Published on April 2025 | Information Technology, Digital Transformation

Digital Transformation in University Extension Services: Evaluating UTAUT Constructs in the Adoption of a SMART Extension Management System

The digital transformation of extension services in higher education institutions is pivotal in enhancing operational efficiency, optimizing resource allocation, and improving service delivery. This study examines the adoption and acceptability of the SMART Community Extension Management System (SMART-CEMS) at Cebu Technological University (CTU) using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The research evaluates the impact of key UTAUT constructs—performance expectancy, effort expectancy, social influence, and facilitating conditions—on system adoption among faculty members, extension coordinators, and administrators. A descriptive-correlational research design was employed, utilizing a structured survey questionnaire and inferential statistical analyses to assess user perceptions, adoption drivers, and challenges. Findings indicate that performance expectancy and effort expectancy significantly influence adoption, as users perceive the system as a transformative tool that enhances efficiency and streamlines extension service operations. Social influence and facilitating conditions were also found to play crucial roles, with institutional support and access to training serving as key determinants of adoption success. Despite its benefits, challenges such as data migration complexities, system integration issues, and user training gaps were identified as barriers to full implementation. The study provides strategic recommendations to mitigate these challenges, including targeted training programs, policy integration, and continuous technical support. These findings contribute to the broader discourse on digital transformation in higher education, offering valuable insights for institutions seeking to modernize extension services through technology-driven solutions. Future research should explore longitudinal adoption trends and the integration of advanced analytics and artificial intelligence to further optimize system capabilities.