Center for Digital Transformation

Prof. Dr. Martin Meißner


How can we use virtual and augmented reality to better understand consumer behavior? How do consumers make decisions in virtual and real shopping environments? How can marketers best make use of social media data?


Professor Meißner and his team investigate these and related questions. The Digital Marketing team focuses on three main areas of research: social media and digitalization, information processing and decision research as well as virtual and augmented reality. Most research projects follow a quantitative-empirical research approach and use latest methods, such as eye tracking, virtual reality, and text mining. We work closely with leading national and international research institutions as well as with companies from different industries.


Curriculum vitae




Areas of interest

Selected current research projects

Recommender systems in retailing (virtual and augmented reality)

Research in the area of recommender systems in retailing is still in its infancy. In an interdisciplinary research project with Jella Pfeiffer, Thies Pfeiffer and Christian Peukert, we work on developing a recommender system based on attentional information, i.e., mobile eye-tracking information. Results of first empirical studies were recently published in the Journal of Management Information Systems, Information Systems Research, and Journal of Business Research. The development of such a recommender system is a long-term project that requires overcoming technical obstacles and working on many open questions regarding the interpretation of mobile eye-tracking data. Our research group is among the first investigating attentional processes in virtual retail environments. Our approach allows us to automatically analyze the respective data, i.e., allocate fixations to the respective objects in virtual reality. In virtual reality, we can analyze attentional processes “on the fly” and change the environment in real time based on the respondent’s gaze behavior.

Investigate decision making process using eye-tracking

Research in this area focusses on better understanding information search processes and decision making. We are particularly interested in understanding the extensive information search processes for digital innovations. We use eye tracking to measure visual attention of decision makers. PhD courses on how to use eye tracking are offered on a regular basis. Results of empirical studies were published in the Journal of Marketing Research and Organizational Research Methods. We collaborate with scholars from international universities, such as Duke University (US, Joel Huber), Monash University (Australia, Harmen Oppewal) and Andrés Musalem (University of Chile).

Social media and digitization

Social media have become an integral part of digital marketing strategies. Companies must rethink their communication strategies and learn how to use social media in a targeted manner. A particular challenge is also the fact that the relevance of social media is constantly changing. The marketing discipline is faced with the task of developing and empirically testing theories that justify the effective use of social media. Our research focuses on investigating new phenomena in social media.

Key publications

  • Pfeiffer, J., T. Pfeiffer, M. Meißner, E. Weiß (2020). Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments. Information Systems Research, 31(3), 675-691.
  • Peukert, C., J. Pfeiffer, M. Meißner, T. Pfeiffer, C. Weinhardt (2020). Shopping in Virtual Reality Stores: The Influence of Immersion on System Adoption. Journal of Management Information Systems, 36(3), 1-34.
  • Meißner, M., J. Oll (2019). The Promise of Eye-Tracking Methodology in Organizational Research: Best Practice Recommendations and Future Avenues. Organizational Research Methods, 22(2), 590-617.
  • Meißner, M., A. Musalem, J. Huber (2016). Eye-Tracking Reveals a Process of Conjoint Choice that is Quick, Efficient and Largely Free from Contextual Biases. Journal of Marketing Research, 53(1), 1-17.
  • Scholz, S. W., M. Meißner, R. Decker (2010). Measuring Consumer Preferences for Complex Products: A Compositional Approach Based on Paired Comparisons. Journal of Marketing Research, 47(4), 685-698.