Beyond the hype: understanding barriers to AI adoption through lens of protection motivation theory

Ahyar Yuniawan, Hersugondo Hersugondo, Fuad Mas’ud, Hengky Latan, Murad Ali and Moacir Godinho Filho ()
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Ahyar Yuniawan: UNDIP - Diponegoro University
Hersugondo Hersugondo: UNDIP - Diponegoro University
Fuad Mas’ud: UNDIP - Diponegoro University
Hengky Latan: FTD Institute
Murad Ali: Northumbria University [Newcastle]
Moacir Godinho Filho: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: Purpose This study explores how perceived self-threat, skepticism and distrust influence employees' intentions to adopt artificial intelligence (AI), both directly and indirectly, through anticipated adverse outcomes. Design/methodology/approach Using protection motivation theory (PMT) as a theoretical framework, data were gathered from 597 employees. This study employed covariance-based structural equation modeling (CB-SEM) to evaluate the proposed hypotheses. Findings The results indicate that perceived self-threat, skepticism and distrust significantly and negatively impact employees' intention to use AI. Specifically, elevated levels of these psychological factors heighten concerns about privacy and job security, which in turn diminish the likelihood of AI adoption. Originality/value This study provides new insights into how perceived self-threat, skepticism and distrust affect AI adoption intentions through anticipated adverse outcomes. It enriches the literature by highlighting the psychological barriers to AI adoption and underscores the need for targeted managerial strategies to address these challenges.

Keywords: Protection motivation theory; Distrust; Skepticism; Perceived self-threat; Anticipated adverse outcomes; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
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Published in Aslib Journal of Information Management, In press, ⟨10.1108/AJIM-08-2024-0619⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05384006

DOI: 10.1108/AJIM-08-2024-0619

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