Bibliographic record
Abstract
本案例讲述了飞书从团队诞生、海外商业化到将经营重心转移回国内并进行持续优化的过程。飞书是抖音、今日头条母公司字节跳动旗下的企业协同办公软件平台。2016年，字节跳动成立团队自主研发协同办公软件以满足公司内部的需要，飞书团队由此诞生，并在2017年底在字节跳动全面推广。考虑到产品日渐成熟，2019年飞书在海外市场开始商业化，2020年将市场重心从海外移到国内。本案例主要讨论以下两个管理主题。 第一，企业文化在企业发展中的作用。具体来说，一是字节跳动的企业文化——字节范儿是如何影响飞书产品设计及商业化进程的？与钉钉和企业微信等主要竞争对手不同，飞书采用套件（all in one）的产品设计理念，各种功能在飞书内无需切换便可使用；使用单租户的解决方案，在系统内弱化等级差别而重视信息平等与透明。这些带有强烈“字节跳动”风格的产品特点被互联网公司所推崇，但推广到以管控和精益运营文化主导的传统企业就出现了障碍。那么，要实现商业化价值，未来飞书的产品设计是否还应与字节范儿绑定？二是字节跳动的管理制度特别是绩效管理制度在引导和塑造员工行为的社会化过程中发挥了什么作用？字节跳动通过OKR工作法、360绩效考核以及激励等制度对员工在行为上展现字节范儿起到了引导和强化的作用，从而落实了企业所倡导的价值观，这种作用是如何发挥的？ 第二，绩效管理是组织管理的核心机制之一。企业应当如何进行绩效管理制度的设计及工具选择以确保企业文化和战略落地？要实现产品的商业化目标，飞书应当进行怎样的绩效管理制度设计？与其他绩效管理工具相比，OKR能否助力飞书产品的价值实现？如何能够评估员工的价值贡献？OKR是最适合飞书的绩效管理工具吗？
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 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".