<![CDATA[This study set out to extend UTAUT2 through the construct of policy trust, examining how it shapes behavioral intention and use behavior around mobile money among Ghanaian MSMEs after the E-Levy's repeal. Policy trust was tested in two roles—as a direct predictor and as a moderator. The study followed a quantitative explanatory approach, running a cross-sectional survey with 400 MSMEs across Accra, Kumasi, and Sekondi-Takoradi through purposive sampling. Analysis relied on PLS-SEM in SmartPLS 4.0, covering the measurement model, structural model, and a two-stage moderation test. As many as 11 out of 12 hypotheses were supported. Performance expectancy, effort expectancy, hedonic motivation, price value, and habit significantly influence behavioral intention, while social influence does not. Facilitating conditions and habit influence use behavior, with behavioral intention as the strongest predictor. Policy trust significantly affects behavioral intention and also moderates two main relationships. The model explains 61.2% of behavioral intention and 68.7% of use behavior. This study introduces policy trust as an extension of UTAUT2, built on an integration with policy feedback theory. Policy trust proved significant for usage intention, and it also moderated two key relationships—price value to behavioral intention, and facilitating conditions to use behavior. UTAUT2 gains an institutional dimension it previously lacked. Several recommendations follow from these findings. Bank of Ghana and the Ghana Revenue Authority should strengthen policy communication. Mobile money providers need to pair infrastructure investment with cost certainty. And Fintech Growth Fund administrators would benefit from factoring policy trust into how they segment beneficiaries. This study has its constraints—a cross-sectional design, self-report data, a sample limited to urban areas, and a policy trust construct still awaiting broader validation. Future work could take a longitudinal approach, extend the analysis across countries, and draw on actual transaction data rather than survey responses alone.]]>
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