Service operations: what’s next?
Bibliographic record
Abstract
Purpose The purpose of this paper is to present exciting and innovative research questions in service operations that are aligned with eight key themes and related topics determined by theJournal of Service Management(JOSM) Service Operations Expert Research Panel. By offering a good number of such research questions, this paper provides a broad range of ideas to spur conceptual and empirical research related to service operations and encourage the continued creation of deep knowledge within the field, as well as collaborative research across disciplines that develops and incorporates insights from service operations. Design/methodology/approach Based on a Delphi study, described in the companion article, “Service Operations: What Have We Learned?,” the panel identified eight key research themes in service operations where leading-edge research is being done or has yet to be done (Victorinoet al., 2018). In this paper, three or four topics within each theme are selected and multiple questions for each topic are proposed to guide research efforts. The topics and questions, while wide-ranging, are only representative of the many ongoing research opportunities related to service operations. Findings The field of service operations has many interesting research topics and questions that are largely unexplored. Furthermore, these research areas are not only increasingly integrative across multiple themes within operations but often transcend functional disciplines. This creates opportunities for ever more impactful research with a greater reach throughout the service system and suggests that service researchers, regardless of functional affiliation, can contribute to the ongoing conversation on the role of service operations in value creation. Originality/value Leveraging the collective knowledge of theJOSMService Operations Expert Research Panel to expand on the research themes generated from the Delphi study, novel questions for future study are put forward. Recognizing that the number of potential research questions is virtually unlimited, summary questions by theme and topic are also provided. These questions represent a synopsis of the individual questions and can serve as a quick reference guide for researchers interested in pursuing new directions in conceptual and empirical research in service operations. This summary also serves as a framework to facilitate the formulation of additional research topics and questions.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.026 | 0.028 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".