Abstract
Web 2.0 technologies have attracted an increasing number of active online writers and viewers. A deeper understanding of when customers will review and what motivates them to write online reviews is of both theoretical and practical significance. In this paper, we present a novel methodological framework, which consists of theoretical modeling and text-mining technologies, to study the relationships among customers’ review promptness, their review opinions, and their review motivations. We first study customers’ online “purchase-review” behavior dynamics; then, we introduce the LDA method to mine customers’ opinion from their review text; finally, we propose a theoretical model to explore some motivations for those people publishing review online. The analytical and experimental results with real data from a Chinese B2C website demonstrate that the behavior dynamics of customers’ online review are influenced by the multidimensional motivations, and some of them can be observed from their review behaviors, such as review promptness.
Highlights
Online customer review is a review made by a customer who has purchased a product or service online
We study the customers’ online behavior dynamics by exploring the distribution of customers’ “purchase-review” time interval; second, we introduce a LDA-based method to solve the problem of opinion mining from the online reviews varying in length, detail, and quality
“Rating2” (β2 = −0.1013) is in a marginal manner (p < 0.1), while “Rating” (β1 = 0.8900; p < 0.05), “Member” (β3 = 0.5754; p < 0.001), “Member2” (β4 = −0.0545; p < 0.001), “Cost” (β7 = −0.1262; p < 0.01), and “Service” (β8 = −0.4438; p < 0.001) were statistically significant. These variables explained about 8 percent of the review promptness (R2 = 0.08275)
Summary
Online customer review is a review made by a customer who has purchased a product or service online. It is a form of customers’ feedback on e-commerce and online shopping sites. Consumers would make comments, or reviews, about the products they had purchased, and about everything that they had experienced during the whole process of online shopping. If T follows a typical non-Poisson process and is characterized by a powerlaw distribution, it means that the review behavior on a B2C website has been affected by extrinsic motivations, intrinsic ones, or both [36]. To verify the assumption that the time interval between two consecutive customers’ behaviors, that is, purchase and Extracted data MEMBERSHIP LEVEL PURCHASE TIME SCORE REVIEW REVIEW TIME Online product.
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