How humanoid robots influence consumer preferences in the foodservice industry
Bibliographic record
Abstract
Companies in the foodservice industry are investing heavily into robots, attracted by gains in efficiency and lower labor costs. The appearance of these robots varies significantly. Some companies are investing in generalized robots that mimic the human form, while others are looking into specialized robots that look nothing like a human. This distinction in form may seem trivial when it comes to large-scale manufacturing, but it might be quite relevant to consumers when it comes to replacing vulnerable populations, such as the ones employed in food service. To that end, we investigate whether and how humanoid versus non-humanoid robots impacts consumer patronage and restaurant evaluations in a food service context across two online studies. Study 1 ( N = 303, M age = 34.9 years) shows that consumers evaluate restaurants less favorably when robots prepare food instead of humans and that this reaction is stronger when the robot has the humanoid form. Study 2 ( N = 307, M age = 39.6 years) explores the underlying cause and reveals that robots with humanoid form generate a stronger inference that they are adopted with the intent to replace human workers, which in turn reduces the perceived morality of the restaurant. Together, these findings reveal the moral inferences that consumers make about robotic labor in foodservice and offer actionable insights for restaurateurs who are considering the transition towards automation.
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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 | 0.001 | 0.006 |
| 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) | 0.005 | 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".