Bringing employee learning to AI stress research: A moderated mediation model

Qiwei Zhou, Keyu Chen and Shuang Cheng

Technological Forecasting and Social Change, 2024, vol. 209, issue C

Abstract: While a substantial portion of the literature characterizes artificial intelligence (AI) stress as a hindrance, our focus diverges by probing employee learning as an active response to this challenge. We highlight the role of employee knowledge and skills development amidst an enterprise's digital transformation. Drawing on the active learning perspective of the Job Demand-Control model, we investigate why and when AI stress promotes employee learning and subsequent adaptive coping behaviors. We propose that AI stress can create opportunities and resources for employee learning, leading to improved job performance and supportive behavior for digital transformation. Additionally, we examine how employee trust in AI moderates these relationships, finding that higher levels of AI trust are associated with greater use of active learning strategies when faced with AI stress. Our findings, based on a two-wave survey of 224 employees from a motor-vehicle testing company in China, are further supported by post-hoc interview data collected from 32 employees of the same company. Overall, our study contributes to the understanding of AI adoption, digital transformation, and stress learning.

Keywords: AI stress; Learning; AI trust; Digital transformation (search for similar items in EconPapers)
JEL-codes: M00 M10 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524005717

DOI: 10.1016/j.techfore.2024.123773

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