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<p>While AI usage is increasingly embedded in employees’ core work tasks, its implications for task performance remain inconsistent in the literature. Drawing on self-determination theory and regulatory focus theory, this study develops a sequential dual-path model to explain how AI usage is linked to employee task performance through distinct motivational and behavioral mechanisms, with core self-evaluations (CSE) as a boundary condition. Using three-wave, multi-source data from 409 employees and 53 supervisors in China, the results show that AI usage is associated with task performance through both facilitative and inhibitive indirect pathways. Specifically, AI usage is positively associated with task performance through autonomous motivation and promotion-focused job crafting, but negatively associated with task performance through controlled motivation and prevention-focused job crafting. Furthermore, CSE strengthens the positive indirect pathway while weakening the negative indirect pathway, indicating that employees’ deeper self-evaluations shape how they respond motivationally and behaviorally to AI usage. By uncovering a sequential motivational–behavioral mechanism underlying the AI usage–performance paradox, this study extends research on AI and employee outcomes and offers practical implications for designing human-centered AI usage practices in organizations.</p>

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When AI enables and undermines: dual mechanisms linking AI usage to task performance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

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  • Wenhui Zhang,
  • Po-Chien Chang,
  • Xinqi Geng

摘要

While AI usage is increasingly embedded in employees’ core work tasks, its implications for task performance remain inconsistent in the literature. Drawing on self-determination theory and regulatory focus theory, this study develops a sequential dual-path model to explain how AI usage is linked to employee task performance through distinct motivational and behavioral mechanisms, with core self-evaluations (CSE) as a boundary condition. Using three-wave, multi-source data from 409 employees and 53 supervisors in China, the results show that AI usage is associated with task performance through both facilitative and inhibitive indirect pathways. Specifically, AI usage is positively associated with task performance through autonomous motivation and promotion-focused job crafting, but negatively associated with task performance through controlled motivation and prevention-focused job crafting. Furthermore, CSE strengthens the positive indirect pathway while weakening the negative indirect pathway, indicating that employees’ deeper self-evaluations shape how they respond motivationally and behaviorally to AI usage. By uncovering a sequential motivational–behavioral mechanism underlying the AI usage–performance paradox, this study extends research on AI and employee outcomes and offers practical implications for designing human-centered AI usage practices in organizations.