A Review Of Mediating And Moderating Mechanisms Shaping Consumer Trust And Loyalty In Digital Marketing

Abstract

This paper offers an integrative overview of mediating and moderating influence which determines consumer trust and loyalty in the context of an AI-driven digital marketing setting. Based on the Stimulus-Organism-Response (S-O-R) model and theory known as privacy calculus, the paper uses the synthesis of recent empirical studies of recommendation systems, chatbots, and predictive models. The perceived personalization quality, customer satisfaction, privacy risk, and perceived control can be defined as the key mediating variables that play a critical role in defining how the AI systems impact the user trust and the behavioral loyalty. Moreover, these effects are also shown to be moderated by some variables such as the sensitivity to privacy, type of product, and brand reputation such that these effects lead to a cue-consistent behavior at some levels but cue-shifted behavior at other levels of consumer groups. The offered conceptual framework combines them in order to provide the comprehensive view on how AI technologies might strengthen the consumer relationships or impede them. Its results have practical implications to be used by marketers, AI designers, and polymakers interested in ethical, personalized, and user-centric digital marketing strategies. The review helps to develop the knowledge base because of describing the psychological and situation-related aspects of interactions between AI and consumers; due to the discussed issues, the consideration may be used in developing AI systems in which the primary concern is the level of their integrity, user control, and the long-term promotion of their loyalty.