Empowering Social Welfare Through Zakatech: A Meta-analysis of Drivers Behind Digital Zakat Adoption

This study conducts the first meta-analysis of digital zakat platform adoption, synthesizing 23 empirical studies (96 effect sizes, N = 36,054) to evaluate classical and contextual predictors. Drawing from established behavioural models – Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Theory of Reasoned Action (TRA) – as well as emerging constructs specific to digital zakat (e.g., trust and self-efficacy), this analysis evaluates the strength and consistency of predictors influencing digital adoption across diverse Muslim-majority markets. Findings indicate that social influence (SI) (r = 0.36), performance expectancy (PE) (r = 0.31), and facilitating conditions (FC) (r = 0.29) exhibit moderate associations and remain robust drivers of digital zakat adoption. Contextual constructs, such as trust (r = 0.40) and self-efficacy (r = 0.59), demonstrate comparable or even stronger predictive strength, emerging as particularly influential factors overall. High between-study heterogeneity (I2 > 90% for several constructs) underscores the importance of potential moderators – such as demographic, regional, and platform-specific differences – in shaping digital zakat adoption behaviour. The study contributes theoretically by advancing a hybrid adoption framework that integrates faith-aligned and user-centred factors to enhance digital zakat adoption. It also offers actionable insights for policymakers and fintech developers. By systematically identifying behavioural determinants within a faith-sensitive and user-centred context, this meta-analysis provides an evidence-based foundation for improving user adoption and fostering innovation in Shariah-compliant digital zakat platforms.