Toward an Understanding of Human Trust in Organizational Generative Artificial Intelligence (GenAI)

M. Max Evans () and Anthony K. P. Wensley ()
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M. Max Evans: School of Information Studies, McGill University
Anthony K. P. Wensley: The University of Toronto, Department of Management, University of Toronto Mississauga and J.L. Rotman School of Management

A chapter in Managing Human and Artificial Knowledge, 2026, pp 201-221 from Springer

Abstract: Abstract Organizational adoption and use of artificial intelligence (AI), and more specifically generative AI (GenAI), has seen remarkable growth in the last few years, with nearly every Fortune 500 company using it or exploring its use. GenAI comes with significant benefits (e.g., task/process automation; improved decision-making/planning; better knowledge management) and considerable risks (e.g., information biases/inaccuracies, misapplications, labor displacement, legal/security threats). This is why researchers and practitioners have signalled the importance of developing an understanding of how and why individuals in organizations place their trust in these technologies and the information they provide. Unless researchers provide a robust and validated model of trust, the implementation of GenAI is foolhardy at best, and fundamentally irresponsible and potentially dangerous at worst. Failure to model how individuals come to assess the trustworthiness of AI also jeopardizes all knowledge management initiatives. It is hoped that this chapter provides some initial steps toward the development of a nuanced model of trust (MoT) by extending a MoT proposed by Mayer, Davis, and Schoorman. Each dimension in the MoT is discussed in the context of GenAI, as well as the interactions between them. The chapter concludes by discussing the role of emotion and organizational culture, then proposes directions for future research.

Keywords: Trust; Generative artificial intelligence (GenAI); Trustworthiness; Model of trust; Organizational (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-14721-9_10

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