Social-related factors in integrated UTAUT model for ZakaTech acceptance during the COVID-19 crisis

Purpose: Despite the significant growth in Islamic economies and the increasing number of Muslim youths inclining digital services, empirical-based research addressing the adoption of digital Islamic services is still scarce. Particularly, as a new term in the Islamic finance industry, ZakaTech has recently emerged as a modern term describing novel technologies adopted by zakat (compulsory levy on all believing and practicing high-net-worth Muslims) institutions; yet, it has largely been neglected in the literature. Therefore, this paper aims to propose an integrated model that scrutinizes the factors of unified model of acceptance and use of technology (UTAUT) of ZakaTech, combined with social cognitive theory (SCT), especially in a time of COVID-19 social distancing. Design/methodology/approach: The UTAUT–SCT model was validated via SmartPLS structural equation modeling by using a valid sample of 510 users (individual zakat payers) from Saudi Arabia. Findings: The results demonstrated the suitability of the integrated UTAUT–SCT model in predicting zakat payers’ intention to use ZakaTech services. This proposed model has 70% explanatory power to explain variance in intention. All UTAUT constructs are statistically significant, except for effort expectancy. Social isolation caused by the pandemic and trust in e-zakat system exerted a significant influence on the inclination to uptake ZakaTech services. Originality/value: To the best of the authors’ knowledge, this research is among the first research that studies Muslims’ adoption of ZakaTech during COVID-19. Particularly, this study could add value to FinTech acceptance literature by empirically examining an integrated framework of UTAUT–SCT in a context as modern and unique as ZakaTech.