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Record W2148874657 · doi:10.5465/amj.2012.0508

Status and Corporate Illegality: Illegal Loan Recovery Practices of Commercial Banks in India

2014· article· en· W2148874657 on OpenAlexaff
Rekha Krishnan, Rajiv Krishnan Kozhikode

Bibliographic record

VenueAcademy of Management Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLoanAsset qualityBusinessAsset (computer security)Quality (philosophy)Commercial bankCorporate social responsibilityFinanceAccountingFinancial systemEconomicsPublic relationsMarket economy

Abstract

fetched live from OpenAlex

Why might high-status organizations, presumably secure in their positions, resort to illegality? This study considers the possibility that status theory might have overestimated the relative security of high-status organizations. We examine our theory that an inability to meet associates’ expectations about quality might be the source of insecurity, using data on the illegal loan recovery practices employed by commercial banks in India between 2005 and 2009. High-status banks were found to be particularly likely to engage in illegal recovery practices. This was especially true when a high-status bank had experienced a decline in its financial asset quality or had fallen behind the financial asset quality of its peers. However, when a bank’s business partners placed greater emphasis on corporate social responsibility (CSR), it minimized a bank’s tendency to resort to illegal loan recovery practices.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.331
Teacher spread0.268 · 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

Citations72
Published2014
Admission routes1
Has abstractyes

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