Breadth and Depth of International Diversification: Interactions, Trade-offs and Profitability
Bibliographic record
Abstract
All firms have to decide on a strategic issue when they diversify into foreign markets: that is, what is the optimal level of the breadth and depth of diversification? In isolation, the breadth and the depth have been widely discussed in the existing business literature, but their relationships remain unknown. This study explores how the breadth and depth interact with each other to affect firm performance. Evidence collected in this study shows that the interaction effect is positive and significant when the level of both breadth and depth is moderate. When either dimension increases further, the interaction effect is still positive and grows even more significant. However, the positive and significant effect reverses and becomes negative (although non-significant) when a high level of both dimensions is reached. These relationships suggest that the adoption of an international diversification strategy should take into consideration breadth and depth simultaneously as they affect each other mutually in determining firm performance. Findings of the study shed light on an effective mechanism to design strategies in uncertain environments – an important issue in general management.
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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 | 0.002 |
| 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".