TY - JOUR
T1 - A Case Study on How Reference Staffing and Visibility Models Impact Patron Behaviors
AU - Bridgeman, Matthew
N1 - Publisher Copyright:
© 2022 Bridgeman.
PY - 2022
Y1 - 2022
N2 - Objective – To determine if reference staffing models are a predictor of reference question rates and if academic library patrons’ reference behaviors are linked to reference staffing models and desk visibility. Design – A retrospective case study. Setting – Two academic libraries at a large R3 public university in the state of Georgia, United States of America. Subjects – 10,295 service transactions (chat and in-person, including non-reference transactions related to directional and technology questions) from the 2016 fiscal year and 6,568 service transactions (chat and in-person, including only chat non-reference transactions) from FY 2017. Methods – Analysis of two years of service transaction data (July 2015 to June 2017) recorded by librarians using the reference analytics module of Springshare’s LibAnswers at three locations (virtual 24/7 chat and two libraries with different physical locations, such as centrally-located or harder-to-find service points) for three kinds of reference service modes: chat, fully-staffed in-person services, and occasional “on-call” services. “Reference” transactions were classified using the Reference & User Services Association (RUSA) definition. Email, SMS/text, and Facebook inquiries were excluded from this study. One library, which had the same service model for the 2016-2017 fiscal years, served as the study’s “control” so that an analysis of service model alterations could be conducted.
AB - Objective – To determine if reference staffing models are a predictor of reference question rates and if academic library patrons’ reference behaviors are linked to reference staffing models and desk visibility. Design – A retrospective case study. Setting – Two academic libraries at a large R3 public university in the state of Georgia, United States of America. Subjects – 10,295 service transactions (chat and in-person, including non-reference transactions related to directional and technology questions) from the 2016 fiscal year and 6,568 service transactions (chat and in-person, including only chat non-reference transactions) from FY 2017. Methods – Analysis of two years of service transaction data (July 2015 to June 2017) recorded by librarians using the reference analytics module of Springshare’s LibAnswers at three locations (virtual 24/7 chat and two libraries with different physical locations, such as centrally-located or harder-to-find service points) for three kinds of reference service modes: chat, fully-staffed in-person services, and occasional “on-call” services. “Reference” transactions were classified using the Reference & User Services Association (RUSA) definition. Email, SMS/text, and Facebook inquiries were excluded from this study. One library, which had the same service model for the 2016-2017 fiscal years, served as the study’s “control” so that an analysis of service model alterations could be conducted.
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U2 - 10.18438/eblip30084
DO - 10.18438/eblip30084
M3 - Review article
AN - SCOPUS:85126963577
VL - 17
SP - 140
EP - 142
JO - Evidence Based Library and Information Practice
JF - Evidence Based Library and Information Practice
SN - 1715-720X
IS - 1
ER -