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Record W4388703120 · doi:10.4236/ti.2023.144017

The Impact Path of Executive Team Heterogeneity and Environmental-Social-Governance on Corporate Performance

2023· article· en· W4388703120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTechnology and Investment · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative comparative analysisCorporate social responsibilityCorporate governanceBusinessPath (computing)Sample (material)Path analysis (statistics)Antecedent (behavioral psychology)Set (abstract data type)StakeholderAccountingIndustrial organizationProcess managementComputer scienceEconomicsManagementPublic relationsPsychologyPolitical scienceFinance

Abstract

fetched live from OpenAlex

Taking pharmaceutical manufacturing companies listed in Shanghai and Shenzhen A-share in 2021 as a sample, this paper explores the synergistic effects and driven paths of ESG and five dimensions of executive team heterogeneity on corporate performance by applying fuzzy-set Qualitative Comparative Analysis (fsQCA). This paper finds that: 1) A single factor is not necessary to drive corporate performance, and there is asymmetry in the impact of each antecedent condition on corporate performance; 2) There are three driven paths for high corporate performance: ESG-driven path, social interaction-driven path, and team conflict-driven path.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.069
GPT teacher head0.375
Teacher spread0.305 · 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