Developing an employee turnover risk evaluation model using case-based reasoning

Xin Wang, Li Wang (), Li Zhang, Xiaobo Xu, Weiyong Zhang and Yingcheng Xu
Additional contact information
Xin Wang: Dalian Maritime University
Li Wang: Beihang University
Li Zhang: Beijing Jiaotong University
Xiaobo Xu: American University of Sharjah
Weiyong Zhang: Old Dominion University
Yingcheng Xu: China National Institute of Standardization

Information Systems Frontiers, 2017, vol. 19, issue 3, No 10, 569-576

Abstract: Abstract All enterprises are concerned with employee turnover risk due to the significant impact on their effectiveness and competitiveness. Evaluation of the risk is a frequent topic in the literature. However, the majority of past work has not incorporated the advancement of modern information technology, particularly in the era of Internet of Things (IoT). In this paper, we propose to use an artificial intelligence method, case-based reasoning (CBR), to develop a multi-level employee turnover risk evaluation model. The proposed model adopts multiple CBR techniques including case representation, organization and management, and retrieval and matching to evaluate employee turnover risk. Specifically, we employ an object-oriented method in case knowledge expressing, utilize relational database in case organization and management, and follow a tree-hash algorithm to retrieve the best cases. Both theoretical and practical implications of the proposed model are discussed.

Keywords: Employee turnover risk; Case-based reasoning (CBR); IoT (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-015-9615-9

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