Journal Packages: Another Look at Predicting Use

Grabowsky, Adelia; Weisbrod, Liza; Fan, Shirley; Gaillard, Philippe

doi:10.1080/01462679.2019.1607643

Cited by 1 publication

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adelia Grabowsky et al studied predictors of future use for a package of SAGE journals and found that there was a strong correlation between ILL requests and future use. 5 They found that considering the subject area of journals could improve predictive models of use. Gail Perkins Barton, George E. Relyea and Steven A. Knowlton, on the other hand, found that ILL requests only slightly positively correlated to later full-text use.…”

Section: Interlibrary Loanmentioning

confidence: 99%

“…Eva Stowers and Cory Tucker conducted a review of SFX reports that included an examination of unsuccessful full text requests. 10 They created a list of 577 titles with ten or more requests and found 23 percent of them had a document deliver request in 2006 and 39 percent had a request in 2007. While both of these studies generated lists of titles that were candidates for subscription, they did not look at use of any titles that were added to the collection.…”

Section: Failed Link Resolver Requestsmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Potential Electronic Serials Use

Jabaily¹

2020

The Serials Librarian

View full text Add to dashboard Cite

When assessing electronic serials, librarians often review usage reports for subscribed serials but know relatively little about the potential use of unsubscribed titles. This paper discusses methods for predicting future use, including examining use of similar materials, citation metrics, interlibrary loan requests, denial/turnaway reports, and failed link resolver requests. Each indicator can tell librarians something different about how users are discovering and attempting to access materials. This paper also shares preliminary data from two case studies that detail an attempt to correlate prior year indicators of demand with first year use data of new electronic serials acquisitions.

show abstract

Section: Interlibrary Loanmentioning

confidence: 99%

Section: Failed Link Resolver Requestsmentioning

confidence: 99%