Comparative Analysis of Business Performance of Cross Border Acquisitions in Serbia
Bibliographic record
Abstract
This paper investigates performance of cross-border acquisitions in Serbia. The paper includes two parts. The first part considers the results of the hitherto done research into cross-border acquisitions as a value creation strategy. The success rate cross-border acquisitions have had and the factors behind post-acquisition business performance of target firms are presented. The second part of this paper gives the analysis of the performance of cross-border acquisitions in Serbia based on the sample drawn from 78 target firms. The analysis comprises three business performance ratios whose values before and after acquisition were compared. The average of performance ratios of the most profitable companies in Serbia is used as a benchmark against which the performance of cross-border acquisitions is then compared. The analysis shows that, in spite of the global economic crisis, foreign investors in Serbia enhanced business performance of a significant number of target firms. Additionally, the analysis shows that cross-border acquisitions in Serbia failed to meet the business performance benchmark due to post-acquisition duties that were to be met under the economic crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".