Mingers, John, Yang, Liying (2016) Evaluating Journal Quality: A Review of Journal Citation Indicators and Ranking in Business and Management. European Journal of Operational Research (ABS 4), 257 (1). pp. 323-337. ISSN 0377-2217. (doi:10.1016/j.ejor.2016.07.058) (KAR id:55131)
Microsoft Word
Author's Accepted Manuscript
Language: English |
|
Download this file (Microsoft Word/573kB) |
![]() |
Request a format suitable for use with assistive technology e.g. a screenreader | |
Official URL: http://dx.doi.org/10.1016/j.ejor.2016.07.058 |
Abstract
Abstract: Evaluating the quality of academic journal is becoming increasing important within the context of research performance evaluation. Traditionally, journals have been ranked by peer review lists such as that of the Association of Business Schools (UK) or though their journal impact factor (JIF). However, several new indicators have been developed, such as the h-index, SJR, SNIP and the Eigenfactor which take into account different factors and therefore have their own particular biases. In this paper we evaluate these metrics both theoretically and also through an empirical study of a large set of business and management journals. We show that even though the indicators appear highly correlated in fact they lead to large differences in journal rankings. We contextualise our results in terms of the UK’s large scale research assessment exercise (the RAE/REF) and particularly the ABS journal ranking list. We conclude that no one indicator is superior but that the h-index (which includes the productivity of a journal) and SNIP (which aims to normalize for field effects) may be the most effective at the moment.
Item Type: | Article |
---|---|
DOI/Identification number: | 10.1016/j.ejor.2016.07.058 |
Uncontrolled keywords: | OR in scientometrics; ABS journal list; Eigenfactor; h-index; impact factor; journal indicators; journal ranking; normalisation; REF; SJR; SNIP; |
Subjects: | Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | John Mingers |
Date Deposited: | 25 Apr 2016 09:54 UTC |
Last Modified: | 04 Mar 2024 16:46 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/55131 (The current URI for this page, for reference purposes) |
- Link to SensusAccess
- Export to:
- RefWorks
- EPrints3 XML
- BibTeX
- CSV
- Depositors only (login required):