Hyper-personalization, artificial intelligence, and customer loyalty in Islamic banking: The mediating role of Minangkabau cultural congruence

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Type of the article: Research Article

Abstract
This study addresses the growing importance of integrating advanced digital technologies with local cultural values in enhancing customer loyalty in Islamic banking. The study aims to examine the mediating role of Minangkabau culture in the relationship between hyper-personalization, artificial intelligence (AI), and customer loyalty. A quantitative cross-sectional survey was conducted among 206 Islamic bank customers in Padang City, Indonesia, during 2025. Data were collected using structured questionnaires distributed via online platforms and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results reveal that hyper-personalization (β = 0.387, p < 0.001) and AI (β = 0.183, p < 0.01) have significant positive effects on customer loyalty. Both variables also significantly influence Minangkabau culture (β = 0.360 and β = 0.369, respectively), which in turn has a strong effect on loyalty (β = 0.409, p < 0.001). The mediating analysis confirms that Minangkabau culture significantly mediates the relationships between hyper-personalization and loyalty (β = 0.147, p < 0.01) as well as AI and loyalty (β = 0.151, p < 0.01). The model demonstrates substantial explanatory power, with an R² value of 0.773 for customer loyalty. The findings indicate that aligning digital personalization and AI with local cultural values enhances customer trust and strengthens long-term loyalty. This study highlights the importance of culturally embedded digital strategies in Islamic banking and provides practical insights for developing ethically grounded and culturally relevant financial services.

Acknowledgement
This research was funded by Yayasan UPI YPTK Padang under the Penelitian Unggulan Grant 2024/2025 (Contract No. 062/UPI-YPTK/LPPM/P/KP/VII/2025). The authors gratefully acknowledge the financial support provided for this study.

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    • Figure 1. Research framework
    • Figure 2. Outer loading after elimination
    • Table 1. Respondent characteristics
    • Table 2. Results of Average Variance Extracted (AVE) test
    • Table 3. Results of construct reliability test
    • Table 4. R-square results
    • Table 5. Result of hypothesis testing – direct effects
    • Table 6. Result of hypothesis testing – indirect effects
    • Conceptualization
      Susriyanti, Fitri Yeni
    • Data curation
      Susriyanti, Fitri Yeni
    • Formal Analysis
      Susriyanti, Fitri Yeni
    • Funding acquisition
      Susriyanti, Fitri Yeni
    • Investigation
      Susriyanti, Fitri Yeni
    • Methodology
      Susriyanti, Fitri Yeni
    • Project administration
      Susriyanti, Fitri Yeni
    • Resources
      Fitri Yeni
    • Software
      Fitri Yeni
    • Supervision
      Fitri Yeni
    • Validation
      Fitri Yeni
    • Visualization
      Fitri Yeni
    • Writing – original draft
      Fitri Yeni
    • Writing – review & editing
      Fitri Yeni