Sports operations management: examining the relationship between environmental uncertainty and quality management orientation
Bibliographic record
Abstract
Research question: The outcome of a sporting competition is uncertain and one of the key reasons for the sustained popularity of spectator sport. Whilst unique and exciting, this context poses challenges for the management of the sporting experience as there is no control over the outcome of the competition; a disappointing result on-field may translate to a disappointing overall experience for the spectators. We wish to understand if and how quality management practices can be used in off-field operations to mitigate on-field uncertainty, and thus have greater control over spectator perception of the sporting experience.Research methods: A multi-country survey of operations managers of sporting stadia in the United Kingdom, United States, Canada, Australia and New Zealand was conducted. We operationalize environmental uncertainty as spectator co-creation and enforced collaboration, and assess quality management orientation from both a customer and process perspective. Linear regression is used for data analysis.Results and Findings: Surprisingly, we find that environmental uncertainty does not encourage the orientation of quality management practices towards the customer. Instead, we find a greater application of process focus. In considering sporting fans as passive customers rather than active co-creators of value, quality management practices seem to have skewed towards process rather than person.Implications: Customer satisfaction appears as secondary to process performance in the sample of stadia examined. This is in contrast to studies that have encouraged a focus on the customer in contexts of environmental uncertainty. We suggest a renewed focus on the customer for the longevity of sporting stadia.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".