Content uploaded by Maria Elena Aramendia-Muneta
All content in this area was uploaded by Maria Elena Aramendia-Muneta on Nov 08, 2017
Content may be subject to copyright.
Maria Elena Aramendia-Muneta
4.2 Spread the Word –The Eﬀect of Word of
Mouth in e-Marketing
Abstract: Word of mouth (WOM) is an extraordinary mechanism with which to
spread information and disinformation. There is an interaction between WOM
and eWOM, creating diﬀerent channels for the dissemination of information.
However, this information cannot be controlled by marketers; at least this is
seldom the case. Positive and negative comments are found in eWOM and they
have a powerful inﬂuence on credibility, trust and persuasiveness, where inﬂu-
encers play a main role. Brand reputation is shaped by the ﬂow of information
and disinformation on the Internet. Social networks are a real tool with which to
create and place information. A comment may greatly beneﬁt consumers, pre-
venting uncertainty and boosting sales. eWOM disseminates both information
and disinformation, and so internet users and marketers are faced with the
problem of how to turn this to their own beneﬁt.
Word-of-mouth (WOM) is “an interpersonal communication in which a sender
spreads a message to receivers”(Bao, Chang 2014, p. 21). The same idea when
applied to the Internet becomes electronic word of mouth (eWOM), a 21st century
The power of WOM is so well known that in 2004 the Word of Mouth
Marketing Association (WOMMA) was founded in order to lead the WOM industry
through advocacy, education, and ethics. According to WOMMA (2014), WOM is
the driving force behind 13% of sales, while paid marketing in total accounts for
20-30% of sales.
Online consumers have more power than do other consumers and this
diﬀerence may even be increasing. In fact, thanks to the Internet, the relation
between ﬁrms and consumers has changed dramatically and now this relation
is more favourable to consumers (Kucuk, Krishnamurthy 2007). Everyone, any-
where, at any time is able to express their opinion.
Above all, WOM has a huge impact on the service industry, where the poten-
tial customer needs information in order to choose which service to purchase.
The tourist industry is one of the main sectors where WOM has proved its poten-
tial, as a service, people need opinions so that they can choose a hotel, a
restaurant or cruise.
DOI 10.1515/9783110416794-013, © 2017 Maria Elena Aramendia-Muneta, published by
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
Electronic WOM (eWOM) plays an important role in electronic marketing
(e-marketing) nowadays because comments can evoke emotion and aﬀect
behaviour. The real motivation to generate information on the Internet (Ho,
Dempsey 2010) is the need: to be part of a group, to be individualistic, to be
altruistic and for personal growth. Being part of a group and being altruistic
are more relevant to the beneﬁt of the group, and being individual and personal
growth are more related to personal values. Consumers generate and distribute
information and disinformation, which can lead to a response from other
consumers or enterprises.
In this scenario, the purpose of this chapter is to clarify the main points for
both parties (companies and consumers) and to provide a holistic perspective on
the eWOM world that aﬀects marketing strategies. Firstly, by means of a com-
parison between WOM and eWOM so as to comprehend the diﬀerences and rela-
tionships between oﬄine and online channels and how information is conveyed.
Secondly, the role of inﬂuencers through online comments is shown, as well as
the value of positive and negative comments. Thirdly, the three key variables
(trust, credibility and persuasiveness) are examined together with their inter-
connection in eWOM. Fourthly, the impact of eWOM in brand reputation is out-
lined, where disinformation is highlighted as well as the use of social networks.
Finally, an insight is provided into how information is presented in eWOM and
the beneﬁts derived.
2 Comparison between WOM and eWOM
User-generated content is an ensemble of positive or negative comments (Cheong,
Morrison 2008; Gretzel, Yoo 2008). Overall, WOM is the provider of this content.
It can be argued that there are two diﬀerent ways of spreading information
and we can classify them into two distinct groups: WOM in-group (close friends
or family) and WOM out-of-group (actors near a person’s social, familial, work
and collegial circles) (Brown, Reingen 1987). Moving one-step forward, eWOM
could aﬀect the WOM out-of-group in a more extensive way, because it has
evolved in reaction to technology, and an individual has access to any informa-
tion written by any other individual in the world. Actually, eWOM out-of-group
also makes reference to contact with people whom the individual does not
know (Abrantes et al. 2013).
The number of internet users is sharply increasing as well as the number of
connections between them. The possibilities of augmenting eWOM out-of-group
is more relevant because of its much larger audience. The mathematical relation
is that of one individual to the population at large.
228 Maria Elena Aramendia-Muneta
The media used to spread information is diﬀerent between WOM and eWOM.
The main channel involved in eWOM is social media. In contrast, WOM favours
face-to-face connection as direct interaction rather than more distanced means
of spreading information.
In the study “Global Trust in Advertising”by Nielsen (2015), consumers
respond more to “recommendations from people I know”as a ﬁrst choice. In
second place comes “the branded websites”and in third “consumer opinions
posted online”. As table 1 shows, among the top six trusted ways of advertising,
only the ﬁfth aﬀects WOM directly, while eWOM solely applies to the second,
third and sixth positions.
Table 1: Top Six Positions in Trust Advertising
Position Type WOM or eWOM
1 Recommendations from people I know WOM and eWOM
2 Branded websites eWOM
3 Consumer opinions posted online eWOM
4 Brand sponsorships WOM and eWOM
5 Editorial content, such a newspaper articles WOM
6 Emails I signed up for eWOM
Keller and Fay (2012) reinforced Nielsen’s results regarding “recommendations
from people I know”. As human beings, interpersonal relationship is the key to
WOM. However, 90% of conversations are oﬄine (WOM through face-to-face
conversations) and only 8% online (eWOM). Is it possible to conﬁrm that
eWOM has a deeper impact than WOM?
WOMMA (2014) asserted that oﬄine WOM is responsible for 2/3 of the
impact, while online only accounts for 1/3. However, they both amplify the eﬀect
of paid media by 15%. Compared to traditional advertising, WOM has an im-
mediate impact, especially in the ﬁrst two weeks. In the movie industry, eWOM
particularly aﬀects the ﬁrst week’s box-oﬃce revenues and after the second week
both WOM and eWOM have a direct inﬂuence on revenues (Bae, Kim 2013).
The “go viral”eﬀect is higher in eWOM than WOM. As mentioned before, the
huge number of users on the Internet is a useful means of spreading information
and thus the “go viral”has a higher potential in eWOM than WOM. However, it
is worth noticing the close interconnection between WOM and eWOM, where the
omnichannel is latent, because a face-to-face comment can be included on a
website and online information can be broadcast in a TV programme. The same
information and disinformation may be spread through diverse channels.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 229
If there is a trend to use omnichannels, how can enterprises keep track of
where information concerning them is located? Unfortunately, there is no direct
tool for marketers to control both online and oﬄine channels (Allsop et al.
2007). In the case of eWOM, there are several social media monitoring bodies
such as e.g. Netvibes, Datashif, UberVU. Unfortunately there is no consensus
on which is the most appropriate tool to receive feedback and various com-
panies, marketers and researchers have tackled the problem without any relevant
Both WOM and eWOM studies on communication agree that a favourable
comment increases the probability of purchase and unfavourable word of mouth
has the opposite eﬀect (Arndt 1967; Ho, Dempsey 2010). Reading comments is a
mean to reducing uncertainty. As a general rule, in the oﬀand online world, the
principle of saving time is of paramount importance, consumers try to cut down
on the time spent on searches by using the comments of other like-minded users
or by asking for information.
Communication through both channels has the same direction: one-to-one,
one-to-many and many-to-many. While in face-to-face communication, informa-
tion assumes an oral format, in electronic communication information is written
and endurable (Barreto 2014). However, written communication is more eﬀective
because it leads senders to describe products and brands more accurately
through a self-enhancement eﬀect (Berger, Iyengar 2013).
Several researchers agreed that anonymity is a unique characteristic in
eWOM (Breitsohl et al. 2010; Cheung et al. 2008). However, in WOM the sentence
“someone told me”, also implies anonymity, and this feature is made even
clearer on the Internet. It is also worth noticing that anonymity provides a
greater opportunity to formulate misleading information, because the source is
anonymous and so cannot be veriﬁed.
Besides, people often make their decisions after consulting online informa-
tion (Lee 2009). Therefore, eWOM aﬀects not only online purchases, but also
those in the real world (ﬁgure 1). The opposite eﬀect is also well known by com-
panies and mostly aﬀects local commerce, being detrimental to their sales. It is
common practice for consumers to visit a small retailer only for the purpose of
seeing a product recommended online or oﬄine physically and then to purchase
Although eWOM is gaining in popularity, marketers should not believe that
eWOM has replaced WOM (Fulgoni, Lipsman 2015). Oﬄine and online communi-
cations are complementary and marketers need to see both channels in a holistic
way. Enterprises are adapting themselves to the present situation because con-
sumers use cross-channel information. Years ago, business focused on developing
multi-channel selling approaches and now they are turning into omnichannel
230 Maria Elena Aramendia-Muneta
strategies, where consumers check information on oﬄine and online channels
and then they decide where to buy. The IBM Omnichannel Capability Index
(2015) pointed out the need of providing a shopping experience independent
of the chosen channel. In that case, the interconnection of eWOM and WOM is
crucial to consumers due to their omnichannel behaviour.
3 eWOM: Negative vs Positive Comments
Negative comments are more persuasive than positive reviews, particularly,
among females (Bae, Lee 2011a; Jeong, Jang 2011). Negative reviews have a great
impact on consumer-based brand equity. Contrary to popular belief, a bad
comment not only deeply aﬀects a small company’s equity, but also that of
renowned enterprises (Bambauer-Sachse, Mangold 2011). Thus the size of a
company does not render it immune to this eﬀect.
Negative eWOM (eNWOM) postings have a greater linguistic impact on
users. This language normally expresses anger on the part of the poster and
connects more to their frustrations (Gheorghe, Liao 2012). Negative emotional
expressions tend to have less of an impact than might be expected because
consumers believe that those expressions are based on an irrational disposition
(Kim, Gupta 2012). eNWOM is sometimes perceived as disinformation, especially
when it is placed among numerous positive eWOM (ePWOM).
Negative eWOM has a more signiﬁcant eﬀect on experience goods than on
search goods and even more so if the website’s reputation is taken into account.
These negative comments are more reliable on established websites (Park,
Gretzel 2007; Park, Lee 2009).
The real problem with eNWOM is the fact that enterprises do not perceive
a negative comment as a chance to improve. The geniality will transform an
eNWOM into an ePWOM and create a real asset for the company. Kikumori and
Ono (2013) identiﬁed that an eNWOM may have a positive impact.
Figure 1: Relation between eWOM, WOM, and purchase
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 231
However, if ePWOM appears together with information connected to a pro-
motion, it is more persuasive than negative eWOM (Zhang et al. 2010). Positive
expression in a single positive comment does not lead to positive impact (Kim,
A basic human motive (self-enhancement) guides consumers to create
ePWOM, while NWOM is merely transmitted (De Angelis et al. 2012). Statistically
speaking an ePWOM occurs three times more often (3 to 1) than an eNWOM (East
et al. 2007). Customers spread eNWOM when there is a serious failure (Wang
et al. 2014). A satisﬁed consumer is more prone to writing a positive comment
than an unsatisﬁed customer is prone to writing a negative comment. However,
in the study by Cheung et al. (2007) cultural diﬀerences were found: Chinese
eNWOM is based on venting anger and punishing the business organization,
while US eNWOM will seek compensation and the correction of a bad situation.
Through positive comment, the sender has a deep inﬂuence on the receiver.
The two-step ﬂow communication (Katz 1957) also applies to eWOM. In this
case, there is a push communication relation (ﬁgure 2), where the aim is to
spread information through intermediaries and to push consumers to buy or
even to get them to advise other consumers to do so. Both manufacturers and
producers need to ﬁnd the best inﬂuencer with the best impact on potential
customers (Arenas-Gaitan et al. 2013). When inﬂuencers really believe that a
service or product could beneﬁt consumers, their comments improve the credi-
bility of the brand and increase purchase intention. Fashion bloggers are
well known as a best inﬂuencer. A good example is Chiara Ferragni with more
than three million followers on Instagram and 500,000 unique visitors of her
renowned blog “the blonde salad”every month. Each time she recommends a
certain product it sells out in a very short space of time. Harvard Business
School wrote a business case based on Chiara Ferragni’s success.
Figure 2: Push relation between positive comment, credibility, and purchase intention
Such inﬂuencers are on the whole considered as leaders. When their messages
are accurate and comprehensive, followers react more strongly to this infor-
mation (Bao, Chang 2014; Godes, Mayzlin 2009). Inﬂuencers have the role of
leadership and that is why manufacturers and producers should ﬁnd a leader
to spread their information so as to proﬁt from it (return on investment in e-
marketing) or generate a multiplier or ripple eﬀect. Overall, an authoritative
232 Maria Elena Aramendia-Muneta
opinion maker entices future customers or fosters a behavioural outcome. How-
ever, it remains unclear whether inﬂuencers have been paid by the enterprise to
write positive comments. The market rate for inﬂuencers is around €6,000 to
promote a product or a brand.
A problem with the inﬂuence of eWOM arose when it was discovered that
some hotels created fake comments, which is an illegal practice and forbidden
in Britain, Ireland, France, Italy and Germany. This malpractice continues
because the average payment for hiring somebody willing to make such com-
ments is around €8 per comment. Li et al. (2014) demonstrated some patterns
that reveal the authenticity of a message. A fake comment may include exces-
sive use of superlatives and a lack of detail and description.
To sum up, eWOM is created through comments, where the information can
constitute eNWOM or ePWOM. Both can aﬀect purchases, eNWOM reduces the
percentage of sales, whereas ePWOM has the opposite eﬀect.
4 eWOM and Variables
The key elements to eﬀective persuasion on the Internet are credibility and trust
(Teng et al. 2014). Credibility, trust and persuasiveness are variables that are
closely interconnected (ﬁgure 3). Although some researchers have tried to focus
1The blond Salad by Anat Keinan, Kristina Maslauskaite, Sandrine Crener and Vincent Dessain
available at http://www.hbs.edu/faculty/Pages/item.aspx?num=48520
Figure 3: Interconnection between trust, credibility, and persuasiveness
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 233
their studies on one variable (Cheung et al. 2009; Pan, Chiou 2011), their ﬁnd-
ings proved that one variable cannot survive without the other. If a comment
is credible, readers rely on it and the message can persuade them to react by
following its recommendation, as for example, encouraging the buying of a
product or just improving the brand’s reputation. That way, the overlap of the
three variables is shown and they act as a chain of events.
4.1 eWOM and Credibility
As a rule, there is a push relation between positive comments, credibility and
purchase intentions. A positive comment has a positive impact on credibility
and inﬂuences purchase intention (see ﬁgure 4, Chih et al. 2013). However, this
simple model has some moderators that enhance or temper credibility.
Figure 4: Push relation between positive comment, credibility, and purchase intention
On the Internet, the credibility of the creator of any information might suﬀer
due to lack of face-to-face contact. The more positive comments are presented
on a website, the higher its potential to undermine the credibility of the site,
although it may result in higher scores (Doh, Hwang 2009; Reichelt et al. 2013).
Not only negative, but also positive comments could be detrimental to the
credibility of the source.
However, if eWOM is customized (visual cues, numbers), it becomes more
trustworthy, due to the fact that consumer experiences are relevant (Ha 2002;
Teng et al. 2014). Users’acceptance of online reviews as reliable increases for
comments if the user perceives the post as useful or recognises social ties with
the poster (Teng et al. 2014).
Another variable aﬀecting credibility is product type. A review of an ex-
perience product is most credible when it appears in an online community and
is given more credit than a search product comment when it appears in a
consumer-developed review site (Bae, Lee 2011b).
234 Maria Elena Aramendia-Muneta
The previous experiences of participants as well as the quantity and articu-
lation of posts are major factors. Readers of information may give it more credit
when they are able to compare diﬀerent opinions, as in that way they have the
opportunity of choosing which is more convincing (O’Reilly, Marx 2011).
The initial model in ﬁgure 4 has moderators that aﬀect credibility and the
ﬁnal decision (ﬁgure 5).
Figure 5: Moderators aﬀecting credibility
4.2 eWOM and Trust
Gender is a moderating factor in online trust and the intention to shop online.
Women attach more value to responsive participation (e.g. a real conversation,
where there are interactions between comments), while men give more value to
the ability to post online content (Awad, Ragowsky 2008).
Trust is the foundation of success in the banking sector, where consumers
have to entrust their own money to a speciﬁc institution. A positive word of
mouth has a positive eﬀect on electronic banking use and enhances trust in a
particular bank, while a negative comment inﬂuences the consumer, creating
risk and potential mistrust (Ashtiani, Iranmanesh 2012).
What really undermines the trust of a ﬁrm when it receives eNWOM is their
failure to take immediate action (Audrain-Pontevia, Kimmel 2008). Users are
wary of those ﬁrms that do not act in a proactive manner.
Researchers generally show a connection between levels of trust and credi-
bility. In fact, the variables present in credibility also aﬀect trust. Cheung et al.
(2009) showed that consumers are more likely to trust eWOM when comments
are more credible.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 235
4.3 eWOM and Persuasiveness
When it comes to persuasiveness, inﬂuences play a major role. In fact, the repu-
tation of a reviewer directly aﬀects followers and laggards’purchase behaviour
due to their trust of expertise.
The information given by residents is more persuasive in the accommoda-
tion, food and beverage sectors than travellers’experiences, whose opinion has
more inﬂuence in the destination category (Arsal et al. 2010).
Evaluating a product with a promotion goal has a positive impact on the
target reader. Actions that are more negative tend to generate negative eﬀects,
whereas positive actions tend to have positive eﬀects as a response (Zhang et al.
Without trust in the credibility of a comment, there is no persuasiveness.
Persuasion relates closely to consumer beliefs. The conclusions of the study by
Teng et al. (2014) found that credibility (trustworthiness) is a main characteristic
of persuasion in eWOM messages.
5 eWOM and Brand Reputation
In social discussion eWOM is an indicator of the reputation of a brand, be it
a company or the author of a book, and it even aﬀects complementary goods
(Amblee, Bui 2011).
In the banking sector, loyalty and ePWOM derive from user-friendly web
development and satisfaction on the part of the customer (Casaló et al. 2008).
However, several researchers have drawn similar conclusions regarding the rela-
tionship between technology readiness and eWOM (Chen 2011).
Gender is a variable that directly aﬀects brand reputation, because women
are more willing to post brand-related content on Facebook than men (Choi,
In an eWOM group, if there is a strong connection between the stake-
holders, they react less aggressively when their brand receives eNWOM and
tend to downplay criticism, because they regard the brand as if it were an
integral part of the group (Chang et al. 2013).
Dissatisfying product or service experiences produce eNWOM, which is
strongly detrimental to the company’s reputation and sales when the company
does not take immediate action to solve the problem of unsatisfactory product
or service experiences (Nyer, Gopinath 2005; Burton, Khammash 2010). How-
ever, if the company treats the complaint adequately, this rapid reaction increases
consumer loyalty and satisfaction level (Hong, Lee 2005). After eNWOM, prompt
feedback reduces the overall impact on the brand (Shimabukuro Sandes, Torres
236 Maria Elena Aramendia-Muneta
The better known and bigger a company is, the higher the number of anti-
brand sites that appear, which are deleterious to the brand image (Krishnamurthy,
Kucuk 2009). In those anti-brand sites, the language used has a marked ideolog-
ical and transactional component.
Post-purchase reviews written by e-communities can create harmful rumours.
Thus, companies have to pay particular attention to negative feedback and
eNWOM in order to safeguard both their brand and their online reputation.
Brands really need to promote e-interactivity with their potential consumers
or potential eWOM senders to protect their image. Harmful rumours might be
prevented if the brand automatically reacts reasonably. Otherwise, the snowball
eﬀect (Brooks 1957) may impact them and disinformation can increase in a way
that the brand cannot control.
6 eWOM and Social Networks
Social networks can be considered as a broadcast medium and, of course, they
play a main role in eWOM and create consumer-to-consumer communication
(C2C). However, there is a diﬀerence in the way information is presented: in the
typical way as on Facebook or Twitter, and discussion communities such as e.g.
TripAdvisor. Researchers consider the ﬁrst group as a provider of information
in a social context while the second is considered a source of information
(Arenas-Gaitan et al. 2013). The main diﬀerence is the interconnection among
readers on Facebook (replies are possible) and this is not the case on TripAdvisor.
There has been much debate (de Cristofaro et al. 2014) regarding recent
practices where a company is able to buy e.g. 10,000 likes for around €36, which
is why even the comments a potential customer may ﬁnd within a social net-
work make them wonder whether they have been bought.
Individuals are primary actors in social networks (SNs) and, they construct
online communities around their opinions and contributions where they can
identify themselves as part of a group (Brown et al. 2007).
Social networks provide the likelihood of expanding social circles, but when
community members use the fan page its main purpose is to collect information
to be used by enterprises. Facebook collects personal data from users which they
then sell to attract advertisers. However, users tend to express their preferences
by clicking on “Like”rather than sharing the information within their social
circle (Chen 2011).
What happens with those social networks such as Facebook where the user
can only “Like”or do nothing? Facebook users tend to be more positive than
negative (Chen et al. 2013), because they do not have the possibility to express
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 237
a simple “Not Like”when they do not agree. They only have the chance to
express “Like”to a negative comment.
In SNs like Twitter, those users who are motivated by the brand, the so
called “brand followers”, serve as role models and tweet or retweet brands’links
(Chu, Sung 2015). In fact, the information on a SN has higher impact on inten-
tion and trust than the information included on a ﬁrms’website (Meuter et al.
However, up to now there are a number of questions left unanswered which
presents challenges for new research: how can a company monitor each fan in
terms of revenue? What is the best way of measuring social networks? How can
thousands of fans be converted into proﬁt?
7 eWOM and Information
As a rule, eWOM is based on recommends, shares, likes and comments. How-
ever, comments can be classiﬁed not only according to positive or negative
information, but also according to their content. After analysing information on
numerous conversations in discussion forums, Andreassen and Streukens (2009)
considers that, they can be divided into four main categories:
a) Information request
b) Usage experience
c) Business practice issues
d) Comments pertaining to new product launches.
As can be seen, these four main categories relate to a ﬁnal business purpose.
Information request aims at a prospective purchase, usage experiences and com-
ments relating to new products are a response from buyers and can encourage
potential consumers. Finally, business practice issues are closely related to the
image of the enterprise and can boost sales.
Other researchers such as Hung and Li (2007) have highlighted four cate-
gories for the responses found in virtual communities:
a) Sources of social capital
b) Brand choice facilitation
c) Persuasion knowledge development
d) Consumer reﬂexivity.
There is not much consensus about how to generalize information in eWOM. It
seems apparent that a comment can facilitate the choice of brand and product
and is the driving force behind consumer persuasion. In this process, experience
is used as a mediator as well as business practice.
238 Maria Elena Aramendia-Muneta
eWOM has changed the buying environment. The amount of information
that a consumer can access has developed new ways of organizing information
and specialized sites such as e.g. TripAdvisor, where the buyer can compare
prices, evaluate other visitors’opinions and classify a service not only according
to the price variable. e-marketing strategists see the need to identify more
variables in order to be able to apply the best strategy. An example might be
the motivation behind the creation of fake comments that create disinformation
(Levine et al. 2010).
8 eWOM and Beneﬁts
Who really beneﬁts from the information on the Internet?
On the one hand, individuals transmitting information satisfy self-needs
such as being part of something noticeable. They also increase their social needs
and intentions, such as helping the community and bonding socially (Alexandrov
et al. 2013). By helping out another member of a virtual community the com-
municator enhances their self-worth (O’Reilly, Marx 2011).
Consumers use eWOM because they need to avoid uncertainty and risk in
their purchases (Chan, Ngai 2011; Goldsmith, Horowitz 2006; O’Reilly, Marx 2011).
In contrast, customer participation through eWOM inﬂuences behavioural
outcomes, which also aﬀects ﬁrms’outcomes by increasing eﬃciency and
generating higher revenues (Bolton, Saxena-Iyer 2009). When a product receives
a positive evaluation from consumers, as for example on Amazon, it is more
likely to be purchased (Leskovec et al. 2007).
eWOM has been recognized as a powerful driver of purchaser intention that
really beneﬁts enterprises. In fact, virtual communities foster attitudes and
purchase intentions (Huang et al. 2012). As various researchers have conﬁrmed
purchases, as well as brand image and reputation, are much inﬂuenced and
enriched by eWOM (Jalilvand, Samiei 2012).
Regarding the relation between consumers and ﬁrms, eWOM helps to enhance
conative and action loyalty levels (Roy et al. 2014). The largest beneﬁt of loyalty
is an increase in long-term sales as well as the fostering of repurchases.
Although there are several factors that inﬂuence eWOM, this chapter tries to take
a holistic approach, because ultimately the various eﬀects are interconnected
and it is very diﬃcult to assert that any single factor is the trigger in eWOM.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 239
EWOM creates both information and disinformation, because as consumers we
cannot clearly identify valid information, and ﬁrms cannot reliably identify
whether a comment is genuine or a fake.
In eWOM, inﬂuencers have a leading role and are a mediator between enter-
prises and potential customers, but they can also create disinformation because
some may be paid to assert something positive even when they themselves are
not convinced of it.
ENWOM needs to be followed and attended to, because it directly aﬀects
e-reputation. A prompt response may prevent a potentially undesirable snowball
eﬀect. However, too much ePWOM is also suspicious and can project a distorted
image of a company.
Virtual communities are very relevant to companies and they can be a
crucial asset for them in critical situations. Social networks can be a tool to
promote such virtual communities.
The information within individual comments is diﬃcult to classify, because
the amount of information on the Internet is so huge and there are few studies
in this area.
There are many unknown factors in this ﬁeld which need to be examined
and clariﬁed that are fundamental for companies in order for them to convert
the information into a source of revenue. Although there are several studies of
eWOM, there remain gaps that require further research.
Abrantes, José L.; Seabra, Cláudia; Lages, Cristiana R.; Jayawardhena, Chanaka. 2013. Drivers
of in-group and out-of-group electronic word-of-mouth (eWOM). European Journal of
Marketing 47 (7), pp. 1067–1088. DOI: 10.1108/03090561311324219.
Alexandrov, Aliosha; Lilly, Bryan; Babakus, Emin. 2013. The eﬀects of social- and self-motives
on the intentions to share positive and negative word of mouth. In Journal of the Academy
of Marketing Science 41 (5), pp. 531–546. DOI: 10.1007/s11747-012-0323-4.
Allsop, Dee T.; Bassett, Bryce R.; Hoskins, James A. 2007. Word-of-mouth research. Principles
and applications. In Journal of Advertising Research 47 (4), pp. 398–411. DOI: 10.2501/
Amblee, Naveen; Bui, Tung. 2011. Harnessing the inﬂuence of social proof in online shopping.
The eﬀect of electronic word of mouth on sales of digital microproducts. In International
Journal of Electronic Commerce 16 (2), pp. 91–114. DOI: 10.2753/JEC1086-4415160205.
Andreassen, Tor W.; Streukens, Sandra. 2009. Service innovation and electronic word‐of‐
mouth. Is it worth listening to? In Managing Service Quality 19 (3), pp. 249–265. DOI:
Arenas-Gaitan, Jorge; Rondan-Cataluña, Francisco Javier; Ramírez-Correa, Patricio Esteban.
2013. Social identity, electronic word-of-mouth and referrals in social network services.
In Kybernetes 42 (8), pp. 1149–1165. DOI: 10.1108/K-04-2013-0081.
240 Maria Elena Aramendia-Muneta
Arndt, Johan. 1967. Role of product-related conversations in the diﬀusion of a new product. In
Journal of Marketing Research 4 (3), pp. 291–295. DOI: 10.2307/3149462.
Arsal, Irem; Woosnam, Kyle. M.; Baldwin, Eliwabeth D.; Backman, Sheila J. 2010. Residents as
travel destination information providers. An online community perspective. In Journal of
Travel Research 49 (4), pp. 400–413. DOI: 10.1177/0047287509346856.
Ashtiani, Peyman G., & Iranmanesh, Ali. 2012. New approach to study of factors aﬀecting adop-
tion of electronic banking services with emphasis on the role of positive word of mouth. In
African Journal of Business Management 6 (11). DOI: 10.5897/AJBM11.2921.
Audrain-Pontevia, Anne-Françoise; Kimmel, Allan J. 2008. Negative word-of-mouth and redress
strategies. An exploratory comparison of French and American managers. In Journal of
Consumer Satisfaction, Dissatisfaction & Complaining Behavior 21, pp. 124–136. Available
online at http://jcsdcb.com/index.php/CSD_and_CB/article/view/50.
Awad, Neveen F.; Ragowsky, Arik. 2008. Establishing trust in electronic commerce through
online word of mouth. An examination across genders. In Journal of Management Informa-
tion Systems 24 (4), pp. 101–121. DOI: 10.2753/MIS0742-1222240404.
Bae, Jungho; Kim, Byung-Do. 2013. Is the electronic word of mouth eﬀect always positive on the
movie? In Academy of Marketing Studies Journal 17 (1), pp. 61–78.
Bae, Soonyong; Lee, Taesik. 2011a. Gender diﬀerences in consumers’perception of online
consumer reviews. In Electronic Commerce Research and Applications 11 (2), pp. 201–
214. DOI: 10.1007/s10660-010-9072-y.
Bae, Soonyong; Lee, Taesik. 2011b. Product type and consumers’perception of online consumer
reviews. In Electronic Markets 21 (4), pp. 255–266. DOI: 10.1007/s12525-011-0072-0.
Bambauer-Sachse, Silke; Mangold, Sabrina. 2011. Brand equity dilution through negative
online word-of-mouth communication. In Journal of Retailing and Consumer Services
18 (1), pp. 38–45. DOI: 10.1016/j.jretconser.2010.09.003.
Bao, Tong; Chang, Tung-lung Steven. 2014. Finding disseminators via electronic word of mouth
message for eﬀective marketing communications. In Decision Support Systems 67, pp. 21–
29. DOI: 10.1016/j.dss.2014.07.006.
Barreto, Ana Margarida. 2014. The word-of-mouth phenomenon in the social media era. In
International Journal of Market Research 56 (5), pp. 631–654.
Berger, Jonah; Iyengar, Raghuram. 2013. Communication channels and word of mouth. How the
medium shapes the message. In Journal of Consumer Research 40 (3), pp. 567–579. DOI:
Bolton, Ruth; Saxena-Iyer, Shruti. 2009. Interactive services. A framework, synthesis and
research directions. In Journal of Interactive Marketing 23 (1), pp. 91–104. DOI: 10.1016/j.
Breitsohl, Jan; Khammash, Marwan; Griﬃths, Gareth. 2010. E‐business complaint management.
Perceptions and perspectives of online credibility. In Journal of Enterprise Information
Management 23 (5), pp. 653–660. DOI: 10.1108/17410391011083083.
Brooks, Robert C. 1957. Word-of-mouth advertising in selling new products. In Journal of
Marketing 22 (2), pp. 154–161. DOI: 10.2307/1247212.
Brown, Jacqueline Johnson; Reingen, Peter H. 1987. Social ties and word-of-mouth referral
behavior. In Journal of Consumer Research 14 (3), p. 350. DOI: 10.1086/209118.
Brown, Jo; Broderick, Amanda J.; Lee, Nick. 2007. Word of mouth communication within online
communities. Conceptualizing the online social network. In Journal of Interactive Market-
ing 21 (3), pp. 2–20. DOI: 10.1002/dir.20082.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 241
Burton, Jamie; Khammash, Marwan. 2010. Why do people read reviews posted on consumer-
opinion portals? In Journal of Marketing Management 26 (3-4), pp. 230–255. DOI:
Casaló, Luis V.; Flavián, Carlos; Guinalíu, Miguel. 2008. The role of satisfaction and website
usability in developing customer loyalty and positive word‐of‐mouth in the e‐banking
services. In International Journal of Bank Marketing 26 (6), pp. 399–417. DOI: 10.1108/
Chan, Yolanda Y.Y.; Ngai, E.W.T. 2011. Conceptualising electronic word of mouth activity. In
Marketing Intelligence & Planning 29 (5), pp. 488–516. DOI: 10.1108/02634501111153692.
Chang, Aihwa; Hsieh, Sara H.; Tseng, Timmy H. 2013. Online brand community response to
negative brand events. The role of group eWOM. In Internet Research 23 (4), pp. 486–
506. DOI: 10.1108/IntR-06-2012-0107.
Chen, Chen-Yuan; Chen, Tsung-Hao; Chen, Ying-Hsiu; Chen, Chieh-Lian; Yu, Shang-En. 2013.
The spatio-temporal distribution of diﬀerent types of messages and personality traits
aﬀecting the eWOM of Facebook. In Natural Hazards 65 (3), pp. 2077–2103. DOI: 10.1007/
Chen, Shih-Chih. 2011. Understanding the eﬀects of technology readiness, satisfaction, and
electronic word of mouth on loyalty in 3C products. In Australian Journal of Business and
Management Research 1 (3), pp. 1–9.
Cheong, Hyuk Jun; Morrison, Margaret A. 2008. Consumers’reliance on product information
and recommendations found in UGC. In Journal of Interactive Advertising 8 (2), pp. 38–
49. DOI: 10.1080/15252019.2008.10722141.
Cheung, Christy M.K.; Lee, Matthew K.O.; Rabjohn, Neil. 2008. The impact of electronic word‐
of‐mouth. In Internet Research 18 (3), pp. 229–247. DOI: 10.1108/10662240810883290.
Cheung, Man Yee; Luo, Chuan; Sia, Choon Ling; Chen, Huaping. 2009. Credibility of electronic
word-of-mouth. Informational and normative determinants of on-line consumer recommen-
dations. In International Journal of Electronic Commerce 13 (4), pp. 9–38. DOI: 10.2753/
Cheung, Mee-Shew; Anitsal, M.; Anitsal, Ismet. 2007. Revisiting word-of-mouth communications.
A cross-national exploration. In The Journal of Marketing Theory and Practice 15 (3),
pp. 235–249. DOI: 10.2753/MTP1069-6679150304.
Chih, Wen-Hai; Wang, Kai-Yu; Hsu, Li-Chun; Huang, Su-Chen. 2013. Investigating electronic
word-of-mouth eﬀects on online discussion forums. The role of perceived positive elec-
tronic word-of-mouth review credibility. In Cyberpsychology, Behavior, and Social Network-
ing 16 (9), pp. 658–668. DOI: 10.1089/cyber.2012.0364.
Choi, Jayoung; Kim, Yongbum. 2014. The moderating eﬀects of gender and number of friends on
the relationship between self-presentation and brand-related word-of-mouth on Facebook.
In Personality and Individual Diﬀerences 68, pp. 1–5. DOI: 10.1016/j.paid.2014.03.040.
Chu, Shu-Chuan; Sung, Yongjun. 2015. Using a consumer socialization framework to under-
stand electronic word-of-mouth (eWOM) group membership among brand followers on
Twitter. In Electronic Commerce Research and Applications 14 (4), pp. 251–260. DOI:
De Angelis, Matteo; Bonezzi, Andrea; Peluso, Alessandro M.; Rucker, Derek D.; Costabile,
Michele. 2012. On braggarts and gossips. A self-enhancement account of word-of-mouth
generation and transmission. In Journal of Marketing Research 49 (4), pp. 551–563. DOI:
242 Maria Elena Aramendia-Muneta
de Cristofaro, Emiliano; Friedman, Arik; Jourjon, Guillaume; Kaafar, Mohamed Ali; Shaﬁq, M.
Zubair. 2014. Paying for likes? Understanding Facebook like fraud using honeypots. In
Carey Williamson, Aditya Akella, Nina Taft (Eds.): IMC ’14 proceedings of the 2014 con-
ference on internet measurement conference. Vancouver, BC, Canada. New York, pp. 129–
Doh, Sun-Jae; Hwang, Jang-Sun. 2009. How consumers evaluate eWOM (electronic word-of-
mouth) messages. In CyberPsychology & Behavior 12 (2), pp. 193–197. DOI: 10.1089/
East, Robert; Hammond, Kathy; Wright, Malcolm. 2007. The relative incidence of positive and
negative word of mouth. A multi-category study. In International Journal of Research in
Marketing 24 (2), pp. 175–184. DOI: 10.1016/j.ijresmar.2006.12.004.
Fulgoni, Gian M.; Lipsman, Andrew. 2015. Digital word of mouth and its oﬄine ampliﬁcation. In
Journal of Advertising Research 55 (1), pp. 18–21. DOI: 10.2501/JAR-55-1-018-021.
Gheorghe, Iuliana-Raluca; Liao, Mei-Na. 2012. Investigating romanian healthcare consumer be-
haviour in online communities. Qualitative research on negative eWOM. In Procedia –
Social and Behavioral Sciences 62, pp. 268–274. DOI: 10.1016/j.sbspro.2012.09.043.
Godes, David; Mayzlin, Dina. 2009. Firm-created word-of-mouth communication. Evidence from
aﬁeld test. In Marketing Science 28 (4), pp. 721–739. DOI: 10.1287/mksc.1080.0444.
Goldsmith, Ronald E.; Horowitz, David. 2006. Measuring motivations for online opinion seek-
ing. In Journal of Interactive Advertising 6 (2), pp. 2–14. DOI: 10.1080/15252019.2006.
Gretzel, Ulrike; Yoo, Kyung Hyan. 2008. Use and impact of online travel reviews. In Peter
O’Connor, Wolfram Höpken, Ulrike Gretzel (Eds.): Information and communication technol-
ogies in tourism 2008. Vienna: Springer Vienna, pp. 35–46.
Ha, Hong-Youl. 2002. The eﬀects of consumer risk perception on pre-purchase information in
online auctions. Brand, word-of-mouth, and customized information. In Journal of Com-
puter-Mediated Communication 8 (1), p. 0. DOI: 10.1111/j.1083-6101.2002.tb00160.x.
Ho, Jason Y.C.; Dempsey, Melanie. 2010. Viral marketing. Motivations to forward online content.
In Journal of Business Research 63 (9-10), pp. 1000–1006. DOI: 10.1016/j.jbusres.2008.
Hong, Ji-Young; Lee, Wei-Na. 2005. Consumer complaint behavior in the online environment. In
Yuan Gao (Ed.): Web systems design and online consumer behavior. Hershey, PA: Idea
Group Publishing, pp. 90–106.
Huang, Jen-Hung; Hsiao, Teng-Tai; Chen, YiI-Fen. 2012. The eﬀects of electronic word of mouth
on product judgment and choice. The moderating role of the sense of virtual community.
In Journal of Applied Social Psychology 42 (9), pp. 2326–2347. DOI: 10.1111/j.1559-
Hung, Kineta H.; Li, Stella Yiyan. 2007. The Inﬂuence of eWOM on virtual consumer communi-
ties. Social capital, consumer learning, and behavioral outcomes. In Journal of Advertising
Research 47 (4), pp. 485–495. DOI: 10.2501/S002184990707050X.
IBM. 2015. IBM Omnichannel Capability Index (OmCI) Survey 2015. Available online at http://www-
01.ibm.com/software/it/pdf/6855_EU_OmCi_2015_ﬂyer_v3.pdf, checked on 6/18/2016.
Jalilvand, Mohammad Reza; Samiei, Neda. 2012. The eﬀect of electronic word of mouth on brand
image and purchase intention. In Marketing Intelligence & Planning 30 (4), pp. 460–476.
Jeong, EunHa; Jang, SooCheong. 2011. Restaurant experiences triggering positive electronic
word-of-mouth (eWOM) motivations. In International Journal of Hospitality Management
30 (2), pp. 356–366. DOI: 10.1016/j.ijhm.2010.08.005.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 243
Katz, Elihu. 1957. The two-step ﬂow of communication. An up-to-date report on an hypothesis.
In Public Opinion Quarterly 21 (1), p. 61. DOI: 10.1086/266687.
Keller, Edward B.; Fay, Brad. 2012. The face-to-face book. Why real relationships rule in a
digital marketplace. New York: Free Press.
Kikumori, Mai; Ono, Akinori. 2013. Positive eﬀects of negative word-of-mouth on consumer
attitude. Considering the ratio and order of E-wom message. In Aric Rindﬂeisch, Jim
Burroughs (Eds.): Challenging the bounds of marketing thought. AMA winter marketing
educators’conference 2013. Red Hook, NY: Curran Associates, pp. 2–3.
Kim, Junyong; Gupta, Pranjal. 2012. Emotional expressions in online user reviews. How they
inﬂuence consumers’product evaluations. In Journal of Business Research 65 (7), pp. 985–
992. DOI: 10.1016/j.jbusres.2011.04.013.
Krishnamurthy, Sandeep; Kucuk, S. Umit. 2009. Anti-branding on the internet. In Journal of
Business Research 62 (11), pp. 1119–1126. DOI: 10.1016/j.jbusres.2008.09.003.
Kucuk, Umit S.; Krishnamurthy, Sandeep. 2007. An analysis of consumer power on the internet.
In Technovation 27 (1-2), pp. 47–56. DOI: 10.1016/j.technovation.2006.05.002.
Lee, Sheng-Hsien. 2009. How do online reviews aﬀect purchasing intention? In African Journal
of Business Management 3 (10), pp. 576–581. DOI: 10.5897/AJBM09.204.
Leskovec, Jure; Adamic, Lada A.; Huberman, Bernardo A. 2007. The dynamics of viral marketing.
In ACM Transactions on the Web 1 (1), pp. 5–39. DOI: 10.1145/1232722.1232727.
Levine, Timothy R.; Kim, Rachel K.; Hamel, Lauren M. 2010. People lie for a reason. Three
experiments documenting the principle of veracity. In Communication Research Reports
27 (4), pp. 271–285. DOI: 10.1080/08824096.2010.496334.
Li, Jiwei; Ott, Myle; Cardie, Claire; Hovy, Eduard. 2014. Towards a general rule for identifying
deceptive opinion spam. In Kristina Toutanova, Hua Wu (Eds.): Proceedings of the 52nd
Annual Meeting of the Association for Computational Linguistics. Baltimore, Maryland.
Association for Computational Linguistics, pp. 1566–1576.
Meuter, Matthew L.; McCabe, Deborah Brown; Curran, James M. 2013. Electronic word-of-mouth
versus interpersonal word-of-mouth. Are all forms of word-of-mouth equally inﬂuential? In
Services Marketing Quarterly 34 (3), pp. 240–256. DOI: 10.1080/15332969.2013.798201.
Nielsen. 2015. Global trust in advertising. Winning strategies for an evolving media landscape.
Available online at httpsː//www.nielsen.com/content/dam/nielsenglobal/apac/docs/
reports/2015/nielsen-global-trust-in-advertising-report-september-2015.pdf, checked on
Nyer, Prashanth U.; Gopinath, Mahesh. 2005. Eﬀects of complaining versus negative word of
mouth on subsequent changes in satisfaction. The role of public commitment. In Psychology
and Marketing 22 (12), pp. 937–953. DOI: 10.1002/mar.20092.
O’Reilly, Kelley; Marx, Sherry. 2011. How young, technical consumers assess online WOM credi-
bility. In Qualitative Market Research 14 (4), pp. 330–359. DOI: 10.1108/13522751111163191.
Pan, Lee-Yun; Chiou, Jyh-Shen. 2011. How much can you trust online information? Cues for per-
ceived trustworthiness of consumer-generated online information. In Journal of Interactive
Marketing 25 (2), pp. 67–74. DOI: 10.1016/j.intmar.2011.01.002.
Park, Cheol; Lee, Thae Min. 2009. Information direction, website reputation and eWOM eﬀect. A
moderating role of product type. In Journal of Business Research 62 (1), pp. 61–67. DOI:
Park, Young. A.; Gretzel, Ulrike. 2007. Success factors for destination marketing web sites. A
qualitative meta-analysis. In Journal of Travel Research 46 (1), pp. 46–63. DOI: 10.1177/
244 Maria Elena Aramendia-Muneta
Reichelt, Jonas; Sievert, Jens; Jacob, Frank. 2013. How credibility aﬀects eWOM reading. The
inﬂuences of expertise, trustworthiness, and similarity on utilitarian and social functions.
In Journal of Marketing Communications 20 (1-2), pp. 65–81. DOI: 10.1080/13527266.
Roy, Sanjit Kumar; Butaney, Gul; Sekhon, Harjit; Butaney, Bhupin. 2014. Word-of-mouth and
viral marketing activity of the on-line consumer. The role of loyalty chain stages theory.
In Journal of Strategic Marketing 22 (6), pp. 494–512. DOI: 10.1080/0965254X.2014.
Shimabukuro Sandes, Fabio; Torres Urdan, Andre. 2013. Electronic word-of-mouth impacts
on consumer behavior. Exploratory and experimental studies. In Journal of International
Consumer Marketing 25 (3), pp. 181–197. DOI: 10.1080/08961530.2013.780850.
Teng, Shasha; Khong, Kok Wei; Goh, Wei Wei; Chong, Alain Yee Loong. 2014. Examining the
antecedents of persuasive eWOM messages in social media. In Online Information Review
38 (6), pp. 746–768. DOI: 10.1108/OIR-04-2014-0089.
Wang, Chih-Chien; Wang, Pei-Hua; Yang, Yolande Y. H. 2014. Opinion leadership and negative
word-of-mouth communication. In Leon Shyue-Liang Wang, Jason J. June, Chung-Hong Lee,
Koji Okuhara, Hsin-Chang Yang (Eds.): Multidisciplinary social networks research. Berlin,
Heidelberg: Springer, pp. 36–47.
Word of Mouth Marketing Association (WOMMA). 2014. Return on word of mouth. Available
online at http://womma.org/wp-content/uploads/2015/09/STUDY-WOMMA-Return-on-WOM-
Executive-Summary.pdf, checked on 3/3/2016.
Zhang, Jason Q.; Craciun, Georgiana; Shin, Dongwoo. 2010. When does electronic word-of-
mouth matter? A study of consumer product reviews. In Journal of Business Research
63 (12), pp. 1336–1341. DOI: 10.1016/j.jbusres.2009.12.011.
Spread the Word –The Eﬀect of Word of Mouth in e-Marketing 245