Ambush Marketing Via Social Media: The Case of the Three Most Recent Olympic Games
Bibliographic record
Abstract
This study explored the practices and strategies of ambush marketing via social media (SM) during the 2014 Sochi, 2016 Rio, and 2018 PyeongChang Olympic Games. An observational netnography method was adopted to investigate direct industry competitors’ (of the Olympic sponsors) use of SM for the purpose of ambush marketing during the 2014, 2016, and 2018 Games. Data were gathered from the official Twitter accounts of 15 direct industry competitors over the three most recent Games. Despite a series of SM guidelines released by IOC for the 2014, 2016, and 2018 Games, the findings showed that the practice of ambush marketing via SM was evident during each of the Games. Direct industry competitors were found employing four specific ambush strategies, namely, associative, values, coattail, and property infringement. Theoretical and practical implications, as well as an impetus for future research, are suggested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".