Bibliographic record
Abstract
Any company might one day face the test of a grave crisis that puts its very existence at risk. How can it survive the darkest moments and rise from the ashes? What is key to a company's longevity? The case of Luckin provides one possible answer. This case illustrated changes in Luckin's corporate strategy, business model, and operations strategy after its accounting fraud scandal. Luckin had previously adopted an aggressive expansion strategy. This approach featured prolific store openings, huge levels of financing, big customer discounts, and operations supported by a data-driven "new retail" system that acquired customers online and delivered products and services offline. This signature system took Luckin years to develop and refine. Customers didn't instantly abandon Luckin the aftermath of the scandal. Instead, customers rushed to Luckin stores to use up all their coupons just in case the company went out of business. While ensuring its stores could continue operating normally to keep up with this surge in demand, Luckin's new management team suspended its previous strategy and pivoted towards business performance. It focused on younger consumers and refined its operations strategy. The company launched new initiatives, including establishing private-domain traffic, introducing new products rigorously, and finetuning store operations to drive continued improvements in business performance. In August 2022, Luckin claimed to have "risen from the ashes and completely reinvented itself" as it announced second-quarter earnings. At that point in time, however, China's coffee segment was witnessing many emerging brands backed by deep-pocketed investors. Could Luckin sustain its growth amid such competition? How could it stay competitive?
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 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".