Skip to main navigation Skip to search Skip to main content

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

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
Early online date29 Feb 2024
DOIs
Publication statusPublished - 1 May 2024

Keywords

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

Fingerprint

Dive into the research topics of 'Dark side of algorithmic management on platform worker behaviors: A mixed-method study'. Together they form a unique fingerprint.

Cite this