Business groups and the study of international business: A Coasean synthesis and extension
Bibliographic record
Abstract
Abstract This paper harmonizes the business group literature in international business and across relevant fields within a unified theoretical framework. Business groups (firms under common control but with different, if overlapping, owners) are economically important in much of the world. Business groups’ economic significance co-evolves with their economies' institutions and market environments, patterns of particular interest to international business scholars. The vast literature on business groups raises discordant perspectives. This paper first proposes a unifying definition and provides a list of stylized historical observations on business groups across different parts of the world. It then develops a Coasean framework to harmonize seemingly disparate views from the literature by building on recent surveys and the stylized historical patterns of business groups. We enlist two concepts – fallacies of composition/decomposition and time inconsistency – to harmonize these perspectives. This yields a theoretical framework for understanding business groups that mobilizes concepts long-used to understand multinational enterprises: the economy's market and hierarchical transaction costs, openness, and their dynamic interactions. We then apply this framework to globalization and business group internationalization. This work leads to an overarching research agenda encompassing seemingly inconsistent prior work.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".