Intra- and inter-regional expansion: a nonlinear model
Bibliographic record
Abstract
Purpose The purpose of this paper is to differentiate between intra- and inter-regional diversification and explore how each affects firm performance. Existing studies show that both intra- and inter-regional expansion provide benefits and incur costs but the findings are mixed. This study aims to explain the mixed findings. Design/methodology/approach This study uses secondary data and quantitative methodologies to test hypotheses. Findings Using data from 663 Canadian firms over a six-year period (2006–2011), the authors find that the relationship between firm performance and the depth and width of intra-regional expansion is nonlinear. The authors also find a sigmoid-shaped relationship between firm performance and inter-regional diversification, i.e., performance initially increases with home regional diversification, decreases with bi-regional diversification and finally increases again with multi-regional diversification. Originality/value The findings of this study shed light on the current debate on the merits of inter- and intra-regional diversification and have important theoretical and managerial implications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".