Structural equation modeling reveals effort expectancy and relative advantage drive AI adoption intention among library professionals, highlighting key targets for digital transformation.
Purpose Artificial intelligence (AI) can transform library services by enhancing efficiency and user experience. The adoption of AI in Pakistani libraries remains underexplored. Design/methodology/approach This study aims to identify the determinants of AI adoption intention among library professionals using an extended unified theory of acceptance and use of technology (UTAUT) model integrated with combined importance-performance map analysis (cIPMA). Findings The key constructs, such as effort expectancy, performance expectancy, relative advantage, top management support, trust and competitive pressure, were assessed using partial least squares structural equation modeling. The findings highlight effort expectancy and relative advantage as significant predictors. Originality/value The study contributes to theoretical advancement by extending UTAUT in a library context and offers practical insights to help administrators facilitate AI adoption.
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Jan et al. (2026) studied this question.
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