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Enregistrement W3032884934 · doi:10.1108/jaar-06-2019-0093

Network analysis in accounting research: an institutional and geographical perspective

2020· article· en· W3032884934 sur OpenAlexaboutno aff
Ali Uyar, Merve Kılıç, Mehmet Ali Köseoğlu

Notice bibliographique

RevueJournal of Applied Accounting Research · 2020
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueAccounting Education and Careers
Établissements canadiensnon disponible
Organismes subventionnairesNational Taiwan UniversityHong Kong Polytechnic UniversityUniversitat de ValènciaUniversity of QueenslandCardiff UniversityMonash UniversityUniversity of New South WalesNanyang Technological UniversityMacquarie University
Mots-clésCentralityPublicationDominance (genetics)AccountingAccounting researchAccreditationRegional scienceRanking (information retrieval)Social network analysisLibrary sciencePolitical scienceSociologyBusinessSocial scienceComputer science

Résumé

récupéré en direct d'OpenAlex

Purpose The objective of this study is to explore the accounting research network among institutions and countries globally and to contribute to the knowledge development in accounting discipline across regions with a novel and original approach. Design/methodology/approach This study has been conducted by manually collecting data from 10,863 papers published in 22 accounting journals indexed in the Web of Science (WoS) for the period 2000–2016. Analyses and visualizations of collaborative networks across institutions and regions were performed by using network analysis software packages, including Pajek, UCINET 6, NetDraw and VOSviewer. Findings The study finds that the most productive five universities are the University of New South Wales, University of Sydney, University of Texas, University of California and University of Manchester worldwide. In accordance with the institution ranking, the five most productive countries in all periods are the USA, the UK, Australia, Spain and Canada. However, in addition to these countries, it is important to note that some European and Asian countries and New Zealand from Oceania are among the most productive countries which host prolific institutions. Furthermore, network indicators show that the UK is the most influential actor in centrality and brokerage within the research network. We should note that Australia is also among the most influential nations with its influential institutions. In all research metrics, the dominance of Anglophone countries (e.g. the USA, the UK and Australia) is observable on which language advantage might play a role since most internationally accredited journals publish scientific articles in English. Research limitations/implications The study is bounded with several main limitations. First, due to collecting the data manually, there might be some inherent limitations. Second, the study is constrained by the time frame between 2000 and 2016. The study does not answerwhyandhowquestions in investigating research productivity and effectiveness in the network. Our study might inspire new studies to complement ours by considering these constraints. Practical implications Our findings indicated the prominent institution-wide and country-wide actors; thus, the results provide a global perspective on the collaboration network. Second, our findings guide job seekers, who are particularly research-oriented, to potential recruiters around the world both at the institution level and country level. Third, the results might play an important role in forming institution-based and country-based research policies. The USA, among others, is a particularly important actor in productivity, whereas the UK, among others, is a remarkable country in centrality and brokerage in the research network. By examining the policies of these two countries, other nations might shape their research strategies, promotion policies and support and reward schemes. Fourth, cross-institution and in particular cross-country collaborations are imperative in the diversity of accounting research as they blend culturally diverse researchers. Fifth, prominent institutions highlighted in this study might be adopted as role models by other institutions in the same country and benefit their expertise in productivity and cooperation by scrutinizing their approaches. Sixth, our findings and metrics might be adopted as benchmarks for institutions and nations for performance evaluation. Considering our 5-year period indicators, institutions can set targets for their improvement and for measuring the progress. We provide other important implications in the conclusion section of the study. Originality/value To the best knowledge of the authors, no study yet investigated the collaboration across academic institutions, regions, and countries in accounting discipline to this extent. Therefore, our research provides a significant contribution to the literature by seeking a comprehensive network analysis of authorship patterns from an institutional and geographical perspective. Doing so, we contribute to knowledge development in accounting discipline with institutional and geographical network analyses.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesBibliométrie
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,986
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,031
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0140,022
Études des sciences et des technologies0,0020,003
Communication savante0,0070,008
Science ouverte0,0010,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,085
Tête enseignante GPT0,362
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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