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Record W7132363833

CIFI Group (A): Liquidity Crisis

2024· other· en· W7132363833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCEIBS Institutional Repository · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsCentre Casa
Fundersnot available
KeywordsMarket liquidityReal estateFinancial crisisResilience (materials science)Crisis managementLiquidity crisisPsychological resilience
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.064

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.

Opus teacher head0.015
GPT teacher head0.258
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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