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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".