Influencing Factors of Agricultural Supply Chain Resilience Based on ISM-MICMAC

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Abstract
With the rapid development of Internet of Things (IoT) technology, the digital transformation of the agricultural sector has gradually become a key means to improve the efficiency and effectiveness of the supply chain. IoT technology helps to optimize agricultural production, logistics and supply chain management through real-time data collection, monitoring, and analysis. However, despite the huge potential brought by the application of IoT, the agricultural supply chain still faces challenges such as decentralized production, seasonality, and regionality. Coupled with the complexity of the agricultural supply chain network and the volatility of the agricultural market environment, the agricultural supply chain shows weak resistance to exogenous shocks. Based on the context of China’s agricultural supply chain, according to the Technology-Organization-Environment (TOE) framework, this paper applies the Interpretive Structural Modeling (ISM)-Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis method to construct a multilevel recursive structural model of the factors influencing the resilience of the agricultural supply chain and to excavate the deep-rooted influencing factors of the strength of the agricultural supply chain. The results indicate that among the factors influencing the resilience of China’s agricultural product supply chain, government policy support, supply chain structure, and infrastructure development are fundamental factors. These factors exert a strong driving effect on other elements and are less susceptible to influence from other factors. Therefore, government departments should formulate and implement policies conducive to the production, processing, and marketing of agricultural products, strengthen the construction of infrastructure, promote the application of digital technology and IoT in the farm supply chain, and improve the resilience of the agricultural supply chain.
Funding Information
  • 2024 Social Science Development Research Project of Hebei Province (202402059)
  • Big Data Management and Application Based on Elliptic Model, Talent Demand Analysis and Training Program Construction (2023GJJG645)

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