Road freight e-commerce platforms, as a specialized form of e-commerce in the road transportation sector, face complex operational risks due to their unique service positioning and business models. This study employs a comprehensive methodology to examine the risk framework of these platforms. Through the development of three distinct questionnaires, 20 critical risk factors are identified, which were subsequently analyzed using a combination of Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) approaches. Furthermore, a systematic risk assessment is conducted by integrating the Analytic Hierarchy Process (AHP) with Fuzzy Comprehensive Evaluation (FCE) methods. The research reveals that the 20 core risk factors affecting road freight e-commerce platforms exhibit a systematic and hierarchical structure, with clearly defined transmission pathways between different levels of risk factors. This investigation uncovers the internal interaction mechanisms among these risk factors. The study demonstrates that a thorough consideration of risk factor mechanisms, coupled with a systematic risk assessment, can significantly enhance the effectiveness and precision of risk control strategy formulation. This methodological approach not only provides a robust framework for understanding the complex risk landscape of online freight platforms but also offers practical insights for developing targeted risk mitigation strategies in this evolving sector.

Purpose: This paper investigates the complexities of a multimodal dry port transportation network, operating under the challenging conditions posed by the availability of transportation modes. Design/methodology/approach: Our study focuses on a network structure that includes multiple foreign seaports, local seaports, dry ports and manufacturers. We identify a significant research gap in the existing literature on dry port–based multimodal transportation networks. We first develop a mixed-integer non-linear programming (MINLP) model that adeptly integrates freight departure and demand scheduling, inventory management and backlog, while considering the availability of transportation modes in every node. To account for disruptions from uncertain events like extreme weather, we extend this deterministic foundation to a two-stage stochastic programming model. This stochastic model explicitly incorporates uncertainty in transportation mode availability, allowing proactive and adaptive strategies. Findings: The computational study, conducted across two distinct setups, demonstrates our models effectively avoiding substantial backlog penalties as well as delivering robust, cost-balanced solutions to handle uncertainty and complexity. In conclusion, the paper presents a comprehensive analysis including sensitivity analysis, dynamic planning evaluation and cross-regional validation, providing insightful guidance for strategic decision-making and demonstrating the model’s adaptability across different operational contexts. Originality/value: The insights gained from this analysis not only underline the practicality and strategic significance of our framework but also suggest key strategies for businesses.