The Role of Technology Incubators in Fostering Interorganizational Networks A Case Study of MaRS
Bibliographic record
Abstract
Business Incubators (BI) emerged in the early 1980s as a result of previous experiences with different services aimed at developing businesses. Their main objective is to support the process of creating new ventures. They offer cost- effective workspace along with shared amenities, counseling, training, data, and connections to external networks for entrepreneurial collaborations. In this research, we employed a case study design to theorize on the network’s nature and strategies of incubated firms at MaRS Innovation Centre in Canada. This research was carried out over two years (2021– 2023) in the form of 29 interviews with the representatives of the MaRS tenants, including senior executives (20) and managers (7). The interviews that explored business development, R&D strategies and networking at MaRS were audio- taped, transcribed, and analyzed using conventional qualitative data analysis procedures. Tenants were grouped according to business type and incubator functions, while networks were further coded based on functions and partners. The analysis revealed three types of networks such as advisory, spin-off, and strategic, with different degrees of engagement of support organizations, large enterprises, and SMEs. Enablers incorporated the community setting, MaRS business services, close access to advisers, clients, and partners, and MaRS identification, while barriers consisted of distinct business orientations, restricted resources, numerous tenants at MaRS, and approaches to recruitment. MaRS had a strong collaborative culture and offered a wide range of services that greatly supported network development and usage among the tenants
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".