Bibliographic record
Abstract
Abstract: Investment in the Chinese market presents a challenge and an opportunity for international investors. This is especially true for Canadian firms. Canadian firms may initially be at a disadvantage in terms of cultural, language, legal and political differences, however they bring a reputation of historically successful institutional financial performance and as reliable suppliers of natural resources to this new business environment. As such, Canadian firms can synchronize with China’s increasingly open financial system and demand for natural resources. While Canada’s entry into the Chinese marketplace is still in its infancy, forecasts generally indicate the Canadian investment in China will continue to grow. The purpose of this study is two fold: firstly, to investigate the degree to which entry modes and firm size influence financial performance; secondly, to determine whether overseas investment experience exerts a moderating effect on these two relationships. Contact information of Canadian companies investing in China was attained from the CCBC Directory of Canadian Companies and Professionals in China (2006) and the 2002 Directory of Standing Representative Organizations of Foreign and Territorial Companies in China. Questionnaires were sent to 283 firms operating in the Chinese marketplace, the final analysis was based on the responses of 32 firms representing 13 separate industries. The variables employed in this study were: firm size, overseas investment experience, entry mode and financial performance. Multiple regression analysis indicates that a significant relationship exists between entry mode and financial performance as well as firm size and financial performance. Overseas investment experience was found to have a moderating effect on the relationship between entry mode and financial performance; that is, wholly owned ventures with no overseas experience were found to demonstrate higher levels of financial performance than joint ventures with no overseas experience. For firms with overseas experience, though, there was no significant relationship between entry mode and financial performance. Overseas investment experience was shown to not exert a moderating effect on the relationship between firm size and financial performance.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".