Your winning business model agenda: four questions to shape growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it