A Conceptual Framework on the Relationship between Artificial Intelligence Adoption, Data-Driven Decision-Making and Zakat Management Efficiency

Izatul Akmar Ismail, Mohd Adib Abd Muin, Norhasyikin Rozali, Noor Syahidah Mohamad Akhir, Daing Maruak Sadek and Amin Che Ahmat
Additional contact information
Izatul Akmar Ismail: Academy of Contemporary Islamic Studies (ACIS), Universiti Teknologi MARA (UiTM), Cawangan Kedah, Kampus Sungai Petani, 08400, Kedah, Malaysia
Mohd Adib Abd Muin: Islamic Business School (IBS), Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
Norhasyikin Rozali: Faculty of Business Management, Universiti Teknologi MARA (UiTM), Cawangan Kedah, Kampus Sungai Petani, 08400, Kedah, Malaysia
Noor Syahidah Mohamad Akhir: Academy of Contemporary Islamic Studies (ACIS), Universiti Teknologi MARA (UiTM), Cawangan Kedah, Kampus Sungai Petani, 08400, Kedah, Malaysia
Daing Maruak Sadek: Academy of Contemporary Islamic Studies (ACIS), Universiti Teknologi MARA (UiTM), Cawangan Kedah, Kampus Sungai Petani, 08400, Kedah, Malaysia
Amin Che Ahmat: Academy of Contemporary Islamic Studies (ACIS), Universiti Teknologi MARA, Cawangan Perak, Kampus Tapah, 35400, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 10, 5490-5500

Abstract: Zakat plays a vital role in Islamic social finance, serving as a mechanism for poverty alleviation and social welfare. However, its management continues to face challenges including inefficiency, limited transparency, and low stakeholder trust. Conventional approaches often hinder timely collection and equitable distribution, underscoring the need for innovative, Shariah-compliant technological solutions. This study develops a conceptual framework that examines the relationship between Artificial Intelligence (AI) adoption, data-driven decision-making (DDDM), and zakat management efficiency. Drawing upon General Systems Theory (GST), the framework positions DDDM as a mediating mechanism that translates AI-driven technological capabilities such as predictive analytics, real-time monitoring, and machine learning into evidence-based, ethical, and transparent decision-making processes. Using a narrative review of Scopus-indexed literature (2019- 2024), the study synthesizes theoretical and empirical insights to demonstrate that AI adoption enhances institutional efficiency primarily through the mediating role of DDDM, which strengthens accountability, fairness, and governance in zakat administration. The study contributes theoretically by extending systems theory into Islamic social finance and practically by providing policymakers and zakat institutions with a Shariah-aligned model for responsible AI integration that promotes transparency, trust, and socio-economic justice.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... -5500-202511_pdf.pdf (application/pdf)
https://www.rsisinternational.org/journals/ijriss/ ... nagement-efficiency/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:9:y:2025:i:10:p:5490-5500

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().