An explorative study of trends, distribution and pattern of bilateral cross-border joint ventures (CBJVs)
Bibliographic record
Abstract
Purpose This paper aims to explore the trends, distribution and pattern of Indian bilateral cross-border joint venture (CBJV) activity with advanced developed nations (G7 nations) which include the USA, the UK, Japan, France, Germany, Italy and Canada over the 2001-2010 period. Design/methodology/approach Longitudinal data on the population of 201 CBJVs are analyzed using Securities Data Company (SDC) platinum database. Chi-square test of independence is conducted on the parameters for CBJVs collected over a span of 10 years to test interrelations between them. Findings The results of explorative trend analysis and test of interdependence are significantly different from developed countries in terms of interrelation between parent’s nationality, industry classification, broad purpose, period of formation and the equity owned. Research/limitations implications Future work may explore the strategic motivation of both developed and developing nation firms, given the dynamics of CBJVs explored in this paper. The study could also be extended to other developed and developing nation firms CBJVs with Indian firms. Practical implications This study provides a broad-based objective exploratory study of trends and distribution of CBJVs from the standpoint of the developing nations. This helps managers to identify the dynamic industries of CBJVs in India as far as G7 nations are concerned. Social implications The possibility of asymmetric motives of partners in CBJV could not be negated. The role of Indian policymakers also becomes much larger to regulate the monopolistic and anti-competitive practices. Originality/value The longitudinal study serves to present first of its kind systematic analysis of detailed activity of Indian firms in bilateral CBJV formation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".