Offshore Outsourcing of Manufacturing SMEs and Developing Value Ladder
Bibliographic record
Abstract
Research on production efficiency from offshore outsourcing is abundant.In a hyper-competitive business environment, the SMEs need not only efficiency related strategy but also growth oriented strategy by improving their dynamic capabilities that lead towards Sustainable Competitive advantages (SCA).However, there are insignificant research that addresses the offshore outsourcing as a medium of dynamic capabilities development.The objective of this paper is to explore on how manufacturing SMEs enhance their dynamic capabilities through offshore outsourcing in addition to the efficient related advantages that firms gain from this strategy.Organizational dynamic capabilities development process includes among others increasing focus on Core competency of the focal firm, developing innovation capabilities, increasing market share in existing and/or new markets, and improving flexibility of the firm to match with the volatile market trends.Results from the case study on ten manufacturing SMEs from Quebec show that offshore outsourcing contributes to the development of dynamic capabilities with varying degrees of success.It shows an evolutionary path of dynamic capability development process.This article open-up a new horizon on offshore outsourcing research and shed light on growth perspective and sustainable competitive advantages (SCA) that offshore outsourcing bring to manufacturing SMEs despite the size and resource constraints that they inherit.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".