System Quality and Perceived Usefulness in Shaping Smart Learning Effectiveness and Smart Campus Design

Abstract

Smart campus initiatives are increasingly adopted to support the digital transformation of higher education, yet the extent to which system quality translates into meaningful learning benefits remains insufficiently understood. This study examines how key system quality dimensions are associated with smart learning effectiveness and investigates the role of perceived usefulness in this relationship. Drawing on the Information System Success Model and the Technology Acceptance Model, a research model was developed incorporating accessibility, system efficiency, information quality, and real-time capability as system-related antecedents of perceived usefulness and smart learning effectiveness. Data were collected through a questionnaire survey of 420 students and faculty members at the School of Light Industry, North China University of Science and Technology, and analyzed using partial least squares structural equation modeling with 5,000 bootstrap resamples. The results indicate that all four system quality dimensions are positively associated with perceived usefulness and smart learning effectiveness. Information quality exhibited the strongest relationship with both perceived usefulness (β = 0.569, p < 0.001) and smart learning effectiveness (β = 0.342, p < 0.001), while perceived usefulness was positively associated with smart learning effectiveness (β = 0.255, p < 0.001). The findings further indicate differences between students and faculty in selected relationships involving information quality and real-time capability. Building on these empirical findings, the study develops an empirically informed smart campus design framework that translates system quality requirements into architectural and service-design priorities. The study contributes to smart campus research by connecting system-level characteristics, users’ perceptions of usefulness, and learning effectiveness within an integrated framework, while providing evidence-based priorities for the design and improvement of smart campus environments.