Bibliographic record
Abstract
Revenue deserves more attention in mergers; indeed, a failure to focus on this important factor may explain why so many mergers don't pay off. Too many companies lose their revenue momentum as they concentrate on cost synergies or fail to focus on postmerger growth in a systematic manner. Yet in the end, halted growth hurts the market performance of a company far more than does a failure to nail costs. Some balance may have to be restored. The belief that mergers drive revenue growth could be a myth. A Southern Methodist University (SMU) study of 193 mergers, worth $100 million or more, from 1990 to 1997 found that revenue growth was fairly elusive. Measured against industry peers, only 36 percent of the targets maintained their revenue growth in the first quarter after the merger announcement. By the third quarter, only 11 percent had avoided a slowdown; the median lag was 12 percent. When McKinsey joined the SMU researchers to take a closer look, it turned out that the targets' continuing underperformance explained only half of the slowdown; unsettled customers and distracted staff explained the rest. Moreover, these revenue shortfalls don't represent the beginnings of a J-curve. Further McKinsey research sampled more than 160 acquisitions by 157 publicly listed companies across 11 industry sectors in 1995 and 1996 (Exhibit 1). Only 12 percent of these companies managed to accelerate their growth significantly over the next three years. In fact, most sloths remained sloths, while most solid performers slowed down. Overall, the acquirers managed organic growth rates that were four percentage points lower than those of their industry peers; 42 percent of the acquirers lost ground. Exhibit 2, on the next page, shows how one company with apparently solid growth rates actually fell well short of the revenue it could have expected had it and its targets stayed apart and maintained industry-average growth rates. These results held across the board. Mergers in high-tech and other so-called growth sectors were as susceptible to the burden of mergers as any. Nor, oddly enough, did size matter--small companies risking a large acquisition were no less successful than larger companies swallowing a start-up or two. On average, experienced acquirers didn't have better success rates than novices. Why worry so much about revenue growth in mergers? Because, ultimately, it is revenue that determines the outcome of a merger, not costs; whatever the merger's objectives, revenue actually hits the bottom line harder. As Exhibit 3 shows, fluctuations in revenue can quickly outweigh fluctuations in planned cost savings. Given a 1 percent shortfall in revenue growth, a merger can stay on track to create value only if a company achieves cost savings that are 25 percent higher than those it had anticipated. Beating target revenue-growth rates by 2 to 3 percent can offset a 50 percent failure on costs. Furthermore, cost savings are hardly as sure as they appear: up to 40 percent of mergers fail to capture the identified cost synergies. [1] The market penalizes this slippage hard: failing to meet an earnings target by only 5 percent can result in a 15 percent decline in share prices. …
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".