As a consequence of progressively accelerating digital transformation, determining and advancing customer satisfaction is emerging as a central strategic issue for emerging markets. The traditional approaches to measuring customer satisfaction, specifically SERVQUAL and the expectations-performance paradigm, provide valuable theoretical frameworks; however, they are inadequate for fully comprehending the dynamic complexities of customer behaviour that continuously evolve within these environments. This article will address this shortcoming by presenting a conceptual framework that illustrates how artificial intelligence (AI)-based technologies (e.g., machine learning and natural language processing) can be integrated into current customer satisfaction modelling. Through a critical evaluation of the literature, this study employs a theoretical and exploratory approach to the addition and enhancement of existing models, rather than the outright replacement of them. The proposed conceptual framework illustrates how the use of AI facilitates a more precise, fluid, and current understanding of customer expectations and perceptions when integrating local cultural and infrastructural particularities specific to the emerging markets context, using Morocco as a representative case in this regard. The principal contributions of this research are the proposal of an adaptable and flexible structure for understanding customer satisfaction; the description and approval of practical suggestions for incorporating AI into marketing initiatives in under-resourced settings; and the generation of theoretical contributions concerning the relationship between “smart” technologies and marketing in transition economies. This conceptual framework opens up avenues for future empirical research and offers valuable recommendations for marketers and policymakers seeking to enhance customer satisfaction through intelligent, contextualized strategies tailored to local realities.
Rais et al. (Fri,) studied this question.