As the rate of vaccination against COVID-19 is increasing, demand for overseas travel is also increasing. Despite people’s preference for duty-free shopping, previous studies reported that duty-free shopping increases impulse buying behavior. There are also self-reported tools to measure their impulse buying behavior, but it has the disadvantage of relying on the human memory and perception. Therefore, we propose a Brain–Computer Interface (BCI)-based brain signal processing methodology to supplement these limitations and to reduce ambiguity and conjecture of data. To achieve this goal, we focused on the brain’s prefrontal cortex (PFC) activity, which supervises human decision-making and is closely related to impulse buying behavior. The PFC activation is observed by recording signals using a functional near-infrared spectroscopy (fNIRS) while inducing impulse buying behavior in virtual computing environments. We found that impulse buying behaviors were not only higher in online duty-free shops than in online regular stores, but the fNIRS signals were also different on the two sites. We also achieved an average accuracy of 93.78% in detecting impulse buying patterns using a support vector machine. These results were identical to the people's self-reported responses. This study provides evidence as a potential biomarker for detecting impulse buying behavior with fNIRS.

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A time interval can be presented as a duration (e.g., 10 days) or a calendar date (e.g., May 10). This study examines the influence of time descriptions on task scheduling.Results of 10 studies (N = 2,129) present evidence that people tend to schedule their tasks earlier when the time interval is described as a duration than a date. This time description effect is accounted for by different mindsets. People tend to have an implemental mindset when time is described as a duration (vs. date) because of stronger feelings of being inside the time frame. This mindset affects decisions on scheduling when to initiate future tasks.

Background Although previous research has demonstrated that ingredient information plays a significant role in consumers’ healthier food choices, none of the research has investigated how the number of ingredients (ie, single vs multiple) influences consumer perceptions of food healthiness. However, this research argues that consumers have lay belief that the greater the number of (un)healthy ingredients in food, the more (un)healthy the food is perceived and they rely on the lay belief when judging food healthiness. Thus, this paper proposes and examines the multiple ingredients effect that people make inferences about food (un)healthiness based on the diversity of (un)healthy ingredients. Methods and Results Four studies test our hypotheses using ANOVAs and regression analyses. Study 1 examines that people indicate a higher perception of food unhealthiness when the number of unhealthy ingredients is presented as multiple rather than single. Study 2 replicates the multiple ingredients effect in the healthy food domain and eliminates an alternative explanation based on the subadditivity effect. Study 3 also finds that the difference in food healthiness perception between people who have high and low health consciousness is driven by the salience of each ingredient. Finally, Study 4 identifies the evaluability of the nutritional value as a boundary condition for our effect in an AI-based self-service context. Conclusion The current research demonstrates the multiple ingredients effect that people perceive higher food (un)healthiness when the number of (un)healthy ingredients is greater although nutritional information is identically presented. Moreover, this effect is moderated by the extent to which people are conscious of health-related issues. This finding is because ingredient information is highly accessible and salient for health-conscious people .

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