A comparison of academic libraries: an analysis using a self‐organizing map

Damien Ennis (Department of Mathematics, Truckee Meadows Community College, Reno, Nevada, USA)
Ann Medaille (Mathewson‐IGT Knowledge Center, University of Nevada, Reno, Reno, Nevada, USA)
Theodore Lambert (Department of Mathematics, Truckee Meadows Community College, Reno, Nevada, USA)
Richard Kelley (Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, USA)
Frederick C. Harris Jr (Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, USA)

Performance Measurement and Metrics

ISSN: 1467-8047

Publication date: 19 July 2013

Abstract

Purpose

This paper aims to analyze the relationship among measures of resource and service usage and other features of academic libraries in the USA and Canada.

Design/methodology/approach

Through the use of a self‐organizing map, academic library data were clustered and visualized. Analysis of the library data was conducted through the computation of a “library performance metric” that was applied to the resulting map.

Findings

Two areas of high‐performing academic libraries emerged on the map. One area included libraries with large numbers of resources, while another area included libraries that had low resources but gave greater numbers of presentations to groups, offered greater numbers of public service hours, and had greater numbers of staffed service points.

Research limitations/implications

The metrics chosen as a measure of library performance offer only a partial picture of how libraries are being used. Future research might involve the use of a self‐organizing map to cluster library data within certain parameters and the identification of high‐performing libraries within these clusters.

Practical implications

This study suggests that libraries can improve their performance not only by acquiring greater resources but also by putting greater emphasis on the services that they provide to their users.

Originality/value

This paper demonstrates how a self‐organizing map can be used in the analysis of large data sets to facilitate library comparisons.

Keywords

Citation

Ennis, D., Medaille, A., Lambert, T., Kelley, R. and Harris, F. (2013), "A comparison of academic libraries: an analysis using a self‐organizing map", Performance Measurement and Metrics, Vol. 14 No. 2, pp. 118-131. https://doi.org/10.1108/PMM-07-2012-0026

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2013, Emerald Group Publishing Limited

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