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Record W4214696689 · doi:10.3390/jrfm15030096

Vinculum of Sustainable Development Goal Practices and Firms’ Financial Performance: A Moderation Role of Green Innovation

2022· article· en· W4214696689 on OpenAlexvenueno aff
Parvez Alam Khan, Satirenjit Kaur Johl, Shakeb Akhtar

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
FundersYayasan UTPUniversiti Teknologi Petronas
KeywordsModerationProsperitySustainable developmentBusinessOrder (exchange)Empirical researchEmpirical evidenceFinanceEnvironmental economicsEconomic growthEnvironmental resource managementEconomicsPolitical sciencePsychology

Abstract

fetched live from OpenAlex

The 2030 Agenda for Sustainable Development (SDGs) has been established to alter our world by addressing the challenges faced by humanity in order to promote wellbeing, economic prosperity, and the protection of the environment. The SDGs provide a holistic and multi-dimensional approach to development compared to conventional development plans that focus on a limited range of dimensions. As a result, linkages between the SDGs may result in differing outcomes. This research is the first to investigate the direct relationship of environmental and social SDGs with firms’ financial performance and the moderating role of green innovation. Data from 67 companies from five continents (Europe, Australia and New Zealand, Asia, North America, and Africa) and their top five blue-chip firms were collected through content analysis. Generalized least squares (GLS) were used to test for direct relationships. The results showed a positive correlation between environmental SDGs and the negative significance of social SDGs on firms’ financial performance. However, mixed findings regarding the moderation variable green innovation over SDGs and firms’ financial performance were found. The new findings extend the SDG literature and provide empirical evidence to practitioners and policymakers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations116
Published2022
Admission routes1
Has abstractyes

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