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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jennifer Wieland & Lauren Keating & Alwine Mohnen, 2026. "From understanding resistance to fostering acceptance: how mindsets impact GenAI adoption," Post-Print hal-05724498, HAL.
  • Handle: RePEc:hal:journl:hal-05724498
    DOI: 10.1080/0144929X.2026.2706666
    Note: View the original document on HAL open archive server: https://hal.science/hal-05724498v1
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