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Managing academic performance by optimal resource allocation

Author

Listed:
  • Alexander Grigoriev

    (Maastricht University School of Business and Economics
    Novosibirsk State University)

  • Olga Mondrus

    (HSE University)

Abstract

In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.

Suggested Citation

  • Alexander Grigoriev & Olga Mondrus, 2022. "Managing academic performance by optimal resource allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2433-2453, May.
  • Handle: RePEc:spr:scient:v:127:y:2022:i:5:d:10.1007_s11192-022-04342-5
    DOI: 10.1007/s11192-022-04342-5
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    References listed on IDEAS

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    1. Glock, C. H. & Jaber, M. Y., 2014. "A group learning curve model with and without worker turnover," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 61829, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Schaubroeck, John & Lam, Simon S. K., 2004. "Comparing lots before and after: Promotion rejectees' invidious reactions to promotees," Organizational Behavior and Human Decision Processes, Elsevier, vol. 94(1), pages 33-47, May.
    3. Wai Fong Boh & Sandra A. Slaughter & J. Alberto Espinosa, 2007. "Learning from Experience in Software Development: A Multilevel Analysis," Management Science, INFORMS, vol. 53(8), pages 1315-1331, August.
    4. Nijs, Sanne & Gallardo-Gallardo, Eva & Dries, Nicky & Sels, Luc, 2014. "A multidisciplinary review into the definition, operationalization, and measurement of talent," Journal of World Business, Elsevier, vol. 49(2), pages 180-191.
    5. Scott M. Shafer & David A. Nembhard & Mustafa V. Uzumeri, 2001. "The Effects of Worker Learning, Forgetting, and Heterogeneity on Assembly Line Productivity," Management Science, INFORMS, vol. 47(12), pages 1639-1653, December.
    6. Gerard M. Campbell, 1999. "Cross-Utilization of Workers Whose Capabilities Differ," Management Science, INFORMS, vol. 45(5), pages 722-732, May.
    7. Youngsoo Kim & Ramayya Krishnan & Linda Argote, 2012. "The Learning Curve of IT Knowledge Workers in a Computing Call Center," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 887-902, September.
    8. Lai, Yi-Ling & Ishizaka, Alessio, 2020. "The application of multi-criteria decision analysis methods into talent identification process: A social psychological perspective," Journal of Business Research, Elsevier, vol. 109(C), pages 637-647.
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