LEAN AND GLOBAL TECHNOLOGY START-UPS: LINKING THE TWO RESEARCH STREAMS
Bibliographic record
Abstract
In this paper, the authors introduce the concept of the lean global start-up (LGS) as a way of emphasising the problems for new technology start-ups when dealing separately with business development, innovation and early internationalisation. The paper has two components — an introductory conceptual part and an empirical part that should be considered as basis for the preliminary validation of the conceptual insights. The research sample includes six firms — three from Canada and three from Denmark. Two different early internationalisation paths have been identified: Lean-to-global (L2G start-ups) and lean-and-global (L&G start-ups). Both types of start-ups were found to have faced significant problems with the complexity, uncertainties and risks of being innovative on a global scale. They have however found ways of addressing these problems by a disciplined knowledge sharing and IP protection strategy and the efficient use of business and supporting and public funding mechanisms. The Danish firms have pivoted around the ways of delivering their value proposition and not around the specific value propositions themselves. The Canadian firms have actively pivoted their value proposition motivated by the degree of innovativeness of their products and the insights from business supporting organisations. The analysis of the results justifies the introduction of the LGS concept and opens the opportunity for future research focusing on the articulation of more practical LGS entrepreneurial frameworks.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".