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

Author

Listed:
  • Xin Wang

    (Dalian Maritime University)

  • Li Wang

    (Beihang University
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation)

  • Li Zhang

    (Beijing Jiaotong University)

  • Xiaobo Xu

    (American University of Sharjah)

  • Weiyong Zhang

    (Old Dominion University)

  • Yingcheng Xu

    (China National Institute of Standardization)

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.

Suggested Citation

  • Xin Wang & Li Wang & Li Zhang & Xiaobo Xu & Weiyong Zhang & Yingcheng Xu, 2017. "Developing an employee turnover risk evaluation model using case-based reasoning," Information Systems Frontiers, Springer, vol. 19(3), pages 569-576, June.
    • Handle: RePEc:spr:infosf:v:19:y:2017:i:3:d:10.1007_s10796-015-9615-9
    DOI: 10.1007/s10796-015-9615-9
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    References listed on IDEAS

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    1. Shancang Li & Li Da Xu & Shanshan Zhao, 2015. "The internet of things: a survey," Information Systems Frontiers, Springer, vol. 17(2), pages 243-259, April.
    2. Xin Wang & Li Wang & Xiaobo Xu & Ping Ji, 2014. "Identifying Employee Turnover Risks Using Modified Quality Function Deployment," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 398-404, May.
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    Cited by:

    1. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Olivera Marjanovic & Vijaya Murthy, 2022. "The Emerging Liquid IT Workforce: Theorizing Their Personal Competitive Advantage," Information Systems Frontiers, Springer, vol. 24(6), pages 1775-1793, December.
    3. Yuming Zhai & Lixin Zhang & Mingchuan Yu, 2024. "AI in Human Resource Management: Literature Review and Research Implications," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 16227-16263, December.

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