The influence loop: how perceived norms drive academic librarians' use and advocacy of generative AI for information-seeking

Although generative AI tools are becoming increasingly popular in the workplace, owing partly to the belief that they can boost efficiency and productivity, empirical evidence is limited on the norms that drive actual usage and advocacy behaviour among academic librarians, particularly in developing countries. The current study aims to determine the influence of two types of perceived norms (injunctive norms and descriptive norms) on academic librarians' generative AI use and advocacy behaviours while examining the mediating role of behavioural intention and the role of digital information-seeking skills. This study applies the integrated behavioural model to correlational research among academic librarians in three Federal Universities in South-Western Nigeria. A sample of 133 participants was selected using simple random sampling, and responses were obtained using a self-report questionnaire. Partial Least Squares structural equation modelling validated the research model. Results suggest that injunctive norms and information-seeking skills directly influence Generative AI use and advocacy behaviours. Descriptive norms directly influence use behaviour, but not advocacy behaviour and behavioural intention mediates the relationship between injunctive norms and generative AI use behaviour. The Findings have implications for learning and technology adoption among library and information science practitioners, particularly for improving skills through targeted training programmes and social interaction in the workplace. The study offers insights into the potential of Generative AI for information discovery, advocacy, and service delivery by academic librarians in a non-Western context, and provides valuable evidence relevant to policymaking, training, and development.

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