scispace - formally typeset
Search or ask a question
Journal ArticleDOI

More Than Words: The Influence of Affective Content and Linguistic Style Matches in Online Reviews on Conversion Rates

01 Jan 2013-Journal of Marketing (American Marketing Association)-Vol. 77, Iss: 1, pp 87-103
TL;DR: In this article, the authors employ text mining to extract changes in affective content and linguistic style properties of customer book reviews on Amazon.com, and find that positive changes of affective cues and increasing congruence with the product interest group's typical linguistic style directly and conjointly increase conversion rates.
Abstract: 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.

Summary (6 min read)

Introduction

  • The influence of affective content and linguistic style matches in online reviews on conversion rates.
  • 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 light of these gaps and concerns, the authors aim to provide theoretical and managerial guidance on the influence of textual properties of consumer reviews on online retailers’ conversion rates in three ways.

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.
  • Existing research has accordingly focused on the diagnosticity of readily extractable, quantifiable customer review information cues, such as quality ratings (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 popularity (Zhu and Zhang 2010) and price (Yong 2006).
  • 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 conversants’ personalities (Bird, Franklin, and Howard 2002).
  • Recent research drawing on communication accommodation theory (Giles and Smith 1979) has also suggested that common ground may be an automatic outcome of LSM, irrespective of content (Ireland and Pennebaker 2010).

Affective Content

  • Affect drives evaluation and decision making (Lench, Flores, and Bench 2011).
  • Previous theory and research thus suggest that affective cues elicit automatic affective responses, which require few processing resources, emerge rapidly, and guide attitudes and actions (Baumeister et al. 2007; Cohen et al. 2008).
  • The authors test this relationship to corroborate previous research and to demonstrate the ability of text analytic techniques to detect theoretically meaningful, well-established relationships.
  • Accordingly, the authors posit the following: H1a: There is a quadratic relationship between changes in aggregate positive affective content in a product’s reviews and changes in conversion rate.
  • At the extremes of positive affect change, each additional increase should have a smaller impact on conversion rate change.

LSM

  • Third, in line with Ireland and Pennebaker (2010), the authors derived the overall LSM score of a particular review by taking the average LSM score across all function words.
  • If a review used “despite” four times in a text of 100 words, it would yield an intensity of .04.
  • Therefore, the authors obtained a measure of overall LSM for reviews published about a particular product in a given week.
  • The authors conducted a pilot study to confirm the validity of the LSM measure, in terms of actually eliciting social identification by the target audience and strengthening the impact of the reviews on purchase intentions.

Setting

  • The authors gathered data using automated JavaScripts to access and parse HTML and XML pages describing books available for sale on Amazon.com, the leading electronic retailer.
  • The authors chose this research context because of the unique traceability features of customer review information and their influence on conversion behavior on retail sites.
  • All information is publicly accessible and updated frequently on the retailer’s webpage.
  • Therefore, in addition to the information conveyed through the customer reviews, the authors could collect and control for other product- and review-related information, such as price, review volume, review helpfulness, and advertising, all of which may affect consumers’ purchase decisions.
  • Similar data about the conversion rates of websites could be obtained from software packages designed to track or retrace online visitor behavior.

Sample

  • The authors initial sample included 641 unique books across all subgenres, released between April 15 and May 5, 2010, which received at least one customer review during the observation period.
  • The authors selected only books launched in this time period to ensure that the sample books were in approximately the same stage in their product life cycle.
  • To explore how changes in customer reviews influence the conversion behavior dynamics of visiting customers, the authors retrieved conversion rates and customer reviews, along with general product and price information, at weekly intervals for 17 consecutive weeks.
  • 92 / Journal of Marketing, January 2013 the authors eliminated 36 books that were unavailable for purchase (out of stock) for some period of time during the data collection.
  • In a second stage, two independent coders assigned the books to subgenres using Amazon.

Measurement Development

  • The authors obtained conversion behavior data from the conversion rate information provided by Amazon.com, namely, the “What Do Customers Ultimately Buy After Viewing This Item” information, which listed the percentage of customers who bought the product featured on the retail page (Bray and Martin 2011).
  • To assess the affective content and LSM of the review texts, the authors then conducted a content analysis of the reviews’ qualitative text comments.
  • Content analysis is an increasingly popular method to study user-generated posts online (Singh, Hillmer, and Ze 2011).
  • To transform text comments into quantitative data, content analysis uses automated, systematic procedures that ensure the objectivity, reproducibility, and reliability of the data analysis (Chung, Pennebaker, and Fiedler 2007).
  • Preparing the data for automated text analysis entails archiving the review texts and converting them into text files, a process that produced several review text files for each product (minimum = 1, maximum = 503).

Fiction

  • Originally developed to analyze emotional writing, LIWC dictionaries offer strong, reliable convergence between the dimensions they extract and content ratings performed by human coders (Pennebaker et al. 2007).
  • Using word counts for a given text, LIWC calculates the proportion of words that match predefined dictionaries.
  • Research into collective settings has examined group similarities using direct consensus models, which take the group average as the preferred mode of aggregation (Bliese 2000).
  • The authors results empirically justify the data aggregation of individual linguistic styles in reviews to derive a common “genre” linguistic style; for all the function word variables, the ICC 1 values are significant (F-values, p < .05), ranging from .62 to .94.

Control Measures

  • The authors constructed their control variables from observed Amazon.
  • In addition to the traditional star rating measure, which offers a five-point product quality rating per review, the authors controlled for the effects of increased review quantity posted in a particular week for each book.
  • Because price dynamics influence online purchase decision making (Xinxin and Hitt 2010), the authors also considered the impact of changes in discounts (percentage margin of the original price in week t).
  • Using the same measure, Mudambi and Schuff (2010) show that the percentage of people who find a review helpful is related to the diagnosticity of the retail site.
  • Finally, the authors collected data about additional advertising of the books, but because there were virtually no such occurrences and the effect was highly insignificant, they did not include this variable in the final model.

Analyses and Results

  • To capture the influence of their explanatory variables on changes in product site conversion rates, the authors specified a 94 / Journal of Marketing, January 2013 dynamic panel data model.
  • The authors assessed the impact of the preceding changes in the explanatory variables on subsequent changes in the product site’s conversion rates, to reduce potential problems associated with autocorrelation and remove the impact of time-invariant unobservable factors.
  • By studying the within-product changes rather than absolute levels, the authors could eliminate observed and unobserved differences between the books, which might influence conversion rates.
  • This feature does not change over time, so the authors removed (by statistically controlling for) it with the general method of moments first-difference transformation.
  • The initial condition was zero for all products; the launch date, as a first observation, produced a conversion rate of zero.

Overall Model

  • The authors also assume that E [uit |t] = 0, where the expectation of change in the error term, given the information More Than Words / 95 TA bL e 3.
  • De sc rip tiv e set at time t, is 0, instrumented using the lagged value from the previous week (t – 1); uit is the change in the random error term (the authors excluded fixed effect errors from the model by first differencing); and t denotes the week.
  • First, on the basis of past conversion behavior, Amazon.
  • Past changes in conversion behavior may influence the amount of helpfulness votes that reviews on that site accumulate in a subsequent week.
  • Collectively, the traditional fixed effects estimators therefore must be biased.

Hypothesis Testing

  • Table 4 outlines the results of the models.
  • Because the authors used first differencing and the lagged values for conversion rate, the sample size for the models fell to 4763 observations (591 books).
  • There was insufficient evidence to reject the assumption of no autocorrelation in the differences, so their generalized method estimators likely yielded unbiased and constituent estimates (Arellano and Bond 1991).
  • The exogenous variables indicated frequency (percentages of affect-laden positive and negative content words in the review text; match percentage in function words), so the authors mean-centered the variables and calculated the interaction term by multiplying mean-centered variable scores.

Variables Model 1 Model 2 Model 3 Model 4 Model 5

  • The authors compared the models by computing the chi-square difference test, which confirmed that the main, interaction, and squared effects added explanatory power to the original Model 1 (p < .01).
  • In Model 5, the authors additionally split up affective content into positive and negative affective content.
  • The authors use the estimates reported from Model 4, including all hypothesized effects, to discuss their results.

Results

  • The negative coefficient of the squared term for positive affective content indicates that the effect tapered off, so in line with H1a, overtly positive changes in the affective content in customer reviews had a smaller positive impact on conversion rate, compared with moderate changes in positive affective tone.
  • The coefficients of positive and negative changes in affective content differed significantly (p < .001), and negative changes had stronger impacts on the conversion rate.
  • The authors chose values for LSM to be one standard deviation below the mean (low), at the mean , and one standard deviation above the mean (high).
  • Changes in review quantity also had significant effects on changes in conversion rates (review quantity = .002, p < .05), in line with prior studies that have indicated that product sales can be explained by review volume (Godes and Mayzlin 2004).

The Combined Impact of Changes to Affective Content and LSM on Conversion Rate

  • Changes in helpfulness were weakly significant, positive predictors of subsequent conversion rate changes (helpfulness = .006, p < .10).
  • The authors tested the effect of changes in star ratings on subsequent conversion rate changes, but in line with Yong (2006), they found no significant relationship.
  • The descriptive information in Table 3 reveals that the average star rating for all products was mildly positive (4.15) and did not change much over time, in line with recent research that suggests star ratings converge to an average within a few weeks (Moe and Trusov 2011).
  • In contrast, review texts are nuanced and still provide new and relevant information over time that may color overall product evaluations more than the overall star rating dynamics.
  • In contrast, variance in the weekly changes of affective content was not significantly related to subsequent conversion rate changes (affective content variation = .002, p < .411).

Extending Extant Research

  • This study contributes to contemporary research on customer reviews by outlining a method to dissect customer review texts to reveal their semantic content and style properties, as well as demonstrating the dynamic influence of text properties on conversion rates in online retail sites.
  • Various concerns persist about the validity of reviews (Mudambi and Schuff 2010), and the authors confirm that in the case of sharp increases in positive affective content, the conversion rate increases are smaller than if the positive affective content increase were more moderate.
  • Yet the authors fail to find a similar attenuating effect for extremely negative changes.
  • This finding, though unexpected, is consistent with previous research that indicates that negative affective cues can be more powerful than positive ones for driving judgment and behavior.
  • Online review settings remove the faceto-face contacts that traditionally have informed word-ofmouth recommendations, but their research reveals that the contents of reviews have significant effects when their linguistic style elicits source similarity perceptions.

Corroborating Extant Research

  • Customer review phenomena have stimulated exceptional research studies aimed at uncovering relationships between the information diagnostics provided in such settings and retail performance, and yet the field still lacks a good synthesis and reconciliation of evidently divergent findings (Zhu and Zhang 2010).
  • Their results highlight that price dynamics remain a decisive purchase criterion, irrespective of any other information.
  • Third, in line with Mudambi and Schuff (2010), who argue that more helpful reviews increase the diagnosticity of an online retail page, the authors find that increases in helpfulness perceptions of featured customer reviews enhance subsequent conversion rates by enhancing site diagnosticity.
  • By collecting longitudinal weekly data and converting their model into first differences, the authors statistically removed fixed product effects, such as inherent quality, which could account for previous mixed findings (Zhu and Zhang 2010).
  • Similar to Chen, Wu, and Jungsun (2004), the authors find no significant impact of changes in star ratings on the retail site’s conversion rate for that product.

Limitations and Further Research

  • The authors results are consistent with the proposition that customers read and rely on review text information in their purchase decision making (Chevalier and Mayzlin 2006).
  • Online retailers, such as Amazon.com, increasingly prompt customers with complementary or alternative product choices to cross- and up-sell, so ongoing research should investigate how these products might affect the conversion success of a focal product.
  • Fifth, following CAT (Giles 2009), greater LSMs reflect greater identification with a conversant (Ireland and Pennebaker 2010).
  • This study’s insights into people’s emotional states and their interaction with the development of a shared language, retrievable through function word usage, suggest that text-based sentiment analysis of customer reviews should incorporate these words.
  • These purchasers could improve their return on the significant investments in customer reviews by selecting those reviews that are most impactful in terms of conversion rates, as determined by the elicited affective content and linguistic style convergence.

Method

  • For the pilot study, the authors solicited the cooperation of 230 business students who were in at least their third year of study (119 women [51.7%], mean age = 21 years [SD = 2.32]), to ensure that they had substantial exposure to business jargon over the course of their studies.
  • The self-reported mean number of business books read was 15 (SD = 5).

Procedure

  • Participants were invited by e-mail to an online experiment.
  • After instructing them to picture themselves in a situation in which they needed to buy a new business book, they were randomly assigned to one of four experimental groups and sequentially shown two typical Amazon.com pages featuring a business book with two customer reviews respectively, taken from their review sample.
  • The dependent variables in their pilot study consisted of purchase intention and identification with the reviewer.
  • The authors used four multiple-item, seven-point Likert-type scales (coefficient = .952) to measure participants’ perceived identification with the reviewers (Prendergast, Ko, and Yuen 2010).

Results and Discussion

  • The goal of the study was to determine the degree to which LSM (1) influenced participants’ identification with the reviewers and (2) interacts with affective content to increase (or attenuate in the case of low LSM) the impact of the reviews on purchase intention.
  • The main effect of affective content and the interaction effect were not significant.
  • Chen, Pei-Yu, Shin-Yi Wu, and Yoon Jungsun (2004), “The Impact of Online Recommendations and Consumer Feedback on Sales,” in Proceedings of the International Conference on Information Systems, ICIS 2004.
  • Clark, Herbert H. and Susan E. Brennan (1991), “Grounding in Communication,” in Perspectives on Socially Shared Cognition, L.B. Resnick, J.M. Levine, and S.D. Teasley, eds.

Did you find this useful? Give us your feedback

Content maybe subject to copyright    Report

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
This is the published version of the paper.
This version of the publication may differ from the final published
version.
Permanent repository link: https://openaccess.city.ac.uk/id/eprint/15756/
Link to published version: http://dx.doi.org/10.1509/jm.11.0560
Copyright: City Research Online aims to make research outputs of City,
University of London available to a wider audience. Copyright and Moral
Rights remain with the author(s) and/or copyright holders. URLs from
City Research Online may be freely distributed and linked to.
Reuse: Copies of full items can be used for personal research or study,
educational, or not-for-profit purposes without prior permission or
charge. Provided that the authors, title and full bibliographic details are
credited, a hyperlink and/or URL is given for the original metadata page
and the content is not changed in any way.
City Research Online

City Research Online: http://openaccess.city.ac.uk/ publications@city.ac.uk

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:

Citations
More filters
Journal ArticleDOI
TL;DR: In this article, a multi-dimensional analysis of electronic word-of-mouth (eWOM) communication has been conducted based on a systematic review of 190 studies and the key issues in current and emerging literature and propose important questions for future research.

800 citations


Cites background from "More Than Words: The Influence of A..."

  • ...For instance, Ludwig et al. (2013) find that linguistic styles can positively affect source perceptions and lead to higher conversion rates....

    [...]

Journal ArticleDOI
TL;DR: It is argued that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided.
Abstract: The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumer-produced digital text by enabling discovery or by providing ecological validity.

359 citations

Journal ArticleDOI
TL;DR: An integrated model based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and the features of MFOAs are proposed and support the role of online review, online rating, online tracking, performance expectancy, hedonic motivation, and price value on e-satisfaction and continued intention to reuse.

329 citations


Cites background from "More Than Words: The Influence of A..."

  • ...Online rating also facilitates the purchasing experience of other customers who wish to compare alternatives before ordering, since the ratings visually present the feedback of previous customers (King et al., 2014; Ludwig et al., 2013; Qiu et al., 2012)....

    [...]

  • ...com demonstrated a direct relationship between online features, such as linguistic style and star rating, and conversion rates (Ludwig et al., 2013)....

    [...]

Journal ArticleDOI
TL;DR: The authors found that words are part of almost every marketplace interaction, including online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data.
Abstract: Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But...

321 citations


Cites background from "More Than Words: The Influence of A..."

  • ...For example, Ludwig et al. (2013) include price discount in the model when studying the relationship between product reviews and conversion rate to control for this factor....

    [...]

  • ...Berger and Packard (2018), for example, compare lyrics from different genres, and Ludwig et al. (2013) include reviews of both fiction and nonfiction books....

    [...]

  • ...For example, a temporal sequence of documents or a portfolio of documents across a group or community of communicators can be examined for interdependencies (Ludwig et al. 2013, 2014)....

    [...]

  • ...In some cases, the researcher is interested in measuring the similarity between documents (e.g., Ludwig et al. 2013)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors used a sample of 127,629 reviews from tripadvisor.com to predict overall customer satisfaction using the technical attributes of online textual reviews and customers' involvement in the review community.

300 citations


Cites background or methods from "More Than Words: The Influence of A..."

  • ...Customer ratings supported by lengthy textual reviews containing rich information are favored by customers, and thus hotels should identify and promote the most influential reviews and provide instructions to motivate customers to write powerful reviews (Ludwig et al., 2013)....

    [...]

  • ...The relationship between the technical attributes of online textual reviews and customers’ ratings also influences future customers’ demands because customers tend to read both textual reviews and ratings to justify their consistency (Chevalier and Mayzlin, 2006; Ludwig et al., 2013)....

    [...]

  • ...The authors employed a text mining approach to find the influence of the linguistic style of online reviews on customers’ conversion rates among product websites (Ludwig et al., 2013)....

    [...]

  • ...Customers’ linguistic style is influenced by their motivation for writing online reviews (Ludwig et al., 2013)....

    [...]

  • ..., 2018), customer conversion rates (Ludwig et al., 2013), and hotel performance (Blal and Sturman, 2014)....

    [...]

References
More filters
Journal ArticleDOI
TL;DR: In this article, the generalized method of moments (GMM) estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
Abstract: This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.

26,580 citations


"More Than Words: The Influence of A..." refers methods in this paper

  • ...Therefore, we used a general method of moments estimator (Arellano and Bond (1991)....

    [...]

  • ...There was insufficient evidence to reject the assumption of no autocorrelation in the differences, so our generalized method estimators likely yielded unbiased and constituent estimates (Arellano and Bond 1991)....

    [...]

Journal ArticleDOI
TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.

10,792 citations


"More Than Words: The Influence of A..." refers background in this paper

  • ...The pooled augmented Dickey-Fuller test verified that our series in conversion rates was stationary (p < .01); the conversion rate observations were independent of time (Levin, Lin, and Chu 2002)....

    [...]

Journal ArticleDOI

7,489 citations

Journal ArticleDOI

6,503 citations

Journal ArticleDOI
James A. Russell1
TL;DR: At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated, which influence reflexes, perception, cognition, and behavior.
Abstract: At the heart of emotion, mood, and any other emotionally charged event are states experienced as simply feeling good or bad, energized or enervated. These states--called core affect--influence reflexes, perception, cognition, and behavior and are influenced by many causes internal and external, but people have no direct access to these causal connections. Core affect can therefore be experienced as free-floating (mood) or can be attributed to some cause (and thereby begin an emotional episode). These basic processes spawn a broad framework that includes perception of the core-affect-altering properties of stimuli, motives, empathy, emotional meta-experience, and affect versus emotion regulation; it accounts for prototypical emotional episodes, such as fear and anger, as core affect attributed to something plus various nonemotional processes.

4,585 citations


"More Than Words: The Influence of A..." refers background in this paper

  • ...…diagnostic cues and the corresponding automatic responses may be best understood according to a positive–negative continuum (Baumeister et al. 2007; Russell 2003); thus, research examining the content of online text has applied this approach to understand links between the mood of online messages…...

    [...]

  • ...…thus recognize the effectiveness of customer reviews for converting 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)…...

    [...]

  • ...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)....

    [...]

Frequently Asked Questions (15)
Q1. What are the future works mentioned in the paper "More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates" ?

Nonetheless, further research might investigate linguistic properties that characterize ironic statements, especially in higher-involvement purchase situations. Such disidentification and its implications present a worthwhile avenue for further research ( Elsbach and Bhattacharya 2001 ). Although this approach offers a computationally simple tool for establishing linguistic synchrony by computing the differences between individual-level and group-level function word usage, further research could develop and validate alternate computational means of deriving LSM in group settings. The authors illustrate the use of text analytics to systematically analyze specific aspects of customer reviews, a method that can be applied while monitoring customer opinions and subsequent impacts in real time. 

The exogenous variables indicated frequency (percentages of affect-laden positive and negative content words in the review text; match percentage in function words), so the authors mean-centered the variables and calculated the interaction term by multiplying mean-centered variable scores. 

Because the authors used first differencing and the lagged values for conversion rate, the sample size for the models fell to 4763 observations (591 books). 

Changes in helpfulness were weakly significant, positive predictors of subsequent conversion rate changes (helpfulness = .006, p < .10). 

In an online customer review context, readers often have little but the review text to use to form their perceptions of the review’s diagnosticity, so linguistic styles may serve as identity-descriptive information that, as a heuristic cue, shapes consumers’ evaluations of the review and thus of the product. 

Evidence from previous studies that use experimental manipulations to prime affective states suggests that exposure to affective cues influences evaluations and/or judgments of attitude objects, such as brands and products: Positive (negative) affective cues lead to more positive (negative) evaluations and judgments (e.g., Lau-Gesk and Meyers-Levy 2009). 

Two facets of diagnosticity documented in prior literature seem relevant to (conversion) behavior: (1) perceived 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) perceived validity, or whether affective cues appear consistent with other cues and across multiple sources (Gasper and Clore 1998). 

Because review titles are particularly prominent, the authors mined and conducted separate calculations for title and text intensities similar to Cao, Duan, and Gan (2011). 

The anonymous nature of online review settings 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). 

Various concerns persist about the validity of reviews (Mudambi and Schuff 2010), and the authors confirm that in the case of sharp increases in positive affective content, the conversion rate increases are smaller than if the positive affective content increase were more moderate. 

Online review settings remove the faceto-face contacts that traditionally have informed word-ofmouth recommendations, but their research reveals that the contents of reviews have significant effects when their linguistic style elicits source similarity perceptions. 

The linear relationship tests the notion from the affect transfer and priming literature that predominantly negative (positive) reviews over time increase the negative (positive) affect conveyed through reviews, leading to reduced (increased) product conversion rates. 

The figure illustrates how a change in the reviews’ content toward more positive affect leads to higher predicted changes in conversion rate and yet tapers off at extreme degrees of change. 

Such perceived rapport provides readily accessible diagnostic information, which directly influences consumer judgments and behaviors if they process information heuristically, as is the case for online information searches (Chaiken and Maheswaran 1994; Jones, Ravid, and Rafaeli 2004). 

Humans are highly attentive to the conveyance of messages (Giles and Smith 1979), and prior work in several scientific disciplines has demonstrated the importance of function words for determining conversational outcomes (Huffaker, Swaab, and Diermeier 2011).