A self‐verification perspective on customer mistreatment and customer‐directed organizational citizenship behaviors
Bibliographic record
Abstract
Summary Customer mistreatment events play a major role in employees' subsequent customer service behaviors, and is believed to have implications for employees' sense of self. We extend this line of research by developing a self‐verification account of the relationship between customer mistreatment and customer‐directed OCBs (OCB‐Cs) by examining theoretically prescribed novel mechanisms (i.e., self‐verification) and boundary conditions (i.e., self‐esteem and entity customer appreciation) for this relationship. We conducted a programmatic series of studies using daily diary (Study 1), audio vignette (Study 2), and behavioral experiment (Study 3) designs to test the proposed model. The overall pattern of results showed that customer mistreatment led employees to feel less self‐verified, especially among those with higher trait self‐esteem. These employees in turn were more likely to withhold OCB‐Cs, especially among those perceiving lower levels of entity customer appreciation. Overall, these results deepen our understanding of the role of the self‐concept in how employees experience and react to customer mistreatment‐‐depending on how employees see themselves and how they see their customers in general.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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".