A Framework for the Analysis of Strategic Approaches Employed by Non-profit Sport Organisations in Seeking Corporate Sponsorship
Bibliographic record
Abstract
Despite a continually growing body of literature that investigates the nature of corporate sponsorship of sport from the perspective of the donor, it is suggested that very little is known about how sport organisations are positioning themselves in their efforts to attract sponsors. Additionally, we argue that there has been limited effort in relating the sponsorship endeavours of sport organisations to the broader strategic management literature. This paper develops a framework that highlights the primary factors that underpin the ability of non-profit sport organisations to generate funding from the corporate sector. The analysis is based on data obtained from semi-structured interviews with marketing personnel in thirty-four Canadian national sport organisations (NSOs). Analysis of the data reveals two key environmental factors that appear to contribute to the ability of NSOs to raise sponsorship funds: media exposure and participation rates. The framework classifies sport organisations as belonging to one of five categorisations, based on their relative levels of these two factors. The discussion of the results provides an assessment of the ability of NSOs to influence these primary sponsorship success determinants. We suggest ways in which the framework developed here could be used in the future to further our understanding of the strategic nature of sponsorship acquisition.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".