The Chinese enterprise secret: sustained advantage in labor‐intensive industries
Bibliographic record
Abstract
Purpose In the past decade, Chinese enterprises have achieved superior cost advantages in the labor‐intensive industries. This paper explores the valuable resources that Chinese enterprises use to develop such advantages and the effective mechanisms they employ to sustain the advantages. Design/methodology/approach The study used a multiple case design that allows a replication logic, in which a series of cases is treated as a series of experiments with each case serving to confirm or disconfirm the inferences that are drawn from the others. Twenty‐nine cases were collected. The data analysis consisted of three steps (1): within‐case analysis; (2) cross‐case analysis; and (3) proposition‐shaping analysis. Findings Evidence from this study indicates that the Chinese enterprises employ a complicated multi‐step framework to develop and sustain their cost advantages. The framework consists of various resources at different levels. Resources at the same level fit, support, and reinforce each other and they work together to achieve certain competitive advantages. The advantages are not constants. They are renewed frequently, and the advantages at previous step serve as the foundation for generating the next round of advantages. The contextual and historical causality between these resources and the advantages result in their sustainability. Originality/value The findings of this study make contributions to the existing strategy literature on two fronts. First, the sustainability of a competitive advantage results from the contextual and historical causality between various resources in combination. Second, in addition to physical, human, and organizational resources, valuable resources may also include intangibles, such as culture, norms, large home market size, tough domestic competition, and flexible organizational structures, etc.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".