Is Seeing Eye to Eye Always Beneficial? How and When (Dis)agreement on Service Climate Influences Store Turnover and Sales Performance
Bibliographic record
Abstract
This study examines the effect of (dis)agreement between the employees and their store manager regarding service climate on store-level turnover and subsequently sales performance. In addition, we test the moderating effect of perceived employee fit with customers on these relationships. Using polynomial regression and response surface methodology with data from 753 frontline employees and 125 managers nested in 125 stores, we found that collective turnover is lower when the store manager and the employees both perceive (vertical agreement) that customer service is prioritized at moderate levels. However, turnover is higher when managers and employees do not agree on the level of the service climate (vertical disagreement). The results indicate that the beneficial effect of vertical service climate agreement on turnover was higher when perceived employee-customer fit was high. The detrimental effect of vertical service climate disagreement on turnover was reduced when the strength of employees’ service climate was strong (high horizontal agreement). Furthermore, our examination found that the level of turnover in stores was negatively related to sales performance and that the effect of vertical service climate agreement on sales performance was conditional on the degree of perceived employee-customer fit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.021 |
| 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) | 0.004 | 0.001 |
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".