Opportunity-based growth management: enabling a company-wide effort to proactively take advantage of new business prospects
Bibliographic record
Abstract
Purpose The growth imperative dominating in today’s market economy implies that shareholder value creation can happen only through constant, profitable growth of the business. The article offers a process for increasing the effectiveness of a firm’s strategy by improving the quality and number of growth opportunities it enables managers to take advantage of. Design/methodology/approach To address the problem of bridging the strategizing process with emerging opportunity landscapes, the current paper offers a practical approach for establishing opportunity-based growth management (OGM) system, comprising six basic components: Understanding, Scanning, Articulating, Testing, Choosing, and Organizing. Findings The presented approach allows the management to notice and exploit the emerging market opportunities before competitors, to leverage the full information available within the company (particularly among front-line employees), and to assess the current company’s business model and make the necessary adjustments. Practical implications A case study of the process in action is presented. Originality/value The proposed OGM framework enables higher-level linking of the “strategy-as-learning” with “strategy-as-planning” paradigms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".