When Your Co‐Worker Is a Robot: Intergroup Performance Status and Its Consequences for Workplace Attitudes
Bibliographic record
Abstract
ABSTRACT Perceiving robots as outperforming humans in the workplace can be particularly damaging to employees’ workplace attitudes (e.g., organizational commitment and job satisfaction) because they create intergroup comparisons of performance status. We conducted five experiments with college students and working adults, using both scenario‐based designs and simulated work environments to test this idea. Participants’ perception of lower performance status than robots led to greater realistic threat to job security, which further lower workplace attitudes. Robots’ humanlike features did not moderate these effects. Using a benchmark as the comparison target had no significant effect on perceived threat or workplace attitudes, suggesting that the social comparison process—not low performance status itself—drives these effects. Comparisons with robots revealed both parallels and distinctions from comparisons with humans. Team‐based incentive policy, as an intervention, reduced the negative effects on workplace attitudes but not on realistic threats, compensating for rather than mitigated robot threat.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".