|dc.description.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.||en_US