AI credibility and consumer-AI experiences: a conceptual framework

Abstract

Purpose: This study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize. Design/methodology/approach: This study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews. Findings: This study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences. Research limitations/implications: This study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research. Practical implications: The authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility. Originality/value: This study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.