Uwarunkowania Kulturowe A Powiązania Międzyfirmowe W Sektorach Globalnych. Perspektywa Empiryczna [Culture Factors And Interfirm Ties In Global Sectors. Empirical Perspective]
Bibliographic record
Abstract
I explore the relationship in partnering strategy [exploration – based on weak ties vs. exploitation – based on strong ties], national culture and and firms’ organizational model in the context of global ICT Industry. In the highly uncertain global environment, partnering is the one of the most effective ways to access a broad set of knowledge and resources. In such an environment, an exploration strategy [based on the multiplicity of weak ties] is more effective than an exploration strategy [based on strong ties]. Using a sample of 30 firms and 10,247 ties I found that national culture that enhances the organizational model also impacts firms’ partnering strategy. Exploration strategy is most characteristic for firms from certain emerging economies [e.g., Indian and Chinese firms] as well as some mature economies [e.g., the US, Sweden, Norway, Canada, and the UK]. Exploration strategy is typical for firms from countries such as France, Spain, and Japan as well as Poland. The results support the importance of institutions in international strategic management and entrepreneurship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".