Introduction The integration of artificial intelligence (AI) into pricing systems is reshaping global markets, yet its implications for societal sustainability and inclusive growth in emerging economies remain largely overlooked. This study examines how AI-driven dynamic pricing influences customer satisfaction within Jordan’s electric vehicle (EV) sector—a context marked by evolving trust in technology and distinct socio-cultural norms. Methods A survey of 454 EV users was conducted, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model tested direct, mediating, and moderating effects among AI-supported dynamic pricing, purchasing decision quality, customer satisfaction, and trust in AI. Results The analysis reveals that algorithmic pricing does not directly enhance customer satisfaction. Instead, its effect is fully mediated by the consumer’s perceived decision confidence. Furthermore, trust in AI negatively moderates the relationship between dynamic pricing and decision quality, suggesting that heightened trust may paradoxically reduce proactive price evaluation in this context. Discussion These findings challenge Western-centric narratives and underscore the nuanced, context-dependent role of technology in sustainable consumption. The study highlights that in emerging markets, satisfaction is derived less from price perception and more from the psychological confidence of making an informed choice. For marketers and policymakers, this underscores the need for transparent, confidence-building communication strategies that align algorithmic pricing with broader goals of social inclusion and psychological well-being—key pillars of sustainable business performance in evolving economies. The research offers a rich context-driven model that positions decision confidence as the critical psychological route between algorithmic pricing and satisfaction.
Mousa et al. (Mon,) studied this question.