Beyond humans: Consumer reluctance to adopt zoonotic artificial intelligence
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
Abstract In addition to humanoid‐robotic designs, an increasing number of artificial intelligence (AI)‐powered services are being represented by animals, referred to as zoonotic design. Yet, little is known about the consequential effects of such zoonotic AI on consumer adoption of these services. Drawing on the concepts of prototypicality, Cognitive Load Theory, and the “Match‐up” Hypothesis, the current research uncovers how the use of zoonotic designs, as opposed to robotic ones, may negatively influence consumers’ adoption of AI over a human provider. The results of seven studies suggest that consumers are less likely to choose an AI over a human provider for performing tasks when the AI has a zoonotic embodiment rather than a robotic embodiment. This negative effect is mediated by the increased cognitive difficulty associated with linking the AI prototype to the task. However, such a negative effect decreases when the characteristics of the animal are congruent with the task and is even reversed when the congruent task is of a hedonic nature. These findings advance the understanding of consumer–AI interactions in the context of zoonotic embodiment and provide valuable managerial insights into when and how firms should use zoonotic design for AI‐powered services.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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