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Putting the social into social network sites

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Putting the social into social network sites a knowledge sharing perspective
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Choi, Jae Hoon ( author )
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English
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1 electronic file (105 pages). : ;

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Online social networks -- Psychological aspects ( lcsh )
Online social networks -- Social aspects ( lcsh )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Social network sites (SNSs) have changed the way people interact and network, and many users have integrated SNSs into their daily practices. Then, are SNSs able to be used for the benefits of the society and organizations as well as for personal daily usage? If SNSs are able to be used for the society and organizations, how do we utilize them and what factors influence the utilization of them? This dissertation examines whether the use of SNSs is positively related to users' relational social capital (trust, norms, obligations, and identification), and knowledge sharing and electronic Word of Mouth (eWOM). This dissertation also examines the factors, specifically social rewards, altruism, and social influence, affecting knowledge sharing in the SNSs. The results show that the SNS usage increases trust and identification, and trust and identification are positively related to eWOM quality which is positively related to knowledge sharing. The results also show that reputation, altruism, and subjective norms are the motivators for knowledge sharing intention in SNSs, and knowledge sharing intention influences users' perceived knowledge sharing in SNSs. One interesting result is that women experience stronger relationship between trust and eWOM quality, between identification and eWOM quality, and between eWOM quality and knowledge sharing in SNSs than men. In addition, subjective norms have a positive effect more strongly for women than men. These results indicate that women emphasize maintaining relationships more than men. This dissertation provides a theoretical model for researchers to test a new framework examining the relationships between social capital, eWOM and knowledge sharing in SNSs; and a new framework examining the factors, specifically, reputation, altruism, and subjective norms, influencing knowledge sharing in SNSs. This dissertation also provides rationale for practitioners to utilize SNSs internally for organizational intra-networking and organizational learning, and externally for marketing purposes.
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Thesis (Ph.D.)--University of Colorado Denver.
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Includes bibliographic references.
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Department of Computer Science and Engineering
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by Jae Hoon Choi.

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University of Colorado Denver
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Auraria Library
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910884341 ( OCLC )
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PUTTING THE SOCIAL INTO SOCIAL NETWORK SITES: A KNOWLEDGE SHARING PERSPECTIVE by JAE HOON CHOI B.A. in Economics, Yonsei University, Seoul, 2001 M.S. in Information Management, Syracuse University, 2007 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Computer Science and Information Systems 2015

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ii This thesis for the Doctor of Philosophy degree by Jae Hoon Choi has been approved for the Computer Science and Information Systems Program by Dawn Gregg, Chair Judy Scott, Advisor Ilkyeun Ra Ronald Ramirez April 22, 2015

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iii Choi, Jae Hoon (Ph.D., Computer Science and Information Systems) Putting the Social into Social Network Sites: A Knowledge Sharing Perspective Thesis directed by Associate Professor Judy Scott. ABSTRACT Social network sites (SNSs) have changed the way people interact and network, and many users have integrated SNSs into their daily practices. Then, are SNSs able to be used for the benefits of the society and organizations as well as for personal daily usage? If SNSs are able to be used for the society and organizations, how do we utilize them and what factors influence the utilization of them? This dissertation examines whether the use of SNSs is positively related to usersÂ’ relational social capital (trust, norms, obligations, and identification), and knowledge sharing and electronic Word of Mouth (eWOM). This dissertation also examines the factors, specifically social rewards, altruism, and social influence, affecting knowledge sharing in SNSs. The results show that the SNS usage increases trust and identification, and trust and identification are positively related to eWOM quality which is positively related to knowledge sharing. The results also show that reputation, altruism, and subjective norms are the motivators for knowledge sharing intention in SNSs, and knowledge sharing intention influences usersÂ’ perceived knowledge sharing in SNSs. One interesting result is that women experience stronger relationship between trust and eWOM quality, between identification and eWOM quality, and between eWOM quality and knowledge sharing in SNSs than men. In addition, subjective norms have a positive effect more strongly for women than men. These results indicate that women emphasize maintaining relationships more than men. This dissertation provides a theoretical mode l for researchers to test a new framework examining the relationships between social capital, eWOM and knowledge sharing in SNSs; and

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iv a new framework examining the factors, specifically, reputation, altruism, and subjective norms, influencing knowledge sharing in SNSs. This dissert ation also provides rationale for practitioners to utilize SNSs internally for organizational intra-networking and organizational learning, and externally for marketing purposes. The form and content of this abstract ar e approved. I recommend its publication. Approved: Judy Scott

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v DEDICATION I dedicate this work to my wife, Yu Rok, and my son, Eiden Jeehu. I also dedicate this work to my father, mother, father-in-law, and mother-in-law.

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vi ACKNOWLEDGMENTS I would like to thank my advisor Dr. Judy Scott, and dissertation committee, Dr. Dawn Gregg, Dr. Ilkeyun Ra, and Dr. Ronald Ramirez. The first study was presented at the 17th Americas Conference on Information Systems (Aug, 2011) entitled “Social Network Sites and Digital Word of Mouth: A Social Capital Perspective, and has been published in the Journal of Theoretical and Applied Electronic Commerce Research (2013) entitled “Electronic Word of Mouth and Knowledge Sharing on Social Network Sites: A Social Capital Perspective.

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vii TABLE OF CONTENTS CHAPTER I.INTRODUCTION ....................................................................................................... 1II.LITERATURE REVIEW ............................................................................................ 8Social Network Sites (SNSs) .................................................................................. 8Social Capital ........................................................................................................ 11Electronic Word of Mouth Quality ....................................................................... 13Social Exchange Theory (SET) ............................................................................ 15Behavioral Economics .......................................................................................... 17Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) ...... 19Knowledge Sharing ............................................................................................... 21III.SOCIAL NETWORK SITES AS SOCIAL CAPITAL (STUDY 1) ......................... 24SNS Usage and Social Capital .............................................................................. 24Social Capital, eWOM, and Knowledge Shari ng in SNSs ................................... 28Methodology ................. ................ ................ ................ ................ ................ ........ 32Measurement development ............................................................................. 32Control variables ............................................................................................. 35Survey administration ..................................................................................... 36Results ................................................................................................................... 37Discussion ............................................................................................................. 41IV.FACTORS AFFECTING KNOWLEDGE SHARING IN SNSS (STUDY 2) ......... 44Reputation and Intention to Share Knowledge in SNSs ....................................... 44Altruism and Intention to Share Knowledge in SNSs .......................................... 46Subjective Norms and Intention to Share Knowledge in SNSs ............................ 48

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viii Intention to Share Knowledge and Knowledge Sharing in SNSs ......................... 49Methodology ................. ................ ................ ................ ................ ................ ........ 50Measurement development ............................................................................. 51Control variables ............................................................................................. 54Survey administration ..................................................................................... 55Results ................................................................................................................... 57Discussion ............................................................................................................. 61V.CONCLUSION .......................................................................................................... 65REFERENCES ................................................................................................................. 73APPENDIX ...................................................................................................................... 84A. Initial Items of Study 1..................................................................................... 84B. Questionnaire of Study 1 .................................................................................. 87C. Initial Items of Study 2 ..................................................................................... 90D. Questionnaire of Study 2.................................................................................. 92E. Introductory Remarks and Concluding Rema rks of the Survey ....................... 94

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ix LIST OF TABLES Table 1 Research about SNSs. ................................................................................................... 102 Descriptive Statistics (Study 1). .................................................................................... 373 Reliability of Constructs (Study 1). .............................................................................. 384 Correlations of Constructs (Study1). ............................................................................ 385 Hypotheses Supported (Study 1). ................................................................................. 396 Constructs and References (Study 2). ........................................................................... 527 Descriptive Statistics (Study 2). .................................................................................... 568 Reliability of Constructs (Study 2). .............................................................................. 589 Correlations of Constructs (Study 2). ........................................................................... 5910 Hypotheses Supported (Study 2). ............................................................................... 6011 Hypotheses Supported – Gender Effect (Study 2). ..................................................... 60

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x LIST OF FIGURES Figure 1 Conceptual Model of SNSs as Social Capital. .............................................................. 322 Structural Model of SNSs as Social Capital. ................................................................ 403 Conceptual Model of Factors Affecting Know ledge Sharing in SNSs. ........................ 504 Structural Model of Factors Affecting Knowledge Sharing in SNSs. .......................... 60

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xi LIST OF ABBREVIATIONS AVE Average Variance Extracted EWOM Electronic Word of Mouth PLS Partial Least Squares SEM Structural Equation Model SET Social Exchange Theory SNS Social Network Sites TPB Theory of Planned Behavior TRA Theory of Reasoned Action

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1 CHAPTER I INTRODUCTION The rise of social network sites (SNSs) has changed the way people interact and network. SNSs are Internet Websites “in which participants create a self-descriptive profile and make links to other members” (Donath and Boyd, 2004, p.71). There are many SNS services including Facebook, MySpace, Twitter, LinkedIn, CyWorld, and so on. SNSs make it possible for users to keep track of their existing, even alienated, relationships and to build new ones. SNSs also make it possible to overcome the traditional richness-reach trade-off. Richne ss means “the quality of information,” and reach means “the number of people who participate in the sharing of that information” (Evans and Wurster, 2000, p. 23 ). In the traditional richness-r each tradeoff, the quality of the information decreases with increasing the number of people who participate in the sharing of that information. However, SNSs allow users to have rich interactions with many more people. Thus, SNSs have attracte d millions of users since their introduction, and many of these users have integrated thes e sites into their daily practices (Boyd and Ellison, 2008). Then, are SNSs able to be used for the benefits of the society and organizations as well as for personal daily usage? If SNSs are able to be used for the society and organizations, how do we utilize them and what factors influence the utilization of them? This dissertation starts with exploring these questions. Even though it was not a major purpose, SNSs have been used for the benefits of the public. For example, SNSs have supported backchannel communications during

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2 natural disasters (Sutton, Palen, and Shlovski, 2008). SNSs have also been a good medium for raising donations after the huge disasters and reporting social absurdities and injustices. And, public opinions expressed on SNSs have influenced election results. Companies have been using SNSs for disseminating their products and services, and collecting information from customers. Be sides, organizations are utilizing SNSs to encourage customer communication which builds brand loyalty. SNSs allow organizations to increase interactions with their customers for crowdsourcing, such as integrating customers into their innovation processes (Leimeister, Huber, Bretschneider, and Krcmar, 2009). At Starbucks, SNSs have be en used for connecting with customers, notifying them of promotions, receiving product suggestions, and monitoring consumerto-consumer conversation. In MyStarbucks Idea and Starbucks Digital Network, SNSs have been used for suggestions, discussions, and votes on thousands of new product and service ideas from a large customer community (Gallaugher and Ransbotham, 2010). Customers exchange their experiences and opinions through SNSs. Organizations have also been utilizing SNSs for intra-networking and collaboration. For example, SNSs have strengthened weak ties among colleagues at IBM and Microsoft (Huang, Choi, and Horowitz, 2010). IBM has used SNSs for keeping employees connected, bridging the generation gaps, and innovating through collaboration (Majchrzak, Cherbakov, and Ives, 2009). These facts suggest that SNSs have the potential to (1) be associated with social capital, since they have been used for the benefits of the public; (2) facilitate knowledge sharing, since they have been used for innovating through collaboration; (3) be an effective medium of electronic word of mouth (eWOM), since they encourage customer

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3 communication; and (4) be utilized by organi zations for internal and external outcomes, since they allow organizations to increase interactions with their customers and intranetworking among employees. Social capital is “the resource available to actors as a function of their location in the structure of their social relations” (Adler and Kwon, 2002, p. 18). Knowledge sharing is the process between a person who contributes knowledge and a person who seeks knowledge. Knowledge sharing usually involves a systematic process for acquiring, organizing, and communicating knowledge (K ankanhalli, Tan, and Wei, 2005). WOM is one of the most important interpersonal communication methods among varied channels for receiving information (Godes and Mayzlin, 2004). As above, SNSs could be used to benefit the users, and organizations have been utilizing SNSs to receive the benefits. However, we have limited understanding of the underlying factors for receiving the benefits, which prevents organizations from utilizing SNSs more effectively. For example, how do organizations facilitate communication between the organization and customers, and among customers? How do organizations encourage their employees to share knowledge for collaboration? Since employees use SNSs to share knowledge for problem solving their tasks (Majchrzak et al., 2009), and customers use SNSs to share information and access shared knowledge on products and services (Heinonen, 2011) mostly in the form of eWOM (Chu and Kim, 2011; Kabaday and Alan, 2012); we focus on knowledge sharing related to products and services in SNSs. To leverage SNSs effectively, we first need to understand if the use of SNSs could build social capital; since organizations are interested in building social capital, and

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4 social capital generates a positive effect of interaction among employees (Putnam, 2000) and customers. We also need to understan d how knowledge is shared and how eWOM leads to knowledge sharing in SNSs, since SNSs can be an effective and economical medium for knowledge sharing. However, knowledge sharing in SNSs has not been researched much. Even though SNSs are an ideal tool for eWOM (Chu and Kim, 2011), the relationship between eWOM and knowledge sharing in SNSs has also not been published in academic journals. In addition, we need to understand the factors influencing knowledge sharing in SNSs to leverage SNSs effectively. First, people may share knowledge in SNSs, because they expect rewards from sharing knowledge In traditional economics, people are rational and they try to maximize their welfare. Thus, people do something for others when they expect rewards from them. The rewards may include economic rewards, reputation, anticipated rewards in the near future, and so on. We are interested in social perspectives of exchange. Social Exchange Theory (SET) views exchange as a social behavior which results in both economic and social outcomes. Similar to economic exchange, social exchange assumes that people participate in an exchange when they expect the benefits outweighin g the costs. Mostly, SET has analyzed human interactions in the marketplace. However, it is possible to analyze human interactions in other social relations (Blau, 1964). Second, contrary to traditional economics researchers in behavioral economics argue that people are not always rational. They argue that people have limited information, people tend to avoid losses, or people want to contribute to the public good without improving their private welfare. We are interested in selfless behavior or

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5 altruism, since altruism is an essential part of the social instincts (Darwin, 1871). People may share knowledge in SNSs, because they want to help others or to improve society. Finally, we are interested in social influence. People usually behave as expected by others (Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975). The Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) provide a theoretical basis for the relationship between social influence and behavior. The effect of social influence on intention to use Internet-related services has been verified (Hsu and Lin, 2008; Morris, Venkatesh, and Ackerman, 2005). These suggest that people may share knowledge in SNSs, since they need to conform to the expectations of others. Researchers from various disciplines have examined SNSs, their meaning and usage, within cultural differences, and their practical usage. However, few studies have examined SNSs from the perspective of th e public good. Besides, few studies have examined knowledge sharing in SNSs. Even though Ellison, Steinfield, and Lampe (2007) explore the relationship between the use of Facebook and the formation and maintenance of social capital, whether and how the usage of SNSs facilitates the formation of social capital has not been resolved. The impact of knowledge sharing through eWOM in the context of SNSs also remains unresolved. Furthermore, the antecedents of knowledge sharing in SNSs have not been studied widely. There is little research that examines eWOM’s underlying sources of effectiveness, which limits understanding of the medium (Hung and Li 2007). In addition, there has been little discussion on the concept of eWOM quality. This dissertation contributes to closing these research gaps. The first study answers the questions “Are SNSs able to be used for the benefits of the society and

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6 organizations? If so, how do we utilize them?” Specifically, it explores whether the use of SNSs is positively related to users’ relation al social capital, and knowledge sharing and eWOM. The second study answers the question “What factors influence the utilization of SNSs for knowledge sharing?” Specifically, it explores the factors, social rewards, altruism, and social influence, affecting knowledge sharing in SNSs. The research questions are (1) Does the use of SNSs build social capital? (2) Does social capital facilitate knowledge sharing in SN Ss? (3) Is social capit al positively related to eWOM quality in SNSs? (4) Is eWOM qua lity positively related to knowledge sharing in SNSs? (5) Does the expectation of social rewards facilitate knowledge sharing in SNSs? (6) Does altruism positively affect knowledge sharing in SNSs? (7) Is social influence positively related to knowledge sharing in SNSs? (8) Is there difference between how men and women share knowledge in SNSs? This dissertation introduces a theoretic al model for researchers to study the utilization of SNSs. We expect that the model will provide support for the utilization of SNSs as social capital. It could also provide support for the utilization of SNSs internally for organizational intra-networking and organizational learning such as enhancing organizational loyalty, and externally for marketing purposes such as increasing customers’ loyalty and disseminating information about products. This dissertation is organized as follows. The literature review of the research of SNSs, social capital, eWOM quality, SET, behavioral economics, TRA and TPB, and knowledge sharing is presented in the next section, followed by the first study exploring SNSs as social capital and SNSs as a medi um for knowledge sharing. Then, the second

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7 study regarding the factors influencing knowledge sharing in SNSs is presented. Finally, the conclusion with contributions and limitations of this dissertation is discussed.

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8 CHAPTER II LITERATURE REVIEW The literature review of the research on SNSs, social capital, eWOM quality, SET, behavioral economics, TRA and TPB, and knowledge sharing is discussed. Social Network Sites (SNSs) SNSs are defined as web-based services that allow users to “(1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” (Boyd and Ellison, 2008, p. 211). SNSs allow social networks to be visible to people, and enable people to articulate their social networks (Haythornthwaite, 2005). SNSs support the existing social networks, and some support various interests and practices such as Facebook, Twitter, LinkedIn, and Blogs. Even though some SNSs allow users to conn ect to others by interests or practices, the primary level is based on people, not on in terests or practices. This is different from online virtual communities. While SNSs are individual-centered (Boyd and Ellison, 2008), online virtual communities are group-centered (Ridings and Gefen, 2010). Usually, groups of people are brought together by shared interests in online virtual communities (Ridings and Gefen, 2010). Besides, there are some SNSs, such as CyWorld, which do not allow users to connect to others by interests. While there are non-business SNSs as stated above, there are corporate SNSs, such as ThinkPlace, SmallBlue, and Beehive. These corporate SNSs are for the

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9 employees and the employees of allied or related companies. Users may be forced to use these corporate SNSs, or there may be tangible and/or intangible incentives for users to use them. We focus on non-business SNSs, since we are interested in general SNSs. We are also interested in users' voluntariness to use them. A theoretical lens for viewing SNSs is that of social ties. Social ties are “the links that bind individuals to other individuals, as manifested in the frequency and kinds of communications among individuals” (Pickering and King, 1995, p. 480). A social tie exists between individuals wherever they exchange goods and services, or share information (Haythornthwaite, 2005). One can distinguish between strong and weak social ties by four dimensions: time, emotional intensity, mutual confidence and reciprocity (Granovetter, 1973). Strong ties are maintained by frequent and emotional communication, shared confidences, and reciprocity between individuals over time. On the contrary, weak ties are maintained by less frequent and less emotional communication, and it does not require shared confidences or reciprocity (Granovetter, 1973). Computer networks draw on weak ties connecting people across time and distance, and make it easier to reach more people with weak-tie contacts (Constant, Sproull, and Kiesler, 1996). SNSs have been examined along multiple dimensions and factors including usage (Donath and Boyd, 2004; Gallaugher and Ransbotham, 2010; Heinonen, 2011; Jarrahi and Sawyer, 2013; Lee and Paris, 2013; Majchrzak et al., 2009; Pi, Chou, and Liao, 2013), satisfaction (Sibona and Choi, 2012), security and privacy (Gross and Acquisti, 2005; Jagatic, Johnson, Jakobsson, and Menczer, 2007), social ties (Ellison et al., 2007; Haythornthwaite, 2002; Lampe, Ellison, and St einfield, 2007), culture (Hjorth and Kim,

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10 2005), and natural disaster management (Sutto n et al., 2008). Table 1 shows the previous research about SNSs. Although, Ellison et al. (2007) identified a strong relationship between the use of SNSs and social capital, the relationship was mediated by bridging and bonding rather than the theoretical relational dimension. Table 1 Research about SNSs. Authors Context Focus Research Gallaugher and Ransbotham (2010) Business Usage Social media and customer dialog management at Starbucks Jarrahi and Sawyer (2013) Business Usage The ways in which social technologies facilitate informal knowledge sharing in workplace Majchrzak et al. (2009) Business Usage The way how IBM harness the power of the crowds with corporate social networking tools Donath and Boyd (2004) Non-business Usage Social implications of the public display of one’s social network Heinonen (2011) Non-business Usage Conceptualizing consumers’ activities in social media by examining the motivations behind the activities Lee and Paris (2013) Non-business Usage Facebook page “events” as a medium for promoting special events to customers Pi et al. (2013) Non-business Usage Factors promoting Facebook group users’ willingness to share knowledge Sibona and Choi (2012) Non-business Satisfaction Factors affecting end-user satisfaction on Facebook Ellison et al. (2007) Non-business Social ties A strong association between use of Facebook and social capital, with the strongest relationship being to bridging social capital Lampe et al. (2007) Non-business Social ties The relationship between profile structure and number of friends, and how it works to encourage connections and articulated relationships between users Gross and Acquisti (2005) Non-business Privacy The patterns of information revelation in online social networks and their privacy implications Hjorth and Kim (2005) Non-business Culture The use of mobile phone for SNS usage in South Korea Jagatic et al. (2007) Non-business Security The way how phishing attacks can be honed by means of publicly available personal information from social networks Sutton et al. (2008) Non-business Natural disaster management The use of SNSs in the 2007 southern California wildfires

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11 Social Capital Social capital refers to a resource for persons accumulated through the relationships among people (Coleman, 1988). Social capital is developed through interactions when the parties in the relationsh ip facilitate those interactions. Thus, social capital cannot be separated from the relationships among people (Nahapiet and Ghoshal, 1998). Social capital increases the quality and quantity of knowledge transfer through these constant social interactions (Huang et al., 2010). Social capital is different from physical capital and human capital. Physical capital is ordinarily a private good that a pers on who invests in physical capital is able to get the benefits out of the investments. Also for human capital, a person who invests time and resources in it is able to get the benefits in the form of higher payment and/or higher status. However, for social capital, a person who puts efforts in it would not necessarily get benefits for herself/himself. Instead, all those who are influenced by social capital get benefits. This public goods quality of social capital differentiates the position with regard to purposive action from that of other capital (Coleman, 1988). Nahapiet and Ghoshal (1998) define three dimensions of social capital. The structural dimension includes network ties (access, timing, and referrals), network configuration, and appropriable organization. The cognitive dimension includes shared codes and language, and shared narratives. Finally, the relational dimension includes trust, norms, obligations and identification. The organizational conditions which enable the creation of social capital are four fold: (1) creating opportunities for combinations and exchange, (2) creating an anticipation of value through combinations and exchanges, (3)

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12 creating motivation for both sides to participate, and (4) creating structural norms and symmetries to support combination capability (Nahapiet and Ghoshal, 1998). Social capital resides in relationships which are created through exchange, and the pattern of the relationships is the foundation for social capital (Nahapiet and Ghoshal, 1998). The components of the organizational enablers construct are: (1) Bridging, where individuals are brought together purposely for collective work, (2) Bonding, where cognitive norms and implicit understanding is developed by personnel on both sides, and (3) Linking, where structural connections are established for jointly owning ongoing activities (DeLone, Espinosa, Lee, and Carmel, 2005). The resources from relationships can differ in form and function. Social capital is considered both as a cause and an effect (Williams, 2006). Social capital may induce negative influences, but in general social capital is seen as a positive effect of interaction among participants in a social network (Putnam, 2000). Bridging social capital is linked to weak ties (Haythornthwaite, 2002; Putnam, 2000). In this perspective, weak ties are loose connections between individuals who may provide information or new perspectives for each other without emotional connection (Granovetter, 1973). The importance of Internet-based linkages for weak ties has been emphasized, which can serve as the foundation of bridging social capital (Wellman, Haase, Witte, and Hampton, 2001). Bridging social capital may be increased by SNSs (Donath and Boyd, 2004; Wellman et al., 2001). SNSs can increase the number of connections, since technology enables such connections cheaply and enables them to be easily maintained (Donath and Boyd, 2004). However, “not all dimensions of social capital are mutually reinforcing” (Nahapiet and Ghoshal, 1998, p. 251). One of the barriers to the transfer of best practice

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13 within organizations is the existence of constant relations between the source and the recipient (Szulanski, 1996). Key aspects of social capital, which are related to the context for knowledge exchange, belong to the relational dimension (Kankanhalli et al., 2005). Thus, we focus on the relational dimension of social capital which concerns why and when people share knowledge. In the next section, we discuss eWOM quality. Electronic Word of Mouth Quality There are various forms of WOM such as traditional offline WOM and Internet facilitated online WOM (eWOM) (Steffes and Burgee, 2009). Even though the motives for traditional WOM can be expected to be relevant for eWOM (Hennig-Thurau, Gwinner, Walsh, and Gremler, 2004), there are differences between them. While traditional WOM involves an immediate conversation, eWOM involves asynchronous interactions among people separated by time and space (Steffes and Burgee, 2009). This study focuses on eWOM, since it examines social networking environments, and SNSs are an ideal tool for eWOM (Chu and Kim, 2011). eWOM is “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-Thuraru et al., 2004, p. 39). eWOM conveys users’ experiences both positive and negative (Sweeney, Soutar, and Mazzarol, 2012). WOM is informal, person-to-person communication between an individual and another in regard of a product, brand, organization, or service (Anderson, 1998; Arndt, 1967; Harrison-Walker, 2001); which can be expected for eWOM.

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14 WOM quality has largely been studied in marketing, especially in retailer websites. However, there is little research on eWOM quality, while there are many studies about eWOM. WOM quality has been viewed as the degree to which the WOM platform on the Website is considered to be relevant, helpful, or useful (Awad and Ragowsky, 2008); which can also be expected for eWOM quality. Besides, WOM including eW OM has been studied rarely in the context of knowledge sharing. WOM usually involves information (Anderson, 1998; Arndt, 1967; Harrison-Walker, 2001; Hennig-Thurau et al., 2004; Sun, Youn, Wu, and Kuntaraporn, 2006; Sweeney et al., 2012). However, eWOM involves more than information. eWOM usually involves personal experiences and opinions (Dellarocas, 2003; Sun et al., 2006) through the written word (Sun et al., 2006). Written communication is more logical than oral communication; since a wo rd follows a word in an or derly manner in writing, and logic is embedded in the step-by-step linear process (Griffin, 2003). Opinion-passing behavior is an enhanced dimension of eWOM in SNSs (Chu and Kim, 2011). SNS users tend to get valuable information about products from others knowledge of the products, which facilitates eWOM behavior in SNSs (Chu and Kim, 2011). Furthermore, professional knowledge has been embedded in eWOM when a product is related to electronics (Kabaday and Alan, 2012). Information from personal sources is custom-tailored in eWOM (Sun et al., 2006). People willingly exchange tacit knowledge about personal experiences of products and brands in virtual communities (Hung and Li, 2007). In the next section, we discuss SET.

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15 Social Exchange Theory (SET) SET explains social exchange as a process of two-sided transactions which involves interpersonal interactions within social environments (Blau, 1964; Emerson, 1962; Homans, 1958; Thibaut and Kelley, 1959). Similar to economic exchange theory, SET views that people are rational that they tend to maximize their welfare. Therefore, human behavior is based on a cost-benefit analysis. Individuals evaluate the various costs and benefits associated with different behaviors, and choose the most profitable behavior for them. That is people calculate the worth of a particular behavior by subtracting its costs from the benefits it provides (Homans, 1958 ). If the worth is positive, then it is a positive relationship. On the contrary, if the worth is negative, then it is a negative relationship. The worth of a relationship influences whether people will continue the behavior or terminate it. However, unlike economic exchange, social exchange involves only intangible costs such as assistance in time and efforts; and intangible benefits such as respect, recognition, or honor (Gefen and Keil, 1998). There are no explicit rules or regulations of reciprocity in return for the costs in social exchange (Gefen and Ridings, 2002). If there are explicit rules or regulations of reciprocity involving intangible goods, then it is an economic exchange of intangible goods. Although there is no guarantee of reciprocity under social exchange, an individual expects that the other party will reciprocate his/her favor (Blau, 1964; Thibaut and Kelley, 1959). Homans (1958) summarizes five propositions about human behaviors based on costs and rewards. The success proposition states that an individual is likely to perform a behavior when the behavior is rewarded. The stimulus proposition states that an

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16 individual continues a previous behavior if the behavior was rewarded in the past. The deprivation-satiation proposition states that if an individual has received a same reward in the recent past, the value of the reward diminishes. The value proposition states that if the result of a behavior is valuable to an individual, the individual is more likely to perform the behavior. Finally, the rationality proposition states that the individual chooses a behavior among alternatives when the value of the reward multipli ed by the probability of getting the reward is greatest. SET has been used to explain various business areas including IT-related phenomena (Gefen and Ridings, 2002; Gefen and Keil, 1998; Hall, 2003). Knowledge sharing in SNSs can be viewed as a social exchange. In terms of resources exchanged in social exchange, information and knowledge are considered as an exchange resource, even though original SET did not take them into account (Hall, 2003). Mostly, information and knowledge are private goods, since a person invests time and effort to get them. Thus, it is the individual owning them who decides whether to share them or not. An individual who shares knowledge in SN Ss incurs costs such as the investment in time and effort to post in SNSs as well as to have acquired the knowledge. The individual may expect recognition or resp ect from others. Reputation is a potential benefit from sharing knowledge that an indivi dual can expect (Wasko and Faraj, 2005), especially when the participation is voluntary. The decision to share knowledge cannot be explained by economic exchange. The interaction between an individual who shares knowledge and an individual who gets the knowledge does not involve an explicit contract. There is no guarantee that an indi vidual who shares know ledge will receive any

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17 rewards, or an individual who gets the knowledge will respect the individual who shares knowledge. Moreover, the costs and benefits cannot be easily quantified due to their characteristics of intangibles. Therefore, the in dividualsÂ’ decision to participate in sharing knowledge in SNSs is a social exchange. In the next section, we discuss behavioral economics. Behavioral Economics Behavioral economics combines psychology and economics to explore human limitations and complications (Kahneman, 2003). It poses a question on the basic assumption of traditional economics or rati onality. In traditional economics theory, human beings are rational and they try to maximize their benefits and minimize their costs. However, people may not always maximize their benefits and minimize their costs; since there is insufficient information, there is cognitive limitation, or they choose not to. First, Simon (1955) proposes bounded rationality to explain limited information processing capabilities of people, and proposes a model of satisfaction rather than utility maximization. In the real world, there is imperfect information, and the amount of time people have to make decisions is not infinite. In addition, when the situation is complex and there are many options, it is very difficult to process and compute the expected benefits of every alternative option. Delibe ration costs may be high, and there may be other activities requiring decisions at the same time (Williamson, 1981). Based on incomplete information, finite amount of time, the complexity of the situation, and concurrent activities; people are un able to make an optimal decision.

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18 Second, Kahneman and Tversky (1979) explain how people make decisions under uncertainty in prospect theory. In prospect th eory, individuals subjectively frame a result, and it affects the utility they expect. The decisions are based on the potential value of losses and gains rather than the final results, and that people value gains and losses differently (Kahneman, 2003; Kahneman and Tversky, 1979). The decision is based on perceived gains rather than perceived losses. If people were given two equal options, one in terms of possible gains and the other in terms of possible losses, they would choose the one in terms of possible gains. Furthermore, it is likely that people may choose the worse option, if the possible gains of the worse option are illuminated. Finally, contrary to traditional economic th eory stating that people are interested in their own benefits, people are not always selfish. Under the traditional economics assumptions, selfish individuals are fitter than altruists and altruists will disappear (Simon, 1992). However, selfish motives or goals may be socially induced to contribute to the benefits in society (Simon, 1992; Simon, 1993). People show that they sacrifice their own interests to help others. For example, there has been a lot of evidence that consumers help others, especially in the provision of market information (Price, Feick, and Guskey, 1995). We focus on altruistic behaviors, since altruism is an essential part of the social instincts (Darwin, 1871). Altruism has been studied in various disciplines including biology, sociology, psychology, and economics. In general, altruism is explained that an individual is willing to sacrifice oneÂ’s own bene fits in order to incr ease the benefits of others (Simon, 1992; Simon, 1993). Altruism also involves some costs to an individual who does something for others without intention to enhance his/her own benefits

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19 (Ozinga, 1999). For example, one of the main motives that people involve in WOM communication is concern for others to help their friends and relatives make a better purchase decision (Engel, Blackwell, and Mi niard, 1993). People also involve in WOM communication to do something for other people without anticipating any reward or to prevent others from experiencing the problems they may encounter (Sundaram, Mitra, and Webster, 1998). When people donate something to other people, they, generally, do not expect any returns when showing altruistic behavior. People may share their knowledge without expecting the benefits that they may gain fr om it. An individual who shares knowledge in SNSs with others incur costs at his/her own expense such as time, effort, and other opportunity costs. However, people may spend their time and effort to share knowledge in SNSs only to help others. In the next section, we discuss TRA and TPB. Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) TRA and TPB have explained a wide range of human behavior including IT adoption and usage (Morris et al., 2005). Especially, TRA and TPB influenced the Technology Acceptance Model (TAM) which has been widely applied to IS contexts (Venkatesh, Morris, Davis, and Davis, 2003). Even though TRA has not often been applied to the research in the area of knowledge sharing in private environments, Bock and Kim (2002) suggest its good applicability in the area of knowledge sharing in public organizations. Besides, TRA and TPB have verified extensive empirical support for

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20 explaining behaviors in individual as well as organizational environments (Morris et al., 2005). TRA and TPB propose that peopleÂ’s actual behavior is driven by their behavioral intention. Attitude toward behavior and subjective norms are the precursor to behavioral intention (Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975). TPB extends TRA by adding perceived behavioral control which also influences behavioral intention (Ajzen, 1991). Attitude toward behavior is an individualÂ’s judgment whether to perform a behavior or not (Fishbein and Ajzen, 1975). Perceived behavioral control is an individualÂ’s perception of how easy or difficult to perform a behavior (Ajzen, 1991). Finally, subjective norms are oneÂ’s perception of social pressure about whether to perform a behavior or not (Morris et al., 20 05). That is the personÂ’s perception that people who are important to him/her think he/she should or should not perform the behavior. We are interested in social influence or subjective norms, since SNSs are based on usersÂ’ social relations. Subjective norms are different from reciprocity. Unlike reciprocity, individuals have not always received benefits from others. Even though individuals do not receive any benefits, subjective norms may play a role as social pressure to share knowledge in SNSs. Furthermore, the role of social pressure is more important when the motivation to follow the pressure is higher (Morris et al., 2005). Besides, subjective norms are different from obligations, since obligations are differentiated from subjective norms by re lating to more personal relationships. Subjective norms are also different from altruistic behaviors. Unlike altruism, there is less

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21 spontaneity in subjective norms. Even though individuals may spend their time and effort to share knowledge in SNSs, it would not be voluntary if they feel social pressure. An individual may share knowledge in SNSs, since the individual may think friends in SNSs want him/her to share knowle dge. An individual may think that he/she himself/herself lags behind others if he/she does not share knowledge in SNSs. Or an individual may think he/she will be alienated if he/she does not share knowledge in SNSs. Thus, it is important to explore if social influence plays a role in sharing knowledge on SNSs. In the next section, we discuss knowledge sharing. Knowledge Sharing Knowledge is defined as “a justified belief that increases an entity’s capacity for effective action” (Alavi and Leidner, 2001, p. 109). Knowledge is a valuable resource that helps organizations sustain their compe titive advantage (Bock, Zmud, Kim and Lee, 2005). Knowledge resources are outcome of knowledge sharing behavior (Chiu, Hsu, and Wang, 2006; Wasko and Faraj, 2000). Knowledge sharing is defined as “the combination of one or both parties seeking knowledge in response to the request, such that one or both parties are affected by the experience” (Ghosh and Scott, 2007, p. 4). Knowledge sharing has been regarded as motivation for using virtual communities (Wasko and Faraj, 2000). In knowledge sharing, a person is affected by the experience of others. Besides, a person helps them do better and more efficiently through knowledge sharing (Hsu and Lin, 2008). Social network members are exposed to a wide variety of knowledge which

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22 can be valuable, and social networks act as an instrument for processing and moving knowledge (Inkpen and Tsang, 2005). Individuals need to be willing to share knowledge, since it cannot be forced effectively (Bock et al., 2005). Communities are a knowledge sharing entity, and communities and social networks are where people feel membership and commitment (Huysman and Wulf, 2005). Social capital provides the conditions that facilitate knowledge sharing (Gulati, Nohria, and Zaheer, 2000; Inkpen and Tsang, 2005; Kankanhalli et al., 2005), and knowledge benefits are derived from a high level of social capital (Nahapiet and Ghoshal, 1998). The use of the Internet supplements social capital (Wellman et al., 2001). Especially, the relational dimension of social capital concerns the motivation to share knowledge (Putnam, 2000). Besides, people are more likely to share knowledge when social relationships are strong (Szulanski, 1996). Knowledge sharing has widely been studied in the organizational level regarding the motivators of knowledge sharing (Bock and Kim, 2002; Bock et al., 2005; Chang, Hsu, Cheng, and Lo, 2010; Chiu et al., 2006; Hsu, Ju, Yen, and Chang, 2007; Inkpen and Tsang, 2005; Jarrahi and Sawyer, 2013; Kankanhalli et al., 2005; Majchrzak et al., 2009; Szulanski, 2000; Wasko and Faraj, 2000). In an organization, it is likely that employees are forced to use the organizationÂ’s system for their work including knowledge sharing. For an organization, knowledge sharing enables an organization to possess and utilize employeesÂ’ knowledge and experience for increased productivity, even after they leave the organization. Thus, organizations often encourage employees to share their knowledge and experience by providing incentives.

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23 Jarrahi and Sawyer (2013) identify five knowledge practices which enable knowledge sharing in organizations. First is expe rtise locating. This practice is motivated by employeesÂ’ lack of knowledge to complete a task. Second is expert locating, a social process through which employees seek advice from others. Third is reaching-out. This practice is similar to expert locating, but employees like immediate social contacts to locate the required knowledge. Fourth is in strumental socializing. This is motivated by the need for generating and maintaining social contacts. Finally, horizon broadening motivates employees to learn about things beyond their immediate work problems. Unlike organizationsÂ’ knowledge sharing systems, people voluntarily use SNSs. They are not forced to share their knowledge, and they do not seem to have obvious incentives to do so. Still, there is potential for SNSs to be a good medium for knowledge sharing. Customer activity in SNSs is a good example. By sharing experiences and knowledge, customers create new content which plays an important role for others in decision making (Heinonen, 2011). They share knowledge, which influences othersÂ’ decision to purchase products and give more information to others about the products. This kind of shared knowledge is considered to be reliable (Heinonen, 2011). Actually, WOM has been found to be the number one influencer in electronics and apparel purchases, and SNSs are one of the main so urces of it (National Retail Foundation, 2008). In the next section, we discuss the first study, SNSs as social capital.

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24 CHAPTER III SOCIAL NETWORK SITES AS SOCIAL CAPITAL (STUDY 1) The first study explores whether the use of SNSs is positively related to usersÂ’ relational social capital, and knowledge sharing and eWOM. It was published in the Journal of Theoretical and App lied Electronic Commerce Research in April, 2013; titled as Electronic Word of Mouth and Knowledge Sharing on Social Network Sites: A Social Capital Perspective. SNS Usage and Social Capital Social capital can be increased or decreased by the Internet. The Internet decreases face-to-face time with others, which may diminish an individualÂ’s social capital. However, the Internet increases online interactions with others, which may increase an individualÂ’s soci al capital. Computer-mediated interactions have had positive effects on community interactions and social capital in communities supported by online networks (Ellison et al., 2007). Besides, social networks allow their members to access knowledge (Nieves and Osorio, 2013). These suggest that social capital can occur in online communities such as SNSs, and SNSs can be a medium for knowledge sharing. SNSs are web-based communities (Ellison et al ., 2007; Lampe et al., 2007), and are a good tool to keep personal relationships su pported by technology (Boyd and Ellison, 2008). SNSs encourage social capital and relationship building (Donath and Boyd, 2004). Trust is the main source for the social capital that contributes to the value of relationship (Zahedi, Bansal, and Ische, 2010). Trust has been studied in various

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25 disciplines (Bhattacherjee, 2002), and there have been many definitions of trust (Hsu et al., 2007). This study concentrates on interpersonal trust, since we are interested in the trustworthiness between persons on SNSs not the trustworthiness on the technology or platforms of SNSs. It is important for a person to believe that others will behave in a dependable and socially appropriate way in SNSs. Thus, we adopt the definition of trust that trust is “an expectation that others one chooses to trust will not behave opportunistically by taking advantage of the situation” (Gefen, Karahanna, and Straub, 2003, p. 54). Trust has also been viewed as beha vioral intentions that result from specific beliefs in competence, integrity, and benevolence (McKnight, Choudhury, and Kacmar, 2002). People are more willing to engage in so cial interactions, when trust is high in relationships (Putnam, 2000). On the contrary, as members participate in the community, it is positively related for members to trust each other (Sherif, Hoffman, and Thomas, 2006). These indicate that there is a positive relationship between social interactions and trust. In online forums, users tend to trust others when they spend time to write their comments and read others’ comments (Awad and Ragowsky, 2008). Connections in SNSs are based on users’ existing networks, and they join each other’s networks by mutual agreement. These may influence the credibility of their ne tworks, and increase trust. Based on these connections, more SNS usage could have a positive effect on trust. Accordingly, we hypothesize that the use of SNSs increases trust. H1a: The SNS usage increases trust in SNSs.

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26 A norm is an accepted belief about how members should behave in a community (Nahapiet and Ghoshal, 1998). A norm constitutes a form of social capital when it exists and is effective (Coleman, 1988). A norm exists when others, not an actor, hold the socially defined right to control an action (Nahapiet and Ghoshal, 1998). Norms motivate members to participate in virtual communities (Hung and Li, 2007). On the contrary, as members participate in the community, it is positive for them to evolve social norms that govern their interactions (Sherif et al., 2006). These indicate that there is a positive relationship between participation and norms. People use SNSs to participate and inte ract with others. Even though, there is little research which exactly examines the influence of participation in online communities on norms, we believe that it is related positively for SNS users to evolve social norms similar to members’ participation in the community influencing social norms (Sherif et al., 2006). Collaborative norms facilitate coordination and cooperation for mutual benefit (Putnam, 2000). Facebook has recently been found as a preferred ITdriven collaboration platform (Flood, 2013). These suggest that more SNS usage could have a positive influence on collaborative norm s. Thus, we hypothesize that the use of SNSs increases collaborative norms. H1b: The SNS usage increases collaborative norms in SNSs. Obligations are like credit and represent “a commitment or duty to undertake some activity in the future” (Nahapiet and Ghoshal, 1988, p. 255). People feel durable obligations from feelings of thankfulness, and respect or from feelings of the membership

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27 in a family, a school, or an organization (Nahapiet and Ghoshal, 1998). Obligations are differentiated from norms by relating to more personal relationships. There is little research which specifically examines the influence of participation in online communities on obligations. However, as members participate in the community, it is positively related for them to define expectations and obligations (Sherif et al., 2006). SNSs offer users their own un ique space (Gangadharbatla, 2008), which can be oriented towards obligations in personal re lationships such as connecting to those with common interests (Ellison et al., 2007). Weak ti es, which are maintained by less frequent and less emotional communication, do not require shared confidences or reciprocity (Granovetter, 1973). Thus, we believe that th ere is a positive relationship between the usage of SNSs and obligations similar to the relationship between participation in the community and obligations (Sherif et al., 2006). Consequently, we hypothesize that the use of SNSs increases obligations. H1c: The SNS usage increases obligations in SNSs. Identification is “the process whereby i ndividuals see themselves as one with another person or group of people” (Nahapiet and Ghoshal, 1998, p. 256). Identification motivates members to participate in virtua l communities (Hung and Li, 2007). On the contrary, as members participate in the comm unity, they develop an identity for the community (Sherif et al., 2006). These facts indicate that there is a positive relationship between participation and identification. People join SNSs for feelings of affiliation, belonging, and goal achievement (Ridings and Gefen, 2004). Users join each other’s networks from their existing networks, and/or they go through profiles to find any

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28 similarities between them; which may increase identification. These indicate that there is a positive relationship between the usage of SNSs and identification. Therefore, we hypothesize that the use of SNSs increases identification. H1d: The SNS usage increase s identification in SNSs. Social Capital, eWOM, and Knowledge Sharing in SNSs Recently, research is emerging on how social media and online communities change knowledge processes such as creation and sharing, and these changes may prove substantial (von Krogh, 2012). SNS users tend to get valuable information about products from others knowledge of the products, which facilitates eW OM behavior in SNSs (Chu and Kim, 2011). Social capital influences knowledge management in organizations and virtual communities (Boer and Berends, 2003; Ch iu et al., 2006; Wasko and Faraj, 2000). An online medium may be an important communication channel for social capital. These suggest that social capital can influence knowledge sharing and eWOM quality in SNSs. Trust is necessary for knowledge sharing and eWOM quality, since trust is crucial in social interactions, especially in onlin e environments (Gefen et al., 2003). Trust contributes to the exchange of knowledge between two parties (Inkpen and Tsang, 2005). Trust promotes mutual understanding and increases information exchange (Zahedi et al., 2010). Trust influences the effectiveness of knowledge sharing and organizational learning (Szulanski, 2000). Mutual trust has a positive influence on knowledge transfer (Huysman and Wulf, 2005), and mutual trust among the members of an organization is a critical factor for knowledge sharing (Chow and Chan, 2008). Trust also protects against opportunism and obstruction of sharing knowledge (Szulanski, 1996). Trust is important

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29 in knowledge sharing in virtual communities (Chiu et al., 2006; Hsu, Chang, and Yen, 2011; Hsu et al., 2007). In the same way, there may be a positive relationship between trust and knowledge sharing when people use SNSs. Trust is positively associated with knowl edge quality (Chiu et al., 2006). An online medium is an important communication channel for building customer trust, which facilitates eWOM (Dellarocas, 2003). Trust is a key determinant of eWOM in SNSs (Chu and Kim, 2011). Moreover, trust has a positive effect on WOM among customers when they share their experience on products (Ranaweera and Prabhu, 2003). Trust also provides means of generating effective eWOM (Hung and Li, 2007). Accordingly, we hypothesize that trust influences knowledge sharing and eWOM quality when people use SNSs. H2a: Trust increases knowledge sharing in SNSs. H2b: Trust is positively relate d to eWOM quality in SNSs. Collaborative norms are important, since norms are suggested to have a moderating role in knowledge exchange (Constant, Kiesler, and Sproull, 1994). Collaborative norms have played a critical ro le in facilitating knowledge seeking (Bock, Kankanhalli, and Sharma, 2006). Similarly, norms may play a role in knowledge sharing in online social networking environments. There has been little research studying the direct relationship between collaborative norms and eWOM quality in SNSs. However, social structure and cooperation are effective for WOM in information transfers (Frenzen and Nakamoto, 1993). Norms are found to be a source of effective eWOM in a beauty forum (Hung and Li, 2007). These indicate that norms may influence eWOM quality in

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30 SNSs. Thus, we hypothesize that norms in fluence knowledge sharing and eWOM quality when people use SNSs. H3a: Norms increase knowledge sharing in SNSs. H3b: Norms are positively related to eWOM quality in SNSs. Obligations are important in this study, since membersÂ’ knowledge sharing is motivated by moral obligation in online communities (Wasko and Faraj, 2000). A newcomer in an organization feels an obligation to reciprocate when she/he receives knowledge (Gouldner, 1960). Even though most of the interactions in SNSs are private, people may feel an obligation to reciprocate when they receive knowledge in SNSs. There have been very few studies examining the relationship between obligations and eWOM quality. However, obligations may play a role to influence eWOM quality, since eWOM activity may address the need to give something to the receiver (Dichter, 1966). For example, customers are motivated to engage in eWOM to express a good experience in online forums (Hennig-Thurau et al., 2004). This suggests that there is a positive relationship between obligations and eW OM. Consequently, we hypothesize that obligations influence knowledge sharing and eWOM quality when people use SNSs. H4a: Obligations increase knowledge sharing in SNSs. H4b: Obligations are positively related to eWOM quality in SNSs. Identification is an important antecedent of knowledge sharing and eWOM quality, since it has been studied as a major motivator to share knowledge (Chiu et al., 2006). Nahapiet and Ghoshal (1998) argue that identification influences the motivation to

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31 exchange knowledge. In SNSs, users tend to search for people with whom they have connections (Lampe et al., 2007). From these, people would not unsparingly share knowledge unless they identify themselves with other people. There is a positive relationship between social identification and knowledge sharing (Van der Vegt and Bunderson, 2005). Customer identification leads to positive WOM through customer commitment (Brown, Barry, Dacin, and Gunst, 2005). Customers may engage in eWOM communication for reasons of identification and social integration. They perceive these benefits when they participate in and belong to online communities (McWilliams, 2000). Thus, we hypothesize that identifica tion influences knowledge sharing and eWOM quality when people use SNSs. H5a: Identification increases knowledge sharing in SNSs. H5b: Identification is positively related to eWOM quality in SNSs. Even though the relationship between eWOM quality and knowledge sharing has not been studied widely in any environments, we believe that eWOM quality plays an important role in sharing knowledge, especially in online social networking environments. People are not likely to shar e knowledge unless they think the knowledge is worth something (Bock and Kim, 2002). Knowledge quality is positively related to system trust, and system trust is positively related to knowledge sharing behavior through usersÂ’ intention to share knowledge (Chang et al., 2010). We posit that if SNS users feel that SNS content is relevant, accurate, reliable or helpful for them, then they may want their friends to have the same benefits. In turn, they may want to spread the words. As a result, we hypothesize the following:

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32 H6: eWOM quality is positively rela ted to knowledge sharing in SNSs. Figure 1 shows the conceptual model of SNSs as social capital. Figure 1 Conceptual Model of SNSs as Social Capital. Methodology Measurement development As with other research, this study is s ubject to biases resulting from item and measurement. To reduce the common method bias, we tried to develop more concrete survey items. The measures used to operationalize the seven constructs, which are SNS Usage, Trust, Norms, Obligations, Identification, eWOM Quality, and Knowledge Sharing, were adapted and modified from previous research. The items were examined in SNSs, virtual communities, or online environments. This is important, since the way how

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33 people interact in online social networking environments may be different from that in traditional face-to-face environments. SNS Usage focused on the number of friends and time spent in SNSs, which were based on Ellison et al. (2007). Trust was assessed with items adapted to reflect an individualÂ’s beliefs in other usersÂ’ non-oppor tunistic behavior, honesty, reliability, and consistency; which were based on Chiu et al. (2006), Hsu et al. (2007), and Williams (2006). The initial items on No rms were based on Kankanhalli et al. (2005) focusing on collaboration, cooperation, and team work. Obligations were assessed with items adapted to reflect an individualÂ’s perception about the extent to which sh e/he fulfill her/his obligations and others fulfill their obligations; which were based on Chang et al.Â’s (2010) and Kankanhalli et al. (2005). Identification wa s assessed with items adapted to reflect an individualÂ’s sense of belonging and feeling of togetherness, which were based on Ellison et al. (2007), Chiu et al. (2006), and Hsu et al. (2011). Knowledge Sharing is measured by an individualÂ’s perceived ability to contri bute her/his knowledge and the frequency of knowledge transfer, which were based on Chang et al. (2011), Hsu et al. (2007), Hsu et al. (2011), and Kankanhalli et al. (2005). Finally, eWOM Quality was assessed with items adapted to reflect an individualÂ’s perception of quality of eWOM such as relevancy, helpfulness, timeliness, comple teness, reliability, and completeness; which were based on Awad and Ragowsky (2008), Chang et al. (2011), Chiu et al. (2006), and Hsu et al. (2011). The first step of checking the construct validity which is to make sure that the operationalization accurately reflects its construct is to check face validity and content validity. In face validity, it is checked th at the operationalization seems like a good

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34 translation of the construct. In content vali dity, the operationalization is checked against the relevant content domain for the construct (Trochim, 2006). Face validity and content validity are satisfied, since the initial items were adapted and modified from previous research. The next step of checking the construct validity is to check predictive validity and concurrent validity. In predictive validity you assess the operationalizationÂ’s ability to predict something it should theoretically be able to predict. In concurrent validity, you assess the operationalizationÂ’s ability to di stinguish between groups that it should theoretically be able to distinguish between (Trochim, 2006). For checking predictive validity and concurrent validity, experienced researchers in the disciplines were involved to assess their logical consistencies and contextual relevance. A pretest of fifty-one items, excluding three demographic items, was performed by five IS researchers (one assistant professor, four doctoral students) and four marketing researchers (one associate professor, two assistant professors, one doctoral student). They categorized and prioritized the fifty-one initial items. They evaluated how favorable (from 1 as the lowest to 7 as the highest) each item was with regard to the construct. Three items with one asterisk mark (*) in the Appendix A were removed from the questionnaire, since five of the judges categorized them differently. Besi des, twelve items with two asterisk marks (**) in the Appendix A were removed from the questionnaire, since the judges gave low scores for them with regard to the construct. We removed any items from the questionnaire that even one judge had categorized differently, or even one judge had given equal or below 4. We used strict criteria for selecting the items to make the survey items clear, even though the initial items were adapted and modified from previous

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35 research. As a result, thirty-nine items including three demographic items were selected for the questionnaire in the Appendix B. A Seven-point Likert scale with anchors ranging from Strongly Disagree (1), Neutral (4), to Strongly Agree (7) was used. The final questionnaire is in the Appendix B The final step of checking the construct validity is to check convergent validity and discriminant validity. In convergent validity, you examine the degree to which the operationalization is similar to other operationalizations that it theoretically should be similar to. In discriminant validity, you examine the degree to which the operationalization is not similar to other opera tionalizations that it theoretically should be not similar to (Trochim, 2 006). We looked at correlations among items to check convergent validity and discriminant validity. Each of the items actually did reflect the construct to which it belonged shown by th e fact that the items were highly intercorrelated. In addition, the cross-construct correlations were low. However, one item of Trust and two items of eWOM Quality with three asterisk marks (***) in the Appendix A were removed from the analysis, since they substantially affected reliability (CronbachÂ’s alpha). Two items of Obligations and one item of Knowledge Sharing with four asterisk marks (****) in the Appendix A were removed from the analysis, since they substantially affected reliability (factor loadings). Control variables Culture determines the identity of a human group (Hofstede, 1984). There are differences and similarities among the culture patterns among countries (Hofstede, 1984). Facebook was launched in the U.S., and it is the first SNS which has been spread over the world. Koreans had intensively used their SNS, CyWorld, since 1999 before Facebook

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36 became a fad. However, increasingly Koreans have been spending more time on Facebook than CyWorld. We think it would be interesting to compare the users who spread Facebook to the world and the users who have had longer experience in using SNSs. Therefore, we controlled for the two regions to find whether there would be differences between them. We also controlled for gender and age. Survey administration The study sample was taken from undergraduate and graduate students who were using Facebook in two university campuses during January 2012 in South Korea, and three mid-western university campuses during February 2012 in the U.S. Facebook is the most popular SNS in the world that it would be a logical platform for this study. Students are frequent users of SNSs. Thus, we assume that they are representative of the populations we are interested in. The questionnaire had been originally developed in English, and distributed in English to the U.S. subjects. It had been translated in Korean for the Korean subjects. However, two English-Korean bilinguals confirmed the translation to ensure the meanings of the questions held true. Researchers stood on campuses and asked people if they wanted to take the survey and if they were using Facebook, then handed them the survey to complete on paper and give back to researchers at that time. Responses were received from 227 students. However, six responses were removed because of missing values. Responses from 221 students were analyzed. 52.5% of the respondents were female students (116) and 47.5% of the respondents were male students (105). 19.9% of the respondents were younger than 21 (44), 53.8% of the respondents were between 21 and 30 years old (51), 23.1% of the respondents were between 31 and 40 years old (17),

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37 and 3.2 % of the respondents were older than 40 (7). Finally, 45.7 % of the respondents lived in the U.S. (101), and 54.3% of the respondents lived in South Korea (120). Table 2 shows the descriptive statistics. Table 2 Descriptive Statistics (Study 1). Gender Response Female 116 (52.5%) Male 105 (47.5%) Age Response Younger than 21 44 (19.9%) 21 – 30 119 (53.8%) 31 – 40 51 (23.1%) Older than 40 7 (3.2%) Location Response US 101 (45.7%) South Korea 120 (54.3%) Results The partial least squares (PLS) method was used to examine the hypotheses, since PLS focuses on prediction of the constructs rather than explanation of the relationships between items and it works with a wider range of sample sizes than the structural equation model (SEM) (Hair, Black, Babin, and Anderson, 2010). The data were analyzed with PASW version 18 and PLS-Graph build 1130. Table 3 shows the reliability of constructs The composite reliability ranges from .71 to .93. The reliability of all construc ts is adequate since they are above .7.

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38 These results support construct validity of the model. Table 3 Reliability of Constructs (Study 1). Construct Composite Reliability Number of Items SNS Usage .93 3 Trust .84 5 Norms .71 3 Obligations .91 4 Identification .84 5 Knowledge Sharing .86 5 eWOM Quality .84 5 Table 4 shows the correlation estimates between the constructs, and the square root of average variance extracted (AVE) on diagonal values. Table 4 Correlations of Constructs (Study1). Construct SNS Usage Trust Norms Obligations Identification Knowledge Sharing eWom Quality SNS Usage .90 Trust .51 .72 Norms .11 -.19 .71 Obligations -.11 -.01 .17 .85 Identification .51 .70 -.16 -.02 .71 Knowledge Sharing .45 .61 -.15 .03 .65 .74 eWOM Quality .56 .72 -.19 .01 .70 .69 .71

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39 The AVEs are between .50 and .82, which are above the required value of .50. The square roots of AVEs which are from .71 to .90 are higher than the absolute value of correlation estimates which are from .01 to .70. Only the correlation estimate between Trust and eWOM Quality (.72) is high. However, there is no cross-loading among the measured variables. Taken all together, these results support the discriminant validity of the model. Table 5 summarizes the results of the hypotheses supported. Table 5 Hypotheses Supported (Study 1). Hypothesis Supported Estimates t -statistics SNS Usage Trust Yes .51 t = 10.02 SNS Usage Norms No .11 t = 1.32 SNS Usage Obligations No -.11 t = 1.22 SNS Usage Identification Yes .5 t = 9.90 Trust Knowledge Sharing No .03 t = 0.12 Trust eWOM Quality Yes .66 t = 12.52 Norms Knowledge Sharing No -.02 t = .0.25 Norms eWOM Quality No -.02 t = 0.57 Obligations Knowledge Sharing No .02 t = 0.32 Obligations eWOM Quality No .03 t = 1.09 Identification Knowledge Sharing No .16 t = 1.00 Identification eWOM Quality Yes .30 t = 6.05 eWOM Quality Knowledge Sharing Yes .82 t = 5.01 Hypothesis 1a, the SNS usage increases trust in SNSs, is supported; .51, t = 10.02. Hypothesis 1d, the SNS usage increases identification in SNSs, is supported; .51, t =

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40 9.90. Hypothesis 2b, trust is positively related to eWOM quality in SNSs, is supported; .66, t = 12.52. Hypothesis 5b, identification is positively related to eWOM quality in SNSs, is supported; .30, t = 6.05. Finally, hypothesis 6, eWOM quality is positively related to knowledge sharing in SNSs, is supported; .82, t = 5.01. The R2 values show that eWOM Quality is explained the most ( R2 = .91), Knowledge Sharing the second most ( R2 = .49), Identification the third most ( R2 = .26), and Trust the fourth most ( R2 = .25). For control variables, there is no significant difference among age. There is no significant difference between the U.S. and South Korea, either. In addition, gender does not affect the relationship between the SNS us age intensity and trust, and the relationship between the SNS usage intensity and identification. However, female students experience stronger relationship between trust and eWOM quality in SNSs, t = 10.34, r = .57. Female students also experience stronger relationship between identification and eWOM quality in SNSs, t = 6.88, r = .42. Finally, female students experience stronger relationship between eWOM quality and knowledge sharing in SNSs, t = 3.53, r = .23. Figure 2 Structural Model of SNSs as Social Capital.

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41 Figure 2 shows the structural model of SNSs as social capital. Discussion The results show that the SNS usage increa ses trust and identification. The results also show that trust and identification are positively related to eWOM quality. In addition, eWOM quality is positively related to knowledge sharing. Therefore, returning to our hypotheses, we are able to state that there is a positive relationship between SNS use and some of the aspects of relational social capital (trust and identification). However, the results show that people, students in this study, do not feel that they use their SNS for norms or obligations. This could be because they are using SNSs in their private environments, rather than in work environments. Norms are more important in organizations since they form the basis for the organizational culture. Obligations might be more important in formal relationship than amongst casual friends and family in SNSs. We are also able to state that the trus t and identification aspects of relational social capital have positive influence on eWOM quality in SNSs. However, norms and obligations do not have influence on eWOM quality in SNSs. There are two types of relational social capital. One is based on identification and trust and is relevant to the use of SNSs. The other is based on norms and obligations and is not relevant to the student sample used in this study. Previous stud ies found the same results in traditional communities and in online communities (Gefen et al., 2003; Hung and Li, 2007; Ranaweera and Prabhu, 2003; Sherif et al., 2006). People feel the same in SNSs. Connections in SNSs are based on usersÂ’ existing networks, and/or they go through profiles to find any similarities between them for joining each otherÂ’s networks. These

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42 may increase trust and identification, and in tu rn, increase the reliability of their personal experiences and opinions. The results also show that people do not feel the need to share knowledge just because of trust, norms, obligations, or identifi cation; when they use their SNS. This also could be because they are using SNSs in their private environments, rather than in work environments. Even though, there is no rela tionship between the relational social capital and knowledge sharing in SNSs directly, eWOM quality has a positive effect on knowledge sharing in SNSs. When students in th is study feel that the content in SNSs are relevant, helpful, accurate, reliable, or timely; they want their friends to have the same benefits. Consequently, they spread the bene fits, and they perceive that they share knowledge in SNSs. This indicates that ther e is no direct relationship between the relational social capital and knowledge sharin g in SNSs. However, some of the relational social capital factors (trust, identification) have an influence on knowledge sharing through eWOM quality in SNSs. One interesting result is that female students experience stronger relationship between trust and eWOM quality, between identification and eWOM quality, and between eWOM quality and knowledge sharing in SNSs than male students. This result indicates that female students feel more st rongly about eWOM quality when they trust others, or when they perceive that they belong to their SNS community when they use their SNS. In addition, female students feel more strongly about knowledge sharing when they perceive that eWOM quality is good. This may be because females emphasize maintaining relationships more and communicate with one another more than males (Brannon, 1999; Tannen, 1990). Besides, females are more likely to share their

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43 information with others and change their behavior based on interactions with others (Brannon, 1999). In the next section, we discuss the second study, factors affecting knowledge sharing in SNSs.

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44 CHAPTER IV FACTORS AFFECTING KNOWLEDGE SHARING IN SNSS (STUDY 2) We have found that there is a positive re lationship between SNS use and some of the aspects of relational so cial capital (trust and identification), and they influence knowledge sharing through eWOM quality in SNSs. In this section, we discuss the factors affecting knowledge sharing in SNSs. Specifically, we are interested in knowledge sharing about electronics in SNSs. In the first study, we find that SNSs can be a medium for knowledge sharing through eWOM. This may be especially im portant when the eWOM is related to electronics products (Kabaday and Alan, 2012). According to the National Retail Foundation (2008), WOM is the number one influencer in electronics purchases, and SNSs are one of the main sources which influence electronics purchases. Reputation and Intention to Share Knowledge in SNSs Social psychologists consider that knowledge sharing has an egotistic aspect explained by economic and social exchange theory (Deci, 1975). We are not interested in economic rewards in this study, since participation in SNSs is voluntary, with no economic rewards. However, users who shar e knowledge in SNSs may expect others’ feedback to obtain recognition or reputation which provide perceptions of social rewards. Reputation is “the degree to which a person believes that participation could enhance personal reputation through knowledge sharing” (Hsu and Lin, 2008, p.68). Reputation is

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45 important for an individual to achieve and maintain status in a group (Wasko and Faraj, 2005). Reputation is also how others perceive you as an individual. Reputation is the critical extrinsic mo tivator for volunteers of open-source software projects (Wu, Gerlach, and Young 2013). The opportunity to improve oneÂ’s reputation is an important motivation for providing advice to others in organizations (Constant et al., 1996), and knowledge contributors improve their reputation in organizations (Ba, Statlaert, and Whinston, 2001; Constant et al., 1994; Constant et al., 1996). Knowledge contributors gain benefits from displaying their valuable knowledge, which improves their recognition as an expert (B a et al., 2001). Reputation has also been found as a strong motivator for sharing know ledge in organizations (Kankanhalli et al, 2005; Wasko and Faraj, 2005), and reputation is a strong motivator for increasing the quality of shared knowledge in virtual communities (Chang and Chuang, 2011). This applies not only within organizations, but also in any social environment, since reputation acts different levels of social entities (Fombr un, 1996). In addition, anticipated reputation has a positive influence on attitude toward knowledge sharing in Facebook (Pi et al., 2013). Thus, the perception that sharing k nowledge will enhance oneÂ’s reputation may motivate individuals to share their knowledge with others in SNSs. Men have a higher degree of concern for ego than women (Miller and Karakowsky, 2005), and men show an increasing awareness of social status (Tannen, 1990). Knowledge contributors show others that they have valuable expertise and they receive recognition; which establishes reputati on (Ba et al, 2001). Building reputation enhances oneÂ’s status (Chang and Chuang, 2011). These may indicate that men consider

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46 sharing knowledge in SNSs as an opportunity to show their exper tise, thereby receive recognition from others. Accordingly, we hypothesize that reputation is positively related to knowledge sharing intention in SNSs, and the relationship is stronger for men than women. H1a: Reputation is positively related to knowledge sharing intention in SNSs. H1b: Reputation is positively related to knowledge sharing intention in SNSs more strongly for men than women. Altruism and Intention to Share Knowledge in SNSs Altruism is usually demonstrated through pro-social behaviors such as sharing, helping, cooperation, and community service (Batson, 2012). Altruism has long been associated with information sharing (Staples and Jarvenpaa, 2000). People who show altruism share information with others because they want to give something to others, they want to express concerns and care, or they want to reduce othersÂ’ distress (Price et al., 1995). Social psychologists also argue that kn owledge sharing has altruistic motives (Deci, 1975). Altruism has been considered as the motivator for knowledge sharing (Hsu and Lin, 2008), and altruism is the critical perceived benefits which increase knowledge sharing in virtual communities (Chang and C huang, 2011). An individual is willing to contribute to othersÂ’ benefits without expecting any returns. For example, altruism intrinsically motivates programmers to partic ipate in open-source projects (Hars and Ou, 2002; Wu et al., 2013), and altruism has been an important motivator for knowledge contribution in peer-to-peer communities (Kwok and Gao, 2004). People actively

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47 participate in sharing knowledge in a blog, sinc e they enjoy helping others (Hsu and Lin, 2008). Furthermore, knowledge contributo rs feel satisfied by demonstrating their altruistic behavior (Ba et al., 2001; Wasko and Faraj 2000). The above examples have something in common, which is voluntary participation. Knowledge belongs to individuals, who cannot be forced to share it. Extrinsic rewards or incentives are likely to be important motives for knowledge sharing in task-oriented organizations (Bock et al., 2005; Kankanhalli, et al., 2005). On the other hand, people may willingly engage in sharing knowledge to increase welfare of others without expecting rewards in SNSs, since they use SNSs voluntarily. By sharing knowledge in SNSs, they have the opportunity to help others, and it has been demonstrated that people enjoy and gain satisfaction from helping others (Deci, 1975). Women are more concerned with helping others than men are (Bridges, 1989), and women consider pleasing others more hi ghly than men (Miller, 1986). These may indicate that women understand the needs of others, and they value behaviors that help others. In addition, altruism related to know ledge sharing has been found to be stronger for women than men in organizations (Lin, 2008). We expect that altruismÂ’s influence on knowledge sharing is stronger for women than men not only in working environments, but also in SNSs. Thus, we hypothesize that altruism positively influences intention to share knowledge in SNSs, and altruism on knowledge sharing is stronger for women than men. H2a: Altruism positively influences intention to share knowledge in SNSs. H2b: Altruism positively influences intention to share knowledge in SNSs more strongly for women than men.

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48 Subjective Norms and Intention to Share Knowledge in SNSs Subjective norms are defined as “the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1975, p.302). It does not necessarily imply that an individual views a behavior or its result favorably. Subjective norms positively affect an individual’s behavior (Venkatesh et al., 2003). Previous studies have verified that subjective norms have a strong and positive influence on the intention to perform a behavior (Morris et al., 2005; Sheppard, Hartwick, and Warshaw, 1988; Venkatesh and Morris, 2000; Venkatesh et al., 2003). Subjective norms have also been found as an important factor for intention to share knowledge in organizations (Bock and Kim, 2002; Bock et al., 2005; Chow and Chan, 2008). Subjective norms are important not only in organizations, but also in private environments (Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975). In this study, subjective norms stem from the influence of reference groups which are a user’s friends in SNSs. People use SNSs to maintain and articulate their social networks which are important to them. Even though not all the SNS friends are important to them, people who are important to them are in SNSs. Thus the perception of the important people in SNSs may still influence one’s behavior. Even though the contexts of sharing knowledge in this research are voluntary, pressures from friends in SNSs may play an important role in intention to share knowledge in SNSs. This is further supported by research that has found subjective norms affect intention to share knowledge in Facebook (Pi et al., 2013). However, women and men differ in the extent to which they are influenced by others (Becker, 1986; Eagly and Carli, 1981) Women tend to be more people-oriented

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49 while men tend to be more independent (Minton and Schneider, 1985). This may imply that women value the others’ opinions as op portunities to learn something more than men do. In turn, women may consid er that others would learn something from their sharing. Women are also strongly motivated by affiliation needs (Hoffman, 1972). Women also tend to be more network oriented (Tannen, 1990). They may feel that they are separated from their friends if they do not sh are anything in SNSs. In addition, they may be more responsive to a favorable reaction (e.g. more ‘Like’ in Facebook) from their friends in SNSs. These indicate that women ma y weigh the opinions of others in sharing knowledge in SNSs, and the opinions may influence on their intention to share knowledge in SNSs. Accordingly, we hypothesize that subjective norms have a positive effect on intention to share knowledge in SNSs, and they have stronger effect for women than men. H3a: Subjective norms have a positive effect on intention to share knowledge in SNSs. H3b: Subjective norms have a positive effect on intention to share knowledge in SNSs more strongly for women than men. Intention to Share Knowledge a nd Knowledge Sharing in SNSs A wide range of studies has verified a strong and significant relation between behavioral intention and actual behavior (Sheppard et al., 1988) including knowledge sharing in organizations (Morris et al., 2005). TRA and TPB do not specify a particular behavior nor a particular environment. They, rather, explain a general social behavior (Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975). Understanding knowledge sharing

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50 behavior can be valuable to understand Facebook usage behavior (Lee and Paris, 2013). Thus, they may explain the relationship between the intention to share knowledge and knowledge sharing in SNSs. Accordingly, we hypothesize that intention to share knowledge in SNSs is positively re lated to knowledge sharing in SNS. H4: Intention to share knowledge in SNSs is positively related to knowledge sharing in SNSs. Figure 3 shows the conceptual model. Figure 3 Conceptual Model of Factors Affecting Knowledge Sharing in SNSs. Methodology A survey was employed to examine the relationship between reputation, altruism, subjective norms, intention to share knowledge, and knowledge sharing in SNSs.

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51 Measurement development As with other research, this study is s ubject to biases resulting from item and measurement. To reduce the common method bias, we developed more concrete survey items. The measures used to operationalize the five constructs, which are Reputation, Altruism, Subjective Norms, Intention to Share Knowledge, and Knowledge Sharing were adapted and modified from previous research. The items were examined in SNSs, virtual communities, or online environments. This is important, since the way how people interact in online social networking environments may be different from that in traditional face-to-face environments. Reputation was assessed with items adapted to reflect an opinion about an individual based on the result of social evaluation; from Kankanhalli et al. (2005), and Wasko and Fara j (2005). Altruism was assessed with items adapted to reflect an individualÂ’s spontaneous helping someone other than oneself, which were based on Hsu and Lin (2008); Podsakoff, MacKenzie, Moorman, and Fetter. (1990); Price et al. (1995); and Wasko and Faraj (2005). Subjective norms were assessed with items adapted to reflect an individualÂ’s pe rception that Facebook friends think he/she should share knowledge in Facebook, which we re based on Bock and Kim (2002), Bock et al. (2005), and Hsu and Lin (2008). Intention to Share Knowledge is measured by an individualÂ’s willingness and ability to contribut e her/his knowledge and the frequency of knowledge transfer, which were based on Bock and Kim (2002), Bock et al. (2005), and Hsu and Lin (2008). Finally, we adopted K nowledge Sharing measures from our first study. Table 6 shows the five constructs and their references. The initial thirty-four items are in the Appendix C.

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52 Table 6 Constructs and References (Study 2). Constructs References Reputation Kankanhalli et al. (2005), Wasko & Faraj (2005) Altruism Hsu & Lin (2008), Podsakoff et al. (1990), Price et al. (1995), Wasko & Faraj (2005) Subjective Norms Bock & Kim (2002), Bock et al. (2005), Hsu & Lin (2008) Intention to Share Knowledge Bock & Kim (2002), Bock et al. (2005), Hsu & Lin (2008) Knowledge Sharing Kankanhalli et al. (2005), Hsu et al. (2011) The first step of checking the construct validity which is to make sure that the operationalization accurately reflects its construct is to check face validity and content validity. Face validity checks that the operationalization seems like a good translation of the construct. In content valid ity, the operationalization is checked against the relevant content domain for the construct (Trochim, 20 06). For face validity and content validity, the initial items were adapted and modified from previous research. In addition, experienced researchers in the disciplines were involved to assess their logical consistencies and contextual relevance. A pretest of thirty-one items, excluding four general items, was performed by five IS re searchers (two assistant professors, one instructor, two doctoral students) and four marketing researchers (one associate professor, two assistant professors, one doctoral stud ent). They categorized and prioritized the thirty-one initial items. They evaluated how fa vorable (from 1 as the lowest to 7 as the highest) each item was with regard to the cons truct. Four items with one asterisk mark (*) in the Appendix C were removed from the questionnaire, since the judges gave low scores for them with regard to the construct. We removed any items from the

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53 questionnaire that even one judge had categorized differently, or even one judge had given equal or below 4. We used strict criteria for selecting the items to make the survey items clear, even though the initial items were adapted and modified from previous research. The next step of checking the construct validity is to check predictive validity and concurrent validity. Predictive validity assesses the operati onalizationÂ’s ability to predict something it should theoretically be able to predict. Concurrent validity assesses the operationalizationÂ’s ability to distinguish be tween groups that it should theoretically be able to distinguish between (Trochim, 2006). For checking predictive validity and concurrent validity, a pilot test was used. The questionnaire was distributed to thirty eight undergraduate students in a mid-western uni versity. Eighteen responses were collected without missing values. Three items with two as terisk marks (**) in the Appendix C were removed from the questionnaire, since they lower the reliability of the constructs. As a result, twenty seven items including four demographic items were selected for the questionnaire. A Seven-point Likert scale with anchors ranging from Strongly Disagree (1), Neutral (4), to Strongly Agree (7) was used. The final questionnaire is in the Appendix D. The final step of checking the construct validity is to check convergent validity and discriminant validity. In convergent validity, you examine the degree to which the operationalization is similar to other operationalizations that it theoretically should be similar to. In discriminant validity, you examine the degree to which the operationalization is not similar to other opera tionalizations that it theoretically should be not similar to (Trochim, 2 006). We looked at correlations among items to check

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54 convergent validity and discriminant validity. Each of the items actually did reflect the construct to which it belonged shown by th e fact that the items were highly intercorrelated. In addition, the cross-construct correlations were low. However, one item of Reputation and one item of Altruism with three asterisk marks (***) in the Appendix C were removed from the analysis, since they substantially affected reliability (CronbachÂ’s alpha). Two items of Subjective Norms and two items of Knowledge Sharing with four asterisk marks (****) in the Appendix C were also removed from the analysis, since they substantially affected reliability (factor loadings). Control variables Culture determines the identity of a human group (Hofstede, 1984). There are differences and similarities among the culture patterns among countries (Hofstede, 1984). Facebook was launched in the U.S., and it is the first SNS which has been spread over the world. Koreans had intensively used their SNS, CyWorld, since 1999 before Facebook became popular. However, increasingly Koreans have been spending more time on Facebook than CyWorld. Thus, comparing th e users who spread Facebook to the world and the users who have had longer experience in using a SNS may provide useful insights, since there is difference between the early adopter market and the big market of IT (Hjorth and Kim, 2005). Therefore, we control for the users in two regions to find whether there are differences between them. We also check whether there are differences among age groups and among users with differing numbers of friends in SNSs. Age differences have been studied widely, and proven to be important variables in a technology context (Morris et al., 2005).

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55 Survey administration The study sample was taken from undergraduate and graduate students who are using Facebook in three university campuses in South Korea and three mid-western and one western university campuses in the U.S. Facebook was chosen as the platform being used for this study, since it is the most popul ar SNS in the world. It is also the only SNS which is extensively used both in the US and Korea. Student samples have been used in previous studies such as Ellison et al. (2007) and Lampe et al. (2007) exploring SNSs. Students are frequent users of SNSs, and are frequent contributors of eWOM; and as such represent a suitable population to study for the purposes of this study. The questionnaire was originally developed in English, and was distributed in English to the U.S. subjects. It was translated in Korean for the Korean subjects. In order to insure that the survey questions retain their original underlying meaning, two EnglishKorean bilinguals confirmed the translation to ensure the meanings of the questions held true. Researchers created an online survey in SurveyMonkey for U.S. subjects. Researchers asked some of the judges who had involved in the pretest of developing the survey items to distribute and collect the survey for Korean subjects. And then, researchers collected the survey from the judges. In the introductory statement, researchers stressed that the questions were regarding their perspectives on knowledge sharing regarding electronics products. Table 7 shows the descriptive statistics. Responses were received from 271 students. However, 14 responses were remove d because of missing values or the same values for all the question items. Respon ses from 257 students were analyzed.

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56 Table 7 Descriptive Statistics (Study 2). Gender Response Female 136 (52.9%) Male 121 (47.1%) Age Response Younger than 21 70 (27.2%) 21 – 30 123 (47.9%) 31 – 40 57 (22.2%) Older than 40 7 (2.7%) Location Response US 125 (48.6%) South Korea 132 (51.4%) Number of Friends Response Less than 51 14 (5.4%) 51 – 100 54 (21.0%) 101 – 150 31 (12.1%) 151 – 200 43 (16.7%) 201 – 250 28 (10.9%) 251 – 300 25 (9.7%) 301 – 350 23 (8.9%) 351 – 400 23 (8.9%) More than 400 16 (6.2%) 52.9% of the respondents were female students (136) and 47.1% of the respondents were male students (121). 27.2% of the respondents were younger than 21 (70), 47.9% of the respondents were betwee n 21 and 30 years old (123), 22.2% of the

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57 respondents were between 31 and 40 years old (57), and 2.7 % of the respondents were older than 40 (7). 48.6 % of the respondents lived in the U.S. (125), and 51.4% of the respondents lived in South Korea (132). Finally, 5.4% of the respondents had less than 51 friends in Facebook (14), 21.0% of the res pondents had between 51 and 100 friends in Facebook (54), 12.1% of the respondents had between 101 and 150 friends in Facebook (31), 16.7% of the respondents had between 15 1 and 200 friends in Facebook (43), 10.9% of the respondents had between 201 and 250 friends in Facebook (28), 9.7% of the respondents had between 251 and 300 friends in Facebook (25), 8.9% of the respondents had between 301 and 350 friends in Facebook (23), 8.9% of the respondents had between 351 and 400 friends in Facebook (23), and 6.2% of the respondents had more than 400 friends in Facebook (16). Results To examine separate relationships for each of a set of dependent variables, SEM was used. The SEM provides efficient estimation technique for a series of separate multiple regression equations estimated simu ltaneously (Hair et al., 2010). It is particularly useful to test theories that contain multiple equations involving dependence relationships. In this study, we believe that reputation, altruism, and subjective norms are positively related to knowledge sharing intention, and knowledge sharing intention is positively related to knowledge sharing in SNSs. In other words, knowledge sharing intention is both an independent variable and a dependent variable in the same model. Thus, a hypothesized dependent variable becomes an independent variable in a subsequent dependence relationship.

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58 However, we were more interested in prediction of the constructs rather than explanation of the relationships between items. Thus, the PLS approach to SEM was used to examine the hypotheses, since PLS focuses on prediction of the constructs rather than explanation of the relationship between items (H air et al., 2010). Besides, it works with a wider range of sample sizes than the SEM (Hair et al., 2010). The data were analyzed with PASW version 18, AMOS 18, and PLS-Graph build 1130. Table 8 shows the reliability of constructs The composite reliability ranges from .79 to .91. The reliability of all constructs is adequate since they are above .7. These results support construct validity of the model. Table 8 Reliability of Constructs (Study 2). Construct Composite Reliability Number of Items Reputation .79 4 Altruism .81 4 Subjective Norms .86 3 Intention to Share Knowledge .90 4 Knowledge Sharing .91 3 Table 9 shows the correlation estimates between the constructs, and the square root of AVE on diagonal values. The AVEs are between .62 and .84, which are above the required value of .50. The square roots of AVEs which are from .79 to .92 are higher than the absolute value of correlation estimates which are from .11 to .71. Even though the square roots of AVEs are higher than the abso lute value of correlation estimates, it seems that Intention to Share Knowledge and Know ledge Sharing correlate highly. However, because Intention to Share Knowledge is a predictor variable and Knowledge Sharing is a

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59 response variable, multicollinearity is not pres ent in this model. Multicollinearity exists when there is a strong correlation between predictors (Hair et al., 2010). Taken all together, these results support the discriminant validity of the model. Table 9 Correlations of Constructs (Study 2). Construct Reputation Altruism Subjective Norms Intention to Share Knowledge Knowledge Sharing Reputation .79 Altruism .35 .80 Subjective Norms .22 .17 .89 Intention to Share Knowledge .33 .24 .22 .88 Knowledge Sharing .29 .11 .17 .71 .92 Table 10 and table 11 summarize the results of the hypotheses supported, and Figure 4 shows the analysis of the structural model. Hypothesis 1a, reputation is positively related to knowledge sharing intention in SNSs, is supported; .24, t = 3.12. Hypothesis 2a, altruism positively influences intention to share knowledge in SNSs, is supported; .13, t = 1.98. Hypothesis 3a, subjective norms have a positive effect on intention to share knowledge in SNSs, is supported; .14, t = 2.36. Hypothesis 3b, subjective norms have a positive effect on in tention to share knowledge in SNSs more strongly for women than men, is supported; the estimates of male .06 and the estimates of female .23, t = 2.02. Finally, hypothesis 4, intention to share knowledge in SNSs is positively related to knowledge sharing in SNSs, is supported; .70, t = 14.52. The R2 values show that Knowledge Sharing is explained the most ( R2 = .50), and Intention to Share Knowledge the second most ( R2 = .19).

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60 Table 10 Hypotheses Supported (Study 2). Hypothesis Supported Estimates t -statistics Reputation Intention to Share Knowledge Yes .24 3.12 Altruism Intention to Share Knowledge Yes .13 1.98 Subjective Norms Intention to Share Knowledge Yes .14 2.36 Intention to SK Knowledge Sharing Yes .70 14.52 Table 11 Hypotheses Supported – Gender Effect (Study 2). Hypothesis Supported Estimates(M), Estimates (W) t -statistics Reputation (Men > Women) No .28, .21 0.64 Altruism (Men < Women) No .13, .14 0.05 Subjective Norms (Men < Women) Yes .06, .23 2.02 Figure 4 shows the structural model of factors affecting knowledge sharing in SNSs. Figure 4 Structural Model of Factors Affecting Knowledge Sharing in SNSs.

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61 Testing the impact of the control variables shows that there is no statistically significant difference between students that are different ages. There is no statistically significant difference between the U.S. and So uth Korean students, either. In addition, there are no statistically significant differe nces between students with differing numbers of friends in SNSs. Discussion The results show that reputation is positively related to knowledge sharing intention in SNSs. This is consistent with the results of previous studies exploring the relationship between reputation and knowledge sharing in organizations (Ba et al., 2001; Chang and Chuang, 2011; Constant et al., 1994; Constant et al., 1996; Kankanhalli et al., 2005; Wasko and Faraj, 2005). This indicates that people, students in this study, feel that sharing knowledge in SNSs improves oneÂ’s reputation; and reputation is an important motivator for knowledge sharing not only in organizations, but also in private environments. However, men donÂ’t consider reputation to a greater extent than women in making their decisions regarding the intention to share knowledge in SNSs. This may be because building reputation which enhances oneÂ’s status (Chang and Chuang, 2011) is important not only for men, but also for women. In addition, the fact that electronics is a product category relevant to both women an d men suggests both groups are willing to share knowledge on electronics. The results show that altruism is anothe r important motivator for the intention to share knowledge in SNSs. This indicates that pe ople, students in this study, like to share

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62 their knowledge in SNSs, since they expect their knowledge to be of some help to others. This could be because their friends in SNSs include people who are important to them such as their family, relatives, and close friends. Besides, Facebook is their private environment. However, altruism does not have a stronger effect for women than men in the intention to share knowledge in SNSs. One possible explanation to this result is that men care for their family, relatives, and close friends to the same extent as women. The results also show that social factors (subjective norms) are important for the intention to share knowledge in SNSs. This is consistent with the previous studies that found subjective norms as an important factor for the intention to share knowledge in organizations (Bock and Kim, 2002; Bock et al., 2005; Chow and Chan, 2008), and in Facebook (Pi et al., 2013). This indicates that people, students in this study, pay attention to their friends in SNSs; and feel affiliation by using SNSs. They may feel that sharing nothing will separate them from their friends in SNSs. When their friends ask something about electronics, or post a photo about electronics with a question mark (?); they may feel that they are expected to share knowledge about them. In addition, subjective norms are more salient to women compared with men in the intention to share knowledge in SNSs. This may be because women tend to be more people-oriented (Minton and Schneider, 1985). They may be more motivated by affiliation. Another possible explanation is that women may be more responsive to a favorable reaction (e.g. more ‘Like’ in Fa cebook) from their friends in SNSs. Women may weigh the opinions of others in sharing knowledge in SNSs, and the opinions may influence their intention to share knowledge in SNSs.

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63 Finally, the results show that the intention to share knowledge in SNSs has a positive effect on usersÂ’ perceived knowledge sharing in SNSs. This is consistent with a wide range of studies verifying strong and significant relations between behavioral intention and actual behavior (Sheppard et al ., 1988). It is also consistent with the previous study which found the intention to share knowledge had a positive relationship with knowledge sharing in organizations (Morris et al., 2005). Returning to our hypotheses, we are able to state that reputation, altruism, and subjective norms are important factors for th e intention to share knowledge in SNSs. And then, subjective norms effect knowledge sharing intentions more strongly for women than men. We are also able to state that the inte ntion to share knowledge in SNSs influences usersÂ’ perceived knowle dge sharing in SNSs. One interesting result is that subjective norms have a positive effect more strongly for women than men. This is consistent with the results of the first study. In the first study, women experience stronger relationship between identification and eWOM quality. These results indica te that women emphasize main taining relationships more than men. However, there is no difference related to age, the number of friends in SNSs, and between the U.S. students and Korean students. One limitation of survey sample is that it does not include mid-teens. Usually, mid-teens are more responsive to reputation and social pressures. The results could have been different if they had been added to the survey sample. We are able to state that the number of friends in SNSs does not affect knowledge sharing behavior in SNSs. We are also able to state that knowledge sharing behavior in SNSs is not significantly statistically different between the U.S. students and

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64 Korean students in this study. In other disciplines, there are differences among culture patterns. However, there is no difference between the U.S. and Korea regarding knowledge sharing in SNSs in this study. In the next section, we discuss the bene fits of these studies, limitations, and conclusion.

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65 CHAPTER V CONCLUSION The results of this study provide a theoretical model for researchers to test a new framework examining the relationships between social capital, eWOM and knowledge sharing in SNSs; and a new framework examining the factors, specifically, reputation, altruism, and subjective norms, influencing knowledge sharing in SNSs. This study examines trust, norms, obligations, and identif ication, rather than examining relational social capital as a whole. This enables us to understand the relationship between the relational social capital knowledge sharing, and eWOM more clearly. As far as we know, this study is the first to find empirical results for the effect of eWOM quality on knowledge sharing in SNSs. This framework also shows that different factors influence knowledge sharing in SNSs. People share knowledge for rewards (s ocial rewards in this study), due to the motivation to help others, and because of social pressure. This shows that the motivation to share knowledge in SNSs is complex, and there needs to be further research on this topic. With these models, further research on this topic would enable a theoretical understanding of the potential to leverage eWOM and knowledge sharing in SNSs for business, education, and societ y. In addition, further rese arch using the models would enable a theoretical understanding of the issues that need to be resolved regarding knowledge sharing in SNSs. We discuss the implications for practitioners in the following paragraphs. Although this study uses students, employees in organizations are likely to similarly develop trust and identification using SNSs internally; which would help

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66 employees to become more interested in the business of the organization (McWilliams, 2000). Further research could provide rationale for practi tioners to utilize eWOM in SNSs internally for organizational networking and organizational learning. Actually, social networking environments offer an opportunity for organizations and customers to have rich interactive content (Sweeney et al., 2012). If an organization facilitates use of SNSs amongst its employees, then it could use SNSs as a tool to promote trust among employees and enhance identification to the organization. It is similar to implications of (Gangadharbatla, 2008), which indicate that there is a positive relationship between th e need to belong and attitudes toward SNSs. This could help employees to extend and main tain their ties, and build a strong sense of emotional support and identity. It may not be immediately beneficial for organizations such as resolving immediate work-related problems. However, they may make it possible for organizations to have more useful work-related knowledge sharing in the future. Employees could also use SNSs to locate references when they encounter workrelated problems such as software coding and technical support, which would facilitate solving their work-related tasks. When they fail to locate an expert inside the organization, SNSs could be a good platform to locate experts outside the organization. Younger knowledge workers, especially, are more likely to use public social media for reaching out to their strong ties, and they consider public social media as a useful venue for sharing advice (Jarrahi and Sawyer, 2013). EmployeesÂ’ personal SNSs could be beneficial for organizations, since they could help provide a clearer understanding of the broader marketplace issues. Someone unrelated to an organization, neither an employee nor a customer, may have different

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67 perspectives on the products, services, and/or the market. Their feedback could improve the products and services, and give new ideas for the organization. For example, Jarrahi and Sawyer (2013) found that work-related publications posted on an employeeÂ’s Facebook page served as a valuable means for keeping updated social channels. This may not be beneficial immediately, but it could be useful for receiving feedback or extending the network in the future. Organizations benefit from employeesÂ’ knowledge sharing in the public SNSs as well as in corporate SNSs. However, many organizations restrict using public SNSs during work hours, some even block them; since they consider that work would be interrupted by using public SNSs. That is true when employees use public SNSs excessively, which causes many problems such as addiction to SNSs and decline in oneÂ’s concentration (SOGETILabs, 2013). Besides, employees should be careful about workrelated postings, since there are concerns on co nfidentiality of organi zationsÂ’ information. When they use public SNSs properly, there are more advantages than disadvantages, internally, as discussed above. Practitioners also have the potential to utilize eWOM in SNSs, externally, as a marketing tool. Customers perceive SNSs as a more reliable source of product information than companies (Chu and Kim, 2011), and shared knowledge in SNSs is considered to be reliable and value adding (Heinonen, 2011). These imply that organizations could utilize SNSs to increa se customersÂ’ loyalty to its brand by communicating through SNSs. People want to try products or services that their friends or family recommend, and SNSs could be a good medium for conveying those recommendations.

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68 SNS use could also facilitate knowledge sharing through eWOM among customers. Managers are interested in knowl edge sharing through eWOM, since it affects customer behavior (Banerjee, 1992; Godes and Mayzlin, 2004; Hennig-Thurau et al., 2004), and the user-generated content redu ces the expense of traditional marketing communications by influencing other custom ers’ behavior (Heinonen, 2011). Companies could reduce the expense of customer surveys and focus groups, since feedback is provided by potentially large numbers of customers spontaneously. Customers share their experiences and knowledge, which influence decision making (Heinonen, 2011). In addition, customers do not passively consume products anymore. They are active producers of the business value for organizations (Heinonen, 2011). Organizations need to participate more in their customers’ social media activities, and encourage them. Some organizations are taking notice of feedback from customers. The feedback has influenced correction of product flaws, and provided inspiration for new product development and new services. One possible way to facilitate knowledge sharing is that organizations design reward systems for customers’ contribution, and put them into effect. For example, organizations name contributors as expert such as “Zeus,” “Poseidon,” “Hades,” and so on based on their contribution; since reputation is a strong motivator for knowledge sharing in SNSs. This acknowledges their effort and performance. This could also improve the quality of knowledge; which enhances the eWOM quality, and in turn, facilitate knowledge sharing. Naver, a web port al in Korea, uses th is strategy. Knowledge contributors who answer the questions from others are ranked based on the quality and

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69 quantity of their contribution. Users who pos t questions also rate the answers. All together, the expert rank is given to the knowledge contributors. In addition to naming contributors as expert, organizations could introduce them to other customers. This could build trust among customers, and enhance identification to the organizations. Another possible way to facilitate knowledge sharing is that organizations display images which inspire empathy and emphasize human touch, since altruism is another strong motivator for knowledge sharing in SNSs. Organizations also could donate a certain amount to charity organizations, when a customerÂ’s contribution reaches a certain level. This could also move customers to share their knowledge. Furthermore, when more customers participate in knowledge sharing, it could produce social pressure for them to participate in the activities. On the other hand, challenges need to be faced. For example, organizations should recognize that they are engaging in interactions, not controlling the customersÂ’ opinions (Gallaugher and Ransbotham, 2010). If they try to manipulate customersÂ’ opinions, it is unethical. Even more, they will encounter adverse criticism. For example, companies which made their employees post good things about their products and services, and remove bad postings experienced serious criticism and brand weakening; when their actions were revealed. In schools, teachers could encourage their students to use SNSs, which could build trust and identification among students. When students have trust among classmates, and feel identification to the class; it could reduce bullying among students. It could also encourage students to share knowledge in SNSs, which could help their classmates. Teachers may praise a student who contributes to help his/her classmates in

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70 front of the class to facilitate knowledge sh aring. Teachers may also tell students about the positive influence of helping others, frequently. When more students participate in knowledge sharing, it could be social pressu re for students to share their knowledge. There are limitations to this study. First, this study focuses on SNS use in private non-corporate environments using students. Future research should find out whether there are similar results in corporate and other working environments. Second, Facebook was chosen as the platform being used for this study. Future research could also consider other SNSs. If the connection among users is primarily based on interests or practices, then the results may be different. Third, this study focuses on the relational dimension of social capital, leaving future research to investigate the structural dimension (network ties, network configuration, and appropriable organization) and cognitive dimension (shared codes and languages, and shared narratives) of social capital in the context of SNSs. Fourth, even though there was no cross-loading among items, the absolute value of correlation estimate between Trust and eWOM Quality was higher than one of the square roots of AVEs in the first study. However, because Trust was a predictor variable and eWOM quality was a response variable mutlicollinearity was not present. Multicollinearity exists when there is a strong correlation between predictors (Hair et al., 2010). Although the absolute value of correlation estimate between Trust and Identification was not higher than the square roots of AVEs, it was not low. This may pose multicollinearity which causes a proble m because it becomes difficult to determine the unique contribution to a factor of the variables. However, there were no very high correlations among the items of Trust and Identification ( r > .8) (Hair et al., 2010). Fifth, there are motivators other than individual behavioral motivators including reputation,

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71 altruism, and subjective norms for intention to share knowledge in SNSs ( R2 =.19). For example, motivators regarding technology usage need to be studied further. Sixth, unlike using information systems in organizations, there may be less concern and resistance using public SNSs. Thus, future research may refine the construct regarding knowledge sharing in SNSs, and future research may be necessary to more precisely measure the impact of intention to share knowledge and knowledge sharing in public SNSs. In this dissertation, the knowledge sharing construct measured perceptions on knowledge sharing, not actual knowledge sharing. Both “perceptions on knowledge sharing” and “actual knowledge sharing” contribute to our understanding of the knowledge sharing construct. Nevertheless, further research is needed to clarify the measurement of the knowledge sharing construct in SNSs. Finally, this study focuses on social rewards. Future research needs to examine economic rewards, since SNSs are becoming more associated with advertising involved in monetary activities. To conclude, we would like to answer the questions that we posed in Introduction. Are SNSs able to be used for the benefits of the society and organizations as well as for personal daily usage? According to the results, we believe that there is a strong possibility for SNSs to be social capital, si nce the SNS usage could develop some aspects of social capital (trust and identification). This indicates that we can get benefits from using SNSs, if we use SNSs properly. Then, if SNSs are able to be used for the society and organizations, how do we utilize them and what factors influence the utilization of them? We believe that SNSs are a good medium for knowledge sharing, especially through eWOM. Knowledge is a valuable intangi ble asset that could benefit the receivers.

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72 We also believe that expectation of reputation, altruism, and social influence can facilitate knowledge sharing in SNSs. This dissertation provides a theoretical framework examining the relationships between social capital, eWOM and knowledge sharing in SNSs; and a new framework examining the factors, specifically, reput ation, altruism, and subjective norms, influencing knowledge sharing in SNSs. Our contribution is important because despite SNSs being used widely through the world, theoretical understanding is limited. This dissertation also provides rationale for prac titioners to utilize SNSs internally for organizational intra-networking and organizational learning, and externally for marketing purposes.

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84 APPENDIX A Initial Items of Study 1 General What is your gender? What is your age? Where is your location? SNS Usage About how many total Facebook friends do you have? Ellison et al., 2007 In the past week, on average, approximately how many minutes per day have you spent on Facebook? Ellison et al., 2007 Facebook has become part of my daily routine Ellison et al., 2007 Trust Members in Facebook will not take advantage of others even when the opportunity arises Chiu et al., 2006* Members in Facebook will always keep the promises they make to one another Chiu et al., 2006** Members in Facebook are truthful in dealing with one another Chiu et al., 2006** There are several people in my Facebook I trust to help solve my problems Williams, 2006 There is someone in my Facebook I can turn to for advice about making very important decisions Williams, 2006*** When I feel lonely, there are several people in my Facebook I can talk to Williams, 2006 I can talk freely to my Facebook friends about my personal issues Hsu et al., 2007 If I share my problems with my Facebook friends, I know they will respond constructively and caringly Hsu et al., 2007 I know most of my Facebook friends are honest Hsu et al., 2007 I know most members of Facebook will do everything within their capacity to help others Hsu et al., 2007* Norms

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85 It seems to me that there is a norm of cooperation when interacting with people in my Facebook Kankanhalli et al, 2005 It seems to me that there is a norm of collaboration when interacting with people in my Facebook Kankanhalli et al, 2005 It seems to me that there is a norm of teamwork when interacting with people in my Facebook Kankanhalli et al, 2005 Obligations I feel committed to undertake activities of my Facebook groups when interacting with my Facebook friends Kankanhalli et al, 2005**** I feel obliged to contribute to my Facebook groups when interacting with my Facebook friends Kankanhalli et al, 2005**** I feel my Facebook friends expect contribution from me when interacting with them Kankanhalli et al, 2005 My Facebook friends believe that helping others is part of being Facebook members Chang et al., 2011 My Facebook friends believe that helping Facebook community to operate is part of being members Chang et al., 2011 My Facebook friends believe that helping Facebook community to accumulate or enrich knowledge is part of being members Chang et al., 2011 Identification I feel a sense of belonging towards my Facebook community Chiu et al., 2006 I have the feeling of togetherness or closeness in my Facebook community Chiu et al., 2006 I have a strong positive feeling toward Facebook Chiu et al., 2006* I am proud to be a member of Facebook Chiu et al., 2006** I feel I am part of my Facebook community Ellison et al., 2007 I have close relationships with my Facebook friends Hsu et al., 2011 I know my Facebook friends on a personal level Hsu et al., 2011 Knowledge Sharing I enjoy sharing my knowledge with my Facebook friends Kankanhalli et al, 2005 It seems to me that my Facebook friends enjoy sharing their knowledge with others Kankanhalli et al, 2005 It seems to me that Facebook facilitates sharing knowledge among people Kankanhalli et al, 2005 It seems to me that my Facebook friends share the best knowledge that they have Kankanhalli et al, 2005

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86 I frequently participate in knowledge sharing activities in Facebook Hsu et al., 2011** I usually spend a lot of time conducting knowledge sharing activities in Facebook Hsu et al., 2011** When participating in Facebook, I usually actively share my knowledge with others Hsu et al., 2011** I feel that members in Facebook provide valuable knowledge to others Chang et al., 2011** I feel that members in Facebook provide their experience to others Chang et al., 2011** I come to my Facebook community to share knowledge I know about a particular subject Hsu et al., 2011 I come to my Facebook community to share my skills Hsu et al., 2011**** eWOM Quality Facebook content is accurate Chiu et al., 2006 Facebook content is complete Chiu et al., 2006*** Facebook content is reliable Chiu et al., 2006 Facebook content is timely Chiu et al., 2006 Facebook content is easy to understand Chiu et al., 2006** Facebook content is relevant for me Awad & Ragowsky, 2008 Facebook content is helpful Awad & Ragowsky, 2008 Facebook content is usually the information I need Awad & Ragowsky, 2008*** Facebook content is consistent Chang et al., 2011** Facebook content is meaningful Hsu et al., 2011** Facebook content is important Hsu et al., 2011**

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87 APPENDIX B Questionnaire of Study 1 General What is your gender? What is your age? Where is your location? SNS Usage About how many total Facebook friends do you have? In the past week, on average, approximately how many minutes per day have you spent on Facebook? Facebook has become part of my daily routine Trust There are several people in my Facebook I trust to help solve my problems There is someone in my Facebook I can turn to for advice about making very important decisions When I feel lonely, there are several people in my Facebook I can talk to I can talk freely to my Facebook friends about my personal issues If I share my problems with my Facebook friends, I know they will respond constructively and caringly I know most of my Facebook friends are honest Norms It seems to me that there is a norm of cooperation when interacting with people in my Facebook It seems to me that there is a norm of collaboration when interacting with people in my Facebook It seems to me that there is a norm of teamwork when interacting with people in my Facebook Obligations I feel committed to undertake activities of my Facebook groups when interacting with my Facebook friends

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88 I feel obliged to contribute to my Facebook groups when interacting with my Facebook friends I feel my Facebook friends expect contribution from me when interacting with them My Facebook friends believe that helping others is part of being Facebook members My Facebook friends believe that helping Facebook community to operate is part of being members My Facebook friends believe that helping Facebook community to accumulate or enrich knowledge is part of being members Identification I feel a sense of belonging towards my Facebook community I have the feeling of togetherness or closeness in my Facebook community I feel I am part of my Facebook community I have close relationships with my Facebook friends I know my Facebook friends on a personal level Knowledge Sharing I enjoy sharing my knowledge with my Facebook friends It seems to me that my Facebook friends enjoy sharing their knowledge with others It seems to me that Facebook facilitates sharing knowledge among people It seems to me that my Facebook friends share the best knowledge that they have I come to my Facebook community to share knowledge I know about a particular subject I come to my Facebook community to share my skills eWOM Quality Facebook content is accurate Facebook content is complete Facebook content is reliable Facebook content is timely

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89 Facebook content is relevant for me Facebook content is helpful Facebook content is usually the information I need

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90 APPENDIX C Initial Items of Study 2 General What is your gender? What is your age? About how many total Facebook friends do you have? Where is your location? Reputation I earn respect from others by sharing knowledge in Facebook Wasko & Faraj, 2005** I feel that sharing knowledge in Facebook improves my reputation Wasko & Faraj, 2005 I share knowledge in Facebook to improve my reputation Wasko & Faraj, 2005*** Sharing knowledge in Facebook improves others recognition of me Kankanhalli et al, 2005 When I share knowledge in Facebook, my friends in Facebook respect me Kankanhalli et al, 2005** When I share knowledge in Facebook, my friends in Facebook praise me Kankanhalli et al, 2005 My friends in Facebook who share their knowledge in Facebook have more prestige than those who do not Kankanhalli et al, 2005 Altruism I like helping other people in Facebook Wasko & Faraj, 2005 I am willing to help others to solve problems in Facebook Wasko & Faraj, 2005 It is important to me to help others in Facebook Price et al., 1995* It is important to me to give to others in Facebook Price et al., 1995* Sharing knowledge in Facebook can help others with similar problems Hsu & Lin, 2008*** I enjoy helping others through sharing knowledge in Facebook Hsu & Lin, 2008* When I have the opportunity, I help others solve their problems in Facebook Podsakoff et al., 1990

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91 When I have the opportunity, I give my time to help others when needed in Facebook Podsakoff et al., 1990 Subjective Norms People who are important to me think that I should share knowledge in Facebook Hsu & Lin, 2008**** People who influence my behavior think that I should share knowledge in Facebook Hsu & Lin, 2008**** My Facebook friends think that I should share knowledge in Facebook Bock et al., 2005 My Facebook friends encourage me to share knowledge in Facebook Bock and Kim, 2002 It is expected of me that I share knowledge in Facebook Bock and Kim, 2002 Intention to Share Knowledge I plan to share knowledge with my Facebook friends in Facebook Bock and Kim, 2002 I will make an effort to share knowledge with my Facebook friends in Facebook Bock and Kim, 2002** I will try to share knowledge with my Facebook friends in Facebook Bock et al., 2005 I intend to share knowledge with my Facebook friends in Facebook Bock et al., 2005 I will provide my knowledge at the request of my Facebook friends in Facebook Bock et al., 2005 It is worth sharing knowledge in Facebook Hsu and Lin, 2008* Knowledge Sharing I enjoy sharing my knowledge with my Facebook friends Kankanhalli et al, 2005 It seems to me that my Facebook friends enjoy sharing their knowledge with others Kankanhalli et al, 2005 It seems to me that Facebook facilitates sharing knowledge among people Kankanhalli et al, 2005 It seems to me that my Facebook friends share the best knowledge that they have Kankanhalli et al, 2005**** I come to my Facebook community to share knowledge I know about a particular subject Hsu et al., 2011****

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92 APPENDIX D Questionnaire of Study 2 General What is your gender? What is your age? About how many total Facebook friends do you have? Where is your location? Reputation I feel that sharing knowledge in Facebook improves my reputation I share knowledge in Facebook to improve my reputation Sharing knowledge in Facebook improves others recognition of me When I share knowledge in Facebook, my friends in Facebook praise me My friends in Facebook who share their knowledge in Facebook have more prestige than those who do not Altruism I like helping other people in Facebook I am willing to help others to solve problems in Facebook Sharing knowledge in Facebook can help others with similar problems When I have the opportunity, I help others solve their problems in Facebook When I have the opportunity, I give my time to help others when needed in Facebook Subjective Norms People who are important to me think that I should share knowledge in Facebook People who influence my behavior think that I should share knowledge in Facebook My Facebook friends think that I should share knowledge in Facebook

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93 My Facebook friends encourage me to share knowledge in Facebook It is expected of me that I share knowledge in Facebook Intention to Share Knowledge I plan to share knowledge with my Facebook friends in Facebook I will try to share knowledge with my Facebook friends in Facebook I intend to share knowledge with my Facebook friends in Facebook I will provide my knowledge at the request of my Facebook friends in Facebook Knowledge Sharing I enjoy sharing my knowledge with my Facebook friends It seems to me that my Facebook friends enjoy sharing their knowledge with others It seems to me that Facebook facilitates sharing knowledge among people It seems to me that my Facebook friends share the best knowledge that they have I come to my Facebook community to share knowledge I know about a particular subject

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94 APPENDIX E Introductory Remarks and Conc luding Remarks of the Survey Dear Sir/Madam, This is Jae Hoon Choi, doctoral candidate at the University of Colorado Denver. This survey is for the dissertation, “Putting the so cial into social network sites: A knowledge sharing perspective.” Your perspectives on knowledge sharing regarding electronics products will be asked. Your voluntary pa rticipation is extremely important. This survey is anonymous, and no personal identif ication information will be collected. The information you provide will be treated co nfidentially. Your participation will be greatly appreciated. Affiliation: University of Colorado Denver Researcher: Jae Hoon Choi Email: jae.choi@ucdenver.edu Thank you for taking your time to complete this questionnaire. Your assistance in providing this information is highly appreciated.