How excited are you supposed to be?
Company’s reaction to positive eWOM and its effect on brand attitude and virality, mediated by skepticism, trust in the brand and brand warmth.
Elisabeth Carolina van Duist (Ellis)
10671005
MSc in Business Administration – Marketing Track
Amsterdam Business School - UvA
29 January, 2016
Final draft
First supervisor: dr. Alfred Zerres
Statement of originality
This document is written by Elisabeth Carolina van Duist who declares to take
full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and
that no sources other than those mentioned in the text and its references have
been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision
of completion of the work, not for the contents.
Table of Contents
Abstract ... 4
Introduction ... 5
Literature Review ... 7
Defining the Types of eWOM ... 7
Strategies ... 8
Defining Virality ... 10
Skepticism Towards eWOM ... 11
Trust in the Brand ... 13
Brand Warmth ... 14
Data and Method ... 16
Sample and Procedure ... 16
Measures ... 17
Analysis ... 18
Results ... 18
Discussion and Conclusions ... 23
Limitations and Future Research ... 26
Managerial Implications ... 26
References ... 27
Abstract
Current literature focuses primarily on negative eWOM. Positive eWOM lacks focus and
no guidelines or strategies have been constructed nor tested. Therefore this study aims to extend
the knowledge regarding positive eWOM, using the theory of mimicry as a strategy. A
company’s reaction to positive eWOM is proposed to affect brand attitude and virality, through skepticism, trust in the brand and brand warmth.
An experiment was conducted to determine what the best way would be to react to
positive eWOM, based on four treatments with different levels of excitement. Data were
collected via Facebook and email, using an online survey (N = 122). A conceptual model was
tested using bootstrapping.
Opposite to what was predicted, none of the treatments had a significant effect on the
proposed mediators nor outcome variables in the first analysis. The results from the mediation
model using brand warmth as a mediator was found to be significant.
Overall it appears that a high level of excitement in a company’s reaction was the most beneficial for brand attitude and virality.
How excited are you supposed to be?
Company’s reaction to positive eWOM and its effect on brand attitude and virality, mediated by
skepticism, trust in the brand and brand warmth.
Introduction
The importance and relevance of the effects of consumer Word-of-Mouth (WOM) in
research have been noticeable for the past decades (e.g. Richins & Root-Shaffer, 1988). But with
the emergence of internet and especially the rise of social media, the initial concept expanded and
it has since become an even greater point of interest. WOM before the digital era was limited to
the circle of family, friends and occasional interactions with people one vaguely knew. Now,
consumers air their experiences, opinions and arguments regarding products or services online.
Electronic WOM (eWOM) essentially has no limitations to its potential audience yet these
connections are also more distant in affiliation. Through public social media pages consumers are
more easily reached by other consumers who are unknown to them, yet whose opinions regarding
a product or service is still much valued. Hennig-Thurau, Gwinner, Walsh and Gremler (2004)
have shown that eWOM compared to traditional WOM is observed as more powerful by
consumers because it is perceived as more credible, instant and publicly available and thus have a
more significant reach. Bickart and Schindler (2001) in addition have also shown that product
information generated by other consumers has greater credibility, relevance and is more likely to
evoke empathy among consumers than information which is generated by the company itself on
their own website. It is therefore only appropriate that researchers should focus on the effects of
eWOM on the company or brand by researching the reasons for consumers to create online
content of their own volition (e.g. Cheung & Lee, 2012; Gruen, Osmonbekov & Czaplewski,
2006; Hennig-Thurau et al., 2004) and how company’s should react to certain types of eWOM
companies should respond to eWOM since the satisfaction with the company’s response influences purchase intention and even expressing positive eWOM after the initial negative
experience (Gelbrich and Roschk, 2011). What these and other recent articles have in common is
their focus on the information direction, namely negative eWOM.
Research concerning positive eWOM focuses on the importance of customer commitment
created in the physical marketplace before consumers might engage in positive eWOM (Tsao &
Hsieh, 2012), the (negative) effect of multiple positive eWOM messages on credibility (Reichelt,
Sievert & Jacob, 2014) and consumer behavioral intention (D.H. Park & J. Lee, 2008), and how
to turn the initial negative eWOM into a possible expression of positive eWOM (Gelbrich &
Roschk, 2011).
The clear emphasis on negative eWOM in academic research can be explained through
the backlash a company can have when they receive negative eWOM, which they do not have
when positive eWOM is expressed. Multiple articles have found evidence of the greater effect
which negative eWOM can have over positive eWOM (Chevalier & Mayzlin, 2006; Melnik &
Alm, 2002; C. Park & T.M. Lee, 2009; Pavlou & Dimoka, 2006). Although these findings are
important and they have contributed greatly to the effective management of negative eWOM,
they have also influenced the direction in which research concerning eWOM is generally geared.
Thus leaving positive eWOM neglected in its wake. In this research the focus is on positive
eWOM, specifically looking into the effect of the company’s reaction towards positive eWOM. The following research question was formulated to further the research concerning the
To what extent does the level of excitement (high or low) in the company’s response to positive eWOM have an effect on an observer’s brand attitude and virality of the conversation?
Since most research concerning eWOM and response strategies focus on negative eWOM
and its possible effects (e.g. Hennig-Thureau et al., 2010; Lee & Song, 2010), an experiment was
conducted to establish what the effect ofa company’s reaction to positive eWOM have on an observer’s brand attitude and virality.
In the following section of this article a clear definition of eWOM and strategies to
cope with positive eWOM are provided. Next, the variables through which a company’s reaction
to positive eWOM affects an observer’s brand attitude and the likeliness of it going viral is
examined and hypotheses are drawn from existing literature.
Literature Review Defining the Types of eWOM
Whenever eWOM is analyzed, the general division between positive and negative eWOM
is distinguished (Lee & Youn, 2009; C. Park & T.M. Lee, 2009). Negative eWOM is consumer
generated online content which in its core criticizes a brand for its product/service, befitting a
consumer’s negative experience. Positive eWOM is the opposite as a consumer’s positive experience with a brand’s product/service results in commending and/or complimenting them. There are several ways people can express their sentiment in a message, there is not one clear
distinction between expressing your positive or negative feelings regarding a company or its
products. Kietzmann and Canhoto (2013) distinguish three levels as they add neutral to the range,
Rodgers and Kim (2009) are one of few who view eWOM as an almost gradual scale instead of
two extremities. They speak of the level of extremity in either positive or negative eWOM and
have thus distinguished three levels: extremely negative, moderately negative and extremely
positive. As can be seen, the scale is not used for variation within positive eWOM, nor did they
include the neutral level. Berger and Milkman (2012) first generally refer to positive and negative
eWOM based on existing news articles, but they dissect these further after their findings show
clear distinctions within these two. The different emotions which they had first identified from
these articles and had put under either positive or negative eWOM, had different effects. This
made them conclude that both positive and negative emotions which are stimulating (e.g. awe
and anger) lead to greater virality. For further distinction and to be able to determine the type of
positive eWOM which could be used, we will need to look at message characteristics of
positivity.
According to Walther, Loh and Granka (2005) and Walther (2007) there are several ways
in which people signal positive affect in Computer-Mediated Communication (CMC). For the
current research two levels of positive eWOM were used for both the consumer narrative and the
company’s reaction to the positive eWOM: high or low in displaying excitement in the message. To portray a higher level of excitement a greater use of verbiage is used in affective language, as
is proposed by Walther et al. (2005).
Strategies
Kietzmann and Canhoto (2013) found that positive eWOM does not receive much
attention from brand managers. The only literature which can be found which addresses company
responses to consumers are all purely focused on negative eWOM (e.g. Gelbrich & Roschk,
2011; Hennig-Thurau et al., 2010; Lee & Song, 2010). Their aim is for the most part to dampen
eWOM as companies will want to leverage the positive feelings of their consumers to eventually
have this positively affect their brand attitude and existing strategies do not take this into
consideration. Instead of using predetermined response strategies, the theory of mimicry was
applied to the level of excitement displayed in the consumer’s appreciation.
Mimicry is first and foremost the unconscious act of copying someone’s posture
(Chartrand & Bargh, 1999), tone of voice (Neumann & Strack, 2000), and other characteristics in
face-to-face interaction (Derks, Fischer & Bos, 2007). Neumann and Strack (2000) have also
researched the mood and emotion aspect within mimicry and found there to be a natural reaction
to mimic the emotion which was displayed first. Van Baaren, Holland, Kawakami and Van
Knippenberg (2004) investigated the effect of mimicry on the person who is being mimicked.
Their results suggest that mimicry enacted in face-to-face communication between two humans
increases pro-social behavior in the person who is being mimicked, and this behavior was not
only directed towards the mimicker. Since mimicking is mostly done unintentionally, it should be
done subtly, and overdoing the action might kill the positive effect it would normally have.
When adapting this theory to CMC, it should be taken into account that the observer will
identify the consumer as human and the company still as a company, not specified as a human.
This distinction could negatively influence the brand attitude since consumers might not
appreciate the attempt of being more amicable towards them than necessary. Kwon and Sung
(2011) however show that humanizing a brand when replying to consumers on social media
could “influence consumers to elevate the status of the brand from a passive object to a full-fledged relationship partner.” (p. 14). Assuming that every brand strives to achieve a positive brand attitude and increase their brand familiarity, keeping to the theory of mimicry in case of
Displaying excitement in eWOM, even if a brand is just responding to a message, affects
certain variables. The three which are measured in this article are discussed below. In Figure 1
the conceptual model is shown, with the proposed main effect if excitement in both narratives is
high.
Figure 1. Proposed effect of excitement level in company’s reaction to positive eWOM on brand
attitude and virality, through skepticism towards the conversation, trust in the brand and brand
warmth.
Defining Virality
According to the Oxford dictionary virality is the “[t]endency of an image, video, or piece
of information to be circulated rapidly and widely from one Internet user to another.” The word itself is derived from viral which also indicates the spreading of a virus among people or the
digital type which is spread among computers. Kaplan and Haenlein (2011) actually go as far as
to compare an instance of virality with an epidemic, clearly the consequences of contracting a
virus are greater and much worse than simply being exposed to an image, video or information
which is circulating on the internet. The definition for both are, however, similar since they are
both spread amongst the population through types of interaction (physical vs. digital).
Porter and Golan (2006) show the importance of virality for companies’ marketing
communication of provocative content originating from an identified sponsor using the internet to
persuade or influence an audience to pass along the content to others” (p.33). Any kind of information regarding a product or brand which is beneficial to the image and which consumers
are spreading gleefully to other people, is now one of the desired outcomes marketers long for. It
is not only restricted to the carefully constructed communication efforts directly from the brand.
If anything, consumers regard information from other consumers more credible than directly
from the company, as stated before (Bickart & Schindler, 2001).
Skepticism Towards eWOM
In this research skepticism is the doubt and uncertainty a consumer has with the
truthfulness of the conversation (Bruner, 2013). Reichelt et al. (2014) show that consumers doubt
the credibility of the messages if it is mostly positive. In this research the higher level of
excitement displayed by both the consumer as the company could therefore increase the
skepticism towards the conversation as a whole.
The effect of skepticism on brand attitude was researched by Yoo and MacInnis (2005).
They researched the effect of emotional versus informational ads on brand attitude. Their
findings show that when an emotional format is used in an ad, brand attitude is driven through
feeling responses. More specifically, negative feelings, such as skepticism, result in negative
evaluations of the brand. Leading to the belief that increased skepticism has a negative effect on
brand attitude.
As for the effect skepticism has on virality of the conversation, it is important to establish
what type of content is most likely to be shared online. Berger and Milkman (2012) looked into
the effect of emotion on virality and discovered that positive online content is more likely to be
shared. This would mean that since the conversation is already positive, even if this positivity
eWOM. But Berger and Milkman looked further and discovered that (positive and negative)
emotions which are characterized by arousal or activation are also linked to virality. Since high
display of excitement in both narratives will increase skepticism, the negative feeling of being
skeptical is hardly perceived as arousing or exciting to experience, this manipulation will
therefore have a negative effect on virality. Bringing this analysis back to Reichelt et al. (2014), it
is furthermore unlikely that there would be a direct effect between the level of excitement
displayed in both narratives and virality.
Since these articles seem to agree that the display of more emotion, and in this case
excitement, the greater the effect will be. This led to the first set of hypotheses:
H1a. The effect of excitement on brand attitude is negatively mediated by skepticism. H1b. The effect of excitement on virality is negatively mediated by skepticism.
What should also be mentioned is the possibility of viewing the response of the company
as overreacting or even a form of teasing instead of simply showing or copying great excitement.
Alberts, Kellar-Guenther and Corman (1996) showed that teasing, though inherently meant to be
humorous, can be aggressive for the other person receiving or observing this. It may even go as
far as observing such interactions as insults. In this research that would be a step too far as it
might be viewed as overreacting, but nothing from the first message is ridiculed. It would still
have a positive effect on skepticism (Qiu et al., 2012).
Even though both hypotheses are backed by different articles, it still seems contradictory
statements as adhering to the theory of mimicry was thought to have an overall positive effect,
especially on brand attitude. This would leave indications open for the adaptability of the theory
Trust in the Brand
The second variable which is measured for the effect of the level of excitement in
positive eWOM is the trust in the brand. In this research trust in the brand means the degree in
which the observer finds the brand reliable and trustworthy (Bruner, 2013). The difference
between skepticism and trust is first and foremost that the first concerns the attitude towards the
conversation as a whole and the second is regarding the positive feelings one has towards the
brand after reading the conversation but solely directed towards the brand. Besides that, it should
be noted that skepticism is negative in itself whilst trust has a more positive angle.
According to Stewart, Pavlou and Ward (2002) interactive communication on the internet
(amongst which social media) facilitates trust building through the possibility of communicating
to and with the company. Especially compared to the one-way communication it used to be when
companies mainly advertised to the people through traditional media. Since the brand pages
allow people to comment on their products or brand and it is visible for everyone that they
respond to this praise, it will positively affect the trust in the brand.
The effect of the increased trust in the brand on virality is hard to determine. From
existing literature this relationship has not been researched. This makes somewhat sense since the
attributes of a message, what it makes an observer feel (awe, sadness, anxiety, anger etc.), seem
to be the main antecedents for the effect on virality (Berger & Milkman, 2012). Going back to
Porter and Golan’s article about virality and what could increase viral marketing, it can be seen that provocative content has to be part of the information for it to become viral. Provocative in
this context meaning extraordinary content which is very emotional or funny. They then go as far
as to imply that the product itself does not have to provide exceptional value for the consumer for
the content to go viral. In the current research only positive eWOM is tested with a low and high
agreement with Kietzmann and Canhoto’s (2013) findings. Yet the initial post is purely about the positive experience, not including extraordinary funny or emotional elements. The company’s narrative is also lacking in these elements. Even though the trust is positively affected, this trust
is in no way based on provocative feelings. It can also be said that being trustworthy is not
something which is viewed as exciting. It is therefore unlikely that even a heightened feeling of
trust in the brand would increase the virality. Even though this analysis might be viewed as
far-fetched, it is the only literature which slightly implies that trust on the brand can have a certain
effect on virality. These articles led to the second set of hypotheses:
H2a. The effect of excitement on brand attitude is positively mediated by trust in the
brand.
H2b. The effect of excitement on virality is negatively mediated by trust in the brand.
Brand Warmth
Brand warmth in this research is the perceived kindness of the brand by the observer
(Bruner, 2013). At first this variable seems consistent with brand attitude, but it is not. Brand
warmth specifically concerns the almost human aspect of the brand, and thus its personality as
such, focused on a more emotional level. Besides this distinction, brand warmth influences brand
attitude as well.
The theory of mimicry will apply for the level of excitement and its effect on brand
warmth since this concerns the human aspect of the brand. As discussed previously, mimicking a
person results in greater pro-social behavior (Van Baaren et al., 2004). By humanizing a company
on social media with respect to responding to eWOM affects the consumer’s brand attitude positively (Kwon & Sung, 2011). A greater display of emotional cues, in this case excitement,
increases the level of informality between the consumer and the company. It is therefore
proposed that the high level of excitement in the company’s narrative will have a greater positive effect on brand warmth, no matter the level of excitement displayed by the consumer, compared
to when the company displays a low level of excitement in their narrative.
Aggarwal (2004) researched the effect of brand relationship norms on consumer attitudes
and behavior. The two types which are distinguished are the communal and exchange
relationship. Generally the relationship between a consumer and company is viewed as an
exchange relationship: give something and get/expect something in return. Yet positive eWOM
generated by a consumer is generally done for the benefit of other consumers, a more communal
type of relationship. The response by the company is also not specifically needed in positive
eWOM which it would be when it is negative eWOM. When it comes to positive eWOM the
relationship is more of a communal nature which results in more affective feelings, such as
warmth, towards the brand. An observer will also perceive such an interaction more as communal
in nature, which will only get stronger if the company displays a greater amount of excitement in
their response. Aggerwal (2004) shows that communal relationships have a more positive effect
on brand attitude.
The effect of brand warmth on virality is discussed by Bergman and Milkman (2012). In
all three of their studies it was evident that the more emotion is present in online content, and the
more arousing the emotion which is felt by the observer, the more likely people are inclined to
share it. The key element here is the emotion which is felt by the observer since brand warmth is
and additional positive feeling which they experience if the excitement in the narratives is high.
This led to the following hypotheses:
H3a. The effect of excitement on brand attitude is positively mediated by brand warmth. H3b. The effect of excitement on virality is positively mediated by brand warmth.
Data and Method Sample and Procedure
The sample consisted out of 122 participants, of which 61.5% were female and the mean
age was 26.74 (SD = 9.201). All participants were active on social media, with the most used
platforms being Facebook (95%, N = 116) and Instagram (59.8%, N = 73).
The data were collected through an online survey (Qualtrics), spread primarily via
Facebook pages (personal and public) and email. The treatment which the participants received
was staged in a Facebook setting. This specific social media was chosen as it had to be accessible
for everyone and had to allow consumers to react and other consumers to view this interaction
without needing to be related to them through a social media. The message should thus be ‘open’ for the public to see, not limited to certain groups. Brand pages on Facebook are generally open
to the public and display the messages which they receive on their pages. This medium is also
most often used by consumers to investigate what other people have to say about a specific brand,
instead of finding out what message the brand itself wants to deliver onto people (Kwon & Sung,
2011).
Participants were randomly shown one of four treatments, followed by a questionnaire
which was the same for every treatment. The experiment had a 2x2 design. Treatments varied in
the level of excitement (high or low) in both the initial post by the customer as the response by
the company. The four treatments were identified as follows: high level of excitement in both
narratives (HH), high level of excitement in consumer’s narrative with a low level of excitement
in company’s narrative (HL), low level of excitement in consumer’s narrative with a high level of excitement in company’s narrative (LH) and low level of excitement in both narratives (LL). Treatment HH and LL were based on the theory of mimicking as they portrayed the same amount
of excitement in both narratives. The other treatments (HL and LH) had opposing levels of
excitement. The four treatments and questionnaire are attached in Appendix A.
A fictional brand was made up to make sure that the variables were not influenced by
preexisting connotations which would be the case for existing brands. The fictional brand was
shortly introduced in the questionnaire as being a company which ‘designs state of the art photo cameras’.
Measures
The variables which were used to measure the effect of the treatments on the participants
all had a 7-point Likert scale division.
To measure brand attitude the scale which was developed by Spears and Singh (2004)
was used. This seven-point semantic differential scale consisted out of five items;
unappealing/appealing, bad/good, unpleasant/pleasant, unfavorable/favorable and
unlikable/likable. The scale was reliable (α=.84).
Virality was measured by asking ‘How likely are you to share this exchange with others?’ (Berger & Milkman, 2012), ranging from very unlikely (1) to very likely (7).
All three scales which were used to measure the mediators skepticism, brand warmth and
trust in the brand, were taken from the Marketing scales handbook (Bruner, 2013). Skepticism
was chosen to be measured to see whether participants trusted the exchange and the reaction by
the company. In other words; how credible do they find the exchange (Bickart & Schindler,
2001). It consisted out of three items: skeptical, suspicious and distrustful ranging from strongly
disagree (1) to strongly agree (7). The scale was reliable (α=.92). Warmth and trust in the brand
were measured to evaluate the effect of the level of excitement displayed by the company on the
participant’s perception of the company. Trust in the brand existed out of dependable, reliable and trustworthy, ranging from strongly disagree (1) to strongly agree (7). The scale was not
reliable (α=.56). Warmth consisted out of warm, kind and generous. The scale was reliable
(α=.78).
The control variables were gender, age and specifically for virality, liking behavior on
social media. For the latter the scale by Allsop, Bassett and Hoskins (2007) which specifically is
aimed towards the liking and forwarding behavior on social media, was used. Participants were
asked how often they would like posts/images on social media and how often they forward
information they find on the internet to friends, family, colleagues, etc. A seven-point
Likert-scale was used, ranging from never to several times per hour.
Analysis
Bootstrapping was used in this study as recommended by Hayes (2009) and Zhao, Lynch
and Chen (2010) for measuring mediation and indirect effects.
Results
The significance of the proposed mediators on the relationship between a high level of
excitement in the consumer’s eWOM and the company’s reaction and the outcome variables, brand attitude and virality, was tested. First, a one-way between subjects ANOVA was conducted
to analyze whether the separate treatments of excitement had an effect on the mediators directly.
After the reliability check only two variables were left to be tested: skepticism and brand warmth.
As the variable trust in the brand was found to be unreliable, it should be noted that hypothesis 2a
and 2b cannot be rejected nor accepted since no data could be used to test this hypotheses. As for
the other variables; there was no significant effect of excitement on skepticism, F(3, 118) = 1.52,
p = .213, thus seemingly rejecting the first path between predictor and mediator (a) of hypothesis
1a and 1b. There was a significant effect of excitement on brand warmth, F(3, 118) = 2.75, p =
HSD test, however, did not indicate that the mean score for any of the four treatments was
significantly different than any of the other treatments’ means. The means, standard deviations and p-values for these comparisons are provided in Table 1. Since the comparison between LL
and LH treatment is the closest to being significant (p = .10), it does imply that the level of
excitement displayed by the company could have had an effect on brand warmth when they react
with the opposite level of excitement (high vs low). This is however speculating since there was
no significant effect found.
Table 1.
Post Hoc comparisons of treatments, using Tukey HSD
Treatments M SD HH HL LH LL
HH 4.74 1.16 -
HL 4.35 .73 .34 -
LH 4.86 .89 .96 .13 -
LL 4.32 .81 .28 1.00 .10 -
Note. N =122; M, mean; SD, Standard deviation; HH, high excitement in consumer’s and company’s narrative; HL, high excitement in consumer’s narrative and low excitement in company’s narrative; LH, low excitement in
consumer’s narrative and high excitement in company’s narrative; LL, low excitement in consumer’s and company’s narrative.
Since the two mediators are both continuous variables, separate regression analyses were
executed to see if they were significantly associated with the outcome variables. Skepticism was
negatively related to brand attitude (b = -.25, p < .001), which was expected (hypothesis 1a), but
not significantly related to virality (b = -.11, p = .268) hence the second path between mediator
and outcome variable (b) of hypothesis 1b is rejected. Brand warmth was positively related to
both brand attitude (b = .52, p < .001) and virality (b = .45, p = .003), which was expected
The last step before the mediation analysis is to analyze whether there is a direct effect of
the independent variable on the outcome variable. Another ANOVA-test was performed to
compare the effect of excitement on brand attitude and virality in high and low conditions for
both narratives. Excitement had no significant effect on brand attitude F(3, 118) = .624, p = .601.
As for virality, results showed that there was no significant effect of excitement on virality, F(3,
118) = .570, p = .636.
According to Hayes (2009) and Zhao et al. (2010), finding no effect in the first steps of
mediation does not signify that no effect will also be found when testing the mediation model as a
whole. So, in spite of the direct effects not being significant, the mediation model was tested for
both variables, skepticism and brand warmth.
Since excitement is not continuous, the four treatments were split in two contrasts with
the intention to compare the effect of excitement displayed by the company, regardless of the
consumer’s level of excitement. The following contrasts were formed: HL and LL (contrast 0) and HH and LH (contrast 1).
To test the mediator model, the PROCESS macro written by Hayes (2012) was used.
5,000 bootstrap samples were drawn and a level of confidence of 95 per cent was used for the
analyses. All the specific indirect pathways and direct effects are given in Table 2.
Skepticism (M) was twice tested in the mediation model where X was contrast and Y was
in the first analysis brand attitude and in the second virality. Neither for brand attitude, 95% BC
CI [-.2173, .0604] nor Virality, 95% BC CI [-.1907, .0213] was skepticism found to mediate the
relation. The independent b path was found to be significant between skepticism and brand
attitude b = -.2513, 95% BC CI [-.3546, -.1480] which shows skepticism to have had a negative
effect on brand attitude. Nevertheless, both direct and indirect paths for this mediation model
Table 2.
Specific indirect pathways using bootstrapping
Bootstrapping BC 95% CI
Effect SE Lower Upper
Indirect effect X M Y
Contrast brand warmth brand attitude .2536 .1029 .0647 .4692
Contrast skepticism brand attitude -.0626 .0689 -.2173 .0604
Direct effect -.1610 .1432 -.4445 .1225
Contrast brand warmth virality .2462 .1310 .0553 .5863
Contrast skepticism virality -.0249 .0449 -.1907 .0213
Direct effect -.5989 .2829 -1.1590 -.0388
Note. N = 122; BC, bias corrected; CI, confidence interval; displayed in bold the effect is statistically significant; SE, standard error; Contrast, excitement level (high or low) in company’s narrative.
For the second mediator the mediation model was similar; X was contrast, Y was brand
attitude and M was brand warmth. From the complete diagram of this mediation model (Figure 2)
it can be seen that there appeared to be a significant indirect only effect of excitement on this
model b = 0.254, 95% BC CI [.065, .469], since the confidence interval does not contain zero.
This effect remained significant controlled for gender, age and liking behavior on social media b
= 0.256, 95% BC CI [.078, .461]. Which represents a medium to large effect,
ᴋ
2 = .157, 95% BC CI [.044, .274]. Even though it implies that mediation has occurred based on the confidenceinterval, which was also predicted (hypothesis 3a), results further showed a non-significant direct
effect, 95% BC CI [-.445, .123]. This type of effect is known as an indirect-only mediation (Zhao
Figure 2. Mediation model of excitement to brand attitude via brand warmth
For the second analysis X and M stayed the same but the outcome variable (Y) was virality
(see Figure 3). The same was found for this model as the analysis showed that there was a
significant indirect effect of excitement on virality, through brand warmth b = 0.246, 95% BC CI
[.055, .586]. This effect remained significant when controlled for gender, age and liking behavior
on social media b = 0.269, 95% BC CI [.069, .626]. This represents a relatively medium effect,
ᴋ
2= .082, 95% BC CI [.020, .178]. In addition, the direct effect was significant betweenexcitement and virality b = -.599, 95% BC CI [-1.159, -.039]. This effect also remained
significant when controlled for gender, age and liking behavior on social media b = -.612, 95%
BC CI [-1.179, .045]. Even though both direct and indirect effect were found to be significant,
indicating a partial mediation, the total effect of the model was non-significant, 95% BC CI
Figure 3. Mediation model of excitement to virality via brand warmth.
Discussion and Conclusions
The purpose of this study was to address the shortcomings which were perceived in the
existing literature regarding positive eWOM, specifically how companies should react towards
specific variations of positive eWOM. Existing literature on strategies coping with eWOM are
focused on negative eWOM and how to dampen the negative backlash a company might endure
from it (e.g. Gelbrich and Roschk, 2011; Hennig-Thurau et al., 2010; Kietzmann & Canhoto,
2013; Lee & Song, 2010). By attempting to show the positive effects a company could gain from
responding to positive eWOM, this study embarked on a not often entered domain. Positive
eWOM was specified by choosing one commonly used characteristic in such narratives, namely
excitement. The level in the company’s reaction was either high or low which was hypothesized to affect brand attitude and virality, through three mediators (skepticism, trust in the brand and
brand warmth). Trust in the brand was not analyzed since this mediator scored an insufficient
score for reliability.
From the first results it was apparent that the individual treatments of excitement (HH,
HL, LH, LL) did not have a significantly different effect between each other on the mediators,
consumers severely doubt the message when it is overtly positive. Considering that both
narratives would display double the amount of excited positivity in HH, it was expected to have a
greater negative effect on the mediators and outcome variables. This statement was supported by
the possible misinterpretation of teasing instead of sincerity of showing excitement (Alberts et al.,
1996). In contrast, yet also without support from these findings, both the mimicking treatments
(HH and LL) did not show any greater positive effect over the other treatments. The reason for
the lack of an effect between these four treatments could be that they were not extreme enough,
thus resulting in similar outcomes. From this study the theory of mimicking seemed to not be the
most effective way to react to positive eWOM.
Even though they did not significantly differ from each other individually, a small trend
was found between two treatments pertaining to the level of excitement and brand warmth (LL
and LH). Although later rejected, the trend was found between two treatments with opposing
excitement levels in company’s narrative. As this could mean that there was indeed a significant effect between high and low excitement in the company’s narrative on the mediators. This led to the comparison between two treatments in which the company showed a high level of excitement
(HH and LH) with the remaining two treatments, showing a low level of excitement (HL and
LL). The findings of these contrasts are discussed below.
Skepticism did not significantly mediate the relationship between excitement and either
outcome variable. Even though this was not expected, this finding does strengthen the existing
believe of eWOM, regardless of type, being perceived the same as being highly sensitive to
manipulation by the company (e.g. Lee & Youn, 2009; Qiu et al., 2012; Zhang et al., 2016). As
all eWOM is thus viewed skeptically, it would be plausible for an emotional additive such as
between excitement and virality was also expected according to previous literature (Reichelt et al.
2014).
Results did show a significant negative effect of skepticism on brand attitude. This is
consistent with findings in previous literature (Yoo & MacInnis, 2005) where negative emotions
affect brand attitude.
Brand warmth contributed significantly to the mediation model for both outcome
variables, as was expected. In the first model excitement had only an indirect effect on brand
attitude via brand warmth. According to Zhao et al. (2010) this would mean that there was an
indirect-only mediation for this relationship, also known as a full mediation (Baron & Kenny,
1986). Since there is no direct effect between excitement and brand attitude this indicates that this
relation is only observed when measured for brand warmth.
Consistent with the last hypothesis, brand warmth positively mediated the relationship
between excitement and virality. This relationship is known as a partial mediation since both the
direct and indirect relation was shown to be significant. This finding supports Berger & Milkman
(2012) findings concerning the positive effect of arousing emotions on virality. From the
mediation model it was observed that there was a direct effect which was not expected since the
first ANOVA test showed no direct effects between excitement and outcome variables. As
indicated before, in the mediation model two contrasts were created to compare the high and low
excitement displayed by the company. Individually none of the four treatments showed a greater
significant relationship with virality, which is still the case. The mediation model however,
showed the difference between a company either responding with high excitement or low
excitement. Between these two contrasts the relationship was found of greater excitement having
Limitations and Future Research
There are some limitations that need to be taken into account when interpreting the results
of this study. To determine how a company could and should react to positive eWOM, the theory
of mimicry was believed to obtain the most beneficial results. As no strategies concerning
positive eWOM have been determined, let alone tested, this seemed like a reasonable base for a
possible strategy for companies. This was indirectly derived from the literature and from the
results it implies that applying this theory to this situation is not the most beneficial approach.
Future research should rather focus on company’s level of emotion in their reaction to positive eWOM.
Excitement was tested in this study as the independent variable, but other signals which
are used to portray positive affect should be tested as well (Walther et al., 2005; Walther, 2007).
This will provide a wider arrange of positive eWOM types which a company may encounter and
should reciprocate.
Furthermore, the contrasting effect of reacting to positive eWOM was found to be
significant which strengthens the idea of eWOM in general making an observer more skeptical.
To continue this by accepting or rejecting this train of thought, future research could compare the
effect of a company’s reaction to both high and low levels of emotional negative eWOM and also positive eWOM. Current literature are still conflicted on this presumption of all eWOM
increasing skepticism among consumers.
What also should be derived from this study is the significant finding of the indirect-only
effect between excitement and brand attitude. Future research regarding positive eWOM’s effect on brand attitude should probably measure brand warmth to check for this relationship.
A practical implication for this study’s finding concerning high or low level of excitement in company’s narrative, is the necessity of showing high excitement, regardless of the positive eWOM generated by the consumer. This could2 directly affect the chance of the interaction going
viral (free publicity) and indirectly affect the brand warmth positively. It is a win-win situation.
References
Aggarwal, P. (2004). The effects of brand relationship norms on consumer attitudes and behavior.
Journal of Consumer Research, 31(1), 87-101.
Alberts, J.K., Kellar-Guenther, Y., & Corman, S.R. (1996). That's not funny: Understanding
recipients' responses to teasing. Western Journal of Communication, 60(4), 337-357.
Allsop, D.T., Bassett, B.R., & Hoskins, J.A. (2007, December). Word-of-mouth research:
Principles and applications. Journal of Advertising Research, 398-411.
Baron, R.M. & Kenny, D.A. (1986). Moderator-mediator variables distinction in social
psychology research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51(6), 1173-1182.
Berger, J. & Milkman, K.L. (2012). What makes online content viral? Journal of Marketing
Research, 49(2), 192-205.
Bickart, B. & Schindler, R.M. (2001). Internet forums as influential sources of consumer
Information. Journal of Interactive Marketing, 15(3), 31-40.
Bruner, G.C. (2013). Marketing Scales Handbook: Multi-item Measures for Consumer Insight
Research. Fort Worth, Texas: GCBII Productions, LLC.
Chartrand, T.I. & Bargh, J.A. (1999). The chameleon effect: The perception-behavior link and
social interaction. The Journal of Personality and Social Psychology, 76(6), 893-910.
mouth in online consumer-opinion platforms. Decision Support Systems, 53, 218-225.
Chevalier, J.A. & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book
reviews. Journal of Marketing Research, 43(3), 345-354.
Derks, D., Fischer, A.H., & Bos, A.E.R. (2008). The role of emotion in computer-mediated
communication: A review. Computers in Human Behavior, 24, 766-785.
Gelbrich, K. & Roschk, H. (2011). A meta-analysis of organizational complaint handling and
customer responses. Journal of Service Research, 14(1), 24-43.
Gruen, T.W., Osmonbekov, T., & Czaplewski, A.J. (2006). eWOM: The impact of customer-to-
customer online know-how exchange on customer value and loyalty. Journal of Business
Research, 59, 449-456.
Hayes, A.F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new
millennium. Communication Monographs, 76, 408-420.
Hayes, A.F. (2012). PROCESS for Mediation and Moderation Analysis (Draft). The Ohio State University, Ohio.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-
mouth via consumer-opinion platforms: What motivates consumers to articulate
themselves on the internet? Journal of Interactive Marketing, 18, 38-52.
Hennig-Thurau, T., Malthouse, E.C., Friege, C., Gensler, S., Lobschat, L., Rangaswamy, A., &
Skiera, B. (2010). The impact of new media on customer relationships. Journal of Service
research, 13(3), 311-330.
Kaplan, A.M. & Haenlein, M. (2011). Two hearts in three-quarter time: How to waltz the social
media/viral marketing dance. Business Horizons, 54(3), 253-263.
Kietzmann, J. & Canhoto, A. (2013). Bitter sweet! Understanding and managing electronic word
Kwon, E.S. & Sung, Y. (2011). Follow me! Global marketers’ twitter use. Journal of Interactive
Advertising, 12(1), 4-16.
Lee, M. & Youn, S. (2009). Electronic word of mouth (eWOM) – How eWOM platforms
influence consumer product judgement. International Journal of Advertising: The Review
of Marketing Communications, 28(3), 473-499.
Lee, M., Rodgers, S., & Kim, M. (2009). Effects of valence and extremity of eWOM on
attitude toward the brand and website. Journal of Current Issues & Research in
Advertising, 31(2), 1-11.
Lee, Y.L. & Song, S. (2010). An empirical investigation of electronic word-of-mouth:
Informational motive and corporate response strategy. Computers in Human Behavior, 26,
1073-1080.
Melnik, M.I. & Alm, J. (2002). Does a seller’s ecommerce reputation matter? Evidence from eBay auctions. Journal of Industrial Economics, 50(3), 337-349.
Neumann, R. & Strack, F. (2000). “Mood contagion”: The automatic transfer of mood between persons. Journal of Personality and Social Psychology, 79(2), 211-223.
Park, C. & Lee, T.M. (2009). Information direction, website reputation and eWOM effect: A
moderating role of product type. Journal of Business Research, 62(1), 61-67.
Park, D.H. & Lee, J. (2008). eWOM overload and its effect on consumer behavioral intention
depending on consumer involvement. Electronic Commerce Research and Applications,
7(4), 386-398.
Pavlou, P.A. & Dimoka, A. (2006). The nature and role of feedback text comments in online
marketplaces: Implications for trust building, price premiums, and seller differentiation.
Information System Research, 17(4), 391-412.
viral advertising to television advertising. Journal of Interactive advertising, 6(2), 30-38.
Qiu, L., Pang, J., & Lim, K.H. (2012). Effects of conflicting aggregated rating on eWOM review
credibility and diagnosticity: The moderating role of review valence. Decision Support
System, 54(1), 631-643.
Reichelt, J., Sievert, J., & Jacob, F. (2014). How credibility affects eWOM reading: The
influences of expertise, trustworthiness, and similarity on utilitarian and social functions.
Journal of Marketing Communications, 20(1-2), 65-81.
Richins, M.L. & Root-Shaffer, T. (1988). The role of involvement and opinion leadership in
consumer word-of-mouth: An implicit model made explicit. Advances in Consumer
Research, 15, 32-36.
See-To, E.W.K. & Ho, K.K.W. (2014). Value co-creation and purchase intention in social
network sites: The role of electronic word-of-mouth and trust - a theoretical analysis.
Computers in Human Behavior, 31, 182-189.
Spears, N. & Singh, S.N. (2004). Measuring attitude toward the brand and purchase intentions,
Journal of Current Issues & Research in Advertising, 26(2), 53-66.
Stewart, D.W., Pavlou, P., & Ward, S. (2002). Media influences on marketing communications.
In J. Bryant & D. Zillmann (Eds.), Media Effects: Advances in Theory and Research (2nd
ed.) (353-395). New Jersey: Lawrence Erlbaum Associates, Inc.
Tsao, W.C. & Hsieh, M.T. (2012). Exploring how relationship quality influences positive
eWOM: The importance of customer commitment. Total Quality Management &
Business Excellence, 23(7-8), 821-835.
Van Baaren, R.B., Holland, R.W., Kawakami, K., & Van Knippenberg, A. (2004). Mimicry and
prosocial behavior. American Psychological Society, 15(1), 71-74.
com/definition/english/virality
Walther, J.B. (2007). Selective self-presentation in computer-mediated communication:
Hyperpersonal dimensions of technology, language, and cognition. Computers in Human
Behavior, 23, 2538-2557.
Walther, J.B., Loh, T., & Granka, L. (2005). Let me count the ways: The interchange of verbal
and nonverbal cues in computer-mediated and face-to-face affinity. Journal of Language
and Social Psychology, 24(1), 36-65.
Yoo, C. & MacInnis, D. (2005). The brand attitude formation process of emotional and
informational ads. Journal of Business Research, 58, 1397-1406.
Zhang, X., Ko, M., & Carpenter, D. (2016). Development of a scale to measure skepticism
toward electronic word-of-mouth. Computers in Human Behavior, 56, 198-208.
Zhao, X., Lynch Jr., J.G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths
Appendix A
Dear participant,
Thank you for taking time to participate in this survey. This survey is part of my Master thesis,
which aims to give more insight into consumer attitude in the digital age.
You will be presented with a fictional exchange that takes place on Facebook. The fictional
company Corion designs state of the art photo cameras. A consumer has posted something on
their fan page to which Corion has replied. Based on this scenario you will be asked to fill in the
survey. No answer is right or wrong, this is solely about your opinion regarding the exchange.
Participation in this survey is completely anonymous and on a voluntary basis.
This survey will only take about 5 minutes of your time. All information provided by you will be
kept confidential and will only be used for academic purposes.
Thank you for your time,
Ellis van Duist
Student at the University of Amsterdam, Business Administration – Marketing
Treatment 1 - High/High
Treatment 3 – Low/High
The following questions should be answered regarding the exchange between John Tucker and Corion. There is no correct answer. Please answer all the questions truthfully, according to your sentiments!
Questionnaire
1. How likely are you to share this exchange with others
Very Unlikely Unlikely Somewhat Unlikely Undecided Somewhat Likely Likely Very Likely
2. When reading this, I felt:
Strongly Disagree Disagree Somewhat Disagree Neither Agree nor Disagree Somewhat Agree Agree Strongly Agree Skeptical Suspicious Distrustful
3. To what extent do you believe that Corion is:
Strongly Disagree Disagree Somewhat Disagree Neither Agree nor Disagree Somewhat Agree Agree Strongly Agree Warm Kind Generous Dependable Unreliable Trustworthy
4. Please indicate your overall feelings about Corion. Unappealing Appealing Bad Good Pleasant Unpleasant Unfavorable Favorable Unlikable Likable
5. If you are active on social media, for which platforms do you have an account?
o Facebook o Twitter o Google+ o Instagram
o Other:………
6. Which platform do you visit most often?
o Facebook o Twitter o Google+ o Instagram
o Other: ______________________
7. Please indicate how frequently you perform the following activities
Never Once a week Several times a week Once a day Several times a day Once per hour Several times per hour I like posts/images on social
media
I like to forward information I find on the internet to friends, family, colleagues, etc.
Demographics
Please provide the following information
Sex: Male/female
Age:_______________
Nationality: __________________
What is the highest degree or level of school you have completed/are following now?
o No schooling completed o Elementary school
o High school (VMBO, HAVO, VWO) o MBO.
o Bachelor’s degree (HBO)
o Bachelor’s degree (University/WO) o Master’s degree
o PHD
