City, University of London Institutional Repository
Citation: Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E., Wetzels, M. and Pfann, G.
(2013). More than words: The influence of affective content and linguistic style matches in
online reviews on conversion rates. Journal of Marketing, 77(1), pp. 87-103. doi:
10.1509/jm.11.0560
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Permanent repository link: https://openaccess.city.ac.uk/id/eprint/15756/
Link to published version: http://dx.doi.org/10.1509/jm.11.0560
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City Research Online
Stephan Ludwig, Ko de Ruyter, Mike Friedman, Elisabeth C. Brüggen,
Martin Wetzels, & Gerard Pfann
More Than Words: The Influence of
Affective Content and Linguistic
Style Matches in Online Reviews on
Conversion Rates
Customers increasingly rely on other consumers’ reviews to make purchase decisions online. New insights into the
customer review phenomenon can be derived from studying the semantic content and style properties of verbatim
customer reviews to examine their influence on online retail sites’ conversion rates. The authors employ text mining
to extract changes in affective content and linguistic style properties of customer book reviews on Amazon.com. A
dynamic panel data model reveals that the influence of positive affective content on conversion rates is
asymmetrical, such that greater increases in positive affective content in customer reviews have a smaller effect
on subsequent increases in conversion rate. No such tapering-off effect occurs for changes in negative affective
content in reviews. Furthermore, positive changes in affective cues and increasing congruence with the product
interest group’s typical linguistic style directly and conjointly increase conversion rates. These findings suggest that
managers should identify and promote the most influential reviews in a given product category, provide instructions
to stimulate reviewers to write powerful reviews, and adapt the style of their own editorial reviews to the relevant
product category.
Keywords: online customer reviews, affective content, linguistic style match, conversion rate, Internet marketing
Stephan Ludwig is a postdoctoral researcher and research consultant,
InSites Consulting ForwaR&D Lab (e-mail: s.ludwig@ maastrichtuniversity. nl),
Ko de Ruyter is Professor of Interactive Marketing and Professor of Inter-
national Service Research (e-mail: k.deruyter@ maastrichtuniversity. nl), Elis-
abeth C. Brüggen is Assistant Professor of Marketing (e-mail: e.bruggen@
maastrichtuniversity.nl), and Martin Wetzels is Professor of Marketing and
Supply Chain Research (e-mail: m.wetzels@ maastrichtuniversity. nl),
Department of Marketing and Supply Chain Management, School of Busi-
ness and Economics, Maastricht University. Mike Friedman is Assistant
Professor of Marketing, Louvain School of Management, Center for
Research on Consumers and Marketing Strategy (e-mail: mike.friedman@
uclouvain.be). Gerard Pfann is Professor of Econometrics of Markets and
Organizations, Departments of Quantitative Economics and Organiza-
tional Strategy, School of Business and Economics, Maastricht University,
(e-mail: G.Pfann@maastrichtuniversity.nl). The authors thank the three
anonymous JM reviewers for comments that greatly improved the article.
They also thank Utpal M. Dholakia, Rice University; Neeraj Bharadwaj,
Temple University; Dhruv Grewal, Babson College; Michel Tuan Pham,
Columbia University; and Richard B. Slatcher, Wayne State University for
their friendly revisions. The authors are especially grateful for the financial
support of InSites Consulting in this project and are indebted to their
research assistant, Hannes Datta, Maastricht University, for his contribu-
tion in collecting the data used in this study and assistance during the
data analysis. John Hulland served as area editor for this article.
© 2013, American Marketing Association
ISSN: 0022-2429 (print), 1547-7185 (electronic)
Journal of Marketing
Volume 77 (January 2013), 87 –103
87
C
ustomer reviews have become one of the most fre-
quently accessed online information sources, as con-
sumers appear to be weary of traditional, marketer-
dominated information channels (Godes and Mayzlin 2004).
Online shoppers put 12 times more trust in peers’ opinions
than in marketer-initiated sources (eMarketer 2010), and
according to a recent market study (ChannelAdvisor 2010),
92% of online customers read and use verbatim review
comments in their purchase decisions. Online retailers thus
recognize the effectiveness of customer reviews for con-
verting customer visits into sales; Roku, the market leader
in innovative applications for digital media, attributes a
20% lift in its online conversion rates to the appearance of
approximately 17,000 (both positive and negative) cus-
tomer reviews on its website (Bronto.com 2011). Yet the
sheer volume and lack of structure of qualitative informa-
tion in customer reviews continues to present a formidable
challenge (Cao, Duan, and Gan 2011; Singh, Hillmer, and
Ze 2011). Most online retailers believe their performance is
hampered because they cannot efficiently decipher or reli-
ably assess how online customers use the informational
cues from their online conversations at a manageable, prod-
uct category level (Bonnet and Nandan 2011). A recent
market study by Econsultancy (2011) even shows that 81%
of online retail sites have “limited” or “no understanding”
of why customers leave without purchasing. Thus, there is a
clear managerial need to develop insights into the influence
of text-based customer reviews, to improve understanding
of conversion behavior.
Current research on online reviews offers little guid-
ance. Most studies focus nearly exclusively on “quantitative
surrogates” of review contents (Mudambi and Schuff 2010,
p. 195), and in this emerging stream, the empirical support
for the predictive influence of numerical quality diagnos-
tics, such as review volume or star ratings, on sales remains
equivocal (Chen, Wu, and Jungsun 2004; Chevalier and
Mayzlin 2006; Dellarocas, Xiaoquan, and Awad 2007;
Duan, Gu, and Whinston 2008). Therefore, researchers are
turning to the reviews’ textual properties and assessing their
impact on retail performance (Chevalier and Mayzlin
2006). In particular, affective cues provided in verbatim text
(e.g., “I love the book,” “worst book I ever read”) might
influence respondents’ attitudes (Cohen et al. 2008), and the
heuristic nature of online information processing seems
likely to allow for the affective content contained in review
texts to drive behavior (Das, Martinez-Jerez, and Tufano
2005; Jones, Ravid, and Rafaeli 2004). However, it is still
unclear whether affective word cues serve as straightfor-
ward predictors of the collective impact of customer
reviews on retail success, considering the limited evidence
of nonlinear relationships between affective activation and
product evaluations (Andrade 2005; Roehm and Roehm
2005). In particular, extreme (positive and negative) review
content is prevalent and may threaten review diagnosticity
(Streitfeld 2011). Beyond review content, recent theorizing
in social psychology points to linguistic style, as manifested
in an author’s profile, as likely to shape the impact of infor-
mation contained in reviews (Ireland and Pennebaker
2010). The inherent inseparability of content and style in
reviews suggests that insights into linguistic style may
extend beyond verbatim content to enhance understanding
of their overall impact on customer decision making and
thus retail performance.
In addition, reviews have long been related to sales (or
its proxies), and yet there is growing consensus that online
conversion rate offers a better metric for gauging online
retail performance (Gurley 2000; Moe and Fader 2004).
Small gains in conversion rates have powerful implications
for firm performance, through increasing revenue and
decreasing marketing costs as a percentage of sales. The
impact of user-generated content on performance metrics
also should be assessed dynamically rather than statically
(Tirunillai and Tellis 2012) because conversion rate dynam-
ics give a continuous indication of the potential business
that retailers lose when customers leave the site without
making a purchase. However, few efforts have explored the
dynamics of customer conversion rates as a primary success
metric (Moe and Fader 2004).
In light of these gaps and concerns, we aim to provide
theoretical and managerial guidance on the influence of tex-
tual properties of consumer reviews on online retailers’ con-
version rates in three ways. First, we examine the collective
impact of affective content from a dynamic perspective, by
noting how changes in affective content influence changes
in conversion rates over time. Little previous work on affect
has featured longitudinal measures or analyses; to address
this shortcoming, we study the impact of aggregate, weekly
changes in the affective content of product reviews on shifts
in product conversion rates. This novel approach to investi-
gating affect in marketing is particularly important in the
context of reviews because new reviews typically take
prominent spots on the product display page, so changes in
88 / Journal of Marketing, January 2013
affective content likely provide strong drivers of changes in
product conversion rate. We focus on their nonlinear
impact, taking into account extreme positive and negative
changes. Research into manipulations of affective states and
their influence on responses to various stimuli (e.g., ads,
products; Cohen et al. 2008) usually focuses on mean-level
differences across experimental conditions. While experi-
mental manipulations provide suggestive evidence of non-
linear relationships between affect and consumer thought
and behavior (for demonstrations of nonlinear relationships
between manipulated affect activation and product evalua-
tions, see, e.g., Andrade 2005; Roehm and Roehm 2005), a
rigorous test of this notion requires studying the effect of
affect across a range of values, rather than at specific points
on a spectrum primed by experimental procedures.
Second, we add to recent research by noting the impact
of linguistic style of customer reviews on online conversion
rates. Human communication theory (e.g., Giles 2009)
posits that conversation style can elicit perceptions in con-
versational dyads. Furthermore, recent research has shown
that synchronization in conversational style, or linguistic
style match (LSM), irrespective of content, increases rap-
port, credibility, and shared perceptions among conversants
(Ireland and Pennebaker 2010). Yet previous research on
the impact of customer review texts has focused on content
and has ignored linguistic style as a potential diagnostic
cue. Beyond the importance of recommender similarity per-
ceptions, as prior research has suggested (Menon and
Blount 2003), we posit that the degree to which reviewers
accommodate the linguistic style of the product interest
group may determine the influence of the reviews on
changes in customers’ conversion behavior.
Third, content and linguistic style are inherently insepa-
rable and may reinforce the impact of a review (Chaiken
and Maheswaran 1994; Menon and Blount 2003), and their
collective impact demands more empirical examination.
Verbatim comments assume a pivotal role as the primary
means to establish source perceptions and indicate review-
ers’ product experience. We supplement prior research on
customer reviews by assessing how changes in the reviews’
affective content and style jointly relate to subsequent con-
version rate dynamics. In customer review settings, such a
joint impact highlights the need to study content and style
collectively when assessing the impact of customer reviews
on retail success.
Conceptual Foundations
Feldman and Lynch (1988) posit that the relative weight of
heuristic inferences, as decision inputs, depends on two
context-dependent facets: their relative accessibility and
their diagnosticity compared with alternative inputs. The
sheer volume of online peer reviews often leads consumers
to process information heuristically. We posit that at an
aggregate level, this has a decisive influence on their online
purchase decisions and website conversion rates (Jones,
Ravid, and Rafaeli 2004). Existing research has accordingly
focused on the diagnosticity of readily extractable, quantifi-
able customer review information cues, such as quality rat-
ings (Chevalier and Mayzlin 2006), volume (Duan, Gu, and
Whinston 2008), and reviewer identity information (e.g.,
name, location; Forman, Ghose, and Wiesenfeld 2008), as
well as on product-related aspects such as product popular-
ity (Zhu and Zhang 2010) and price (Yong 2006). However,
empirical investigations into the influence of numerical
cues (e.g., star ratings) on sales often provide mixed or
inconclusive results, which suggest some doubts about their
diagnosticity and predictive ability (Yong 2006). Chevalier
and Mayzlin (2006) find that additional favorable review
ratings on Amazon.com increase book sales, whereas incre-
mental negative ratings decrease them. Yet Chen, Wu, and
Jungsun (2004) find no significant impact of positive rat-
ings on sales, and Berger, Sorensen, and Rasumussen
(2010) suggest that even negative ratings increase sales for
products with lower awareness. In the movie industry, Del-
larocas, Xiaoquan, and Awad (2007) indicate that numerical
ratings are positively related to box office revenue, irre-
spective of the volume of reviews, whereas Duan, Gu, and
Whinston (2008) and Yong (2006) find that review volume,
not ratings, drives sales.
These mixed findings might stem from (1) methodologi-
cal shortcomings, such as a cross-sectional context and
inability to control for unobserved differences, including
product quality (Zhu and Zhang 2010), or (2) the inability
of numeric cues to do justice to the nuanced, fine-grained,
and expressive nature of verbatim reviews (Cao, Duan, and
Gan 2011; Pavlou and Dimoka 2006; Singh, Hillmer, and
Ze 2011). Making use of recent advances in text analytics to
systematically analyze large volumes of collections of cus-
tomer review verbatim scripts and taking a dynamic per-
spective, which is more reflective of the rapid, continual
changes in user-generated content (Tirunillai and Tellis
2012), may clarify the impacts of review content on conver-
sion rates (Chevalier and Mayzlin 2006; Mudambi and
Schuff 2010).
Emerging research on text-based communication sug-
gests that both content and style elements of verbatim
reviews are relevant decision inputs that help determine
relative diagnosticity and accessibility (Huffaker, Swaab,
and Diermeier 2011). This research distinguishes linguistic
content and style: At a word level, “content words are gen-
erally nouns, regular verbs, and many adjectives and
adverbs. They convey the content of a communication”
(Tausczik and Pennebaker 2010, p. 29). Yet no content can
be communicated without style words. As Tausczik and
Pennebaker (2010, p. 29) state, “intertwined through these
content words are style words, often referred to as function
words. Style or function words are made up of pronouns,
prepositions, articles, conjunctions, auxiliary verbs, and a
few other esoteric categories.” These categories identify not
only what people convey (i.e., sentential meaning) but also
how they write (sentential style), so both have diagnostic
value that affects decisions (Bird, Franklin, and Howard
2002).
Affective content words (e.g., conveying emotions such
as happiness, sadness, anger) reveal the intent of a text
(Bird, Franklin, and Howard 2002; Das, Martinez-Jerez,
and Tufano 2005). Affect in and of itself is not a linguistic
property but refers to an “internal feeling state” (Cohen et
al. 2008, p. 297) that is “consciously accessible as the sim-
More Than Words / 89
plest raw (nonreflective) feelings evident in moods and
emotions” (Russell 2003, p. 148). The use of word cues
may be the most effective way to make affect accessible
(Ortony, Clore, and Foss 1987). In line with accumulating
empirical support for treating feelings as information
(Schwarz and Clore 1996), we find a clear underlying ratio-
nale for mining affectively laden content words in relation
to online customer reviews. At the individual level, affec-
tive content words should be particularly likely to influence
consumers whose motivation to engage in detailed cogni-
tive processing is low and those with limited access to pro-
cessing resources (e.g., because they are distracted or under
time pressure), as well as when other bases of evaluation
are ambiguous or unrevealing and when consumers lack
expertise in the target domain (Cohen et al. 2008;
Greifender, Bless, and Pham 2011; Lau-Gesk and Meyers-
Levy 2009). The online purchase process reflects these con-
ditions (Jones, Ravid, and Rafaeli 2004), in that text-based
affective content words provide rapidly accessible and diag-
nostic signals about targets (Cohen et al. 2008). We argue
that, at the aggregate level, affective content will influence
conversion rates. Regarding accessibility, Zajonc’s (1980)
well-documented hypothesis on the primacy of affect in
evaluative judgments indicates that affective cues are more
accessible than factual or descriptive information. Pham et
al. (2001) demonstrate that affective cues are registered
more rapidly than cognitive assessments; the relative acces-
sibility of affective cues also increases with their volume
and evaluative clarity or intensity (Gorn, Pham, and Sin
2001). In addition to accessibility, affective cues provide
decision inputs only if they are perceived as sufficiently
diagnostic. Two facets of diagnosticity documented in prior
literature seem relevant to (conversion) behavior: (1) per-
ceived representativeness, which is related to the extent to
which consumers believe that affective content reflects the
target and whether the representation of the sender indicates
qualifications to express his or her opinions, and (2) per-
ceived validity, or whether affective cues appear consistent
with other cues and across multiple sources (Gasper and
Clore 1998). The anonymous nature of online review set-
tings makes it difficult to establish sender qualifications,
but extreme deviations in affective cues lower the value of
feelings as information and elicit counterproductive effects
by reducing diagnosticity (Andrade 2005). We investigate
whether this phenomenon extends to the aggregate level.
In addition to affective content, the accessibility and
diagnosticity of customer reviews and their impact on cus-
tomer purchasing behavior is likely related to their linguis-
tic style (i.e., the particular usage style of function words
employed). Humans are highly attentive to the conveyance
of messages (Giles and Smith 1979), and prior work in sev-
eral scientific disciplines has demonstrated the importance
of function words for determining conversational outcomes
(Huffaker, Swaab, and Diermeier 2011). There are only
approximately 500 function words in the English language,
but this deceptively small category comprises roughly 55%
of people’s daily word usage and provides insight into con-
versants’ personalities (Bird, Franklin, and Howard 2002).
Consider three different descriptions of book experiences at
Amazon.com: