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

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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
Publication statusPublished - 1 May 2024

Keywords

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

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