Making Decisions: Using Electronic Data Collection to Re-Envision Reference Services at the USF Tampa Libraries

Abstract

Declining reference statistics, diminishing human resources, and the desire to be more proactive and embedded in academic departments, prompted the University of South Florida Library to create a taskforce for re-envisioning reference services. The taskforce was charged with examining the staffing patterns at the desk and developing recommendations to give librarians greater flexibility and to better respond to the information-seeking needs of users. These recommendations were based on statistics of desk usage, collected with the newly adapted online tool Desk Tracker, and structured interviews with library administrators. The taskforce was interested in how these stakeholders use quantitative data in decision-making.

Staffing an Information Desk for Maximum Responsiveness and Effectiveness in Meeting Research and Computing Needs in an Academic Library

“…Since then, many academic libraries have begun migrating to a tiered model of reference service in various combinations offering circulation, reference, and/or computing services (Bugg & Odom, 2009;Dempsey, 2011;Todorinova, Huse, Lewis, & Torrence, 2011). In 2011, on the basis of the peer reference model at Coastal Carolina University's Kimbel Library (Faix et al, 2010), Addlestone Library's Reference Department and Information Technology's Student Computing Support at the College of Charleston integrated research and student computing support services at the Information Desk.…”

Literature Review of Data Mining Applications in Academic Libraries

“…This study is part of a bigger project to examine the efficacy of using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) to construct a search and discovery service focused on information resources in the domain of cultural heritage. Todorinova et al (2011) examine the staffing patterns at the reference desk in order to give librarians greater flexibility as well as to allow better responding to the information-seeking needs of users. Furthermore, 19 out of 41 articles (46%) deal with the application of data mining models in collection analysis, 12 articles (29%) with service analysis, and four articles (10%) with quality analysis, thus covering various aspects of library services and collection.…”