Bibliographic record
Abstract
This chapter examines Ghemawat and Ghadar's idea that global M&A transactions usually do not make economic sense. The authors note several management biases that lead to inefficient M&As, and they recommend several alternative strategies as superior to global M&As. These ideas will be examined and then criticized using the framework presented in Chapter 1. Significance Pankaj Ghemawat and Fariborz Ghadar wrote a classic HBR article in 2000, criticizing the observed trend towards international mergers and acquisitions (M&As), especially those among large MNEs from different regions of the world (the so-called ‘global mega-mergers’). Such M&As typically aim to create a company with a much wider geographic reach than that commanded by each partner individually. Ghemawat and Ghadar ask whether such large-scale M&A transactions between MNEs, attempting to create firms with interregional or even worldwide market coverage, make economic sense. According to the authors, a general belief persists in many industries that increasing internationalization, in the sense of growing interdependence of markets in the world economy, will ultimately lead to industry consolidations whereby only a few large firms, commanding impressive scale economies, will survive. The obvious implication for senior managers is to get big in order to survive. This view is exemplified by the main strategy rule introduced at General Electric by former CEO Jack Welch. This rule, which still prevails in this highly diversified, US-based MNE, states that the firm should be active only in businesses where it can be the number one or two in the world in terms of size, and should divest businesses in which it cannot achieve that goal.
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 | 0.000 | 0.002 |
| 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 | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.004 |
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".