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Record W2768876008 · doi:10.5539/ijef.v9n12p101

Conflicts of Interest in Financial Distress: The Role of Employees

2017· article· en· W2768876008 on OpenAlexvenueno aff
Wenchien Liu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyFinancial distressCreditorGoing concernBusinessDebtorPower (physics)European unionAsset (computer security)FinancePossession (linguistics)EconomicsAccountingAuditDebtFinancial systemEconomic policyAuditor's report

Abstract

fetched live from OpenAlex

The interests of employees are not consistent with those of other stakeholders when firms are in financial distress. Hence, conflicts of interest among stakeholders are more severe, especially for those firms with strong union power, as news is reported in the media. However, little attention has been paid to the impacts of employees on bankruptcy resolutions. This study examines the impacts of employees (i.e., union power) on the conflicts of interest of distressed firms in the United States from 1983 to 2015. We find that union power has strong effects on conflicts of interest related to employees, such as asset sales, debtor-in-possession financing, successful emergence from bankruptcy, CEO replacement, and refiling for bankruptcy. On the contrary, for conflicts of interest unrelated to employees, including the costs of bankruptcy resolution, choice of bankruptcy resolution method, and conflicts of interest between creditors and debtors, we find no significant relationships. Finally, we also find a positive impact of union power on the probability of refiling for bankruptcy in the future after emerging successfully.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.034
GPT teacher head0.229
Teacher spread0.196 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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