Bibliographic record
Abstract
This case describes how FreshFresh, a Shanghai-based online fresh food platform, worked to develop a sustainable business model by following the lean startup approach. After coming up with the idea to create FreshFresh at the end of 2013, Bin (Leo) Shen and Baiyuan Fang conducted round after round of hypothesis formulation and testing. After 2 years of efforts, FreshFresh became a online fresh food platform well regarded by both customers and investors. As of June 2016, FreshFresh had 700,000 registered users, and the majority had a repeat purchase rate (i.e., making two or more purchases) of around 45%. In March 2016, FreshFresh announced that it had raised USD 20 million in Series A round financing. On June 30, FreshFresh won the 2016 TopDigital award, making it the only fresh food e-commerce platform recognized at that year's innovation publication ceremony among all the TMT players. However, like 99% of companies in the online fresh food industry, FreshFresh hasn't built a profitable business model yet. In 2015, it lost about RMB 20 million, accounting for 20% of its total revenue. After a series of adjustments carried out in the previous years, Shen saw signs of hope that FreshFresh would make a profit in the following months. Yet looking at FreshFresh's performance report in July 2016, Shen decided to change the expansion strategy and give up entering other cities. In his mind, working in the online fresh food industry would be a marathon. Looking into the future, how should FreshFresh lay out its strategic resources? How could this new startup develop a profitable and sustainable business model?
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.010 |
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".