Multidimensional factors influencing continuance usage intention of university library self-service systems: An empirical analysis based on an extended TAM-UTAUT

Abstract

With the advancement of smart library initiatives in higher education institutions, the automation of circulation services has emerged as a critical component. Automated literature services alleviate librarians’ repetitive workloads and enhance the efficiency of faculty and student resource utilization. Existing research on the continued usage intention of library self-service systems often adopts singular sociological or technological perspectives, presenting limitations. This study integrates the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to construct a multidimensional analytical framework. It systematically investigates the mechanisms influencing university faculty and students’ sustained use of self-service systems, encompassing system characteristics (navigation, terminology, relevance), technological readiness (retrieval knowledge), accessibility, and individual attributes.Valid questionnaire data from 365 faculty and students across Qinghai University, Qinghai Normal University, and Qinghai Minzu University were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) for path analysis and hypothesis testing. Results indicate that system characteristics, technological readiness, accessibility, perceived ease of use, and perceived usefulness significantly impact sustained system usage. Furthermore, variables including occupation, gender, ethnicity and usage frequency exhibit moderating effects within the model.This research extends the application dimensions of traditional TAMs by elucidating the interactive mechanisms of multifactorial influences on self-service system continuance usage intention. It provides actionable insights for library administrators, end-users, and system vendors, contributing valuable references for advancing smart library development. The findings refine theoretical models of user behavior in library automation and underscore the necessity of contextualizing technological interventions within diverse sociocultural and demographic landscapes.