The Research on Joint Effect of Online Reviews and Sales Volume in New E-Commerce Context


Student thesis: Doctoral Thesis

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Award date4 May 2018


Online reviews/ratings offer more product information for consumers to shop online and for sellers to target different market. The thesis focuses on the different joint impact of online review and sales volume information on both consumers’ purchase decision and sellers’ pricing strategies under different online context.

Specifically, we first focus on the information goods such as mobile application (app), and explore how a monopolistic app developer offers different quality level of apps and set prices under the online rating and network effect of app consumers. We build a two-period economic model and find that when the consumers overestimate the app quality, the online rating is less than 1 and reflects the true quality efficiently. In this case, the developer’s optimal response is to set a skim pricing strategy if the network effect intensity is small; otherwise, a penetration pricing strategy is optimal. When the consumers underestimate the app quality, the online rating is restricted to reflect the true quality. In this case, the optimal app quality is equal to consumers’ expected quality. We also discusses the limitation of the fixed rating interval. To improve profits, developer needs some marketing strategies such as platform promotion to improve consumers’ willingness to pay.

In the second study, we consider the optimal policy from platform (such as Apple Store) and risk-averse consumers. In a monopolistic setting, we find that online review has bidirectional word-of-mouth effect relying on consumers’ ex post expected utility. When consumers’ ex post expected utility is positive, the word-of-mouth leads to a positive effect, vice versa. We also find that when the sales volume increases, the equilibrium quality increases while the change trend of equilibrium price and profits are ambiguous. Only positive word-of-mouth leads the price and profits to increase with sales volume and negative word-of-mouth results in a counter trend. The optimal revenue sharing policy between the developers and platform is that revenue-sharing percentage decreases with the app development costs, which motivates the developers to offer high quality app voluntarily.

Finally, we consider how online reviews as well as past sales volume information, jointly affect consumers’ choice and firms’ competition. We build a two-period duopoly model in a market with herding consumers, who have different preferences and are unsure of the quality difference between the products, and “irregular” consumers, who result in the sales volume uncertainty. We find that the impact of online reviews and sales volume information on firms’ profits are mutually enhancing. The impact of these two types of information depends on both product characteristics (the level of misfit costs for consumers for the non-preferred product), and how consumers a priori perceive the quality difference between the products. In addition, we find that such information can be detrimental to firms, as firms adjust prices to induce, or react to, favorable market-generated information. Accordingly, consumers may not benefit from such market-generated information if the gain from less uncertainty cannot offset the loss from higher product prices.

The thesis makes several contributions in both theoretical and practical level. Theoretically, we enrich the joint impact of online review and sales volume information on consumers with multi-perspective explanations. Meanwhile, we broaden the studies of online pricing strategies when incorporating both online review and sales volume information together, in a monopoly, a duopoly setting, or interaction with platform policy. Lastly, we offer new insights into precisely why the more/accurate information cannot always be beneficial if the online review and sales volume information are influenced by each other. Practically, the major findings offer guidelines for both monopolistic and competing firms to set better pricing strategies, for the platform to design optimal revenue sharing as well as information policies, such as whether or not to promote more informative reviews.

    Research areas

  • online rating/review, sales volume, network effect, quality, pricing strategy, risk preference, revenue share, informativeness, Competition