Exploring Critical Success Factors of Competence-Based Synergy in Strategic Alliances: The Renault–Nissan–Mitsubishi Strategic Alliance
Bibliographic record
Abstract
This paper aims to unbundle the antecedents of competence-based synergy in the strategic alliance formation process by employing the ARCTIC framework. The current research provides a new empirical application of the ARCTIC framework to reveal the success factors of reciprocal synergies of the Renault–Nissan–Mitsubishi strategic alliance in the automotive industry. By taking a resource-based view on the sources of competitive advantage, the current paper contributes to theoretical and practical issues of global strategic alliances as part of the existing literature on strategic management, international business, and corporate finance. By bridging qualitative and quantitative research methods, the paper provides validity to the ARCTIC framework with an application of the real option valuation. A conceptual model of research helps practitioners and scholars to explore critical success factors of alliance formation and to predict a competence-based synergy of strategic alliances. Future research may explore the institutional context of strategic alliances, specifically, exploring the impact of the French and Japanese governments on the Renault–Nissan–Mitsubishi alliance’s synergies.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".