Bibliographic record
Abstract
Purpose This paper aims to examine the case of Canadian manufacturers involved in the Chinese market. In particular, it seeks to look at the challenges of entering a new export destination, including access to market intelligence. It also aims to analyze recent performance. Design/methodology/approach A postal survey of Canadian manufacturers that examined the myriad challenges and strategies for manufacturers serves as the basis for this research. Findings The findings show that, for these manufacturers, face‐to‐face contact is important in the Chinese market. The group of exporters, on average, was not as dependent on the US market. Perhaps most importantly, export success is not limited solely to larger manufacturers. Research limitations/implications The small sample size and survey structure limited statistical analysis. Firm‐level interviews need to be conducted in order to examine unique export success strategies in this booming market. Practical implications The findings show that in‐person business relationships are important in the China market. Also, export success is not limited solely to larger manufacturers. Companies involved in implementing lean techniques tended to view China as an opportunity (rather than a threat) at a much higher rate than other manufacturers. Originality/value The paper provides an examination of manufacturers attempting to enter a relatively new market after years of regionally focused sales to a mature customer base.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".