The Level of Involvement with the Olimpic Games and its Influence in Sport Sponsorship.
Bibliographic record
Abstract
In this research, intend to demonstrate the influence of the consumer involvement in a finer brand purchase intent of the sponsor, and generate an effect on the consumers "goodwill" towards the sponsor and also to perceive a greater fit between sponsor and event, and finally cause the consumer better exposure in the event. This was chosen the Rio 2016 Olympic Games. In this study, different analyses have been conducted to verify reliability and factorial scales of charges. As well as analyses to contrast the hypotheses: ANOVAS and structural equations using the SmartPLS program (is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method[1]), to check the fit for the model. It is interesting to highlight the contribution to this research, because if organizations look for sporting events with a public involved with them. Consequently will get a bigger intention to purchase the sponsor's brand, a finer perception of both the goodwill and the fit between the event and the sponsor, and finally a larger exposure in the event and accordingly, to the promotions made by sponsor brands. [1] Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".