Building a framework for issues management in sport through stakeholder theory
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
Sport managers are continually challenged by changing constituent environments as they work toward short‐term and long‐term organizational goals. At any given time, decision‐makers may have several issues that must be addressed in order to satisfy the demands of their organization's constituents. As such, managers need robust methods with which to analyze the organization's environment in order to develop strategic planning initiatives. This paper reviews the basic tenets of stakeholder theory and discusses/suggests applications to sports‐related issues, in an effort to show that stakeholder theory has descriptive and prescriptive value for sport management practitioners and academics alike. Stakeholder analysis can be used to identify stakeholders, stakeholder claims, motivations and relative importance, by evaluating stakeholders’ levels of power, legitimacy and urgency related to the issue (Mitchell, Agle & Wood, 1997). These attributes exist at varying levels as an issue develops and solutions are presented over time. In classifying stakeholders based on the attributes of power, legitimacy and urgency, and identifying their underlying needs and expectations, sport managers can more efficiently allocate resources. This paper provides a framework for issue analysis based on the tenets of stakeholder theory and issues management. It also proposes a research agenda to evaluate the framework, as well as considerations for managers wishing to use the framework. In doing so, stakeholder theory allows for new insight into issues management, from both research and practical perspectives.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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