Issue #1 (Volume 17 2026)

Type of the article: Research Article

Abstract
Growing 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
The integration of AI-enabled medical robots into the medical field has increased the potential risks to which patients may be exposed. To protect patients’ rights, this study aims to explore and analyze the need for mandatory insurance against civil liability of medical robots operating with AI technologies in the United Arab Emirates. Such insurance is intended to ensure adequate compensation, reinforce legal protection, and uphold confidence in medical practice, while also contributing to societal stability and supporting the growth of the insurance sector. The study employed a combination of descriptive and analytical methods. It concludes that smart medical robots are neither inanimate objects nor irrational beings. It recommends legislative regulations granting them digital legal personality under specific controls, recognizing their independent financial status, and enabling them to bear civil liability for actions causing harm. The study showed an upward trend in the number of insurance companies providing liability coverage for damages caused by AI-operated medical robots, increasing from two in 2020 to ten in 2025, and expected to rise further if full legal personality is granted. The research findings suggest amending the UAE Civil Transactions Code and the Medical Liability Law to codify civil liability provisions for autonomous smart medical robots and to mandate liability insurance. Furthermore, as insurers’ obligations depend on establishing the insured’s liability, UAE law should grant the injured party a direct right of action against the insurer.

Type of the article: Research Article

Abstract
InsurTech adoption faces challenges in emerging markets, where consumers are concerned about the fairness of insurance claims and the security of InsurTech systems. The aim of this study is to test the InsurTech adoption intention in Jordan based on trust, perceived risk, and perceived regulatory protection. The research design is based on a survey of insurance consumers in Jordan (N = 346).
Adoption intention is positively correlated with trust (r = 0.71) and regulatory protection (r = 0.60) and is negatively correlated with perceived risk (r = −0.38). Regulatory protection is positively correlated with trust (r = 0.65) and negatively correlated with perceived risk (r = −0.52). The model explains 57% of the variance in adoption intention (R-squared = 0.57). Trust has the strongest influence on adoption intention (beta = 0.520, p < 0.001), whereas perceived risk has a negative effect (beta = −0.180, p < 0.001). Regulatory protection also positively influences adoption intention (beta = 0.120, p = 0.012) and trust (beta = 0.650, p < 0.001), and negatively influences perceived risk (beta = −0.530, p < 0.001). Regulatory protection also indirectly influences adoption intention through trust (beta = 0.340, p < 0.001) and perceived risk (beta = 0.100, p < 0.001).
InsurTech adoption intention in Jordan is primarily based on confidence and assurance, where trust is the most important factor, and the influence of regulation is manifested through trust and risk perceptions.