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Showing 2 results for Interpretive Structural Modeling

Mehdi Nojavan, Babak Omidvar, Mehdi Sahba, Hamid Karimi Kivi,
Volume 16, Issue 4 (10-2024)
Abstract

INTRODUCTION: One of the environmental issues faced by the majority of large human settlements in the world is natural disasters and their effects. Thus, the purpose of this paper is to present a model using Interpretive Structural Modeling (ISM) for explaining the relationship between the factors affecting disaster management in order to improve its effectiveness.
METHODS: In this study, quantitative method were used. For identifying the factors influencing disaster management, thematic analysis and second-order confirmatory factor analysis were used and confirmed through SmartPLS. Then the main model of the study was developed based on ISM using the views of experts in the field of disaster management.
FINDINGS: The findings showed that risk evaluation, risk management, and management actions were the fundamental factors in the disaster management model which consisted of 19 sub-factors. Convergent validity of the study was found to be higher than 0.5 based on Average Variance Extracted (AVE) and reliability was higher than 0.7 based on Cronbach’s alpha, also Composite Reliability (CR) was calculated to be larger than 0.6, which showed that the suggested factors completely measure the intended concept in the study.
CONCLUSION: According to the results, the proposed model shows the relation between factors affecting reduction of damages caused by disasters using the ISM. It can be used in different stages of disaster management because it explains the relation between 12 levels of different factors and enables managers and planners to clearly understand what activities need to be taken for more effective disaster management.
 

Ali Sibevei, Samira Ebrahimi, Moein Khazaei,
Volume 17, Issue 2 (4-2025)
Abstract

INTRODUCTION: The COVID-19 pandemic has revealed major shortcomings in healthcare data systems worldwide, particularly the need for accessible and transparent data sharing. In Iran, these shortcomings were particularly visible due to the lack of a structured open data network in the healthcare sector. Hence, this study addresses the barriers to open data networks in healthcare.
METHODS: This study used Interpretive Structural Modeling (ISM) supported by MICMAC analysis to examine and prioritize barriers to the establishment and use of open data platforms in the Iranian healthcare system. Data were collected through expert consultations with eight experts in the field of health information and policy.
FINDINGS: The analysis revealed significant barriers to implementation, including lack of government coordination, high startup costs, and inadequate technology infrastructure. For use, the most prominent barriers included the lack of data standards, poor data management, and uncontrolled growth of unstructured data. Many of these barriers were interrelated, and some acted as root causes that hindered systemic progress.
CONCLUSION: Addressing these challenges requires coordinated strategic efforts focused on increasing ICT competencies, upgrading infrastructure, and strengthening institutional support. Establishing a functional open data network is essential to improve public health outcomes and enable faster responses to future health crises in Iran.

 


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