Entrepreneurial business intelligence and green innovation in international banks: The mediating role of sustainability practices

  • 3 Views
  • 0 Downloads

Creative Commons License DMCA.com Protection Status
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
The study aims to examine the relationship between entrepreneurial business intelligence and green innovation, as well as the mediating effect of sustainability practices. Data were collected in July and August 2025, focusing on managers and departmental heads at seven major international banks: HSBC, Citibank, Standard Chartered, Deutsche Bank, Barclays, Bank of America, and BNP Paribas. A total of 400 questionnaires were distributed online, of which 378 valid responses were retained for analysis, and participants were mainly from selected branches and regional offices in Europe and North America. PLS-SEM was conducted using SmartPLS 4. The findings showed that entrepreneurial business intelligence had a positive and statistically significant effect on green innovation (β = 0.228, p < 0.001). Additionally, entrepreneurial business intelligence exerted a significant positive effect on sustainability practices (β = 0.365, p < 0.001). Sustainability practices also exerted a positive and statistically significant effect on green innovation (β = 0.405, p < 0.001). Sustainability practices were found to mediate the relationship between entrepreneurial business intelligence and green innovation, resulting in a significant indirect effect (β = 0.148, p < 0.001). Moreover, the total effect of entrepreneurial business intelligence on green innovation was positive (β = 0.376, p < 0.001). The findings imply that international banks leveraging data-driven entrepreneurial business intelligence together with sustainability practices are better positioned to enhance green innovation in support of green finance.

view full abstract hide full abstract
    • Figure 1. Structural model
    • Table 1. Organizational and demographic characteristics of the respondents
    • Table 2. Common method bias assessment
    • Table 3. Item-level descriptive statistics
    • Table 4. Measurement model assessment
    • Table 5. Fornell–Larcker criterion
    • Table 6. HTMT ratios and bootstrap confidence intervals
    • Table 7. Inner-model VIF values
    • Table 8. ICC and design-effect diagnostics
    • Table 9. Structural path estimates
    • Table 10. Indirect effect and mediation assessment
    • Table 11. Explanatory and predictive relevance
    • Table 12. Effect sizes
    • Table 13. Bank- and country-adjusted sensitivity analysis
    • Table 14. Control-variable model
    • Table 15. Country-level measurement invariance assessment
    • Table 16. PLSpredict-style construct-score assessment
    • Table 17. Alternative model comparison
    • Table A1. Measurement scale
    • Conceptualization
      Fawwaz Tawfiq Awamleh, Hussein M. AL Hawamdeh, Ahmad Albloush
    • Funding acquisition
      Fawwaz Tawfiq Awamleh, Ahmad Albloush
    • Investigation
      Fawwaz Tawfiq Awamleh
    • Resources
      Fawwaz Tawfiq Awamleh
    • Software
      Fawwaz Tawfiq Awamleh, Ahmad Albloush
    • Validation
      Fawwaz Tawfiq Awamleh, Hussein M. AL Hawamdeh
    • Writing – original draft
      Fawwaz Tawfiq Awamleh
    • Data curation
      Sami F. Aldejwi
    • Formal Analysis
      Sami F. Aldejwi, Hussein M. AL Hawamdeh
    • Methodology
      Sami F. Aldejwi
    • Project administration
      Sami F. Aldejwi, Ahmad Albloush
    • Writing – review & editing
      Sami F. Aldejwi, Hussein M. AL Hawamdeh
    • Visualization
      Hussein M. AL Hawamdeh
    • Supervision
      Ahmad Albloush