Service climate in knowledge-intensive, internal service settings
Bibliographic record
Abstract
Purpose This study aims to extend service climate research from its existing focus on routine service for external clients into a knowledge-intensive, internal (KII) service setting. This extension was important because internal knowledge workers may operate from a monopolistic perspective and not view themselves as service providers because of the technical/professional nature of their work. Design/methodology/approach Two surveys were distributed in participating organizations. One survey, completed by employees in information technology (IT) service units, contains measures of service climate, climate antecedents and technical competence. The second survey, filled out by members of their corporate customer units, taps their evaluations of service quality. Findings Service climate in IT service units significantly predicted service evaluations by their respective customer units. Importantly, service climate was more predictive than IT service employees’ technical competency. Role ambiguity, empowerment and work facilitation were also found to be significant service climate antecedents. Research limitations/implications These results provided strong empirical evidence supporting an extension of the existing service climate research to KII service settings. To the extent that front-line service employees rely on internal support to deliver quality service to external customers, managers should work to enhance the service climate in internal support units, which ultimately improves external service quality. Originality/value This is the first study that establishes the robustness of the service climate construct in KII service settings. It makes service climate a useful managerial tool for improving both internal and external service quality.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".