## 1 Introduction  
With the growing popularity of voice assistants (i.e., smart speaker, e.g., Amazon's Alexa, Google's Siri, Microsoft's Cortana), the size of voice shopping market is also rapidly increasing [1]. Voice shopping, which allows users to shop by talking with a voice assistant, offers several advantages in terms of user experience during the shopping process. For example, according to survey form Bert and Ava [2], voice shopping is hands-free, it makes multi-tasking more convenient, and it enables providing quick answers and results [2, 3]. Due to such benefits, voice shopping is expected to become a USD 40 billion market across the United States and the United Kingdom by 2022 [1]. However, despite the high expectations of voice shopping market, the number of voice shopping users is increasing slower than expected. For instance, in 2018, only 2% of Amazon Echo members used voice shopping to purchase items [4]. In addition, among the consumers aged 18-44 and over 45, only 1.32% and 0.16%, respectively, preferred voice shopping over other shopping methods [2]. Note that perceived trust in the purchase process has a significant impact on shopping [5, 6, 7, 8], while it is assumed that still many customers do not easily feel trust in the purchase process when voice shopping. Indeed, according to a report by eMarketer [9], users of voice shopping cannot obtain visual information of various products, due to the lack of a screen in voice assistants, and it lacks cues to judge goods before purchase, making it difficult to build trust in vendors and products. To overcome the problems caused by consumer's lack of trust in their purchases, present online shopping provides reviews, and previous studies revealed that customers rely heavily on others' reviews before making purchasing decisions [10; 11]. Having the ability to consider various information and reviews about certain products can lower the level of perceived risk and help users make purchase decisions [12], whereas being unable to consider such information can cause difficulties in making purchase decisions. However, since current voice user interfaces (VUIs) rarely deliver reviews, little is known about the effect of such reviews on voice shopping. Moreover, for voice shopping service companies, determining how to provide proper information in voice shopping remains a significant challenge. The current study investigates the effects of reviews in voice shopping; whether providing reviews in voice shopping improves purchasing intention and trust. Considering that the types of online reviews are organized by ratings or by features [13; 14], we examined the three types of reviews in voice shopping (i.e., review by rating, review by feature, and no review). Since the VUI and online sites have different modalities, it is an important problem to recognize the type of review that fits the VUI. Specifically, voice shopping as a lean media [15], could be negatively impacted by providing too much information to users because it requires a higher cognitive load [16; 17]. Thus, we expect that the types of reviews (i.e., no review, review by rating, and review by feature) which is suitable for voice shopping may differ from those for online commerce. In addition, we consider the product types in the study. The review types that users desire are mainly dependent on the types of products that they are shopping for [10; 18]. For example, users aiming to purchase search goods (e.g., a cell phone), as opposed to experience goods (e.g., a hotel), tend to consider detailed reviews to be more trustworthy compared to other types of reviews [18]. Additionally, convenience goods, which have a short purchase cycle, benefit more from electronic word-of-mouth (eWOM) through visually intensive social networking services (SNSs) (e.g., Instagram). This is in contrast to durable goods, which have a longer lead time before purchase, and benefit more from text-heavy SNSs (e.g., Twitter) [19].