Modeling the adoption of AI-driven data visualization for service innovation in university libraries

Purpose The study examines the adoption of general-purpose generative artificial intelligence (GenAI) tools (e.g. ChatGPT, DeepSeek, Gemini) for data visualization tasks among academic librarians and its subsequent impact on library services innovation. Design/methodology/approach A quantitative research method was used, in which a cross-sectional survey on the extended model of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs was chosen in 100 universities of the Punjab province (both public and private sectors) in Pakistan with the help of a purposive sampling technique. Statistical Package for Social Sciences and Analysis of Moment Structure (SPSS-AMOS v-26) were applied to collect data and analyze it. Findings The findings of the study demonstrated that effort expectancy, social influence and facilitating conditions have a statistically significant influence on the behavioral intention to adopt GenAI tools for data visualization. Furthermore, the GenAI adoption has a significant positive impact on library service innovation. However, performance expectancy, hedonic motivation and price value have statistically insignificant impact on library professionals' behavioral intention to adopt GenAI tools. Originality/value This research provides original quantitative evidence from Pakistan, identifying the key factors influencing AI data visualization adoption in university libraries and its significant positive impact on service innovation.