Chew, KatherineStemper, JamesLilyard, CarolineSchoenborn, Mary2013-02-182013-02-182013-02-18https://hdl.handle.net/11299/144461Proceedings of the 2012 Library Assessment Conference, Charlottesville, VA, Oct 29-31Purpose: Building on the work done by the California Digital Library (CDL), the University of Minnesota Libraries is developing a set of user-defined value-based electronic journal usage metrics. User value is assessed in three overall categories: (1) utility or reading value, (2) quality or citing value, and (3) cost effectiveness. In addition to analyzing vendor-generated usage metrics, also included were Affinity String data, derived from the University of Minnesota’s central authentication system that anonymously captures a user’s academic department and degree program or position at the university and combined with vendor-generated usage data, provides a granular picture of journal use down to the title level. Collection management librarians and library users can benefit from a viable, more accurate metric for use and value of library resources than cost-per-download, which would ensure that the most needed/valued resources are available to further research and learning. Methodology: Metrics were identified that are utilized to determine e-journal retainability: OpenURL link resolver requests for article views, COUNTER-compliant downloads, JCR Impact Factors, Eigenfactor Scores, local citations from Thomson Reuters Local Journal Use Reports and Affinity String requests for article views. Two years of usage data were assessed using Pearson correlation coefficients to compare the different metrics. Affinity String data is correlated with the results to determine any discipline or degree level differences. A composite score is assigned to each journal to assess its overall value in comparison to other journals within the same broad subject category. Findings: This project found SFX clickthroughs a more consistent predictor than COUNTER downloads of the journals our faculty will cite in their articles, with Eigenfactor a more consistent predictor of citation behavior than Impact Factor.en-USJournal Usage MetricsElectronic JournalsValue MetricsAffinity StringsCollection AssessmentCorrelation AnalysisConference Paper