Generative Artificial Intelligence Adoption for Reference Services in University Libraries: A Technology Acceptance Perspective

The integration of Generative Artificial Intelligence (GenAI) into university libraries has created new opportunities for improving reference services through enhanced information retrieval, personalised user support and increased operational efficiency. Despite these prospects, the adoption of GenAI in many university libraries remains uneven due to technological, organisational and human-related factors. This study examined the adoption of Generative Artificial Intelligence for reference services in university libraries from a Technology Acceptance perspective. Specifically, the study investigated the factors influencing adoption, identified the opportunities and benefits of GenAI, examined the challenges affecting its implementation, assessed librarians' readiness and perceptions and determined the relationship between technology acceptance and GenAI adoption. The descriptive survey research design was adopted and data were collected from university librarians using a structured questionnaire. Descriptive statistics and Partial Least Squares Structural Equation Modelling (PLS-SEM) were employed for data analysis. The findings revealed that perceived usefulness, perceived ease of use, organisational support and AI literacy significantly influenced the adoption of GenAI for reference services. The study further established that GenAI enhances information accessibility, improves service delivery and increases librarians' productivity, although challenges such as inadequate technological infrastructure, ethical concerns, insufficient training and limited institutional support continue to impede widespread adoption. The structural model demonstrated a significant positive relationship between technology acceptance and the adoption of Generative Artificial Intelligence for reference services (β = 0.691, p < 0.001), supporting the Technology Acceptance Model The study concludes that the adoption of Generative Artificial Intelligence for reference services is largely influenced by perceived usefulness, perceived ease of use, organisational support and AI literacy. It recommends that university libraries invest in robust digital infrastructure, provide continuous AI training and capacity-building programmes for librarians, formulate clear institutional policies and ethical guidelines for AI implementation, and strengthen organisational support to facilitate the effective and responsible adoption of GenAI for reference services.