Market challenges, learning and customer orientation, and innovativeness in IJVs
Bibliographic record
Abstract
Purpose The purpose of this paper is to advance a theoretical framework that incorporates the relationship between market challenge and learning and customer orientations, and the influence of these orientations on innovativeness in an international joint venture (IJV) context. Design/methodology/approach The authors estimate a structural equation model utilizing survey data collected from 199 IJVs in the Republic of Korea. Findings The authors found that while market challenge does not influence learning orientation in IJVs, it does have a significant positive influence on customer orientation. Further, the authors’ findings support that both learning orientation and customer orientation have positive impacts on IJV innovativeness. Another interesting finding shows that the impact of learning orientation on IJV innovativeness is significant only when IJVs have high levels of interaction with parent firms. The study also reveals that having a strong learning orientation amplifies the impact of customer orientation on innovativeness in IJVs. Originality/value Despite increased interest in IJVs, there has been relatively little work linking IJV innovativeness with learning and customer orientations. The study contributes to recent streams of research that seek to understand the role of these orientations in IJV innovativeness.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".