The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Why Does Algorithmic Management Undermine Employee Creativity?: A Perspective Focused on AMO Theory
Open accessAbstract
With the rapid development of artificial intelligence technology, algorithmic management is increasingly prevalent in enterprises. Despite the considerable scholarly attention given to the impact of algorithmic management, a research gap remains regarding its influence on employee creativity. To address this gap, the authors developed a theoretical model using ability-motivation-opportunity (AMO) theory. This model aims to investigate the direct impacts of algorithmic management (opportunity) on employee creativity (performance) while also considering the mediating roles played by knowledge combination capability (ability) and achievement goal (motivation). Using a sample of 327 paired leader-employee data from an information technology service company, the findings reveal that algorithmic management has a negative effect on employee creativity. Furthermore, the results demonstrate that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.
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Citations
Cites
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Amabile, T. M. (1993) · Human Resource Management Review · doi:10.1016/1053-4822(93)90012-s · Cited by 1,150
Automation, Algorithms, and Beyond: Why Work Design Matters More Than Ever in a Digital World
Parker, S. K., & Grote, G. (2022) · Applied Psychology · doi:10.1111/apps.12241 · Cited by 960
Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512
Algorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00764-w · Cited by 180
Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring
Kelan, E. K. (2024) · Human Resource Management Journal · doi:10.1111/1748-8583.12511 · Cited by 106
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Metadata
- Authors
- Li, D., Liu, M., Zhao, Y., Li, Y., Zhang, T., Zhang, W., Xia, D., & Lv, B.
- Affiliations
- Qingdao University (CN)
- Shandong University, China
- Beijing Wuzi University (CN)
- Venue
- Journal of Organizational and End User Computing
- Publisher
- IGI Global Scientific Publishing
- Published
- 2024-03-12
- Volume
- 36
- Issue
- 1
- Pages
- 1-16
- ISSN
- 1546-2234 (ISSN-L)
- 1546-5012
- OpenAlex
- W4392684118
- Cited by
- 12
- References
- 7
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 1
- FWCI
- 6.03
- Type
- journal article
- Language
- ng
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Organizational and End User Computing (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- algorithmic management, employee creativity, ability-motivation-opportunity theory, achievement goals, leader-employee relationship
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