Objectification at work: The impact of algorithmic management on employee work engagement

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Authors: Na Zhang, Yu Zhang, Chunhua Jin
Cited by: 1
References: 49

Abstract

With the increasing prevalence of digital and intelligent management, the impact of algorithmic management on employees' psychology and behavior has become a focus of scholarly attention. However, existing research predominantly views algorithmic management as a stressor from cognitive or emotional perspectives, leaving its profound impact on employees' self-concept underexplored. Drawing on social identity theory, this paper constructs a model of "algorithmic management-workplace objectification perception-employee work engagement" from the perspective of identity threat and empirically tests it using two-wave questionnaire data from 383 respondents. The findings reveal that algorithmic management is significantly negatively correlated with work engagement and that workplace objectification perception mediates this relationship. Specifically, algorithmic management is associated with employees' perception of workplace objectification by reinforcing their sense of instrumental value and powerlessness, which may deny their uniqueness and agency as human beings and is associated with diminished work engagement. This study not only enriches theoretical insights into algorithmic management and employee work engagement but also offers a new theoretical lens for understanding the potential side effects of technology-enabled management. Furthermore, it provides empirical references for organizations to optimize algorithmic management practices and balance efficiency enhancement with employee care, offering valuable implications for developing a technology-for-good paradigm in modern organizational management.

Fields of Study & Subjects

Technostress in Professional Settings Cyberloafing and Workplace Behavior Digital Economy and Work Transformation

Publication details

Journal / source
Acta Psychologica
Publisher
Elsevier BV
Volume
268
Pages
107350–107350
Publication date
4 July 2026
Language
EN