Motivating Vietnamese Employees and Managers in an American Joint-Venture: What a Challenge!
Bibliographic record
Abstract
Paul Fierman, an American expatriate, was the General Director of the Vietnamese subsidiary of Chicago Food and Beverage Company (CFB Co.) in Haiphong. His three-year mandate consisted of preparing and executing, in collaboration with the Asia Pacific Regional Director, a strategy of creating synergy among three Asian subsidiaries, which should allow CFB Co. to conquer the three markets. Six months after his arrival, Paul was facing many problems, including a decrease in the sales of non-alcoholic beverages, the general lack of motivation of management in the subsidiary and unrest among the factory workers who wanted better wages and were threatening to unionize. Now, to save the subsidiary and also his career, Paul needed a rapid action plan to put the subsidiary back on track. He had to present and defend such an action plan during his next meeting with the Asia Pacific Regional Director of CFB Co. This case is useful in examining the challenges in managing in a foreign environment.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".