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Stakeholder Actions and Their Impact on the Organizational Cultures of Two Tobacco Companies

2010· article· en· W1539252299 on OpenAlexaff
Achilles A. Armenakis, Jeffrey Wigand

Bibliographic record

VenueBusiness and Society Review · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsPhysicians for a Smoke-Free Canada
Fundersnot available
KeywordsDisengagement theoryOrganizational cultureStakeholderPublic relationsBusinessGovernment (linguistics)Tobacco industryPolitical scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT We link the behaviors of executives and lawyers in two tobacco companies, in defending their tobacco products to the actions of stakeholders (e.g., the U.S. Government and Congress, medical researchers, consumers, public‐health organizations, tobacco‐control advocates, and insiders who have spoken out). Included in our analysis, which is based on publicly available documents spanning over a period of almost six decades, are critical incidents in which moral disengagement tactics were applied in the decision‐making process. We infer that the disengagement tactics applied by tobacco decision makers are indicative of what Schein and other organizational scientists describe as organizational culture. We equate the critical incidents to the espoused beliefs and values and underlying assumptions which comprise organizational culture and explain that the cultures of these two tobacco companies are not consistent with the stakeholder theory of management. We conclude that the critical incidents we analyze were immoral and the representatives were indeed accountable for these behaviors. From an organizational change perspective, we discuss how analyzing these critical incidents can serve to assess the extent to which an organizational culture is ethical. Furthermore, these critical incidents can be fed back to organizational decision makers and can then be used to initiate organizational changes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.534

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.265
GPT teacher head0.435
Teacher spread0.170 · 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.

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

Citations15
Published2010
Admission routes1
Has abstractyes

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