Lean Startup Supporting Sustainability-as-Flourishing during the Early Stages of Enterprise Development
Bibliographic record
Abstract
Purpose: Many startups face the complex anticipation of offering value sustainably over the long term yet must test market engagement to evaluate an economically viable business model in the near term. This study aims to capture the usefulness of a business model innovation method (Flourishing Startup Method) aligned with sustainability-as-flourishing thinking as perceived by entrepreneurs during the early stages of enterprise development. Design/Methodology/Approach: Through action research, the utility of the approach was evaluated through multiple applications across two events with a total of 64 entrepreneurs taking place 2017-2018. Findings: The research revealed insights related to the reaction to the Flourishing Startup Method and its facilitation including the intention to use beyond the events, their perceived learning utility, as well as the overall perceived utility in terms of usefulness and ease of use. The study also showed that to fully leverage the Flourishing Startup Method, entrepreneurs must have time and facilitated opportunities to develop a minimum level of proficiency in a set of entrepreneurial competencies that support business model for sustainability-as-flourishing. Originality/Value: This research contributes to understanding the process of business model innovation towards novel and impactful business models aligned with sustainability-as-flourishing and offers one of few empirical investigations on a business model innovation method aligned with sustainability-as-flourishing to identify its utility and fit with the needs and expectations during the early stages of startup development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".