One Size Fits All? The Differential Impact of Online Reviews and Coupons

Abstract

U sing a unique dataset that includes individually identifiable consumer online browsing and purchase data in the restaurant industry, this paper examines how individual consumer shopping characteristics and product characteristics moderate the effect of online reviews on individual consumer choice based on information search theory and signaling theory. We find that different dimensions of reviews have distinctive impacts on consumers with different information search needs and behaviors based on their shopping experience, inertia tendency and proneness to coupon promotions. Online reviews also influence consumers' purchase decisions differently depending on the available information and signals indicated by the product's ongoing coupon promotions and other signaling characteristics such as the overall price level. Specifically, the valence of reviews primarily affects the consumers who have more shopping experience, who demonstrate less inertia, or who are less prone to coupons, whereas the volume of reviews more significantly affects the highly coupon prone consumers or consumers with high inertia tendency. When the product is of low price or the seller offers a coupon for the product, the number of reviews plays a more important role in affecting consumers' decision. This research not only enriches the online review literature by highlighting the differential effects of online reviews but also offers practical guidelines to sellers and platforms that wish to use online reviews together with coupon promotions to stimulate sales. Our research also highlights how companies can take advantage of the powerful combination of consumers' shopping behavior data with online review browsing data to design timely and innovative marketing activities.

Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews

“…By investigating these novel effects of OPV, our research extends the work on user-generated content and digital platforms in the OM-IS interface (Ba et al, 2020;Cui et al, 2018;Guan et al, 2020;Kumar et al, 2018cKumar et al, , 2018aLau et al, 2018;Mallipeddi et al, 2021;Mejia et al, 2021), and, in particular, OM-IS research on response bias and fake reviews (Chen et al, 2016;Kumar et al, 2018bKumar et al, , 2019Lappas et al, 2016;Lee et al, 2018). Closely relevant research to ours compared platforms that require verified reviews (e.g., Expedia) with platforms that do not offer any type of purchase verification (e.g., TripAdvisor) to find that fake reviews are more prevalent in platforms without purchase verification (Mayzlin et al, 2014).…”

Do Experts or Crowd-Based Models Produce More Bias? Evidence from Encyclopedia Britannica and Wikipedia

“…Wikia uses principles and norms similar to Wikipedia's (Greenstein et al 2009). Knowledge communities have also been based on other technologies, such as online bulletin boards and review systems (e.g., Ba et al 2014;Bin and Ye 2014;Wasko and Faraj 2005). Our results imply two normative pieces of advice for such community sites: (a) representing multiple sides of an issue typically takes many contributions and revisions and (b) length by itself is not usually sufficient to guarantee a balanced view without considerable revision.…”

Joint Effect of Different Dimensions of eWOM on Product Sales

“…To further test for multicollinearity, we calculate the VIFs for all the independent variables. Table 3 shows that the maximum VIF is below 5, indicating that no strong multicollinearity exists in the data set [44].…”

“…The disagreement in the academic community is probably due to the particular categories of goods in previous studies, such as books [4,35,36], movies [11,26,27,33,35], digital cameras [3], software [37], video games [9,38], hotels [39][40][41][42][43], restaurants [44], and fashion products [45]. Prior studies tend to focus on the online sales of nondurable goods or product categories that involve the online consumption of digital products.…”