Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
The importance of online reviews for consumers' decision-making engages fraudsters to game the review system by writing or buying fake reviews. Fake reviews are a main threat to consumers since they are hardly distinguishable from genuine human-made reviews. Moreover, advances in generative AI like ChatGPT foster the simple creation of persuasive text, such as high-quality fake reviews. While prior studies primarily focused on automatic fake review detection, little is known about how consumers react to AI-generated fake reviews. Based on a quantitative-qualitative study with 151 consumers (906 review classifications), we found that humans cannot reliably distinguish between genuine and AI-generated fake reviews (accuracy= 53.2%). They are especially worse at detecting negative AI-generated fake reviews. Our findings extend prior research by examining consumers' ability to detect AI-generated fake reviews, identifying a set of cues they use for review classification, and investigating the cues' effectiveness for detection. Further, we derive practical implications.
Recommended Citation
Fröhnel, Kim; Santelmann, Bennet; and Zarnekow, Rüdiger, "Genuine or Fake? Explaining Consumers’ Perception and Detection of AI-Generated Fake Reviews" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 6.
https://aisel.aisnet.org/hicss-58/in/impacts/6
Genuine or Fake? Explaining Consumers’ Perception and Detection of AI-Generated Fake Reviews
Hilton Waikoloa Village, Hawaii
The importance of online reviews for consumers' decision-making engages fraudsters to game the review system by writing or buying fake reviews. Fake reviews are a main threat to consumers since they are hardly distinguishable from genuine human-made reviews. Moreover, advances in generative AI like ChatGPT foster the simple creation of persuasive text, such as high-quality fake reviews. While prior studies primarily focused on automatic fake review detection, little is known about how consumers react to AI-generated fake reviews. Based on a quantitative-qualitative study with 151 consumers (906 review classifications), we found that humans cannot reliably distinguish between genuine and AI-generated fake reviews (accuracy= 53.2%). They are especially worse at detecting negative AI-generated fake reviews. Our findings extend prior research by examining consumers' ability to detect AI-generated fake reviews, identifying a set of cues they use for review classification, and investigating the cues' effectiveness for detection. Further, we derive practical implications.
https://aisel.aisnet.org/hicss-58/in/impacts/6