Aspects of Online Reviews and their Effects on Consumer Decisions

Abstract

This dissertation examines different aspects of online reviews and their effects in consumer decisions. Online reviews are proliferating at a tremendous rate, with most consumers now stating that online reviews are the most important product attribute in online purchase decisions (BrightLocal 2017). As such, it is important to understand how various aspects of reviews affect consumers’ decisions, and outline the conditions by which some of these attributes may have conditional influences. To that end, we begin this dissertation by first investigating two numerical attributes of online reviews, average product ratings and review volumes. Furthermore, because online reviews are becoming such an influential tool, firms have begun to attempt exploiting consumers via fake reviews (Mayzlin, Dover, and Chevalier 2014; Luca and Zervas 2016). Thus, the second essay in this dissertation investigates how consumers respond when a website discloses that they have caught fake reviews being written for a specific brand. In Essay I, we investigate how average product ratings and review volumes influence consumers’ decisions when faced with a choice set in which there is no dominant option (i.e., when one option has a higher rating, but fewer reviews relative to another option). We argue that the diagnosticity (i.e., influence) of both average product ratings and review volumes are conditionally influenced by the other attribute, and as such, the choice between the higher-rated, fewer reviews option and lower-rated, more reviews option is dependent on the specific values of each attribute. While prior research has demonstrated the relative influence of both attributes, the findings are still debated (Floyd et al. 2014; You, Vadakkepatt, and Joshi 2015). By investigating the conditional effects of these attributes on choice, we help to rectify the divergent findings. We argue that average product ratings are inherently more diagnostic than review volumes due to the bound versus unbound nature of their scales, respectively. Whereas average product ratings have stable scale boundaries (e.g., one to five stars), review volumes do not (e.g., zero to infinity). As such, review volumes are more susceptible to relative comparisons made within the choice set. We demonstrate how the relative diagnosticity of these attributes are a function of the review volumes contained within the choice set, and how this ultimately governs choice. We conclude Essay I with the theoretical implications as well as a series of simulations demonstrating the practical implications for managers. In Essay II, we demonstrate the consequence of websites informing consumers that they have identified fake reviews for brands featured on their website. While a growing body of literature has investigated the characteristics of fake reviews (Mukherjee et al. 2013; Ott et al. 2013), as well as the firms which are likely to solicit them (Mayzlin, Dover, and Chevalier 2014; Luca and Zervas 2016), to the best of our knowledge, this is the first investigation into the effect of disclosing this information to consumers. While fake review alerts inform consumers that websites are monitoring the reviews for fraudulent information, we argue that the alerts also activate consumers’ persuasion knowledge (Friestad and Wright 1994), leading to attempts to correct for perceived biased information, as well as justice against the brand when it is the source of the fake reviews. We demonstrate that fake reviews lead consumers to not only attempt correction in their perception of the brand, but also in the information that they acquire (i.e., the reviews they read). Furthermore, we show that reducing consumers’ perceptions of inaccurate information attenuates their corrections. As such, this research holds relevance for website managers which provide reviews for their consumers. In both essays, we demonstrate the consequences of review information in consumers’ judgments and decisions. We argue that managers must carefully consider what information to provide consumers, and how to present it, in order to avoid biasing their consumers’ decisions.