Using Interactive Management to Identify, Rank and Model Entrepreneurial Competencies as Universities’ Entrepreneurship Curricula
Bibliographic record
Abstract
While the strong influence of entrepreneurial competencies on business performance is recognised, despite some doubts about the teachability of these competencies, a fundamental question has remained unanswered: what are the key entrepreneurial competencies that need to be developed in an entrepreneurship curriculum and how are these competencies interrelated? The current study used Interactive Management (IM) with a group of seven successful entrepreneurs to identify, clarify, rank order and build a consensus model describing the interdependencies between 12 entrepreneurial competencies. Results indicated that positivity and competitiveness are fundamental drivers of all other competencies in the consensus model. At the same time, determination and inquisitiveness were the most highly ranked competencies. Results are discussed in light of the ongoing challenges of defining the optimal scope and sequence of training in entrepreneurial curricula. This article fulfils an identified need to study specific entrepreneurial competencies that are to be promoted in university students.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".