Locating experts using social media, based on social capital and expertise similarity

ABSTRACT

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.

KEYWORDS:

• Expert location system
• HITS
• knowledge management system
• recommender system
• semantic similarity
• social capital
• social network

Additional information

Notes on contributors

Shiu-Li Huang

Shiu-Li Huang is an associate professor in the Department of Business Administration at National Taipei University, Taiwan. He holds a doctoral degree in information management from National Sun Yat-sen University. Dr. Huang’s research interests are e-commerce, online advertising, recommender systems, and knowledge management. His papers have appeared in Decision Support Systems, Expert Systems with Applications, International Journal of Electronic Commerce, Electronic Commerce Research and Applications, International Journal of Information Management, Computers & Education, and several other journals. He is also recipient of Best Paper Awards at international conferences.

Sheng-Cheng Lin

Sheng-Cheng Lin is an assistant professor in the Department of Information Management at Tunghai University. His research interests are knowledge management, virtual communities, and social network analysis. He published related articles in the IEEE Transactions on Engineering Management, Computers & Education, Journal of Organizational Computing and Electronic Commerce, and Information Processing & Management.

Ren-Jie Hsieh

Ren-Jie Hsieh received her master’s degree in the Department of Information Management from Ming Chuan University. Her research interests are knowledge management systems and e-commerce.