Bibliographic record
Abstract
本案例讲述了中国民营房地产企业旭辉控股（集团）有限公司（以下简称“旭辉”）成立以来通过各阶段的组织和人力资源管理工作逐步提升组织能力的实践历程，并重点讲述了其在2022年进行的一次组织变革。2022年初，旭辉首席人力官葛明提出进行组织变革，但并没有立刻得到公司领导人林中和绝大部分区域总经理的支持。葛明在分析当时的形势后仍然坚持变革。在两个业绩最好的区域进行成功试点后，葛明的变革提议也获得了林中的同意。葛明组织变革的方案包括架构与人员调整以及薪酬调整两个方面。在架构和人员调整上，旭辉首先压缩层级、精简机构；其次重新梳理岗位体系；最后以岗定人，竞聘上岗。通过这些措施，旭辉实现了人员缩编并提高了战斗力。在激励体系上，旭辉重新设计了基于岗位的“宽带薪酬”制度，让畸高的薪酬回归常态，这一举措帮助旭辉降低了管理费率。在改革方案执行的过程中，葛明也遇到各种许多问题，最终通过坚持原则、及时沟通、给予一定弹性等方式解决。在变革过程中，以林中、林峰为代表的最高管理者与首席人力官（CHRO）葛明发挥了各自不同的作用，展现了变革的“道”与“术”。对本案例的分析和讨论有助于学员掌握组织能力的概念与打造、组织变革的动因、阻力及推行策略。
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.100 |
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".