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Shifting attitudes and trust in AI: Influences on organizational AI adoption

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  • Daly, Sarah J.
  • Wiewiora, Anna
  • Hearn, Greg

Abstract

This paper investigates how trust in artificial intelligence (AI) influences its adoption in organizational settings, emphasizing the dynamic nature of attitudes towards AI. Using qualitative data from 29 interviews with AI developers, managers, and users, the study identifies three attitudinal positions: positive, negative, and instrumental. The findings reveal that attitudes towards AI are changing, often shifting from negative or instrumental to positive as individuals gain knowledge and experience with AI technologies. For example, we found evidence that instrumental attitudes, which require evidence before trust is established, become more positive when people become more familiar with AI. Negative attitudes, rooted in perceived threats like job displacement or privacy concerns, tend to shift when people begun to realize AI benefits. Building on organizational trust and trust in AI theory, this paper extends the understanding of differences in how AI developers, managers and users develop trust in AI.

Suggested Citation

  • Daly, Sarah J. & Wiewiora, Anna & Hearn, Greg, 2025. "Shifting attitudes and trust in AI: Influences on organizational AI adoption," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:tefoso:v:215:y:2025:i:c:s0040162525001398
    DOI: 10.1016/j.techfore.2025.124108
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    References listed on IDEAS

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    1. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    2. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    3. Raut, Rakesh D. & Priyadarshinee, Pragati & Gardas, Bhaskar B. & Jha, Manoj Kumar, 2018. "Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach," Technological Forecasting and Social Change, Elsevier, vol. 134(C), pages 98-123.
    4. Michael C Horowitz & Lauren Kahn, 2021. "What influences attitudes about artificial intelligence adoption: Evidence from U.S. local officials," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-20, October.
    5. Roman Lukyanenko & Wolfgang Maass & Veda C. Storey, 2022. "Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 1993-2020, December.
    6. Matthias Söllner & Axel Hoffmann & Jan Marco Leimeister, 2016. "Why different trust relationships matter for information systems users," European Journal of Information Systems, Taylor & Francis Journals, vol. 25(3), pages 274-287, May.
    7. Frey, Carl Benedikt & Osborne, Michael A., 2017. "The future of employment: How susceptible are jobs to computerisation?," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 254-280.
    8. Tatjana Vasiljeva & Ilmars Kreituss & Ilze Lulle, 2021. "Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries," JRFM, MDPI, vol. 14(8), pages 1-17, July.
    9. Melany Horsfall & Merijn Eikelenboom & Stasja Draisma & Johannes H. Smit, 2021. "The Effect of Rapport on Data Quality in Face-to-Face Interviews: Beneficial or Detrimental?," IJERPH, MDPI, vol. 18(20), pages 1-10, October.
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    1. Jacob Dominski & Christopher Hoy & Cassandra Merritt & Yong Suk Lee, 2026. "Managers as gatekeepers in the age of AI," IFS Working Papers W26/23, Institute for Fiscal Studies.

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