Innovation drivers, barriers, and strategies of organizing committees for the Olympic games: an embedded single-case study approach
Bibliographic record
Abstract
Research question Using organizational innovation as a framework, this empirical study explores the drivers of innovation within Organizing Committees for the Olympic Games (OCOGs), highlights barriers that may hamper their abilities to innovate, and discusses strategies to overcome these barriers and enhance innovation capabilities.Research method A qualitative embedded single-case study approach focusing on two OCOGs (i.e. 2024 Paris Organizing Committee for the Olympic Games and the 2028 Los Angeles Organizing Committee for the Olympic Games) as the embedded units of analysis was conducted through an analysis of archival materials and interviews with key informants (n = 16) regarding innovation.Results and findings Results suggest OCOGs experience various environmental, organizational, and individual drivers toward innovation but also encounter certain barriers (e.g. resistance to change, organizational characteristics, and knowledge limitations) that hinder the implementation of new practices. Suggestions are provided for ways OCOGs can enhance their innovation capabilities.Implications This study adds a new dimension to sport event management literature by applying innovation concepts (i.e. organizational innovation) to the unique context of OCOGs, where innovation has become increasingly important in meeting stakeholder expectations. In doing so, this study contributes to the literature on innovation-related strategies and offers insight on how mega-sport event organizers can enhance their innovative capabilities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".