Dichotomizing salient stakeholders through organizational hypocrisy: evidence from an Argentinian agro-giant
Bibliographic record
Abstract
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.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".