Enhancing resilience and sustainable operational performance in food hubs business model framework for disaster-prone areas
Food systems in disaster-prone areas face recurrent systemic disruptions that threaten sustainability in a long-term, especially in disaster-prone areas and agricultural production centres. While the Triple Layered Business Model Canvas (TLBMC) integrating economic, environmental, and social dimensions, its sustainability-oriented focus can be further enhanced by incorporating resilience mechanisms to address multiple disruptions. This research aims to fill this gap by proposing the integration of the resilience layer into TLBMC. Food hubs are placed as aggregation nodes that have a dual-function, i.e., humanitarian response and commercial food distribution. This study uses an empirical qualitative case study in West Java Province, Indonesia to develop and refine the proposed framework. Empirical findings are used to test the applicability, contextual relevance, and practical implications of the resiliences, to enhanced TLBMC framework. The proposed framework incorporates of resilience layer consisting of robustness, adaptability, recovery, and reconfigurability. These resilience dimensions extend the logic of conventional business model beyond efficiency optimization to adaptive system capable of maintaining their functionality under uncertainty. The findings suggest that achieving sustainability requires not only the integration of economic, environmental, and social value dimensions but also resilience capabilities that enables organizations to mitigate disruptions, adjust operations, and accelerate recovery. The findings indicate that food hubs may contribute to improving coordination, supporting food accessibility, and enhancing supply chain resilience during disruptions. This study contributes to business model and food system resilience literature by extending the TLBMC with a resilience layer and provides practical guidance for designing resilient, inclusive, and sustainable food hubs in high-risk areas. Further research should validate the proposed framework through simulations-based modelling and develop measurable operational indicators for each resilience dimension.