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Record W2101060893 · doi:10.5539/jms.v4n3p37

Exploring the Relationships among Sustainable Manufacturing Practices, Business Performance and Competitive Advantage: Perspectives from a Developing Economy

2014· article· en· W2101060893 on OpenAlexaffvenue
Suzana N. Russell, A. Harvey Millar

Bibliographic record

VenueJournal of Management and Sustainability · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsCompetitive advantageSustainabilityBusinessProductivityIndustrial organizationSustainable businessMarketingKnowledge managementEconomicsComputer scienceEconomic growthEcology

Abstract

fetched live from OpenAlex

This study aims to empirically test the relationships among the adoption of sustainability practices, businessperformance and competitive advantage in Caribbean manufacturing firms. Seven dimensions of sustainablemanufacturing practices are conceptualized and tested against measures of business performance andcompetitive advantage. Three hypothesized relationships are tested using the partial least squares structuralequation modeling (PLS-SEM) technique. The results show a negative relationship between the adoption ofsustainability practices and business performance. We also find that there is no significant relationship betweenthe adoption of sustainability practices and competitive advantage. However, we observe a significant positiverelationship between competitive advantage and business performance. Based on these findings, manufacturingfirms in developing regions, such as the Caribbean, are advised to pursue sustainability strategy implementationwith some vigor, but should base their choices on strategies that will enhance sustainability through improvedresource productivity, while improving business performance and competitive advantage.

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 score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.007
Open science0.0000.001
Research integrity0.0000.001
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 head0.027
GPT teacher head0.225
Teacher spread0.198 · 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

Citations43
Published2014
Admission routes2
Has abstractyes

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