Mergers and Acquisitions as Vital Instruments of Corporate Strategy: Current and Historical Perspective
Bibliographic record
Abstract
In this paper our main focus is to provide insight into the history of M&A's for this purpose we have analysed the different waves of M&A. We have analysed these waves in context of available literature and fact and figures. During the study we realised that almost all of the waves of M&A's ended because of financial crises, although impact and severity of that crises may differ. We analysed the impact of current crises on M&A in global context and in order to establish how companies have and in post crises era i.e. after crises of 2007 onwards how the companies have changed their corporate strategies to accommodate M&A's. We have also analysed which factors fuelled M&A's in past and were these factors present in post crises era M&A activities. By first quarter of 2011 the many firms saw new growth opportunities in M&A activities seemed to rebound as large companies used M&A's as part of their corporate strategy but this was cut short by events like US debt ceiling, down grade of USA's credit ratings along with fears about Eurozone's financial health and their impact on future prospects of M&A's would they continue to prosper or would they be weighed down by these events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".