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Record W3043438799 · doi:10.32479/ijefi.9959

THE IMPACT OF INTELLECTUAL CAPITAL EFFICIENCY ON BANK RISKS: EMPIRICAL EVIDENCE FROM THE SAUDI BANKING INDUSTRY

2020· article· en· W3043438799 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueInternational Journal of Economics and Financial Issues · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInsolvencyCapital adequacy ratioEconomic capitalIntellectual capitalQuarter (Canadian coin)EconomicsRisk-adjusted return on capitalHuman capitalStructural capitalCredit riskCapital (architecture)Actuarial scienceCapital requirementBusinessFinancial capitalFinanceCapital formationMicroeconomicsIndividual capital

Abstract

fetched live from OpenAlex

The main purpose of conducting this research is to investigate the impact of intellectual capital efficiency (ICE) and its components – human capital efficiency (HCE) and structural capital efficiency (SCE) - on bank credit and insolvency risks in the Saudi banking industry. To assess such a relationship, value added intellectual coefficient model (VAIC) along with a couple of panel data techniques were utilized by using quarterly observations spanning from the first quarter of 2009 to the fourth quarter of 2018. The carried out empirical results confirm the existence of a significant negative relationship between ICE, in particular HCE, and bank credit and insolvency risks. Keywords: Intellectual Capital Efficiency, Human Capital, Structural Capital, Credit Risk, insolvency risk, risk managementJEL Classifications: O34, E24, J21, G32, C23, G21DOI: https://doi.org/10.32479/ijefi.9959

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)0.000
metaresearch head score (Gemma)0.001
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 score0.198
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.077
GPT teacher head0.309
Teacher spread0.232 · 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