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A machine learning analysis of citation impact among selected Pacific Basin journals
Author(s) -
Jones Stewart,
Alam Nurul
Publication year - 2019
Publication title -
accounting and finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.645
H-Index - 49
eISSN - 1467-629X
pISSN - 0810-5391
DOI - 10.1111/acfi.12584
Subject(s) - citation , citation impact , citation analysis , accounting , sample (material) , pacific basin , impact factor , business , library science , computer science , political science , oceanography , chemistry , chromatography , law , geology
This study uses a machine learning approach to identify and predict factors which influence citation impacts across five Pacific Basin journals: Abacus , Accounting & Finance , Australian Journal of Management , Australian Accounting Review and the Pacific Accounting Review from 2008 to 2018. The machine learning results indicate that citation impact is mostly influenced by: length of a journal article; the field of research (particularly environmental accounting), sample size; whether the sample is local or international; choice of research method (e.g., archival vs survey/interview); academic rank of the first author; institutional status of the first author; and number of authors of the article. The results may be useful for predicting future trends in citation impact as well as providing strategies for authors and editors to improve citation impact.