The Effects of Digital Capability on Firms’ Export-Financial Performance Under Immigrant Ownership
Bibliographic record
Abstract
Firms are increasingly investing in digital technologies amidst accelerating technological change in developed countries. They could reap benefits that compensate for the costs and risks involved. For internationalizing firms operating under immigrant ownership, this could mean improved export and financial performance relative to what is possible under non-immigrant ownership. This perspective treats digital capability development (DCD) as a strategically enabling factor underpinned by performance-enhancing complementarities between digital and non-digital capabilities. On the contrary, DCD could be a strategically inhibiting factor associated with performance-inhibiting digital market misfit linked to the global digital divide. To resolve these conflicting views, we articulate a dynamic capabilities framework that explains why and how DCD could improve firms’ export-financial performance, and whether immigrant ownership amplifies or reduces export-financial gains from DCD. We use a representative sample of 7,761 Canadian small and medium-sized enterprises (SMEs) to validate a strategically enabling view of DCD: internationalizing firms realize larger export-financial gains from DCD under immigrant than non-immigrant ownership. Our novel insights and findings contribute to multiple research streams spanning international business and entrepreneurship.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".