Bibliographic record
Abstract
Universities must secure stakeholder support to ensure the successful implementation of most initiatives. However, given the shared governance structures and collegial cultures of many universities, what strategies do university leaders enact to obtain stakeholder support? Although several stakeholder management and organizational response models have been proposed, there is limited empirical research on the actual strategies university leaders use to secure stakeholder support. This study focuses mainly on university academics - a powerful, autonomous, and intelligent stakeholder group whose support for most higher education initiatives is essential. Guided by a theoretical stakeholder management model, this research examines the strategies university leaders employ to manage this salient and sometimes adversarial group with respect to a major organizational change initiative. The evidence shows that university leaders use strategies that centre mostly on themes of shared goals, consensus, partnerships and engagement, which align with the strategies proposed by the theoretical model. However, to manage non-supportive stakeholders peer influence is enacted rather than the defend strategy recommended by the theoretical model. As a result, this study contributes to stakeholder management theory and proposes a revised stakeholder management model that is particularly applicable to the higher education sector.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 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".