Florian’s research uses self-reported and behavioral data to examine how digital technologies affect users and how users' experiences with technology can be improved. He uses a mix of innovative, rigorous statistical methodology and qualitative research to shed new light on the challenge of understanding and quantifying users' experiences. He also advocates a broader adoption of Open and Reproducible Science.
Florian is a PostDoc and the Head of the Human-Computer Interaction research group (mmi-basel.ch) at the Faculty for Psychology, University of Basel.
PhD in Cognitive Psychology / Human-Computer Interaction, 2019
University of Basel
MSc in Psychology, 2015
University of Basel
BSc in Psychology, 2013
University of Basel
Multimedia electronics technician EFZ, 2007
Player motivation is a key research area within games research, with the aim of understanding how the motivation of players is related to their experience and behavior in the game. We present the results of a cross-sectional study with data from 750 players of League of Legends, a popular Multiplayer Online Battle Arena game. Based on the motivational regulations posited by Self-Determination Theory and Latent Profile Analysis, we identify four distinct motivational profiles, which differ with regards to player experience and, to a lesser extent, in-game behavior. While the more self-determined profiles "Intrinsic" and "Autonomous" report mainly positive experience-related outcomes, a considerable part of the player base does not. Players of the "Amotivated" and "External" profile derive less enjoyment, experience more negative affect and tension, and score lower on vitality, indicating game engagement that is potentially detrimental to players' well-being. With regards to game metrics, minor differences in the rate of assists in unranked matches and performance indicators were observed between profiles. This strengthens the notion that differences in experiences are not necessarily reflected in differences in behavioral game metrics. Our findings provide insights into the interplay of player motivation, experience, and in-game behavior, contributing to a more nuanced understanding of player-computer interaction.
Despite recent concerns about data quality, various academic fields rely increasingly on crowdsourced samples. Thus, the goal of this study was to systematically assess carelessness in a crowdsourced sample (N = 394) by applying various measures and detection methods. A Latent Profile Analysis revealed that 45.9% of the participants showed some form of careless behavior. Excluding these participants increased the effect size in an experiment included in the survey. Based on our findings, several recommendations of easy to apply measures for assessing data quality are given.
Motivation is a fundamental concept in understanding people’s experiences and behavior. Yet, motivation to engage with an interactive system has received only limited attention in HCI. We report the development and validation of the User Motivation Inventory (UMI). The UMI is an 18-item multidimensional measure of motivation, rooted in self-determination theory (SDT). It is designed to measure intrinsic motivation, integrated, identified, introjected, and external regulation, as well as amotivation. Results of two studies (total N = 941) confirm the six-factor structure of the UMI with high reliability, as well as convergent and discriminant validity of each subscale. Relationships with core concepts such as need satisfaction, vitality, and usability were studied. Additionally, the UMI was found to detect differences in motivation for people who consider abandoning a technology compared to those who do not question their use. The central role of motivation in users’ behavior and experience is discussed.
Selected lectures and seminars
Human-friendly explanations of artificial intelligence decisions and predictions