MétaCan
Menu
Back to cohort
Record W2605881945 · doi:10.5539/ibr.v10n5p39

A Conceptual Model of Forces Driving the Introduction of a Sustainability Report in SMEs: Evidence from a Case Study

2017· article· en· W2605881945 on OpenAlexvenueno aff
Fabio Caputo, Stefania Veltri, Andrea Venturelli

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityConstruct (python library)Conceptual modelBusinessOutcome (game theory)Knowledge managementConceptual frameworkCorporate social responsibilityProcess managementCorporate sustainabilitySustainable developmentComputer sciencePublic relationsEconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

The paper aims to depict the forces responsible for an effective introduction of a sustainability report within SMEs. The paper’s aim is addressed employing the case study methodology. In detail an SME considered a best practice in introducing sustainability innovations has been selected and analyzed. The main outcome of the paper is to use the case study evidence to construct a conceptual model highlighting the forces that drive companies to introduce innovative sustainable management tools. The conceptual model emphasizes as driving forces the capability of the firm to engage with its stakeholders, together with some relevant managerial and organizational features. The adoption of sustainable management tools is the outcome of a strategic alignment of corporate and sustainable strategy, and of the organizational capability to carry on effectively social and environmental responsibility (SER) activities based on the firm’s SER critical dimensions.

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)0.015
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.005
Science and technology studies0.0040.010
Scholarly communication0.0080.008
Open science0.0020.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.166
GPT teacher head0.432
Teacher spread0.266 · 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 designQualitative
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

Citations24
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicCorporate Social Responsibility ReportingFrench-language works237,207