The impact of ownership on global strategy: Owner diversity and non‐financial objectives
Bibliographic record
Abstract
Abstract Research Summary In this special issue introduction, we analyze how a firm's international ownership affects its global strategy. We reinterpret the literature by grouping dominant owners into four categories: (1) individuals (entrepreneurs and families), (2) labor (managers and employees), (3) state (national and subnational governments), and (4) institutions (pension funds, mutual funds, hedge funds, private equity, venture capital, and impact investors). We argue that although all seek financial returns from their investments, they differ markedly in their non‐financial objectives, resulting in differences in strategies for expanding abroad. We also propose that the home country context modifies the impact of ownership on global strategy, directly by influencing the prevalence of owner types, and indirectly by affecting owners' incentives and constraints in their pursuit of non‐financial objectives. Managerial Summary Although all firms' owners search for financial returns from their investments, differences across dominant owners in their non‐financial objectives result in significant diversity in the global strategies of invested firms. We clarify these differences by grouping owners into four categories: (1) individuals (entrepreneurs and families), (2) labor (managers and employees), (3) state (national and subnational governments), and (4) institutions (pension funds, mutual funds, hedge funds, private equity, venture capital, and impact investors). We explain how their specific non‐financial objectives influence the global strategies of invested firms. We also discuss how the characteristics of the home country affect both the prevalence of types of owners and owners' strategies. The special issue articles illustrate some of these ideas.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".