CROWDSOURCING MOTIVATION AND PERFORMANCE: A SOCIAL IDENTITY PERSPECTIVE

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1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2016 Proceedings Pacific Asia Conference on Information Systems (PACIS) Summer CROWDSOURCING MOTIVATION AND PERFORMANCE: A SOCIAL IDENTITY PERSPECTIVE Jeonghun Seo Korea Advanced Institute of Science and Technology (KAIST), hidark7@kaist.ac.kr Hangjung Zo Korea Advanced Institute of Science and Technology (KAIST), joezo@kaist.ac.kr Follow this and additional works at: Recommended Citation Seo, Jeonghun and Zo, Hangjung, "CROWDSOURCING MOTIVATION AND PERFORMANCE: A SOCIAL IDENTITY PERSPECTIVE" (2016). PACIS 2016 Proceedings. Paper This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2016 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org.

2 CROWDSOURCING MOTIVATION AND PERFORMANCE: A SOCIAL IDENTITY PERSPECTIVE Jeonghun Seo, Department of Business and Technology Management, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea hidark7@kaist.ac.kr Hangjung Zo, Department of Business and Technology Management, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea joezo@kaist.ac.kr Abstract Crowdsourcing has emerged as a new online collaborative paradigm that employs a crowd of users to build long-lasting value beneficial to communities. Crowdsourcing has the potential to support a collaboration culture for society. This study is to examine key motivators to intention to participate in crowdsourcing. The VIST model is employed to identify the motivators for participating in virtual groups and the social identity theory is used to examine why individuals join virtual groups and how individual motivation can influence group performance. The research results are expected to contribute to both academia and industry to understand crowdsourcing with various perspectives. Keywords: Crowdsourcing, VIST model, Social identity theory, Virtual group, Group performance

3 1 INTRODUCTION Crowdsourcing is a kind of system that enlists multiple public audiences to engage in business activities, such as production and services. This term is a compound word derived from crowd and outsourcing. In simple terms, crowdsourcing is defined as sharing work with a crowd. When a company opens a business process to an undefined network of people in the form of an open call, consumers who voluntarily participate raise the income of the company and share the profits (Howe 2006). However, defining crowdsourcing exactly in the manner stated above does not seem to be aligned with the current trends. Originally, the concept of crowdsourcing leaned toward an association with business, but it has become gradually understood in broader sense. At present, many people view collective intelligence, crowd funding, and open source software projects as representative crowdsourcing examples. Others that can be included are web-based services, as well as open hardware, such as Arduino and Smart-citizen kit. Thus, we can say that crowdsourcing utilizes a crowd of users who are experts for collaboration to build a long-lasting artifact that is beneficial to the whole community (Doan et al. 2011). In recent years, views emerged of crowdsourcing as a general-purpose problem-solving and production model beyond just the meaning of business or information systems (Brabham 2008; Yuxiang Zhao 2012). As mentioned above, crowdsourcing encompasses a relatively broad range of meanings based on various perspectives. This diversity blurs the boundaries of crowdsourcing, such that it may be identified with any type of internet-based collaborative activity, such as co-creation or user innovation (Estellés & González 2012). The majority of crowdsourcing services are based on open-source software. People mostly form or join certain teams to participate in crowdsourcing contests and to share information or human assets, such as ICT skills, in virtual communities. This presents possibilities to embrace social and collaborative innovation, thus encouraging users and communities to participate voluntarily. Therefore, crowdsourcing could enable users or communities to access one another and share their resources to contribute to public goods or social values while collaborating on various fields ranging from economic projects to social issues. Thus, the driver of crowdsourcing is, quite literally, the crowd. Participation in collective effort is the main factor. The participation of a crowd can be defined as the act of trying to create new knowledge while collectively contributing new perspectives to issues, diffusing knowledge to others, and learning together (Hanna 2000). Most crowdsourcing services are provided through online web sites and information technology to enable online interactions. The online web, such as online open platforms and virtual communities, enables us to harness collective intellect through online interactions, even when collaborators have never seen each other previously (Brabham 2009). With this background, it could be said that online platforms, including virtual groups like teams or communities, are key to collective efforts by encouraging individuals to network and pool their resources to support the true meaning of a collaboration culture for the social good. These features of collaborative participation significantly differentiate crowdsourcing services from common crowdfunding, open-source software (OSS) projects, or existing business models. Although there are many possibilities to apply it to the creation of social value, academic research on the crowdsourcing model remains rare (Peng & Zhang 2010). Crowdsourcing could spread the culture of collaboration widely. Furthermore, it could contribute to collaboration innovation and digital social innovation. To derive the full potential of crowdsourcing, we need to determine why people participate in crowdsourcing and study the main principles of this concept so that it could truly serve as a foundation of collaboration, learning, and activation of communities. Although several studies have been conducted on motivations behind participation in crowdsourcing, most previous works mainly draw conclusions in conceptual terms. Existing studies on the motivation to participate in crowdsourcing include similar models, such as crowd funding and OSS projects, which mostly focus on the dichotomy of motivation with intrinsic and extrinsic motivation. Indeed, these studies focused on personal motivation rather than the collective perspective. In other words, the reasons

4 why people form or join certain teams, as well as why virtual communities to participate or are active in crowdsourcing, have not been sufficiently explored. To identify the features of crowdsourcing and to determine why people participate, it is essential to understand motivation from the social and collective perspectives. To fill the research gaps, this study aims to shed light on the motivation to participate in crowdsourcing using conceptual terms related to the collective and main principles of crowdsourcing through the social perspective. First, given that the majority of participants or users of crowdsourcing act as a virtual group, similar to teams or communities, a better understanding of personal motivational processes that encourage these people to participate in virtual teams or communities might be helpful. We will present a theoretical model called the VIST model from the social sciences. Second, as previously mentioned, collaboration is one of main factors contributing to the successful operation of crowdsourcing. Thus, it is essential to understand how individual motivation can be expanded to collective motivation for collaboration. We will explain the relationship between individual motivation and group performance. We assess the degree of collaboration with constructs of group performance. Drawing upon the social identity theory, this study sets out to determine how private motivational factors link to group performance with the concept of individual motivation to identify what makes team or community members collaborate while participating in crowdsourcing. 2 LITERATURE REVIEW & THEORECTICAL BACKGROUND 2.1 Conceptual framework In this section, we will discuss theories and literature relevant to the topic. Given that most crowdsourcing services and platforms stem from OSS projects, investigating motivators in OSS are helpful. However, these motivators may not exactly be transferrable to crowdsourcing cases because of the differences between these two concepts (Zhao & Zhu 2012). Thus, we will look into how the motivators in OSS could be applied to crowdsourcing in the social context based on the group and social identity perspectives. Moreover, we present the private-collective innovation model of Hippel and Krogh (2003). Borrowing from the social sciences, we then introduce the VIST model to identify the motivators behind participating in teams or communities (Hertel 2002). We adopt social identity theory as a theoretical framework to explain how individual motivation can be expanded to collective motivation and group performance. To do this, we review the overall content of the social identity theory. (see Figure 1.) Figure 1. Conceptual Framework 2.2 Motivators in OSS and private-collective model of innovation There have been a number of studies that examined why open-source programmers contribute freely (Raymond 2001; Lakhani et al. 2002; Lerner & Tirole 2002; Lakhani & Wolf 2003; Bitzer et al. 2007). Raymond (2001) and Lakhani & Wolf (2003) present three basic motives: direct benefit from the development itself, enjoyment of programming, and a better reputation among members of OSS

5 communities. Lerner & Tirole (2005) observed that adequate rewards could motivate the participants. However, merely comprehending these motivational factors is insufficient to examine how it connects to the intention to collaborate. Hippel and Krogh (2003) argued that the private-collective model, which is the combined model of private investment and collective action, could describe the phenomenon such as OSS development. First, the private investment model assumes that innovators will be rewarded with private returns, such as patents and copyrights. Moreover, the innovators naturally tend to avoid any spillover because this reduces the profit and eventually decreases the innovator s will. Second, the collective action model applies to the provision of public goods and the fact that innovators collaborate to produce public goods. This requires innovators to transfer their control of knowledge to the common pool. This knowledge transfer makes it possible for the public to access the knowledge easily. However, this gives rise to problems related to the motivating potential of innovators. The private-collective model eliminates some assumptions from the private investment and collective action models to explain the principles of OSS. From the private investment model, the assumption that the free transfer of any knowledge results in a loss of private profit should be eliminated. Moreover, the assumption that any free rider could benefit from the public goods at a level equal to the original innovators is eliminated from the collective action model. For instance, the benefits from participation in communities could be considered as valuable as private benefits. In detail, social incentives, such as social connection between members, respect from other peers, and social status within communities, could be included as private benefits from participating in communities (Yue & Blevis 2011). These benefits might be key clues in the examination of the collective motives of participants. If we apply the private-collective model to crowdsourcing, it could explain why people participate in crowdsourcing to some degree. The model is incapable of completely explaining the user s motivation despite eliminating assumptions about private loss and why users collaborate to pursue social benefits. From the perspective of our study aims, we will describe the application of the private-collective model to crowdsourcing with a focus on teams and communities. 2.3 VIST model of individual motivation in virtual teams or communities To explore the motivators of individuals when they work in a team or community and to determine whether team work or seamless collaboration exists, we present the VIST model designed by Hertel (2002). We employ this model because most crowdsourcing projects are conducted by teams or multiple members of communities, and our research aims to study whether crowdsourcing has the potential of spreading a culture of collaboration. The VIST model is a compound of abbreviation of valence, instrumentality, self-efficacy, and trust. The VIST model presents four main motivators that determine why people work spontaneously in virtual teams or communities. In the VIST model, virtual teams refer to members who collaborate through digital technology even while working in different places. A community involves mass participation and is open to almost anyone who agrees to implicit rules (Hertel et al. 2003). Strictly speaking, a team is a relatively small group with stable boundaries. However, crowdsourcing involves both types of virtual collaboration, but this model might still be acceptable. According to the VIST model (Hertel 2002), valence refers to an individual s subjective evaluation of team goals. The VIST model assumes that a team s collective motive is directly (inversely) proportional to an individual s subjective evaluation. The VIST model s other three components approach motivators from the perspective of expectancy concepts. Instrumentality describes the perceived importance or indispensability of his or her contribution that an individual recognizes. If a member of the team perceives higher instrumentality, he or she will exert efforts to achieve team goals with higher motivation. Self-efficacy is one s perceived capability to attain one s goal (Bandura 1994). If an individual strongly believes that he or she is able to accomplish his or her goal or contribute to the team s

6 goal strongly, this individual will exert effort, which leads to improved performance. Lastly, trust includes both interpersonal trust and systemic trust. In terms of interpersonal trust, individual efforts will be affirmative and reciprocated. Trust in the system refers to the assumption that any digital support system works reliably, and an online platform could be a mediator of trust. These four components positively affect individual motivation and will of participation. The VIST model is very suggestive, and it introduces new perspectives on motivators in addition to subjective and expectancy concepts. Furthermore, it provides additional support to the private-collective model. Despite eliminating the assumption that the free transfer of innovation results in a loss of private profit, why people could use their private resources even if they lose private returns to a certain level remains unclear. 2.4 Social identity theory We adopted the social identity theory as a theoretical framework because it is capable of reflecting how individual motivation can be expanded to collective motivation. This concept was originally introduced by Tajfel et al. (1971). Tajfel et al. (1971) stated that social identity refers to an individual s perceived value and feelings by recognizing his or her belongingness to a specific group. Abrams & Hogg (1990) believed that social identity could emerge when individuals become emotionally involved with particular social groups. In the social identity theory (hereinafter, SIT), people have a personal identity based on their own distinctiveness and social identity, and this motivates them to achieve the improvement of their own identity (Ashforth & Mael 1989). This phenomenon is attributed to people s instinct to improve self-esteem. People tend to classify themselves into various social groups (Mael & Ashforth 1992). According to SIT, there are two kinds of information that motivate people to form their social identity through affiliated groups. One is perceived pride associated with affiliation to the group, and the other is perceived respect through an appreciation of one s status or reputation within the group. Tyler et al. (1999) argued that if people perceive pride associated with being affiliated with the group and the respect for one s status from peers through the collective experience, people will have a tendency to cooperate with the group, such that sharing information affects one s social identity positively. Moreover, the perceived pride and respect could alter members working attitude and facilitate internal changes. It also affects voluntary cooperation attitude immensely. Moreover, it has a greater influence than extrinsic motivation, such as monetary rewards (Mael & Ashforth 1992). In this context, this study applies this SIT as a theoretical framework. 3 RESEARCH MODEL AND HYPOTHESES 3.1 Research Model Drawing upon SIT, our study proposes a research model to explain the intention to participate in virtual teams or communities to collaborate on crowdsourcing and to highlight crowdsourcing s potential to support a collaboration culture through social open platforms. Based on our conceptual framework and theoretical support, the factors of the VIST model could affect motivation to join crowdsourcing teams or communities. Moreover, social identity causes the members of a team to assume collective motivation, and this contributes to the intention to collaborate and work on crowdsourcing. We developed a research model, and we propose the hypotheses given below (see Figure 2):

7 Figure 2. Theoretical model of group s contribution to Crowdsourcing 3.2 Research hypotheses Based on our previous review, we have defined each construct of our research model. The definition of each construct is presented in Table 1. The details of each construct and the relevant hypotheses are discussed in the following subsections. Construct Definition Reference Perceived Valence Perceived Instrumentality Self-Efficacy Trust Identification Individual Motivation Group Performance Social Identity Table 1. The degree to which an individual perceives the attractiveness and importance of the group s goal in terms of satisfying his or her goals and needs. The degree to which an individual believes that his or her contribution toward specific tasks is important and indispensable. The degree to which an individual perceives his or her capability to perform a specific job to attain a goal. The degree to which an individual believes that other members will exert adequate effort and that his or her efforts will be reciprocated. Moreover, any support system or platform is expected to work reliably and perform well as a trusted third party. The degree to which an individual perceives himself or herself as a representative of a specific group, which causes him or her to perceive the group s features as selfdescriptive. The degree to which an individual wants to participate in a group and perceives the desire to perform a specific task better. The degree to which a group, such as a certain team, performs jobs effectively through collaboration based on interactions with peers. The degree to which an individual perceives the selfconcept derived from his or her membership in a group, along with emotional significance. Definition of Constructs Hertel (2002), (2003) Frijda (1986) Hertel (2002), (2003) Bandura (1993), (1994) Hertel (2002), (2003) Hertel (2002), (2003) McKnight et al. (1998) McKnight et al. (2002) Van Knippenberg (2000) Ellemers et al. (2004) Hertel (2002), (2003) Ardichvili et al. (2003) Guzzo & Shea (1992) Karau & Hart (1998) van Knippenberg (2003) Tajfel et al. (1971) Ashforth (1989) Tyler et al. (1999)

8 3.2.1 Motivators to participate in a team and individual motivation As stated in previous studies, there are four motivators that encourage people to participate in teams or communities. These four VIST components are believed to be motivators that contribute positively to individual motivation (Hertel 2002, 2003). Perceived valence is the degree of an individual s subjective evaluation of the team goal s attractiveness and importance. A higher perceived importance of the team goals by an individual causes his or her motivation to take part in the team for the achievement of these goals to likewise be higher. Perceived instrumentality refers to the perceived importance of one s contribution to the team s goals. It could be described as one s sense of how indispensable one s commitment to team goals is. In other words, an individual could be motivated by his or her commitment as recognized by others (Kollock 1999). Self-efficacy is an individual's belief about his or her capabilities to perform a specific task. Considerable empirical evidence shows a positive relationship between perceived self-efficacy and contributions to collective goods (Stajkovic & Luthans 1979; Bandura 1993; Kollock 1999; Osterloh & Rota 2007). Therefore, if the members of a group or team believe that they are capable of performing the task, individual motivation will increase. In the VIST model and in this study, trust involves interpersonal trust and trust in technology. If an individual believes that other peers will exert adequate effort for the team and that member s efforts will be equal, he or she will have motivation to participate in the team (Jarvenpaa et al. 1998; McKnight et al. 1998, Osterloh & Rota 2004; Heavey et al. 2011). In terms of trust of technology, studies have revealed that digital support systems, such as online platforms or virtual communities, have numerous trust factors, such as high reputation, that induce people s participation, arguing that these factors should be treated as a mediator of trust (Ba & Pavlou 2002; Osterloh & Rota 2004). In the social sciences, individual motivation is regarded as one of the important factors affecting group performance. Identification is one of the significant factors affecting individual motivation, according to SIT (Hogg 1992; Ellemers et al. 1999; Van Knippenberg 2000). Proposition of identification, such as social or organizational identification, affects work motivation. Moreover, identification weakens the distinction between the self and the group. These social concepts stemmed from the fact that identification has behavioral effects on individual motivation (Van Knippenberg 2000). Thus, we hypothesize that: H1a: Higher perceived valence leads to higher individual motivation. H1b: Higher perceived instrumentality leads to higher individual motivation. H1c: Higher self-efficacy leads to higher individual motivation. H1d: Higher trust leads to higher individual motivation. H1e: Higher identification leads to higher individual motivation Relationship between individual motivation and group performance In practice, many tasks are performed by units of groups or teams, and many groups perform collective tasks requiring an aggregation of individual efforts. At present, with the development of online technology, collective tasks are increasingly performed through electronic communication media, even though members are distributed globally. Thus, regardless of the location barrier, the motivation level of individual members becomes accepted as a key determinant of group performance (Karau & Williams 2001). In social psychology, individual members are believed to be motivated by psychological factors, such as pride and trust, and these individual task motivation factors link to group performance (Berkowitz & Levy 1956; Dirks 1999). In this context, we hypothesize that: H2: Higher individual motivation leads to better group performance.

9 3.2.3 Social identity and the relationship between individual motivation and group performance As previously mentioned, individual motivation affects group performance additively. According to research on SIT, social identity facilitates the cohesion of individual motivation for group performance (Hogg 1992; Karau & Hart 1998; van Knippenberg & Ellemers 2003). Tyler et al. (1999) argued that social identity is motivated by perceived pride in membership in a group and perceived respect from group members, and these motivators could also affects members working attitude, such as voluntary cooperation, while also improving group performance. Thus, we hypothesize that: H3: Social identity strengthens the relationship between individual motivation and group performance. 4 RESEARCH METHOD To test our model and proposed hypotheses, we will conduct a survey for data collection because of the ease in gathering data from numerous users of crowdsourcing from around the world. Our study s primary sample target includes crowdsourcing contributors. As regards data collection, we will develop a survey instrument, in which measurements of constructs that had been adapted in previous studies will be applied. Four constructs of the VIST model will be measured based on the items used by Hertel et al. (2003). Some modifications will be applied to the items used by Hertel (2003) to make them suitable for our study s context. Other measurement items will be developed based on the definitions of constructs suggested on Table 1. The collected data will be analysed using SPSS and Smart PLS to examine the structural equation model. 5 EXPECTED RESULTS AND FUTURE RESEARCH Our study s main objective is to understand the potential of crowdsourcing to support the true meaning of collaboration by identifying the factors behind the motivation to participate in virtual teams or communities and through the application of the social identity theory. Crowdsourcing is a relatively recent concept, and many of the most recent developments in crowdsourcing give rise to the need to understand why people participate in this process. However, only a few studies have focused on the motivation to participate in crowdsourcing, which has only been examined from the individual perspective. In this context, the proposed research model could contribute to the examination of motivation from the group perspective. Compared with existing studies on motivation relative to crowdsourcing, we consider virtual groups as a key factor behind participation. SIT stated that social or group identification affects individual motivation, and social identity strengthens it. By grafting SIT on to the existing VIST model, our empirical research suggests that the motivation to participate in virtual groups consequently leads to improved group performance. The result of this study is expected to suggest a new perspective grounded on SIT regarding the motivation behind the intention to participate in crowdsourcing. Therefore, our research on the importance of motivational aspects related to virtual group and social identity could serve as a basis for the recommendation that virtual teams or groups should have or develop attractiveness to induce participation for the success of crowdsourcing. Nevertheless, this study has some limitations. Although there are numerous studies that demonstrate the relationship between individual motivation and group performance, the suggested research model does not show how individual motivation transitions into work motivation or collective motivation and the consequent group performance. Thus, an explanation of the process may be needed with consideration of other constructs to explain collective motivation in a future study.

10 References Abrams, D. E. and M. A. Hogg (1990). Social identity theory: Constructive and critical advances, Springer-Verlag Publishing. Ardichvili, A., et al. (2003). "Motivation and barriers to participation in virtual knowledge sharing communities of practice." Journal of Knowledge Management 7(1): Ashforth, B. E. and F. Mael (1989). "Social identity theory and the organization." Academy of management review 14(1): Ba, S. and P. A. Pavlou (2002). "Evidence of the effect of trust building technology in electronic markets: Price premiums and buyer behavior." MIS quarterly: Bandura, A. (1993). "Perceived self-efficacy in cognitive development and functioning." Educational Psychologist 28(2): Bandura, A. (1994). Self efficacy, Wiley Online Library. Berkowitz, L. and B. I. Levy (1956). "Pride in group performance and group-task motivation." The Journal of Abnormal and Social Psychology 53(3): 300. Bitzer, J., et al. (2007). "Intrinsic motivation in open source software development." Journal of Comparative Economics 35(1): Brabham, D. C. (2008). "Crowdsourcing as a Model for Problem Solving." The International Journal of Research into New Media Technologies. Brabham, D. C. (2009). "Crowdsourcing the Public Participation Process for Planning Projects." Planning Theory 8(3): Dirks, K. T. (1999). "The effects of interpersonal trust on work group performance." Journal of Applied Psychology 84(3): 445. Doan, A., et al. (2011). "Crowdsourcing systems on the World-Wide Web." Commun. ACM 54(4): Ellemers, N., et al. (2004). "Motivating individuals and groups at work: A social identity perspective on leadership and group performance." Academy of management review 29(3): Ellemers, N., et al. (1999). "Self categorisation, commitment to the group and group self esteem as related but distinct aspects of social identity." European journal of social psychology 29(23): Estellés-Arolas, E. and F. González-Ladrón-de-Guevara (2012). "Towards an integrated crowdsourcing definition." Journal of Information Science 38(2): Frijda, N. H. (1986). "The emotions: Studies in emotion and social interaction." Edition de la. Guzzo, R. A. and G. P. Shea (1992). "Group performance and intergroup relations in organizations." Handbook of industrial and organizational psychology 3: Hanna, K. S. (2000). "The Paradox of Participation and the Hidden Role of Information: A Case Study." Journal of the American Planning Association 66(4): Heavey, C., et al. (2011). "Enhancing performance: Bringing trust, commitment and motivation together in organisations." Journal of General Management 36(3). Hertel, G. (2002). Management of virtual teams based on social psychology models, Pabst Publisher, Lengerich. Hertel, G., et al. (2003). "Motivation of software developers in Open Source projects: an Internetbased survey of contributors to the Linux kernel." Research Policy 32(7): Hippel, E. v. and G. v. Krogh (2003). "Open Source Software and the Private-Collective Innovation Model: Issues for Organization Science." Organization Science 14(2): Hogg, M. A. (1992). The social psychology of group cohesiveness: From attraction to social identity, Harvester Wheatsheaf. Howe, J. (2006). The Rise of Crowdsourcing. Wired Magazine. Jarvenpaa, S. L., et al. (1998). "Is Anybody out There? Antecedents of Trust in Global Virtual Teams." Journal of Management Information Systems 14(4): Karau, S. J. and J. W. Hart (1998). "Group cohesiveness and social loafing: Effects of a social interaction manipulation on individual motivation within groups." Group Dynamics: Theory, Research, and Practice 2(3): 185.

11 Karau, S. J. and K. D. Williams (2001). "Understanding individual motivation in groups: The collective effort model." Groups at work: Theory and research: Kollock, P. (1999). "The economies of online cooperation." Communities in cyberspace 220. Lakhani, K., et al. (2002). "The boston consulting group hacker survey." Boston, The Boston Consulting Group. Lakhani, K. and R. G. Wolf (2003). "Why hackers do what they do: Understanding motivation and effort in free/open source software projects." Lerner, J. and J. Tirole (2002). "Some simple economics of open source." The journal of industrial economics 50(2): Lerner, J. and J. Tirole (2005). "The scope of open source licensing." Journal of Law, Economics, and Organization 21(1): Mael, F. and B. E. Ashforth (1992). "Alumni and their alma mater: A partial test of the reformulated model of organizational identification." Journal of organizational Behavior 13(2): McKnight, D. H., et al. (2002). "The impact of initial consumer trust on intentions to transact with a web site: a trust building model." The Journal of Strategic Information Systems 11(3): McKnight, D. H., et al. (1998). "Initial trust formation in new organizational relationships." Academy of management review 23(3): Osterloh, M. and S. Rota (2004). "Trust and Community in Open Source Software Production" Analyse und Kritik 26(1): 279. Osterloh, M. and S. Rota (2007). "Open source software development Just another case of collective invention?" Research Policy 36(2): Peng, L. and M. Zhang (2010). An Empirical Study of Social Capital in Participation in Online Crowdsourcing. E-Product E-Service and E-Entertainment (ICEEE), 2010 International Conference on. Raymond, E. S. (2001). The Cathedral & the Bazaar: Musings on linux and open source by an accidental revolutionary, " O'Reilly Media, Inc.". Stajkovic, A. D. and F. Luthans (1979). "Social Cognitive Theory and Self efficacy: Implications for Motivation Theory and Practice." Tajfel, H., et al. (1971). "Social categorization and intergroup behaviour." European journal of social psychology 1(2): Tyler, T. R., et al. (1999). "Justice, social identity, and group processes." The psychology of the social self: Van Knippenberg, D. (2000). "Work motivation and performance: A social identity perspective." Applied psychology 49(3): van Knippenberg, D. and N. Ellemers (2003). "Social Identity and Group Performance." Social identity at work: Developing theory for organizational practice: 29. Yue, P. and E. Blevis (2011). A survey of crowdsourcing as a means of collaboration and the implications of crowdsourcing for interaction design. Collaboration Technologies and Systems (CTS), 2011 International Conference on. Yuxiang Zhao, Q. Z. (2012). A Conceptual Model for Participant's Motivation in Crowdsourcing Contest. Wuhan International Conference on e-business. Zhao, Y. and Q. Zhu (2012). "Evaluation on crowdsourcing research: Current status and future direction." Information Systems Frontiers 16(3):