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

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  • Sulin Ba
  • Yuan Jin
  • Xinxin Li
  • Xianghua Lu

Abstract

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.

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  • Sulin Ba & Yuan Jin & Xinxin Li & Xianghua Lu, 2020. "One Size Fits All? The Differential Impact of Online Reviews and Coupons," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2403-2424, October.
    • Handle: RePEc:bla:popmgt:v:29:y:2020:i:10:p:2403-2424
    DOI: 10.1111/poms.13233
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    6. Hu, Li & Zhang, Mengwei & Wen, Xin, 2023. "Optimal distribution strategy of coupons on e-commerce platforms: Sufficient or scarce?," International Journal of Production Economics, Elsevier, vol. 266(C).
    7. Xinxue (Shawn) Qu & Aslan Lotfi & Dipak C. Jain & Zhengrui Jiang, 2022. "Predicting upgrade timing for successive product generations: An exponential‐decay proportional hazard model," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2067-2083, May.
    8. Bingsheng Liu & Wenwen Zhu & Yinghua Shen & Yuan Chen & Tao Wang & Fengwen Chen & Maggie Wenjing Liu & Shi‐Hao Zhou, 2022. "A study about return policies in the presence of consumer social learning," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2571-2587, June.
    9. Wang, Siqi & Lim, Xin-Jean & Luo, Xi & Cheah, Jun-Hwa, 2024. "To hesitate or not to hesitate: Can popularity cues minimize the hesitation to checkout in e-commerce?," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    10. Zhanfei Lei & Dezhi Yin & Sabyasachi Mitra & Han Zhang, 2022. "Swayed by the reviews: Disentangling the effects of average ratings and individual reviews in online word‐of‐mouth," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2393-2411, June.

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