Weapons of mass intrusion: the leveraging of ambush marketing strategies
Bibliographic record
Abstract
Purpose – The study presented in this article aims to examine the impact of the leveraging of three distinct ambush marketing strategies that are under-researched in the literature: Promotion, Event, and Broadcast. Design/methodology/approach – An experiment was conducted where the type of ambush strategy was manipulated (i.e. Promotion, Event, Broadcast, no ambush) as well as the market dominance of the sponsor (i.e. dominant or non-dominant) and the congruence level between the event and the sponsor (i.e. high or low congruence). Findings – Ambush strategies' impacts differ widely. The Broadcast strategy is the most harmful to the identification of the actual sponsor; the Event strategy favors the identification of the pseudo-sponsor as the sponsor, while the Promotion strategy is both harmful to the actual sponsor and beneficial for the pseudo-sponsor. Furthermore, although dominant brands benefit more from their sponsorships, they are more affected by an ambush than non-dominant brands. Research limitations/implications – Only one sponsor and one pseudo-sponsor were considered at a time. In addition, digital media were not investigated as vectors of ambush marketing. Further research where multiple sponsors and pseudo-sponsors are leveraging their associations to an event, using both off and on-line media, needs to be undertaken. Practical implications – Against the Promotion strategy sponsors need to create not only strong but also unique associations with the event. The Event strategy can be circumvented with preemptive smaller scale events. Exclusive access to the program broadcast for event sponsors can protect against pseudo-sponsors. Originality/value – This study is the first to provide empirical evidence regarding the impact of the Promotion, Event, and Broadcast strategies. Previous studies had focused almost exclusively on another strategy: the airing of commercials by pseudo-sponsors during event broadcast against which most sponsors are now effectively protected.
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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 | 0.017 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 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".