Beyond individual justice facets: How (Mis)alignment between justice climates affects customer satisfaction through frontline customer extra-role service behavior
Bibliographic record
Abstract
This study examines the combined effect of organizational justice facets on store-level customer extra-role service behavior, and subsequently on customer satisfaction. Hypotheses were tested on a sample of 1,951 employees in 121 business units from four countries, and on 55,731 customers of an international retailer. The results of polynomial regression and response surface analysis revealed that unit customer service performance and customer satisfaction are higher when justice facets are aligned at high levels, compared to when they are aligned at low levels. Moreover, we found evidence that the consequences of misalignment between justice facets are asymmetrical. Unit outcomes were higher when distributive justice (DJ) and procedural justice (PJ) climates were both higher than the interpersonal justice climate, compared to when the inverse was true. Conversely, unit outcomes increased when informational justice (INF-J) climate was higher than DJ and PJ climates, compared to when DJ and PJ climates were higher than INF-J climates. The observed effects of misalignment between justice facets were non-linear, as complex curvilinear relationships were moderator-dependent. Customer satisfaction was higher in stores with higher team customer service behavior, and team service behavior was found to be a significant conduit by which justice facets (mis)alignment influence customer satisfaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".