Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the impact of governance quality on firms with multiple voting structures. Design/methodology/approach – The sample includes 487 acquisitions undertaken by dual-class firms from 1996 to 2009. The author used event studies (Patell, 1976) for short-term performance analysis around merger announcement dates; Berkovitch and Narayanan (1993) methods to identify the motive behind these transactions; and standard benchmark adjusted return on assets (and return on sales) (Barber and Lyon, 1996) and BHAR (Mitchell and Stafford, 2000) to analyze long-term post-acquisition performance. Findings – First, dual-class acquirers with better governance quality show stronger performance around takeovers which indicates that these firms make better acquisition decisions. These results hold even after controlling for different firm and deal characteristics. Second, transactions undertaken by acquirers with good governance show little or no sign of agency motive. This reinforces the findings in first. Third, the author reports that acquirers with above-median governance quality display stronger long-term post-acquisition operating as well as stock performances. These results are robust to different benchmarks used for this study. Originality/value – This paper expands the literature on dual-class firms by showing the impact of governance quality on acquisition activities undertaken by these firms. This is the first study to show that despite agency issues inherent in the dual-class structure, improving governance quality would have a positive impact, at least in the case of corporate takeovers.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".