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The Influence of Stakeholders on the Environmental Strategy of Service Firms: The Moderating Effects of Complexity, Uncertainty and Munificence<sup>*</sup>

2007· article· en· W2100067416 on OpenAlexaff
Antonio Rueda‐Manzanares, Juan Alberto Aragón Correa

Bibliographic record

VenueBritish Journal of Management · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsStakeholderBusinessService (business)Industrial organizationResource (disambiguation)MarketingResource-based viewKnowledge managementEnvironmental resource managementProcess managementEconomicsManagementComputer scienceCompetitive advantage

Abstract

fetched live from OpenAlex

Drawing on the resource‐based view of the firm, we examine how complexity, uncertainty and munificence in the general business environment moderate the association between a firm's stakeholder integration capability and its environmental strategy. Our data were drawn from 134 ski resorts in 12 countries in western Europe and North America. Our study finds that (1) an organizational capability of stakeholder integration is associated with a service firm's adoption of a proactive environmental strategy; (2) an uncertain business environment has a direct positive influence and a complex business environment has a direct negative influence on a firm's environmental strategy; and (3) complexity has a negative moderating influence on the relationship between a firm's stakeholder integration capability and its environmental strategy.

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)0.002
metaresearch head score (Gemma)0.014
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
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 head0.021
GPT teacher head0.213
Teacher spread0.192 · 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

Citations293
Published2007
Admission routes1
Has abstractyes

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