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Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency

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  • El Bhilat, El Mehdi
  • El Jaouhari, Asmae
  • Hamidi, L. Saadia

Abstract

The urge for greater agri-food supply chain efficiency (AFSCE) has been gaining in prominence increasingly, spurred on by escalating logistics costs and advances in industry 4.0 technologies (e.g artificial intelligence AI). The latter significant contributions in logistics have led academics to point their attention more to AI utility in operations management. Drawing on dynamic capability view (DCV) and organizational information-processing theories, this study aims to examine the effect of AI based technologies on reducing wastes and minimizing afri-food supply chain costs, hence increasing organization profitability. It also analyzes how outbound logistics and distribution network efficiency (DNE) (warehousing, transportation, packaging, etc.) mediates the relationship between AI and AFSCE. This research investigates the moderating impact of AI adoption impediments (AIAI) on the mediating correlation between AI, DNE and AFSCE as well. Using Partial Least Square-Structural Equation Modeling (PLS-SEM) approach, conceptual model and hypothesis were analyzed and tested with 348 responses collected from executives and managers in the Moroccan agri-food industry. As a novel result, distribution network efficiency and by way of mediation AFSCE and organization performance are found to be directly and positively affected by AI integration. The researchers find also that with more AIAI, the relationship between the aforementioned variables weakens.

Suggested Citation

  • El Bhilat, El Mehdi & El Jaouhari, Asmae & Hamidi, L. Saadia, 2024. "Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s004016252300834x
    DOI: 10.1016/j.techfore.2023.123149
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