The Influence of Relational Learning of a Transnational Egocentric Network on Innovation Competence for Internationalization
Bibliographic record
Abstract
The purpose of this article is to evaluate the influence of a transnational egocentric network on the innovation competence for internationalization for a transnational focal organization—considered one of the main players in the telecommunications sector worldwide. The theoretical–empirical framework relies on the perspectives of innovation, relational learning, and relational embeddedness in inter-organizational networks. With regard to methodology, the design chosen for this research was data collection, since it deals with relational data treated dyadically, using the network analysis technique to identify the egocentric network configuration with the use of UCINET software. We conclude that the studied network is oriented toward innovation and the analyzed variables show a high degree of complexity, especially in relation to the wide-reaching treatment of innovation as overlapping with the traditional indicators and with an emphasis on technology or research and development. In this sense, the main contribution of this research is to show that the traditional conception of organizational competence as an internal resource can be extended to the perspective of relational embeddedness by considering innovation for internationalization as an inter-organizational level of competence, creating the need to undertake new studies that will complement the explanatory power, in addition to the learning dimensions, that are contemplated in this research.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".