Technology parks and entrepreneurial outcomes around the world
Bibliographic record
Abstract
Purpose – The purpose of this paper is to study factors that affect the success of technology parks in terms of fostering entrepreneurial firm formation, growth, and financing. Design/methodology/approach – Based on a new international dataset of technology parks (tech parks) from 13 countries (eight developing countries and five developed countries), the paper relates the success of technology transfer to the legal environment within which the tech park operates, as well as the characteristics of the tenants in the tech park and the services provided by the tech park. Findings – The data indicate entrepreneurial success is more likely to be facilitated when there is better legal protection offered to companies in the jurisdiction within which the tech park is located, when there is a greater presence of foreign university- and government-affiliated companies in tech parks, and a smaller presence of foreign private companies in tech parks, particularly foreign subsidiaries. The data further indicate entrepreneurial success is more likely when tech park tenants have greater testing/analysis focus, and when tenants have less assembly- and service-focussed activities. Also, entrepreneurial success is more likely to be facilitated by tech parks with on- and off-site technology licensing offices that promote trade shows, provide access to funds for commercialization and distribute information on the R&D outcomes of tech park tenants. Research limitations/implications – The data offer insights into efficient design of tech parks. Coarse measures from survey data are limitations yet offer scope for further examination in future research. Originality/value – The paper provides guidance for entrepreneurs and their investors in terms of ways maximize value in terms of entrepreneurial growth and financing from selecting appropriate tech parks.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".