Post-Pandemic Digital Health in Indonesia: A Dual-Model Study of Task–Technology Fit and UTAUT on Telemedicine Use

This study investigates the determinants of continuance intention to use telemedicine applications in post-pandemic Indonesia by integrating the Task–Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT) models. Using a quantitative approach, data were collected through an online questionnaire, yielding 200 valid responses, which were analyzed using Structural Equation Modeling (SmartPLS 4.1.1.2). The model explained 40.6% of the variance in continuance intention and 43.2% of the variance in task–technology fit. Results showed that technology and task characteristics significantly influence TTF, while effort expectancy has a positive impact on performance expectancy. Social influence and TTF emerged as key predictors of continuance intention, whereas effort expectancy had no direct effect. These findings highlight the importance of task alignment and social endorsement in sustaining telemedicine usage. The study provides practical insights for developers and policymakers to design user-centered, functionally aligned digital health services in Indonesia’s diverse healthcare landscape.