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Meike Zehnle, M.Sc.

Doctoral Research Associate

Meike Zehnle is a Research Associate and PhD Candidate at the Institute of Behavioral Science and Technology at the University of St. Gallen. She has a main research focus on the psychological mechanisms and behavioral consequences of human interaction with conversational AI. In her current projects, she particularly examines how such interactions (e.g., with chatbots) affect financial decision-making, the acceptance of advice and written communication.

Meike received a Bachelor of Science in Business Administration and Economics with a specialization in Management and Marketing from the University of Passau and a Master of Science in Consumer Science with a specialization in Consumer, Technology and Innovation from the Technical University of Munich.

Selected Publications

  • Zehnle, M. & Hildebrand, C. A. (2022) Conversational Interfaces Reduce Financial Planning Stress. European Marketing Academy Conference (EMAC).
  • Zehnle, M. & Hildebrand, C. A. (2022) Conversational Interfaces Reduce Financial Planning Stress. Summer Conference on Consumer Financial Decision Making.
  • Zehnle, M. & Hildebrand, C. A. (2021). Less is more? How Conversational Interfaces Alter Survey Outcomes. Technology, Mind & Society (TMS).
  • Zehnle, M. (2021). New Directions in Conversational AI: The Impact on Linguistic Style, Task Experience, and Firm Perception. Swiss Academy of Marketing Science (SAMS).
  • Zehnle, M. & Hildebrand, C. A. (2021). Conversational Interfaces Reduce Financial Planning Stress. Association for Consumer Research (ACR).
  • Zehnle, M. & Hildebrand, C. A. (2021). The Impact of Conversational Survey Interfaces on Consumers’ Written Self-Expression. European Marketing Academy (EMAC).
  • Zehnle, M. (2020). The Role of Conversational Interfaces for Consumer Self-Expression, Well-Being, and Advice Acceptance. European Marketing Academy (EMAC).
  • Maier, M., Elsner, D., Marouane, C., Zehnle, M., & Fuchs, C. (2019). DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (pp. 1415-1421).