Sports operations management: examining the relationship between environmental uncertainty and quality management orientation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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 it