Trouble in Paradise (HBR Case Study)
Bibliographic record
Abstract
Life in Shanghai has been more than comfortable for Mike Graves, the general manager of a U.S. apparel company's 50/50 joint venture with a Chinese manufacturer. His children go to the best school, he lives in a beautiful expat neighborhood, and his company pays for a chauffeur and a nanny. Mike has made the joint venture into a big success, at least in the eyes of its Chinese executives and local officials. Zhong-Lian Knitting has turned around three money-losing businesses and has increased its payroll from 400 to 2,300 employees. But Mike's boss, the CEO of the U.S. company, Heartland Spindle, doesn't share the rosy view. "A 4% ROI is pathetic," he says. "The numbers should be better by now." He's looking for a 20% ROI, which he says will require laying off 1,200 Chinese workers. He also wants to aim at the high end of the clothing market, meaning the JV will have to meet much tougher standards of quality than it has been able to do so far. To make matters worse, the Chinese executives now want to make a fourth acquisition, which they hope will position the venture to start its own brand of apparel--a move that could eat into profits for years. Can Mike keep the joint venture from unraveling? Four commentators offer expert advice in this fictional case study: Eric Jugier, the chairman of Michelin (China) Investment in Shanghai; Dieter Turowski, a managing director in Mergers & Acquisitions at Morgan Stanley in London; David Xu, a principal at McKinsey in Shanghai; and Paul W. Beamish, the director of the Asian Management Institute at the University of Western Ontario's Richard Ivey School of Business in Canada.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".