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Value Trade-Offs in Matching Functions: Evidence From Online Labor Markets

Author

Listed:
  • Abdulaziz Alshalfan

    (Kuwait University, Kuwait)

  • Lian Qi

    (Rutgers University, USA)

  • Alok Baveja

    (Rutgers University, USA)

Abstract

Online labor markets (OLMs) face challenges in refining match functions to connect clients with suitable service providers. This study examines how platform-controlled factors (number of assigned providers, matching speed) and project-specific attributes (description length, requested hours) affect matching effectiveness across four outcomes: hiring probability, hiring time, billed hours, and profit per contracted project. Using data from a premium OLM platform, the study reveals important operational and financial trade-offs. Expanding the provider pool improves hiring probability and project profitability but lengthens hiring time. Faster matching boosts hiring likelihood without compromising quality. Larger requested workloads reduce hiring probability but raise profitability. These findings offer actionable insights into how calibrated matching strategies can improve OLM performance across the project lifecycle, contributing to platform governance and digital operations literature.

Suggested Citation

  • Abdulaziz Alshalfan & Lian Qi & Alok Baveja, 2025. "Value Trade-Offs in Matching Functions: Evidence From Online Labor Markets," International Journal of E-Business Research (IJEBR), IGI Global Scientific Publishing, vol. 21(1), pages 1-27, January.
    • Handle: RePEc:igg:jebr00:v:21:y:2025:i:1:p:1-27
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