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Faculty of Management, Economics and Social SciencesBusiness Analytics Group

Behavioral Analytics

We use behavioral data to infer user characteristics. For example, trace data is time-stamped behavioral data that allows us to study decision dynamics. Such data can be used to get real-time feedback from users, for example, on their emotional state. Since customers are often "hidden" behind websites, studying behavioral data is particularly relevant to get insights into their preferences. As such, trace data can be seen as a proxy for digital body language and can be used for predictive purposes, such as identifying hidden cognitive states. In our research, we use trace data such as mouse movements that we gather in controlled studies or derive from websites. 

Examples of Projects and Research Questions:

  • How do emotions influence mouse movements?
  • Can mouse movements be used to predict fraud?
  • How do keystroke dynamics and emotions correlate?

Selected Publications:

  • Weinmann, M., Valacich, J.S., Schneider, C., Jenkins, J.L., & Hibbeln, M. (2022).  The path of the righteous: Using trace data to understand fraud decisions in real time.  MIS Quarterly, 46 (4), 2317–2336.
  • Hibbeln, M., Jenkins, J. L., Schneider, C. Valacich, J. S., & Weinmann, M. (2017).  How is your user feeling? Inferring emotion through human-computer interaction devices.  MIS Quarterly, 41 (1), 1–21.