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

Bo Jiang, Xuecheng Tian (), King-Wah Pang, Qixiu Cheng, Yong Jin and Shuaian Wang
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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

Mathematics, 2024, vol. 12, issue 13, 1-12

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.

Keywords: performance assessment; equitable resource allocation; data-driven optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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