Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations
Bibliographic record
Abstract
Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psychology remains largely unexplored. We present four experiments that demonstrate how food service automation leads to negative downstream effects (i.e., diminished taste perceptions, decreased willingness to pay, less favorable attitudes towards food items) across multiple food categories. This stems in part from two distinct contagion effects, whereby automation appears to undermine the food's ability to contain symbolic love (positive contagion from human contact) while simultaneously increasing feelings of disgust (negative contagion from machine contact). Moreover, we highlight how communicating the consumer-oriented benefits of automation can suppress the disgust associated with automation and subsequently mitigate the deleterious effects on consumer evaluations. Our findings suggest that service retailers should consider the psychological impact on consumers when shifting away from human involvement in a category as intimate and consequential as the production of our food.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".