Relationship Marketing: Revisiting the Scholarship in Sport Management and Sport Communication
Bibliographic record
Abstract
Relational perspectives have influenced marketing theory and practice over the past 40 years, with a volume of relationship marketing (RM) research accumulating over this time. In sport management specifically, a number of RM research articles have been published since the late 1990s. Although an influx has been seen, a review of said literature informs us that RM is a diverse field with no single best explanation, no clear domain and scope, and no universally accepted definition and that, most particularly, the literature is a melting pot of various concepts. This circumstance creates frustration and confusion among new researchers. Additionally, as strategic communication strategies rely on clear and consistent messaging, it is pivotal to holistically address the issue. Therefore, adopting an integrative literature review approach, this commentary revisits the RM scholarship to present, brings attention to the complex nature of the RM literature, and identifies a point of departure for researchers attempting to find a fitting “home” for their research.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| 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".