Eliminating data silos with business intelligence: The role of organizational culture and leadership in Jordan’s insurance sector

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Type of the article: Research Article

Abstract
Business intelligence systems are becoming vital in Jordan’s insurance sector, driving efficiency, compliance, and data-driven decisions. This study investigates how institutional, technical, and cultural conditions influence the effectiveness of business intelligence implementation, especially in overcoming persistent data silos and fragmented legacy systems. A structured survey was conducted between September and December 2024 across major Jordanian cities, targeting BI managers, IT specialists, compliance officers, and operations analysts within insurance companies. A stratified sampling approach was used to ensure representation by firm size, BI maturity, and data silo severity, yielding 260 valid responses from 360 distributed questionnaires (72% response rate). This focus on professionals directly involved in BI implementation and evaluation ensured the relevance and depth of insights.
Partial Least Squares Structural Equation Modeling revealed that BI integration significantly reduced data silos (β = –0.482, p < 0.0001), improved operational efficiency (β = 0.413, p = 0.0003), strengthened regulatory compliance (β = 0.391, p = 0.0005), and enhanced decision-making effectiveness (β = 0.428, p < 0.0002). Mediation analysis confirmed that improved data quality partially explained BI’s impact on decision-making (β = 0.216, p = 0.0012). Moreover, the positive effects of BI were amplified in organizations with strong data-driven cultures (β = 0.183, p = 0.0026) and active top management support (β = 0.194, p = 0.0021). These findings underscore that technological solutions alone are insufficient; effective BI outcomes rely on an alignment of systems, culture, and leadership, offering critical insights for digital transformation in regulated industries.

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    • Table 1. Respondent profiles
    • Table 2. Descriptive statistics of study variables
    • Table 3. Measurement model analysis
    • Table 4. Hypothesis testing results
    • Table 5. Mediation analysis results
    • Table 6. Moderation analysis results
    • Table 7. Multi-group analysis (MGA) results
    • Conceptualization
      Ibrahim A. Abu-AlSondos
    • Data curation
      Ibrahim A. Abu-AlSondos
    • Formal Analysis
      Ibrahim A. Abu-AlSondos
    • Funding acquisition
      Ibrahim A. Abu-AlSondos
    • Investigation
      Ibrahim A. Abu-AlSondos
    • Methodology
      Ibrahim A. Abu-AlSondos
    • Resources
      Ibrahim A. Abu-AlSondos
    • Software
      Ibrahim A. Abu-AlSondos
    • Visualization
      Ibrahim A. Abu-AlSondos
    • Writing – original draft
      Ibrahim A. Abu-AlSondos
    • Writing – review & editing
      Ibrahim A. Abu-AlSondos