Journal of Service Theory and Practice at age 30: past, present and future contributions to service research
Bibliographic record
Abstract
Purpose In 2020, the Journal of Service Theory and Practice (JSTP), previously titled Managing Service Quality, celebrates its 30th anniversary. This study provides a retrospective of the evolution and contribution of the journal to service research by identifying its major trends, research constituents, factors contributing to citations and thematic structure over its 29 active years (1991–2019). The paper concludes by providing directions and ideas for progressing service research Design/methodology/approach The study uses the Scopus database to extract JSTP's bibliographic data. It employs bibliometric methods to study the trends of the journal, such as the citation structure and most-contributing authors, institutions and countries. Bibliographic coupling and keyword co-occurrence analyses are used to study the intellectual structure of the journal. Regression analysis discloses the factors influencing citations of JSTP articles. Factors explaining the citation count of JSTP articles include article age, number of author keywords, article length, title length and number of references. Findings JSTP's influence has grown significantly in the scientific community, which is evidenced by findings relating to the citation counts, the thematic scope/variety and authorship features of the JSTP papers published during the last 30 years. JSTP attracts publications from around the globe, but most contributions come from the United States, United Kingdom and Australia. Although JSTP has continuously evolved with new and varied themes, a bibliographic coupling analysis clustered JSTP articles into five major clusters. Research limitations/implications The limitations of the Scopus database may impact the study's results. Originality/value This study is the first to provide a comprehensive review of JSTP since its launch. It is useful to the editorial board and other JSTP stakeholders as well as service scholars alike.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".