How to get more from your quantitative LibQUAL+TM dataset: making results practical

Purpose - The purpose of this paper is to outline three analytic tools utilized in the analysis and interpretation of LibQUAL+™ quantitative data.Design methodology approach - D-M scores, value rankings, and split-file cross-tabulations were used to assess the service items from the 2004 LibQUAL+™ quantitative data. The D-M score is methodologically superior to other methods used in that it is a single score that takes into account all three LibQUAL+™ perception expectation scores as dictated by the theoretical model LibQUAL+™ is based upon.Findings - The paper finds that these tools provide a way to more easily utilize LibQUAL+™ results in taking actions and developing strategic plans designed to improve patrons' perceptions of service quality. These tools also allow for the continuous evaluation of implemented plans.Practical implications - The paper discusses how these tools helped produce findings that were informative and in a format that decision makers could easily comprehend and utilize.Originality value - This paper outlines three approaches and offers practical recommendation of how to analyze and interpret LibQUAL+™ quantitative data as well as present findings to strategic stakeholders.