Disasters are severe disruptions of community functions. Building a resilient community could reduce the impacts of disasters, which requires the collaboration of community stakeholders. Interactions among community stakeholders act as a buffer against disasters, can alleviate the shock caused by disasters, and present a way to build community resilience. However, despite the relevant benefits of these interactions, they are difficult to improve since stakeholders have conflicting points of view and different priorities. These conflicts usually revolve around how to improve resilience, how to manage the financial resources needed to implement these improvements, and how to evaluate their efficacy. These conflicts add to the complexity of the interactions between different stakeholders and make disaster-related decisions prone to ineffectiveness and inefficiency. Decision support systems and tools have the potential to deal with these conflicts by developing analytical models that map the various operations and include all stakeholders. Several decision support tools have been proposed in the literature to improve community resilience, but none focus on interactions among stakeholders. This study's objectives are two-fold. First, identify the areas of stakeholders’ interaction. Second, build a decision support tool to guide decision-makers in prioritizing areas of interaction. The proposed tool is based on two sets of criteria, first the interdependencies among the interaction areas, and second the practical implementation side of the interaction areas considering measures such as human and nonhuman resources, regulatory changes, and time of implementation. To achieve the first objective, we conducted a literature review to identify areas of interaction among community stakeholders; resulting in identifying 27 factors that reflect the various interaction areas. Then we followed a two-phase approach to build the decision support tool. In both phases, we combined experts’ knowledge with quantitative modeling. The first phase considered the interdependencies among the various areas. In this phase, we utilized a Delphi study to capture the dependency among the different areas. Then to prioritize the identified areas of interaction, we used network analysis techniques to understand the propagating impacts of a change in one area on the others. This approach was applied to Spain, utilizing data provided by Spanish resilience experts. Our findings indicate a high degree of interdependence among all areas of interaction. Decentralization of the decision-making process and effective leading capabilities of emergency organizations have been identified as top priority areas. Then we extended the tool to cover the second set of criteria, applying a feasibility-based prioritization, assessing the practicality of interventions designed to improve the significant areas of interactions identified in phase 1. We surveyed Spanish emergency experts to gather data on the interaction areas and their evaluations against the decision-making criteria. We applied the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyze data. The results indicate that initiatives focusing on enhancing the leadership skills of emergency managers emerge as the most feasible and impactful interventions in our case study. On the other hand, initiatives for improving emergency response functionality, and disaster risk management plans are less feasible to implement. To ensure the reliability of our findings, we conducted a sensitivity analysis using various methods. The analysis confirmed the proposed tool's stability under minor weight and criteria adjustments. However, significant changes can influence rankings (especially for mid-ranked alternatives), highlighting the significance of accounting for diverse criteria framings during data collection and result interpretation. Additionally, we evaluated the usability and practicality of the tool together with emergency experts from different sectors. The tool received an overall positive evaluation from the experts, highlighting the significance of human factors such as status quo bias and structuring human judgment in decision-support tools while acknowledging potential resistance from users in utilizing such tools due to lack of education and training. The tool is designed to be utilized during the preparedness phase of the disaster life cycle, functioning at a strategic level. It serves as an empowering resource for policymakers and practitioners (emergency managers and city managers), providing a systematic approach to prioritize context-specific interventions aimed at enhancing community resilience. By employing this tool, decision-makers can proactively and effectively contribute to the development of resilient communities, ensuring a strategic and well-informed foundation for disaster preparedness efforts.
