Creating Personas: For the Enrichment of the User Experience Community

Editor's Notes

• #5: https://www.interaction-design.org/literature/article/personas-why-and-how-you-should-use-them
• #17: For the journey to finding who LITA members were, the first step that the LITA Persona Task Force took was basically to gather and analyze existing datasets. Often times, when we think about creating personas, we tend to start from scratch like jumping into user research right away and ignore valuable existing datasets generated from others or other user research. We were really lucky that LITA has kept and managed multiple datasets with useful metadata which means those datasets were well-documented and coded. Therefore, we didn’t need to reorganize or clean up datasets in order to answer our initial questions. So we gathered different datasets like sanitized LITA membership data which didn’t contain any personal information of individual; rather, it was a cluster of information like job title, organization type, and work specialty. Other datasets included job descriptions, LITA annual reports, assessment and research, and surveys done by LITA. I will go in detail about each dataset like how we analyzed, what we learned from it, and what we came up initially.
• #18: We started from analyzing the sanitized 2016 LITA membership information. As I mentioned in my previous slide, it was well-documented and coded, basically reusable, so it gave us a good snapshot or overview of the current LITA members by organization type (where they work), position description, and work specialty. We also cross-checked between these related-fields in order to be accurate. From the LITA membership data, what we learned was almost 40% of the members hold an administrative role either as dean, director, manager, or supervisor. Then this is followed by librarians without any supervisory role. Although few members work at consortium, school, or special libraries, the majority of them work for academic library, followed by public library.
• #19: We also considered job postings as our datasets because they tell us general trends on skills and technologies required or wanted in the job market. It would be perfect if we looked at all published job postings, but honestly, due to time constraints and because the task force was all voluntary-based, in reality, we couldn’t do that. Rather, we focused only on Massachusetts library job postings from the last five years with the word “technologies” either in the job title or description. The reason why Massachusetts was chosen is simply one of the members was from there. We were able to identify type of libraries, full or part-time status, required education as well as popular terms which were “Digital”, “Information”, “Technology”, “Web” to name a few.
• #20: Then, other documents produced by other committees in LITA were provided such as LITA dropped member survey conducted in 2010, 2011 satisfaction survey, feedback, annual reports and etc. Again, LITA does a good job at managing datasets, so we could see a big picture from these datasets in terms of overall LITA activities in the last couple of years, members’ satisfaction as well as overall satisfaction and feedback on LITA activities.
• #21: From analyses on available datasets, we were able to find patterns and use those to group similar people together. Therefore, we drafted 6 different archetypical models based on the patterns found: New member between 2015 and 2016, LITA involvement member who are actively involved with LITA committees, interest groups, or task forces, Administrator member who holds an administrative role, Techie member who works with technology like systems implementation or coding, Librarian member who is interested in technologies for their daily work, and Non-member including dropped members, social media followers and etc. As you may notice, some of the members could fall into more than one category. If so, they are categorized in a hierarchical order in the table here. For example, a librarian does application development so she could be a techie member, but she also serves on the LITA committees. In this case, this person could be categorized as LITA involvement member for data analysis purpose. The reason why we made this hierarchical table was that the patterns we found from the existing datasets indicated differences based on these types. We wanted to make sure whether this hypothesis was correct so we did quick and dirty in-person interviews at 2016 ALA Annual.
• #22: Although we couldn’t meet people who were not at 2016 ALA Annual, we thought that it was a good idea to test out our persona model quickly by conducting a small group of in-person interviews. We’ve interviewed a total of 10 people and all of them were a LITA member: 4 new members, 3 active LITA members who serve at least one of the committees, interest groups, or task forces, 1 admin member and 2 librarian members. The individual interview lasted between 30 and 60 minutes and we recruited them via online invitation sent out before ALA. This quick and dirty user research allowed us to actually meet LITA members, listen to their stories, and connect member’s profile to our persona model in terms of their needs, thoughts, and career.
• #23: For developing a hypothesis of our persona model, it was a good start from analyzing existing datasets and conducting in-person interviews with a small group of people. However, in order to better describe a persona, it was not enough and there were still questions to be answered. Are they still accurate? A lot of research like satisfaction survey was done a long time ago so we were not sure if this still well described LITA members in 2016. Even though existing datasets enabled us to see a big picture, it was difficult to link between their profile and their stories. Therefore, we decided to conduct more in-depth user research and Lynne will discuss about it in detail.
• #26: https://www.surveymonkey.com/r/litapersonas
• #30: . Expected goals and outcomes -ELs will create a useful guide for themselves and for the division. -ELs will help the division understand how to better support members. -ELS will create a focused plan for themselves to gain their desired leadership experience. 2. Next steps for the project -ELs will review the personas created by the Persona Task Force and the project actions described by the committees and task forces. They will create user stories based on their own experiences and conversations with others. 3. How we intend to incorporate the project -The project will build on work already completed by the division, committees, and interest groups. The project will provide important context for members on how to focus their engagement with the division to meet their own career goals. It will also help the division to change their practices to improve member engagement. 4. The name of our member guide Someone from the Personas Task Force?