Enhancing professional skepticism through simulation-based learning: Evidence from the UAE insurance industry

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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.

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    • Figure 1. Conceptual model
    • Table 1. Constructs, measures, and sources
    • Table 2. Hypotheses and statistical tests
    • Table 3. Descriptive statistics of key variables (N = 160)
    • Table 4. Multilevel regression results for direct effects (H1, H2)
    • Table 5. Mediation analysis: Indirect effect of training via analytical reasoning (H3)
    • Table 6. Moderation analysis: Cross-level interaction with learning climate (H4)
    • Conceptualization
      Amer Morshed, Hanadi A. Salhab
    • Data curation
      Amer Morshed
    • Formal Analysis
      Amer Morshed, Hanadi A. Salhab
    • Investigation
      Amer Morshed, Hanadi A. Salhab
    • Methodology
      Amer Morshed, Hanadi A. Salhab
    • Project administration
      Amer Morshed
    • Resources
      Amer Morshed
    • Software
      Amer Morshed, Hanadi A. Salhab
    • Supervision
      Amer Morshed
    • Validation
      Amer Morshed, Hanadi A. Salhab
    • Visualization
      Amer Morshed
    • Writing – original draft
      Amer Morshed
    • Funding acquisition
      Hanadi A. Salhab
    • Writing – review & editing
      Hanadi A. Salhab