Towards Conceptual Framework for Analysing the Impact of Artificial Intelligence on Fresh-Food-Produce Wholesalers' Transport Logistics Performance

AI applications in transport logistics, particularly within fresh-food-produce wholesale, optimise processes such as route planning, freight management, and real-time tracking. However, research on AI's impact on fresh-food-produce wholesale transport logistics, especially in France, remains underexplored. This study addresses the gap by investigating key factors that influence AI-enhanced fresh-food-produce wholesalers' transport logistics performance through a qualitative survey among a relevant target group of French professionals. The contribution of this research is twofold. First, the authors provide an overview of the current state of AI and its application in fresh-food-produce wholesale transport logistics within the French context. Second, the authors present a conceptual framework that emerges from their primary qualitative data analysis results, illustrating the relationships between higher-level constructs presented by J. C. Perez-Mesa, L. Piedra-Munoz, M. C. Garcia-Barranco, and C. Giagnocavo, including Production-Quality-Health Programming, Production-Sustainability Programming, Transport-Quality- Health, and Transport-Sustainability which in turn could be linked to lower-level constructs with AI enhancement potential identified in this study. The resulting concepts with AI enhancement potential are: Forecasting demands, Services quality of transport, Certification quality, Communication, Environmental responsibility, Social responsibility, Groupage transport, Product classification and Routes improvement. In future research, the proposed hypothesised relationships could be statistically tested based on a large enough dataset to establish the reliability and validity of the resulting framework, ideally within and beyond France.