From understanding resistance to fostering acceptance: how mindsets impact GenAI adoption

Jennifer Wieland, Lauren Keating () and Alwine Mohnen
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Jennifer Wieland: TUM - Technische Universität Munchen = Technical University Munich = Université Technique de Munich
Lauren Keating: EM - EMLyon Business School
Alwine Mohnen: TUM - Technische Universität Munchen = Technical University Munich = Université Technique de Munich

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Abstract: Individual resistance to adopting generative artificial intelligence (GenAI) jeopardises its successful implementation in organisations. Across two studies, this paper aims to explain why some individuals embrace GenAI while others oppose it, and investigates whether a growth mindset can facilitate its adoption in organisations. In Study 1, we surveyed 159 German employees to establish a Theory of Planned Behavior (TPB) model explaining the variance in GenAI adoption. The structural equation modelling results revealed that attitudes, subjective norms, and perceived behavioural control predict the intention to adopt GenAI, with attitudes exhibiting the strongest association. While Study 1 enhances understanding of GenAI adoption, Study 2 seeks to promote it. Given the challenges posed by GenAI, we tested whether a growth mindset – the belief that abilities can be developed – can boost GenAI adoption, as it fosters openness to challenges and change. In a randomised experiment with 389 German employees, we observed that a growth mindset intervention positively influences attitudes, subjective norms, and perceived behavioural control. Overall, the findings provide insight into the role that mindsets play in approaching or avoiding digital transformations, as well as offer a path for diminishing people's reluctance to embrace them.

Keywords: Mindsets; Generative AI; Technology adoption; Theory of Planned Behavior (TPB) (search for similar items in EconPapers)
Date: 2026-08-11
New Economics Papers: this item is included in nep-exp
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Published in Behaviour and Information Technology, inPress, pp.24. ⟨10.1080/0144929X.2026.2706666⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05724498

DOI: 10.1080/0144929X.2026.2706666

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