A Conceptual Framework of Personalization, Trust, and Perceived Risk for Artificial Intelligence in Consumer Decision-Making

Abstract

Artificial Intelligence (AI) has emerged as a key building block of a digital consumer environment through its ability to offer sophisticated levels of personalization, intelligent recommendations, and automated decision support. Though AI technologies have gained considerable popularity, the related literature has focused on the isolated constructs of adoption intention, trust, and perceived risks, providing a shallow appreciation of the complex psychological mechanisms underlying how AI-powered personalization influences the process of consumer decision-making. To this effect, the present conceptual piece attempts to build a more complete framework of understanding on how AI-powered personalization influences consumer decision outcomes via the joint mediating roles of AI-trust and perceived risks. Drawing from the insights of the Technology Acceptance Model (TAM), the Stimulus-Oriented Organism-Response (S-O-R) framework, AI-trust theory, and perceived risks theory, the proposed framework conceptualizes AI-powered personalization as a stimulus input triggering complex organismic responses, AI-trust beliefs, and perceived risks, both of which then drive the critical phases of the consumer decision-making process, namely, information search, alternatives evaluation, and purchase intentions. Moderated by variables of consumer technology literacy and product involvement, the proposed framework attempts a more complete conceptualization of AI-powered personalization influencing the mentioned phases of the consumer decision-making process through certain precise testable hypotheses related to technological developments in future research streams.