Mapping globally branded business schools: a strategic positioning analysis
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
Purpose The purpose of this paper is to investigate the strategic profiles and differences across globally leading business schools. Design/methodology/approach This paper used the concepts of strategic group identity and domain consensus to examine the differences across the business schools. Cluster analysis is applied to identify strategic groups among 82 global schools from the USA, Canada, Europe, Asia and Australia. Findings Ten strategic groups – essentially similar strategic “clusters” – are identified by the clustering analysis. The results demonstrate that the groups do have different resource and reputation profiles. Research limitations/implications Future research can improve the research base by collecting data on financial variables such as endowments, providing metrics by which a school's efficiency can be assessed, or collecting longitudinal data. Furthermore, a form of cognitive strategic mapping could be achieved through survey and interview mechanisms in order to highlight the perspectives of deans and senior managers of business schools. Originality/value This research contributes to the literature in two aspects. First, this research provides a clear mapping of the strategic “bands” across globally branded business schools. The results are highly timely in today's debate about the nature and future of business schools. Second, this research demonstrates that strategic group theory can be applied in the business school context.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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