When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity

Yunjian Li, Yixiao Song, Yanming Sun and Mingzhuo Zeng

Technology in Society, 2024, vol. 77, issue C

Abstract: Based on social learning theory, this paper empirically analyzed the effect of employee artificial intelligence (AI) use frequency on employee learning from AI, and explored the moderating effects of employee perceived enjoyment and task-related complexity in this context using a questionnaire-based approach. The study showed that employee AI use frequency can promote employee learning from AI. Employee perceived enjoyment can facilitate employee to learn from AI, and employee perceived enjoyment positively moderates the effect of employee AI use frequency on employee learning from AI. Task-related complexity positively influences employee learning from AI and enhances the positive effect of employee AI use frequency on employee learning from AI, as does employee perceived enjoyment on employee learning from AI. Significant three-way interaction effects among employee AI use frequency, employee perceived enjoyment, and task-related complexity on employee learning from AI are observed. In this paper, a scale for measuring employee learning from AI is developed that extends the learning model from ‘human learning from humans’ to ‘human learning from AI’, broadens the scope of application and theoretical connotations of social learning theory, and opens the black box of the relationship between employee AI use and employee learning from AI.

Keywords: Employee learning from AI; Employee AI use frequency; Employee perceived enjoyment; Task-related complexity; Social learning theory (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24000666

DOI: 10.1016/j.techsoc.2024.102518

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