Analysis of factors influencing the adoption of artificial intelligence in academic libraries

Purpose This exploratory study examines the key factors influencing artificial intelligence (AI) adoption in academic libraries. Drawing on the technology–organization–environment (TOE) framework and elements of Unified Theory of Acceptance and Use of Technology (UTAUT), it investigates the roles of transformational leadership, technological infrastructure, financial support, organizational culture, human resource training and user engagement in the AI adoption process. Design/methodology/approach An explanatory sequential mixed-methods design was employed. First, a systematic literature review (23 included studies) informed the development of a 45-item questionnaire, which was completed by 26 experts in academic librarianship and AI selected through purposive sampling. Quantitative data were analyzed using SPSS and SmartPLS. Subsequently, a 10-member expert panel was convened to contextualize and validate the quantitative findings through thematic analysis. Findings Transformational leadership emerged as the strongest predictor of AI adoption (β = 0.966, p < 0.001), followed by user engagement (β = 0.426, p = 0.018), financial support (β = 0.410, p = 0.015) and organizational culture (β = 0.389, p = 0.022). The model explained 94% of the variance (R2 = 0.94; adjusted R2 = 0.92); however, this high explanatory power should be interpreted cautiously due to the small sample size (N = 26) and risk of overfitting. Qualitative findings revealed that leadership operates through visible commitment and psychological safety, while financial barriers extend beyond procurement to include system integration and staff retention costs nuances that the quantitative model alone could not capture. Originality/value AI adoption in academic libraries is increasingly essential for enhancing service quality and user experience. Successful implementation requires strong transformational leadership, sustained financial support, cultural change and applied staff training. While general awareness of AI alone is insufficient without supportive leadership and resources, the study provides a preliminary integrated framework to guide university administrators and policymakers, particularly in developing countries. Given the exploratory nature and sample constraints, the findings require replication with larger, more diverse samples to confirm their generalizability.