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Rightful Rewards: Refining Equity in Team Resource Allocation through a Data-Driven Optimization Approach

Author

Listed:
  • Bo Jiang

    (Institute of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Xuecheng Tian

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • King-Wah Pang

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Qixiu Cheng

    (Business School, University of Bristol, Bristol BS8 1PY, UK)

  • Yong Jin

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Shuaian Wang

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

Abstract

In group management, accurate assessment of individual performance is crucial for the fair allocation of resources such as bonuses. This paper explores the complexities of gauging each participant’s contribution in multi-participant projects, particularly through the lens of self-reporting—a method fraught with the challenges of under-reporting and over-reporting, which can skew resource allocation and undermine fairness. Addressing the limitations of current assessment methods, which often rely solely on self-reported data, this study proposes a novel equitable allocation policy that accounts for inherent biases in self-reporting. By developing a data-driven mathematical optimization model, we aim to more accurately align resource allocation with actual contributions, thus enhancing team efficiency and cohesion. Our computational experiments validate the proposed model’s effectiveness in achieving a more equitable allocation of resources, suggesting significant implications for management practices in team settings.

Suggested Citation

  • Bo Jiang & Xuecheng Tian & King-Wah Pang & Qixiu Cheng & Yong Jin & Shuaian Wang, 2024. "Rightful Rewards: Refining Equity in Team Resource Allocation through a Data-Driven Optimization Approach," Mathematics, MDPI, vol. 12(13), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2095-:d:1428454
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    References listed on IDEAS

    as
    1. Imtiaz Ahmed & Ineen Sultana & Sanjoy Kumar Paul & Abdullahil Azeem, 2013. "Employee performance evaluation: a fuzzy approach," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 62(7), pages 718-734, September.
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