Research on supply chain risk evaluation of online to offline e-commerce enterprises based on catastrophe progression method

Nian Zhang, Yue Zhu, Shuo Zheng and Qiang Pan

Mathematics and Computers in Simulation (MATCOM), 2026, vol. 241, issue PB, 542-572

Abstract: Risk assessment of online-to-offline (O2O) e-commerce enterprises holds significant strategic value for improving supply chain collaborative governance mechanisms and enhancing overall operational efficiency. The research objective of this study is to construct a practically applicable and theoretically grounded risk evaluation model that reflects the unique structural and behavioral complexities of O2O e-commerce supply chain. The research problem is the lack of comprehensive, behavior-sensitive frameworks for systematically evaluating the multidimensional, nonlinear, and abrupt risks inherent in O2O e-commerce supply chains. To mitigate the shortcoming, the research method integrates Social Network Analysis (SNA), semantic clustering, and grounded theory to systematically develop a robust risk index system. The system comprises well-structured indicators evaluated through linguistic variables and expert judgment, capturing complex risk characteristics across the supply chain. The indicators are classified into five categories: environmental, market, operational, collaborative, and technological, corresponding to factors such as policy uncertainty, demand fluctuations, financial instability, partner conflicts, and digital vulnerabilities. In the assessment phase, an improved catastrophe progression method is applied. The maximizing deviation method is employed to determine objective weights and capture the nonlinear and abrupt nature of supply chain risks. A case study focusing on a travel-oriented O2O e-commerce platform is conducted to validate the model. Moreover, the research findings confirm the model’s effectiveness in identifying and prioritizing key risk dimensions, particularly in areas such as information security and customer satisfaction. Despite its contributions, the limitation of the study lies in the constrained scope of semantic data and the relatively small sample size used in the questionnaire survey for empirical validation. As a future direction, expanding data sources and applying the model across diverse O2O sectors would enhance its generalizability and practical relevance. This study contributes a structured and behavior-aware approach to O2O supply chain risk evaluation, with both theoretical and managerial implications.

Keywords: Supply chain risk management; Social network analysis; Semantic clustering; Catastrophe progression method (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:241:y:2026:i:pb:p:542-572

DOI: 10.1016/j.matcom.2025.10.017

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