Sports Sponsorship Announcements and Marketing Capability
Bibliographic record
Abstract
Sports sponsorships are almost a $20 billion business in North America alone. Yet, despite the significant academic and corporate interest in such high financial stakes, the literature is equivocal on several key aspects. While some papers report that sports sponsorships enhance shareholder value, others dispute this. Furthermore, the marketing determinants of this value are unclear, particularly the role of firms’ marketing capabilities. To address these, the authors first created a database of sports sponsorship announcements over 19 years by Canadian and U.S. firms, complementing it with the stock market and firm-level financial and marketing data. The authors then conducted an event study and found that investor response to sports sponsorship announcements is, on average, positive. The authors found that investors not only credit firms with higher marketing capabilities, amplifying their positive reaction, but that they also seem to use firms’ marketing capabilities to offset the potential barriers to the value generated from these announcements. Specifically, for investors, the firms’ marketing capabilities can compensate for the dampening effect of financial risk. Our results are robust to considerations of sample selection bias, endogeneity, and outliers.
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 | 0.001 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".