be powerful indicators of when customers expected high value, they express ed strong intentions to patronize the restaurant.

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38 In the tourism literature, a significant relationship among tourist satisfaction, intention to return and positive word of mouth communication has been found (Hutchinson, et al., 2009). In addition, satisf ied tourists are most likely to recommend destinations they have visited to their friends and relatives (Yoon & Uysal, 2005). Kozak and Remington (2000), in a study of tourists visiting Mallorca, Spain, found that the more satisfied the tourists were with their visits, the more likely they were to return and recommend the destination to others. Further, satisfied tourists were more likely to recommend holidays in Mallorca that replicated their visits to the destination. A significant correlation also was fo und between intention to recommend and intention to visit the destination. Thus, tourists were more likely to recommend the destination to others if they intended to revisit. In a study of entertainment travelers, Petrick et al. (2001) reported that perc s to revisit an entertainment destination. In a study using a sample of golf travelers, Petrick and Bachman (2002) used simple bivariate correlation to determine that overall satisfa ction was highly correlated with intention s to revisit. Word of M outh The importance of word of mouth (WOM) communications for service firms has been well established (Mangold & Miller, 1999). Although WOM can be positive and negative, marketers are mor e interested in promoting positive WOM, such as recommendations to others. Service quality has been suggested to have a direct effect on word of mouth communication s Thus, when customers have positive and/or beneficial service experiences, they should be motivated to encourage their friends and family members to have the same experience (Babin et al., 2005) In a study of hotel guests, Hartline and Jones (1996) reported that high quality service increased word of mouth intentions. Boudling, Karla, Staelin, and Zeithmal (1993) empirically

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39 likely they were to engage in behaviors beneficial to the strategic health of the firm. Thus, service quality should be positivel y related to WOM praise. Perceive d value and satisfaction also are suggested to influence WOM directly (Brown et al., 2005; Fornell et al., 1996 ; Lee et al., 2007; Oh, 1999). Consumers who are satisfied with their service experiences may be motivated to spread positive WOM communication s to encourage others to have the same experience. Correspondingly, satisfaction has been suggested to relate positively to WOM (Mangold & Miller, 1999). It has been found that when WOM is more positive, consumers are more likely to make purchase recommendation s ( Mittal, Kumar, & Tsirosm, 1999 ; Wirtz & Chew, 2002). Conceptual M odel The proposed model is based on the comprehensive and critical literature review above First, the proposed model simultaneously examines the rela tionships of destination image, service quality, perceived value, satisfaction, and destination loyalty. The model suggests that destination image, service quality, perceived value, and satisfaction all have directional relationship s with each other and al so serve as antecedents to destination loyalty outcome. Second, in addition to the frequently used service evaluation constructs of quality, val ue, and satisfaction, the propose d model also posits that destination image is a consequent variable to perceive d value and an antecedent variable to both satisfaction and loyalty. Third, a relationship between the three service evaluation constructs (i.e., service quality, value, and satisfactio n) and two destination loyalty indicators (i.e., revisit intentions wo rd of mou th referrals) are also proposed.

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40 Figure. 2 1 Antecedents of Destination Loyalty Con ceptual B ackground and H ypotheses B ased on a comprehensive review of previous literature, destination image is defined as an individual mental representation of knowledge (beliefs), feeling s and overall perception s of a particular destination. Destination image affects the destination choice decision making process and also after decision making behaviors including on site experience, sat isfaction and future behavioral intentions (Bigne et al., 2001; Lee et al., 2005). An o n site experience can be mainly represe nted as perceived travel quality. However, these studies indicate the influence of destination image on after decision making beha viors has been limited. Following a marketing perspective, Lee et al. (2005) argued that individuals having a favorable destination image would perceive their on site experience (i.e. perceived quality) positively, which in turn would lead to greater satis faction level s and behavioral intentions. The first four hypotheses, therefore, would be: Destination Loyalty Service Quality Destination Image Perceived Value Satisfaction H7 H1 H2 H3 H8 H9 H10 H4 H6 H5

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41 H 1 : The more favorable the destination image, the higher the overall satisfaction H 2 : The more favorable the destination image, the higher the perceived value H3: T he more favorable the destination image, the higher the service quality H 4 : The more favorable the destination image, the more the destination loyalty. As aforementioned, service quality and perceive d value have been recognized as the antecedent s of sati sfactio n and behavioral intentions in the service field s In addition, quality, perceived value and satisfaction have been recognized as the antecedents of behavioral intentions The next nine hypotheses, therefore, would be: H 5 : Service quality has a dire ct positive effect on perceived value H 6 : Service quality has a direct positive effect on customer satisfaction. H 7 : Service quality has a direct positive effect on destination loyalty H 8 : Perceived value is a direct antecedent of overall satisfaction H 9 : Perceived value is a direct antecedent of destination loyalty. H 10 : Satisfaction has a direct positive effect on destination loyalty. H11: Destination image has an indirect influence on destination loyalty through satisfaction H12: Service quality has an indirect influence on destination loyalty through satisfaction H13: Perceived value has an indirect influence on destination loyalty through satisfaction

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42 CHAPTER 3 METHODOLOGY The method s used in this study are presented in the followin g four s ections: (1) construct measurement, (2) s tudy site and sample (3) procedures, and (4) data analyses. A survey was constructed to assess the influence of satisfaction on destination loyalty as mediated b y perceived value and service quality in the context of a tourist destination. Construct M easurement This study employed a causal research design using a cross sectional sample survey. The survey questionnaire was composed of the following major s ections: questions that measure the following constructs de stination image, service quality, perceived value, overall satisfaction, travel behavior s In order to avoid personal biases and suitably quantify qualitative data, 7 point scales were used in this study (Um, Chon, & Ro, 2006). Although a 5 point scale could be acceptable, a wider range allows more effective comparison analyses to more clearly show the differences between scores (Kozak 2001). (1) Destination I mage. A combination of structure d and unstructured techniques will be used to capture various s of Orlando as a travel destination, including a thorough literature revi ew of previous destination studies, content analyse s of tourism literature, promotion brochures, and Orlando websites Through this process, thirty two cognitive/perceptual evaluation items were generated and were measured on 1(s trong ly disagree) to 7 (s trongly agree) point Likert scale s A lso four affe c tive d estination image items were employed fro m Baloglu and McCleary (1999 ) with semantic differential scale s (Pleasant Unpleasant, Arousing Sleepy, Relaxing Distressing, and Exciting Gloomy) A composite score

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43 of four bipolar scales provides an overall affectiv e evaluation of a given destination (Baloglu & McCleary, 1999). Then, unstructured image questions were asked with respondents writing down the first three adjectives or nouns th at ca me to their mind s for Orlando (2) Service Q uality Service quality is m easured based on fifteen aspects of service quality, which are adapted from both Cronin et al. (2000), and Gallarza an d Saura (2006). The selected fifteen serv ice qualit y items wer e rated on 7 point Liker t scale s where 1 = very low and 7 = v ery high. Then the second measure consisted of three overall direct measure s of service quality that are adapted used elsewhere (Cronin & Taylor, 1992). These three items w ill be measured using semantic differential scale s (Excellent Poor, Superior Inferior, and High standards Low standards) (3) Perceive d V alue Perceived value measures were ad apted from Lee et al. (2007). Thirteen items divided into functional, emotional, a nd overall values were presented The respondents were asked to indicate the degree to which they agree d based on their visit to the area, and to what extent they agree d that their visit g a ve them su perior net value on each of thirteen items on a 7 point s cale, strongly disagree to strongly agree. (4 ) Overall S atisfaction To measure overall satisfaction, there have been debates on using multiple item measures vs. single item measures. A number of studies have used a summative overall measure of satisfacti on ( Lee et al., 2007 ; Oliver & Swan, 1989; Yoon & Uysal, 2005 ). M any studies have addressed overall satisfaction, using a single item scale ranging from very unsatisfied to very satisfied (Cronin and Taylor, 1992; Howat, Murray, and Crilley, 1999; Parasura man et al., 1994). However, a multiple item overall satisfaction scale was used in this study to capture more

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44 variance in explaining overall satisfaction The r esponden ts were asked to rate their satisfa ction with the ir overall travel experience on a 7 poi nt Liker t scale with 1 being strongly disagree and 7 being strongly agree (5 ) Destination L oyalty Attitudinal measurement s including revisit intentions and recommendations are usually used to infer consumer loyalty, and were found to be the pertinent m easure (Chi & Qu, 2008). Prior research has shown that loyal customers are more likely to repurchase a product/service in the future (Petrick et al., 2001; Sonmez & Graefe, 1998). It has also been suggested that loyal visitors are more willing to recommend the product/service to others (Shoemaker & Lewis, and positive WOM referrals (Oh, 2000; Oh & Parks, 1997). Therefore, repurchase and referral intentions make up t he Customer Destination Index (Taylor, 1998). In this study, six item measures were used to assess tourist destination loyalty as the ultimate dependent construct: s to revisit Orlando and their willingness to recommend Orlando as a favo rable destination to others, with 7 point Likert scale s (1 = strongly disagree ; 7 = strongly agree ). (6 ) Demographic I nformation For the purpose of sample description, demographic background variables are included in t he questionnaire, which consisted of the following variables: gender, ethnicity, age, marital status, household inco me, and education. Questions were phrased in a close ended multiple choice format. Study S ite and S ample The data for this study was collected using a self administered question naire in an Orlando area commercial airport Magic Kingdom; 12. Universal Studios Orlando/Islands of Adventure at Universal Orlando; 13.

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45 SeaWorld Orlando) in Orlando were listed on the top 25 most visited tourist destinations in the USA. Metro Orlando hosted 47.8 million visitors in 2006 In 2006, visitors contributed $29.8 billion in spending to the Metro Orlando area. According to the report, domestic leisure travel volume in 2006 was 34.5 millio n visitors The most popular reason reported for visiting Orlando was for a general va cation (31%), followed by a geta way weekend (14%), and visiting friends and relatives (13%). The average length of stay among overnight domestic leisure visitors was 4.2 nights. Non Florida residents stayed longer in Orlando (5.7 nights) than their Florida counterparts (2.3 nights). Metro Orlando also welcomed 2.7 million international visitors in 2006, of which 2.0 million were from oversea markets. The United Kingdom rem s top oversea s purposes (81%) while 8% were traveling for business or convention purposes. The population of this study consisted of tourists who travel e d individu ally or in groups during the Spring season To improve the representativeness of the sample, surveys were distributed at 12 different gates and different times (e.g., weekdays and weekends) and the interviewer used quota sampling to control for a ge, gender, and ethnic background s Given the experiential nature o f the study, respondents were only asked to respond to the survey if the y had travel experiences in the Orlando area. Procedures Following the development of items the preliminary question naire was submitted to a panel of eight experts for content vali dity testing. The panel included five university professors and three practitioners. Among the university professors, one specialized in natural resources, two in tourism and hospitality manag ement, and two in sport management. Among the practitioners, all three worked in Orlando travel agencies. Each panel member was asked to examine the relevance, representativeness, clarity, test format, and item content throughout the

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46 questionnaire. Followi ng the feedback, the preliminary questionnaire was modified, revised, and improved, mainly in the areas of item adequacy, factor relevance, and wording clarity. With the modified version of the questionnaire, a pilot study was conducted with Dunnellon (a small city 76 miles northwest of Orlando) Presby terian Chu rch members who had experience visiting Orlando within the past 6 months ( n = 26). The purpose of this pilot study was to further examine the content validity comprehension and estim ate the time needed to fill out the questionnaire by the general public After the pilot study suggested changes and improvements were minor and they were primarily related to wording clarifications. It took an average of 11 minute s to fill out the questi onnaire. The researcher first contacted the data collection location ( Sanford International Airport just north of Orlando ) to obtain permission to conduct the study. In a meeting with the marketing and public relation manager, a n action plan was submitted including a copy of the survey and agreement that results would be s hared with them. The main complication was having the data collector located beyond security checkpoint s This required getting the data collector badged; necessitating a full background check, fingerprinting, etc. It took two weeks for the proper authorizations to be acquired. Then the data collector took four hours of security classes. After passing those classes, he was finally permitted to interview travelers beyond security checkpoin t s (i.e., in boarding lounges near departure gates) over a four week period Data A nalyse s The Statistical Pa ckage for the Social Sciences 17 .0 (2008 ) was employed to calculate descriptive statistics for sociodemographics and normality checks in which va lues of skewness and kurtosis were evaluated. Explorato ry factor analysis (EFA) was employed to derive the underlying dimensions of destination image, and perceived value, primarily to identify unique and reliable simple factor structures that have the pot ential to be generalized to a universe of

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47 variables from a sample of variables, so as to reduce any redundant data. Analysis of Mom ent Structure (AMOS) 7.0 was also utilized to examine psychometric properties of the hypothesized model via confirmatory fact or analysis (CFA) and test the theoretical relationships of the model through structural equation modeling (SEM). A CFA using the Maximum Likelihood (M L) estimation method was employed f or the constructs of destination image service quality, perceived va lue, overall satisfaction, and destination loyalty respectively, to examine factor structures of the overall model (Bollen, 1989; Hair, Anderson, Tatham, and Black, 1998). In order to examine o verall goodness of mode l fit, the researcher follow ed the sugg estion of Hair et al. (1998 ) on using multiple fit indexes, which inclu ded the chi 2 ), the normed chi 2 / df ), root mean square error of approximation (RMSEA) standardized root mean residual (SRMR), and comparative fit index (CFI) (Ben tler, 1990; Bollen, 1989; Hu & Bentler, 1999; Steiger, 1990). The chi 2 ) examines the difference between the expected model and observed model (if any). The normed chi square is the chi square statistic per degree of freedom 2 / df ) (Kline, 2005). Bollen (1989 ) suggested that cutoff values of less than 5.0 for the normed chi square are considered a reasonable fit. Hu and Bentler (1999) indicated that any RMSEA values less than .06 indicate good fit. Any RMSEA values between .06 and .08 are considered an acceptable fit and values of RMS EA between .0 8 and .10 indicate a mediocre fit. Lastly, a cutoff value of .95 or higher for CFI in combination with a cutoff value (less than) .09 for SRMR was utilized (Hu & Bentler, 1999). A total of three reliability tests were used to determine internal consiste ncy of the items ), (b) Construct Reliability (CR), and (c) Average Variance Extracted (AVE). The suggested .70 was applied to

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48 determine internal consistency of and CR (Forne ll & Larcker, 1981; Nunnally & Bernstein, 1994 ) and .50 was adopted for evaluating AVE value. In order to examine convergent validity of the hypo the sized model, two methods were carried out: (a) examination of factor loadings and (b) examination of criti cal ratios (Kline, 2005). Generally, item loadings equal to or greater than .707 are considered good convergent validity since it indicates that more than 50% of the variance is associated with common variance (Anderson & Gerbing, 1988). Another way to d etermine convergent validity is to evaluate critical ratio values. Any values exceeding 2.58 for a two tail test are considered statistically significant at the .001 level (Arbuckle, 2006). An examinat ion of discriminant validity was conducted using two m ethods: (a) examination of interfactor correlation (Kline, 2005 ) and (b) comparing AVE values with squared correlation of any of two latent constructs ( Fornell & Larcker, 1981). Discriminant validity is established when interfactor correlation does not ex ceed .85. A more conservative discriminant validity test is when the squared correlation between any of two constructs is lower than the AVE value of either construct. Finally, a SEM test was conducted to examine the hypothesized structural rel ationships a mong destination image, service quality, perceived value, overall satisfaction, and destination lo yalty. Path coefficients were used to determine the direct and indirect relationships among the factors.

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49 CHAPTER 4 RESULTS The results of the study are prese nted in the following order: (a) descriptive statistics (b) data screening and test of assumptions (c) exploratory factor analyses (d) confirmatory factor analyses, and (e) structural equation model analyses. Descriptive Statistics Demographics P articipant s ( n = 581) demographic characteristics are presented in Table 4 1. The majority of the participants were women (59%). The average age was 38 years old ( M = 38.37, SD = 14) and 94 % of part icipants were Caucasian The average household income was above $60 ,000. However, one third of the average household income was above $100,000 (32%). Well less than half (38%) of the participants were visiting Orlando for the first ti me in the last three years, and 27% of participants visited the destination twice during the same period. The type of travel companions most often reported were family with children (53%), followed by family without children (22%), and friends (19%). The most popular r easons reported for visiting Orlando were: vacation/pleasure (80%), followed by visit ing friend s and relatives (9.3%), and business and professional event s (3%). P a rticipants reported visiting Disneyworld, Universal Studios Orlando, and SeaWorld during their visit The more frequent duration of stay s i n Orlando were 5 nights (21%) followed by 4 nights (17%), 7 nights (15%), 3 nights (14%), and 6 nights (11%). Destination I mage Descriptive statistics including mean and standard deviation for the destination image variables are presented in Table 4 2. The means of the destination image variables ranged from 4.73 (Orlando had many traffic jams) to 6.26 (Orlando was a pleasing travel destination) with 7

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50 All variables (32) had mean score s greater than 4.0 (i.e., midpoint on the 7 point Likert scale), indic ating that destination image variables were considered important when making a decision to visit the Orlando area Standard deviations ranged from .89 to 1. 71 Service Q uality Table 4 3 displays the descriptive statistics for service quality. Means of al l items for Service Quality were above 5.00 (4.0 mid point), indicating that service quality variables were evaluated positively by Orlando visitors and ranged from 5.43 ( SD = 1.14) for the item SD = .87) for ranged from .87 to 1.14. Perc eived V alue Descriptive statistics for perceived value are depicted in Table 4 4. All items h ad mean score s greater than the midpoint of 4 on the 7 point Likert scale, representing that perceived value s were generally agreed with when evaluating their Orlando trip The M = 6. 08; SD = .91) M = 4.74; SD = 1.38), though it was generally agreed with. Standard deviations for the items ranged from .91 to 1.41. Satisfaction Descriptive statistics for satisfaction are sho wn in Table 4 5. Means of all items for Satisfaction were above 5.00 (4.0 mid point) on the 7 point Liker t scale, indicating that the three satisfaction measures indicated overall satisfaction with their Orlando area experience. The M = 5.88; SD = .99) and

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51 score ( M = 5.65; SD = 1.02), which was still positive. Standard deviations for the items ra nged from .99 to 1.02. Desti nation L oyalty Table 4 6 displays the descriptive statistics for destination loyalty. The m eans of all six destination loyalty items were above the mid poin t on the 7 point Likert scale, demonstrating that the respondents ten ded to agree with indicators of loyalty to Orlando which ranged from 5.25 ( SD 5 91 ( SD = 1.13 I will say positive things about visiting Orlando to other people andard deviations for the items ranged from 1 13 to 1. 85 Data Screening and Test of Assumptions for Structural Equation Modeling (SEM) The assumptions of multivariate normality and linearity were evaluated through descriptive statistics using SPSS and CF A using AMOS The analyses were performed on the total sample ( N = 581). No standardized score for any variable was above 3.29 and no standardized score was below 3.29, which were the suggested cut off values for potential outliers (Tabachnick & Fidell, 2 007). Skewness and kurtosis values for the 69 manifest variables ranged from .459 to 1.490 and .055 to 2.904, respectively, which were well within the acceptable threshold s There was no evidence that both univariate and multivariate normality assumpti ons for observed variables were violated. Exploratory Factor Analyses Destination I mage An exploratory factor analysis using a principal component extraction met hod and a varimax rotation of 33 destination image items was conducted on a random sample ( n = 290) from the 581 total respondents Prior to running the a nalysis with SPSS, the data was screened by examining descriptive statistics on each item, interitem correlation, and possible univari ate and

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52 multivariate assumption violations. From this initial assessment, all variables were found to be interval like, variable pairs appeared to be normally distributed, and all cases were independent of one another. The Kaiser Meyer Olkin measure (KMO) of sampling adequacy was .922, indicating that the present dat a were suitable for principal component analysis. Similarly, p < .001), indicating sufficient correlation between the variables to proceed with the analysis. Using the Kaiser Guttman retention criterion o f eigenvalues greater than 1.0, a six factor solution provided the extraction. The scree plot test also suggested that a six factor model was the most interpretable. These six factors accounted for 64% of the total variance. Table 4 7 presents 24 items, th eir factor loadings communality estimates lpha s Communalities were fairly high for each of the 24 items, with a range of .54 to .80 c oefficient alpha ranged from .81 to .91 among the six factors, indicating that they were all internally consistent and reliable. Based on the predetermined criterion of an item loading equal to or greater than .40, one item was eliminated (i.e., Orlando was a safe place to visit). Eight other items were removed due to hav ing only one or two item s load on the respective factors (i.e., Orlando had many interesting places, Orl ando offered good nightlife and entertainment Orlando offered easy access to the area where I wanted to visit, Orlando offered appealing local food, Orlando offered convenienc e of local transportation, Orlando was a relaxing place, Orlando was a restful place, and Orlando offered variety of special events/festivals). Consequently, the six remaining factors were labeled as Travel Atmosphere (8 items), Travel Information (3 items ), Travel Envi ronment (4 items), Shopping (3 items), Community Attitude (3 items), and Accessibility (3 items). The rational used in naming these six factors was guided in part by the recommendations of Chi and Qu (2008) in

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53 which sorted factor loadings in interpreting each factor. The present six factor model was deemed the best solution because of its conceptual clarity and ease of interpretability. Service Q uality An exploratory factor analysis u sing a principal component extraction met hod and a varimax rotation of 15 service quality items was conducted on a random sample ( n = 290) of the 581 total respondents Prior to running the a nalysis with SPSS, the data was screened by examining descriptive statistics on each item, interitem correlation, and possible univari ate and multivariate assumption violations. From this initial assessment, all variables were found to be interval like, variable pairs appeared to be normally distributed, and all cases w ere independent of one another. The Kaiser Meyer Olkin measure (KMO) of sampling adequacy was .947 of Sphericity (BTS) was 3218.98 ( p < .001), indicating s ufficient correlation between the variables to proceed with the analysis. Using the Kaiser Guttman retention criterion of eigenvalues greater than 1.0, a two factor solution provided the extraction. The scree plot test also suggested that a two factor mo del was the most interpretable. These two factors accounted for 68 % of the total variance. Table 4 8 presents 15 items, their factor loadings communality estim lphas. Communalities were fairly high for each of the 15 items, with a rang e of .57 to .76 coefficient alpha for the factors were .95 and .89, respectively, indicating that they were both internally consistent and reliable. Perceived V alue An exploratory factor analysis using a principal component extraction method a nd a varimax rotation of 13 service quality items was conducted on a random sample ( n = 290) of

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54 the 581 total respondents Prior to running the analysis with SPSS, the data were screened by examining descriptive statistics on each item, interitem correlati on, and possible univariate and multivariate assumptions violations. From this initial assessment, all variables were found to be interval like, variable pairs appeared to be normally distributed, and all cases were independent of one another. The Kaiser M eyer Olkin measure (KMO) of sampling adequacy was .910, indicating that the present data were suitable for principal component analysis. Similarly, p < .001), indicating sufficient correlation between the va riables to proceed with the analysis. Using the Kaiser Guttman retention criterion of eigenvalues greater than 1.0, a two factor solution provided the extraction. The scree plot test also suggested that a two factor model was the most interpretable. Thes e two factors accounted for 62% of the total variance. Table 4 9 presents 11 items, their factor loadings, commun lphas. coeffici ent alpha for the factors were .92 and .79, respectivel y, indicating that they were both internally consistent and reliable. Two items were removed due to having only one or two items loaded on the respective factors (i.e., T he level of service I experien ced was a good value, and Compared to other tourism destinations I have visited, Orlando was a good value for the money ). Consequently, the two factors w ere labeled as Overall Value ( 8 items), and Financial Value (3 items). Loyalty An exploratory factor an alysis using a principal component extraction method and a varimax rotation of 6 loyalty items was conducted on a random sample ( n = 290) of the 581 total respondents Prior to running the a nalysis with SPSS, the data was screened by examining descriptive statistics on each item, interitem correlation, and possible univari ate and multivariate

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55 assumption violations. From this initial assessment, all variables were found to be interval like, variable pairs appeared to be normally distributed, and all cases we re independent of one another. The Kaiser Meyer Olkin measure (KMO) of sampling adequacy was .844, indicating that the (BTS) was 1673.84 ( p < .001), indicating su fficient correlation between the variables to proceed with the analysis. Using the Kaiser Guttman retention criterion of eigenvalues greater than 1.0, a two factor solution provided the extraction. The scree plot test also suggested that a two factor mod el was the most interpretable. These two factors accounted for 89% of the total variance. Table 4 10 presents 6 items, their factor loadings, communality estimates and Cr lphas. Communalities were extremely high for each of the 6 items, with a ran ge of .85 to .92 rs were .94 and .92 respectivel y, indicating that they were both internally consistent and reliable. Measurement M odel s : Confirmatory Factor Analyses Destination I mage Prior to testing the struct ural equation model of destination image, service quality, perceived value, satisfaction, and loyalty, confirmatory factor analysis (CFA) of each construct was used to determine which variables should be included in the models based on good fits. The secon d data set for the destination image variables was submitted to a CFA, using Maximum Likelihood (ML) estimation Goodness of fit indexes revealed that the six factor and 24 item measurement model did not fit the data well (Table 4 11). The chi square stati stic had a significance level of 0.0 1 This statistic failed to support the criterion that the differences between the proposed model and the observed data were non significant The chi square statistic is used to test for difference s between the predic ted and the observed relationships

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56 (correlations/covariances). However, it is generally agreed that the 2 value should be used as a guide rather than an absolute index of fit due to its sensitivity to sample size and model complexity (Kline, 2005) Therefore alternative fit indices were examined, including the normed ch i square, RMSEA, SRMR, CFI and G FI. A value of the normed chi square ( 2 / df = 3.03 ) was slightly above the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the six factor model showed a mediocre fit (RMSEA = .8 to .10; Hu & Bentler, 1999). Although the value ine, 2005), the CFI value of .89 was lower tha n the recommended cut off ratio (>.90; Hu & Bentler, 1999), indicating an overall lack of fit to the data. The model fit tests suggested a need f or respecification. According to Tabachnick and Fidell (2007) model respecification is need ed if the proposed model does not fit the data well. Poor indicator loadings also supported model respecification. Adopting a conservative criterion in order for a scale to possess good convergent validity, an indicator loading should be equal to or greater than .707 (Anderson & Gerbing, 1982 ). Of 24 items, three were below .707, indicating a lack of conve rgent validity. Therefore, t hree items were eliminated ( Orl and o was a good place to travel, E verything was fascinating and Orlando had limited parking area s ). After careful consideration of both statistical and theoretical justifications, a decision was made to remove two more items, which were items (Orlando had ma ny traffic jams and Orlando was crowded) that formed the Accessibility factor, which is now eliminated As a result of t he model respecification, a five factor model with 19 items was concep tualized: Destination Atmosphere (6 items), Travel I nformation (3 items), Travel Environment (4 items), Shopping (3 items), and Community Attitude (3 items). This was consistent with the recommendations made by Bollen (1989) that each factor consists of at least

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57 three items. A five fa ctor model with 19 items was submitte d to a CFA. Overall goodness of fit revealed that the five factor model fit the data reasonably well (Table 4 11 ). The c hi square statistic was non significant ( 2 = 414.089 p < .079 ). The normed chi square ( 2 / df = 2.91 ) was lower than the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the f ive factor model had an acceptable fit (RMSEA = .069 ; Hu & Bentler, 1999). The SRMR (.0 65 ) was of a magnitude ( .10; Kline, 2005). CFI was .931 which was considered acceptable (Kline). TLI was 9 23, indicative of an acceptable mode l. Overall the model fit for the five f actor model improved substantially indicating acceptability. The con vergent validity of the measurement scale was examined via the following tests. F or each variable the t value associated with each of the loadings was significa nt at the 0.01 level (Table 4 16 ). The results indicated that all variables were significantly related to their specific constructs, verifying the posited relationships among indicators and constructs. The construct reliability (CR) and the average variance extracted (AVE) were also computed for the latent constructs. For both CR and AVE, all cons tructs surpassed the threshold value of .70 and .50, respectively. Therefore, it can be concluded that the five factors for the destination image construct were significant in terms of how the measurement model was specified. According to Fornell and Lar cker (1981), discriminant validity can be established when the AVE values were compared to the squared correlations between the corresponding constructs, and none of th e squared correlations surpass the AVE. These tests indicated that t he discriminant validity was upheld for the destination image construct. Service Q uality The second data set for the service quality variables was submitted to a CFA, using Maximum Likelihood (ML) estimation: Performance based quality (10 items) and Prod uct based

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58 quality (5 items). Overall goodness of fit revealed that the two factor model fit the data reasonably well (Table 4 12). The c hi square statistic was nonsignificant ( 2 = 215.979 p < .1 79 ), indicating that the model fits the data. The normed chi square ( 2 / df = 2. 43 ) was lower than the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the two factor model had an acceptable fit (RMSEA = .073 ; Hu & Bentler, 1999). The SRMR (.0 50 ) was of sufficient GFI was .9 34 as indicative of an acceptable model. Also NFI was .922, supporting the finding that this model fits the data. The convergent validity of the measurement scale was examined via the following tests. For each variable the t value associated with each of the loadings was significa nt at the 0.01 level (Table 4 18 ). The results indicated that all variables wer e significantly related to their specific constructs, verifying the posited relationships among indicators and constructs. The construct reliability (CR) and the average variance extracted (AVE) were also computed for the latent constructs. For both CR a nd AVE, all constructs surpassed the threshold value of .70 and .50, respectively. Therefore, it can be concluded that the two factors for service quality construct were significant in terms of how the measurement model was specified. According to Fornel l and Larcker (1981), discriminant validity can be established when the AVE value s for the latent constructs are compared to the squared correlations between the corresponding constructs, and none of th e squared correlations surpass the AVE. These tests in dicated that the discriminant validity was upheld for the service quality construct. Perceive d V alue The second data set for the perceived value variables was submitted to a CFA, using Maximum Likelihood (ML) estimation. Goodness of f it indexes revealed t hat the two factor and 11 item measurement model did not fit the data well (Table 4 13 ). The chi square statistics had a

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59 significance level of 0.0 1 This statistic failed to support concluding that the differences between the proposed model and the observed data were non significant. The chi square statistic is used to test the difference between the predicted and the observed relationships (c orrelations/covariances). Howeve r, it is generally agreed that 2 value should be used as a guide rather than an absolute fit index due to its sensitivity to sample size and model complexity (Kline, 2005). Therefore, alternative fit indices were further e xamined, including the normed chi squ are, RMSEA, SRMR, CFI and GFI. The v alue of the normed chi square ( 2 / df = 3. 386) was above the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RM SEA value indicated that the two factor model showed a mediocre fit (RMSEA = .8 to .10; Hu & Bentler, 1999). Although the value of SRMR (.09 ine, 2005), the CFI value of .902 was lower tha n the recommended cut off ratio (>.90; Hu & Bentler, 1999), indicating an overall lack of fit to the data. The model fit tests suggested a need for respecification. According to Tabachnick and Fidell (2007), model respec ification is need ed if the proposed model does not fit the data well. Poor indicator loadings also supported model re specification. Adopting a conservative criterion for the scale to have good convergent validity, an indicator loading should be equal to or greater than .707 (Anderson & Gerbing, 1982). Of the 11 items, four items were below .707, indicating a lack of conv e rgent validity. Therefore, four items were eliminated ( V isiting Orlando gave me pleasure, Orlando was a destination that I enjoyed, v isiting Orlando made me feel better, and after visiting Orlando my image of Orlando has improved. ). After careful consider ation of both statistical and theoretical justifications, a decision was made to keep the other 7 items. As a result of the model respecification, a two factor model with 7 items was conceptualized: Overall value ( 4 items), an d Financial value (3 items). This was consistent with

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60 the recommendatio ns made by Bollen (1989) since each factor consisted of at least three items. A two factor model with 7 items was further submitted to a CFA. Overall goodness of fit revealed that the two factor model fit the data reasonably well (Table 4 13 ). The c hi square statistic was nonsignificant ( 2 = 35.75 p < .058 ). The normed chi square ( 2 / df = 2. 75 ) was lower than the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the two factor model had an acceptable fit (RMSEA = .069; Hu & Bentler, 1999). The SRMR (.0 87) was acceptable 26 which acceptable (Kline 2005 ). GF I was .9 22 indicative of an acceptable mode l. Overall, the model fit for the two factor model improved drastically, indicating acceptability. The convergent validity of the measurement scale was examined via the following tests. For each variable the t value associated with each of the loadings was significant at the 0.01 level (Table 4 20 ). The results indicated that all variables were significantly related to their sp ecific constructs, verifying the posited relationships among indicators and constructs. The construct reliability (CR) and the average variance extracted (AVE) were also computed for the latent constructs. For both CR and AVE, all constructs surpassed th e threshold value of .70 and .50, respectively. Therefor e, it can be concluded that two factors for perceived value construct were significant in terms of how the measurement model was specified. According to Fornell and Larcker (1981), discriminant vali dity can be established when the AVE value s for the latent constructs are compared to the squared correlations between the corresponding constructs, and none of th e squared correlations surpass the AVE. These tests indicated that the discriminant validity was upheld for the perceived valu e construct. Loyalty The second data set for the loyalty variables was submitted to a CFA, using Maximum Likelihood (ML) estimation: Revisit Intentions (3 items) and WOM (3 items). The o verall

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61 goodness of fit revealed tha t the two factor model fit the data reasonably well (Table 4 14). The c hi square statistic was nonsignificant ( 2 = 15.235, p < .065), indicating that the model fits the data. The normed chi square ( 2 / df = 1.904) was lower than the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the two factor model had a good fit (RMSEA = .056; Hu & Bentler, 1999). The SRMR (.047) was acceptable CFI was .971, which was good (Kline 2005 ). GFI was .953 indicative of a good model. Also NFI was .965, supporting the finding that this model fits the data. The convergent validi ty of the measurement scale was examined via the following tests. For each variable the t value associated with each of the loadings was significant at the 0.01 level (Table 4 2 2 ). The results indicated that all variables were significantly related to the ir specific constructs, verifying the posited relationships among indicators and constructs. The construct reliability (CR) and the average variance extracted (AVE) were also computed for the latent constructs. For both CR and AVE, all constructs surpass the threshold value of .70 and .50, respectively. Therefore, it can be concluded that the two factors of the loyalty construct were significant in terms of how the measurement model was specified. According to Fornell and Larcker (1981), discriminant va lidity can be established when the AVE value s for the latent constructs are compared to the squared correlations between the corresponding constructs, and none of th e squared correlations surpass the AVE. These tests indicated that the discriminant validit y was upheld for the loyalty construct. Structural Model The most obvious examination of the structural model involves the significance tests for the estimated coefficients (paths), which provide the basis for accepting or rejecting the proposed relation ships between latent constructs. Prior to estimating path coefficients for the hypothesized structural model, a structural model with five constructs was estimated using Maximum

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62 Likelihood (ML) estimation. Although the chi square test was significant ( 2 = 1729.128, p < .001), the results yielded acceptably high good ness of fit indices (Table 4 15), indicating that the hypothesized model fit the observed data. The normed chi square ( 2 / df = 2.802) was lower than the suggested cut off value (i.e., < 3.0; Bollen, 1989). The RMSEA value indicated that the structural model had a good fit (RMSEA = .058; Hu & Bentler, 1999). The SRMR (.047) was of h was good (Kline). GFI was .931 as indicative of a good mode l. Also NFI was .930 supporting this model fits the data. The convergent validity of the structural model was examined via the follo wing tests. For each factor, the t value associated with each of the loadings was significant at the 0.01 level. The resu lts indicated that all factors were significantly related to their specific constructs, verifying the posited relationships among indicators and constructs. The construct reliability (CR) and the average variance extracted (AVE) were also computed for th e latent constructs. For both CR and AVE, all constructs surpassed the threshold value s of .70 and .50, respectively. Therefore, it can be concluded that all factors in the hypothesized structural model showed acceptable reliability. According to Fornell and Larcker (1981), discriminant validity can be established when the AVE value s for the latent constructs are compared to the squared correlations between the corresponding constructs, and none of th e squared correlations surpass the AVE. These tests indi cated that the discriminant validity was upheld for the destination image construct. Having satisfied the psychometric properties of the measurement model, it was appropriate to proceed to examine the structural relationship among the different set s of fac tors. The hypothesized structural model was estimated to examine the hypotheses with regard to the effect of destination image, service quality and perceived value factors on destination

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63 loyalty as medi ated by satisfaction (Table 4 24 ). The tested model i ncluded a total of 11 latent constructs and 3 manifest variables (Figure 4 5). More specifically, there were five latent variables representing destination image, two latent variables for service quality, perceived value and destination loyalty, respective ly. Nine out of ten direct effect of hypotheses (paths) were found to be significant, excepting the effect of destination image on satisfaction ( t value = 3.29). The standardized direct effect s of destination image had a positive influence on service t value = 6.47), t value = 12.29), and destination t value = 6.72), respe ctively. These results indicated that when destination image increased by one standard deviation, service quality also increased by .567 standard deviations as well as perceived value .725 and destination loyalty .476 Therefore, h ypothesis 2, 3 and 4 we re supported. The standardized direct effects of service quality were found to exert a positive influence on perceived value ( t t value = 2.29), and 1.55, t value = 2.14), respectively, indicating that when service quality increased up by one standard deviation, perceived value increased also by .271 stan dard deviation as well as satisfaction .171, but destination loyalty decreased by 1.55. Therefore, h ypotheses 5, 6, and 7 were all supported. Hypotheses 8 and 9 dealt with the direct effects of perceived value on satisfaction and destination loyalty. Th e findings revealed that the direct effects of perceived value had a positive relationship with satisfaction ( t t value = 5.89), respectively, indicating that when perceived value increased by one standard deviation, satisfaction increased also by .735 as well as destination loyalty by .445.

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64 Hypothesis 10 dealt with the direct effect of satisfaction on destination loyalty. The finding revealed that the direct effect of satisfaction had a positive relationship with destination t value = 6.359), indicating that when satisfaction increase d by one standard deviation, destination loyalty increased also by .344. One of major purposes of this study was to examine the mediating effect of satisfaction. A total of three mediating analyses were conducted. It was found that satisfaction played a mediating role in all three in the relationship s between destination image and destination loyalty ( 0 34, p value < .05 ), p value < .0 5 ) and p value < .01). These result s indicat ed that destination loyalty was expected to enhance by .034, .011 and 098 standard deviations for every increase in destination image, service quality, and perceived value through its prior effe ct on satisfaction. Therefore, h ypotheses 11 12 and 13 were supported.

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65 Figure 4 1. First order confirmatory factor analysis for Destination Image

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66 Figure 4 2. First order confirmatory factor analysis for Service Quality Pe Pr Pr1 e1 Pr2 e2 Pr3 e3 Pe1 e1 Pe2 e2 Pe3 e3 Pe4 e4 Pe5 e5 Pe6 e6 Pe7 e7 Pe8 e8 Pe9 e9 Pe1 0 e1 0

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67 Figure 4 3. First order confirmatory factor analysis for Perceived Values OV FV OV 1 e1 O V 2 e2 OV 3 e3 OV 4 e4 FV 1 e1 FV 2 e2 FV 3 e3

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68 Figure 4 4. First order confirmatory factor analysis for Loyalty RI WOM RI1 e1 RI2 e2 RI3 e3 WOM 1 e1 WOM 2 e2 WOM 3 e3

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69 Figure 4 5. Tested Structured Model Destination Loyalty Service Quality Destination Image Perceived Value Satisfaction .155** .097 .725** .567** .735** .445** .344* .476** .171* .271**

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70 Table 4 1. Frequency distributions for the sociodemographic variables ( N = 581) Variables Category Frequency (%) (N = 581) Cumulative % Gender Male Female 241 (41.5) 340 (58.5) 41.5 100.0 Ethnicity Caucasian African American Hispanic/Latin America Asian American/Pacific Islander Native American Other 546 (94.0) 9 (1.5) 15 (2.6) 1 (0.2) 8 (1.4) 94.0 95.5 98.1 98.3 99.7 100.0 Age 18 25 26 32 33 40 41 50 51 65 66 or above 160 (27.6) 57 ( 9.8) 88 (15.1) 149 (25.8) 119 (20.5) 7 (1.2) 27.6 37.4 52.5 78.3 98.8 100.0 Marital Status Single Married Divorced Widowed Other 181 (31.2) 356 (61.3) 27 (4.6) 6 (1.0) 11 (1.9) 31.2 92.4 97.1 98.1 100.0 Income Below $20,000 $20,000 39,999 $40,000 59,999 $60,000 79,999 $80,000 99,999 Above $100,000 47 (8.1) 65 (11.2) 98 (16.9) 80 (13.8) 102 (17.6) 189 (32.6) 8.1 19.3 36.1 49.9 67.5 100.0 Education Some high S chool High School Graduate Some College College Graduate Graduate degree Other 17 (2.9) 112 (19.3) 188 (32.4) 188 (32.4) 72 (12.4) 4 (0.7) 2.9 22.2 54.6 86.9 99.3 100.0

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71 Table 4 1 Continued Variables Category Frequency (%) (N = 581) Cumulative % Groups Family with no children Family with children Tour group Friends Others 125 (21.5) 307 (52.8) 3 (.5) 110 (18.9) 36 (6.2) 21.5 74.4 74.9 93.8 100.0 Primary Purpose Vacation/pleasure Business/professional Visit friends/relatives Convention/exhibition En route to somewhere else Leisure Others 465 (80.0) 17 (2.9) 54 (9.3) 12 (2.1) 5 (.9) 5 (.9) 22 (3.8) 80.2 83.1 92.4 94.5 95.3 96.2 100.0 Duration 1 2 3 4 5 6 7 9 Over 10 60 (10.4) 1 79 (30.8) 186 (32.1) 124 (21.3) 32 (5.5) 10.4 41.2 73.3 94.6 100.0

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72 Table 4 2. Descriptive statistics for the Destination Image Variables ( N = 581) Variable M SD Skewness Kurtosis 1.Orlando was a good place to shop 5.642 1.19304 0.651 0.1 36 2.Local people were helpful 5.3046 1.33202 0.795 0.553 3.Orlando offered wide variety of shops 5.9071 1.13029 1.017 0.541 4.Orlando was a restful place 5.0637 1.56615 0.584 0.443 5.Orlando had many traffic jams 4.7332 1.71084 0.337 0.813 6.Or lando was an enjoyable traffic destination 6.1601 0.95241 0.149 2.904 7.Orlando offered convenience of local transportation 5.3121 1.31585 0.690 0.109 8.Orlando offered good nightlife and entertainment 5.8176 1.11159 1.003 1.064 9.Orlando was a safe place to visit 5.5611 1.15302 0.874 0.985 10.Orlando had pleasant weather 6.3787 0.8893 1.822 3.895 11.Orlando was a pleasing travel destination 6.2582 0.89565 1.730 4.752 12.Orlando was crowded 5.6386 1.27583 0.919 0.611 13.Orlando was family orie nted destination 6.2203 0.89423 1.303 2.221 14.Orlando offered va rious events information 5.7745 1.0966 0.804 0.406 15.Orlando had high standards for sanitation and cleanliness 5.4286 1.11748 0.605 0.32 16.Orlando was advanced and developed city 5.71 08 0.95721 0.554 0.09 17.Orlando had suitable accommodations 6.0637 0.89697 0.960 0.945 18.Orlando offered appealing local food 5.8657 1.05498 0.888 0.543 19.Orlando was a good atmosphere to visit 6.1067 0.89383 1.039 1.268 20.Orlando had high stan dards of living 5.5439 1.0087 0.459 0.04 21.Orlando offered good tourism information 6.0293 0.95457 0.894 0.918 22.Orlando offered easily accessible tourism information 6.0516 0.93813 0.857 0.467 23.Orlando had wide variety of products 5.8795 1.001 35 0.760 0.524 24.Everything was fascinating 5.3477 1.21327 0.499 0.147 25.Orlando offered convenient shopping 5.8055 1.04965 0.691 0.187 26.Orlando had many interesting places 6.0809 0.90896 0.921 0.735 27.Orlando offered easy access to the a rea where I want to visit 5.8675 1.14981 1.125 1.115 28.Orlando was an exciting travel destination 6.0275 1.00306 1.156 1.809 29.Orlando was a relaxing place 5.3597 1.41288 0.912 0.454 30.A holiday in Orlando is a real adventure 5.6282 1.22734 1.053 1.293 31.Local people were friendly 5.4768 1.1926 0.722 0.446 32.Orlando was a good place to travel 6.0776 0.98302 1.327 2.525 33.Orlando had limited parking area 5. 3652 1.1 524 0.895 0.721

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73 Table 4 3. Descriptive statistics for the Service Quality Variables ( N = 581) Variable M SD Skewness Kurtosis 1.Generally, the employees provided service reliably and consistently 5.5164 1.02477 0.777 1.008 2.Ge nerally, the employees provided services in a timely manner 5.4974 1.06775 0.906 1.198 3.Generally, the employees were competent (knowledgeable and skillful) 5.5250 1.08667 0.841 1.100 4.Generally, the physical facilities were clean 5.5783 1.07934 0.7 69 0.847 5.Generally, the employees were courteous, polite and respectful 5.6403 1.05194 0.748 0.571 6.Generally, the employees listened to me and we understood each other 5.4303 1.13747 0.786 0.760 7.Generally, the employees were trustworthy, believa ble and honest 5.5697 1.05080 0.699 0.450 8.Generally, the employees made the effort to understand my needs 5.6179 1.04813 0.927 1.436 9.Generally, the employees were neat and clean 5.7091 1.02203 0.833 0.908 10.Generally, the employees were approach able and easy to contact 5.7057 1.06728 0.989 1.513 11.Compared to other destinations, I got high quality from visiting Orlando 5.7040 0.93687 0.616 0.596 12.Orlando offered good quality tourism product 5.9346 0.92617 0.784 0.585 13.Orlando offered g ood quality of merchandise 5.7745 0.97155 0.668 0.303 14.Orlando offered good quality of lodging facilities 6.0413 0.87190 0.879 1.182 15.Orlando offered good quality of food 5.9208 0.97057 1.033 1.854

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74 Table 4 4. Descriptive statistics for th e Perceived Value Variables ( N = 581) Variable M SD Skewness Kurtosis 1. Visiting Orlando made me feel better 5.7969 1.01373 0.950 1.370 2.Orlando was a destination that I enjoyed 6.0792 0.91006 1.231 2.439 3.Compared to other tourism destinations I h ave visited, 5.2582 1.26305 0.815 0.770 4.Orlando was a good value for the money 5.5095 1.04979 0.765 0.660 5.The level of service I experienced was a good value 5.7625 0.96062 0.763 0.763 6.I obtained good results while visiting Orlando 4.8107 1.40 269 0.506 0.055 7.The quality per dollar spent while visiting Orlando was more than what I expected 5.2375 1.36913 0.750 0.284 8.Orlando was a place where I wanted to travel 5.4200 1.12306 0.580 0.229 9.Overall, my Orlando experience were better tha n I expected 5.8141 1.10946 0.943 0.705 10.The choice to visit Orlando was the right decision 5.8795 1.05337 1.037 1.187 11.Visiting Orlando gave me pleasure 4.7367 1.38259 0.525 0.112 12.Orlando was reasonably priced 5.2943 1.24060 0.594 0.214 13 .After visiting Orlando, my image of Orlando has improved 5.3115 1.44831 0.667 0.273 14.Orlando was expensive 4.7367 1.38259 0.525 0.112

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75 Table 4 5. Descriptive statistics for the Satisfaction Variables ( N = 581) Variable M SD Ske wness Kurtosis 5.8793 0.99355 0.975 1.604 expectations before traveling 5.6523 1.02197 0.635 0.522 time and effort I investe d 5.7573 1.01096 0.826 0.962

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76 Table 4 6. Descriptive statistics for the Loyalty Variables ( N = 581) Variable M SD Skewness Kurtosis 1. I will revisit Orlando within three years for a vacation(s) 5.4441 1.63340 1.034 0.381 2.I will recommend visiting O rlando to others (family or friends) 5.8382 1.22671 1.269 1.808 3.I will refer Orlando to other people who want advice on travel destination 5.7741 1.23661 1.181 1.538 4.I have a high likelihood of revisiting Orlando within three years of vacation 5.50 43 1.63435 1.092 0.452 5.I will say positive things about visiting Orlando to other people 5.9053 1.12633 1.295 2.267 6.I have plans to revisit Orlando in the near future 5.2582 1.85383 0.888 0.284

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77 Table 4 7. EFA for Destination Image variabl es: varimax rotation using first half data ( n = 290) Original subscale item name Factor loading Communality Destination Atmosphere (8 items) .91 Orlando was an exciting travel destination .76 .75 Orlando was a good place to t ravel .75 .75 Orlando was a pleasing travel destination .72 .68 Orlando was an enjoyable travel destination .7 .65 A holiday in Orlando was a real adventure .67 .58 Orlando was a good atmosphere to visit .62 .66 Orlando had a pleasant weather .6 .55 Everything was fascinating .55 .65 Travel Information (3 items) .84 Orlando offered easily accessible tourism information .83 .80 Orlando offered good tourism information .77 .72 Orla ndo offered various events information .63 .66 Travel Environment (4 items) .82 Orlando was advanced and developed city 8 0 .72 Orlando had high standards for sanitation & cleanliness .71 .66 Orlando had suitable accommodations .66 .61 Orlando had high standard of living .47 .57 Shopp ing (3 items) .90 Orlando was a good place to shop .76 .68 Orlando offered wide variety of shops .75 .68 Orlando offered convenient shopping .71 .54 Community Attitude (3 items) .85 Local people were helpful .66 .74 Orlando was family or iented destination .59 .64 Local people were friendly .54 .64 Accessibility (3 items) .81 Orlando had many traffic jams .82 71 Orlando was crowded .76 .64 Orlando had limited parking area .59 .59

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78 Table 4 8. EFA for Service Qualit y variables: varimax rotation using first half data ( n = 290) Original subscale item name Factor loading Communality Performance based quality (10 items) .95 Employees were competent .86 .71 Employees listened to me and we understand each other .84 .64 Employees provided service reliably & consistently .83 .72 Employees provided services in a timely manner .8 0 .67 Employees were trustworthy, believable and honest .80 .67 Employees made the effort to understand my needs .79 .72 Employees were courteous, polite and respectful .78 .72 Employees were approachable and easy to contact .78 .71 Employees wer e neat and clean .70 .63 Physical facilities were clean .60 .57 Product based quality (5 items) .89 Orlando offered good quality of merchandise .81 .70 Orlando offered a good quality of tourism product .80 .76 Orlando offered good quality o f food .75 .66 Orlando offered good quality of lodging facilities .72 .68 Compared to other travel destination, I got high quality from visiting Orlando .54 .68

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79 Table 4 9. EFA for Perceived Value variables: varimax rotation using first half d ata ( n = 290) Original subscale item name Factor loading Communality Overall Value (8 items ) .92 Visiting Orlando gave me pleasure .82 .68 The choice to visit Orlando was the right decision .81 .68 Orlando was a destination that I enjoyed .80 .65 Visiting Orlando made me feel better .79 .65 I obtained good res ults while visiting Orlando .70 .60 Orlando is a place where I always wanted to travel .68 .49 Orlando experiences were better than I expected .67 .59 After visiting Orlando, my image of Orlando has improved .60 .46 Financial Value (3 items) .79 Orlando was reasonably priced .76 .72 Orlando was expensive .75 .66 The quality per dollar spent while visiting Orlando was more than what I expected .61 .51

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80 Table 4 10. EFA for Loyalty variables: varimax rotation using first half data ( n = 290) Original subscale item name Factor loading Communality Revisit intentions (3 items) .94 I have a high likelihood of revisiting Orlando within 3 years for vacation .91 .92 I will revisit Orlando within 3 years for a v acation .91 .91 I have plans to revisit Orlando in the near future .86 .92 WOM (3 items) .92 I will say positive things about visiting Orlando to other people .89 .85 I will recommend visiting Orlando to others (family or friends) .88 .90 I wil l refer Orlando to other people who want advice on travel destinations .87 .89

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81 Table 4 11. Model Fit comparison between the six factor model and five factor model of destination image using second half data ( n = 291 ) Model 2 df 2 / df RMSEA SRMR CFI TLI Six Factor Model (24 items) 718.447 237 3.03 .089 .087 .79 .880 Five Factor Model (19 items) 414.089 142 2.91 069 .065 .931 .9 23

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82 Table 4 12. Model Fit of service quality using second half data ( n = 291) Model 2 df 2 / df RMSEA SRMR GFI CFI NFI Two Factor Model (15 items) 215.979 89 2.43 .073 .050 .934 .931 .922

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83 Table 4 13. Model Fit comparison between the eleven item model and seven item model of perceived value using second half da ta ( n = 291) Model 2 df 2 / df RMSEA SRMR CFI GF I Two Factor Model (11 items) 145.620 43 3. 386 .0 98 .0 97 902 .8 99 Two Factor Model (7 items) 35.75 13 2. 75 .0 78 .0 87 .9 26 .92 2

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84 Table 4 14. Model Fit of loyalty using second half data ( n = 291) Model 2 df 2 / df RMSEA SRMR GFI CFI NFI Two Factor Model (6 items) 15.235 8 1.904 .056 .047 .953 .971 .965

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85 Table 4 15. Overall model fit of hypothesized structural model using second half data ( n = 291 ) Model 2 df 2 / df RMSEA SRMR GFI CFI NFI Structural Model 1729.128 617 2.802 .05 8 .047 .9 31 .9 43 .9 30

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86 Table 4 16 r the destination image ( n = 291) Variables Indicator Loadings Critical Ratios Alpha Construct Reliability Average Variance Extracted Destination Atmosphere (6 items) .87 .84 .62 Orlando was an exciting travel destination .88 Orlando was a pleasing travel destination .75 14.15 Orlando was an enjoyable travel destination .77 15.54 A holiday in Orlando was a real adventure .85 12.78 Orlando was a good atmosphere to visit .86 13.84 Orlando had a pleasant weather .91 14.52 Travel Information (3 items) .85 .83 .60 Orlando offered easily accessible tourism information .92 Orlando offered good tourism information .88 11.23 Orlando offered various events information .89 10.79 Travel Environment (4 items) .80 .80 .59 Orlando was advanced and developed city .89 Orlando had high standards for sanitation & cleanliness .78 13.52 Orlando had suitable accommodations .85 12.44 Orlando had high standard of living .75 13.62 Shoppin g (3 items) .89 .87 .57 Orlando was a good place to shop .90 Orlando offered wide variety of shops .88 9.54 Orlando offered convenient shopping .87 10.11 Community Attitude (3 items) .81 .80 .58 Local people were helpful .78 Orlando was family oriented destination .83 14.85 Local people were friendly .88 15.13

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87 Table 4 17. Correlations among destination image constructs ( n = 291) 1 2 3 4 5 1. Destination Atmosphere 1.00 2. Travel Information .61 1.00 3. Travel Env ironment .75 .71 1.00 4. Shopping .59 .78 .68 1.00 5. Community Attitude .69 .58 .73 .65 1.00

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88 Table 4 18 Indica tor loadings, critical ratios, C ( n = 291) Variables Indicator Loadings Critical Ratios s Alpha Construct Reliability Average Variance Extracted Performance based quality (10 items) .96 .94 .63 Employees were competent .89 16.60 Employees listened to me and we understand each other .95 17.83 Employees provided service reliably & consistently .88 18.04 Employees provided services in a timely manner .90 17.81 Employees were trustworthy, believable and honest .92 18.10 Employees made the effort to understand my needs .93 18.32 Employees were courteous, polite and respectful .94 18.76 Employees were approachable and easy to contact .92 18.84 Employees were neat and clean .86 18.63 Physical facilities were clean .79 14.91 Product based quality (5 items) .90 .91 .57 Orlando offered good quality of merchandise .81 17.08 Orlando offered a good quality of tourism product .76 17.42 Orlando offered good quality of food .69 13.20 Orlando offered good quality of lodging facilities .77 15.73 Compa red to other travel destination, I got high quality form visiting Orlando .78 16.82

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89 Table 4 19. Correlations between service quality constructs ( n = 291) 1 2 1. Performance based quality 1.00 2. Product based quality .59 1.00

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90 Table 4 20 Indicator l o adings, critical ratios, C value ( n = 291) Variables Indicator Loadings Critical Ratios s Alpha Construct Reliability Average Variance Extracted Overall Value (4 items) .88 .90 .58 The choice to visit Orlando was the right decision .76 19.644 I obtained good results while visiting Orlando .83 16.88 Orlando is a place where I always wanted to travel .71 13.24 Orlando experiences were better than I e xpected .69 16.42 Financial Value (3 items) .76 .77 .52 Orlando was reasonably priced .77 16.09 Orlando was expensive .80 15.88 The quality per dollar spent while visiting Orlando was more than what I expected .69 18.84

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91 Table 4 21. Correlations between perceived value constructs ( n = 291) 1 2 1. Overall Value 1.00 2. Financial Value .69 1.00

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92 Table 4 22 Indica tor loadings, critical ratios, C n = 2 91) Variables Indicator Loadings Critical Ratios s Alpha Construct Reliability Average Variance Extracted Revisit intentions (3 items) .93 .91 .5 2 I have a high likelihood of revisiting Orlando within 3 years for vacation .93 14.26 I will revisit Orlando within 3 years for a vacation .91 13.84 I have plans to revisit Orlando in the near future .86 11.83 WOM (3 items) .95 .93 .57 I will say positive things about visiting Orlando to other people .86 15.90 I will recommen d visiting Orlando to others (family and friends) .90 16.78 I will refer Orlando to other people who want advice on travel destinations .87 16.01

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93 Table 4 23. Correlations between destination loyalty constructs ( n = 291) 1 2 1. Revisit Intentions 1.00 2. Word of Mouth .72 1.00

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94 Table 4 24 (SE), and t values for the hypothesized structural model using second half data ( n = 291) Path Coefficients between Factors CR SE t Direct Effect (S) .567 13.094 .046 6.47** (S) .725 15.829 .044 12.29** (NS) .097 1.561 .069 3.29 Destina (S) .476 8.324 .033 6.72.** (S) .271 4.732. .053 3.23** (S) .171 3.678. .040 2.29* (S) .155 3.45 9 .077 2.14** (S) .735 10.879 .078 11.789** (S) .445 4.720 .151 5.89** (S) .344 3.612 .112 6.359** Path Coefficients between Factors p Indirect Effect Image (S) .034 .028 Quality (S) (S) .011 .098 .033 .001 Note. S = sig nificant; NS = not significant **Correlation significant at .01 level *Correlation significant at .05 level

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95 CHAPTER 5 DISCUSSION The primary objective s of this study were to investigate and develop a theoretical relationship among destination image, se rvice quality, and perceived value, and to empirically test the construct s that are likely to affect tourist satisfaction, which in turn influence revisit intentions a nd Word of Mouth (WOM) To achieve these purposes, measurement scales for destination ima ge, service quality, perceived value, loyalty were developed relying on previous studies across various contexts. Then, the measurement scales were tested a nd validated through multiple CFAs. Next, the structural nature of the relationship of destination l oyalty, service quality, perceived value, satisfaction, and destination loyalty constructs were explored. T his discussion chapter is organized into t he following sections: (a) hypotheses testing (b) conceptual/theoretical implications, (c) managerial impl ications, (d) delimitations, (e) limitations and recommendations for the future studies. Hypotheses Testing As destination competit ion is becoming more intense, the process of selecting a destination is also more complex so it is crucial for both practiti oners and researchers to identify those variables that directly and indirectly influence destination loyalty. An in depth understanding of what factors influence tourists to decide to return to a des tination, and how they refer a destination to others, is of paramount importance for dest ination marketers to better understand tourist behavior s The SE M analysis supported the existence of statistically significant relationships between destinatio n image and perceived value (H2 ), destination image and service quality (H3 ), destination image and destination loyalty (H4), service quality and perceived value (H5), service quality and satisfaction (H6), service quality and destination loyalty (H7), perceived value and

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96 satisfaction (H8), perceived value and destinat ion loyalty (H9), and satisfaction and destination loyalty (H10). The SEM analysis also confirmed the mediation role that satisfaction played between destination image and destination loyalty (H11), service quality and destination loyalty (H12), and percei ved value and destination loyalty (H13). T he only hypothesis (H1 ) that was not supported pointed to no significant relationship between perceived desti nation image and overall satisfaction Through these results, it is believed that the destination loyalt y model outlined in the conceptual framework was corroborated. Therefore it can be said that tourist overall satisfacti on was affected by perceptions of service quality, and perceived value, which were also directly influenced by perceived destination i mage, and destination loyalty was in turn influenced by overall satisfaction. In addition, the newly proposed direct path from service quality to destination loyalty and perceived value to destination loyalty were shown to be significant; thus service qua lity and perceived value were also direct antecedents of destination perceived destination image, high perceived service quality, perceived value, and overall satis fa ction First, this study does not support a conclusion that destination image directly influences satisfaction however, destination image has both direct and indirect relationship s with destination loyalty through satisfaction as a moderating variable. The t otal effect of destination image on destination loyalty, (i.e., sum of direct and indirect effect through satisfaction, and satisfaction on destination loyalty) wa s found to be 0.51, and 0.34. This indicates that destination image and satisfa ction are important variables influencing destination loyalty. T his finding

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97 confirms the conclusions of previous studies (Bigne et al., 2001; Chen & Tsai, 2007; Lee et al., 2005). Although different studies have verified that destination image leads to overall sat isfaction, Chen and Tsai (2007) point out that perceptions of positive destination image does not always imply satisfaction. T he literature has shown that a generally positive relationship between destination image and satisfaction, but some authors sugges ted that destination image may not be enoug h to explain satisfaction (Bigne et al., 2001 Chen & Tsai, 2007 ). In general, the explanation may be related to the type of travelers. The type of companions most often reported were family with children (53%) fo r the purpose of vacation and pleasure in the current study Parents may be dissatisfied with overall experiences at Orlando, but their priority was to choose a vacation destination for the children. In addition, satisfied children were not part of the sur vey respondents. W hen the researcher interviewed some respondents (i.e., parents taking care of children), they were often close to exhaustion with their Orland o stay but the children were still excited and seemed to be happy, expecting to visit again. D feelings and overall perception s of a particular destination (Crompton, 1979; Fakeye & Crompton, 1991). Destination image plays two important roles in behaviors: (1) to infl uence the destination choice decision making proces s and (2) to condition post decision making behaviors including participation (on site experience), evaluation (satisfaction) and future behavioral intentions (intention to revisit and willingness to recom mend) (Bigne et al., 2001; Chen & Tsai, 2007; Lee et al., 2005). The result s of the current study are consistent with past studies; in particular, destination image not only influences the decision making proces s but also conditions post decision making

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98 b ehaviors of tourists. In other words, the influence of destination image is not limited to the destination selection stage, but also affects the onsite behavior s of tourists in general (Bigne et al., 2001; Chen & Tsai, 2007). Hence, endeavors to build o r i mprove a destination facilitate loyal visitors revisiting or recommending behaviors, thus being critical to the success of destination tourism development. Second, perceived service quality is an immediate antecedent of satisfaction, and affects d estination loyalty both directly and indirectly through satisfaction as a moderating variable. In addition, perceived service quality was positively influenced by destination image. T his finding of the current study is consistent with past studies (Castro, Armario, & Ruiz, 2007; Baker & Crompton, 2000; McDougall & Levesque; 2000; Hutchinson, Lai, & Wang, 2009; Murra & Howat, 2002; Shonk & Chelladurai, 2008). Therefore, service quality measurement and improvement are essential aspect s for those wishing to en hance destination l oyalty. It should be noted that the current study measures perceived service quality, referring to employee performance based quality and product based quality experiences. Service quality is a widely studied, and debated, construct (Cr onin, Brady, & Hult, 2000; Cronin & Taylor, 1992; Parasuraman, Zeithaml, & Berry, 1988). However, for the purpose of explaining variance in dependent constructs, the weight of the evidence in the extant literature supports the use of performance based serv ice quality (Cronin, Brady, & Hult, 2000). As a result, two multiple factors (employee performance based service quality and product based quality) were included in the current study. Because of the comprehensive nature of the study, the number of items us ed to measure each variable became a major concern. Thus, the first employee performance based service quality measure consisted of 10 questions derived from (2000), which created the basis to conclude that perceived service

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99 quali ty is an immediate antecedent of satisfaction, and affects destination loyalty both directly and indirectly through satisfaction as a moderating variable. Third, the results are consistent with those of prior studies (Cronin et al., 2000; Eggert & Ulaga, 2002; Lee et al., 2007; Woodruff, 1997) in that, perceived value has an effect on customer satisfaction, which in turn influences destination loyalty. These findings indicate that the respondent tourists consider ed v isiting Or lando to be a valuable and co rrect decision, which likely affect ed their level of travel satisfaction expressed for Orlando As for travel expenses, most respondents indicated satisfaction with prices being reasonable during their Orlando visit. Most also characterized Orlando as a pl easurable an d enjoyable tourism destination, adding to their satisfaction levels. In a similar vein, results provide acceptable evidence that measures of value and financial value can be expanded to include perceived value in visiting a family oriented destination. Implications Theoretical/Conceptual Implications There are a number of theoretical implications of the findings. First, a newly developed comprehensive model was tested to simultaneously analyze the relat ionship between destination image, service quality, perceived value, and satisfaction and to concurrently explore these four constructs in the prediction of intention to re visit and share positive word of mouth impressions with others. Although destination ima ge, service quality, value, and satisfaction studies have dominated, in a variety of field s; in the tourism and hospitality literature, these constructs have usually been studied fragmentarily. Second, considerable research has focused on the nature of service quality, and there is general acceptance that service quality is composed of a number of underlying dimensions. However, there is a lack of agreement on the exact nature of these dimensions. T hus, previous

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100 research consistently tailored service quality dimensions based on the context being examined (Murray & Howat, 2002). In the same token service quality studies in tourism and recreation stressed products/programs and/or destinations (Baker & Crompton, 2000; Getty & Thompson, 1994; Howat, Murra y, & Crilley, 1999) However, the current study developed two different factors with eighteen items to capture the complexity of perceptions. Third although marketing literature has suggested that perceived value is the leading predictor of customer loy alty and repurchase intentions (Parasuraman & Grewal, 2000; in the hospitality and marketing literature. As shown in table 4 22, the current study operationalized perce ived value with two factors (overall perceived value and financial value) and seven variables which attempted to better measure the complexities of perceived value. In addition, table 4 25 explained that the total effect of perceived value on destination l oyalty (i.e., sum of direct and indirect effect through satisfaction) was found to be .543. This finding suggests that perceived value has a significant influence on des tination loyalty, as has been also reported by past researches (Lee, Yoon, & Lee, 2007) Managerial Implications The major findings of this study have significant managerial implica tions for Orlando tourism management and travel destination marketers as well as for other destinations. First the exploratory factor analyses showed that tou rists pursue six different destination images (destination atmosphere, travel information available travel environment, shopping, community attitude, and accessibility) two different service qualities (employee performance based quality and product based quality) and perceived value s (overall value and financial value) Thus, it is suggested that destination marketers consider the practical implications of these variables,

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101 because they can be elementary with various products as well as enhancing destination loyalty. Second, the implications for tourism an d hospitality managers in any destination are that expectations for service quality. Employee performan ce based and product based quality must be delivered appropriately to the customers, as these are significant drivers of customer satisfaction, which is directly related to their intentions to re visit and express positive word of mouth comments to others. In particular, hospitality managers need to understand what their basic promise is to the customer and how deliver on that promise. This promise generates the basic expectations that cu stomers have with respect to desired levels of service quality. As an e xample, at a restaurant, customers expect the provider to perform the a ctivities involved in taking food order s delivering the food a nd any other promises the provider has implied in their advertizing or based on their advertized quality rankings These p romises could include high employee performance based service quality and /or high food quality. The promises could also include timely service. Customers will evaluate service quality based on the promises made, which may include core aspects of service. T hus, hospitality managers need to train their employees to deliver on all the promises made to meet customer expectations. Further, hospitality organizations should provide excellent guest services, which may be represented by the friendliness, courtesy, w illingness to help, pr ofessionalism, and knowledge levels of front line personnel. Finally, this study supports the idea that the general theory of consumer loyalty c an apply tourism destination. Thus, destination managers can e post purchase behavior s and consider this information in their decision making.

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102 Limitations and Recommendations for Future R esearch Even though the c urrent research findings are based on good indicators of the antecedents of destination loyalty the understanding of limited. P articularly, previous research has rarely explored temporal issues related to destination loyalty (Jang & Feng, 2007; Oppermann, 2000). Oppermann (1999, p. 58) suggested that time is significant in tourist intention s and loyalties appropriate time intervals during which a purchase m a In addition, Jang is necessary to understand how the revisit intention changes over time and identify appropriate time intervals understand temporal destination revisit intentions, future research should include the temporal perspective of d estination loyalty which would be measured utilizing short term, mid term, and long term intentions. Second, the design of this study (post visit assessment of image) made it impossible to measure the pre visit image of the destination, which would have made it feasible to measure the extent to which secondary information sources influence the formation of the pre visit image and the way in which primary information sources could alter this image. Also, Fakeye and Crompton (1991) revealed that the image h eld of destinations by nonvisitors di ffered from that of visitors Empirical studies have found that people change their image about a destination after they visit. In addition, the number of visits or the extent of previous experience at a specific destin ation seems to have a positive influence on the image of that destination. Therefore, it would seem desirable to carry out longitudinal studies that deal with the specific destination image, measured before and after visiting a destination. Third most ea rly research work focused on overall satisfaction with the on site tourism experience (Anderson et al., 1994; Babin et al., 2005; Baker & Crompton, 2000; Bigen, 2001;

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103 Chen & Tsai, 2007; Chon, 1989; Cronin et al., 2000; Gallarza & Saura, 2006; Getty & Thomp son, 1994; Gotlieb et al., 1994; Howat, 1999; Kozak & Remington, 2000; Lee et al., 2004, 2007; Oliver, 1980). Researchers only recently directed attention to attribute level conceptualization s of the antecedents of overall satisfaction (Chi & Qu, 2008; Oli ver, 1993). According to Oliver (1993), overall satisfaction and attribute satisfaction are distinct but related constructs. ttribute satisfaction has significant, positive and direct effects on overall satisfaction; an d it capture s a significant amount of variation in overall with individual component s of destination s leads to their satisfaction with the ove rall destination ( Danaher & Arweiler, 1996; Hsu, 2003; Mayer, Johnson, Hu, & Chen, 1998; Chen & Tsai, 2008). It is time for researchers in tourism and hospitality to distinguish overall satisfaction from satisfaction with individual attribute s since the pa rticular characteristics with in tourism and hospitality service have a prominent effect on tourist overall satisfaction (Chen & Tsai, 2008). Chen and Tsai (2008) also noted that services should be distinguished from products, because they have generic ch aracteristics such as intangibility, inseparability, heterogeneity, and perishability (Zeithaml, Parasuraman, & Berry, 1985). Further, tourism and hospitality products are considered to be interdependent between sub sectors. T experiences at desti nations encompass satisfaction at hotels, restaurants, shops, attractions, etc.; they may evaluate each service element separately. These individual components of satisfaction with a destination lead to overall satisfaction. From this perspective Chen and Tsai (2008) further contended that overall satisfaction with a hospitality experience is a function of satisfaction with the individual attributes of all the services and products that make

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104 up the experience, such as accommodation, weather, natural enviro nment s social environment s etc. Therefore, future studies should explore satisfaction with various components of the destination in order to give in depth managerial implications to destination marketers or managers.

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117 BIOGRAPHICAL SKETCH Soon Ho Kim completed his Doctoral of Philosophy degree in health and human performance (concentration: tourism) from the University of Florida in May 2010. While pursuing his degree at UF, he received the University of Florida Alumni Fellow Award and engaged in three phases funded research project such as assessing the recreational facilities and programs of the Suwannee River Water Management District. In addition, his teaching evaluation from students was in top 3% (4.78 out of 5.00) in college level. Furthermore, He has served as lecturer at J. Mack Robinson College of Business from Georgia State University since August, 2009