Generative AI streamers in action: a source credibility perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generative AI technologies are reshaping online virtual personas with hyper-realism, accelerating their adoption across online business contexts. This research investigates audience responses to hyper-realistic generative AI streamers versus human streamers within livestream commerce, drawing on source credibility theory to explain potential differences. Across four online scenario-based experiments, we find that audiences generally exhibit lower purchase intentions toward AI streamers compared to their human counterparts. This disparity is largely attributed to the lower perceived credibility of AI streamers. Moreover, this credibility gap is moderated by audience traits, such as novelty-seeking and social overload, with AI streamers appealing more to audiences characterized by higher levels of these traits. This research enriches scholarly understanding of audience response to AI-generated personas in dynamic online environments and offers practical insights for marketers deploying AI streamers in their businesses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it