Cross-cultural issues in M&As: experiences and future agenda from Asia-Pacific deals
Bibliographic record
Abstract
In the era of global competition, enterprises have adopted a strategic route on Mergers and acquisitions (M&As) for growth. This has been more profound since the liberalisation and openness programme adopted by many countries. Research literature on M&As has attributed failure of deals primarily to cross-cultural factors. Hence, researchers remained interested in understanding the nuances of cross cultural issues for the success of integration. Further, literature on cultural issues suggests that due to the complexity, uniqueness and largely tacit nature of organisational culture, it is difficult to imitate and adopt any specific organisational culture. This becomes even more difficult in the international settings where organisations forced to merge with different nationalities, background, and languages. This study examines the cross-cultural issues associated with Indian cross border deals in the Asia-Pacific region in order to find out the key research dimensions. It attempts to seek answers to these issues based on the Indian evidence which may aid successful cultural integration.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".