Canadian National Sport Organizations’ Use of the Web for Relationship Marketing in Promoting Sport Participation
Bibliographic record
Abstract
Sport-participation development requires a systematic process involving knowledge creation and dissemination and interactions between national sport organizations (NSOs), participants, clubs, and associations, as well as other agencies. Using a relationship-marketing approach (Grönroos, 1997, Gummesson, 2002, Olkkonen, 1999), this article addresses the question, How do Canadian NSOs use the Web, in terms of functionality and services offered, to create and maintain relationships with sport participants and their sport-delivery partners? Ten Canadian NSOs’ Web sites were examined. Functionality was analyzed using Burgess and Cooper’s (2000) eMICA model, and NSOs’ use of the Internet to establish and maintain relationships with sport participants was analyzed using Wang, Head, and Archer’s (2000) relationshipbuilding process model for the Web. It was found that Canadian NSOs were receptive to the use of the Web, but their information-gathering and -dissemination activities, which make up the relationship-building process, appear sparse and in some cases are lagging behind the voluntary sector in the country.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".