Factors influencing the adoption of machine learning in libraries: a systematic literature review

Purpose This study aims to identify the factors influencing the adoption of machine learning in libraries. It also aims to reveal challenges linked to implement machine learning in library settings. Design/methodology/approach Systematic literature review methodology was applied to address the study’s objectives. Twelve digital databases and Google Scholar were used to conduct the study. Findings The findings showed that enhanced user experience, operational efficiency, strategic value, technological innovation and capacity building were the key factors influencing the adoption of machine learning in libraries. Results also revealed that financial constraints, skills deficiency, ethical concerns and integration challenges negatively affected the adoption of machine learning in library settings. Originality/value This study has developed a framework based on evidence-based findings to adopt machine learning-based systems and services in libraries. It has provided significant theoretical, managerial, methodological, economic and policy and social implications through the addition of valuable literature to the existing body of knowledge.