Bibliographic record
Abstract
In the VUCA (volatility, uncertainty, complexity, and ambiguity) era defined by ever-present risks and increasing uncertainty, reshaping society to withstand major shocks has become a key global issue. Similarly, building the resilience to adapt and recover quickly has become essential for driving company growth. Cases A & B describe in detail how CIFI Holdings (Group) Co., Ltd. (hereinafter “CIFI”), a leading real estate developer in China, solved its liquidity crisis that erupted in September 2022. These two cases focus on resilient leadership and organizational resilience, providing insights into how to enhance resilient leadership and build organizational resilience while also highlighting the importance of these qualities for company development and crisis management. The time span of Case A covers the period from the establishment of CIFI to the outbreak of its liquidity crisis in October 2022. The case consists of three parts: 1) CIFI’s soft power, including the founder and top management’s entrepreneurial ventures and management styles, as well as the corporate culture of expedition; 2) CIFI’s hard power, including its strategic planning capabilities, organizational structure, talent pool, capital, and other resources or capabilities built up over the past two decades; 3) CIFI’s liquidity crisis or, more specifically, the causes of the crisis and potential solutions. Students are required to put themselves in the shoes of Lin Zhong and consider both the soft and hard power of CIFI when determining the best solution. Through case analysis, students will understand the manifestations and benefits of resilient leadership and organizational resilience. The time frame of Case B spans from November 2022, when CIFI publicly acknowledged its liquidity crisis, to July 2023. The case introduces CIFI’s three-step approach to crisis management: Step 1: “Hunker down”. This represented CIFI’s initial response; Step 2: “Live on”. This was an emergency measure to deal with the crisis itself; Step 3: “Stand up”. This involved planning and preparing for the later stages and aftermath of the crisis. Through case discussion, students will develop a better understanding of organizational resilience-building. In summary, Cases A and B outline CIFI’s responses to the liquidity crisis. By analyzing these cases, students will understand how resilient leadership and organizational resilience come into play during critical decision-making moments, and how to make improvements in these two areas.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".