Digital effectiveness and adoption intention in Islamic banking: Evidence from Saudi Arabia, the UAE, and Jordan

  • 16 Views
  • 1 Downloads

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

Type of the article: Research Article

Abstract
Rapid digitalization is redefining how consumers evaluate Islamic banks, where technological progress must align with Shariah principles to ensure transparency, fairness, and credibility. In this context, digital marketing serves as a critical bridge between technological innovation and ethical communication. This study investigates how digital marketing effectiveness shapes trust and engagement, and how these factors, in turn, influence adoption intention in Islamic banking. It further examines the moderating role of religiosity and compares structural relationships across Saudi Arabia, the United Arab Emirates, and Jordan. A quantitative, cross-sectional survey conducted from January to April 2025 collected data from 824 clients of Islamic banks (Saudi Arabia = 297, United Arab Emirates = 205, Jordan = 322). The data were analyzed using partial least squares structural equation modeling, measurement invariance testing, multi-group analysis, and moderation-mediation procedures. All respondents were Muslim account holders who had interacted with an Islamic bank’s digital marketing campaign within the preceding six months. Digital marketing effectiveness significantly increased trust (β = 0.662, t = 15.42) and engagement (β = 0.628, t = 13.88). Adoption intention was jointly predicted by trust (β = 0.422, t = 10.17) and engagement (β = 0.377, t = 9.83), explaining 60.2 percent of the variance. Religiosity strengthened both relationships, with stronger effects in Saudi Arabia and the United Arab Emirates than in Jordan. Transparent, interactive, and ethically consistent digital marketing enhances trust and engagement, providing the behavioral foundation for Islamic digital banking adoption.

view full abstract hide full abstract
    • Figure 1. Conceptual framework
    • Table 1. Demographic profile of respondents
    • Table 2. Reliability and convergent validity results
    • Table 3. HTMT matrix
    • Table 4. Multicollinearity assessment (VIF values)
    • Table 5. Predictive accuracy (R² and Q²)
    • Table 6. Structural path coefficients
    • Table 7. Effect sizes (f²) of structural paths
    • Table 8. Descriptive statistics of constructs
    • Table 9. MICOM results for measurement invariance
    • Table 10. MGA test framework
    • Table 11. Comparison of path coefficients across countries (PLS-MGA)
    • Table 12. Country-specific predictive accuracy (R² and Q²)
    • Table 13. Predictive relevance (Q²predict) and error estimates
    • Table 14. Mean differences by religiosity segment
    • Table 15. Moderating effects of religiosity
    • Table 16. Mediation analysis
    • Table 17. Importance-performance matrix analysis (target = adoption intention)
    • Table 18. Model-fit statistics
    • Table 19. Robustness and cross-validation statistics
    • Conceptualization
      Amer Morshed, Ayman Bader
    • Data curation
      Amer Morshed
    • Formal Analysis
      Amer Morshed, Ayman Bader, Hanadi A. Salhab
    • Investigation
      Amer Morshed, Ayman Bader
    • Methodology
      Amer Morshed, Ayman Bader, Hanadi A. Salhab
    • Project administration
      Amer Morshed
    • Resources
      Amer Morshed
    • Software
      Amer Morshed, Ayman Bader
    • Supervision
      Amer Morshed
    • Validation
      Amer Morshed, Ayman Bader
    • Visualization
      Amer Morshed, Ayman Bader
    • Writing – original draft
      Amer Morshed, Hanadi A. Salhab
    • Writing – review & editing
      Ayman Bader
    • Funding acquisition
      Hanadi A. Salhab