Internationalization of Renewable Energy Companies: In Search of Gestalts
Bibliographic record
Abstract
Internationalization is an essential component of corporate strategy. The present study, which focuses on therenewable energy sector, is an empirical investigation into the prognostic strength of the Gestalt Approach ofInternational Business Strategies (GAINS approach) for the identification of potentially successful strategies forinternationalization, focusing on the mode of market entry.The results show that (1) the GAINS approach has a high prognostic potential for successful internationalizationstrategies, since (a) FIT configurations represent Gestalts, i.e. they show significantly higher success ofinternationalization than other configurations, (b) consistent configurations show significantly higher successthan congruent configurations and (c) MISFIT configurations prove to be the least successful configuration. (2)Although FIT configurations promise the highest success of internationalization, the results indicate that manycompanies do not enter into fit configurations. Yet, it was proven, that (3) CONSISTENT configurations arepreferred compared to CONGRUENT configurations and (4) MISFIT configurations are significantly beingavoided.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".