Understanding Member Adoption Decisions in Micro-Islamic Financing: The Interplay of Knowledge, Service Quality, and Margin Perceptions - Syafa Khoirur Rizal; Mutia Pamikatsih (Institut Agama Islam Syubbanul Wathon Magelang, 2026)
This study aims to analyze the determinants influencing members' adoption decisions regarding Murabahah financing at KSPPS BMT BIMA Temanggung, focusing on the interplay of customer knowledge, service quality, and perceived margin levels. Utilizing a quantitative design, primary data were collected via structured questionnaires from 85 active members selected through a purposive sampling mechanism. Quantitative estimation was executed using multiple linear regression under the Ordinary Least Squares (OLS) framework via EViews 13 software. The empirical diagnostics indicate that all three independent variables exert a positive and statistically significant partial effect on members' financing adoption choices. Under the analytical lens of social exchange theory, members' knowledge acts as a form of social capital that compresses information asymmetry costs (p = 0.013), while superior service quality functions as a relational reward that stimulates organizational attachment (p = 0.039). Crucially, fair perceived margin levels are framed not merely as economic utility but as an expression of distributive justice within social exchange, guaranteeing non-exploitative reciprocity and psychological equity (p = 0.014). Simultaneously, the F-test confirms that all predictors concurrently direct the consumer decision-making process (p = 0.000), with an adjusted R-squared value demonstrating that the structural model successfully accounts for 60.08% of the total behavioral variance. This study concludes that a balanced microfinance ecosystem driven by information transparency, relational rewards, and equitable pricing architectures accelerates consumer adoption. Future scholarly efforts are encouraged to incorporate external variables such as localized religiosity and digital fintech readiness to expand model generalizability.