Recognizing and Prioritizing Economic Criteria Affecting Ticket Sales Management in World Sport Mega Events
Bibliographic record
Abstract
Purpose: The aim of this study was to identify and prioritize economic indicators affecting ticket sales management in important sporting events of the world.Method: According to its goal, this research was applied, and it was based on a mixed (qualitative-quantitative) method. The statistical population of the qualitative part included all countries around the world, among which the United States, Australia, China, South Korea, United Kingdom, Finland, Canada, Qatar, Germany, Turkey and Iran were selected as the statistical sample. The sample of quantitative part consisted of 12 members of Iranian sports marketing elite. In this research, various internet sites, databases, books and articles, as well as taking notes and recording documentary observations have been used. Also, in the quantitative part, first a questionnaire was designed for pairwise comparison of indicators and distributed among 12 sports marketing elites to be analyzed through hierarchical analysis.Results: The results showed that the smart and multi-purpose ticket index and ticket counter index with a weight of 0.138 and 0.020 were in the first and last rank, respectively. The results of qualitative part showed what indicators have been used to manage, prepare and distribute tickets and earn economic income in selected countries of the world.Conclusion: According to the findings, it can be stated that each of these factors has brought about results and achievements such as preventing the black market, ticket sales, event control and security, revenue generation as well as other benefits.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".