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

Ying Lu; Miles M. Yang; Jianhua Zhu; Ying Wang

doi:10.1002/hrm.22211

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

Ying Lu¹; Miles M. Yang¹; Jianhua Zhu²; Ying Wang³

¹ Centre for Applied Artificial Intelligence, & Health & Wellbeing Research Unit, Department of Management, Macquarie Business School Macquarie University Sydney Australia · ² School of Economics and Management Harbin Institute of Technology Weihai China · ³ School of Management Beijing Institute of Technology Beijing China

Human Resource Management 2024

AbstractThis 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.

DOI: 10.1002/hrm.22211
Volume: 63 (3)
Pages: 477-498
Language: en
Export: BibTeX
Sources: crossref