Canonical correlation analysis between collaborative networks and innovation: A case study in information technology companies in province of Tehran, Iran
Bibliographic record
Abstract
The increase competitions as well as technological advancements have created motivation among business owners to look for more innovative ideas from outside their organizations.Many enterprises collaborate with other organizations to empower themselves through innovative ideas.These kinds of collaborations can be observed as a concept called Regional Innovation System.These collaborations include inter-firm collaborations, research organizations, intermediary institutions and governmental agencies.The primary objective of this paper is to evaluate relationships between Collaborative Networks and Innovation in information technology business units located in province of Tehran, Iran.The research method utilized for the present study is descriptive-correlation.To evaluate the relationships between independent and dependent variables, canonical correlation analysis (CCA) is used.The results confirm the previous findings regarding the relationship between Collaborative Networks and Innovation.Among various dimensions of Collaboration, Collaboration with governmental agencies had a very small impact on the relationship between collaboration networks and innovation.In addition, the results show that in addition to affecting product innovation and process innovation, collaboration networks also affected management innovation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".