Post-Acquisition Performance of Emerging Market Firms: A Multi-Dimensional Analysis of Acquisitions in India
Bibliographic record
Abstract
M&A performance is a multifaceted, compound construct with no overarching factor that captures all different dimensions. This paper examines the concept of acquisition performance and proposes a model that links firm-level factors and transaction parameters with firms’ short-term and long-term performance, extending to financial-, market- and innovation measures. Building on past empirical studies on the influence of various factors on M&A performance, a multi-dimensional structural equation model has been developed and it has been tested with a dataset on acquisitions in the Indian technology sector over a period of ten years. The results suggest that: (a) smaller acquirers with higher book value and leveraged firms demonstrate better long-term performance; (b) contrary to established understanding, short-term market returns are not influenced by deal parameters; (c) majority stake purchases show relatively lesser gains—suggesting the possible presence of post-acquisition integration issues and, (d) acquirers with high intangible assets continue to do well on innovation performance post-acquisition. By indicating situations and conditions under which an acquisition would potentially lead to a performance gain for the acquirer, these results provide significant insight to practitioners pursuing M&As for growth opportunities.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".