Accelerating employee-related scholarship in service management
Bibliographic record
Abstract
Purpose The purpose of this paper is to accelerate research related to the employee-facets of service management by summarizing current developments in multiple research streams, providing propositions, and articulating new directions for theory and empirical inquiry. Design/methodology/approach Seven scholars provide short reviews of the core topics and findings from four employee-related research streams – collective turnover, service climate, emotional labor, and occupational stress; and generate propositions to guide future theoretical and empirical work. Four distinguished service scholars – David Bowen, Ray Fisk, Christian Grönroos, and Jochen Wirtz comment upon these research streams and provide future directions for accelerating employee-related research in service management. Findings All four research-streams yield insights that have the potential to advance service management research. Commentaries from the distinguished scholars further integrate this work with key concerns within service management including technology-enablement, transformative services, and service strategy. Originality/value This paper is unique in its scope of coverage of management topics related to service and its aim to promote interdisciplinary dialog between service management scholars and researchers conducting employee-related research relevant to services.
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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".