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Autonomous vehicles (AVs) face major adoption barriers in emerging markets as a result of weak infrastructure, limited user exposure, and regulatory uncertainty. To explain adoption in such contexts, this study extends the Task-Technology Fit (TTF) model by integrating a User-Technology Fit (UTF) construct that captures the psychological and capability alignment between users and AV technology, while examining trust in automation as a moderating factor. A cross-sectional online survey was conducted with 227 licensed drivers in Ethiopia (18 to 56+ years; 74% male; average driving experience = 7.5 years). The questionnaire incorporated validated scales corresponding to the proposed research model and was distributed over three months. Results indicate that higher UTF significantly enhances perceptions of TTF, and both fit constructs positively influence the intention to adopt Level 3 AVs. Mediation analysis confirms that TTF partially mediates the effect of UTF on adoption intention. Trust strengthens the positive influence of TTF on adoption but does not significantly moderate the UTF-intention link. Integrating UTF into the TTF framework advances adoption theory by offering a holistic “technology fit” (FIT) perspective that unites psychological and functional alignment. The findings also highlight trust’s conditional influence and underscore the importance of designing technologies that align with both user and task dimensions to foster adoption in low-exposure markets.
Belay et al. (Wed,) studied this question.