Identifying and Managing Risks Inherent to Cultural Differences in the Case of Production Internationalization
Bibliographic record
Abstract
Abstract Ignoring cultural differences can cause unfavourable situations and negatively influence the success of production internationalization projects. A decision to subcontract part of the production or to produce in a foreign country, requires identifying and controlling the risks related to cultural differences. Our research objective was to study these risks and the business practices used to face them. In order to reach this objective, we studied eight (8) Canadian manufacturing companies, that outsourced part of their production to China. Results show that the risks related to cultural differences, can provoke an important divergence between requirements of the Canadian company and the resulting products manufactured by the Chinese subcontractor and cause an additional unexpected manufacturing cost. The risk of misunderstanding and difficulty communicating in addition to difficulty applying quality control practices were critical for most of the studied companies’ managers. However, the identified risks could be controlled with different business practices, which we classified according to their role of mitigation or contingency. Frequent interaction with Chinese partners, developing a good trust-based relationship with them and finding ways of encouraging them to get more involved in the partnership and to suggest solutions and innovations represent some of the important practices to adopt. JEL classification numbers: M1. Keywords: Cultural differences, Production internationalization, International outsourcing, Outsourcing in China, Risk management, Cross-cultural management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".