Proving Value and Improving Practice: Using System Data to Analyze User Behaviors

Abstract

Today's library systems collect and supply a wealth of transaction data. The unobtrusive data collected by these systems can help us understand our users' search and information-retrieval behaviors. Analyzing user behaviors challenges or reinforces our practices, and the changes we make should not be simply consumer preferences but based upon the analysis of usage patterns and search behavior. Institutions of any size can use a cyclical grounded theory model to look at the unobtrusive data that is available from systems such as WorldCat Local, Google Analytics, and others to reveal our users' information-seeking behaviors.

The author's model shows how system-generated data can be a part of an assessment strategy for ongoing improvement that can be implemented by small academic libraries. By articulating inputs consisting of goals, users, and performance indicators, and by utilizing a grounded-theory approach, libraries can observe behaviors that can inform, as well as reveal, outputs.

KEYWORDS:

• Search behavior
• analytics
• statistical analysis
• decision making
• assessment
• system data

Acknowledgments

© Terry Huttenlock and David B. Malone