How to win a bid for major sporting events? A stakeholder analysis of the 2018 Olympic Winter Games French bid
Bibliographic record
Abstract
While understanding the planning and hosting of major sporting events is a popular research area, less is known about the bid process despite the potential economic and political spinoffs. Some studies offer criteria for successful bids and even consider the stakeholder network as a key factor. Considering the importance of the stakeholder network, we delve deeper into this area. Using the power, legitimacy and urgency framework by CitationMitchell et al. (1997), we examine the 2018 Olympic Winter Games’ French national bid competition (four candidacies) to analyse the stakeholder relationships, identify their salience and then determine stakeholder-based bid key success factors. Archival material and 28 interviews were analysed. We notably found that to increase the probability of winning, no actor alone should have a definitive status, the sport stakeholder group should have at least the expectant status, and no strategic stakeholder should have the latent status. We also find that a three-level analysis of the stakeholder network allows for a greater understanding of the bid governance and process dynamics at play, which help to elucidate a successful bid. We contribute to the literature by (a) showing how stakeholder salience analysis can assist in understanding the bid network governance structure; (b) demonstrating that stakeholder salience depends on the level which is analysed (local, between bids, and with the event owner), the stage (deciding to bid, national bid competition, national bid win/international competition), and the case/context; and (c) determining stakeholder-based key bid success factors such as who should and should not be more salient in the bid process.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".