Predicting the intention to adopt e-zakat payment services: a machine learning approach

Abstract

The technology evolution in the zakat collection and payment services has brought about a profound transformation in the global processes of gathering and distributing charitable contributions. Despite witnessing a positive trend in annual zakat collection in Malaysia, it has yet to reach its optimal level. Therefore, predictions regarding performance and comparisons across multiple models for online zakat collection hold crucial significance in improving the overall collection rate. This paper, utilizing data from 230 zakat payers, presents an empirical assessment of various machine learning algorithms aimed at predicting zakat payer intentions when utilizing online platforms for zakat payments. Additionally, this paper presents the analysis of machine learning features importance to justify the effect of technology acceptance model (TAM) and theory of technology readiness (TR) attributes in the machine learning algorithms for predicting e-zakat payment service adoption intention. The findings show that many of the machine learning models are able to perform for highly accurate results, with most achieving over 80% accuracy. The most crucial attribute influencing these predictions was found to be the TAM. This study's methodology is designed to be easily replicable, allowing for further detailed exploration of both the influencing factors and the machine learning algorithms used.