Bibliographic record
Abstract
在巨头们鏖战的成熟市场，还有创业空间吗？新创企业如何进入并在其中立足？艾优用自己的实践回答了这个问题。 艾优是一家具有4年历史的消费品创业企业，它创业的与众不同之处在于，大部分创业企业在创业初期都只打造一个品牌，而它则陆续推出了分布在成熟市场不同赛道的7个品牌。切入每一个赛道，它都采取了颜值差异化战略。经过4年发展，2020年艾优年营收达12亿元，但它却将2025年的年营收目标设为100亿元。尽管艾优凭颜值差异化战略已取得了一定成果，但是消费者对颜值的偏好是感性易变的，它如何才能持续保持这种战略的竞争优势？要用这种战略在5年内实现超过8倍的增长，艾优将会遇到什么样的挑战？请同学站在艾优创始人和董事长曾瑞的角度来思考这些问题。
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.007 |
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; both teacher heads agree on what is shown here.
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".