Breaking rules yet helpful for all: Beneficial effects of pro‐customer rule breaking on employee outcomes
Bibliographic record
Abstract
Summary Pro‐customer rule breaking refers to employees' breaking of organizational rules with the primary intention of helping customers or providing better customer service. In spite of its prosocial nature, it is unclear whether and how pro‐customer rule breaking also benefits employees who engage in this behavior. Drawing on self‐determination theory, we examine employees' well‐being and voice at work as outcomes of pro‐customer rule breaking. Across a simulation study and a critical incident‐based survey study, we found that pro‐customer rule breaking was positively related to employee psychological need fulfillment, which, in turn, was associated with lower emotional exhaustion, higher job satisfaction, and increased voice. Furthermore, normative conflict with organizational rule moderated the positive relationship between pro‐customer rule breaking and psychological need fulfillment such that employees with high normative conflict with organizational rule (i.e., employee perception that their existing organizational rule results in inefficiency and that their organization could be much better if it changed its practices) benefited more from their pro‐customer rule breaking. We discuss the theoretical and practical implications of these findings and offer directions for future research.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".