Enhancing Interpretive Structural Modeling (ISM) Through the Integration of Influence Signs: A Novel Approach for Advanced Systemic Assessment and Validation via an Illustrative Case Study

Abstract

This paper examines the limitations of traditional Interpretive Structural Modeling (ISM), which is widely used to structure and prioritize interconnected elements in complex systems. Although ISM is effective, it inadequately accounts for the directional nature of influences (positive or negative) between variables. To address this gap, an extended version of the ISM methodology is proposed, incorporating influence signs into these relationships. This new approach enhances ISM's ability to capture the nuances of complex interactions, thereby improving support for strategic decision-making. The paper provides a detailed comparison of the traditional and new ISM methodologies, focusing on critical decision-making phases. A practical application is conducted on a nine-variable system describing population dynamics, demonstrating the improved efficacy of the new ISM method and offering a better understanding of the impact of influential relationships in complex systems.