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Record W4312335844 · doi:10.54063/ojc.2022.v43i02.08

Debt Financing and Capital Structure Influencing the Firm’s Financial Performance: A Bibliometric Analysis

2022· article· en· W4312335844 on OpenAlexaboutno aff
Ms. Surbhi, Priti Sharma

Bibliographic record

VenueOrissa Journal of Commerce · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsCapital structureFinanceLeverage (statistics)DebtChinaProfitability indexBusinessFinancial capitalDebt financingCorporate financeEconomicsPolitical scienceEconomic growthHuman capital

Abstract

fetched live from OpenAlex

Debt financing plays a prominent role in deciding future growth and the earning capacity of any company. Debt financing is a part of capital structure; hence both terms are interlinked with each other. The main aim of this study is to provide widespread view of previous studies associated with debt financing and capital structure. For this purpose, a Bibliometric analysis is performed with the aid of Bibliometrix Library along with BiblioShiny tools in R Studio software. Web of Science is elected as main database consisting data of 21 years from 2002-2022, gathered by using keywords “debt financing”, “capital structure”, “financial leverage”, “financial performance” and “profitability”. USA and China has been found as top contributing countries. Most of the authors belong to the USA, China, Canada, UK etc. Texas Christian University from United Statusis discovered as most active institute in writing documents/articles related to this field.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0880.110
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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.

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
Published2022
Admission routes1
Has abstractyes

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