The role of mergers and acquisitions in mitigating the effects of corporate fraud in the pharmaceutical sector
Bibliographic record
Abstract
Purpose In today’s highly comparative pharmaceutical sector, multiple humanitarian and pricing issues are prevalent within the industry. Mergers and acquisitions (M&A) are perceived to be an essential method for organizational consolidation and value generation. The purpose of this paper is to illustrate via descriptive methodology and t -tests, how a merger can mitigate the effects of fraud in the pharmaceutical sector. Design/methodology/approach The research focuses on secondary data. This research paper explores the differences in these organizations’ financial metrics using the t -test regression analysis, both pre and post-merger. Secondary data have been used to compile separate financial ratios for five years before and five years after the scandal. Findings The results indicate a positive outlook for both organizations after the merger. Mergers appear to have a favorable impact on the performance of a company, with the only exception of external variables (laws, controversies, fines, etc.) affecting its post-merger performance. Originality/value The paper uses secondary data to test the impact that mergers have on pharmaceutical companies after they have been implicated in corporate malfeasance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".