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Record W1826171902 · doi:10.1002/bse.1861

Measuring Enterprise Sustainability

2014· article· en· W1826171902 on OpenAlexafffund
Cory Searcy

Bibliographic record

VenueBusiness Strategy and the Environment · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSustainabilityProcess managementContext (archaeology)Key (lock)Sustainability organizationsComputer scienceKnowledge managementConceptual frameworkBusinessManagement scienceEngineeringSociology

Abstract

fetched live from OpenAlex

Abstract The purpose of this paper is to identify the key requirements for measuring enterprise sustainability. The paper argues that measuring enterprise sustainability requires the explicit consideration of a focal firm, its supply chain and the sustainability context within which the firm operates. Building on this notion, original definitions of enterprise sustainability and enterprise sustainability performance measurement systems (ESPMSs) are proposed. The definitions provide the basis for the development of a novel conceptual framework. The framework is used to identify seven key requirements and 35 associated sub‐requirements for an ESPMS. Overall, the requirements highlight that sustainability performance measurement requires a systematic, structured and integrated approach that considers all aspects of enterprise sustainability. The framework presented in this paper is a conceptual model. In recognition of this point, the paper provides discussions on the potential application of the framework and guidance for further research. The academic, managerial and societal implications of the paper are also discussed. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment

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)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0010.002
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.174
Teacher spread0.164 · 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 designNot applicable
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

Citations145
Published2014
Admission routes2
Has abstractyes

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