AI Labels, Perceived Authenticity, and Consumer Trust in User-Generated Reviews

With growing interest in the effects of AI disclosure on user-generated content, empirical studies have produced mixed results. While some studies report negative consequences of disclosure, others suggest that transparent AI use does not necessarily reduce perceived authenticity or product evaluations. There is still limited knowledge about how AI disclosure in online reviews influences consumer perceptions when AI is presented as a support tool rather than a replacement for human input. To address this gap, the present study examines how AI disclosure and AI-related review cues influence consumer trust. The study compares three labeled review scenarios—reviews without AI-related information, AI-assisted labeled reviews, and AI-generated labeled reviews. The textual content of the reviews remained constant across conditions, while only AI-related labels and images were varied. This study also examines how these labeled scenarios relate to perceived authenticity and whether perceived authenticity mediates the relationship between labeled review scenarios and consumer trust. Based on survey data from 370 users of digital marketplaces in Latvia, analyzed using repeated-measures ANOVA, pairwise comparisons, and mediation analysis, this study found that: (1) reviews labeled as AI-generated showed the lowest levels of consumer trust and perceived authenticity, whereas AI-assisted labeled reviews were evaluated more favorably than AI-generated labeled reviews; (2) differences across the three scenarios were statistically significant for both consumer trust and perceived authenticity; and (3) perceived authenticity significantly mediated the relationship between labeled review scenarios and consumer trust. This study contributes to the literature by providing a more nuanced understanding of how AI disclosure and AI-related review cues shape consumer trust. It suggests that the key issue is not AI disclosure alone, but how AI-related cues shape perceived authenticity and, in turn, consumer trust.

Authors

Institutions

Publication Details

Journal
Journal of theoretical and applied electronic commerce research
Published
2026-05-14
DOI
https://doi.org/10.3390/jtaer21050154
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Labels, Perceived Authenticity, and Consumer Trust in User-Generated Reviews

Klaus Solberg Söilen, Dariia Drozd
Journal of theoretical and applied electronic commerce research
AI in Service Interactions
article

AI Labels, Perceived Authenticity, and Consumer Trust in User-Generated Reviews

Klaus Solberg Söilen, Dariia Drozd
article en

Abstract

With growing interest in the effects of AI disclosure on user-generated content, empirical studies have produced mixed results. While some studies report negative consequences of disclosure, others suggest that transparent AI use does not necessarily reduce perceived authenticity or product evaluations. There is still limited knowledge about how AI disclosure in online reviews influences consumer perceptions when AI is presented as a support tool rather than a replacement for human input. To address this gap, the present study examines how AI disclosure and AI-related review cues influence consumer trust. The study compares three labeled review scenarios—reviews without AI-related information, AI-assisted labeled reviews, and AI-generated labeled reviews. The textual content of the reviews remained constant across conditions, while only AI-related labels and images were varied. This study also examines how these labeled scenarios relate to perceived authenticity and whether perceived authenticity mediates the relationship between labeled review scenarios and consumer trust. Based on survey data from 370 users of digital marketplaces in Latvia, analyzed using repeated-measures ANOVA, pairwise comparisons, and mediation analysis, this study found that: (1) reviews labeled as AI-generated showed the lowest levels of consumer trust and perceived authenticity, whereas AI-assisted labeled reviews were evaluated more favorably than AI-generated labeled reviews; (2) differences across the three scenarios were statistically significant for both consumer trust and perceived authenticity; and (3) perceived authenticity significantly mediated the relationship between labeled review scenarios and consumer trust. This study contributes to the literature by providing a more nuanced understanding of how AI disclosure and AI-related review cues shape consumer trust. It suggests that the key issue is not AI disclosure alone, but how AI-related cues shape perceived authenticity and, in turn, consumer trust.

Journal of theoretical and applied electronic commerce researchVol. 21(5)
Halmstad University (SE), University of Latvia (LV)
Openalex Percentile: Top 3%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.