UTAUT and TTF Reveal Healthcare Technology Drivers
An integrated systematic review and meta-analysis published in 2025 (J Med Internet Res) analyzed 50 studies including 24,764 participants to assess determinants of health care technology adoption using UTAUT and TTF. It found performance expectancy is the strongest UTAUT predictor of usage intention (β=.304), and technology characteristics most strongly drive task-technology fit (β=.445), with high heterogeneity across settings (I²≈82%–95%). The results guide implementers to prioritize usefulness, usability, and alignment.
Key Points
- 1Identify performance expectancy as strongest UTAUT predictor of usage intention (β=.304, P<.001).
- 2Demonstrate technology characteristics strongly drive task-technology fit (β=.445), indicating feature alignment matters.
- 3Advise implementers to prioritize performance expectancy, usability, training, and task-technology alignment to increase adoption.
Scoring Rationale
Strong meta-analytic evidence across 50 studies supports actionable predictors, but high heterogeneity limits cross-context generalizability.
Sources
Public references used for this report.
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