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Record W2997132482 · doi:10.20525/ijrbs.v9i1.579

Does institutional ownership moderate the effect of intellectual capital and company value?

2020· article· en· W2997132482 on OpenAlexfundno aff
Rahmita Dwinesia Paputungan, Bambang Subroto, Abdul Ghofar

Bibliographic record

VenueInternational Journal of Research in Business and Social Science (2147-4478) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
FundersMcMaster University
KeywordsIntellectual capitalStock exchangeBusinessNonprobability samplingSample (material)Value (mathematics)Enterprise valuePopulationAccountingRegression analysisBook valueFinanceEarnings

Abstract

fetched live from OpenAlex

This study aims to empirically examine the influence of intellectual capital towards company value and also its influence while being moderated by institutional ownership. This study uses purposive sampling to determine samples from manufacturing companies listed in Indonesia Stock Exchange during the year of 2014–2018. The total sample obtained in this study is 301 from the 720 population of data throughout the research year. Data analysis techniques use multiple regression and moderated regression analysis (MRA) methods. The results of this study show that Intellectual Capital has a positive significant effect on company value while institutional ownership does not have a significant effect on moderating the influence of intellectual capital towards company value. The practical implication of this study is to provide information to managers or owners of public manufacturing companies and investors about the importance of intangible assets investment like intellectual capital as the competitive strategies to achieve more optimal company value, as well as for regulator to make clear regulations about the disclosures of intangible assets.

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)0.014
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.053
GPT teacher head0.327
Teacher spread0.274 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of Research in Business and Social Science (2147-4478)Same topicIntellectual Capital and Performance AnalysisFrench-language works237,207