South Beauty Group: In Search of a “Beautiful” Growth Story (A)
Bibliographic record
Abstract
The restaurant segment was the largest in China's catering industry, which was divided into four categories: fine dining (mainly Chinese cuisine), hot pot, fast food (Western and Chinese) and tea houses (and coffee houses).Surprised by the immense popularity of Western fast food in China in the 1990s and early 2000s, the leading Chinese fine-dining restaurants, including South Beauty Group, were exploring how to expand their scale through the development of restaurant chains. Given the difficulties of standardizing Chinese cuisine, the scale expansion of the Chinese fine-dining segment was slower than that of the hot pot and Chinese fast-food segments.The (A) case provides an introduction to South Beauty Group's founder, Ms. Zhang Lan; the Group's origins, development and business model in 2007; consumer trends and opinions; China's restaurant market; and the Group's future plans and challenges. The (B) case, to be read by students after the (A) case is taught, describes more recent developments at the Group, which faced significant challenges in 2011.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".