Digital learning, big data analytics and mechanisms for stabilizing and improving supply chain performance


  • Aziz Barhmi Mohammed V University, Rabat
  • Fahd Slamti Mohammed V University, Rabat
  • Soulaimane Laghzaoui Ibn Tofail University
  • Mohamed Reda Rouijel Sidi Mohamed Ben Abdellah University, Fez



digital learning, supply chain, data analytics, capabilities, disruptions


This study attempts to shed light on the nature of the contribution of digital learning orientation (DLO), as an intangible resource, to the development of the dynamic capability of supply chain data analytics powered by artificial intelligence (SCDA-AI) as well as to the moderation of its effects on the enhancement of the operational capabilities of supply chain flexibility (SCFL), supply chain resilience (SCRE) and supply chain responsiveness (SCRES) in order to stabilize and improve supply chain performance (SCPER) in times of uncertainties and disruptions. The study was based on survey data collected from 200 foreign companies based in Morocco. Respondents were mainly senior and middle managers with experience in general management and supply chain (SC). Validity and reliability analyses and hypothesis testing were carried out using structural equation modelling (SEM) with SPSS Amos. The results revealed that DLO acts as an antecedent to SCDA-AI without moderating its effects on the three operational capabilities of SCFL, SCRE and SCRES. In addition, this study provides further empirical evidence that dynamic capabilities can produce significant results in terms of stabilizing and improving performance through the generation and/or reconfiguration of operational capabilities in situations of uncertainties and disruptions.




How to Cite

Barhmi, A., Slamti, F., Laghzaoui, S., & Rouijel, M. R. (2024). Digital learning, big data analytics and mechanisms for stabilizing and improving supply chain performance. International Journal of Information Systems and Project Management, 12(2), 30–47.