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How Employees Respond to AI Performance Shortfalls: A Within-Person Investigation
Abstract   Open access

How Employees Respond to AI Performance Shortfalls: A Within-Person Investigation

Wenqing Yu, Yiduo Shao and Shenjiang Mo
Academy of Management Annual Meeting Proceedings, Vol.2026(1)
07/2026
DOI: 10.5465/AMPROC.2026.15518abstract
url
https://doi.org/10.5465/AMPROC.2026.15518abstractView
Published (Version of record) Open Access

Abstract

As artificial intelligence (AI) becomes increasingly embedded in daily work, prior research has focused primarily on AI adoption and use, offering limited insight into how employees respond when AI fails to meet expectations. Drawing on expectancy violations theory, we examine how employees react when AI performance falls below their expectations. Across three studies, we found that such performance shortfalls elicited both adaptive and maladaptive responses, which in turn exerted beneficial effects on employee performance and detrimental effects on well-being, respectively. We further found that these responses were contingent on employees’ beliefs about their future opportunities. By shifting attention from AI adoption to performance variability, this study advances understanding of human-AI collaboration and offers new insights into employee experiences in AI-integrated workplaces.

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