Bulletin of Electrical Engineering and Informatics - 2025 : Predicting the Intention to Adopt E-Zakat Payment Services: A Machine Learning Approach

Abd Samad, Nor Hafiza and Abdul Rahman, Rahayu and Masrom, Suraya and Omar, Norliana (2025) Bulletin of Electrical Engineering and Informatics - 2025 : Predicting the Intention to Adopt E-Zakat Payment Services: A Machine Learning Approach. Bulletin of Electrical Engineering and Informatics, 14 (3). pp. 2330-2337. ISSN 2302-9285

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Abstract

The technology evolution in zakat collection and payment services has transformed the processes of collecting and distributing charitable contributions. This study evaluates various machine learning algorithms to predict Malaysian zakat payers' intention to adopt online zakat payment services using data from 230 respondents. It also examines the importance of Technology Acceptance Model (TAM) and Technology Readiness (TR) attributes. Most machine learning models achieved over 80% prediction accuracy, with TAM identified as the most influential predictor of e-zakat payment adoption.

Item Type:	Article
Uncontrolled Keywords:	E-zakat payment; Machine learning; Malaysia; Technology acceptance model; Technology readiness
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Institute of Graduate Studies (IGS)
Depositing User:	LIBRARY2 UPTM
Date Deposited:	05 Aug 2026 08:26
Last Modified:	05 Aug 2026 08:26
URI:	http://eprints.uptm.edu.my/id/eprint/5945

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