Your winning business model agenda: four questions to shape growth
Bibliographic record
Abstract
Purpose Defines clear steps for growth planning that support answers to the crucial question: How and where are you planning to scale up the business and what talent do you need to implement this? Design/methodology/approach As the “Business model value matrix” shows, having ‘happy customers’ is only one determinant of a business model’s success. The other essential block of diagnostic questions deals with the current state and prospects of the firm’s growth. Findings We found that companies that have found ways to keep their business models in a winner’s state can provide clear, evidence-based answers to questions about growth opportunities and risks, while their less successful peers have difficulty addressing the issues. Continuous collecting and analyzing of this information allows successful companies to embrace the strategy-as-learning model of development, built around active learning and proactive adjustment to evolving environment. Practical implications To develop a strategy for moving to and sustaining the Winner state, managers must clearly articulate and test a set of hypotheses about the mechanisms of their company’s growth. The first step on this path is related to obtaining a clear view on the factors that underpin the current financial performance. Originality/value High-performance cultures make sure that each manager has the clear answers to the questions of value, growth and digitization in order to learn, experiment and implement the company business model agenda. The unproductive cultures, on the other hand, are sustained by managerial teams that usually do not have the answers to these crucial questions, but are very good at political games.
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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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".