Analyzing the Determinants of Healthcare Technology Adoption Using the Task-Technology Fit (TTF) Model: A Systematic Review and Meta-Analysis - Thanthrige, Ayesha; Wickramasinghe, Nilmini (38th Bled eConference: Empowering Transformation: Shaping Digital Futures for All: Conference Proceedings, 2025)
This study aims to investigate the determinants of healthcare technology adoption using an extended Task-Technology Fit (TTF) model through a Systematic Literature Review (SLR) and meta-analysis, focusing on healthcare-specific tasks and their alignment with technology characteristics. While TTF has been widely applied across various domains, its application within healthcare is limited, with inconsistent findings. Addressing this research gap, the study provides a clearer understanding of how healthcare-specific tasks align with Technology Characteristics (TechC) to influence adoption among individuals. The extended model includes Behavioral Intention (BI) to assess users’ intention to adopt healthcare technologies. The analysis reveals that TTF is a significant predictor of technology use, offering novel insights into the factors that drive successful healthcare technology adoption. The findings contribute to both theoretical advancements in TTF and offer practical implications for improving the design and implementation of digital healthcare solutions. Healthcare solution designers are encouraged to apply the TTF framework when evaluating new technologies to guide technology design and evaluation in real-world healthcare environments.