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<p>Despite the critical importance of occupational health and safety (OHS), limited research has employed structural equation modeling (SEM) to systematically examine the impact of occupational accident risk factors on business performance. This study addresses this gap by integrating SEM and Bayesian network (BN) analysis to examine both the direct and indirect effects of occupational risk factors on organizational outcomes, providing a comprehensive and probabilistic framework for decision-making in occupational risk management. A conceptual framework was developed based on an extensive literature review. Data were collected from 200 respondents representing different textile manufacturing firms. The results indicate that occupational risk factors significantly influence production efficiency and business performance. Among the factors examined, psychosocial risks were identified as the most impactful, followed by chemical risks. The BN model validated the SEM findings and provided complementary probabilistic insights, enhancing the results’ robustness.</p>

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Prioritizing occupational risks: a decision model for manufacturing firms using SEM and Bayesian networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月25日

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  • Ömer Faruk Efe,
  • Ahmet Baki Engi̇n,
  • Burak Efe

摘要

Despite the critical importance of occupational health and safety (OHS), limited research has employed structural equation modeling (SEM) to systematically examine the impact of occupational accident risk factors on business performance. This study addresses this gap by integrating SEM and Bayesian network (BN) analysis to examine both the direct and indirect effects of occupational risk factors on organizational outcomes, providing a comprehensive and probabilistic framework for decision-making in occupational risk management. A conceptual framework was developed based on an extensive literature review. Data were collected from 200 respondents representing different textile manufacturing firms. The results indicate that occupational risk factors significantly influence production efficiency and business performance. Among the factors examined, psychosocial risks were identified as the most impactful, followed by chemical risks. The BN model validated the SEM findings and provided complementary probabilistic insights, enhancing the results’ robustness.