Event leveraging of mega sport events: a SWOT analysis approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the strengths, weaknesses, opportunities and threats (SWOT) that a host city can experience to utilize these for future strategy planning and event leveraging. Design/methodology/approach Five phone interviews were conducted with administrators of the Athens Olympic Games. Respondents were asked four questions relating to the SWOT of hosting the Olympic Games. Responses collected were transcribed and analyzed using a content analysis. Findings Findings suggest that the strengths lie in having certain infrastructures in place, volunteers, a strong economy and good political standing. Weaknesses stem from a lack of infrastructure, the size of the country, uncertain political and economic stability. Opportunities included the growth of the tourism industry, business developments, increase in the quality of life, the use of legacies post‐event, and the improvement and development of infrastructures. Threats included the cost of the event, pollution, relying on the event to rejuvenate the economy and the displacement of residents. Originality/value The SWOT analysis conducted in this paper laid the foundation for strategic planning for future host cities' organizers while taking into consideration the weaknesses and problems that have been experienced by the organization of former Olympic Games host cities. Moreover, the SWOT analysis conducted in this paper goes one step further by incorporating Chalip's leveraging model in order to identify what strengths and weaknesses need to be addressed in order for a host city to leverage the opportunities and threats of hosting a sport event.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".