Start-ups’ scaling-up strategies at the regional periphery
Bibliographic record
Abstract
Purpose Our understanding of the strategies that lead to the success of start-ups when they scale-up is limited when it occurs at the regional periphery. The main purpose of this study is to explore the specific strategies that start-ups employ to scale-up, specifically in contexts with high resource constraints at the regional periphery. Design/methodology/approach Analyzing the data from personal in-depth interviews with engineering and science start-up founders in peripheral regions of upstate New York USA bordering the Canadian Ontario, we explored a combination of internal and external strategies that start-ups employed to scale-up. Findings The study found that start-ups prioritize building internal scaling capacity in their human capital, organizational structure, scalable business model, finance and business ownership. To foster the scaling process further, start-ups develop new effective external strategies that target the business environment. Practical implications Policymakers and regional governments can use our research to develop more effective industrial policies for supporting start-ups’ growth and subsiding strategic industry clusters for rebooting new competition policy, which is a current debate in many industrialized economies including the US. This targeted regional industrial policy is specially needed when scaling-up at the regional periphery. Social implications Our study is specially need it when scaling-up at the regional periphery and with limited resources. Originality/value This study enriches our understanding of the growth of start-ups and small ventures by providing context-based insights into how firms build the capacity to scale-up in highly challenging and uncertain business environments in a peripheral bordering region between the USA and Canada. It also offers useful managerial and policy implications.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".