The Heterogeneous Impact of the Age of Sports Star Spokespersons on Brand Promotion and Its Underlying Mechanism: From the Perspective of Age Differences
Bibliographic record
Abstract
Athletes play an important role in the business market, and their signings influence fans’ attention to brands. The main goal of cooperation is mutual benefit and win-win, and the interests of both parties are the primary consideration. Players’ behavior and popularity growth will directly affect brand reputation and market response. Young players have room to develop but receive lower salaries, while older players are valued for their experience and impact. Brands need to be cautious when choosing spokespersons, including age, image, etc. Research shows that spokespersons have a significant impact on brand equity and consumer attitudes. There are differences in the choice of spokespersons between the two brands, reflecting different brand strategies. The age of sports stars has a profound impact on brands. Young stars can attract more attention and recognition, while older spokespersons have a mature and stable image. Companies need to carefully consider spokesperson selection and crisis management, establish diversified marketing strategies, and ensure healthy brand development. With the rapid spread of social media, the image and reputation of the spokesperson determine the success of the brand, so it is crucial to reduce the risk of relying on a single spokesperson and comprehensively plan the strategy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".