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Record W2139921014 · doi:10.1002/bse.545

Implementing a corporate environmental strategy: establishing coordination and control within multinational companies

2006· article· en· W2139921014 on OpenAlexaff
Marc J. Epstein, Marie‐Josée Roy

Bibliographic record

VenueBusiness Strategy and the Environment · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMultinational corporationBusinessControl (management)Sample (material)Key (lock)Empirical researchIndustrial organizationProcess managementEnvironmental resource managementMarketingEnvironmental economicsEconomicsManagementComputer scienceFinance

Abstract

fetched live from OpenAlex

Abstract This article presents the results of an empirical study that investigated coordination and control within multinational companies over six key aspects of their environmental strategy. Specifically, it first examines the level of centralization of these key issues. Second, it investigates whether there is a positive relation between three variables associated with organizational complexity and the level of centralization of the environmental issues. Statistical analyses of the data obtained from a sample of 98 North American multinationals were conducted. The study's results revealed that most companies have adopted a global environmental standard to govern their worldwide business activities that is supported by strong central controls over both environmental performance evaluation of facilities and decisions regarding the development of environmental programs. Results also revealed that there are limited links between organizational complexity and most aspects of the environmental strategy. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.

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)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.010
GPT teacher head0.186
Teacher spread0.176 · 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 designNot applicable
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

Citations83
Published2006
Admission routes1
Has abstractyes

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