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Record W2085520290 · doi:10.5465/ambpp.2004.13857751

MANAGERIAL PERSPECTIVES ON CORPORATE ENVIRONMENTAL AND SOCIAL RESPONSIBILITIES IN 22 COUNTRIES.

2004· article· en· W2085520290 on OpenAlexaff
Carolyn P. Egri, David A. Ralston, Laurie P. Milton, Irina Naoumova, Ian Palmer, Prem Ramburuth, FLORIAN WANGEHEIM, María Teresa de la Garza Carranza, Liesl Riddle, Ilya Girson, Detelin Elenkov, Marina Dabić, Arif Nazir Butt, S. Narasimhan, Vojko Potočan, Olivier Furrer, Tevfik Dalgić

Bibliographic record

VenueAcademy of Management Proceedings · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCollectivismCorporate social responsibilityIndividualismSocial responsibilityUniversalismPublic relationsBusinessPolitical science

Abstract

fetched live from OpenAlex

This study investigated perspectives on corporate environmental and social responsibilities of 5539 managers and professionals in 22 countries. In particular, we studied the influence of personal values (individualism, collectivism, universalism), personal characteristics (age, gender, education and organizational position level), organizational characteristics (company size and industry), and country level of economic development on the relative importance attributed to corporate environmental and social responsibilities. Country level of economic development was found to be a significant factor in cross-cultural differences in perspectives on corporate environmental and social responsibility. We also found that personal values influence environmental orientations more than social orientations, and that personal characteristics have more influence than organizational contexts.

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)0.003
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations24
Published2004
Admission routes1
Has abstractyes

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