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Record W4328024743 · doi:10.5267/j.uscm.2023.2.002

Exploring the effect of corporate environmental management responsibility on firm performance

2023· article· en· W4328024743 on OpenAlexvenueno aff
Ahmad Yacoub Nasereddin

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEnterprise valueStrategic managementModerated mediationMediationStakeholderCompetitive advantageMarketingSimilarity (geometry)Stakeholder theoryAccountingManagementEconomicsPsychologySociologySocial psychology

Abstract

fetched live from OpenAlex

Business executives and scholars are increasingly interested in learning how corporate environmental responsibility (CER) and firm success are related. However, prior research in this field requires consistent and contradictory findings. This study fills this gap by suggesting and verifying a moderated mediation theory that better explains the association between CER and company performance by considering organizational slack and strategy similarity. From 2015 to 2017, 260 listed Chinese companies provided the data for this study, yielding 780 firm-year observations. According to the multivariate analysis findings, the association between CER and company effectiveness is mediated by strategic similarity. The relation between CER and strategic identity and the direct effect of CER on organizational value via strategic similarity are both moderated by organizational slack. These findings imply that CER efforts and pursuing strategic similarities are crucial for coping with stakeholder pressure and being competitive in the market. They offer useful insights for corporate managers looking to make educated decisions regarding CER.

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)0.004
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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