[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117490-en":3,"doc-seo-117490-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117490,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Predicting the Intention to Adopt e-Zakat Payment Services - A Machine Learning Approach","Technology evolution in zakat collection and payment services has reshaped how charitable contributions are gathered and distributed. Despite positive growth in Malaysia’s annual zakat collection, collection performance has not reached optimal levels. This study uses data from 230 zakat payers to empirically evaluate machine learning algorithms for predicting e-zakat payment adoption intention. It also analyzes feature importance to explain how technology acceptance model (TAM) and technology readiness (TR) attributes affect predictions.","Predicting the intention to adopt e-zakat payment services: a  \nmachine learning approach  \nNor Hafiza Abd Samad1, Rahayu Abdul Rahman2, Suraya Masrom3, Norliana Omar2, Haslinawati  \nChe Hasan2  \n1Faculty of Computing and Multimedia, Universiti Poly-Tech Malaysia, Kuala Lumpur, Malaysia 2Faculty of Accountancy, Universiti Teknologi MARA, Tapah, Malaysia 3College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA, Tapah, Malaysia  \nArticle history:  \nReceived Mar 27, 2024 Revised Nov 27, 2024 Accepted Dec 25, 2024  \nKeywords:  \nE-zakat payment Machine learning Malaysia  \nTechnology acceptance model  \nTechnology readiness  \nCorresponding Author:  \nThe 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.  \nThis is an open access article under the CC BY-SA license.  \nSuraya Masrom  \nCollege of Computing, Informatics, and Mathematics, Universiti Teknologi MARA Perak Branch, Tapah, Malaysia  \nEmail: [suray078@uitm.edu.my](suray078@uitm.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nAdvancements in zakat collection and payment services, encompassing technologies such as mobile applications, online payment gateways, and blockchain [1]-[3], have sparked a profound evolution in the way charitable contributions are gathered and distributed globally. These transformative technologies have not only modernized zakat collection and payment services but have also increased their accessibility, security, and adaptability to the changing needs of zakat payers and beneficiaries on a global scale [4] . For instance, according to [5], mobile banking apps have streamlined the zakat payment process, making it exceptionally convenient for contributors to fulfill their obligations through secure and hassle-free transactions.  \nIn Islam, zakat operates as a sustainable force for social and economic upliftment, aligning with Islamic principles of social justice and equity. Zakat is a form of compulsory charity that aims to purify wealth and redistribute it among those in need [6] . Indeed, Zauro et al. [7] stress that zakat, a fundamental aspect of Islamic financial and social responsibility, serves as a pivotal force in alleviating poverty and  \nbolstering economic stability. Mandated as a charitable obligation, zakat involves the systematic giving of a portion of one's wealth to assist the less fortunate. Through systematic wealth redistribution within the Muslim community, zakat channels resources into essential social welfare programs such as healthcare, education, and financial aid for the less fortunate. This targeted assistance not only directly aids those in need but also fosters economic stability by","cbCaibGVnxF8SlDH","https://ap.wps.com/l/cbCaibGVnxF8SlDH","pdf",367652,1,"English","en",105,"# Introduction\n## Zakat and Online Payment Services\n## Research Gap and Motivation\n## Machine Learning for Intention Prediction","[{\"question\":\"What does the study predict regarding e-zakat payment services?\",\"answer\":\"The study predicts zakat payers’ intention to adopt online e-zakat payment services using machine learning models.\"},{\"question\":\"What data and models are used in the analysis?\",\"answer\":\"The work uses data from 230 zakat payers and evaluates multiple machine learning algorithms to estimate adoption intention.\"},{\"question\":\"Which factors most influence the prediction results?\",\"answer\":\"Feature-importance analysis shows that TAM attributes are the most crucial influence on the prediction outcomes.\"}]","Predicting the Intention to Adopt e-Zakat Payment Services - A Machine Learning Approach | PDF",1785676170,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predicting-the-intention-to-adopt-e-zakat-payment-services-a-machine-learning-approach","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/predicting-the-intention-to-adopt-e-zakat-payment-services-a-machine-learning-approach/117490/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What does the study predict regarding e-zakat payment services?","Question",{"text":74,"@type":75},"The study predicts zakat payers’ intention to adopt online e-zakat payment services using machine learning models.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data and models are used in the analysis?",{"text":79,"@type":75},"The work uses data from 230 zakat payers and evaluates multiple machine learning algorithms to estimate adoption intention.",{"name":81,"@type":72,"acceptedAnswer":82},"Which factors most influence the prediction results?",{"text":83,"@type":75},"Feature-importance analysis shows that TAM attributes are the most crucial influence on the prediction outcomes.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]