Dine in or take out dataset: user behavior in an interactive virtual reality café

Area of Science:

• Human-Computer Interaction
• Virtual Reality
• Behavioral Science

Background:

• Understanding consumer behavior in virtual environments is crucial for service design.
• Virtual reality (VR) offers a controlled setting to study decision-making processes.
• Gen Z's digital nativity makes them ideal participants for VR-based studies.

Purpose of the Study:

• To introduce the Dine In or Take Out Dataset, a novel VR dataset.
• To investigate how wait times and environmental factors influence dine-in vs. take-out decisions in VR.
• To analyze the impact of these decisions on user-reported anxiety, loneliness, and frustration.

Main Methods:

• Collected data from 35 participants in a VR café using Meta Quest Pro.
• Implemented four randomized wait-time treatments (maître d' and food).
• Measured psychological states (anxiety, loneliness, frustration) and collected behavioral/biometric data (eye-gaze, movement).

Main Results:

• The dataset allows analysis of how wait times affect dine-in/take-out choices.
• Changes in anxiety and frustration levels correlated with different wait time conditions.
• VR sickness and usability were assessed post-immersion.

Conclusions:

• The Dine In or Take Out Dataset provides valuable insights into virtual consumer behavior.
• Future research can utilize this dataset with deep learning models to predict responses.
• Findings can inform the design of virtual and physical service environments.