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Record W4404145888 · doi:10.5267/j.uscm.2024.7.018

The effect of intellectual capital on competitive advantage and financial performance

2024· article· en· W4404145888 on OpenAlexvenueno aff
Made Kusuma Wardani, Ni Luh Putu Wiagustini, Ica Rika Candraningrat, Luh Gede Sri Artini

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIntellectual capitalCompetitive advantageFinanceMarketing

Abstract

fetched live from OpenAlex

Financial performance is an evaluation of a company's assets, liabilities, equity, expenses, revenues, and profitability. This evaluation provides an overview of the company's overall financial health during a certain period. Resource Based Theory states several factors that can affect financial performance, namely intellectual capital and competitive advantage. This research was conducted with a quantitative approach. The observed population of this research is all BPRs in Bali Province which are still operating in 2017 - 2021. Determination of the sample in this study using the census method or saturated sample so that the sample used in this study was 133 BPRs in Bali Province. The analysis technique used in this research is path analysis with Eviews version 9.0 software. The test results show that intellectual capital has no effect on financial performance, intellectual capital has a positive effect on competitive advantage, competitive advantage has a positive effect on financial performance and competitive advantage is able to mediate the effect of intellectual capital on financial performance. The results of this study are expected to contribute to various interested parties, namely the development of Resource Based Theory as a theoretical benefit and BPR management, government, OJK, society and the banking industry as practical beneficiaries.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.728

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 studies0.0000.000
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 head0.005
GPT teacher head0.210
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 teacher head, not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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