Fixation and confusion: Investigating eye-tracking participants' exposure to information in personas

• Main Authors: SALMINEN, Joni, JANSEN, Bernard J., AN, Jisun, JUNG, Soon-Gyo, NIELSEN, Lene, KWAK, Haewoon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Artificial Intelligence and Robotics; Computer and Systems Architecture; Data Storage Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6531; https://ink.library.smu.edu.sg/context/sis_research/article/7534/viewcontent/Fixation_and_Confusion_Investigating_Eye_Tracking_Participants__Exposure_to_Information_in_Personas.pdf
• Institution: Singapore Management University

To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed from non-confused users, what information of the personas caused confusion, and what were the predictors of confusion of end users of personas. We consolidate our various findings into a confusion ratio measure, which highlights in a succinct manner the most confusing elements of the personas. Findings show that inconsistencies among the informational elements of the persona generate the most confusion, especially with the elements of images and social media quotes. The research has implications for the design of personas and related information products, such as user profiling and customer segmentation.