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Analysis of barriers to adopting Industry 4.0 to achieve agri‐food supply chain sustainability: A group‐based fuzzy analytic hierarchy process

Author

Listed:
  • Guoqing Zhao
  • Chenhui Ye
  • Denis Dennehy
  • Shaofeng Liu
  • Antoine Harfouche
  • Femi Olan

Abstract

Agri‐food supply chains (AFSCs) need to become more sustainable, and Industry 4.0 (I4.0) is a crucial enabler. However, various barriers make adopting I4.0 technologies to achieve AFSC sustainability challenging. Few previous studies have examined China's agri‐food industry. Through a literature review and consultation of Chinese experts, we identify 27 barriers in six categories and prioritize these using a group‐based fuzzy analytic hierarchy process to produce novel results. First, we identify six new I4.0 adoption barriers closely linked with China's economic, social and cultural environments, including acquisition of intelligent agricultural equipment subsidies based on guanxi. Second, our prioritization of barriers reveals that the key challenges to applying I4.0 are the increased cost of terminal logistics, acquisition of intelligent agricultural equipment subsidies based on guanxi, low compatibility of I4.0 technologies with existing agricultural equipment, and problems with the government subsidy model. These results have managerial implications for governments and knowledge dissemination organizations.

Suggested Citation

  • Guoqing Zhao & Chenhui Ye & Denis Dennehy & Shaofeng Liu & Antoine Harfouche & Femi Olan, 2024. "Analysis of barriers to adopting Industry 4.0 to achieve agri‐food supply chain sustainability: A group‐based fuzzy analytic hierarchy process," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 8559-8586, December.
    • Handle: RePEc:bla:bstrat:v:33:y:2024:i:8:p:8559-8586
    DOI: 10.1002/bse.3928
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    References listed on IDEAS

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    1. Ma, Qun & Li, Xinrui, 2025. "The impact of fiscal-financial synergistic support for agriculture on agricultural total factor productivity: Based on provincial panel data in China," International Review of Economics & Finance, Elsevier, vol. 103(C).

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