# Consumer-Driven Usability Test of Mobile Application for Tea Recommendation Service
## 1 Introduction  
The paradigm of decision-making in consumer behavior is gradually shifting from people to machines [1]. The advance of information communication technology (ICT), and machine learning and artificial intelligence (AI) technology have been driving this paradigm shift, causing many changes in the purchasing behavior of consumers [1; 2]. Consumers conveniently order products at online shopping malls without having to check them directly at offline stores. They receive automatically generated recommendations on the products, films, music, and food that they want, and complete their purchases via recommendation algorithms, without the assistance of a sales person. Consumers are increasingly relying on ICT-based decision-making systems based on recommendation algorithms and big data analyses, such as recommender systems, for minimized shopping expenses and maximized convenience.  
While there are many items that recommender systems target, such as books, movies, songs, and foods [3; 4], there has rarely been a recommender system specialized for tea, which is one of the oldest drinks in human history. According to the report from Zion Market Research [5], the global tea market is forecasted to show significant growth; the market value was around USD 49,456.52 million in 2017 and it will grow at a compound annual growth rate (CAGR) of about 4.5% between 2018 and 2024. The rapidly growing interest in pursuing a healthy lifestyle, including the health benefits of consuming certain foods [6; 7], and the growing tea-consuming population are driving the growth of tea industry [5]. Young consumers such as Millennials are also an important factor contributing to this growth [8; 9].  
Blended teas are positioning themselves among teas enjoyed by the young generation [9; 10]. Blended teas, i.e., a mix of two or more types of tea materials and herbs [11], have a richer flavor than single teas. Consumers can expect not only the synergistic effects of their flavors, but also the health benefits from the ingredients of blended teas. However, there are more than a few hundred different types of blended teas. This makes it very difficult for consumers to choose the one that best matches their preferences. Also, it is not easy for consumers to enjoy blended teas unless they are high-involvement consumers who have a lot of experience with tea consumption.  
In this paper, we study the feasibility of recommendation services for blended teas, which has not been addressed well by existing recommendation services. To this end, we design a mobile application, _TeaPickTM_. The application suggests certain blends of tea that are suited to the preferences of individual users, while providing useful information on blended tea. We adopt a content-based approach that allows us to provide recommendations on tea even without the initial preference data from a large number of consumers. We build a tea database and recommendation logic for the recommendation service based on the collaboration with experts in the tea industry and consumer research.  
To investigate whether the developed application is useful for consumers and the tea industry, we conduct an evaluation regarding consumer behavior with 31 participants. Our user study covers the usability and usefulness of the application, user satisfaction, intention to use, brand attitude, and consumer preference for the recommended tea. The results show that our study participants were positive about the recommendation service provided by our application and that they were generally satisfied with the recommended tea.  
The rest of this paper is organized as follows. Section 2 discusses related work. Section 3 presents our mobile application for blended tea recommendation and experimental setup for the user study. Section 4 presents the results of the user study. Finally, Section 5 concludes the paper.