A machine learning analysis of citation impact among selected Pacific Basin journals
Stewart Jones and
Accounting and Finance, 2019, vol. 59, issue 4, 2509-2552
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.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:acctfi:v:59:y:2019:i:4:p:2509-2552
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0810-5391
Access Statistics for this article
Accounting and Finance is currently edited by Robert Faff
More articles in Accounting and Finance from Accounting and Finance Association of Australia and New Zealand Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().