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

A comparison of academic libraries: an analysis using a self‐organizing map 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Performance Measurement and Metrics Emerald Publishing

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

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Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
1467-8047
DOI
10.1108/PMM-07-2012-0026
Publisher site
See Article on Publisher Site

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.

Journal

Performance Measurement and Metrics – Emerald Publishing

Published: Jul 19, 2013

Keywords: Self‐organizing map; Machine learning; Neural network; Academic libraries; Cluster analysis; Performance

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