Academic journal article Journal of Electronic Commerce Research

Do Only Review Characteristics Affect Consumers' Online Behaviors? a Study of Relationship between Reviews

Academic journal article Journal of Electronic Commerce Research

Do Only Review Characteristics Affect Consumers' Online Behaviors? a Study of Relationship between Reviews

Article excerpt

(ProQuest: ... denotes formulae omitted.)


With the widespread adoption of mobile phones, mobile commerce has become increasingly popular. In particular, Alibaba, Jingdong, and Amazon will strategically upgrade m-commence. The Nilson Report states that more and more Chinese people have begun to buy goods and services through mobile devices, particularly those in 3rd and 4th tier cities. With a low price, small volume, and powerful functionality, smart phone industry gains breakthroughs. In China, smart phone penetration among those who are less than 30 years old was 62% by 2014; computer penetration is only 35%. In contrast to other platforms, mobile phones have two distinct characteristics [Shankar & Balasubramanian 2009; Clarke 2001]. First, mobile phones have real-time functionality and high flexibility as they can be used anywhere and at any time due to their small size and powerful functions. Second, using mobile phones could enhance personalized service and, in particular, improve the interaction between retailers and consumers and the repeat behavior after the geographical location is considered. However, the screen size of a smart phone is usually 3.5-5 inches; for example, the screen size of iPhone 6 is only 4.7 inches. The different screen size causes users to behave differently. With the increasing cost for consumers to search and understand products by using mobile phones, it is urgent to guide them to efficient e-shopping by offering more shopping references, which is not only a problem of the mobile phone user interface but also an optimization problem of the whole shopping procedure, including the recommendation searching system and the recommending system.

For instance, Figures 1 and 2 reflect the users' reviews displayed on mobile phones and computers, respectively. Figure 1 shows that there are only 2 review comments displayed on iPhone 6s, one of which cannot be fully displayed, while Figure 2 shows that there are 5 review comments fully displayed on a computer screen. Therefore, it is impossible for consumers to read thousands of reviews with a 4.7-inch mobile phone.

Traditionally, the product reviews are ordered on the basis of helpful votes, from the most helpful to the least helpful on a traditional e-commerce platform. Consequently, not all the reviews could be efficiently voted on. In this case, the traditional recommending systems do not work effectively. However, if the reviews on an e-commerce platform are ordered by review time from most recent to least recent, certain useless reviews would restrain consumers from buying the product, which is more significant when a consumer uses a mobile phone to shop.

Most literature has studied how product sales are influenced by product reviews and which factors of the reviews constructively contribute to helpful votes. Nevertheless, the review is treated as an independent entity, and interactions between different reviews are ignored in most previous studies. For instance, certain researchers apply characters from an independent review, such as readability, objectivity, and reviewer's reputation to explain why it receives helpful votes. However, the researchers neglect the influence caused by the relationship between reviews. Assuming an extreme case, there are two similar reviews describing the same attribute of a product with the same sentiment. Intuitively, the first review that ranks higher will gain more helpful votes, since consumers have previously acquired product information from it, which has provided more valuable information. Consequently, the second review only receives fewer helpful votes or no helpful vote. Conversely, the second review will gain more helpful votes if the first one is not read by consumers. Similarly, the previous studies on the influence of the reviews on product sales address the reviews independently, rather than analyze the logical relationship of the reviews from their semantic and perceptive aspects. …

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