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Record W2481429296 · doi:10.3390/su8070694

Does Adoption of Management Standards Deliver Efficiency Gain in Firms’ Pursuit of Sustainability Performance? An Empirical Investigation of Chinese Manufacturing Firms

2016· article· en· W2481429296 on OpenAlexafffund
Xiaoling Wang, Haiying Lin, Olaf Weber

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaChina Postdoctoral Science Foundation
KeywordsCertificationSustainabilityBusinessCertificateIndustrial organizationEmpirical researchEnvironmental economicsMarketingEconomicsManagementComputer science

Abstract

fetched live from OpenAlex

Building on longitudinal data from 73 Chinese manufacturing firms during 2009–2012, we assess whether and how firms gain higher efficiency in achieving their sustainability goals by adopting management practice standards (ISO 9001, ISO 14001, and/or OHSAS 18001). We propose four pathways for firms to gain sustainability efficiency in their certification journey: participation, qualitative integration, quantitative expansion, and temporal accumulation. Our results confirm that firms certifying management standards gain higher efficiency in pursuing their sustainability goals than firms without these standards. We also find some support for increased efficiency effect in firms with diverse management systems over firms with only a single certificate in 2011. Finally, our results highlight the experiential and temporal accumulation effect of such efficiency gains, that is, firms with prior certification experience or having a longer certification history demonstrate higher efficiency gains in pursuing their sustainability goals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.250
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 teacher head, not a consensus.

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

Citations22
Published2016
Admission routes2
Has abstractyes

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