As a quantitative user researcher, I’m fascinated by the psychological motivations of user behavior and the relationship between those motivations and interaction with the Internet browser. Conducting research into browser usage patterns allows us to gain valuable insight into user needs and leads to an improved user experience. Existing literature on user typologies has focused mostly on why people use media but not on how they do so (for a thorough review, see (Brandtzæg, 2010)). Furthermore, no research has concentrated on users’ interaction with the browser. My internship project focuses on exploring the relationship between psychological factors of user behavior and browser usage patterns. To that end, I designed a study that incorporated both a survey (attitudes and beliefs) and actual browser interaction (behavioral) data.
As a starting point in identifying individual variations in attitudes and beliefs toward online behavior, we used the three factors proposed by Johnson & Kulpa (2007) in determining a user typology: sociability, reciprocity, and utility. Building on that research, we added privacy as a fourth factor as well as rating scale items for each factor. Our survey instrument also collected general demographic data about Firefox users. The answers that respondents give to each of the questions place them in a four-dimensional space, theoretically grouping them with users of similar psychological motivations. In addition to the survey, we used the Test Pilot add-on to collect behavioral data, which allows Firefox users to opt in their anonymous browser usage data for analysis. The data builds on previous heat map studies and captures user interaction with the browser interface.
Our overarching theory is that users with similar psychological motivations will also exhibit similar interactions with their browser. In other words, users who score similarly on the four factors of the survey will exhibit similar behaviors to each other, while users far apart in our four-dimensional space will have very different behaviors from each other. We further hypothesize variations based on demographics, interactions between the four factors, and related metrics. We are currently working on analyzing the data and will post the results in a future entry.
References
Brandtzæg, P. B. (2010). Towards a unified Media-User Typology (MUT): A meta-analysis and review of the research literature on media-user typologies. Computers in Human Behavior, 26(5), 940–956.
Johnson, G. M., & Kulpa, A. (2007). Dimensions of online behavior: Toward a user typology. CyberPsychology & Behavior, 10(6), 773–780.