Online review hasalready been recognized as an important sales assistant for consumers to make their purchase decision. However, with the rapid development of electronic commerce,overwhelming informationoverloads and review manipulation make consumers lost in ocean of reviews and face huge cognitive stress. To address this issue, different types of online review have been developed by online marketplaces. Especially, except traditional types of online reviews (positive, neutral and negative), several new types of online review (review with picture and additional review) do not only contain plain text, but also pictures. Consumers could attach additional reviews to the original reviews to further share their experience sometimes later. Few studies have focused on which types of online reviews are able to influence consumers’ decisions more efficiently. Especially, research on new types of reviews is still unanswered.Using data from Taobao.com, the biggest electronic marketplace in China,this study conducts an empirical investigation to bridge the gap. Weinvestigatethat whether and howtraditional text reviewsand new types of reviews influence consumers’ purchase decision making. The results show that under the context of information overload and review manipulation, traditional reviewsare still influential, but less effective than new types of reviews. Although review with picture and additional review don’t show valence directly, they present more reliable references towards product quality and attract consumers’ attention more efficiently.And it is more interesting that new types of online review provide an effective channel for consumers to alleviate their dissatisfaction to effect potential consumers purchase decision making. The findings of this study can provide useful implications for researchers by highlighting the roles of different types of online review in consumers’ decision making. Also, the empirical investigation in this paper will remind business vendors to focus on online reviews especially new types of online reviews and conduct targeted marketing strategies to increase competitive advantage and improve their sales performance.
Zhang, Jin; Ma, Baojun; Zhang, Jilong; Ren, Ming; and Ma, Chong, "UNDERSTANDING THE MASSIVE ONLINE REVIEWS: A NOVEL REPRESENTATIVE SUBSET EXTRACTION METHOD" (2016). PACIS 2016 Proceedings. 299.