Role of mergers and acquisitions on corporate performance: emerging perspectives from Indian IT sector
Bibliographic record
Abstract
This article is an attempt to investigate the impact of mergers and acquisitions (M&As) on corporate performance of Indian IT sector for the period 2007–2015. The focus of this article is on Indian IT sector as it has been the growth engine of the economy and emerging as its most internationalised sector. This article has engaged, fixed and random effect panel data models to measure the impact of M&As on various financial variables with comparative analysis of domestic and cross border deals. The findings reveal that the M&As have significant positive impact on Return on Net worth and Revenue of IT companies in India when both domestic and cross-border regions were considered together. Whereas, Earnings Before Interest, Taxes, Depreciation and Amortisation experienced significant decline. Return on Capital Employed did not experience any significant impact due to M&As. When the impact of M&As was analysed separately for domestic and cross-border deals, it was obtained that the impacts are in different directions for domestic and cross-border deals in two out of four variables. The significant inference is that domestic deals per se are much better than the cross border deals.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".