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On the Spillover Effects of Online Product Reviews on Purchases:
Evidence from Clickstream Data
Abstract
We analyze the spillover effect of the online reviews of related products on the purchases of a focal product
using clickstream data from a large retailer by investigating (a) whether the related, co-visited products are
complementary or substitutive; (b) whether the related products are from the same or a different brand, and
(c) the choice of media channel (mobile or PC) used. To identify complementary and substitutive products,
we used a text-mining approach of topic modeling on the product descriptions to quantify the functional
similarity of pairwise products. Our empirical analysis shows that the mean rating of the online reviews of
substitutive products has a negative effect on the purchasing of the focal product, while the mean rating of
complementary products has a positive effect. Moreover, we find that the negative spillover effect of the
online reviews of substitutive products across different brands on the purchasing of a focal product is
significantly higher than those of the same brand, and for consumers who viewed online product reviews
on mobile devices versus traditional PCs. Our study has theoretical and managerial implications on
leveraging the spillover effects of the online product reviews of related products on purchases.
Key words: Online product reviews, Substitutive products, Complementary products, Brand Spillover,
WOM Spillover, Topic modeling
1. Introduction
Online commerce has been growing in popularity because of the convenience in searching for and
purchasing products. The surge in popularity has also produced user-generated product information or
“word of mouth,” termed online product reviews. Online product reviews have received much interest from
academics and practitioners alike, and there is rich body of literature on the effect of online product reviews
on aggregate product sales (demand) of a focal product (e.g., Cone 2010; Chevalier and Mayzlin 2006).
This is because consumers often rely on online product reviews written by other consumers to reduce
product uncertainty (e.g., Dimoka et al. 2012), and the effect of online product reviews on product sales
has been extensively studied in the literature (e.g., Chevalier and Mayzlin 2006; Zhu and Zhang 2010).
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While the process in which a consumer purchases a product from a number of related products on the same
shopping trip, called “market basket choice,” is well established in the literature (Russell and Petersen 2000),
the literature on online product reviews has ignored that, when consumers search for products, they do not
only consider the online reviews of a focal product that is of interest to them, but they also consider the
online reviews of other, related products. While the importance of examining the effects of related products
on the purchase of a focal product has been touted (e.g., Shocker et al. 2004), to our knowledge, there is a
lack of research on the spillover effects of online product reviews of other, related, co-visited products on
a consumer’s purchasing of a focal product.1 In particular, how the online product reviews of related
(complementary or substitutive) products within the set of products that an individual consumer takes into
account shape the consumer’s purchasing decision is unknown. Because of the association among products
that are viewed together in a market basket, we examine how the consumer’s purchasing decision may be
affected by the online product reviews of related, co-visited products. In this study, using clickstream data,2
we examine the spillover effect of online product reviews across related, co-visited products in a market
basket in the purchase of a product. In sum, we examine the relationship between online product reviews
and sales based on: (a) whether the products are complementary or substitutive; (b) whether the products
are of the same or a different brand, and (c) which media channel (mobile device or traditional PC) is used.
We aim at answering two research questions: (1) How do the online product reviews of other, related (i.e.,
complementary or substitutive) products in a consumer’s consideration set affect the purchasing of a focal
product? (2) How does the association among products (i.e., complementary or substitutive), brand, and
media channel moderate the role of the online product reviews of other products in a focal product purchase?
While spillover effects across products (e.g., advertising spillovers) have attracted attention in marketing
(e.g., Anderson and Simester 2013; Peres and Van den Bulte 2014; Libai et al. 2009; Lewis and Nguyen
1 For instance, when a consumer wants to purchase a HD LED TV, it is likely that she searches many TVs with similar
specifications that match her interests to find the best TV for her among all substitutive TVs. She is also likely to
search for TV wall brackets and/or sound bars together, which can complement the use of the focal TV.
2 Clickstream data of individual consumers who shop on a retailer’s website allow us to observe complete records of
the products each consumer has viewed and the online product reviews of these products, enabling us to clearly define
a consumer’s consideration set and examine whether the online product reviews of other related (substitutive or
complementary) products that attract the consumer’s attention affect her likelihood of purchasing a focal product.
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2015), including some work on word-of-mouth communication (e.g., Krishnan et al. 2012; Libai et al. 2009;
Parker and Gatignon 1994), to our knowledge, the spillover effects of online product reviews among related
products on a consumer’s purchasing decision have not been examined. Extending these studies, we use a
scalable text-mining approach to identify substitutive/complementary products from a large set of products
within an individual consumer’ market basket to examine the spillover effect of online product reviews on
an individual consumer’s purchasing behavior by examining the role of online product reviews of other,
related products in the consumer’s consideration set that are co-visited during the consumer’s online session.
To define related (complementary and substitutive) products, we use a text-mining approach called topic
modeling to measure product similarity in a given consumers’ consideration set (co-viewed products in a
market basket during an online session). In a consumer’s market basket, products with high similarity in
product usage are viewed as substitutes, while products with co-usage or simultaneous needs in
consumption are viewed as complements. The Instrumental Variable (IV) estimation approach further
confirms the validity of our measures of complementary and substitutive products. We also examine how
brand and channel media (mobile or PC) moderate the role of the online product reviews of related products.
Brand spillover of related products is of interest for the marketing decisions of firms and the placement
decisions of retailers, but research on brand spillover resulting from online product reviews on purchases
has been limited. The extensive use of mobile devices by consumers has attracted the attention of marketers,
but the nature and role of online product reviews viewed on mobile devices is still largely unexplored.
Taken together, our emphasis is on the substitutability/complementarity of related, co-visited products, and
the moderating factors of brand and channel media, to understand how consumer-generated information,
online product reviews, can spillover across related products in an individual consumer’s consideration set.
The results show a significant negative spillover role of online product reviews for substitutive products
and a significant positive spillover role for complementary products in the purchase of a focal product.
These results are also practically relevant: if the mean review rating of substitutive products increases by 1,
on average, there will be at least a 7% decrease in the purchase probability of a focal product; if the mean
rating of the reviews of complementary products increases by 1, on average, there will be at least a 14%
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increase in the purchase probability of a focal product. Notably, the magnitude of the spillover effects from
the review ratings of related products is about the same, or even larger, than the magnitude of the effect of
the mean rating of the focal product. This result highlights the importance of spillover effects. Interestingly,
we find a significantly higher (negative) spillover role of online product reviews for substitutive products
from different brands than those of the same brands, whereas we do not find a significant difference in the
positive spillover effect of the online reviews for complementary products. We also find a significantly
higher spillover effect of online product reviews viewed on mobile devices compared to traditional PCs.
Our main contributions are as follows: First, we propose and validate a text-mining-based measure for
classifying substitutive and complementary products by using product similarity across an individual
consumer’s co-visited products (i.e., a consumer’s consideration set). Using clickstream data, a consumer
views several product pages in the same shopping session mainly because of the following two scenarios:
(a) comparing several products that are interchangeable and choosing the best one among them (substitutes);
and (b) considering purchasing several products that work together for a specific goal or task (complements).
We thus contribute to the literature on the association among products (i.e., substitutive and complementary)
in a consumer’s consideration and the effect of this association on consumers’ purchasing decision. Second,
we examine the spillover role of online product reviews of related products in an individual consumer’s
purchase decision for a focal product, contributing to the literature on online product reviews by examining
associations among the online product reviews of related products. Third, we show how brand moderates
the spillover role of online product reviews, contributing to the literature on brand spillover in a consumer’s
market basket choice and informing the marketing strategies of retailers. Fourth, we show how these effects
differ when a consumer’s shopping session is made across channel media (mobile device or traditional PC),
which is of growing importance to marketers because of the increased popularity of mobile devices among
consumers. We thus contribute to the emerging literature on mobile devices by examining the role of
spillover of online product reviews on a consumer’s purchase decisions across media channels.
We also contribute to managerial practice by showing how consumer decisions across co-visited products
are inter-related, which can prescribe to marketing managers how to use cross-product linkages to develop
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their marketing strategies (e.g., Russell et al. 1999). For practitioners, understanding the interaction among
related, co-visited products in a consumer’s market basket, such as what other products are substitutive or
complementary to their own products, is critical to their marketing strategies. Our results regarding the role
of online product reviews depending on media channel used will also inform marketing managers how to
leverage marketing strategies depending on channel (mobile device versus traditional PC) through which a
consumer views products. The reported stronger effects of online product reviews on mobile devices are
particularly important as more businesses consider mobile devices to reach consumers (Fong et al. 2015),
thus contributing to marketing channel strategies with emphasis on whether and how mobile devices can
play an important role in consumers’ purchasing decisions by showing the consumer’s heightened reliance
on online product reviews on mobile devices despite their limited information presentation.
2. Literature Review
2.1 Online Product Reviews
There has been extensive research on the effect of online product reviews, a common form of user-generated
content, on the sales of a focal product (e.g., Archak et al., 2011; Clemons et al., 2006; Forman et al., 2008;
Goes et al., 2014). Studies in general have shown various effects of online product reviews. For example,
Chevalier and Mayzlin (2006) found that online product reviews significantly influence book sales.
Dellarocas et al. (2007) obtained similar results for movies. Liu (2006) studied movie reviews and found
that online movie reviews offer significant explanatory power for both aggregate and weekly box office
revenues. Godes and Mayzlin (2004) showed a positive relationship between online product reviews and
television show viewership. Dellarocas et al. (2007) found that adding online movie reviews to their
revenue-forecasting model significantly improves the model’s predictive power. The effect of online
product reviews is because consumers perceive the information delivered by other consumers to be valuable,
thus reducing product uncertainty (e.g., Dimoka et al. 2012; Hong and Pavlou 2014). Sellers strategically
respond to online product reviews (e.g., Chen and Xie 2005 and 2008; Dellarocas 2006; Kwark et al. 2014),
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and they consider online product reviews to be another important component of their overall marketing mix
(e.g., Chen and Xie 2005, 2008; Godes and Mayzlin 2009; Libai et al. 2013).3
There is rich body of literature on the average effect of online product reviews on aggregate sales (demand).
Several characteristics of online product reviews were shown to be influential on sales --- rating valence
(e.g., Chevalier and Mayzlin 2006, Clemons et al. 2006, Duan et al. 2008), volume of ratings (e.g. Liu 2006),
variance (Clemons et al. 2006), text reviews (Archak et al. 2011), reviewer identity (Forman et al. 2008),
and product and review characteristics (Zhu and Zhang 2010). Further, recent research has examined the
role of characteristics of online reviews and product type in review helpfulness (Mudambi and Schuff 2010),
negative emotions embedded in online product reviews on review helpfulness (Yin et al. 2014), how online
product reviews reduce product uncertainty (Hong and Pavlou 2014), interactions of online product reviews
with promotional marketing (Lu et al. 2013) and product recommendations (Jabr and Zheng 2014; Lee and
Hosanagar 2016), herding behavior in online product reviews (Duan et al. 2009; Lee and Hosanagar 2015),
potential bias formed in the generation of online product reviews (Li and Hitt 2008; Hu et al. 2016), plus
other factors that were shown to affect the posting, generation, development, and potential biases of online
product reviews (Gao et al. 2015; Goes et al. 2014; Dellarocas et al. 2010; Zhu and Zhang 2010; Rice 2012).
The valence of the online product reviews is widely regarded as the most influential factor on product sales
(Dellarocas et al. 2007; Chintagunta et al. 2010). We focus on the mean rating of online reviews, which
refers to the valence of online product reviews and denotes the overall evaluation score reported by
reviewers about a product. Although some studies did not find a positive association between the mean
rating and sales (Liu 2006), research has mostly shown that the higher the mean rating of online reviews
for a product, the more positive the consumers’ attitudes are toward the product, which leads to higher sales
(Lu et al. 2013; Chevalier and Mayzlin 2006, Clemons et al. 2006, Duan et al. 2008; Zhu and Zhang 2010).
3 There is also an emerging literature on the diffusion of user-generated content through digital networks (Susarla et
al. 2012), of consumer attention through a recommendation network (Oestreicher-Singer and Sundararajan 2012a;
Oestreicher-Singer and Sundararajan 2012b), of celebrity endorsement through co-purchase recommendation
networks (Carmi et al. 2012), and of the expert reviews on the general consumers’ perception (Luo et al. 2016). Overall,
these papers focus on the propagation of the digital content or opinion throughout the network and the effect of
placement of visible product networks on the contagion process (e.g., network proximity). In this study, we focus on
online product reviews viewed by individual consumers and the effect on the purchase decision.
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Extending the studies that focused on the average effect of online product reviews on aggregate sales
(product demand), we examine the effect of online product reviews on an individual consumer’s single
purchasing decision. We complement this stream of research by specifically examining the spillover effect
of online product reviews of related (substitutive or complementary) products in the individual consumer’s
purchases of a focal product. Also, in our study, online product reviews are those of both the focal product,
and also of other related products of the same and of different brands, in which the individual consumers
show interest by co-visiting in a given time period using either a traditional PC or a mobile device.
2.2 Spillover Role of Word-of-Mouth (WOM)
Spillover refers to the extent to which a message affects beliefs related to attributes that are not contained
in the message (Ahluwalia et al. 2001). The spillover effects across products have been studied in the
literature. For example, researchers have studied WOM spillover among consumers for marketing decisions
on WOM externalities (Peres and Van den Bulte 2014), new product diffusion (Libai et al. 2009), seeded
marketing campaigns (Chae et al. 2016), and product growth (Krishnan et al. 2012). Positive WOM
spillover can make a firm’s exclusivity unprofitable (Peres and Van den Bulte 2014), while WOM spillover
can lead to a shorter takeoff of a later entrant (Libai et al. 2009). In studies on the effects of WOM and
marketing mix within and across brands/categories (e.g., Parker and Gatignon 1994; Shankar et al. 1998;
Krishnan et al. 2000; Krishnan et al. 2012; Libai et al. 2009), the effect of WOM is in general estimated by
indirect measures (i.e., survey or category-level market data). Different from the stream of research that
indirectly estimates the interpersonal communication across brands/categories, in this study, we directly
measure online product review ratings that have been reported by other consumers, and we examine the
spillover role of online product reviews of related products in an individual consumer’s purchase decision.
2.3 Brand Spillover
Research has shown the existence of spillover effects to rivals in advertising (Anderson and Simester 2013),
brand scandals (Roehm and Tybout 2006), and product recalls (Borah and Tellis 2015). Spillover effects
across products vary depending on their brand. Studies showed that consumers’ product quality perceptions
are correlated when products share a common brand name, while the correlation is negative across brands
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(Erdem 1998; Erdem and Winer 1999; Seetharaman et al. 2005). Erdem and Sun (2002) found advertising
and sales promotion spillovers within the same brand. Companies with positive brand equity (brand value)
can benefit from a positive spillover from other products that share the same brand name. For such benefit,
marketers often extend their product lines to increase product demand and respond to competitive threats
(Smith and Park 1992), which can even lead to cannibalization and reduction of the firm’s total profits
(Desai 2001; Aaker 1990). Regarding the spillover effect of marketing activities and publicity across brands,
mixed results have been reported because of moderating effects, such as brand loyalty, familiarity, and
product similarity (e.g., Ahluwalia et al. 2001; Janakiraman et al. 2009).
Research has explained the mixed results of spillover based on the accessibility–diagnosticity framework
(Feldman and Lynch 1988). In brief, this framework suggests that information spillover (spillover effect of
online product reviews in our context) can occur when information is accessible and diagnostic for the other
product (e.g., Roehm and Tybout 2006; Janakiraman et al. 2009; Ahluwalia and Gurhan-Canli 2000).
Accessibility is such that concepts, such as brand, product attributes, and product categories, reside in a
network and can activate one another when having strong links (Anderson 1983; Collins and Loftus 1975).
Diagnosticity is a function of consumers’ implicit theories about how things relate in the world
(Broniarczyk and Alba 1994a, b). Thus, the accessibility–diagnosticity framework suggests that if one
brand is perceived as diagnostic for another brand, observation about one can be applied to another. Roehm
and Tybout (2006) extended this theory to the context of brand scandal: spillover to a competitor is more
likely to occur if the scandal for one brand is diagnostic to the competitive brand. Researchers have found
spillover effects to be especially salient for negative versus positive information, which supports the
moderating role of accessibility-diagnosticity (Ahluwalia et al. 2000; Ahluwalia and Gurhan-Canli 2000).
Strong ties further accelerate this spillover effect. For example, brands as a member of a category have
strong linkages to the category, and thus any scandal of one brand can be seen diagnostic on the product
category (Barsalou 1985). Distinct from these studies of brand spillover effect of marketing activities and
publicity on sales of products in a common market, we examine the brand spillover role of online product
reviews on an individual consumer’s purchase decision among other related products in the consumer’s
consideration set that are co-visited during the consumer’s online session.
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2.4 Product Similarity and Complementarity/Substitutability
Substitutive products are those goods that can be interchanged among each other, whereas complementary
products are those goods that are often purchased together (Mass-Colell et al. 1995). Substitutive products
have a positive cross-price elasticity, while complementary products have a negative cross-price elasticity;
accordingly, as price of one increases, demand for the other product increases (or decreases) in substitutes
(or complements) (e.g., Bucklin et al. 1998; Russell and Bolton 1988; Russell and Petersen 2000). Cross
effects of prices among products (Wedel and Zhang 2004) and among retail price promotions (Walters 1991)
have been empirically studied following these definitions. Product substitutability/complementarity have
long been a natural way to view inter-product associations (Shocker et al. 2004; Seetharaman et al. 2005).
Product substitution is found in success or failure of a new product (e.g., Cooper 2001) and technological
obsolescence (e.g., Christensen 1997; Utterback 1994). Product complementarity is addressed by research
on product bundles (e.g., Eppen et al. 1991; Gaeth et al. 1991; Guiltinan 1987; Yadav 1994). Recently,
some studies examined the effect of product networks of co-views and/or co-purchases on product demand
(e.g., Oestreicher-Singer and Sundararajan 2012; Lin et al. 2015). Several studies on online co-viewing or
co-purchasing behavior (e.g., McAuley et al. 2015; McAuley et al. 2015) have borrowed these concepts of
product substitutability/complementarity. The key questions in these papers, for instance, are whether the
multiple decisions in the consumers’ consideration set are related, and how marketing managers can use
cross-product linkages to develop effective marketing strategies (Russell et al. 1999). The interdependence
in demand relationships across products in the consumers’ consideration set is pointed out as the key feature
of the consumer’s market basket choice (Russell and Petersen 2000).
It is important for online retailers and manufacturing firms that sell their products online to identify the
products that consumers search together and/or purchase together. In a similar vein, recent research in
marketing has shown emphasis on understanding cross-category effects of marketing activities and several
aspects of cross-category effects of marketing activities have been examined (Manchanda et al. 1999;
Russell and Petersen 2000; Wedel and Zhang 2004). In general, these studies show how the price promotion
of one product is affected by the demand of within-category and/or across-category products (e.g.,
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detergents and softeners/cake mix and frostings). In this study, we seek to explain the
substitutability/complementarity distinction, along with other several factors, such as budget constraint and
value transfer, across brands (e.g., Song and Chintagunta 2006; Mehta 2007; Niraj et al. 2008). We also
seek to contribute to this stream of research by studying the “spillover effects” of online product reviews
of different, but other, related products among the co-visited products.
Shocker et al. (2004) documented the rationale for the (complementary and substitutive) effects of the
marketing efforts of other products on the purchase of a focal product. They suggested a lack of research
on the effects of other, related products, and they called for a research of the explicit effects of other products
(i.e., complements and substitutes). They pointed out that it is primarily the “person” in the three P’s
framework (person, products, and purpose) that determines the extent of substitutability. Day et al. (1979)
also offered a customer-oriented definition of a product market as the set of products judged to be substitutes,
within those usage situations in which similar patterns of benefits are sought. Srivastava et al. (1981) offered
a managerially useful framework within which a measure of inter/brand substitutability/competitiveness is
based on substitution-in-use or similarity of product-usage patterns (Srivastava et al. 1984; Day et al. 1979;
Shocker and Srinivasan 1979; Urban and Hauser 1980). Products are perceived as similar whenever they
are perceived as substitutable as means for the same purpose or usage (Ratneshwar and Shocker 1991).
Product complementarity can be originated from consumers’ perceptions regarding complementary needs
(i.e., consumers' perception of the necessity of one product for the performance/use of the second product),
which can be found by advertising alliances for joint advertisement (e.g., Samu et al. 1999), product bundles
(e.g., Eppen et al. 1991; Gaeth et al. 1991; Guiltinan 1987; Yadav 1994), and co-purchase online behavior
(e.g., McAuley et al. 2015; McAuley et al. 2015). Complements are products that are used in conjunction
with one another to satisfy a particular need (Henderson and Quandt 1958). Complements-in-use enhance
the growth prospects of one another, and their coexistence is affected by user purpose (Shocker et al. 2004).
A well-known example is hardware and software, which have exhibited a positive, reinforcing effect on
each other (e.g., Gates 1998). In many cases, complements are products that essentially have limited value
without the other (e.g., hardware and software; television sets and programming). In other situations,
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complementary products can be used independently, but they usually are not because a superior result can
be achieved jointly. Because of the positive inter-category relationships, firms and retailers have often
followed a product-bundling strategy (e.g., Eppen et al. 1991; Guiltinan 1987). Airlines and travel web sites
also often offer mixed bundles that include air travel, lodging, and rental cars.
Walters (1991) conceptualized that the substitutability of one product for another product as a continuum.
As the perceived attributes of a set of products become increasingly similar, substitutability of one product
for another increases. The perceived similarity in product attributes that satisfy the same consumer needs
is an important aspect to define substitutability. For example, canned juices and soft drinks are beverages
that both quench thirst, and dry and liquid detergents that both clean clothes are another example. For brands
in a product category, the degree of substitution is believed to be high because of similarity. Similarly,
complementarity of products is a continuum. For some products, complementarity depends on the specific
purchase occasion and the needs of the consumer. For example, peanut butter and jelly can be used together
to create a sandwich or can be used separately in a recipe or on a piece of toast. Certain other products are
strict complements because they must be used together to yield another distinct product. Thus, the key
aspect of the substitutive products is the perceived similarity in functions that may serve a similar purpose
for similar consumers whereas the key of the complementary products is in a consumer’s expectation of
superior results achieved from co-usage by improving the functionality and satisfying the needs together.
2.5 Mobile Channel
Mobile devices have become popular for online shopping, however, higher search costs with limited
functionalities (Ghose et al. 2013) can limit the overall information access and the effectiveness of learning
(Maniar et al. 2008). There are a few aspects of mobile devices that distinguish them from other types of
Internet devices, such as personal computers (PCs). In contrast to using PCs, mobile devices can be
available anytime and anywhere, without limitations on geographical mobility and access and screen sizes
are smaller on mobile devices, thereby rendering higher search costs (Ghose and Han 2011). With this
limitation in search, it has been shown that a small screen increases the effects of product ranking, which
might suggest more pronounced role of information delivery on a mobile device. That is, increased efforts
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required for information processing on mobile devices might be costly; however, consumers can pay more
attention to messages delivered on mobile devices because of the easy and timely access to information
(Ghose and Han 2011; Ghose and Han 2014). As mobile channel media are increasingly considered an
influential marketing tool (Fong et al. 2015; Friedrich et al. 2009), in this study, we explore this relatively
new aspect of online product reviews on mobile devices---how the effects of online product reviews and
their spillover effects across products on a consumer’s purchase decision are different in mobile devices.
3. Theory and Hypotheses
First, the role of the mean rating of online product reviews of substitutive and complementary products in
the purchase of the focal product is discussed (H1) (accounting for other aspects of online product reviews).
Second, the role of the mean rating of online product reviews of substitutive and complementary products,
when products are of the same or a different brand, in the purchase of the focal product, is hypothesized
(H2). Third, the different role of the rating of online product reviews in the purchase of the focal product
depending on channel media (traditional PC or a mobile device) is hypothesized (H3).
3.1 Complementary / Substitutive Products and Product Purchases
Substitutes are products that a consumer perceives as similar or comparable, so substitutive products may
replace each other. Complements are products that the coexistence of other products is affected by their
purpose, exhibiting a positive reinforcing effect on each other (Shocker et al. 2004). Formally, in economics,
products are substitutes if, demand for a product increases when the price of another product increases (i.e.,
a positive cross elasticity of demand). Products are complements if, the demand of a product decreases
when the price of another product increases (Mass-Colell et al. 1995). This classification of products and
their association have been noted as a key feature of the market basket choice (Russell and Petersen 2000).
The negative (positive) relationship in demand changes between substitutive (complementary) products has
been found. For example, marketing activities, such as promotions, for one product negatively (positively)
affect other substitutive (complementary) products (e.g., Walters 1991; Chintagunta and Haldar 1998;
Manchanda et al. 1999; Kamakura and Kang 2007; Mulhern and Leone 1991).
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Studies have shown a significant positive association between the mean rating of online product reviews
and product sales (e.g., Chevalier and Mayzlin 2006; Clemons et al. 2006; Duan et al. 2008; Lu et al. 2013).
A higher mean rating of online product reviews has a positive effect on the demand of the product because
a higher mean rating of the online reviews of a product leads to more positive attitudes toward the product,
and thus higher sales. Researchers have shown that online product reviews serve as an effective marketing
tool for retailers (Chen and Xie 2008). For substitutive products, however, this effect can be opposite.
Because most positive reviews lead to the better evaluation of a product, the relative attractiveness of other
substitutive products should be undermined, thus shifting the preference against the substitutive products
(Luo et al. 2016). Substitutive products are more diagnostic to one another because of the similarity in their
attributes and functionalities, which enhances their relative attractiveness due to online product reviews.
On the other hand, a highly rated product from online reviews can create and further increase the needs of
other complementary products because the benefit from joint consumption is enhanced. Studies have found
that the promotion of one product leads to higher sales of complementary products, while it decreases the
sales of the substitutive products (e.g., Walters 1991; Mulhern and Leone 1991). Altogether, given the
positive association between the mean rating of online product reviews and product demand, and the
negative (positive) relationship of substitutive (complementary) products in a consumer’s purchase decision,
we expect the negative (positive) role of the mean rating of online product reviews of substitutive
(complementary) products on the purchase of the focal product. Hence, we hypothesize:
H1a: The mean review ratings of substitutive products have a negative role in the purchase
of a focal product.
H1b: The mean review ratings of complementary products have a positive role in the purchase
of a focal product.
3.2 The Moderating Role of Brand
Substitutes serve a similar purpose, and thus may have similar customers (e.g., Srivastava et al. 1981, 1984).
When products cater to consumers with similar needs and compete in the same or similar category,
consumers are likely to perceive information about one product to be highly related to another substitutive
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product. When substitutive products are sold in the same market, the negative influence of one product can
accelerate switching to another substitutive product. In price promotions, for instance, the promotion of one
brand increases its own sales at the expense of those of substitutive brands (Dodson et al. 1978; Kumar and
Leone 1988; Mulhern and Leone 1991; Walters 1991). When products are substitutes, overall, the role of
the mean rating of online product reviews of substitutive products on the purchase of the focal product
should be negative, regardless of brand. On the other hand, we propose an opposite effect when substitutive
products are of the same brand. Products within a brand benefit from one another in terms of advertising,
and they have shown positive spillover effects (e.g., Aaker 1996; Balachander and Ghose 2003). This is
because consumers transfer their perception about the focal product to another similar product, and this
positive spillover effect across similar products within a same brand is generally stronger. Aaker (1996)
suggests that advertising of the brand extension is beneficial to the other products of the same brand.
Balachander and Ghose (2003) also found a positive reciprocal effect from advertising among the products
of the same brand because advertising of other products with the same brand makes consumers aware of
the brand bond, increasing quality perceptions and thus sales of unadvertised products of the same brand.
While we expect either a positive or a negative effect of the mean rating of the online product reviews of
substitutes of the same brand in the purchase of a focal product, research has shown a strong negative effect
of marketing promotion for a product on the demand of substitutive products across different brands
(Mulhern and Leone 1991; Walters 1991; Kumar and Leone 1988). For example, Kumar and Leone (1988)
found that the promotion of one product has a negative effect on the sales of other substitutive products of
different brands. Luo et al. (2016) found that reviews of a focal brand have a negative effect on consumer
perceptions of its competing brands because reviews of a focal brand may undermine consumer perceptions
of its competing brands. When reviews are favorable on the one brand, the online product reviews signal
to consumers the preference over the brand, and this signal induces a preference shift in consumers
(Aggarwal et al. 2012; Spence 1973). Given the core nature of substitutive products, the consumer’s choice
should be made among these competing products, which can attract one product more at the expense of
another competing product with similar attributes/functionalities. With favorable reviews of the one product,
15
the substitutive nature of competing products can accelerate the customer’s propensity to select the superior
product among substitutive products, and further shift the preference against the other competing brand.
Therefore, considering that direct competitors have strong connections with one another in the consumer’s
mind because of frequent juxtapositions during the purchasing process, we expect that the negative spillover
effects among substitutive products to be stronger among different brands than within the same brand.
Complementary products can be promoted jointly, and retailers often follow product bundling strategies by
offering a group of products as a bundle (e.g., Eppen et al. 1991; Guiltinan 1987). The positive relationship
among complementary products has been consistently shown (Walters 1991; Chintagunta and Haldar 1998;
Manchanda et al. 1999). Because of this reciprocal interaction, we expect a universally (regardless of brand)
positive role of the mean rating of online product reviews among complementary products. However, for
complementary products across brands, studies have shown that complementary effects are mixed because
complementary products across brands may work as substitutes to each other (Mulhern and Leon 1991;
Walters 1991; Kamakura and Kang 2007), and the strength of the complementarity among products can
vary depending on other factors (e.g., proximity in displayed position, brand competition, brand strength)
and product pairs (Walters 1991; Manchanda et al. 1999; Niraj et al. 2008). Although complementary
products generally exhibit choice dependence within a market basket, the lack of a bond among products
across brands may reduce the magnitude of their complementarity. Thus the additional needs created by the
online product reviews can be weaker among different brands. In contrast, when complementary products
belong to the same brand, we expect a strong positive effect of the mean rating of online product reviews
among complementary products on the purchase of another product, notably because of the complementary
effects in consumer co-usage/co-purchase needs and the value transfer within the same brand. First, given
the reciprocal nature of complementary products in consumer needs, the consumer’s purchasing decision
of one product can positively affect the purchasing of other complementary products. Hence, the role of the
online product reviews can be reinforcing for complementary products. Second, the online reviews of a
product can affect, not only the value of the product, but other complementary products of the same brand.
The positive effect of promotion across the sales of complementary products within a same brand was found
16
(Mulhern and Leon 1991; Walters 1991; Kamakura and Kang 2007) since it is easier to adopt products by
reducing product uncertainty when products belong to the same brand (Erdem 1998; Erdem and Sun 2002).
Information about a product is diagnostic in a consumer’s purchasing decision for other products of the
same brand because it reinforces the meaning of brand, thus helping to build brand equity (Aaker 1991;
Keller 1993). Accordingly, it is not surprising that the literature has shown spillover effects from one
product to another within the same brand (e.g., Balachander and Ghose 2003; Aaker 1996). In summary,
combining these two effects---the complementary effects from co-purchase and the transferred value within
the same brand, we expect a positive spillover effect of the online product reviews of complementary
products of the same brand. For complementary products across different brands, however, the effect can
be mixed, as elaborated above. Given that the joint promotion of products that belong to same brand can be
more pronounced than the joint promotion of products that belong to different brands, we expect the positive
spillover effect of the mean rating of online product reviews among complementary products to be larger
within the same brand compared to among products that belong to different brands. We thus hypothesize:
H2a: The negative role of the mean review ratings of substitutive products in the purchase of the
focal product is larger among different brands than within the same brand with the focal product.
H2b: The positive role of the mean review ratings of complementary products in the purchase of the
focal product is larger within the same brand than across different brands with the focal product.
3.3 The Moderating Role of Media Channel
As discussed earlier (H1), with respect to the role of online product reviews on product purchases, the
consumer’s purchase of one product will negatively (positively) affect the purchase intention of other
substitutive (complementary) products. Nonetheless, there are reasons to believe that the role of the mean
rating of online product reviews on mobile devices can be larger than that on traditional PCs. Mobile devices
have become popular for online shopping, still, the limited search functionalities and higher search costs
(Ghose et al. 2013) reduce the overall amount of information that consumers can access by liming learning
effectiveness (Maniar et al. 2008). A simplified mobile user interface on small mobile screens makes it
17
more challenging for consumers to locate and collect information, which leads to the higher ranking effects
because of a higher degree of effort required in mobile devices (Ghose et al. 2013). Therefore, when
consumers use mobile devices, the limited search functionalities can make the role of the mean rating of
online product reviews more visible. It has been shown that a small screen increases the effects of ranking
on a click. That is, higher ranking effect implies the higher value to be ranked near the top because a small
screen causes more cognitive effort for information processing (Nunamaker et al. 1987; Ghose et al. 2013).
Consumers are likely to draw more attention to the numerical ratings than other formats of information,
such as textual reviews. Practitioners find that mobile advertising becomes powerful with a lack of clutter
on the page and the proportionally larger advertising units to the screen (Butcher 2010). At the same time,
mobile devices increase consumer accessibility on information with less time and location constraints.
While increased cognitive effort required for information processing is costly on mobile devices, marketers
increasingly consider mobile advertisement as a means to interact with the customer anywhere because of
higher accessibility. Higher accessibility to consumers promotes sales. For example, mobile phone
promotions sent to in-store consumers, increase their unplanned purchases (Hui et al. 2013). Researchers
noted that one critical point of mobile ads, contextual relevance, (i.e., temporal/geographical) matters
(Kenny and Marshall 2000; Bart et al. 2014). Consumers pay attention to messages delivered through
mobile devices because they can have an easy, flexible, and timely access to information with mobility
(Ghose and Han 2011; 2014). For instance, advertising on mobile devices was found to be more effective
to commuters in more crowded subway trains (Andrews et al. 2015), and mobile promotion is an influential
marketing tool to increase response rates (Fong et al. 2015; Friedrich et al. 2009; Shankar et al. 2010).
Similarly, mobile consumers tend to better respond to location-based services and time-sensitive offerings
due the mobility and personal nature of mobile devices with ubiquitous reach (Kenny and Marshall 2000;
Luo et al. 2014; Shankar 2012). Mobile promotion at closer proximity to the cinema or at closer scheduled
movie times increased the odds of movie purchases (Luo et al. 2014).
Mobile users turn to their devices for a purchase may be on the move or multi-tasking, which may make
them feel pressured on time, causing the “narrowing effect” where people channel or tunnel their focus only
18
toward a task and examine less information for decision making under time pressure (Svenson et al. 1985;
Entin et al. 1990). The narrowing effect is considered critical for mobile marketing in behavioral sciences.
Mobile on-the-go mindset narrows the consumers’ focus and makes them rely on what is informed by the
mobile devices as an exclusive information source (Ariely 2016). Marketers try to grab the mobile users’
attention and direct their choices (Ariely 2016). According to Industry surveys (e.g., Insight Express),
mobile display campaigns were up to five times more effective than comparable online display campaigns
in shifting brand awareness, attitudes, and intentions (Butcher 2010). Nielsen (2012) reports that a large
proportion of consumers exposed to mobile advertisements are likely to make a subsequent purchase.
Despite the scant academic research on mobile information (Shankar 2012), the few studies on mobile
advertising formats have shown the positive effect on consumer attitudes and behaviors (Bart et al. 2014;
Barwise and Strong 2002; Drossos et al. 2007; Luo et al. 2014; Tsang et al. 2004). We thus expect that the
effect of online product review ratings can be more salient to consumers because, first, they can search the
ratings anywhere, anytime, in which the mobile on-the-go mindset can push consumers to make a decision
quickly under time pressure, and second, the narrowing focus and limited search of mobile devices can
make consumers rely more on the mean rating. In sum, we expect that in a situation where consumers
consider a product on mobile device, the effect of the online product review ratings to be more pronounced.
Therefore, when a consumer searches on a mobile device versus a traditional PC, the spillover effect of the
mean rating of online product reviews on a consumer’s purchase decision will be stronger. We hypothesize:
H3a: The mean review ratings of substitutive products have a stronger negative role in the focal
product purchase of consumers of mobile devices than of PCs.
H3b: The mean review ratings of complementary products have a stronger positive role in the focal
product purchase of consumers of mobile devices than of PCs.
4. Method
4.1 Clickstream Data
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Our dataset is from a large U.K. online retailer. The clickstream data provide complete website visits
(including product page views, reviews read, and purchases) from individual consumers, giving us the
opportunity to analyze how the online product reviews (from both the focal and related products) affect
customer’s purchase behavior at the individual level. The dataset is divided into two partitions based on
two product categories: Home/Garden and Technology.
Table 1. Clickstream Data Description
Variables Home/Garden Technology
# of consumers 97,852 60,695
# of products 25,662 7,393
# of sessions 229,577 118,051
# of product-session pairs 493,928 202,454
# of sessions with multiple products 100,675 (43.85%) 40,572 (34.37%) avg. session duration (minutes) 57.57 25.37
# of products per session (min) 1 1
# of products per session (max) 213 72
# of products per session (average) 2.151 1.715 # of products per session (STD) 2.362 1.525
Table 1 provides the description of the two datasets. First, the Home/Garden data contain information on
97,852 unique consumers browsing product description and review pages of 25,662 products in 229,577
web sessions in a two-month period. A consumer can visit multiple product pages in a shopping session (an
online session expires after 30 minutes of inactivity), and there are a total of 493,928 product-session
observations. Second, the Technology data include records on 60,695 consumers accessing 7,393 product
pages in 118,051 sessions. There are 202,454 product-session observations. Note that we only observe user
identities for login sessions, and that there are sessions without user credentials due to private browsing or
non-login. To capture consumer purchases across different products (e.g., the market basket), we identified
co-visited products within a single session. For the Home/Garden products, on average, a consumer
accessed 2.15 products in a session and each session lasted about an hour. For Technology products, on
average, consumers spent less time (25 minutes) and checked 1.71 products in a session. Note that a
significant fraction of sessions (43% in Home/Garden data and 34% in the Technology data) involved
multiple products. This empirically motivates this study to investigate the spillover effects of online product
reviews among related (or co-visited) products.
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4.2 Text-Mining Based Identification of Substitutive and Complementary Products
Our paper analyzes how the focal product purchases are affected by the online reviews of related products,
specifically, substitutive and complement products. Thus, a critical task is to identify complementary versus
substitutive product pairs in an automated and scalable manner. Below is the detailed procedure on how
substitutive versus complementary product pairs were identified. In Section 5.5, we validate the procedure
with the economic definition based on price elasticity (Mas-Colell et al. 1995).
We designed the text mining process following the consumer perception based on the classic definition of
substitutive and complementary products from the marketing literature (Walters 1991). In other words,
related products are defined by the individual consumer’s perception of them. Thus, we first created related
product pairs that are co-viewed within a single consumer purchasing session. Most recommender systems
also rely on this approach since co-visited products are likely to be related in a certain way. Some consumers
may visit multiple substitutive products (e.g., mobile phones from different brands) to choose the best one,
while other consumers may visit multiple complementary products that work together for a specific goal
(e.g., a mobile phone and a protection cover).
The next step was to distinguish substitutive product pairs from complementary ones among the co-visited
product pairs. A conventional way used in the retail industry is to use pre-defined product categorizations.
Online retail websites often have a tree-based product category structure. A pair of products is regarded as
substitutes if the two products belong to the same subcategory; and as complements if they belong to the
same root category but to different subcategories. However, this method is prone to misclassification errors.4
In addition, a product can only be categorized into a single subcategory. Moreover, the category-based
classification method has limited expressive power, as the resulting value is binary: two products are either
substitutes or complements. To overcome these issues, we used a text-mining based approach to identify
substitutive and complementary products. Each product page includes detailed text description about the
product from which various product features can be extracted. We classified a pair of co-visited products as
4 The authors communicated with an industry expert team supporting multiple online retail stores to hear anecdotal
cases where the retails or the merchants misclassified products.
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substitutes if the product descriptions have significant overlaps in their product features; versus as
complements otherwise.5 Given its successful applications of many tasks in the management literature
(Singh et al. 2014, Tirunillai and Tellis 2014, Shi et al. 2016), we adopted the Latent Dirichlet Allocation
(LDA) (Blei et al. 2003) to extract product topics from product descriptions. LDA is a statistical method that
discovers abstract “topics” from a large collection of text documents. In our case, we treated each topic as a
product feature. We developed two separate 50-topic models from the 27,714 Home/Garden products and
7,492 Technology products.6 Tables B.1 and B.2 in Online Supplementary Appendix B show partial topic
model results from the Home/Garden and Technology product categories, respectively.
Based on the constructed topic model, each product can be represented as a 50-dimensional product feature
(topic) vector where each element corresponds to the weight with respect to a feature. Then, given a pair of
co-visited products, the cosine similarity between the two topic vectors is calculated. The resulting value
ranges from 0.0 to 0.1, where 0.0 indicates no common feature between the two and 1.0 indicates complete
overlap of features. Then we used two thresholds to identify complementary and substitutive product pairs.
If the similarity was below 0.2, we termed the pair as complements; if the value was above 0.8, substitutes.
To test the sensitivity of such thresholds, we varied the thresholds (0.1/0.9, 0.2/0.8, 0.3/0.7, 0.4/0.6), and
we found that our results are quite robust across different thresholds (please see Section 5.7 for details).
4.3 Variable Construction
Given the identification of substitutive and complementary product pairs, we constructed relevant variables.
Each variable was constructed based on web visits data from each individual user session. The dependent
variable is purchase, indicating if the user purchased the focal product in the session. The key independent
variable was the mean rating, while the volume of online product reviews was treated as a control variable,
and we controlled for the price of the focal product and the other products co-visited within the same
session. There are two dummy variables – mobile and ios -- to control for device type used in the session.
The detailed description of all variables is given in Table 2.
5 We can exclude the cases where two products are totally unrelated, since these pairs are co-visited by the consumer. 6 We varied the number of topics to find the optimal value. The empirical results are robust with alternative choices.
22
Table 2. The Primary Variables in the Regressions
Variables Descriptions
purchase whether a consumer purchase a product in an online session (unit: 0, 1)
rate_focal mean rating of the focal product (range: 1~5) vol_focal volume of the focal product reviews (range: 0+)
vol_subs volume of the reviews of the substitutes (range: 0+)
vol_comp volume of the reviews of the complements (range: 0+)
rate_subs mean rating of the substitutes (range: 1~5) rate_comp mean rating of the complements (range: 1~5)
rate_subs_samebrand mean rating of the substitutes produced by the same brand (range: 1~5)
rate_comp_diffbrand mean rating of the complements produced by the same brand (range: 1~5)
rate_subs_diffbrand mean rating of the substitutes produced by different brands (range: 1~5)
rate_comp_diffbrand mean rating of the complements produced by different brands (range: 1~5) price_focal price of the focal product (unit: £) price_comp mean price of the complements of the focal product (unit: £) price_subs mean price of the substitutes of the focal product (unit: £) mobile whether a mobile device is used in an online session (unit: 0, 1)
rate_subs_mobile rate_subs * mobile rate_comp_mobile rate_comp * mobile
ios whether an iOS device is used in an online session (unit: 0, 1)
rate_subs_ios rate_subs * platform_ios
rate_comp_ios rate_comp * platform_ios
Table 3. Summary Statistics of Home/Garden Data
Variables Min Max Avg Std
purchase 0 1 0.042 0.199
rate_focal 1.0 5.0 4.162 0.667
vol_focal 0 4,779 112.0 277.1
rate_subs 1.0 5.0 4.133 0.600
rate_comp 1.0 5.0 4.128 0.562 rate_subs_samebrand 1.0 5.0 4.201 0.635
rate_comp_samebrand 1.0 5.0 4.089 0.616
rate_subs_diffbrand 1.0 5.0 4.117 0.609
rate_comp_diffbrand 1.0 5.0 4.132 0.565 price_focal 0.0 869.99 34.01 65.69
price_comp 0.0 699.99 32.94 59.30
price_subs 0.0 869.99 42.82 72.53
mobile 0 1 0.350 0.476
ios 0 1 0.260 0.438
We provide summary statistics in two product categories. Tables 3 and 4 are for Home/Garden and
Technology, respectively. Among the total product-session observations (493,928 for Home/Garden and
202,454 for Technology), we have some observations with missing variables. For example, in case of
products without any online product reviews, we counted 0 for vol_focal but treated them as missing data
for variables related to the average rating (e.g., rate_focal, rate_subs, rate_comp, etc.).
23
Table 4. Summary Statistics of Technology Data
Variables Min Max Avg Std
purchase 0 1 0.037 0.190
rate_focal 1.0 5.0 4.349 0.590 vol_focal 0 866 65.33 116.9
rate_subs 1.0 5.0 4.321 0.524
rate_comp 1.0 5.0 4.304 0.578
rate_subs_samebrand 1.0 5.0 4.371 0.554 rate_comp_samebrand 1.0 5.0 4.296 0.589
rate_subs_diffbrand 1.0 5.0 4.272 0.539
rate_comp_diffbrand 1.0 5.0 4.367 0.532
price_focal 0.0 1049.0 63.98 112.3
price_comp 0.0 1049.0 52.59 89.74 price_subs 0.0 1049.0 67.35 100.6
mobile 0 1 0.287 0.452
ios 0 1 0.204 0.403
For the purchase dummy variable, the purchase probability was 4.1% and 3.7% for Home/Garden and
Technology, respectively. Data show that all ratings variables have high averages (above 4.0 out of 5.0):
the average rating of Home/Garden products is 4.16 and that of Technology products is 4.34. Skewed
distributions are observed from the review volume (vol_focal) as well. A Home/Garden product has 112
total reviews, on average, with a standard deviation of 277. The most popular Home/Garden product has a
total of 4,779 reviews, while there are 14,243 products (55.5%) with no reviews. A product in the
Technology category received 65.3 reviews, on average, with a 116.9 standard deviation. The most popular
Technology product has 866 reviews, while 3,974 (53.8%) products do not have any reviews.
In terms of price data (e.g., price_focal, price_subs, price_comp), we observed that, on average,
Technology products (£63.98) were more expensive than Home/Garden products (£34.01). There were
observations with £0 price tag because of various marketing events. The most expensive items cost £869.99
in the Home/Garden category and £1,049 for the Technology category.
We used a dummy variable for mobile observations (mobile). Please note that mobile observations included
user activities from tablet devices (e.g., Apple iPad). We observed more mobile device data for
Home/Garden (35%) than for Technology (28.7%) products. For mobile sessions, we used another dummy
ios for iOS devices. There are more iOS observations for Home/Garden (26%) than for Technology (20%).
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5. Data Analysis and Empirical Results
5.1 Spillover Effects of Online Product Reviews
To examine the spillover effect of online product reviews on the focal product, we considered a pair-wise
relationship of the focal product with other products in a consumer’s co-visited set. The four types of other
products are: (i) substitutive products of the same brand, (ii) substitutive products by a different brand, (iii)
complementary products of the same brand, and (iv) complementary product by a different brand.
5.2 Measurement of Substitutive and Complementary Products
The first method is the text-mining approach (Section 4.2). We define substitute or complementary products
using pair-wise product similarity measures based on topic modeling. For example, for product pairs that a
consumer co-visits during a visit session, the product with high topic similarity is defined as a substitutive
product, while the product with low topic similarity is defined as a complementary product. We note that a
substitutive or a complement product in our context is defined using the clickstream data of an individual
consumer i. If a product pair is co-visited by consumer i, and the topic similarity is greater than 0.8, then
the two products are classified as substitutes; if a product pair is co-visited by consumer i, and the topic
similarity is less than 0.2, the two products are classified as complements.7 To confirm the validity of our
similarity measures based on topic modeling, we also examined the definition of substitutes/complements
using cross-price elasticity in the economics literature (Mas-Colell et al. 1995): an increase in the price of
substitutes should increase the purchase of a focal product; whereas an increase in the price of complements
should reduce the purchase of a focal product. The estimation results on cross-price elasticity are shown in
Section 5.5. As a robustness check, we adopted another definition of substitutive/complementary products
that is commonly used in the industry: if two products are viewed in the same shopping session and they
belong to the same product subcategory, then they are classified as substitutes; if two products are viewed
in the same shopping session but they belong to different product subcategories, then they are complements.
The estimation results are presented in Section 5.7.
7 We also varied the topic similarity threshold of the definitions in Section 5.7, and the results are robust.
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5.3 Econometric Model and Identification Strategy
We summarize our empirical models as follows. First, we focus on the overall effects of the mean rating of
online product reviews of substitutive and complementary products in Equation 1 (H1a & H1b). Second,
we examine the moderating role of brand in Equation 2 (H2a & H2b). Third, we confirm the validity of our
text-mining based measures of substitutes/complements and to address price endogeneity in Equation 3.
Finally, we investigate the moderating role of media channel in regression Equations 4 and 5 (H3a & H3b).
Our benchmark econometric specification is a linear regression model as follows:8
𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛽0 + 𝛽1𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽2𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡
+𝛽3𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 + 𝛽4𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡 , [1]
where i represents a consumer, j represents a product page viewed by a consumer in an online session, and
t represents an online shopping session identified by the data. An online session expires after 30 minutes of
inactivity. The binary dependent variable, 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡, indicates whether consumer i purchases product j
in online session t (purchase: 1, not purchase: 0), 𝑎𝑖 is the fixed effect of a consumer capturing unobserved
individual heterogeneity, 𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 is the mean rating of the focal product that consumer i views in
online session t, which measures the direct impact of the focal product’s word of mouth, and 𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡
is the volume of the focal product reviews. We are particularly interested in the coefficients on
𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 and 𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡. The independent variables 𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 and 𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 represent
the mean rating of the substitutes and complements of product j, respectively. In the estimation, we used
the robust t-statistics to deal with the concerns about the failure to meet standard regression assumptions,
such as unknown heteroskedasticity and possible cluster correlations in error terms. In our context, same
consumers are involved in different shopping sessions. Failure to control for within-cluster error
correlations can greatly understate true standard errors (Cameron et al. 2008).
8 A typical concern in estimating demand side models is price endogeneity. We address this concern in Section 5.5
using the Instrumental Variable (IV) approach.
26
Our fixed-effects estimation results for Equation 1 are presented in Columns 1 (Home/Garden category)
and 2 (Technology category) of Table 5. The results are consistent: the coefficient on rate_subs is negative
and significant, and the coefficient on rate_comp is positive and significant. These findings suggest that the
mean rating of substitutes has a negative role in the purchase probability of the focal product, while the
mean rating of complements has a positive role in the purchase probability of the focal product. In sum,
these findings support hypotheses H1a and H1b, respectively.
Besides statistical significance, we also looked at the practical significance of the coefficients. For instance,
in Column 1 of Table 5, the coefficient on rate_comp is 0.00573, which means that if rate_comp increases
by 1, the focal product purchase probability will increase by 0.573%. At the first glance, the magnitude
seems modest. However, the mean of our dependent variable purchase is about 4.2% in Home/Garden data,
which means the average purchase probability is only 4.2%. Therefore, if rate_comp increases by 1, on
average the purchase probability of a focal product will increase from 4.2% to 4.773% (4.2% + 0.573%),
which represents a 14% (0.573/4.2) increase in purchase probability. The magnitude is substantial given
the volume of product purchases. As expected, we found that the coefficient on rate_focal is positive and
significant, which reflects the role of online word of mouth. The volume of reviews, vol_focal, also has a
positive role in purchase probability. A possible reason is that a large volume of reviews may reduce the
uncertainty inherent in a product purchase, consistent with the literature. In Columns 3 and 4 in Table 5,
we also controlled for the volume of the reviews of the substitutes/complements (vol_subs and vol_comp)
to examine the spillover role of review rating. Results were robust. In Online Supplementary Appendix A
(Table A1), we also conducted a logit regression and a fixed effect logit regression and found that the results
were robust.
5.4 The Moderating Role of Brand
In the next estimation, we investigate the role of the same brand versus different brands (Equation 2):
𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛽0 + 𝛽1𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽2𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡
+𝛽3𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠_𝑠𝑎𝑚𝑒𝑏𝑟𝑎𝑛𝑑𝑖,𝑗,𝑡 + 𝛽4𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝_𝑠𝑎𝑚𝑒𝑏𝑟𝑎𝑛𝑑𝑖,𝑗,𝑡
+𝛽5𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠_𝑑𝑖𝑓𝑓𝑏𝑟𝑎𝑛𝑑𝑖,𝑗,𝑡 + 𝛽6𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝_𝑑𝑖𝑓𝑓𝑏𝑟𝑎𝑛𝑑𝑖,𝑗,𝑡 +
𝛽7𝑝𝑟𝑖𝑐𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽8𝑝𝑟𝑖𝑐𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 + 𝛽9𝑝𝑟𝑖𝑐𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡, [2]
27
Table 5. The Spillover Effects of Online Reviews
(1) (2) (3) (4) (5) (6)
VARIABLES FE Home FE Tech FE Home FE Tech Brand Effects Home Brand Effects Tech
rate_focal 0.00256*** 0.00284*** 0.00262*** 0.00288*** 0.00309*** 0.00267**
[5.172] [3.397] [5.272] [3.403] [3.273] [1.963]
vol_focal 0.0000102*** 1.16e-05** 6.30e-06*** 8.50e-06 3.73e-06** 4.63e-06
[7.051] [2.510] [2.935] [1.132] [2.384] [0.882]
rate_subs -0.00283*** -0.00327*** -0.00279*** -0.00321***
[-8.351] [-6.645] [-7.805] [-6.045]
rate_comp 0.00573*** 0.00827*** 0.00642*** 0.00847***
[6.524] [4.725] [7.209] [4.682]
vol_subs -1.97e-06 -3.09e-06
[-0.517] [-0.269]
vol_comp -2.36e-05*** -1.18e-05
[-3.714] [-0.668]
rate_subs_samebrand -0.000272 -0.00106***
[-0.710] [-2.714]
rate_comp_samebrand 0.00234** 0.00616***
[2.351] [3.903]
rate_subs_diffbrand -0.00396*** -0.00290***
[-7.878] [-3.842]
rate_comp_diffbrand 0.00462*** 0.00399**
[3.737] [2.277]
price_focal -1.72e-05 5.80e-06
[-0.948] [0.624]
price_subs 0.000132*** 1.67e-05
[3.152] [0.791]
price_comp -7.81e-05*** -2.78e-05**
[-3.224] [-2.103]
Observations 380,798 171,581 380,798 171,581 277,670 137,563
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
28
where rate_subs_samebrand and rate_comp_samebrand are the mean rating of substitutes/complements
produced by the same brand, rate_subs_diffbrand and rate_comp_diffbrand are the mean rating of the
substitutes/complements produced by different brands, price_focal is the price of the focal product, and
price_subs/price_comp is the mean price of the substitutes/complements of the focal product.
In Column 5 of Table 5, we present the estimation results for the Home/Garden category. Similarly, we
found that both the focal product mean rating and the focal product review volume have positive roles in
the purchase of the focal product, consistent with the literature. Coefficients rate_subs_diffbrand and
rate_subs_samebrand are negative and significant, while coefficients rate_comp_diffbrand and
rate_comp_samebrand are positive and significant. Moreover, we conducted statistical tests on whether the
coefficient on rate_subs_diffbrand equals the coefficient rate_subs_samebrand, and whether the
coefficient on rate_comp_diffbrand equals the coefficient on rate_comp_samebrand. The F statistics are
35.91 (p value = 0.00) and 2.98 (p value = 0.14), respectively. The test results show that (i)
rate_subs_diffbrand has a significantly larger negative impact than rate_subs_samebrand. The implication
is that the negative spillover effect of the online reviews of substitutive products from different brands is
significantly greater than that of the same brand, supporting hypothesis H2a; (ii) rate_comp_diffbrand is
not statistically different from rate_comp_samebrand, and the implication is that the positive spillover
effect of the online reviews of complementary products from different brands is not statistically different
from the effect of the online reviews of complementary products from the same brand, not supporting H2b.
A possible explanation is that we expect two positive effects of review ratings of a complementary product
on the purchase of another focal product for products within the same brand---the complementary effects
from co-purchase and the transferred value within the same brand. In the Home/Garden category, the effect
of the transferred value in a same brand might be weak. Summarizing our empirical results, we show that
the magnitude of the spillover effects of online product reviews depends on whether the product is a
substitute or a complement, and whether the product belongs to the same brand or to a different brand.
Column 6 of Table 5 shows the results for the Technology category, which are consistent with the
Home/Garden category. We found that the negative spillover effect of online reviews of substitute products
29
across different brands to be significantly greater than that of the same brand (p = 0.02), while the positive
spillover effect of the online reviews of complementary products among different brands is not statistically
different from the effect of online reviews of complementary products within the same brand (p = 0.30).
5.5 Price Endogeneity
In our regression model, prices are endogenous because producers change their price in response to demand,
and consumers change their demand in response to prices. To confirm the validity of our text mining based
measures of substitutes/complements and to address price endogeneity, we estimated Equation 3:
𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛽0 + 𝛽1𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽2𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽3𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 +
𝛽4𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝛽5𝑝𝑟𝑖𝑐𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽6𝑝𝑟𝑖𝑐𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 + 𝛽7𝑝𝑟𝑖𝑐𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡 , [3]
In Columns 1 and 2 of Table 6, we find that in both the Home/Garden and Technology categories, the
coefficient on price_subs is positive, while the coefficient on price_comp is negative. These findings are
consistent with the definitions of substitutes and complements using cross-price elasticity in economics
(Mas-Colell et al. 1995). These results confirm the validity of our similarity measures: an increase in the
price of substitutes affects the purchase of a focal product positively; and an increase in the price of
complements affects the purchase of a focal product negatively. These findings are merely “suggestive”
because the prices of substitutes and complements are not exogenously determined in our context. Some
unobserved factors can drive both the demand of the focal product and the prices of its substitutes or
complements, and therefore, the correlation shown in columns 1 and 2 in Table 6 may not capture the true
causal effect of the prices of substitutes or complements on the focal product purchases. More specifically,
in our context, we want to estimate the response of a focal consumer’s purchase decision to exogenous
changes in price_focal, price_comp, and price_subs. However, prices are not exogenously determined since
they are determined in part by market demand. Following Berry et al. (1995), Granados et al. (2012), and
Chung et al. (2013), we addressed this potential endogeneity problem by performing a two-stage least
squares (2SLS) regression with instrumental variables (IVs) for price_focal, price_comp, and price_subs.
30
The intuition is that we look for variables that shift cost or margins that are not correlated with product
demand shock. More specifically, Chung et al. (2013) used the total number of products from a firm as an
IV for price. Similarly, in our context, we adopted the total number of products from a brand as an IV for
price_focal. As for price_comp and price_subs, we first identified the complements/substitutes of a focal
product i. We denote C(i) as the set of focal product i’s complements, and S(i) as the set of focal product
i’s substitutes. For each substitute good j ∈ S(i), we identified j’s brand and calculated the total number of
products from this brand. Then, we used the average number of products within a brand as an IV for
price_subs. Similarly, we defined an IV (price_comp) for the complementary products.
Table 6. Addressing Price Endogeneity Using Instrumental Variables
(1) (2) (3) (4)
VARIABLES FE Home FE Tech FE + IV Home FE + IV Tech
rate_focal 0.00316*** 0.00404*** 0.002662*** 0.00336**
[3.337] [2.904] [3.125] [2.336]
vol_focal 3.87e-06** 4.87e-06 1.42e-06** 9.04e-06
[2.473] [0.925] [2.267] [0.556]
rate_subs -0.00423*** -0.00404*** -0.00615*** -0.00420***
[-8.600] [-5.728] [-9.238] [-4.425]
rate_comp 0.00618*** 0.0129*** 0.00859*** 0.0218**
[4.581] [4.606] [5.274] [2.436]
price_focal -1.35e-05 7.52e-06 -3.82e-05** -9.34e-05
[-0.740] [0.808] [-2.215] [0.125]
price_subs 0.000132*** 2.40e-06 0.000461*** 1.23e-05**
[3.154] [0.115] [2.854] [2.125]
price_comp -6.92e-05*** -2.17e-05 -9.63e-05** -8.25e-05**
[-2.764] [-1.614] [-2.056] [-2.208]
Observations 277,670 137,563 277,670 137,563
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
For the total number of products from a brand to be a valid IV for prices in our regression model, it has to
be (i) correlated with price and (ii) uncorrelated with the error term so that the total number of products
from a firm influences individual i’s purchase decision on product j only through price. Chung et al. (2013)
pointed out that a firm’s product count is a standard instrument in the empirical industrial organization
literature and satisfies both conditions (i) and (ii). It is also well known that if the correlation specified in
condition (i) is weak, IV methods can be ill behaved and can cause severe inconsistency (Stock et al. 2002).
31
To address this concern, we tested whether our IVs are weak instruments in the Home/Garden category
(results are robust for the Technology category), by calculating the first-stage F statistics based on the
method proposed by Stock et al. (2002) and modified by Angrist and Pischke (2008). Please note that the
conventional first-stage F statistic is not appropriate in our case because we have multiple endogenous price
variables in our regression, so we adopted the multivariate first-stage F statistics (Angrist and Pischke 2008).
A high multivariate F statistic (22.84) suggests that our IVs are not weak. Additionally, in our context, the
exclusion restriction is plausible: our IV, the total number of products from a brand, should affect our
dependent variable, individual i’s purchase decision on product j only indirectly, through the correlation
with prices. Following Angrist and Krueger (2001) and Acemoglu et al. (2001), we conducted a test on the
exclusion restriction by including the number of products from a brand as an independent variable in our
model, and the coefficient was not statistically significant. This result is encouraging and shows no evidence
for a direct effect of a firm’s product count on the purchase decision of the focal product. The intuition is
based on the assumption that if the only impact of a firm’s product count on the purchase decision is through
price, then the firm’s product count should be insignificant in a regression equation that also includes price.
If we do not have an endogeneity problem on prices, both the fixed effect estimator and the fixed effect IV
estimator would be consistent, but the fixed effect IV estimator may be inefficient. In other words, the fixed
effect estimation is preferred to the fixed effect IV estimation if the model does not suffer from endogeneity
(Wooldridge 2002). Therefore, we also conducted the Hausman test (1978) to check for endogeneity, and
we could reject the null hypothesis that the fixed effect estimators of prices presented in Columns 1 and 2
in Table 6 are consistent estimators. Columns 3 and 4 in Table 6 show the estimation results of 2SLS with
IVs. Once again, the coefficient on price_subs is positive, and the coefficient on price_comp is negative.
These IV estimation results further confirm the validity of our measures of complementary and substitutive
products: a negative cross-price elasticity of demand denotes that two products are complements, while a
positive cross-price elasticity of demand denotes two substitute products. Prior econometrics literature
showed that even if IVs do not perfectly satisfy the exclusion restriction, one can still draw valid statistical
32
inferences using the Anderson and Rubin (AR) test and the fractionally resampled Anderson Rubin (FAR)
test (Riquelme et al. 2013). As a final robustness check, we also conducted these two tests in our IV
regression, and we found that the p-values of the coefficients on price_subs and price_comp are less than
0.01, which further confirms our results in Table 6.
5.6 The Moderating Effect of Channel Media
Next, we investigate how the spillover effect is moderated by other factors, such as multi-channel media
(whether the product review is viewed on a mobile device or PC). To examine these issues, we added the
interaction terms into regression Equation 1. Accordingly, we obtained the following specifications:
𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛽0 + 𝛽1𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽2𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑖,𝑗,𝑡 + 𝛽3𝑚𝑜𝑏𝑖𝑙𝑒𝑖,𝑗,𝑡
+𝛽4𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 + 𝛽5𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝛽6𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠_𝑚𝑜𝑏𝑖𝑙𝑒𝑖,𝑗,𝑡 + 𝛽7𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝_𝑚𝑜𝑏𝑖𝑙𝑒𝑖,𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡, [4]
where mobile is whether or not a mobile device is used in an online session, rate_subs_mobile and
rate_comp_mobile are interaction terms rate_subs * mobile and rate_comp * mobile. The coefficients of
the interactions terms in Equation 4 specify how the spillover effect is moderated by whether the product
review is viewed on a mobile device or a PC. The estimation results are shown in Table 7.
Table 7. The Moderating Role of Mobile Devices
(1) (2)
VARIABLES
Mobile
(Home)
Mobile
(Tech)
rate_focal 0.00254*** 0.00278***
[5.113] [3.302]
vol_focal 1.02e-05*** 1.15e-05**
[7.023] [2.496]
rate_subs -0.00183*** -0.00260***
[-5.620] [-5.490]
rate_comp 0.00421*** 0.00722***
[4.792] [4.131]
rate_subs_mobile -0.00321*** -0.00290*
[-3.679] [-1.921]
rate_comps_mobile 0.00473** 0.00393
[2.167] [0.818]
Observations 380,798 171,581
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
33
The coefficients of rate_subs_mobile are negative, and the coefficients of rate_comp_mobile are positive,
supporting hypotheses H3a and H3b, respectively. The estimation results show that for consumers who use
mobile devices, the spillover effect of online product reviews is much stronger: the negative impact of the
online reviews of substitutes is 2-3 times larger; the positive impact of the online reviews of complements
is about two times larger. The moderating role of channel media is more prominent in the Home/Garden
than in the Technology category. These results suggest that mobile devices make the spillover effects of
online product reviews on purchases more pronounced to consumers.
5.7 Robustness Checks
In the previous analysis, the threshold of the topic similarity of substitutes/complements was chosen to be
above 0.8 or below 0.2. We varied these thresholds and checked the robustness of our results. In Tables 8
and 9, we show that the spillover effects are robust when the thresholds of the topic similarity of
substitutes/complements are above 0.9 / below 0.1, above 0.7 / below 0.3, and above 0.6 / below 0.4. The
results are robust, irrespective of the exact thresholds chosen.
Table 8. The Spillover Effects of Online Product Reviews: Robustness Check I
(1) (2) (3) (4) (5) (6)
VARIABLES
0.9/0.1
(Home)
0.9/0.1
(Tech)
0.7/0.3
(Home)
0.7/0.3
(Tech)
0.6/0.4
(Home)
0.6/0.4
(Tech)
rate_focal 0.00266*** 0.00350*** 0.00257*** 0.00285*** 0.00259*** 0.00313***
[5.412] [2.592] [5.129] [3.350] [5.172] [3.621]
vol_focal 1.01e-05*** 4.98e-06 1.02e-05*** 1.15e-05** 1.02e-05*** 1.15e-05**
[7.004] [0.946] [7.066] [2.500] [7.061] [2.489]
rate_subs -0.00252*** -0.00404*** -0.00309*** -0.00318*** -0.00313*** -0.00323***
[-8.187] [-6.075] [-8.615] [-6.179] [-8.384] [-6.048]
rate_comp 0.00572*** 0.0104*** 0.00618*** 0.00766*** 0.00644*** 0.00881***
[6.824] [4.439] [6.748] [4.094] [6.992] [4.622]
Observations 380,798 171,581 380,798 171,581 380,798 171,581
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
We also conducted statistical tests on whether the coefficient on rate_subs_diffbrand equals the coefficient
on rate_subs_samebrand, and whether the coefficient on rate_comp_diffbrand equals the coefficient on
rate_comp_samebrand when we vary the thresholds. The results are consistent with our previous analysis:
34
the negative spillover effect of online reviews of substitutive products from different brands is significantly
greater than that from the same brand (p value < 0.05), while the positive spillover effect of online reviews
of complementary products from different brands is not statistically different from the positive spillover
effect of online reviews of complementary products from the same brand (p value > 0.05).
Table 9. The Spillover Effects of Online Reviews: Robustness Check II
(1) (2) (3) (4) (5) (6)
VARIABLES 0.9/0.1
(Home)
0.9/0.1
(Tech)
0.7/0.3
(Home)
0.7/0.3
(Tech)
0.6/0.4
(Home)
0.6/0.4
(Tech)
rate_focal 0.00331*** 0.00238* 0.00310*** 0.00266* 0.00309*** 0.00291** [3.515] [1.783] [3.257] [1.924] [3.254] [2.109]
vol_focal 3.67e-06** 4.63e-06 3.76e-06** 4.62e-06 3.73e-06** 4.54e-06 [2.345] [0.883] [2.404] [0.881] [2.388] [0.866]
rate_subs_samebrand -0.000244 -0.00108*** -0.000286 -0.000966** -0.000309 -0.000949** [-0.630] [-2.747] [-0.747] [-2.421] [-0.807] [-2.372]
rate_comp_samebrand 0.00219** 0.00658*** 0.00203** 0.00604*** 0.00198** 0.00615*** [2.249] [4.026] [2.095] [3.996] [2.085] [4.158]
rate_subs_diffbrand -0.00343*** -0.00347*** -0.00413*** -0.00285*** -0.00425*** -0.00240*** [-7.562] [-5.030] [-7.836] [-3.605] [-7.768] [-3.040]
rate_comp_diffbrand 0.00480*** 0.00336** 0.00486*** 0.00310* 0.00493*** 0.00399** [4.066] [2.128] [3.780] [1.674] [3.809] [2.126]
price_focal -9.64e-06 7.13e-06 -1.72e-05 2.66e-07 -1.17e-05 2.66e-07 [-0.561] [0.827] [-0.909] [0.0265] [-0.589] [0.0265]
price_subs 0.000143*** 1.19e-05 0.000132*** 1.15e-05 0.000142*** 1.15e-05 [3.517] [0.585] [3.033] [0.536] [3.232] [0.536]
price_comp -6.40e-05*** -2.27e-05* -7.73e-05*** -3.80e-05*** -6.66e-05** -3.80e-05*** [-2.705] [-1.853] [-2.995] [-2.714] [-2.491] [-2.714]
Observations 277,670 137,563 277,670 137,563 277,670 137,563
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
A common practice in industry is to use subcategories to identify substitute and complementary products.
We adopted this method as an additional robustness check. Under this method, two products are considered
substitutes if they are viewed within the same consumer session and belong to the same product subcategory;
two products are regarded as complementary if they are viewed within the same consumer session but
belong to different product subcategories. Table 10 presents the empirical results based on this subcategory-
based definition. The results are robust: the coefficient on rate_subs is negative and significant, while the
coefficient on rate_comp is positive and significant. This finding confirms our previous results: the average
35
review ratings of substitutive products have a negative role in the purchase of the focal product, while the
average review ratings of complementary products have a positive role in the purchase of the focal product.
Table 10. The Spillover Effects of Online Product Reviews Using Subcategory-based
Substitute/Complementary Definition: Robustness Check III
(1) (2) (3) (4)
VARIABLES FE Home FE Tech FE Home FE Tech
rate_focal 0.00300*** 0.00362*** 0.00306*** 0.00369***
[5.814] [4.116] [5.919] [4.162]
vol_focal 1.02e-05*** 1.13e-05** 7.76e-06*** 5.95e-06
[7.053] [2.445] [3.496] [0.713]
rate_subs -0.00566*** -0.00289*** -0.00559*** -0.00280***
[-16.75] [-6.208] [-15.70] [-5.563]
rate_comp 0.0103*** 0.00903*** 0.0106*** 0.00929***
[9.392] [4.825] [9.558] [4.885]
vol_subs -2.07e-06 -5.73e-06
[-0.623] [-0.475]
vol_comp -8.75e-06 -1.52e-05
[-1.583] [-0.890]
Observations 380,798 171,581 380,798 171,581
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
In Online Supplementary Appendix A, we conducted additional robustness checks by controlling for: (1)
individual consumer learning (Table A2), (2) whether a complementary or a substitutive product was
purchased (Table A3), (3) by controlling for the volume and variance of the review ratings of substitutive
and complementary products (Table A4), and (4) by using similarity-weighted average rating of substitutes
and complements (Table A5). Besides, we ran a baseline regression without distinguishing complements
and substitutes (rate_nonfocal is the mean rating of all co-visited products in an online session), and we
found that the coefficient on rate_nonfocal is not statistically significant (Table A6). This finding suggests
that if we do not distinguish between complements and substitutes, then the aggregate spillover effects will
cancel out. Besides, we examined the moderating role of iPhones (Table A7), and the results were robust.
Finally, to account for the dynamics between online review ratings and product sales, we conducted a vector
autoregression (VAR) analysis. We found a significant spillover effect from the mean ratings of substitutive
products (Table A8).
36
6. Contributions and Implications for Theory and Practice
6.1 Key Findings
First, we found that the mean ratings of the online reviews of substitutive products have a negative role in
the purchase probability of a focal product, while the mean ratings of complementary products have a
positive role in the purchase probability of a product. Second, the spillover effect of online product reviews
is moderated by brand and channel media; specifically, the negative spillover effect of the mean ratings of
substitutive products across different brands is significantly higher than those within the same brand;
besides, the spillover effect of the mean ratings of online product reviews is significantly higher for
consumers who conduct their purchasing session using mobile devices versus traditional PCs. Table 11
shows the practical significance of the spillover effects estimated in our main regressions. It is worth noting
that the average purchase probability in Home/Garden or Technology data is only about 4.2% and 3.7%
(and thus the increase/decrease in purchase probability is based on dividing by 4.2 and 3.7), respectively.
In the interpretation of the practical effects in Table 11, we focus on the change (decrease or increase) in
the average purchase probability of a focal product shaped by the online reviews of related products
(complementary and substitutive from the same or different brands). In sum, a star-rating increase in
substitute products leads to a 7% - 9% decrease in the purchase probability of a focal product; a star-rating
increase in complements leads to a 14% - 22% increase in the purchase probability of a focal product.
Table 11. Practical Significance of the Spillover Effects
Regression
(Table 5) Independent Variable Category Coefficient
Change in Purchase
Probability
Column 1 rating of substitutes Home -0.00283 7% decrease
Column 2 rating of substitutes Tech -0.00327 9 % decrease
Column 1 rating of complements Home 0.00573 14% increase
Column 2 rating of complements Tech 0.00827 22% increase
Column 5 rating of substitutes of same brand Home -0.000272 0.6% decrease
Column 6 rating of substitutes of same brand Tech -0.00106 4% decrease
Column 5 rating of complements of same brand Home 0.00234 6% increase
Column 6 rating of complements of same brand Tech 0.00616 17% increase
Column 5 rating of substitutes of different brand Home -0.00396 9% decrease
Column 6 rating of substitutes of different brand Tech -0.00290 8% decrease
Column 5 rating of complements of different brand Home 0.00462 11% increase
Column 6 rating of complements of different brand Tech 0.00399 11% increase
Note: The figures state the magnitude of the spillover effects when an independent variable (rating) increases by 1.
37
6.2 Contributions and Implications for Theory
First, we contribute to the literature on online product reviews and user-generated content by examining the
spillover effect of online product reviews among complementary and substitutive products on a consumer’s
purchasing decision. Studies have shown the mean rating to be a strong predictor of product sales for a
focal product. To the best of our knowledge, however, this is the first study to show the spillover effect of
the mean rating of online product reviews of other related (i.e., substitutive and complementary) products.
We find a negative spillover effect of online product reviews among substitutive products and a positive
effect of the online reviews among complementary products, while the extent of the spillover depends on
the brand of the products and the channel media used. We measured the consumer’s product consideration
set co-visited in a session, which consists of related products and the corresponding online product reviews
of these products, to examine the spillover effect of the mean rating of online product reviews on a
consumer’s purchase decision. The literature in online product reviews largely focuses on the prediction of
aggregate sales of a product from the review ratings. We contribute to this stream of research by showing
the effect of the reviews on an individual consumer’s purchasing decision in a co-visited consideration set,
and we show that the spillover role of the reviews depends on the relationship of these co-visited products.
Second, we show the spillover effect of the online product reviews within the same and different brands.
Recent marketing literature has emphasized the importance of brand on the consumer’s purchase decision
(e.g., Lovett et al. 2013; Luo et al. 2016; Tirunillai and Tellis 2015). However, efforts to enhance the
understanding of the role of brand on an individual consumer’s purchase decision, together with the
spillover effect of online product reviews, have been limited. Our results show that the mean rating of the
online reviews of the products in a consumer’s market basket has a spillover effect on one another, and the
effects depend on the association among products (substitutive or complementary) and among brand names
(same or different). We extend the literature on brand spillover by showing the overall negative effect of
the rating spillover for substitutive products and the larger negative effect among different brands versus
the same brands. We also find the positive role of the rating spillover for complementary products but the
38
difference among same versus different brands is not supported. We attribute the different results on brands
to the functional similarity across products in the consumers’ minds. The value transfer within the same
brands in the spillover effects of the online product reviews may be very high in substitutive products
because of the perceived similar functions in a same brand, which may offset the competitive substitution
effects and derive the significant difference between same and different brands of the substitute products.
The literature on branding mainly focuses on the effect of marketing promotion or the publicity on demand
changes of the limited set of chosen products. We contribute to this research stream by examining the effect
of consumer-generated information and measuring the effect on an individual decision among the co-visited
products. Our results show that the spillover effect of the online product reviews across brands may depend
on the association of the products, and how the reviews play a role as information to an individual consumer
to evaluate these related products of same or different brands. Third, we contribute to the emerging literature
on mobile devices by examining the role of spillover across channels. We find that the spillover effect is
more salient on mobile devices versus PCs because the on-the-go mindset can push consumers to make
decisions under pressure while the limited search can make consumers rely more on quantitative ratings.
6.3 Implications and Prescriptions for Practice
Marketing practitioners have considered WOM or user-generated content as influential marketing tools
because of the influence on consumers’ perception. We have shown that the effect of the mean rating of
online product reviews, as a representative form of a user-generated content, can spillover across other
products in a consumer’s market basket, and that this spillover effect works differently across products of
the same or different brands and across channel media (mobile or PC). The perception of brands and effect
of WOM across brands have been important for marketers to leverage the marketing mix. Our paper
contributes to marketing practitioners by helping understand these brand effects on the consumer’s decision.
We also shed light on the moderating role of media channel. Because of the increased usage of mobile
devices, how influential role the user-generated content has should attract more attention to the marketers.
We believe that our results show the consumer’s salient focus on WOM with mobile channel media.
39
Our study has managerial implications for marketing practitioners to stand out from the competition by
leveraging online product reviews, shedding light on how to take account the spillover effects and design a
better online review system. For instance, online reviews of a focal product may enhance or hurt the sales
of other substitutive or complementary products, and brands and channel media can moderate this spillover
effect. Accordingly, retailers may want to feature the online review ratings of substitutive or complementary
products differently on their websites. In most e-commerce websites, consumers can only write reviews on
a single product. However, given our finding of spillover effects of related products, retailers can re-design
their review systems to enable consumers to write comparative reviews, where multiple substitutive
products purchased together can be evaluated together. For instances where consumers purchased multiple
complementary products together, the market basket (e.g., multiple products purchased together) can
inform other consumers about the synergy between those co-purchased products. Finally, retail websites
may also dynamically generate a comparative review page that overviews the reviews from other products
in the consumer’s consideration set, such as those products suggested by recommendation systems.
6.4 Methodological Contributions
It has long been suggested to understand the effects of substitutive and complementary products in the
economics and marketing literature (e.g., Shocker et al. 2004; Seetharaman et al. 2005), however, the
empirical measurement of these types of products has not been well captured. Applying the traditional
definition discussed in the literature to describe substitutive and complementary products focusing on the
functional similarity of the products in consumer usage and consumer’s simultaneous needs of multiple
products. To define related (complementary and substitutive) products, we used product similarity in a
given consumers’ market basket (co-viewed items for shopping) using text-mining methodology. We
developed and validated a text-mining-based approach to measure substitution and complementarity.
Compared to the conventional method using subcategory match, our approach minimizes misclassification
errors since product features are algorithmically extracted from the product descriptions. In addition, the
topic modeling algorithm converts each product description into a distribution of multiple product features.
40
In other words, a product can be categorized into multiple categories with different weights. Finally, the
product similarity values produced by our method are continuous. Thus, we can measure to which degree
a pair of products is substitutive or complementary to each other, instead of making a black-and-white call.
6.5 Limitations
First, our empirical investigation is limited to each product category (Technology or Home/Garden)
provided by the corporate partner. Each category has a large number of different subcategories under which
the number of different products is large, but inter-category relationship between these two is not captured.
Second, our measure of complements and substitutes is not perfect. Some products can be simultaneously
complements and substitutes, which is not captured by our approach. For instance, a notepad can serve as
a complement to a laptop, but at the same time, it can be a substitute as well. Likewise, our model may not
identify the products that have low (high) similarity in their functions but can work as substitutes
(complements). An example includes the purchase of an e-book reader instead of home movie theater.
Third, we only considered the short-term effect of related products. In our analysis, only products co-visited
within a session (usually spanning within an hour) were considered. A consumer may investigate a product
over multiple sessions in multiple days. In this case, the reviews of substitutive products accessed in the
previous session may also affect the purchase of the focal product in the current session.
Fourth, our measure of online product reviews is limited to quantitative ratings (and not text reviews). Since
there is a distinction between ratings and text reviews (Pavlou and Dimoka 2006), how consumers process
the distinction in their purchasing decisions and the net effect of the online product reviews are not captured.
Finally, future research could use crowdsourcing to verify the substitutive and complementary product pairs
produced by our text-mining approach and the conventional subcategory-based approach. Specifically, we
can use Amazon Mechanical Turk to evaluate each of the product pairs generated by the two approaches.
If the substitutes/complements based on topic modeling is more likely to be confirmed by the Turkers, then
it shows that our topic modeling based measure outperforms the subcategory match-based measure.
41
6.6 Concluding Remark
Using clickstream data from consumers who shop on a retail website where we can observe full records of
which online product reviews consumers viewed, we are able to specify a consumer’s consideration set and
offer a more complete picture on how online product reviews take effect at the individual consumer level.
Specifically, we examined whether review ratings of substitute and complementary products a consumer
viewed affect her likelihood of purchasing a focal product. The prior literature has mainly focused on the
direct impact of online reviews on a focal product. However, in a typical process of purchasing, a consumer
may look at multiple product pages because she wants to choose the best product from several or she is
considering purchasing several products that work together for a specific goal. In these settings, we show
that online review ratings of other products have a significant spillover effect on focal product purchase,
and this spillover effect can be moderated by brand and channel media. Our results complement the existing
studies on online product reviews and provide insights into designing a more effective online review system.
References
Aaker, D. A. 1990. Brand Extensions: The Good, the Bad and the Ugly. Sloan Management Review 31(4)
47-56.
Aaker, D. A. 1991. Managing Brand Equity. The Free Press, New York.
Aaker, D. A. 1996. Building Strong Brands. The Free Press, New York.
Acemoglu, D., S. Johnson, J. A. Robinson. 2001.The Colonial Origins of Comparative Development: An
Empirical Investigation. American Economic Review 91(5) 1369-1401.
Aggarwal, R., R. Gopal, A. Gupta, and H. Singh. 2012. Putting Money Where the Mouths Are: The Relation
between Venture Financing and eWOM. Information Systems Research 23(3) 976–992.
Ahluwalia, R., H.R. Unnava, R.E. Burnkrant. 2001. The Moderating Role of Commitment on the Spillover
Effect of Marketing Communications. Journal of Marketing Research 38(4) 458-470.
Ahluwalia, R., R. E. Burnkrant, H. R. Unnava. 2000. Consumer Response to Negative Publicity: The
Moderating Role of Commitment. Journal of Marketing Research 37(2) 203–214.
Ahluwalia, R., Z. Gurhan-Canli. 2000. The Effects of Extensions on the Family Brand Name: An
Accessibility-Diagnosticity Perspective. Journal of Consumer Research 27(3) 371–81.
Anderson, J. R. 1983. The Architecture of Cognition. Harvard University Press, Cambridge, MA.
Anderson, E. T., D. Simester 2013. Advertising in a Competitive Market: The Role of Product Standards,
Customer Learning, and Switching Costs. Journal of Marketing Research 50(4) 489-504.
Andrews, M., Luo, X., Fang, Z., Ghose, A. 2016. Mobile ad Effectiveness: Hyper-contextual Targeting
with Crowdedness. Marketing Science 35(2) 218-233. Angrist, J. D., A. B. Krueger. 2001. Instrumental Variables and the Search for Identification: From Supply
and Demand to Natural Experiments. Journal of Economic Perspectives 15(4) 69-85.
Angrist, J. D., J. S. Pischke. 2008. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton
University Press.
42
Archak, N., A. Ghose, P.G. Ipeirotis. 2011. Deriving the Pricing Power of Product Features by Mining
Consumer Reviews. Management Science 57(8) 1485-1509.
Balachander, S., S. Ghose. 2003. Reciprocal Spillover Effects: A Strategic Benefit of Brand
Extensions. Journal of Marketing 67(1) 4-13.
Barsalou, L. W. 1985. Ideals, Central Tendency, and Frequency of Instantiation as Determinants of Graded
Structure in Categories. Journal of Experimental Psychology: Learning, Memory, and Cognition 11
(4) 629–54.
Bart, Y., Stephen, A.T. and Sarvary, M., 2014. Which Products Are Best Suited to Mobile Advertising? A
Field Study of Mobile Display Advertising Effects on Consumer Attitudes and Intentions. Journal
of Marketing Research 51(3) 270-285.
Barwise, Patrick and Colin Strong. 2002. Permission-Based Mobile Advertising. Journal of Interactive
Marketing 16(1) 14–24.
Berry, S., J. Levinsohn, A. Pakes. 1995. Automobile Prices in Market Equilibrium. Econometrica 63(4)
841-890.
Blei, D. M., A. Y. Ng, M. I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning
Research 3 993-1022.
Borah, A., G. J. Tellis. 2016. Halo (Spillover) Effects in Social Media: Do Product Recalls of One Brand
Hurt or Help Rival Brands? Journal of Marketing Research. 53(2) 143-160.
Broniarczyk, S. M., J. W. Alba. 1994a.The Importance of the Brand in Brand Extension. Journal of
Marketing Research 31(2) 214–28.
Broniarczyk, S. M., J. W. Alba. 1994b. The Role of Consumers’ Intuitions in Inference Making. Journal
of Consumer Research 21(4) 393–407.
Bucklin, R. E., G. J. Russell, V. Srinivasan. 1998. A Relationship Between Market Share Elasticities and
Brand Switching Probabilities. Journal of Marketing Research 35(1) 99-113.
Bucklin, R. E., C. Sismeiro. 2003. A Model of Web Site Browsing Behavior Estimated on Clickstream
Data. Journal of Marketing Research 40(3) 249-267.
Butcher, Dan. 2010. Mobile Ad Campaigns 5 Times More Effective Than Online: Insight Express Study.
MobileMarketer.com, (February 5), (accessed August 10, 2016), [available at
http://www.mobilemarketer.com/cms/news/research/5308.html].
Cameron, A. C., J. B. Gelbach, and D. L. Miller. 2008. Bootstrap-based Improvements for Inference with
Clustered Errors. Review of Economics and Statistics 90(3) 414-427.
Carmi, E., G. Oestreicher-Singer, A. Sundararajan. 2012. Is Oprah Contagious? Identifying Demand
Spillovers in Online Networks. Identifying Demand Spillovers in Online Networks (August 3,
2012) .NET Institute Working Paper 10-18. Available at SSRN.
Chae, I., A. Stephen, Y. Bart, D. Yao. 2016. Spillover Effects in Seeded Word-of-Mouth Marketing
Campaigns. Marketing Science Forthcoming.
Chen Y., J. Xie. 2005. Third-party Product Review and Firm Marketing Strategy. Marketing Science 24(2)
218–240.
Chen Y., J. Xie. 2008. Online Consumer Review: Word-of-mouth as a New Element of Marketing
Communication Mix. Management Science 54(3) 477–491.
Chevalier, J. A., D. Mayzlin. 2006. The Effect of Word of Mouth on Sales: Online Book Reviews. Journal
of Marketing Research 43(3) 345-354
Chintagunta, P. K., S. Haldar. 1998. Investigating Purchase Timing Behavior in Two Related Product
Categories. Journal of Marketing Research 35(1) 43-53.
Chintagunta, P.K., Gopinath, S. and Venkataraman, S., 2010. The effects of online user reviews on movie
box office performance: Accounting for sequential rollout and aggregation across local markets.
Marketing Science 29(5) 944-957.
Christensen, C. 1997, The Innovator’s Dilemma: When Technologies Cause Great Firms to Fail. Harvard
Business School Press, Boston.
Chung, K. Y., T. P. Derdenger, K. Srinivasan. 2013. Economic Value of Celebrity Endorsements: Tiger
Woods' Impact on Sales of Nike Golf Balls. Marketing Science 32(2) 271-293.
43
Clemons, E. K., G. Gao, L. M. Hitt. 2006. When Online Reviews Meet Hyper Differentiation: A Study of
the Craft Beer Industry. Journal of Management Information Systems 23(2) 149-171.
Conecomm.com. 2010. Consumers Look Online to Verify Purchase Recommendations, even from Their
Most Trusted Sources. Retrieved April 09, 2016.
http://www.conecomm.com/stuff/contentmgr/files/0/57cbc4124b1ea562f11290ad6dda9277/files/2010_on
line_influence_trend_tracker_factsheet_final.pdf
Collins, A. M., E. F. Loftus. 1975. A Spreading Activation Theory of Semantic Processing. Psychological
Review 82(6) 407–428.
Cooper, R.G. 2001, Winning at New Products: Accelerating the Process from Idea to Launch. Addison-
Wesley, New York.
Dan Ariely. 2016. Time Pressure: Behavioral Science Considerations for Mobile Marketing. Research
Report (accessed August 10, 2016), [available at https://www.thinkwithgoogle.com/articles/time-
pressure-behavioral-science-considerations-mobile-marketing.html].
Day, G. S., A. D. Shocker, R. K. Srivastava. 1979. Customer-oriented Approaches to Identifying Product-
Markets. Journal of Marketing 43(4) 8-19.
Dellarocas, C. 2006. Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and
Firms. Management Science 52(10) 1577-1593.
Dellarocas, C., G. Gao, R. Narayan. 2010. Are Consumers More Likely to Contribute Online Reviews for
Hit or Niche Products? Journal of Management Information Systems 27(2) 127-157.
Dellarocas, C., X. Zhang, N. F. Awad. 2007. Exploring the Value of Online Product Reviews in Forecasting
Sales: The Case of Motion Pictures. Journal of Interactive Marketing 21(4) 23-45.
Desai, P.S. 2001. Quality Segmentation in Spatial Markets: When Does Cannibalization Affect Product
Line Design? Marketing Science 20(3) 265-283.
Dimoka, A., Y. Hong, P. A. Pavlou. 2012. On Product Uncertainty in Online Markets: Theory and Evidence.
MIS Quarterly 36(2) 395-426.
Dodson, J., A. Tybout, B. Sternthal. 1978. Impact of Deals and Deal Retraction on Brand Switching.
Journal of Marketing Research 15(1) 72-81.
Drossos, D., Giaglis, G.M., Lekakos, G. Kokkinaki, F. and Stavraki, M.G. 2007. Determinants of Effective
SMS Advertising: An Experimental Study. Journal of Interactive Advertising 7 (2), 16–27.
Duan, W., B. Gu, A. B. Whinston. 2009. Informational Cascades and Software Adoption on the Internet:
An Empirical Investigation." MIS Quarterly 33(1) 23-48.
Duan, W., B. Gu, A. B. Whinston. 2008. The Dynamics of Online Word-of-mouth and Product Sales: An
Empirical Investigation of the Movie Industry. Journal of Retailing 84(2) 233-242.
Entin, E. E., Serfaty, D., & Alphatech Inc., Burlington, M.A. 1990. Information Gathering and Decision
Making under Stress.
Eppen, G. D., W. A. Hanson, R. K. Martin. 1991. Bundling-New Products, New Markets, Low Risk.
Working paper, Krannert Graduate School of Management. Purdue University.
Erdem, T., B. Sun .2002. An empirical Investigation of the Spillover Effects of Advertising and Sales
Promotions in Umbrella Branding. Journal of Marketing Research 39(4) 408-420.
Erdem, T. 1998. An Empirical Analysis of Umbrella Branding. Journal of Marketing Research 35(3) 339-
351.
Erdem, T., R. S. Winer. 1999. Econometric Modeling of Competition: A Multi-Category Choice-Based
Mapping Approach. Journal of Econometrics 89(1) 159-175.
Feldman, J. M., J. G. Lynch. 1988. Self-Generated Validity and Other Effects of Measurement on Belief,
Attitude, Intention, and Behavior. Journal of Applied Psychology 73(3) 421.
Fong, N. M., Z. Fang, X. Luo. 2015. Geo-Conquesting: Competitive Locational Targeting of Mobile
Promotions. Journal of Marketing Research 52(5) 726-735.
Forman, C., A. Ghose, B. Wiesenfeld. 2008. Examining the Relationship Between Reviews and Sales: The
Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research 19(3)
291-313.
44
Friedrich, R., F. Gröne, K. Hölbling, M. Peterson. 2009. The March of Mobile Marketing: New Chances
for Consumer Companies, New Opportunities for Mobile Operators. Journal of Advertising
Research 49(1) 54-61.
Gates, B. 1998. Compete, Don’t Delete. The Economist 1998(19) 19-21.
Gaeth, G. J., I. P. Lewin, G. Chakraborty, A. M. Levin. 1991. Consumer Evaluation of Multi-Product
Bundles: An Information Integration Analysis. Marketing Letters 2(1) 47-58.
Gao, G., B. N. Greenwood, R. Agarwal., J. S. McCullough. 2015. Vocal Minority and Silent Majority: How
Do Online Ratings Reflect Population Perceptions of Quality? MIS Quarterly 39(3) 565-589.
Ghose, A., S. P. Han. 2011. Ghose, A. and Han, S.P., 2011. An Empirical Analysis of User Content
Generation and Usage Behavior on the Mobile Internet. Management Science 57(9) 1671-1691.
Ghose, A., A. Goldfarb, S. P. Han. 2013. How Is the Mobile Internet Different? Search Costs and Local
Activities. Information Systems Research 24(3) 613-631.
Ghose, A., S. P. Han. 2014. Estimating Demand for Mobile Apps in the New Economy. Management
Science 60(6) 1470–1488.
Godes, D., D. Mayzlin. 2004. Using Online Conversations to Study Word-of-mouth
Communication. Marketing Science 23(4) 545-560.
Godes, D., D. Mayzlin. 2009. Firm-created Word-of-mouth Communication: Evidence from a Field
Test. Marketing Science 28(4) 721-739.
Goes, P. B., M. Lin, C. A. Yeung. 2014. Popularity Effect in User-generated content: Evidence from Online
Product Reviews. Information Systems Research 25(2) 222-238.
Granados, N., A. Gupta, R. J. Kauffman .2012. Online and Offline Demand and Price Elasticities: Evidence
from the Air Travel Industry. Information Systems Research 23(1) 164-181.
Guiltinan, J. P. 1987. The Price Bundling of Services: A Normative Framework. Journal of Marketing
51(2), 74–85.
Hausman, J. 1978. Specification Tests in Econometrics. Econometrica 46(6) 1251-71.
Henderson, J. B., R. Quandt. 1958. Micro Economics Theory: A Mathematical Approach. McGraw-Hill
Book Company. New York.
Hong, Y. (Kevin), P. A. Pavlou. 2014. Product Fit Uncertainty in Online Markets: Nature, Effects, and
Antecedents. Information Systems Research 25(2) 328-344.
Hu, N., P. A. Pavlou, J. Zhang. 2016. On Self-Selection Biases in Online Product Reviews. MIS Quarterly.
Forthcoming.
Hui, S. K., J. Jeffrey Inman, Yanliu Huang, and Jacob Suher. 2013. The Effect of In-Store Travel Distance
on Unplanned Spending: Applications to Mobile Promotion Strategies. Journal of Marketing 77
(March), 1–16.
Jabr,W., Z. Zheng. 2014. Know Yourself and Know Your Enemy: An Analysis of Firm Recommendations
and Consumer Reviews in a Competitive Environment. MIS Quarterly 38(3) 635-654.
Janakiraman, R., C. Sismeiro, S. Dutta. 2009. Perception Spillovers across Competing Brands: a
Disaggregate Model of How and When. Journal of Marketing Research 46(4) 467-481.
Kamakura, W. A., W. Kang. 2007. Chain-Wide and Store-Level Analysis for Cross-Category Management.
Journal of Retailing 83(2) 159–70.
Keller, K. L. 1993. Conceptualizing, Measuring, and Managing Consumer-Based Brand Equity. Journal of
Marketing 57(1) 1–22.
Kenny D. and Marshall, J.F. (2000) Contextual Marketing: The Real Business of the Internet. Harvard
Business Review 78(6) 119–125.
Krishnan, T.V., F. M. Bass, V. Kumar. 2000. Impact of a Late Entrant on the Diffusion of a New
Product/Service. Journal of Marketing Research 37(2) 269-278.
Krishnan, T., P. B. Seetharaman, D. Vakratsas 2012. The Multiple Roles of Interpersonal Communication
in New Product Growth. International Journal of Research in Marketing 29(3) 292-305.
Kumar, V., R. Leone.1988. Measuring the Effect of Retail Store Promotions on Brand and Store
Substitution. Journal of Marketing Research 25(2) 178-85.
45
Kwark, Y., J. Chen, S. Raghunathan. 2014. Online Product Reviews: Implications for Retailers and
Competing Manufacturers. Information Systems Research 25(1) 93-110.
Lee, D., K. Hosanagar. 2016. When Do Recommender Systems Work the Best? The Moderating Effects of
Product Attributes and Consumer Reviews on Recommender Performance. Proceedings of the 25th
International Conference on World Wide Web 85-97.
Lee, Y.-J., K. Hosanagar, Y. Tan. 2015. Do I Follow my Friends or the Crowd? Information Cascades in
Online Movie Ratings. Management Science 61(9) 2241-2258.
Lewis, R. A., D. Nguyen. 2015. Display Advertising's Competitive Spillovers to Consumer Search.
Quantitative Marketing and Economics 13(2) 93-115.
Li X., L. M. Hitt. 2008. Self-selection and Information Role of Online Product Reviews. Information
Systems Research 19(4) 456–474.
Li, X., L. M. Hitt, Z. J. Zhang. 2011. Product Reviews and Competition in Markets for Repeat Purchase
Products. Journal of Management Information Systems 27(4) 9-42.
Libai, B., E. Muller, R. Peres. 2009. The Role of Within-brand and Cross-brand Communications in
Competitive Growth. Journal of Marketing 73(3) 19-34.
Libai, B., E. Muller, R. Peres. 2013. Decomposing the Value of Word-of-mouth Seeding Programs:
Acceleration versus Expansion. Journal of Marketing Research 50(2) 161-176.
Lin, Z., K. Y. Goh, C. S. Heng. 2015. The Demand Effects of Product Recommendation Networks: An
Empirical Analysis of Network Diversity and Stability. MIS Quarterly. Forthcoming.
Liu, Y. 2006. Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of
Marketing 70(3) 74-89.
Lovett, M. J., R. Peres, R. Shachar. 2013. On Brands and Word of Mouth. Journal of Marketing
Research 50(4) 427-444.
Lu, X., S. Ba, L. Huang, Y. Feng. 2013. Promotional Marketing or Word-of-mouth? Evidence from Online
Restaurant Reviews. Information Systems Research 24(3) 596-612.
Luo, X., J. Zhang, B. Gu, C. Phang. 2016. Expert Blogs and Consumer Perceptions of Competing Brands.
MIS Quarterly. Forthcoming.
Luo, X., Andrews, M., Fang, Z. and Phang, C.W., 2014. Mobile Targeting. Management Science 60(7)
1738-1756.
Manchanda, P., A. Ansari, S. Gupta. 1999. The “Shopping Basket”: A Model for Multicategory Purchase
Incidence Decisions. Marketing Science 18(2) 95-114.
Maniar, N., E. Bennett, S. Hand, G. Allan. 2008. The Effect of Mobile Phone Screen Size on Video Based
Learning. Journal of Software 3(4) 51-61.
Mass-Colell, A., M. D. Whinston, J. R. Green. 1995. Microeconomic Theory, Oxford University press, New
York.
Mayzlin, D. 2006. Promotional Chat on the Internet. Marketing Science 25(2) 155-163.
Mehta, N. 2007. Investigating Consumers’ Purchase Incidence and Brand Choice Decisions Across
Multiple Product Categories: A Theoretical and Empirical Analysis. Marketing Science 26(2) 196-
217.
Mehta, N., Y. Ma. 2012. A Multicategory Model of Consumers' Purchase Incidence, Quantity, and Brand
Choice Decisions: Methodological Issues and Implications on Promotional Decisions. Journal of
Marketing Research 49(4) 435-451.
McAuley, J., R. Pandey, J. Leskovec. 2015. Inferring Networks of Substitutable and Complementary
Products. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining 785-794.
McAuley, J., C. Targett, Q. Shi, A. van den Hengel .2015. Image-based Recommendations on Styles and
Substitutes. Proceedings of the 38th International ACM SIGIR Conference on Research and
Development in Information Retrieval 43-52.
Moorthy, K.S., I. P. Png. 1992. Market Segmentation, Cannibalization, and the Timing of Product
Introductions. Management Science 38(3) 345-359.
Morein, J. A. 1975. Shift from Brand to Product Line Marketing. Harvard Business Review 53 (5) 56-64.
46
Mudambi, S. M., D. Schuff. 2010. What Makes a Helpful Online Review? A Study of Customer Reviews
on Amazon.com. MIS Quarterly 34(1) 185-200.
Mulhern, F. J., 1989. An Econometric Analysis of Consumer Response to Retail Price Promotions. Doctoral
dissertation, University of Texas at Austin.
Mulhern, F. J., R. P. Leone. 1991. Implicit Price Bundling of Retail Products: A Multiproduct Approach to
Maximizing Store Profitability. Journal of Marketing 55 (4) 63–76.
Nielsen. 2012. State of the Media: U.S. Digital Consumer Report, Q3-Q4 2011. Research Report, (accessed
August 10, 2016), [available at http://www.nielsen.com/us/en/insights/reports/2012/us-digital-
consumer-report.html].
Niraj, R., V. Padmanabhan, P.B. Seetharaman. 2008. A Cross-Category Model of Households' Incidence
and Quantity Decisions. Marketing Science 27(2) 225-235.
Nunamaker Jr, J. F., L. M. Applegate, B. R. Konsynski. 1987. Facilitating Group Creativity: Experience
with a Group Decision Support System. Journal of Management Information Systems 3(4) 5-19.
Oestreicher-Singer, G., B. Libai, L. Sivan, E. Carmi, O. Yassin. 2013. The Network Value of
Products. Journal of Marketing 77(3) 1-14.
Oestreicher-Singer, G., A. Sundararajan. 2012. Recommendation Networks and the Long Tail of Electronic
Commerce. MIS Quarterly 36(1) 65-83.
Oestreicher-Singer, G., A. Sundararajan. 2012. The Visible Hand? Demand Effects of Recommendation
Networks in Electronic Markets. Management Science 58(11) 1963-1981.
Parker, P., H. Gatignon. 1994. Specifying Competitive Effects in Diffusion Models: An Empirical Analysis.
International Journal of Research in Marketing 11(1) 17-39.
Pavlou, P. A., and Dimoka, A. 2006. The Nature and Role of Feedback Text Comments in Online
Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation.
Information Systems Research 17(4) 391-412.
Peres, R., C. Van den Bulte. 2014. When to Take or Forgo New Product Exclusivity: Balancing Protection
from Competition against Word-of-mouth Spillover. Journal of Marketing 78(2) 83-100.
Raeder, T., N. V. Chawla. 2011. Market Basket Analysis with Networks. Social Network Analysis and
Mining 1(2) 97-113.
Ratneshwar, S., A.D. Shocker. 1991. Substitution in Use and the Role of Usage Context in Product Category
Structures. Journal of Marketing Research 28(3) 281-295.
Rice, S. C. 2012. Reputation and Uncertainty in Online Markets: An Experimental Study. Information
Systems Research 23(2) 436-452.
Riquelme, A., D. Berkowitz, M. Caner. 2013. Valid Tests When Instrumental Variables Do Not Perfectly
Satisfy the Exclusion Restriction. Stata Journal 13(3) 528-546.
Roehm, M.L., A.M. Tybout .2006. When Will a Brand Scandal Spill Over, and How Should Competitors
Respond? Journal of Marketing Research 43(3) 366-373.
Russell, G. J., A. Petersen .2000. Analysis of Cross-Category Dependence in Market Basket Selection.
Journal of Retailing 76(3) 367–392.
Russell, G. J., R. N. Bolton .1988. Implications of Market Structure for Elasticity Structure. Journal of
Marketing Research 25(3) 229–241.
Russell, G. J., S. Ratneshwar, A. D. Shocker, D. Bell, A. Bodapati, A. Degeratu, L. Hildebrandt, N. Kim,
S. Ramaswami, V. H. Shankar. 1999. Multiple-category Decision-making: Review and Synthesis.
Marketing Letters 10(3) 319-332.
Samu, S., H. S. Krishnan, R. E. Smith .1999. Using Advertising Alliances for New Product Introduction:
Interactions between Product Complementarity and Promotional Strategies. Journal of Marketing
63(1) 57-74.
Shankar, V., G. S. Carpenter, L. Krishnamurthi. 1998. Late Mover Advantage: How Innovative Late
Entrants Outsell Pioneers. Journal of Marketing Research 35(1) 54-70.
Shankar, V., Venkatesh, A., Hofacker, C., & Naik, P. 2010. Mobile Marketing in the Retailing Environment:
Current Insights and Future Research Avenues. Journal of Interactive Marketing 24(2), 111-120.
47
Shankar, Venkatesh. 2012. Mobile Marketing Strategy in Handbook of Marketing Strategy, Venkatesh
Shankar and Gregory S. Carpenter, eds. Northampton, MA: Edward Elgar Publishing, 217–30.
Seetharaman, P. B., S. Chib, A. Ainslie, P. Boatwright, T. Chan, S. Gupta, N. Mehta, V. Rao, A. Strijnev,
2005. Models of Multi-Category Choice Behavior. Marketing Letters 16(3-4) 239-254.
Shocker, A. D., B. L. Bayus, N. Kim .2004. Product Complements and Substitutes in the Real World: The
Relevance of “Other Products”. Journal of Marketing 68(1) 28-40.
Shocker, A. D., V. Srinivasan.1979. Multi-attribute Applications for Product Concept Valuation and
Generation: A Critical Review. Journal of Marketing Research 16(2) 159-180.
Smith, D. C., C. W. Park. 1992. The Effects of Brand Extensions on Market Share and Advertising
Efficiency. Journal of Marketing Research 29(3) 296.
Shi, Z. G. M. Lee, A. B. Whinston. 2016. Towards a Better Measure of Business Proximity: Topic Modeling
for Industry Intelligence. MIS Quarterly. Forthcoming.
Singh, P. V., N. Sahoo, T. Mukhopadhyay. 2014. How to Attract and Retain Readers in Enterprise Blogging?
Information Systems Research 25(1) 35-52.
Song, I., P. K. Chintagunta. 2006. Measuring Cross-category Price Effects with Aggregate Store Data.
Management Science 52(10) 1594-1609.
Spence, M. 1973. Job Market Signaling. Quarterly Journal of Economics 87(3) 355–374.
Srivastava, R. K., M. I. Alpert, A. D. Shocker.1984. A Customer-oriented Approach for Determining
Market Structures. Journal of Marketing 48(2) 32-45.
Srivastava, R.K., R.P. Leone, A.D. Shocker. 1981. Market Structure Analysis: Hierarchical Clustering of
Products Based on Substitution-in-use. Journal of Marketing 45(3) 38-48.
Stock, J. H., J. H. Wright, M. Yogo. 2002. A Survey of Weak Instruments and Weak Identification in
Generalized Method of Moments. Journal of Business & Economic Statistics 20(4) 518-529
Susarla, A., J. H. Oh, Y. Tan. 2012. Social Networks and the Diffusion of User-generated content: Evidence
from YouTube. Information Systems Research 23(1) 23-41.
Svenson, O., Edland, A., & Karlsson, G. 1985. The Effect of Verbal and Numerical Information and Time
Stress on Judgements of the Attractiveness of Decision Alternatives. In L.B. Methlie & R. Sprague
(Eds.), Knowledge Representation for Decision Support Systems. (134-144).
Tirunillai, S., G. J. Tellis. 2012. Does Chatter Really Matter? Impact of User Generated Content on Stock
Market Performance. Marketing Science 31(2) 198-215.
Tirunillai, S., G. J. Tellis. 2014. Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis
of Big Data Using Latent Dirichlet Allocation. Journal of Marketing Research 51(4) 463-479.
Tsang, Melody M., Shu-Chun Ho, and Ting-Peng Liang. 2004. Consumer Attitudes Toward Mobile
Advertising: An Empirical Study. International Journal of Electronic Commerce 8(3) 65–78.
Urban, G.L., J.R. Hauser. 1980. Design and Marketing of New Products and Services. Prentice-Hall,
Englewood Cliffs, New Jersey.
Utterback, J. M. 1994. Mastering the Dynamics of Innovation. Harvard Business School Press, Boston
Walters, R. G. 1991. Assessing the Impact of Retail Price Promotions on Product Substitution,
Complementary Purchase, and Interstore Sales Displacement. Journal of Marketing 55(2) 17-28.
Wedel, M., J. Zhang. 2004. Analyzing Brand Competition Across Subcategories. Journal of Marketing
Research 41(4) 448-456.
Wooldridge, J.M. 2002. Econometrics Analysis of Cross Section and Panel Data. MIT Press, Cambridge,
MA.
Yadav, M. 1994. How Buyers Evaluate Product Bundles: A Model of Anchoring and Adjustment. Journal
of Consumer Research 21(2) 342–53.
Yin, D., S.D. Bond, H. Zhang. 2014. Anxious or Angry? Effects of Discrete Emotions on the Perceived
Helpfulness of Online Reviews. MIS Quarterly 38(2) 539-560.
Zhu, F., X. Zhang. 2010. Impact of Online Consumer Reviews on Sales: The Moderating Role of Product
and Consumer Characteristics. Journal of Marketing 74(2) 133-148.
48
Online Supplementary Appendix A. Additional Robustness Checks
A.1 Logit Regressions
Because our dependent variable, purchase, (purchase or not) is a binary variable, a discrete choice model
might be more appropriate than the linear probability model. Therefore, in Table A.1, we conducted a logit
regression and a fixed effect logit regression, and we found that our results are robust.
Table A.1 Logit Regression and Fixed-Effect Logit Regression
(1) (2) (3) (4)
VARIABLES Logit Home Logit Tech FE-Logit Home FE-Logit Tech
rate_focal 0.139*** 0.0403* 0.196*** 0.252***
[11.32] [1.902] [5.957] [3.582]
vol_focal 0.000184*** 0.000139 0.000494*** 0.000717**
[8.867] [1.460] [6.890] [2.270]
rate_subs -0.213*** -0.245*** -0.124*** -0.170***
[-56.11] [-36.89] [-9.474] [-6.497]
rate_comp 0.142*** 0.0908*** 0.261*** 0.371***
[37.02] [13.17] [8.593] [5.547]
z-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
A.2 Control for Consumer Learning
In our main fixed effects model, we controlled for the unobserved time-invariant effect. However, consumer
learning may occur over time. In Table A.2, we control for two types of learning: (i) individual level
learning: the cumulative number of online sessions each consumer has experienced in session t
(num_prev_sess), and (ii) individual-product level learning: the cumulative number of sessions containing
the focal product (num_product_sess). We find the results to be robust.
Table A.2 Controlling for Learning
(1) (2) (3) (4)
VARIABLES Logit Home Logit Tech Logit Home Logit Tech
rate_focal 0.140*** 0.0438** 0.132*** 0.0442**
[11.38] [2.065] [8.886] [2.088]
vol_focal 0.000177*** 0.000116 0.000181*** 0.000157
[8.504] [1.213] [7.115] [1.643]
rate_subs -0.214*** -0.244*** -0.214*** -0.248***
[-56.27] [-36.71] [-46.30] [-37.36]
rate_comp 0.150*** 0.0995*** 0.141*** 0.0879***
[38.83] [14.38] [30.28] [12.74]
num_prev_sessions -0.0691*** -0.174***
[-19.16] [-16.33]
num_product_sessions -0.0360*** -0.165***
[-3.113] [-8.180]
z-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
49
A.3 Controlling for Whether a Complementary/ Substitutive Product was Purchased
The magnitude of the spillover effect may differ when a complementary or a substitutive product was
purchased. In Table A.3, we further examine the spillover effect of online product reviews after controlling
for whether a complementary/substitutive product was purchased. The dummy variables purchase_comp
and purchase_comp indicate whether other complementary or substitutive products are purchased in the
same session. We find that our results are robust.
Table A.3 Controlling for Whether a Complementary/ Substitutive Product was Purchased
VARIABLES (1)
FE Home
(2)
FE Tech
rate_focal 0.00479*** 0.00390***
[8.479] [5.396]
vol_focal 7.73e-06*** 5.65e-06**
[5.638] [2.251]
rate_subs -0.00310*** -0.00284***
[-7.510] [-6.077]
rate_comp 0.0152*** 0.0151***
[8.937] [6.391]
purchase_subs -0.436*** -0.829***
[-8.702] [-35.26]
purchase_comp 0.282*** 0.544***
[3.598] [12.76]
Robust z-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
A.4 Baseline Regression without Distinguishing Complements and Substitutes
If we do not distinguish complements and substitutes, the spillover effects might intertwine with each other.
In Table A.4, we run a baseline regression without distinguishing complements and substitutes
(rate_nonfocal is the mean rating of all co-visited products in an online session), and find that the coefficient
on rate_nonfocal is not statistically significant. It suggests that if we do not distinguish between
complements and substitutes, then the negative spillover effect from substitutes and the positive spillover
effect from complements will cancel out.
Table A.4 Baseline Regression without Distinguishing Complements and Substitutes
VARIABLES (1)
FE Home
(2)
FE Tech
rate_focal 0.00143* 0.000459
[1.822] [0.345]
vol_focal 9.92e-06*** 1.09e-05**
[6.849] [2.368]
rate_nonfocal -0.00235 -0.00371
[-1.567] [-1.578]
Constant 0.0481*** 0.0479***
[6.723] [4.323]
Observations 380,798 171,581
Robust t-statistics in brackets,
*** p<0.01, ** p<0.05, * p<0.1
50
A.5 Controlling for Volume and Variance of the Reviews of Substitutes and Complements
In our main analysis, we focused on the spillover effect of the average review rating. However, the volume
or the variance of the review ratings may also matter. In Table A.5, we control for the volume of the reviews
of substitutes and complements (vol_subs and vol_comp) and the variance of the review ratings of
substitutes and complements (var_subs and var_comp). We find that the results are robust.
Table A.5 Controlling for Volume and Variance of the Reviews of Substitutes and Complements
(1) (2)
VARIABLES FE Home FE Tech
rate_focal 0.00263*** 0.00335***
[4.798] [3.405]
vol_focal 6.13e-06*** 8.10e-06
[2.861] [1.080]
rate_subs -0.00269*** -0.00312***
[-7.543] [-5.895]
rate_comp 0.00658*** 0.00874***
[7.343] [4.794]
vol_subs -2.45e-06 -4.22e-06
[-0.640] [-0.367]
vol_comp -2.31e-05*** -1.15e-05
[-3.625] [-0.656]
var_subs -0.00351** -0.00467**
[-2.463] [-2.027]
var_comp 0.00644*** 0.00326
[2.881] [0.954]
Robust z-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
A.6 Similarity-Weighted Average Rating of Substitutes/Complements
The spillover effects might differ when the similarity measure varies. In Table A.6, we examine the impact
of the weighted average review rating of complements and substitutes. The variables w_rate_subs and
w_rate_comp are similarity-weighted average rating of substitutes/complements.
Table A.6 Similarity-Weighted Average Rating of Substitutes/Complements
(1) (2)
VARIABLES FE Home FE Tech
rate_focal 0.00204*** 0.00172**
[3.217] [2.154]
vol_focal 1.14e-05*** 1.13e-05**
[6.042] [2.440]
w_rate_subs -0.00265*** -0.00298***
[-7.153] [-7.001]
w_rate_comp 0.0228*** 0.0337***
[5.502] [4.993]
Constant 0.0605*** 0.0652***
[14.28] [9.581]
Robust z-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
51
A.7 The Moderating Role of iPhones
We examine the moderating role of i-Phones in the following regression:
𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑖,𝑗,𝑡 = 𝑎𝑖 + 𝛽0 + 𝛽1𝑟𝑎𝑡𝑒_𝑓𝑜𝑐𝑎𝑙𝑗,𝑡 + 𝛽2𝑣𝑜𝑙_𝑓𝑜𝑐𝑎𝑙𝑗,𝑡 + 𝛽3𝑖𝑜𝑠𝑖,𝑗,𝑡
+𝛽4𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠𝑖,𝑗,𝑡 + 𝛽5𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝𝑖,𝑗,𝑡 + 𝛽6𝑟𝑎𝑡𝑒_𝑠𝑢𝑏𝑠_𝑖𝑜𝑠𝑖,𝑗,𝑡 + 𝛽7𝑟𝑎𝑡𝑒_𝑐𝑜𝑚𝑝_𝑖𝑜𝑠𝑖,𝑗,𝑡 + 𝜀𝑖,𝑗,𝑡 , [A.1]
where ios is whether or not an iOS device is used in an online session, and rate_subs_ios and rate_comp_ios
are interaction terms rate_subs * ios and rate_comp * ios. The coefficients of the interactions terms in
Equation A.1 specify how the spillover effect is moderated by whether the online product review is viewed
on i-Phones.
The estimation results are shown in Table A.7. We found that the coefficients on rate_subs_ios are negative,
and the coefficients on rate_comp_ios are positive. The estimation results show that for consumers who
use iOS devices, the spillover effect of online product reviews is much stronger.
Table A.7 The Moderating Role of i-Phones
(1) (2)
VARIABLES
iOS
(Home)
iOS
(Tech)
rate_focal 0.00255*** 0.00280***
[5.127] [3.318]
vol_focal 1.02e-05*** 1.15e-05**
[7.023] [2.497]
rate_subs -0.00177*** -0.00247***
[-5.765] [-5.551]
rate_comp 0.00406*** 0.00675***
[4.910] [4.141]
rate_subs_ios -0.00448*** -0.00461**
[-4.177] [-2.352]
rate_comp_ios 0.00689** 0.00761
[2.569] [1.254]
Observations 380,798 171,581
Cluster robust t-statistics in brackets: *** p<0.01, ** p<0.05, * p<0.1
52
A.8 Vector Autoregression (VAR) Analysis
We employed a method of VAR to examine the dynamics between online review ratings among substitutive
products. A plausible endogeneity problem in our main model is that ratings of substitutes/complements
could be affected by product sales. For instance, in our context, a positive random shock in past
purchase/sales of a focal product may negatively affect the ratings of substitutes because consumers
compare the focal product with the substitutes. On the other hand, a positive shock in past purchase/sales
of a focal product may positively affect current purchase/sales of a focal product. In order to address this
concern, we use the method of VAR to control for the past ratings and sales.
The specification of our model has the following form:
𝑌𝑡 = 𝐴(𝐿)𝑌𝑡 + 𝜀𝑡 ,
where 𝑌𝑡 is a vector of covariates as follows:
𝑌𝑡 =
[
𝑃𝑟𝑜𝑑𝑢𝑐𝑡1𝑆𝑎𝑙𝑒𝑠𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡1𝑅𝑒𝑣𝑖𝑒𝑤𝑅𝑎𝑡𝑖𝑛𝑔𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡1𝑅𝑒𝑣𝑖𝑒𝑤𝑉𝑜𝑙𝑢𝑚𝑒𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡2𝑆𝑎𝑙𝑒𝑠𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡2𝑅𝑒𝑣𝑖𝑒𝑤𝑅𝑎𝑡𝑖𝑛𝑔𝑡
𝑃𝑟𝑜𝑑𝑢𝑐𝑡2𝑅𝑒𝑣𝑖𝑒𝑤𝑉𝑜𝑙𝑢𝑚𝑒𝑡]
,
where products 1 and 2 are substitutes in a product pair. We investigate the dynamics among product sales,
average review ratings, and volume of reviews in different time periods (each time period is a day in our
analysis). The lag operator L is defined by 𝐿𝑌𝑖𝑡 = 𝑌𝑖,𝑡−1, and we also define the symbol 𝐿𝑝𝑌𝑖𝑡 = 𝑌𝑖,𝑡−𝑝. Let
𝐴(𝐿) be the lag polynomial:
𝐴(𝐿) = 𝑎1𝐿 + ⋯+ 𝑎𝑝𝐿𝑝,
which is defined as an operator such that:
𝐴(𝐿)𝑌𝑖,𝑡 = 𝑎1 ∙ 𝑌𝑖,𝑡−1 + ⋯+ 𝑎𝑝 ∙ 𝑌𝑖,𝑡−𝑝.
In our analysis, we take 𝑝 = 1.
In Table A.8, we show two examples of pairs of substitutive products. From Column 1 in Table A.8 (a),
we can see that L.avg_rate_2 (lagged average review rating of product 2) has a negative impact on its
substitutive product’s sales: sales_1. From Column 4 in Table A.8 (b), L.avg_rate_1 has a negative impact
on substitutive product’s sales: sales_2. These results confirm the negative spillover effects of the review
rating of substitutive products.
53
Table A.8 VAR Results of Substantive Product Pairs
(a) Example 1
(1) (2) (3) (4) (5) (6)
VARIABLES sales_1 avg_rate_1 rate_vol_1 sales_2 avg_rate_2 rate_vol_2
L.sales_1 -0.0192 0.000151 -0.323 -0.226 -0.000103 -0.115
[-0.147] [0.812] [-1.397] [-1.609] [-0.00149] [-0.0138]
L.avg_rate_1 1.705* -0.00557*** -15.93*** -1.684* 0.000133 -0.488
[1.898] [-4.356] [-10.04] [-1.744] [0.000282] [-0.00853]
L.rate_vol_1 -0.0985** 0.000311*** 0.836*** 0.0451 -8.71e-06 0.0120
[-2.394] [5.298] [11.50] [1.019] [-0.000403] [0.00460]
L.sales_2 0.0560 -0.000267* -0.338* -0.0576 -5.25e-05 -0.0392
[0.506] [-1.692] [-1.729] [-0.484] [-0.000903] [-0.00556]
L.avg_rate_2 -14.15** 1.040*** -7.689 4.839 0.872 -13.82
[-2.183] [112.6] [-0.671] [0.694] [0.256] [-0.0335]
L.rate_vol_2 0.122** -0.000244*** 0.228** -0.0314 2.30e-05 0.993
[2.280] [-3.205] [2.414] [-0.546] [0.000820] [0.292]
Constant -0.000681 -8.00e-07 5.49e-06 0.125 0.598*** 72.37***
[-0.00169] [-0.00140] [7.73e-06] [0.289] [2.828] [2.828]
Observations 56 56 56 56 56 56
z-statistics in brackets *** p<0.01, ** p<0.05, * p<0.1
(b) Example 2
(1) (2) (3) (4) (5) (6)
VARIABLES sales_1 avg_rate_1 rate_vol_1 sales_2 avg_rate_2 rate_vol_2
L.sales_1 -0.194 -0.00261 -0.227 0.196 0.0179 -0.106*
[-1.412] [-0.723] [-0.612] [1.136] [0.809] [-1.722]
L.avg_rate_1 3.567 0.800*** 8.813 -22.65*** 0.0728 -0.622
[0.976] [8.326] [0.894] [-4.942] [0.124] [-0.381]
L.rate_vol_1 -0.0166 7.18e-05 0.750*** 0.112** 0.000699 0.0289
[-0.382] [0.0629] [6.404] [2.052] [0.100] [1.491]
L.sales_2 -0.0293 -0.000623 -0.254 -0.450*** 0.0247 -0.000302
[-0.296] [-0.239] [-0.950] [-3.621] [1.549] [-0.00682]
L.avg_rate_2 -0.316 0.000398 -1.799 4.973*** 0.785*** 0.340
[-0.424] [0.0203] [-0.894] [5.317] [6.541] [1.022]
L.rate_vol_2 0.0536 0.00254 0.992* 0.298 -0.0258 0.853***
[0.267] [0.481] [1.828] [1.183] [-0.798] [9.502]
Constant -13.34 0.860** -24.46 72.88*** 0.663 1.060
[-0.890] [2.184] [-0.605] [3.877] [0.275] [0.158]
Observations 50 50 50 50 50 50
z-statistics in brackets *** p<0.01, ** p<0.05, * p<0.1
54
Online Supplementary Appendix B. Topic Modeling
B.1 Constructed Topic Models from Home/Garden and Technology Products
Table B.1. 10 topics from the 50-topic model of Home/Garden products
Topic Top five keywords
00 curtain, bath, fittings, tap, basin
01 drawer, handles, metal, drawers, wood
02 plan, care, breakdown, purchase, years
03 clock, microwave, power, pressure, watts
04 kettle, function, boil, settings, heat
05 blind, drop, unit, safety, fittings
06 table, chairs, dining, oak, wood
07 floor, nozzle, hose, brush, cord
08 hood, led, rated, colour, chimney
09 polyester, machine, washable, duvet
Table B.2 10 topics from the 50-topic model of Technology products
Topic Top five keywords
00 microphone, recording, built, pack, batteries
01 xqisit, material, protection, easy, apple
02 port, dvd, remote, record, digital
03 resolution, led, contrast, hdmi, ratio
04 galaxy, Samsung, tab, tablet, viewing
05 set, sims, ages, warriors, kitty, hello
06 print, speed, printer, priting, pages, minute
07 range, number, facility, indicator, recording
08 bluetooth, speaker, wireless, ipod, 5mm
09 tablet, bluetooth, enabled, pixels, life