Interlibrary Loan (ILL) is a service offered by libraries to supply patrons with materials which are not immediately available for lending. This could be for many reasons; the library might not own the item, the library may own a copy but it is already checked out to another patron, or the assignment of a required but expensive textbook spurs high demand for a particular title.

Analysis of historical ILL request data is a useful exercise to undertake as each request represents a patron with an information need which was not able to be immediately filled by the library’s collection. Each ILL request comes with a guaranteed circulation of at least one interested patron, and the request information is compiled in a dataset and preserved. Loans which are not able to be filled are still recorded and included in the dataset. Investigating trends and tendencies of a user base through this data can lead to more informed collection development practices, and understanding these data sets can reveal gaps in coverage or highlight areas where the user community may find the collection lacking.

This study is an analysis of five years worth of Iowa State University’s ILL requests of print books, spanning calendar years 2013-2017. 18,841 borrowing requests were analyzed, and monograph title data available for conducting this analysis include loan author, title, year, publisher, edition, and lender library. Patron information includes department affiliation and status; no further identifying information is recorded in the dataset used here.

This analysis focuses mostly on requests made by patrons from engineering departments, and it analyzes trends over time by constructing visualizations to look at:

• the most active academic departments and their request activity over time
• the most heavily requested titles
• requests by patron status (undergraduate, graduate, faculty, staff, unaffiliated)
• the total number of requests made over time
• what peer libraries are used to fill the requests

This work focuses on requests for print books only; the scope does not include electronically delivered PDF journal articles, book chapters, or conference proceedings.

The analysis is done in the statistical software JMP, and the procedure to automatically create the plots which appear in this paper has been coded, saved, and uploaded for others to use or adapt to their home institution’s ILL data sets at: https://github.com/eschares/ILL-analysis

This study is intended to illuminate the ILL request activity at a large, public, land-grant university in the United States, demonstrate the tendencies and trends of the campus community, and discover where users’ information needs are not immediately being met through the print collection. This work can inform future collection development activities not only at the local institution but also at other universities worldwide.