Mapping of <i>Journal of Services Marketing</i> themes: a retrospective overview using bibliometric analysis
Bibliographic record
Abstract
Purpose Journal of services marketing ( JSM ) is a leading journal that has published cutting-edge research in services marketing over the past 34 years. The main objective of this paper is to provide a retrospective of the thematic structure of papers published in JSM over its publication history. Design/methodology/approach This study uses bibliometric methods to present a retrospective overview of JSM themes between 1987 and 2019. Using keywords co-occurrence analysis, this paper unveils the thematic structure of JSM ’s most prolific themes. Bibliographic coupling analysis uncovers the research trends of the journal. Findings Leading authors, leading institutions, authors’ affiliated countries and critically, the dominant themes of JSM are identified. As its founding, JSM has published approximately 40 papers each year, with 2019 being its most productive year. On average, lead JSM authors to collaborate with 1.30 others. Keywords co-occurrence analysis identifies nine prominent thematic clusters, namely, “marketing to service”, “quality, satisfaction and delivery systems”, “service industries”, “relationship marketing”, “service failure, complaining and recovery”, “service dominant logic”, “technology, innovation and design”, “wellbeing” and “service encounters”. Bibliographic coupling analysis groups JSM papers into four clusters, namely, “brand & customer engagement behaviour”, “service co-creation”, “service encounters & service recovery” and “social networking”. Research limitations/implications This study is the first to analyse the thematic structure of JSM themes over its history. The themes are analysed across time periods and then compared to dominant themes identified in contemporary service research agendas. Recommendations are made based on the gaps found. This retrospective review will be useful to numerous key stakeholders including the editorial board and both existing and aspiring JSM contributors. The selection of literature is confined to Scopus. Originality/value JSM ’s retrospection is likely to attract readership to the journal. The study’s recommendations regarding which areas have matured and which are still ripe for future contributions will offer useful guidelines for all stakeholders.
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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.181 | 0.193 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".