How the Firm networks Affect the foundation and development of NTBF: Empirical evidence on the Propositions of Hite and Hesterly
Bibliographic record
Abstract
Network has drawn attention from different fields and its contributions on the businesses have been discussed; however, Hite and Hesterly (2001) propose that it is significant for companies to change from closed networks to dispersed networks as the firm grows. Similarly, scholars argue that firm network, development and resources are “co-evolved”. These authors further state that changing of networks are affected by (a) individual difference of entrepreneurs, (b) industrial differences for resources, and (c) difference in the compositional quality. Thus, this paper tests their propositions to see “why” it is essential to change networks and to pinpoint “what”, “when” and “how” the necessity is needed in the formation and development of New Technology Based Firms (NTBFs). To achieve the objectives, four case studies are developed and the empirical results show that the origin of firm network determines the necessity for changing networks, the quality of network member affects the changes but individual difference of technology-based entrepreneur may or may not affect the changes.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".