Service climate in knowledge-intensive, internal service settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it