A Mixed-Methods Methodology for Constructing Integrated Shariah and Economic Evaluation Frameworks
DOI:
https://doi.org/10.61212/Keywords:
Diabetes mellitus, Machine learning, Data mining, Predictive modelAbstract
This study develops a rigorous methodological foundation for constructing an integrated evaluation framework for Islamic financial products, addressing the longstanding absence of standardized, replicable, and analytically coherent assessment tools in the field. Existing literature largely separates Shariah compliance analysis from economic performance evaluation, resulting in fragmented methodologies and inconsistent regulatory practices. This study adopts a mixed-methods design that combines deductive reasoning rooted in Islamic jurisprudence with inductive refinement derived from empirical contract analysis. The methodological structure integrates qualitative tools—such as clause-based checklists and structured suspicion scoring—with quantitative financial indicators capturing risk, liquidity, return, and capital preservation. The paper demonstrates how theoretical assumptions are transformed into conceptual components and operational procedures within a dual-pillar system comprising Shariah compliance and economic efficiency. The proposed methodology allows Shariah boards, regulatory authorities, institutional investors, and financial analysts to evaluate Islamic financial products using standardized tools rather than case-specific judgments. This contributes to reducing interpretive inconsistency across products and facilitating more transparent decision-making processes for market participants. The framework also provides a replicable foundation that can be expanded across asset classes and regulatory contexts.
Keywords: Economic Efficiency; Shariah Compliance; Mixed-Methods; Islamic Financial Product Evaluation; Suspicious Elements; Evaluation Frameworks.
References
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Scientific Development for Studies and Research (JSD)

This work is licensed under a Creative Commons Attribution 4.0 International License.