Business Incubators: A Need-Heed Gap Analysis of Technology-based Enterprises
Bibliographic record
Abstract
As technology-based enterprises (TBEs) are more promising than non-technology firms, there is a strong case for their incubation and promotion. However, TBEs use incubators not as support providers for idea incubation but to control costs incurred to develop and implement their ideas, thus defying the objective of incubation. Different types of incubators, such as commercial, social and university business incubators (UBIs), have different types of tangible and intangible resource offerings for their incubatees. Entrepreneurs utilize these resources based on their needs to save costs and reduce risks. Drawing from the conversations with the serial incubatees it was evident that once their access to resources at one incubation centre is exhausted, they move to another incubation centre for further fulfillment of their enterprise’s needs. Our analysis based on 20 interviews with the entrepreneurs of TBEs reveals a need-heed gap between the incubatees and incubators, which will have to be reduced for incubators to remain relevant to firms and effective for entrepreneurs.There is a need for a ‘hybridized incubation’ arrangement for TBEs in which incubators will have to pay heed to their roles as providers of specialized resources from both academia and industry.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".