Perceived trust: Do all of its dimensions matter for insurance inclusion?

  • 215 Views
  • 49 Downloads

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

The study aimed to examine the significance of perceived trust dimensions in explaining insurance inclusion in Uganda. Insurance inclusion remained very low in Uganda. Although trust is vital for insurance inclusion, it is not known whether all of its dimensions are relevant for insurance inclusion. As such, hierarchical regression analysis was adopted to investigate the predictive power of the individual dimensions of perceived trust on insurance inclusion. The significance of the individual components was attained by determining the change in the adjusted R2 and the significance of the change. Hence, the results showed that integrity (β = 0.316, p < 0.01), credibility (β = 0.252, p < 0.01) and reliability (β = 0.211, p < 0.01) were significant positive predictors of insurance inclusion. However, the results showed benevolence (β = 0.018, p > 0.05) to have an insignificant positive influence on insurance inclusion in Uganda. The effect of benevolence on insurance inclusion was practically and statistically insignificant. Overall results showed that independent variables explained 50.6% of the variance in insurance inclusion in Uganda when combined. Unlike prior studies that have investigated the general effect of trust as the global variable, the current study examined the impact of the independent dimensions of trust in explaining insurance inclusion. Besides, earlier studies ignored the trust theory, which provides key dimensions for understanding trust. The current study reveals that not all dimensions of perceived trust are significant for insurance inclusion in Uganda.

view full abstract hide full abstract
    • Table 1. Exploratory factor analysis for perceived trust
    • Table 2. Exploratory factor analysis for insurance inclusion
    • Table 3. Descriptive statistics for study variables
    • Table 4. Correlation analysis results
    • Table 5. Hierarchical regression analysis
    • Conceptualization
      Archillies Kiwanuka, Athenia Bongani Sibindi
    • Data curation
      Archillies Kiwanuka
    • Formal Analysis
      Archillies Kiwanuka
    • Investigation
      Archillies Kiwanuka
    • Methodology
      Archillies Kiwanuka, Athenia Bongani Sibindi
    • Project administration
      Archillies Kiwanuka, Athenia Bongani Sibindi
    • Software
      Archillies Kiwanuka
    • Validation
      Archillies Kiwanuka, Athenia Bongani Sibindi
    • Writing – original draft
      Archillies Kiwanuka
    • Writing – review & editing
      Archillies Kiwanuka, Athenia Bongani Sibindi
    • Supervision
      Athenia Bongani Sibindi