ChapterPDF Available

Spread the Word - The Effect of Word of Mouth in E-Marketing

Authors:

Abstract and Figures

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 influencers 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, preventing 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.
Content may be subject to copyright.
Maria Elena Aramendia-Muneta
4.2 Spread the Word The Eect 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 dierent 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 inuence on credibility, trust and persuasiveness, where inu-
encers play a main role. Brand reputation is shaped by the ow of information
and disinformation on the Internet. Social networks are a real tool with which to
create and place information. A comment may greatly benet 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 benet.
1 Introduction
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
phenomenon.
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
dierence may even be increasing. In fact, thanks to the Internet, the relation
between rms 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
De Gruyter.
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 aect
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 benet 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 aects marketing strategies. Firstly, by means of a com-
parison between WOM and eWOM so as to comprehend the dierences and rela-
tionships between oine and online channels and how information is conveyed.
Secondly, the role of inuencers 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 benets 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 dierent 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 persons social, familial, work
and collegial circles) (Brown, Reingen 1987). Moving one-step forward, eWOM
could aect 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 dierent 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 Advertisingby Nielsen (2015), consumers
respond more to recommendations from people I knowas a rst choice. In
second place comes the branded websitesand in third consumer opinions
posted online. As table 1 shows, among the top six trusted ways of advertising,
only the fth aects 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 Nielsens results regarding recommendations
from people I know. As human beings, interpersonal relationship is the key to
WOM. However, 90% of conversations are oine (WOM through face-to-face
conversations) and only 8% online (eWOM). Is it possible to conrm that
eWOM has a deeper impact than WOM?
WOMMA (2014) asserted that oine WOM is responsible for 2/3 of the
impact, while online only accounts for 1/3. However, they both amplify the eect
of paid media by 15%. Compared to traditional advertising, WOM has an im-
mediate impact, especially in the rst two weeks. In the movie industry, eWOM
particularly aects the rst weeks box-oce revenues and after the second week
both WOM and eWOM have a direct inuence on revenues (Bae, Kim 2013).
The go viraleect 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 viralhas 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 Eect 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 oine 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
success.
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 eect (Arndt 1967; Ho, Dempsey 2010). Reading comments is a
mean to reducing uncertainty. As a general rule, in the oand 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 eective
because it leads senders to describe products and brands more accurately
through a self-enhancement eect (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 veried.
Besides, people often make their decisions after consulting online informa-
tion (Lee 2009). Therefore, eWOM aects not only online purchases, but also
those in the real world (gure 1). The opposite eect is also well known by com-
panies and mostly aects 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 oine physically and then to purchase
it online.
Although eWOM is gaining in popularity, marketers should not believe that
eWOM has replaced WOM (Fulgoni, Lipsman 2015). Oine 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 oine 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 aects a small companys equity, but also that of
renowned enterprises (Bambauer-Sachse, Mangold 2011). Thus the size of a
company does not render it immune to this eect.
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 signicant eect on experience goods than on
search goods and even more so if the websites 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) identied that an eNWOM may have a positive impact.
Figure 1: Relation between eWOM, WOM, and purchase
Spread the Word The Eect 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,
Gupta 2012).
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 satised consumer is more prone to writing a positive comment
than an unsatised customer is prone to writing a negative comment. However,
in the study by Cheung et al. (2007) cultural dierences 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 inuence on the receiver.
The two-step ow communication (Katz 1957) also applies to eWOM. In this
case, there is a push communication relation (gure 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 nd the best inuencer with the best impact on potential
customers (Arenas-Gaitan et al. 2013). When inuencers really believe that a
service or product could benet consumers, their comments improve the credi-
bility of the brand and increase purchase intention. Fashion bloggers are
well known as a best inuencer. 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 saladevery 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 Ferragnis success.
Figure 2: Push relation between positive comment, credibility, and purchase intention
Such inuencers 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). Inuencers have the role of
leadership and that is why manufacturers and producers should nd a leader
to spread their information so as to prot from it (return on investment in e-
marketing) or generate a multiplier or ripple eect. Overall, an authoritative
232 Maria Elena Aramendia-Muneta
opinion maker entices future customers or fosters a behavioural outcome. How-
ever, it remains unclear whether inuencers have been paid by the enterprise to
write positive comments. The market rate for inuencers is around 6,000 to
promote a product or a brand.
1
A problem with the inuence 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 aect purchases, eNWOM reduces the
percentage of sales, whereas ePWOM has the opposite eect.
4 eWOM and Variables
The key elements to eective persuasion on the Internet are credibility and trust
(Teng et al. 2014). Credibility, trust and persuasiveness are variables that are
closely interconnected (gure 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 Eect of Word of Mouth in e-Marketing 233
their studies on one variable (Cheung et al. 2009; Pan, Chiou 2011), their nd-
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 brands 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 inuences purchase intention (see gure 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 suer
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). Usersacceptance 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 aecting 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 dierent opinions, as in that way they have the
opportunity of choosing which is more convincing (OReilly, Marx 2011).
The initial model in gure 4 has moderators that aect credibility and the
nal decision (gure 5).
Figure 5: Moderators aecting 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 specic institution. A positive word of
mouth has a positive eect on electronic banking use and enhances trust in a
particular bank, while a negative comment inuences the consumer, creating
risk and potential mistrust (Ashtiani, Iranmanesh 2012).
What really undermines the trust of a rm when it receives eNWOM is their
failure to take immediate action (Audrain-Pontevia, Kimmel 2008). Users are
wary of those rms 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 aect trust. Cheung et al.
(2009) showed that consumers are more likely to trust eWOM when comments
are more credible.
Spread the Word The Eect of Word of Mouth in e-Marketing 235
4.3 eWOM and Persuasiveness
When it comes to persuasiveness, inuences play a major role. In fact, the repu-
tation of a reviewer directly aects followers and laggardspurchase behaviour
due to their trust of expertise.
The information given by residents is more persuasive in the accommoda-
tion, food and beverage sectors than travellersexperiences, whose opinion has
more inuence 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 eects,
whereas positive actions tend to have positive eects as a response (Zhang et al.
2010).
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 aects 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 aects brand reputation, because women
are more willing to post brand-related content on Facebook than men (Choi,
Kim 2014).
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 companys 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
Urdan 2013).
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
eect (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 dierence 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 rst 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 dierence 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 nd 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 Likerather than sharing the information within their social
circle (Chen 2011).
What happens with those social networks such as Facebook where the user
can only Likeor 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 Eect of Word of Mouth in e-Marketing 237
a simple Not Likewhen they do not agree. They only have the chance to
express Liketo 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 brandslinks
(Chu, Sung 2015). In fact, the information on a SN has higher impact on inten-
tion and trust than the information included on a rmswebsite (Meuter et al.
2013).
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 prot?
7 eWOM and Information
As a rule, eWOM is based on recommends, shares, likes and comments. How-
ever, comments can be classied 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 nal 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 reexivity.
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 visitorsopinions 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 Benets
Who really benets 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 (OReilly, Marx 2011).
Consumers use eWOM because they need to avoid uncertainty and risk in
their purchases (Chan, Ngai 2011; Goldsmith, Horowitz 2006; OReilly, Marx 2011).
In contrast, customer participation through eWOM inuences behavioural
outcomes, which also aects rmsoutcomes by increasing eciency 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 benets enterprises. In fact, virtual communities foster attitudes and
purchase intentions (Huang et al. 2012). As various researchers have conrmed
purchases, as well as brand image and reputation, are much inuenced and
enriched by eWOM (Jalilvand, Samiei 2012).
Regarding the relation between consumers and rms, eWOM helps to enhance
conative and action loyalty levels (Roy et al. 2014). The largest benet of loyalty
is an increase in long-term sales as well as the fostering of repurchases.
9 Conclusion
Although there are several factors that inuence eWOM, this chapter tries to take
a holistic approach, because ultimately the various eects are interconnected
and it is very dicult to assert that any single factor is the trigger in eWOM.
Spread the Word The Eect of Word of Mouth in e-Marketing 239
EWOM creates both information and disinformation, because as consumers we
cannot clearly identify valid information, and rms cannot reliably identify
whether a comment is genuine or a fake.
In eWOM, inuencers 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 aects
e-reputation. A prompt response may prevent a potentially undesirable snowball
eect. 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 dicult 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 eld which need to be examined
and claried 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.
Publication Bibliography
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. 10671088. DOI: 10.1108/03090561311324219.
Alexandrov, Aliosha; Lilly, Bryan; Babakus, Emin. 2013. The eects 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. 531546. 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. 398411. DOI: 10.2501/
S0021849907070419.
Amblee, Naveen; Bui, Tung. 2011. Harnessing the inuence of social proof in online shopping.
The eect of electronic word of mouth on sales of digital microproducts. In International
Journal of Electronic Commerce 16 (2), pp. 91114. DOI: 10.2753/JEC1086-4415160205.
Andreassen, Tor W.; Streukens, Sandra. 2009. Service innovation and electronic wordof
mouth. Is it worth listening to? In Managing Service Quality 19 (3), pp. 249265. DOI:
10.1108/09604520910955294.
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. 11491165. DOI: 10.1108/K-04-2013-0081.
240 Maria Elena Aramendia-Muneta
Arndt, Johan. 1967. Role of product-related conversations in the diusion of a new product. In
Journal of Marketing Research 4 (3), pp. 291295. 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. 400413. DOI: 10.1177/0047287509346856.
Ashtiani, Peyman G., & Iranmanesh, Ali. 2012. New approach to study of factors aecting 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. 124136. 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. 101121. DOI: 10.2753/MIS0742-1222240404.
Bae, Jungho; Kim, Byung-Do. 2013. Is the electronic word of mouth eect always positive on the
movie? In Academy of Marketing Studies Journal 17 (1), pp. 6178.
Bae, Soonyong; Lee, Taesik. 2011a. Gender dierences in consumersperception 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 consumersperception of online consumer
reviews. In Electronic Markets 21 (4), pp. 255266. 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. 3845. DOI: 10.1016/j.jretconser.2010.09.003.
Bao, Tong; Chang, Tung-lung Steven. 2014. Finding disseminators via electronic word of mouth
message for eective 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. 631654.
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. 567579. DOI:
10.1086/671345.
Bolton, Ruth; Saxena-Iyer, Shruti. 2009. Interactive services. A framework, synthesis and
research directions. In Journal of Interactive Marketing 23 (1), pp. 91104. DOI: 10.1016/j.
intmar.2008.11.002.
Breitsohl, Jan; Khammash, Marwan; Griths, Gareth. 2010. Ebusiness complaint management.
Perceptions and perspectives of online credibility. In Journal of Enterprise Information
Management 23 (5), pp. 653660. DOI: 10.1108/17410391011083083.
Brooks, Robert C. 1957. Word-of-mouth advertising in selling new products. In Journal of
Marketing 22 (2), pp. 154161. 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. 220. DOI: 10.1002/dir.20082.
Spread the Word The Eect 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. 230255. DOI:
10.1080/02672570903566268.
Casaló, Luis V.; Flavián, Carlos; Guinalíu, Miguel. 2008. The role of satisfaction and website
usability in developing customer loyalty and positive wordofmouth in the ebanking
services. In International Journal of Bank Marketing 26 (6), pp. 399417. DOI: 10.1108/
02652320810902433.
Chan, Yolanda Y.Y.; Ngai, E.W.T. 2011. Conceptualising electronic word of mouth activity. In
Marketing Intelligence & Planning 29 (5), pp. 488516. 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 dierent types of messages and personality traits
aecting the eWOM of Facebook. In Natural Hazards 65 (3), pp. 20772103. DOI: 10.1007/
s11069-012-0468-y.
Chen, Shih-Chih. 2011. Understanding the eects 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. 19.
Cheong, Hyuk Jun; Morrison, Margaret A. 2008. Consumersreliance 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
ofmouth. In Internet Research 18 (3), pp. 229247. 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. 938. DOI: 10.2753/
JEC1086-4415130402.
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. 235249. DOI: 10.2753/MTP1069-6679150304.
Chih, Wen-Hai; Wang, Kai-Yu; Hsu, Li-Chun; Huang, Su-Chen. 2013. Investigating electronic
word-of-mouth eects 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. 658668. DOI: 10.1089/cyber.2012.0364.
Choi, Jayoung; Kim, Yongbum. 2014. The moderating eects of gender and number of friends on
the relationship between self-presentation and brand-related word-of-mouth on Facebook.
In Personality and Individual Dierences 68, pp. 15. 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. 251260. DOI:
10.1016/j.elerap.2015.04.002.
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. 551563. DOI:
10.1509/jmr.11.0136.
242 Maria Elena Aramendia-Muneta
de Cristofaro, Emiliano; Friedman, Arik; Jourjon, Guillaume; Kaafar, Mohamed Ali; Shaq, 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
136.
Doh, Sun-Jae; Hwang, Jang-Sun. 2009. How consumers evaluate eWOM (electronic word-of-
mouth) messages. In CyberPsychology & Behavior 12 (2), pp. 193197. DOI: 10.1089/
cpb.2008.0109.
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. 175184. DOI: 10.1016/j.ijresmar.2006.12.004.
Fulgoni, Gian M.; Lipsman, Andrew. 2015. Digital word of mouth and its oine amplication. In
Journal of Advertising Research 55 (1), pp. 1821. 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. 268274. DOI: 10.1016/j.sbspro.2012.09.043.
Godes, David; Mayzlin, Dina. 2009. Firm-created word-of-mouth communication. Evidence from
aeld test. In Marketing Science 28 (4), pp. 721739. 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. 214. DOI: 10.1080/15252019.2006.
10722114.
Gretzel, Ulrike; Yoo, Kyung Hyan. 2008. Use and impact of online travel reviews. In Peter
OConnor, Wolfram Höpken, Ulrike Gretzel (Eds.): Information and communication technol-
ogies in tourism 2008. Vienna: Springer Vienna, pp. 3546.
Ha, Hong-Youl. 2002. The eects 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. 10001006. DOI: 10.1016/j.jbusres.2008.
08.010.
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. 90106.
Huang, Jen-Hung; Hsiao, Teng-Tai; Chen, YiI-Fen. 2012. The eects 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. 23262347. DOI: 10.1111/j.1559-
1816.2012.00943.x.
Hung, Kineta H.; Li, Stella Yiyan. 2007. The Inuence of eWOM on virtual consumer communi-
ties. Social capital, consumer learning, and behavioral outcomes. In Journal of Advertising
Research 47 (4), pp. 485495. 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_yer_v3.pdf, checked on 6/18/2016.
Jalilvand, Mohammad Reza; Samiei, Neda. 2012. The eect of electronic word of mouth on brand
image and purchase intention. In Marketing Intelligence & Planning 30 (4), pp. 460476.
DOI: 10.1108/02634501211231946.
Jeong, EunHa; Jang, SooCheong. 2011. Restaurant experiences triggering positive electronic
word-of-mouth (eWOM) motivations. In International Journal of Hospitality Management
30 (2), pp. 356366. DOI: 10.1016/j.ijhm.2010.08.005.
Spread the Word The Eect of Word of Mouth in e-Marketing 243
Katz, Elihu. 1957. The two-step ow 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 eects of negative word-of-mouth on consumer
attitude. Considering the ratio and order of E-wom message. In Aric Rindeisch, Jim
Burroughs (Eds.): Challenging the bounds of marketing thought. AMA winter marketing
educatorsconference 2013. Red Hook, NY: Curran Associates, pp. 23.
Kim, Junyong; Gupta, Pranjal. 2012. Emotional expressions in online user reviews. How they
inuence consumersproduct 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. 11191126. 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. 4756. DOI: 10.1016/j.technovation.2006.05.002.
Lee, Sheng-Hsien. 2009. How do online reviews aect purchasing intention? In African Journal
of Business Management 3 (10), pp. 576581. 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. 539. 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. 271285. 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. 15661576.
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 inuential? In
Services Marketing Quarterly 34 (3), pp. 240256. 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
6/18/2016.
Nyer, Prashanth U.; Gopinath, Mahesh. 2005. Eects of complaining versus negative word of
mouth on subsequent changes in satisfaction. The role of public commitment. In Psychology
and Marketing 22 (12), pp. 937953. DOI: 10.1002/mar.20092.
OReilly, Kelley; Marx, Sherry. 2011. How young, technical consumers assess online WOM credi-
bility. In Qualitative Market Research 14 (4), pp. 330359. 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. 6774. DOI: 10.1016/j.intmar.2011.01.002.
Park, Cheol; Lee, Thae Min. 2009. Information direction, website reputation and eWOM eect. A
moderating role of product type. In Journal of Business Research 62 (1), pp. 6167. DOI:
10.1016/j.jbusres.2007.11.017.
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. 4663. DOI: 10.1177/
0047287507302381.
244 Maria Elena Aramendia-Muneta
Reichelt, Jonas; Sievert, Jens; Jacob, Frank. 2013. How credibility aects eWOM reading. The
inuences of expertise, trustworthiness, and similarity on utilitarian and social functions.
In Journal of Marketing Communications 20 (1-2), pp. 6581. DOI: 10.1080/13527266.
2013.797758.
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. 494512. DOI: 10.1080/0965254X.2014.
885990.
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. 181197. 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. 746768. 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. 3647.
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. 13361341. DOI: 10.1016/j.jbusres.2009.12.011.
Spread the Word The Eect of Word of Mouth in e-Marketing 245
... With more than half of the world's population being active social media users today (Johnson, 2021), social media is expanding the reach of conservation messaging, as information can be spread easily and quickly. Word of mouth (or spreading the word) is a powerful mechanism for spreading information, and is an important and established marketing tool that evolved into "electronic word of mouth" in the digital space with the advent of social networking (Aramendia-Muneta, 2017;Groeger & Buttle, 2014). Social media is also diversifying the ways in which people can contribute to conservation funding. ...
Article
Full-text available
Conservation needs adequate support and funding to address our ecological crises. People support conservation in different ways, from social media engagement to donating money. Various factors influence how people choose to support conservation, including social norms and ecological status. The rise of social media has provided people with an easy and low‐cost way to support conservation: sharing information online. How valuable is social media engagement and activism for conservation funding and outcomes? Here, we develop an evolutionary game‐theoretic framework to understand the complex interactions between individuals in the context of social media information sharing, conservation philanthropy, and how these interactions ultimately impact ecological outcomes. From a game theory perspective, we can consider donors to be hard‐cooperators, sharers of information on social media to be soft‐cooperators, and those who do nothing to be non‐cooperators. Our model shows that soft‐cooperators can help stabilize conservation funding flows and develop social norms. Supporting conservation through social media sharing can ultimately contribute to conservation success. Our study conceptualizes the complex decision‐making processes of conservation funding and affirms the importance and value of mobilizing all types of supporters in conservation. In this manuscript, we develop a novel game‐theoretic framework to understand the complex interactions between individuals in the context of information sharing on social media, conservation philanthropy, and how these interactions ultimately impact ecological outcomes. Our results demonstrate the importance of mobilizing people who are weakly engaged in conservation action to stabilize funding flow and ecological status.
... Thus, graduates who participated in a futures market simulation game will have a comparative advantage when looking for jobs because their abilities, knowledge, and experience were gained through real trading simulation. Therefore, this study attempts to contribute more findings from the study conducted by the previous research as these studies accentuated the need for future research to accommodate more insight on trading simulation and to understand more the effects of ePWOM on other categories, exploring the constructs on cultural and geographical diversity, conducting research on the other platform and employs different quantitative technique (Aramendia-Muneta, 2017;Liew et al., 2019;Rao et al., 2021;Sharif & Naghavi, 2021). ...
... The budgets dedicated to influencer marketing actions are increasingly more prominent. They produce more measurable results, have a direct impact on sales, a real return on investment, and a more immediate effect than traditional advertising in both the sectors where it has been more common (fashion and beauty), and in others where its use is gradually spreading, among which is undoubtedly the equestrian sector (Venegas, 2015;Aramendía-Muneta, 2017;Buttle and Groeger, 2017). ...
Article
Full-text available
Influencers have gained an important place in many companies’ business strategies since they convey trust and confidence to those who follow them. Their use as promotion tools is widespread in the sports field. However, they are only beginning to be introduced in the equestrian sector. The main objective of this paper is to analyse the opinion that potential target markets have about social networks and influencers as new communication tools. Quantitative methodologies have been used (sector data analysis, survey descriptive and inferential analyses) and qualitative methodologies (interviews). 80% of the current or potential audience admits that if there were more influencers, they would follow them. 80% of users who follow equestrian content profiles mainly follow informative ones, and 65.1% use them as a training and learning resource. 80.7% of the participants state that they have found new information thanks to an influencer, while 55.4% admit to making a purchase or applying one of the techniques because of them. Despite the sector being in a development phase, the use of influencers as a communication tool is highly recommended in the equestrian sector. DOI: https://doi.org/10.31921/doxacom.n32a6
... De hecho, los presupuestos dedicados a acciones de marketing con influencers son cada vez más grandes y producen resultados medibles, con un impacto directo en ventas, un retorno de inversión real y un efecto más inmediato que la publicidad tradicional, tanto en los sectores en los que ha venido siendo más habitual (moda y belleza), como en otros a los que paulatinamente se va generalizando su utilización, entre los que, sin duda, se encuentra el ecuestre (Venegas, 2015;Aramendía-Muneta, 2017;Buttle y Groeger, 2017). ...
Article
Full-text available
Los influencers han alcanzado un lugar preminente en las estrategias comerciales de muchas empresas, ya que transmiten seguridad y confianza a quienes les siguen. Su empleo como herramientas de promoción está bastante generalizado en el ámbito deportivo y están empezando a introducirse en el sector ecuestre en particular. El principal objetivo de este artículo es analizar la opinión que el potencial público receptor del sector tiene sobre las redes sociales y los influencers como nuevas herramientas de comunicación. Se han empleado metodologías de tipo cuantitativo (análisis de datos de fuentes secundarias del sector, estudio y análisis bivariado de los resultados de una encuesta) y cualitativas (entrevistas). El 80% del público potencial admite que si hubiera más influencers los seguirían. Un 80% de los usuarios que siguen perfiles de contenido ecuestre siguen principalmente perfiles de tipo informativo y un 65,1% los aprovechan como fuente de aprendizaje y formación. El 80,7% declara haber hecho nuevos descubrimientos gracias a un influencer, mientras que un 55,4% manifiesta haber realizado una compra gracias a ellos. Se concluye que, a pesar de estar en fase de desarrollo en el sector, la utilización de los influencers como herramienta de comunicación resulta muy recomendable.
Conference Paper
This study intends to distinguish hidden factors and causal associations between cognitive rationality and misinformation that influence panic buying and price hikes. It utilizes consumer behavior theory consolidating cognitive rationality and misinformation concept for anticipating the complementary or unidirectional impact of price hikes and frenzy purchasing. Hierarchical regression analysis will be conducted to distinguish the connections between four builds: misinformation, cognitive rationality, price hikes, and panic buying. Since SNS, WOM, and print media are as of now the most broadly utilized wellsprings of data and for spreading deception, that is the reason we think about SNS, WOM, and Print Media as our primary apparatuses which connect the mediator and moderator. A survey questionnaire is used to examine all presumptions. We are expecting that negative misinformation will direct hoarders' mental objectivity, which will intervene in price hikes, and panic buying. Likewise, it is also expected that price hikes and panic buying will have a reciprocal relationship.
Article
Full-text available
Social media platforms provide easy access to the public opinion (called electronic word-of-mouth), which can be collected and analyzed to extract knowledge about the reputation of an organization. Monitoring this reputation in the public sector may bring several benefits for its institutions, especially in supporting decision-making and developing marketing campaigns. Thus, to offer a solution aimed at the needs of this sector, the goal of this research was to develop a methodology capable of extracting relevant information about eWOM in social media, using text mining and natural language processing techniques. Our goal was achieved through a methodology capable of handling the small amount of information regarding public state organizations in social media. Additionally, our work was validated using the context of the Portuguese Army and revealed the potential to provide indicators of institutional reputation. Our results present one of the first cases of the application of this type of techniques to an Army organization and to understand its negative reputation among the population.
Article
Full-text available
The snowballing use of web-mediated communications has amplified the influence that electronic word of mouth has on information adoption and purchase decisions. In view of this, the primary objective of this research has been directed towards the investigation of the effect of electronic word of mouth (E-Wom) on the intention to visit the Algerian Sahara. For that, a sample of foreigners and local Algerians was targeted through an online questionnaire. As to the findings, there was a significant correlation between the E-Wom credibility and valence and the intention to visit the Algerian Sahara. Likewise, there was no significant correlation between E-Wom quantity and visit intention. We also found that both-Wom credibility and valence influence the intention to visit, meanwhile, the E-Wom quantity was found to have no significant impact. Résumé: l'utilisation massive des communications médiées par le Web a amplifié l'influence du bouche à oreille électronique sur l'adoption des informations et la décision d'achat. Dans ce contexte, l'objectif principal de cette recherche est d'étudier l'effet du bouche à oreille électronique (E-Wom) sur l'intention de visiter le Sahara algérien. Pour cela, un échantillon d'étrangers et des Algériens locaux a été ciblé via un questionnaire en ligne. Les résultats indiquent qu'il existe une corrélation significative entre la crédibilité et la valence du bouche à oreille électronique et l'intention de visiter le Sahara algérien. Alors qu'il n'y a pas de corrélation significative entre la quantité et l'intention de visite. Nous avons également constaté que la crédibilité et la valence du bouche à oreille électronique influent sur l'intention de visiter, tandis que la quantité n'a pas eu d'impact significatif. Mots clés: bouche à oreille électronique, crédibilité, quantité, la valence, l'intention de visiter le Sahara algérien
Article
This article spotlights the relationship between likes and comments and the content of tourism photographs on Instagram with the aim of understanding users’ behavior and, thus, helping destination management organizations. Based on the stimulus-organism-response model, a content analysis was conducted of 1,094 pictures that received 131,116,800 likes and 2,859,448 comments. By combining content analysis and regression analysis, the results show that Instagrammers’ responses are influenced differently by different picture attrib- utes, resulting in dissimilar behavior with regard to likes and comments. Specifically, likes, as immediate reactions, tend to be driven by content featuring people, views, or common habits. In contrast, comments, which require greater effort on the part of the Instagrammer, are elicited by the topic of festi- vals or hotels, colors such as cream, green, orange, or yellow, images of water or animals, and images featuring tourist activ- ities, mostly at night. Multi-image or fake pictures negatively impact likes. By analyzing the content of the information pro- vided by the uploaded photographs, a typology of photo- graphic attributes is developed to offer clues for destination management organizations to enhance engagement with potential customers and Instagram users.
Article
This article spotlights the relationship between likes and comments and the content of tourism photographs on Instagram with the aim of understanding users’ behavior and, thus, helping destination management organizations. Based on the stimulus-organism-response model, a content analysis was conducted of 1,094 pictures that received 131,116,800 likes and 2,859,448 comments. By combining content analysis and regression analysis, the results show that Instagrammers’ responses are influenced differently by different picture attributes, resulting in dissimilar behavior with regard to likes and comments. Specifically, likes, as immediate reactions, tend to be driven by content featuring people, views, or common habits. In contrast, comments, which require greater effort on the part of the Instagrammer, are elicited by the topic of festivals or hotels, colors such as cream, green, orange, or yellow, images of water or animals, and images featuring tourist activities, mostly at night. Multi-image or fake pictures negatively impact likes. By analyzing the content of the information provided by the uploaded photographs, a typology of photographic attributes is developed to offer clues for destination management organizations to enhance engagement with potential customers and Instagram users.
Article
Full-text available
Purpose – Numerous electronic word-of-mouth (eWOM) studies have been conducted to examine the effectiveness of persuasive eWOM messages. Despite the impact of eWOM messages in decision-making processes, few researches have directly tested potential antecedents of persuasive eWOM messages among message recipients in social media context. The purpose of this paper is to critically discuss and examine the determinants of persuasive eWOM messages when message recipients intend to accept and use eWOM messages. Design/methodology/approach – The authors reviewed extant literature of eWOM and proposed hypotheses regarding persuasive eWOM messages in social media context. A survey of 78 respondents was conducted and the data were analysed using SmartPLS. Findings – This study found that argument quality, source credibility, source attractiveness, source perception and source style are critical antecedents of persuasive eWOM messages. The PLS results suggested that source credibility (trustworthiness), source perception (usefulness, social ties) and source style (visual cues, number) are main characteristics of credible eWOM messages in relation to users’ intention to accept and use online reviews. The variance of information acceptance and intention to use were also explained in the findings. Practical implications – This paper identified critical antecedents of persuasive eWOM messages and suggested eWOM messages as a credible source. An integrated conceptual framework was developed to illustrate comprehensive antecedents of persuasive eWOM messages, and the relationships between these messages, information acceptance and intention to use. Originality/value – The significance of the study is to identify the effectiveness of eWOM messages and its impact on intention to accept and use these messages. Moreover, this study will provide insightful guidelines for marketers with practical implications in approaching emerging markets via eWOM initiatives.
Chapter
The emergence of the Internet and its communication capabilities have changed the way consumers communicate their negative experiences with products and services. This chapter offers a comprehensive assessment of the Internet as a viable complaint communication channel and details its related threats and opportunities. An integrated conceptual model of consumer complaint behavior is proposed. It is suggested that an in-depth understanding of the psychological mechanisms that underlie consumer complaint behavior and the characteristics of online communication as well as the characteristics of the business may be essential in taking advantage of the Internet as a complaint communication channel. Managerial implications and recommendations for practical implementation are also suggested.
Conference Paper
Customers may feel negative emotion when they experience service failure. The negative emotion may induce unsatisfactory customers to spread negative word-of-mouths (WOM). However, not all unsatisfactory will spread negative WOM. The current study conducted an experimental design to explore the influence of opinion leadership tendency to negative word-of-mouth communication intention. The results revealed that customers will spread negative WOMs when the service failure is serious. However, when the service failure is minor, customers with a higher opinion leadership tendency are with higher intention to spread negative WOMs. The findings of the current are useful in exploring the role of opinion leadership tendency in negative WOM communication.
Article
Previous studies have been arguing that the movie revenue is affected by the volume or the valence or both of electronic word of mouth (Liu, 2006; Chintagunta et al., 2010, Bae et al. 2010). Those studies analyzed the effect of the electronic word of mouth (eWOM) in the film industry with the assumption that the volume of eWOM unilaterally affects revenue. However, they overlooked that box-office revenue, in turn, can influence the volume of eWOM. Therefore, in this study, a simultaneous equation that considers the influence of box-office revenue on eWOM as well is formulated. The authors found that the valence of eWOM affects box-office revenue and the volume of eWOM is not cause of the revenue except the first week. The volume of eWOM is affected by the revenue on the contrary. The authors also found that eWOM effect in the result above does not work on the successful movie. Because the audiences have a lot of information sources about the successful movie, they don't have to check out the eWOM on the website before going to movie theater. But, in the case of the unsuccessful movie the audiences don't have enough sources to gather the information about the movie except eWOM on the website, so that eWOM is critical to them. The authors conclude that the movie companies have to use the eWOM as an important communication channel especially if it is expected to be the unsuccessful movie.
Article
With the real-time brand communications prevalent in Twitter, it has emerged as an increasingly important social technology that facilitates brand-focused electronic word-of-mouth (eWOM) activities. The objective of the current research is to examine the factors which discriminate between Twitter brand followers’ decisions to engage in eWOM behaviors on the site. Specifically, this study proposes that peer communication, brand-related factors, and Twitter usage motivate brand followers to share eWOM messages on Twitter. Results from an online survey showed that brand followers who serve as role-models to others, those with positive attitudes toward and relationships with brands on Twitter, those who most heavily use Twitter and follow many brands, were most likely to tweet brands. Similar patterns were found in terms of retweeting the links of brands. This study contributes to the literature by demonstrating that Twitter is a socialization agent that facilitates eWOM and provides useful insights for social media marketers.