A Mathematical Programming Approach for Imputation of Unknown Journal Ratings in a Combined Journal Quality List

Date
2019-06-18
Authors
Kim, Jinhak
Park, Young-Woong
Park, Young-Woong
Williams, Alvin
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Department
Information Systems and Business Analytics
Abstract

The quality of faculty scholarship and productivity is one of the primary measures for faculty evaluation in most academic institutions. Due to the diversity and interdisciplinary nature of modern academic research fields, it is increasingly important to use journal quality lists, with journal ratings, that offer credible measures of the worth of faculty scholarship. Despite the existence of such metrics, journal lists, by their very nature, exclude some well‐recognized journals. Consequently, academic institutions expend inordinate resources to assess the quality of unrated journals appropriately and equitably across disciplines. The current research proposes mathematical programming models as a path to determining unknown ratings of multiple journal quality lists, using only their known rating information. The objective of the models is to minimize the total number of instances where two journals are rated in opposite order by two different journal quality lists. Computational results based on journal quality list data in https://harzing.com/ indicate that the proposed methods outperform existing imputation algorithms with most realistic test data sets in terms of accuracy, root mean square error, and mean absolute deviation.

Comments

This accept article is published as J. Kim, Y.W. Park*, and A.J. Williams (2019), “A Mathematical Programming Approach for Imputation of Unknown Journal Ratings in a Combined Journal Quality List,” Decision Sciences, 52(2);455-482. doi: 10.1111/deci.12400. Posted with permission

Description
Keywords
Citation
DOI
Source
Collections