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Record W2150529496 · doi:10.1504/ijssm.2014.064350

Linkage between firm's sustainability strategies and corporate performance: a meta-analysis of global studies

2014· article· en· W2150529496 on OpenAlexaff
Amir Gabriel, Jatin Nathwani

Bibliographic record

VenueInternational Journal of Sustainable Strategic Management · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSustainabilityCorporate sustainabilityTriple bottom lineLinkage (software)BusinessCorporate social responsibilitySustainability organizationsBalance (ability)Industrial organizationMarketingEnvironmental economicsEconomicsPublic relationsPsychologyEcology

Abstract

fetched live from OpenAlex

The balance between reasonable return on investment and long-term organisational viability has fuelled a significant amount of research to evaluate the effectiveness of corporate sustainability strategies. Several factors influence the relationship between a firm’s performance and its sustainable development strategies, and there is no clear ‘line of sight’ between performance and strategic behaviour. Most theoretical attempts to describe the relationship have concluded that there is insufficient evidence to produce robust conclusions for general guidance. Using a meta-analysis methodology, this article evaluates the aggregated performance outcomes of different corporate sustainability strategies that draw on data from 18 studies and more than 20,000 firms. The findings indicate a medium to strong positive relationship between sustainability-oriented strategies and a firm’s ‘triple bottom line’. Additionally, proactive sustainability-oriented strategies tend to result in higher payoff.

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)0.035
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.077
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.026
Bibliometrics0.0120.014
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.001
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 head0.089
GPT teacher head0.333
Teacher spread0.244 · 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 designMeta-analysis
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

Citations4
Published2014
Admission routes1
Has abstractyes

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