Harness the Wisdom of Crowds: The Importance of We-Intention in Social Computing Research

Harness the Wisdom of Crowds: The Importance of We-Intention in Social Computing Research

Aaron X. L. Shen (City University of Hong Kong, Hong Kong), Matthew K.O. Lee (City University of Hong Kong, Hong Kong) and Christy M. K. Cheung (Hong Kong Baptist University, Hong Kong)
Copyright: © 2011 |Pages: 17
DOI: 10.4018/978-1-61692-904-6.ch002
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Today, the growth and popularity of social computing greatly facilitate online collaboration in creating user-centered networked content. This chapter explores participation behaviors in social computing communities, conceptualizing them as group-referent intentional social actions. The authors identify the concept of “we-intention”, which reflects an individual’s perception of the extent to which all participants in a collectivity will engage in the joint action and act together, as a topic of theoretical and practical interest. A preliminary conceptual framework was further developed and, in particular, the collectively shared beliefs (i.e., collective attitude and collective efficacy) and the social influence processes (i.e., subjective norms, group norms and social identity) were regarded as the key predictors of participation we-intention in social computing communities. This chapter finally concludes with a discussion on future research directions in the areas of we-intention and social computing.
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The usage and diffusion of social computing applications have been growing dramatically in the past few years. According to Alexa’s latest ranking (Alexa, 2009), five of the top ten global websites are social computing systems, including Facebook, YouTube, Windows Live, Wikipedia and Blogger.com. The advent and spread of social computing tools greatly change the fundamental way people communicate and share information, and further create opportunities for business world to improve its competitiveness in today’s knowledge economy. Nowadays, lots of initiatives have been undertaken in the organizations from diverse industries. For example, in a recent report released by The Gilbane Group, the evolution of social computing is believed to enable a new generation of team collaboration in organizations (Bock & Paxhia, 2008). The Gilbane Group has investigated the use of social computing applications across seventeen different industries and it identified five industry trends for collaboration with the use of social media – the growing appetite for innovative technologies, the focus on vertical solutions, the restrained role for IT, the advent of rich media, and the importance of building communities.

It is commonly believed that the success of social computing depends not only on the technology itself but on a strong online community (Parameswaran, 2007). It is thus important and necessary to incorporate community-oriented motivational factors in understanding the adoption and usage of social computing technologies. In addition, prior studies have demonstrated that decision interdependence and collective efforts are essential for the successful implementation of social computing communities (Li et al., 2007). This is because the benefits of social computing applications can be achieved only when the majority of its users accept and voluntarily use the system together. Especially in the new era of social computing, collective action plays an increasingly significant role in creating and maintaining a virtual community, and decision making often involves two or more people who are jointly engaged in the collective action. The participation intention in social computing communities thus can be social in some sense. In this regard, it is recommended that researchers should explore new theories and methodologies from more disciplines to address questions raised by social computing phenomenon, and extend them to the realm of social endeavor (Parameswaran & Whinston, 2007).

The traditional intention-based models primarily focus on individual behavioral intention, but neglect the collective perceptions and efforts involved in the intention formation processes. Individual behavioral intention is often defined as a “person’s motivation in the sense of his or her conscious plan to exert effort to carry out a behavior” by himself or herself (Eagly & Chaiken, 1993, p. 168). In the current investigation context of social computing communities, the traditional individual intention may not be appropriate because the value and power of social computing greatly depend on the simultaneous play of all participants. Some recent studies also have demonstrated that it is necessary to re-specify intention when two or more people are involved and when decision involves mutual, shared and joint processes (Bagozzi, 2007). In this regard, we-intention may be a more appropriate construct, which is rooted in “a person’s self-conception as a member of a particular group or social category, and action is conceived as either the group acting as a unit or the person acting as an agent of, or with, the group” (Bagozzi, 2007, p. 248) in social computing research.

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