Artificial intelligence as a transformation catalyst: Modeling and optimizing digital customer experience in Moroccan e-commerce

L'intelligence artificielle comme catalyseur de transformation: Modélisation et optimisation de l'expérience client digital dans le e-commerce Marocain

Mohamed Salim Thamir (), Ibtissam Lakhlili and Aya Sehmani ()
Additional contact information
Mohamed Salim Thamir: LEG - Laboratoire d'économie et de gestion (LEG), Faculté pluridisciplinaire de Khouribga (FPK), Université Sultan Moulay Slimane (USMS), Maroc
Ibtissam Lakhlili: LEG - Laboratoire d'économie et de gestion (LEG), Faculté pluridisciplinaire de Khouribga (FPK), Université Sultan Moulay Slimane (USMS), Maroc
Aya Sehmani: UH2C - Université Hassan II de Casablanca = University of Hassan II Casablanca = جامعة الحسن الثاني (ar)

Post-Print from HAL

Abstract: Abstract: Digital transformation is reshaping the e-commerce landscape in Morocco, forcing companies to strategically rethink customer experience. This study presents a critical narrative review of recent literature (2018–2025) exploring the transformative contributions of artificial intelligence (AI) in modeling online customer journeys. Our approach was based on a systematic analysis of key scientific publications, including reference articles, meta-analyses, and existing systematic reviews on this emerging field. The analytical framework developed deconstructs the relationship between AI and customer experience along several interrelated dimensions. The study first maps the theoretical applications of AI in customer experience optimization and evaluates the proposed conceptual framework to measure its impact on platform performance. It then reveals how the theoretical cultural characteristics of Moroccan consumers interact with intelligent interfaces, presenting unique design and adaptation challenges. Finally, the theoretical implementation model is tested using critical success factors documented in the emerging market literature. The results of this critical synthesis show that strategically adopting AI in the customer journey can generate dual conceptual added value: operational through process optimization and transformational through the creation of hyper-personalized contextual experiences. This study makes a significant contribution to the theoretical corpus of digital transformation in emerging market companies and offers a comprehensive conceptual framework. Its academic significance opens new perspectives for the development of cultural adaptation models for the integration of artificial intelligence technologies in the Moroccan digital environment. Keywords: Artificial intelligence, Customer experience, e-commerce, Customer journey, Digital transformation. Classification JEL: M39 Paper type: Theoretical Researc

Keywords: E-commerce; Parcours client; Transformation numérique. JEL Classification : M39 Type du papier : Recherche Théorique Artificial intelligence; Customer experience; e-commerce; Customer journey; Digital transformation. Classification JEL: M39 Paper type: Theoretical Research; Expérience client; Intelligence artificielle; Intelligence artificielle Expérience client E-commerce Parcours client Transformation numérique. JEL Classification : M39 Type du papier : Recherche Théorique Artificial intelligence Customer experience e-commerce Customer journey Digital transformation. Classification JEL: M39 Paper type: Theoretical Research (search for similar items in EconPapers)
Date: 2025
New Economics Papers: this item is included in nep-ara
Note: View the original document on HAL open archive server: https://hal.science/hal-05405390v1
References: Add references at CitEc
Citations:

Published in International Journal of Accounting, Finance, Auditing, Management and Economics, 2025, 6, pp.44-81. ⟨10.5281/zenodo.17821133⟩

Downloads: (external link)
https://hal.science/hal-05405390v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05405390

DOI: 10.5281/zenodo.17821133

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().