The Olympic Games and associative sponsorship
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the brand relationships between a mega-sports event, the Olympic Games, and its branded main sponsors, using the lens of brand personality. Design/methodology/approach The study uses the internet-based website communications of the sponsor and event brands to assess congruence in brand personality identity exhibited in the communications of sponsors and how these relate to the event brand itself. A lexical analysis of the website text identifies and graphically represents the dominant brand personality traits of the brands relative to each other. Findings The results show the Olympic Games is communicating excitement as a leading brand personality dimension. Sponsors of the Olympics largely take on its dominant brand dimension, but do not adapt their whole brand personality to that of the Olympics and benefit by adding excitement without losing their individual character. The transference is more pronounced for long-running sponsors. Practical implications Sponsorship of the Olympic Games does give brands the opportunity to capture or borrow the excitement dimension alongside building or reinforcing their own dominant brand personality trait or to begin to subtly alter their brand positioning. Originality/value This study is the first to examine how the sponsor’s brand aligns with the event being sponsored as a basis for developing a strong shared image and associative dimensions complimentary to the positioning of the brand itself.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".