The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2692
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:01:23.775402Z
status: published
lens: p_space
sector: science
headline: Why AI Looks Less Real: The Role of Cultural Learning Cues in Tourism Destination Imagery
dek: Although generative artificial intelligence (AI) has increasingly been used to create destination marketing images, tourists’ responses to such images remain unclear. Prior research has often attributed negative reactions to the visual characteristics of AI-generated images. However, limited attention has been paid to how tourists interpret these images within broader cultural contexts. Drawing on authenticity theory and cultural learning theory, this research examines the effect of image type (AI vs. human) on…
gain_title: (none)
problem_title: In three experiments, AI-generated destination marketing images reduced tourists' perceived authenticity and visit intention compared with images taken by humans, and heritage cultural cues made authenticity concerns worse.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In three experiments, AI-generated destination marketing images reduced tourists' perceived authenticity and visit intention compared with images taken by humans, and heritage cultural cues made authenticity concerns worse.
problem_evidence: AI-generated images reduce perceived authenticity and visit intention compared with images taken by humans | heritage cultural cues amplify authenticity concerns
quick_read: A peer-reviewed study in the Journal of Theoretical and Applied Electronic Commerce Research ran three experiments on how tourists respond to destination marketing images made by generative AI. Compared with images taken by humans, the AI images reduced perceived authenticity and visit intention.

Earlier research mostly blamed negative reactions on the visual look of AI images. This study instead examined how tourists read the images within cultural contexts, drawing on authenticity theory and cultural learning theory.

The type of cultural cue mattered. Salient cultural learning cues raised perceived authenticity and visit intentions overall. Commodified cultural cues softened negative responses to AI images, while heritage cultural cues amplified authenticity concerns.
limitation: 
tag: Evidence-backed problem
key_points: The research used three experiments comparing AI-generated destination images with images taken by humans. | AI-generated images reduced both perceived authenticity and visit intention relative to human-taken images. | Salient cultural learning cues raised tourists' perceived authenticity and visit intentions. | Commodified cultural cues mitigated tourists' negative responses to AI-generated images. | Heritage cultural cues amplified authenticity concerns about AI-generated images. | The authors drew on authenticity theory and cultural learning theory to explain the effects.
rundown: Generative AI is increasingly used to create destination marketing images, yet how tourists respond has been unclear. The study tested the effect of image type, AI versus human, on perceived authenticity and visit intention.

Across three experiments, AI images scored lower than human-taken images on both measures. The authors also tested whether cultural learning cues change this pattern, treating them as a moderating factor.

The cue type produced different outcomes. Commodified cultural cues reduced the penalty for AI images, while heritage cues made authenticity concerns stronger. The authors say the findings give destination marketers guidance on where AI images can be deployed effectively.
sources:
- peer_reviewed | Journal of Theoretical and Applied Electronic Commerce Research | https://doi.org/10.3390/jtaer21060193 | 2026-06-19
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

f8a42e353020f3afb73881c697614590b7823984fc8ec5653861186c5e73832d

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000