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Record W4382243096 · doi:10.22495/cocv20i3siart3

Sustainability, corporate governance, and firm performance: Evidence from emerging markets

2023· article· en· W4382243096 on OpenAlexaff
Mohamed A. K. Basuony, Angie Abdel Zaher, Mohammed Bouaddi, Neveen Noureldin

Bibliographic record

VenueCorporate Ownership and Control · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsAssociation of Universities and Colleges of CanadaUniversity of Prince Edward Island
FundersAmerican University in Cairo
KeywordsEmerging marketsCorporate governanceSustainabilityIndex (typography)AccountingReturn on assetsBusinessTobin's qEnterprise valueEnvironmental Sustainability IndexSample (material)Diversity (politics)PillarSustainability reportingFinance

Abstract

fetched live from OpenAlex

The purpose of this paper is to explore and investigate the influence of sustainability especially the environmental pillar and corporate board diversity on the financial performance in emerging markets. This study examines the effect of sustainability and board composition on firm performance. The sample of this study comprises 1382 firms with a total of 19199 firm-year observations covering a period from 2008 to 2021. These firms are listed in the MSCI emerging markets index representing 24 emerging countries. The results show that the main index of sustainability (ESG index) and other sub-indices (environmental score, emission score and CO2 equivalent emission) of sustainability that are used as measures of climate change have an effect on accounting-based performance (return on assets, ROA) and market-based performance (Tobin’s Q and book-to-market value, BTMV). Also, the results show that age, nationality and education as board diversity components affect the firm performance; however, the female directors on the board did not affect the firm performance.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.250
Teacher spread0.204 · 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 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

Citations11
Published2023
Admission routes1
Has abstractyes

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