It is important for participants and owners of virtual communities to find out which participants are trustworthy. However, this challenge has not been adequately addressed in practice and/or in research. Following a well-known tenet that past behavior tends to predict future behavior, we applied social network analysis to examine how participants' past interactions in the MyAV virtual community predict trustworthiness. According to the social network indices derived from a social network matrix, which represents past interactions in the community, we found (1) the ”out-degree centrality” index significantly predicts the competence dimension of trustworthiness (β=0.28, p<0.01); while the ”in-closeness centrality” index significantly predicts the integrity dimension of trustworthiness (β=0.35, p<0.01). Foremost, both indices outperform other conventional variables (e.g., frequencies of posting messages in the virtual community) in predicting trustworthiness.
It is important for participants and owners of virtual communities to find out which participants are trustworthy. However, this challenge has not been adequately addressed in practice and/or in research. Following a well-known tenet that past behavior tends to predict future behavior, we applied social network analysis to examine how participants' past interactions in the MyAV virtual community predict trustworthiness. According to the social network indices derived from a social network matrix, which represents past interactions in the community, we found (1) the ”out-degree centrality” index significantly predicts the competence dimension of trustworthiness (β=0.28, p<0.01); while the ”in-closeness centrality” index significantly predicts the integrity dimension of trustworthiness (β=0.35, p<0.01). Foremost, both indices outperform other conventional variables (e.g., frequencies of posting messages in the virtual community) in predicting trustworthiness.