Data Quality, AI Effectiveness, and Food Supply Chain Traceability: A Theory-Driven Framework for Resilience

Abstract

AI is becoming more used to improve the traceability and transparency of Moroccan food supply chains. Still, the efficiency of AI-powered traceability systems is strongly based on the quality of the data used. While research has flourished in the fields of AI, digital traceability, and supply chain resilience, the importance of data quality as an eminent antecedent of AI efficiency has not been adequately studied.

This paper adopts a model-focused conceptual design, based on an integrative synthesis of complementary theoretical perspectives to reveal the varieties of ways through which the technical, semantic, and governance dimensions of a high-quality data contribute to improve AI analytical ability and the eventual realization of transparency, traceability, and resilience of the Moroccan food supply chain. Based on the theoretical lenses of Information Quality theory, the Resource-Based View, Dynamic Capabilities Theory, and Socio-Technical Systems theory, the setup regards AI effectiveness as a mediating factor and supply chain complexity and digital maturity as contextual factors which may impact the relationships. A set of research propositions specifies the proposed direct, mediating, and moderating relationships. The proposed framework is conceptual and has not yet been empirically validated; therefore, the proposed relationships should be interpreted as theoretically derived rather than empirically established.

The paper contributes to the literature by integrating data quality, AI effectiveness, transparency, traceability, and resilience within a unified theoretical model and by positioning data quality as a strategic foundation for AI-enabled supply chain capabilities. The framework provides a basis for future empirical validation in Moroccan food supply chain contexts.

Classification JEL : Q13, O33, L23, L15

Paper type: Theoretical Research