Coffee has become one of the most popular beverages since the 1960s. These days, coffee is more about pleasure, experience, success, lifestyle, high concentration, and social status. Bhumiratana et al. (2014) state that individuals consume coffee with the intention of eliciting positive high-energy emotions, which can help in reducing fatigue, increasing alertness, improving work performance, and promoting a focused mental state, such as feeling motivated, productive, in-control, or clear-minded. Aguirre (2016) and Bhumiratana et al. (2014) also suggest that people drink coffee to stay awake. As coffee has popularized, café has become important as a place for people meetings. However, customers may tend to evaluate coffee as a service product depending on what service encounter they faced. They might perceive the taste and quality of coffee differently by following their mindset and stereotypes about perfect coffee. Customers pay attention to many factors in consuming coffee depending on service encounters. They may pay less for coffee prepared by robots and evaluate coffee prepared by humans as more precious. Therefore, this study proposes a specific factor theoretical framework of customers’ mindset about coffee prepared by human baristas and robot-barista. The study aims to determine how customers evaluate coffee prepared by baristas and robot-barista. Specifically, to which characteristics they pay attention in barista or robot-barista cases. Then, the study examines relationship between expected and perceived coffee characteristics and intentions to revisit café. Finally, the study compares customers' expectations about coffee depending on contact (barista) or untact (robot-barista) service.

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.