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Record W2765597360 · doi:10.1177/0007650317745636

The Influence of External and Internal Stakeholder Pressures on the Implementation of Upstream Environmental Supply Chain Practices

2017· article· en· W2765597360 on OpenAlexfundno aff
Stephanie Graham

Bibliographic record

VenueBusiness & Society · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsAntecedent (behavioral psychology)Upstream (networking)Supply chainBusinessStakeholderDownstream (manufacturing)Sample (material)Industrial organizationCompetitive advantageMarketingMultilevel modelBest practiceEconomicsManagementPsychology

Abstract

fetched live from OpenAlex

This study examines the independent and combined influences of internal and external antecedents to upstream environmental practices. Proactive environmental strategy is considered as an internal antecedent and competitive pressure as an external antecedent. Multiple hierarchical regression analysis is used to test the hypothesized relationships using data from a sample of 149 manufacturing companies located within the U.K. food industry. The results suggest that proactive strategy and competitive pressure exert both independent and combined influences on environmental supply chain practices. Proactive strategy appears to be a stronger driver of these practices, suggesting that internal stakeholders such as directors, managers, and employees may be more influential in the adoption of certain practices than external stakeholder pressures. This article builds upon the recent wave of research highlighting the potential for internal and external factors to generate a combined influence on the adoption of environmental practices within companies and their supply chains.

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)0.017
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 score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
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 head0.019
GPT teacher head0.262
Teacher spread0.243 · 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

Citations73
Published2017
Admission routes1
Has abstractyes

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