Journal of Retailing and Consumer Services 41 (2018) 288–295
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Journal of Retailing and Consumer Services
journal homepage: www.elsevier.com/locate/jretconser
Incentivized reviews: Promising the moon for a few stars
a,⁎
a
b
Maria Petrescu , Kathleen O’Leary , Deborah Goldring , Selima Ben Mrad
a
b
a
T
Nova Southeastern University, USA
Stetson University, USA
A R T I C L E I N F O
A B S T R A C T
Keywords:
Incentivized reviews
Exchange Theory
Word-of-mouth
Influencer marketing
This paper studies the motivations behind incentivized consumer reviews generated via influencer marketing
campaigns. Exchange theory is applied as a theoretical framework to analyze, in a qualitative and a quantitative
study, the relationship between incentivized reviews and the satisfaction ratings assigned by consumers to a
product. The main contributions of the study find that incentivized campaigns can contribute to a sustained
increase in the number of reviews and have the potential to lead to higher purchase potential. Moreover, this
study also uncovers that incentivized electronic word-of-mouth, in the form of consumer reviews, leads to
increased consumer interest and desire to find out more about the product through search engines. Our findings
also show that the scope of exchange theory can be broader, from an exchange between two parties to more
complex relationships, between brands, influencers, and consumers, through an emerging, specialized word-ofmouth technique.
1. Introduction
Incentives are a necessary component of an influencer marketing
campaign because only a small fraction of social media influencers will
write a positive or negative review without even the slightest incentive,
which may include both money and product samples. On the influencer
side, it is hard to self-identify, join, stay motivated and maintain a longterm commitment to brands, as incentives are regularly needed and
real-life consumption is expected to be aligned with influencer communication. High-value influencers will only work with brands they
perceive to be authentic and trustworthy. Influencers can find that their
online fame also results in an ‘always on’ lifestyle, which can be
physically and emotionally exhausting. Incentives are an effective
method to engage with influencers and get them to recommend a
product or service (Wirtz and Chew, 2002).
Despite the efforts of businesses to legitimately engage in influencer
marketing, the level of integrity and unethical conduct in fake reviews
is still a problem. Amazon has recently filed a lawsuit against more than
1000 unidentified individuals who were allegedly selling fake reviews
on Fiverr.com for products sold on Amazon (Soper, 2015). Businesses
have started campaigns to incentivize buyers with a variety of discounts
and promotions for posting positive recommendations. However, some
companies still have disguised business-generated reviews as consumer
recommendations in the anonymity of the Internet (Mayzlin, 2006,
2014). An entire industry has developed around influencer marketing,
including managing both the brand side and the influencer side with
sophisticated databases to manage campaigns and track influencer
activity. These companies can support brands by finding the best
Word-of-mouth marketing is a brand-initiated strategy of intentionally persuading consumer-to-consumer conversations (Kozinets, 2010).
Social media enabled word-of-mouth marketing is a powerful mechanism for quickly disseminating positive message about brands. Some
examples of WOM marketing tools include viral videos, contests,
challenges, teaser advertisements, and influencer marketing. Its purpose is to encourage organic word-of-mouth, without intervening in the
posting process and the content of consumer generated conversations.
Influencer marketing, a relatively new word-of-mouth marketing
technique, goes even further, by compensating connected social media
participants who have the credibility, following, and motivation to
drive positive word-of-mouth to a broader and salient segment of the
market. Influencer marketing focuses on consumer-to-consumer campaigns in which the identified influencer receives incentives to post
positive messages about a brand so that it permeates throughout their
valuable network via electronic word-of-mouth (eWOM). In this case,
consumer reviews are not organic. Reimer and Benkestein (2016) found
that eWOM is positively impacted by consumer incentives; however,
this also entails a possible risk for the perception of the company,
especially when reviewers receive material rewards.
Influencer marketing is not without challenges. On the brand side, it
is difficult to identify, recruit, activate, and retain high-value influencers. Brands want influencers who have an authentic personal brand
and are trustworthy. Influencer outreach is costly and time-consuming.
⁎
Corresponding author.
E-mail addresses: mpetresc@nova.edu (M. Petrescu), koleary@nova.edu (K. O’Leary), goldring@stetson.edu (D. Goldring), sbenmrad@nova.edu (S. Ben Mrad).
http://dx.doi.org/10.1016/j.jretconser.2017.04.005
Received 23 December 2016; Received in revised form 21 March 2017; Accepted 22 April 2017
Available online 02 May 2017
0969-6989/ © 2017 Elsevier Ltd. All rights reserved.
Journal of Retailing and Consumer Services 41 (2018) 288–295
M. Petrescu et al.
campaigns on consumers’ search for information, as well as purchase
intent. The study also provides more questions for future research in
detecting the differences between fake product reviews, incentivized
product reviews, and organic/non-incentivized product reviews.
The paper is organized as follows. First, the literature related to
incentivized online reviews is summarized. An exploratory, qualitative
study is performed on reviews from Amazon.com, to examine the
differences between reviews from consumers who purchased the
product and those who received incentives. Then, a conceptual framework is proposed to enable our hypothesized relationships, and a multimethods study is conducted to test the hypotheses. Finally, the results
are presented with managerial implications.
influencers and providing small incentives in exchange for posting on
social media or product review sites. BzzAgent (www.bzzagent.com) is
one of the largest influencer marketing agencies.
This type of campaigns through third-party firms are seeded
marketing campaigns (SMCs) (Chae et al., 2016). They consist of
potential influencers responding to surveys, being matched with
products and then directed to post their comments on different social
media sites and online retailing websites. Smiley360 (smiley.
socialmedialink.com) offers products and coupons in exchange for
posting on personal blogs or social media. Influenster (influenster.
com) requires that influencers have a blog, YouTube channel, or Twitter
account to receive product samples.
While posting fake consumer reviews is hard to control, this
situation has resulted in manufacturers, retailers and third-party
companies developing technology platforms for systematically managing customer reviews (Zhou and Duan, 2015). Research on the
effectiveness of these marketing campaigns is scarce. There are few
studies (Anderson and Simester, 2014; Chae et al., 2016; Zhou and
Duan, 2015) that offer an explanation on how consumers respond to
these techniques or if they might perceive any of them as disingenuous.
Not a lot is known about how product reviews on a manufacturer's
website influence those on a retailer's portal or if fake reviewers
contribute to sales and positive word-of-mouth, although reviews, in
general, have a positive impact on consumers. Most of the research has
looked at the characteristics of ideal seeds (Hinz et al., 2011), the
relationship between loyalty and consumer seeds (Godes and Mayzlin,
2004), the spillover effect of seeded WOM on marketing campaigns
(Chae et al., 2016), and the types of brands that will generate more
WOM. However, to our knowledge, there have not been studies looking
at the consequences of word-of-mouth seeds on consumers.
Therefore, this research examines the consequences of consumer
reviews generated by influencer marketing campaigns as a result of
receiving a product for free. We draw on exchange theory as the
theoretical framework (Gatignon and Robertson, 1986; Ryu and Feick,
2007). Our study is the first attempt using exchange theory as the main
framework for understanding the motivation behind being involved in
incentivized reviews. Exchange theory has been used for referral and
reward programs (Ryu and Feick, 2007), but not in the case of online
incentivized reviews. We analyze influencers’ motivations behind
involvement in incentivized reviews using both a qualitative and a
quantitative study.
Our objective in the qualitative study is to differentiate between
verified reviews and incentivized reviews regarding the positive versus
negative sentiments that consumers show, as well as regarding the
primary characteristics of the product, brand, and purchasing process
that are important, including price and quality, in the two types of
reviews. These differences are then further explored in a quantitative
analysis in which we analyze the relationship between incentivized
reviews and the satisfaction ratings assigned by reviewers to the
respective product. Also, we focus on the receiving end of the
communication model and analyze the reactions of consumers who
are the recipients of the message. Considering the word-of-mouth
literature, this paper looks at the relationship between incentivized
review campaigns and their effect on the quantity and the sentiment of
reviews. It also explores the relationship between incentivized review
campaigns and consumers’ further search for information about the
product, as well as their purchase intentions.
Overall, this study characterizes several aspects of incentivized
consumer-generated reviews, an area of considerable importance to
sellers of products and services online and to consumers who rely on the
authenticity of these reviews. We also analyze the differences in the
number of reviews and the satisfaction ratings that happen even after
the campaign, as well as their impact on purchases. From a theoretical
standpoint, this paper fills a gap in the literature by providing an
understanding of incentivized reviews motivations. From a managerial
perspective, this article can show the effect of seeded marketing
2. Incentivized online reviews
A wide range of information is available to consumers during the
buying decision process, including electronic word-of-mouth and consumer-generated content. Because of this array of information, consumers have gained considerable power, by communicating their
satisfaction or disappointment with a product or purchase experience
in the online environment, usually by clicking from one to five stars in
addition to writing about their product experiences. Even though there
are many tools through which consumers can communicate, including
social media posts, blogs, and forum discussions, the most widely used
are online customer reviews (Casaló et al., 2015; Kostyra et al., 2016;
Moon and Kamakura, 2016).
There are different platforms where customers can post comments
and reviews (positive and negative), including the manufacturer's
website, the online retailer's website (Walmart, Amazon, and others),
as well as on review aggregators, whose primary purpose is to host
reviews, such as Yelp and TripAdvisor (Anderson and Simester, 2014;
Chevalier and Mayzlin, 2006; Godes and Mayzlin, 2004; Munzel, 2015).
Research has even found that customers show more interest in usergenerated product information on the Internet than toward information
provided by businesses, while online reviews are the second most
trusted source of product information, after family and friends (Salehan
and Kim, 2016). Online customer reviews have been defined as “peergenerated product evaluations posted on a company or third–party
websites” (Mudambi and Schuff, 2010). Customer reviews generate
more sales (Mudambi and Shuff, 2010; Chen et al., 2008; Clemons et al.,
2006), affect consumers’ trust (Pavlou and Gefen, 2014) and create
more word-of-mouth spillover effect (Chae et al., 2016; Kostyra et al.,
2016; Moon and Kamakura, 2016).
Some reviews are posted spontaneously by non-seeded consumers,
and some others are posted by seeded users, which are usually
incentivized by manufacturers, retailers or third-party companies
offering free samples or products free of charge in exchange for
consumer reviews and electronic word-of-mouth in social media. In
return for these seeded WOM reviews, consumers can have access to
free trials or sampling of electronic products, such as software
programs, either with limited functionality or for a limited period,
and free book previews on Amazon and Google (Zhou and Duan, 2015).
Influencers who participate in these programs are usually motivated
to be as active as possible to be included in other future incentivized
opportunities. For example, each consumer with an account at
BzzAgent receives points for reviews posted and messages shared on
social media, and the standing and ranking of the account changes
according to this activity. The better the ranking of the account, the
more incentive options the particular consumer will receive. In
summary, incentivized reviews are based on an exchange between the
manufacturer, distributor or a third-party company and the influencer.
Influencers are motivated to post reviews when they receive a sample, a
discount coupon or another material incentive in return for their posts.
Therefore, we define incentivized reviews or seeded reviews as online
product and service reviews posted on e-tailers or review websites as a
result of an incentive received by the reviewer.
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reactions to deceptive attempts. For example, Amazon has recently filed
a lawsuit against more than 1000 unidentified individuals selling fake
reviews for products sold on amazon.com (Soper, 2015). Moreover,
Amazon has also started deleting incentivized reviews that seem to
have higher ratings than the average review (Anderson and Simester,
2014; Perez, 2016).
Many companies who have used incentives to create biased user
reviews have encountered the risk of reducing their usefulness,
especially in the case of branded, well-known products (Mayzlin,
2006, 2014). Even the use of manipulated reviews from third-party
companies, who provide influencer marketing and online reputation
management services represents a risk of deceiving consumers (Munzel,
2015; Zhou and Duan, 2015). Studies have shown that external user
reviews, including reviews from retailer websites, can be more influential on sales than internal comments from a manufacturer's web page.
Research also noted that the volume of consumer reviews on a retailer's
website increases consumer awareness of the brand and contributes to
the bottom line (Petrescu, 2011; Zhou and Duan, 2015). The question
that arises is how can we distinguish between the incentivized review
and the non-incentivized one. Understanding the differences between
these two types of reviews is paramount, and an exploratory study is
conducted for this purpose.
Research has found that around 10% of goods are subject to online
review manipulation (Hu et al., 2012). Moreover, an analysis of
Amazon by ReviewMeta has also shown that incentivized reviews tend
to have a higher rating than non-incentivized ones, which pushed
Amazon to begin deleting them, even retroactively (Perez, 2016). Other
studies found that deceptive reviews are a growing problem overall, but
with different growth rates across communities. The growth rates are
influenced by signaling costs associated with deception for each review
community, including posting requirements (Ott et al., 2012). Researchers also noted that the prevalence of useful comments depends on
the platform used, media type and the degree of polarization among
commenters (Momeni et al., 2013). For example, reviews posted via
mobile devices are more timely, shorter, and more negative than web
reviews. At the same time, positive reviews are on average shorter than
negative reviews (Piccoli and Ott, 2014).
Even though researchers have focused on electronic word-of-mouth
and consumer reviews, they did not consider all incentivized reviews
aspects. They have not studied extensively the impact of online
customer reviews on the consumption decision process or the consequences of online reputation marketing as well as corporate meddling
in the reviewing process, by posting fake reviews or paying for
consumer reviews (Anderson and Simester, 2014; Kostyra et al.,
2016; Reimer and Benkestein, 2016). For example, Dellarocas (2006)
found that firms are locked in a competitive ‘rat race’ and are forced to
spend resources on review posting activities, in the hopes of minimizing
perceptions of brand damage. However, the author also notes that the
social cost of this online manipulation can be reduced by a filtering
technology that allows the elimination of biased or dishonest reviews.
Moon and Kamakura (2016) also analyzed ways to deal with reviewer
bias and writing style to obtain consumer insights from reviews, such as
identifying common words and writing styles that these reviewers
would use.
At the same time, there are also deterrents that uncover and
penalize fake reviews, including fines for businesses that publish fake
reviews. In 2013, Samsung was fined $340,300 for hiring people to
write fake positive reviews about their products and posting negative
ones on their competitors (Munzel, 2015). For example, during the
month of May 2016, Yelp issued 59 new Consumer Alerts, which are
notices that are placed on a business page, indicating that they
attempted to pay for better reviews (Streitfeld, 2016). A website,
reviewskeptic.com, was developed by Cornell University researchers
to identify fake hotel reviews with approximately 90% accuracy. Some
businesses are even trying to remove their name from review websites
or take action against the trend of fake reviews, by encouraging their
customers to post funny, unfavorable reviews for them (Streitfeld,
2016). Finally, in May 2015, the FTC issued new guidelines for both
brands and influencers entitled The FTC's Endorsement Guides: What
People Are Asking, which provides detailed information on solicitation
and disclosure that provides greater transparency to the consumer on
incentivized or compensated influencer marketing campaigns. The FTC
suggests tagging incentivized reviews with words like sponsored,
promotion, or ad with or without a hashtag (#).
Research has shown that online review websites are among the
Internet platforms that are most affected by deceptive communication
efforts (Munzel, 2015). Mayzlin (2006) analyzed firms who manipulate
the anonymity of online reviews that promoted their products. The
study provides a model for a unique equilibrium where the promotional
chat remains credible despite the use of deceptive messages. Studies
also show that using deceptive messages is costly to the firm, which
means that it is not optimal to produce high volumes of these messages
(Mayzlin, 2006). Articles have also noted that using deceptive practices
is much easier in the online environment than offline since deception
detection in electronic media is usually harder than in face-to-face
circumstances (Anderson and Simester, 2014).
Nevertheless, this topic requires more attention and research, due to
new technologies and new business initiatives, innovations, as well as
3. Exploratory analysis
In a qualitative study, we analyzed incentivized consumer reviews
and their positive or negative tone, versus reviews posted by customers
who bought the product and posted their reviews organically and
spontaneously. We explored similarities and differences among reviews
to identify any possible trends that might be further helpful in the
quantitative analysis. This type of analysis is especially important,
considering the difficulties faced by major e-tailers, such as Amazon,
which started deleting incentivized reviews. Consumers are starting to
become aware of review manipulations and are paying more attention
to the text of the review and noticing any positive/negative sentiment
(Bambauer-Sachse and Mangold, 2013). Therefore, text mining methods to analyze consumer text data regarding associated sentiment and
word usage are considered a promising approach to take advantage of
the potential of this information (Agnihotri and Bhattacharya, 2016;
Rese et al., 2014).
We collected consumer reviews from Amazon for a facial hair
trimmer from a major brand. Amazon includes both qualitative reviews,
in the form of comments, and summary statistics, in the form of fivestar ratings (Kostyra et al., 2016). The incentivized review campaign
ran through BzzAgent. Influencers received the product for free and
were asked to post comments on social media websites, as well as
reviews on Amazon. They were instructed to write in their reviews that
they received the product for free in a “program run” by the major
brand company. The incentivized reviews were posted on Amazon
during a 30-day period. For our qualitative analysis comparison, we
used a total of 1558 incentivized reviews and 305 verified reviews
posted from actual buyers from Amazon.
We performed a content analysis using NVIVO, version 11, and the
first technique used was a simple, yet efficient standard term frequency
measure (Hu et al., 2012). We created a word cloud illustrated in Fig. 1,
based on verified and incentivized reviews. Regarding the most
frequent words, it can be observed in Fig. 1 and 2 that the discussions
are centered around the same issues, such as the battery, blades, the
attachment tools and the price. Incentivized reviews are also focused on
the fact that influencers received the product for free in exchange for a
review.
Based on our qualitative analysis, we found that price has a higher
importance for consumers who bought the product than the ones who
received the sample for free. The product's quality and its potential to
last for years are also more important for actual buyers, as seen in the
word map in Table 1. Actual product customers discuss price, money,
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to only 27% for incentivized reviews. The same difference is encountered regarding very positive sentiment, with 30% for verified reviews
versus 40% for incentivized reviews. The top negative words were
identified in reviews as being: bad, duped, expensive, disappointed,
disappointing, broke, poor, waste, weak and terrible. These adjectives
were more commonly encountered in the text for the reviews posted by
buyers (0.37% of text) than the reviews posted by influencers who
received incentives (0.14%).
In summary, the qualitative analysis shows that influencers who
participate in the incentivized campaign are more likely to focus on the
positive aspects of the products and to use positive words in their
reviews, and are recommending the product reviewed more than the
non-incentivized ones. Overall, this exploratory study shows differences
that will be further explored on a theoretical basis.
4. Conceptual framework
Fig. 1. Verified Reviews.
The relationship between the influencer and the company in the
incentivized process is based on the exchange between both. As found
in our qualitative study, influencers tend to recommend the product
when they receive an incentive. Also, positive words are being used in
their posts to express their positive feelings towards the product.
Therefore, we use exchange theory framework to examine how
consumers respond to incentivized review programs (Gatignon and
Robertson, 1986; Ryu and Feick, 2007). This theory states that the
transmission of information is based on a cost/benefit analysis by the
communicator or influencer, following the same rules of utility maximization in economic decisions (Gatignon and Robertson, 1986). In
other words, reviewers will engage in an incentive program by
weighing the rewards and the costs of such a post. The potential
rewards for the communicator from this exchange of information come
from decision support and justification, social status and power. While
influencers engage in such a relationship for an economic gain, they
also share their experience to reduce cognitive dissonance, help others
in their choices, and be perceived well by others (Ryu and Feick, 2007).
However, the greatest risk in the incentivized program is mainly
economic since the influencer does not have a direct relationship with
the consumer. iInfluencers might spend time and energy posting their
reviews, responding to others reviewers' comments (Ryu and Feick,
2007) and even dealing with dissatisfaction in case the receiver of the
information is unhappy. However, the economic cost that the influencer
will encounter is much greater than the social cost. In case the review is
found to be inaccurate, the influencer might be perceived as biased, and
therefore he might encounter a low internal rating by the company.
Therefore, the influencer might face a risk in which his relationship
with him/her and the receiver might suffer, but also an economic one
since he won’t be able to access free or highly discounted products (Ryu
and Feick, 2007).
This framework was used by previous research to analyze the
impact of rewarded referral programs. Studies found that rewards
increase consumers’ referral likelihood and referrals without any
extrinsic rewards may create feelings of inequity (Ryu and Feick,
2007). In the context of eWOM, Reimer and Benkestein (2016)
concluded that monetary and material incentives increase the likelihood of online recommendations. An increase in reviews can also be
obtained through altruistic incentives, such as donations to a social
Fig. 2. Incentivized Reviews.
and how expensive the product is. Moreover, buyers also talk about
their customer service experiences when contacting company representatives regarding the problems they encountered when using the
product. However, service experience, durability, and quality seem not
to be topics of interest for the participants in the incentivized campaign
(Table 2). Nevertheless, it is worth noticing that recommending the
product appears to be more important for individuals who received the
shaver for free in the review campaign.
Our qualitative research did not focus only on the most important
attributes for each user of the product, but also on the feelings
expressed by the two types of reviewers. Therefore, sentiment analysis
is used to identify the positive and negative language in the text, using a
classical text mining method. The method consists of employing a
technique of machine learning and natural language processing performed by NVIVO. The negative versus positive sentiments were
automatically generated based on sentiment lexicons of negative and
positive words (Hu et al., 2012). Thus, we found that 34% of the text
analyzed for verified reviews exhibited negative sentiments, compared
Table 1
Word map verified reviews.
like
works
battery
love
good
well
easy
price
bought
call
blade
time
blades
recommend
manager
nice
money
291
quality
batteries
best
attachment
expensive
cheap
shower
expect
Journal of Retailing and Consumer Services 41 (2018) 288–295
M. Petrescu et al.
Table 2
Word map incentivized reviews.
free
like
good
nice
battery
time
works
shower
love
price
attachment
smooth
trimmers
size
work
quality
recommend
easy
well
best
blade
blades
handle
attachments
razors
influencers are significantly motivated to post reviews that are considered of higher quality since they do not want to be publicly
accountable for comments such as “did not find it useful” or "report
this comment." Even though they are not explicitly told to post positive
reviews, social desirability bias might intervene in this type of situation
and influencers will tend to post favorable reviews. In fact, based on the
exploratory study previously performed, influencers who receive a gift
to write a review tend to use a more positive tone and a more favorable
formulation of the review. The qualitative analysis of Amazon reviews
showed that incentivized influencers tended to recommend the product
more often and had a more positive sentiment than buyers of the
product.
Therefore, we believe that influencers on average are more likely to
post positive reviews due to the incentives they receive, especially if
their activity can lead to more free products and rewards. While
influencers seem to be still bound by the social risks of posting bad
reviews, the potential benefits they get outweigh the potential hazards.
Thus, we posit that incentivized reviews will tend to be more positive
than regular reviews.
project and the incentive to help other consumers and the company
itself.
Considering exchange theory in the context of incentivized user
reviews, the exchange is even more complicated than in the case of
word-of-mouth, because it includes communication between multiple
parties. The influencer will communicate with both consumers who
read the review, and with the manufacturer, retailer or influencer
marketing company who manages the incentivized campaign.
Influencers who receive a particular incentive to engage in word-ofmouth about a product will base their decisions and actions on the
perceived costs and benefits of the exchange between them and the
influencer marketing company (Gatignon and Robertson, 1986; Ryu
and Feick, 2007). Potential costs included in the transaction involve a
low internal rating received from the influencer marketing company
managing the campaign, if the review is not considered acceptable,
which might put the reviewer on hold regarding future campaigns and
free products. Potential benefits, however, will consist in an economic
gain with free or discounted products. Incentivized reviews managed by
third-party companies are usually based on offering free samples or
products free of charge in exchange for consumer reviews and electronic word-of-mouth in social media, such as from BzzAgent. The digital
format also allows for free trials or sampling of electronic products,
such as software programs, either with limited functionality or for a
limited period, on CNET download and free book previews on Amazon
and Google (Zhou and Duan, 2015). In this case, considering the
Exchange Theory framework and the cost/benefit ratio, we hypothesize
that there will be a significant increase in the number of reviews from a
third-party free sampling action, even though consumers are not paid to
post reviews and access to free products is not conditioned by posting of
reviews.
H2. There is a positive relationship between the incentives provided for
reviews and the average overall positive ratings assigned to the
reviewed product.
At the same time, researchers and practitioners are interested not
only in how influencers respond when being asked to write incentivized
reviews, but also on whether their posts will contribute to the creation
of more organic word-of-mouth and, more importantly, sales (Duan
et al., 2008a; Verlegh et al., 2013; Zhou and Duan, 2015). Positive
word-of-mouth reviews with positive sentiment are more read and
perceived to be helpful (Salehan and Kim, 2016). Previous research has
also demonstrated the positive relationship between online consumer
reviews and sales (Chevalier and Mayzlin, 2006; Kostyra et al., 2016).
One of the risks that incentivized reviews might present is deception, or, as the persuasion knowledge model shows, a possible change in
the effectiveness of the review if the reader feels a persuasion attempt
(Godes and Mayzlin, 2004). The persuasion knowledge model is
focused on how people use their knowledge of persuasion motives
and tactics to interpret, evaluate and respond to influence attempts
from marketers and others. This can be especially applied in the type of
incentivized reviews where influencers state that they received the
product for free to write a review about it, an occurrence very common
on different online platforms. In these cases, if consumers perceive an
ulterior motive of the influencer for the review or recommendation, it
might even have negative consequences, depending on many factors,
such as the profile of the company (Bambauer-Sachse and Mangold,
2013; Godes and Mayzlin, 2004; Mayzlin, 2006; Reimer and
Benkenstein, 2016; Verlegh, 2013). Moreover, research on online
product reviews found that users employ different information cues,
such as disclosed information about the reviewer, the consensus
between a review and the average ratings of the product (Munzel,
2016). In the case of incentivized reviews, a consensus among reviewers
due to higher and positive ratings could have an impact on trustworthiness and further purchases. The sentiment of reviews, the quantity, and
H1. There is a positive relationship between offering incentives and the
number of reviews posted online.
While influencers are usually driven by the economic gain of the
exchange relationship between them and other consumers, they are also
driven by social obligations and do not want to be accountable for their
untruthful recommendation (Gatignon and Robertson, 1986). Reviewers on some platforms, such as Amazon, are easier to identify
and hold accountable, as it not as easy to open an Amazon account
every day. For other platforms, it is more facile to use throw-away
accounts just for particular review purposes, with a lower social risk.
From this point of view, consumers who post online reviews are
held within ethical and truthful boundaries by their social obligations,
but also by the possibility of being held accountable publicly with
mechanisms such as “report this comment,” “did not find useful,”
down-voting and other tools. Also, based on the exchange theory
model, consumers use word-of-mouth for their desire to help and
reduce any post-purchase dissonance (Ryu and Feick, 2007). That is
why influencers are usually driven by posting reviews as complete and
honest as possible (Gatignon and Robertson, 1986; Ryu and Feick,
2007). Influencers who participate in incentivized campaigns have a
profile with the company managing the incentivized campaign and are
evaluated based on the number and quality of their reviews. Then,
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M. Petrescu et al.
consumers rate the satisfaction with the product from one to five
“stars,” with five being the highest level of satisfaction. The number of
verified reviews was used as a proxy variable for purchase potential, as
verified reviewers are only persons who already purchased the product.
We also measured daily consumer search for information related to the
product by using the Google Insights index for the exact name of the
product, reflecting the searches performed for that keyword in the
Google search engine.
To test the effect of the intervention and to see if the incentivized
review campaign will lead to an increase in organic reviews even after
the campaign, we started our analysis with an event study, a time-series
analysis in the form of an autoregressive integrated moving average
model (ARIMA) (Box et al., 2008; Kling et al., 2009). ARIMA is a type of
model that accounts for autocorrelation and divides the pattern of a
time series into three components: the autoregressive component,
which describes how observations are related to each other because
of being close together in time, the differencing component, which is
used to make a time series stationary, and the moving average
component, which describes outside “shocks” or interventions to the
system.
After coding the period when the incentivized reviews were posted
as the intervention period, the ARIMA analysis was performed in SPSS,
for the variable representing the number of reviews. The results of the
analysis are presented in Table 3. As the results show, the intervention
is significant (p < 0.001) for the number of reviews, which underlines
the fact that the incentivized campaign had a significant effect
(p < 0.001) on the number of reviews posted about the respective
product during the period when the campaign was run.
We continue the quantitative analysis with a MANOVA analysis in
which we test the differences as a function of the period (pre, during
and post-campaign), in the number of incentivized reviews and the
number of verified reviews, the satisfaction stars assigned to the
reviewed product and the Google search index representing consumers’
external search for information. We analyzed 74 days before the
campaign, 39 days during, and 57 days post-campaign. The results of
the MANOVA procedure are presented in Table 4.
The four core indicators (Pillai's Trace, Wilks' Lambda, Hotelling's
Trace and Roy's Largest Root) showed significance for the overall model
analyzed, with p < 0.001. Regarding the variables of interest, the
number of satisfaction stars awarded to the product was the only
variable not found significant in the model, which will be discussed in
the results section. We also performed a multiple comparisons test to
assess the differences in the values of the variables analyzed as a
function of the three time periods examined. This test is shown in
Table 5. These results will be discussed in the following section.
quality of information in reviews are also determinants that affect
consumer purchase intentions (Agnihotri and Bhattacharya, 2016; Floh
et al., 2013; Plotkina and Munzel, 2016).
However, research has found that online word-of-mouth can be used
successfully as a marketing tool because consumers have the power to
regulate it through comments and votes. Research has underlined an
active feedback mechanism between electronic word-of-mouth and
sales because word-of-mouth leads to sales, which then leads to more
word-of-mouth (Dellarocas, 2006; Duan et al., 2008a, 2008b; Godes
and Mayzlin, 2004). In this case, word-of-mouth is seen not only as an
exogenous variable but also as an endogenous one. For example,
research considers the effect of online user reviews as an indicator of
the intensity of word-of-mouth, which plays a significant role in box
office revenues (Duan et al., 2008b). In this case, an incentivized
campaign will also tend to have an effect of attenuation on possible
organic negative reviews, decreasing the impact of negative reviews on
sales.
Therefore, despite the potential risks, and since more word-ofmouth can generate more sales, we hypothesize that there is a positive
relationship between running a review campaign and further purchases.
H3. There is a positive relationship between incentivized review
campaigns and purchase potential.
Nevertheless, the number of reviews available online has a significant effect on sales and can also lead to increased awareness (Duan
et al., 2008b; Petrescu, 2011). Awareness is a critical outcome because
even if consumers are not convinced of the review or recommendation
they read, the Internet offers them a wide range of other sources of
information to verify the objectivity of what they read. Exchange theory
posits that the degree to which information from one source is
compatible with the receiver's information as will enhance the acceptance of the information by the receiver (Gatignon and Robertson,
1986).
Since online reviews, especially incentivized, can be unclear
regarding their source characteristics, including credibility, which is
an essential element in exchange theory, consumers might want to
search for further information about the product before making a
purchase decision. Therefore, we estimate that consumer information
search for the product subjected to an incentivized review campaign
will increase so that the receiver verifies whether the source is a
credible one.
H4. There is a positive relationship between incentivized review
campaigns and consumer information search.
After performing a qualitative analysis in the previous section and
considering the theoretical support provided by literature, this study
will now focus on a quantitative analysis of the relationship between
offering rewards in exchange for reviews and the outcome of these
campaigns.
6. Results and discussion
The results of the ARIMA procedure show that the intervention of
the incentivized reviews campaign has created an effect that lasted
within the post-campaign period when the reviews posted on Amazon
regarding the analyzed product came primarily from the influencers
who purchased the product. The ARIMA and MANOVA procedures both
provide support for Hypothesis 1 and show that the campaign had a
positive effect in creating word-of-mouth and buzz around the product.
The multiple comparisons analysis indicates that the total number of
reviews is the highest during the campaign, followed by the post-
5. Methodology and analysis
We continue the exploratory study performed with data collected
from Amazon reviews with a quantitative analysis. We manually
downloaded and analyzed the reviews posted for a shaving and
trimming product from a major brand on Amazon from January 1st
to September 30th. During this time, the incentivized review campaign
managed by a third-party company was run between May and June,
and it was reflected between May 21st – June 28th on Amazon's
website. During the nine months analyzed, 1558 reviews posted were
incentivized, and 305 were from verified shoppers. According to
Amazon's online information, when a product review is marked
Amazon Verified Purchase, it means that the customer who wrote the
review purchased the item at Amazon.com.
We quantified the number of reviews and the number of verified
reviews per day and the average satisfaction “stars” per day, where
Table 3
ARIMA Results.
Non-Seasonal Lags
Regression Coefficients
AR1
Period
Estimates
Std. Error
t
Approx. Sig
−0.222
44.488
0.076
7.458
−2.934
5.965
0.004
0.001
Melard's algorithm was used for estimation.
293
Journal of Retailing and Consumer Services 41 (2018) 288–295
M. Petrescu et al.
other individuals or avoiding criticism and negative comments from
other consumers (Gatignon and Robertson, 1986). Nevertheless, while
the satisfaction rating might not be significantly different from one
period to another, as the qualitative study showed, the text of the
reviews posted by incentivized influencers had a positive sentiment in a
higher proportion than that of actual buyers. The non-significant
difference in review “stars” can also be due to consumers’ importance
placed on the text of the reviews, especially considering that consumers
are starting to be aware of manipulated reviews and need more
information than just a “star” rating (Bambauer-Sachse and Mangold,
2013; Petrescu, 2011). Moreover, research has also found that consumers can discover the manipulation of numeric ratings, which might
be another reason why numeric rating alone is no longer a significant
indicator of potential sales (Hu et al., 2012).
Looking over the remaining results of the MANOVA procedure and
the multiple comparisons, we also find support for Hypothesis 4. The
results show that consumers’ search for information on Google differs as
a function of the period pre, during and post-campaign. Consumer
interest in the product and external search for information is significantly higher in the third period, during the post-campaign months.
This might be influenced by word-of-mouth from other sources,
including social media posts that were encouraged during the same
incentivized campaign from BzzAgent.
Table 4
MANOVA results.
Dependent Variable
Mean Square
F
Sig.
Nr. reviews
Satisfaction
Nr. verified reviews - Purchase potential
Information search
22835.98
1.768
4.254
4329.394
209.037
1.901
8.53
17.838
0.001
0.153
0.001
0.001
campaign months.
The campaign also influenced an increasing trend in sales, leading
to a steady increase in the number of reviews even after the campaign
has ended. This applies in the context of verified reviews too, used as a
proxy for purchase potential, since the results of the MANOVA analysis
provides support for Hypothesis 3. This shows a positive relationship
between running an incentivized review campaign and possible purchases, once consumers have a significant amount of positive reviews to
read about the product.
The multiple comparisons analysis underlines the fact that the
number of verified purchases is significantly higher during the postcampaign period. The results confirm previous literature noting that
electronic word-of-mouth has a significant effect on product purchases,
if we consider that there is a very clear correlation between product
purchase potential and the number of verified reviews (Chevalier and
Mayzlin, 2006; Dellarocas, 2006; Duan et al., 2008b; Godes and
Mayzlin, 2004). The multiple comparisons test also confirms previous
studies that found that the number of reviews can positively influence
consumers’ opinion about a seller, as a surrogate for the seller's
experience and time on market (Petrescu, 2011).
An interesting observation that can make the subject of future
studies is related to the fact that the number of verified reviews is the
lowest during the campaign, lower even than during the pre-campaign
months. Looking over previous literature, including the exchange
theory (Gatignon and Robertson, 1986) and research related to free
sampling (Zhou and Duan, 2015), there could be two main explanations
for this occurrence: product buyers were less motivated to post a review
due to negative influences from the campaign or the hundreds of free
products offered through this campaign cannibalized potential sales.
The fact that incentivized reviewers had to disclose that they received
the product for free might have influenced this outcome. This can bring
further support for some studies which found that the potential interest
of the review writer might decrease its credibility, create some
skepticism for review readers and reduce trust (Bambauer-Sachse and
Mangold, 2013; Reimer and Benkenstein, 2016).
However, when analyzing the MANOVA results, the satisfaction
ratings assigned to the product in the form of “stars” from one to five do
not differ as a function of the period, despite some signs that there are
potential linguistic and sentiment differences found in the qualitative
analysis. This might be explained by the exchange theory model of
interpersonal communication and the reviewers’ process of weighing
costs versus benefits (Gatignon and Robertson, 1986). Some of the
factors that could have determined whether incentivized influencers
provided low ratings might have been social, including impressing
7. Conclusions
This study analyzes the evolution and outcomes of incentivized
review campaigns, in a context where more companies are starting to
use revolutionary marketing techniques to increase the number of
product reviews on internal and external websites. The main contributions of the study are both theoretical and managerial. From a
theoretical standpoint, this paper is the first one using exchange theory
to understand the relationship between an influencer and other
consumers when using seeded reviews. Exchange theory has been used
in word of mouth reward programs (Ryu and Feick, 2007), but not in
incentivized reviews. Our work shows that the scope of exchange
theory can be broader, from an exchange between two parties to more
complex relationships between influencers, consumers, and influencer
marketing agencies, and it can also be applied to specialized online
word-of-mouth situations.
Our findings show that incentivized campaigns, even managed by
third-party companies, result in influencers posting positive reviews
because of the benefit they are getting from posting the reviews and the
potential for further incentives and campaigns. Engagement in the
incentivized reviews depends on a cost/benefit analysis similar to
reward programs. In an incentivized review, an influencer is motivated
by an economic gain (the sample he/she is getting in return). As a
result, the influencer can contribute to a lasting increase in the number
of reviews with a more positive sentiment and with the potential to lead
to higher sales.
Regarding the practical implications of these results, managers
should know that dealing with business-generated word-of-mouth or
consumer-generated reviews based on rewards is a process that may
have the appearance of deception. There are different interpersonal
Table 5
Multiple Comparisons.
Mean
Variable
Nr. reviews (per day)
Satisfaction (number of stars)
Nr. verified reviews (per day) – Purchase Potential
Information search (Google search index)
Pre-campaign (0)
1.324
4.1655
1.176
49.649
Mean differences
Campaign (1)
40.410
4.5295
0.872
57.795
* Significant at 0.001 level.
294
Post-campaign (2)
1.561
4.3547
1.474
66.018
(0–1)
−39.086
−0.3639
0.304
−8.146*
(1–2)
*
(0–2)
*
38.849
0.1748
−0.602*
−8.223*
−0.237
−0.1892
−0.298
−16.369*
Journal of Retailing and Consumer Services 41 (2018) 288–295
M. Petrescu et al.
Duan, W., Gu, B., Whinston, A.B., 2008b. The dynamics of online word-of-mouth and
product sales—An empirical investigation of the movie industry. J. Retail. 84 (2),
233–242.
Floh, A., Koller, M., Zauner, A., 2013. Taking a deeper look at online reviews: the
asymmetric effect of valence intensity on shopping behavior. J. Mark. Manag. 29
(5–6), 646–670.
Gatignon, H., Robertson, T.S., 1986. An exchange theory model of interpersonal
communication. Adv. Consum. Res. 13, 534–538.
Godes, D.B., Mayzlin, D., 2004. Using online conversation to study word-of-mouth
communication. Mark. Sci. 23 (4), 545–560.
Hinz, O., Skiera, B., Barrot, C., Becker, J.U., 2011. Seeding Strategies for Viral Marketing:
an Empirical Comparison. J. Mark. 75, 55–71.
Hu, N., Bose, I., Koh, N.S., Liu, L., 2012. Manipulation of online reviews: an analysis of
ratings, readability, and sentiments. Decis. Support Syst. 52, 674–684.
Kling, G., Ghobadian, A., O'Regan, N., 2009. Organic growth and shareholder value: a
case study of the insurance industry. Int. J. Res. Mark. 26 (4), 276–283.
Kostyra, D.S., Reiner, J., Natter, M., Klapper, D., 2016. Decomposing the effects of online
customer reviews on brand, price, and product attributes. Int. J. Res. Mark. 33 (1),
11–26.
Kozinets, R.V., de Valck, K., Wojnicki, A.C., Wilner, S.J.S., 2010. Networked narratives:
understanding word-of-mouth marketing in online communities. J. Mark. 74 (2),
71–89.
Mayzlin, D., 2006. Promotional chat on the Internet. Mark. Sci. 25 (2), 155–163.
Mayzlin, D., 2014. Promotional reviews: an empirical investigation of online review
manipulation. Am. Econ. Rev. 104 (8), 2421–2455.
Momeni, E., Cardie, C., Ott, M., 2013. Properties, Prediction, and Prevalence of Useful
User-generated Comments for Descriptive Annotation of Social Media Objects.
Association for the Advancement of Artificial Intelligence (〈www.aaai.org〉).
Moon, S., Kamakura, W.A., 2016. A picture is worth a thousand words: Translating
product reviews into a product positioning map. International Journal of Research in
Marketing, http://dx.doi.org/http://dx.doi.org/10.1016/j.ijresmar.2016.05.007.
Mudambi, S.M., Schuff, D., 2010. What makes a helpful online review? A study of
customer Reviews on Amazon.com. MIS Q. 34 (1), 185–200.
Munzel, A., 2015. Malicious practice of fake reviews: experimental insight into the
potential of contextual indicators in assisting consumers to detect deceptive opinion
spam. Rech. Et. Appl. En. Mark. 30 (4), 24–50.
Munzel, A., 2016. Assisting consumers in detecting fake reviews: the role of identity
information disclosure and consensus. J. Retail. Consum. Serv. 32, 96–108.
Ott, M., Cardie, C., Hancock, J., 2012. Estimating the Prevalence of Deception in Online
Review Communities. WWW 2012 – Session: Fraud and Bias in User Ratings, April
16–20, 2012, Lyon, France, 201-210.
Pavlou, P.A., Gefen, D., 2014. Building effective online Marketplaces with institutionbased Trust. Inf. Syst. Res. 15 (1), 37–59.
Perez, Sarah, 2016. Amazon makes good on its promise to delete incentivized reviews.
Tech Crunch. Retrieved from 〈https://techcrunch.com/2016/11/23/amazon-madegood-on-its-promise-to-delete-incentivized-reviews/〉.
Petrescu, M., 2011. Online price dispersion – more than imperfect information. J.
Product. Brand Manag. 20 (7), 541–548.
Piccoli, G., Ott, M., 2014. Impact of mobility and timing on user-generated content. MIS
Q. Exec. 13 (3), 147–157.
Plotkina, D., Munzel, A., 2016. Delight the experts, but never dissatisfy your customers! A
multi-category study on the effects of online review source on intention to buy a new
product. J. Retail. Consum. Serv. 29, 1–11.
Reimer, T., Benkenstein, M., 2016. Altruistic eWOM marketing: more than an alternative
to monetary incentives. J. Retail. Consum. Serv. 31, 323–333.
Rese, A., Schreiber, S., Baier, D., 2014. Technology acceptance modeling of augmented
reality at the point of sale: can surveys be replaced by an analysis of online reviews?
J. Retail. Consum. Serv. 21, 869–876.
Ryu, G., Feick, L., 2007. A penny for your thoughts: referral reward programs and referral
likelihood. J. Mark. 71, 84–94.
Salehan, M., Kim, D.J., 2016. Predicting the performance of online consumer reviews: a
sentiment mining approach to big data analytics. Decis. Support Syst. 81 (1), 30–40.
Soper, S., 2015. Amazon targets web freelancers peddling fake customer reviews.
Bloomberg. October 16th. Retrieved from 〈http://www.bloomberg.com/news/
articles/2015-10-16/amazon-targets-web-freelancers-peddling-fake-customerreviews〉.
Streitfeld, D., 2016. Online reviews? Researchers give them a low rating. The New York
Times, June 7, 2016. Retrieved from 〈http://nyti.ms/1WDOSbn〉.
Verlegh, P.W.J., Ryu, G., Tuk, M.A., Feick, L., 2013. Receiver responses to rewarded
referrals: the motive inferences framework. J. Acad. Mark. Sci. 41, 669–682.
Wirtz, J., Chew, P., 2002. The effects of incentives, deal proneness, satisfaction and tie
strength on word‐of‐mouth behavior. Int. J. Serv. Ind. Manag. 13 (2), 41–162.
Zhou, W., Duan, W., 2015. An empirical study of how third-party websites influence the
feedback mechanism between online word-of-mouth and retail sales. Decis. Support
Syst. 76, 14–23.
communication issues that contribute to consumers’ decision to review
a product, including reviewers’ social motivations to seek power and
recognition or to help others. This study noted many aspects that need
to be considered regarding these consumer decisions.
Regarding how consumers process an incentivized review, deception is also an important factor. In our example, potential deception was
mitigated by asking reviewers to announce that they received the
product for free; however, this has the potential to negatively influence
consumers’ intentions to pay for the product. Regarding the effectiveness of such a review, even if consumers might not trust a review, they
may have a higher interest in the product and search for more
information. Nevertheless, as the results of this study show, an
incentivized review campaign, even led by a third-party influencer
marketing company, has significant positive benefits on consumers’
word-of-mouth, interest, and sales.
Future studies can further develop this model and use primary data
to assess influencers’ review behavior when participating in an incentivized campaign, as well as focus on ways to identify this potentially
deceptive word-of-mouth, by analyzing the content of reviews and the
different types of technologies already used in practice. Data from other
websites and incentivized campaigns, with different product types, can
make these results more generalizable can also help. Even though
Amazon is screening fake reviews, inflated reviews seem to be also
another problem that additional research needs to focus on. Also, future
studies can analyze consumer response to the reviews posted as a result
of this campaign, differences in attitude formation, as well as their
interest and ability to make distinctions between regular and incentivized reviews. In addition, future studies should focus on understanding
whether the trustworthiness of reviews submitted is directly influenced
by higher review ratings provided by incentivized reviewers. Indeed,
trustworthiness merits further examination. Research can study at what
point the "value" of the product to be reviewed motivates/demotivates
prospective users to participate in the review process, or determines
them to "adjust" their commitment to providing an in‐depth versus a
superficial review. Future research can also focus on the type of
incentives being used and their impact on the quality and quantity of
the reviews.
References
Agnihotri, A., Bhattacharya, S., 2016. Online Review Helpfulness: role of Qualitative
Factors. Psychol. Mark. 33 (11), 1006–1017.
Anderson, E.T., Simester, D.I., 2014. Reviews without a purchase: low ratings, loyal
customers, and deception. J. Mark. Res. 51 (June), 249–269.
Bambauer-Sachse, S., Mangold, S., 2013. Do consumers still believe what is said in online
product reviews? A persuasion knowledge approach. J. Retail. Consum. Serv. 20,
373–381.
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 2008. Time Series Analysis: Forecasting and
Control, 4th edition. John Wiley and Sons, Inc., New York.
Casaló, L.V., Flavián, C., Guinalíu, M., Ekinci, Y., 2015. Avoiding the dark side of positive
online consumer reviews: enhancing reviews' usefulness for high risk-averse
travelers. J. Bus. Res. 68, 1829–1835.
Chae, I., Stephen, A.T., Bart, Y., Yao, D., 2016. Spillover effects in seeded word-of-mouth
marketing campaigns. Mark. Sci. 36 (1), 89–104. http://dx.doi.org/10.2139/ssrn.
2676047. https://ssrn.com/abstract=2676047.
Chevalier, J.A., Mayzlin, D., 2006. The effect of word of mouth on sales: online book
reviews. J. Marketing Res. 43. pp. 345–354.
Clemons, E., Gao, G., Hitt, L., 2006. When Online Reviews Meet Hyperdifferentiation: a
Study of the Craft Beer Industry. J. Manag. Inf. Syst. 23 (2), 149–171.
Dellarocas, C., 2006. Strategic manipulation of Internet opinion forums: implications for
consumers and firms. Manag. Sci. 52 (20), 1577–1593.
Duan, W., Gu, B., Whinston, A.B., 2008a. Do online reviews matter? — An empirical
investigation of panel data. Decis. Support Syst. 45, 1007–1016.
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