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Record W3097587936 · doi:10.5430/ijfr.v11n4p493

The Effects of Managerial Ownership, Institutional Ownership, and Profitability on Capital Structure: Firm Size as the Moderating Variable

2020· article· en· W3097587936 on OpenAlexvenueno aff
Muhammad Khafid, Rida Prihatni, Ira Safitri

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexBusinessStock exchangeCapital structureDescriptive statisticsNonprobability samplingCapital callVariablesAccountingPopulationEconomicsFinanceHuman capitalStatisticsEconomic capitalMarket economy

Abstract

fetched live from OpenAlex

This study was to analyze the effects of managerial ownership, institutional ownership, and profitability on capital strucuture with firm size as the moderating variable. All manufacturing companies of basic industry and chemical sector listed on Indonesia Stock Exchange during the period of 2014-2017 were the population of the study. There were 66 taken as the samples by using purposive sampling technique. There were 39 companies as research samples and 115 as unit of analysis. Data were collected by documentation method. Then, data were analyzed by using descriptive statistics and inferential statistics. The results of the study indicated that managerial ownership and institutional ownership did not significantly affect capital structure, but profitability had a negative and significant effect on capital structure. Firm size did not have any moderating effect between managerial ownership and profitability on capital strucuture, but firm size moderated the effect between institutional ownership and capital structure. It was concluded that only profitability significantly influenced capital structure, and firm size was able to moderate the effect between institutional ownership and capital structure.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
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 integrity0.0000.001
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 head0.028
GPT teacher head0.283
Teacher spread0.254 · 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.

Study designTheoretical or conceptual
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

Citations19
Published2020
Admission routes1
Has abstractyes

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