How online reviews with different influencing factors affect the diffusion of new products

Online reviews are becoming increasingly important for the diffusion of new products. Based on the content of online reviews on JD.com , the study utilizes the Latent Allocation model to identify the critical factors of interest to consumers and categorized them into two categories, namely, product quality and supporting service factors. Thereafter, we use the system generalized method of moments to analyse the dynamic model of the panel data and further study the relationship between online reviews and new product diffusion under the two types of factors. We further investigate how the interaction between the critical metrics of online reviews affects the diffusion of new products. The results indicate that as various factors are considered, online reviews have a significantly different impact on the diffusion of new products. Moreover, compared with supporting service factors, online reviews of product quality factors have a more significant impact. In addition, the interactions between online review metrics demonstrate complex characteristics, especially the volume and positive emotional tendency, which does not promote new product diffusion, as expected.     

Mining the text of online consumer reviews to analyze brand image and brand positioning

The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of “text mining”; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.     

Delight the experts,but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product

Online reviews are a pervasive form of electronic word-of-mouth (eWOM) that potentially accelerate—or slow down—the diffusion of recently launched services in the marketplace. While empirical research largely supports the effects of online reviews on attitudinal and behavioral outcomes, less is known about the impact the source of the review—i.e., if it comes from a peer consumer or an expert—has on the recipient. Two experiments that combine reviewer- (expert, consumer), service type- (mobile package, restaurant, car repair), consumer- (level of general innovativeness), and review-related (positive, negative) characteristics reveal a challenging interaction between the review’s source and its valence: while—compared to an established baseline—a positive expert review seems more effective in increasing the recipient's intention to purchase than a review by a peer consumer, a negative consumer review lowers the recipient's intentions to a larger extent than a negative expert review. We further find effects of the consumer's innovativeness and the service category across the experiments. Our research contributes to the topical and increasing body of empirical research on the effects of involved characteristics within online reviews across several product types.