The emergence of increasingly autonomous Artificial Intelligence (AI) agents is reshaping how consumers search for information, evaluate alternatives, and make purchasing decisions. While existing consumer journey models have incorporated AI primarily as a decision-support technology, they provide limited conceptual guidance for understanding situations in which AI agents perform purchasing activities on consumers’ behalf. Drawing on consumer journey theory, AI-enabled commerce, and human–AI collaboration research, this conceptual paper adopts a theory-adaptation approach to examine how established frameworks may be extended to account for emerging forms of delegated decision-making. We propose the Human–AI Collaborative Consumer Journey, a framework that conceptualizes purchasing as a collaborative process involving consumers and consumer-authorized AI agents across different stages of the journey. Its central contribution is the concept of Delegated Consumer Agency (DCA), defined as the configuration of authority consumers grant AI agents to perform cognitive or operational purchasing activities within predefined goals and constraints. The framework highlights how human and AI participation may vary across purchasing contexts and identifies implications for consumer autonomy, decision-making, and e-commerce management. Finally, six research propositions and a future research agenda are presented to guide empirical investigation.
The rapid advancement of networks has facilitated the widespread dissemination of online reviews derived from user-generated content. These information-dense reviews have emerged as a critical decision-support resource in various domains such as product selection and recommendation. Although prior studies have integrated numerical ratings with textual reviews, effective methods for aggregating these data in a multi-platform consensus analysis remain underexplored, especially in multi-platform settings where reviews and ratings may be scattered and inconsistent. This oversight not only results in an incomplete utilization of the available decision-relevant information but may also introduce bias and reduce the reliability of decision outcomes. To address this gap, this paper proposes a novel consensus-driven group decision-making framework that integrates online reviews and ratings collected from multiple platforms. The proposed framework combines textual reviews, numerical ratings and information credibility by employing an improved representation of basic uncertain linguistic information, aggregates review information at the platform level, and treats each platform as an individual decision-maker. A consensus model is then used to reduce inconsistencies among platform-level evaluations and obtain an acceptable collective product ranking. Finally, a product-selection case study based on real-world online data validates the applicability and effectiveness of the proposed framework through comprehensive comparative analyses. The results indicate that incorporating coupled multi-platform online reviews and ratings through a consensus-oriented analytical framework yields more comprehensive and transparent decision support compared to reliance on a single information source.
AI assistance and augmented reality (AR) can make shopping easier without necessarily improving the resulting decision. This conceptual article develops a model of when consumers can inspect, redirect, or reject assistance and when those opportunities may fail. A targeted literature synthesis connects marketing research on autonomy and algorithm overdependence with human–AI reliance studies. The model separates effective decision authority from perceived agency, and distinguishes confidence in advice before acceptance from confidence in the final choice. Three central predictions concern cosmetic controls, checking versus delegation, and persuasive presentation that encourages reliance on inferior advice. Shopping-specific extensions examine whether corrected requirements propagate across a product shortlist, conversation, and AR display, and whether repeated claims from one source are mistaken for independent verification. Decision quality is evaluated only where independent product facts and defensible preference benchmarks permit comparison with an initial unaided choice. A staged preference-review protocol identifies ambiguous cases rather than assigning them arbitrary correctness labels. The evidence table records access depth, and the research agenda specifies results that would count against the central predictions. Practical and governance implications are proposals to evaluate. The model is unvalidated and makes no claim to demonstrate consumer-welfare effects.
E-commerce enables suppliers to bypass traditional retailers and sell directly to consumers, but such encroachment intensifies channel competition. Although price matching commitments can mitigate this conflict, their strategic role has primarily been studied under the assumption of profit-maximizing retailers. We develop a game-theoretic model to examine how a retailer’s valuation of consumer surplus alongside accounting profit affects a supplier’s pricing choice and direct-channel sales. The supplier chooses whether to adopt an exact commitment to match the retailer’s price. We show that the supplier adopts price matching only when the retailer’s weight on consumer surplus is sufficiently large and the direct-selling cost falls within an intermediate range. Under supplier-preferred selection among retail-only pricing equilibria and without a fixed entry cost, the supplier opens the direct channel throughout the maintained cost range. Under this selection, the price matching option changes the pricing arrangement rather than the entry decision. Greater retailer altruism improves consumer surplus and social welfare within each conditional outcome yet can lower either measure in equilibrium by inducing the supplier to adopt matched pricing or stop direct sales while retaining unmatched pricing. This interaction can give rise to an altruism paradox. Numerical extensions show that welfare losses can persist with positive entry costs or asymmetric channel demand. These findings offer implications for retailers that value consumer surplus alongside profit and suppliers considering direct-to-consumer expansion.
Fitness influencers increasingly convert follower trust into purchasing behavior through seeding, group-buying, and social selling arrangements within South Korea’s hyperconnected, appearance-oriented digital ecosystem. Yet how they assess brand fit, manage authenticity, and navigate follower expectations remains underexamined. This study uses grounded theory to examine how fitness influencers with 500,000 or more followers navigate brand collaborations across these commercial formats. Semi-structured in-depth interviews were conducted with 16 fitness influencers, and open, axial, and selective coding were applied to identify a paradigm model of collaborative decision-making. The findings suggest, from the influencers’ side, that brand-fit assessment is not an influencer–brand dyadic negotiation but a triadic process in which the follower community functions as an active third party, simulated in advance and verified through comments, direct messages, and purchase-verification behavior. Influencers who experienced authenticity crises subsequently institutionalized stricter, measurable disclosure and verification rules, while the deliberate disclosure of product flaws was reported by one participant to coincide with restored trust and better sales. These findings propose a reconceptualization of influencer–brand fit in social commerce as a dynamic, community-mediated negotiation rather than a static dyadic attribute, with implications for how brands and social commerce platforms may design influencer partnerships and disclosure strategies.
