A Systematic Literature Review on Mergers and Acquisitions: A Bibliometric Analysis Approach
Bibliographic record
Abstract
Many studies have examined the effects of mergers and acquisitions, but their results vary significantly. Thus, mergers and acquisitions are one of the most popular corporate restructuring activities undertaken by various organizations, institutes, companies (both private and public), agencies, and establishments, all in a quest to achieve the desired aim of the companies. But a pertinent question exists as to if mergers and acquisitions (M&A) have produced the desired goals and objectives of the companies. Therefore this study aims to examine the results, empirical preference, and author's opinions of existing literature as to if M&A had produced synergy gains or not. Bibliometric analysis is the methodological procedure used in this study. A total number of fifty (50) high-profile literatures were qualitatively examined, and it shows that M&A produced synergy gains to the level of 48%. From the analysis, M&A was also found not to produce synergy gain to the level of 28%, thus making the remaining 24% to be associated with authors whose views weren't explicit and those that are undecided to M&A producing synergy gains. This review study recommends that horizontal mergers and acquisitions be encouraged to increase the companies' market share, leading to their desired goals and objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.150 | 0.118 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".