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A Conceptual Framework on the Relationship between Artificial
Intelligence Adoption, Data-Driven Decision-Making and Zakat
Management Efficiency
Izatul Akmar Ismail
1
*, Mohd Adib Abd Muin
2
, Norhasyikin Rozali
3
, Noor Syahidah Mohamad Akhir
4
,
Daing Maruak Sadek
5
, Amin Che Ahmat
6
1,4,5,6
Academy of Contemporary Islamic Studies (ACIS), University Teknologi MARA (UiTM),
Cawangan Kedah, Kampus Sungai Petani, 08400, Kedah, Malaysia Islamic Business School, University
Utara Malaysia, 06010, Sintok, Malaysia
2,3
Faculty of Business Management, University Teknologi MARA (UiTM), Cawangan Kedah, Kampus
Sungai Petani, 08400, Kedah, Malaysia
*Corresponding author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000449
Received: 12 October 2025; Accepted: 20 October 2025; Published: 15 November 2025
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.
Keywords— Zakat, Artificial Intelligence (AI), Data-Driven Decision-Making (DDDM), General Systems
Theory (GST), Zakat Management Efficiency
INTRODUCTION
The integration of Artificial Intelligence (AI) into financial and social sectors has transformed traditional
practices, introducing advanced mechanisms that enhance efficiency, transparency, and accountability. Within
Islamic social finance, particularly zakat management, AI adoption presents transformative opportunities to
address persistent challenges in fund collection, distribution, and governance. As one of the five pillars of
Islam, zakat serves not only as a religious obligation but also as a socio-economic instrument for wealth
redistribution and poverty alleviation. However, zakat institutions have long faced inefficiencies in
identifying eligible recipients, ensuring transparency, and maintaining reliable reporting mechanisms. These
challenges underscore the urgent need for technological innovation that complements the principles of
Islamic ethics and accountability (Beik et al., 2021).
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Previous studies indicate that AI has significantly enhanced organizational efficiency across industries,
including project management, strategic decision-making, and zakat operations by improving identification
accuracy and institutional trust (Kozhakhmetova et al., 2024; Awang Abu Bakar et al., 2024). For example,
BAZNAS in Indonesia has utilized AI technologies such as the Rice Automatic Telling Machine (ATM
Beras) and structured digital identification systems to ensure more efficient zakat fund allocation (Beik et al.,
2021). Similarly, Farrokhvar et al. (2018) demonstrated that predictive analytics and machine-learning
models can effectively forecast charitable giving behaviour, suggesting similar potential for zakat institutions
to anticipate donor intentions and optimise online collection strategies. These advancements underscore the
potential of AI in reshaping zakat operations by embedding data-driven approaches into institutional
practices.
Despite these positive developments, the adoption of AI in Zakat management remains limited and
fragmented. Persistent challenges include issues of data quality, resistance to organizational change, and
ethical considerations such as fairness and transparency in decision-making (Hangl et al., 2023).
Furthermore, while studies acknowledge the broad advantages of AI in finance and management, here
remains a lack of integrated frameworks exploring how AI adoption, when combined with data-driven
decision-making, can systematically improve zakat management efficiency (Ashurov et al., 2020; Ashurov et
al., 2022). This study addresses this gap by proposing a conceptual framework that illustrates the relationship
between AI adoption, data-driven decision-making, and zakat management efficiency, thereby offering a
structured pathway for enhancing institutional effectiveness.
The significance of this study lies in aligning technological innovation with the principles of Islamic social
finance. By adopting AI-driven solutions, zakat institutions can enhance operational efficiency, foster greater
transparency, and improve trust among stakeholders. These elements are particularly important because they
represent key factors that contribute to strengthening governance and increasing the socio-economic impact
of zakat distribution (Hadi et al., 2024). More importantly, the proposed framework emphasizes how
datadriven decision-making mediates the link between AI adoption and improved efficiency, ensuring
evidencebased, equitable, and Shariah-compliant fund allocation. This integration of technology with
religious obligations strengthens the credibility of zakat institutions, thereby encouraging higher compliance
among payers and maximizing contributions toward poverty alleviation and sustainable development goals.
Theoretically, this study adopts General Systems Theory (Von Bertalanffy, 1968) to explain how AI
technologies and decision-making processes function as interdependent components of a larger
sociotechnical system. The conceptual framework integrates predictive analytics, real-time monitoring, and
machine learning as tools to optimize zakat collection, distribution, and reporting. The paper is structured as
follows: the introduction outlines the context and research problem; the literature review synthesizes relevant
studies on AI and zakat; the conceptual framework illustrates the relationships between AI adoption,
datadriven decision-making, and zakat management efficiency; the methodology describes the research
design; the findings and discussion present key implications; and the conclusion highlights the study’s
contributions and directions for future research. By doing so, this paper provides both theoretical insights and
practical recommendations for leveraging AI in zakat management.
To address the identified research gaps, it is essential to examine previous studies that explore how
technological innovations particularly Artificial Intelligence (AI) and data-driven decision-making (DDDM)
influence the efficiency, transparency, and governance of zakat institutions. From an Islamic perspective, the
integration of AI should not only optimise operational outcomes but also uphold the ethical principles of
maqaṣid al-shariʿah, ensuring justice (ʿadl), fairness, and equitable wealth distribution. Accordingly, the
following section reviews the theoretical foundations, empirical findings, and ethical considerations that
underpin the development of the proposed Shariah-aligned conceptual framework for enhancing zakat
management efficiency.
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LITERATURE REVIEW
The adoption of Artificial Intelligence (AI) in zakat management is influenced by a combination of
technological, organizational, and environmental factors. Prior studies applying the Technology
Organization-Environment (TOE) framework highlight that relative advantage, system compatibility,
technological complexity, top management support, and organizational readiness significantly shape AI
adoption (Khan et al., 2024; Pathak & Bansal, 2024). In zakat institutions, these determinants are especially
crucial given the dual requirement of ensuring both technological innovation and compliance with Shariah
principles. For example, the implementation of AI tools for donor and recipient identification demonstrates
the importance of organizational readiness and infrastructure support to achieve institutional goals efficiently.
Data-driven decision-making (DDDM) has emerged as a critical enabler of efficiency in zakat management.
By leveraging advanced data analytics, predictive models, and machine learning algorithms, institutions can
improve governance, optimize resource allocation, and enhance transparency in distribution (Awang Abu
Bakar et al., 2024). In this context, DDDM strengthens institutional accountability by transforming raw data
into actionable insights, supporting evidence-based strategies in collection and disbursement. Machine
learning models, for instance, can predict zakat payer intentions with high accuracy, thereby streamlining
online zakat collection and improving donor engagement. This illustrates how DDDM mediates the
relationship between AI adoption and improved zakat management efficiency.
Nevertheless, integrating AI into zakat management is not without challenges. Issues related to data quality,
employee resistance, lack of technical expertise, and limited institutional trust in AI remain persistent barriers
(Hangl et al., 2023). Ethical concerns further complicate adoption, as biases in datasets, lack of transparency
in automated decision-making, and fairness in fund allocation raise questions about accountability and
legitimacy (Ferrell et al., 2024). Overcoming these barriers requires not only technical solutions but also
organizational change management and strong regulatory frameworks to ensure that AI adoption aligns with
both efficiency goals and Islamic ethical principles.
From a conceptual standpoint, the relationship between AI adoption, DDDM, and zakat management
efficiency can be illustrated as an interdependent framework. AI adoption provides the technological
infrastructure, while DDDM mediates by converting data into strategic insights that enhance operational
transparency, trust, and efficiency. The synergy of these elements creates opportunities for zakat institutions
to improve governance, reduce costs, and enhance stakeholder confidence (Panduro-Ramirez et al., 2023).
However, realizing this potential requires addressing both structural and ethical challenges, ensuring that
AIdriven zakat management not only improves efficiency but also upholds the fairness and justice central to
Islamic social finance.
METHODOLOGY
Research Design
This study adopts a narrative review methodology to synthesize existing literature and propose a conceptual
framework that illustrates the relationship between Artificial Intelligence (AI) adoption, data-driven
decisionmaking (DDDM), and zakat management efficiency. Unlike systematic reviews that rely on rigid
protocols, narrative reviews emphasize a broader and more flexible approach in analyzing, summarizing, and
integrating findings from diverse sources (Snyder, 2019). The narrative review is particularly suited to this
study as it enables the exploration of theoretical, empirical, and contextual insights regarding AI adoption
and its applications in Islamic social finance.
The design of this narrative review is underpinned by a conceptual orientation, aiming to identify themes,
relationships, and research gaps in the existing literature. The focus is not merely descriptive but also
analytical, seeking to align technological developments in AI with the socio-religious requirements of zakat
management. The narrative review design enables the integration of perspectives across domains such as
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information systems, decision sciences, and Islamic economics, which is essential to build a comprehensive
conceptual framework (Ferrari, 2015; Green et al., 2006).
Key Steps in Conducting a Narrative Review
The data collection for this study was conducted primarily using the Scopus database, selected due to its wide
coverage of peer-reviewed and high-impact journals across multidisciplinary fields. Scopus is recognized for
its comprehensive indexing of academic works, particularly in management, information technology, and
Islamic finance, thereby ensuring access to credible and authoritative sources. The search strategy employed
relevant keywords and Boolean operators such as “artificial intelligence adoption”, “data-driven
decisionmaking”, “zakat management”, “Islamic finance efficiency”, and combinations thereof. To ensure
relevance and rigor, only publications indexed between 2019 and 2024 were considered, aligning with the
recency of AI applications in financial and social welfare contexts.
The narrative review process involved several key steps. First, identification of relevant articles was carried
out using Scopus search strings, and duplicates were removed. Second, screening and eligibility checks were
applied, focusing on journal articles, conference proceedings, and book chapters directly addressing AI
adoption, data-driven approaches, and Islamic finance applications. Third, data extraction was conducted by
coding key information such as study objectives, methodologies, findings, and implications. Finally,
synthesis and interpretation were performed, whereby insights were compared, contrasted, and organized
thematically to highlight determinants of AI adoption, the mediating role of DDDM, and its impact on zakat
management efficiency. This multi-step process ensured that the review was comprehensive, coherent, and
aligned with the aim of developing a conceptual framework (Snyder, 2019; Green et al., 2006).
The methodological approach thus provides a structured yet flexible means of consolidating knowledge,
identifying research gaps, and developing theoretical propositions. Through the use of a narrative review
with Scopus as the main data source, the study not only draws on robust academic literature but also
contextualizes the findings within the framework of Islamic social finance. This methodology ensures that
the conceptual framework proposed is grounded in scholarly evidence while addressing the unique
requirements of zakat management. This process is illustrated in Figure 1, which summarizes the narrative
review steps adopted in this study:
Figure 1: Narrative Review Process for Developing the Conceptual Framework
Data Collection and Review Strategy
The data collection for this study was carried out using the Scopus database as the primary source due to its
comprehensive coverage of peer-reviewed journals, conference proceedings, and book chapters across
multidisciplinary domains. A structured search strategy was employed, incorporating Boolean operators and a
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carefully designed search string to ensure inclusivity of relevant literature. The following search string was
applied:
("artificial intelligence" OR "AI" OR "machine learning" OR "deep learning") AND ("adoption" OR
"implementation" OR "integration" OR "utilization") AND ("zakat" OR "charity" OR "almsgiving" OR
"donation") AND ("management" OR "efficiency" OR "effectiveness" OR "performance")
This search string was designed to capture studies at the intersection of AI adoption, data-driven
decisionmaking, and zakat or charity-related management practices. The terms ensured coverage of both
technological concepts (e.g., AI, machine learning, deep learning) and institutional outcomes (e.g., efficiency,
effectiveness, performance). To maintain academic rigor, the review was limited to studies published
between 2019 and 2024, reflecting the recency of AI applications in financial technology and Islamic social
finance. Only English-language publications indexed in Scopus were included to ensure quality and
accessibility.
The review process followed three systematic steps: identification, screening, and synthesis. During the
identification phase, all articles retrieved using the search string were compiled. In the screening phase,
duplicates, non-academic sources, and studies irrelevant to the focus of AI adoption or zakat management
were excluded. Articles were further assessed based on titles, abstracts, and keywords to ensure alignment
with the study’s objectives. Finally, full-text reviews were conducted on shortlisted articles, with data
extraction focusing on author details, study context, methodology, findings, and theoretical perspectives.
To analyze the collected literature, this study employed an integrative thematic analysis approach. Unlike
traditional thematic analysis that may be confined to qualitative studies, integrative thematic analysis allows
for the synthesis of findings across diverse methodological traditions, including empirical, conceptual, and
theoretical works (Snyder, 2019). The approach consisted of three phases: (1) familiarization and coding, (2)
theme development, and (3) synthesis and interpretation. In the first phase, recurring patterns, keywords, and
constructs related to AI adoption, data-driven decision-making, and zakat management efficiency were
coded. In the second phase, these codes were clustered into higher-order themes, such as determinants of AI
adoption, mediating role of DDDM, ethical considerations, and institutional efficiency outcomes. Finally, in
the synthesis phase, themes were compared and integrated to construct a holistic understanding of how AI
adoption, mediated by data-driven decision-making, influences zakat management efficiency.
The combination of a rigorous search strategy and integrative thematic analysis ensured that this narrative
review was both comprehensive and conceptually robust. By drawing on a wide range of studies across
information systems, management, and Islamic finance, the review identified key themes, conceptual
linkages, and theoretical perspectives that inform the development of the proposed conceptual framework.
Key Findings from the Narrative Review
Table 1 Key Findings from the Narrative Review
Theme / Component
Description of Findings
Key References
AI Adoption in
Zakat Management
AI technologies (e.g., predictive analytics, chatbots,
realtime monitoring) enhance efficiency in zakat collection
and distribution. BAZNAS uses AI to generate
donor/recipient IDs and implement automated distribution
such as ATM Beras.
Beik et al., 2021
Theme / Component
Description of Findings
Key References
Data-Driven
Decision-Making
(DDDM)
Predictive analytics and machine learning enable zakat
institutions to analyze donor behavior, predict payer
intentions, and optimize online zakat collection. Improved
data management enhances transparency and
Awang Abu Bakar et
al., 2024
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accountability.
Efficiency
Improvements
Integration of AI and DDDM increases efficiency by
automating complex processes, ensuring timely reporting,
and improving organizational performance. It also
strengthens trust and enhances governance in zakat
institutions.
Kozhakhmetova et
al.,
2024; Ashurov et al.,
2020; Ashurov et al.,
2022
Challenges of AI
Integration
Barriers include poor data quality, resistance to
organizational change, lack of expertise, and limited trust in
AI-driven systems. These issues hinder effective adoption
in zakat institutions.
Hangl et al., 2023;
Mkhize et al., 2023
Integrating AI and
DDDM within zakat
institutions
The necessity for ethical governance in AI deployment,
advocating for transparency, accountability, and fairness as
guiding principles.
Khan et al., 2024;
Ferrell et al., 2024
Ethical
Considerations
Concerns include data bias, fairness, privacy, transparency,
and accountability of AI-driven decisions. Ethical
governance frameworks are needed to ensure Shariah
compliance and just outcomes.
Ferrell et al., 2024;
Panduro-Ramirez et
al.,
2023
The findings of the narrative review highlight that AI adoption in zakat management offers significant
potential in improving both collection and distribution processes. Tools such as predictive analytics, chatbots,
and real-time monitoring not only streamline operational tasks but also improve accuracy in identifying
eligible recipients. A practical example can be seen in BAZNAS Indonesia, which utilizes AI for donor and
recipient identification alongside innovative distribution technologies like ATM Beras (Beik et al., 2021).
Equally important, the role of data-driven decision-making (DDDM) emerges as a critical mediator between
AI adoption and management efficiency. Advanced data analytics and machine learning models enable zakat
institutions to analyze donor patterns, predict zakat payer intentions, and optimize online collection channels.
These applications strengthen transparency, accountability, and evidence-based decision-making in zakat
administration (Awang Abu Bakar et al., 2024).
The integration of AI and DDDM translates directly into efficiency improvements across zakat institutions.
By automating complex processes and enabling real-time monitoring, these technologies enhance
organizational performance, improve public trust, and ensure more equitable fund allocation
(Kozhakhmetova et al., 2024; Ashurov et al., 2020, 2022). However, the review also underscores the
challenges of AI integration, including poor data quality, organizational resistance, and lack of technical
expertise, which remain significant barriers to implementation (Hangl et al., 2023; Mkhize et al., 2023).
Integrating Artificial Intelligence (AI) and data-driven decision-making (DDDM) within zakat institutions
necessitates adherence to Islamic ethical principles to ensure justice (ʿadl) and the avoidance of bias or
uncertainty (gharar). From a Shariah perspective, algorithmic fairness is essential to guarantee that
automated systems do not inadvertently disadvantage specific groups of zakat recipients or donors. Bias in
data collection, model training, or algorithmic outputs may lead to unjust allocation, contradicting the
Qur’anic command to give everyone their due right (al-Qur’an, 16:90).
To ensure compliance, AI algorithms applied in zakat management should be designed to operate
transparently, incorporating explainable AI (XAI) methods that allow for human oversight and ethical
auditing. Such systems should use validated datasets that represent diverse socio-economic profiles of zakat
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recipients to prevent discriminatory outcomes. Furthermore, Shariah governance frameworks can be
embedded into the data lifecycle, from data acquisition to decision deployment ensuring that every analytical
decision aligns with the maqaṣid al-shariʿah, particularly ḥifẓ al-mal (protection of wealth) and ḥifẓ al-ʿadl
(preservation of justice).
Scholars such as Khan et al. (2024) and Ferrell et al. (2024) have emphasized the necessity for ethical
governance in AI deployment, advocating for transparency, accountability, and fairness as guiding principles.
Translating these principles into the Islamic context, zakat institutions can implement Shariah-supervised
data protocols and algorithmic audits (hisbah al-taṭbiqat al-dhakiyyah) to ensure that decision outcomes
remain equitable and free from gharar or ẓulm (injustice). This integration of AI ethics with Shariah values
forms the foundation for a trustworthy, spiritually aligned technological ecosystem within Islamic social
finance.
Finally, ethical considerations represent a crucial dimension in adopting AI for zakat management. Issues
such as data bias, privacy, transparency, and fairness must be addressed to ensure the responsible use of AI. A
robust governance framework is essential to align AI-driven decision-making with Shariah principles and
guarantee fair, just outcomes for recipients and stakeholders alike (Ferrell et al., 2024; Panduro-Ramirez et
al., 2023). Collectively, these findings provide the foundation for proposing a conceptual framework that
links AI adoption, DDDM, and zakat management efficiency.
Theoretical Framework Development
The theoretical foundation of this study is grounded in General Systems Theory (GST), which posits that an
organization can be understood as an interconnected system where technological, social, and organizational
subsystems interact to achieve efficiency and balance (Von Bertalanffy, 1968). GST provides a lens to
explain how the adoption of Artificial Intelligence (AI) technologies can influence organizational outcomes
by functioning as a catalyst for systemic improvements. In the context of zakat institutions, GST helps to
conceptualize zakat management as a complex socio-technical system in which AI adoption and data-driven
decision-making (DDDM) interact to produce enhanced efficiency, transparency, and accountability. By
employing GST, this study positions AI not merely as a technological tool, but as a subsystem that
harmonizes with organizational processes, human decision-making, and ethical governance.
Applying GST in this study enables a holistic understanding of how AI adoption contributes to efficiency in
zakat management through the mediating role of DDDM. The conceptualization of the framework illustrates
that AI adoption (e.g., predictive analytics, machine learning, real-time monitoring) strengthens the capacity
of zakat institutions to collect and distribute funds efficiently, while DDDM serves as the mechanism through
which raw data is transformed into actionable insights for decision-making (Awang Abu Bakar et al., 2024).
This mediating role bridges the technological input with organizational outcomes, ensuring that zakat
distribution is not only operationally efficient but also ethically aligned with principles of transparency and
fairness (Ferrell et al., 2024). By synthesizing literature across information systems, management, and
Islamic social finance, the framework integrates technological determinants of AI adoption with socio-
religious imperatives of zakat, offering a comprehensive theoretical model.
The integration of GST and empirical insights provides both theoretical and practical implications.
Theoretically, the framework extends prior research on AI adoption by emphasizing its role in faith-based
social finance, where efficiency is inseparable from ethical and religious considerations (Khan et al., 2024;
Hangl et al., 2023). Practically, the framework offers zakat institutions a structured pathway to adopt AI
responsibly, ensuring that efficiency gains are balanced with accountability, transparency, and trust. It
provides policymakers with a model to design guidelines that facilitate responsible AI integration in zakat
systems, while practitioners can utilize the framework to strengthen operational processes and enhance
stakeholder confidence. In conclusion, the theoretical framework consolidates AI adoption, DDDM, and
zakat management efficiency into a systemic model that not only advances scholarly discourse but also offers
practical strategies for improving Islamic social finance management in the digital era.
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The following figure illustrates the conceptual integration of these components, demonstrating how AI
adoption interacts with data-driven decision-making to enhance zakat management efficiency:
Figure 2: Theoretical Framework of the Relationship between AI Adoption, Data-Driven Decision-
making and Zakat Management Efficiency
PROPOSITION DEVELOPMENT
Building upon the conceptual relationships established in Section 3.5, the following propositions are
formulated.
Proposition Development: AI Adoption Affects Data-Driven Decision-Making
The adoption of Artificial Intelligence (AI) enhances the capacity of organizations to engage in effective
datadriven decision-making. As shown by Ines et al. (2024), AI tools such as machine learning and data
analytics enable enterprises to transform large volumes of data into actionable insights that improve strategic
and operational decisions. In the zakat context, AI facilitates the accurate identification of donors and
recipients, forecasts payer behaviour, and optimises collection processes, thereby strengthening evidence-
based and transparent decision-making (Beik et al., 2021; Awang Abu Bakar et al., 2024). Hence, AI
adoption is expected to positively influence DDDM in zakat institutions.
Proposition Development: Data-Driven Decision-Making Affects Zakat Management Efficiency
DDDM plays a pivotal role in enhancing zakat management efficiency by converting data into insights that
support transparency, accountability, and optimal resource allocation. Effective data management practices,
as highlighted by Bakar et al. (2024), underpin strategies that ensure accurate and equitable fund distribution.
Consistent with findings in Malaysian zakat institutions, structured data utilisation improves governance and
operational decision-making, leading to higher efficiency in collection and distribution (Awang Abu Bakar et
al., 2024). Therefore, DDDM is proposed to have a positive effect on zakat management efficiency.
Proposition Development: AI Adoption Affects Zakat Management Efficiency
AI adoption contributes directly to zakat management efficiency through process automation, improved
accuracy, and greater transparency. Empirical studies have demonstrated that AI-enabled systems enhance
organisational performance and decision quality across multiple sectors (Kozhakhmetova et al., 2024; Xiao et
al., 2024). Within zakat institutions, such technologies minimise administrative delays, strengthen public
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trust, and maximise social impact in line with Islamic social finance objectives (Ashurov et al., 2020;
Ashurov et al., 2022). Accordingly, AI adoption is proposed to have a direct positive influence on zakat
management efficiency.
Proposition Development: AI Adoption Affects Data-Driven Decision-Making Affects Zakat
Management Efficiency
The relationship between AI adoption and zakat management efficiency is mediated by DDDM. AI
technologies enable institutions to collect and analyse large datasets, providing insights that guide fund
allocation, donor engagement, and beneficiary identification (Awang Abu Bakar et al., 2024). When these
insights are embedded into structured decision-making, operational efficiency and accountability improve.
Prior research indicates that efficiency gains from AI depend on the presence of robust data-driven processes
that translate technological inputs into actionable strategies (Usmani et al., 2023). Therefore, DDDM
mediates the effect of AI adoption on zakat management efficiency.
CONCLUSIONS
This study has explored the relationship between Artificial Intelligence (AI) adoption, data-driven
decisionmaking, and zakat management efficiency, resulting in the development of a conceptual framework
that integrates these dimensions. The key findings indicate that AI adoption plays a critical role in automating
processes, enhancing transparency, and providing real-time insights, while data-driven decision-making
mediates this relationship by transforming technological inputs into actionable strategies that improve
operational efficiency. Together, these elements contribute to more effective zakat collection, distribution,
and reporting, ultimately strengthening trust and accountability in Islamic social finance institutions.
Theoretically, the framework enriches existing scholarship by demonstrating how technological innovation
intersects with decision-making theories to improve institutional efficiency in the context of zakat
management. It extends the application of systems theory by showing how AI functions as an enabler of
structured decision-making, thereby advancing the understanding of technology’s role in enhancing
governance within faith-based financial institutions. This theoretical contribution provides a foundation for
future empirical studies that seek to validate the mediating role of data-driven decision-making.
In terms of practical implications, the framework offers actionable insights for zakat institutions and
policymakers. By adopting AI-enabled solutions and embedding data-driven decision-making practices,
institutions can significantly enhance efficiency, reduce administrative bottlenecks, and improve donor and
beneficiary satisfaction. Furthermore, this approach aligns technological adoption with the ethical and social
objectives of zakat, ensuring that resources are distributed equitably and with greater transparency. The
framework also provides a roadmap for practitioners to integrate emerging technologies consistent with the
principles of Islamic social finance.
Despite these contributions, the study is not without limitations. The conceptual framework is developed
primarily through a narrative review, meaning its propositions remain theoretical and require empirical
validation across diverse contexts. Moreover, potential challenges such as data quality, institutional readiness,
and ethical considerations in AI adoption remain areas of concern. Future research should therefore test the
framework using quantitative and qualitative approaches, examine variations across countries and zakat
institutions, and explore strategies for addressing ethical and operational challenges in AI-enabled zakat
management.
In addition to enhancing operational efficiency, the integration of AI and DDDM within zakat institutions
must be guided by Shariah-compliant principles to maintain ethical integrity. Future frameworks should
incorporate algorithmic transparency, bias mitigation, and Shariah governance mechanisms that align with
maqaṣid al-shari’ah. By ensuring fairness (ʿadl) and avoiding uncertainty or bias (gharar), AI-driven
systems can strengthen trust among stakeholders and reinforce the moral foundations of zakat management,
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transforming technological advancement into a means of achieving socio-economic justice in accordance
with Islamic values.
ACKNOWLEDGMENT
The authors would like to express their sincere gratitude to the Kedah State Research Committee, UiTM
Kedah Branch, for the generous funding provided under the Tabung Penyelidikan Am. This support was
crucial in facilitating the research and ensuring the successful publication of this article.
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