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

dc.contributor.author Kim, Jinhak
dc.contributor.author Park, Young-Woong
dc.contributor.author Park, Young-Woong
dc.contributor.author Williams, Alvin
dc.contributor.department Information Systems and Business Analytics
dc.date 2021-05-04T11:02:04.000
dc.date.accessioned 2021-08-14T18:12:00Z
dc.date.available 2021-08-14T18:12:00Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.embargo 2021-05-03
dc.date.issued 2019-06-18
dc.description.abstract <p>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 <a href="https://harzing.com/">https://harzing.com/</a> 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.</p>
dc.description.comments <p>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,” <em>Decision Sciences, 52(2);455-482. d</em>oi: <a href="https://doi.org/10.1111/deci.12400">10.1111/deci.12400</a>. Posted with permission</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/isba_pubs/4/
dc.identifier.articleid 1001
dc.identifier.contextkey 22762683
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath isba_pubs/4
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/JvNVQo9v
dc.source.bitstream archive/lib.dr.iastate.edu/isba_pubs/4/2019_ParkYW_A_Mathematical_Programming_Approach_for.pdf|||Sat Jan 15 00:06:36 UTC 2022
dc.subject.disciplines Business Analytics
dc.subject.disciplines Entrepreneurial and Small Business Operations
dc.subject.disciplines Journalism Studies
dc.subject.disciplines Management Information Systems
dc.subject.keywords Business‐Engineering Interface
dc.subject.keywords Mathematical Programming
dc.subject.keywords Performance Measurement
dc.subject.keywords Predictive Models
dc.title A Mathematical Programming Approach for Imputation of Unknown Journal Ratings in a Combined Journal Quality List
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 401090a0-a926-4926-87ea-dc3aea9919d8
relation.isOrgUnitOfPublication 0099bcd5-3121-4f25-813d-0ec68d96243f
File
Original bundle
Now showing 1 - 1 of 1
Name:
2019_ParkYW_A_Mathematical_Programming_Approach_for.pdf
Size:
1.4 MB
Format:
Adobe Portable Document Format
Description:
Collections