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

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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.

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Authors and Affiliations

  1. Transportation Management College, Dalian Maritime University, Dalian, China

    Xin Wang

  2. School of Economics and Management, Beihang University, Beijing, China

    Li Wang

  3. Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing, China

    Li Wang

  4. Beijing Jiaotong University, Beijing, China

    Li Zhang

  5. Department of Marketing and Information Systems, American University of Sharjah, Sharjah, United Arab Emirates

    Xiaobo Xu

  6. Department of Information Technology and Decision Sciences, Strome College of Business, Old Dominion University, Norfolk, VA, USA

    Weiyong Zhang

  7. China National Institute of Standardization, Beijing, China

    Yingcheng Xu

Authors
  1. Xin Wang
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  2. Li Wang
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  3. Li Zhang
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  4. Xiaobo Xu
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  5. Weiyong Zhang
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  6. Yingcheng Xu
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Correspondence to Li Wang.

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Wang, X., Wang, L., Zhang, L. et al. Developing an employee turnover risk evaluation model using case-based reasoning. Inf Syst Front 19, 569–576 (2017). https://doi.org/10.1007/s10796-015-9615-9

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  • DOI: https://doi.org/10.1007/s10796-015-9615-9

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