An Entrepreneurial Management System for established companies
Bibliographic record
Abstract
Purpose The article introduces the Entrepreneurial Management System (EMS), which delineates a strategic process within an organizational context that is aimed at encouraging and supporting the pursuit of opportunities that have the potential to create value through innovative strategic actions. It is designed to stimulate entrepreneurial thinking and corporate venturing at all levels. Design/methodology/approach The authors offer an approach to organization-wide continuous innovation that incorporates proven concepts from existing research with lessons learned from the authors’ research analysis and consulting experience in helping large and medium companies across different industries and markets to establish effective entrepreneurial management. Findings Given the spectre of constant disruption from new technologies or business models, management teams will be judged on how they proactively respond to these challenges by turning them into value-creating opportunities. Practical implications Multidisciplinary teams allow employees to become familiar with other domains and see possible solutions, share their problems and ideas and vet their insights with the input of colleagues. Originality/value An Entrepreneurial Management System allows a firm to "internalize" the marketplace’s evolutionary processes so that a company can generate, develop and implement ideas that will have value for customers. It should be a clear decision for all of today’s business leaders and investors to have an implementation plan to ensure continuous innovation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".