Human or artificial? How AI review labeling shapes trust and consumer interaction in food delivery platforms

Purpose This study examines how artificial intelligence (AI) labeling reviews on food delivery platforms influence consumer trust and purchase intentions, focusing on the moderating role of product price. Design/methodology/approach The study draws on signaling and cue utilization theories to conduct two controlled experiments. Experiment 1 tests how review origin cues—human-authored, incentive-driven, and AI-assisted—shape trust and purchase intentions across price levels. Experiment 2 isolates the effect of AI-assisted labeling by holding review content constant and manipulating a transparency-based disclosure. We also test whether perceived trustworthiness mediates the effect of AI labeling on purchase intentions. Findings Across both experiments, AI labeling reduces trust and purchase intentions, although contextual cues shape this effect. Consistent with prior research linking higher prices to greater perceived risk, Experiment 1 shows that premium pricing amplifies this negative effect. In contrast, Experiment 2 shows that although AI labeling lowers trust and indirectly reduces purchase intentions, price does not significantly moderate this relationship. This result suggests that trust remains influential even when price signals premium quality. Finally, trust mediates the relationship between labeling and purchase intentions in both experiments. Originality/value This study demonstrates that AI-assisted review labels function as context-dependent credibility signals rather than uniformly negative cues. It contributes to AI transparency and interactive marketing literature by identifying when AI labeling undermines or sustains trust. By showing that consumers integrate review origin cues and price signals differently across service contexts, the study advances theory and highlights the need for more nuanced transparency strategies on digital platforms.