A multi-dimensional framework for assessing emergency preparedness effectiveness in industrial settings using the Delphi method and fuzzy AHP

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Background Industrial environments face significant risks due to hazardous materials, machinery, and complex supply chains. Effective emergency preparedness is essential for minimizing human, environmental, and economic impacts during crises. Objective This study develops a comprehensive tool to assess emergency preparedness in industrial settings that integrates expert insights with quantitative analysis. Methods The study was conducted in four phases. A three-round Delphi study involved 24 subject matter experts (SMEs). Content validity was assessed using the content validity index (CVI) and content validity ratio (CVR). The fuzzy analytic hierarchy process (FAHP) was applied to assign weights to the dimensions and factors. SMEs also participated in validating the final tool. Cronbach's alpha was calculated to evaluate internal reliability. Results The Delphi process identified eight dimensions and 40 key factors. The training & competency dimension received the highest weight (0.200), followed by exercises/drills/simulations (0.131) and risk assessment & hazard identification (0.125). Low inconsistency rates (maximum consistency ratio (CRm) = 0.002, geometric consistency ratio (CRg) = 0.007) confirmed the robustness of expert feedback. The CVI values for all 200 items exceeded 0.79, confirming their relevance, while CVR values were above 0.37, demonstrating good reliability (Cronbach's alpha = 0.81). The tool validation process, which includes expert consensus and psychometric testing, confirms its effectiveness for practical application. Conclusion The developed tool provides a scientifically reliable, and comprehensive framework for assessing emergency preparedness in industrial environments. Future research should aim to extend the tool's validation to additional industrial sectors and integrate technological advancements to enable real-time data analysis and decision-making.