Manufacturing Small And Medium Size Enterprises Offshore Outsourcing And Competitive Advantage: An Exploratory Study On Canadian Offshoring Manufacturing SMEs
Bibliographic record
Abstract
This paper explores whether and how theoffshore outsourcing of the manufacturing SMEs creates competitive advantagesfor these firms. The offshore outsourcing strategy is widely criticized in thedeveloped countries for allegedly reducing job opportunities, missing scaleeconomy, diminishing innovation potentialities and creating various socialproblems. The present article with empirical data from thirteen Canadianoffshoring manufacturing SMEs attempted to address that the world-widedistributed co-production network could instead increase profit and marketshare, boost investment in R&D, raise focus on core competency and enhancecompetitivity of offshoring SMEs. This strategy enables companies to enhancetheir competitiveness by allowing them to have access to the competitiveproduction factors and new markets for their products. This paper contributesto the existing body of knowledge by showing that not only the largemultinationals but also the SMEs can achieve competitive advantages fromoffshoring part of their activities to foreign firms where those tasks can beperformed more competitively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".