Family ownership and acquisition behavior in publicly‐traded companies
Bibliographic record
Abstract
Abstract Much of the literature on corporate acquisitions has focused on managerial incentives for making acquisitions but has underemphasized the role played by the social context of major shareholders. This study of Fortune 1000 firms argues that the priorities and risk preferences of family owners can have important implications not only for the volume but also for the diversifying nature of their acquisitions. Agency and family business perspectives are used to derive expectations concerning the acquisitions behavior of family owners. Consistent with both perspectives, and owners' desire to reduce business risk, we find that family ownership is inversely related to the number and dollar volume of acquisitions. However, whereas agency theorists differ about how ownership concentration influences whether acquisitions are diversified, the family firm literature is more definitive. The latter suggests that given family owners' desire to retain control of their firms for offspring, their wealth must remain concentrated. Hence they can most easily reduce the risk of their wealth portfolio by diversifying the business—that is, through diversifying acquisitions. Consistent with this logic, we found the propensity to make diversifying acquisitions to increase with the level of family ownership. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".