Helping to reduce fights before flights: How environmental stressors in organizations shape customer emotions and customer–employee interactions
Bibliographic record
Abstract
Abstract Previous examinations of environmental stressors in organizations have mostly emphasized their dysfunctional effects on individuals’ emotions and behaviors. Extending this work by drawing from the social functional perspective on emotion, we propose that customers’ negative emotional responses to environmental stressors in organizations can exert both dysfunctional and functional effects on customer–employee interactions. Specifically, we theorize that situational and physiological forms of environmental stressors can be dysfunctional by incurring customer anger, precipitating customer aggression, and diminishing employee helpfulness. We further theorize that situational relative to physiological stressors can exert functional effects in inducing customer fear that elicits empathy and helpfulness from employees. We test our model via an archival, observational, and critical incident yoked experimental study set in the airport context. This research contributes to stress theory and its organizational application by integrating theory from the social functional approach to emotion with appraisal‐based theories of stress in organizations.
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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 | 0.001 | 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) | 0.002 | 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".