Resource Adjustment Costs, Cost Stickiness, and Value Creation in Mergers and Acquisitions*
Bibliographic record
Abstract
ABSTRACT We examine whether resource adjustment costs, such as installation and disposal costs for fixed assets, or hiring and firing costs for employees, impede value creation in mergers and acquisitions (M&A). We focus on M&A deals because they are major corporate investment decisions. As a proxy for adjustment costs, we use a firm‐level measure of cost stickiness. We predict that acquirers with high adjustment costs have less flexibility in restructuring resources following the acquisition and will find it more costly to merge the target firm's operations. Consistent with this prediction, we find that the acquirer's adjustment costs are negatively associated with abnormal returns around the acquisition announcement. Additionally, adjustment costs are also negatively associated with deal synergies. Relatedly, we find that acquirers with high adjustment costs purchase targets with high adjustment costs. In accordance with this finding, we show that acquirers with high adjustment costs purchase intangible‐intensive targets. Collectively, our results highlight the important implication of adjustment costs in M&A deals for managers and capital market participants.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".