Beyond humans: Consumer reluctance to adopt zoonotic artificial intelligence
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".