Objective: This study aims to develop an integrated model for identifying and analyzing the factors affecting the performance of fast food service units and evaluating their efficiency under conditions of uncertainty. The proposed model is intended not only to support managerial decision-making but also to provide an evidence-based foundation for the formulation and improvement of industrial policies in the fast foods.
Methods: Initially, 48 factors affecting performance were identified through literature review and expert consultation. These factors were screened using the Fuzzy Delphi, resulting in the selection of 18 key factors. The structural and hierarchical relationships among the selected factors were analyzed using Interpretive Structural Modeling, while their driving and dependence powers were examined through MICMAC. The identified factors were then employed as inputs to a Two-Stage Stochastic Data Envelopment Analysis, and the efficiency of 400 fast foods was evaluated.
Findings: Indicate that strengthening managerial factors, developing regional infrastructure, and improving supportive policies play a significant role in enhancing the performance of fast foods under conditions of uncertainty. Results suggest that industrial policies aimed at regional infrastructure development, investment support, business environment facilitation, and supply chain strengthening can substantially improve the performance and resilience of fast foods.
Conclusion: In addition to its managerial applications, the proposed model provides significant implications for industrial policymaking. The findings offer an evidence-based foundation for formulating policies on targeted resource allocation, regional infrastructure development, digital transformation support, supply chain strengthening, business environment improvement, and the enhancement of productivity, resilience, and competitiveness in the fast foods.