Capability sequencing: strategies by township and village enterprises in China
Bibliographic record
Abstract
Purpose This study aims to identify and assess the strategies of township and village enterprises in China to capture competitive advantages. Design/methodology/approach The paper employs a self‐administered questionnaire survey approach, involving a sample of managing directors of township and village enterprises in the Fujian Province, China. Findings The analysis identifies the causal linkages across time between firms' different capabilities. Labor‐intensive industries and rural locations offer township and village enterprises (TVEs) opportunities to create capabilities to minimize costs. The cost minimization and systematic learning capabilities, in turn, lead to low‐priced innovator positioning. It also suggests that the co‐evolution and co‐existence of different capabilities contribute to capability inimitability. Research limitations/implications Future studies on Chinese TVEs should expand the empirical database and include TVEs in underdeveloped areas and to investigate how firms survive within severe resource limitations. Practical implications The findings of this study indicate that dynamic capabilities are important not only for firms in rapidly changing environments, but also for those in relatively stable industries, such as labor‐intensive industries. Firms should develop different capabilities over time and combine these into complex capabilities bundles. Originality/value The findings from this study indicate that firms in developing countries can achieve cost leadership and differentiation, but the route to the destination has a path‐dependent history.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".