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Dark side of algorithmic management on platform worker behaviors: A mixed-method study

  • Ying Lu
  • , Miles M. Yang
  • , Jianhua Zhu
  • , Ying Wang*
  • *Corresponding author for this work
  • Macquarie University
  • School of Economics and Management, Harbin Institute of Technology Weihai
  • Beijing Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

This research investigates the impact of algorithmic management on worker behaviors, focusing on workers' commitment to service quality and referral tendencies. Drawing upon the job demands-resources model, we argue that high levels of algorithmic management could create hindrance demands that impede service quality and demotivate referral behaviors. We propose that high workload, as a challenge demand, buffers the negative effects of algorithmic management on worker outcomes. We find support for our proposed research model in an experiment with a sample of 1362 platform-based food-delivery riders. We also conduct a qualitative study with 21 riders, which provides a more nuanced understanding of how algorithmic management affects workers' attitudes, behaviors, and referral tendencies.

Original languageEnglish
Pages (from-to)477-498
Number of pages22
JournalHuman Resource Management
Volume63
Issue number3
DOIs
StatePublished - 1 May 2024
Externally publishedYes

Keywords

  • algorithmic management
  • commitment to service quality
  • job demands-resources model
  • platform worker
  • referral tendencies

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