Transformative Marketing Education: Drivers, Outcomes, and Research Agenda
Bibliographic record
Abstract
The perseverance and agility to be transformative marketing educators have been identified to be critical for the future of marketing education. In developing this observation further, this study conceptualizes the transformative marketing education (TME) concept and defines it. Furthermore, adopting a management educational institution perspective, this study proposes a framework for the implementation of TME. The proposed framework identifies the drivers of TME to be the marketing education triad, comprised of the management education institution, the marketing learners, and the external environment. The study identifies the outcomes of implementing TME as fourfold: (a) an interdisciplinary activity, (b) a value-creating activity, (c) an insights-centric activity, and (d) a “brain retrain” activity. The study also offers some variables that could moderate the outcomes of implementing TME. For the drivers and outcomes, this study offers propositions that can be tested. In addition, potential strategic implications of the TME outcomes are identified as (a) personalizing marketing education, (b) enhancing student engagement, and (c) establishing closer stakeholder connections. The study concludes by identifying a research agenda for TME.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".