The factors integrating the learning analytics into academic libraries: Finding from SmartPLS-NCA and cIPMA

With the rapid advancement of the digital environment, the learning behaviors of digital natives have undergone significant transformation, and academic libraries are increasingly expected to draw on learner-related data to inform their services. This study examines the factors associated with librarians' behavioral intention to use, and their actual use of, learning analytics in academic libraries. The research is anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT), which was extended with three additional structural paths (performance expectancy → effort expectancy, effort expectancy → social influence, and social influence → facilitating conditions). A cross-sectional questionnaire was analyzed with PLS-SEM in Smart-PLS 4, supplemented by Necessary Condition Analysis (NCA) and Combined Importance-Performance Map Analysis (cIPMA). Performance expectancy (β = 0.467) and effort expectancy (β = 0.199) were positively associated with behavioral intention, whereas the social-influence path was not statistically significant (β = 0.118, p = 0.216). Behavioral intention (β = 0.565) and facilitating conditions (β = 0.143) were associated with reported use. The model accounted for 36.8% of the variance in behavioral intention and 37.7% in use behavior. According to the NCA, performance expectancy was the only antecedent that met the necessity criterion for behavioral intention. Given the cross-sectional design and the composition of the sample, the results describe associations among the constructs for this group of respondents and should not be read as causal effects or generalized to all academic librarians. The study extends UTAUT to the context of learning analytics in academic libraries and offers preliminary, evidence-informed insights for library administrators and policymakers.