Journal Article10.4018/joeuc.340037
Why Does Algorithmic Management Undermine Employee Creativity?
Daiheng Li,Mingyue Liu,Yun Zhao,Yuzhu Li,Tao Zhang,Wenjia Zhang,Dongrui Xia,Bo Lv +7 more
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TL;DR: Algorithmic management undermines employee creativity by negatively impacting knowledge combination capability and achievement goal.
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Abstract: 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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TL;DR: This study examines how algorithmic management influences gig workers' job crafting through gameful experience and perceived job autonomy, finding that it increases promotion-focused and prevention-focused job crafting behaviors, moderated by core self-evaluation.
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The consequences and theoretical explanation of workplace AI on employees: a systematic literature review
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Abstract: Abstract In the digital and intelligent era, the application of artificial intelligence (AI) in the workplace presents both numerous opportunities and challenges for employees. The complex interactions between AI technologies and employees necessitate a comprehensive understanding of how workplace AI implementation affects employee outcomes. This study analyzes publication trends in the related literature, identifying key publishing journals and research hotspots in this emerging field. Building on this foundation, we synthesize representative concepts and measures related to workplace AI, and explore its effects on employees through four theoretical perspectives: resource, stress, cognitive, and motivational perspectives. Additionally, we outline the moderating mechanisms that influence these effects. Finally, we construct an integrated research framework and propose directions for future research, aiming to provide theoretical guidance for ongoing workplace AI research and practical insights for organizational decision-making.
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