Managing academic performance by optimal resource allocation

Alexander Grigoriev*, Olga Mondrus

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Number of pages21
JournalScientometrics
Issue number5
DOIs
Publication statusE-pub ahead of print - 19 Mar 2022

JEL classifications

  • c00 - Mathematical and Quantitative Methods: General

Keywords

  • Incentive
  • Performance Monitoring
  • Resource Allocation
  • STRATEGIC BEHAVIOR
  • Talent Performance
  • Talent Recognition
  • DEFINITION
  • LEARNING-CURVE
  • TASK
  • Incentives
  • Talent performance
  • EMPLOYEE REACTIONS
  • WORKERS
  • Resource allocation
  • TALENT MANAGEMENT
  • Strategic behavior
  • MODEL
  • ENVY
  • INDIVIDUAL-DIFFERENCES
  • Talent recognition
  • Performance monitoring

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