Issue #2 (Volume 16 2025)

Type of the article: Research Article

Abstract
The study aims to evaluate the impact of organizational support, customer awareness, perceived security, and regulatory compliance on e-commerce adoption within the insurance sector of Jordan. A structured questionnaire was administered to 400 participants from executive management, IT, customer service, and compliance departments who worked in ten Amman-based insurance companies. Believing that a quantitative research design matched the analysis requirements, 372 valid responses were gathered and analyzed through structural equation modeling (SEM) operated by AMOS 24. Organizational support, along with customer awareness, was found to have strong effects on adoption behavior because perceived security functions as the primary determining factor. The research results indicated that regulatory compliance failed to have a direct effect on adoption behavior. The study validated construct reliability and validity through confirmatory factor analysis (CFA) since all Cronbach’s alpha values surpassed 0.80 and the composite reliability and average variance extracted measurements fell within acceptable ranges. The study model demonstrated an acceptable fit, as indicated by RMSEA (0.045), CFI (0.942), TLI (0.930), and χ²/df (2.18). Digital transformation in insurance requires organizational programs that provide team-based customer education while maintaining robust privacy measures.

Type of the article: Research Article

Abstract
This study examines the influence of socio-economic factors on life insurance demand in Bangladesh using annual data from 18 life insurance companies between 2014 and 2023. Life insurance demand is assessed using life insurance penetration and life insurance density; GDP per capita, inflation, healthcare spending to GDP, and education spending to GDP serve as proxies for socio-economic variables. This study employs a dynamic Panel-Corrected Standard Errors (PCSE) method to handle cross-sectional dependence in panel data. Stepwise regression is further applied as a robustness check. The findings exhibit that GDP per capita has a statistically significant negative impact on insurance density (β = –0.0003, P < 0.001) and insurance penetration (β = –0.000002, P < 0.001). This suggests that income growth does not facilitate increased insurance adoption. In contrast, inflation has a significant positive influence on both insurance density (β = 0.0310, P < 0.001) and insurance penetration (β = 0.0001, P < 0.001), emphasizing the influence of inflationary pressure on life insurance demand. Similarly, healthcare expenditure exhibits a significant positive effect on life insurance demand, influencing both insurance density (β = 2.0560, P < 0.01) and insurance penetration (β = 0.0024, P < 0.05), possibly due to rising healthcare costs prompting individuals to seek financial security. However, education spending does not show a statistically significant effect on life insurance demand. The results indicate that demand for life insurance in Bangladesh is influenced more by financial insecurity than by income increases, emphasizing the impact of inflation and healthcare expenses on insurance adoption.

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.

Type of the article: Research Article

Abstract
Accurate prediction of motor insurance claim frequency is necessary for efficient risk management, underwriting, and policy pricing. Predictive performance of Poisson Generalized Linear Models (GLMs), Decision Trees, and Generalized Additive Models (GAMs) is investigated using 108,699 motor third-party liability insurance contracts, representing the French Motor TPL dataset from the CASdatasets R package widely used in actuarial research. These models’ predictability, explainability, and flexibility on training and testing sets are compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Poisson Deviance metrics. Results indicate that, although GLM offers an interpretable, accurate baseline, GAM slightly surpasses GLM and Decision Trees under all performance measures. Results demonstrate that GAM achieves superior performance across all metrics, with the lowest MSE (0.0506), RMSE (0.2251), and Poisson Deviance (36.41% training, 37.76% test), compared to GLM (MSE: 0.0509, RMSE: 0.2257, Poisson Deviance: 36.83% training, 38.08% test) and Decision Trees (MSE: 0.0582, RMSE: 0.2413, Poisson Deviance: 37.12% training, 38.31% test). The GAM model reduces prediction error by approximately 0.6% compared to GLM and 13.1% compared to Decision Trees based on MSE. Empirical findings reveal how GAMs achieve an optimum balance between model explainability and prediction flexibility, rendering them best suited for insurers who want to refine risk segmentation without compromising on regulatory compliance and business transparency. This study joins other research calling for interpretable state-of-the-art statistical techniques in insurance analytics and presents worthwhile observations for actuaries and data scientists who wish to refine motor insurance frequency modeling frameworks.