Integrating higher degree education with practice: exploring the value proposition of executive MBA apprenticeships
Bibliographic record
Abstract
Purpose The study aims to explore how the collective representations of stakeholders associated with an Executive MBA (EMBA) apprenticeship are conceptualised, and what value elements are perceived as relevant for personal, professional and organisational development. Design/methodology/approach Forty stakeholders participated in a mixed methods-based group concept mapping (GCM) approach, representing the tripartite relationship of an EMBA apprenticeship. This approach allowed the deconstruction and shared understanding of the value proposition of higher degree apprenticeships. Findings Analysis identified seven conceptualised value clusters around an eight central cluster “Professional Development”. Two regions of meaning were identified: the personal dimensions of apprentices and their contribution within and to their organisation. Findings showed that a work-based designed curriculum improves confidence and communication skills and that much of the value proposition is personal and self-managed by the apprentices. The tripartite relationship was shown to be incomplete and inconsistent, particularly regarding mentoring and reflective practice. Practical implications The pandemic has brought about significant changes in business practice. This study identifies several areas for improving higher degree apprenticeship pedagogy in readiness for the new business landscape, as well as identifying limitations of the tripartite relationship. Originality/value The study addresses a gap in the literature by exploring the value proposition an EMBA apprenticeship programme. The interrelatedness of cluster concepts has identified value elements missing from previous apprenticeship delivery. Deconstructing the senior leader apprenticeship enables educators to redesign and refine models of engagement for improving the apprenticeship experience.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 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".