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Volume 16, Issue 2 (Summer-in press 2026)
Disaster Prev. Manag. Know. 2026, 16(2): 344-379 |
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Saffari Darberazi A, Malekinejad P, Ahmadi Ghavamabad D. Designing a Structural Model of Factors Affecting the Establishment of Quality Management in Natural Disasters. Disaster Prev. Manag. Know. 2026; 16 (2) :344-379
URL: http://dpmk.ir/article-1-800-en.html
URL: http://dpmk.ir/article-1-800-en.html
1- Department of Industrial Engineering, Faculty of Technical and Engineering, Bam Higher Education Complex, Bam, Iran.
2- Department of Industrial Management, Faculty of Economics, Management and administrative sciences, Semnan University, Semnan, Iran.
3- Department of Business Administration, Ya.C., Islamic Azad University, Yazd, Iran.
2- Department of Industrial Management, Faculty of Economics, Management and administrative sciences, Semnan University, Semnan, Iran.
3- Department of Business Administration, Ya.C., Islamic Azad University, Yazd, Iran.
Abstract: (770 Views)
Background and objective Despite the importance of quality management in disaster response, an integrated, structured framework explaining the relationships among the key factors required for its establishment remains underdeveloped, particularly in Iran. This study aims to identify the factors affecting the establishment of quality management in natural disasters and to develop and validate a structural model that explains their hierarchical and causal relationships.
Methods This is a descriptive survey. First, we reviewed the literature to identify the key factors and indicators affecting quality management in natural disasters, which led to the identification of 9 main factors subsequently evaluated by 13 experts from Iran’s National Disaster Management Organization. Interpretive structural modeling (ISM) was then employed to determine the hierarchical relationships among the identified factors and develop the conceptual model. The proposed structure was subsequently validated using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, based on 161 valid responses from experts. Finally, MICMAC analysis classified the factors by driving power and dependence.
Results The ISM analysis organized the 9 identified factors into six hierarchical levels. “Strategic planning” occupied the most fundamental and principal driving factor, whereas “speed of relief and safety” was the final outcome in the hierarchical model. The PLS-SEM results confirmed the reliability and validity of the measurement model and supported all structural relationships examined in the study. The model also demonstrated acceptable overall fit and strong predictive relevance. MICMAC analysis classified policy framework, governmental support, strategic planning, and access to financial resources as driving factors; organizational commitment and public–private participation as linkage factors; and information transparency/information sharing, continuous improvement, and speed of relief and safety as dependent factors. No factor was located in the autonomous quadrant, indicating a highly interconnected structural system.
Conclusion Effective quality management in natural disasters should be approached as an interconnected and hierarchical system rather than as a set of isolated operational practices. Strategic planning can facilitate governmental support, appropriate policymaking, and access to financial resources, which in turn can strengthen organizational commitment and public–private organization participation, which contribute to greater information transparency, continuous improvement, and ultimately faster and safer disaster relief operations.
Methods This is a descriptive survey. First, we reviewed the literature to identify the key factors and indicators affecting quality management in natural disasters, which led to the identification of 9 main factors subsequently evaluated by 13 experts from Iran’s National Disaster Management Organization. Interpretive structural modeling (ISM) was then employed to determine the hierarchical relationships among the identified factors and develop the conceptual model. The proposed structure was subsequently validated using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3, based on 161 valid responses from experts. Finally, MICMAC analysis classified the factors by driving power and dependence.
Results The ISM analysis organized the 9 identified factors into six hierarchical levels. “Strategic planning” occupied the most fundamental and principal driving factor, whereas “speed of relief and safety” was the final outcome in the hierarchical model. The PLS-SEM results confirmed the reliability and validity of the measurement model and supported all structural relationships examined in the study. The model also demonstrated acceptable overall fit and strong predictive relevance. MICMAC analysis classified policy framework, governmental support, strategic planning, and access to financial resources as driving factors; organizational commitment and public–private participation as linkage factors; and information transparency/information sharing, continuous improvement, and speed of relief and safety as dependent factors. No factor was located in the autonomous quadrant, indicating a highly interconnected structural system.
Conclusion Effective quality management in natural disasters should be approached as an interconnected and hierarchical system rather than as a set of isolated operational practices. Strategic planning can facilitate governmental support, appropriate policymaking, and access to financial resources, which in turn can strengthen organizational commitment and public–private organization participation, which contribute to greater information transparency, continuous improvement, and ultimately faster and safer disaster relief operations.
Keywords: Quality management, Natural disasters, Interpretive structural modeling (ISM), Structural equation modeling
Type of Study: Research |
Subject:
Special
Received: 2025/08/13 | Accepted: 2026/04/11 | ePublished: 2026/09/19
Received: 2025/08/13 | Accepted: 2026/04/11 | ePublished: 2026/09/19
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