Bibliographic record
Abstract
In this study, we examine the differentiating characteristics of acquiring firms and focus on the M&A (Mergers and Acquisitions) motives from the perspective of agency theory. We use an out-of-sample dataset that involves all completed Canadian M&A deals between 1997 and 2002. With respect to firm-specific financial and technical variables, we found that firms with higher cash reserves, better past performances, and a higher R&D (Research and Development) focus (high-tech firms) are more likely to be the acquirers. With respect to the firm-specific governance variables, we found that acquiring firms have higher pay ratios (option pay plus option value dividend by cash pay), lower inside director ratios, higher board sizes, and lower blockholder ownerships. However, these results are not supported in multivariate analysis. The results from differentiating characteristics analyses have highlighted at least two motives behind an acquisition decision. First, we found strong support for an ‘empire building’ motive behind M&A. Our results indicate that firms with higher levels of cash reserves are more likely to be acquirers. In other words, firms with more CEO discretion and excess resources tend to grow in size through acquisition. Second, we found support for a strategic motive for high-tech firms behind M&A. High-tech firms were more likely to make an acquisition in order to stay innovative and preempt competition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".