Antecedents of Second Order Outsourcing by Manufacturing Suppliers in Low-Cost Countries
Bibliographic record
Abstract
The objective of this study is to identify and understand the process and factors that influence the ‘second-order’ outsourcing decision in context of manufacturing firms in low cost developing countries. Usually, big manufacturing suppliers receive more foreign manufacturing orders than they can handle with their existing infrastructure, and then, transfer part of their manufacturing process to other smaller firms due to the lack of internal capabilities and production factors. This study aims to identify factors that influence large manufacturing suppliers to go for second-order sub-contracting. A simple questionnaire was developed to collect data and a total of 126 responses were collected from mid-level managers of manufacturing outsourcing suppliers. The findings revealed that access to inter-firm resources, company focus, and internal shortages of certain resources and capabilities have significant effects on second-order outsourcing decisions. Both internal and external factors influence managerial decisions and the managers need to evaluate both internal and external environment for maximizing the benefits before adopting the outsourcing as a business strategy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".