Lenovo 2009 (A): The Role of Board Chairman in a Turnaround
Bibliographic record
Abstract
The case brings readers to February 6, 2009 just after Lenovo, China’s largest PC maker, had replaced its American CEO — the third time in the five years since Lenovo’s acquisition of IBM’s PC business. The leadership shakeup was seemingly caused by the worsening condition in Lenovo’s key target market and the disastrous financial loss that followed, but the deeper causes actually lay within Lenovo. Lenovo had undergone the trickiest internal challenges that a globalizing Chinese company could experience: retaining international executives during a time of severe financial distress, integrating two companies with different cultural roots, changed power relations on the Board, the awkward relationship between a Chinese executive Chairman and a foreign CEO. The case includes four video clips of case author’s interview with Liu Chuanzhi, non-executive Chairman of Lenovo’s Board of Directors, the protagonist of the case. The first two clips contain Liu’s answers to the first two assigned case questions (see Section 2: Suggested Assignment Questions). The third and fourth video clips can be used for the discussion of question 3 and show Liu discussing his view of the Board’s role, and his thoughts on making Lenovo “a family business without family ties” from a governance perspective.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".