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Record W3009844040 · doi:10.18280/ijsdp.150207

Empirical Analysis on the Relationship Between Institutional Pressure, Environmental Strategy and Corporate Environmental Performance

2020· article· en· W3009844040 on OpenAlexvenueno aff
Jun Li, Shu Ji

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEmpirical researchEnvironmental scienceEnvironmental resource managementEnvironmental economicsEnvironmental planningEconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper attempts to disclose the driving effect of institutional pressure on corporate environmental performance in the context of China.Firstly, the institutional pressure was divided into three dimensions: coercive pressure, normative pressure and mimetic pressure.Then, the authors established an analytical model of "institutional pressure-environmental strategy-environmental performance", and verified the model with the structural equation.The results show that corporate environmental performance is positively affected by coercive pressure and mimetic pressure, but not directly influenced by normative pressure; the three dimensions of institutional pressure all promote the environmental strategy; environmental strategy mediates the relationships between each dimension and environment performance.Further discussion and cause analysis were performed in the light of China's institutional environment.Finally, the authors summed up the theoretical contributions of this research and looked forward to the future research.

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)0.006
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.248
Teacher spread0.193 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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