Factors Determining the Quality of the Business Relationships between Korean Exporting Firms and Their Canadian Counterparts
Bibliographic record
Abstract
The purpose of this study is to investigate what factors make the relationships between Korean firms and their Canadian counterparts good or bad, particularly within the context of international trade. This study suggests that the factors affecting the quality of business relationship and relationship performance include relational norms, formalization, and market uncertainty. It also proposes that relational norms are determined by trust, relationship investment, and continuity experience. Data was collected through a mail survey of 412 Korean exporting firms which have transaction relationships with Canadian companies. Data strongly supports the research hypotheses. As hypothesized, the results of data analysis show that relational norms and formalization tend to enhance relationship quality and relationship performance. The impact of formalization is only moderate, however. In addition, we notice that relationship quality and relationship performance seem to decrease as market uncertainty increases. Data also supports the hypotheses regarding the determinants of relational norms. Trust, relationship investment, and continuity experience tend to induce the development of relational norms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".