Sport organizations under tension: dynamic capabilities and resilience building during the COVID-19 pandemic
Bibliographic record
Abstract
Research question Sport management research on resilience has primarily focused on athletes and sport teams. This has led to a limited understanding of how sport organizations can enhance their resilience. Therefore, drawing on the dynamic capabilities framework, the purpose of this study is to explore the processes that contributed to the resilience of sport organizations during the COVID-19 pandemic. Additionally, the present study seeks to investigate whether the type and size of sport organizations influenced their dynamic capabilities and resilience during this large-scale crisis.Research methods Based on a qualitative approach, a thematic analysis was conducted of semi-structured interviews with 41 high-ranking managers from 37 Canadian sport organizations.Results and findings Findings indicate that economic, social, technological, human, and political capital were all important sources of resilience for sport organizations during the COVID-19. Also, we found that the acquisition, reconfiguration, and integration of these five forms of capital are key capabilities through which sport organizations can enhance their crisis resilience. Lastly, segmentation analyses revealed that while most dynamic capabilities were leveraged by all sport organizations, others were only applied by certain types.Implications This study offers a fined-grained analysis of the processes through which sport organizations can enhance their resilience and manage large-scale crises effectively. Specifically, our findings highlight that capabilities that expand, refine, and integrate the capital base of sport organizations are integral to their resilience-building efforts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".