Locating experts using social media, based on social capital and expertise similarity
Shiu‐Li Huang, Sheng-cheng Lin, Ren-Jie Hsieh · Journal of Organizational Computing and Electronic Commerce · 2016
Information and communication technologies boost knowledge activities, both within and across organizations, and in online communities. Determining how to effectively search for and find experts via social media has become a critical issue. Although social capital is a key driver of knowledge contribution, we have not addressed the issue of how to locate experts based on their social capital. Systems designed to locate experts typically recommend such experts based on keywords, thus failing to consider any semantic similarity between their areas of expertise and the problem domain (a.k.a., “expertise similarity”). The system designed and developed in this study recommends experts based on both their social capital and expertise similarity. We measure the social capital of experts based on their consultant service relationships and their friendships. We conduct a field experiment to evaluate user satisfaction and the system’s knowledge-contribution predictive capability. The results show that the proposed system is of high quality and delivers excellent information. Hence, users expressed their intention to use this system. In addition, the positive effect of social capital on the knowledge contribution is verified from the perspective of user behavior.