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Peer-reviewedCC BY 4.0Frontiers in Psychology

Algorithmic management and employees’ work withdrawal behavior: the roles of AI anxiety and perceived algorithmic transparency

  • Yong-hui Gao1
  • Yu-feng Pan1
  1. 1School of Management, Northwest Minzu University, Lanzhou, China
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Abstract

Introduction: As artificial intelligence and algorithmic systems are increasingly incorporated into organizational management, scholarly attention has been directed toward employees’ psychological and behavioral responses. Drawing on technostress theory, this study examines the association between algorithmic management and employees’ work withdrawal behavior, focusing on the indirect association through AI anxiety and the moderating role of perceived algorithmic transparency.

Methods: A cross-sectional questionnaire survey was conducted among corporate employees in China, yielding a final analytical sample of 346 employees. The proposed relationships were examined using statistical analyses of direct, indirect, and moderating associations.

Results: Algorithmic management was positively associated with work withdrawal behavior and AI anxiety. AI anxiety was also positively associated with work withdrawal behavior, and bootstrap analyses indicated a statistically significant indirect association through AI anxiety. Furthermore, perceived algorithmic transparency negatively moderated the association between algorithmic management and AI anxiety. The conditional indirect association through AI anxiety was weaker at higher levels of perceived algorithmic transparency.

Discussion: These findings are consistent with the proposed moderated mediation model and highlight AI anxiety and perceived algorithmic transparency as potentially relevant factors in understanding employees’ responses to algorithmic management. However, the cross-sectional design does not establish temporal precedence or causal relationships.

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Cite

BibTeXRIS
BibTeX
@article{gao2026algorithmic,
  title = {{Algorithmic management and employees’ work withdrawal behavior: the roles of AI anxiety and perceived algorithmic transparency}},
  author = {Yong-hui Gao and Yu-feng Pan},
  journal = {Frontiers in Psychology},
  year = {2026},
  month = oct,
  doi = {10.3389/fpsyg.2026.1916936},
  url = {https://doi.org/10.3389/fpsyg.2026.1916936},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Algorithmic management and employees’ work withdrawal behavior: the roles of AI anxiety and perceived algorithmic transparency
AU  - Yong-hui Gao
AU  - Yu-feng Pan
PY  - 2026
DA  - 2026/10/21
JO  - Frontiers in Psychology
DO  - 10.3389/fpsyg.2026.1916936
UR  - https://doi.org/10.3389/fpsyg.2026.1916936
AB  - Introduction: As artificial intelligence and algorithmic systems are increasingly incorporated into organizational management, scholarly attention has been directed toward employees’ psychological and behavioral responses. Drawing on technostress theory, this study examines the association between algorithmic management and employees’ work withdrawal behavior, focusing on the indirect association through AI anxiety and the moderating role of perceived algorithmic transparency.  Methods: A cross-sectional questionnaire survey was conducted among corporate employees in China, yielding a final analytical sample of 346 employees. The proposed relationships were examined using statistical analyses of direct, indirect, and moderating associations.  Results: Algorithmic management was positively associated with work withdrawal behavior and AI anxiety. AI anxiety was also positively associated with work withdrawal behavior, and bootstrap analyses indicated a statistically significant indirect association through AI anxiety. Furthermore, perceived algorithmic transparency negatively moderated the association between algorithmic management and AI anxiety. The conditional indirect association through AI anxiety was weaker at higher levels of perceived algorithmic transparency.  Discussion: These findings are consistent with the proposed moderated mediation model and highlight AI anxiety and perceived algorithmic transparency as potentially relevant factors in understanding employees’ responses to algorithmic management. However, the cross-sectional design does not establish temporal precedence or causal relationships.
SN  - 1664-1078
ER  -