Predictors of AI-Assisted Metadata Adoption Intentions Among Cataloguing Librarians in South-West Nigerian University Libraries

Existing studies on artificial intelligence in libraries have largely focused on general adoption, with limited attention to cataloguing librarians and AI-assisted metadata creation. This study examined predictors of AI-assisted metadata adoption intentions among cataloguing librarians in South-West Nigerian university libraries using an extended Technology Acceptance Model. Data were collected from 87 cataloguing librarians and analyzed using descriptive statistics, Pearson correlation, and multiple regression analysis. Findings revealed moderate awareness and knowledge of AI-assisted metadata tools but limited self-assessed functional competence, alongside generally favorable attitudes toward adoption. The regression model explained 64.3% of the variance in behavioral intention to adopt AI-assisted metadata tools. Perceived usefulness was the strongest predictor of behavioral intention, followed by digital competency and institutional support, whereas perceived ease of use was not a significant predictor. The findings indicate relatively favorable adoption intentions alongside comparatively lower perceptions of institutional support and functional competence. The study recommends targeted practical training, infrastructural investment, and responsible implementation frameworks to support effective AI-assisted metadata adoption.