International Joint Ventures and Shareholder Value Creation: Evidence from Manufacturing and Non-manufacturing Sectors
Bibliographic record
Abstract
Numerous empirical studies have suggested that the economic performance of international joint ventures (IJVs) is modest, at best. This suggestion may be inaccurate, however, since the studies' findings are based on ex post managerial perceptions of IJV performance that are found to be biased. Stated differently, there is a need to investigate IJV performance from ex ante “external* perspective—that of capital markets. Consequently, this study investigates the extent to which IJVformation announcements increase the shareholder value pf participating firms. Despite the merits of engaging a capital markets' perspective, it is necessary to investigate the extent to which capital markets are informationally ‘efflcient’- particularly given persistent doubts about whether capital markets really are as efficient as is widely accepted. Hence, this study compares IJV&' expected performance with their actual performance that is reported in other empirical studies. This study engages the event-study methodology to examine the impact of IJV formation announcements on the shareholder value of IJV parents, and tries to circumvent the methodology's principal limitations in doing so. Based on a sample of more than 500 IJVs, the study's findings indicate participation in IJVs increases parents' shareholder value by an average of approximately one per cent, albeit this figure varies across industry sectors (manufacturing; non-manufacturing) and firm size (large; small). Shareholder value is created for about 50 per cent Of firms in the sample. Moreover, the study's findings suggest that capital markets are informationally efficient
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".