Should Chinese new technology SMEs remain independent in overseas markets?
Bibliographic record
Abstract
Purpose Chinese new small‐ and medium‐sized technology enterprises face an important strategic decision when they operate in overseas markets. That is, should they remain independent? Independent small‐ and medium‐sized enterprises (SMEs) rely on their own internal resources while dependent SMEs resort to external resources through partnerships. The paper aims to evaluate various market contexts in which one strategy is preferred to the other. Design/methodology/approach Hypotheses developed from the literature review are tested with the quantitative data which were collected through questionnaires. Findings This paper assesses Chinese new technology SMEs' market environments and their internal resources. Findings from this paper suggest that different market contexts and different internal resources lead to different strategies. Originality/value This paper makes contributions to existing studies on two fronts. First, it investigates Chinese new technology SMEs' performance in the overseas market. Chinese new technology SMEs have been a new phenomenon in the world market and few studies have been reported on these firms' strategies and performance. Second, this paper assesses SMEs' strategic option of independence and dependence against the background of high‐tech industries which require heavy R&D investments and have been highly risky and uncertain.
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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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".