Industry classification with online resume big data: A design science approach

Industry classification is a vital step of industry analysis and competitive intelligence. However, existing schemes and methods are limited by the lagged information of firms’ business and the lack of consideration of the human resource aspects. In this paper, we adopt a design science approach to develop and evaluate a novel industry classification method by constructing a labor mobility network using online resume big data collected from the professional social network. We also propose a hierarchical extension of the community detection algorithm to better discover scalable firm clusters on the constructed network. The evaluation conducted on real-world datasets shows that our method outperforms the existing industry classification schemes and the state-of-the-art methods by improving their explanatory power and enlarging the cross-industry variation. Moreover, two application cases confirm the validity of our method in earlier revealing firms’ action of entering new industries.