Business model innovation in start-ups: an exploratory case study of why and how business models are changed
Bibliographic record
Abstract
It has been long understood that start-ups change their business models. However, research on creating a business model, called business model development, and the change of business model, called business model innovation, has primarily focused on established firms. There is a lack of empirical evidence of why and how start-ups change their business model, and it is unclear to what extent existing literature on established firms can be applied to start-ups. The research question of this thesis is “Why and how do start-ups change their business models?”. This thesis provides a unique contribution to the study of business model innovation by providing an exploratory case study of six versions of a start-up’s business model canvas. While considering the impact of human capital investments and outcomes, it is shown that a) business models are changed because founders believe that the business model is not, or cannot be, profitable, OR that there is a more profitable and scalable option within reach, and b) business models are changed by discovering new markets or customer use cases and then arranging the resources, capabilities, network allies, and operations necessary to achieve the desired impact.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".