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

Using 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.

• 저널Production and Operations Management · 29(10) · 2403–2424
• 토픽Digital Marketing and Social Media · Sociology and Political Science
• DOI10.1111/poms.13233
• 저자Sulin Ba, Yuan Jin, Xinxin Li, Xianghua Lu