Relationship marketing: a strategy for acquiring long-term strategic sponsorships in the disability sport sector
Bibliographic record
Abstract
Since the founding of the National Wheelchair Basketball Association (NWBA) in 1949, wheelchair basketball has expanded to over 200 teams in the U.S. and Canada. Despite the success and growth of wheelchair basketball in the U.S., NWBA programs still face funding challenges. Considering the potential to generate funding through corporate sponsorship, nine semi-structured interviews were conducted with professionals in charge of sponsorship management of NWBA programs to gain insight into the acquisition and relationship management of their sponsorship programs. Findings showed several unique ways NWBA programs attract sponsors, including focusing on sponsors with an existing interest in the disability community, and highlighting the unique assets of NWBA teams such as their compelling stories, the program’s impact, disability expertise, and corporate social engagement opportunities. Communication, evaluation, and cross-marketing opportunities were found to be key in retaining sponsors. Both successful sponsorship acquisition and retention are underlined by relationship marketing efforts to build commitment and trust by establishing an emotional connection and mutually beneficial relationship between the sponsor and the team, as well as having a dense network of relationships between the sport property and sponsor. The results aid current and future programs in successful sponsorship acquisition and retention.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".