Service operations: what have we learned?
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify research themes in service operations that have great potential for exciting and innovative conceptual and empirical work. To frame these research themes, the paper provides a systematic literature review of operations articles published in theJournal of Service Management(JOSM). The thorough review of published work inJOSMand proposed research themes are presented in hopes that they will inspire impactful research on service operations. These themes are further developed in a companion paper, “Service operations: what’s next?” (Fieldet al., 2018). Design/methodology/approach TheJOSMService Operations Expert Research Panel conducted a Delphi study to generate research themes where leading-edge research on service operations is being done or has yet to be done. Nearly 700 articles published inJOSMfrom its inception through 2016 were reviewed and classified by discipline focus. The subset of service operations articles was then further categorized according to the eight identified research themes plus an additional category that primarily represented traditional manufacturing approaches applied in service settings. Findings From the Delphi study, the following key themes emerged: service supply networks, evaluating and measuring service operations performance, understanding customer and employee behavior in service operations, managing servitization, managing knowledge-based service contexts, managing participation roles and responsibilities in service operations, addressing society’s challenges through service operations, and the operational implications of the sharing economy. Based on the literature review, approximately 20 percent of the published work inJOSMis operations focused, with earlier articles predominantly applying traditional manufacturing approaches in service settings. However, the percentage of these traditional types of articles has been steadily decreasing, suggesting a trend toward dedicated research frameworks and themes that are unique to the design and management of services operations. Originality/value The paper presents key research themes for advancing conceptual and empirical research on service operations. Additionally, a review of the past and current landscape of operations articles published inJOSMoffers an understanding of the scholarly conversation so far and sets a foundation from which to build future research.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.023 | 0.036 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".