THE EFFECTS OF E-WORD-OF-MOUTH VIA SOCIAL MEDIA ON DESTINATION BRANDING: AN EMPIRICAL INVESTIGATION ON THE INFLUENCES OF CUSTOMER REVIEWS AND MANAGEMENT RESPONSES  By  Jung-Ho Suh        A DISSERTATION   Submitted to  Michigan State University in partial fulfillment of the requirements  for the degree of   Sustainable Tourism and Protected Area Management!Doctor of Philosophy  2017 ABSTRACT THE EFFECTS OF E-WORD-OF-MOUTH VIA SOCIAL MEDIA ON DESTINATION BRANDING: AN EMPIRICAL INVESTIGATION ON THE INFLUENCES OF CUSTOMER REVIEWS AND MANAGEMENT RESPONSES  By Jung-Ho Suh Destination branding through social media is crucial to touristsÕ decision making in the planning stages of travel. Although social media is becoming more important to both destination promotional mix and customer decision making, the social media literature is still emerging, with many gaps in the knowledge base remaining. By conducting experimental design, this study aims to investigate how tourists perceive a destination on social media through electronic word-of-mouth (eWOM) focusing on effects of customer reviews and management responses. This study recruited a total of 516 subjects from the Qualtrics online panel database. Four different experimental conditions were randomly assigned to the subjects. After comparing the variance of experimental groups and conducting structural equation modeling using multi-group analysis, the results show that there are different characteristics among experimental conditions. The findings of this study may allow tourism policy makers to make wiser decisions about developing more effective strategies for their destination branding using social media. Theoretical and managerial implications are discussed.        Copyright by JUNG-HO SUH 2017!iv TABLE OF CONTENTS   LIST OF TABLES ....................................................................................................................... vi LIST OF FIGURES .................................................................................................................... vii KEY TO ABBREVIATIONS ..................................................................................................... ix CHAPTER 1 .................................................................................................................................. 1 INTRODUCTION ........................................................................................................................ 1 Background ............................................................................................................................... 1 Introduction of Study Constructs ............................................................................................ 3 Destination Brand Image ........................................................................................................ 3 Credibility ............................................................................................................................... 4 Behavioral Intention ................................................................................................................ 5 Problem Statement .................................................................................................................... 6 Purpose of the Study ................................................................................................................. 7 Proposed Research Model ........................................................................................................ 7 Definitions of Terms ................................................................................................................. 8 Delimitations .............................................................................................................................. 9 CHAPTER 2 ................................................................................................................................ 10 LITERATURE REVIEW .......................................................................................................... 10 Social Media in Destination Marketing ................................................................................ 10 Social Media in Tourism ....................................................................................................... 12 Electronic Word-of-Mouth (eWOM) in Destination Marketing .......................................... 12 Social Earned Media and Social Owned Media ................................................................... 13 Social Media Marketing and DMOs ..................................................................................... 15 Destination Branding through Social Media ........................................................................ 16 Destination Marketing and Destination Branding ................................................................ 16 Destination Branding and Brand Image ................................................................................ 20 Credibility in Social Media .................................................................................................... 22 Tourist Behavioral Intentions ................................................................................................ 25 Conceptual Framework .......................................................................................................... 26 Hypotheses Development ........................................................................................................ 29 CHAPTER 3 ................................................................................................................................ 32 METHODOLOGY ..................................................................................................................... 32 Experimental Design ............................................................................................................... 32 Quasi-Experimental Designs ................................................................................................ 34 Research Design ...................................................................................................................... 34 Procedure .............................................................................................................................. 35 Measurements ......................................................................................................................... 47 Credibility ............................................................................................................................. 47 Destination Brand Image ...................................................................................................... 48 !v Behavioral Intentions ............................................................................................................ 49 Data Collection and Sampling ............................................................................................... 51 Data Analysis ........................................................................................................................... 52 CHAPTER 4 ................................................................................................................................ 55 RESULTS .................................................................................................................................... 55 Descriptive Analysis ................................................................................................................ 55 Demographic Profile of Respondents for Each Group ......................................................... 57 Manipulation Checks .............................................................................................................. 65 Comparison of the Effects of Experiments ........................................................................... 65 Measurement Model ............................................................................................................... 73 Structural Model ..................................................................................................................... 77 The Mediating Role of Destination Brand Image ................................................................ 79 The Moderating Role of eWOM Effects ............................................................................... 79 Chi-square difference test ..................................................................................................... 79 CHAPTER 5 ................................................................................................................................ 86 SUMMARY, DISCUSSION AND IMPLICATIONS .............................................................. 86 Summary of the Study ............................................................................................................ 86 Summary of Purpose ............................................................................................................. 86 Summary of Procedure ......................................................................................................... 87 Summary of Data Analysis ................................................................................................... 87 Summary of Significant Findings ......................................................................................... 88 Discussion ................................................................................................................................ 88 Theoretical Contribution ........................................................................................................ 95 Managerial Implications ........................................................................................................ 97 Limitations and Future Research ........................................................................................ 100 REFERENCES .......................................................................................................................... 103        !vi LIST OF TABLES !Table 1 Measurement Items for Credibility .................................................................................. 48 Table 2 Measurement Items for Destination Brand Image ........................................................... 49 Table 3 Measurement Items for Behavioral Intentions ................................................................. 50 Table 4 Demographic Profile of Respondents (N=516) ............................................................... 56 Table 5 Descriptive Analysis (Social Media Use) (N=516) ......................................................... 57 Table 6 Demographic Profile of Respondents for Experimental Group 1 (N=139) ..................... 58 Table 7 Social Media Use for Experimental Group 1 (N=139) .................................................... 59 Table 8 Demographic Profile of Respondents for Experimental Group 2 (N=135) ..................... 60 Table 9 Social Media Use for Experimental Group 2 (N=135) .................................................... 61 Table 10 Demographic Profile of Respondents for Experimental Group 3 (N=123) ................... 62 Table 11 Social Media Use for Experimental Group 3 (N=123) .................................................. 63 Table 12 Demographic Profile of Respondents for Experimental Group 4 (N=119) ................... 64 Table 13 Social Media Use for Experimental Group 4 (N=119) .................................................. 65 Table 14 Descriptive Analysis (Experimental Groups) (N=516) ................................................. 66 Table 15 Comparison of Means and Standard Deviations ............................................................ 67 Table 16 Model Fit Indices ........................................................................................................... 74 Table 17 Factor Reliability ........................................................................................................... 74 Table 18 Results of Confirmatory Factory Analysis .................................................................... 75 Table 19 Standardized Path Coefficients of the Structural Model!Overall Model ...................... 77 Table 20 Mediating Effect!Sobel Test ......................................................................................... 79 Table 21 Invariance Test Results across Experimental Groups .................................................... 80 Table 22 Moderating Role of eWOM Effects and Multi-Group Analysis ................................... 81 !vii LIST OF FIGURES !Figure 1 Conceptual Model ............................................................................................................ 8 Figure 2 Destination Marketing Framework ................................................................................. 19 Figure 3 Dimensions of Brand Knowledge .................................................................................. 22 Figure 4 Conceptual Model .......................................................................................................... 27 Figure 5 Traverse City-Pure Michigan Video Commercial .......................................................... 37 Figure 6 Traverse City Tourism Facebook page for Experimental Group 1 ................................ 39 Figure 7 Reviews of Facebook Users for Experimental Group 1 ................................................. 40 Figure 8 DMO Responses for Experimental Group 1 .................................................................. 40 Figure 9 Traverse City Tourism Facebook page for Experimental Group 2 ................................ 41 Figure 10 Reviews of Facebook Users for Experimental Group 2 ............................................... 42 Figure 11 DMO Responses for Experimental Group 2 ................................................................ 42 Figure 12 Traverse City Tourism Facebook page for Experimental Group 3 .............................. 43 Figure 13 Reviews of Facebook Users for Experimental Group 3 ............................................... 44 Figure 14 DMO Responses for Experimental Group 3 ................................................................ 44 Figure 15 Traverse City Tourism Facebook page for Experimental Group 4 .............................. 45 Figure 16 Reviews of Facebook Users for Experimental Group 4 ............................................... 46 Figure 17 DMO Responses for Experimental Group 4 ................................................................ 46 Figure 18 Mean of Destination Brand Image ............................................................................... 70 Figure 19 Mean of Credibility ...................................................................................................... 71 Figure 20 Mean of Tourists' Behavioral Intentions ...................................................................... 72 Figure 21 The Result of Second-Order CFA for Overall Model .................................................. 76 Figure 22 The Result of SEM with Standardized CoefficientsÑOverall Model ......................... 78 !viii Figure 23 The Result of SEM with Standardized CoefficientsÑExperimental Group 1 ............. 82 Figure 24 The Result of SEM with Standardized CoefficientsÑExperimental Group 2 ............. 83 Figure 25 The Result of SEM with Standardized CoefficientsÑExperimental Group 3 ............. 84 Figure 26 The Result of SEM with Standardized CoefficientsÑExperimental Group 4 ............. 85               !ix KEY TO ABBREVIATIONS !DMO: Destination Marketing Organization eWOM: Electronic Word-of-Mouth CRD: Credibility DBI: Destination Brand Image BI: Behavioral Intentions !1 CHAPTER 1 INTRODUCTION  This chapter includes the following sections: (1) Background; (2) Introduction of Study Constructs; (3) Problem Statement; (4) Purpose of the Study; (5) Proposed Research Model; (6) Definition of Terms; and (7) Delimitations.   Background As the tourism industry has become more competitive and globalized due to technological advancements in information communication (Kim & Lee, 2011; Pike & Page, 2014; UNWTO, 2011), many local and national government authorities have begun to pay attention to destination branding (MacKay & Vogt, 2012; Pew Research Center, 2015b; UNWTO, 2011). Moreover, given the considerable and ever-increasing number of social media users, many destinations have started to utilize social media for their marketing strategies (Leung & Bai, 2013; Leung, Law, van Hoof, & Buhalis, 2013; Tham, Croy, & Mair, 2013). Not surprisingly, destination marketing organizations (DMOs), like many organizations wishing to achieve marketing goals through branding activities, have turned to the use of social media marketing. Destination branding through social media is crucial to DMO practitionersÕ decisions related to developing target market strategies and achieving marketing communication goals (Yumi Lim, Chung, & Weaver, 2012).  Due to ubiquity and cost-effectiveness associated with social media, marketers are starting to shift their communication strategies meeting a new media environment (Kietzmann, !2 Hermkens, McCarthy, & Silvestre, 2011; B. K. Wright, 2015). Notably, unlike traditional mediaÑsuch as newspapers, magazines, radio broadcastings, and TV programsÑsocial media provides marketers with two-way communications, such as customersÕ reviews and companiesÕ responses to them (Aluri, 2012; Bao & Chang, 2014; Plunkett, 2013; B. K. Wright, 2015). Accordingly, many companies have started to pay attention to how to invest their resources into social media marketing. In 2013, companies in the U.S. spent approximately US$5.1 billion on social media advertising, and estimates are that this will reach almost US$15 billion by 2018 (BIA/Kelsey, 2014).  Likewise, social media marketing has also become a crucial part of the promotional mix in destination marketing. Social media platforms such as TripAdvisor or other online review communities facilitate travelersÕ ability to share their experiences by posting reviews and comments on destinations that they have visited or are planning to visit (Ayeh, Au, & Law, 2013a, 2013b; Filieri, 2015; Filieri & McLeay, 2014; Plunkett, 2013). For this reason, when marketers from DMOs develop social media marketing strategies, they should consider that social media plays a significant role in touristsÕ perceived image of destinations and their behavioral intentions. Therefore, prior to launching social media marketing strategies, DMO practitioners should understand the concepts relevant to the new communication landscape.  From the consumersÕ perspectives, social media has an impact on their decisions just as it influences on DMOs on the suppliersÕ side (Bilgihan, Barreda, Okumus, & Nusair, 2016; Cabiddu, Carlo, & Piccoli, 2014; Hudson, Roth, Madden, & Hudson, 2015; Tanford & Montgomery, 2015). For example, Hudson et al. (2015) confirmed that social media interactions among tourists has a significant effect on customer relationships with tourism brands. Additionally, Cabiddu et al. (2014) analyzed combined social media metrics (e.g., the number of !3 Facebook fans, the average responses per post, the average likes per post, and the number of Twitter followers.) and found that social media usage within the tourism context supports customer engagement. This clearly indicates that using social media in a destination branding context, has the potential to positively influence touristsÕ decision making. Previous studies that have investigated electronic word-of-mouth (eWOM) and social media marketing suggest that it is crucial for hospitality organizations and DMOs to develop better understanding of eWOM effects through social media on brand image and customersÕ behavioral intentions (Berezan, Raab, Tanford, & Kim, 2015; Chu & Kim, 2011; Gaikar, Marakarkandy, & Dasgupta, 2015; Jansen, Zhang, Sobel, & Chowdury, 2009; Lee & Cranage, 2014; H. Lee, Reid, & Kim, 2014; Leung, Bai, & Stahura, 2015; Yahya, Azizam, & Mazlan, 2014). As many hospitality organizations and destinations have become focused on social media marketing, destination marketers have been interested in the effects of social media for enhancing image of their destination brands. Utilizing social media platforms for DMOsÕ marketing, practitioners from DMOs also have noticed the significance of eWOM effects conveying information and knowledge in customer engagement including customer behaviors.   Introduction of Study Constructs Destination Brand Image Destination brand image is one of the prominent constructs in the destination brand literature explaining the actual image of the destination brand that tourists hold in their minds (Keller, 1993; Pike & Page, 2014; Qu, Kim, & Im, 2011). Studies regarding destination brand image have been built upon the consumer-based brand equity (CBBE) concept (Aaker, 1996). A !4 CBBE centers on measuring how a consumer assesses the brand value and is determined by brand knowledge (Keller, 1993). Brand image is an important sub-dimension that constitutes brand knowledge based on the association network memory model (Keller, 1993; Zhang, Jansen, & Mattila, 2012). Brand image is defined as Òperceptions about a brand as reflected by the brand associations held in consumer memoryÓ (Keller, 1993, p.3). Brand association and attitudes constitute brand image. Keller (1993) conceptualized that brand associations, which function as informational nodes, connect in consumersÕ memories to a brand node, and consumers associate their memory with the meaning of the brand (Cai, 2002; Zhang et al., 2012). Sub-dimensions of brand image include the favorability, strength, and uniqueness of brand associations. These dimensions differentiate brand image from brand knowledge by playing an important role in determining the differential response that goes into brand equity. Credibility To compare the effect of social earned media with that of social owned media to potential tourists who are planning their travels by measuring their behavior, theories of source credibility and trust are applicable. Source credibility theory explains how the perceived credibility of the message source influences communication receiverÕs acceptance of the message (Hovland & Weiss, 1951). According to Cho, Kwon, and Park (2009), source credibility theory considers four factors that constitute the credibility of an information source: expertise (competency), trustworthiness, co-orientation (similarity), and attraction. The authors described the four factors thus: expertise is the extent to which a source provides correct information a message receiver is able to perceive; trustworthiness is the degree to which a source facilitates information that reveals the sourceÕs actual thoughts or emotions; co-orientation represents the degree to which a !5 source has aspects in common with the target audiences; attraction is the extent to which a source motivates positive reflections - e.g., a desire to imitate the source - from target audience.  In marketing literature, trust is defined as an essential factor that maintains a continuous relationship between customer and provider (Chiu, Hsu, Lai, & Chang, 2012; Han & Hyun, 2015). Also, Morgan and Hunt (1994) conceptualized trust as existing when one party has confidence in an exchange partner's reliability and integrity. In the same vein, Moorman, Deshpand”, and Zaltman (1993, p. 82) defined trust as Òa willingness to rely on an exchange partner in whom one has confidence."  These theories and concepts are worth further investigation because they have the potential to create a good framework to address my third research objective. With proper modification and operationalization, theories of source credibility and trust may be the best way of understanding the effects of social earned and owned media. Behavioral Intention  Numerous studies have supported that destination brand image influences touristsÕ behavioral intentions (Ponte, Carvajal-Trujillo, & Escobar-Rodr™guez, 2015; Chen & Tsai, 2007; Chew & Jahari, 2014; Kang & Gretzel, 2012; Qu et al., 2011). Tourist behavioral intentions consist of intention to visit the destination and intention to recommend.  Intention to visit has been widely investigated in tourism literature for its determinant of credible destination marketing (Ponte et al., 2015; Chen & Tsai, 2007). In general marketing, trust has been extensively examined because trustworthy brands attract more customers (Chiu, Hsu, Lai, & Chang, 2012; Gefen & Straub, 2003). It is important to note that the literature supported that destination brand image impacts intention to visit (Qu et al., 2011).    !6 The intention to recommend a destination has been emphasized since the word-of-mouth (WOM) effects have proved its influence on creating positive destination image (Baloglu & McCleary, 1999; Simpson & Siguaw, 2008; Tham et al., 2013). More notably, much of the destination marketing literature has indicated WOM reduces perceived risk, as well as increases credibility, when tourists make decisions for purchasing destinations (Beerli & Martõ«n, 2004; Litvin, Goldsmith, & Pan, 2008; Qu et al., 2011; Tham et al., 2013) . Additionally, based on previous literature, it would be assumed that tourists who have positive images of a destination are more likely to recommend the destination to others.  Problem Statement Social media is becoming more important to both destination promotional mix and customer decision making, the social media literature is still emerging, with many studies being exploratory and many gaps in the knowledge base remaining. There appears, for example, to be a gap in knowledge about the effects of social media on destination branding as evidenced by mixed outcomes of social media marketing initiatives. Some of these initiatives have led to successful outcomes for the marketing organizations (Filieri, 2015; Z. Liu & Park, 2015; Tanford & Montgomery, 2015), while others have been less effective (Gallivan, 2014; Gallup, 2014). A strong need exists to examine the impact of social media on destination branding and to develop an understanding of how policy makers and entrepreneurs apply this information to their decision making processes.  !7 Purpose of the Study The purpose of this study is to identify the electronic word-of-mouth (eWOM) effects caused by social earned media (e.g., online reviews generated by customers on Facebook) and social owned media (e.g, responses from companies/brands to customersÕ reviews on Facebook) on potential tourists by measuring their behavioral intentions. This study also aims to extend the current understanding of destination marketersÕ social media usage by examining how destination branding through social media impacts touristsÕ perceptions and behavioral intentions. The measurements in this study examine touristsÕ perceived images of destination brands in order to investigate how tourists perceive a destination through social media. Moreover, this study seeks to examine the effects destination branding has on the perceived credibility of a destination branding strategy and investigates its sequential effects on perceived destination brand images and touristÕs behavioral intentions (e.g., Òintention to visit the destination,Ó Òintention to recommend the destinationÓ).  Proposed Research Model  To accomplish the research objectives, this study suggests the proposed model illustrated in Figure 1.  !8 !Note: This model proposes the relationship between independent variables (i.e., credibility: ÒI feel that the Traverse City Tourism's social media marketing activities are credibleÓ), mediating variables (i.e., destination brand image: ÒBased on your experiences with the video commercial, online reviews and management responses your impression of the image of Traverse City, MI is positiveÓ) and dependent variables (i.e., behavioral intentions: ÒI am likely to visit Traverse City, MIÓ). The relationships of moderating variables (i.e., experimental conditions of eWOM: negative online reviews/best practices of DMOÕs responses) also are included in this model.  Figure 1 Conceptual Model Definitions of Terms  The following terms are defined to clarify their use in this study. Social Earned Media: Social media activity related to a company or brand that is not directly generated by the company or its agents but rather by other entities such as customers or journalists (Bao & Chang, 2014; DiStaso & Brown, 2015; Stephen & Galak, 2012). For example, online reviews generated by customers on TripAdvisor or Facebook can be considered as social earned media.  Social Owned Media: Social media activity related to a company or brand that is generated by the company or its agents in channels it controls (Bao & Chang, 2014; DiStaso & Brown, 2015; !9 Stephen & Galak, 2012). For instance, management responses from organizations to customersÕ reviews on TripAdvisor or Facebook can be defined as social owned media.   Delimitations  This study is delimited to the following: 1. All subjects are potential tourists who were planning their travel while they are on vaction. Those subjects who didnÕt plan their travel during the vacation were excluded by the screening question.  2. Study subjects are segmented into four experimental groups and one control group. The experiment employed a 2 (Reviews of Facebook users: 5-star reviews vs. 1-star reviews) " 2 (DMOÕs responses: best practices vs. poor practices) between-subjects full factorial design. A control group will be added to provide baseline measures for the dependent variables.   3. Study subjects were recruited from the Qualtrics online panel database. This study used U.S. General Population as a sampling frame. These sample respondents were opt-in panel participants (i.e., The sampling method can be regarded as a quota sampling).   !10 CHAPTER 2 LITERATURE REVIEW This chapter is organized into the following sections: (1) Social Media in Destination Marketing; (2) Destination Branding through Social Media; (3) Credibility in Social Media; (4) Tourists Behavioral Intentions; (5) Conceptual Framework; and (6) Hypotheses Development.   Social Media in Destination Marketing Social media has become a crucial communication tool for destination marketing. For tourism consumers and providers alike, social media use has been gaining in popularity, especially for the purpose of sharing information. In addition, an increasing number of marketers and DMOs have started to promote their destinations on social media (Plunkett, 2013; Wright, Khanfar, Harrington, & Kizer, 2010). Furthermore, past studies on social media have provided many definitions that can be employed to consider the usefulness of social media as a powerful communication tool in the Web 2.0 era. Kaplan and Haenlein (2010) define social media as Òa group of Internet based applications that builds a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of User Generated Contents,Ó (p. 61). In the context of destination marketing, Milano, Baggio, and Piattelli (2011) have suggested Travel 2.0, the touristic version of Web 2.0, which indicates that social media utilizes such applications to facilitate interactive information sharing, collaboration, and the formation of virtual communities via both mobile and web-based platforms.  In a study examining how people use social media, the Pew Research Center (2015a) !11 highlighted the fact that between February 2005 and January 2015, the use of social media among Internet users jumped from 8% to 74%. During the last few years, while the growth of Facebook users has slowed, other social media platforms such as Twitter, Instagram, Pinterest, and LinkedIn have continued to see significant growth in usership (Duggan et al., 2015; Pew Research Center, 2015a). However, the existing literature has indicated that Facebook still remains the most popular social media platform (Duggan et al., 2015; Ma & Chan, 2014; Statista, 2016a). According to Facebook (2016), there were still 1.59 billion daily active users of Facebook on average in April 2016 (Facebook, 2016; Statista, 2016b), whereas Instagram and Snapchat were the fastest-growing competitors to reach 400 million and 200 million monthly active users, respectively (Statista, 2016b).  Research on social media in destination marketing has taken a number of forms, and it is important to consider the impact of social media on touristsÕ behavioral intentions and perceived image tourists have of destination brand. Bolton et al. (2013) argued that social media marketing would successfully influence Generation YÐthose born between 1981 and 1999Ðwhich is the first generation to have experienced early and frequent exposure to technology and electronic devices. Due to Generation YÕs familiarity with and acceptance of technology, marketers and researchers pay attention to this cohortÕs social media use because it can serve as a barometer of how consumers will behave in the future (Bolton et al., 2013). Tham et al. (2013) suggested that many case studies in tourism support widespread adoption of social media use by DMOs, hotels and other suppliers in the tourism and hospitality industry for the purpose of customer engagement. Leung et al. (2013) also emphasize the importance of social media marketing in tourism research: ÒBeing one of the Òmega trendsÓ that has significantly impacted the tourism system, the role and use of social media in travelersÕ decision making and in tourism operations and !12 management have been widely discussed in tourism and hospitality researchÓ (p.3). Social Media in Tourism Recent studies have investigated the significant role of social media in tourism and hospitality research, which, in most cases, has shown distinct implications depending on the categorization of social media platforms (Baka, 2016; Harrigan, Evers, Miles, & Daly, 2017; Ketter, 2016; Leung, Bai, & Stahura, 2015b; Luo & Zhang, 2016; Mariani, Di Felice, & Mura, 2016). In studies examining how tourism organizations utilize social networking sites such as Facebook and Twitter to promote their destinations, social media experiences would impact touristsÕ attitude toward destination brand image as well as touristsÕ behavioral intentions (Harrigan et al., 2017; Ketter, 2016; Mariani et al., 2016). To achieve successful social media marketing goals, DMOs and tourism organizations should understand the marketing effectiveness of social networking sites in terms of enhancing customer loyalty, trust, and engagement (Harrigan et al., 2017). Moreover, it is important to note that reputation management for tourism organizations through social media platforms such as online communities (e.g. tripadvisor.com) are essential to earn usersÕ trust to effectively manage reviews, rankings, and ratings (Baka, 2016; Luo & Zhang, 2016). The emergence of social media outlets has led to a dramatic increase in research interest. Accordingly, rapid growth in the popularity of social media has extended into destination marketing. While there is a great need for research into social media in destination marketing, there is a lack of existing literature investigating the eWOM effects of social media on destination branding.  Electronic Word-of-Mouth (eWOM) in Destination Marketing  According to Katz and Lazarsfeld (1966), word-of-mouth (WOM) is defined as Òthe act of exchanging marketing information among consumers, and plays an essential role in changing !13 consumer attitudes and behavior towards products and services.Ó Based on the definition of WOM, electronic word-of-mouth (eWOM) is defined as Òany positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the InternetÓ (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004, p.39). In recent years, research on eWOM has taken a number of forms; it is important to consider what influence eWOM has on destination marketing (e.g., Litvin et al., 2008; Tseng, Wu, Morrison, Zhang, & Chen, 2015; Wu, Wall, & Pearce, 2014). For example, Litvin et al. (2008) discuss that eWOM is an effective marketing tool for destination marketing in terms of cost reduction. Also, Tseng et al. (2015) examine how eWOM used in travel blogs influences international touristsÕ destination image formation. Wu et al. (2014) show that touristsÕ reviews on TripAdvisor affect international touristsÕ shopping experiences in Beijing. Thus, much of the previous literature confirms the importance of eWOM in destination marketing, which identifies that eWMO is positively associated with touristsÕ decision making. Accordingly, this study extends a current stream of eWOM literature to social media studies.  Social Earned Media and Social Owned Media In the marketing communication research, researchers have taken different approach. The typology of social media comprises social earned, social paid and social owned media (Bao & Chang, 2014; DiStaso & Brown, 2015; Stephen & Galak, 2012). Social earned media can be defined as media activities relevant to a company or a brand that is not directly undertaken by the company or its agents but rather by a customer. A good example of this is online reviews posted by customers on social media sites. Social paid media refers to advertising on social media platforms created by a company or its agents and distributed via channels controlled by other entities (e.g., social media sites or search engines), which may include social network !14 advertising. Social owned media refers to media activities conducted by a company on its own channel, such as a company-owned page on a social media site (Bao & Chang, 2014; DiStaso & Brown, 2015; Stephen & Galak, 2012; Titan SEO, n.d.). Social earned media is also a form of electronic Word of Mouth (eWOM), which includes referrals and online customer reviews on sites such as a companyÕs Facebook page, Yelp.com, or TripAdvisor.com. Social paid media involves advertisements on social networks (e.g., Facebook ads), whereas social owned media involves company-generated content on social media platforms. An example of this would be the DMOÕs responses to customer reviews on the Pure MichiganÕs Facebook page (i.e., the official brand of the travel authority in the state of Michigan). Previous studies have distinguished social earned and social owned media from social paid media based on which type of social media allows people to spread out a message most effectively (i.e., eWOM effects) and how credible the social media method is (Bao & Chang, 2014; DiStaso & Brown, 2015; Stephen & Galak, 2012). DiStaso and Brown (2015) argue that social earned media and social owned media are more credible and suitable channels that deliver less controlled content than social paid media. Further, as many companies are recognizing the increasing impact of online reviews on social media, it is becoming common for marketers to turn to social earned and social owned media when developing marketing communications strategies (Bao & Chang, 2014; Bruce, Foutz, & Kolsarici, 2012; Feng & Papatla, 2011; Lovett & Staelin, 2012; Onishi & Manchanda, 2012; Trusov, Bucklin, & Pauwels, 2009). While marketing literature has paid attention to the effects of paid mediaÑin both social and traditional media outletsÑon companiesÕ marketing performance and related outcomes, relatively little research has examined the marketing-related impact of social earned and social owned media. In their examination of cost reduction in marketing expenditure, Bruce et al. (2012) found that !15 increased social earned media activities are more effective at stimulating new product demand at a later stage of distribution when the attention customers give to advertising campaigns begins to wane. Trusov et al. (2009) advanced the literature on social earned media by comparing online referrals resulting from earned media and owned media in traditional marketing vehicles (i.e., newsletters, company events, or press releases). Their research reflects the relative advantage of social earned media in both the short run and in the long run over traditional earned and owned media in terms of new customer acquisition and retention.  Destination marketing is no exception to these findings. Despite only a handful of the academic evidence, these studies clearly indicate that it is crucial to study the impact of social earned and social owned media in the context of destination marketing. This study investigates the influences of customer reviews and management responses. As discussed above, customer reviews can be regarded as social earned media, and company responses to these reviews can be described as social owned media. The effects of social paid media on marketing have been extensively studied in the marketing literature. The effects of social earned and social owned media, however, have received limited attention. Therefore, the current study aims to fill a gap in the recent destination marketing literature by examining the effects of destination branding messages on social earned and social owned media. Social Media Marketing and DMOs This evidence shows that it is necessary to study the way in which DMOs can utilize social media as a powerful marketing tool to encourage tourist engagement with their destination brands. The rationale behind this is that there is academic evidence that social media does impact consumer behavior (e.g., Bolton et al., 2013; Chan & Guillet, 2011; Leung et al., 2013; Tham et al., 2013). Moreover, these academic studies emphasize the fact that social media is an important !16 topic of research because it is also crucial to studies of corporate brand strategy (Burson-Marsteller, 2010; Chan & Guillet, 2011; Leung & Bai, 2013; Moore, 2011). DMOs are able to develop successful target strategies with destination branding through social media. It is evident that successful target marketing can be achieved by attracting more targeted groups of visitors to the destinations. For example, the literature supports the suggestion that DMOs should use branding strategies to promote their destination marketing (Cai, 2002; Garc™a, GŠmez, & Molina, 2012a; Qu et al., 2011). A destination with a strong brand can appeal to its target markets more effectively than other destinations, and thus, it will expect to attract more visitors than others (Cai, 2002; Hankinson, 2005; Yumi Lim et al., 2012).  Destination Branding through Social Media Destination Marketing and Destination Branding  Globally, tourism destinations must contend with a highly competitive marketing environment (Bornhorst, Ritchie, & Sheehan, 2010). The United Nations World Tourism Organization (UNWTO) forecasts that international tourist arrivals worldwide will show an annual growth of 3.3 % from 2010 to 2030 (UNWTO, 2014). By 2030, international tourist arrivals worldwide are projected to reach 1.8 billion (UNWTO, 2014). In addition, the tourism industry has been transformed over the past several decades by a number of macro-environmental factors, including transportation developments (i.e., the growth of low cost carriers [LCCs]) and information communication technologies (ICT) advancements (Kim & Lee, 2011; Pike & Page, 2014; UNWTO, 2014). Such factors have opened a broader range of options for tourists and increased their bargaining power.  As the tourism market has grown more competitive and globalized, a critical issue for government authorities of tourism destinations has become destination marketing (UNWTO, !17 2011). Destination marketing is conceptualized as a comprehensive process facilitating touristic experiences for visitors to destinations (Wang, 2011). In other words, destination marketing requires a holistic approach to tourism systems, which comprise services and activities by various entities - tourists, tourism-related industry, and government organizations of host communities (Goeldener & Ritchie, 2010). A relatively-early definition (Wahab, Crampson, & Rothfield, 1976), defined destination marketing as:  The management process through which the National Tourist Organizations and/or tourist enterprises identify their selected tourists, actual and potential, communicate with them to ascertain and influence their wishes, needs, motivations, likes and dislikes, on local, regional, national and international levels, and to formulate and adapt their tourist products accordingly in view of achieving optimal tourist satisfaction thereby fulfilling their objectives. (p. 24) Although this definition has been criticized for its idealistic approach to practical issues (Pike & Page, 2014), Uysal et al. (2011) contended that the subsequent studies in destination marketing (e.g., Gartell, 1994; Morrison, 2010; Pike, 2008) support the definition by Wahab et al. (1976), and agree with this definition in its focus on a diverse marketing environment surrounding tourism destinations. Accordingly, this study concentrates on a holistic conceptualization view of destination marketing. To narrow the research topic, this study focuses on the framework for destination marketing in Figure 2, which reveals limitations in the existing literature with a holistic perspective.  Pike and Page (2014) proposed a destination marketing framework providing a fundamental structure for destination marketing. This framework maps out strategic plans for destination marketing by discussing the goal of destination marketing, core requirements to achieve this goal, the role of destination marketing organizations (DMOs), and destination marketing organizational effectiveness. As shown in Figure 2, every DMO aims to achieve !18 sustained destination competitiveness; this could be viewed as the ultimate goal of destination marketing.  Two core elements are required to reach this goal. The first is resources that offer comparative advantages. The second is effective destination management. Pike & Page (2014) identified resources to strengthen comparative advantage of a destination; four germane considerations for this were adopted from a V.R.I.O. model of resource-based theory of competitive advantage (Barney, 1991, 1996). According to that model, a resource should be a) ÔValuableÕ in terms of cost reduction and revenue increase; b) ÔRareÕ compared to rival destinationsÕ resources; c) ÔInimitableÕ to be distinct from competing destinations;  and d) DMOs should be ÔOrganizedÕ to maximally enhance marketplace effect.  As shown Figure 2, important success factors are proposed for an efficiently-managed destination. An effective management organization, in this case, a DMO, is essential for destination marketing activities. DMO effectiveness is essential to achieve a destinationÕs marketing goal, namely sustained destination competitiveness. And, DMO effectiveness can be considered via internal perspectives emphasizing appropriate and efficient use of resources, and via external perspectives highlighting efficacy in the marketplace. Destinations compete against each other in global markets to place themselves in strong market positions (Pike & Page, 2014). To be well positioned as competitive players, destinations should develop brand identities and coordinate brand positioning marketing communications, evaluation of which necessitates performance measurement and tracking. !19 !Note. Destination marketing framework. Reprinted from ÒDestination Marketing Organizations and Destination Marketing: A Narrative Analysis of the Literature,Ó by Pike, S. and Page, S. J., 2014, Tourism Management, 41, p. 208. Copyright 2013 by Elsevier. Figure 2 Destination Marketing Framework  Among researchers and practitioners, inconsistencies exist in definitions of destination branding (see Blain, Levy, & Ritchie, 2005; Pike & Page, 2014; Ritchie & Ritchie, 1998). Berthon, Hulbert, and PittÕs (1999) model of a brand emphasized its functions for both the buyer and the seller. In line with this, Blain et al. (2005) defined destination branding from a comprehensive perspective:   !20 the set of marketing activities (1) that support the creation of a name, symbol, logo, word mark or other graphic that readily identifies and differentiates a destination; that (2) that consistently convey the expectation of a memorable travel experience; that (3) serve to consolidate and reinforce the emotional connection between the visitor and the destination; and that (4) reduce consumer search costs and perceived risk. Collectively, these activities serve to create a destination image that positively influences consumer destination choice. (p.237) Destination Branding and Brand Image Despite destination brandingÕs significance in the destination marketing framework (Morgan, Pritchard, & Pride, 2003; Pike & Page, 2014), few empirical studies have measured its effects. Since the 1990s, to achieve a strong market position and to attract more tourists, DMOs have put their efforts into differentiating the products and services that their destinations offer (Marzano & Scott, 2009; Pike & Page, 2014; Tasci, Gartner, & Cavusgil, 2007). Branding theories and concepts in general marketing first began to proliferate in the mid-20th Century, but it was not until the late 1990s that destination branding studies began to spread (Blain et al., 2005; Hampf & Lindberg-Repo, 2011; Pike & Page, 2014). How we measure the value of a brand is through consumer-based brand equity (CBBE) (Aaker, 1996) and since the early 1980s CBBE has been considered as the most important concept in branding studies. Nonetheless, only a handful of CBBE studies apply to destination branding (see Boo, Busser, & Baloglu, 2009; Chen & Myagmarsuren, 2010; Gartner & Konecnik Ruzzier, 2011).  From a consumer-oriented perspective, consumer-based brand equity centers on measuring how a consumer assesses brand value (Hampf & Lindberg-Repo, 2011; Keller, 1993). Keller (1993) defined consumer-based brand equity as Òthe differential effect of brand knowledge on consumer response to the marketing of the brandÓ (p. 2). In this sense, destination CBBE would be investigated in terms of brand knowledge, constituted by two sub-dimensions - brand awareness and brand image - built on the association network memory model (Keller, !21 1993; Zhang, Jansen, & Mattila, 2012). According to the literature, brand awareness is related to how either brand node or trace in memory are strongly associated with a consumerÕs ability to identify a brand among different brands within a category (see Keller, 1993; Percy & Rossiter, 1992; Zhang et al., 2012).  As shown in Figure 3, brand awareness, the first dimension of brand knowledge, consists of brand recognition and brand recall.  As Keller (1993) suggested, brand image, the second dimension of brand knowledge, is the actual image of the brand that consumers hold in their minds (see Figure 2; Pike & Page, 2014). Brand image is defined as Òperceptions about a brand as reflected by the brand associations held in consumer memoryÓ (Keller, 1993, p. 3). Brand association and attitudes constitute brand image. Keller (1993) conceptualized that brand associations which function as informational nodes are connected in consumersÕ memories to a brand node, and consumers associate their memory with the meaning of the brand (Cai, 2002; Zhang et al., 2012). Sub-dimensions of brand image include the favorability, strength, and uniqueness of brand associations. These dimensions differentiate brand image from brand knowledge by playing an important role in determining the differential response that goes into brand equity. !22 !Note. Reprinted from ÒConceptualizing, Measuring, and Managing Customer-Based Brand Equity,Ó by Keller, K. L., 1993, Journal of Marketing, 57, p. 7. Copyright 1993 by American Marketing Association. Figure 3 Dimensions of Brand Knowledge  Credibility in Social Media Destination marketing essentially aims to persuade potential tourists to purchase unknown experience goods with a high risk (Tham et al., 2013). When it comes to transactions and recommendations in an online marketing environment, it is important to note that user- generated content regarding destinations exchanged via social media should be trustworthy. More importantly, owing to the fact that the communicators are independent from the media channels, which allows them a greater autonomy in terms of capability to manage and operate their messages, tourists are more likely to consider marketing communication on social media platforms more reliable and credible (Lim & Van Der Heide, 2014). For this reason, when DMOs develop social media marketing strategies, they should consider that source credibility !23 plays a significant role in consumersÕ decision making processes. Therefore, prior to launching social media marketing strategies, DMO practitioners are necessary to understand the concepts relevant to source credibility. Credibility has been studied to explain to suggest how consumers accept a senderÕs message to be trustful and believable (Lim & Van Der Heide, 2014). In other words, credibility refers to determine the extent of touristsÕ intention to change their attitudes about a destination to be credible (Filieri, 2015; Lim & Van Der Heide, 2014; Veasna, Wu, & Huang, 2013). Therefore, this commonly accepted concept indicates how crucial credibility is with regard to the impact of social media. Source credibility theory explains that the perceived credibility of the message source influences communication receiverÕs acceptance of the message (Hovland & Weiss, 1951). Source credibility is a comprehensive concept with multiple dimensions, and expertise (competency), trustworthiness, co-orientation (similarity), and attraction constitute the concept (Cho et al., 2009; Filieri, 2015; Hawkins & Mothersbaugh, 2004; Young-shin Lim & Van Der Heide, 2014; Schweitzer, 1969). Expertise/competency is the perceived receiverÕs credibility that the message sender provides correct information; trustworthiness is the extent to which the receiverÕs level of confidence in the message senderÕs ability to reveal his or her actual thoughts or emotions; co-orientation/similarity refers to the extent to which the message sender facilitates aspects in common with the receiver; attraction represents the extent to which the message sender motivates positive reflections and feelings from the receiver, such as a desire to imitate the message sender.   In marketing literature, trust is defined as an essential factor that maintains a continuous relationship between customer and provider (Chiu et al., 2012; Han & Hyun, 2015). Also, Morgan and Hunt (1994) conceptualized trust as existing when one party has confidence in an !24 exchange partner's reliability and integrity. In the same vein, Moorman, Deshpand”, and Zaltman (1993, p.82) defined trust as Òa willingness to rely on an exchange partner in whom one has confidence." In terms of relation marketing (i.e., establishing, developing, and maintaining successful relational exchanges), Morgan and Hunt (1994) developed the commitment-trust theory. They explained that: commitment and trust are central to successful relation marketing because they encourage marketers to (1) work at preserving relationship investments by cooperating with exchange partners, (2) resist attractive short-term alternatives in favor of the expected long-term benefits of staying with existing partners, and (3) view potentially high-risk actions as being prudent because of the belief that their partners will not act opportunisticallyÓ (Morgan & Hunt, 1994, p.22). When consumers feel both commitment and trust regarding a brand or company, successful factors of relationship marketing, namely efficiency, productivity, and effectiveness, are generated. These theories and concepts are worth further investigation in the context of destination marketing because they have the potential to create a good framework to understand underlying relationships between consumer behavior and social media marketing. Comparing different applications and approaches among theories of source credibility, commitment-trust, and trust in the previous research, with proper modification and operationalization, source credibility may be the best way of understanding the effects of social earned and owned media. Therefore, in the current study, source credibility is adopted to identify how perceived credibility of social media marketing generated by DMOs affects touristsÕ perception of destination brand image and behavioral intentions.  !25 Tourist Behavioral Intentions Since early 1960s and 1970s, social science researchers have extensively studied behavioral intentions to predict consumersÕ future behavior (Aluri, 2012). Behavioral intentions can be defined as the extent to which an individualÕs intention is determined based on a specific behavior (Ajzen & Fishbein, 1969; Fishbein & Ajzen, 1977). There is no exception in tourism and destination marketing studies. It has been generally accepted in the previous studies that destination brand image impacts tourist behavioral intentions (Aluri, 2012; BonsŠn Ponte et al., 2015; Chen & Tsai, 2007; Chew & Jahari, 2014; Kang & Gretzel, 2012; Qu et al., 2011).  Similar to the general knowledge on behavioral intentions, tourist behavioral intentions consist of the two most important behavioral consequences: intention to visit the destination and intention to recommend.  Credibility has been widely studied to examine how trustworthy brands lead to higher customer engagement. Tourist behavioral intentions have been studied to evaluate how credible destination marketing affects tourists decision making when they plan their travel (Ponte et al., 2015; Chen & Tsai, 2007). As Ponte et al. (2015) examine, touristsÕ behavioral intentions depend on perceived trust of online content created by service provider. Chen and Tsai, (2007) also found that destination image has influence on touristsÕ intention to revisit the destination. In consideration of the importance of destination brand image, Qu et al. (2011) supported that destination brand image impacts intention to visit the destination. More notably, the destination marketing literature indicates that increased credibility has proved its influence on creating positive destination brand image (Baloglu & McCleary, 1999; Simpson & Siguaw, 2008; Tham et al., 2013). Additionally, it would be assumed that tourists who have positive images of a destination are more likely to recommend the destination to others (Beerli & Martõ«n, 2004; !26 Bruce et al., 2012; Qu et al., 2011; Tham et al., 2013).  Conceptual Framework Based on a review of literature regarding the relationships among social media marketing, credibility theories, destination branding models, and touristsÕ behavioral intentions, this study reveals the relationship that credibility, perceived by study participants who are exposed to a Michigan DMOÕs social media marketing, influences on destination brand image and touristsÕ behavioral intentions. Moreover, along with the existing literature, this study examines the relationship between destination brand image and touristsÕ behavioral intentions. Figure 4 proposes an illustration of the conceptual model.  H1, H2, and H3 are main effects, H4 is mediating effect, and H5a, H5b, and H5c are moderating effects. Along with the prior studies on credibility and destination branding, the tourism destination branding model created by Veasna et al. (2013) suggested that destination source credibility positively influences destination image. Although Veasna et al. (2013) confirmed the relationship between credibility and destination image, their study has a lack of attention to the social media marketing. In line with previous social media research in tourism and destination marketing (Ayeh et al., 2013b, 2013b; Tham et al., 2013), credibility is adopted to explain the conceptual model of the current study. This conceptual model also uses elements of research models from Cheng and Loi (2014) and Ponte, Carvajal-Trujillo, and Escobar-Rodr™guez (2015), which revealed the causal relation among credibility, destination brand image, and tourists' behavioral intentions. The research model from Cheng and Loi (2014) provided that the credibility of the brand affects touristsÕ intentions to purchase, and emphasized the importance of !27 studying moderating effects of different types of company responses to customersÕ online reviews. Similarly, Ponte et al. (2015) provided that in the tourism and e-commerce field, credible website marketing positively affects the online purchase intention. Derived from these studies, this study uses the credibility construct to confirm the relationship between other constructs (i.e., destination brand image and touristsÕ behavioral intentions) in the conceptual model.  !Note: This model proposes the relationship between independent variables (i.e., credibility: ÒI feel that the Traverse City Tourism's social media marketing activities are credibleÓ), mediating variables (i.e., destination brand image: ÒBased on your experiences with the video commercial, online reviews and management responses your impression of the image of Traverse City, MI is positiveÓ) and dependent variables (i.e., behavioral intentions: ÒI am likely to visit Traverse City, MIÓ). The relationships of moderating variables (i.e., experimental conditions of eWOM: negative online reviews/best practices of DMOÕs responses) also are included in this model.!Figure 4 Conceptual Model !!!28 This study uses destination brand image as a mediator between credibility and touristsÕ behavioral intentions. Qu et al. (2011) found that it made a positive impact on touristsÕ behavioral intentions. Chen and Tsai (2007) also indicated that the more favorable the destination image, the more positive the touristsÕ behavioral intention. However, Qu et al. (2011) only discussed overall image of destination brand, and did not include the other elements relevant to impression and attitudes scales of brand image suggested by Garretson and Burton (1998), Goodstein (1993), and Hsieh, Lo, and Chiu (2016). The destination marketing literature has yet to empirically investigate the influence of destination brand image on touristsÕ behavioral intentions in the context of social media marketing.  Similarly, Ayeh et al. (2013a) suggested that online travelerÕs attitude toward social media mediates the relationship between credibility, comprised of two antecedents which are trustworthiness and expertise, and behavioral intentions. In their research model Ayeh et al. (2013a) confirmed the mediating role of online travelersÕ attitude which links between credibility and behavioral intentions. However, it has limited application to the destination branding concepts. Historically, many researchers in consumer behavior and marketing fields have proven that the brand image scale has been adopted and developed from the traditional attitude scale (e.g., Garretson & Burton, 1998; Garretson & Niedrich, 2004; Goodstein, 1993; Hsieh et al., 2016; Zhang et al., 2012). Therefore, the current study tested the mediating role of destination brand image is derived from the existing research model (e.g., Ayeh et al., 2013a) to explain how credibility is associated with touristsÕ behavioral intentions in the context of social media and destination branding and to extend the research models from the previous literature.  Cheng and Loi (2014) examined how two important factors of management responses (e.g., persuasion effect, financial compensation outcome) relate to the online customer reviews in !29 the hotel industry. These two factors in management responses were treated as moderating variables, and the results confirmed how responses to negative online customer reviews influence hotel customersÕ intention to purchase through a moderating effect. However, they only discussed negative online customer reviews, and did not empirically test the destination brand image construct in the model. The current study extends the model of Cheng and Loi (2014) by adding two different types of online reviews (i.e., positive reviews, negative reviews) and using two types of management responses (i.e., best practice, poor practice) as moderating variables. As discussed above, the recent stream of social media research has proposed that credibility in social media marketing play a crucial role to customer behavioral intentions. However, social media research in destination branding is limited. Therefore, this raises the need to study how credibility influences destination brand image and how destination brand image mediates between credibility and touristsÕ behavioral intentions toward the destination. Most research has focused on identifying experimental treatments that enable customers to post reviews on social media and how these treatments may be moderated or mediated by factors such as persuasion effects, and economic and social compensations, leading to customer satisfaction or intention to purchase (Cheng & Loi, 2014; Gu & Ye, 2014; X. Liu, Schuckert, & Law, 2015). Accordingly, this study also aims to contribute to the existing research model of online reviews and management responses by taking a different approach of adding moderators regarding online customer reviews and management responses.  Hypotheses Development According to the previous literature on social media, credibility, destination brand image, and touristsÕ behavioral intentions, the following hypotheses were developed in an attempt to !30 examine the relationship among credibility in social media marketing, destination brand image, and touristsÕ behavioral intentions.   H1. Credibility in social media marketing has a positive influence on the destination brand image of a destination. H2. Credibility in social media marketing results in positive tourist behavioral intentions.  H3. Destination brand images result in positive tourist behavioral intentions.  H4. Destination brand images mediate the relationships between source credibility and tourist behavioral intentions.  H5a. EWOM effects of social earned media and social owned media moderate the positive relationship between credibility and destination brand image, such that the relationship between credibility and destination brand image is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews.  H5b. EWOM effects of social earned media and social owned media moderate the positive relationship between credibility and touristsÕ behavioral intentions, such that the relationship between credibility and touristsÕ behavioral intentions is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews.  !31 H5c. EWOM effects of social earned media and social owned media moderate the positive relationship between destination brand image and touristsÕ behavioral intentions, such that the relationship between destination brand image and touristsÕ behavioral intentions is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews.   !32 CHAPTER 3 METHODOLOGY The preceding literature review suggests that there is a connection between credibility, destination brand image, and touristsÕ behavioral intentions, and that better destination marketing research is needed in order to understand the impact of eWOM transmitted by social earned media and social owned media on these constructs. The current study builds on the existing literature by using social earned media and social owned media to identify the relationship between credibility, destination brand image, and touristsÕ behavioral intentions. This chapter is organized into the following sections: (1) Experimental Design, (2) Research Design, (3) Measurements, (4) Data Collection and Sampling, and (5) Data Analysis.   Experimental Design Many social media studies in destination marketing literature have used exploratory methods to address their research questions (Hays, Page, & Buhalis, 2013; Yumi Lim et al., 2012; Xiang & Gretzel, 2010). For example, Xiang and Gretzel (2010) conducted content analysis using search queries combined with tourism-related search keywords and destination names in order to investigate role of social media in online tourism planning. The methods used in this study have enabled researchers to suggest definition of social media and to delineate social media categories, which has added to the existing literature connecting this new phenomenon and technology in destination marketing (Xiang & Gretzel, 2010). An exploratory approach has made a strong contribution to research topics on which much light hasnÕt been shed in the previous literature. However, only a handful of studies have used explanatory research to investigate causal relationships between variables associated with social media in general !33 marketing and branding research (e.g., Laroche, Habibi, & Richard, 2013). This indicates that there is a strong need for explanatory research in social media studies in the context of destination branding. Since experimental design methods are efficient and commonly used, especially in media and communication research (Carr, Vitak, & McLaughlin, 2013; Carr & Walther, 2014; Walther, 2011), this study considers using experimental design methods to compare multiple groups under different treatments.  Experimental design in social sciences is Òone of several forms of scientific inquiry employed to identify the cause-and-effect relation between two or more variables and to assess the magnitude of the effect(s) produced (Silva, 2008, p.253).Ó In social science studies focused on establishing causal relationships, experimental designs often are considered as rigorous research methods (Black, 1955; Campbell & Stanley, 1966; Mitchell, 2012). The existing literature shows that in highly controlled experiments an experimental design is more likely to produce the strongest internally valid findings and design (Greenwood, 2004; Mitchell, 2012; Mook, 1983). Further, understanding experimental design is useful in the social sciences as a way to enhance researchersÕ understanding of the general logic (Babbie, 2010). Experimental design is suited especially well for testing hypotheses by controlling and manipulating the research environment and observing information. In other words, the researchers using experimental design anticipate any corresponding change in the dependent variable when they manipulate the degree of the independent variable (i.e., the stimulus) (Babbie, 2010; Burns & Burns, 2008).  Unlike field studies, which are conducted in uncontrolled situations, experimental studies, also called laboratory studies, require strictly controlled research settings (Burns & Burns, 2008).  The classical Òtrue experimentsÓ use strict control over the subjects and conditions !34 in the study. Researchers manipulate the conditions, control types, and the level of stimuli to determine whether they have any impact on the dependent variable (Havitz & Sell, 1991). The experimental design also entails random assignment of subjects to treatment groups. Randomization generates two or more groups that have no critical initial differences prior to any treatments being applied to the experimental group. Thus, random selection guarantees that the measured changes in the dependent variable can be attributed to the influence of independent variable (Babbie, 2010; Burns & Burns, 2008; Kirk, 2009).  Quasi-Experimental Designs However, in many forms of social research, it may not be possible to select a randomized group or to control all possible extraneous variables, which are essential elements in a classical experiment. Due to the constraints of the institutional environment on the research process, quasi-experimental designs are used more often than classic experiments in social sciences and in tourism settings (Kraus & Allen, 1987). Similar to classical experiments, quasi-experiments manipulate treatments. However, quasi-experiments are characterized by less control over the variables (stimuli) involved and non-random assignment of participants to different treatments. Because quasi-experimental studies usually use existing groups, this design is convenient and less disruptive to the participants than are classical experiments (Kraus & Allen, 1987; Rosenberg & Daly, 1993).   Research Design As discussed in the preceding sections, it is important to note that understanding the eWOM effects of social earned media (i.e., touristsÕ online reviews) and social owned media (i.e., DMOÕs responses) on destination brand image and behavioral intentions is necessary for !35 this study. In particular, marketers can formulate their responses to tourists who have read other customersÕ online reviews in order to stimulate positive behavioral intentions and attract more tourists to their destinations. As Cheng and Loi (2014) indicated, management responses can be manipulated as experimental stimuli and moderating variables in experimental design research. As in their study, the current study considers two different management response conditions to differentiate the experimental stimuli from each experimental group. Furthermore, two different types of online reviews (i.e., positive reviews and negative reviews) are included for each experimental group. Procedure The experiment employed a 2 (positive Facebook reviews vs. negative Facebook reviews) " 2 best practices vs. poor practices in DMO responses) between-subjects factorial design to identify the eWOM effects of social earned media and social owned media on touristÕs perceived image of the destination and on their subsequent behavioral intentions. For the purpose of this experiment, a Facebook page of Traverse City, Michigan, was designed to represent the hypothetical Facebook page of the DMO. Traverse City is the largest city in the Northern Michigan region, based on the 2010 U.S. Census, and, in 2009, TripAdvisor ranked it as the number two small-town travel destination in the United States. Traverse City is well known for its beaches, lakes, golf courses, and the wineries. Traverse City Tourism (TCT), the official DMO of this area, was organized in 1981 as the Traverse City Area Convention and Visitors Bureau. Traverse City Tourism focuses on enhancing, reinforcing and developing its destination brand to stimulate economic growth and to create jobs through tourism by attracting more visitors. In line with the preceding literature review, which emphasized the importance of destination branding, Ashton (2014) argued that Òdestination branding is a central topic for !36 academic research and is practically important for all destinations because it is intended to identify and differentiate one destination from othersÓ (p.1). The brand ÒPure MichiganÓ is recognized nationwide as a unique authentic destination brand that differentiates the region (Longwoods International, 2016). In 2006, Travel Michigan, the official DMO of the state, developed and launched the Pure Michigan destination brand campaign using several branding advertisements that were aired on the television and posted on YouTube. Forbes (2009) named Pure Michigan one of its 10 best tourism promotion campaigns of all time, ranked in the top six. As a result of Traverse City TourismÕ efforts to work closely with the Travel Michigan, Traverse City was designated as one of the six destinations that Pure Michigan promotes in the region. As shown in Figure 5, the national Traverse City-Pure Michigan video commercial depicting Traverse City and its associated attractions was embedded in an online survey.  !37 !Figure 5 Traverse City-Pure Michigan Video Commercial Subjects in each experimental group were asked to watch the Traverse City-Pure Michigan National Video commercial, were exposed to online reviews about Traverse City Tourism Facebook page provided by other Facebook users, as well as DMO responses to online reviews. The current study contained four experimental conditions (positive reviews/best practice of DMOÕs response, positive reviews/poor practice of DMOÕs response, negative reviews/best practice of DMOÕs response, negative reviews/poor practices of DMOÕs response). These four different versions of the Traverse City TourismÕs Facebook page represented the four experimental stimuli. All four versions of the Facebook page showed the same user interface of Facebook to control for the effect of other conditions except customer reviews and DMOÕs responses parts. FacebookÕs star-rating system ranges from one to five stars, where five stars is the highest. Following the procedure used by Cheng and Loi, (2014), this study designed !38 fictitious online customer reviews based on the adoption from the existing online reviews of similar tourism destinationÕs Facebook page. DMOÕs responses were designed according to the best practices reported by experts from both industry and academia.  For example, as shown in Figures 6 to 17, the hypothetical Traverse City TourismÕs Facebook pages presented subjects with fictitious reviews and DMOÕs responses as the following format:  !39  !Figure 6 Traverse City Tourism Facebook page for Experimental Group 1    !40 !Figure 7 Reviews of Facebook Users for Experimental Group 1   !Figure 8 DMO Responses for Experimental Group 1   !41 !Figure 9 Traverse City Tourism Facebook page for Experimental Group 2    !42 !Figure 10 Reviews of Facebook Users for Experimental Group 2   !Figure 11 DMO Responses for Experimental Group 2  !43 !Figure 12 Traverse City Tourism Facebook page for Experimental Group 3       !44  !Figure 13 Reviews of Facebook Users for Experimental Group 3   !Figure 14 DMO Responses for Experimental Group 3  !45 !Figure 15 Traverse City Tourism Facebook page for Experimental Group 4     !46 !Figure 16 Reviews of Facebook Users for Experimental Group 4  !Figure 17 DMO Responses for Experimental Group 4   !47 To test the hypotheses, this study utilized experimental design, as many media and communications studies have manipulated type of media to apply an experimental stimulus (treatment) (e.g., Carr & Walther, 2014; Darley & Smith, 1993; Shi, Messaris, & Cappella, 2014; Y. Yoo & Alavi, 2001). The experimental groups were exposed to eWOM effects of social earned media and social owned media as experimental stimuli, and the comments and responses were manipulated in order to identify the different ways in which tourists who view the Traverse City-Pure Michigan branding through a video commercial perceived the destination branding. Experimental design is also characterized by the measurement of dependent variables, so in order to assess touristsÕ perceptions of destination branding, variables related to destination brand image were measured as dependent variables. Destination brand image scales were adopted from the existing literature (Aaker, 1996; Berry, 2000; Garc™a et al., 2012a; Garretson & Burton, 1998; Keller, 1993; Zhang et al., 2012). Additionally, destination brand image was investigated as a mediator to examine whether eWOM effects increase the degree of the credibility in a destination brand message, which, as a result, would lead to greater positive perception and stronger attitudes toward the destination.   Measurements Credibility To evaluate touristsÕ perceptions of credibility in DMOÕs social media marketing, a credibility construct was measured based on the existing literature (Ayeh et al., 2013a, 2013a; Gefen, Karahanna, & Straub, 2003; Ohanian, 1990, 1991; Ponte et al., 2015). Adapting the operationalization used by Ayeh et al. (2013a), this study measured credibility on a five-item scale. Ayeh et al. (2013a) adapted items from the measurements conducted by Ohanian (1990, !48 1991) to design their credibility scale. The scale consists of five items to measure credibility on a seven-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). The statement ÒHow would you evaluate the Traverse City Tourism's social media marketing activities in general?  I feel that the Traverse City Tourism's social media marketing activities are...Ó preceded credibility items.  Table 1 Measurement Items for Credibility Note: All items ranged from 1 (strongly disagree) to 7 (strongly agree)  Destination Brand Image To make the subjectsÕ perception of the destination brand image measurable, perceptions were operationally defined according to five items based on survey instruments used in the literature (Bruce et al., 2012; Garc™a et al., 2012a; Garretson & Burton, 1998; Goodstein, 1993; Constructs Items Source Credibility   How would you evaluate the Traverse City Tourism's social media marketing activities in general?  I feel that the Traverse City Tourism's social media marketing activities are...  (Ayeh et al., 2013a, 2013b; Gefen et al., 2003; Larzelere & Huston, 1980; Morgan & Hunt, 1994; Ponte et al., 2015; Tsfati & Ariely, 2014)  ...honest.  ...trustworthy.  ...reliable.  ...sincere.  ...dependable. !49 Hsieh et al., 2016; Qu et al., 2011; Quintal, Phau, & Polczynski, 2014). As explained in the literature review, destination brand image has been measured using a number of scales. In this study, brand image has been measured using a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), and an adaptation of a scale utilized by Goodstein, (1993), Qu et al. (2011), and Zhang et al. (2012) was used to measure destination brand image. The scale included five attitudinal adjectives to interpret perceptions and impressions generated from subjectsÕ destination brand image: Good, Positive, Likable, Favorable, and Pleasant.   Table 2 Measurement Items for Destination Brand Image Note: All items ranged from 1 (strongly disagree) to 7 (strongly agree)  Behavioral Intentions  To assess touristsÕ behavioral intentions, this study adapted a scale used by Kang and Gretzel, (2012). The scale includes five items to measure touristsÕ willingness to visit and to recommend the destination to others. The five-item scale for touristsÕ behavioral intentions was measured on a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Constructs Items Source Destination Brand Image  Based on your experiences with the video commercial, online reviews and management responses your impression of the IMAGE of Traverse City, MI isÉ    (Garc™a et al., 2012b; Garretson & Burton, 1998; Quintal et al., 2014; Zhang et al., 2012) ...good.  ...positive.  ...likable.  ...favorable. ...pleasant.  !50 The survey includes the following statement, ÒPlease indicate your level of agreement or disagreement with the following statements.Ó preceded by behavioral intentions items. All the measurement items were assessed as detailed in Table 3. Table 3 Measurement Items for Behavioral Intentions Note: All items ranged from 1 (strongly disagree) to 7 (strongly agree)  The final portion of the survey included items regarding demographic information and the frequency of social media use for each subject. The demographic questions in this section included questions related to gender, age, and ethnicity, and the frequency of social media use was measured based on an adaptation of a scale utilized by (Hampton, Rainie, Lu, Shin, & Purcell, 2015).  Constructs Items Source Behavioral Intentions  Please indicate your level of agreement or disagreement with the following statements.   (Chen & Tsai, 2007; Chiu et al., 2012; Kang & Gretzel, 2012) Traverse City, MI seems like a great place to visit.  I would recommend Traverse City, MI to friends and family.  I believe I would enjoy myself on a trip to Traverse City, MI. I am likely to visit Traverse City, MI.  !51 Data Collection and Sampling As Jang, Kim, and Lee (2015) argue, an increasing number of academic and commercial studies have used online panel surveys as a powerful tool to examine consumer behaviors in numerous settings, including the tourism and hospitality industries (e.g., Ayeh et al., 2013b; Lee & Hyun, 2015; Tanford, Baloglu, & Erdem, 2012; Wu, Fan, & Mattila, 2015; Yen & Tang, 2015). Similarly, due to its cost-effectiveness and the ease of collecting data, many researchers in tourism and hospitality have begun to recruit respondents through online panel providers such as Qualtrics and Amazon Mechanical Turk (Hung & Petrick, 2011; Tanford et al., 2012; Yen & Tang, 2015). One of the advantages of using online panel data is that researchers can reach out to survey participants with less spatial and temporal constraints (Babbie, 2010). On the other hand, according to Yen and Tang (2015), one of the disadvantages of using a panel is that some participants are likely to simply click through the questionnaire without seriously taking time to answer the questions. To prevent such a situation, this study has designed attention filters and quality check questions with QualtricsÕ assistance. For example, one question is the prompt, ÒFor quality, select Ô3Õ for this line.Ó If respondents answer other than 3, the responses are deleted from the data set. Although those who have access to the Internet can be overrepresented when researchers use the online panel database, the sampling that has been adopted for this study has been designed to maximize general population representation.  This study recruited a total of 516 subjects from the Qualtrics online panel database. After receiving approval from the Institutional Review Board (IRB), the online questionnaire was distributed through the Qualtrics online survey platforms. These sample respondents were opt-in panel participants. Moreover, this study used the U.S. General Population as a sampling frame, and subjects were randomly assigned to the four experimental groups. Each experimental !52 condition was distributed to the subjects from the Qualtrics online panel database through its online survey links. Data collection was stopped when 1,051 subjects had participated in the survey. Among the collected responses, subjects who did not plan their travel during the vacation were excluded through a screening question. Also, incomplete responses were removed from the dataset. The remaining 516 responses were used for analysis.  Practically, the sample size needed for researchers to run structural equation modeling (SEM) well gets larger when studies use more parameter estimates (Bentler & Chou, 1987; Jıreskog & Sırbom, 1993, 1996). According to Jıreskog & Sırbom (1996), the sample size for a SEM should be approximately five to ten times the number of estimated parameters. Since the model used in this study is estimated to be 45 parameters, it was determined that a total sample of the current study (N=516) met the requirement.  Data Analysis  This study used structural equation modeling (SEM) to test the hypotheses in the conceptual model. Kaplan (2008, p.1) proposes that structural equation modeling can be defined as Òa class of methodologies that seeks to represent hypotheses about the means, variances and covariances of observed data in terms of a smaller number of ÔstructuralÕ parameters defined by a hypothesized underlying modelÓ.  In other words, SEM is a multitude of statistical techniques that allows researchers to take a comprehensive approach to evaluate, modify, and advance theoretical models (Anderson & Gerbing, 1988; Plunkett, 2013).  Previous literature supports that SEM has been gaining popularity, especially for the purpose of testing theoretical models examining how sets of variables explain constructs and how these constructs relate to one !53 another (Schumacker & Lomax, 2004; Wright, 2015). Moreover, one of the main advantages of using SEM is its ability to measure the latent constructs and assess the paths of the hypothesized relationships between those constructs. In particular, SEM is a combination of factor analysis and path analysis, which establishes measurement model and structural model. As Plunkett (2013, p.62) discussed, SEM provides Òthe advantage of estimating a series of multiple regression equations simultaneously with one comprehensive model, integrating latent variables into the analysis while accounting for measurement errors in the estimation processÓ. In the context of the current study, the CFA and SEM analyses were used to confirm the structure of latent constructs and examine the relationships among credibility, destination brand image, and touristsÕ behavioral intentions. Data analysis for structural equation modeling (SEM) was conducted to determine the effects of destination branding messages disseminated via social earned media and social owned media on the perceived credibility and to investigate its sequential effect on destination brand image and on tourist behavioral intentions. The data collected from the experiment was imported to the Statistical Package for Social Science (SPSS) 22.0 and Analysis of Moment Structure (AMOS) 22.0. For the entire statistical analysis, SPSS 22.0 and AMOS 22.0 were used.  Prior to statistical analysis, the data was cleaned, and missing data was dealt with using the expectation-maximization (EM) technique in SPSS to estimate missing values. To test the proposed model, this study used confirmatory factor analysis (CFA) in structural equation modeling (SEM). This was also used to evaluate convergent and discriminant validity of the factor structure. Then, to test the hypothesized relationships, path analysis in SEM was conducted. Data analysis for SEM was conducted to determine the effects of destination branding messages disseminated via social earned media and social owned media on the !54 perceived credibility and to investigate its sequential effect on destination brand image and on touristsÕ behavioral intentions.  !55 CHAPTER 4 RESULTS The overall results of this study support the hypothesized relationships among key constructs (e.g., credibility in social media marketing, perceived image of the destination brand, and touristsÕ behavioral intentions). This study employed a 2 (positive Facebook reviews vs. negative Facebook reviews) " 2 (best practices vs. poor practices in DMO responses) between-subjects experimental design in order to compare groups of subjects according to an experimental stimulus (i.e., eWOM effects) (Birnbaum, 1999; Wright, 2015). Along with previous literature on eWOM effects of social media (Cheng & Loi, 2014; Gu & Ye, 2014; Sparks, So, & Bradley, 2016), it is expected that eWOM effects have a different moderating effect on the relationship between credibility, destination brand image and touristsÕ behavioral intentions. First of all, the current study describes how all the experimental treatments were distributed to each experimental group. This is followed with descriptive analysis providing characteristics of the sample, including demographic information. Moreover, the findings present the impact of credibility on both subjectÕs perceived destination brand image and behavioral intentions. Accordingly, this chapter presents the findings of the current study and includes the following sections: (1) Descriptive Analysis, (2) Manipulation Checks, (3) Comparison of the Effects of Experiments, (4) Measurement Model, and (5) Structural Model.  Descriptive Analysis Table 4 shows that 67.2% of the subjects were female, and 32.8% were male. The age group with the most subjects was 27Ð35 (25.6%). As shown in Table 4, the remaining subjectsÕ fell into the groups 18Ð26 (15.3%), 36Ð45 (18.8%), 46Ð55 (14.1%), 56Ð65 (16.1%), and 66 or !56 older (10.1%). In terms of ethnicity, the majority of subjects were Caucasian/White (77.9%). Table 4 Demographic Profile of Respondents (N=516) !              As indicated in Table 5, a significant majority of subjects (70.7%) indicated that they use Variables Frequency (n) Percentage (%) Gender:   Female  347 67.2 Male 169 32.8 Age groups in years:   18Ð26 79  15.3 27Ð35 132  25.6 36Ð45 97  18.8 46Ð55 73  14.1 56Ð65 83  16.1 66+ 52  10.1 Ethnicity   Black/African American 42 8.1 Hispanic/Latino 34  6.6 Caucasian/White 402  77.9 Asian 24 4.7 Native American/ American Indian 7  1.4 Pacific Islander 0  0.0 Other 7  1.4 !57 social media several times a day. The remaining subjects reported that they use social media about once a day (13.4%), 3Ð5 days a week (6.8%), less often (6.4%) and every few weeks (2.7%).  Table 5 Descriptive Analysis (Social Media Use) (N=516) !!    Demographic Profile of Respondents for Each Group Since the experiment of this study designed to assign subjects each experimental group randomly, differences of demographic information among groups wouldnÕt impact the experimental design which was intended to use experimental stimulus solely impact the results of data analysis. Demographic information of the Experimental Group 1 presented that 59.7% of the subjects were female, and 40.2% were male. The age group with the most subjects was 27Ð35 (28.1%). As indicated in Table 6, the remaining subjectsÕ fell into the groups 18Ð26 (9.4%), 36Ð45 (20.9%), 46Ð55 (13.7%), 56Ð65 (17.3%), and 66 or older (10.8%). In addition, the prevalent subjects were Caucasian/White (76.3%) and Black/African American (10.1%). As indicated in Table 7, the majority of subjects (68.3%) use social media several times a day followed by individuals who reported that they use social media about once a day (15.1%), 3Ð5 days a week (7.2%), less often (7.2%), and every few weeks (2.7%).  Social Media Use Several times a day: 365 (70.7%) About once a day: 69 (13.4%) 3Ð5 days a week: 35 (6.8%) Every few weeks: 14 (2.7%) Less often: 33 (6.4%) !58 Table 6 Demographic Profile of Respondents for Experimental Group 1 (N=139) !    Variables Frequency (n) Percentage (%) Gender:   Female  83 59.7 Male 56 40.3 Age groups in years:   18Ð26 13 9.4 27Ð35 39 28.1 36Ð45 29 20.9 46Ð55 19 13.7 56Ð65 24 17.3 66+ 15 10.8 Ethnicity   Black/African American 14 10.1 Hispanic/Latino 9 6.5 Caucasian/White 106 76.3 Asian 6 4.3 Native American/ American Indian 3 2.2 Other 1 .7 !59 Table 7 Social Media Use for Experimental Group 1 (N=139) !!!!!!!!!!!! As shown in Table 8, demographic information of the Experimental Group 2 indicated that 66.7% of the subjects were female, and 33.3% were male. The age group with the most subjects was 27Ð35 (22.2%). The remaining subjectsÕ ages ranged between 36 and 45 (20.0%), 56 and 65 (18.5%), 18 and 26 (17.0%), 46 and 55 (13.3%), and 66 or older (8.9%). In addition, the prevalent subjects were Caucasian/White (83.0%). As indicated in Table 9, the majority of subjects (71.1%) use social media several times a day followed by individuals who reported that they use social media about once a day (16.3%), 3Ð5 days a week (5.2%), less often (4.4%), and every few weeks (3.0%).       Social Media Use Several times a day: 95 (68.3%) About once a day: 21 (15.1%) 3Ð5 days a week: 10 (7.2%) Every few weeks: 3 (2.2%) Less often: 10 (7.2%) !60 Table 8 Demographic Profile of Respondents for Experimental Group 2 (N=135) !! !!Variables Frequency (n) Percentage (%) Gender:   Female  90 66.7 Male 45 33.3 Age groups in years:   18Ð26 23 17.0 27Ð35 30 22.2 36Ð45 27 20.0 46Ð55 18 13.3 56Ð65 25 18.5 66+ 12 8.9 Ethnicity   Black/African American 8 5.9 Hispanic/Latino 5 3.7 Caucasian/White 112 83.0 Asian 7 5.2 Native American/ American Indian 2 1.5 Other 1 .7 !61 Table 9 Social Media Use for Experimental Group 2 (N=135) !!!!!!!!!!!!! Table 10 shows that demographic profile of the Experimental Group 3 in Table 10 shows that 59.7% of the subjects were female, and 40.3% were male. The age group with the most subjects was 27Ð35 (26.8%) with an average age of 40-years old. As shown in Table 10, the remaining subjectsÕ fell into the groups 18Ð26 (19.5%), 36Ð45 (17.9%), 46Ð55 (15.4%), 56Ð65 (12.2%), and 66 or older (8.1%). Additionally, the majority of subjects were Caucasian/White (75.6%). As presented in Table 11, the prevalent subjects (72.4%) use social media several times a day followed by participants who reported that they use social media about once a day (12.2%), 3Ð5 days a week (6.5%), less often (5.7%), and every few weeks (3.3%). !!!!!!!!!!!Social Media Use Several times a day: 96 (71.1%) About once a day: 22 (16.3%) 3Ð5 days a week: 7 (5.2%) Every few weeks: 4 (3.0%) Less often: 6 (4.4%) !62 Table 10 Demographic Profile of Respondents for Experimental Group 3 (N=123) !!!Variables Frequency (n) Percentage (%) Gender:   Female  90 59.7 Male 33 40.3 Age groups in years:   18Ð26 24 19.5 27Ð35 33 26.8 36Ð45 22 17.9 46Ð55 19 15.4 56Ð65 15 12.2 66+ 10 8.1 Ethnicity   Black/African American 10 8.1 Hispanic/Latino 9 7.3 Caucasian/White 93 75.6 Asian 7 5.7 Native American/ American Indian 1 .8 Other 3 2.4 !63 Table 11 Social Media Use for Experimental Group 3 (N=123) !!!!!!!!!!!!!Demographic profile of the Experimental Group 4 indicated that 70.6% of the subjects were female, and 29.4% were male. Subjects from the Experimental Group 4 ranged in age 18 to 85 with an average age of 43-years old. The age group with the most subjects was 27Ð35 (25.2%). The remaining subjectsÕ ages ranged between 18 and 26 (16.0%), 36 and 45 (16.0%), 56 and 65 (16.0%), 46 and 55 (14.3%), and 66 or older (12.6%). Moreover, the majority of subjects were Caucasian/White (76.5%). As shown in Table 13, the majority of subjects (71.4%) use social media several times a day followed by individuals who reported that they use social media about once a day (9.2%), 3Ð5 days a week (8.4%), less often (8.4%), and every few weeks (2.5%).       Social Media Use Several times a day: 89 (72.4%) About once a day: 15 (12.2%) 3Ð5 days a week: 8 (6.5%) Every few weeks: 4 (3.3%) Less often: 7 (5.7%) !64 Table 12 Demographic Profile of Respondents for Experimental Group 4 (N=119) !!!!!  Variables Frequency (n) Percentage (%) Gender:   Female  84 70.6 Male 35 29.4 Age groups in years:   18Ð26 19 16.0 27Ð35 30 25.2 36Ð45 19 16.0 46Ð55 17 14.3 56Ð65 19 16.0 66+ 15 12.6 Ethnicity   Black/African American 10 8.4 Hispanic/Latino 11 9.2 Caucasian/White 91 76.5 Asian 4 3.4 Native American/ American Indian 1 .8 Other 2 1.7 !65 Table 13 Social Media Use for Experimental Group 4 (N=119)       Manipulation Checks Manipulation checks were conducted to determine whether subjects would perceive the experimental stimuli as realistic and consider DMOÕs responses to the online reviews in the experimental stimuli as a best practice. Scenario realism (e.g., ÒFacebook page described at the beginning of the survey is realistic.Ó) and subjectsÕ consideration of DMOÕs responses as a best practice (e.g., ÒThe response from the Facebook page to the online review can be considered as a best practice.Ó) would be confirmed with a mean value 4 or higher on a 7-point scale. With regard to scenario realism, subjects perceived the hypothetical Facebook page of Traverse City tourism as realistic (M = 5.30). The results of the descriptive analysis indicated that the manipulation check for perceived subjectsÕ consideration on experimental stimuli of eWOM effects was 4.27. Taken together, these results indicate that our manipulations were effective.  Comparison of the Effects of Experiments To compare the different eWOM effects of social earned media and social controlled Social Media Use Several times a day: 85 (71.4%) About once a day: 11 (9.2%) 3Ð5 days a week: 10 (8.4%) Every few weeks: 3 (2.5%) Less often: 10 (8.4%) !66 media on destination brand image, the one-way between-groups analysis of variance (ANOVA) alternative was conducted using SPSS 22.0. As Table 14 showed, there were 139 subjects in Experimental Group 1 (positive reviews/best practice of DMOÕs response); 135 in Experimental Group 2 (positive reviews/poor practice of DMOÕs response); 123 in Experimental Group 3 (negative reviews/best practice of DMOÕs response); and 119 subjects in Experimental Group 4 (negative reviews/poor practices of DMOÕs response) after assessing the missing data. Table 15 displays the means and stand deviations of descriptive analysis. More specifically, since experimental groups have unequal sample sizes and standard deviations, the robust test of equality of means was conducted. According to Tomarken and Serlin (1986), when the result of the test of homogeneity of variances is statistically significant (i.e., the data analysis rejects the assumption of ANOVA test) it is commonly recommended to use an ANOVA alternative to test mean differences under variance heterogeneity. In this study, one of the commonly used ANOVA alternatives, the Brown-Forsythe test, was used to avoid from committing type 1 error (Tomarken & Serlin, 1986).  Table 14 Descriptive Analysis (Experimental Groups) (N=516)    Subjects in Experimental Groups  Experimental Group 1: 139 (26.9%) Experimental Group 2: 135 (26.2%) Experimental Group 3: 123 (23.8%) Experimental Group 4: 119 (23.1%) !67 Table 15 Comparison of Means and Standard Deviations  Source Mean (Standard Deviations)  Experimental  Group 1  Experimental  Group 2  Experimental  Group 3  Experimental  Group 4  Destination  Brand Image  5.98 (1.06) 5.96 (1.05) 4.49 (1.80) 4.57 (1.73) Credibility  5.70 (1.20) 5.57 (1.19) 4.77 (1.50) 4.60 (1.56) Behavioral Intentions  5.33 (1.25) 5.31 (1.15) 4.03 (1.65) 4.33 (1.64) Note. Experimental Group 1 = positive reviews/best practice of DMOÕs response, Experimental Group 2 = positive reviews/poor practice of DMOÕs response, Experimental Group 3 = negative reviews/best practice of DMOÕs response, Experimental Group 4 = negative reviews/poor practices of DMOÕs response. All items were assessed on a 7-point scale (1=strongly disagree, 7=strongly agree). Games-Howell post hoc test conducted.   As presented in Table 15 and Figure 18, the mean value of destination brand image ranged from 4.49 to 5.98. Based on the results of the Brown-Forsythe test, significant differences exist on the dependent variable, destination brand image (i.e., a mean of all the destination brand image items) between experimental stimuli on eWOM effects at the p < .001 level, F = 41.59. In particular, the results showed that subjects exposed to positive Facebook reviews and best practices in DMO responses (Experimental Group 1) were more likely to have positive perceptions on destination brand image than those with treatments of negative Facebook reviews combined with best practices in DMO responses (Experimental Group 3). Likewise, a statistically significant difference in destination brand image was found between Experimental Group 1 and Experimental Group 4 (negative reviews/poor practices of DMOÕs response). Further, there was a significant difference in destination brand image between Experimental Group 2 and Experimental Group 3. Specifically, subjects in Experimental Group 2 who were exposed to positive Facebook reviews and poor practices in DMO responses were more likely to !68 have higher perceptions on destination brand image than subjects in Experimental Group 3. Also, a significant effect of destination brand image was found between Experimental Group 2 and Experimental Group 4. On the other hand, there were not statistically significant differences for destination brand image between experimental groups with same reviews. The results suggest that exposure to the same reviews did not have an effect on touristsÕ perceptions on destination brand image.  The findings showed that the mean value of credibility ranged from 4.60 to 5.70 (see Table 15 and Figure 19). Based on the results of the Brown-Forsythe test, significant differences found on the dependent variable, credibility (i.e., a mean of all the credibility items) between experimental stimuli on eWOM effects at the p < .001 level, F = 20.812. More specifically, the findings showed that subjects exposed to positive Facebook reviews and best practices in DMO responses (Experimental Group 1) were more likely to have positive perceptions on credibility in social media marketing than those with treatments of negative Facebook reviews combined with best practices in DMO responses (Experimental Group 3). Similarly, a statistically significant difference in destination brand image was found between Experimental Group 1 and Experimental Group 4 (negative reviews/poor practices of DMOÕs response). In addition, there was a significant difference in credibility in social media marketing between Experimental Group 2 (positive reviews/poor practices of DMOÕs response) and Experimental Group 3 (negative reviews/best practices of DMOÕs response). Specifically, Experimental Group 2 that exposed to positive Facebook reviews and poor practices in DMO responses were more likely to have higher credibility in social media marketing than subjects in Experimental Group 3. Moreover, a significant effect of credibility was existed between Experimental Group 2 (positive reviews/poor practices of DMOÕs response) and Experimental Group 4 (negative reviews/poor !69 practices of DMOÕs response). However, there were not statistically significant differences for destination brand image between experimental groups with same reviews.  Likewise, as presented in Table 15 and Figure 20, the mean value of touristsÕ behavioral intentions ranged from 4.03 to 5.33. Based on the results of the Brown-Forsythe test, significant differences exist on the dependent variable, touristsÕ behavioral intentions (i.e., a mean of all the tourists behavioral intentions items) between experimental treatments on eWOM effects at the p < .001 level, F = 27.619. Particularly, the results found that subjects exposed to positive Facebook reviews and best practices in DMO responses (Experimental Group 1) were more likely to have positive intentions to visit and to recommend the destination than those with treatments of negative Facebook reviews combined with best practices in DMO responses (Experimental Group 3). In the same way, a statistically significant difference in touristsÕ behavioral intentions was found between Experimental Group 1 (positive reviews/best practices of DMOÕs response) and Experimental Group 4 (negative reviews/poor practices of DMOÕs response). Also, there was a significant difference in touristsÕ behavioral intentions between Experimental Group 2 (positive reviews/poor practices of DMOÕs response) and Experimental Group 3 (negative reviews/best practices of DMOÕs response). Specifically, subjects in Experimental Group 2 who were exposed to positive Facebook reviews and poor practices in DMO responses were more likely to show positive behavioral intentions than subjects in Experimental Group 3. Further, a significant effect of touristsÕ behavioral intentions was found between Experimental Group 2 (positive reviews/poor practices of DMOÕs response) and Experimental Group 4 (negative reviews/poor practices of DMOÕs response). On the other hand, there were not statistically significant differences for touristsÕ behavioral intentions between experimental groups with same reviews  !70 !Note. BI = Behavioral Intentions; IMG = Destination Brand Image; CRD = Credibility. Figure 18 Mean of Destination Brand Image   !71 !Note. BI = Behavioral Intentions; IMG = Destination Brand Image; CRD = Credibility. Figure 19 Mean of Credibility   !72 !Note. BI = Behavioral Intentions; IMG = Destination Brand Image; CRD = Credibility. Figure 20 Mean of Tourists' Behavioral Intentions    !73 Measurement Model Following the recommendations of Anderson and Gerbing (1988), a confirmatory factor analysis (CFA) using maximum likelihood estimation was employed to assess the measurement model. As displayed in Figure 21, a first order-factor model was adopted to examine three key constructs (e.g., Behavioral Intentions, Destination Brand Image, Credibility) in the model. The results showed that standardized factor loading for all items were statistically significant and exceeded the recommended .70 threshold (Hair, Black, Babin, & Anderson, 2013) with most loadings reaching values above .90 (see Figure 21). A second-order CFA was conducted to examine the overall fit of the measurement model (see Figure 21 and Table 8). As Table 16 illustrates, the overall goodness-of- fit indices for second-order CFA suggested that the model presented an acceptable fit for the data: #2/df = (321.818/74) = 4.349; GFI = .917; AGFI = .882; CFI = .978; NFI = .971; RFI = .964. Further, factor reliability was assessed with evaluation of construct reliability, average variance extracted (AVE), and CronbachÕs alpha ($). As shown in Table 17, construct reliability of all three constructs surpass the recommended threshold value of .70 (Hair et al., 2013), with most values above .90 (see Table 17). The results also indicated that AVE and $ exceeded the commonly recommended values of .50 and .80, respectively (see Table 17) as suggested by Fornell and Larcker (1981).      !74 Table 16 Model Fit Indices #2/df GFI  AGFI  CFI  NFI  RFI 4.349  .917 .882  .978 .971 .964 Note. 2 = Chi-square; GFI = goodness-of-fit-index; AGFI = adjusted goodness-of-fit-index; NFI = normed fit index; RFI = relative fit index.  Table 17 Factor Reliability          Note. AVE =average variance extracted;  = CronbachÕs alpha.    Construct Construct  Reliability AVE ! Behavioral Intentions 0.926 0.807 .930 Destination Brand Image 0.984 0.924 .984 Credibility 0.972 0.876 .972 !75 Table 18 Results of Confirmatory Factory Analysis Note. ***p<.001 Construct Item Standardized Factor Loading Behavioral Intentions  Please indicate your level of agreement or disagreement with the following statements.     BI1 Traverse City, MI seems like a great place to visit.  .898*** BI2 I would recommend Traverse City, MI to friends and family.  .892*** BI3 I believe I would enjoy myself on a trip to Traverse City, MI. .905*** BI4 I am likely to visit Traverse City, MI.  .821*** Destination Brand Image Based on your experiences with the video commercial, online reviews and management responses your impression of the IMAGE of Traverse City, MI isÉ  IMG1 ...good.  .961*** IMG2 ...positive.  .963*** IMG3 ...likable.  .959*** IMG4 ...favorable. .959*** IMG5 ...pleasant.  .964*** Credibility  How would you evaluate the Traverse City Tourism's social media marketing activities in general?  I feel that the Traverse City Tourism's social media marketing activities are...  CRD1 ...honest.  .955*** CRD2 ...trustworthy.  .954*** CRD3 ...reliable.  .949*** CRD4 ...sincere.  .886*** CRD5 ...dependable. .934*** !76  Note. BI = Behavioral Intentions; IMG = Destination Brand Image; CRD = Credibility.!Figure 21 The Result of Second-Order CFA for Overall Model  !77 Structural Model Table 19 and Figure 22 provide a summary of the structural model and results used to test the research hypotheses. Overall model fit indicated that all indices satisfied the threshold as suggested by Hair et al. (2013) (#2/df = (321.818/74) = 4.349; GFI = .917; AGFI = .882; CFI = .978; NFI = .971; RFI = .964). As hypothesized, credibility in social media marketing has a positive effect on both destination brand image (% = .780 p < .001) and touristsÕ behavioral intentions (% = .166, p < .001), providing support to Hypotheses 1 and 2. Hypothesis 3 predicting a positive relationship between destination brand image and touristsÕ behavioral intentions is also supported (% = .695, p < .001).  Table 19 Standardized Path Coefficients of the Structural Model!Overall Model Note. ***p < .01; S.E. = Standard Error; C.R. = Critical Ratio.   Hypotheses/path Standardized coefficients S.E. C.R. Structural Model    H1 CRD ! DBI .780*** .036 23.910 H2 CRD ! BI .166*** .048 3.598 H3 DBI ! BI .695*** .047 13.739 !78 !Figure 22 The Result of SEM with Standardized CoefficientsÑOverall Model   Model=Standardized estimates Group=Overall (N=516) Chi-square=321.818 df=74, Chi-square/df=4.349, GFI=.917, AGFI=.882,  NFI=.971, CFI=.978, RMR=.051, RMSEA=.081,P=.000 !79 The Mediating Role of Destination Brand Image As indicated in Table 20, a summary of the tests of mediation and results used to test the research hypotheses were provided. Considering the effects of destination brand image, a mediating test was conducted to analyze its mediating role between credibility and behavioral intentions. The Z score from the Sobel test for the effect of credibility on behavioral intentions through destination brand image (Z =11.77, p < .01) indicated that the mediating effect of destination brand image for the impact of credibility on behavioral intentions was significant. In the relationship between credibility and behavioral intentions, the mediating effect of destination brand image is .542 (p < .01). Therefore, given the results of the Sobel test, the significant effect of credibility on behavioral intentions is partially mediated by destination brand image, thus providing support for H4.  Table 20 Mediating Effect!Sobel Test Note. ***p < .01.   The Moderating Role of eWOM Effects Chi-square difference test  A multi-group analysis in AMOS can be tested using Chi-square differences. As shown in Table 21, an analysis of the entire structural model has found that the 2 difference between the unconstrained model (2 = 678.850, df = 296) and the fully constrained model (#2 = 752.444, Hypotheses/path Standardized coefficients z-test P Mediation Test    H4 CRD ! DBI .542*** 12.213 *** ! BI !80 df = 338) was significant, &#2 (42) = 73.593, p < .01. Therefore, the relationships among the four experimental groups were different. As indicated in Table 22, the strength was higher in Experimental Group 3 than in the other three experimental groups for the path from credibility to destination brand image. The results are as follows: Group 3: % = 0.807, p < .01; Group 1: % = 0.790, p < .01; Group 4: % = 0.765, p < .01; Group 2: % = 0.613, p < .01; &#2 (3) = 24.855, p < .01, thus supporting the moderating role of eWOM effects on the CRD -> DBI path. Likewise, the coefficient estimates for the path from credibility to behavioral intentions across experimental groups shows that the CRD -> BI path was significantly different among the four groups, &#2 (3) = 19.025, p < .01. For the CRD -> BI path, the coefficient estimate was the strongest in Experimental Group 2 (Group 2: % = 0.525, p < .01; Group 4: % = 0.096, p >.05; Group 1: % = 0.061, p > .05; Group 3: % = 0.022, p > .05). For the path from the destination brand image to behavioral intentions, the strength was higher in Experimental Group 3 than in the other three experimental groups (Group 3: % = 0.525, p < .01; Group 4: % = 0.096, p >.05; Group 1: % = 0.061, p > .05; Group 3: % = 0.022, p > .05; &#2 (3) = 6.575, p < .1).  Table 21 Invariance Test Results across Experimental Groups Note. 2 = Chi-square; GFI = goodness-of-fit-index; CFI = comparative fit index  Models #2 df P GFI RMSEA CFI Unconstrained model 678.850 296 .000 .845 .050 .962 Fully constrained model  752.444 338 .000 .830 .049 .959 #2 difference test:! "#2 (42) = 73.593, p < .01 !81 Table 22 Moderating Role of eWOM Effects and Multi-Group Analysis Note. ***p < .01; **p < .05; C.R. = Critical Ratio; CRD = credibility; DBI = destination brand image; BI = Behavioral Intentions.   Experimental Groups Group 1 (N = 139) Group 2 (N = 135) Group 3 (N = 123) Group 4 (N = 119) Paths % (C.R.) % (C.R.) % (C.R.) % (C.R.) CRD -> DBI .790 (12.395***) .613 (7.827***) .807 (12.389***) .765 (11.381***) CRD -> BI .061 (.504) .525 (5.934***) .022 (.233) .096 (1.061) DBI -> BI .620 (4.608***) .406 (4.953***) .823 (7.906***) .755 (7.710***) !82  !Figure 23 The Result of SEM with Standardized CoefficientsÑExperimental Group 1   !83 !Figure 24 The Result of SEM with Standardized CoefficientsÑExperimental Group 2   !84 !Figure 25 The Result of SEM with Standardized CoefficientsÑExperimental Group 3    !85 !Figure 26 The Result of SEM with Standardized CoefficientsÑExperimental Group 4    !86 CHAPTER 5 SUMMARY, DISCUSSION AND IMPLICATIONS As modern travelers search, create, and share information via various social media platforms at the same time, understanding social media marketing from both the theoretical and managerial perspectives has become a major topic in the destination marketing field. This study is to expatiate upon destination branding in an effort to enable destination marketers and tourism researchers to understand a new marketing environment caused by the current social media phenomenon. This chapter will provide a summary of the study, discussion of the findings, both the theoretical and managerial implications of the study, limitations of the study, and recommendations for future studies.   Summary of the Study Summary of Purpose  The main purpose of this study is to investigate the eWOM effects caused by social earned media and social owned media in the context of destination branding and social media marketing. The current study is also to demonstrate the conceptual associations between key constructs (e.g., destination brand image, credibility, touristsÕ behavioral intentions) in the research model and to contribute to the existing theories of destination branding and credibility. To fulfill these research objectives, the measurements of this study investigated credibility, destination brand image, and touristsÕ behavioral intentions to identify how tourists perceive a destination on social media that generates eWOM effects through both social earned media and social owned media.  !87 Summary of Procedure  This study recruited a total of 516 from the Qualtrics online panel database, and the questionnaire was distributed through the Qualtrics online survey platforms. Moreover, this study used U.S. General Population as a sampling frame, and subjects were randomly assigned to the four experimental groups. This study employed a 2 (positive Facebook reviews vs. negative Facebook reviews) " 2 (best practices vs. poor practices in DMO responses) between-subjects experimental design in order to compare groups of subjects according to an experimental stimulus (i.e., eWOM effects of social earned media and social owned media). There were 139 subjects in Experimental Group 1 (positive reviews/best practice of DMOÕs response); 135 in Experimental Group 2 (positive reviews/poor practice of DMOÕs response); 123 in Experimental Group 3 (negative reviews/best practice of DMOÕs response); and 119 subjects in Experimental Group 4 (negative reviews/poor practices of DMOÕs response) after assessing the missing data. Summary of Data Analysis  To compare the eWOM effects of social earned media and social owned media on destination brand image, the one-way between-groups analysis of variance (ANOVA) alternative was conducted. More specifically, in this study, one of the commonly used ANOVA alternatives, the Brown-Forsythe test, was used to compare means of each experimental group. This study also used structural equation modeling (SEM) to test the hypotheses in the conceptual model. In particular, SEM is a combination of factor analysis and path analysis, which establishes measurement model and structural model. In the context of the current study, the CFA and SEM analyses were used to confirm the structure of latent constructs and examine the relationships among credibility, destination brand image, and touristsÕ behavioral intentions. Data analysis for structural equation modeling (SEM) was conducted to determine the effects of destination !88 branding messages disseminated via social earned media and social owned media on the perceived credibility and to investigate its sequential effect on destination brand image and on tourist behavioral intentions.  Summary of Significant Findings Based on the findings from survey-based experiments uniquely designed for potential tourists using social media platforms, this study found many significant findings from ANOVA alternatives and SEM. In an effort to provide comparisons of experimental groups, Brown-Forsythe test identified significant differences exist on the dependent variables between experimental stimuli on social media. Also, SEM using multi-group analysis determined the effects of destination branding messages disseminated via social earned media and social owned media on the perceived credibility and to investigate its sequential effect on destination brand image and on touristsÕ behavioral intentions.  Discussion While the extant literature includes studies that have examined the importance of online reviews (i.e., social earned media) and companiesÕ responses (i.e., social owned media) for tourists in the travel planning context, no attempts have been made to apply these topics to social media marketing and destination branding. To fill this gap, the present study not only confirms the significant effects of eWOM on social earned media and social owned media, but it also extends the development of DMOÕs destination branding strategies to social media communication using a survey-based experimental design approach. This study examined significant findings for tourism destinations that operate their destination branding strategy !89 through social media platforms. The significant findings of the study are discussed along with the related hypotheses.  H1. Credibility in social media marketing has a more positive influence on destination brand image toward destination. Hypothesis 1 suggests that there would be a positive relationship between credibility in social media marketing and destination brand image. When tourists experience credible social media marketing, they are more likely to have strong perceptions on destination brand image.  The credibility construct utilized in the current study was defined as touristsÕ perceptions of credibility that evaluate DMOÕs social media marketing (Ayeh et al., 2013a). Adapted items from the measurements conducted by previous research (e.g., Ayeh et al., 2013a, 2013b; Gefen et al., 2003; Larzelere & Huston, 1980; Morgan & Hunt, 1994; Ohanian, 1990, 1991; Ponte et al., 2015; Tsfati & Ariely, 2014), the credibility construct defined as believability of some information and/or its source (Hovland & Weiss, 1951) is extended to social media marketing. Accordingly, the application of credibility to social media marketing can contribute to the advancement of the extant literature in destination branding. Support for the relationship between credibility in social media marketing and destination brand image confirms the previous research conducted by Veasna et al. (2013) who suggested that credibility positively impacts destination brand image. Along with the tourism destination branding model by Veasna et al. (2013), this study also supported the claim provided by Cheng and Loi, (2014) and Ponte et al. (2015) who revealed the causal relationship between credibility and destination brand image. Therefore, the findings regarding Hypothesis 1 demonstrate the importance of credible social media marketing managed by DMOs in leading tourists to perceive higher positive brand image toward a destination. The implication derived above will help DMOs to put more efforts on developing !90 credible social media marketing as to provide options for tourism destinations. H2. Credibility in social media marketing results in more positive touristsÕ behavioral intentions.  Hypothesis 2 states that the credibility in social media marketing would have a positive relationship with the touristsÕ behavioral intentions, and the findings of the present study support the claim that credibility leads to positive touristsÕ behavioral intentions (# = .166, p < .01). As the credibility theory in social media studies asserts, tourists are likely to have more positive touristsÕ behavioral intentions as a result of touristsÕ experiences with a credible social media marketing based on the results from the current study. Ayeh et al. (2013a) found that online travelersÕ perceptions of the credibility of user-generated content (UGC) sources positively influences the behavioral intentions to use travel-related UGC. Likewise, this study employed the relationship between credibility and behavioral intentions from Ayeh et al. (2013a), and the results suggest that tourists are likely to have more positive touristsÕ behavioral intentions as a result of touristsÕ experiences with a credible social media marketing. Consequently, the confirmed relationship between these two constructs contributes to the development of DMOÕs social media marketing. Also, the findings related to Hypothesis 2 indicate the importance of DMOÕs efforts to operate credible social media marketing, as this will lead to tourists having greater positive behavioral intentions in agreement with the previous research (Ayeh et al., 2013a; Lin & He, 2014; Qu et al., 2011).  H3. Destination brand image results in more positive touristsÕ behavioral intentions.  The findings of the present study also support Hypothesis 3, which indicates that destination brand image would result in more positive touristsÕ behavioral intentions (% = .695, p < .01). The previous research argued that destination brand image influences touristsÕ behavioral !91 intentions (Aluri, 2012; Chen & Tsai, 2007; Chew & Jahari, 2014; Kang & Gretzel, 2012; Ponte et al., 2015; Qu et al., 2011). The findings of the current study confirm that when tourists perceived destination brand image through DMOÕs social media marketing, they are more likely to show positive behavioral intentions toward the destination. Consequently, this study suggests that tourists who have a positive perception of a destinationÕs brand image are more willing to visit and to recommend the destination.  H4. Destination brand image mediates the relationship between source credibility and touristsÕ behavioral intentions.  Consistent with the previous research (Ayeh et al., 2013a, 2013b; Qu et al., 2011; Veasna et al., 2013), destination brand image has been found to be a partial mediating variable in the relationship between credibility and touristsÕ behavioral intentions. According to the Sobel test, the relationship between credibility and touristsÕ behavioral intentions can be partially mediated by destination brand image, which supports Hypothesis 4. This confirms that touristsÕ experiences with more credible social media marketing from DMOs lead to greater positive destination brand image perceptions, which then result in higher intentions to visit destinations.  H5a. EWOM effects of social earned media and social owned media moderate the positive relationship between credibility and destination brand image, such that the relationship between credibility and destination brand image is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews.  Hypothesis 5a indicates that eWOM effects of social earned media and social owned media have a stronger moderating effect on the relationship between credibility and destination !92 brand image for Experimental Group 3 than Experimental Group 2, and the results of the current study supported this claim. The significant moderating role of eWOM effects through social earned media and social owned media has been found on the credibility to destination brand image path among four experimental groups. More specifically, the positive causal effect between credibility and destination brand image is stronger for Experimental Group 3 than for the other three groups (Group 3: % = 0.807, p < .01; Group 1: % = 0.790, p < .01; Group 4: % = 0.765, p < .01; Group 2: % = 0.613, p < .01; &#2 (3) = 24.855, p < .01). This finding highlights the fact that tourists who are exposed to eWOM effects of negative reviews and best practices of DMOÕs responses are likely to show stronger positive effects of the credibility to destination brand image than those who experience eWOM effects of positive reviews and poor practice of DMOÕs responses. Also, from the coefficient comparison between Experimental Group 1 and Experimental Group 3, the results indicate that best practices of DMOÕs responses to negative reviews affect tourists more strongly in the relationship between credibility in social media marketing and destination brand image than those with positive reviews.  In addition, the significant moderating role of eWOM effects caused by social earned media and social owned media by the link between credibility and destination brand image is stronger for subjects exposed to positive reviews with best practices of DMOÕs responses (Experimental Group 1) than those reading positive reviews and poor practices of DMOÕs responses (Experimental Group 2). Investigating the moderating effect between subjects who have experienced negative reviews and best practices of DMOÕs responses (Experiment Group 3) and those who have been exposed to negative reviews with poor practices of DMOÕs responses (Experimental Group 4), eWOM effects of social earned media and social owned media moderate more strongly for Experimental Group 3 within the link between credibility in social !93 media marketing and destination brand image. According to the results of the present study, the effect of credibility on destination brand image can be more evident for tourists who read best practices of DMOÕs responses no matter what online reviews they have read. This extends the approach of Cheng and Loi (2014), who, focusing on strong and quality arguments (central route of Elaboration Likelihood Model), have examined how the use of responses to online reviews moderates the relationship between hotel brand trust and intention to purchase. The findings of this study suggest that when tourists read best practices of DMOÕs responses to online customer reviews, their credibility in social media marketing drives more positive perceptions of destination brand image than when tourists are exposed to poor practices of DMOÕs responses to online customer reviews.  H5b. EWOM effects of social earned media and social owned media moderate the positive relationship between credibility and touristsÕ behavioral intentions, such that the relationship between credibility and touristsÕ behavioral intentions is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews. The findings of the current study do not support Hypothesis 5b, which indicates that eWOM effects of social earned media and social owned media have a moderating effect on the relationship between credibility and touristÕs behavioral intentions between Experimental Group 3 and Experimental Group 2. In particular, the positive causal effect between credibility and touristÕs behavioral intentions is stronger for Experimental Group 2 than for the other three groups (Group 2: % = 0.525, p < .01; Group 4: % = 0.096, p >.05; Group 1: % = 0.061, p > .05; Group 3: % = 0.022, p > .05; &#2 (3) = 19.025, p < .01). This finding emphasizes the fact that tourists who have experiences with eWOM effects of positive reviews and poor practices of !94 DMOÕs responses are likely to indicate stronger positive credibility and behavioral intentions than subjects from the other three groups. This finding may be attributed to the fact that the impact of credibility on behavioral intentions can be more evident for tourists who read poor practices of DMOÕs responses to positive online reviews. H5c. EWOM effects of social earned media and social owned media moderate the positive relationship between destination brand image and touristsÕ behavioral intentions, such that the relationship between destination brand image and touristsÕ behavioral intentions is even more positive for tourists who experience best practices of DMOÕs responses to negative online reviews and less positive for those who experience poor practices of DMOÕs responses to positive online reviews.   The findings support Hypothesis 5c, which states that eWOM effects of social earned media and social owned media would have a stronger moderating effect on the relationship between destination brand image and touristsÕ behavioral intentions for Experimental Group 3 than Experimental Group 2. The significant moderating role of eWOM effects of social earned media and social owned media exist on the destination brand image to touristsÕ behavioral intentions between subjects exposed to negative reviews with best practices of DMOÕs responses (Experimental Group 3) (Group 3:  = 0.823, p < .01; Group 4:  = 0.755, p <.01; Group 1:  = 0.620, p < .01; Group 2:  = 0.406, p < .01; &2 (3) = 6.575, p < .1). Examining the moderating role of different eWOM effects of social earned media and social owned media between subjects who have experienced positive reviews and best practices of DMOÕs responses (Experiment Group 1) and those who have been exposed to positive reviews with poor practices of DMOÕs responses (Experimental Group 2), the significant moderating role of eWOM effects on the link !95 between the destination brand image to touristsÕ behavioral intentions was stronger for subjects from Experimental Group 1. Similarly, the significant moderating role of eWOM effects caused by social earned media and social owned media on the link between destination brand image and touristsÕ behavioral intentions is stronger for subjects who have been exposed to negative reviews with best practices of DMOÕs responses (Experimental Group 3) than those who have read poor practices of DMOÕs responses to the same reviews (Experimental Group 4). Thus, the effect of credibility on destination brand image can be more evident for tourists who read best practices of DMOÕs responses no matter what online reviews they have read.  Theoretical Contribution In terms of theoretical contribution, the primary purpose of the current study is to investigate the significant relationships between credibility, destination brand image, and touristsÕ behavioral intentions in the context of social media marketing. The relationship between destination brand image and tourist behavioral intentions has been studied in the previous research, and the results from the present study support this relationship in a social media and destination marketing setting. In addition to this relationship, credibility was included as a key construct to better grasp the importance of the role of eWOM effects of social earned media and social owned media. This research also extends the current literature of social media by examining, through an empirical approach, how destination branding through social media impacts touristsÕ perceptions and intentions.  First, this study confirms hypothesized overall relationships among latent variables such as credibility, destination brand image, and touristsÕ behavioral intentions, which can lead to !96 crucial extension of the existing theories in credibility and branding studies. Most studies on social media have focused on factors that predict credibility and purchase intentions, but not many studies have attempted to take all three constructs together. Ayeh et al. (2013b) confirmed the importance of examining constructs such as trustworthy user-generated content on social media, attitude toward using user-generated content for travel planning, and behavioral intentions. Ponte et al. (2015) noted the effects of trust and the intention to purchase online. In responding to previous literature, the findings from the measurement model contribute to this line of social media research by focusing on destination brand image. Second, the current study extends the research on structural models on credibility, destination branding and credibility by examining all three concepts together. Previous research has identified the relationships between two constructs respectively, however the current study advances the existing research, which have fortified the foundation of this research stream in social media marketing (Ayeh et al., 2013b, 2013a; Cheng & Loi, 2014; Ponte et al., 2015). By considering the crucial role of credibility to destination branding, the present study added destination brand image to the structural model. The findings from structural modeling indicate that credibility in social media marketing has positive impacts on both destination brand image and behavioral intentions. This study also demonstrates that tourists respond more positively to destination brand image when they perceive credibility in social media marketing. The results also confirm that tourists who indicate increased degrees of destination brand image show more positive behavioral intentions toward the destination. Moreover, the present study supports that greater destination brand image will result in more positive touristsÕ behavioral intentions, and the mediating role of destination brand image is confirmed.  Third, the moderating role of eWOM effects of social earned media and social owned !97 media on the relationships among study constructs (i.e., credibility, destination brand image, touristsÕ behavioral intentions) is detected. Social media research has not attempted to identify the moderating role of eWOM effects via social media. The results of the current study show that different eWOM effects of social media play moderating roles in the relationship between credibility and destination brand image. Further, a significant moderation of eWOM effects on positive online reviews and negative reviews has been found on the path from credibility to destination brand image. Thus, by identifying moderating effects of eWOM through social earned media and social owned media, this study extends the recent stream of social media research in destination marketing (Ayeh et al., 2013a, 2013b; Cheng & Loi, 2014; Hsieh et al., 2016; Lim & Van Der Heide, 2014; Ponte et al., 2015; Veasna et al., 2013) and offers empirical evidence to contribute to the literature on eWOM effects of social media platforms.   Managerial Implications The current study generates several managerial implications derive from the results of the data analysis. Based on the comparison of means for each experimental group using the Brown-Forsythe test, DMOs can expect tourists to be more likely to have positive perceptions of destination branding through social earned media including exposure to 5-Star reviews on Facebook than through experimental groups including exposure to 1-Star reviews on Facebook. This provides insights for marketers who are looking for effective ways to spend their resources on their destination branding using social media platforms. For example, based on the results from the comparison of means between Experimental Group 1 and two experimental groups with 1-Star reviews (e.g., Experimental Group 3, Experimental Group 4), positive social earned media affect touristsÕ perception of destination brand image more effectively than do negative social !98 earned media. In addition, the mean comparisons between Experimental Group 2 and two experimental groups with 1-Star reviews (e.g., Experimental Group 3, Experimental Group 4) also support that positive online reviews lead to greater destination brand images for tourists than tourists do negative reviews on social media. By comparing the different eWOM effects of social earned media and social owned media, the current study enables policy makers from tourism destinations to more effectively invest their resources on social media marketing and branding management. For example, DMOs can include authentic 5-Star customer reviews in their video commercial when developing a branding strategy. In particular, a customer testimonial video can be used to incorporate positive customer reviews into a DMOÕs marketing plan.  The results from the comparisons between experimental groups with the identical social earned media exposure reveal statistically insignificant differences that can explain why DMOÕs responses do not appear to be effective for tourists who are exposed to the same reviews. For instance, the destination brand image of tourists who are exposed to positive reviews and best practice of DMOÕs responses do not differ significantly from that of those who read 5-Star Reviews (Experimental Group 1) and poor practice DMOÕs responses (Experimental Group 2). In the same vein, tourists from Experimental Group 3 (negative reviews/best practice of DMOÕs response) may not indicate significantly different perceptions of destination brand image. This implies that social earned media still has a crucial influence on tourists in increasing destination brand image but that social owned media may not have significant impact on touristsÕ destination brand image when they are under the same condition of social earned media. Therefore, the findings suggest that customer reviews are more important than management responses when it comes to generating positive brand image. The findings from structural equation modeling (SEM) using multi-group analysis suggest that !99 the perception of the credibility of social media marketing would affect more significantly the destination brand image of those tourists who read best practices of DMOÕs responses to online reviews (Experimental Group 1 and 3). This offers crucial implications for marketers who design destination branding campaigns using social media. It is plausible that credible social media marketing can be utilized as an effective marketing tool to turn around tourists exposed to poor practices of DMOÕs responses to customer reviews.  Similarly, the findings of moderating impact of eWOM effects through social earned media and social owned media using multi-group analysis found in the relationship between destination brand image (DBI) and touristsÕ behavioral intentions (BI). According to the results comparison between groups with the same reviews, the strong effect of destination brand image on touristsÕ behavioral intentions can be more salient for tourists who are exposed to best practices to online customer reviews.  The findings suggest that exposure to the best practices of DMOÕs responses have a significant effect on touristsÕ perception on key variables when they are exposed to the same reviews. This finding is meaningful for DMOs as it provides empirical evidence that tourists may be affected by DMOÕs responses if they are best practices. Along with previous literature that examined the importance of management responses (Gu & Ye, 2014; X. Liu et al., 2015), the results provide crucial implications for marketers from DMOs in terms of how to utilize management responses. It is plausible that if DMOs develop better responses to online customer reviews tourists are likely to have more positive destination brand image and behavioral intentions.   !100 Limitations and Future Research There are several limitations of the current study, which lead to further research opportunities. First, this study depends on constructs of branding and general communication theories to examine the relationships in the proposed research model whereas future studies could consider computer-mediated communication theories (e.g., social presence) to offer advanced implications. Future research could consider other factors that influence the decisions of social media users about travel. The current study makes the first attempts to extend social media research to the destination marketing area by adding a destination brand image construct and investigating different eWOM effects of social earned media and social owned media. However, future research can include constructs related to brand theories (e.g., brand loyalty, brand community) to develop the current model. This study depends on constructs of branding and general communication theories to examine the relationships in the proposed research model whereas future studies could consider computer-mediated communication theories (e.g., social presence) to offer advanced implications.  Second, future research can include experimental conditions such as restricting social earned media only to providing more distinctive comparisons between the effects of social earned media and those of social owned media. The current study is limited because each experimental stimulus has a combined design between eWOM effects through via social earned media and social owned media. However, for the future studies, online reviews-only groups can be separated into two groups: one that reads positive online reviews without DMOÕs responses and another that reads negative online reviews only. The moderating role of eWOM effects of social earned media and social owned media can be detected more clearly with the inclusion of the reference group in future research.  !101 Third, although many researchers have increasingly employed online survey tools such as Qualtrics and AmazonÕs Mechanical Turk, there is an ongoing debate on the population of these providersÕ panel database. As Schnorf, Sedley, Ortlieb, and Woodruff (2014) argued that researchers who collected data via online survey database have faced challenges because they might underrepresent the general population. For instance, research participants who have easy accessibility to the Internet may be overrepresented when researchers use online panel samples. As previous literature in hospitality and tourism journals increasingly utilizes Internet-based surveys, it is crucial to include screening questions that identify a reasonable demographic representation.  Fourth, since this study examined only one tourism destination (Traverse City, MI), much should be advised when generalizing the findings. Although the findings of this study confirm that the research framework and variables used in the current research model fit the sample and the data well. The research model and theoretical structure in this study can be applied to other destinations. Moreover, the model presented in the current study can be tested with different samples in other areas of the tourism and hospitality industry (e.g., hotel, resorts, coffee shops, restaurant, airline, cruise). Last, this study used Facebook as a social media platform. It should be cautious for researchers in generalizing the results of the study to other social platforms. Facebook is still the most popular social media outlet based on its number of users, but further studies can extend the current model to other social media platforms which have become strong competitors for Facebook, such as Instagram and Snapchat. Additionally, the particular use of Facebook that this research utilized is less common than the typical social networking use of Facebook. Thus, applying the theoretical frame and constructs used in the current research model to online review !102 sites such as Yelp and TripAdvisor will provide a clear comparison. In future studies, the proposed research model for different social media platforms could be tested to compare the effects of each social media platform. This approach may offer richer insights to DMOs in terms of developing comprehensive social marketing strategies. !103 REFERENCES!!!!104 REFERENCES !Aaker, D. A. (1996). Measuring Brand Equity Across Products and Markets. California Management Review, 38(3), 102Ð120. 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