Beyond success and failure: learning from execution of corporate entrepreneurial actions
Bibliographic record
Abstract
Purpose This article aims to redefine the paradigm of evaluating corporate entrepreneurial actions, moving beyond the simplistic success/failure dichotomy. It underscores the importance of a nuanced understanding of the outcomes of such initiatives, emphasizing the need for organizations to learn from both their successes and failures. The focus is on fostering a learning organization culture, where strategic decision-making is enhanced through a comprehensive analysis of past entrepreneurial endeavors, thus ensuring long-term corporate health and growth. Design/methodology/approach The study is based on the analysis of the existing literature and illustrative real-world cases from prominent corporations. Findings The findings reveal that understanding the reasons behind the success or failure of strategic initiatives is crucial for sustainable corporate growth. The study highlights that mere success can be misleading without comprehending the underlying factors, just as failure can offer valuable lessons if properly analyzed. The 'Learning from Execution Matrix' facilitates this understanding, aiding corporations in fostering a culture of continuous improvement. The DIRS (Decomposition, Interpretation, Rewarding, Scaling) framework further helps in breaking down and scaling these learnings for broader application within the organization. Originality/value This article contributes to the field of strategic entrepreneurship by providing a novel framework for analyzing corporate entrepreneurial actions. The 'Learning from Execution Matrix' and the DIRS framework offer practical, actionable tools for managers and leaders, fostering a culture of learning and strategic adaptation. This approach is original in its emphasis on the nuanced understanding of both successes and failures, making it a valuable guide for corporations seeking sustained growth and innovation in a competitive business environment.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".