MétaCan
Menu
Back to cohort
Record W4389082683 · doi:10.3390/jrfm16120497

Examining the Impact of Agency Issues on Corporate Performance: A Bibliometric Analysis

2023· article· en· W4389082683 on OpenAlexvenueno aff
Vinay Khandelwal, Prasoon Mani Tripathi, Varun Chotia, Mohit Srivastava, Prashant Sharma

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)ScopusCorporate governanceBibliographic couplingPolitical scienceBibliometricsPrincipal–agent problemPublishingPublic relationsBusinessSociologySocial scienceComputer scienceLibrary scienceFinanceLawCitation

Abstract

fetched live from OpenAlex

An agency problem is defined as a conflict of interest arising due to a misalignment of interests among the managers and other stakeholders of the company. This article aims to review the articles addressing the agency problem and their impact on business performance. This article reviews the contributions of prominent theorists on agency problems and agency costs. Using bibliometric attributes of 740 articles from the Scopus database, this study highlights the publishing trend and outlets, along with leading contributors and collaborators in terms of authors, institutions, and countries. This study identifies the clusters through the bibliographic coupling technique and a trend topics analysis. Most researchers have focused on corporate governance and expressed the agency problem as one of the impact areas. This study is unique as no study to date specifically focuses solely on agency theory or the agency problem through the lens of bibliometric analysis. Future research directions on agency problems and their solutions conclude this study.

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)0.013
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.094
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.1910.294
Science and technology studies0.0020.002
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.258
Teacher spread0.219 · 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
DomainMethods
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

Citations25
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicCorporate Finance and GovernanceFrench-language works237,207