The Social Dimension of Service Interactions
Bibliographic record
Abstract
Service interactions run a gamut from an instrumental self-focus to full social appreciation. Observing another customer’s incivility toward a frontline employee can emphasize social concerns as guiding principles for the observer’s own service interaction. Five studies test these dynamics; the results reveal that an incivility incident leads observers to prioritize social over market concerns. This reprioritization becomes manifest in a subsequent service interaction through increased feelings of warmth toward the employee who experienced incivility. In turn, feelings of warmth prompt observers to provide emotional support to the affected employee. Yet such prosocial inclinations are less likely when an employee is held responsible for or reciprocates incivility. Finally, this article also examines the effects of different employee reaction strategies on observers’ inferences about the employee and the service firm, showing that observers are most positively disposed toward the employee and the firm when the former reacts to incivility with a polite reprimand. Together, the results suggest that, contrary to past theorizing, observing customers may contribute to employee well-being, contingent on appropriate employee responses. Notably, the commonly prescribed polite, submissive employee reaction that requires emotional labor may not be the most desirable reaction—neither for the employee nor for the firm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".