Drivers of Islamic financial inclusion among non-Muslims: A rational-emotional-spiritual approach
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DOIhttp://dx.doi.org/10.21511/bbs.21(2).2026.12
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Article InfoVolume 21 2026, Issue #2, pp. 163-175
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Type of the article: Research Article
Abstract
Islamic finance plays a crucial role in promoting inclusive and sustainable economic growth, particularly in emerging economies such as Indonesia. Despite the rapid expansion of Islamic banking, participation from non-Muslim consumers remains relatively low, revealing a gap in understanding the motivational factors that drive their engagement. This study aims to analyze how the Islamic Product Brand Image influences Islamic Financial Inclusion among non-Muslim consumers through rational, emotional, and spiritual motivational factors. A quantitative research approach was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 384 non-Muslim respondents who intend to use Islamic banking products in Indonesia. The analysis included tests of validity, reliability, and hypothesis using bootstrapping procedures. The results show that Islamic Product Brand Image does not directly affect Islamic Financial Inclusion (β = –0.187, p = 0.258), but has a significant indirect effect through Product Features (β = 0.164, p = 0.045) and Religiosity (β = 0.200, p = 0.031). Sustainable Finance, representing emotional motivation, was found to be insignificant (β = 0.093, p = 0.348). These findings indicate that rational and spiritual motivations are stronger determinants than emotional ones in influencing non-Muslim consumers’ inclusion in Islamic banking. Theoretically, this study contributes to understanding cross-religious financial inclusion by integrating multidimensional motivational constructs. Practically, it suggests that Islamic banks should emphasize product innovation, transparency, and ethical trust-building to enhance inclusivity across faith boundaries.
- Keywords
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JEL Classification (Paper profile tab)G41, G21
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References51
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Tables8
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Figures1
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- Figure 1. Conceptual model
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- Table 1. Sample demographics
- Table 2. Reliability and convergent validity indicators
- Table 3. Discriminant validity using the Fornell-Larcker criterion
- Table 4. VIF – Inner model
- Table 5. R-square
- Table 6. Model fit
- Table 7. SEM results with direct effects
- Table 8. SEM output with indirect mediation effect
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