Type of the article: Research Article AbstractArtificial intelligence is diffusing across Gulf Cooperation Council insurance markets, yet disclosure-based evidence remains fragmented on whether adoption is associated with organizational change or localized automation. This study examines a purposive disclosure-based sample of 120 insurers from Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain. Because inclusion required sufficient disclosure of artificial intelligence practices, the sample is not intended to represent the insurance market. The study examines whether disclosed artificial intelligence adoption is associated with organizational change through financial transparency and operational efficiency. The dataset is constructed from annual reports, audited financial statements, governance reports, environmental, social, and governance reports, investor materials, and regulatory documents. Documents from 2017 to 2023 are treated as an observation window, coded at the item level, and aggregated into one firm-level score per insurer for cross-sectional structural equation modeling. Results indicate positive associations from artificial intelligence adoption to financial transparency (β = 0.52, p < 0.001) and operational efficiency (β = 0.49, p < 0.001). Financial transparency (β = 0.41, p = 0.003) and operational efficiency (β = 0.38, p = 0.012) are associated with organizational change. The indirect paths through transparency and efficiency are statistically distinguishable from zero within the model. Because all variables are derived from similar disclosure evidence, the pattern is interpreted as disclosure co-patterning rather than proof of a mechanism. The findings are associational, not causal, representative, or longitudinal.
Type of the article: Research Article AbstractThe study aims to assess the effect of digital maturity, digital marketing, customer engagement, and brand trust on corporate reputation in Jordan’s insurance industry. A structured questionnaire was used to collect the data from 423 people selected from executive management, information technology, marketing, customer service, and digital transformation departments in insurance companies in Jordan. A quantitative research design was employed to cater to the requirement of analysis, and a total of 401 valid responses were gathered and analyzed using structural equation modeling (SEM) by employing the AMOS version 24. The results showed that digital maturity and digital marketing had a significant impact on corporate reputation, and customer engagement turned out to be the most important one. Brand trust also has a positive and significant effect on the development of reputation. Confirmatory factor analysis (CFA) was used to confirm construct reliability and validity findings based on the results of all Cronbach’s alpha values being greater than 0.80, as well as composite reliability and average variance extracted values meeting acceptable thresholds. The results showed an acceptable fit for the structural model in terms of RMSEA (0.047), CFI (0.944), TLI (0.932), and chi-squared values (2.21). The results indicate that reputation building in the insurance business requires coordinated digital solutions that enhance engagement, trust, and technological maturity.
Type of the article: Research ArticleAbstractThis study investigates the determinants influencing life insurance demand across 38 OECD countries over the period 2009 to 2022, with the data sourced from OECD Insurance Statistics, the World Bank, and the World Development Indicators (WDI). The purpose is to identify and analyze the determinants that shape life insurance penetration (premiums as a percentage of GDP) and density (premiums per capita), providing a comprehensive understanding of market dynamics. Using panel data, the study employs a dynamic regression model with Panel-Corrected Standard Errors (PCSE) to ensure accuracy and reliability, complemented by Pooled Ordinary Least Squares (OLS) for robustness. The findings indicate that economic, demographic, and social factors significantly impact life insurance demand. GDP per capita, poverty rates, and healthcare expenditure to GDP significantly stimulate life insurance demand. Life expectancy positively correlates with insurance penetration, whereas a higher dependency ratio adversely affects it. In contrast, inflation and education expenditure to GDP are found to reduce demand. However, urbanization is found to have no significant influence. The study provides actionable insights for policymakers to design strategies that safeguard consumer interests while promoting market expansion. Furthermore, OECD countries stand out as appealing investment destinations within the stable insurance sector. These findings highlight opportunities for insurance companies to adapt offerings to evolving consumer needs, boosting competitiveness and profitability.AcknowledgmentThe authors gratefully acknowledge Dr. Anup Kumar Saha, Lecturer in Accounting at Keele University, United Kingdom, for his valuable intellectual input and constructive feedback.
Type of the article: Theoretical Article AbstractThe functioning of insurance markets during prolonged military conflict remains insufficiently explored in contemporary insurance and risk management literature. The purpose of this study is to develop and apply the Insurance Market Wartime Resilience Index (IMWRI) for assessing insurance market resilience under conditions of military conflict. The empirical calibration combines indicators from both the non-life and life insurance segments; however, the dominant contribution comes from non-life insurance data. Consequently, the resulting IMWRI should be interpreted as a market-wide resilience measure with a stronger sensitivity to developments in the non-life segment. The proposed IMWRI integrates five dimensions of wartime insurance market functioning: premium resilience, claims functionality, reinsurance support, solvency and financial stability, and market structure adaptation. The empirical results demonstrate that the Ukrainian insurance market produces an approximate IMWRI value of 67.9%. The largest contributions are generated by premium resilience (0.2625) and solvency and financial stability (0.2325), followed by market structure and concentration (0.0893) and claims functionality (0.0780). The smallest contribution is provided by reinsurance support (0.0165). This distribution suggests that wartime insurance resilience in Ukraine is driven primarily by internal market capacity and financial stability rather than by external risk-transfer mechanisms. The proposed IMWRI confirms that resilience is generated through a combination of financial stability, premium continuity, institutional adaptation, and selective risk transfer. Therefore, the future development of wartime insurance systems depends not only on strengthening insurers themselves but also on building integrated public-private risk-sharing mechanisms capable of narrowing the gap between market resilience and protection needs.
Type of the article: Research Article AbstractThe global trend towards digital transformation and the adoption of social media marketing as a primary channel for communicating with customers is increasing loyalty in the insurance sector. This study aims to examine the impact of social media marketing on brand reputation, loyalty, and advocacy in the Jordanian insurance sector. The study adopts a quantitative approach and uses a cross-sectional survey design to collect data from a targeted segment of insurance customers in Amman, Jordan. A structured opinion poll was conducted in Jordan between May 1, 2025 and August 15, 2025, targeting participants who follow insurance companies on social media platforms. Convenience sampling was used to ensure consistency across the target groups, with 391 valid responses distributed. The study results demonstrated the significant impact of social media platforms on building brand reputation (β = 0.445, p < 0.0000). The analysis confirmed the effective role of brand reputation in enhancing loyalty (β = 0.380, p < 0.000). Durham’s brand reputation also enhances brand advocacy among the general public (β = 0.339, p < 0.0000), highlighting the importance of brand loyalty in building a loyal audience (β = 0.373, p < 0.0000). The study results confirm the effectiveness of social media marketing strategies in building brand reputation, which enhances loyalty and advocacy, providing critical insights for Jordanian insurance companies.
Type of the article: Research Article AbstractThis study aims to analyze the influence of financial performance, consisting of solvency, profitability, and liquidity, on financial distress moderated by good corporate governance (institutional ownership). The study was conducted at Indonesian joint venture insurance companies. This study used a quantitative method. The population in the study is joint venture insurance companies in Indonesia registered with the Financial Services Authority. The sample in this study amounted to five (5) joint venture insurance companies selected using purposive sampling. The data used are secondary data from the company’s annual financial statements for the period 2019 to 2023. The data were processed using the EViews 13 application to illustrate the relationship between independent, dependent, and moderating variables. The results of the study show that solvency and profitability have a significant negative effect on the financial distress of joint venture insurance companies in Indonesia Liquidity does not affect the financial distress of joint venture insurance companies in Indonesia. Institutional ownership as a moderating variable can strengthen the influence of solvency on financial distress, but it weakens the influence of profitability and liquidity on financial distress. This study offers original value by examining the moderating role of institutional ownership as a proxy for good corporate governance in the relationship between financial performance and financial distress.
Type of the article: Research Article AbstractDigital distribution is reshaping insurance markets, yet people remain cautious about purchasing complex, high-involvement life insurance products through online channels. Prior technology-adoption studies commonly apply UTAUT, but evidence on the relative importance of its core predictors in online life insurance and on whether optimism meaningfully conditions these effects in an emerging-market setting remains limited. This study addresses this gap by testing a UTAUT-based model of online life insurance purchase intention in Guangxi Province, China, and by assessing optimism as a potential moderator. Survey data from 707 responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory model fit (SRMR = 0.026; NFI = 0.934). In the structural model, performance expectancy (β = 0.142, p = 0.001), effort expectancy (β = 0.205, p < 0.001), social influence (β = 0.030, p < 0.001), and facilitating conditions (β = 0.172, p < 0.001) each showed significant positive effects on behavioral intention, with social influence exerting the strongest impact. The model explained a substantial share of variance in intention (R² = 0.90). The results showed that performance expectancy, effort expectancy, social influence, and facilitating condition had significant positive effects on consumers’ behavioral intention to purchase online life insurance. However, none of the four proposed moderation hypotheses involving optimism were supported, indicating that optimism did not significantly moderate these relationships. Overall, the findings suggested that life insurance companies should focus on improving UTAUT predictors to strengthen customers’ purchase intention rather than enhancing customer optimism. AcknowledgmentThe study appreciates Professor Dr Mohamad Bin Bilal Ali for the grammatical advice.
Type of the article: Research Article AbstractSaudi insurers increasingly face demands to analyze huge quantities of underwriting and claims data, but disparate systems may decrease the precision of pricing and the speed of claim settlement, thereby reducing operational efficiency. The purpose of this study is to identify if data warehousing adoption could contribute positively to the underwriting and claims handling operations of insurance companies in Saudi Arabia. The study employed fixed-effect regressions to examine the relationship between data warehousing adoption and underwriting/claims handling operations of insurance companies based on a firm-year fixed-effect panel data set of 2015–2024, including information technology investment intensity interaction terms. The result of this study indicated that data warehousing adoption is positively related to underwriting/claims handling operations of insurance companies, where data warehousing adoption could contribute positively to reducing loss ratio by 4.8 percent (β = –0.048, P < 0.01) and combined ratio by 5.6 percent (β = –0.056, P < 0.01). Data warehousing adoption could also contribute positively to claims handling operations, where average claim settlement time could be reduced by 6.21 days (β = –6.21, P < 0.05). In addition, the data warehousing investment interaction term can provide an additional 3.2 percentage points of improvement (β = –0.032, p < 0.05), implying that data warehousing value can be enhanced by complementary investments in information technology capabilities. Explanatory powers of the model are considerable, with R-squared of 0.41-0.52 for different equations.
Type of the article: Research Article AbstractGrowing regulatory demands and operational complexity in the insurance industry require professionals with strong analytical reasoning and professional skepticism, yet traditional training often fails to develop these competencies. This study aims to evaluate the effectiveness of simulation-based experiential training in enhancing professional skepticism and analytical reasoning among insurance professionals and to examine how the organizational learning climate influences this relationship.A quasi-experimental study was conducted between May and September 2025 across eight insurance companies in the United Arab Emirates, involving 160 early-career professionals (mean age = 27.3 years, SD = 2.5) organized into 40 teams. Teams were randomly assigned to either simulation-based experiential training or conventional instruction. Data were collected at three stages – pre-training, post-training, and eight weeks after training – and analyzed using multilevel structural equation modeling.Participants who received simulation-based training showed a 0.42-point increase in professional skepticism and a 0.78-point improvement in analytical reasoning compared with the control group, both statistically significant at p < 0.001. Analytical reasoning mediated 57% of the training’s total effect on skepticism (indirect effect = 0.24, p < 0.001). The organizational learning climate significantly moderated this relationship (interaction effect = 0.21, p < 0.001), with greater gains observed in firms that promoted reflection and feedback.The findings confirm that simulation-based experiential learning, reinforced by a supportive organizational climate, substantially enhances analytical, skeptical, and ethical judgment essential for accurate claim evaluation, risk assessment, and fraud prevention in the insurance sector.
Type of the article: Research Article Abstract Mergers and acquisitions (M&A) are increasingly popular transactions in financial markets, often seen as a faster and safer way to achieve growth and create value. However, research focusing exclusively on the insurance industry remains limited. This research article aims to synthesize the characteristics of existing studies, describe the methods and variables used, present the results regarding the impact on the value and performance of the companies involved, identify the main determinants of this impact, and finally, highlight the main gaps and directions for future research. Studies published up to 2025 were collected from the Web of Science and Scopus databases, resulting in a final sample of 28 articles. The analysis shows that the Journal of Banking & Finance, Journal of Risk and Insurance, and Journal of Risk Finance are the leading publication outlets, with J. David Cummins emerging as the most influential author. Publication peaks occurred in 2008 and 2011, although no sustained upward trend was observed. Most studies focus on non-life insurers and use U.S. data. Methodologically, event studies and DEA models dominate the literature, focusing respectively on shareholder value creation and firm efficiency. Findings remain mixed, since M&A transactions are theoretically expected to create value, yet empirical evidence shows considerable variation across contexts. Identified determinants of M&A performance include company size, prior M&A experience, geographic or sectoral diversification, payment method, ownership and business type, governance, and human resources.
Type of the article: Research Article AbstractThis study investigates the nexus between Intellectual Capital efficiency (ICE) components and firm value in Sub-Saharan African (SSA) insurance companies. The study employed a modified Value-Added Intellectual Coefficient (VAIC™) model, incorporating components such as Value-Added Capital Coefficient (VACA), Structural Capital Value-Added Coefficient (SCVA), Value-Added Human Capital Coefficient (VAHC), and Innovation Capital Efficiency (VAHC2). These components were integrated to calculate the VAIC, offering a holistic assessment of value-creation efficiency within SSA insurance firms. Static and dynamic panel data analyses were employed to estimate the relationship between the ICE components and Tobin’s Q ratio, serving as a proxy for firm value. A positivist approach and descriptive quantitative methods were used in this study. The study analyzed panel data from 122 insurance firms across 46 SSA countries over the period 2010–2022, sourced from databases including Wharton Research Data Services, S&P CapitalIQ, and Refinitiv Eikon. The VAIC™ model was applied by integrating various ICE components to comprehensively evaluate the value creation efficiency in SSA insurance firms. The findings indicate significant variation in the impact of ICE components on firm value across SSA insurance companies. Specifically, higher VAIC™ values are associated with enhanced firm performance, underscoring the critical role of intellectual capital in value creation within this sector. This research contributes to the body of knowledge by demonstrating the applicability of the VAIC™ Model in SSA’s insurance sector and underscoring the relevance of intellectual capital management in driving financial outcomes. Practical implications include informing policymakers, executives, and investors about optimizing intellectual resources to foster sustainable growth and resilience in SSA insurance.
Type of the article: Research Article AbstractThis study examines how patients’ health insurance claims were denied by different insurance providers at a Saudi Academic Medical Center (AMC), exploring the reasons for these rejections, their relationship to claim characteristics, and the factors that predict health insurance claim rejections. A descriptive study design was employed, involving a retrospective review of all insurance claims submitted by both inpatients and outpatients between January and December 2023 at a tertiary care AMC in Saudi Arabia. Following data screening using the UCAF 2.0 form, all denied insurance claims cases (n = 1,117) were subjected to qualitative analysis. The majority of rejected health insurance claims were submitted by female patients (56.9%) and outpatients (93.6%). Among the insurance companies studied, “Tawuniya” rejects the most insurance claims (n = 730). Variables such as age, gender, and insurance company were significantly associated with the reasons for denying claims (p < 0.05). Furthermore, variables such as age, cost, department type (inpatient/outpatient), and the month of claims are significant predictors of claim rejections (p < 0.05). However, gender, insurance companies, and clinical diagnosis were not significant (p > 0.05). The primary reasons for insurance claim denials in Saudi Arabia are missing medical data, system errors, and non-coverage of specific conditions. This study will help insurance companies and patients identify trends and reasons for claim rejections, enabling them to implement more effective preventive and corrective measures. AcknowledgmentsThe authors expressed their gratitude to Imam Abdulrahman Bin Faisal University for granting permission [IRB-2024-03-188] to conduct this study.
Type of the article: Research Article AbstractThe study aims to explore the insurance profiles of SMEs and to identify access gaps across different firm categories, using the Ipsos European Public Affairs survey dataset, which consists of 8,187 SMEs from Europe. This dataset was analyzed using multiple correspondence analysis, cluster analysis, and discriminant analysis. Results show clear evidence of ownership concentration in a few insurance products: commercial motor (64.3% of SMEs own such an insurance), general liability (54.4%), and workers’ compensation (46.1%). On the other hand, uptake is lowest for cyber insurance (15.3%), non-damage business interruption (14.5%), and commercial insurance with business interruption (20.3%); notably, 8.2% report no insurance product ownership. Furthermore, three behavioral clusters were identified: minimally insured (n = 3,604; mean 1.58 policies), moderately insured (n = 2,603; mean 5.22), and broadly insured (n = 1,980; mean 6.73). Also, portfolios exhibit structured “baskets” with frequent co-ownership of general liability and motor (40%). Findings document systematic, demography-linked disparities and actionable access gaps. The study concludes that persistent disparities in access are linked to firm size, age, and turnover, underscoring the need for tailored policy measures and market solutions to address inclusion gaps. The practical value of this study lies in providing evidence-based insights for insurers, regulators, and policymakers seeking to expand SME risk protection. AcknowledgmentWe gratefully acknowledge EIOPA for providing access to the SME insurance dataset used in this study.
Type of the article: Research Article AbstractThe increasing complexity of insurance fraud in Jordan has unveiled inadequacies of traditional detection mechanisms, calling for advanced technologies. This study investigates drivers and inhibitors of machine learning adoption for fraud detection within Jordan’s insurance sector, with a focus on institutional readiness, ethical concerns, and supporting regulations. By applying quantitative and exploratory research design, Partial Least Squares Structural Equation Modeling serves as an approach to analyze data collected from 291 practitioners of fraud detection, data science, and insurance compliance in the industry.Findings show that both existing fraud detection efforts (coefficient = 0.42, p = 0.012) and knowledge of machine learning (coefficient = 0.55, p = 0.009) have favorable impacts on adoption likelihood, which underlines the relevance of bureau experience and informed professional culture. By contrast, major adoption deterrents such as limited IT capability, budgetary constraints, and moral concerns about fairness and clarity (coefficient = –0.40 and –0.38, respectively) unfavorably decrease adoption intention.Regulatory encouragement has a two-fold role: it has a direct promoting effect on adoption (coefficient = 0.47, p = 0.011) and a buffering effect on negative ethical concerns (interaction = 0.36, p = 0.025) and adoption barriers (interaction = –0.28, p = 0.032). Perceived efficacy also mediates between awareness/experience on the one hand and adoption decisions on the other (coefficients = 0.51 and 0.44, p < 0.05).The results demonstrate successful incorporation of machine learning into fraud detection as depending on the clarity of regulations, ethical protections, and institutional readiness, rather than on technical capability itself.
Type of the article: Research Article AbstractThis study investigates the key factors that shape individuals’ decisions to repurchase voluntary personal auto insurance, with particular attention to the mediating effect of customer satisfaction and the moderating role of perceived risk. Drawing upon the theory of planned behavior, perceived risk theory, and expectation-confirmation theory, the study employs a structured face-to-face survey conducted between January and April 2025 among 496 voluntary personal auto insurance policyholders in the Southeast region of Vietnam. Respondents were randomly selected from customers who had renewed their policies at least once, ensuring that the sample represented active policyholders with actual repurchase experience. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to examine the hypothesized relationships. The analysis reveals that customer satisfaction is a critical driver of repurchase intention, acting both independently and through mediators such as brand image, subjective knowledge, and perceived value. Additional factors, namely price sensitivity and perceived behavioral control, also show positive effects on repurchasing behavior. Interestingly, perceived service quality does not significantly influence repurchase intention, indicating a potential shift in consumer expectations within digital insurance environments. Furthermore, the study finds that perceived risk mitigates the strength of the satisfaction-repurchase link, suggesting that even satisfied clients may hesitate to renew when uncertainty is high. The results contribute to theoretical models of post-purchase behavior and provide practical implications for insurers seeking to enhance customer loyalty through improved satisfaction, trust, and risk communication. AcknowledgmentThis research was conducted as part of the doctoral dissertation approved by Decision No. 1218/QD-DHNCT on December 14, 2023, by Nam Can Tho University, Vietnam. The authors express their gratitude to the reviewers and editor-in-chief for their valuable assistance in preparing this study.
Type of the article: Research Article AbstractThis paper examines the influence of reinsurance strategies and insurance liabilities on the performance and market valuation of Jordanian insurance firms. Using panel data from 2010 to 2023 and employing fixed-effects regression and mediation analysis, we test whether Excess Loss Installments (ELI) mediate these relationships. Based on a balanced panel of 16 listed Jordanian insurers over the period 2010–2023, the study applies SPSS, EViews, and SmartPLS to conduct fixed-effects regression and mediation analysis. The findings reveal that a higher reinsurers’ share is significantly associated with lower return on assets (ROA) (β = –0.18, p < 0.05), suggesting that excessive risk cession may erode underwriting profitability. In contrast, insurance contract liabilities have a strong positive impact on ROA (β = 0.29, p < 0.01) and firm value measured by Tobin’s Q (β = 0.32, p < 0.01), indicating that prudent technical reserve accumulation enhances financial strength and investor perception. Correlation analysis further revealed a negative association between reinsurance share and ROA (r = –0.21), while liabilities showed a moderate positive correlation with Tobin’s Q (r = 0.36). Mediation analysis showed that ELI does not play a statistically significant mediating role in the relationship between the main variables. In some models, ELI even had a minor negative indirect effect on firm value.These findings emphasize the importance of optimizing reinsurance structures and liability management. For Jordanian insurers, effective risk transfer must be balanced against profitability goals. Regulators and firm managers should revisit the strategic use of advanced mechanisms like ELI to reduce inefficiencies and strengthen financial outcomes. Acknowledgment(s)This research was funded through the annual funding track by the Deanship of Scientific Research, from the vice presidency for graduate studies and scientific research, King Faisal University, Saudi Arabia [Grant no. KFU253235].
As health systems worldwide increasingly focus on mitigating the burden of non-communicable diseases, the strategic role of insurance schemes in facilitating early detection and preventive care, thereby reducing the substantial costs associated with advanced-stage treatment, has become a critical area of policy and research attention. This study aims to evaluate the impact of various health financing models, specifically voluntary, compulsory, and social insurance, on the burden of cardiovascular diseases and neoplasms, measured by Disability-Adjusted Life Years (DALYs), across working-age and older populations. The analysis is based on unbalanced panel data from 51 countries covering the period 2000–2021, drawing from the Global Burden of Disease database for DALY rates and the OECD and WHO Global Health Expenditure Database for health financing indicators. Fixed and random effects panel regression models with clustered robust standard errors were employed to estimate the associations. Results show that voluntary private insurance significantly reduces DALY rates from cardiovascular diseases, by approximately 19-28%, among working-age (15-49) and older adults (50-69). Compulsory and social insurance models also exhibit protective effects, but of smaller magnitude. Government health financing schemes similarly correlate with improved outcomes. In contrast, enterprise-based financing is positively associated with higher DALY rates, especially in older age groups. Insurance schemes demonstrate weaker and more inconsistent associations for neoplasms, with compulsory insurance and government schemes showing the most stable links to reduced burden among older adults.
Type of the article: Research Article AbstractAccurate 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.
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 AbstractThis 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.
