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Record W4413953707 · doi:10.1108/arla-04-2024-0054

Dichotomizing salient stakeholders through organizational hypocrisy: evidence from an Argentinian agro-giant

2025· article· en· W4413953707 on OpenAlexaff
Taslima Nasreen, Eliana Werbin, Cecilia Ficco

Bibliographic record

VenueAcademia Revista Latinoamericana de Administración · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsAlgoma University
Fundersnot available
KeywordsHypocrisySalientBusinessPolitical scienceLaw

Abstract

fetched live from OpenAlex

Purpose This study explores how an about-to-bankrupt company, Vicentin, in Argentina, used organized hypocrisy and façade techniques to create dichotomized understanding among salient stakeholders about its financial condition. Design/methodology/approach We applied a qualitative interpretive approach (content analysis) and used the organized hypocrisy and façade theoretical framework to analyze the company’s stakeholder management style during this crisis period. Findings Our study revealed that Vicentin used organized hypocrisy to present a very different reality (dichotomized understanding) to its salient stakeholders, such as creditors and employees. Although mainstream accounting and critical accounting explored organized hypocrisy and façades as tactics for organizational management purposes, this study sheds light on these tactics for creating contrasting understandings employed by an about-to-bankrupt company during its court proceedings to manage salient stakeholders. Practical implications The findings of this study can guide regulators, management, and auditors in understanding the stakeholder management schema of an about-to-bankrupt company. The findings emphasize the significance of fostering a more comprehensive dialogue and interaction within the stakeholder group, particularly when their interests diverge. Originality/value This study represents one of the early attempts to apply the concepts of organized hypocrisy and façade to gain a more nuanced understanding of stakeholder management strategies that reflect the differing realities (dichotomized knowledge) among the most pressing stakeholder groups.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.053
GPT teacher head0.310
Teacher spread0.257 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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