Assessing Source Credibility on Social Media-––– an Electronic Word-Of-Mouth Communication Perspective

ASSESSING SOURCE CREDIBILITY ON SOCIAL MEDIA-––– AN ELECTRONIC WORD-OF-MOUTH COMMUNICATION PERSPECTIVE

Xiao Hu

A Dissertation

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

May 2015

Committee:

Louisa Shu Ying Ha, Advisor

Mary E. Benedict Graduate Faculty Representative

Gi Woong Yun

Michael Albert Horning

Xiao Hu

Louisa Shu Ying Ha, Advisor

This study aimed to discover, develop, and understand people’s perception of others’ credibility in the context of social media in marketing communication. In communication, how people perceive information source directly affects whether or not they accept information, which in turn affects the communication effect. In the offline world, word-of-mouth communication was believed to be extremely credible, therefore its commercial value has been explored since early last century. However, new technologies have expanded communication from offline to cyberspace. Electronic word-of-mouth communication (eWOM), based on social media, brought more variables to marketing communication, considering its communication magnitude. Thus, how people perceived different “hubs” (popular figures on social media), with the intervention of social media, became significant in marketing communication. Based on traditional measures of source credibility, the new tentative measures of source credibility on social media have been proposed. How the perceived source credibility on social media was affected by other factors, e.g., involvement in the Internet and Twitter, and different product settings, has also been examined in this study.

To validate the tentative new measures of source credibility on social media and test the proposed hypotheses, an online survey in a mid-sized university was conducted to collect data.

Structural equation model was employed to analyze data. The results showed that source credibility on social media was a mixed second-order construct. Individual source credibility and brand source credibility included six dimensions–––competence, trustworthiness, social tie, attractiveness (attribute for brand source credibility), dynamism, and technology affordance. iv Although they were all comprised of six dimensions, individual source credibility and brand source credibility had different indicators for each dimension. News organization source credibility was proved to be a five-dimension construct, which embraced competence, trustworthiness, social tie, dynamism, and technology affordance dimensions.

Both Internet and Twitter involvement had positive relationship with perceived source credibility on social media. However, Twitter involvement accounted for, but not all of, the relationship between Internet involvement and perceived credibility. Self-esteem was found having a positive relationship with news organizations and brand source credibility, but there was no relationship between self-esteem and individual source credibility.

On Twitter, family and friends were the most credible sources for people to get information when they needed to make a purchase decision. News organizations and brands were the second and third credible sources; while politicians on Twitter were the least credible sources. Additionally, product type did affect people’s perceived credibility. When considering different types of products, the perceived credibility of the same source often changed. For example, when people were not confident to make a purchase decision, the perceived credibility of entertainment stars was higher when they needed to buy an inexpensive item than an expensive item. However, regardless of the product settings, family and friends were always the most credible sources and politicians were the least credible sources on Twitter.

To my parents and my husband Alex, for their love and support.

To my daughter Emma, without whom this research would have been completed one year earlier. vi ACKNOWLEDGMENTS

It is easier said than done. I never thought that completing a dissertation requires such a substantial commitment of time and effort until now. I would like to express my heartfelt gratitude to my professors, friends, and loved ones. Without the support, guidance, help, and patience of them, this dissertation would not have been completed.

I would like to thank the members of my committee for their full support and encouragement throughout this study. Dr. Louisa Ha, my dissertation advisor, who dedicated so much time and effort to guide me, helped me grow from a novice in academia to a scholar who is capable of doing research independently. She is not only my advisor, but also my life coach. Her kindness, devotion, and knowledge inspired and motivated me. I would like to express my gratitude to Dr. Gi Woong Yun, Dr. Michael Albert Horning, and Dr. Mary E. Benedict for giving me invaluable comments and setting good examples for me as a scholar. I want to thank

Dr. Yun for teaching me how to be a confident scholar; Dr. Horning for his endless support and patience; and Dr. Benedict for showing me what inner strength is through her words and deeds.

I would like to express my deepest gratitude to Jie Wu, who helped me conduct the survey; and Dr. Thomas Mascaro and Dr. Kara Joyner, Dr. Xiaoqun Zhang, Ling Fang, and Yen-

I Lee, who kindly allowed me to collect my data during their classes. I want to thank Dr. Hsueh-

Sheng Wu and Dr. James Gaskin, who have directly and indirectly guided and inspired me in data analysis. I also thank Sriya Chattopadhyay for proof-reading my dissertation. Last, but not the least, I would like to thank my family–––I thank them for always being there for me. I especially want to thank my newborn baby girl Emma. Thanks for being born on time instead of earlier; otherwise, this dissertation would have been completed another half year or one year later.

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TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION: STUDY OVERVIEW ...... 1

Background Overview ...... 3

Purposes of the Research ...... 9

Significance of the Study ...... 11

Organization of the Dissertation ...... 13

CHAPTER II. LITERATURE REVIEW ...... 14

Word-of-Mouth & Electronic Word-of-Mouth …………………………………… 15

Categories of eWOM ...... 17

Social Media as Platform for eWOM ...... 27

Summary ……………………………………………………………………… 29

CHAPTER III. SOURCE CREDIBILITY THEORY & RESEARCH………………………. 31

Persuasion Communication ...... 32

Conceptualization of Source Credibility ...... 35

New Dimensions in Social Media Context ...... 48

Social Tie Strength ...... 51

Technology Affordance ...... 52

Product Type and Perceived Source Credibility ...... 56

Prior Knowledge and Perceived Source Credibility ...... 60

CHAPTER IV. THEORETICAL MODEL OF CREDIBILITY ...... 63 viii

Six Dimensions of Social Media Source Credibility ...... 63

Source Credibility as a Second-Order Formative Construct ...... 68

Research Model ...... 71

CHAPTER V. METHODOLOGY…………………………………………………………… 75

Study Setting ...... 76

Why Twitter ...... 76

Study Population ...... 77

Preliminary Study ...... 78

Selection of Scale ...... 78

Selection of Sources ...... 80

Survey Implementation ...... 80

Operationalization and Measures ...... 81

Individual Source Credibility ...... 81

News Organization Source Credibility ...... 84

Brand Source Credibility ...... 86

Source Credibility Moderated by Product Type ...... 88

Internet Involvement ...... 88

Twitter Involvement ...... 88

Self-esteem ...... 89

Data Screening and Statistical Techniques ...... 90

Data Cleaning and Screening ...... 90

Structural Equation Modeling ...... 91

Multivariate Statistics ...... 96 ix

CHAPTER VI. RESULTS ...... 97

Sample Profile ...... 97

Structural Equation Model Results ...... 99

Construct Validity and Estimation of Reflective Constructs ...... 99

Construct Reliability and Estimation of Reflective Constructs ...... 110

Construct Validity and Estimation of Formative Constructs ...... 112

Source Credibility on Twitter ...... 122

Structural Model Results and Evaluation ...... 129

Collinearity check results ...... 129

Structural model path coefficients ...... 130

Mediation effects ...... 134

Effect size f2 ...... 139

Ranking of Perceived Credibility on Twitter ...... 139

Product Type’s Role in Perceived Credibility ...... 142

CHAPTER VII. DISCUSSION AND CONCLUSION ...... 145

Summary…………………………………………………………………………… 145

Interpretation of Results ...... 150

The New Measure of Individual Source Credibility ...... 151

The New Measure of News Organization Source Credibility ...... 153

The New Measure of Brand Source Credibility ...... 156

Direct and Indirect Effects in the Three Models ...... 158

The Ranking of Perceived Credibility of Different Sources ...... 161

The Role of Product Type ...... 162 x

Theoretical Implications ...... 163

Methodological Implications ...... 166

Practical Implications ...... 167

Limitations of this Study ...... 169

Suggestions for Future Research ...... 171

REFERENCES……………………………………………………………………………… 173

APPENDIX A. MEASURE OF SOURCE CREDIBILITY ...... 201

APPENDIX B. CONSENT LETTER ...... 202

APPENDIX C. QUESTIONNAIRE ...... 204

APPENDIX D. INDIVIDUAL SOURCE CREDIBILITY SCALE ...... 221

APPENDIX E. NEWS ORGANIZATION SOURCE CREDIBILITY SCALE ...... 222

APPENDIX F. BRAND SOURCE CREDIBILITY ...... 223

APPENDIX G. CORRELATION MATRIX OF INDIVIDUAL MODEL ...... 224

APPENDIX H. CORRELATION MATRIX OF NEWS ORGANIZATION MODEL ...... 226

APPENDIX I. CORRELATION MATRIX OF BRAND MODEL ...... 228

APPENDIX J. MEASURES AND ABBREVAITIONS ...... 230

APPENDIX K. INDIVIDUAL STRUCTURAL MODEL WITH PATH COEFFIENTS .... 231

APPENDIX L. NEWS ORGANIZATION STRUCTURAL MODEL WITH PATH ...... 232 COEFFIENTS APPENDIX M. BRAND STRUCTURAL MODEL WITH PATH COEFFIENTS ...... 233

APPENDIX N. STRUCTURAL EQUATION MODELS FIT INDICES ...... 234

LIST OF FIGURES

Figure Page

3.1 Persuasive effect model ...... 33

3.2 Consumer perception of organization ...... 41

3.3 Factor structure of salesperson credibility ...... 42

3.4 Factor structure of company credibility ...... 43

3.5 Factor structure of spokesperson credibility ...... 44

3.6 The main model ...... 54

4.1 Individual social media source credibility dimensions ...... 68

4.2 Formative and reflective measurement models ...... 69

4.3 Source credibility research model ...... 72

6.1 Measurement model of individual source credibility ...... 123

6.2 Measurement model of news organization credibility ...... 124

6.3 Measurement model of brand source credibility ...... 125

6.4 Structural model ...... 131

6.5 Mediator analysis procedure in PLS-SEM ...... 138

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LIST OF TABLES

Table Page

3.1 Source Credibility Scale ...... 40

3.2 Measure of Source Credibility ...... 48

3.3 Source Credibility Scale ...... 50

3.4 Product Types ...... 59

5.1 Individual Source Credibility ...... 83

5.2 News Organization Source Credibility ...... 85

5.3 Brand Source Credibility ...... 87

6.1 Demographic Profile of Sample ...... 98

6.2 Loadings and T-statistics for Convergent Validity of Reflective Constructs in Individual

Source Credibility Model ...... 100

6.3 Loadings and T-statistics for Convergent Validity of Reflective Constructs in News

Organization Source Credibility Model ...... 102

6.4 Loadings and T-statistics for Convergent Validity of Reflective Constructs in Brand Source

Credibility Model ...... 104

6.5 Loadings of the Reflective Measurement Items in Individual Source Credibility

Model ...... 106

6.6 Loadings of the Reflective Measurement Items in News Organization Source Credibility

Model ...... 107

6.7 Loadings of the Reflective Measurement Items in Brand Source Credibility

Model ...... 108 xiii

6.8 Discriminant Validity Through the Square Root of AVE for Individual Source Credibility

Model ...... 109

6.9 Discriminant Validity Through the Square Root of AVE for News Organization Source

Credibility Model ...... 110

6.10 Discriminant Validity Through the Square Root of AVE for Brand Source Credibility

Model ...... 110

6.11 Reliability Test for Reflective Constructs for Individual Source Credibility

Model ...... 111

6.12 Reliability Test for Reflective Constructs for News Organization Source Credibility

Model ...... 111

6.13 Reliability Test for Reflective Constructs for Brand Source Credibility

Model ...... 111

6.14 Weights and T-Statistics for Formative Constructs of Individual Source Credibility

Model ...... 113

6.15 Weights and T-Statistics for Formative Constructs of News Organization Source

Credibility Model ...... 115

6.16 Weights and T-Statistics for Formative Constructs of Brand Source Credibility

Model ...... 116

6.17 VIF for Formative Indicators in Individual Source Credibility Model ...... 118

6.18 VIF for Formative Indicators in News Organization Source Credibility

Model ...... 119

6.19 VIF for Formative Indicators in Brand Source Credibility Model ...... 120 xiv

6.20 New Weights and T-Statistics for Formative Constructs of News Organization Source

Credibility Model ...... 121

6.21 Individual Source Credibility Scale ...... 126

6.22 News Organization Source Credibility Scale ...... 127

6.23 Brand Source Credibility Scale ...... 128

6.24 Collinearity Assessment for Individual Source Credibility Model ...... 129

6.25 Collinearity Assessment for News Organization Source Credibility Model ...... 130

6.26 Collinearity Assessment for Brand Source Credibility Model ...... 130

6.27 Significance Testing Results of the Individual Source Credibility Structural Model Path

Coefficients ...... 132

6.28 Significance Testing Results of the News Organization Source Credibility Structural

Model Path Coefficients ...... 133

6.29 Significance Testing Results of the Brand Source Credibility Structural Model Path

Coefficients ...... 134

6.30 Normality of Distribution ...... 141

6.31 Perceived Credibility Ranking on Twitter ...... 142

6.32 Perceived Credibility on Twitter by Product Type ...... 142

6.33 Compare Means of Perceived Credibility in Four Product Types ...... 143

CHAPTER I.

INTRODUCTION: STUDY OVERVIEW

Twitter has become an important news medium in recent years. The 140-character length limit of Twitter posts not only facilitates rapid expansion and success of fast news in the battle for attention, but also accelerates information diffusion on social media forums. However, given the rapid and uncontrollable spread of information on Twitter, an authoritative and credible information source in this environment is particularly important. Hoaxes on Twitter can have severe consequences. For example, a Twitter hoax in April, 2013 claimed that President Obama was injured in an explosion at the White House, which resulted in a 150 points Dow Jones

Industrial Average fall, evaporating a $136 billion market value in minutes (Chozick & Perlroth,

2013). Even though people soon learned that the fake tweet was posted by hackers through the

Associated Press (AP) account and the domestic market rebounded, it caused panic in the global markets and some investors lost money. Another hoax news was put up in May, 2013. Five-time

NBA champ, Kobe Bryant, was said to retire in the fake news posted by a hoax Twitter account pretending to be Yahoo’s well-known sports writer Adrian Wojnarowski. No market plunge occurred this time. But people did believe that Bryant was going to end his career after 17 seasons. The rumor ended only with Bryant’s rebuttal tweet, “Really?? Me. Retire?? Soon, but not yet! Vino still has work to do.” Fake news on Twitter exerts great influence on us whether we are aware of it or not. In effect, it is the sources that play a vital role in the process of information dissemination because only when people believe the sources will they accept and spread the information provided by such sources.

Then again, why people believe these social media sources to be credible and spread these rumors like they are real news? Now, what if a real sports writer, or AP or ESPN reporter

2 publicized Bryant’s retirement instead of, say, a makeup artist or a mechanical engineering scientist? Whose words sound more credible? Again, if we are talking about makeup and beauty, does a post by a makeup artist make it more authoritative and credible to you? The platform provided by interactive technology does accelerate information diffusion and then increase communication effect, but most of the time, communication effect on social media is more hinged on “hubs”—individuals more connected and visible in the networks (Barabási, 2002).

Thus, the credibility of these hub-sources is more critical to both academia and practitioners in marketing.

According to Barabási (2002), cyberspace is a networked world, which consists of a few highly connected hubs and numerous connectors. Hubs have more links and outreach connections than connectors. Although both hubs and connectors can have their views published online, what matters is whose posts have more chances of being read, or who has a more influential communication effect? In the case of social media, public figures with a huge number of followers/fans are hubs in the virtual social network. They are amplifiers that contribute to effective communication by reaching out to lots of people in just a few seconds. However, public visibility cannot guarantee effective communication; people still need to make judgments on information based on source credibility. In regards to different kinds of information/news, people hold different expectations from different “hubs.” Compared with any popular individual Twitter account, the AP has more credibility when it comes to distributing political news. That’s why the fake news posted by a hacker via the AP Twitter handle caused such a furor.

To put it in a nutshell, although numerous research has been done on persuasion and effective communication, there are new features and attributes of communication in the context of social media. On the one hand, social media facilitates communication in scale and speed; on

3 the other hand, the technology makes online communication more complicated. Within the multiple source layers in online communication, it is harder to estimate information and source credibility than the traditional direct and visible criteria of face-to-face communication.

However, technology affordances such as followers and blue verified badges, do provide some cues for people to make judgments on source credibility. Therefore, the researcher would like to investigate how people’s evaluation criterion of source credibility changes, and which factors influence people’s perceived source credibility in the context of social media.

Background Overview

Though it is remarkable that social media spreads rumors so quickly (Doer, Fouz,

&Friedrich, 2012), its rapid information spreading has also been effective in assisting various communications, such as political campaigns (e.g., Arab Spring, Obama’s success in his

Presidential campaign), and marketing campaigns. Social media was reported as important by over 86% of marketers as a marketing tool to increase exposure and sales for businesses

(Stelzner, 2013).

Within marketing communication, the advent of the Internet and interactive technology promptly shifted an ever-growing barrage of traditional marketing to cyberspace. After “display” and “banner” ads, classified ads, and searching advertising, online marketing communication is stepping into its fourth phase—conversational marketing (“Word of Mouth,” 2007) based on word-of-mouth communication. Given its collaborative, interactive, and social attributes, social media has become the new battlefield of marketing communication.

Social media is “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 content (Kaplan & Haenlein, 2010, p.61).” Examples of social media are social

4 networking sites such as Facebook, microblogging sites such as Twitter, social news sharing sites such as Reddit, and social video sharing sites such as YouTube, and more. The exponential growth in membership worldwide and high popularity made Facebook and Twitter two ideal online marketing tools for retailers. It only took Facebook ten years to grow into a giant which now has 1.39 billion monthly active users as of December 31, 2014 (Company Info, n.d.), while

Twitter, with 284 million monthly active users, is producing 500 million tweets every day

(About Twitter, n.d.). Social media websites with interactive attributes not only enable people to maintain their friendships, but also provide opportunities to create new friendships and expand social networks. Thus, it is much easier to approach and pinpoint specific potential and current consumers with less cost on the huge virtual community, which is based on social media, than through the more traditional marketing channels. Moreover, social media provides a platform for companies to engage their customers to establish “an increased sense of intimacy of the customer relationship” and maintain customer loyalty (Mersey, Malthouse & Calder, 2010; Vinerean,

Cetina, Dumitrescu, & Tichindelean, 2013).

In social marketplaces, no longer relying on company-controlled information, customers share information, seek feedback, create relevant content, and make purchase decisions based on crowd intelligence (Moscato & Moscato, 2011). In effect, the general marketing practice is to use social media to generate consumer-to-consumer word-of-mouth to maximize marketing activities (Bughin, Doogan, & Vetvik, 2010). Marketers encourage consumers to create and spread experiential word-of-mouth communication, based on direct consumption experiences, such as customer reviews, to involve interested potential customers. Word-of-mouth, the recommended communication between receivers and communicators, whom receivers perceived as independent from retailers (Arndt, 1967; Breazeale, 2009), is believed to be an effective

5 marketing strategy for either brand or product promotion (Day, 1971; Goldenberge, Libai, &

Muller, 2001; Herr, Kardes, & Kim, 1991; Katz & Lazarsfeld, 1955).

The Web 2.0 brings social and interactive features into cyberspace, which expands face- to-face WOM communication to text-based electronic word-of-mouth communication. Previous studies indicated that 81 percent of surveyed customers considered online customer reviews as important when they were searching or planning to buy products. Moreover, 63 percent of the surveyed customers indicated they would be more likely to buy products on the websites with customer reviews (iPerceptions, 2006). The passive customers in traditional WOM communication become more active in gathering information to make purchase decisions with the help of the Internet. The giants of social media, such as Facebook and Twitter, “have gotten people to publicly and privately share information on the Web, including their favorite articles, videos, personal interests and whereabouts, and redefine how people discover news articles or get advice on where to shop or travel” (Efrati, 2011). Facebook fan pages even create a space for business, organizations, sports teams, films, TV shows, and all kinds of brands on social networking sites to attract audiences and maintain “long-life relationship[s]” with their fans

(Kryder, 2010). Brands can send links, ads, video, and text to their fans and post updates on their fan pages to engage them. More importantly, fans can respond to the updates and participant in the events created on fan pages, which are automatically posted in their news feeds and fed to their “friends,” turning to be an electronic word-of-mouth (Holahan, 2007). Things spread virally through those connections on social media websites (Holahan, 2007).

However, two concerns were raised amidst the boom of social media marketing communication. First of all, with an abundance of available information online, customers are also facing sheer information overload. Though people can equally publish, share, and seek

6 information at their disposal, the chances of being heard online are not equal. Attention is extremely scarce in this environment. The highly-connected well-known “hubs” are always more visible than the numerous unknown “nodes” (Barabási, 2002). Using celebrity endorsers to increase brand presence and visibility is a common practice in marketing (Li, Lee, & Lien,

2012). Extensive research on marketing communication and social psychology showed that celebrity endorsement generated attention and improved promotion (Agrawal & Kamakura,

1995; Joshi & Ahluwalia, 2008; Kamins, 1990; Ohanian, 1991; Sonwalkar, Kapse, & Pathak,

2011) because of the celebrity’s public recognition, attractiveness, and trustworthiness; the credibility of the celebrity was transferred to the brand (Sonwalkar, Kapse, & Pathak, 2011).

Similarly, in social marketing, targeting “influential” people and finding appropriate advocates for brands are particularly important for brands to generate exponential high-impact buzz

(Bughin, Doogan, & Vetvik, 2010). By cultivating brand advocates, Starbucks, with 33 million fans on Facebook and 3 million followers on Twitter, gained huge success on social media, leading to 38 percent in-store purchase increase. Even when Red Bull could not identify influentials precisely, it identified celebrities and other opinion leaders to send the right messages among consumers (Bughin, Doogan, & Vetvik, 2010). Nevertheless, the celebrity effect is proving to be less reliable today, with customers not easily influenced by a “superstar” and needing more relevant product information to make decisions (Sharma, 2007). This brings us to the second concern—how do people choose information sources from the buzz on social media and why do they trust certain sources?

The second concern is actually about trust networks in electronic word-of-mouth communication (Powers, Advincula, Austin, Graiko, & Snyder, 2012). Different from traditional

WOM communication, which occurs among preexisting social networks such as friends or

7 family, eWOM communication occurs among unknown people who don’t have prior relationships (Xia & Bechwati, 2008). We are not only exchanging information with our close social ties, but also gathering input for purchase decisions from all kinds of sources online.

“Social media are expanding the range of people we trust” (Powers, et al., 2012, p481). But among various and numerous sources online, who is more credible for consumers? In effect, research on source credibility dated back to persuasion communication research. Persuasion theory suggested five key factors—source, message content, media, and contextual factors—that affected communication effects through an impact on attitude corresponding to the basic elements of communication process—source, channel, message, and receiver (O’Keefe, 2002).

Abundant corresponding research on these factors in communication effect field has been done and found that source credibility was a primary factor that influenced persuasive effects. The measure of source credibility with competence and trustworthiness dimensions was developed and tested by scholars to understand how perceived source credibility influenced people’s attitudes towards relevant information and persuasion communication.

Nonetheless, these studies mainly focused on source credibility within public speaking context from interpersonal communication (Tewksbury, Jensen, & Coe, 2011), which was different from communication in social media context. The advent of interactive technology and the popularity of social media enabled the convergence of information production and consumption, and blurred the media and sources (Stewart & Pavlou, 2009). On social media platforms, such as Twitter, everyone is free to create an account as a source. Different from traditional media created by professional organizations and harnessed by a few people, Twitter empowers publishing rights to the public; all people have the same right to publish and inform others. Meanwhile, in this new context, people are both information producers and consumers.

The hybrid phenomenon of producer and consumer was conceptualized as prosumption three decades ago (Toffler, 1980), which directly triggered the shift from the traditional notion of the information/news “gatekeeping” to “gatewatching” role of media (Bruns, 2008). Gatewatchers were not able to control the gate through which information passed, but received co-created content with comments and links from a variety of sources. The complicated media ecosystem was blurring the boundary between medium and source. After all, it’s well to remember that it is ultimately human beings who are behind social media, who post information by using the attributes and multiple functions of social media. Therefore, source credibility on social media cannot be measured correctly without taking social media characteristics into consideration. The measure of source credibility developed in traditional media ecosystem cannot be applicable in the new social media context for a number of reasons.

First, the old source credibility measure was based on face-to-face interpersonal communication. Some indicators of the measure, such as “care about me,” “attractive,”

“sincere,” cannot be used in the new measure of social media source credibility because in mediated communication on social media, we cannot make any such judgments due to the lack of visual and non-verbal cues. It is likely that we don’t even know the sources we refer to; we only know they are popular and credible. So, measures based on physical appearances cannot be applied and generalized to all the sources online. Second, old source credibility measures cannot be generalized to multiple sources. Though specific measures for different sources were developed and could be generalized to some extent (Ohanian, 1990), they were still in the context of offline marketing communication. To date, to the best of the researcher’s knowledge, there is no reliable measure for social media source credibility. Third, online communication on social media platforms provides a lot of new cues for us in terms of source credibility, such as

9 verified badge, followers, and followings. Without integrating these new technology affordances into source credibility measure, we cannot attain an accurate understanding of what constructs social media source credibility.

Purposes of the Research

Developing a new social media source credibility measure is necessary for us to understand online communication because while high-impact hubs, or influentials, are more visible and well known, and thus more credible for customers in marketing communication, the established source credibility measure is outdated in the new context of social media. Therefore, the first purpose of this study is to develop a measurement for source credibility in the context of social media. Scholars who developed and enriched the existing source credibility measure mostly only test credibility of one specific type of source in specific contexts. For example, in the early study of perceived credibility, Haiman (1949) developed his two crude dimensions of source credibility—character and intelligence by examining the effects of high and low credible sources in speech communication. Similarly, Berlo, Lemert, and Mertz (1969) asked people to rate specific public and personal sources and developed three meaningful and statistically independent dimensions for perceived credibility—safety, qualification, and dynamism. These measures could not be generalized to other kinds of sources, such as organization sources, nor could they be applied to other contexts. There is a research gap of generalized source credibility measure. Moreover, personal and institutional source credibility should be measured by different scales for the sake of accuracy and clarity. Thus, in this study the researcher will categorize different sources on Twitter, and develop generalized measures for these different kinds of sources correspondingly.

In addition, according to different information, people might refer to and place more credibility on specific sources. Similarly, within marketing communication, scholars

(Weinberger & Dillon, 1980) found that product type had a significant effect on source credibility; people relied more on outside information (WOM) for services than physical goods.

For some products of which consumers had no prior knowledge, high-credibility sources with prepurchase experience were more persuasive (Jain & Posavac, 2001). Industry research firms such as McKinsey also found that the impacts of different recommendations changed across product categories: consumers who had expertise in cars might only influence car buyers instead of electronics buyers (Bughin, Doogan, & Vetvik, 2010). In a recent study of word-of-mouth, scholars (Hu, Ha, Mo, and Xu, 2014) found that people were more likely to refer to traditional

WOM and eWOM on social media when they considered buying books, movies, music, and games; while they would look for help from experts when it came to electronic products.

Therefore, among different sources on Twitter, when considering different products, people have different preferences for sources when obtaining WOM information to help their purchase decision-making. To identify influentials across product types, the researcher included product type as a moderator in the model of source credibility on Twitter.

To sum up, the media landscape is undergoing huge changes. The rise of interactive and social media makes the media ecosystem more complicated. In the context of marketing communication, to achieve better marketing communication effects, practitioners need to identify high-impact influentials in marketing campaigns; and to understand marketing communication outcomes, scholars need to develop new measures for source credibility to qualify and predict people’s attitude on social media based on word-of-mouth communication.

Hence, the purpose of this study is to develop new credibility measures for different kinds of

11 sources on Twitter, and then investigate how people’s demographic characteristics, online behaviors and product types affect perceived source credibility.

Significance of the Study

Source credibility research has been studied for decades and is still being enriched constantly (Pornpitakpan, 2004). In word-of-mouth communication, source credibility directly affected people’s acceptance of corresponding messages (Dabholkar, 2006; Dou, Walden, Lee,

& Lee, 2012) and consumers’ decision-making (Awad & Ragowsky, 2008). People were more likely to adopt eWOM when sources were perceived to be more credible (Fan, Miao, Fang, &

Lin, 2013). In other words, source credibility played an important role in customers’ purchase decision-making through influencing acceptability of reference information. Thus, how customers construct source credibility was significant in understanding eWOM communication

(Fan, Miao, Fang, & Lin, 2013).

This study advances source credibility theory through developing new measures in the context of social media. Previous endeavors on source credibility have not arrived at an agreement upon its conceptual dimensions. Researchers developed different measures for source credibility based on their research contexts. Hovland, Janis, and Kelly (1953) first identified trustworthiness and expertise as two dimensions of source credibility, while Berlo, Lemert, and

Mertz (1969) argued that source credibility construct included three dimensions in interpersonal communication—safety, qualification, and dynamism. McCroskey along with Teven (1999), brought ‘goodwill’ (intent toward receiver) into the measure in the context of teaching communication. Though in marketing communication, scholars, such as Ohanian (1990) and

Eisend (2006), established particular source credibility for organizations, salespersons, and spokespersons, there is still a lack of research that systematically explores generalized source

12 credibility for each different source. More importantly, not much attention was paid on developing source credibility measures in the new context of social media. The researcher combines the new attributes brought by social media and media convergence with the traditional measures to establish a new set of source credibility measures in the new media ecosystem.

Moreover, this study contributes to a more comprehensive understanding of WOM communication in which social media provides a huge platform for the distribution of eWOM.

WOM was believed to be an effective marketing strategy influencing people’s behaviors, and psychological perceptions (Mangold, 1987; Sheth, 1971). Research about WOM mainly concentrated on five attributes of WOM, namely, valence, focus, timing, solicitation, and intervention (Buttle, 1998). The conceptual framework was only based on traditional face-to-face

WOM communication. Stepping into the interactive era, though both academia and industry paid much attention to eWOM, the relevant research is just beginning. There was no generalized and agreed definition, and no clear classification, which resulted in quantification and measuring problems in both practice and research. This study provides a generalized definition for eWOM and classifies different eWOM based on their platforms and usages. In the well-built eWOM context, the researcher further explores source credibility measures.

Additionally, this study reexamines communication effects from the source credibility perspective in the context of social media. This study is based on new media. The profound changes in the media ecosystem prompted us to rethink information communication and communication effects. Not only applicable to marketing communication, the results and focus of this study can also be expanded to other communication areas. Through analyzing the attributes and characteristics of social media as well as the virtual networked world they have created, this study reveals new communication trends and potential communication outcomes.

Finally, for industry, this study directly benefits marketing practice. To launch an effective WOM campaign, marketers first need to know who those high-impact sources are, and how consumers perceive such “hubs.” The measures of source credibility across product types allow marketers to estimate the tangible effect of different sources during WOM communication on brand equity and sales. Once marketers know who are more likely to be affected by which kinds of sources, they can harness the potential of WOM in social media more specifically with ease, and gain more returns on their marketing investments. These insights are essential for current social marketing.

Organization of the Dissertation

This dissertation consists of seven chapters. Research context and problems are raised in

Chapter I. Chapter II introduces and clarifies the basic concepts and phenomenon, such as WOM and eWOM. Chapter III provides a comprehensive literature review on the theories and relevant studies. Chapter IV presents the research model as well as elaborates on the research questions.

Chapter V explains the research methods, including conceptual operationalization, the survey questionnaire design, the implementation of the survey, and statistical techniques. Chapter VI analyzes the data and reports the results. The last chapter discusses the research results, and provides implications and conclusions of this study as well as suggestions for future research.

CHAPTER II.

LITERATURE REVIEW

The importance of interpersonal communication has been recognized and investigated for decades both in academia and industry. Extensive attention has been paid to effective WOM communication, which was known as an extremely credible and persuasive force, affecting people’s attitude and engagement (Vázquez-Casielles, Suárez-Álvarez, & del Río-Lanza, 2013).

Especially within marketing communication, the commercial value of WOM has been acknowledged in industry since the early 1920s (Butler, 1923). According to a survey conducted by Inc. Magazine in 2006, WOM has been used by 82% of the fastest growing companies

(Ferguson, 2008) and nearly one-third of 23 service industries chose WOM as one of the most important marketing tools (East, Hammond, Lomax, & Robinson, 2005).

WOM communication was shown to have a significant influence on people’s expectations (Anderson & Salisbury, 2003), attitudes (Herr, Kardes, & Kim, 1991; Bone, 1995), and purchase decisions (Arndt, 1967; Leskovec, Aamic, & Huberman, 2007; Iyengar, Van den

Bulte, & Valente, 2011; Whyte, 1954). According to Keller Fay Group’s TalkTrack research during July 2010 and June 2011, 58 percent of consumers believed WOM is highly credible, while 50 percent expressed their intentions to buy as a result of WOM across 15 different product and service categories (Keller & Fay, 2012). As the Internet and interactive technology develop, the marketing battlefield has moved to the virtual community. The highly simulated community created by social media and its interactivity makes social media an ideal platform for buzz spreading. Each account on social media actually is an independent source disseminating information. Traditional WOM marketing based on face-to-face communication has transferred to eWOM marketing. Different from physical offline WOM communication, eWOM, with its

15 unique characteristics, can reach more audiences in a shorter time. But it also brings problems such as overload of information and sources, uncertainty, and credibility issues. Moreover, in the context of social media, the research about offline WOM communication is not sufficient to explain the eWOM mechanism. Though in this study the researcher mainly focused on source credibility in the context of social media, it is important to review relevant WOM literature to lay the foundation for the present study. Thus, the researcher first clarifies WOM and eWOM in terms of their definitions and characteristics. Then the researcher discusses the significance of

WOM in product purchase decision and perception of products and brands, and how product type as a moderator affects WOM communication.

Word-of-Mouth & Electronic Word-of-Mouth

Word-of-Mouth (WOM) generally refers to personal communication and was believed to be an effective marketing strategy for either brand or product promotion. It was defined in various ways in extant studies. Some scholars (Arndt, 1967; Breazeale, 2009) viewed WOM as an oral, person-to-person communication between receivers and communicators, whom receivers perceived as independent from retailers. Others described WOM as “the process whereby consumer who have experienced a product or service pass on their views, both positive and negative, about the product or service to other people.” (Swarbrooke & Horner, 2007, p.416).

WOM was not necessarily about products or services. It could also be about brands, or sellers of particular goods and services (Buttle, 1998; Westbrook, 1987). Though these definitions are different in wording, they are consistent with each other and share two characteristics (Vázquez-

Casielles, et al., 2013). Firstly, WOM senders are perceived as independent from any commercial organizations when they initiate a WOM communication. Secondly, WOM could be either positive or negative for involved products, services, or brands.

The technology growth created a new battlefield for retailers in cyberspace. To promote products, retailers initially bought digital ads and built their own websites in the early years of the Internet. When Web 2.0 became available, retailers also adopted and added the interactive features in online marketing, turning to “electronic word-of-mouth” (eWOM) marketing. The eWOM marketing, employing and distributing consumer-shared content, was different from advertising and distributing retailer-sponsored content. eWOM, expanding the WOM concept from face-to-face oral communication to text-based communication, has been defined as “ any 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 Internet.”

(Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004). Litvin, Godsmith, and Pan (2008) modified the definition as “all informal communications directed at consumers through Internet- based technology related to the usage or characteristics of particular goods or services, or their sellers.” Thus, any computer-mediated communication (CMC), including blogs, emails, bulletin board systems, etc., can be considered as eWOM (Buttle, 1998), given that eWOM communication is delivered by different online media in different manners with different emphasis, such as web-based opinion platforms, discussion forums, boycott web sites, and news groups (Hennig-Thurau, et al., 2004).

Although the given definitions of eWOM seemed to match with the original definition of

WOM, they overlooked the shared/reposted posts of customers or potential customers, which might be created originally by retailers. But when retailers-generated content is shared or forwarded by customers or potential customers, they become customers’ opinions that either implies endorsements or rebuttals. Hence, the researcher believes eWOM should be any information, including not only customers’ own statements but also those shared/forwarded posts

17 from retailers or other published sources, which are exchanged among potential, actual, or former customers about a product or company, available to a multitude of people and institutions via the Internet. According to the different contexts in cyberspace, the researcher classified eWOM into the following categories.

Categories of eWOM

1. Specialized eWOM: This kind of eWOM refers to the customer reviews (or customer- generated/forwarded content) posted on specialized comparison-shopping or rating websites, such as Yelp and Consumersearch. These websites, focusing on product ratings and recommendations, only provide customer reviews of specific products/services rather than purchases and sales service of products. One advantage of these websites is that they offer price comparison services and external links to different online retailers’ websites. Also, their powerful retrieval system and meticulous product classification makes it much easier for customers to find ratings and customer reviews of a specific product or service. However, the major shortcoming of this type of eWOM is that these specialized websites don’t provide detailed customer product reviews by themselves; they only direct customers to ratings and reviews on other online shopping websites, or only focus on certain industry. If customers can find reviews and ratings on other online shopping websites, or if they have a preference for certain online shopping websites, they don’t need to use these specialized websites for eWOM.

2. Affiliated eWOM: Affiliated eWOM, also called retail website eWOM, refers to customer reviews affiliated to retail websites, such as customer reviews on Amazon.com and eBay. This type of eWOM, linked with products, is only one small part of retail websites, which provide both product/service and customer reviews. The major benefit of affiliated eWOM is that reviews are more targeted; they are product/service-specific. It not only delivers detailed

18 information about the specific product/service, but also embraces customer comments on online shopping websites, such as comments on the quality of their shipping service and/or customer service. It is much easier for potential customers to take actions and make purchase decisions on the spot. Additionally, this eWOM is operated under an interesting power dynamic; consumers provide the eWOM, at the same time, it is the online retailers who sell the products that provide and maintain the review features (Yun, Park, & Ha, 2008). However, the overloaded customer reviews can confuse people; customers may have a hard time to filter what they really need from the massive information and the mix of positive and negative reviews.

3. Social eWOM: Social eWOM refers to any information related to products/services exchanged among social networking sites users, which includes customer-generated information as well as information that is created by online retailers but is forwarded/shared by customers.

The biggest difference between social eWOM and the first two types of eWOM is the different contexts in which eWOM is delivered to people. If the first two types of eWOM are considered as closed environment, then social eWOM should be relatively open. Both of the specialized and affiliated eWOM provide relatively closed environments, in which users or potential customers are active in searching for the eWOM of specific products/services and they know exactly that they can get customer reviews of the products/services because customer reviews and product purchases and sales are bound together. In contrast, social eWOM is a community-based eWOM, like Facebook, which does not have specific features for online shopping information communication. People are randomly exposed to information related to products/services. The randomness results in the fragmentation of eWOM, which again makes it hard to gather the information people need efficiently. People are passively receiving eWOM, rather than actively searching for the eWOM they need. However, compared with specialized and affiliated eWOM

19 exchanged among strangers, the major advantage of social eWOM is that they provide a relatively credible environment for eWOM. They are built on a community-network where people who exchange information do know each other.

4. Miscellaneous eWOM: This eWOM includes online shopping-relevant information shared on other online social media such as discussion boards, emails, and blogs. However, due to their different features and functions, it is not easy to form viral marketing in these forums.

For example, a blog is best for long and thoughtful arguments, which may not be appropriate for online shopping information dissemination. Moreover, the influences of these forms of eWOM are different depending on the quantities of users and contributors. For instance, the discussion board is a relatively open forum, which has its users and contributors from the same relatively large population while email only enables communication in a relatively small scope.

Similar to offline interpersonal WOM, scholars (Bickart & Schindler, 2001) have revealed that eWOM had higher credibility, empathy and relevance to customers than retailer- created information online. About 52 percent of online retailers offered online customer reviews or product rating services (“Survey,” 2011). Eight-three percent of Internet shoppers in a study reported that their purchasing decisions were affected by online product evaluations and reviews

(Beal, 2010). Also, scholars found eWOM had an impact on customers’ attitudes (Doh &

Hwang, 2009), information adoption (Cheung, Lee, & Rabjohn, 2008), trust (Awad &

Ragowsky, 2008), purchase intention (Lee & Lee, 2009), awareness (Davis & Khazanch, 2008), and loyalty (Gauri, Bhatnagar, & Rao, 2008) and so on.

On its own terms, WOM was characterized by valence, focus, timing, solicitation, and intervention (Buttle, 1998). The valence of WOM was different for customers and companies.

For example, a negative WOM comment on a company’s underperformance could be taken as a

20 positive one from a customer’s viewpoint. File et al. (1994) suggested that management efforts could not only influence the frequency of WOM, but also changed the direction of WOM.

Despite most relevant research showing that positive WOM encouraged purchase and negative ones discouraged purchase, some studies found a contrary result in certain circumstances

(Fitzsimons & Lehmann, 2004). For example, when people disagreed with the values of senders and when they were directed to do things that they did not want to do, even negative WOM had a positive effect in encouraging purchase. The maximum focus on WOM, either in academia or industry, was WOM exchanged among customers. But it is worth mentioning that WOM could also be operated to influence investment decisions, and company recruitment. Actually, a company is generally concerned with building and maintaining mutually beneficial relationships with six domains: customer, supplier, employees, influencers, recruitment, and referral markets

(Christopher, Payne, & Ballantyne, 1991). Thus, the WOM operation should not necessarily be limited to customer WOM. Previous research on other focused WOM was relatively thin compared to WOM exchanged among customers. The timing of WOM suggested that referral

WOM might be uttered either before or after purchase. Determined by the timing of uttering

WOM, the before-purchase WOM, and recruitment market were viewed as input WOM, and the after-purchase WOM as output WOM. The solicitation of WOM referred to whether WOM was induced or not, and whether it was sought or not. A typical example of unsolicited WOM in the digital era was the automatically recommended product/service on shopping websites, such as

Amazon. People did not actively seek the information; instead, the websites pushed the shopping information automatically and forced people to pay attention. Finally, intervention in WOM mainly referred to company-stimulated and -managed WOM spreading, by adding intervention in the process, such as employing celebrity endorsements.

More broadly, on the terms of WOM communication, previous research mainly focused on the consequences and factors influencing WOM communication. WOM consequence research was also considered in marketing-level analysis (Lee & Lee, 2009), which demonstrated the effectiveness of WOM in marketing and how it triggered exponential sales (Silverman, 2001).

WOM played a greater role in influencing people’s purchase intentions, compared to other marketing strategies, such as newspaper ads, direct sales, radio ads (Day, 1971; Goldenberge,

Libai, & Muller, 2001; Herr, Kardes, & Kim, 1991; Katz & Lazarsfeld, 1955). Additionally,

WOM was demonstrated to influence customers from various aspects. WOM had a more emphatic influence on purchasing decision and raising awareness of an innovation (Mangold,

1987;Sheth, 1971) because personal sources were viewed as more credible and trustworthy (Day,

1971; Murray, 1991). Moreover, referring to WOM was a risk reduction strategy (Buttle, 1998) especially for high-risk or intangible-dominant products (File, Cermak, & Prince, 1994) and affected the perceived value and risk of services (Yin-Hsi, 2012).

Research on factors influencing WOM communication has been conducted to address the problem: How did WOM change people’s attitudes toward purchasing decisions? Namely, which factors contributed to WOM persuasiveness? Studies for this purpose were based on individual levels of analysis, instead of marketing (Lee & Lee, 2009). Relevant research regarded WOM communication as a process of interpersonal communication between senders and receivers, which involved persuasion communication (Yi-Wen, Yi-Feng, Yu-Hsien, & Ruei-Yun, 2013).

There were explanations for WOM communication mechanism from both macro and micro perspectives. Macro research explained how WOM affected people’s intention from the theory of planned behavior perspective. According to the theory of planed behavior (Ajzen, 1991), people’s behavior intention was determined by both prevailing subjective norms and perceptions

22 of behavioral control factors. WOM from friends, acquaintances, experts, or other peers was regarded as external peer influence, which determined subjective norm (Hung, Ku, & Chang,

2003; Pedersen, 2001).

Different from passive customers affected by subjective norm in the theory of planned behavior, the micro explanation mainly concentrated on people’s initiatives to seek information or cues to help them make decisions. Social network theory reasoned that strong ties played an important role within social circles, while weak ties played a crucial role in disseminating information across groups (Granovetter, 1973). Tie strength was a continuous variable measured by several indicators, such as the importance attached to someone, and frequency of social contact (Brown & Reingen, 1987; Granovetter, 1973; Weimann, 1983). People were more likely to consult strong-tie sources than weak-tie ones in WOM communication (Brown & Reingen,

1987) in that strong-tie sources might be perceived as more credible than weak-tie ones (Roger,

1983). Elaboration likelihood model (ELM) explained why people referred to outside sources to help their decision-making (Park, Lee, & Ham, 2007). The ELM proposed two routes for human beings’ cognition in persuasive communication—central and peripheral routes (O’Keefe, 2002).

The central route involved high elaboration, deep issue-relevant analysis, and careful scrutiny of the relevant information. Under the central route to persuasion, receivers’ elaboration on proattitudinal and counterattitudinal messages, and argument strength were two determinants of persuasive effect. The peripheral route required low elaboration. Under the peripheral route to persuasion, people were always employing some shortcut and referring to heuristic principles to make decisions. People under center routes needed more information and deep analysis to make final decisions; while people under peripheral routes with low elaboration of message, just wanted to find some shortcut to help them make decisions. Therefore, people with high

23 involvement and the ability to process information were more likely to be affected by the central arguments of a persuasive communication; the quality and arrangement of the arguments presented were especially important (Dainton & Zelley, 2005). This explanation brought research on eWOM content. Scholars found that eWOM quantity and quality had a significant effect on consumer-perceived eWOM credibility and further affected eWOM adoption (Yi-Wen, et al., 2013). Jungho and Bying-Do (2013) found eWOM volume affected the first week’s movie revenue.

While less involved customers, or those with no motivations, were more affected by persuaders’ credibility and context, they were more likely to use cues to assist their decision- making. For example, using authority as a peripheral cue (Dainton & Zelley, 2005), online opinion leaders played a significant role in WOM diffusion on social media (Samutachak & Li,

2012). Also, using liking messages stressing affinity toward a person or a product as a cue, used the underlying principle of if I like you, I would like your ideas (Dainton & Zelley, 2005). These cues were echoed with empirical research. Previous studies indicated that perceived experience of sources (Bansal & Voyer, 2000), tie strength with sources (Bruyn & Lilien, 2008; Frenzen &

Nakamoto, 1993), similarity (Brown and Reingen, 1987), perceptual affinity (Gilly, Graham,

Wolfinbarger, & Yale, 1998), and even their own emotion (White, 2010), exerted strong influence on WOM communication. Moreover, people using peripheral route to persuasion were more affected by valence of peripheral messages (Dainton & Zelley, 2005). Research suggested that WOM valence directly affected revenue and communication effects (Lee & Koo, 2012).

Positive eWOM helped increase box-office revenue (Jungho & Bying-Do, 2013) and had a positive impact on television programs (Romaniuk, 2007), while negative WOM was disseminated more than positive and neutral WOM (Samutachak & Li, 2012).

In sum, the two cognitive routes in persuasion communication implied that WOM communication persuasiveness could be affected by characteristics of sources, quality and quantity of messages, receivers’ own characteristics, and contexts. These influential factors just consisted of the four basic research focuses within persuasive communication—source factors, message factors, and receiver and context factors. Considerable research has been committed to personal WOM in the aforementioned research areas for decades, some of them could also be used to explain eWOM communication. But they were still not sufficient to understand eWOM communication process in cyberspace. The new communication context of cyberspace not only provided us with infinite imaginary space for eWOM, but also brought several concerns that merited attention to better understand eWOM communication.

Firstly, it is worth noticing that eWOM “posses[es] unprecedented scalability and speed of diffusion” (Cheung & Thadani, 2010). The statements of eWOM are available to a multitude, potentially countless people and organizations, which can be accessed via the Internet (Black and

Kelley, 2009; Chen & Wu, 2012). One customer review could have thousands of millions of click rate, while traditionally personal WOM could only reach one or several persons at once.

The scope and speed of eWOM communication is unrivaled compared with personal WOM communication. The unrivaled communication power of eWOM also aroused the second concern—how could we guarantee the accuracy and authority of the spreading information online in the case of diffusion of false information.

eWOM is typically from unfamiliar sources (Cheung & Thadani, 2010; Black and Kelly,

2009; Bronner and Hoog, 2010; Pan et al., 2007; Xie et al., 2011), while traditional WOM always occurs among preexisting social networks, such as friends and family members. The anonymity attribute of eWOM raises uncertainty of source credibility influencing people’s

25 judgments and decisions. Research indicated that people were more likely to accept high- homophily sources than low-homophily sources. Personal WOM communication always occurred among people from the same social circle, or linked to each other physically in reality, while in most eWOM communication, sources were anonymous; receivers were exposed to more risks to adopt unknown sources (Parry, Kawakami, & Kishiya, 2012). But some research suggested that just because of the anonymity, product comments online were considered more credible than biased marketing ads (Bickart & Schindler, 2001). The Internet and the rise of social media accelerated the expansion of available information. Customers no longer relied on limited acquaintances and controlled messages; instead, they employed online crowd intelligence to help them make purchasing decisions (SAP, 2010). But filtering useful information from numerous sources is another concern aroused from crowd intelligence argument (see the next concern below). What are people’s criteria in selecting credible sources? Though previous research has established reliable measures for source credibility, they were developed and tested in the context of personal communication, rather than in online mediated communication. In the new context, how people perceive different sources in terms of their credibility and which factors contributed to social media source credibility would be interesting to explore.

Moreover, as stated above, despite the abundance of information available online, at the same time, people were bothered by information overload, which forced them to make filtering decisions. From the sender’s perspective, information dissemination could be either intentional or unintentional, while from the receiver’s standpoint, message spreading could be either solicited or unsolicited. In cyberspace, intentional WOM communication could be driven by explicit incentive (e.g., invite a friend, get $25), or solely by a desire to share the product purchase experience with friends (Bruyn & Lilien, 2008). Some scholars (Lovett, Peres, &

Shachar, 2013) classified the purposes of WOM dissemination into three categories: social, emotional, and functional drivers. Social driver emphasized one’s desire to share with others his/her expertise, uniqueness, or social status; the emotional one accentuated sharing one’s good or bad feelings to balance emotional arousal; functional driver referred to incentives that motivated people to spread information. A common example of unintentional message diffusion is Facebook fan page; all activities of fans done through these pages are automatically shown on their news feeds, visible to their friends. In physical reality, people are barely bothered by unsolicited messages; they will say no immediately to unwanted messages. But in cyberspace, eWOM are usually unsolicited (Bruyn & Lilien, 2008); consumers are overwhelmed by overload information, including both a high level of noise and solicited information; spam filtering is necessary in this sense.

Furthermore, eWOM does not vanish immediately as traditional WOM (Stern, 1994), rather, it is saved and traceable through archival threads online (Buttle, 1998) and can be accessed at customers’ convenience (Edwards, 2007). Thus, eWOM is more observable and measurable than traditional WOM in terms of format and quantity (Lee, Park and Hen, 2008;

Park and Kim, 2008). eWOM is always in textual format and saved in achieves, while traditional

WOM is generally oral and fleeting. It is much easier to quantify eWOM than traditional WOM.

Lastly, traditionally personal WOM possessed more information richness than eWOM communication. Information richness was defined “as the potential information-carrying capacity of data” (Tax, Chandrashekaran, & Christiansen, 1993). Lengel (1983) pointed out that the media used to convey information was an important determinant of richness; face-to-face communication was the richest with immediate feedback, facial and body language. Compared to face-to-face traditional WOM, eWOM possesses lower richness. The low information richness

27 directly affects people’s judgments and potential behaviors. Therefore, in the new media ecosystem, the standards people relied on to assess source credibility have been changed. New credibility dimensions need to be developed.

Social Media as Platform for eWOM

As shown above in the classification of eWOM, social eWOM has been classified out of other eWOM to be an independent form of eWOM. More and more attention in marketing communication has been attracted to social media because of its rapid development and ideal nature for marketing compared with other types of eWOM. Furthermore, the early successful business attempts on social media further prompted companies to consider social media as an essential part of their integrated marketing communication plans (Li, Bemoff, Feffer, & Pflaum,

2007; Mikalef, Giannakos, & Pateli, 2013). Social media tactics were more and more embraced as part of Fortune 500 companies’ business and marketing strategies (Barnes, 2011). In marketing, 59 percent of companies used Twitter in 2010 and 71 percent used Facebook, a jump from 61 percent in 2009 (Barnes, 2011). In order to maintain their competitive stances in the market, companies are investing more money in online advertising on social media, especially on

Facebook and Twitter, than ever before. More than 1.5 million organizations have their fan pages on Facebook; and 20 million people “like” Facebook fan pages every day (Jeanjean, 2012).

Electronic commerce and promotion is entering a new stage. Facebook fan pages create a space for business, organizations, sports teams, films, TV shows, and all kinds of brands on social networking sites to attract audiences and maintain “long-life relationship” with their fans (Kryder,

2010). Brands can send links, ads, video, and text to their fans and post updates on their fan pages to engage them. Fans can respond to the updates and participate in the events created on fan pages, which are automatically posted in their news feeds, fed to their “friends,” turning to be

28 ads for brands (Holahan, 2007). Things spread virally through those connection on social media

(Holahan, 2007), which have a significant impact on effective marketing online (Pai & Tsai,

2011). In addition, compared with other eWOM, social media provides a more relatively safe forum for social eWOM. Other eWOM either is highly anonymous, lacking credibility to some extent, such as BBS discussion, or is suspected of independence from manipulation of interest groups, such as affiliated eWOM, and specialized eWOM, because of the interesting power dynamic embedded in the eWOM aforementioned. Social media, featured with relatively real identity, consist of preexisting social networks and new-created “friendship.” Thus, it should be more credible and trustworthy hypothetically.

As per the aforementioned concerns, the virtual community context of social media provides an ideal environment for marketing; meanwhile, it also complicates eWOM communication in various ways. One of them is the link, repost, share, and even news feeds functions blurred the boundary between advertisement and WOM. People might repost a picture/comment of a product/service originally from retailer/company, which was considered as ads before others shared, reposted, or linked it. After people’s participation in the ads post, the ads became WOM automatically. Advertising is “any paid form of nonpersonal presentation of ideas, goods or services by an identified sponsor” (Alexander, 1964). WOM is not paid, nonpersonal, transparently sponsored communication. It is a voluntary behavior, although some

WOM originated from sponsored organizations. The ads became WOM on social media once people voluntarily distributed it. However, there existed a third possibility. According to

Payne’s taxonomy of referrals (Buttle, 1998), there were two referrals: customer referrals and non-customer referrals. In social media, customer referrals WOM might be either customer initiated or company/retailer initialed as the researcher explained above. However, non-customer

29 referrals WOM referred to the one initiated by company/retailer, which had reciprocal relationship with company benefiting. Technically, this kind communication should not be considered as eWOM on social media in that the essential part of the definition of WOM required information should be independent of company/retail. However, it is hard to distinguish because the reciprocal referrals occur “ when two or more organizations agree to cross-refer customers to each other (p.245).” It is like the tacit agreement between an animal hospital and a pet store; a vet will recommend his/her favorite pet store to clients who need to buy pet products, and the pet store will possibly recommend the animal hospital to its clients. Caution must be used to distinguish between customer-posted content and manufacturer/retailer-posted content on social media in this regard. However, the complicated communication environment makes it really hard to tell WOM from ads because everyone is both information producer and consumer; everyone has the potential to be either a source or receiver. Moreover, surrounded by overload information and numerous different sources, customers have to make a filtering choice. Among the four factors influencing WOM persuasiveness—source, message, receiver, and context— the biggest changes of eWOM brought by online forum were within source and context, compared with traditionally personal WOM. How they perceived sources in the context of social media is interesting to investigate. Therefore, given the limited research on eWOM so far, the researcher would like to only concentrate on source factor research, especially on what predicted and constructed social media source credibility, and on factors moderating people’s social media source credibility.

Summary

This chapter reviewed the relevant important concepts of this study. By exploring the definitions of WOM and eWOM, the researcher defined a clear conceptual space for the present

30 study. The classification of eWOM is to better demonstrate the position of this study within the whole conceptual framework. Since this study is based on Twitter and is going to explore different sources on Twitter, it is a study on source credibility of social eWOM.

CHAPTER III.

SOURCE CREDIBILITY THEORY & RESEARCH

In communication, source credibility was one of the most important factors that differentially affected people’s attitude, behavioral intentions (Haley, 1996), and thus affected people’s behavior eventually. As mentioned earlier, if receivers did not trust sources, then information spreading could not be completed, and accordingly communication results would not be as effective as expected. Although the perceived credibility of source was not the only factor that predicted people’s attitudes and behaviors in a communication (other factors like message content and media vehicle), in this present study, the researcher only focused on source credibility.

People’s acceptance of information and ideas was partly dependent on the message source (Berlo, Lemert, & Mertz, 1969). The influence of source on communication effectiveness was labeled variously in previous research, such as prestige, charisma (Berlo et al., 1969), ethos

(Berlo et al., 1969; McCroskey, 1966), credibility (O’Keefe, 2002), source credibility

(McCroskey & Teven, 1999), message source credibility, perceived credibility (Gunther, 1992), information source credibility (Berlo et al., 1969), and perceived source credibility (Spence,

Lachlan, Westerman, & Spates, 2013). Among these labels, the most frequently used term was source credibility (Berlo et al., 1969). To avoid confusion, the researcher used both source credibility and perceived source credibility, which were exchangeable in this study. Source credibility has been widely studied in diverse contexts—such as in public speech, political campaigning, interpersonal persuasion communication, mainstream public health message communication (Spence et al., 2013), psychology, sociology, and education (McCroskey, 1966).

Within the communication field, this construct was widely studied by interpersonal

32 communication scholars (Bracken, 2006) and originally used to measure audiences’ attitudes toward mass media sources (Hovland, Janis, & Kelley, 1953). Despite the fact that source credibility research has been done in diverse circumstances, previous scholarship suggested a consistent finding—people were more likely to accept the transmitted information from a source with more credibility (Berlo, et al., 1969).

Early research on source credibility was mainly conducted within a public speech context which mainly focused on interpersonal communication (Tewksbury, Jensen, & Coe, 2011). With the rise of multimedia in the recent half century, media industry shifted attention to media credibility, or channel credibility (Tewksbury et al., 2011). Media credibility was defined as

“perceptions of a news channel’s believability, as distinct from individual sources, media organizations, or the content of the news itself (Bucy, 2003).” Although it was clearly stated that media credibility differed from source credibility (Bucy, 2003), the intertwist of source, message, and channel/media in the context of interactive technology, as well as terminology misused in some scholarship, confounded people and made the credibility research more challenging

(Tewksbury et al., 2011). Before conceptualizing the construct of source credibility, it was necessary to distinguish source and channel credibility.

Persuasion Communication

As stated in Chapter II, according to the persuasion theory, there were five key factors that affected communication effects through an impact on attitude. These factors corresponded to the basic elements of communication process—source, channel, message, receiver, and context

(see Figure 3.1 below). The research of source in communication paid attention to the influence of various source characteristics on persuasion communication outcomes. The influence of source was source credibility, which was believed to be a changeable perception, not a static

33 intrinsic property/attribute of a source (Berlo et al, 1969; O’Keefe, 2002). Source credibility research focused on how people perceived the characteristics of a source in terms of expertise, trustworthiness, and other aspects. Beyond source credibility, other source factors, such as the liking, similarity, and attractiveness of a source have also been studied as possible influences on persuasive effects. Given the interactions among these source factors, scholars (Cunningham &

Bright, 2012, Ohanian, 1990) suggested some source factors, such as attractiveness and liking, could be considered as conceptual dimensions of source credibility, rather than independent source factors. For example, in the research about athlete endorsement and source credibility, attractiveness was included as one dimension under the conceptual structure of source credibility

(Cunningham & Bright, 2012; Ohanian, 1990).

Source Channel Message Receiver &Context

Attitude

Behavior

Figure 3.1: Persuasive effect model

The research of the influence of channel on perceived credibility mainly stressed the characteristic of a channel/medium and how these characteristics encouraged/discouraged people’s attitudes and behaviors. It was easy to confound source with channel in the

34 technological era, in that media can be defined as either sources or channels. For instance, when we say newspaper, it can be a news organization (source); meanwhile, it can also be a mode of transmission (print media), which differs from digital media. Additionally, it was worth noting that the traditional line between media and source became blurred because of the convergence of information production and consumption in the new media era. In this sense, everyone in social media was both producer and consumer of content; every account was a source producing information; meanwhile, it was also a medium channel disseminating information. The hybrid phenomenon of producer and consumer was conceptualized as prosumption (Toffler, 1980), which directly triggered the shift from the traditional notion of information/news “gatekeeping” to “gatewatching” (Bruns, 2008). Gatewatchers were not able to control the gate through which information passed, but only received co-created content with comments and links from a variety of sources, take Twitter for example. In this context, source credibility was more important for people to form judgment of the outside information. Source credibility on social media was not only determined by the characteristics of the exact person or organization behind an account, but also affected by the attributes of the social media platform, such as interactivity and sociability enabled by social media. They were all playing important roles as integral parts in establishing source credibility on social media. Thus, establishing measures for source credibility on social media could not overlook the attributes of social media.

The other three factors were more distinguishable than source and channel credibility in the persuasion model. Message credibility influenced people’s judgment and decision from content perspective. And it could be examined from message structure, message content, and sequential-request strategies (O’Keefe, 2002). Receiver factors referred to receiver characteristics that influenced people’s perception and attitude. Gender, age, and other individual

35 characteristics have been the focus of attention in the research that investigated whether certain people were more persuasible in communication than others (O’Keefe, 2002). Though there was considerable research on how receiver characteristics affected persuasive communication, few studies investigated the effect of receivers’ characteristics in the context of WOM communication. Moreover, now that perceived source credibility was defined as the evaluation of a source by the information recipient (Haley, 1996), the individual’s characteristics might have direct effect on people’s perception. In other words, individual characteristics might predict people’s perceived source credibility.

Lastly, contextual factors, including primacy-recency, media, and the persistence of persuasion, scarcely received attentions in persuasion communication research (O’Keefe, 2002).

In the context of social media, whether an information recipient was familiar with the Internet and environment in which communication occurred, and whether s/he was involved in social media were the important indicators of contextual factors.

Conceptualization of Source Credibility

Many scholars tried to conceptualize source credibility in previous research. The definitions given were similar, but different in wording. For example, McCroskey (1966) described source credibility/ethos as perceiver’s attitude toward a source. Lafferty and Goldsmith

(1999, p43) defined credibility as “the extent to which the source is perceived as possessing expertise relevant to the communication topic and can be trusted to give an objective opinion on the subject.” O’Keefe (2002) defined it as “judgments made by a perceiver (e.g., a message recipient) concerning the believability of a communicator.” Gunther (1992) argued that perceived source credibility was more audiences’ responses than message source attribute.

Therefore, involvement should be a better predictor of perceived credibility—“a person’s

36 involvement in situations, issues, or groups will show the greatest explanatory power” (p.152).

However, more investigations defined source credibility by identifying the basic underlying dimensions of the construct (O’Keefe, 2002).

The conceptualization of source credibility was stated by Aristotle originally, who named the image of a source in the minds of receivers as ethos (McCroskey & Teven, 1999). The ethos, composed of three elements—intelligence, character, and goodwill— is what is commonly referred to as source credibility construct nowadays. Seminal work on source credibility in academia dated back to Hovland et al.’s research on persuasion in 1950s (Hass & Wearden,

2003). Based on the research of persuasive public speech, Hovland, Janis, and Kelley (1953) pointed out that the source initiating the communication and the cues as to the trustworthiness, intentions, and affiliations of the source in the process of communication, had an important impact on the effectiveness of communication. However, it should be noted that they only identified trustworthiness and expertness as factors of source credibility. In addition, they only used one-item scale for their dimensions in some studies, merely functioning as a check on the validity of the prior high or low credibility values (Berlo et al. 1969).

The most widely cited measure of source credibility was advanced by Berlo, Lemert, and

Mertz in 1969 (Hass & Wearden, 2003). Berlo et al. (1969) followed Osgood, Suci, and

Tannenbaum’s (1957) measurement of meaning to construct the semantic differential of source credibility scales. A set of adjectival pairs being antonyms were rated by respondents for familiarity and then filtered by respondents’ evaluation (generally, adjectives that were rated unfamiliar by over 25% of the respondents should be eliminated). Their study was an extension of the earlier work of Hovland et al. (1953) and aimed to establish a generalizable instrument for indexing of different sources. Two factor analytic studies were conducted to extract and retest

37 dimensions and scales of source credibility. They argued that there were three meaningful and statistically independent dimensions for the source credibility construct. There were five indicators for each dimension. The first dimension was “safety”, which included safe/unsafe, just/unjust, kind/cruel, friendly/unfriendly, and honest/dishonest. The second dimension

“qualification” embraced trained/untrained, experienced/inexperienced, skilled/unskilled, qualified/unqualified, and informed/uninformed. The last dimension “dynamism” included aggressive/meek, emphatic/hesitant, bold/timid, active/passive, and energetic/tired. Berlo et al.’s

“safety” seemed like “trustworthiness” proposed by Hovland et al. (1953), but it was a broader concept than “trustworthiness.” “Safety” did not only include “intent-oriented” items, such as just/unjust, but also contained items having nothing to do with “intent-oriented,” such as safe/unsafe, kind/cruel, friendly/unfriendly, and honest/dishonest. Qualification dimension here corresponded to “expertise.” Proposed by Hovland, which examined the professional capability of a source, dynamism dimension was “a combination of the potency and activity factors of general connotation (Berlo et al., 1969, p. 576).” Although dynamism was independent of the safety and qualification factors statistically, it was not sufficiently stable to be psychologically independent of the other factors. The dynamism dimension could be used as intensifier, intensifying or weakening people’s perception or evaluation of a source.

McCroskey (1966) suspected the significance of the “dynamism” factor in persuasive communication. He reasoned that, given source credibility/ethos was the attitude of a perceiver toward a communicator, the factors of source credibility should be consistent with the factors of other attitudes. However, the “dynamism” factor did not show its explanatory power to other attitudes. But he also objectively stated that, “[W]hile the ‘dynamism’ factor did not appear in the studies reported below, this should not be interpreted as indication that it does not exist

(p.66).” Two factors—“Authoritativeness” and “Character”—were identified by factor analysis in McCroskey’s study. Whether a communication sender was reliable, informed, qualified (on certain topics), intelligent, valuable, and expert were all included under “authoritativeness” dimension as indicators. While, whether a sender was honest, friendly, pleasant, unselfish, nice, and virtuous were selected as indicators of “character.” He excluded “goodwill” (the intention to care perceiver) as one dimension because the indicators of goodwill were subsumed under

“authoritativeness” and “character” dimensions. To justify the validity of Likert scaling approach for source credibility, McCroskey conducted a comparative study by using semantic differential items to measure the construct. Results showed that both Likert and semantic differential scales appeared to measure equally.

However, in his classic instrument for source credibility indexing, McCrokey, along with

Teven (1999), brought “goodwill”/ “intent toward receiver” back into the measure (see Appendix

A). In the context of teaching, they argued that “goodwill” “was represented in the current

‘caring’ construct…But the caring construct did not suggest the opposite of caring is malicious intent. It was just indifference (p.92). Three elements that related to be more caring were: understanding, empathy, and responsiveness. “Understanding” was “knowing another person’s ideas, feelings, and needs.” Empathy referred to “one person’s identification with another person’s feelings.” Responsiveness involved that “one person acknowledging another person’s communicative attempts.” McCroskey and Teven (1999) finally used “caring,” “have my interests at heart,” “self-centered,” “concerned with me,” “insensitive,” and “not understanding,” as indicators of “goodwill”. He retained the other two dimensions—“competence” and

“trustworthiness” in his instrument with some wording changes.

Both McCroskey and Berlo’s measures for source credibility were in the context of face- to-face interpersonal communication within the persuasive communication framework. Realizing the importance and impact of perceived source credibility on people’s attitudes and behaviors, marketing communication scholars started constructing source credibility measures in the field of marketing communication. Based on previous research of persuasion communication,

Ohanian (1990) proposed that celebrity endorser credibility was structured by three dimensions—attractiveness, trustworthiness, and expertise (see Table 3.1 below). He selected attractive/unattractive, classy/not classy, beautiful/ugly, elegant/plain, and sexy/not sexy to measure attractiveness dimension. Dependable-undependable, honest/dishonest, reliable/unreliable, sincere/insincere, and trustworthy/untrustworthy were identified to measure trustworthiness dimension. Expertise dimension included expert/not an expert, experienced/inexperienced, knowledgeable/unknowledgeable, qualified/unqualified, and skilled/unskilled.

Table 3.1 Source-Credibility Scale

Attractiveness Attractive –––––––––––––––– Unattractive Classy –––––––––––––––– Not Classy Beautiful –––––––––––––––– Ugly Elegant –––––––––––––––– Plain Sexy –––––––––––––––– Not sexy Trustworthiness Dependable –––––––––––––––– Undependable Honest –––––––––––––––– Dishonest Reliable –––––––––––––––– Unreliable Sincere –––––––––––––––– Insincere Trustworthy –––––––––––––––– Untrustworthy Expertise Expert –––––––––––––––– Not an expert Experienced –––––––––––––––– Inexperienced Knowledgeable –––––––––––––––– Unknowledgeable Qualified –––––––––––––––– Unqualified Skilled –––––––––––––––– Unskilled Note. From “Construction and Validation of A Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness,” by R. Ohanian, 1990, Journal of Advertising, 19, 3, p.50.

Confirming the three primary credibility dimensions of Ohanian (1990), Haley (1996) proposed the concept of organizational credibility. In his study, consumers identified corporate image and prior performance as important indicators of trustworthiness dimension.

“Recognizability, a reputation of offering a quality product/service, being well managed, treating employees with respect, and a history of prosocial involvement with issues,” were all considered as corporate image and prior performance. Another dimension—expertise—for organization credibility was defined as the extent to which a company was knowledgeable about issues related to itself or consumers. The criterion for expertise was consistent with previous description

(Hovland et al., 1953), which was defined as “the extent to which a communicator is perceived to be source of valid assertions.” Attractiveness, which was measured by “affective components

41 related to consumers’ feeling toward the organization’s reputation and product/service” (Haley,

1996, p.31), was also defined as one dimension of organization credibility in Haley’s study. The following Figure 3.2 illustrates a consumer’s perception of organization.

Organization credibility

Congruent I like I know Like me with my them them values

Figure 3.2. Consumer perception of organization. Adapted from “Exploring the Construct of Organization as Source: Consumers’ Understandings of Organizational Sponsorship of Advocacy Advertising,” by E. Haley, 1996, Journal of Advertising, xxx (2), p.31.

Realizing the inconsistency in the conceptual structure of source credibility, Eisend

(2006) generalized created-measurements of source credibility by reanalyzing existing measures developed in previous studies within marketing communication. The replication design was done via two studies; in the first study he selected 29 different existing credibility scales and filtered the adjectives used in these scales by Osgood et al.’s (1957) semantic differential technique to 64 scales/pairs. Appropriate product, company, spokesperson, and salesperson were carefully selected. Tentative extraction of factors of company, spokesperson, and salesperson credibility were generated. In the second study, reliability analysis, validity analysis, exploratory and confirmatory factor analysis were conducted. Dimensions of three different sources— salesperson, company, and spokesperson—were identified and verified at last. Each source had three dimensions for perceived credibility. Trustworthiness, competence, and attraction

42 structured salesperson credibility (See Figure 3.3). Corresponding indicators were identified for each dimension.

Salesperson credibility

Trust- Attraction worthiness Competence

Honest/dishonest Attractive/unattractive Trained/untrained Sincere/insincere Appealing/unappealing Competent/incompetent Realistic/unrealistic Nice/awful Professional/unprofessional Right/wrong Expressive/inexpressive Experienced/inexperienced Trustworthy/not trustworthy Dynamic/static

Figure 3.3. Factor structure of salesperson credibility. From “Source Credibility Dimensions in Marketing Communication: A Generalized Solution,” by M. Eisend, 2006, Journal of Empirical Generalisations in Marketing Science, 10, 2, p. 20.

Company credibility retained trustworthiness and competence dimensions as included in salesperson credibility, but replaced attraction with dynamism dimension (see Figure 3.4). It was noteworthy that some items under trustworthiness and competence of company credibility were changed because the company was an organizational source. Another three sufficient discriminant factors—sincerity, professionalism, and attraction—were included for spokesperson credibility (see Figure 3.5). According to the investigated source, Eisend (2006) made some changes as to the wording and scale selection as shown in Figure 3.5.

Company credibility

Trust- Dynamism worthiness Competence

Honest/dishonest Expert/inexpert Sincere/insincere Professional/unprofessional Active/passive Believable/unbelievable Competent/incompetent Dynamic/static True/false Organized/chaotic Fair/unfair Useful/useless

Figure 3.4. Factor structure of company credibility. From “Source Credibility Dimensions in Marketing Communication: A Generalized Solution,” by M. Eisend, 2006, Journal of Empirical Generalisations in Marketing Science, 10, 2, p. 21.

Spokesperson credibility

Sincerity Professionalism Attraction

Accurate/inaccurate Dynamic/static Honest/dishonest Informative/uninformative Expressive/inexpressive Sincere/insincere Successful/unsuccessful Appealing/unappealing Believable/unbelievable Skilled/unskilled Attractive/unattractive

Trained/untrained Exiting/dull Professional/unprofessional

Figure 3.5. Factor structure of spokesperson credibility. From “Source Credibility Dimensions in Marketing Communication: A Generalized Solution,” by M. Eisend, 2006, Journal of Empirical Generalisations in Marketing Science, 10, 2, p. 22.

In Eisend’s study, even though he chose different factor labels for salesperson and spokesperson credibility, the factors and corresponding indicators of salesperson and spokesperson credibility were much the same. It was reasonable to have different factors and indicators in organizational source credibility than in individual source credibility. However, to avoid confusion, the factors and indicators used to measure individual source credibility should be consistent.

Although the dimensions for each source in the marketing communication study were described slightly differently and were not exactly measured with identical indicators, the results revealed a consistent structure for source credibility (Eisend, 2006). Actually, the three dimensions were referred to as the “inclination toward truth” (“the source will tell the truth”), the

“potential of truth” (“the source knows the truth”) and “presentation”(appearance attributes

45 facilitating source credibility). The “inclination toward truth” and “potential of truth” dimensions corresponded to “trustworthiness” and “expertise” dimensions identified in previous studies.

“Presentation” dimension embraced visible characteristics of the appearance of a source, such as dynamism, and attraction, or attractiveness. The attempt to generalize instrument of indexing for source credibility in marketing communication was the most applicable and trans-situational practice in that it not only established a common structure beyond one specific circumstance, but also proposed an idea—customize specific measures and scales for specific sources in a study.

Inspired by the idea, the researcher customized source credibility measures according to the specific source categories in this study. For example, an individual should have different credibility measure than an organization on social media.

In effect, besides the studies above, many other endeavors were made to investigate dimensions of perceived source credibility. It seemed that there was no generalized and trans- situational measure for the construct (Berlo, 1969; O’Keefe, 2002); the conceptual structure, or dimensions of source credibility might vary from circumstance to circumstance (King, 1976;

Liska, 1978). But there was an agreement on two dimensions of source credibility (McCroskey

& Teven, 1999; O’Keefe, 2002) as shown in previous studies.

1. Expertise. The expertise dimension, also called “competence,” “expertness,”

“authoritativeness,” or “qualification,” aims to measure if sources have the capability to

know the truth. Hovland, Janis, and Kelley (1953) defined expertise as “the extent to

which a communicator is perceived to be a source of valid assertions.” Measures such as

“experienced/inexperienced, informed/uninformed, trained/untrained,

qualified/unqualified, skilled/unskilled, intelligent/unintelligent, and expert/not expert”

are commonly used to represent this dimension. Multiple-item scales of this dimension

have been proved as having high internal reliability (O'Keefe, 2002). The example of

multiple-item scale (See Table 3.2) of source credibility is demonstrated below. There

were three dimensions in this measure. Each was measured with six separated semantic

differential type items, anchored with two antonyms using a 7-point response scale

ranging from 1 to 7. The items formed unidimensional solutions for each dimension.

2. Trustworthiness. The trustworthiness dimension, also called “character,” “safety,” or

“personal integrity,” measures to what extent a source is inclined to tell the truth if he or

she knows it. Hovland, Janis, and Kelley (1953) defined trustworthiness as “the degree of

confidence in the communicator's intent to communicate the assertions he considers most

valid.” Scales of this dimension are like “honest/dishonest, trustworthy/untrustworthy,

open-minded/closed-minded, just/unjust, fair/unfair, and unselfish/selfish.” (O'Keefe,

2002). Again, multiple-item scales of trustworthiness dimension have demonstrated

higher internal reliability (O’Keeffe, 2002, p183).

Furthermore, the measure proposed by Eisend (2006) was more generalized, trans- situational, and applicable in that it provided specific instruments for different sources, either individuals or organizations. However, his measurement was established in the specific context; particular brands and products were selected to measure credibility in his study. This practice was reasonable, considering the purpose of his study was to test the new generalized measure for source credibility. But it did not establish a credibility indexing which enabled credibility comparisons across different sources. Also, in the digital media era the measures used traditionally were outdated. For the present study, measures of source credibility must be applicable to social media context. Among the scant research on source credibility on social media, a recent study (Spence, Lachlan, Westerman, & Spates, 2013) investigating perceived

47 credibility of sources on social media used health information to test the relationship between perceived source credibility and ethnicity. The measure of credibility, containing three separate dimensions—competence, goodwill/caring, and trustworthiness—was used (McCroskey &

Teven, 1999) in this study. However, as shown in Table 3.2, McCroskey & Teven’s (1999) measure of source credibility in the context of interpersonal communication between student and instructor was insufficient in the social media context, in that social media had created a new communication ecology.

Table 3.2 Measure of Source Credibility

Competence Intelligent –––––––––––––––– Unintelligent Untrained –––––––––––––––– Trained Inexpert –––––––––––––––– Expert Informed –––––––––––––––– Uninformed Incompetent –––––––––––––––– Competent Bright –––––––––––––––– Stupid Goodwill Cares about me –––––––––––––––– Doesn’t care about me Has my interests at heart –––––––––––––––– Doesn’t have my interests at heart Self-centered –––––––––––––––– Not self-centered Concerned with me –––––––––––––––– Unconcerned with me Insensitive –––––––––––––––– Sensitive Not understanding –––––––––––––––– Understanding Trustworthiness Honest –––––––––––––––– Dishonest Untrustworthy –––––––––––––––– Trustworthy Honorable –––––––––––––––– Dishonorable Moral –––––––––––––––– Immoral Unethical –––––––––––––––– Ethical Phoney –––––––––––––––– Genuine Note. Adapted from “Goodwill: A Reexamination of The Construct and Its Measurement,” by J.C. McCroskey & J.J. Teven, 1999, Communication Monographs, 66, p.95.

New Dimensions in Social Media Context

First, in social media, especially on Twitter, where people were more focused on information collection and dissemination, most of their followings and followers were weak ties.

The scalability and diffusion scope on social media was much larger than in traditional communication. In this case, a source was always facing hundreds and thousands of weak-tie friends, instead of just a few strong-tie friends. Thus, the information and subjects they communicated were also expanded, rather than limited in some specific issues. The old dimension “goodwill,” which was measured by “care about me,” “has my interests at heart,”

“self-centered,” “concerned with me,” “insensitive,” and “not understanding,” was no longer

49 accurate and appropriate to use in the context of social media. Because these indicators and goodwill factor were established and validated in the specific context created by the scholars; they could not be applicable to every sources, especially not applicable to weak ties on social media. Therefore, this factor was excluded from measures in this study.

Another study (Cunningham & Bright, 2012) examining the influence of athlete endorsements and source credibility in Twitter, employed the measure created by Ohanian

(1990). This measure included attractiveness as one dimension of source credibility, which was applicable to athlete celebrity. Because the persuasion model (O’Keefe, 2002) suggested that although attractiveness was always considered one source characteristic paralleling with source credibility, they intertwined with each other sometimes. On social media, it was not necessary for people to know each other physically to be friends. For the “hubs” on social media especially, they were well known and followed by many ordinary people, who were mostly strangers to them either online or offline. But for the ordinary people, they knew the “hubs” on social media because the “hubs” were public figures. Therefore, considering this study concentrated on developing individual source credibility by using public figures for example, attractiveness should still be an important factor for individual source credibility on social media. However, for an organization, attractiveness, which mainly measured the physical attributes and personality of an individual, was not that prefect as a dimension of organizational source credibility. But according to Haley, attractiveness for an organization should be measured by “affective components related to consumers’ feeling toward the organization’s reputation and product/service” (Haley, 1996, p.31). Hence, for organizations, attractiveness could be replaced by organization attributes. And corresponding indicators need changing as well.

Table 3.3 Source Credibility Scale

Safety Kind –––––––––––––––– Cruel Safe –––––––––––––––– Dangerous Friendly –––––––––––––––– Unfriendly Just –––––––––––––––– Unjust Honest –––––––––––––––– Dishonest Qualification Trained –––––––––––––––– Untrained Experienced –––––––––––––––– Inexperienced Qualified –––––––––––––––– Unqualified Skilled –––––––––––––––– Unskilled Informed –––––––––––––––– Uninformed Dynamism Aggressive –––––––––––––––– Meek Emphatic –––––––––––––––– Hesitant Bold –––––––––––––––– Timid Active –––––––––––––––– Passive Energetic –––––––––––––––– Tired Note. Adapted from “Dimensions for Evaluating The Acceptability of Message Sources,” by D.K. Berlo, J.B. Lemert, & R.J. Mertz, 1969, Public Opinion Quarterly, 33, p. 574.

Besides the aforementioned, in the context of social media, the dynamism dimension of source credibility (Table 3.3) posited by Berlo et al. (1969) might be brought in combining with

McCroskey & Teven’s three-dimension measure (McCroskey & Teven, 1999) to measure the construct. Because on Twitter people were mainly connected by weak ties, they did not know each other most of time. It was hard to make a judgment of source credibility as to goodwill based on tweets or other content from a source. However, people could judge on the speaking and behavior styles of different sources, which were referred to as “dynamism” by Berlo et al.

(1969). On social media, where people physically knew little about each other, this dynamism factor might have a great influence on source credibility.

Social Tie Strength

Moreover, social tie strength might be considered as another factor of source credibility.

Network analysis (Granovetter, 1973) assumed society was structured into high-connected clusters, which were made up of strong social ties, like family and close friends. Weak links, such as acquaintances, were weak social ties, which connected these clusters together “keeping them from being isolated from the rest of the world (Barabási, 2002).” At most times, weak ties played an important role in people’s social activities by spreading words and getting information from the outside world, although strong ties were more influential for people than weak ties

(Barabási, 2002; Brown & Reingen, 1987). The network theory provided promising explanation for source credibility in social media context. In the virtual community, people were connected by “friend” or “follow” functions of social media. In the virtual network, there were a few

“hubs” that were connected with numerous people and more ordinary people who were connected with a few “friends.” People were all connected for a reason but with different closeness. The relevance of a source (or an account on social media) to people determined the social tie strength. The strength of people’s social tie on social media, or the “logical association” between a source and people had an influence on people’s perceived credibility of this source (Haley, 1996). Taking Twitter for example, people were surrounded by their friends, and more “followers.” Every account could be viewed as a source. Friends and family were strong ties, while “friends” connected by strong ties were weak ties.

Based on network theory, Brown and Reingen (1987) proposed two-level WOM behavior—micro- and macro-level. They argued that people’s WOM behavior occurred either within their strong social ties at the micro-level or among weak social ties in macro-level. They found that social ties strength provided useful explanation as to how information was accepted

52 within and among “clusters/groups.” Tie strength was determined by several variables, such as, perceived importance of social relation, frequency of social contact, and type of social relations

(e.g., friends, family, or acquaintances) (Brown & Reingen, 1987; Granovetter, 1973). In Haley’s study of organizational credibility, he stated that the relationship between the organization and the consumer was the central component of consumers’ perceived credibility of an organization

(Haley, 1996). Similarly, the relevance of an individual to people should also be important factor for them in judging the individual’s credibility.

Technology Affordance

Except ignoring the new social media context for source credibility and social ties’ influences, extant research also overlooked the influence of system-generated cues (or technology) on source credibility. In a study of source credibility on Twitter, based on MAIN model and social information processing theory, researchers (Westerman, Spence, Van Der

Heide, 2012) proposed that system-generated cues, like followers and followings on Twitter, might affect perceived credibility of Twitter page owners. The results indicated that both the dimensions of source credibility—competence and trustworthiness—had an inverted “U” curvilinear pattern with the number of followers. Too few and too many followers both resulted in less credibility in the competence and trustworthiness of Twitter page owners. It was expected that fewer followers triggered less perceived credibility. However, beyond expectation, more followers also reduced source credibility. This finding also conflicted with bandwagon effects (If others think that something is good, it must be good) (Sundar & Nass, 2001). In addition, they found that lesser the ratio of followers to followings, the more credibility given to Twitter page owners. Their study inherited research on source credibility from a technology perspective dated back to the 1990s.

Although credibility research arose mostly from the fields of psychology and communication, researchers (e.g., Tseng & Fogg, 1999) expanded and enriched the research topic from technology perspective as a result of the popularity of computer and the Internet, and computer-related products. Tseng and Fogg (1999) proposed four types of credibility, two of which were referred to superficial aspects of the site (the other two related to people’s experience and assumption to others): reputed credibility and surface credibility. The reputed credibility referred to the labels or markers on the site of the source’s expertise and reputation. In the case of Twitter, it included the verified accounts on Twitter, which indicated accounts’ identity.

Surface credibility was more abstract, which described how much a perceiver believed a source based on inspection of the site and profile. The basic idea was people made their credibility judgment with the help of markers/labels generated by technology/computer/system.

Wathen and Burkell (2002) operationalized surface credibility and pointed out that surface characteristics involved appearance/presentation and information organization, and interface design elements, such as interactivity, navigability, and download speed. Sundar (2008) demonstrated in his study of youth’s assessment of credibility that surface features of the interface, which was referred as technology affordances, had a profound influence on young people’s judgment of credibility. The technology affordances in digital media triggered cognitive heuristics to affect people’s assessments of credibility by offering auto-generated cues or markers on social media. A typical example was the number of followers on Twitter, which suggested whether an account/source was popular or not and affected people’s impression (Tong,

Van Der Heide, Langwell, & Walther, 2008; Utz, 2010). In addition, Sundar (2008) argued that because of the excessive information online, today’s youth more and more depended on cues/markers transmitted by technological features to make snap decisions about the credibility

54 and quality of information they consumed. Based on previous studies and analysis, Sundar (2008) proposed MAIN model embracing four broad technology affordances—Modality (M), Agency

(A), Interactivity (I), and Navigability (N).

Cues Credibility Affordance Heuristics Quality Judgment

Figure 3.6. The MAIN model. Adapted from “The MAIN model: A Heuristic Approach to Understanding Technology Effects on Credibility,” by S.S. Sundar, 2008, in Digital Media, Youth, and Credibility. Edited by M. J. Metzger and A. J. Flanagin. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning. Cambridge, MA: The MIT Press, p.79.

The main argument of MAIN model, as demonstrated in Figure 3.6, was that people’s perceptions were affected by the presence of technology affordances on an interface (Sundar et al., 2015). MAIN model assumed that technology affordances served as cues, which triggered people’s cognitive heuristics. Technology affordances trigger heuristics in two ways: 1) “its presence on the interface can transmit cues, both about its functionality and the designer’s intent; and/or 2) by adaptively gathering information for the user in the form of metrics that reflect its operation (Sundar et al., 2015).” For example, the number of followers to the account could be a criterion to judge the owner’s popularity and credibility. Based on the cognitive heuristics, people made snap decisions on quality, which directly determined their credibility judgment.

Sundar (2008) proposed four broad technology affordances. Firstly, media technology can affect people’s perception of a source through heuristics triggered by modality cues. In other words, how information is conveyed (e.g., text, picture, and audiovisual) can either enhance or diminish perceived credibility of information. For example, people are more likely to trust visual information over textual information (Sundar et al., 2015). Secondly, agency is affordance

55 pertaining to the source of media content. In the traditional media era, professional gatekeepers

(e.g., reporters and editors) served as agency, while in digital era, either the technology itself

(e.g., robot) or the user can serve as a source on social media with the technology affordances

(e.g., modality, interactivity, and navigability). On the one hand, the sense of “user control” provide users greater sense of “self as source,” which shapes users’ perception of content quality and source credibility (Sundar et al., 2015). However, on the other hand, the identity of sources on social media was murky, people were always suspecting whether a source was a real person behind the device. A mass of computer-controlled accounts pervading social media directly resulted in the decreasing credibility and trust in sources and information. The computer- controlled accounts, also called “zombie fans,” pretended to be a real account and disseminated ads and junk information on social media. The social presence heuristic, afforded by the interface cues which imply whether a user is interacting with a real person or intelligent entity, affected people’s perception of source and media content. This was confirmed by several studies in different contexts. For instance, on social media where emphasize communication and interactivity, the accounts operated by a human agent were more attractive than those machine- controlled accounts (Edwards, et al., 2014). A verified blue badge offered by social media as well as active interactions with followers/followings could all boost a source’s credibility.

Moreover, the fact that multiple layers of sources on social media makes perceived source characteristics salient in people’s credibility judgment. In other words, the cues suggested expertise of a source could trigger the authority heuristic, which makes the source more credible in certain subject matter (Hu & Sundar, 2010). Interactivity cue implied both interaction and activity. Great activity engendered great dynamism, which was shown to be related to high credibility in traditional source credibility measure. The interactivity afforded by social media

56 technology not only enable users to customize their own websites, but also support the interactive activities between users and their audiences. The interactivity, modality, and navigability affordances create user a great sense of “self as source.” In the context of social media in this study, interactivity could be the way/style a source communicated with “fans”/

“followers,” such as, whether a source was responsive to conversations on Twitter and how frequently, and whether a source was always telepresent or not. Study (Westerman, Spence, &

Van, 2014) has showed that recency of updates impacted source credibility on Twitter.

Navigability cues mattered how interface features, which suggested transportation from one location to another, influenced people’s credibility assessment. For example, the navigability tools which enable users easily browse a site can trigger help heuristic, which in turn predispose users to be positive toward the account owners or designers (Sundar et al., 2015).

Product Type and Perceived Source Credibility

Besides the characteristics of WOM source—receiver, message, and contextual factors

(Arndt, 1967) —the WOM target, which could be a product, a company, or a brand (Sundaram &

Webster, 1999), was also playing a crucial role in WOM communication. In the present study, the researcher only focused on how products, as the WOM subject, affected perceived source credibility.

A product was considered to be a set of benefits to consumers (Ltifi & Gharbi, 2012). It could be anything exchanged on a market to meet a need. Product could be classified in various ways depending on the selective criteria. For example, products were divided into services and goods due to their intangible and tangible characteristics. And products were also classified into hedonic & utilitarian products in terms of their functions and people’s cognitive feeling when consuming them (Laurent & Kapferer, 1985; Vaughn, 1980, 1986; Zaichkowsky, 1987). But

57 regardless of these product classifications, most of them were derived from the four antecedents of product involvement: product importance, risk importance, product’s symbolic value, and product’s pleasure value (Laurent & Kapferer, 1985). Product involvement referred to people’s perceived relevance of the product based on inherent needs, values, and interests (Zaichkowsky,

1985). According to Laurent and Kapferer (1985), given the perceived importance of a product, or its personal meaning (product importance), the perceived risk associated with the product purchase in terms of poor choice and its probability (risk importance), the cost of the product

(product’s symbolic value), and the hedonic value of the product, or its emotional appeal

(product’s pleasure value), people were either highly or lowly involved in this product.

Although abundant research had acknowledged the effects of product on the WOM sender in terms of spreading WOM (Bansal & Voyer, 2000; Bone, 1992; Buttle, 1998; Derbaix

& Vanhamme, 2003; Moldovan, Goldenberg, & Chattopadhyay, 2011; Smith & Vogt, 1995), not much research had explored how the product influenced WOM receivers’ perceptions of sources during a WOM communication. Weinberger and Dillon (1980) found there was a significant effect of product type treatment on WOM effectiveness; people relied more on WOM for services than goods. Goods always referred to tangible and physical products, such as, clothes, cars, and so on. Services were always considered intangible, such as entertainment, restaurant, lawyer, and accountant services (Heim, 2009). One possible explanation for the finding might be that services were harder to judge (Johnson, 1969), and more risky than goods, therefore, customers placed more reliance on WOM to get information about services. This was confirmed by Murray’s finding (1991), that people placed more importance on WOM when making purchase decision on services than goods because of services’ heterogeneity and intangibility.

And the findings were also consistent with the elaboration likelihood model, which argued that

58 people with strong motivations were more likely to use central route to process information. That meant they would rely more on the quality and quantity of information to help them make decisions.

A recent study (Park & Lee, 2009) confirmed the moderating role of product type in

WOM communication online. Instead of categorized as goods and services, they classified products into search goods and experience goods. Search goods were defined as goods, for which information could be acquired prior to purchase (Nelson, 1974), while experience goods were goods, for which relevant information was unknown until the purchase and after use of the goods, or relevant information was too costly and/or difficult than direct product use experience

(Klein, 1998). Park and Lee (2009) found that for negative eWOM, people were more sensitive to experience goods than search goods in that the negative information magnified their uncertainty and fear initiated by the lack of familiarity, or sufficient cognitive information, of experience goods.

Moreover, research and theoretical framework suggested that product type not only moderated the influence of WOM, but also influenced people’s selection of certain WOM. After investigating experiences of movie watchers, Wang (2005) claimed the perceived similarity of movies to audiences was important in affecting their movie choose. He also stated particularly to some experiential products, third-party endorsements (institutional or experts’ ratings and normal consumers’ ratings) would play a significant role in products evaluating for consumers.

Correlating with the influence of products type on consumers’ decision, Jain and Posavac (2001) argued, to some products which consumers had no prior knowledge, high credibility sources in terms of prepurchase experience were more persuasive than low credibility sources without prepurchase experience. A recent study (Guido & Peluso, 2009) found that the sub-dimensions

59 of perceived source credibility varied in intensity with different product categories. It was consistent with previous studies (Kamins, 1990; Stafford, Spears, & Hsu, 2003), which argued that certain types of sources, like attractive sources, or expert sources, only had influences on corresponding-related product types.

Table 3.4 Product Types

High Cost Product Low Cost Product

High Risk Product CHRH Product: Costly CLRH Product: Cheap product with product with high risk. E.g., high risk. E.g., new brand cheap automobile, house, and product, like new-brand toothpaste. computers Low Risk Product CHRL Product: Costly CLRL Product: Cheap product with product with low risk. E.g., low risk. E.g., soap and snacks. expensive jewelry and vacation. Note. C stands for Cost and R stands for Risk; subscript H stands for High and subscript L stands for Low.

As explained in chapter II, the elaboration likelihood model provided theoretical justification for people’s information seeking behaviors in a persuasion communication. It also validated the empirical findings about product type. People with more motivation and ability to process messages were more likely to adopt the central route to persuasion with more elaboration, while peripheral cues became more determinant and important in the process of persuasion when elaboration decreased (O’Keefe, 2002). But the central and peripheral routes to persuasion were not exhaustive or mutually exclusive (Stiff, 1986); persuasion was a process involving both central and peripheral routes, as well as other complex effects (Petty & Cacioppo, 1986; Petty &

Wegener, 1999). The motivation and ability to process message were two determinant factors influencing elaboration (Petty & Cacioppo, 1984). Personal relevance (the extent people perceived a given topic to be relevant to themselves), or to be highly involved or lowly involved

60 with the topic, and need for cognition (the degree of the tendency for people to engage in effortful cognition) were two main factors affecting elaboration motivation (O’Keefe, 2002).

Accordingly, in WOM communication, product involvement was believed to be one of the greatest causes of motivation (Xue & Zhou, 2011). Scholars had already shown that product involvement moderated consumers’ attitudes toward WOM statements online (Xue & Phelp,

2004). Hence, in this study, based on Laurent and Kapferer’s four antecedents of product involvement (1985), the researcher divided products into four categories according to risk importance and sign value of product by controlling product importance and pleasure importance of product (See Table 3.4).

Prior Knowledge and Perceived Source Credibility

Besides motivation, which was indicated by product involvement, another factor influencing persuasive elaboration process was ability. A bulk of research in interpersonal persuasive communication revealed some possible influences on receivers’ ability to process information, such as message repetition and receiver’s body posture (O’Keefe, 2002). However, one of the factors that received the most extensive attention was prior knowledge. Research found that the more prior knowledge, the more issue-relevant thoughts occur, the more elaborative cognition, and then the less influence of peripheral cues (such as source likability)

(Laczniak, Muehling, & Carlson, 1991; O’Keefe, 2002; Wood & Kallgren, 1988). In the context of eWOM communication on social media, people’s prior knowledge used to make judgments on sources should not only include their overall knowledge and experience of different products, but also include their familiarity to the new technical environment created by the Internet. In this study, since the research subject was the sources on Twitter, the familiarity of the Internet and

Twitter might be important predictors of perceived credibility on Twitter.

Furthermore, communication medium effects research suggested the variations in the medium of communication such as the difference between a face-to-face communication and a computer-mediated communication, directly affected persuasive effects (O’Keefe, 2002). Little empirical research has been conducted about computer-mediated persuasive communication.

Given the context of social media and the rise of eWOM communication, people’s online media involvement might typically reflect how comfortable they were with the new media and eWOM communication, and thus acted as contextual factors influencing people’s perceived source credibility. The researcher employed the constructs of Internet involvement and Twitter involvement to represent people’s familiarity of the Internet and Twitter. The conceptualizations of Internet and Twitter involvement have been described in the next chapter.

Moreover, according to persuasion communication research, receivers’ factors, such as gender, self-esteem, and contextual factors directly affected attitudes and consequently influenced persuasive outcomes (O’Keefe, 2002). Sex difference in persuasibility was much studied in persuasion communication, and women were found more easily persuaded than men

(Becker, 1986; Eagly & Carli, 1981). But in a study of the generalized trust in society, scholars demonstrated that gender and income were not significant predictors of generalized social trust, while educational level positively predicted people’s trust. In this study, gender, income, and class standing have been used as control variables when exploring the relationship between perceived credibility and Internet and Twitter involvement.

There was sufficient research evidence indicating that people’s self-esteem and intelligence predicted influenceability (Rhodes & Wood, 1992). Self-esteem was people’s own evaluation of themselves, whether positive or negative. It measured how much a person viewed the self as worthwhile and competent (Coopersmith, 1967). People high in self-esteem were

62 more confident to make decisions while people low in self-esteem had difficulty in receiving messages. Thus, people with moderate self-esteem were demonstrated to be more influenceable than the other two groups. Moreover, there was an ongoing debate on whether social media use enhanced or diminished one’s self-esteem. Some scholars (Heine, Takemoto, Moskalenko,

Lasaleta, & Henrich, 2008) argued that being exposed to social media, e.g., Facebook, enabled people to be exposed to information of others, which in turn made people aware of their own shortcomings. This objective self-awareness activated discrepancies between one’s self and social standards, and consequently downgraded one’s rating of self (Duval & Wicklund, 1972).

The other scholars (Gonzales & Hancock, 2011; Walther, 1996) contented and demonstrated that social media exposure enhanced self-esteem through selective self-presentation online.

CHAPTER IV.

THEORETICAL MODEL OF CREDIBILITY

WOM communication is one type of persuasive communication, which involves information sending and receiving. The recipients could choose to accept or reject the messages from WOM sources. In the old interpersonal physical context, substantial efforts have been made to understand WOM communication persuasiveness and effects. The persuasive communication effects are affected by various factors within a communication process, such as, source, message, and even the characters of recipient. Of all the factors that are essential to persuasive communication, source credibility earned attention from the very beginning. Plenty of research has been done to establish and develop scales for source credibility from various research areas.

But in the new context of social media, although people have recognized the influence of eWOM on innovation diffusion or product promotion, little research explored social media source credibility from a persuasion theoretical perspective. Despite the developed scales to measure source credibility, none of them were established in the context of social media from WOM communication perspective. Therefore, the researcher aimed to develop a new measure for source credibility on social media, specifically on Twitter, and investigated factors that could predict people’s perceived source credibility on social media. Moreover, the researcher was interested in whether product type affects people’s perceived credibility of different social media sources when making a purchase decision.

Six Dimensions of Social Media Source Credibility

From the literature, source credibility was a perceptual variable, which intervened between a source and a recipient (Cronkhite & Liska, 1976). Different dimensions on which receivers perceived a source were identified in different specific contexts by previous researchers.

For example, in a persuasive setting, Hovland et al. (1953) reported “expertise,” and

“trustworthiness.” Berlo, Lemert and Mertz (1969) reported finding “safety,” “qualification,” and “dynamism.” McCroskey (1966) identified “authoritativeness,” “character”, and “goodwill

(McCroskey & Teven, 1999).” Moreover, in other communication settings, different perceptual structures were found. The dimensions of “agreeableness,” “extroversion (Norman, 1963),” and

“professionalism (Eisend, 2006) were reported in their specific cases. Despite the no commonly agree-upon dimensions derived from different specific research, one notion underlying these research that could reach a consensus is that “receivers have a set of relatively independent attitudes toward sources (Infante, 1980).” The independent attitudes constitute the domains of source credibility dimensions. Namely, source credibility is “a set of attitudes toward a source that influence how receivers behave toward the source” (Infante, 1980). Theoretically, it varied according to the situation: in some cases, certain attitudes/perceptions were more prominently stimulated by specific incentives and the rest of attitudes/perceptions were suppressed accordingly. Consequently, the perceived source credibility changed in different situations.

Fishbein’s (1963) summative model of attitude validated this assumption. The summative model of attitude suggested that people’s attitude to certain source was a sum of the product of one’s beliefs in the source and their strengths. Each new positive belief could add favorability to the total attitude. The summative model of attitude illustrated that one’s attitude to a source was the function of one’s salient beliefs about the source (belief evaluation) and their strengths (belief strength) correspondingly. The model could be described by the following formula:

A=∑ biei, in which A refers to “attitude to a source,” bi is the strength of a given belief, and ei is the evaluation of a given belief.

The strength of a given belief was like a weighting coefficient, determining the proportion of influence of a given belief. The belief evaluation determined if one had such

65 beliefs in the source (Fishbein, 1963). Thus, in different research settings, it was possible that the settings added more, or suppressed some, attributes of a source, which changed the domain of the construct source credibility. Social media as a computer-mediated communication had changed the way people communicated to one another. The speaker-less nature of this communication, along with the technology-intervening design, complicated the use of traditional scales of source credibility. In other words, in the social media environment, the domain of source credibility, or the dimensions of source credibility, was different and worthy of being explored.

From the literature review, the two dimensions of

“expertise”/“competence”/“qualification,” and “trustworthiness”/“safety” were approved by most researchers. “Expertise,” “competence,” and “qualification,” which rated sources’ attributes of professionalism and talents, were believed roughly correspond to each other. “Trustworthiness” and “safety” mainly assessed sources’ moral attributes. In this study, the researcher chose to use

“competence” and “trustworthiness” as two traditional dimensions of source credibility, maintaining the wording consistent with McCroskey and Teven’s study (1999). Modification of the corresponding indicators for each traditional dimension will be introduced in the next chapter.

Besides that, social tie strength had been neglected in developing instrument of source credibility. Literature revealed that the perceived strength of social tie between a source and receiver affected people’s judgment (Brown & Reingen, 1987). Thus, social tie strength should be the third and independent attribute of a source, which could be used to rate perceived credibility.

Although the “attractiveness” and “dynamism” dimensions were not fully acknowledged among source credibility scholars, their potency of being evaluative attributes was recognized

(Cronkhite & Liska, 1976). On the one hand, the technology made communication less physical,

66 lessening the influence and significance of appearance in a communication process; on the other hand, the technology also added more new stimuli to assist computer-mediated communication, such as, emoticons and interactive features. Therefore, the appearance of a source (when it was an individual source) on social media still mattered when assessing the source’s credibility. And the technology also provided us with clues of a source’s dynamism. For example, we could easily tell if a source on Twitter was responsive, active, or even comic from the frequency and wordings of posts. Hence, the “attractiveness” and “dynamism” dimensions were retained in the new scale in this study.

Finally, the new technology brought us lots of affordances, as mentioned in the literature review. In the new setting of social media, the construct of source credibility will not be comprehensive of the entire domain of content without taking these technology affordances into consideration. Thus, technology affordances should be the last dimension of social media source credibility.

To put it in a nutshell, the researcher posited, in the setting of social media, there were six independent dimensions of source credibility (See Figure 4.1 for individual source credibility).

Some of the traditional dimensions of source credibility were retained. But a lot of modifications corresponding to the new context were necessary as well. Since individual source credibility should be different from organization source credibility, the specific indicators to each dimension of the measure of source credibility might vary in source type (e.g., individual and organization sources would have different indicators of source credibility). Considering the power of communication of different “hubs” on Twitter, the researcher selected the following four kinds of individual sources on Twitter–––entertainment stars, sports stars, famous business persons, and famous politicians––– to investigate their source credibility. Meanwhile, the

67 researcher was also interested in news organizations’ credibility on social media, and in the context of social media marketing communication, the credibility of brands (or commercial corporations) on Twitter compared to other sources, which were, or potentially spreading eWOM for commercial corporations is another interesting point of research. Thus, News organizations and brands accounts on Twitter were selected as organizational sources to study. These thoughts brought up the four research questions of this study:

RQ1: What constitutes individual source credibility on social media?

RQ2: What constitutes news organization source credibility on social media?

RQ3: What constitutes brand (commercial corporation) source credibility on social media?

RQ4: Which of these sources on Twitter is the most credible in general?

Moreover, based on the literature and analysis in Chapter III, product type affected people’s attitudes towards sources of a market-based persuasive communication, and then affected their purchase intentions and behaviors eventually. Therefore, in a WOM communication, the researcher was also interested in how different product types affected people’s cognition and responses towards a particular category of sources. Thus, the last research question was proposed as follows:

RQ5: How does product type affect people’s perception of credibility of different sources?

Figure 4.1. Individual social media source credibility dimensions

Source Credibility as a Second-Order Formative Construct

In previous studies, most researchers made efforts to develop and verify reliable indices for measurement of source credibility dimensions, while little attention was paid to the construct of source credibility as a whole. The relationship between measures and the associated construct- source credibility was neglected. In fact, the scales used in traditional source credibility were assumed to be unidimensional, which meant the scales (indicators) that represented a specific dimension were assumed to be highly correlated and interchangeable (Applbaum & Anatol,

1973). However, the evaluative dimensions/factors of source credibility were relatively independent (Infante, 1980), which made source credibility a formative construct. Previous studies (e.g., Jin & Phua, 2014; Gi-Yong, Ruihley, & Dittmore, 2012), assuming source credibility was a reflective construct, did not even consider the construct specification when using developed scales into their own research model. It was important to specify the type of construct and understand if source credibility was a formative or reflective construct, especially when directly using the developed-indices in a specific research. This is because errors in the

69 measurement model might lead to the wrong conclusions of statistical significance of parameter estimates, which would result in incorrect theoretical conclusions (Petter, Straub & Rai, 2007).

(a) Formative Model (b) Reflective Model

Figure 4.2. Formative and reflective measurement models. Adapted from “Conventional Wisdom on Measurement: A Structural Equation Perspective,” by K. Bollen & R. Lennox, 1991, Psychological Bulletin, 110(2), p.306. In the figure 4.2(a), η1 is latent variable; ζ1 is the measurement error associated with η1; X1, X2, X3, and X4 are observed variables; γ11, γ12, γ13, and γ14 express the contributions of the observed variables X1, X2, X3, and X4 respectively to the construct η1. In the figure 4.2 (b), η1 is latent variable and ε1, ε2, ε3, and ε4 are the measurement errors associated with Y1, Y2, Y3, and Y4, which are observed variables. And λ1, λ2, λ3, and λ4 are the expected effect of the latent variable η1 on Y1, Y2, Y3, and Y4, respectively.

When a variable could not be observed directly or indirectly, it was a latent construct and such construct should be properly evaluated (Nunnally, 1978). A construct could be measured by multiple indicators. Items composing a scale and reflecting the cause of the measured construct were called formative (cause, causal) indicators. “Constructs comprised of causal indicators along with a disturbance term are called formative constructs (Petter, Straub & Rai, 2007).” (See

Figure 4.2a) By way of contrast, reflective indicators were derived from the effect of the measured construct. These reflective indicators and error term for each indicator consisted of reflective constructs (Figure 4.2b). Put in another way, a change in the reflective construct affected its corresponding measures/indicators, while changes in the formative indicators caused changes in the formative construct (Jarvis et al. 2003). Previous studies involving source

70 credibility, as other research, often focused on the structural paths between constructs rather than the nature of constructs (the relationship between scales and constructs) (MacKenzie, 2001). By default, constructs were assumed to be reflective (Petter, Straub & Rai, 2007). But, many times, constructs could be formative.

To specify the nature of the latent variable–––source credibility, the researcher adopted four primary decision rules suggested by Jarvis et al. (2003). To determine if source credibility was a formative, reflective, or mixed construct, the researcher considered 1) the theoretical direction of causality between source credibility and its dimensions, and between each dimension and its measures; 2) checked the interchangeability of the dimensions and their measures; and 3) dimensions and measures’ covariance with each other; 4) examined if measures and dimensions had the same antecedents and consequences respectively. In this particular study, source credibility was a multidimensional construct, which was comprised of six independent dimensions (see Figure 4.1). These six independent dimensions were not interchangeable and they made up the construct of source credibility together. Thus, in the second order, source credibility was a formative construct. When going back to the first order to examine the measures of each dimension, the measures used for “competence,” “expertise,” “social ties,” and

“attractiveness (for individual source credibility),” were reflective items, while “attribute (for organization source credibility),” “dynamism” and “tech affordance” were comprised of formative indicators. Take the reflective sub-construct “competence” and formative sub- construct “technology affordance” for example, in individual source credibility “competence” was measured by “intelligent/unintelligent,” “expert/inexpert,” and “informed/uninformed.”

These three items were all measuring whether a source was capable to provide relevant information and they were interchangeable. “Technology affordance” as a formative sub-

71 construct of source credibility on social media was measured by four aspects: 1) whether a source was verified account with the blue check mark next to their Twitter account; 2) whether this source has a lot of followers; 3) whether this account was operated by the real person; 4) whether the account tweeted everyday. These four aspects of technology affordance were four independent components, which could not be substituted by each other. The first order structure of source credibility was neither reflective, nor formative, but mixed. In conclusion, source credibility in this study was a second-order formative construct.

Research Model

In the persuasive process of WOM communication, the attitude towards a source

(perception of source) was especially significant because it directly affected the persuasive effect.

The common practice of previous studies was taking the perceived source credibility as an independent variable (e.g., Chung, Fink & Kaplowitz, 2008; Kalafatis et al., 2012; Kumkale,

Albarracín & Seignourel, 2010) and investigating its possible effects in a persuasive communication. Few studies viewed source credibility as a dependent variable and explored the potential factors, which influenced source credibility. As stated in chapters II & III, persuasion theory identified several factors, such as sources, content, channels, receivers, and contexts, which were important in affecting people’s attitudes. People’s perceived source credibility was a complex construct, which was not only impacted by source itself, but also was influenced by how the message was sent in a communication process, the media that loaded the message, the receivers’ characteristics, and communication contexts. In this study, the researcher was interested in how new media environment affects people’s perceived source credibility on

Twitter. In other words, in acknowledgement of other factors, the researcher narrowed down the research scope and only focused on the influence of social media use on people and their

72 perception of source credibility. By controlling the basic demographics, such as gender, income, and class standing of the college students, a model of social media source credibility, Internet involvement, Twitter involvement, and self-esteem was constructed as below (Figure 4.3).

Figure 4.3. Source credibility research model. Error Terms (δi, gi) for the observed variables and the disturbance terms (ζi) are omitted to simplify the representation of the model.

The basic assumption of this study was people’s perceived source credibility on Twitter was influenced by their Internet and Twitter involvement. Self-esteem was affected by the

Twitter use. The familiarity of the communication environment affected people’s attitudes towards sources. Moreover, the use of new technology and the nature of the technology affordances provided more opportunities and possibilities to people, which had an impact on people’s self-esteem that was shown by previous studies relating to perceived source credibility.

With these factors taken into consideration, the researcher proposed the following research hypotheses, which identified the effect of Internet and Twitter involvement on different sources’ credibility in a social media context.

H1a: The higher Internet involvement, the more perceived individual credibility people hold on Twitter.

H1b: The higher Internet involvement, the more perceived news organization credibility people hold on Twitter.

H1c: The higher Internet involvement, the more perceived brand credibility people hold on Twitter.

H2a: The higher Twitter involvement, the more perceived individual credibility people hold on Twitter.

H2b: The higher Twitter involvement, the more perceived news organization credibility people hold on Twitter.

H2c: The higher Twitter involvement, the more perceived brand credibility people hold on Twitter.

H3a: The higher self-esteem, the more perceived individual credibility people hold on

Twitter.

H3b: The higher self-esteem, the more perceived news organization credibility people hold on Twitter.

H3c: The higher self-esteem, the more perceived brand credibility people hold on Twitter.

H4: The higher Internet involvement, the higher Twitter involvement

H5: The higher Internet involvement, the higher self-esteem.

H6: The higher Twitter involvement, the higher self-esteem.

CHAPTER V.

METHODOLOGY

To understand what constitutes perceived social media source credibility and its influence factors and moderators, a web self-administered survey among college students was employed to collect data for this study. Surveys are believed as valuable research methodology for social scientists to understand “how people influenced, and were influenced by their social environment”

(Visser, Krosnick, & Lavrakas, 2000), especially when the sample generalizability is a central research goal (Schutt, 2011). It was relatively easy and practical to study a small representative sample and then draw a generalized finding to the entire population (Visser, et al., 2000) via survey. Furthermore, survey method was highly valued due to its efficiency and versatility

(Schutt, 2011). It was one of the most common quantitative data collection methods that could cover various topics in mass communication research along with content analysis (Cooper,

Potter, & Dupagne, 1994; Ha et al., forthcoming). A survey can be conducted among a multitude of people at a relatively low cost. Computer technology made survey even more versatile and efficient. However, with the increasing use of the Internet to conduct surveys, scholars were concerned if the survey mode shift from oral administration of questions to online self- administrated survey exerted an impact of survey responses (Chang & Krosnick, 2010).

Sufficient empirical evidence (Chang & Krosnick, 2009; Chang & Krosnick, 2010) showed that online self-administered survey manifested higher concurrent validity and better quality responses than telephone survey and face-to-face survey. Moreover, compared with other survey mode such as telephone, face-to-face surveys, self-administration was superior in terms of providing honest answers to threatening or sensitive questions (Chang & Krosnick, 2010).

People seemed more willing to provide honest answers in online self-administered survey

76 without the presence of a human interviewer (De Leeuw, 1992). With the advantages of survey into consideration, the web self-administered survey of college students’ perception of source credibility on Twitter was conducted. Before discussing data collection implementation, it is necessary to clarify the choice of Twitter as the setting of this study and college students as study population.

Study Setting

Why Twitter?

This study was based on Twitter to develop social media source credibility. The researcher believes Twitter was a good choice as a study setting for source credibility research in eWOM communication for two reasons. First, out of all social media platforms, Twitter stands out with its technological choice and design that makes it an open platform for information sharing. Created in 2006, Twitter is a free social information sharing service that enables anyone to post brief messages, known as “Tweets,” to their self-designated followers (Farhi, 2009). The main part of Twitter’s interface is the 140-character length “Tweets.” Besides that, on Twitter’s home pages, one can find users’ basic information, followers and followings (Stever &Lawson,

2013). Unlike Facebook, Twitter does not impose any restrictions on followers or followings; people can follow anyone they want to follow. Additionally, the follower and following relationship is not reciprocal (Ortutay, 2013); one doesn’t have to be a follower of one’s followings. It is the simplicity in relationship that facilitates Twitter users to reach a broad audience (Twitter, 2013) and made the service especially appealing for eWOM communication with potentially very large number of users. Moreover, There are neither complicated social networks, nor all kinds of features and apps as Facebook provides. The concise page design and ease of tweeting short news has made Twitter a medium specialized in information sharing on a

77 large scale across the networks instantaneously (Jansen, Zhang, Sobel, & Chowdury, 2009). In contrast, its peer Facebook makes more effort to knit and maintain people’s social networks with plentiful features and apps that connect people. Given the technological choice, information diffusion on Twitter is relatively open and unrestrained.

Second, Twitter’s rapid rise in popularity and growing attentions from both industry and academia makes research that takes into account its specific communicative context timely as urgent and meaningful. Since its inception, Twitter has grown up to be a social media giant with more than 284 million monthly active users spanning nearly every country and 500 million tweets per day (About Twitter, n.d.). Twitter users not only include millions of ordinary people from all over the world, but also comprises influential individuals and organizations, such as celebrities, sport superstars, politicians, journalists, and media outlets and companies (Twitter,

2013). As stated in its IPO filing (Twitter, 2013), Twitter is a versatile platform for its users to participate in conversations not only with their friends and family, but also with other people from around the world; and for brands to communicate directly with their followers at no cost.

As a form of eWOM, Twitter’s enormous untapped potential in social commerce arenas has been proposed and discussed in academia and industry (Jansen et al., 2009; Marsden, 2010). But few studies developed source credibility for Twitter from theoretical perspective. Given the nature of eWOM communication, the author believes a new measure for source credibility on Twitter would be representative for social media eWOM source credibility and bridge the gap between practice and theory.

Study Population

The researcher identified college students as study population in this research. The current college students grew up with access to computers and the Internet; they seem to have a

78 natural ability using these technologies (Turan, Tinmaz, &Goktas, 2013). According to previous statistics on social media, young adults were the major users of social media; nearly 75% of young Internet users under age 25 had a social media account (Lenhart, 2009). Though the increase in social media users happened in all age groups, the proportion of social media users in youths aged 12-29 was the highest among different age groups (Lenhart et al., 2010). With this consideration, the researcher believes college students was a good population to measure perceived source credibility on Twitter as they use Twitter more often than other demographic groups.

Preliminary Study

Selection of Scale

A preliminary study, using 22 students in a communication class at the university as subjects, was conducted to select scales. Although the researcher reviewed source credibility literature and maintained the well-built scales, new scales are needed to add to the old ones, considering the new media environment. Besides, limited research on news organization and brand credibility on social media has been conducted and the correspondent scales have not been established yet. To meet this need, it was necessary to explore scales for perceived news organization and brand social media credibility. The new scales used in this study were obtained from a self-administrated survey followed by a short interview. The researcher followed

McCroskey and Young’s (1981) method and the respondents were asked to provide:

Adjectives, or statements to describe, “a person (not your family and offline friends) on

Twitter you would be most likely to believe.”

Adjectives, or statements to describe, “a person (not your family and offline friends) on

Twitter you would be least likely to believe.”

Adjectives, or statements to describe, “a news organization on Twitter you would be most likely to believe.”

Adjectives, or statements to describe, “a news organization on Twitter you would be least likely to believe.”

Adjectives, or statements to describe, “a brand on Twitter you would be most likely to believe.”

Adjectives, or statements to describe, “a brand on Twitter you would be least likely to believe.”

Following the survey, a short interview was conducted to make sure the respondents fully understood the questionnaire and made their most accurate descriptions of the sources they could think of on Twitter.

Based on the feedback from the scale-selection survey and interview, the researcher constructed a set of 27 pairs of polar adjective frequently used to describe most and least credible person on Twitter, a set of 28 pair of polar adjective for most and least credible news organization on Twitter, and a set of 25 pair of polar adjective for most and least credible brand on Twitter. There were three criteria by which the researcher judged and selected the new scales.

The items in the new scale was (1) resulted from new media environment (e.g., technology affordances) and (2) from new research perspective (e.g., social tie), and (3) was most frequently mentioned by the respondents. For the scales that were similar or exactly the same as the old established scales in the literature, the researcher chose to adopt the established scales. Finally, 9 new scales were selected from the preliminary study and combined with 11 old scales, constituting the new scale of individual source credibility on Twitter. Similarly, the new scale of

80 news organization credibility was comprised of 17 new and 7 old scales. Fifteen new and 6 old scales created the new scale of brand source credibility on Twitter.

Selection of Sources

On the one hand, the researcher wanted to develop social media credibility scales for individuals, brands, and news organization on Twitter. On the other hand, the researcher also was interested in the variations of perceive credibility of different sources on Twitter. Thus, besides brand and news organizations, different types of individuals were selected to study based on their recognizability to the college students, and also because of their common roles as spokespersons or endorsers of brands and public issues. The following four individual sources were chosen: (1) entertainment star; (2) sport star; (3) business person; (4) politician.

Survey Implementation

The survey, lasting two weeks, was conducted from Dec 2nd to Dec 16th, 2013 among all the college students (from freshman to graduate students) on main campus in a Midwest state university. This ensured a representative sample of college students from different class standings and majors. Given that the population of college students on the main campus in the university was 16,965, assuming a standard error of .05, and confidence level of 95%, the minimum sample size of this study should be 376 (Krejcie & Morgan, 1970). To optimize this survey research, the researcher referred to the total survey error paradigm (Biemer, 2010) to control survey error while taking the implementation costs into consideration.

Specifically, an invitation email of online surveys with the approval of university institutional research was delivered to all the college students on main campus to avoid the coverage error and sampling error. However, considering the possibility of a low response rate, the researcher also recruited college students from a large introductory class with participation

81 credit rewards as a backup sample to minimize nonresponse error. Students either recruited from the main campus, or from the introductory class, were only allowed to participate once to avoid duplication. A reminder email was sent in the following week to gently encourage students’ participation before the online survey was closed.

The questionnaire, which took 20 minutes to complete, included three sections. The first section examined students’ Internet and Twitter use; the second one investigated students’ perception of different sources on Twitter; the last one covered all kinds of demographical questions, such as gender and household income, and psychographic variables, such as self- esteem.

Operationalization and Measures

Individual Source Credibility

Individual source credibility (see Table 5.1) on Twitter was a second-order formative construct composed of the four reflective and two formative sub-constructs. The four reflective sub-constructs included three traditional dimensions–––competence, trustworthiness, attractiveness–––and one newly-established dimension of social tie. The two formative sub- constructs were dynamism and technology affordance. Semantic differential scales were selected to measure source credibility as it was proved to be valid scale (Infante, 1980). The semantic differential scales were based on the notion that it was a set of receiver’s independent attitudes toward a source that comprised perceived source credibility (Cronkhite, 1969; Ross, 1976). The three traditional dimensions of source credibility (competence, trustworthiness, and attractiveness) were each measured by three bipolar adjective items (See Table 5.1). The indicators chosen for competence and trustworthiness were drawn from McCroskey and Teven’s study (1999) because of their consistently high loadings on the two factors. The three items

82 chosen for attractiveness were drawn from Ohanian’s measurement (1999) for the validity and reliability consideration. Although the computer-mediated communication would not provide as much dynamism cues as in a face-to-face communication, people could still evaluate a source’s dynamism (whether it was aggressive, active, or interactive) based on its activities online. Thus, the two items aggressive/meek and active/passive, which validated by Berlo et al. (1969) as indicators of dynamism, as well as new items comic/not comic, interactive/noninteractive from the preliminary study, were selected as indicators to measure dynamism of individual source credibility in social media context. Moreover, social tie factor was included as a new dimension with three indicators, which were developed from Brown and Reingen’s tie strength scales

(1987). Lastly, given the technological affordance, the researcher proposed another new dimension with four technology-generated items chosen from the preliminary study, verified/unverified, and has a lot of followers/only has a few followers, Real/fake and tweets everyday/doesn’t tweet everyday. These items, as online cues, were also demonstrated to impact perception of credibility in previous studies. The verified badge in social media was associated with high-quality sources and trustable contents (Agichtein et al., 2008). Along with Sundar’s

(2008) MAIN model, Westerman et al. (2014) showed that the immediacy/ recency of updates on

Twitter impacted people’s perception of source credibility. And Utz (2010) demonstrated that number of friends on social media influenced people’s impression formation.

Table 5.1 Individual Source Credibility

Please indicate your impression of a person on Twitter by circling the appropriate number between the pairs of adjective below. The closer the number is to an adjective, the more certain you are of your evaluation Competence Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Informed 1 2 3 4 5 6 7 Uninformed Trustworthiness Honest 1 2 3 4 5 6 7 Dishonest Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honorable 1 2 3 4 5 6 7 Dishonorable Social tie Important to me* 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions* 1 2 3 4 5 6 7 Not interest me Relates to me on certain things* 1 2 3 4 5 6 7 Has no connection with me Attractiveness Attractive 1 2 3 4 5 6 7 Unattractive Classy 1 2 3 4 5 6 7 Not Classy Elegant 1 2 3 4 5 6 7 Plain Dynamism Meek 1 2 3 4 5 6 7 Aggressive Comic* 1 2 3 4 5 6 7 Not comic Active 1 2 3 4 5 6 7 Passive Interactive* 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name)* Has a lot of followers* 1 2 3 4 5 6 7 Only has a few followers Real* 1 2 3 4 5 6 7 Fake Tweets everyday* 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. *Items were developed from preliminary study.

News Organization Source Credibility

Similarly, news organization source credibility (See Table 5.2) is also a second-order formative construct including six dimensions. Though news organization source credibility also included competence, trustworthiness and dynamism factors, some modifications were made specifically for news organization source credibility measure by combining Haley’s (1996) and

Eisend’s (2006) construct of organization credibility and embracing the most recommended scales from preliminary study. Additionally, since news organization source credibility was to measure the credibility of organizations, the attractiveness, which measures the physical attributes and personality of an individual in individual source credibility, was not applicable.

Thus, the researcher replaced “attractiveness” with “attribute” in organization source credibility.

“Attribute” measured people’s affective feeling towards organization’s reputation and performance based on their impression. It could be indicated as huge/small organization, been around for a long time/new business, congruent with my values/not congruent with my values, and good reputation/bad reputation (Haley, 1996). Competence was measured by professional/unprofessional, expert/inexpert, organized/disorganized (Eisend, 2006), intelligent/Unintelligent, informative/uninformative, and Objective/subjective. Trustworthiness included honest/dishonest (Eisend, 2006), recognizable/unrecognizable (Haley, 1996), and trustworthy/untrustworthy. Dynamism retained active/passive proposed by Berlo et al. (1969) and also included fair/unfair, timely/untimely, and interactive/noninteractive. Social tie scales maintained the same as they were in the individual source credibility.

Table 5.2 News Organization Source Credibility

Please indicate your impression of a news organization on Twitter by circling the appropriate number between the pairs of adjective below. The closer the number is to an adjective, the more certain you are of your evaluation Competence Professional 1 2 3 4 5 6 7 Unprofessional Intelligent 1 2 3 4 5 6 7 Unintelligent Informative* 1 2 3 4 5 6 7 Uninformative Expert 1 2 3 4 5 6 7 Inexpert Organized* 1 2 3 4 5 6 7 Disorganized Objective* 1 2 3 4 5 6 7 Subjective Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me* 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions* 1 2 3 4 5 6 7 Not interest me Relates to me on certain things* 1 2 3 4 5 6 7 Has no connection with me Attribute Huge organization* 1 2 3 4 5 6 7 Small organization Been around for a long time* 1 2 3 4 5 6 7 New business Congruent with my values* 1 2 3 4 5 6 7 Not congruent with my values Good reputation* 1 2 3 4 5 6 7 Bad reputation Dynamism Fair* 1 2 3 4 5 6 7 Unfair Timely* 1 2 3 4 5 6 7 Untimely Active Passive Interactive* 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name)* Has a lot of followers * 1 2 3 4 5 6 7 Only has a few followers Real* 1 2 3 4 5 6 7 Fake Tweets everyday* 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. *Items were developed from preliminary study.

Brand Source Credibility

The brand source credibility (see Table 5.3) on Twitter was also a second-order formative construct, which embraced three reflective sub-constructs (competence, trustworthiness, and social tie) and three formative sub-constructs (attribute, dynamism and technology affordances).

Compared with news organization source credibility, the scales of three dimensions (competence, trustworthiness, and dynamism) of brand source credibility were modified according to the preliminary study. For the dimensions of social tie, attribute, and technology affordance, brand source credibility maintained the same scales of news organization source credibility.

Table 5.3 Brand Source Credibility

Please indicate your impression of a brand/corporation on Twitter by circling the appropriate number between the pairs of adjective below. The closer the number is to an adjective, the more certain you are of your evaluation Competence Good quality* 1 2 3 4 5 6 7 Bad quality Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Organized* 1 2 3 4 5 6 7 Disorganized Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me* 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions* 1 2 3 4 5 6 7 Not interest me Relates to me on certain things* 1 2 3 4 5 6 7 Has no connection with me Attribute Huge organization* 1 2 3 4 5 6 7 Small organization Been around for a long time* 1 2 3 4 5 6 7 New business Congruent with my values* 1 2 3 4 5 6 7 Not congruent with my values Good reputation* Bad reputation Dynamism Creative* 1 2 3 4 5 6 7 Uncreative Active 1 2 3 4 5 6 7 Passive Interactive* 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name)* Has a lot of followers* 1 2 3 4 5 6 7 Only has a few followers Real* 1 2 3 4 5 6 7 Fake Tweets everyday* 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. *Items were developed from preliminary study.

Source Credibility Moderated by Product Type

Students were asked to rate seven different familiar sources (family and friends, entertainment stars, sport stars, famous business persons, famous politicians, brands, news organizations) on Twitter on a 7-item Likert scale about their perceived credibility from totally incredible (coded 1) to very credible (coded 7) in four different product settings.

Internet Involvement

According to consumer behavior research, involvement referred to “a person’s perceived relevance of the object based on their inherent needs, values, and interests” (Zaichkowsky, 1985).

Since involvement was the motivation to process information, it embraced the range from cognitive and mental reaction to behavior reaction (Ha & Hu, 2013). People’s Internet involvement referred to the degree to what extent people were involved in the Internet. It needed to include people’s Internet dependence (perceived relevance of the Internet), Internet use experience, and Internet use time (behavior: years of using the Internet). Internet involvement was measured by three items. The last two items gauged the time spent on the Internet. The

Internet dependence consisted of six statements (Ellison, Steinfield, & Lampe, 2007)–––such as

“the Internet is part of my everyday activity,” and “I am proud to tell people I am on the Internet.”

People were asked to rate these statements on a five-item Likert scale from strongly disagree

(coded 1) to strongly agree (coded 5).

Twitter Involvement

Similarly, Twitter involvement needed to include both cognition and behavior measures.

Not only did the researcher retain the three-item measure of Internet involvement, but the researcher also borrowed the concept of social networking sites involvement (SNS involvement) from Ha and Hu’s study (2013) and combined their measure of SNS involvement into the

Twitter involvement. As stated by Ha and Hu (2013), social networking sites involvement should be measured by both activities and time spent on SNS. This is because SNS activity is a sign of active/passive user, while SNS use time is a sign of heavy/light user. To accurately measure people’s involvement in SNS, these two factors should be measured simultaneously. Likewise, besides Twitter dependence developed by Ellison, et al. (2007) and Twitter using experience

(years of using Twitter), in this study the researcher brought another two items, Twitter using time and frequency, from Ha and Hu’s SNS involvement study to gauge Twitter involvement. To summarize, Twitter involvement was measured by four items. The first item asked students’

Twitter use experience; the second one asked students’ Twitter using time per week, from “less than one hour” (coded 1) to “6 hours and more” (coded 7); the third one asked Twitter updating frequency, from “hardly ever update” (coded 1) to “Several times an hour” (coded 7); the last one was the Twitter dependence scale consisting of six statements. Students were asked to rate these statements on a five-item liker scale from strongly disagree (coded 1) to strongly agree

(coded 5).

Self-esteem

Self-esteem was an abstract construct in psychology, which was defined by Rosenberg

(1965) as one’s positive or negative self-image. Studies have showed that the level of one’s self- esteem affected their acceptance of self-generated feedback and others’ feedback. People in low self-esteem were relatively inattentive to self-generated feedback because they did not take themselves as credible sources; they were more likely to accept external sources’ evaluations

(Baumgardner, Kaufman, & Levy, 1989; Josephs, Bosson, & Jacobs, 2003). Moreover, as mentioned in the previous chapter, there was still an ongoing debate on the role of social media use in enhancing or diminishing one’s self-esteem (Heine, Takemoto, Moskalenko, Lasaleta, &

Henrich, 2008; Gonzales and Hancock, 2011; Wilcox & Stephen, 2013). Hence, in this study, the researcher examined the relationship between self-esteem and people’s perceived credibility, and the relationship between self-esteem and people’s Internet and Twitter involvement. Self-esteem was measured by Rosenberg’s classic 10-item self-esteem scale. Students were asked to respond to ten statements on a 4-item scale from strongly agree (coded 1) to strongly disagree (coded 4).

Within the scale, there were five reserve-coded items.

Gender was measured by a dichotomous question by indicating female or male. Class standing and Household income were examined by simply asking people to check an applicable option from multiple-item scales.

Data Screening and Statistical Techniques

Data Cleaning and Screening

Before further statistical analyses were conducted, the data was cleaned by excluding the duplicate cases and reverse coding. Then the data were screened for missing data, outliers, normality, and collinearity. Since there were 17 cases missing data of Twitter use time and

Twitter update frequency and these 17 cases had similar pattern on other relevant variables (e.g.,

Twitter use experience, number of Twitter followers and followings), the researcher chose to handle these missing data by estimating the missing values based on their answers to the relevant questions. For example, the average Twitter use experience of the 17 missing data cases was 0.5 years and they only had few followers and followings (e.g., 2 or 3); they left the questions of

Twitter use time and Twitter update frequency blank, resulting in the missing data. Thus, the researcher deduced these people barely used Twitter and estimated their Twitter use time should be “ Less than 1 hour,” and their Twitter update frequency should be “Hardly ever update.” Then, system-missing data was checked and replaced by mean values.

Structural Equation Modeling

The structural equation modeling was employed to analyze structural relationships of the latent variables (from H1 to H9) and inspect the new measurement instrument of source credibility (RQ1 and RQ2). Structural equation modeling (SEM) “is a collection of statistical techniques that allows examination of a set of relationship between one or more independent variables, either continuous or discrete, and one or more dependent variables, either continuous or discrete” (Ullman, 1996). SEM was demonstrated to have many advantages over first- generation statistical techniques (e.g., linear regression and ANOVA) (Lowry & Gaskin, 2014).

Compared with the first-generation statistical techniques, not only did SEM enable the researcher to include latent variables in a complex model, but also helped in estimating complete causal networks simultaneously (e.g., estimate indirect effects, multiple group moderation of multiple effects at the same time). SEM is a hybrid of factor analysis and path analysis, which allows testing multivariate models (Weston & Gore Jr., 2006). SEM is a full structural model, which includes both measurement and structural model. A measurement model “describes the relationships between observed variables (e.g., instruments) and constructs or constructs those variables are hypothesized to measure,” and help researchers evaluate how well the observed variables represent the latent variables, while a structural model demonstrates the interrelationships among constructs. (Weston & Gore Jr., 2006).

Specifically, partial least squares- (PLS) or variance-based SEM used in SmartPLS was chosen in this study, instead of covariance-based (CB) SEM, for the following reasons: 1) PLS is better for exploratory study than covariance-based SEM, while covariance-based SEM is more appropriate for testing well-established theory because it always ends up with factor indeterminacy (Lowry & Gaskin, 2014). PLS-SEM is an ordinary least squares regression-based

92 method with the objective of minimizing the error terms of the endogenous constructs in the structural model. Thus, PLS-SEM is good at explaining variance and theory development (Hair et al., 2013). CB-SEM employs maximum likelihood estimation procedure to estimate the parameters so that the discrepancy between the sample and the tested model is minimal. So CB-

SEM is better for validating an established model; 2) when dealing with high-order construct models (e.g., second, third- or fourth-order construct), PLS modeling “is not as susceptible to identification problems and improper solutions as covariance-based SEM. (Wetzels, Odekerken-

Schroder, & van Oppen, 2009). It can handle constructs with single and multi-item measures and larger number of indicators “are helpful in reducing the PLSD-SEM bias” (Hair et al., 2013).

Moreover, it can handle complex models with many structural model relations; 3) PLS can account for both formative and reflective indicators in its statistical model while covariance- based SEM assumes that all indicators are reflective, which can result in model errors and inappropriate conclusions if formative constructs or mixed constructs were misspecified (Hair et al., 2013; Lowry & Gaskin, 2014). The using of formative constructs as reflective constructs in

CB-SEM is problematic because on the one hand, converging on a solution in CB-SEM is extremely sensitive to the number of measures in the model constructs, on the other hand, item- reduction and parceling techniques are always utilized in the measurement model of CB-SEM.

That is based on assumptions, such as interchangeability of items, and unidimensionality (Sivo et al., 2006). The items of formative constructs are not interchangeable. The exclusiveness of either item from a formative construct will affect the conceptual domain of that formative construct and then result in model errors.

In this study, the construct of source credibility on social media was conceptualized as a first-order mixed and second-order formative construct. Therefore, PLS-SEM is more

93 appropriate than CB-SEM in this study. Since the most statistical packages, such as SME

LISREL, Amos, EQS, and SAS, etc., are all covariance-based (Petter, Straub, & Rai, 2007;

“Introduction,” n.d.), the researcher finally chose SmartPLS, based on partial least squares method, for the data analysis.

Sample size was assessed before conducting a SEM analysis. Although there is no universally accepted criteria of optimal sample size for a SEM analysis, the sample size of 150 was suggested by many scholars as the minimum sample size (e.g., Anderson & Gerbing, 1988;

Schumacker & Lomax, 2010). The sample size of this study was 530, which was excellent for conducting SEM analysis. Descriptive statistics were conducted to show sample profile of this study.

Model specification was completed by determining the latent dimensionalities and their nature of source credibility, Internet involvement, Twitter involvement, and self-esteem. Since source credibility was a second-order formative construct, directly running a model with the high-order construct would swamp out potential effects from other potential predictors because the first-order sub-constructs have perfectly explained the variances in the construct (Lowry &

Gaskin, 2014). Therefore, a two-step approach proposed by Lowry and Gaskin (2014) was employed to avoid swamping out effect. First, the measurement model was established and the latent variable scores for the first-order sub-constructs were obtained. Then, a new model including the latent variable scores as indicators of the second-order construct was created. The two-step approach and confirmatory factor analyses (CFAs) were conducted on SmartPLS

(Ringle, Wende, & Becker, 2014).

Three models were established in this study: individual source credibility model, news organization source credibility model, and brand source credibility model. Specifying the

94 indicators of study variables as reflective or formative is important before running a PLS analysis because misspecification increases both Type I and Type II errors (Jarvis, MacKenzie, &

Podsakoff, 2003; Petter, Straub, & Rai, 2007). In this study, for the first order of individual source credibility construct, the researcher modeled the three indicators of competence as reflective because these items are interchangeable (e.g., “intelligent,” “expert,” and “informed.”) and any changes in competence would be reflected in these indicators. Similarly, the corresponding three indicators of trustworthiness, social tie, and attractiveness were all specified as reflective indicators. However, the researcher characterized the four indicators of dynamism as formative because these four items represented four independent aspects of dynamism, and dynamism was a composite of these four items (e.g., “meek,” “comic,” “active,” and

“interactive.”). Likewise, the four items representing technology affordances were specified as formative because excluding any of the items would change the domain of technology affordances. As they were not interchangeable, they should be identified as formative indicators.

For the second order of individual source credibility, since the six dimensions (competence, expertise, social tie, attractiveness, dynamism, and technology affordance) represented six independent attitudes of people towards a source, they should be specified as formative.

Therefore, individual source credibility was a mixed second-order construct, which embraces both reflective and formative indicators.

Likewise, news organization credibility was a mixed second-order construct including six dimensions. Although the labels of dimensions for news organization source credibility were exactly the same as those for individual source credibility except the attribute dimension, these two types of source credibility had different indicators of each dimension. The competence dimension of news organization source credibility was a reflective construct, which included

“professional,” et al. five indicators (see Table 5.2). Trustworthiness and social tie were reflective constructs as the indicators of them were interchangeable and the causality was from constructs to indicators. However, the attribute, dynamism, and technology affordance dimensions in news organization source credibility were all formative constructs (see Table 5.2 for the detailed indicators and dimensions).

Following the logic of news organization credibility, the brand source credibility was also a mixed second-order construct including six factors with “competence,” “trustworthiness,”

“social tie,” as reflective sub-constructs, and “attribute,” “dynamism,” “technology affordance” as formative sub-constructs. Scales in these factors were different from those in individual credibility and news organization credibility, though they were same-named factors (see Table

5.3 for detailed scales).

Internet involvement and Twitter Involvement were specified as formative constructs because the represented items of these two constructs were not interchangeable. For example,

Internet involvement was represented by “Internet using experience,” “Internet use time,” and

“Internet dependence.” Twitter involvement was defined by “Twitter using experience,” “Twitter use time,” “Twitter update frequency,” and “Twitter independence.” The construct of self-esteem was characterized as reflective construct because the scales, after reversed coding of half of the indicators, used in this study were interchangeable. They were all asking people’s evaluation of self-image (e.g., “on the whole, I am satisfied with myself,” “I feel that I have a number of good qualities,” and “I take a positive attitude toward myself”).

Convergent validity of the reflective constructs was established by checking if the measurement items were loaded with significant t-values on their corresponding constructs.

Discriminant validity was determined by checking the matrix of loadings and cross-loadings for

96 all reflective items in the model. To confirm the discriminant validity, the average variance extracted (AVE) was calculated. AVE test showed if the correlation of the construct with its measurement items was higher than its correlation with other constructs (Gefen & Straub, 2005).

Reliability scores generated by SmartPLS for reflective constructs were examined.

For formative constructs in this study, since the procedures for determining the validity of formative construct were different from those of reflective construct (Petter, Straub, & Rai,

2007), indicators weights of formative constructs and multicollinearity were examined to assess formative validity.

Finally, the mediation test and the predictive power of the model assessment were performed. A mediator is an intervening variable in a causal chain between an independent variable and a dependent variable (Baron & Kenny, 1986). The researcher followed the mediation test suggested by Baron and Kenny (1986). Predictive power of the model was demonstrated by examining R2 in the model and significant structural paths (Lowry & Gaskins,

2014).

Multivariate Statistics

Paired t-tests were performed to compare the differences of perceived source credibility on each product type. Finally, to examine how product type affect perceived credibility of different source and the most credible source, paired t-tests were also performed. The results are reported in the next chapter.

CHAPTER VI.

RESULTS

Sample Profile

A total of 766 college students participated in this study. Of the respondents, 530 were

Twitter users, who were the main research subjects of this study. The demographic profile of the main subjects is shown in Table 6.1.

The sample well represented students at different levels on main campus, majoring in a wide variety of subjects. Among the 530 respondents, there were many more female (73.6%) than male (26.4%) respondents than that in the whole population (57% female and 43% male).

The sample was distributed equally in different class standings, from freshman to senior. The overall class standing distribution in the sample was roughly in accordance with that in the population. Most of the students were from colleges of Arts & Sciences and Education, similar to the U.S. population. Their annual household incomes before tax were mostly in the “Under

$20,000” bracket (39.1%). The majority of them (80.2%) were Caucasian. Their average age was

21 with the median of 20. The GPA of students were quite high as 44% reported GPA higher than 3.5.

Table 6.1 Demographic Profile of Sample

Variables N (Total N=530) Sample Population Gender Male 140 26.4% 43% Female 390 73.6% 57% GPA 3.5 or above 233 44.0% 3-3.49 164 30.9% 2.5-2.99 98 18.5% 2-2.49 33 6.2% 1.5-1.99 2 .4% Class Standing Freshman 110 20.8% 25.4% Sophomore 109 20.6% 18.3% Junior 107 20.2% 17.1% Senior 128 24.2% 22.7% Graduate Student 76 14.3% 13.0% Major Arts & Sciences 214 40.4% 36.5% Business 46 8.7% 10.3% Education 127 24% 28.1% Health & Human Services 82 15.5% 11.9% Technology 26 4.9% 6.9% Undecided/Other 35 6.6% 6.2% Income Under $20,000 207 39.1% $20,000-$49,999 60 11.3% $50,000-$79,999 96 18.1% $80,000-$109,999 88 16.6% $110,000-$139,999 34 6.4% $140,000-169,999 22 4.2% $170,000 or above 23 4.3% Ethnicity Caucasian 425 80.2% African-American 55 10.4% Hispanic 16 3.0% Asian-Pacific islanders 12 2.3% Native American 4 .8% Others 18 3.4% Age Mean: 21.60 Standard Deviation: 4.839 Median: 20.00

Structural Equation Model Results

Construct Validity and Estimation of Reflective Constructs

Confirmatory factor analyses (CFA) were executed to check convergent validity of all the reflective constructs in this study by examining the reflective indicator loadings and their t- values in SmartPLS. For the individual source credibility model, all of the reflective indicators were significant at the α <0.05 levels. The t-value of each item was much greater than 1.96, which suggested a significant p-value of each item (see Table 6.2). The convergent validity in individual source credibility model was established.

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Table 6.2 Loadings and T-statistics for Convergent Validity of Reflective Constructs in Individual Source Credibility Model

Reflective Construct Indicator Loading T-statistic Competence C1. Intelligent .959 189.012*** C2. Expert .959 229.489*** C3. Informed .965 215.734*** Trustworthiness T1. Honest .964 236.383*** T2. Trustworthy .976 313.807*** T3. Honorable .961 192.351*** Social Tie ST1. ImportantToMe .912 71.306*** ST2. AttractAttentions .924 97.045*** ST3. RelateToMe .891 63.526*** Attractiveness A1. Attractive .858 44.735*** A2. Classy .942 178.552*** A3. Elegant .936 117.136*** Internet Dependence ID1 .758 25.120*** ID2 .677 23.566*** ID3 .797 26.883*** ID4 .664 21.930*** ID5 .723 31.614*** ID6 .662 21.757*** Twitter Dependence TD1 .916 117.124*** TD2 .785 39.844*** TD3 .919 124.247*** TD4 .872 71.923*** TD5 .896 83.745*** TD6 .821 49.353*** Self-esteem SE1 .624 10.276*** SE2 .750 4.222*** SE3 .710 8.758*** SE4 .582 8.210*** SE5 .715 9.043*** SE6 .763 6.486*** SE7 .800 8.612*** SE8 .696 4.234*** SE9 .732 7.091*** SE10 .767 8.965*** Note. *p<.05, **p<.01,***p<.001.

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Although all the reflective indicators in the news organization source credibility model were significant at the α <0.05 level, the loading of the item “objective/subjective” on

“trustworthiness” dimension seemed like an outlier. It was loaded .823 while other items on the same dimension loaded .950 or more. Thus, one could speculate that “objective/subjective” should not belong to the “trustworthiness” dimension. The researcher removed the “objective” item from “trustworthiness” and added it to the scales of “dynamism” for the sake of dynamism definition in source credibility. “Objective/subjective” was one of the attitudes of a news organization, which one could feel in the communication with the news organization. The loadings on “trustworthiness” were boosted after this modification (see Table 6.3) and the inclusiveness of “objective” in “dynamism” construct did improve the explanatory effects of dynamism factor as a formative sub-construct on news organization source credibility (see Table

6.15).

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Table 6.3 Loadings and T-statistics for Convergent Validity of Reflective Constructs in News Organization Source Credibility Model

Reflective Construct Indicators Loadings T-statistic Competence C1. Professional .964 147.725*** C2. Intelligent .975 179.813*** C3. Informative .973 212.801*** C4. Expert .964 172.332*** C5. Organized .967 184.090*** C6. Objective .823 50.266*** Trustworthiness T1. Trustworthy .954 138.096*** T2. Honest .955 136.668*** T3. Recognizable .901 75.107*** Social Tie ST1. ImportantToMe .938 125.869*** ST2. AttractAttentions .934 106.957*** ST3. RelateToMe .896 50.768*** Internet Dependence ID1 .758 25.148*** ID2 .677 23.730*** ID3 .798 24.594*** ID4 .664 22.633*** ID5 .722 31.209*** ID6 .662 20.354*** Twitter Dependence TD1 .916 124.141*** TD2 .785 38.952*** TD3 .919 127.116*** TD4 .872 71.246*** TD5 .896 89.019*** TD6 .821 49.166*** Self-esteem SE1 .723 15.937*** SE2 .712 9.841*** SE3 .739 15.101*** SE4 .654 11.746*** SE5 .759 20.255*** SE6 .768 14.979*** SE7 .702 12.875*** SE8 .664 9.261*** SE9 .749 16.582*** SE10 .773 16.909*** Note. *p<.05, **p<.01,***p<.001; item shaded in grey was removed for low loading.

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Factor analysis for reflective indicators in brand source credibility model was performed

(see Table 6.4). “Organized/disorganized” was removed from scales of “Competence” for low loading. Other items all had consistent high loadings on their assigned constructs. The loadings of indicators after deleting “organized/disorganized” are also shown in Table 6.4 in the “After” column.

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Table 6.4 Loadings and T-statistics for Convergent Validity of Reflective Constructs in Brand Source Credibility Model

Reflective Construct Indicators Loadings T-statistic Before (After) Before (After) Competence C1. GoodQuality .922 (.925) 109.253*** (108.733***) C2. Intelligent .906 (.923) 74.666*** (81.789***) C3. Expert .931 (.946) 85.385*** (105.528***) C4. Organized .484 (–––) 10.093***(–––) Trustworthiness T1. Trustworthy .945 (.945) 145.695*** (141.693***) T2. Honest .944 (.944) 126.652*** (129.026***) T3. Recognizable .870 (.870) 48.310*** (50.222***) Social Tie ST1. ImportantToMe .917 (.917) 80.016*** (79.571***) ST2. AttractAttentions .937 (.937) 85.633*** (83.826***) ST3. RelateToMe .904 (.904) 61.565*** (62.812***) Internet Dependence ID1 .758 (.758) 25.950*** (24.255***) ID2 .677 (.677) 23.343*** (23.832***) ID3 .797 (.797) 25.522*** (24.225***) ID4 .664 (.664) 21,778*** (20.775***) ID5 .723 (.723) 30.990*** (32.202***) ID6 .662 (.662) 21.716*** (22.910***) Twitter Dependence TD1 .916 (.916) 126.610*** (126.904***) TD2 .785 (.785) 39.812*** (39.359***) TD3 .919 (.919) 129.657*** (125.134***) TD4 .872 (.872) 77.620*** (75.478***) TD5 .896 (.896) 90.680*** (87.359***) TD6 .821 (.821) 50.154*** (51.571***) Self-esteem SE1 .760 (.760) 20.303*** (20.307***) SE2 .674 (.674) 8.756*** (9.382***) SE3 .748 (.748) 14.562*** (13.693***) SE4 .670 (.670) 12.898*** (13.763***) SE5 .760 (.760) 20.818*** (20.689***) SE6 .745 (.745) 14.033*** (16.044***) SE7 .717 (.717) 14.480*** (14.751***) SE8 .630 (.630) 8.867*** (10.046***) SE9 .737 (.737) 16.245*** (18.847***) SE10 .781 (.781) 22.414*** (23.343***) Notes. *p<. 05**, p<. 01***, p<. 001; item shaded in grey was removed for low loading; new loadings after deleting “organized” were shown in “After” column.

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The discriminant validity was examined by two established techniques suggested by

Lowry and Gaskins (2014). First, the reflective latent variable scores were obtained by

SmartPLS. Then Pearson’s correlations of all the reflective indicators against the reflective latent variable scores were run. The researcher correlated the latent variable scores against the indicators and created a matrix of loadings and cross-loadings for all items and constructs in the models (see Table 6.5 for reflective constructs and item loadings in individual source credibility model, Table 6.6 for reflective constructs and item loadings in news organization source credibility model, and Table 6.7 for reflective constructs and item loadings in brand source credibility model). These correlations were the actual loadings of the reflective indicators on their corresponding constructs. According to Gefen and Straub (2005), the general rule to establish discriminant validity was to check if the magnitude of all the loadings of the measurement items on their assigned constructs was 0.1 larger than their any other loadings on other constructs. Finally, strong discriminant validity was established for individual source credibility model, news organization source credibility model, and brand source credibility model in this study.

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Table 6.5 Loadings of the Reflective Measurement Items in Individual Source Credibility Model

Competence Trustworthi Social Attractiven Internet Twitter Self- ness Tie ess Dependence Dependence esteem

C1. Intelligent .959 .784 .588 .758 .123 .239 .096 C2. Expert .959 .762 .563 .730 .138 .246 .095 C3. Informed .965 .800 .573 .750 .161 .255 .125 T1. Honest .788 .964 .693 .732 .157 .280 .093 T2. Trustworthy .771 .976 .719 .724 .148 .242 .070 T3. Honorable .802 .961 .726 .753 .137 .238 .091 ST1. ImportantToMe .504 .678 .912 .623 .127 .181 -.013 ST2. AttractAttentions .523 .650 .924 .647 .147 .204 .031 ST3. RelateToMe .599 .682 .891 .673 .095 .168 .042 A1. Attractive .568 .598 .643 .858 .131 .201 .101 A2. Classy .824 .785 .670 .942 .144 .222 .166 A3. Elegant .713 .688 .643 .936 .126 .191 .188 ID1 .059 .066 .064 .086 .758 .133 .041 ID2 .054 .101 .116 .096 .677 .222 .063 ID3 .117 .130 .087 .105 .797 .157 .084 ID4 .151 .125 .120 .123 .664 .268 -.011 ID5 .162 .159 .145 .157 .723 .319 .087 ID6 .086 .074 .050 .062 .662 .202 .005 TD1 .230 .249 .190 .223 .212 .916 .132 TD2 .229 .259 .196 .209 .367 .785 .111 TD3 .223 .224 .176 .220 .201 .919 .108 TD4 .211 .204 .174 .163 .294 .872 .025 TD5 .267 .247 .180 .229 .220 .896 .136 TD6 .177 .183 .143 .121 .297 .821 .038 SE1 .032 .004 -.031 .025 -.081 .024 .624 SE2 .052 .047 -.038 .078 .082 .118 .750 SE3 .046 .041 -.010 .083 .029 .090 .710 SE4 .041 .035 -.019 .038 .057 -.007 .582 SE5 .076 .074 -.028 .048 .031 .067 .715 SE6 .044 .046 -.001 .091 .064 .121 .763 SE7 .141 .114 .099 .161 .103 .111 .800 SE8 .080 .060 .039 .120 .023 .050 .696 SE9 .101 .060 .026 .120 .049 .008 .732 SE10 .099 .065 .013 .138 .001 .050 .767

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Table 6.6 Loadings of the Reflective Measurement Items in News Organization Source Credibility Model

Competence Trustworthiness Social Internet Twitter Self-esteem Tie Dependence Dependence

C1. Professional .964 .831 .705 .063 .149 .150 C2. Intelligent .975 .868 .752 .079 .172 .162 C3. Informative .973 .860 .721 .084 .174 .178 C4. Expert .964 .868 .747 .094 .164 .177 C5. Organized .967 .877 .743 .066 .166 .189 T1. Trustworthy .805 .954 .780 .130 .193 .158 T2. Honest .800 .955 .777 .125 .215 .146 T3. Recognizable .886 .901 .730 .119 .190 .173 ST1. ImportantToMe .709 .787 .938 .159 .207 .139 ST2. AttractAttentions .691 .737 .934 .144 .242 .128 ST3. RelateToMe .698 .728 .896 .111 .152 .125 ID1 .048 .072 .087 .758 .133 .025 ID2 -.001 .029 .056 .677 .222 .048 ID3 .077 .121 .107 .798 .157 .063 ID4 .116 .146 .143 .664 .268 -.025 ID5 .064 .109 .149 .722 .319 .065 ID6 .036 .093 .101 .662 .202 -.009 TD1 .148 .191 .198 .212 .916 .121 TD2 .128 .158 .170 .367 .785 .092 TD3 .161 .201 .193 .201 .919 .098 TD4 .148 .176 .192 .294 .872 .018 TD5 .172 .194 .202 .220 .896 .126 TD6 .130 .186 .177 .297 .821 .029 SE1 .137 .122 .097 -.081 .024 .712 SE2 .127 .130 .083 .082 .118 .759 SE3 .122 .130 .139 .029 .090 .768 SE4 .143 .106 .091 -.057 -.007 .664 SE5 .141 .122 .067 .031 .067 .749 SE6 .072 .066 .054 .064 .121 .723 SE7 .146 .166 .157 .103 .111 .739 SE8 .145 .126 .111 .023 .050 .654 SE9 .111 .093 .089 .049 .008 .702 SE10 .127 .136 .105 .001 .050 .773

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Table 6.7 Loadings of the Reflective Measurement Items in Brand Source Credibility Model

Competence Trustworthiness Social Internet Twitter Self-esteem Tie Dependence Dependence

C1. GoodQuality .925 .853 .729 .068 .171 .139 C2. Intelligent .923 .764 .658 .056 .196 .059 C3. Expert .946 .807 .669 .053 .178 .107 T1. Trustworthy .825 .945 .758 .061 .201 .123 T2. Honest .811 .944 .726 .063 .235 .127 T3. Recognizable .762 .870 .658 .096 .162 .161 ST1. ImportantToMe .678 .720 .917 .116 .187 .115 ST2. AttractAttentions .709 .720 .937 .153 .200 .105 ST3. RelateToMe .647 .703 .904 .122 .128 .095 ID1 .004 .013 .102 .758 .133 .030 ID2 .036 .053 .095 .677 .222 .053 ID3 -.010 0.01 .105 .797 .157 .071 ID4 .069 .082 .096 .664 .268 -.020 ID5 .141 .138 .177 .723 .319 .073 ID6 .041 .055 .028 .662 .202 -.002 TD1 .203 .227 .209 .212 .916 .125 TD2 .204 .201 .199 .367 .785 .101 TD3 .166 .209 .177 .201 .919 .102 TD4 .128 .137 .120 .294 .872 .022 TD5 .197 .199 .163 .220 .896 .130 TD6 .114 .154 .106 .297 .821 .034 SE1 .086 .099 .093 .064 .121 .760 SE2 .075 .102 .097 .103 .111 .748 SE3 .090 .122 .082 .023 .050 .670 SE4 .064 .110 .120 .049 .008 .717 SE5 .115 .132 .116 .001 .050 .781 SE6 .055 .074 .023 -.081 .024 .674 SE7 .087 .107 .076 .082 .118 .760 SE8 .084 .106 .076 .029 .090 .745 SE9 .055 .105 .043 -.057 -.007 .630 SE10 .069 .119 .062 .031 .067 .737

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The second technique, the average variance extracted (AVE) test, was to confirm the discriminant validity further. The AVE was calculated by computing the variances shared by the items of a particular construct. The underlying notion of AVE test was the correlation of the reflective constructs with their assigned indicators should be larger than the correlations with other constructs (Gefen & Straub, 2005). The AVE square roots were calculated and compared with the correlations of the reflective constructs. According to Table 6.8, Table 6.9, and Table

6.10, the AVE square roots on diagonal were greater than the off-diagonal correlations for the same row and column, which showed strong discriminant validity for all the reflective constructs in individual source credibility model, news organization source credibility model, and brand source credibility model in this study.

Table 6.8 Discriminant Validity Through the Square Root of AVE for Individual Source Credibility Model

(1) (2) (3) (4) (5) (6) (7) Competence (1) .923 Trustworthiness (2) .814 .935 SocialTie (3) .598 .737 .826 Attractiveness (4) .777 .762 .714 .833 InternetDependence (5) .146 .153 .135 .147 .512 TwitterDependence (6) .257 .262 .203 .224 .300 .756 Self-esteem (7) .110 .088 .023 .143 .065 .107 .514 Note. The AVE square roots are in bold on diagonal. The off-diagonal values are the correlations between reflective constructs.

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Table 6.9 Discriminant Validity Through the Square Root of AVE for News Organization Source Credibility Model

(1) (2) (3) (4) (5) (7) Competence (1) .938 Trustworthiness (2) .814 .878 SocialTie (3) .757 .814 .852 InternetDependence (4) .080 .133 .150 .512 TwitterDependence (5) .170 .213 .217 .300 .756 Self-esteem (6) .177 .170 .142 .041 .094 .526 Note. The AVE square roots are in bold on diagonal. The off-diagonal values are the correlations between reflective constructs.

Table 6.10 Discriminant Validity Through the Square Root of AVE for Brand Source Credibility Model

(1) (2) (3) (4) (5) (7) Competence (1) .867 Trustworthiness (2) .840 .847 SocialTie (3) .738 .777 .845 InternetDependence (4) .064 .080 .142 .512 TwitterDependence (5) .194 .217 .187 .300 .756 Self-esteem (6) .111 .149 .114 .050 .100 .524 Note. The AVE square roots were in bold on diagonal. The off-diagonal values were the correlations between reflective constructs.

Construct Reliability and Estimation of Reflective Constructs

According to Table 6.11,Table 6.12, and Table 6.13, each reflective construct in the individual source credibility model, news organization source credibility model, and brand source credibility scored high on reliability test (Cronbach’s α >.7; Composite Reliability

Scores> .7), suggesting excellent internal consistency of measurement in these models (Chin,

1998). Composite reliability score, which was similar to Cronbach’s α, were generated by

SmartPLS and confirmed the internal consistency of these measurements in the three models.

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Table 6.11 Reliability Test for Reflective Constructs for Individual Source Credibility Model

Reflective Construct Cronbach’s α Composite Reliability Competence .958 .973 Trustworthiness .965 .977 SocialTie .895 .935 Attractiveness .899 .937 InternetDependence .808 .862 TwitterDependence .935 .949 Self-esteem .900 .913

Table 6.12 Reliability Test for Reflective Constructs for News Organization Source Credibility Model

Reflective Construct Cronbach’s α Composite Reliability Competence .984 .987 Trustworthiness .930 .956 SocialTie .913 .945 InternetDependence .808 .862 TwitterDependence .935 .949 Self-esteem .900 .917

Table 6.13 Reliability Test for Reflective Constructs for Brand Source Credibility Model

Reflective Construct Cronbach’s α Composite Reliability Competence .924 .951 Trustworthiness .909 .943 SocialTie .908 .943 InternetDependence .808 .862 TwitterDependence .935 .949 Self-esteem .900 .916

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Construct Validity and Estimation of Formative Constructs

Formative indicator weights and their t-values were examined for each construct to assess each indicator’s contribution to its assigned formative construct (Loch, Straub, & Kamel, 2003).

Table 6.14 demonstrates the weights and t-values of formative indicators in individual source credibility model. Only the weights of one indicator of technology affordances (“Has a lot of followers/few followers”) and one indicator of Internet involvement (Internet use experience) were not significant (the t-values in bold in Table 6.14). These two indicators were eliminated to establish well construct validity for the measurement model. The new weights of indicators after excluding the non-significant two items are included in parentheses in Table 6.14. The new measurements of technology affordance were strikingly improved because the contribution of the final technology affordance indicators to technology affordance, and the prediction of technology affordance itself to the individual source credibility construct, were both boosted. Overall, the weights of individual source credibility indicators were roughly equal and all had significant t- values, which demonstrated high construct validity (Ringle, Sarstedt, Straub, 2012). For other formative constructs in the model, although their indicators all had significant weights, some of them were relatively low while others were high. The researcher still decided to keep those items with low weights in the measurement model for two reasons: 1) those item with low weights had significant t-values; 2) according to Cenfetelli and Bassellier (2009, p. 701), “very few reasons, if any, would lead to the decision to remove an item after a single study showing some concerns in the results, when the theoretical definition of the construct justifies its inclusion.”

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Table 6.14 Weights and T-Statistics for Formative Constructs of Individual Source Credibility Model

Construct Weight Std. Error T-Statistics Before (After) Before (After) Before (After) Individual Source Credibility 1. Competence .208 (.231) .008 (.009) 27.345*** (24.536***) 2. Trustworthiness .207 (.140) .008 (.007) 26.229*** (20.931***) 3. Social Tie .161 (.172) .006 (.006) 26.934*** (27.532***) 4. Attractiveness .185 (.200) .006 (.006) 33.453*** (32.198***) 5. Dynamism .189 (.201) .007 (.007) 27.153*** (27.980***) 6. Tech Affordances .200 (.208) .007 (.007) 29.342*** (30.112***) Dynamism 1. Meek & Aggressive .172 (.173) .037 (.035) 4.665*** (4.535***) 2. Comic & Not .202 (.196) .037 (.035) 5.392*** (5.297***) Comic 3. Active & Passive .614 (.620) .047 (.044) 13.031*** (14.146***) 4. Interactive & .227 (.224) .050 (.045) 4.520*** (5.084***) Noninteractive Technology Affordances 1. Verified & .296 (.369) .069 (.056) 4.273*** (6.603***) Unverified 2. Lot of followers & .137 .082 1.655 Few followers 3. Real & Fake .479 (.507) .055 (.055) 8.679*** (9.195***) 4. Tweets everyday & .195 (.234) .064 (.058) 3.048*** (4.039***) Barely tweets Internet Involvement 1. Internet Use .032 .022 1.471 Experience 2. Internet Use Time .078 (.077) .022 (.021) 3.553*** (3.650***) 3. Internet Dependence .977 (.982) .011 (.009) 85.366*** (106.261***) Twitter Involvement 1. Twitter Use .081 (.081) .005 (.006) 15.014*** (14.125***) Experience 2. Twitter Use Time .110 (.110) .004 (.004) 26.986*** (27.352***) 3. Twitter Update .147 (.147) .004 (.003) 39.344*** (40.189***) Frequency 4. Twitter Dependence .788 (.788) .008 (.008) 98.313*** (93.613***) Notes. *p<.05, **p<.01,***p<.001; t-values in bold were not significant; the indicators in italic were eliminated from the model for construct validity; the items in parentheses were the new values after eliminating the two non-significant indicators.

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Similarly, the weights and t-values of formative indicators in news organization source credibility model and brand source credibility are demonstrated in Table 6.15 and Table 6.16 respectively. Except the “Internet use experience” of Internet involvement, other indicators all showed proper weights and significant t-values on their corresponding constructs in these two models. Construct validity in news organization and brand models was well established by the high significant weights and t-statistics.

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Table 6.15 Weights and T-Statistics for Formative Constructs of News Organization Source Credibility Model

Construct Weight Std. Error T-Statistics Before (After) Before (After) Before (After) News Organization Source Credibility 1. Competence .227 (.227) .010 (.010) 23.487*** (23.655***) 2. Trustworthiness .108 (.108) .010 (.010) 10.371*** (10.312***) 3. Social Tie .125 (.125) .007 (.007) 18.439*** (17.796***) 4. Attractiveness .197 (.197) .010 (.010) 18.779*** (20.351***) 5. Dynamism .221 (.221) .009 (.009) 24.657*** (23.622***) 6. Tech Affordances .184 (.184) .007 (.007) 25.505*** (25.871***) Attribute 1. HugeOrg&SmallOrg .226 (.226) .026 (.026) 32.437*** (32.996***) 2. BeenLong&StartUp .132 (.132) .022 (023) 39.544*** (38.004***) 3. CongruentValue&None .208 (.208) .018 (.018) 47.569*** (48.008***) 4. GoodRep&BadRep .544 (.544) .008 (.008) 117.269*** (119.902***) Dynamism 1. Fair & Unfair .268 (.268) .020 (.020) 43.508*** (43.390***) 2. Timely & Untimely .394 (.394) .008 (.008) 123.431*** (117.413***) 3. Active & Passive .270 (.270) .013 (.014) 70.934*** (64.733***) 4. Interactive & Noninteractive .081 (.081) .036 (.038) 19.572*** (18.557***) 5. Objective & Subjective .106 (.106) .025 (.025) 31.880*** (32.016***) Technology Affordances 1. Verified & Unverified .081 (.081) .039 (.040) 19.858*** (19.314***) 2. Lot of followers & Few .220 (.220) .011 (.011) 89.223*** (88.608***) followers 3. Real & Fake .432 (.432) .011 (.011) 87.332*** (88.465***) 4. Tweets everyday & Barely .334 (.334) .010 (.010) 96.251*** (96.380***) tweets Internet Involvement 1. Internet Use Experience .035 .022 1.550 2. Internet Use Time .082 (.085) .023 (.023) 3.563*** (3.653***) 3. Internet Dependence .975 (.979) .013 (.011) 76.713*** (91.953***) Twitter Involvement 1. Twitter Use Experience .081 (.081) .006 (.006) 12.645*** (12.639***) 2. Twitter Use Time .110 (.110) .004 (.004) 28.905*** (28.754***) 3. Twitter Update Frequency .147 (.147) .003 (.003) 42.767*** (45.337***) 4. Twitter Dependence .789 (.789) .008 (.007) 101.048*** (105.523***) Notes. *p<.05, **p<.01,***p<.001; t-values in bold were not significant; the indicators in italic were eliminated from the model for construct validity; the items in parentheses were the new values after eliminating the two non-significant indicators.

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Table 6.16 Weights and T-Statistics for Formative Constructs of Brand Source Credibility Model

Construct Weight Std. Error T-Statistics Brand Source Credibility 1. Competence .159 .008 20.551*** 2. Trustworthiness .169 .010 16.910*** 3. Social Tie .145 .007 20.535*** 4. Attractiveness .219 .007 31.025*** 5. Dynamism .162 .008 21.181*** 6. Tech Affordances .232 .008 30.214*** Attribute 1. HugeOrg&SmallOrg .218 .025 34.246*** 2. BeenLong&StartUp .124 .021 40.485*** 3. CongruentValue&None .318 .017 51.085*** 4. GoodRep&BadRep .446 .017 55.440*** Dynamism 1. Creative & Uncreative .567 .009 113.834*** 2. Active & Passive .318 .013 74.393*** 3. Interactive & Noninteractive .182 .034 24.474*** Technology Affordances 1. Verified & Unverified .142 .033 25.091*** 2. Lot of followers & Few followers .306 .016 58.867*** 3. Real & Fake .402 .014 68.227*** 4. Tweets everyday & Barely tweets .255 .018 50.901*** Internet Involvement 1. Internet Use Experience .030 .022 1.383 2. Internet Use Time .081 .022 3.614*** 3. Internet Dependence .977 .012 81.845*** Twitter Involvement 1. Twitter Use Experience .078 .006 12.652*** 2. Twitter Use Time .111 .004 29.438*** 3. Twitter Update Frequency .148 .003 43.399*** 4. Twitter Dependence .788 .008 98.388*** Notes. *p<. 05, **p<. 01, ***p< .001; t-values in bold were not significant; the indicators in italic were eliminated from the model for construct validity.

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As the weights of formative indicators were the partial coefficients by controlling other indicators, multicollinearity test was performed for construct validity. To diagnose multicollinearity, the researcher examined values for variance inflation factor (VIF) for each formative indicator. The VIF for a particular indicator suggested whether a strong linear correlation between it and other indicators (Stevens, 2001). Although there was no rule of thumb, values of VIF should be 10 at a maximum (Stevens, 2001). As shown in Table 6.17 and Table

6.19, all of the VIFs of the formative factors in individual source credibility model and brand source credibility were below 10, indicating sufficient construct validity in these two models.

However, in the news organization source credibility model (see Table 6.18), both VIFs of

“attribute” and “dynamism” were beyond 10, which caused multicollinearity problem. Based on theoretical consideration, the “attribute,” which measured the affective attitude of people toward a news organization based on its reputation, was not a factor of perceived news organization source credibility in social media (see Table 5.2 for the scales). Thus, the researcher removed

“attribute” factor from the measure of news organization source credibility. The removal of

“attribute” did improve VIFs of “dynamism” and other factors of news organization source credibility (see VIFs in parentheses in Table 6.18). Therefore, the construct of news organization source credibility ended up with five factors including competence, trustworthiness, social tie, dynamism, and technology affordance. Since one factor was dropped at this point, the researcher restarted the factor analysis in SEM analysis of news organization model (see Table 6.20). The formative construct validity in news organization source credibility model was established by eliminating factor of “attribute” and its scales.

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Table 6.17 VIF for Formative Indicators in Individual Source Credibility Model

Construct VIF Individual Source Credibility 1. Competence 4.692 2. Trustworthiness 4.341 3. Social Tie 2.707 4. Attractiveness 3.912 5. Dynamism 3.433 6. Tech Affordances 2.813 Dynamism 1. Meek & Aggressive 1.231 2. Comic & Not Comic 1.760 3. Active & Passive 2.298 4. Interactive & 2.654 Noninteractive Technology Affordances 1. Verified & Unverified 2.885 2. Lot of followers & Few ––– followers 3. Real & Fake 2.526 4. Tweets everyday & Barely 1.962 tweets Internet Involvement 1. Internet Use Experience ––– 2. Internet Use Time 1.042 3. Internet Dependence 1.042 Twitter Involvement 1. Twitter Use Experience 1.208 2. Twitter Use Time 1.526 3. Twitter Update Frequency 2.466 4. Twitter Dependence 2.569 Note. Items in italic were removed from the original scales.

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Table 6.18 VIF for Formative Indicators in News Organization Source Credibility Model

Construct VIF With Attribute Without Attribute News Organization Source Credibility 1. Competence 8.027 7.470 2. Trustworthiness 8.187 7.574 3. Social Tie 3.400 3.186 4. Attribute 10.657 ––– 5. Dynamism 10.120 9.595 6. Tech Affordances 5.685 5.344 Attribute 1. HugeOrg&SmallOrg 3.511 ––– 2. BeenLong&StartUp 4.012 ––– 3. CongruentValue&None 2.407 ––– 4. GoodRep&BadRep 2.825 ––– Dynamism 1. Fair & Unfair 2.659 2.659 2. Timely & Untimely 6.098 6.098 3. Active & Passive 5.547 5.547 4. Interactive & Noninteractive 1.879 1.879 5. Objective & Subjective 2.646 2.646 Technology Affordances 1. Verified & Unverified 2.560 2.560 2. Lot of followers & Few followers 6.537 6.537 3. Real & Fake 4.652 4.652 4. Tweets everyday & Barely tweets 4.794 4.794 Internet Involvement 1. Internet Use Experience ––– ––– 2. Internet Use Time 1.042 1.042 3. Internet Dependence 1.042 1.042 Twitter Involvement 1. Twitter Use Experience 1.209 1.209 2. Twitter Use Time 1.526 1.526 3. Twitter Update Frequency 2.467 2.467 4. Twitter Dependence 2.566 2.566 Note. Items in italic were removed from the original scales.

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Table 6.19 VIF for Formative Indicators in Brand Source Credibility Model

Construct VIF News Organization Source Credibility 1. Competence 4.710 2. Trustworthiness 6.764 3. Social Tie 2.866 4. Attractiveness 6.354 5. Dynamism 6.545 6. Tech Affordances 5.266 Attractiveness 1. HugeOrg&SmallOrg 5.348 2. BeenLong&StartUp 5.659 3. CongruentValue&None 2.757 4. GoodRep&BadRep 3.346 Dynamism 1. Creative & Uncreative 3.829 2. Active & Passive 4.555 3. Interactive & 2.523 Noninteractive Technology Affordances 1. Verified & Unverified 2.762 2. Lot of followers & Few 4.246 followers 3. Real & Fake 3.098 4. Tweets everyday & Barely 3.224 tweets Internet Involvement 1. Internet Use Experience ––– 2. Internet Use Time 1.059 3. Internet Dependence 1.049 Twitter Involvement 1. Twitter Use Experience 1.209 2. Twitter Use Time 1.526 3. Twitter Update Frequency 2.468 4. Twitter Dependence 2.566 Note. Items in italic were removed from the original scales.

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Table 6.20 New Weights and T-Statistics for Formative Constructs of News Organization Source Credibility Model

Construct Weight Std. Error T-Statistics News Organization Source Credibility 1. Competence .295 .011 27.506*** 2. Trustworthiness .162 .010 16.566*** 3. Social Tie .150 .007 22.004*** 5. Dynamism .252 .008 32.374*** 6. Tech Affordances .207 .008 25.490*** Dynamism 1. Fair & Unfair .279 .021 41.562*** 2. Timely & Untimely .356 .009 111.433*** 3. Active & Passive .285 .013 71.387*** 4. Interactive & Noninteractive .084 .036 19.395*** 5. Objective & Subjective .119 .023 35.332*** Technology Affordances 1. Verified & Unverified .050 .044 17.477*** 2. Lot of followers & Few .206 .012 77.527*** followers 3. Real & Fake .455 .011 90.303*** 4. Tweets everyday & Barely .349 .010 99.028*** tweets Internet Involvement 2. Internet Use Time .085 .023 3.769*** 3. Internet Dependence .979 .010 94.338*** Twitter Involvement 1. Twitter Use Experience .081 .007 12.402*** 2. Twitter Use Time .110 .004 29.259*** 3. Twitter Update Frequency .147 .003 42.171*** 4. Twitter Dependence .789 .008 100.407*** Notes. *p<.05, **p<.01,***p<.001.

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Source Credibility on Twitter

Finally, the measurement model of individual source credibility was established and validated, as shown in Figure 6.1. And the correlation matrixes of the three models were attached in the appendices for reference (See Appendix H., I., and J.). Thus, to answer the first research question, the final individual source credibility in social media consisted of six dimensions as shown in Figure 6.1: 1) competence; 2) trustworthiness; 3) social tie; 4) attractiveness; 5) dynamism; and 6) technology affordance. The detailed scales of individual source credibility in social media are shown in Table 6.21. The measurement model of news organization source credibility was validated and is shown in Figure 6.2. For the second research question, news organization source credibility included five factors: 1) competence; 2) trustworthiness; 3) social tie; 4) dynamism; and 5) technology affordance. The detailed scales of news organization source credibility in social media is shown in Table 6.22. For the third research question, the brand source credibility consisted of six factors as well as individual source credibility: 1) competence;

2) trustworthiness; 3) social tie; 4) attribute; 5) dynamism; and 6) technology affordance.

However, the scales for each factor were different than individual source credibility. The detailed scales of brand source credibility in social media are shown in Table 6.23.

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Figure 6.1. Measurement model of individual source credibility

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Figure 6.2. Measurement model of news organization source credibility

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Figure 6.3. Measurement model of brand source credibility

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Table 6.21 Individual Source Credibility Scale

Dimensions Indicators Competence Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Informed 1 2 3 4 5 6 7 Uninformed Trustworthiness Honest 1 2 3 4 5 6 7 Dishonest Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honorable 1 2 3 4 5 6 7 Dishonorable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Attractiveness Attractive 1 2 3 4 5 6 7 Unattractive Classy 1 2 3 4 5 6 7 Not Classy Elegant 1 2 3 4 5 6 7 Plain Dynamism Meek 1 2 3 4 5 6 7 Aggressive Comic 1 2 3 4 5 6 7 Not comic Active 1 2 3 4 5 6 7 Passive Interactive 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Has a lot of followers 1 2 3 4 5 6 7 Only has a few followers Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. The row highlighted in grey was deleted from the final scale.

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Table 6.22 News Organization Source Credibility Scale

Dimensions Indicators Competence Professional 1 2 3 4 5 6 7 Unprofessional Intelligent 1 2 3 4 5 6 7 Unintelligent Informative 1 2 3 4 5 6 7 Uninformative Expert 1 2 3 4 5 6 7 Inexpert Organized 1 2 3 4 5 6 7 Disorganized Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Attribute Huge organization 1 2 3 4 5 6 7 Small organization Been around for a long time 1 2 3 4 5 6 7 New business Congruent with my values 1 2 3 4 5 6 7 Not congruent with my values Good reputation Bad reputation Dynamism Fair 1 2 3 4 5 6 7 Unfair Timely 1 2 3 4 5 6 7 Untimely Active 1 2 3 4 5 6 7 Passive Interactive 1 2 3 4 5 6 7 Noninteractive Objective 1 2 3 4 5 6 7 Subjective Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Has a lot of followers 1 2 3 4 5 6 7 Only has a few followers Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. The rows highlighted in grey were deleted from the final scale.

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Table 6.23 Brand Source Credibility

Dimensions Indicators Competence Good quality 1 2 3 4 5 6 7 Bad quality Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Organized 1 2 3 4 5 6 7 Disorganized Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Attribute Huge organization 1 2 3 4 5 6 7 Small organization Been around for a long time 1 2 3 4 5 6 7 New business Congruent with my values 1 2 3 4 5 6 7 Not congruent with my values Good reputation Bad reputation Dynamism Creative 1 2 3 4 5 6 7 Uncreative Active Passive Interactive 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Has a lot of followers 1 2 3 4 5 6 7 Only has a few followers Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday Note. The row highlighted in grey was deleted from the final scale.

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Structural Model Results and Evaluation

Collinearity check results. Each set of predictors in the three structural models for collinearity was examined because “the estimation of path coefficients in the structural models is based on OLS regressions of each endogenous latent variable on its corresponding predecessor constructs” (Hair et al., 2013). Thus, any significant collinearity among the predictors results biased path coefficients. The latent variable scores of each set of predictors in the three models were obtained respectively from SmartPLS. And then collinearity checks of the three models in this study were performed on IBM SPSS 22.0 as the assessment of formative measurement. The researcher examined each set of predictor variables separately for each component of the three structural equation models. Specifically, the researcher assessed the following sets of predictors for collinearity in the three models: (1) gender, class standing, income, Internet involvement,

Twitter involvement, self-esteem as predictors for perceived source credibility on Twitter; (2)

Internet involvement and Twitter involvement as predictors for self-esteem. The values of VIF, which were all below 3, for the predictor variables showed there was no collinearity problem in these three models (see Table 6.24, 6.25, and 6.26).

Table 6.24 Collinearity Assessment for Individual Source Credibility Model

First Set: DV=Source Credibility Second Set: DV=Self-esteem Independent Variables VIF Independent Variables VIF Gender 1.012 Internet Involvement 1.086 Class Standing 1.181 Twitter Involvement 1.086 Income 1.099 Internet Involvement 1.130 Twitter Involvement 1.179 Self-esteem 1.040

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Table 6.25 Collinearity Assessment for News Organization Source Credibility Model

First Set: DV=Source Credibility Second Set: DV=Self-esteem Independent Variables VIF Independent Variables VIF Gender 1.013 Internet Involvement 1.085 Class Standing 1.182 Twitter Involvement 1.085 Income 1.098 Internet Involvement 1.130 Twitter Involvement 1.177 Self-esteem 1.038

Table 6.26 Collinearity Assessment for Brand Source Credibility Model

First Set: DV=Source Credibility Second Set: DV=Self-esteem Independent Variables VIF Independent Variables VIF Gender 1.013 Internet Involvement 1.086 Class Standing 1.181 Twitter Involvement 1.086 Income 1.099 Internet Involvement 1.130 Twitter Involvement 1.178 Self-esteem 1.039

Structural model path coefficients. The general structural model of the three source credibility models is shown as below (Figure 6.4). After running the PLS algorithm and bootstrapping (5,000 subsamples), estimates and their significances were obtained for the structural model relationships (Appendix L, M, and N). Fitness indices generated from LISREL for the three models were attached in the Appendix O for reference.

In the individual source credibility model, although the R2 values of individual source credibility (.111), Twitter involvement (.079), and self-esteem (.013) were rather weak by general standards, there was no rules of thumb for acceptable R2 values “as this depends on the model complexity and the research discipline” (Hair et al., 2013). In this study, it was not surprising to get a R2 of .111 for individual source credibility, considering the complexity of the

131 construct perceived credibility (which varied in different specific contexts and could be affected by multiple communication factors, i.e., the source itself, message content, media, and communicator); the researcher only concentrated on the contribution of the new media environment to people’ perceived source credibility in social media. Moreover, the construct of perceived individual source credibility itself had already embraced plenty of components that could explain the variance in this construct. The R2 of .111 represented 11% of variance in this construct, explained by all of the exogenous constructs linked to it in the individual source credibility model. In other words, the new media environment/context roughly contributed 11% of variances to people’s perceived individual source credibility on Twitter.

Figure 6.4. Structural model

The hypothesized path coefficients and their significances of individual source credibility model are shown in Table 6.27. H1a was rejected; there was no relationship between Internet involvement and individual source credibility. However, Twitter involvement was positive

132 related to individual source credibility; H2a was supported. Self-esteem did not predict individual source credibility; H3a was rejected. The higher Internet involvement, the higher

Twitter involvement; H4 was supported. Although Internet involvement was not positive related to people’s self-esteem (H5 rejected), Twitter involvement was found positively related to self- esteem (H6 supported). The three controls gender, class standing, and household income, were not significantly related to perceived individual source credibility.

Table 6.27 Significance Testing Results of the Individual Source Credibility Structural Model Path Coefficients

Paths Path T-values Hypotheses Coefficients Internet Involvementà Individual Source .090 1.948 (n.s.) H1a: Rejected Credibility Twitter Involvementà Individual Source .266 6.216*** H2a: Supported Credibility Self-esteemà Individual Source Credibility .098 1.823 (n.s.) H3a: Rejected Internet Involvementà Twitter Involvement .281 6.506*** H4: Supported Internet Involvementà Self-esteem .026 .591 (n.s.) H5: Rejected Twitter involvement à Self-esteem .106 2.330* H6: Supported Control 1(gender)à Individual Source .037 .759 (n.s.) Credibility Control 2(class standing)à Individual -.011 .270 (n.s.) Source Credibility Control 3 (income)à Individual Source -.011 .270 (n.s.) Credibility Note. *p< .05, **p<.01, ***p<.001.

In the news organization source credibility model, the R2 values of news organization source credibility, Twitter involvement, and self-esteem were .080, .079, and .010 respectively.

Thus, only 8% of variance in news organization source credibility was explained by the six exogenous variables linked to it in the news organization source credibility model. Obviously, individual source credibility could be explained better by the predictors than news organization source credibility by comparing their R2 values. Table 6.28 shows the hypothesized path

133 coefficients and significances. Similarly, there was no relationship between Internet involvement and news organization source credibility (H1b rejected), the three controls were not found any relationships with news organization source credibility. But the difference from the individual source credibility model was that in the news organization source credibility model, self-esteem was positively related to news organization source credibility (H3b supported). Likewise, the higher Twitter involvement, the more perceived news organization source credibility (H2b supported).

Table 6.28 Significance Testing Results of the News Organization Source Credibility Structural Model Path Coefficients

Paths Path T-values Hypotheses Coefficients Internet Involvementà News Organization .041 .970 (n.s.) H1b: Rejected Source Credibility Twitter Involvementà News Organization .205 4.540*** H2b: Supported Source Credibility Self-esteemà News Organization Source .157 3.424*** H3b: Supported Credibility Internet Involvementà Twitter Involvement .280 6.452*** H4: Supported Internet Involvementà Self-esteem .003 .079 (n.s.) H5: Rejected Twitter involvement à Self-esteem .100 2.165* H6: Supported Control 1(gender)à News Organization -.011 .284 (n.s.) Source Credibility Control 2(class standing)à News .056 1.298 (n.s.) Organization Source Credibility Control 3 (income)à News Organization .011 .246 (n.s.) Source Credibility Note. *p< .05, **p<.01, ***p<.001.

In the brand source credibility model, the R2 values of brand source credibility, Twitter involvement, and self-esteem were .081, .079, and .011 respectively. The path coefficients and their significances in brand source credibility model (see Table 6.29) followed the same pattern of those in the news organization source credibility model. There was no relationship between

Internet involvement and brand source credibility (H1c rejected). Both Twitter involvement and

134 self-esteem positively predicted brand source credibility (H2c and H3c supported). The three controls did not significantly relate to brand source credibility.

Table 6.29 Significance Testing Results of the Brand Source Credibility Structural Model Path Coefficients

Paths Path T-values Hypotheses Coefficients Internet Involvementà Brand Source .058 1.357 (n.s.) H1c: Rejected Credibility Twitter Involvementà Brand Source .192 4.292*** H2c: Supported Credibility Self-esteemà Brand Source Credibility .144 3.005*** H3c: Supported Internet Involvementà Twitter Involvement .281 6.401*** H4: Supported Internet Involvementà Self-esteem .012 .276 (n.s.) H5: Rejected Twitter involvement à Self-esteem .103 2.220* H6: Supported Control 1(gender)à Brand Source .027 .717 (n.s.) Credibility Control 2(class standing)à Brand Source -.062 1.414 (n.s.) Credibility Control 3 (income)à Brand Source -.006 .133 (n.s.) Credibility Note. *p< .05, **p<.01, ***p<.001.

Mediation effects. Structural models in this study showed there were potential mediation effects in these three models. Thus, in addition to the path coefficients check, a mediation check was necessary. There was a mediation effect when the effect of A on C was transferred by B, in which B was a mediator between A and C (Lowry & Gaskins, 2014).

In this study, the researcher followed the mediator analysis procedure suggested by Hair et al. (2013). First, the significance of the direct effect without a mediator was assessed. Then the mediator was added in the PLS model and the significance of the indirect effect was assessed by a bootstrap test. Finally, the variance accounted for (VAF) the mediation effect was evaluated to determine the size of the indirect effect in relation to the total effect. VAF provided us the extent to which the variance of the dependent variable was directly explained by the independent variable and by the indirect relationship via the mediator variable. According the structural

135 model (see Figure 6.5), there were three potential mediation relationships in each structural model: 1) Internet involvementàTwitter involvementàPerceived source credibility; 2) Twitter involvementà Self-esteemà Perceived source credibility; 3) Internet involvementàSelf- esteemà Perceived source credibility. The researcher examined the three mediation effects in individual source credibility model, news organization source credibility model, and brand source credibility model respectively, and the results were as follows.

Firstly, in the individual source credibility model, the mediation effect of Twitter involvement between Internet involvement and individual source credibility was checked first.

The direct effect from Internet involvement to individual source credibility without the potential mediator Twitter involvement was .171 (p< .001). When the mediator Twitter involvement was included, the relationship between Internet involvement and Twitter involvement (i.e., .281), as well as between Twitter involvement and individual source credibility (i.e., .266) were both significant with p< .000. Thus, the indirect effect’s size was .281*.266= .075, and its significance was tested using bootstrapping results (The bootstrapping sample scores of the two relationships between Internet involvement and Twitter involvement, and between Twitter involvement and individual source credibility, were obtained from SmartPLS and the products of these two scores for the samples and their standard deviation value (.016) were calculated). Hence, the empirical t value of the indirect effect of Internet involvement on individual source credibility was .075/

.016= 4.597. So, the mediation effect of Twitter involvement between Internet involvement and individual source credibility was significant (p< .000). Finally, the strength of this mediation was assessed by using the VAF, which equals the indirect effect divided by the total effect and had a value of .075/(.171+ .075)=0.304. Consequently, 30% of the Internet involvement effect on individual source credibility was explained via the Twitter involvement mediator. Since the VAF

136 was larger than 20% but smaller than 80%, this mediation was a partial mediation effect (Hair et al., 2013).

Following the same procedure, the direct effect from Twitter involvement to individual source credibility without potential mediator self-esteem was .279 (p< .000). And after including the mediator, the direct effects from Twitter involvement to self-esteem and from self-esteem to individual source credibility were .106 (p< .05) and .098 (n.s.). Thus, the indirect effect’s size was .016. Both bootstrapping test and VAF (0.053) suggested there was no mediation effect taking place between Twitter involvement and individual source credibility. Hence, we could conclude that there was no mediation effect between Internet involvement and individual source credibility via self-esteem.

Secondly, the mediation effects in news organization source credibility model were examined. The direct effect from Internet involvement to news organization source credibility without mediator Twitter involvement was .171 (p< .05), while after including the mediator the direct effect became .090 (n.s.). However, the direct effects from Internet involvement to mediator Twitter involvement (i.e., .280) and from Twitter involvement to news organization source credibility (i.e., .205) were both significant. Thus, the indirect effect size was .057.

Bootstrapping indicated that the indirect effect standard deviation was .016, which suggested a significant mediation effect. The value of VAF was .25, showing the mediation between Internet involvement and news organization source credibility via Twitter involvement was a partial mediation.

Similarly, the direct effect from Twitter involvement to news organization source credibility without potential mediator self-esteem was .226 (p< .000). After including the mediator, the direct effect decreased to .205 (p< .000). The direct effect from Twitter

137 involvement to self-esteem (i.e., .100, p< .05) and from self-esteem to news organization source credibility (i.e., .157, p< .01) were both significant, which resulted the indirect effect was .016.

The bootstrapping showed the empirical t value of the indirect effect of Twitter involvement on news organization source credibility via self-esteem was 1.695 (n.s.). So the mediation was not significant, and this was confirmed by its VAF score (.065) as well. There was no mediation effect between Internet involvement and news organization source credibility via self-esteem as well.

Finally, the mediation effect tests in brand source credibility model were performed. The direct effect from Internet involvement to brand source credibility without the potential mediator

Twitter involvement was .117 (p< .01). After including the mediator, this value decreased to .058

(n.s.). And the path coefficients from Internet involvement to Twitter involvement and from

Twitter involvement to brand source credibility were .281(p< .000) and .192 (p< .000) respectively. The bootstrapping test showed the mediation was significant. The VAF was 0.316, which indicated that 31.6% of the Internet involvement effect on brand source credibility was explained via the Twitter involvement mediator. This mediation was also a partial mediation.

The direct effect from Twitter involvement to brand source credibility without mediator self-esteem was significant (i.e., .212, p< .001). It decreased to 0.192 (p< .000) when the mediator was included. Although the path coefficients between Twitter involvement and self- esteem (i.e., .103, p< .05), and between self-esteem and brand source credibility (i.e., .144, p<

.01) were both significant, the bootstrapping test showed the mediation was not significant. The

VAF was only 0.065, which suggested almost no mediation took place. Moreover, there was no mediation between Internet involvement and brand source credibility via self-esteem as well because the direct effect from Internet involvement to brand source credibility without the

138 mediator self-esteem was not significant (i.e., .057) and the relationship between Internet involvement and self-esteem was not significant (i.e., .012) when the mediator was included in the model.

In summary, in these three structural models of this study, each model only had one mediation effect, which was the mediation from Internet involvement to perceived source credibility on Twitter via Twitter involvement.

Figure 6.5. Mediator analysis procedure in PLS-SEM. Adapted from Hair, J.F., Hult, G.T.M., Ringle, C.M., and Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage. p. 224.

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Effect size f2. The effect size f2 of the exogenous constructs in the three structural models were examined to evaluate whether a specified exogenous construct has a substantive impact on the endogenous constructs. The effect size was calculated as:

! ! 2 !!"#$%&'&!!!"#$%&!! f = ! !!!!"#$%&'&

2 2 2 where R included and R excluded are the R values of the endogenous variable when a specified exogenous variable is included in or excluded from the model (Hair et al., 2013). 0.02, 0.15, and

0.35, represented small, medium, and large effects of the exogenous variable on an endogenous construct (Cohen, 1988). By using this formula, in the individual source credibility model, the effect sizes of Twitter involvement, Internet involvement, and self-esteem were, respectively,

.067, .008, and .010. Twitter involvement contributed the most to the endogenous construct individual source credibility. Similarly, in the news organization source credibility model, the effect sizes of Twitter involvement, Internet involvement, and self-esteem were, respectively,

.038, .001, and .025. Although the Twitter involvement still had the most size effect to the news organization source credibility, the size effect of self-esteem also could not be overlooked. In the brand source credibility model, the effect sizes of Twitter involvement, Internet involvement, and self-esteem were, respectively, .034, .003, and .022. Twitter involvement still had the most size effect to the brand source credibility and the self-esteem had relevant size effect on brand source credibility as well.

Ranking of Perceived Credibility on Twitter

To answer research question 4, the researcher compared means of perceived credibility for seven sources on Twitter (family& friend, entertainment stars, sports stars, businessmen, politicians, brands, and news organizations). As shown in Table 6.30, most of the studied variables involved in this research question did not violate the normality distribution except

140 those related to perceived credibility of family& friend and politicians. The scores of family& friend credibility in four product types were left skewed, which indicated that most responses gathered together and valued high on family& friend credibility. In contrast, the scores of politician credibility in the four product settings were right skewed, which suggested most people assessed low scores for politician credibility. The results were not surprising considering the fact of Americans’ skepticism of the government (Bennett et al., 2013). Moreover, it was either from commonsense or from empirical research that people placed more trust in their close relationships than in strangers (Freitag & Traunmuller, 2009).

As shown in Table 6.31, the most credible source in general on Twitter to the college students was their family and friends, followed by news organizations, brands, businessmen, entertainment stars, and sports stars. Politicians were the group that people would least trust on

Twitter. Paired t-test showed that the differences between every paired sources were statistically significant (p<. 001) except the difference between businessmen and brands.

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Table 6.30 Normality of Distribution

Product Type Mean S.D. Skewness Kurtosis Std. Statistic Statistic Statistic Error Statistic Std. Error Family&Friends 1 4.10 .968 -1.309 .106 1.797 .212 2 4.22 .934 -1.460 .106 2.275 .212 3 3.97 1.061 -1.048 .106 .693 .212 4 3.98 1.080 -1.082 .106 .771 .212 Entertainment 1 2.52 1.016 .047 .106 -.592 .212 Stars 2 3.10 1.099 -.238 .106 -.516 .212 3 2.83 1.088 .010 .106 -.528 .212 4 2.98 1.104 -.171 .106 -.475 .212 Sports Stars 1 2.41 1.006 .048 .106 -.732 .212 2 2.91 1.030 -.258 .106 -.348 .212 3 2.71 1.016 -.031 .106 -.292 .212 4 2.82 1.042 -.117 .106 -.309 .212 Businessmen 1 3.02 1.101 -.350 .106 -.583 .212 2 3.16 1.011 -.422 .106 -.042 .212 3 2.94 1.035 -.180 .106 -.275 .212 4 3.00 1.065 -.209 .106 -.291 .212 Politicians 1 1.31 .792 2.597 .106 6.028 .212 2 1.38 .881 2.276 .106 4.227 .212 3 1.36 .852 2.419 .106 5.087 .212 4 1.37 .889 2.339 .106 4.520 .212 Brands 1 2.88 1.149 -.122 .106 -.818 .212 2 3.21 1.126 -.387 .106 -.526 .212 3 3.02 1.116 -.205 .106 -.623 .212 4 3.10 1.131 -.257 .106 -.523 .212 News 1 3.14 1.088 -.493 .106 -.375 .212 Organizations 2 3.30 1.024 -.586 .106 .014 .212 3 3.04 1.033 -.359 .106 -.266 .212 4 3.15 1.057 -.399 .106 -.207 .212 Note. Product 1= CHRH Product; Product 2= CLRH Product; Product 3= CHRL Product; Product 4= CLRL Product (C stands for Cost and R stands for Risk; subscript H stands for High and subscript L stands for Low).

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Table 6.31 Perceived Credibility Ranking on Twitter

Ranking Perceived Credibility Mean S.D. 1 Family & Friends 16.2585 3.49 2 News Organizations 12.6264 3.56 3 Brands 12.2075 3.83 4 Businessmen 12.1189 3.51 5 Entertainment Stars 11.4264 3.60 6 Sports Stars 10.8491 3.42 7 Politicians 5.4245 3.24

Product Type’s Role in Perceived Credibility

To answer the last research question (RQ5), the researcher compared the general perceived credibility scores in four product settings. Table 6.32 shows that people’s perceived credibility on Twitter did change in different product settings. And pair t-test confirmed that these differences were all significant (p< .05). Thus, product type did moderate people’ perceived credibility. When purchasing low-cost and high-risk product, people were most likely to trust outside information from others. People were most skeptical of information from outside sources when making purchase decisions of high-cost and high-risk product.

Table 6.32 Perceived Credibility on Twitter by Product Type

Perceived Credibility by product type Mean S.D. CHRH Product (Expensive, not confident) 19.3698 4.68185 CLRH Product (Inexpensive, not confident) 21.2698 4.81852 CHRL Product (Expensive, confident) 19.8736 5.10431 CLRL Product (Inexpensive, confident) 20.3981 5.50631 Note. For product type, C stands for Cost and R stands for Risk; subscript H stands for High and subscript L stands for Low.

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Table 6.33 Compare Means of Perceived Credibility in Four Product Types

CHRH Product CLRH Product CHRL Product CLRL Product

Mean S.D. Mean S.D. Mean S.D. Mean S.D. Family & Friends 4.10 .968 4.22 .934 3.97 1.06 3.98 1.08 Entertainment Stars 2.52 1.02 3.10 1.10 2.83 1.09 2.98 1.10 Sports Stars 2.41 1.01 2.91 1.03 2.71 1.02 2.82 1.04 Business 3.02 1.10 3.16 1.01 2.94 1.04 3.00 1.07 Politicians 1.31 .79 1.38 .88 1.36 .85 1.37 .89 Brands 2.88 1.15 3.21 1.13 3.02 1.12 3.10 1.13 News Organizations 3.14 1.09 3.30 1.02 3.04 1.03 3.15 1.06 Note. For product type, C stands for Cost and R stands for Risk; subscript H stands for High and subscript L stands for Low.

To further investigate how product type affect perceived credibility of different sources on Twitter, pair t-test was performed to each source in different product settings. Results showed that for family and friends, there was no significant difference between people’s perceived credibility in CHRL product and CLRL Product. However, their perceived credibility of family and friends in other product settings was significantly different (see Table 6.33). In other words, people most relied on family and friends to make decisions when they did not have much confidence to make a good choice (Meanaverage= 4.16). However, when they had much confidence to make a good choice, their perceived credibility of family and friends decreased, whether it was an expensive or inexpensive item to buy (Meanaverage=3.97). For entertainment stars, the general perceived credibility in four product settings was relative low, but the differences were all significant. It indicated that people would consider entertainment stars for purchase information only when they did not have much confidence to make good choice and when it was an inexpensive item (Mean=3.10). When turning to an expensive item and people did not have much confidence to make good decision (Mean=2.52), they probably would not refer to entertainment stars’ recommendations. The perceived credibility of sports stars followed

144 the same pattern as that of the entertainment stars. The most common condition in which people would refer to sports stars for purchase decisions was when it was an inexpensive item and they did not have much confidence (Mean=2.91). The more expensive the items were, the less credibility they placed on sports stars (Meanaverage of low cost product=2.56 vs Meanaverage of high cost product =2.86). For businessmen, they were most credible to provide purchase information when people did not have much confidence to make good purchase decisions of inexpensive items (Mean=3.16). In other product settings, the differences of their perceived credibility were not significantly different. Politicians were the group that had the least perceived credibility scores (Meanaverage=1.35). The less confidence people had to make purchase decisions and the more expensive items they planned to buy, the less likely they would be to trust a politician to make a wise decision (Mean of high-cost & -risk product=1.31 vs Mean of low-cost &-risk product =1.38). For brands on Twitter, people placed the most credibility on brands to get purchase information when they planned to buy inexpensive items (Meanaverage=3.15), followed by the condition that they planned to buy expensive items and had confidence to make good decisions (Mean=3.02). They thought brands were least credible when they planned to buy expensive items and at the same time they did not have much confidence to make a good choice

(Mean=2.88). However, for news organization sources, there was no pattern to explore. From the results, it seemed that news organizations were most valued when people bought inexpensive item and did not have much confidence to make a good choice (Mean=3.30). News organizations had the least credibility when people had much confidence to make good decisions to purchase expensive items (Mean=3.04). News organization sources did not make significant differences between when people had no confidence to make good decisions to buy expensive items and when people had confidence to make good decisions to buy inexpensive items.

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CHAPTER VII.

DISCUSSION AND CONCLUSION

Technology changes the world. Not only is social media changing the media landscape irreversibly, it is also influencing the ways in which people communicate from every aspects of life. Traditional personal communication is moving from offline to online and traditional marketing communication is also shifting from offline to online. Given the increasing popularity of social media and the infiltration of its accompanying influences on communication in our life, the author became interested in learning the nature of social media, the mediating role it plays in marketing communication, and whether and how this mediation has influenced people’s judgments or perceptions since communication expanded from offline to online. Therefore, the purpose of this study was to discover, develop, and understand people’s perception of others’ credibility in social media marketing communication. This brought out the main research question–––what constitute source credibility on social media and how does the measure of source credibility on social media differ from the traditional measure? Also, how the perceived source credibility on social media was affected by other factors, e.g., involvement in the Internet and Twitter in general, and specifically different product settings, was the other question this dissertation attempted to answer by the measurement and structural models.

Summary

One of the most important elements in communication effect is recipient. Whether the recipient accepts the information, or how s/he perceives the information sender has an enormous impact on the communication effect. To discover what the social media environment brought to the traditional marketing communication in terms of perceived credibility, the researcher reviewed the studies of traditional WOM communication. In the offline world, WOM

146 communication was believed to be extremely credible, therefore its commercial value had been explored since early last century. WOM was an effective marketing strategy because in the process of communication, the sender was perceived as independent from any commercial organizations and thus more credible.

Then, the eWOM based on social media was examined. The definition of eWOM proposed by Hennig-Thurau et al. (2004) was adapted in this study. The researcher believes eWOM should be any information, including not only customers’ own statements but also those shared/forwarded posts from retailers or other published sources, which are exchanged among potential, actual, or former customers about a product or company, available to a multitude of people and institutions via the Internet. Either the customer reviews on online retailers’ websites

(e.g., Amazon) or statements related to certain products/brands on social networking sites (e.g.,

Twitter or Facebook) are all eWOM. According to the different platforms of eWOM, four types of eWOM were classified. The first was specialized eWOM, which referred to the eWOM on specialized comparison-shopping or rating websites. The second was known as affiliated eWOM, which was affiliated to retail websites and specific products/services. The first two types of eWOM enabled people to find comments about specific products/brands immediately because they were both bundled with specific products/brands. The third type was social eWOM and the research subject of this study. It was not as organized as the first two types of eWOM; it was scattered over the whole social media platform. And it was not necessarily comments of specific products/brands; it could be any mention or shared posts of any products/brands. What made it more unpredictable was the nature of social networking sites–––people who had more followers on these websites were more influential in spreading eWOM because they were like highly connected “hubs,” which could make eWOM more visible and spread faster. The other

147 miscellaneous types of eWOM fell into the fourth category. For example, the eWOM on discussion boards, in emails, and blogs.

In this study, the researcher focused on the third type of eWOM–––social eWOM. In a communication, how people perceive information source directly affects whether or not they accept information and then affects the communication effect. Thus, specifically, the researcher was interested in how people perceived different “hubs” (popular figures on social media) on social media for marketing communication. Twitter was selected as the studied platform because its technology design makes it a platform specialized in information sharing and its popularity would make this study more representative. The common hubs on Twitter, for example, popular celebrities, well-known news organizations, and brands, were chosen as product information sources for consumers and in comparison to their own friends and family. Understanding the perceived credibility of these typical hubs on Twitter is of great significance in understanding the mediation influence of social media in marketing communication. Twitter even complicates

WOM communication in various ways. The virtuality of the Internet, the magnitude of audience size of posted or reposted eWOM, and the variety of sources/hubs on Twitter etc. make it necessary to reevaluate perceived source credibility in a communication.

To better understand perceived source credibility on social media, the traditional source credibility theory and measures were then reviewed. The classic source credibility theory believed source credibility was “judgments made by a perceiver (e.g., a message recipient) concerning the believability of a communicator (O’Keefe, 2002).” Source credibility was a multiple-dimension construct, which embraced at least safety (or trustworthiness), and qualification (or expertise/competence). Additionally, some researchers suggested source credibility should also include “dynamism” (Berlo et al., 1969), “attractiveness” (Ohanian,

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1990), and “goodwill ” (McCroskey & Teven, 1999). The classic source credibility theory was established for a better persuasive effect in interpersonal communication. However, based on the classic source credibility theory, the successors (Eisend, 2006; Haley, 1996) proposed additional measures for organizational credibility. Although it was suggested that perceived organization credibility held the same names for each dimension with individual credibility (e.g., perceived organization credibility also included trustworthiness and competence dimensions, etc.) except the “attribute” dimension in organization source credibility, the indicators in each dimension in organization credibility were different from those in individual credibility. For example, competence dimension of individual credibility was measured by whether or not a message sender was intelligent, expert, or informed; while measuring a news organization’s source credibility, its competence was measured by whether or not the news organization was professional, informative, or organized, etc.

However, the new technology expanded communication from offline to the cyberspace.

The traditional measures were not sufficient and accurate to evaluate individual or organization credibility in the context of social media. The construct of social tie proposed in network analysis theory (Granovetter, 1973) was added as a new dimension of source credibility on social media because tie strength was shown to affect people’s acceptance of information (Brown & Reingen,

1987). Considering the new communication environment in social media, some changes were made to the traditional measure of social tie strength to make it suitable in the new measure of source credibility on social media.

Moreover, the researcher believes that technology affordances, which referred to surface features of the interface, should be another new dimension of source credibility on social media.

The technology affordances affect people’s judgment of credibility by offering auto-generated

149 cues or markers on social media. For example, the blue check mark next to a Twitter account suggested it was a verified official account, which then implied high credibility.

Therefore, combining traditional measure with new considerations raised from the social media platform, the researcher proposed three types of source credibility in social media and tested the measure for each type of source credibility–––individual credibility, news organization credibility, and brand credibility respectively (see the tentative measures).

Besides developing new measures for source credibility on social media, the researcher was also interested in how much the involvement of new media technology (e.g., Twitter involvement and Internet involvement) contributed to perceived credibility and whether different product settings affect perceived credibility of sources in marketing communication on social media and how.

To validate the tentative new measures of source credibility on social media and test the proposed hypotheses, the researcher conducted a survey in a university to collect data. Structural equation model was employed to analyze data. Source credibility on social media was a mixed second-order construct. Individual source credibility and brand source credibility comprised six dimensions–––competence, trustworthiness, social tie, attractiveness, dynamism, technology affordance. Although they were both comprised of six dimensions, individual source credibility and brand source credibility had different indicators for each dimension from each other (see

Figures 6.1 & 6.3). News organization source credibility was confirmed to be a five-dimension only construct, which embraced competence, trustworthiness, social tie, dynamism, and technology affordance dimensions (see Figure 6.2).

Both Internet and Twitter involvement had positive relationship with perceived source credibility on social media. However, Twitter involvement accounted for some, but not all, of the

150 relationship between Internet involvement and perceived credibility. Self-esteem was found having a positive relationship with news organization and brand source credibility, but there was no relationship between self-esteem and individual source credibility.

On Twitter, family and friends were the most credible sources for people to get information when they needed to make a purchase decision. This is similar to the findings of another study of the research on choice of WOM source in which family and friends were the most commonly consulted WOM across major online product categories for online shoppers (Hu

& Ha, forthcoming). News organization and brands were the second and third credible sources; while politicians on Twitter were the least credible sources. Additionally, product type did affect people’s perceived credibility. When considering different types of product, the perceived credibility of the same source might change. For example, when people were not confident to make a purchase decision, the perceived credibility of entertainment stars was higher for an inexpensive item than an expensive item. Nevertheless, across all product settings, family and friends were always the most credible sources and politicians were the least credible sources on

Twitter.

Interpretation of Results

The results of this study shows that the credibility measures for individuals and organizations on social media are different from those in the offline world in a communication.

One of the reasons is the mediation of social media between information sender and recipient as added additional variables when people perceive information senders in terms of credibility.

Also, on social media, every account represents either an individual or an organization. This makes establishing a concrete credibility measure for an organization possible, because its social media account is like a representative of the organization. So, we can treat it as a person and

151 evaluate it from the impression of its account performance, which is more concrete than assessing an organization in general. Finally, three types of source credibility have been developed and validated in this study.

The traditional measure for source credibility was created in the context of face-to-face communication. It included scales measuring people’s expertise, ethics, and physical appearance.

However, on social media, an individual’s credibility not only hinges on the dimensions created previously in an offline communication, but is also determined by people’s perceived relevance of a source, and others factors arising from the technology. The measure for organization credibility on social media is also different from individual social media credibility. It measures an organization’s credibility from its professionalism, and prior reputation and image, etc..

The New Measure of Individual Source Credibility

Individual source credibility on social media was defined as the extent to which an individual on social media was perceived believable in a communication context. It was examined as a second-order formative construct, consisted of six dimensions: competence, trustworthiness, social tie, attractiveness, dynamism, and technology affordance. These dimensions were latent variables measured by specific indicators respectively. The six dimensions were independent components of individual source credibility and could not be interchangeable; thus the construct of individual source credibility was formative in the second order. The first order of the individual source credibility construct was composed of indicators of each dimension. The competence referred to an individual’s attribute of professionalism and talents. It was measured by the extent to which an individual was intelligent, expert, and informed. Trustworthiness was the rating of an individual’s moral attributes––to what extent did people perceive an individual in a communication context to be honest, trustworthy, and

152 honorable. The social tie dimension measured how much an individual was perceived as close and relevant to the individual recipient in a communication context. It was assessed by the extent to which people perceived an individual to be important, relevant to their interests, and worthy of their attention. The attractiveness dimension measured the extent to which people perceived an individual’s appearance as attractive, classy, and elegant. The dynamism dimension examined the perceived dynamism of an individual in communication. How much an individual was perceived to be meek, comic, active, and interactive was evaluated to measure his/her dynamism.

The last dimension, technology affordance, was measured by the surface attributes (markers) of an individual’s Twitter profile. For instance, whether the individual account was a real and verified account on Twitter, and whether the individual tweeted every day or not.

Since the indicators of competence, trustworthiness, social tie, and attractiveness were the effects of their latent constructs and could be interchangeable, these four dimensions were reflective constructs. The convergent and discriminant validity were both established, and reliability was also high with the average Cronbach’s α of .929, for the four reflective constructs.

Dynamism and technology affordance were measured formatively, thus they were formative constructs. Therefore, the first order of individual source credibility was mixed with both reflective and formative constructs. It is worth noting that only after dropping the item of “has a lot of followers” in technology affordance dimension did the formative construct validity achieved adequate validity. “Has a lot of followers” was dropped off because of its little contribution to explain the variances in technology affordance. It suggested that for individual sources on Twitter, the number of their followers could not make them more or less credible. In other words, having a lot of followers on Twitter did not make a person more credible. An individual’s credibility on Twitter was established by other attributes as well. It, in turn,

153 confirmed that the construct of individual source credibility was a formative construct. Also, this suggested that these college students were mostly rational on Twitter and probably did not solely rely on surface cues or others’ opinions.

The new measure of individual source credibility on social media retained the three classic dimensions of source credibility–––competence, trustworthiness, and attractiveness––– and were confirmed to be valid by the data. This meant that these dimensions were also applicable to the individual source credibility on social media. The perceived competence, trustworthiness, and appearance of an individual, whether face to face or online on social media, was more or less the same. The perceived competence of a person concerning communication topic, perceived moral attributes, and appearance still contributed a lot to his/her credibility on social media. Additionally, a close relationship or high relevance between each other made a person more credible. Also, people who were active, interactive, meek, and humorous on Twitter were believed to be more credible. People were more likely to trust an officially recognized account. For instance, the individual on Twitter with the little blue verified account badge were perceived as more credible. And people who operated their own accounts and tweeted frequently, rather than handing them over to a third party and barely tweeted, were more credible.

The New Measure Of News Organization Source Credibility

News organization source credibility referred to the extent to which a news organization on social media was perceived believable in a communication context. It was also a second-order formative construct. Although it had the five same dimension labels (one different was

“attribute” dimension, which was eliminated in the data analysis process) as individual source credibility, the indicators for each dimension in the news organization source credibility

154 construct were mostly different than those in the individual source credibility construct. Another difference was in the tentative measure of news organization source credibility, the attribute dimension was defined as a formative latent sub-construct. However, in the process of identifying and validating the measurement model of news organization source credibility, the attribute dimension caused a multicollinearity problem and thus was excluded from the tentative measure resulting the five-dimension construct of news organization source credibility. Another reason that made the researcher remove the attribute dimension was from a theoretical consideration. For organizations, the attribute dimension was defined as the affective attitudes of people towards an organization based on its prior performance and reputation. For a news organization, its professionalism and ethics might be the most important factors people considered when assessing its credibility. Its prior reputation and image were more or less determined by its professionalism and ethics. This might be the possible reason why the attribute dimension did not work well with other dimensions in evaluating a news organization’s credibility. The remaining five dimensions were sufficient to measure the perceived news organization source credibility, which again suggested that for a news organization, focusing on increasing professional relevance, and stressing on the operation of its social media account, could boost its credibility.

The competence, trustworthiness, and social tie dimensions in news organization source credibility construct were reflective sub-constructs, while dynamism and technology affordance were formative sub-constructs. These dimensions consisted of the second order of news organization source credibility. Since these five dimensions represented five separate components and could not be exchanged, news organization source credibility was a formative construct in the second order.

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The first order of news organization source credibility was made of indicators of the five dimensions. The competence dimension in news organization source credibility was defined as people’s impression of a news organization’s attribute of professionalism and talents. To judge a news organization in terms of its competence, people not only assessed whether it was an intelligent and expert news organization, but also evaluated whether it was professional, informative, and organized considering its specialty. The trustworthiness rated a news organization’s moral attributes––to what extent did people perceive a news organization in a communication context to be honest, trustworthy, and recognizable. The social tie dimension in news organization source credibility was exactly the same measure as it was in individual source credibility. It measured how much a news organization was perceived as close and relevant to the individual in a communication context. It was assessed by the extent to which people perceived a news organization to be important, relevant to their interests, and worthy of their attention. The dynamism of a news organization was measured by the extent to which a news organization was fair, timely, active, interactive, and objective in responding and communicating with the public.

The technology affordance in the news organization source credibility also aimed to estimate the effect of technology affordance on perceived source credibility. The excluded indicator “has a lot of followers” in individual source credibility measure was confirmed to be an indispensable indicator of technology affordance in the measure of news organization source credibility, working with other technology affordances. This item explained 22% variances in the technology affordance dimension. It suggested that the fact that a news organization had many followers on its Twitter page seemed more genuine and credible than the fact that an individual had many followers. One possible explanation was that news organizations being public organizations, it was reasonable that a news organization had a lot of followers. Also, the

156 credibility of news organizations was established on their audience size. The more audience followed a news organization, the more credible it was shown.

Based on the traditional measures for source credibility, the new measure of news organization source credibility on social media was developed and validated by the data (The convergent and discriminant validity of the three reflective dimensions were both established, and reliability was also high with the average Cronbach’s α of .942). It was a first-order mixed, second-order formative construct, including five dimensions. Overall, the five aspects determined whether a news organization was perceived credible or not on social media.

Regardless of communication topic, a news organization’s credibility was built through enhancing its professional competences and features, media ethics, and relevance to the public.

Additionally, being a long time telepresent and active on social media, and verified officially as a real news organization account, increased its credibility. The number of its followers also contributed to people’s perceived news organization credibility on social media. A news organization with a large number of followers implied higher credibility.

The New Measure of Brand Source Credibility

Brand source credibility on social media was the extent to which a brand (a commercial corporation) on social media was perceived believable in a communication context. It was also a multi-order and multidimensional construct. Since the second order of this construct was also made of six non-interchangeable sub-constructs, the brand source credibility was also a second- order formative construct. The first dimension of brand source credibility was competence, which was a reflective sub-construct and rated by whether a brand on social media was perceived to have good quality, be intelligent, and expert. Trustworthiness and social tie dimensions were both reflective sub-constructs, which had the exactly same indicator as trustworthiness in news

157 organization source credibility. The perceived brand image and the relevance of a brand to the public directly affected its credibility on social media. The attribute dimension, which was dropped off in the news organization source credibility measure, was validated as the fourth dimension of brand source credibility. The attribute was defined by four aspects of a brand––– the size of business, whether it was a new or old brand, whether the brand values were congruent with people’s own values, and its reputation. Thus, attribute was a formative sub-construct.

Dynamism was also a formative sub-construct, which was estimated by whether a brand on social media was creative, and whether it was active and interactive on social media. Finally, the technology affordance dimension in brand source credibility was the same as in news organization source credibility. The authenticity of a brand account affected people’s perception of the brand’s credibility on social media. Furthermore, large number of followers and being active on social media also could increase a brand’s credibility. The convergent and discriminant validity were both established for the three reflective dimensions, and reliability was also high with the average Cronbach’s α of .914.

The indicator “organized” was eliminated from the competence dimension because of the low loading of this item. It suggested that whether organized or not was not relevant to a brand’s credibility. The perceived brand competence was mostly affected by product quality and its professionalism. A brand’s integrity and its relevance to the public’s needs affected people’s perceived credibility of the brand. Furthermore, it was worth noting that if an older brand was a big business organization with a good reputation, it was more likely to gain credibility. And corporate values also played an important role in affecting its credibility. Finally, being a creative corporation and being active and responsive on social media boosted a brand’s perceived credibility.

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Direct and Indirect Effects in the Three Models

The results of this study showed that there were positive significant direct effects of

Internet involvement on individual, news organization, and brand source credibility respectively without the mediator of Twitter involvement. However, when including the Twitter involvement mediator, the indirect effects of Internet involvement on individual, news organization, and brand source credibility were all significant, while the direct relationships between Internet involvement and individual, news organization, and brand source credibility became weaker and non-significant respectively. Especially, the variance in brand source credibility accounted by mediator Twitter involvement (.316), rather than by independent variable Internet involvement, was larger than those in individual (.30) and news organization source credibility (.25). It suggested that the mediation effect of Twitter involvement was most prominent to brand source credibility. People’s Twitter involvement could better predict their perceived brand source credibility than individual and news organization source credibility. Thus, for corporations in particular, they should target their social media effort at people who are highly involved in

Twitter, rather than those highly involved in the Internet in general.

The direct effect of Twitter involvement on individual source credibility was positively significant; while including the mediator self-esteem, the indirect effect was non-significant because there was no relationship between self-esteem and individual source credibility. Twitter involvement positively affected people’s self-esteem, which confirmed previous studies’ argument that social media use enhanced one’s self-esteem (Gonzales & Hancock, 2011;

Walther, 1996). These results implied that whether people’s self-esteem was high or low, it did not have an influence on their perceived individual source credibility on social media. Only their social media involvement directly predicted their perception of an individual on social media.

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The more social media they were involved in, the more credible they perceived an individual on social media. Additionally, in news organization and brand source credibility models, the results showed that there were no mediation effects of self-esteem from Twitter involvement to news organization and brand source credibility respectively. Although the direct effect of Twitter involvement on news organization and brand source credibility were both positively significant, the indirect effects were both non-significant because of the strong relationships between Twitter involvement and news organization source credibility, and between Twitter involvement and brand source credibility. However, with the inclusion of the mediator self-esteem, in news organization source credibility model, the relationship between Twitter involvement and self- esteem, and between self-esteem and news organization source credibility, were both significant.

The same pattern was found in the brand source credibility model. To sum up, people who more highly involved in Twitter had higher self-esteem. However, higher self-esteem did not predict their perception of an individual on social media; instead, it positively predicted their perceived news organization and brand source credibility. There results were contradicted with previous study (Rhodes & Wood, 1992), which suggested people with higher self-esteem were more skeptical. In this study, the researcher found that social media had a positive effect on people’s self-esteem, and those with higher self-esteem perceived organization sources on social media, e.g., news organizations or corporations, rather than individual sources, more credible. This result needed verifying and validating by further research.

Additionally, it is necessary to interpret the R2 in the three structural models of individual

(.111), news organization (.080), and brand (.081) source credibility. By general standard, the R2 values in these three models were rather weak. However, low R2 values were not necessarily bad.

It depended on models and disciplines. In effect, it was not surprising to have such low R2 values

160 in the structural models in this study. Firstly, the nature of perceived source credibility was psychological construct, part of which was always going to have measurement error because the responses of perceived source credibility varied a great deal; everyone had their own standards.

Secondly, the complexity of the source credibility construct, which embraced five to six dimensions (20 indicators totally), brought much more errors in the model. It might contribute to these “low” R2 values. Moreover, perceived credibility could be affected by many factors, e.g., the source itself, message content, and media. The researcher only measured the source credibility on social media, not other settings. It was recognized that many potential predictor variables had not been included in this study. Also, It is possible that the first order indicators have perfectly explained the variances in the source credibility construct, which swamped out the potential effects of other predictors. Additionally, from the empirical experience in statistics, when categorical variable(s) are included in a model on the left side, it always has low R2 because the variances are wide in this situation. Finally, since a lot of variance was explained by unobservable variation across individuals, it was impossible to capture all the predictor variables of people’s perceived source credibility on social media. One of the purposes of this study was to examine how much the mediation of social media contributes to people’s perceived credibility.

Thus, for example, it was not surprising that only people’s involvement on social media and the

Internet could explain 11% variance in individual source credibility. In other words, the new media context roughly contributed 11% variances to people’s perceived individual source credibility on social media, 8% variances to people’s perceived news organization source credibility, and 8.1% variances to people’s perceived brand source credibility.

Furthermore, it is worth mentioning that gender, income, and class standing were not related to people’s perceived source credibility on social media. This finding contradicted

161 previous research. To further test this conclusion, future studies might want to try different method to collect data, for example, experiment, or using different scales.

The Ranking of Perceived Credibility of Different Sources

Family & friends was rated as the most credible source, and the politician was the least credible source on Twitter. It was not surprising considering the fact that Americans were skeptical of the government (Bennett et al., 2013). Strong social tie, such as family and friends, was the most credible sources even in the virtual community. The credibility of news organization ranked the second. People still placed lot of trust on traditional media organizations.

Brand and businessmen were the third credible sources in people’s impression. Entertainment and sport stars had fair credibility. This ranking gave us an idea of the influential hubs online, and it was meaningful when considering social media marketing campaign.

Although family and friends were the most credible sources for people, they were not the public figures that could reach a large number of people just by a click. They could only most influence a few close relationships. The social media marketing campaigns still need to rely on public sources, which are credible and known to everyone, to promote their products, services, or ideas. For example, considering the high ranking of news organization in perceived credibility, establishing public relations with news organizations is important. This is because we can promote brands, products, services, or even ideas through event marketing by news organizations. Through increasing exposure of certain products/services/ideas by creating events, this kind of marketing can gain public’s affection invisibly yet effectively.

Additionally, since brands were perceived as credible by people on Twitter, brands should have their own official Twitter pages to establish credibility. Their Twitter pages are places where people can get direct and accurate information about their products/services. But it

162 is worth noting that this only means that the brand page is a reliable source; it does not mean it is superior to other information. Even though businesspersons had fairly high credibility on

Twitter, they barely endorsed other products/services for commercial purpose, but they probably would like to promote public information for non-profit organizations.

The Role of Product Type

Product type was shown to affect people’s perceived source credibility on social media in this study. With different product settings, people’s general perceived credibility of sources on social media changed. When people considered to purchase an inexpensive item, but did not have much confident to make a wise choice, they had the most perceived credibility of sources on social media; while when they needed to buy an expensive item, and did not have much confident to make a wise choice, the sources held the least perceived credibility. The results implied that it is most likely that people will consult with WOM for purchase decision-making help when they have little knowledge about a low-cost product. So commercial advertising would be most successful when launching low-cost new products. And it was not surprising that people held the least perceived credibility on social media sources when facing an expensive and high-risk item because in this circumstance it was reasonable that people would be more skeptical. Whether they were confident or not to make a smart decision did not affect their perception of sources on social media. It implied that the cost of product was their primary consideration in seeking others’ opinions and directly influenced their perception of sources in terms of credibility. This implication provided new idea for social media marketing. One can speculate that eWOM marketing on social media would be most successful in low-cost products.

Additionally, this study provided more details of how product types affected the specific sources’ credibility on social media. When the cost of product was relatively low and people

163 were lacking confidence to make a wise purchase decision, people were most credulous and had the most perceived credibility of each source (e.g., family and friends, entertainment stars, businessmen, etc.) However, the perceived credibility of family and friends, businessmen, and news organization decreased when people were confident to make purchase decisions, no matter the cost of product. For entertainment, sport stars, politicians, and brand sources on social media, they were perceived most credible when the cost of product was low and people were not confident; they were least credible when the cost of product was high and people were not confident. The results implied that for these four types of sources on social media, people who were not confident in the decision might seek information from them when the cost was low. But when the cost was high and people knew little about the product, they might never consider these four types of sources as credible sources. But for the family & friend, businessmen, and news organization sources on social media, whether the cost was low or high, people might seek information from them to help make purchase decision. This result again supported the finding that the most credible sources were family& friends, and news organizations.

Theoretical Implications

Numerous studies of eWOM have been conducted to examine the effect of eWOM communication; however, few studies concentrated on measuring the development of source credibility on social media. The recent studies on eWOM and source credibility were fragmented. Either they treated source credibility as an independent variable (Xu, 2013) investigating its communication results on social bookmarking websites, or they focused on the credibility of eWOM content, rather than eWOM source (Chih, Wang, Hsu & Huang, 2013).

This study was exploratory and developed the six dimensions for individual source credibility, five dimensions for news organization source credibility, and six dimensions for brand source

164 credibility in social media. The new measures of source credibility in social media was based on the traditional measures and combined with the recent new findings in social media and source credibility research. The conceptualization of credibility as formative construct method used in this study might be a direction for future research. There is still a debate on the precise factor- structure of traditional source credibility. Now, with the presence of computer and the Internet, this debate has expanded and has been transferred online. The new measures of source credibility in social media suggested that in the new media environment, the dimensions of source credibility increased with the new considerations and interventions. Whether a source was verified, had a number of followers, real, and updated frequently, impacted people’s judgments of source credibility. They were all considered as technology affordances because they were not the attributes of sources; instead, they were only the heuristic cues created by the social media platform. These automatic system-generated cues might not directly impact source credibility. It was cognitive elaboration that transferred the impact from the technology affordances to source credibility (Westerman, Spence, & Van Der Heide, 2014). Technology affordances became a dimension of source credibility construct and were inseparably connected with sources’ credibility in social media because people relied on these system-created cues to make credibility judgments. Social tie strength was, for the first time, being considered as a dimension of the source credibility construct. It could not be overlooked because the perceived closeness of a source directly affected people’s perception of source credibility. The perceived closeness was an attribute of a source. Dynamism, though an old dimension, was given a new explanations in the new environment. It more referred to the way people telepresented and communicated online.

Therefore, the traditional two- or three-, or four-dimension source credibility measure is shown not applicable in social media environment in this study.

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Also, this study is the first attempt to develop measures of news organization and brand source credibility on social media. The measures of these organizations were mostly different from the measure of individuals in social media because the assessment criterion which people used was different. People placed lot of emphasis on the quality of organizations’ products/services, which resulted in different indicators of competence dimension than individual source credibility.

Another finding of this study was that people’s Twitter involvement was associated with their perception of source credibility in social media. However, the Internet involvement was shown influencing people’s perceived credibility through Twitter involvement. The demographic factors, e.g., age, gender, economic status, etc., had nothing to do with people’s perceived credibility of different sources. Their familiarity and engagement of the environment (Twitter involvement) was the crucial factor impacting people’s perceived credibility on Twitter. Rhodes and Wood (1992) assumed that people with moderate self-esteem were more easily persuaded than people high and low in self-esteem. This was because high self-esteem persons were more confident to make decisions while people low in self-esteem were less confident. However, their finding was not consistent with the results of this study. People’s self-esteem had nothing to do with their perception of individual source credibility, but was positively correlated with their perceived news organization and brand source credibility.

Lastly, product type affected people’s perceived source credibility in social media. In an eWOM communication where people needed external information from others on social media to help them make a purchase decision, different product settings resulted in different perceived credibility of the same sources. Generally speaking, perceived source credibility decreased when people faced expensive products and increased when facing inexpensive products. When it was

166 an inexpensive product and people did not have much confident to make a wise decision, they were most likely to trust others on social media. In particular, people’s perception of credibility of family & friends, businessmen, and news organization increased when they were not confident of making a purchase decision. However, for the sources of entertainment and sport stars, politicians, and brands on social media, people’s perceived credibility increased only when they considered buying an inexpensive product. One possible explanation was that these sources were not as credible as family & friends, and news organizations. So, only when the cost was affordable did people place trust on these sources.

Methodological Implications

This study developed the tentative scales of source credibility based on the traditional scales, reviewing the recent relevant research, and by a preliminary study among undergraduates.

The traditional scales of source credibility were from preliminary interviews of people by asking them to provide the words to describe a credible person. The combined method used in this study possibly brought in measurement error, but it also ensured comprehensive perspective of source credibility in social media based on both qualitative and quantitative confirmation.

The selection of college students as the sample was appropriate and decreased the measurement error in this study because young people were demonstrated as the heavy and active users of social media; they needed to be the targets of this study. The researcher contacted the students via email and requested them to respond to an online survey. This inevitably brought in nonresponse error.

The application of the structural equation modeling technique by partial least squared method in this study had an advantage over previous relevant studies in this field. Although the structural equation modeling used in this study was not new, it has not been employed previously

167 in similar studies. First, the researcher discussed the formative and reflective nature of source credibility construct. Identifying and specifying the nature of construct was the first step to establish a valid model and arrive at a precise result. Previous source credibility studies only took all dimensions of source credibility as its factors without specifying the nature the construct.

After specifying that source credibility was a first order mixed, second order formative construct, the researcher chose to use Smart PLS to run the structural equation models. The structural equation model not only validated the measures of source credibility construct, but also demonstrated the relationships between the exogenous variables and endogenous variables. Also, with the full picture of the measurement and structural models, the mediation effect was clearly showed. Future studies can apply the established scales of source credibility especially in online social media settings to other communication contexts, rather than eWOM communication.

Practical Implications

The communication effects of eWOM and social media have been recognized; however, in-depth and precise research on eWOM is still a pressing need. The attempt to establish the measures of source credibility in social media in this study not only advanced the theoretical development, but also, more importantly, guided and improved practice.

eWOM marketers might benefit from this study by reviewing the dimensions of source credibility in social media. The six dimensions of individual and brand source credibility and the five dimensions of news organization source credibility might serve as evaluative measure of people’s perceived credibility in social media. For practitioners who were choosing higher- credible sources in social media for their eWOM marketing, the dimensions included in source credibility should be their first consideration. Specifically, the following questions should be asked when considering a source’s credibility in social media: 1) Do the targeted audiences

168 consider a source competent and trustworthy? 2) Do they relate to the source with their own needs? 3) And how does the source manage its account on social media and respond to the conversations on social media? These dimensions are potential standards that practitioners could use to make credibility judgment of online sources and then choose an appropriate source (either an individual or an organization) for their commercial endorsement. Secondly, these dimensions might be direction for practitioners to improve their own credibility on social media. For example, a brand on Twitter could increase its credibility by applying the verified blue badge from Twitter, increasing its social media presence/visibility, responding promptly to its audiences, or be more active online. It is noteworthy that for individual sources, the number of follower did not add any credits to their credibility; while for news organization and brand in social media, the number of followers are cues for their credibility. Thus, marketers need to pay attention to this phenomenon, that when making a decision about choosing a source to disseminate information, an individual source with plenty of followers does not necessary have high credibility. But it is safe to choose an organization source with a large number of followers to disseminate information. One possible explanation of the difference between individual source and organization source is that “likes” and followers don’t necessarily equate with credibility in individual, the fake “followers” undermine individual account credibility. For organization accounts, it is more credible that they have lots of real “followers.” Moreover, these are general credibility measures, which provide practitioners with an idea of credibility ranking in the social media environment. The credibility ranking helps marketers to identify effective “hub” in social media.

In addition, for the audiences, their Twitter involvement was proved to directly impact their perceived credibility in this study. Generally, people more involved in Twitter perceived the

169 different sources as more credible. Therefore, marketers should expect a better persuasive communication effect in people who are more involved on Twitter when conducting an eWOM marketing campaign in Twitter, while people less involved on Twitter are the audiences who are probably less affected by an outside source on social media.

Finally, since product type does impact people’s perception of source credibility, practitioners should carefully choose appropriate sources for endorsement or to disseminate product information on social media considering their own specific products/services. When their products/services are expensive and/or involve more elaborations to make purchase decisions, marketers should realize that family & friend, and news organizations are more credible sources for them to disseminate relevant product information. Practitioners should avoid celebrities, such as sports and entertainment stars, for their relative low credibility on social media. Most importantly, they should not try to make their products/services relate to any politicians or politics because politicians are shown to have the least credibility among sources on social media. Relating products/services with politicians would only undermine their own credibility and destroy their marketing campaigns.

Limitations of this Study

There were several limitations of this study. Firstly, the sampling procedures used college students of a university, who are not representative of the all the college students or the general population. Since the researcher was interested in people’s perceived credibility in Twitter, the research subjects of this study should be those who used Twitter. On the one hand, the recruitment of college students helped the researcher understand the heavy and active users of

Twitter. On the other hand, the purposive sampling decreased the generalizability of the findings in this study. Moreover, the online survey brought non-response error into this study. The

170 researcher sent an invitation email to all the students requesting their participation in this online survey. This resulted in non-response error. Only those who were interested in this topic or wanted to help the researcher completed the online survey. And the non-response error in turn brought another limitation in that the sample was over-represented by female students (73.6%).

Although it was not surprising because women were more likely to participate in online surveys than men (Curtin, Presser, & Singer, 2000), the biased data may have affected the accuracy of the findings. However, there was no gender difference in perceived credibility of social media in this study.

Secondly, the preliminary pretest conducted among 22 students in a communication class needs expanding to a broader scope. The scales and wordings suggested by the 22 students were acceptable, but the researcher believes more participants in the pretest could generate more comprehensive and accurate scales and wordings.

Thirdly, the primary interests of this study were developing new measures for source credibility on social media and exploring the determinants of perceived source credibility. To avoid overcomplexity of the research structural model and focus on the effect of new media environment, the researcher only included the Internet involvement, Twitter involvement, and self-esteem as exogenous variables. With the inclusiveness of other exogenous variables, such as personality variables, the R squared value of the model would probably improve.

Finally, the categories of product types in this study probably brought more measurement errors because everyone has their own standards of “expensive” and “confident.” More specific classification of product setting will help respondents answer questions more accurately, thus improving the final result.

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Suggestions for Future Research

The results and limitations of this study have several implications for future research.

First, future study on source credibility on social media may want to use the random sampling in the general population, which will provide generalizability. However, the cost and time management may be the first barrier and will need careful planning.

Secondly, more participants in the pretest generating scales and wordings for source credibility measures are recommended for future study. The scales of news organization and brand source credibility are still exploratory and need testing and improving in future studies. It is possible that the insignificance of the attribute dimension in news organization source credibility in the study was because of the chosen invalid statements. Thus, it is worthy making the effort to find the attribute dimension for news organization source credibility on social media. Moreover, the variance inflation factor score of the dynamism dimension in news organization source credibility was relatively high, which suggests that the scales of the formative sub-construct were not as good as expected. So, improving the scales of dynamism dimension of news organization source credibility is also recommended in future research.

Thirdly, to avoid the complexity of research model, the researcher only took source credibility construct as a whole, not decomposed it, and examined its relationship with other variables. For future study, decomposing the source credibility and exploring how each dimension of the source credibility is associated with other variables would be interesting. Also, the interactions among these dimensions would be another direction for future study.

Furthermore, as mentioned before, the inclusiveness of other creative variables in the research model might expand research scope.

Lastly, the product setting created in this study was limited to four different and abstract

172 product settings. To test the findings of this study, future research could choose a specific product category to make the findings applicable to specific products. And the scales of source credibility developed in this study can be tested in other non-marketing communication settings in future research. For instance, one can examine different sources’ credibility in a public event on social media.

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Journal of Interactive Marketing, 22, 3, 40-50.

Zaichkowsky, J. L. (1985a). Familiarity: Product use, involvement, or expertise? Advances in

Consumer Research, 12, 296-299.

Zaichkowsky, J.L. (1985b). Measuring the involvement construct in marketing. Journal of

Consumer Research, 12, 341-352.

Zaichkowsky, J. L. (1987). The emotional aspect of product involvement. Advances in Consumer

Research, 14, 32-35.

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APPENDIX A.

MEASURE OF SOURCE CREDIBILITY

Instructions: Please indicate your impression of the person noted below by circling the appropriate number between the pairs of adjectives below. The closer the number is to an adjective, the more certain you are of your evaluation.

Competence Intelligent 1 2 3 4 5 6 7 Unintelligent Untrained 1 2 3 4 5 6 7 Trained Inexpert 1 2 3 4 5 6 7 Expert Informed 1 2 3 4 5 6 7 Uninformed Incompetent 1 2 3 4 5 6 7 Competent Bright 1 2 3 4 5 6 7 Stupid

Goodwill Cares about me 1 2 3 4 5 6 7 Doesn’t care about me Has my interests at 1 2 3 4 5 6 7 Doesn’t have my interests at heart heart Self-centered 1 2 3 4 5 6 7 Not self-centered Concerned with me 1 2 3 4 5 6 7 Unconcerned with me Insensitive 1 2 3 4 5 6 7 Sensitive Not understanding 1 2 3 4 5 6 7 Understanding

Trustworthiness Honest 1 2 3 4 5 6 7 Dishonest Untrustworthy 1 2 3 4 5 6 7 Trustworthy Honorable 1 2 3 4 5 6 7 Dishonorable Moral 1 2 3 4 5 6 7 Immoral Unethical 1 2 3 4 5 6 7 Ethical Phoney 1 2 3 4 5 6 7 Genuine Note. From “Goodwill: A Reexamination of The Construct and Its Measurement,” by J. C. McCroskey & J. J. Teven, 1999, Communication Monographs, 66, p.95. expertise—for organization credibility was defined as the extent to which a company was knowledgeable about issues related to itself or consumers.

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APPENDIX B.

CONSENT LETTER

School of Media & Communication Xiao Hu, Ph.D. Candidate

Online Survey Consent Form

Dear Students:

You are invited to participate in a research study on college students' Twitter usage and how students use different sources on Twitter. This academic study is conducted by Xiao Hu, Ph.D. candidate from School of Media and Communication at Bowling Green State University.

This survey will take approximately 10 minutes of your time. You will be asked to complete an online survey about your usage of Twitter and the Internet, how you use different sources on Twitter, and your basic information such as gender, and major, and etc.

You must to be 18 years old or older to participate in this study. Your participation is voluntary and your completion of the questionnaire constitutes your consent to participate in this study. You have the right to terminate your participation at any time without penalty. There is no risk to you for participating in this study. Your decision to participate, decline, or withdraw from participation will have no effect on your current status at BGSU.

The risk of participation is no greater than that experienced in daily life. Your individual information will be kept conﬁdential and not identiﬁed in the report. But to further protect your privacy, you should be aware of the possible use of tracking software by computer owners and complete the survey on your personal computer. Make sure you clear the browser cache and page history after completing this survey. To thank you for your participation, you will be given participation or extra course credit by your instructor. If you cannot complete this study, you can request your instructor for alternative form of getting the same participation or extra course credit. You will not get the participation or extra credit if you only submit the consent document not the survey. To enable you to receive your participation or extra course credit, please write your name and instructor's name at the end of the survey before hitting the submit button. Once the name list is sent to your instructor, Xiao Hu will delete your identity information immediately. The survey data will be secured in a

203 separate database. Only the principal investigator, Xiao Hu, has access to the database. participants' identity information will not be revealed because Xiao Hu will destroy identifying information after instructors are notiﬁed. The results will only be used for dissertation.

Your opinion is VERY IMPORTANT because previous studies suggested that young people are heavy users of social media. The results of this study can help media scholars understand how social media affect word-of-mouth communication, and thus affect the future development of online social media marketing. Moreover, social media professionals can use the results of this study to improve their media services to their users. You may get better services from Twitter, or even other social media, as a result of getting your voices heard. You may also get a better understanding of how media research is conducted through the participation of this study.

If you have any question or concern regarding the study, you are welcome to contact Xiao Hu at (617) 981-0658 (e-mail: [email protected]). If you have questions about participant rights, you may contact the Human Subjects Review Board at 419-372-7716 or e-mail at [email protected].

Please print a copy of this consent form for your records, if you so desire.

I have read and understand the above consent form, I certify that I am 18 years old or older and, by clicking the submit button to enter the survey, I indicate my willingness voluntarily take part in the study.

Agree

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APPENDIX C.

QUESTIONNAIRE

School of Media & Communication Xiao Hu, Ph.D. Candidate

College Students' Twitter Usage and Twitter Source Research

Section 1: Internet and Twitter Usage

1. How many years have you used the Internet? (Only write a number)

2. In the past week, on average, approximately how much time per day have you spent on the Internet?

Less than 1 hour 1-1.9 hours 2-2.9 hours 3-3.9 hours 4-4.9 hour 5-5.9 hours 6 hours or More

3. Please rate the following statements about the Internet from 1(strong disagree) to 5 (strongly agree). Strong Strongly Disagree Neutral Agree Disagree Agree The Internet is part of my everyday activity I am proud to tell people I am on the Internet The Internet has become part of my daily routine I feel out of touch when I could not get online for a while I feel I am part of the Internet community I would be sorry if the Internet shut down

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4. How many years have you used Twitter? (Only write a number)

5. In the past week, on average, approximately how much time per day have you spent on Twitter?

Less than 1 hour 1-1.9 hour 2-2.9 hours 3-3.9 hours 4-4.9 hour 5-5.9 hours 6 hours and more N/A

6. How many Twitter followers do you have?(Only write a number)

7. How many accounts are you following on Twitter?(Only write a number)

8. How frequently do you update your Twitter (any activities on Twitter including tweet, repost, comment, link, and follow etc.)?

Hardly ever update less than once a month Between once a week and once a month Once to several times a week Everyday Every several hours Several times an hour N/A

9. Please rate the following statements about Twitter from 1(strong disagree) to 5 (strongly agree).

Strong Strongly Disagree Neutral Agree Disagree Agree Twitter is part of my everyday activity I am proud to tell people I am on Twitter Twitter has become part of my daily routine I feel out of touch when I haven't logged onto Twitter for a while

I feel I am part of the Twitter community

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I would be sorry if Twitter shut down

10. What kinds of Twitter accounts are you following? (Check all that apply. If you don't use Twitter, please check "Others" option)

Family and friends Entertainment celebrities (including music, movie stars) Sport Stars Successful business people Politicians Religion leaders News organizations Commercial companies (or brands) Others, please specify

11. What kinds of Twitter account do you follow the most?

12. What is your purpose for using Twitter? (Check all that apply. If you don't use Twitter, please check "Others" option)

Keep informed about news Self-presentation Keep in touch with my friends Follow my idol(s) Catch up with my peers Others, please specify

13. How frequently do you shop online?

Several times a week Once a week Once to several times a month

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Less than once a month Several times a year Less than once a year Hardly shop online

14. Approximately, what percentage of your disposal income do you spend on online shopping?

Less than 10% of the total income 10%-19% 20%-29% 30%-39% 40%-49% 50% or over

15. What kind of the following products do you spend the most time making a purchase decision?

Electronics & Computers Beauty & health Movies, Music & Games Home Garden & Tools Clothing, Shoes & Jewelry Sports & Out doors Others, please specify

Section 2: Perception of Twitter Sources

Now you are asked to indicate your impression of Twitter accounts by checking the appropriate number between the pairs of adjective below. The closer the number is to an adjective, the more certain you are of your evaluation. Numbers 1 and 7 indicate a very strong feeling. Numbers 2 and 6 indicate a strong feeling. Numbers 3 and 5 indicate a fairly weak feeling. Number 4 indicates you are undecided. If you don't use Twitter, check "Undecided" option.

16. Recall an entertainment star on Twitter that you are familiar with, please rate him/her below based on your impression.

Very Very Strong Weak Weak Strong Strong Undecided Strong Feeling Feeling Feeling Feeling Feeling Feeling

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Intelligent 1 2 3 4 5 6 7 Unintelligent

Expert 1 2 3 4 5 6 7 Inexpert

Informed 1 2 3 4 5 6 7 Uninformed

Honest 1 2 3 4 5 6 7 Dishonest

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Honorable 1 2 3 4 5 6 7 Dishonorable

Important to 1 2 3 4 5 6 7 Unimportant to me me Attracts lots of my 1 2 3 4 5 6 7 Not interest me attentions Relates to Has no me on 1 2 3 4 5 6 7 connection certain things with me Attractive 1 2 3 4 5 6 7 Unattractive

Classy 1 2 3 4 5 6 7 Not Classy

Elegant 1 2 3 4 5 6 7 Plain

Meek 1 2 3 4 5 6 7 Aggressive

Comic 1 2 3 4 5 6 7 Not comic

Active 1 2 3 4 5 6 7 Passive

Interactive 1 2 3 4 5 6 7 Noninteractive Veriﬁed (with a blue Unveriﬁed (no check mark 1 2 3 4 5 6 7 blue check next to mark) twitter name)

Has only a few Has a lot of 1 2 3 4 5 6 7 followers followers Real 1 2 3 4 5 6 7 Fake Tweets 1 2 3 4 5 6 7 Barely tweets everyday

17. Recall a sports star on Twitter that you are familiar with, please rate him/her below based on your impression.

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Very Very Strong Weak Weak Strong Strong Undecided Strong Feeling Feeling Feeling Feeling Feeling Feeling

Intelligent 1 2 3 4 5 6 7 Unintelligent

Expert 1 2 3 4 5 6 7 Inexpert

Informed 1 2 3 4 5 6 7 Uninformed

Honest 1 2 3 4 5 6 7 Dishonest

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Honorable 1 2 3 4 5 6 7 Dishonorable

Classy 1 2 3 4 5 6 7 Not Classy

Elegant 1 2 3 4 5 6 7 Plain

Meek 1 2 3 4 5 6 7 Aggressive

Comic 1 2 3 4 5 6 7 Not comic

Active 1 2 3 4 5 6 7 Passive

Interactive 1 2 3 4 5 6 7 Noninteractive Veriﬁed (with a blue check mark Unveriﬁed (no next to 1 2 3 4 5 6 7 blue check twitter name) mark)

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Has a lot of 1 2 3 4 5 6 7 Has only a few followers followers Real 1 2 3 4 5 6 7 Fake Tweets 1 2 3 4 5 6 7 Barely tweets everyday

18. Recall a famous business person on Twitteryou are familiar with, please rate him/her below based on your impression.

Very Very Strong Weak Weak Strong Strong Undecided Strong Feeling Feeling Feeling Feeling Feeling Feeling

Intelligent 1 2 3 4 5 6 7 Unintelligent

Expert 1 2 3 4 5 6 7 Inexpert

Informed 1 2 3 4 5 6 7 Uninformed

Honest 1 2 3 4 5 6 7 Dishonest

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Honorable 1 2 3 4 5 6 7 Dishonorable Unimportant to Important to 1 2 3 4 5 6 7 me me Attracts lots of my 1 2 3 4 5 6 7 Not interest me attentions Relates to Has no me on 1 2 3 4 5 6 7 connection certain things with me Attractive 1 2 3 4 5 6 7 Unattractive

Classy 1 2 3 4 5 6 7 Not Classy

Elegant 1 2 3 4 5 6 7 Plain

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Meek 1 2 3 4 5 6 7 Aggressive Comic 1 2 3 4 5 6 7 Not comic

Active 1 2 3 4 5 6 7 Passive

Interactive 1 2 3 4 5 6 7 Noninteractive Veriﬁed (with a blue Unveriﬁed (no check mark 1 2 3 4 5 6 7 blue check next to mark) twitter name) Has a lot of 1 2 3 4 5 6 7 Has only a few followers followers Real 1 2 3 4 5 6 7 Fake Tweets 1 2 3 4 5 6 7 Barely tweets everyday

19. Recall a famous politican on Twitteryou are familiar with, please rate him/her below based on your impression.

Very Very Strong Weak Weak Strong Strong Undecided Strong Feeling Feeling Feeling Feeling Feeling Feeling

Intelligent 1 2 3 4 5 6 7 Unintelligent

Expert 1 2 3 4 5 6 7 Inexpert

Informed 1 2 3 4 5 6 7 Uninformed

Honest 1 2 3 4 5 6 7 Dishonest

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Honorable 1 2 3 4 5 6 7 Dishonorable Unimportant to Important to 1 2 3 4 5 6 7 me me Attracts lots of my 1 2 3 4 5 6 7 Not interest me

212 attentions Relates to Has no me on 1 2 3 4 5 6 7 connection certain things with me Attractive 1 2 3 4 5 6 7 Unattractive

Classy 1 2 3 4 5 6 7 Not Classy

Elegant 1 2 3 4 5 6 7 Plain

Meek 1 2 3 4 5 6 7 Aggressive

Comic 1 2 3 4 5 6 7 Not comic

Active 1 2 3 4 5 6 7 Passive

20. Recall a brand account on Twitteryou are familiar with, please rate it below based on your impression.

Very Very Strong Weak Weak Strong Strong Undecided Strong Feeling Feeling Feeling Feeling Feeling Feeling

Intelligent 1 2 3 4 5 6 7 Unintelligent

Expert 1 2 3 4 5 6 7 Inexpert Good quality 1 2 3 4 5 6 7 Bad quality

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Honest 1 2 3 4 5 6 7 Dishonest

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Recognizable 1 2 3 4 5 6 7 Unrecognizable Important to 1 2 3 4 5 6 7 Unimportant to me me Attracts lots of my 1 2 3 4 5 6 7 Not interest me attentions Relates to me Has no on certain 1 2 3 4 5 6 7 connection things with me Huge 1 2 3 4 5 6 7 Small company company Been around for a long 1 2 3 4 5 6 7 New business time Congruent Not congruent with my 1 2 3 4 5 6 7 with my values values Good 1 2 3 4 5 6 7 Bad reputation reputation Creative 1 2 3 4 5 6 7 Uncreative

Active 1 2 3 4 5 6 7 Passive

Interactive 1 2 3 4 5 6 7 Noninteractive Veriﬁed with Unveriﬁed (no a blue check 1 2 3 4 5 6 7 blue check mark mark) Has only a few Has a lot of 1 2 3 4 5 6 7 followers followers Real 1 2 3 4 5 6 7 Fake Tweets 1 2 3 4 5 6 7 Barely tweets everyday Organized 1 2 3 4 5 6 7 Disorganized

21. Recall a news organization on Twitteryou are familiar with, please rate it below based on your impression.

Very Very

214

Strong Strong Weak Undecided Weak Strong Strong Feeling Feeling Feeling Feeling Feeling Feeling

Professional 1 2 3 4 5 6 7 Unprofessional

Intelligent 1 2 3 4 5 6 7 Unintelligent

Informative 1 2 3 4 5 6 7 Uninformative

Expert 1 2 3 4 5 6 7 Inexpert

Organized 1 2 3 4 5 6 7 Disorganized

Fair 1 2 3 4 5 6 7 Unfair

Trustworthy 1 2 3 4 5 6 7 Untrustworthy

Honest 1 2 3 4 5 6 7 Dishonest

Recognizable 1 2 3 4 5 6 7 Unrecognizable Good 1 2 3 4 5 6 7 Bad reputation reputation Important to 1 2 3 4 5 6 7 Unimportant to me me Attracts lots of my 1 2 3 4 5 6 7 Not interest me attentions Relates to me Has no on certain 1 2 3 4 5 6 7 connection things with me Huge 1 2 3 4 5 6 7 Small organization organization Congruent Not congruent with my 1 2 3 4 5 6 7 with my values values Been around for a long 1 2 3 4 5 6 7 New business time Timely 1 2 3 4 5 6 7 Untimely

Active 1 2 3 4 5 6 7 Passive Interactive 1 2 3 4 5 6 7 Noninteractive

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Veriﬁed with Unveriﬁed (no a blue check 1 2 3 4 5 6 7 blue check mark mark) Has a lot of 1 2 3 4 5 6 7 Has only a few followers followers Real 1 2 3 4 5 6 7 Fake Tweets 1 2 3 4 5 6 7 Barely tweets everyday Objective 1 2 3 4 5 6 7 Subjective

22. Imagaine a situation where you are considering buying an EXPENSIVE ITEM, but you DON'T HAVE MUCH CONFIDENCE in your ability to make a good choice (for example a house, automobile, etc.). If the following sources on Twitter had information on this item, how credible would these sources be to you?

Totally Very Incredible Neutral Credible Incredible Credible Your family and friends Entertainment celebrity that you are familiar with Sport stars that you are familiar with Successful business people that you are familiar with Important politicians on Twitter that I am familiar with The brand of the ofﬁcial Twitter account of this item News organizations that you are familiar with

23. Imagaine a situation where you are considering buying an INEXPENSIVE ITEM, but you DON'T HAVE MUCH CONFIDENCE in your ability to make a good choice (for example, a new brand product, or some products that you never used before). If the following sources on Twitter had information on this item, how credible would these sources be to you?

Totally Very Incredible Neutral Credible Incredible Credible Your family and friends Entertainment celebrity that you are

216 familiar with Sport stars that you are familiar with Successful business people that you are familiar with Important politicians on Twitter that I am familiar with The brand of the ofﬁcial Twitter account of this item News organizations that you are familiar with

24. Imagaine a situation where you are considering buying an EXPENSIVE ITEM, but you HAVE CONFIDENCE in your ability to make a good choice (for example limited edition products, or luxury.). If the following sources on Twitter had information on this item, how credible would these sources be to you?

25. Imagaine a situation where you are considering buying an INEXPENSIVE ITEM, and you HAVE CONFIDENCE in your ability to make a good choice (for example toothpaste, soap, or snacks). If the following sources on Twitter had information on this item, how credible would these sources be to you?

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Important politicians on Twitter that I am familiar with The brand of the ofﬁcial Twitter account of this item News organizations that you are familiar with

Section 3: Demographics

26. Are you Male or Female?

Male Female

27. What is your age? (Only write a number)

28. What is your GPA?

3.5 or above 3—3.49 2.5 — 2.99 2 — 2.49 1.5 —1.99 1—1.49 Lower than 1

29. What is your class standing?

Freshman Sophomore Junior Senior Graduate Student

30. What type of major are you in?

Arts & Humanities (Including music) Social Science (Including telecommunications) Science Business Education Health & Human Services Technology Undecied/Other

31. What is your total household income before tax per year?

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Under $ 20,000 $20,000-$49,000 $50,000-$79,999 $80,000-$109,999 $110,000-$139,999 $140,000-$169,999 $170,000 or over

32. What is your ethnicity?

Caucasian African-American Hispanic Asian-Paciﬁc islanders Native American Other, Please specify____

33. What is your religious afﬁliation?

Protestant Christian Roman Catholic Evangelical Christian Jewish Muslim Hindu Buddhist Other, please specify

34. My religious faith is:

Important for my life, but no more important than certain other aspects of my life Only of minor importance for my life, compared to certain other aspects of my life Of central importance for my life, and would, if necessary come before all other aspects of my life

35. Everyone must make many important life decisions, such as which occupation to pursue, what goals to strive for, whom to vote for, what to teach one's children, etc. When you have made, or do make decisions such as these, to what extent do you make the decisions on the basis of your religious faith?

I seldom if ever base such decisions on religious faith I sometimes base such decisions on my religious faith but deﬁnitely not most of the time I feel that most of my important decisions are based on my religious faith, but usually in a general, unconscious way I feel that most of my important decisions are based on my religious faith, and I usually consciously attempt to make them so

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36. Without my religious faith, the rest of my life would not have much meaning to it

Strongly disagree Disagree Agree Strongly agree

37. What is the highest level of education your Mother has completed?

Less than high school High school/GED Some college 2-year college degrees (Associates) 4-year college degrees (BA, or BS) Master's degrees Doctoral degrees Professional degrees (MD, JD)

38. What is the highest level of education your Father has completed?

Less than high school High school/GED Some college 2-year college degrees (Associates) 4-year college degrees (BA, or BS) Master's degrees Doctoral degrees Professional degrees (MD, JD)

39. Below is a list of statements dealing with your general feelings about yourself. Please rate the statements from 1(strong disagree) to 4 (strongly agree).

Strong Disagree Agree Strongly Agree Disagree On the whole, I am satisﬁed with myself At times, I think I am no good at all I feel that I have a number of good qualities I am able to do things as well as most other people I feel I do not have much to be proud of I certainly feel useless at times I feel that I'm a person of worth, at least on an equal plane with others I wish I could have more respect for myself; All in all, I am inclined to feel that I am a failure I take a positive attitude toward myself

220

40. Your name (Last name last, ﬁrst name ﬁrst):

41. Your instructor's name:

Please make sure you have ﬁnished the survey before submitting! Thank you for participating!

Submit

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APPENDIX D.

INDIVIDUAL SOURCE CREDIBILITY SCALE

Dimensions Indicators Competence Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Informed 1 2 3 4 5 6 7 Uninformed Trustworthiness Honest 1 2 3 4 5 6 7 Dishonest Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honorable 1 2 3 4 5 6 7 Dishonorable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Attractiveness Attractive 1 2 3 4 5 6 7 Unattractive Classy 1 2 3 4 5 6 7 Not Classy Elegant 1 2 3 4 5 6 7 Plain Dynamism Meek 1 2 3 4 5 6 7 Aggressive Comic 1 2 3 4 5 6 7 Not comic Active 1 2 3 4 5 6 7 Passive Interactive 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday

222

APPENDIX E.

NEWS ORGANIZATION SOURCE CREDIBILITY SCALE

Dimensions Indicators Competence Professional 1 2 3 4 5 6 7 Unprofessional Intelligent 1 2 3 4 5 6 7 Unintelligent Informative 1 2 3 4 5 6 7 Uninformative Expert 1 2 3 4 5 6 7 Inexpert Organized 1 2 3 4 5 6 7 Disorganized Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Dynamism Fair 1 2 3 4 5 6 7 Unfair Timely 1 2 3 4 5 6 7 Untimely Active 1 2 3 4 5 6 7 Passive Interactive 1 2 3 4 5 6 7 Noninteractive Objective 1 2 3 4 5 6 7 Subjective Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Has a lot of followers 1 2 3 4 5 6 7 Only has a few followers Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday

223

APPENDIX F.

BRAND SOURCE CREDIBILITY

Dimensions Indicators Competence Good quality 1 2 3 4 5 6 7 Bad quality Intelligent 1 2 3 4 5 6 7 Unintelligent Expert 1 2 3 4 5 6 7 Inexpert Trustworthiness Trustworthy 1 2 3 4 5 6 7 Untrustworthy Honest 1 2 3 4 5 6 7 Dishonest Recognizable 1 2 3 4 5 6 7 Unrecognizable Social tie Important to me 1 2 3 4 5 6 7 Unimportant to me Attracts lots of my attentions 1 2 3 4 5 6 7 Not interest me Relates to me on certain things 1 2 3 4 5 6 7 Has no connection with me Attribute Huge organization 1 2 3 4 5 6 7 Small organization Been around for a long time 1 2 3 4 5 6 7 New business Congruent with my values 1 2 3 4 5 6 7 Not congruent with my values Good reputation Bad reputation Dynamism Creative 1 2 3 4 5 6 7 Uncreative Active Passive Interactive 1 2 3 4 5 6 7 Noninteractive Technology affordance Verified (with a blue check 1 2 3 4 5 6 7 Unverified (no blue check mark) mark next to twitter name) Has a lot of followers 1 2 3 4 5 6 7 Only has a few followers Real 1 2 3 4 5 6 7 Fake Tweets everyday 1 2 3 4 5 6 7 Doesn’t tweet everyday

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APPENDIX G.

CORRELATION MATRIX OF INDIVIDUAL MODEL

C1/I C2/I C3/I T1/I T2/I T3/I ST1 ST2 ST3 A1/I A2/I A3/I D1/I D2/I D3/I D4/I TA1 TA2 TA3 TA4 II1 II2 C1/I 1 0.87 0.89 0.75 0.75 0.77 0.49 0.5 0.6 0.56 0.8 0.69 0.33 0.47 0.68 0.61 0.64 0.68 0.68 0.51 -0 -0 C2/I 0.87 1 0.89 0.74 0.72 0.75 0.49 0.49 0.56 0.53 0.77 0.67 0.28 0.45 0.71 0.62 0.68 0.71 0.71 0.57 0.03 -0 C3/I 0.89 0.89 1 0.78 0.75 0.79 0.48 0.51 0.57 0.54 0.8 0.7 0.29 0.49 0.74 0.65 0.68 0.73 0.71 0.57 0.01 -0 T1/I 0.75 0.74 0.78 1 0.92 0.87 0.63 0.61 0.65 0.58 0.75 0.66 0.36 0.56 0.65 0.63 0.58 0.56 0.66 0.47 -0 -0 T2/I 0.75 0.72 0.75 0.92 1 0.91 0.67 0.63 0.66 0.56 0.75 0.65 0.38 0.54 0.62 0.6 0.52 0.51 0.6 0.43 0 -0 T3/I 0.77 0.75 0.79 0.87 0.91 1 0.67 0.65 0.67 0.59 0.78 0.68 0.41 0.54 0.65 0.63 0.58 0.57 0.64 0.49 -0 -0 ST1 0.49 0.49 0.48 0.63 0.67 0.67 1 0.8 0.7 0.55 0.59 0.57 0.39 0.54 0.45 0.43 0.32 0.27 0.36 0.22 -0 0.02 ST2 0.5 0.49 0.51 0.61 0.63 0.65 0.8 1 0.72 0.6 0.6 0.58 0.37 0.53 0.48 0.49 0.38 0.32 0.39 0.32 -0.1 0.01 ST3 0.6 0.56 0.57 0.65 0.66 0.67 0.7 0.72 1 0.6 0.64 0.61 0.38 0.54 0.52 0.54 0.37 0.34 0.42 0.31 -0 -0 A1/I 0.56 0.53 0.54 0.58 0.56 0.59 0.55 0.6 0.6 1 0.7 0.69 0.38 0.53 0.57 0.49 0.46 0.47 0.42 0.4 -0 0 A2/I 0.8 0.77 0.8 0.75 0.75 0.78 0.59 0.6 0.64 0.7 1 0.86 0.44 0.49 0.72 0.64 0.6 0.61 0.62 0.48 -0 -0 A3/I 0.69 0.67 0.7 0.66 0.65 0.68 0.57 0.58 0.61 0.69 0.86 1 0.48 0.51 0.67 0.61 0.53 0.52 0.51 0.45 -0 -0 D1/I 0.33 0.28 0.29 0.36 0.38 0.41 0.39 0.37 0.38 0.38 0.44 0.48 1 0.42 0.26 0.33 0.17 0.1 0.14 0.18 -0.1 -0.1 D2/I 0.47 0.45 0.49 0.56 0.54 0.54 0.54 0.53 0.54 0.53 0.49 0.51 0.42 1 0.52 0.6 0.37 0.35 0.36 0.41 -0 0.01 D3/I 0.68 0.71 0.74 0.65 0.62 0.65 0.45 0.48 0.52 0.57 0.72 0.67 0.26 0.52 1 0.75 0.66 0.72 0.65 0.62 -0 -0 D4/I 0.61 0.62 0.65 0.63 0.6 0.63 0.43 0.49 0.54 0.49 0.64 0.61 0.33 0.6 0.75 1 0.53 0.54 0.55 0.56 -0 -0.1 TA1 0.64 0.68 0.68 0.58 0.52 0.58 0.32 0.38 0.37 0.46 0.6 0.53 0.17 0.37 0.66 0.53 1 0.85 0.77 0.68 -0 -0 TA2 0.68 0.71 0.73 0.56 0.51 0.57 0.27 0.32 0.34 0.47 0.61 0.52 0.1 0.35 0.72 0.54 0.85 1 0.81 0.72 0.01 -0 TA3 0.68 0.71 0.71 0.66 0.6 0.64 0.36 0.39 0.42 0.42 0.62 0.51 0.14 0.36 0.65 0.55 0.77 0.81 1 0.62 0.02 -0 TA4 0.51 0.57 0.57 0.47 0.43 0.49 0.22 0.32 0.31 0.4 0.48 0.45 0.18 0.41 0.62 0.56 0.68 0.72 0.62 1 -0 -0 II1 -0 0.03 0.01 -0 0 -0 -0 -0.1 -0 -0 -0 -0 -0.1 -0 -0 -0 -0 0.01 0.02 -0 1 0.15 II2 -0 -0 -0 -0 -0 -0 0.02 0.01 -0 0 -0 -0 -0.1 0.01 -0 -0.1 -0 -0 -0 -0 0.15 1 ID1 0.04 0.04 0.09 0.08 0.06 0.05 0.06 0.08 0.04 0.1 0.09 0.05 -0 0.04 0.05 0.06 0.1 0.15 0.09 0.11 0.07 0.21 ID2 0.04 0.08 0.04 0.1 0.1 0.09 0.12 0.12 0.08 0.11 0.08 0.08 0.04 0.02 0.05 0.07 0.05 0.09 0.09 0.12 0.01 0.11 ID3 0.1 0.1 0.14 0.13 0.12 0.12 0.08 0.09 0.07 0.07 0.12 0.09 -0 0.04 0.06 0.12 0.12 0.15 0.13 0.11 0.08 0.17 ID4 0.14 0.14 0.16 0.13 0.12 0.12 0.09 0.14 0.09 0.1 0.11 0.13 0.04 0.06 0.1 0.13 0.13 0.14 0.12 0.14 0.12 0.11 ID5 0.14 0.15 0.17 0.15 0.16 0.15 0.13 0.17 0.1 0.15 0.14 0.14 0.05 0.08 0.1 0.16 0.12 0.14 0.16 0.17 0.05 0.14 ID6 0.07 0.1 0.09 0.08 0.08 0.06 0.07 0.05 0.03 0.03 0.07 0.07 -0.1 0.03 0.11 0.07 0.13 0.1 0.12 0.09 0.12 0.12 TI1 0.21 0.19 0.18 0.16 0.14 0.17 0.16 0.19 0.14 0.2 0.21 0.22 0.12 0.11 0.16 0.08 0.21 0.23 0.2 0.18 0.01 0.14 TI2 0.13 0.13 0.12 0.12 0.11 0.12 0.14 0.16 0.1 0.13 0.12 0.12 0.2 0.16 0.14 0.13 0.16 0.14 0.12 0.17 -0.1 0.27 TI3 0.19 0.21 0.2 0.22 0.18 0.21 0.12 0.17 0.13 0.18 0.19 0.2 0.14 0.14 0.18 0.17 0.29 0.26 0.29 0.28 -0.2 -0 TD1 0.22 0.21 0.23 0.27 0.22 0.24 0.16 0.2 0.15 0.2 0.22 0.19 0.16 0.19 0.2 0.18 0.3 0.27 0.28 0.29 -0.1 -0.1 TD2 0.22 0.21 0.23 0.27 0.26 0.23 0.17 0.19 0.18 0.19 0.21 0.17 0.09 0.13 0.21 0.17 0.22 0.25 0.29 0.25 -0.1 0.03 TD3 0.21 0.22 0.22 0.25 0.2 0.2 0.16 0.18 0.15 0.2 0.2 0.2 0.14 0.2 0.2 0.17 0.28 0.27 0.28 0.29 -0.1 -0.1 TD4 0.19 0.21 0.21 0.22 0.19 0.19 0.16 0.19 0.13 0.15 0.15 0.14 0.11 0.14 0.17 0.17 0.21 0.19 0.21 0.25 -0.1 0.02 TD5 0.25 0.26 0.27 0.26 0.22 0.23 0.15 0.19 0.16 0.19 0.23 0.2 0.15 0.13 0.23 0.23 0.29 0.28 0.29 0.29 -0.1 -0 TD6 0.17 0.18 0.17 0.2 0.18 0.16 0.16 0.12 0.11 0.1 0.13 0.1 0.02 0.08 0.14 0.09 0.22 0.19 0.2 0.19 -0 0.03 SE1 0.02 0.06 0.05 0.04 0.04 0.06 -0 0 0.01 0.06 0.1 0.09 0.05 -0 0.09 0.09 0.1 0.08 0.07 0.13 0.06 -0.1 SE2 0.02 0.03 0.05 0 0 0.01 -0 -0 -0 0.03 0.03 0.01 0.05 -0.1 0.04 0.03 0.09 0.07 0.05 0.14 0.09 -0.1 SE3 0.12 0.13 0.16 0.11 0.1 0.12 0.05 0.11 0.11 0.11 0.18 0.14 0.1 0.04 0.12 0.16 0.11 0.14 0.1 0.16 0.14 -0.1 SE4 0.08 0.07 0.08 0.07 0.05 0.06 0 0.05 0.06 0.11 0.12 0.1 0.07 0.05 0.12 0.13 0.11 0.14 0.08 0.15 0.08 -0.1 SE5 0.05 0.03 0.08 0.05 0.03 0.06 -0 -0 -0 0.05 0.1 0.06 0.01 -0 0.1 0.07 0.13 0.13 0.1 0.16 -0 -0.1 SE6 0.04 0.04 0.06 0.05 0.02 0.05 -0 0.02 -0 0.07 0.09 0.07 0.09 -0 0.07 0.05 0.12 0.1 0.07 0.15 0.03 -0 SE7 0.1 0.08 0.11 0.08 0.05 0.04 -0 0.03 0.06 0.08 0.14 0.1 -0 0.02 0.16 0.13 0.14 0.15 0.12 0.17 0.11 -0 SE8 0.03 0.02 0.07 0.04 0.03 0.03 -0 -0 -0 -0 0.06 0.05 0.05 -0.1 0.12 0.06 0.1 0.06 0.05 0.1 0.08 -0.1 SE9 0.06 0.06 0.09 0.09 0.05 0.07 -0 -0 -0 0.02 0.08 0.03 -0 -0 0.1 0.07 0.12 0.14 0.12 0.14 0.05 -0.1 SE10 0.1 0.09 0.1 0.07 0.06 0.06 -0 0.01 0.04 0.11 0.16 0.11 0.05 -0 0.15 0.12 0.13 0.11 0.1 0.19 0.01 -0.1

225

CONTINUTED

ID1 ID2 ID3 ID4 ID5 ID6 TI1 TI2 TI3 TD1 TD2 TD3 TD4 TD5 TD6 SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 C1/I 0.04 0.04 0.1 0.14 0.14 0.07 0.21 0.13 0.19 0.22 0.22 0.21 0.19 0.25 0.17 0.02 0.02 0.12 0.08 0.05 0.04 0.1 0.03 0.06 0.1 C2/I 0.04 0.08 0.1 0.14 0.15 0.1 0.19 0.13 0.21 0.21 0.21 0.22 0.21 0.26 0.18 0.06 0.03 0.13 0.07 0.03 0.04 0.08 0.02 0.06 0.09 C3/I 0.09 0.04 0.14 0.16 0.17 0.09 0.18 0.12 0.2 0.23 0.23 0.22 0.21 0.27 0.17 0.05 0.05 0.16 0.08 0.08 0.06 0.11 0.07 0.09 0.1 T1/I 0.08 0.1 0.13 0.13 0.15 0.08 0.16 0.12 0.22 0.27 0.27 0.25 0.22 0.26 0.2 0.04 0 0.11 0.07 0.05 0.05 0.08 0.04 0.09 0.07 T2/I 0.06 0.1 0.12 0.12 0.16 0.08 0.14 0.11 0.18 0.22 0.26 0.2 0.19 0.22 0.18 0.04 0 0.1 0.05 0.03 0.02 0.05 0.03 0.05 0.06 T3/I 0.05 0.09 0.12 0.12 0.15 0.06 0.17 0.12 0.21 0.24 0.23 0.2 0.19 0.23 0.16 0.06 0.01 0.12 0.06 0.06 0.05 0.04 0.03 0.07 0.06 ST1 0.06 0.12 0.08 0.09 0.13 0.07 0.16 0.14 0.12 0.16 0.17 0.16 0.16 0.15 0.16 -0 -0 0.05 0 -0 -0 -0 -0 -0 -0 ST2 0.08 0.12 0.09 0.14 0.17 0.05 0.19 0.16 0.17 0.2 0.19 0.18 0.19 0.19 0.12 0 -0 0.11 0.05 -0 0.02 0.03 -0 -0 0.01 ST3 0.04 0.08 0.07 0.09 0.1 0.03 0.14 0.1 0.13 0.15 0.18 0.15 0.13 0.16 0.11 0.01 -0 0.11 0.06 -0 -0 0.06 -0 -0 0.04 A1/I 0.1 0.11 0.07 0.1 0.15 0.03 0.2 0.13 0.18 0.2 0.19 0.2 0.15 0.19 0.1 0.06 0.03 0.11 0.11 0.05 0.07 0.08 -0 0.02 0.11 A2/I 0.09 0.08 0.12 0.11 0.14 0.07 0.21 0.12 0.19 0.22 0.21 0.2 0.15 0.23 0.13 0.1 0.03 0.18 0.12 0.1 0.09 0.14 0.06 0.08 0.16 A3/I 0.05 0.08 0.09 0.13 0.14 0.07 0.22 0.12 0.2 0.19 0.17 0.2 0.14 0.2 0.1 0.09 0.01 0.14 0.1 0.06 0.07 0.1 0.05 0.03 0.11 D1/I -0 0.04 -0 0.04 0.05 -0.1 0.12 0.2 0.14 0.16 0.09 0.14 0.11 0.15 0.02 0.05 0.05 0.1 0.07 0.01 0.09 -0 0.05 -0 0.05 D2/I 0.04 0.02 0.04 0.06 0.08 0.03 0.11 0.16 0.14 0.19 0.13 0.2 0.14 0.13 0.08 -0 -0.1 0.04 0.05 -0 -0 0.02 -0.1 -0 -0 D3/I 0.05 0.05 0.06 0.1 0.1 0.11 0.16 0.14 0.18 0.2 0.21 0.2 0.17 0.23 0.14 0.09 0.04 0.12 0.12 0.1 0.07 0.16 0.12 0.1 0.15 D4/I 0.06 0.07 0.12 0.13 0.16 0.07 0.08 0.13 0.17 0.18 0.17 0.17 0.17 0.23 0.09 0.09 0.03 0.16 0.13 0.07 0.05 0.13 0.06 0.07 0.12 TA1 0.1 0.05 0.12 0.13 0.12 0.13 0.21 0.16 0.29 0.3 0.22 0.28 0.21 0.29 0.22 0.1 0.09 0.11 0.11 0.13 0.12 0.14 0.1 0.12 0.13 TA2 0.15 0.09 0.15 0.14 0.14 0.1 0.23 0.14 0.26 0.27 0.25 0.27 0.19 0.28 0.19 0.08 0.07 0.14 0.14 0.13 0.1 0.15 0.06 0.14 0.11 TA3 0.09 0.09 0.13 0.12 0.16 0.12 0.2 0.12 0.29 0.28 0.29 0.28 0.21 0.29 0.2 0.07 0.05 0.1 0.08 0.1 0.07 0.12 0.05 0.12 0.1 TA4 0.11 0.12 0.11 0.14 0.17 0.09 0.18 0.17 0.28 0.29 0.25 0.29 0.25 0.29 0.19 0.13 0.14 0.16 0.15 0.16 0.15 0.17 0.1 0.14 0.19 II1 0.07 0.01 0.08 0.12 0.05 0.12 0.01 -0.1 -0.2 -0.1 -0.1 -0.1 -0.1 -0.1 -0 0.06 0.09 0.14 0.08 -0 0.03 0.11 0.08 0.05 0.01 II2 0.21 0.11 0.17 0.11 0.14 0.12 0.14 0.27 -0 -0.1 0.03 -0.1 0.02 -0 0.03 -0.1 -0.1 -0.1 -0.1 -0.1 -0 -0 -0.1 -0.1 -0.1 ID1 1 0.4 0.73 0.29 0.39 0.38 0.13 0.02 0.07 0.13 0.19 0.11 0.09 0.08 0.11 0.02 -0.1 0.07 -0 0.05 0.03 0.04 -0 0.05 0 ID2 0.4 1 0.4 0.34 0.45 0.36 0.15 0.1 0.16 0.13 0.4 0.12 0.19 0.15 0.19 0.05 -0.1 0.08 0.09 0.05 0.07 0.01 -0 -0 0.02 ID3 0.73 0.4 1 0.4 0.43 0.38 0.12 0.04 0.08 0.15 0.2 0.13 0.14 0.1 0.11 0.07 -0 0.11 0.02 0.1 0.01 0.09 -0 0.08 0.02 ID4 0.29 0.34 0.4 1 0.47 0.4 0.09 0.06 0.1 0.16 0.22 0.17 0.39 0.2 0.29 0.01 -0.1 0.04 -0.1 0.01 -0 -0 -0.1 -0 -0 ID5 0.39 0.45 0.43 0.47 1 0.37 0.16 0.17 0.2 0.24 0.36 0.24 0.28 0.32 0.24 0.1 -0 0.13 0.09 0.08 0.04 0.04 -0 -0 0.01 ID6 0.38 0.36 0.38 0.4 0.37 1 0.08 -0 0.07 0.1 0.22 0.1 0.2 0.11 0.36 0.02 -0.1 -0 -0 0.06 -0 0.02 -0.1 0.05 -0 TI1 0.13 0.15 0.12 0.09 0.16 0.08 1 0.34 0.35 0.32 0.27 0.3 0.25 0.32 0.27 0.07 -0 0.08 0.07 0.06 0.06 -0 0.02 0.01 0 TI2 0.02 0.1 0.04 0.06 0.17 -0 0.34 1 0.54 0.53 0.38 0.51 0.46 0.49 0.36 0.02 -0.1 0.05 0.02 0.04 0.04 -0.1 -0 0.01 0.02 TI3 0.07 0.16 0.08 0.1 0.2 0.07 0.35 0.54 1 0.77 0.54 0.74 0.6 0.67 0.54 0.09 0.08 0.13 0.08 0.16 0.15 0.04 0.05 0.11 0.06 TD1 0.13 0.13 0.15 0.16 0.24 0.1 0.32 0.53 0.77 1 0.64 0.94 0.74 0.77 0.65 0.13 0.04 0.14 0.06 0.14 0.13 0.03 0.02 0.08 0.06 TD2 0.19 0.4 0.2 0.22 0.36 0.22 0.27 0.38 0.54 0.64 1 0.63 0.6 0.67 0.61 0.12 -0 0.11 0.11 0.14 0.06 0.02 -0 0.06 0.04 TD3 0.11 0.12 0.13 0.17 0.24 0.1 0.3 0.51 0.74 0.94 0.63 1 0.76 0.78 0.65 0.11 0.04 0.11 0.04 0.11 0.1 0.02 0 0.09 0.06 TD4 0.09 0.19 0.14 0.39 0.28 0.2 0.25 0.46 0.6 0.74 0.6 0.76 1 0.74 0.71 0.07 0 0.03 -0 0.04 0.04 -0 -0 0.03 0 TD5 0.08 0.15 0.1 0.2 0.32 0.11 0.32 0.49 0.67 0.77 0.67 0.78 0.74 1 0.7 0.13 0.05 0.13 0.07 0.14 0.1 0.03 0.02 0.09 0.09 TD6 0.11 0.19 0.11 0.29 0.24 0.36 0.27 0.36 0.54 0.65 0.61 0.65 0.71 0.7 1 0.08 -0 0.04 0.03 0.05 0.03 -0 -0 -0 0.01 SE1 0.02 0.05 0.07 0.01 0.1 0.02 0.07 0.02 0.09 0.13 0.12 0.11 0.07 0.13 0.08 1 0.38 0.55 0.48 0.49 0.45 0.5 0.39 0.45 0.61 SE2 -0.1 -0.1 -0 -0.1 -0 -0.1 -0 -0.1 0.08 0.04 -0 0.04 0 0.05 -0 0.38 1 0.35 0.3 0.51 0.68 0.4 0.56 0.57 0.48 SE3 0.07 0.08 0.11 0.04 0.13 -0 0.08 0.05 0.13 0.14 0.11 0.11 0.03 0.13 0.04 0.55 0.35 1 0.64 0.45 0.4 0.6 0.32 0.41 0.51 SE4 -0 0.09 0.02 -0.1 0.09 -0 0.07 0.02 0.08 0.06 0.11 0.04 -0 0.07 0.03 0.48 0.3 0.64 1 0.39 0.35 0.51 0.27 0.32 0.43 SE5 0.05 0.05 0.1 0.01 0.08 0.06 0.06 0.04 0.16 0.14 0.14 0.11 0.04 0.14 0.05 0.49 0.51 0.45 0.39 1 0.57 0.45 0.42 0.64 0.48 SE6 0.03 0.07 0.01 -0 0.04 -0 0.06 0.04 0.15 0.13 0.06 0.1 0.04 0.1 0.03 0.45 0.68 0.4 0.35 0.57 1 0.41 0.58 0.56 0.51 SE7 0.04 0.01 0.09 -0 0.04 0.02 -0 -0.1 0.04 0.03 0.02 0.02 -0 0.03 -0 0.5 0.4 0.6 0.51 0.45 0.41 1 0.35 0.45 0.54 SE8 -0 -0 -0 -0.1 -0 -0.1 0.02 -0 0.05 0.02 -0 0 -0 0.02 -0 0.39 0.56 0.32 0.27 0.42 0.58 0.35 1 0.5 0.54 SE9 0.05 -0 0.08 -0 -0 0.05 0.01 0.01 0.11 0.08 0.06 0.09 0.03 0.09 -0 0.45 0.57 0.41 0.32 0.64 0.56 0.45 0.5 1 0.53 SE10 0 0.02 0.02 -0 0.01 -0 0 0.02 0.06 0.06 0.04 0.06 0 0.09 0.01 0.61 0.48 0.51 0.43 0.48 0.51 0.54 0.54 0.53 1

226

APPENDIX H.

CORRELATION MATRIX OF NEWS ORGANIZATION MODEL

C1/N C2/N C3/N C4/N C5/N C6/N T1/N T2/N T3/N ST1 ST2 ST3 A1/N A2/N A3/N A4/N D1/N D2/N D3/N D4/N TA1 TA2 TA3 TA4 II1 II2 C1/N 1 0.93 0.93 0.9 0.92 0.65 0.75 0.73 0.85 0.66 0.65 0.65 0.76 0.76 0.67 0.84 0.73 0.84 0.83 0.6 0.63 0.82 0.81 0.79 0.05 0.01 C2/N 0.93 1 0.94 0.93 0.93 0.69 0.8 0.79 0.85 0.7 0.67 0.71 0.72 0.71 0.72 0.88 0.77 0.83 0.81 0.56 0.6 0.78 0.82 0.78 0.04 -0 C3/N 0.93 0.94 1 0.93 0.92 0.67 0.77 0.77 0.87 0.67 0.67 0.65 0.75 0.75 0.69 0.87 0.74 0.86 0.84 0.59 0.64 0.81 0.84 0.83 0.04 -0 C4/N 0.9 0.93 0.93 1 0.92 0.68 0.8 0.8 0.84 0.7 0.68 0.69 0.74 0.73 0.72 0.86 0.77 0.82 0.81 0.59 0.63 0.78 0.82 0.79 0.02 -0 C5/N 0.92 0.93 0.92 0.92 1 0.67 0.79 0.79 0.88 0.7 0.67 0.68 0.74 0.74 0.72 0.87 0.77 0.84 0.84 0.59 0.64 0.8 0.8 0.82 0.04 -0 C6/N 0.65 0.69 0.67 0.68 0.67 1 0.72 0.7 0.69 0.63 0.65 0.6 0.56 0.58 0.65 0.71 0.73 0.7 0.65 0.61 0.48 0.62 0.67 0.63 0.02 -0 T1/N 0.75 0.8 0.77 0.8 0.79 0.72 1 0.93 0.76 0.76 0.71 0.69 0.6 0.62 0.76 0.83 0.88 0.75 0.71 0.52 0.49 0.64 0.71 0.65 0.06 -0 T2/N 0.73 0.79 0.77 0.8 0.79 0.7 0.93 1 0.76 0.76 0.69 0.7 0.58 0.6 0.75 0.84 0.85 0.74 0.69 0.52 0.5 0.64 0.71 0.65 0.02 -0.1 T3/N 0.85 0.85 0.87 0.84 0.88 0.69 0.76 0.76 1 0.69 0.67 0.65 0.82 0.77 0.69 0.87 0.71 0.86 0.85 0.59 0.71 0.86 0.82 0.84 0 -0 ST1 0.66 0.7 0.67 0.7 0.7 0.63 0.76 0.76 0.69 1 0.85 0.75 0.56 0.57 0.73 0.75 0.69 0.68 0.66 0.51 0.46 0.59 0.61 0.61 0.04 -0 ST2 0.65 0.67 0.67 0.68 0.67 0.65 0.71 0.69 0.67 0.85 1 0.74 0.59 0.61 0.71 0.7 0.66 0.7 0.66 0.58 0.48 0.6 0.64 0.64 -0 -0 ST3 0.65 0.71 0.65 0.69 0.68 0.6 0.69 0.7 0.65 0.75 0.74 1 0.57 0.57 0.79 0.7 0.67 0.67 0.61 0.52 0.43 0.56 0.63 0.56 -0 0 A1/N 0.76 0.72 0.75 0.74 0.74 0.56 0.6 0.58 0.82 0.56 0.59 0.57 1 0.83 0.65 0.7 0.6 0.77 0.76 0.55 0.75 0.82 0.72 0.78 0.05 0.02 A2/N 0.76 0.71 0.75 0.73 0.74 0.58 0.62 0.6 0.77 0.57 0.61 0.57 0.83 1 0.69 0.74 0.6 0.84 0.81 0.59 0.74 0.8 0.74 0.78 0.05 0.03 A3/N 0.67 0.72 0.69 0.72 0.72 0.65 0.76 0.75 0.69 0.73 0.71 0.79 0.65 0.69 1 0.73 0.73 0.75 0.67 0.53 0.55 0.61 0.65 0.61 0.06 -0 A4/N 0.84 0.88 0.87 0.86 0.87 0.71 0.83 0.84 0.87 0.75 0.7 0.7 0.7 0.74 0.73 1 0.78 0.84 0.79 0.58 0.61 0.77 0.8 0.75 0.02 -0 D1/N 0.73 0.77 0.74 0.77 0.77 0.73 0.88 0.85 0.71 0.69 0.66 0.67 0.6 0.6 0.73 0.78 1 0.72 0.7 0.52 0.47 0.61 0.69 0.64 0.04 -0 D2/N 0.84 0.83 0.86 0.82 0.84 0.7 0.75 0.74 0.86 0.68 0.7 0.67 0.77 0.84 0.75 0.84 0.72 1 0.9 0.63 0.68 0.83 0.81 0.83 0.04 0.02 D3/N 0.83 0.81 0.84 0.81 0.84 0.65 0.71 0.69 0.85 0.66 0.66 0.61 0.76 0.81 0.67 0.79 0.7 0.9 1 0.63 0.67 0.83 0.81 0.86 0.03 0.03 D4/N 0.6 0.56 0.59 0.59 0.59 0.61 0.52 0.52 0.59 0.51 0.58 0.52 0.55 0.59 0.53 0.58 0.52 0.63 0.63 1 0.48 0.59 0.6 0.59 -0.1 -0 TA1 0.63 0.6 0.64 0.63 0.64 0.48 0.49 0.5 0.71 0.46 0.48 0.43 0.75 0.74 0.55 0.61 0.47 0.68 0.67 0.48 1 0.77 0.67 0.72 0.02 0.01 TA2 0.82 0.78 0.81 0.78 0.8 0.62 0.64 0.64 0.86 0.59 0.6 0.56 0.82 0.8 0.61 0.77 0.61 0.83 0.83 0.59 0.77 1 0.87 0.87 0.05 0.01 TA3 0.81 0.82 0.84 0.82 0.8 0.67 0.71 0.71 0.82 0.61 0.64 0.63 0.72 0.74 0.65 0.8 0.69 0.81 0.81 0.6 0.67 0.87 1 0.84 0.03 -0 TA4 0.79 0.78 0.83 0.79 0.82 0.63 0.65 0.65 0.84 0.61 0.64 0.56 0.78 0.78 0.61 0.75 0.64 0.83 0.86 0.59 0.72 0.87 0.84 1 0.05 0.02 II1 0.05 0.04 0.04 0.02 0.04 0.02 0.06 0.02 0 0.04 -0 -0 0.05 0.05 0.06 0.02 0.04 0.04 0.03 -0.1 0.02 0.05 0.03 0.05 1 0.15 II2 0.01 -0 -0 -0 -0 -0 -0 -0.1 -0 -0 -0 0 0.02 0.03 -0 -0 -0 0.02 0.03 -0 0.01 0.01 -0 0.02 0.15 1 ID1 0.05 0.05 0.06 0.03 0.04 0.07 0.05 0.05 0.11 0.09 0.06 0.09 0.09 0.07 0.09 0.08 0.03 0.08 0.04 0.02 0.03 0.09 0.07 0.06 0.07 0.21 ID2 -0 -0 -0 0.02 0 0.04 0.03 0.02 0.03 0.07 0.07 0.02 0 -0 0.04 0.01 0.03 -0 0.01 0.05 -0 -0 0 0.03 0.01 0.11 ID3 0.05 0.1 0.08 0.1 0.05 0.12 0.12 0.13 0.1 0.12 0.08 0.1 0.05 0.06 0.1 0.13 0.11 0.09 0.05 0.03 0 0.07 0.12 0.05 0.08 0.17 ID4 0.11 0.11 0.12 0.13 0.1 0.12 0.15 0.14 0.12 0.14 0.15 0.11 0.09 0.1 0.1 0.14 0.12 0.13 0.08 0.15 0.08 0.1 0.1 0.09 0.12 0.11 ID5 0.04 0.06 0.06 0.08 0.07 0.11 0.12 0.11 0.08 0.14 0.16 0.11 0.06 0.05 0.14 0.08 0.11 0.09 0.06 0.07 0.03 0.06 0.05 0.08 0.05 0.14 ID6 0.03 0.03 0.05 0.04 0.04 0.01 0.1 0.09 0.07 0.12 0.1 0.05 0.03 0.07 0.08 0.08 0.04 0.09 0.06 0.07 0.05 0.03 0.03 0.05 0.12 0.12 TI1 0.17 0.18 0.17 0.18 0.18 0.16 0.18 0.17 0.18 0.23 0.2 0.18 0.13 0.17 0.17 0.19 0.17 0.2 0.2 0.14 0.1 0.18 0.19 0.2 0.01 0.14 TI2 0.08 0.06 0.07 0.08 0.08 0.07 0.07 0.09 0.11 0.09 0.12 0.08 0.1 0.1 0.09 0.07 0.08 0.1 0.11 0.06 0.09 0.08 0.1 0.1 -0.1 0.27 TI3 0.14 0.16 0.17 0.15 0.16 0.2 0.16 0.2 0.17 0.17 0.19 0.15 0.1 0.12 0.15 0.17 0.14 0.17 0.16 0.14 0.12 0.15 0.2 0.19 -0.2 -0 TD1 0.12 0.16 0.15 0.14 0.15 0.2 0.18 0.2 0.16 0.18 0.22 0.14 0.11 0.1 0.16 0.17 0.15 0.15 0.14 0.14 0.09 0.13 0.19 0.18 -0.1 -0.1 TD2 0.11 0.12 0.13 0.13 0.13 0.14 0.14 0.15 0.16 0.17 0.19 0.11 0.13 0.12 0.14 0.15 0.14 0.14 0.14 0.15 0.09 0.11 0.15 0.16 -0.1 0.03 TD3 0.14 0.17 0.16 0.15 0.16 0.21 0.18 0.21 0.18 0.19 0.21 0.14 0.11 0.1 0.16 0.18 0.16 0.16 0.15 0.13 0.1 0.16 0.21 0.2 -0.1 -0.1 TD4 0.13 0.15 0.15 0.15 0.14 0.2 0.17 0.19 0.14 0.17 0.22 0.14 0.11 0.1 0.14 0.17 0.17 0.16 0.15 0.16 0.1 0.12 0.16 0.15 -0.1 0.02 TD5 0.14 0.18 0.17 0.17 0.17 0.22 0.17 0.19 0.19 0.19 0.22 0.15 0.16 0.13 0.16 0.17 0.16 0.17 0.18 0.16 0.12 0.15 0.19 0.2 -0.1 -0 TD6 0.12 0.12 0.15 0.13 0.12 0.16 0.18 0.19 0.16 0.18 0.2 0.11 0.13 0.1 0.12 0.15 0.13 0.17 0.15 0.11 0.11 0.12 0.15 0.18 -0 0.03 SE1 0.06 0.06 0.07 0.07 0.09 0.1 0.06 0.05 0.08 0.05 0.05 0.05 0.05 0.07 0.06 0.05 0.06 0.1 0.07 0.02 0.01 0.06 0.06 0.05 0.06 -0.1 SE2 0.11 0.13 0.14 0.14 0.14 0.14 0.13 0.09 0.12 0.09 0.11 0.08 0.12 0.1 0.09 0.12 0.1 0.12 0.14 0.08 0.11 0.12 0.11 0.17 0.09 -0.1 SE3 0.14 0.13 0.14 0.15 0.15 0.18 0.15 0.17 0.15 0.16 0.13 0.15 0.09 0.12 0.16 0.14 0.16 0.15 0.13 0.06 0.04 0.12 0.13 0.12 0.14 -0.1 SE4 0.14 0.13 0.14 0.14 0.15 0.12 0.11 0.11 0.13 0.12 0.08 0.11 0.08 0.12 0.12 0.1 0.14 0.15 0.15 0.04 0.06 0.11 0.1 0.14 0.08 -0.1 SE5 0.09 0.12 0.14 0.12 0.14 0.12 0.13 0.12 0.12 0.1 0.07 0.06 0.07 0.11 0.09 0.13 0.13 0.13 0.14 0.04 0.07 0.12 0.12 0.11 -0 -0.1 SE6 0.1 0.12 0.12 0.13 0.13 0.17 0.13 0.1 0.13 0.13 0.15 0.11 0.12 0.13 0.11 0.14 0.11 0.15 0.17 0.08 0.11 0.12 0.14 0.16 0.03 -0 SE7 0.09 0.09 0.11 0.12 0.13 0.12 0.07 0.07 0.13 0.09 0.07 0.08 0.1 0.11 0.09 0.09 0.09 0.13 0.12 0.04 0.07 0.11 0.1 0.11 0.11 -0 SE8 0.12 0.14 0.14 0.14 0.16 0.14 0.1 0.07 0.13 0.09 0.08 0.08 0.13 0.1 0.14 0.12 0.08 0.14 0.16 0.04 0.12 0.12 0.11 0.13 0.08 -0.1 SE9 0.11 0.13 0.16 0.13 0.15 0.11 0.11 0.11 0.13 0.07 0.07 0.06 0.08 0.12 0.05 0.14 0.1 0.15 0.15 0.03 0.09 0.14 0.14 0.11 0.05 -0.1 SE10 0.12 0.1 0.12 0.13 0.14 0.18 0.13 0.11 0.14 0.09 0.09 0.11 0.14 0.13 0.12 0.12 0.12 0.18 0.13 0.1 0.14 0.13 0.12 0.1 0.01 -0.1

227

CONTINUTED

ID1 ID2 ID3 ID4 ID5 ID6 TI1 TI2 TI3 TD1 TD2 TD3 TD4 TD5 TD6 SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 C1/N 0.05 -0 0.05 0.11 0.04 0.03 0.17 0.08 0.14 0.12 0.11 0.14 0.13 0.14 0.12 0.06 0.11 0.14 0.14 0.09 0.1 0.09 0.12 0.11 0.12 C2/N 0.05 -0 0.1 0.11 0.06 0.03 0.18 0.06 0.16 0.16 0.12 0.17 0.15 0.18 0.12 0.06 0.13 0.13 0.13 0.12 0.12 0.09 0.14 0.13 0.1 C3/N 0.06 -0 0.08 0.12 0.06 0.05 0.17 0.07 0.17 0.15 0.13 0.16 0.15 0.17 0.15 0.07 0.14 0.14 0.14 0.14 0.12 0.11 0.14 0.16 0.12 C4/N 0.03 0.02 0.1 0.13 0.08 0.04 0.18 0.08 0.15 0.14 0.13 0.15 0.15 0.17 0.13 0.07 0.14 0.15 0.14 0.12 0.13 0.12 0.14 0.13 0.13 C5/N 0.04 0 0.05 0.1 0.07 0.04 0.18 0.08 0.16 0.15 0.13 0.16 0.14 0.17 0.12 0.09 0.14 0.15 0.15 0.14 0.13 0.13 0.16 0.15 0.14 C6/N 0.07 0.04 0.12 0.12 0.11 0.01 0.16 0.07 0.2 0.2 0.14 0.21 0.2 0.22 0.16 0.1 0.14 0.18 0.12 0.12 0.17 0.12 0.14 0.11 0.18 T1/N 0.05 0.03 0.12 0.15 0.12 0.1 0.18 0.07 0.16 0.18 0.14 0.18 0.17 0.17 0.18 0.06 0.13 0.15 0.11 0.13 0.13 0.07 0.1 0.11 0.13 T2/N 0.05 0.02 0.13 0.14 0.11 0.09 0.17 0.09 0.2 0.2 0.15 0.21 0.19 0.19 0.19 0.05 0.09 0.17 0.11 0.12 0.1 0.07 0.07 0.11 0.11 T3/N 0.11 0.03 0.1 0.12 0.08 0.07 0.18 0.11 0.17 0.16 0.16 0.18 0.14 0.19 0.16 0.08 0.12 0.15 0.13 0.12 0.13 0.13 0.13 0.13 0.14 ST1 0.09 0.07 0.12 0.14 0.14 0.12 0.23 0.09 0.17 0.18 0.17 0.19 0.17 0.19 0.18 0.05 0.09 0.16 0.12 0.1 0.13 0.09 0.09 0.07 0.09 ST2 0.06 0.07 0.08 0.15 0.16 0.1 0.2 0.12 0.19 0.22 0.19 0.21 0.22 0.22 0.2 0.05 0.11 0.13 0.08 0.07 0.15 0.07 0.08 0.07 0.09 ST3 0.09 0.02 0.1 0.11 0.11 0.05 0.18 0.08 0.15 0.14 0.11 0.14 0.14 0.15 0.11 0.05 0.08 0.15 0.11 0.06 0.11 0.08 0.08 0.06 0.11 A1/N 0.09 0 0.05 0.09 0.06 0.03 0.13 0.1 0.1 0.11 0.13 0.11 0.11 0.16 0.13 0.05 0.12 0.09 0.08 0.07 0.12 0.1 0.13 0.08 0.14 A2/N 0.07 -0 0.06 0.1 0.05 0.07 0.17 0.1 0.12 0.1 0.12 0.1 0.1 0.13 0.1 0.07 0.1 0.12 0.12 0.11 0.13 0.11 0.1 0.12 0.13 A3/N 0.09 0.04 0.1 0.1 0.14 0.08 0.17 0.09 0.15 0.16 0.14 0.16 0.14 0.16 0.12 0.06 0.09 0.16 0.12 0.09 0.11 0.09 0.14 0.05 0.12 A4/N 0.08 0.01 0.13 0.14 0.08 0.08 0.19 0.07 0.17 0.17 0.15 0.18 0.17 0.17 0.15 0.05 0.12 0.14 0.1 0.13 0.14 0.09 0.12 0.14 0.12 D1/N 0.03 0.03 0.11 0.12 0.11 0.04 0.17 0.08 0.14 0.15 0.14 0.16 0.17 0.16 0.13 0.06 0.1 0.16 0.14 0.13 0.11 0.09 0.08 0.1 0.12 D2/N 0.08 -0 0.09 0.13 0.09 0.09 0.2 0.1 0.17 0.15 0.14 0.16 0.16 0.17 0.17 0.1 0.12 0.15 0.15 0.13 0.15 0.13 0.14 0.15 0.18 D3/N 0.04 0.01 0.05 0.08 0.06 0.06 0.2 0.11 0.16 0.14 0.14 0.15 0.15 0.18 0.15 0.07 0.14 0.13 0.15 0.14 0.17 0.12 0.16 0.15 0.13 D4/N 0.02 0.05 0.03 0.15 0.07 0.07 0.14 0.06 0.14 0.14 0.15 0.13 0.16 0.16 0.11 0.02 0.08 0.06 0.04 0.04 0.08 0.04 0.04 0.03 0.1 TA1 0.03 -0 0 0.08 0.03 0.05 0.1 0.09 0.12 0.09 0.09 0.1 0.1 0.12 0.11 0.01 0.11 0.04 0.06 0.07 0.11 0.07 0.12 0.09 0.14 TA2 0.09 -0 0.07 0.1 0.06 0.03 0.18 0.08 0.15 0.13 0.11 0.16 0.12 0.15 0.12 0.06 0.12 0.12 0.11 0.12 0.12 0.11 0.12 0.14 0.13 TA3 0.07 0 0.12 0.1 0.05 0.03 0.19 0.1 0.2 0.19 0.15 0.21 0.16 0.19 0.15 0.06 0.11 0.13 0.1 0.12 0.14 0.1 0.11 0.14 0.12 TA4 0.06 0.03 0.05 0.09 0.08 0.05 0.2 0.1 0.19 0.18 0.16 0.2 0.15 0.2 0.18 0.05 0.17 0.12 0.14 0.11 0.16 0.11 0.13 0.11 0.1 II1 0.07 0.01 0.08 0.12 0.05 0.12 0.01 -0.1 -0.2 -0.1 -0.1 -0.1 -0.1 -0.1 -0 0.06 0.09 0.14 0.08 -0 0.03 0.11 0.08 0.05 0.01 II2 0.21 0.11 0.17 0.11 0.14 0.12 0.14 0.27 -0 -0.1 0.03 -0.1 0.02 -0 0.03 -0.1 -0.1 -0.1 -0.1 -0.1 -0 -0 -0.1 -0.1 -0.1 ID1 1 0.4 0.73 0.29 0.39 0.38 0.13 0.02 0.07 0.13 0.19 0.11 0.09 0.08 0.11 0.02 -0.1 0.07 -0 0.05 0.03 0.04 -0 0.05 0 ID2 0.4 1 0.4 0.34 0.45 0.36 0.15 0.1 0.16 0.13 0.4 0.12 0.19 0.15 0.19 0.05 -0.1 0.08 0.09 0.05 0.07 0.01 -0 -0 0.02 ID3 0.73 0.4 1 0.4 0.43 0.38 0.12 0.04 0.08 0.15 0.2 0.13 0.14 0.1 0.11 0.07 -0 0.11 0.02 0.1 0.01 0.09 -0 0.08 0.02 ID4 0.29 0.34 0.4 1 0.47 0.4 0.09 0.06 0.1 0.16 0.22 0.17 0.39 0.2 0.29 0.01 -0.1 0.04 -0.1 0.01 -0 -0 -0.1 -0 -0 ID5 0.39 0.45 0.43 0.47 1 0.37 0.16 0.17 0.2 0.24 0.36 0.24 0.28 0.32 0.24 0.1 -0 0.13 0.09 0.08 0.04 0.04 -0 -0 0.01 ID6 0.38 0.36 0.38 0.4 0.37 1 0.08 -0 0.07 0.1 0.22 0.1 0.2 0.11 0.36 0.02 -0.1 -0 -0 0.06 -0 0.02 -0.1 0.05 -0 TI1 0.13 0.15 0.12 0.09 0.16 0.08 1 0.34 0.35 0.32 0.27 0.3 0.25 0.32 0.27 0.07 -0 0.08 0.07 0.06 0.06 -0 0.02 0.01 0 TI2 0.02 0.1 0.04 0.06 0.17 -0 0.34 1 0.54 0.53 0.38 0.51 0.46 0.49 0.36 0.02 -0.1 0.05 0.02 0.04 0.04 -0.1 -0 0.01 0.02 TI3 0.07 0.16 0.08 0.1 0.2 0.07 0.35 0.54 1 0.77 0.54 0.74 0.6 0.67 0.54 0.09 0.08 0.13 0.08 0.16 0.15 0.04 0.05 0.11 0.06 TD1 0.13 0.13 0.15 0.16 0.24 0.1 0.32 0.53 0.77 1 0.64 0.94 0.74 0.77 0.65 0.13 0.04 0.14 0.06 0.14 0.13 0.03 0.02 0.08 0.06 TD2 0.19 0.4 0.2 0.22 0.36 0.22 0.27 0.38 0.54 0.64 1 0.63 0.6 0.67 0.61 0.12 -0 0.11 0.11 0.14 0.06 0.02 -0 0.06 0.04 TD3 0.11 0.12 0.13 0.17 0.24 0.1 0.3 0.51 0.74 0.94 0.63 1 0.76 0.78 0.65 0.11 0.04 0.11 0.04 0.11 0.1 0.02 0 0.09 0.06 TD4 0.09 0.19 0.14 0.39 0.28 0.2 0.25 0.46 0.6 0.74 0.6 0.76 1 0.74 0.71 0.07 0 0.03 -0 0.04 0.04 -0 -0 0.03 0 TD5 0.08 0.15 0.1 0.2 0.32 0.11 0.32 0.49 0.67 0.77 0.67 0.78 0.74 1 0.7 0.13 0.05 0.13 0.07 0.14 0.1 0.03 0.02 0.09 0.09 TD6 0.11 0.19 0.11 0.29 0.24 0.36 0.27 0.36 0.54 0.65 0.61 0.65 0.71 0.7 1 0.08 -0 0.04 0.03 0.05 0.03 -0 -0 -0 0.01 SE1 0.02 0.05 0.07 0.01 0.1 0.02 0.07 0.02 0.09 0.13 0.12 0.11 0.07 0.13 0.08 1 0.38 0.55 0.48 0.49 0.45 0.5 0.39 0.45 0.61 SE2 -0.1 -0.1 -0 -0.1 -0 -0.1 -0 -0.1 0.08 0.04 -0 0.04 0 0.05 -0 0.38 1 0.35 0.3 0.51 0.68 0.4 0.56 0.57 0.48 SE3 0.07 0.08 0.11 0.04 0.13 -0 0.08 0.05 0.13 0.14 0.11 0.11 0.03 0.13 0.04 0.55 0.35 1 0.64 0.45 0.4 0.6 0.32 0.41 0.51 SE4 -0 0.09 0.02 -0.1 0.09 -0 0.07 0.02 0.08 0.06 0.11 0.04 -0 0.07 0.03 0.48 0.3 0.64 1 0.39 0.35 0.51 0.27 0.32 0.43 SE5 0.05 0.05 0.1 0.01 0.08 0.06 0.06 0.04 0.16 0.14 0.14 0.11 0.04 0.14 0.05 0.49 0.51 0.45 0.39 1 0.57 0.45 0.42 0.64 0.48 SE6 0.03 0.07 0.01 -0 0.04 -0 0.06 0.04 0.15 0.13 0.06 0.1 0.04 0.1 0.03 0.45 0.68 0.4 0.35 0.57 1 0.41 0.58 0.56 0.51 SE7 0.04 0.01 0.09 -0 0.04 0.02 -0 -0.1 0.04 0.03 0.02 0.02 -0 0.03 -0 0.5 0.4 0.6 0.51 0.45 0.41 1 0.35 0.45 0.54 SE8 -0 -0 -0 -0.1 -0 -0.1 0.02 -0 0.05 0.02 -0 0 -0 0.02 -0 0.39 0.56 0.32 0.27 0.42 0.58 0.35 1 0.5 0.54 SE9 0.05 -0 0.08 -0 -0 0.05 0.01 0.01 0.11 0.08 0.06 0.09 0.03 0.09 -0 0.45 0.57 0.41 0.32 0.64 0.56 0.45 0.5 1 0.53 SE10 0 0.02 0.02 -0 0.01 -0 0 0.02 0.06 0.06 0.04 0.06 0 0.09 0.01 0.61 0.48 0.51 0.43 0.48 0.51 0.54 0.54 0.53 1

228

APPENDIX I.

CORRELATION MATRIX OF BRAND MODEL

C1/B C2/B C3/B T1/B T2/B T3/B ST1 ST2 ST3 A1/B A2/B A3/B A4/B D1/B D2/B D3/B TA1 TA2 TA3 TA4 TA5/B II1 II2 C1/B 1 0.84 0.76 0.74 0.76 0.61 0.6 0.64 0.57 0.54 0.55 0.68 0.64 0.67 0.64 0.6 0.49 0.58 0.65 0.62 0.63 -0.1 -0 C2/B 0.84 1 0.81 0.77 0.77 0.69 0.61 0.64 0.59 0.61 0.62 0.67 0.71 0.73 0.7 0.64 0.59 0.63 0.7 0.63 0.69 -0.1 -0 C3/B 0.76 0.81 1 0.76 0.78 0.82 0.67 0.69 0.65 0.65 0.67 0.74 0.84 0.82 0.76 0.68 0.64 0.76 0.77 0.72 0.78 -0 -0 T1/B 0.74 0.77 0.76 1 0.91 0.7 0.68 0.67 0.65 0.6 0.62 0.74 0.73 0.75 0.7 0.67 0.58 0.64 0.71 0.62 0.69 -0.1 -0 T2/B 0.76 0.77 0.78 0.91 1 0.7 0.7 0.71 0.68 0.59 0.61 0.73 0.74 0.75 0.69 0.65 0.6 0.63 0.74 0.64 0.69 -0.1 -0 T3/B 0.61 0.69 0.82 0.7 0.7 1 0.61 0.6 0.6 0.79 0.76 0.68 0.81 0.82 0.77 0.66 0.69 0.81 0.76 0.73 0.81 -0 -0 ST1 0.6 0.61 0.67 0.68 0.7 0.61 1 0.8 0.73 0.53 0.53 0.72 0.65 0.62 0.61 0.52 0.48 0.54 0.58 0.58 0.62 -0 -0 ST2 0.64 0.64 0.69 0.67 0.71 0.6 0.8 1 0.77 0.57 0.58 0.71 0.67 0.66 0.62 0.54 0.48 0.57 0.59 0.61 0.66 -0.1 -0 ST3 0.57 0.59 0.65 0.65 0.68 0.6 0.73 0.77 1 0.52 0.54 0.69 0.63 0.64 0.6 0.54 0.45 0.54 0.6 0.58 0.61 -0 -0 A1/B 0.54 0.61 0.65 0.6 0.59 0.79 0.53 0.57 0.52 1 0.89 0.64 0.73 0.73 0.74 0.58 0.72 0.79 0.67 0.67 0.71 -0 -0 A2/B 0.55 0.62 0.67 0.62 0.61 0.76 0.53 0.58 0.54 0.89 1 0.69 0.74 0.71 0.74 0.58 0.7 0.77 0.69 0.66 0.7 -0 -0 A3/B 0.68 0.67 0.74 0.74 0.73 0.68 0.72 0.71 0.69 0.64 0.69 1 0.78 0.74 0.71 0.6 0.56 0.65 0.65 0.66 0.73 -0 -0.1 A4/B 0.64 0.71 0.84 0.73 0.74 0.81 0.65 0.67 0.63 0.73 0.74 0.78 1 0.86 0.77 0.67 0.66 0.74 0.75 0.74 0.8 -0 -0 D1/B 0.67 0.73 0.82 0.75 0.75 0.82 0.62 0.66 0.64 0.73 0.71 0.74 0.86 1 0.85 0.72 0.68 0.78 0.79 0.74 0.81 -0 -0 D2/B 0.64 0.7 0.76 0.7 0.69 0.77 0.61 0.62 0.6 0.74 0.74 0.71 0.77 0.85 1 0.77 0.67 0.79 0.77 0.76 0.77 -0.1 -0 D3/B 0.6 0.64 0.68 0.67 0.65 0.66 0.52 0.54 0.54 0.58 0.58 0.6 0.67 0.72 0.77 1 0.58 0.68 0.66 0.66 0.67 -0.1 -0 TA1 0.49 0.59 0.64 0.58 0.6 0.69 0.48 0.48 0.45 0.72 0.7 0.56 0.66 0.68 0.67 0.58 1 0.79 0.69 0.64 0.67 -0.1 -0.1 TA2 0.58 0.63 0.76 0.64 0.63 0.81 0.54 0.57 0.54 0.79 0.77 0.65 0.74 0.78 0.79 0.68 0.79 1 0.76 0.79 0.79 -0 0.01 TA3 0.65 0.7 0.77 0.71 0.74 0.76 0.58 0.59 0.6 0.67 0.69 0.65 0.75 0.79 0.77 0.66 0.69 0.76 1 0.77 0.81 -0 -0 TA4 0.62 0.63 0.72 0.62 0.64 0.73 0.58 0.61 0.58 0.67 0.66 0.66 0.74 0.74 0.76 0.66 0.64 0.79 0.77 1 0.81 -0 -0 TA5/B 0.63 0.69 0.78 0.69 0.69 0.81 0.62 0.66 0.61 0.71 0.7 0.73 0.8 0.81 0.77 0.67 0.67 0.79 0.81 0.81 1 -0 -0 II1 -0.1 -0.1 -0 -0.1 -0.1 -0 -0 -0.1 -0 -0 -0 -0 -0 -0 -0.1 -0.1 -0.1 -0 -0 -0 -0 1 0.15 II2 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -0.1 -0 -0 -0 -0 -0.1 0.01 -0 -0 -0 0.15 1 ID1 -0 -0 0.03 -0 -0 0.05 0.08 0.11 0.09 0.09 0.12 0.07 0.04 0.03 0.05 0.02 -0 0.08 0.04 0.04 0.03 0.07 0.21 ID2 0.03 0.04 0.03 0.04 0.04 0.07 0.07 0.11 0.09 0.1 0.13 0.08 0.03 0.02 0.05 0.07 0.03 0.1 0.01 0.1 0.05 0.01 0.11 ID3 -0 -0 0.01 0.01 -0 0.02 0.09 0.1 0.1 0.04 0.06 0.05 0.01 0.02 0.02 0.06 -0 0.04 0.05 0.01 0.02 0.08 0.17 ID4 0.07 0.07 0.06 0.08 0.06 0.08 0.06 0.14 0.07 0.09 0.12 0.08 0.08 0.05 0.06 0.14 0.06 0.11 0.07 0.06 0.1 0.12 0.11 ID5 0.14 0.13 0.13 0.13 0.13 0.13 0.17 0.17 0.15 0.17 0.2 0.16 0.14 0.11 0.15 0.18 0.11 0.19 0.15 0.14 0.13 0.05 0.14 ID6 0.03 0.04 0.04 0.03 0.05 0.07 0.03 0.03 0.02 0.09 0.11 0.05 0.03 0.02 0.06 0.05 0.04 0.09 0.02 0.03 0.02 0.12 0.12 TI1 0.13 0.11 0.18 0.11 0.11 0.15 0.12 0.12 0.09 0.1 0.1 0.16 0.15 0.17 0.12 0.14 0.1 0.14 0.07 0.11 0.12 0.01 0.14 TI2 0.15 0.15 0.14 0.16 0.16 0.1 0.13 0.16 0.11 0.11 0.1 0.14 0.12 0.14 0.11 0.12 0.14 0.12 0.1 0.07 0.12 -0.1 0.27 TI3 0.19 0.17 0.18 0.23 0.21 0.19 0.21 0.2 0.13 0.18 0.19 0.24 0.18 0.18 0.18 0.17 0.19 0.2 0.16 0.19 0.21 -0.2 -0 TD1 0.21 0.17 0.18 0.25 0.22 0.16 0.21 0.21 0.16 0.18 0.18 0.24 0.19 0.17 0.18 0.19 0.18 0.18 0.16 0.19 0.17 -0.1 -0.1 TD2 0.19 0.2 0.18 0.2 0.18 0.18 0.21 0.19 0.14 0.22 0.22 0.21 0.18 0.15 0.2 0.19 0.18 0.23 0.15 0.21 0.15 -0.1 0.03 TD3 0.18 0.14 0.15 0.24 0.2 0.14 0.18 0.19 0.12 0.16 0.16 0.22 0.16 0.13 0.16 0.17 0.16 0.16 0.14 0.17 0.15 -0.1 -0.1 TD4 0.15 0.13 0.08 0.17 0.13 0.08 0.1 0.17 0.06 0.13 0.14 0.16 0.13 0.09 0.12 0.12 0.13 0.12 0.1 0.11 0.14 -0.1 0.02 TD5 0.19 0.19 0.18 0.21 0.17 0.17 0.16 0.17 0.12 0.18 0.19 0.19 0.19 0.17 0.18 0.18 0.17 0.18 0.16 0.18 0.19 -0.1 -0 TD6 0.1 0.1 0.11 0.16 0.14 0.12 0.11 0.11 0.07 0.15 0.16 0.14 0.12 0.08 0.1 0.13 0.13 0.15 0.12 0.14 0.12 -0 0.03 SE1 0.06 0.1 0.09 0.08 0.1 0.1 0.08 0.09 0.09 0.13 0.11 0.1 0.13 0.12 0.11 0.12 0.12 0.12 0.1 0.12 0.08 0.06 -0.1 SE2 0.04 0.05 0.06 0.06 0.06 0.09 0.02 0.03 0.02 0.09 0.1 0.06 0.09 0.08 0.09 0.06 0.09 0.06 0.11 0.09 0.11 0.09 -0.1 SE3 0.03 0.06 0.11 0.08 0.07 0.13 0.12 0.09 0.06 0.13 0.14 0.12 0.1 0.1 0.09 0.07 0.11 0.1 0.1 0.08 0.08 0.14 -0.1 SE4 0.05 0.08 0.12 0.11 0.1 0.13 0.11 0.07 0.06 0.16 0.16 0.1 0.12 0.13 0.14 0.14 0.15 0.14 0.1 0.12 0.1 0.08 -0.1 SE5 0.06 0.07 0.11 0.1 0.09 0.11 0.07 0.07 0.07 0.11 0.11 0.1 0.12 0.12 0.11 0.11 0.1 0.1 0.11 0.11 0.09 -0 -0.1 SE6 0.07 0.08 0.08 0.1 0.1 0.1 0.06 0.08 0.06 0.13 0.15 0.07 0.1 0.11 0.13 0.13 0.1 0.1 0.12 0.11 0.1 0.03 -0 SE7 0.02 0.07 0.09 0.09 0.08 0.13 0.13 0.1 0.1 0.13 0.13 0.08 0.12 0.13 0.14 0.09 0.14 0.09 0.14 0.11 0.12 0.11 -0 SE8 0.02 0.04 0.09 0.1 0.09 0.1 0.04 0.04 0.05 0.07 0.1 0.07 0.13 0.12 0.09 0.06 0.07 0.05 0.08 0.1 0.12 0.08 -0.1 SE9 0.02 0.07 0.1 0.09 0.08 0.16 0.07 0.05 0.06 0.1 0.12 0.05 0.1 0.15 0.13 0.05 0.11 0.12 0.11 0.09 0.12 0.05 -0.1 SE10 0.05 0.12 0.15 0.12 0.12 0.13 0.11 0.12 0.09 0.12 0.13 0.09 0.13 0.14 0.13 0.13 0.16 0.13 0.15 0.15 0.16 0.01 -0.1

229

CONTINUTED

ID1 ID2 ID3 ID4 ID5 ID6 TI1 TI2 TI3 TD1 TD2 TD3 TD4 TD5 TD6 SE1 SE2 SE3 SE4 SE5 SE6 SE7 SE8 SE9 SE10 C1/B -0 0.03 -0 0.07 0.14 0.03 0.13 0.15 0.19 0.21 0.19 0.18 0.15 0.19 0.1 0.06 0.04 0.03 0.05 0.06 0.07 0.02 0.02 0.02 0.05 C2/B -0 0.04 -0 0.07 0.13 0.04 0.11 0.15 0.17 0.17 0.2 0.14 0.13 0.19 0.1 0.1 0.05 0.06 0.08 0.07 0.08 0.07 0.04 0.07 0.12 C3/B 0.03 0.03 0.01 0.06 0.13 0.04 0.18 0.14 0.18 0.18 0.18 0.15 0.08 0.18 0.11 0.09 0.06 0.11 0.12 0.11 0.08 0.09 0.09 0.1 0.15 T1/B -0 0.04 0.01 0.08 0.13 0.03 0.11 0.16 0.23 0.25 0.2 0.24 0.17 0.21 0.16 0.08 0.06 0.08 0.11 0.1 0.1 0.09 0.1 0.09 0.12 T2/B -0 0.04 -0 0.06 0.13 0.05 0.11 0.16 0.21 0.22 0.18 0.2 0.13 0.17 0.14 0.1 0.06 0.07 0.1 0.09 0.1 0.08 0.09 0.08 0.12 T3/B 0.05 0.07 0.02 0.08 0.13 0.07 0.15 0.1 0.19 0.16 0.18 0.14 0.08 0.17 0.12 0.1 0.09 0.13 0.13 0.11 0.1 0.13 0.1 0.16 0.13 ST1 0.08 0.07 0.09 0.06 0.17 0.03 0.12 0.13 0.21 0.21 0.21 0.18 0.1 0.16 0.11 0.08 0.02 0.12 0.11 0.07 0.06 0.13 0.04 0.07 0.11 ST2 0.11 0.11 0.1 0.14 0.17 0.03 0.12 0.16 0.2 0.21 0.19 0.19 0.17 0.17 0.11 0.09 0.03 0.09 0.07 0.07 0.08 0.1 0.04 0.05 0.12 ST3 0.09 0.09 0.1 0.07 0.15 0.02 0.09 0.11 0.13 0.16 0.14 0.12 0.06 0.12 0.07 0.09 0.02 0.06 0.06 0.07 0.06 0.1 0.05 0.06 0.09 A1/B 0.09 0.1 0.04 0.09 0.17 0.09 0.1 0.11 0.18 0.18 0.22 0.16 0.13 0.18 0.15 0.13 0.09 0.13 0.16 0.11 0.13 0.13 0.07 0.1 0.12 A2/B 0.12 0.13 0.06 0.12 0.2 0.11 0.1 0.1 0.19 0.18 0.22 0.16 0.14 0.19 0.16 0.11 0.1 0.14 0.16 0.11 0.15 0.13 0.1 0.12 0.13 A3/B 0.07 0.08 0.05 0.08 0.16 0.05 0.16 0.14 0.24 0.24 0.21 0.22 0.16 0.19 0.14 0.1 0.06 0.12 0.1 0.1 0.07 0.08 0.07 0.05 0.09 A4/B 0.04 0.03 0.01 0.08 0.14 0.03 0.15 0.12 0.18 0.19 0.18 0.16 0.13 0.19 0.12 0.13 0.09 0.1 0.12 0.12 0.1 0.12 0.13 0.1 0.13 D1/B 0.03 0.02 0.02 0.05 0.11 0.02 0.17 0.14 0.18 0.17 0.15 0.13 0.09 0.17 0.08 0.12 0.08 0.1 0.13 0.12 0.11 0.13 0.12 0.15 0.14 D2/B 0.05 0.05 0.02 0.06 0.15 0.06 0.12 0.11 0.18 0.18 0.2 0.16 0.12 0.18 0.1 0.11 0.09 0.09 0.14 0.11 0.13 0.14 0.09 0.13 0.13 D3/B 0.02 0.07 0.06 0.14 0.18 0.05 0.14 0.12 0.17 0.19 0.19 0.17 0.12 0.18 0.13 0.12 0.06 0.07 0.14 0.11 0.13 0.09 0.06 0.05 0.13 TA1 -0 0.03 -0 0.06 0.11 0.04 0.1 0.14 0.19 0.18 0.18 0.16 0.13 0.17 0.13 0.12 0.09 0.11 0.15 0.1 0.1 0.14 0.07 0.11 0.16 TA2 0.08 0.1 0.04 0.11 0.19 0.09 0.14 0.12 0.2 0.18 0.23 0.16 0.12 0.18 0.15 0.12 0.06 0.1 0.14 0.1 0.1 0.09 0.05 0.12 0.13 TA3 0.04 0.01 0.05 0.07 0.15 0.02 0.07 0.1 0.16 0.16 0.15 0.14 0.1 0.16 0.12 0.1 0.11 0.1 0.1 0.11 0.12 0.14 0.08 0.11 0.15 TA4 0.04 0.1 0.01 0.06 0.14 0.03 0.11 0.07 0.19 0.19 0.21 0.17 0.11 0.18 0.14 0.12 0.09 0.08 0.12 0.11 0.11 0.11 0.1 0.09 0.15 TA5/B 0.03 0.05 0.02 0.1 0.13 0.02 0.12 0.12 0.21 0.17 0.15 0.15 0.14 0.19 0.12 0.08 0.11 0.08 0.1 0.09 0.1 0.12 0.12 0.12 0.16 II1 0.07 0.01 0.08 0.12 0.05 0.12 0.01 -0.1 -0.2 -0.1 -0.1 -0.1 -0.1 -0.1 -0 0.06 0.09 0.14 0.08 -0 0.03 0.11 0.08 0.05 0.01 II2 0.21 0.11 0.17 0.11 0.14 0.12 0.14 0.27 -0 -0.1 0.03 -0.1 0.02 -0 0.03 -0.1 -0.1 -0.1 -0.1 -0.1 -0 -0 -0.1 -0.1 -0.1 ID1 1 0.4 0.73 0.29 0.39 0.38 0.13 0.02 0.07 0.13 0.19 0.11 0.09 0.08 0.11 0.02 -0.1 0.07 -0 0.05 0.03 0.04 -0 0.05 0 ID2 0.4 1 0.4 0.34 0.45 0.36 0.15 0.1 0.16 0.13 0.4 0.12 0.19 0.15 0.19 0.05 -0.1 0.08 0.09 0.05 0.07 0.01 -0 -0 0.02 ID3 0.73 0.4 1 0.4 0.43 0.38 0.12 0.04 0.08 0.15 0.2 0.13 0.14 0.1 0.11 0.07 -0 0.11 0.02 0.1 0.01 0.09 -0 0.08 0.02 ID4 0.29 0.34 0.4 1 0.47 0.4 0.09 0.06 0.1 0.16 0.22 0.17 0.39 0.2 0.29 0.01 -0.1 0.04 -0.1 0.01 -0 -0 -0.1 -0 -0 ID5 0.39 0.45 0.43 0.47 1 0.37 0.16 0.17 0.2 0.24 0.36 0.24 0.28 0.32 0.24 0.1 -0 0.13 0.09 0.08 0.04 0.04 -0 -0 0.01 ID6 0.38 0.36 0.38 0.4 0.37 1 0.08 -0 0.07 0.1 0.22 0.1 0.2 0.11 0.36 0.02 -0.1 -0 -0 0.06 -0 0.02 -0.1 0.05 -0 TI1 0.13 0.15 0.12 0.09 0.16 0.08 1 0.34 0.35 0.32 0.27 0.3 0.25 0.32 0.27 0.07 -0 0.08 0.07 0.06 0.06 -0 0.02 0.01 0 TI2 0.02 0.1 0.04 0.06 0.17 -0 0.34 1 0.54 0.53 0.38 0.51 0.46 0.49 0.36 0.02 -0.1 0.05 0.02 0.04 0.04 -0.1 -0 0.01 0.02 TI3 0.07 0.16 0.08 0.1 0.2 0.07 0.35 0.54 1 0.77 0.54 0.74 0.6 0.67 0.54 0.09 0.08 0.13 0.08 0.16 0.15 0.04 0.05 0.11 0.06 TD1 0.13 0.13 0.15 0.16 0.24 0.1 0.32 0.53 0.77 1 0.64 0.94 0.74 0.77 0.65 0.13 0.04 0.14 0.06 0.14 0.13 0.03 0.02 0.08 0.06 TD2 0.19 0.4 0.2 0.22 0.36 0.22 0.27 0.38 0.54 0.64 1 0.63 0.6 0.67 0.61 0.12 -0 0.11 0.11 0.14 0.06 0.02 -0 0.06 0.04 TD3 0.11 0.12 0.13 0.17 0.24 0.1 0.3 0.51 0.74 0.94 0.63 1 0.76 0.78 0.65 0.11 0.04 0.11 0.04 0.11 0.1 0.02 0 0.09 0.06 TD4 0.09 0.19 0.14 0.39 0.28 0.2 0.25 0.46 0.6 0.74 0.6 0.76 1 0.74 0.71 0.07 0 0.03 -0 0.04 0.04 -0 -0 0.03 0 TD5 0.08 0.15 0.1 0.2 0.32 0.11 0.32 0.49 0.67 0.77 0.67 0.78 0.74 1 0.7 0.13 0.05 0.13 0.07 0.14 0.1 0.03 0.02 0.09 0.09 TD6 0.11 0.19 0.11 0.29 0.24 0.36 0.27 0.36 0.54 0.65 0.61 0.65 0.71 0.7 1 0.08 -0 0.04 0.03 0.05 0.03 -0 -0 -0 0.01 SE1 0.02 0.05 0.07 0.01 0.1 0.02 0.07 0.02 0.09 0.13 0.12 0.11 0.07 0.13 0.08 1 0.38 0.55 0.48 0.49 0.45 0.5 0.39 0.45 0.61 SE2 -0.1 -0.1 -0 -0.1 -0 -0.1 -0 -0.1 0.08 0.04 -0 0.04 0 0.05 -0 0.38 1 0.35 0.3 0.51 0.68 0.4 0.56 0.57 0.48 SE3 0.07 0.08 0.11 0.04 0.13 -0 0.08 0.05 0.13 0.14 0.11 0.11 0.03 0.13 0.04 0.55 0.35 1 0.64 0.45 0.4 0.6 0.32 0.41 0.51 SE4 -0 0.09 0.02 -0.1 0.09 -0 0.07 0.02 0.08 0.06 0.11 0.04 -0 0.07 0.03 0.48 0.3 0.64 1 0.39 0.35 0.51 0.27 0.32 0.43 SE5 0.05 0.05 0.1 0.01 0.08 0.06 0.06 0.04 0.16 0.14 0.14 0.11 0.04 0.14 0.05 0.49 0.51 0.45 0.39 1 0.57 0.45 0.42 0.64 0.48 SE6 0.03 0.07 0.01 -0 0.04 -0 0.06 0.04 0.15 0.13 0.06 0.1 0.04 0.1 0.03 0.45 0.68 0.4 0.35 0.57 1 0.41 0.58 0.56 0.51 SE7 0.04 0.01 0.09 -0 0.04 0.02 -0 -0.1 0.04 0.03 0.02 0.02 -0 0.03 -0 0.5 0.4 0.6 0.51 0.45 0.41 1 0.35 0.45 0.54 SE8 -0 -0 -0 -0.1 -0 -0.1 0.02 -0 0.05 0.02 -0 0 -0 0.02 -0 0.39 0.56 0.32 0.27 0.42 0.58 0.35 1 0.5 0.54 SE9 0.05 -0 0.08 -0 -0 0.05 0.01 0.01 0.11 0.08 0.06 0.09 0.03 0.09 -0 0.45 0.57 0.41 0.32 0.64 0.56 0.45 0.5 1 0.53 SE10 0 0.02 0.02 -0 0.01 -0 0 0.02 0.06 0.06 0.04 0.06 0 0.09 0.01 0.61 0.48 0.51 0.43 0.48 0.51 0.54 0.54 0.53 1

230

APPENDIX J.

MEASURES AND ABBREVAITIONS

Individual Model News Organization Model Brand Model Abbr. Measures Abbr. Measures Abbr. Measures C1/I Intelligent C1/N Professional C1/B Intelligent C2/I Expert C2/N Intelligent C2/B Expert C3/I Informed C3/N Informative C3/B Good Quality C4/N Expert C5/N Organized C6/N Objective T1/I Honest T1/N Trustworthy T1/B Honest T2/I Trustworthy T2/N Honest T2/B Trustworthy T3/I Honorable T3/N Recognizable T3/B Recognizable ST1 Important to me ST1 Important to me ST1 Important to me ST2 Attracts lots of my attentions ST2 Attracts lots of my attentions ST2 Attracts lots of my attentions ST3 Relates to me on certain things ST3 Relates to me on certain things ST3 Relates to me on certain things A1/I Attractive A1/N Huge org A1/B Huge org A2/I Classy A2/N Been for long A2/B Been for long A3/I Elegant A3/N Congruent values A3/B Congruent values A4/N Good reputation A4/B Good reputation D1/I Meek D1/N Fair D1/B Creative D2/I Comic D2/N Timely D2/B Active D3/I Active D3/N Active D3/B Interactive D4/I Interactive D4/N Interactive TA1 Verified TA1 Verified TA1 Verified TA2 Has a lot of followers TA2 Has a lot of followers TA2 Has a lot of followers TA3 Real TA3 Real TA3 Real TA4 Tweets everyday TA4 Tweets everyday TA4 Tweets everyday II1 Internet use experience II1 Internet use experience II1 Internet use experience II2 Internet use time II2 Internet use time II2 Internet use time The Internet is part of my everyday The Internet is part of my everyday The Internet is part of my everyday ID1 activity ID1 activity ID1 activity I am proud to tell people I am on the I am proud to tell people I am on the I am proud to tell people I am on the ID2 Internet ID2 Internet ID2 Internet The Internet has become part of my daily The Internet has become part of my daily The Internet has become part of my daily ID3 routine ID3 routine ID3 routine I feel out of touch when I could not get I feel out of touch when I could not get I feel out of touch when I could not get ID4 online for a while ID4 online for a while ID4 online for a while I feel I am part of the Internet I feel I am part of the Internet I feel I am part of the Internet ID5 community ID5 community ID5 community I would be sorry if the Internet shut I would be sorry if the Internet shut I would be sorry if the Internet shut ID6 down ID6 down ID6 down TI1 Twitter use experience TI1 Twitter use experience TI1 Twitter use experience TI2 Twitter use time TI2 Twitter use time TI2 Twitter use time TI3 Twitter use frequency TI3 Twitter use frequency TI3 Twitter use frequency TD1 Twitter is part of my everyday activity TD1 Twitter is part of my everyday activity TD1 Twitter is part of my everyday activity TD2 I am proud to tell people I am on Twitter TD2 I am proud to tell people I am on Twitter TD2 I am proud to tell people I am on Twitter Twitter has become part of my daily Twitter has become part of my daily Twitter has become part of my daily TD3 routine TD3 routine TD3 routine I feel out of touch when I haven't logged I feel out of touch when I haven't logged I feel out of touch when I haven't logged TD4 onto Twitter for a while TD4 onto Twitter for a while TD4 onto Twitter for a while TD5 I feel I am part of the Twitter community TD5 I feel I am part of the Twitter community TD5 I feel I am part of the Twitter community TD6 I would be sorry if Twitter shut down TD6 I would be sorry if Twitter shut down TD6 I would be sorry if Twitter shut down SE1 I am satisfied with myself SE1 I am satisfied with myself SE1 I am satisfied with myself SE2 I think I am no good at all SE2 I think I am no good at all SE2 I think I am no good at all SE3 I have a number of good qualities SE3 I have a number of good qualities SE3 I have a number of good qualities I am able to do things as well as most I am able to do things as well as most I am able to do things as well as most SE4 other people SE4 other people SE4 other people SE5 I feel I do not have much to be proud of SE5 I feel I do not have much to be proud of SE5 I feel I do not have much to be proud of SE6 I certainly feel useless at times SE6 I certainly feel useless at times SE6 I certainly feel useless at times I feel that I'm a person of worth, at least I feel that I'm a person of worth, at least I feel that I'm a person of worth, at least SE7 on an equal plane with others SE7 on an equal plane with others SE7 on an equal plane with others I wish I could have more respect for I wish I could have more respect for I wish I could have more respect for SE8 myself SE8 myself SE8 myself SE9 I am inclined to feel that I am a failure SE9 I am inclined to feel that I am a failure SE9 I am inclined to feel that I am a failure SE10 I take a positive attitude toward myself SE10 I take a positive attitude toward myself SE10 I take a positive attitude toward myself

231

APPENDIX K.

INDIVIDUAL STRUCTURAL MODEL WITH PATH COEFFIENTS

Note. *p< .05, **p<.01, ***p<.001.

232

APPENDIX L.

NEWS ORGANIZATION STRUCTURAL MODEL WITH PATH COEFFIENTS

Note. *p< .05, **p<.01, ***p<.001.

233

APPENDIX M.

BRAND STRUCTURAL MODEL WITH PATH COEFFIENTS

Note. *p< .05, **p<.01, ***p<.001.

234

APPENDIX N.

STRUCTURAL EQUATION MODELS FIT INDICES

Models χ2 df GFI AGFI CFI RMSEA NFI Individual model 3688.83 933 .74 .71 .95 .08 .93 News organization model 5633.67 978 .69 .65 .93 .09 .92 Brand model 5063.96 977 .70 .67 .94 .09 .92 Note. The three structural models did not include control variables.