Comparison of personas between two academic libraries

Abstract

Purpose: A persona describes a group of library patrons as a single person to better identify and describe user patterns and needs. Identifying personas in academic libraries can assist in library planning by focusing on patrons. Initially personas were thought to be unique to each library; additional insights led the researchers to rethink this assertion. This article seeks to determine if personas, developed in one library, are unique or more universal than previously thought. Design/methodology/approach: In this study, 903 surveys were completed across two institutions asking library patrons to identify use patterns within each library. Mean score responses were analyzed using an ANOVA, principal component analysis (PCA), and RapidMiner technology. All analyses were used to identify personas with common interests and places personas in groups or neighborhoods. Findings: The findings provide evidence for the universality of academic library personas. However, differences occur in how the personas are grouped and use different library services and resources. Originality/value: Personas allow librarians to view patrons in a more personal way as they connect personas to specific library spaces. While the personas appear to be universal, their interactions with each other depends on specific library amenities.

Strengths and Weaknesses of Persona Creation Methods: Guidelines and Opportunities for Digital Innovations

“…SUPPORT: Profiles are in a better position to provide evidence of representing the users via the convergence of findings. SR: [76,92]. S7.…”

A Survey of 15 Years of Data-Driven Persona Development

“…In terms of improving validation, several authors have suggested ways of going beyond mere persona development and testing the aptitude of the developed personas in meeting stakeholder goals (Goodman-Deane et al, 2018;Miaskiewicz & Luxmoore, 2017;Rahimi & Cleland-Huang, 2014;Tanenbaum et al, 2018;Watanabe et al, 2017). Some emerging studies have shown promise in this regard, such as the use of longitudinal data and a standardized algorithmic approach to compare persona sets over time (Jung et al, 2019) and between different organizational units (Zaugg & Ziegenfuss, 2018). In the healthcare sector, researchers design tailored medical interventions to subpopulations represented by the personas and investigate how patient adherence and health outcomes are subsequently affected (Tanenbaum et al, 2018;Vosbergen et al, 2015).…”