Mapping globally branded business schools: a strategic positioning analysis
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".