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FROM HERO TO ZERO: BRAND LOVE'S MODERATING EFFECT ON BRAND HATE ANTECEDENTS AND OUTCOMES
Bruno Tiago Barbosa Ribeiro
Dissertation
Master in service Management
Oriented by
Amélia Maria Pinto da Cunha Brandão
Co-Oriented by Teresa Maria Rocha Fernandes Silva
2019
Agradecimentos Em primeiro lugar, um grande obrigado à Professora Amélia que, especialmente nesta última
fase, foi incansável e super atenciosa.
Em segundo lugar, outro grande obrigado à Professora Teresa por toda a disponibilidade
que demonstrou e por me ter ajudado a encontrar o rumo certo para este projeto.
Era muito mau se não agradecesse aos meus pais? Era, pois. No entanto, apesar de eles não
saberem o tema desta dissertação nem sequer do mestrado que escolhi tirar, foram eles que
pagaram as propinas. Além disso, fizeram o favor de me educar da melhor maneira possível
e sempre colocaram o meu bem à frente do resto. “Acaba o mestrado e depois vai trabalhar,
não sejas burro.”, ai, como eles tinham razão.
Não esquecendo o resto dos membros da família, obrigado Bea! Sem a tua lasanha e o teu
gelado nunca teria cruzado a meta!
Aos meus avós, que apesar de desejarem que tivesse estudado para médico, são uns chatinhos
que prezo muito.
À Catarina, por me aturar quando mais ninguém me atura e por estar sempre disponível para
os meus devaneios.
A todos os meus amigos, e ainda são alguns, além dum obrigado uma grande desculpa por
ter, muitas vezes, falhado com eles e mesmo assim encherem o meu dia com a melhor
disposição possível.
Obrigado FEP, quem sabe se em vez de um adeus não é um até já.
Abstract
The evolution and growing relevance of consumer-brand relations have made the study of
this theme dynamic and in constant metamorphosis, with the emergence of new perspectives
and approaches at an incessant pace. Love for a brand is the treasure that all managers seek,
and its unpleasant counterpart, hatred for it, is, on the other hand, largely responsible for the
insomnia they suffer.
As such, the present study focuses on the hateful relationships formed between a consumer
and a brand, with special attention to those that were formed after the existence of a close
affinity with the brand, in the past. This study aims to ascertain whether the formation and
consequent repercussions of a hate relationship with a brand, present substantial differences
if the consumer has nurtured love for the brand in the past. To this end, a research model
was developed based on studies that focused on the Brand Hate concept, its antecedents and
consequences, and created an online survey, where each respondent answered some
questions about their relationship with a brand they currently hate. Thus, 207 valid answers
were obtained, later used for analysis.
The results show that participants with higher Brand Love values in the past are more
resilient to corporate wrongdoings and violation of expectations episodes. However, it was
not possible to verify the moderating impact of Brand Love on behaviors derived from the
established hate relationship, namely negative word of mouth, consumer complaints and
patronage reduction/cessation.
In a world where people use brands to express their feelings and to present themselves to
their peers, using them as an integral part of themselves, this study contributes to the
understanding of the polarizing power that emotions such as love and hate take and the
implications, they may have in a business context. The results provide some interesting
conclusions, however, given the high complexity and constant mutation of this theme, it is
important that further studies are done to test new perspectives and update previously tested
ones, allowing brands to make their decisions in a sustainable way, enhancing the good
relationship with its consumers.
Keywords: Brand Hate; Brand Love; Corporate Wrongdoings; Violation of Expectations;
Negative Word of Mouth; Consumer Complaining; Patronage Reduction/Cessations.
Resumo
A evolução e crescente relevância das relações consumidor-marca têm tornado o estudo
desta temática dinâmico e em constante metamorfose, verificando-se o nascimento de novas
perspetivas e abordagens a um ritmo incessante. O amor à marca é o tesouro que todos os
gestores procuram e, a sua contraparte desagradável, o ódio à mesma, é, por sua vez, o grande
responsável pelas insónias que sofrem.
Como tal, o presente estudo foca-se nas relações de ódio formadas entre um consumidor e
uma marca, com especial atenção para aquelas que foram formadas após ter existido um
vínculo de estreita afinidade com a dita marca, no passado. Este estudo tem o objetivo de
averiguar se a formação e consequentes repercussões de uma relação de ódio para com uma
marca, apresentam diferenças substanciais caso o consumidor tenha nutrido amor pela marca
no passado. Para tal, foi desenvolvido um modelo de investigação com base em estudos que
se debruçam sobre o conceito de Brand Hate, os seus antecedentes e consequentes e realizado
um inquérito online, onde cada inquirido respondeu a algumas questões sobre o seu
relacionamento com uma marca que atualmente odeiem. Deste modo, obtiveram-se 207
respostas válidas, posteriormente utilizadas para análise.
Os resultados mostram que os participantes com valores mais elevados de Brand Love
registados no passado, apresentam uma maior resistência a más práticas por parte das marcas
e a episódios de violação de expetativas. No entanto, o mesmo não foi possível verificar
quanto ao impacto moderador do Brand Love nos comportamentos derivados da relação de
ódio estabelecida, nomeadamente word of mouth negativo, reclamações de consumidores e
redução/cessação do consumo de produtos/serviços da marca.
Num Mundo onde as pessoas se servem das marcas para expressarem o que sentem e se
apresentarem aos seus pares, utilizando-as como parte integrante delas mesmas, este estudo
contribui na medida em que permite entender o poder polarizador que emoções como o
amor e ódio tomam e as implicações que podem ter num contexto comercial. Os resultados
fornecem algumas conclusões interessantes, no entanto, dada a alta complexidade e
constante mutação desta temática é importante que sejam feitos mais estudos de modo a
testar novas perspetivas e atualizar as previamente testadas, permitindo que as marcas tomem
as suas decisões de forma sustentada, potenciando o bom relacionamento com os seus
consumidores.
Keywords: Ódio às marcas; Amor às marcas; Más práticas corporativas; Violação de
expetativas; Word of mouth negativo; Reclamações de consumidores, Redução/cessação do
consumo.
Index
1. Introduction and Topic Relevance ......................................................................................... 1
2. Literature Review....................................................................................................................... 4
2.1. Brand Love ............................................................................................................................. 4
2.2. Brand Hate .............................................................................................................................. 5
3. Hypotheses Development ........................................................................................................ 8
3.1. Antecedents of Brand Hate .................................................................................................. 8
3.2. Outcomes of Brand Hate ..................................................................................................... 9
3.3. Antecedents of Brand Love ............................................................................................... 10
3.4. Outcomes of Brand Love ................................................................................................... 11
4. Methodology ............................................................................................................................ 14
4.1. Research model .................................................................................................................... 14
4.2. Methodology ........................................................................................................................ 15
4.3. Data Collection .................................................................................................................... 15
4.4. Questionnaire’s structure .................................................................................................... 15
4.5. The Sample ........................................................................................................................... 16
4.6. Data Analysis ........................................................................................................................ 17
4.7. Sample Characterization ..................................................................................................... 17
4.8. Descriptive Analysis ............................................................................................................ 20
4.9. Model Validation ................................................................................................................. 22
4.9.1. Factor analysis .............................................................................................................. 22
4.10. Structural Model Validation ............................................................................................. 29
4.11. Hypothesis Test Results and Discussion ....................................................................... 30
5. General Considerations .......................................................................................................... 40
6. Contributions, Limitations and Future Research Suggestions .......................................... 41
7. Bibliographic References ........................................................................................................ 42
8. Attachments ............................................................................................................................. 48
8.1. Annex 1 - Questionnaire .................................................................................................... 48
8.2. Annex 2 – Hate target brands ............................................................................................ 49
8.3. Hated brands – Word Cloud .............................................................................................. 51
8.4. Annex 4 – Brand Hate SPSS Output ................................................................................ 52
8.5. Annex 5 – Brand Love SPSS Output ............................................................................... 53
8.6. Annex 6 – Corporate Wrongdoings SPSS Output ......................................................... 55
8.7. Annex 7 – Violation of Expectations SPSS Output ....................................................... 56
8.8. Annex 8 – Negative Word of Mouth SPSS Output ....................................................... 57
8.9. Annex 9 – Patronage Reduction/Cessation SPSS Output ............................................ 58
8.10. Annex 10 – Consumer Complaining SPSS Output ...................................................... 59
Index of Figures Figure 1 – Antecedents and Outcomes of Brand Hate Model ................................................. 14
Figure 2 – Research Model............................................................................................................. 14
Figure 3 – Reasons that lead respondents to hate the brands they mentioned ...................... 20
Figure 4 – Moderation Model 1..................................................................................................... 29
Index of Tables Table 1 – Brand Hate definitions .................................................................................................... 7
Table 2 – Brand Love Outcomes .................................................................................................. 11
Table 3 – Sociodemographic Data ................................................................................................ 18
Table 4 – Hated brands mentioned by respondents .................................................................. 18
Table 5 – Type of product related to the hated brands mentioned by respondents ............. 19
Table 6 – Dimension item analysis of: Brand Hate, Experienced Brand Love, Brand Hate Antecedents and Brand Hate Outcomes ..................................................................................... 21
Table 7 – Cronbach’s Alpha value description ........................................................................... 23
Table 8 – Cronbach’s Alpha, Composite Reliability and Average Variance Extracted Analysis ............................................................................................................................................. 24
Table 9 – Discriminant validity Test ............................................................................................. 25
Table 10 – Factor Analysis ............................................................................................................. 27
Table 11 – Hypothesis Results ...................................................................................................... 30
Table 12 – Model Summary for H1 and H6................................................................................ 31
Table 13 – Direct effect coefficient for H1 ................................................................................. 31
Table 14 – Moderation coefficient analysis for H6 .................................................................... 32
Table 15 – Model Summary for H2 and H7................................................................................ 33
Table 16 – Direct effect coefficient for H2 ................................................................................. 33
Table 17 – Moderation coefficient for H7 ................................................................................... 34
Table 18 – Model summary for H3 and H8 ................................................................................ 35
Table 19 – Direct effect coefficient for H3 ................................................................................. 35
Table 20 – Model summary for H4 and H9 ................................................................................ 36
Table 21 – Direct effect coefficient for H4 ................................................................................. 36
Table 22 – Model summary for H5 and H10 .............................................................................. 37
Table 23 – Direct effect coefficient for H5 ................................................................................. 38
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1. Introduction and Topic Relevance Relationships between consumers and brands are a topic that is in an incessant discussion,
due to rapid technological innovation and the ease of information sharing. This will be an
ever-changing interaction that will never find an optimal approach. Offering a good product
is no longer enough. The buying process no longer starts at the time the value exchange
happens, it begins way earlier than this practical part. The utilitarian content of acquiring a
product/service is increasingly seen as secondary, to the detriment of its hedonic counterpart
(Hirschman and Holbrook 1982).
Brands play a big part here. All their effort revolves in pleasing and making them more
desired than the rest of the field. They aim to take their consumers to such a high level of
affinity that, for some authors, can be compared to interpersonal love(Shimp and Madden
1988, Whang, Allen et al. 2004, Sirianni and Lastovicka 2011). This threshold is what is more
commonly referred to as brand love, a very actual topic of research (Sirianni and Lastovicka
2011, Batra, Ahuvia et al. 2012, Rossiter and Bellman 2012).
It is of greater interest on the part of the brands to focus their efforts in this direction because
some of the advantages of a "passionate consumer” may be manifested in the form of: a
greater predisposition to rebuy (Carroll and Ahuvia 2006), positive word of mouth (Carroll
and Ahuvia 2006, Munnukka, Karjaluoto et al. 2016) and also, a greater willingness to pay a
premium price (Bauer, Heinrich et al. 2009, Albert and Merunka 2013).
The term "anthropomorphism" comes from psychology and is the phenomenon of imbuing
non-human agents with human characteristics (such as emotions, intentions, or
motivations)(Epley, Waytz et al. 2013). Although this term is used in a perspective of
recognizing certain characteristics to inanimate objects (as in the shape of a car’s headlights
who looks like a pair of eyes), there is another concept that derives from the previous one:
mentalizing (Willard and Norenzayan 2013). Mentalizing is the tendency to attribute human
characteristics to several things that are not human. Curiously, human beings love to atrribute
human features to brands (Kiesler 2006, Aggarwal and McGill 2007, Delbaere, Phillips et al.
2011).
Even more curious is that, one of the catalysts of brand love is, when a brand, through its
strategic plan, successfully promotes the values by which its consumers are driven, making
them identify even more with it (Batra, Ahuvia et al. 2012). Therefore, if consumers "bring
2
their favourite brands to life”, they are to some extent comparable to humans. And if there
is one thing that is intrinsic to the human condition is error: to err is human.
When we like a person and that person hurts us, it is much more intense than if we were a
person with whom we do not share as much affinity (Grégoire and Fisher 2008).
When we really like something and it fails us, we are doubly disappointed. The barrier
between love and hate is extremely tenuous, and what we love in one moment, we might be
hating in the next. Psychologically, hatred is not the absence of love, nor its opposite, as is
commonly discussed. In fact, love and hate are highly related and this can be seen in the ease
with which a loving relationship can turn into disproportionate hatred (J. Sternberg 2003).
It is highly desirable for brands to build good relationships with their consumers (Keller
2001). However, this can be a double-edged sword. Research on whether creating good
relationships can lead to anti-brand behavior by consumers presents very disparate results
(Johnson, Thomson et al. 2011). Whilst good relations with consumers can offer advantages
for brands, the risks to which they are exposed are still relatively unknown topics (Johnson,
Thomson et al. 2011). A consumer who once was one of the biggest lovers of a brand, can
easily become his worst enemy and not only a marginal decrease in sales volume. One of the
consequences of brand love is the willingness and predisposition to invest resources other
than money, such as time or energy (Batra, Ahuvia et al. 2012). In this sense, if a consumer
with this level of commitment is confronted with a side of the brand that is not accustomed
or with which is unable to relate to (ideological differences) (Kozinets and Handelman 2004)
or even the brand’s own personality (Aaker, Fournier et al. 2004)), it is normal that this
consumer does not respond well and even, in extreme cases, feels betrayed. The good
relationship that has been built could be the root of many undesirable problems (Grégoire,
Tripp et al. 2009) and contrarily to what is expected, the brand is in a delicate position, dealing
with a consumer who feels insecure and ashamed (Johnson, Thomson et al. 2011), ready to
retaliate much more pro-actively than any consumer with a weaker consumer-brand
relationship.
According to (Grégoire and Fisher 2006), when the consumer-brand relationship is strong
and the brand commits some kind of error, consumer reaction may take two forms: on the
one hand, the consumer does not take what has happened as relevant and stands on the side
of the brand, event ("love is blind"); on the other hand, when the brand fails to correct its
3
error, the consumer takes what happened to heart and starts to hate the brand ("love
becomes hate").
Given that a lot of studies focus on the recovery relationship that can be built on a customer
who hates a brand and ways to make him fall in love again, this research will go on an
opposite direction. The main questions this study aims to answer are:
• Does Brand Love previously experienced softens the creation of a Brand Hate
Relationship?
• When there is a Brand Hate relationship, does previously experienced Brand Love
alleviate the repercussions?
To this end, the study was developed by asking respondents to answer an online survey. In
order to analyze the data obtained from 207 valid answers, a quantitative methodology based
on a model that relates previous behaviors to a hating relationship with a given brand and
the consequences that these behaviors may cause, adding Brand Love as moderator for those
relationships. The model in question was presented by (Zarantonello, Romani et al. 2016).
This study is divided in four parts. Initially, the introduction and pertinence of the theme is
presented, addressing the relevance of the theme, the gap found in the literature regarding
relationships of this kind and its main objectives. Following the first chapter, the literature
review will be presented regarding the concepts of Brand Hate and Brand Love, their
antecedents and consequences. In the third part, the sample is described and the factor
analysis of the variables is made. On the same point, we will also explain the empirical study,
which exposes the questions and the research context, the respective theoretical model and
the research methodology. Finally, chapter four presents and discusses the conclusions and
limitations of the present study, as well as suggestions for future investigations.
4
2. Literature Review Relationships that consumers establish with brands are dynamic (Langner, Bruns et al. 2016,
Zarantonello, Romani et al. 2018), contrarily to what several studies, with meritorious
contributions to the matter of branding, that were formulated based on a more static
assumption of these relationships (eg. (Batra, Ahuvia et al. 2012, Rossiter and Bellman 2012,
Zarantonello, Romani et al. 2016, Hegner, Fetscherin et al. 2017). The first impression with
a brand is different for each consumer. Each person values different things and perceives
this initial contact differently, which causes different consumers to have different levels of
brand affection, from the start. However, after this initial contact, the relationship continues
to develop and, over time, becomes more complex and the attitude towards the brand begins
to take a more defined shape.
Like A. Fishbein and Ajzen (1975) said, attitude is the amount of affection that nourishes for
or against something. This attitudes, as A. Krosnick and Petty (1995) referred, have four
characteristics: they are persistent over time, resist change, have a strong impact on
information processing and behavior. The stronger the attitude of a consumer towards a
brand, the more pronounced and obvious these characteristics are. In the context of this
study, the attitudes that interest us are the most intense, that is, the most positive and the
most negative, brand love and brand hate, respectively. It is also important to mention that
attitude towards the brand is different from the feelings caused by it. Feelings are transitory
while attitude is lasting (Spears and Singh 2004).
2.1. Brand Love
Since Shimp and Madden (1988) introduced the topic of brand love to the World, this has
been under the radar of several brand managers (Albert and Merunka 2013). The study of
Thomas Shimp e Thomas Madden, among others, more recently like Langner, Bruns et al.
(2016), have established a comparison between a loving relationship between two human
beings with the relationships that consumers establish with brands, through the triangular
love theory (J. Sternberg 1986). However, according to Batra, Ahuvia et al. (2012), there are
different kinds of love, and while it is possible, for example, to associate the desire created
by the products/services offered by a certain brand we love, parental love is a completely
different kind of love that cannot be replicated. Love as emotion is a singular feeling similar
to affection (Richins 1997). When we talk about a loving relation we are referring to
something that endures, does not have a momentary aspect and is imbued with several
5
affective, cognitive and behavioural episodes that happen over time (Fournier 1998). The
problem with this analysis is that, while some studies focus on love as an emotion, others
mention love as the one that exists on a lover’s relationship. It’s also important to point that
the distinction between the two is rarely pointed out (Batra, Ahuvia et al. 2012).
Beyond this theoretical current based on triangular theory, other approaches define that
Brand Love consists of passion, belonging, positive emotions and declarations of love
towards the brand (Carroll and Ahuvia 2006). However, this conceptualization is one-
dimensional and may not be able to comprise all the complexity that a feeling like love entails
(Albert, Merunka et al. 2008).
Finally, we have the chain of thought that considers Brand Love as something
multidimensional and that must be analysed in several layers. Batra, Ahuvia et al. (2012)
points out that a consumer's love for a brand consists of 7 dimensions: perceived functional
quality – there are no customer that can feel love towards a brand if he does attribute good
qualities to the brands’ products/services; self-related cognitions – if a brand is able to
make the customer self-relate with their offer they are aiming straight to his heart; positive
affections and negative affections (the reverse of the first one) – a brand that is capable
of generating good affectivity in a consumer (and the absence of bad cognitions on the other
side) will have an above average level of intimacy compared to the competition; satisfaction
– you can have a satisfied customer that does not feel love for a brand but you can’t have a
customer in love with a brand without feeling satisfied; attitude strength – a brand must
make the customer have intense feeling towards their products/services, otherwise they
won’t ever make it out of the neutral undesired threshold; loyalty – a consumer with
preferential on a brand over the rest is a good indicator that he is in love.
Regarding this study, we will be using this last definition when addressing Brand Love related
topics.
2.2. Brand Hate
While marketing research has focused heavily on the phenomenon of positive feelings that
consumers share with brands (such as brand liking (Spears and Singh 2004), brand devotion
(Pichler and Hemetsberger 2007), brand passion (Albert, Merunka et al. 2013) and brand
love (Carroll and Ahuvia 2006, Batra, Ahuvia et al. 2012, Rossiter and Bellman 2012)), the
negative counterpart does not share the same spotlight (Fetscherin and Heinrich 2015),
6
despite the research provided by different areas (such as consumer behaviour (Banister and
Hogg 2004), neuroscience (Zeki and Romaya 2008) and psychology (A. Ito, Larsen et al.
1998)). A person is more likely to remember a negative event over a positive one, and also
to be more prompt to share negative experiences rather than positive experiences
(Baumeister, Bratslavsky et al. 2001).
To understand what the brand hate is, it is necessary to analyse hatred as a feeling, through
the lens of psychology. Roughly 50 years, the literature on emotions, rarely regarded hatred
as a primary emotion (Arnold 1960). Few years after that, an article where 525 different terms
for emotions are taken into account shows up and we can finally find hatred as a subcategory
of hostile, abhor and antipathy (Storm and Storm 1987). Recently, an approach that takes
hatred as a more powerful emotion, we are told that it is constituted by three components:
repulsion and aversion, anger and fear, disdain (J. Sternberg 2003).
This feeling can arise in several different ways. According to J. Sternberg (2003), any form
of hatred is created by combining any of the three components. It may come up due to a
violation of the rights relating to the freedom of an individual or a community. Although
this is one of the main catalysts of hate, other scholars point out some more possible causes.
Aumer (2007) points out 6 main causes for hate: unpleasant personality – a personality
whose drivers and values don’t correlate with the ones shared by the hatting individual; lack
of respect – inconsideration and misconduct; treason; psychological or physical attacks;
hate target – reciprocating hate because individuals perceived inequity or that the target
hated them first; injury susceptibilities – when individuals get into a situation where they
could have been hurt.
The ways in which people digest this emotion are also quite distinct. According to
(Zarantonello, Romani et al. 2016), 3 types of strategy can be identified as ways of managing
hate: attack strategies, detachment strategies and confrontation strategies.
Attack strategies occur when hate is associated with the desire for destruction, with the
purpose of harming and devaluing the target (J. Sternberg 2003, Tileaga 2015).
In detachment strategies, hate is seen as a waste of time and an unnecessary energy
expenditure. The individual tries to suppress the feeling and deal with the situation as if
nothing happened (Aumer 2007).
7
Confrontation strategies approaches use hate as justification to confront the target and
seek for the justice they deserve (Aumer 2007). The main difference between confrontation
and attack strategies is that, even though in both strategies the individual is looking for some
kind of brand devaluation, in confrontation strategy the hating subject confronts directly the
brand, without fear of them recognizing him and making sure they know who he is and what
he is looking for; in attack-like strategies, the individual acts behind the scenes.
The definition of brand hate is recent and, since 2009, several definitions have been
presented by different researchers, as we can notice in the Table 1.
Table 1 – Brand Hate definitions
Brand Hate Definitions Authors
"The need for a consumer to punish and harm companies for the damages they caused". Here, the term prejudice is seen as "the need for a consumer to cut off all interactions with the company".
(Grégoire, Tripp et al. 2009)
Brand Hate is a strong opposition to a brand by a consumer that is mostly manifested by vindictive actions derived from critical incidents experienced.
(Johnson, Thomson et al.
2011)
“True brand disgust”. Brand Hate is used to describe a situation in which, the consumer is obliged to use a particular brand, to be a customer of a certain company because there aren’t any other options, being unable to change a rival company (for example, when a company detains a monopoly over some type of service).
(Alba and Lutz 2013)
"The deliberated intention to avoid or reject a brand, evidencing this aversion"
(Hultén, Bryson et al. 2013)
Brand Hate consists of two components: active Brand Hate (fed by anger, aversion and contempt) and passive Brand Hate (which is driven by fear, shame, dehumanization, and frustration).
(Zarantonello, Romani et al.
2016)
For the purposes of this study, we will be using the last definition present on the table above
when addressing Brand Hate related topics, given that it is formulated on the basis of a
multidimensional study, offering a broader view on the subject.
8
3. Hypotheses Development
3.1. Antecedents of Brand Hate
In the literature concerning brand hate antecedents we can find 3 main drivers.
Corpoate Wrongdoings
Ideological incompatibility, as presented by Hegner, Fetscherin et al. (2017), is related to
badly perceived behaviors by the brand. This can be related to moral misconducts, badly
perceived communication or a bad brand set of values. The corporate wrongdoings are very
contextual-dependent, given that they are highly sensitive to the set of values present within
a specific society (Lee, Motion et al. 2009). That said, consumers perceive ideological
incompatibility regarding subjects like human rights’ disrespect , environment hurting actions
and unethical practices (Sandıkcı and Ekici 2009). All of those behaviors go against the
intrinsic social responsibility that brands should be promoting (Zarantonello, Romani et al.
2016). Hegner, Fetscherin et al. (2017) points out that ideological incompatibility is the
strongest propeller of brand hate.
Therefore, we hypothesize that:
H1: Corporate Wrongdoings has a positive effect on Brand Hate.
Violation of Expectations
When consumers try a new product or service, they always do it with some kind of
expectations in mind. Consumers chose brands for a lot of different reasons but, the main
one, is that they expect that, given their budget, that brand will have a better performance
than the others on display (Lee, Conroy et al. 2009). If the consumer expectations meet or
exceed the actual performance of what he bought he will often be satisfied. On the other
hand, when negative disconfirmation occurs he will be really unpleased (Lee, Conroy et al.
2009) and that might be the start of a Brand Hate relationship (Zarantonello, Romani et al.
2016).
Therefore, we hypothesize that:
H2: Violation of Expectations has a positive effect on Brand Hate.
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Symbolic Incongruity
Symbolic Incongruity or “Taste System”, as Zarantonello, Romani et al. (2016) presents in
his article, is the third antecedent of Brand Hate mentioned in this study. Taste system is the
negative perception of a brand (i.e., negative brand image) and the people that use that brand,
the incongruity between the symbolic meanings of a brand and the prospects of a consumer
(Hegner, Fetscherin et al. 2017). The theory of disidentification suggests that people can
develop a self-concept by disidentifying themselves with brands that are inconsistent with
their own image (Lee, Motion et al. 2009).
Given that this is a very subjective and complex subject to analyze, we are not going to
include it in our research, focusing on the other two antecedents mentioned.
3.2. Outcomes of Brand Hate
With regard to the literature concerning the consequences of the brand hate we can identify
three different emerging behaviours.
Negative Word of Mouth
Negative word of mouth is the extent to which an individual speaks or writes poorly about
a brand, motivated by negative feelings that make him feel the need to externalize due to bad
treatment (Bonifield and Cole 2007). This concept can be divided in two types: private
complaining (in those cases where the consumer only talks negatively about the brand to his
family and circle of friends) and the public complaining (where the consumer manifests his
negative opinion on website or social media for everyone to see) (Zeithaml, Berry et al. 1996).
Therefore, we hypothesize that:
H3: Brand Hate has a positive effect on nWOM.
Negative Consumer Complaints
The customer manifests in direct actions and complaints to brand’s employees, stealing from
the brand or damaging the brand’s property. J. Sternberg (2003) argues that, hate triggers
people to shorten the distance to the object of hate and to retaliate for the wrongdoings the
brand has committed. Therefore and, in line with other researches, we consider negative
consumer complaints as an outcome for Brand Hate (Grégoire, Laufer et al. 2010,
Zarantonello, Romani et al. 2016).
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The main cause for this, is not a service failure or the product itself, it’s actually the failed
service recovery. Things don’t go right 100% of the time, there are things brands can’t
control and, most of the time, the consumer is aware of that. That said, he is also expecting
the companies to come through and reward their consumers for the failed experience (Ward
and Ostrom 2006).
Therefore, we hypothesize that:
H4: Brand Hate has a positive effect on Consumer Complaining.
Cease/Decrease interactions
This type of behaviour implies two different approaches: brand avoidance and brand
switching. Even though the concepts are distinct, both of them lead to non-consumption
(Hegner, Fetscherin et al. 2017). The first one is conceptualized as “a desire for avoidance is
defined as customers’ need to withdraw themselves from any interactions with firms”
(Grégoire, Tripp et al. 2009). The second one, unlike brand avoidance where the consumer
completely ceases consumption of that type of product/service due to the bad relations
cultivated with the brand, he simply changes to a competing brand (A. Dodson, Tybout et
al. 1978).
Therefore, we hypothesize that:
H5: Brand Hate has a positive effect on Patronage Reduction/Cessation.
3.3. Antecedents of Brand Love
A key criterion for a consumer to feel passionate about a brand is satisfaction. Although not
all satisfied consumers are in love, there is no passionate consumer who is not
satisfied(Carroll and Ahuvia 2006, Roy, Eshghi et al. 2013). Gammoh and Long‐Tolbert
(2012) argues that a successful service delivery experience has a strong impact on the
formation of brand love because this good impression results in a feeling of gratitude and
camaraderie, a good impression that is achieved through good communication between the
consumer and the employee who attended the customer (Yim, Tse et al. 2008). Not only in
services, but in general, positive experiences with good emotional load are good drivers of
brand love (Roy, Eshghi et al. 2013). Ahuvia (2006) states that, pleasure, confidence, esteem
and achievement predict Brand Love. In order for a brand to be loved, it must be valued and
taken in high regard (Batra, Ahuvia et al. 2012).
11
Most consumers have a long history with brands that they love (Albert, Merunka et al. 2008,
Ahuvia, Batra et al. 2009) and it is possible to see the importance of these when analysing
the time that consumers spend thinking and getting in touch with those same brands (Batra,
Ahuvia et al. 2012). They help consumers expressing their identity (Munnukka, Karjaluoto
et al. 2016) and, when the brand demonstrates sharing the same values as the consumer
concerned, their relationship becomes more solid (Carroll and Ahuvia 2006, Ahuvia, Batra
et al. 2009). Brands can reflect what the consumer is, what he aspires to be and what he
desire to become (Batra, Ahuvia et al. 2012). It is also important to note that higher levels of
brand love are registered when the consumer is part of a community of other brand lovers
(Albert and Merunka 2013).
3.4. Outcomes of Brand Love
Different authors reported different behaviours associated with the outcomes of Brand
Love, as can be seen in the Table 2.
Table 2 – Brand Love Outcomes
Brand Love Outcome Authors
Increased Loyalty to the Brand. (Thomson, MacInnis et al.
2005, Kaufmann, Loureiro et al. 2016)
Greater repurchase commitment. (Carroll and Ahuvia 2006)
Strong desire for the brand and willingness to maintain that affinity.
(Batra, Ahuvia et al. 2012)
Higher predisposition for positive word of mouth.
(Carroll and Ahuvia 2006, Batra, Ahuvia et al. 2012,
Munnukka, Karjaluoto et al. 2016)
Promotion of positive attitudes towards the brand through memories and nostalgia, making the consumer dream.
(Albert, Merunka et al. 2008)
Negative word of mouth resistance. (Ahuvia, Batra et al. 2009)
Predisposition to forgive the brand for actions that are not in line with the behaviour to which the consumer is accustomed.
(Bauer, Heinrich et al. 2009)
Predisposition to pay a premium price. (Bauer, Heinrich et al. 2009, Albert and Merunka 2013)
12
Willingness to invest non-monetary resources, such as time or energy.
(Ahuvia, Batra et al. 2009, Batra, Ahuvia et al. 2012)
A consumer who achieves love for a brand, shows a greater predisposition to forgive the
brand for its mistakes (Bauer, Heinrich et al. 2009) and also a greater resistance to bad
critiques by third parties about the brand they value (Ahuvia, Batra et al. 2009), something
that does not happen in consumers who take a neutral position before the brand. These
consumers are building a relationship with the brand, spending time talking about it to those
whom they cherish (Carroll and Ahuvia 2006), building memories and dreaming next to this
(Albert, Merunka et al. 2008). As such, it is to be expected that to break a relationship with
this level of intimacy requires something stronger than what could drive away a consumer
without ties to the brand. That said, it is expected that consumers who demonstrate Brand
Love, who identify with brand values, who have the brand in high consideration, might be
sensitive to experiences where his usual expectations might not be fullfiled. This breach of
expectations here might not be a one big terrible episode. It may also be the accumulation
of several events of a smaller magnitude which, over time, become relevant when taken
together (Johnson, Thomson et al. 2011).
One of the outcomes mentioned above is a greater predisposition on the part of consumers
to invest their time and energy to promote the brand they love (Ahuvia, Batra et al. 2009,
Batra, Ahuvia et al. 2012). Given the emotional and psychological bond that consumers can
create with brands, it seems reasonable to suggest that the ending of such relationships may
result in a long-standing bitterness that manifests itself in actions against the brand (Johnson,
Thomson et al. 2011). When these consumers are confronted with actions that are not in
line with what they are accustomed to, they will experience shame, humiliation and, in
extreme cases, even anger, feelings that tend to lead to attack or confrontation strategies (J.
Sternberg 2003). As such, it is expected that consumers with a greater meaningful
involvement with brands (consumers that attained brand love previously) might adopt
strategies of attack and confrontation, to the detriment of passive strategies, when a bad
episode pops up (Johnson, Thomson et al. 2011). As such, the number of possible negative
word of mouth events is expected to be higher in consumers who have felt embarrassed and
humiliated and higher numbers of protests and complaints in those who have experienced
rage. In consumers where emotional and psychological involvement is weaker or non-
existent, it is to be expected that, if there is a turning point and the consumer starts to hate
the brand, he does not have enough reasons to want revenge or even feel the need to express
13
himself negatively (Johnson, Thomson et al. 2011). For this type of consumer, it is easier to
cease relationships and replace the brand with another and, as such, it is expected that the
adoption of a passive strategy, in this case the cessation of relations with the brand, will be
the most frequent behaviour.
Therefore, we hypothesize that:
H6: Experienced Brand Love has a moderating effect on Corporate Wrongdoings.
H7: Experienced Brand Love has a moderating effect on Violation of Expectations.
H8: Experienced Brand Love has a moderating effect on nWOM.
H9: Experienced Brand Love has a moderating effect on Consumer Complaining.
H10: Experienced Brand Love has a moderating effect on Patronage Reduction/Cessation.
14
4. Methodology
4.1. Research model
The model presented in Figure 1 is the base model we will use in order to develop ou research
model.
By adding the variable Brand Love as moderator for both, antecedents and outcomes of Brand Hate we were able to create our research model, as it is possible to see in Erro! A
origem da referência não foi encontrada..
Figure 1 – Antecedents and Outcomes of Brand Hate Model
Source: Zarantonello, Romani et al. (2016)
Figure 2 – Research Model
15
.
4.2. Methodology
Since this study is based on a deductive approach, focusing on a theoretical basis to explain
the relations (or non-relations) between the various proposed variables, the research will be
based on a quantitative methodology. Respondents will answer a set of survey questions to
understand their level of hatred for a mentioned brand, the reasons for that hatred and the
way they dealt with it. Thus, it will be possible to verify the proposed relationships through
hypotheses via statistical treatment.
To analyse and validate the theoretical model previously proposed, we used the IBM SPSS
Statistics 26 software using Hayes' Process Macro, relying on the principles of ordinary least
squares regression (Bolin 2014).
4.3. Data Collection
As mentioned, given the need to create correlations and in order to analyse the results from
a wider angle, it was decided to use an online questionnaire, to obtain the sample we needed.
The questionnaire contains closed response questions order to increase efficiency and speed
as it is possible to collect many responses in a short time(Saunders, Lewis et al. 2000). This
type of study is one of the most widely used (Malhotra and Df 2007). It also contains some
open answer questions, in order to provide some additional insight over specific cases. These
types of questions are very helpful in the way that, they provide some good examples to
illustrate some specific behaviours. It is also proven that they increase the response rates of
questionnaires, given that the respondent gets more involved with the topic he is being
addressed and feels more freedom to express his feeling in a more concrete way (O'Cathain
and Thomas 2004).
4.4. Questionnaire’s structure
The introduction to the questionnaire will be brief and incisive, without mentioning any kind
of concept that is central to the study, thus preventing the occurrence of the common
method bias (Podsakoff, MacKenzie et al. 2003)
In the survey, we instructed respondents to first think about a brand that they hate, preferably
one that they loved in the past. After that, we asked them to rate three items on overall hate
toward the brand (adapted from a four item scale used by Zarantonello, Romani et al. (2016))
by the use of 7-point Likert scales (1 = “strongly disagree” and 7 = “strongly agree”).
16
In the next part, if the respondents picked a brand they previously love, they would have to
respond to an eight item scale adapted from Bagozzi, Batra et al. (2015) to validate if they
actually felt love or merely liking for the brand by the use, once again, of 7-point Likert scale
(1 = “strongly disagree” and 7 = “strongly agree”).
The third block is related to brand hate antecedents, where we’re only considering two main
reasons, adapted from a three scale item from Zarantonello, Romani et al. (2016), using
another 7-point Liker scale.
After the respondents rated the antecedents that led them to hating the brand they
mentioned, they were asked to write a little summary of the episode, or episodes, that
triggered their discontent.
The last block is related to the outcomes from the hate generated towards those brands and
the type of behaviours consumers might adapt given their specific situation. We are
considering three different behaviours and a three scale item for each one of them, all
adapted from Grégoire and Fisher (2006).
To wrap up the survey, sociodemographic questions were asked, in this case: sex, age and
school level. Even though the three variables we are taking into consideration are not very
intrusive, some respondents might feel like they are intrusive and we don’t want to scare
them away. Those questions are also easier to answer so the respondents don’t feel as much
fatigue and that’s why they come up in the end (Albert, Tullis et al. 2010).
4.5. The Sample
In order to be able to analyse different profiles, a sample must be used. This sample will
provide data that can be used to draw conclusions about the behaviours under study.
Non-probabilistic sampling was the chosen option. Although it does not guarantee a
representative sample, it is a simple method to put in place and with little to no burden, both
in time and resources (Malhotra and Df 2007).
The sampling technique chosen was convenience sampling, where the sample elements are
selected for their convenience, and complemented with the snowball effect, where
respondents share with individuals close to them, in order to increase the sample.
The sample used was obtained by sending the survey via email to all colleges of the University
of Porto and making some individual contacts, between June 14 and 25, 2019.
17
It is recommended that the number of observations is, at least, 5 times the number of
variables (F. Hair, Black et al. 2010). The survey has 26 questions, so the appropriate
minimum number would be 130 valid answers. The final number was 207 valid answers
obtained; therefore, the minimum value was exceeded.
4.6. Data Analysis
In order to answer the proposed research questions, a deductive approach was the chosen
one. Focusing on the theoretical basis proposed by the literature to explain the relationships
between the variables, the hypotheses of this study were formulated and later submitted to
statistical test, thus, confirming (or not) the proposed hypotheses. A quantitative
methodology was the chosen approach.
The data were gathered through a Google Form, and spread out via social networks and e-
mail.
Initially, using the Excel software, a descriptive analysis, as well as a characterization of the
sample, was performed in order to prepare a preliminary analysis of the data.
With that done, a factor analysis was performed. Consisting of an exploratory data analysis
technique, it allows to determine the structure of a set of interrelated variables, reducing them
to a single factor (Marôco 2014).
For analysis and validation of the proposed theoretical model, we opted for the software
IBM - SPSS Statistics using the Hayes' Process macro, relying on the principles of ordinary
least squares regression. This macro is extremely easy to use and model 1 is very well suited
for moderation models, as is the case. In addition, unlike software that uses graphical
demonstrations for structural equation models, this macro can provide results without
having to create a path diagram. (Bolin 2014).
Finally, the results obtained were discussed.
4.7. Sample Characterization
Through a descriptive analysis of the demographic characteristics of the 207 respondents, it
was possible to verify that the majority of respondents belonged to the female gender 61%,
with only 39% being male, as can be seen in Table 3.
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Regarding the age of respondents, the majority of respondents (75%) are between 18 and 25
years old, followed by the age group from 36 to 65 years of age with a percentage of 14%
and, finally, the range of 26 to 35 (11%).
Regarding the educational level of the sample in question, 73% had higher education and
25% finished high school. There are no records of basic education level.
Table 3 – Sociodemographic Data
Sex Age School Level
Male Female 18-25 26-35 36-65 Basic High
School Higher
Education
61% 39% 75% 11% 14% 0 27% 73%
In the initial question, also used as a filter question, subjects had to mention a brand they
hated and thus, answer the rest of the questionnaire with that brand in mind. There was a
great diversity of responses, with 106 different brands registered. The winner was Apple (19
entries), followed by MacDonald’s and Zara (10 entries for both of them). Table 4 shows the
most frequently mentioned brands. The full list and also a word cloud, can be found on the
attachments 2 and 3.
Table 4 – Hated brands mentioned by respondents
Chosen Brand Quantity
Apple 19
McDonald's 10
zara 10
Nestlé 7
NOS 7
Nokia 6
Adidas 5
MEO 5
Nutella 5
Primark 5
Bershka 4
Nike 4
Samsung 4
When we take a closer look at the full brand list, we can also notice that, some types of
products are mentioned more often than others. We can see that, brands within the clothing
or shoe industry get 25% of the hatred from our sample. The technology sector 21,26% and
the food industry 15,94%, also relevant hate targets. Table 5 shows the sectors indicated
more frequently.
19
Table 5 – Type of product related to the hated brands mentioned by respondents
Type of Product Quantity
(%)
Clothing/Shoes 25,12%
Technology 21,26%
Food Products 15,94%
Cosmetics 9,18%
Telecommunications 8,21%
Retail 4,83%
Other 15,46%
Figure 3 represents the reasons that led each respondent to hate the brand they initially chose.
Those answers were analyzed and cataloged in 9 different categories. Each answer could be
part of more than one category.
The category with the most weight is “Poor Quality”, with 22% of the overall sample. This
category is mostly filled with episodes of bad purchases.
After that, with 16%, we have the “Social Inappropriate Attitudes” category. In this category
we see two reprehensible attitudes by a large share of respondents: abuse of workers and
child labour exploration.
The “Bad Working Politics” category (13%) is related to brands that operate in the service
businesses and that left a bad image through unfriendly employees or because they failed to
provide the service were hired to do. A good example of this is that of a participant who
pointed out his hatred brand as “Espírito Santo - Autocarros de Gaia”, a public transport
company operating in the city of Vila Nova de Gaia, who points out: “Unfriendly employees,
constant delays and no good conditions for a comfortable trip.”.
In the “Lack of Environmental and Animal Zeal” category, most registrations relate to
brands that test new products on animals. A good example of this is a participant who chose
“Nars”, a cosmetic brand, as his hate brand, and says: “Nars was a brand known to be against
animal testing, which is why it gained many followers in the US. However, when the
opportunity to enter in the Chinese market appeared, by regional law, they had to perform
tests on animals. And so, they did. They preferred to earn the money that this expansion
would bring, and did not live up to the moral values on which the brand was based.”. This
contribution, besides being considered as “Lack of environmental and Animal Zeal” was also
included in the “Brand Identity Change” category.
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The remaining categories are self-explanatory. However, it’s also appropriate to point out
that in the “Bad Aftersales Services” category, most registrations are related to
telecommunications companies and the poor service provided from the moment the
subscription started, either due to long waiting queues or the inability to solve problems.
Figure 3 – Reasons that lead respondents to hate the brands they mentioned
4.8. Descriptive Analysis
In the first phase of data processing, a descriptive statistical analysis was performed,
characterizing the variables by observing the mode, mean and standard deviation of the
obtained answers. The sampling mode corresponds to the most common value found in a
data set - in this case, the most frequent answer in each of the items. Looking at Table 6, we
can see that “Brand Hate Antecedents” and “Brand Love Experienced” were the ones that
brought together the most representative mode values, indicating that a considerable part of
the participants had a high level of brand affection in the past. and also, that they were well
aware of the reasons that led them to dislike the brand. In the “Brand Hate Outcomes”
section we can see a high disparity, showing in 4 items, the maximum value and in the
remaining 5, the minimum value, which leads us to conclude that there are brand hate
behaviors that take place more often than others. In the “Brand Hate” part, we see that BH1
reaches the maximum value, BH2 a value of 5 and BH3 reaches the minimum value. This
tells us that a considerable part of the participants is well aware that they do not like the
5% 6% 6% 7% 7%8% 8%
13%16%
22%
0%
5%
10%
15%
20%
25%
Reasons that lead to Hate
Violation of Expectations Bad AfterSales Service
Brand Identity Change Misleading Communication
Personal Taste Change Overpricing
Lack of Environmental and Animal Zeal Bad Working Politics
Social Innapropriate Attitudes Poor Quality
21
brand, but a good amount of them does not actually dislike it that much to call it hate. Most
participants also see no reason to feel the need for revenge on the brand.
Table 6 – Dimension item analysis of: Brand Hate, Experienced Brand Love, Brand Hate Antecedents and Brand Hate Outcomes
Questions Mode Mean Standard Deviation
Brand Hate
BH1: I don’t like this brand. 7 5,52 1,51
BH2: I hate this brand. 5 4,03 1,77
BH3: I would like to get revenge on this brand. 6 4,97 1,45
Experienced Brand Love
BL1: Using products/services from this brand used to show something of me as a person.
5 4,48 1,79
BL2: Using products/services from this brand used to make me feel good.
6 4,90 1,74
BL3: Using products/services of this brand used to give my life meaning.
6 3,86 1,89
BL4: Without noticing, I caught myself daydreaming about this brand.
1 3,37 1,94
BL5: I was willing to spend more money than what should be reasonable to be able to use this brand over another.
6 4,07 2,15
BL6: I used to wish to use this brand’s products/services. 6 3,85 1,91
BL7: The products/services of this brand used to be a perfect fit for my taste and preferences.
6 4,49 1,85
BL8: I used to feel emotionally connected to this brand. 4 3,92 1,88
Brand Hate Antecedents
CWD1: The products/services of this brand are produced/provided in a reprehensible manner.
7 4,96 2,19
CWD2: This brand had improper conduct regarding the preservation of the environment and its sustainability.
1 3,06 2,26
CWD3: This brand had improper conduct regarding social issues.
7 4,15 2,57
VEX1: Given the brand in question, I expected better of the products/services they featured.
7 4,96 2,07
VEX2: The products / services of this brand had an inappropriate price for their quality.
7 4,35 2,04
VEX3: Given what I knew from the competition, I expected more from this brand’s produtcts/services.
7 4,97 2,49
Brand Hate Outcomes
NW1: I told my family and friends how bad I feel this brand is.
7 4,46 2,05
NW2: I did negative reviews of this brando n on-line platforms.
1 3,77 2,51
NW3: When I noticed my friends or family were about to buy products/services from this brand I tried to change their mind.
1 3,16 2,44
PR1: I don’t want to spend any more money on this brand.
7 6,14 1,58
PR2: I try to have no connection of any kind with this brand (publicity, for example).
7 6,03 1,51
PR3: If I must user products/services from this brand, I’ll use them as little as possible
7 5,63 1,61
22
CP1: I felt the need to make the brand representatives have a hard time.
1 2,15 1,85
CP2: I felt the need to be unpleasant to the brand representatives.
1 2,06 1,83
CP3: I felt the need to make someone of the brand pay for their bad performance.
1 1,76 1,56
The sample mean indicates where respondents' answers are concentrated on each item, and
is calculated by dividing the sum of responses by the number of participants. The items
where the respondents came closer to full agreement were: BH1 (5.52) PR1 (6.14), PR2 (6.03)
and PR3 (5.63). With the lowest values, stand out BH3 (1.88), CP1 (2.15), CP2 (2.06) and
CP3 (1.76). The sample standard deviation, in turn, is a measure of data dispersion relative
to the sample mean. In this investigation, this measure assumes a certain relevance, telling us
that the answer to some items are quite heterogeneous. The items with the highest standard
deviation values are: CWD3 (2.57), VEX3 (2.49), NWM2 (2.51) and NWM3 (2.44).
Considering the sample mean, the results show that, for “Patronage Reduction”, individuals
share the highest agreement. The 3 items related to “Patronage Reduction” have the highest
mean values and the lowest standard deviations values. It can also be inferred that, in the
“Brand Love Experienced” is where there is the greatest heterogeneity of answers, where the
mean values are not high for most items and standard deviations assume significant values.
4.9. Model Validation
4.9.1. Factor analysis
After the descriptive analysis, it is necessary to analyse the quality of the adjustment of the
study model to the structure of the variables and their correlations. In order to do this, we
will run a factor analysis, a pertinent analysis for Structural Equation Models which allows to
determine the structure of a set of variables that relate to each other, reducing them to just
one factor (Marôco 2014). Through this analysis it is possible to understand if the proposed
variables for each of the model dimensions interrelate and form a latent common factor, that
is, the respective dimension.
Initially, a reliability analysis was performed through IBM SPSS Statistics 26, using Microsoft
Excel 2010 to obtain certain values that couldn’t be directly extracted from SPSS. In order
to confirm the reliability of the results, the internal consistency of the variables organized by
groups was tested by calculating: Cronbach's Alpha, Extracted Mean Variance (AVE) and
Composite Reliability (CR), obtaining the values that can be found in Table 8.
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Cronbach's Alpha values range between 0 and 1, and only values equal to or greater than 0.6
should be considered. (Pestana 2003).
Pestana (2003) present a reliability scale, associating the range of values that the alpha of
Cronbach takes with the quality of internal consistency, as we can see in Table 7.
Table 7 – Cronbach’s Alpha value description
Coeficient Values
α Quality of Internal
Consistency
0,9 - 1,0 Very Good
0,8 - 0,9 Good
0,7 - 0,8 Acceptable
0,6 - 0,7 Weak
< 0,6 Disconsider
As we can see in the table Table 8, “Brand Hate” and “Complaining” variables have a very
good consistency quality, with values of 0.903 and 0.924, respectively. Next, with good
consistency we have “Negative Word of Mouth” and “Patronage Reduction / Cessation”
with 0.806 and 0.893, respectively. In the reasonable consistency range, with 0.751 we have
the variable “Corporate Wrongdoings”. Finally, the only variable left is “Violation of
Expectations”. The Cronbach’s alpha value of this variable is 0.58, which is, unfortunately,
unacceptable. Cronbach's alpha is an indicator that is highly sensitive to the number of items
on the scale and generally tends to underestimate the reliability of internal consistency. In
non-exploratory studies, we would discard this variable, given that the Cronbach’s Alpha
minimum value is not reached. However, when we are performing an exploratory study, it is
valid to opt for the composite reliability coefficient (Nunnally 1994). This coefficient offers
acceptable values when its result is between 0.6 and 0.7 and it provides a great alternative
option for situations like this. (Nunnally 1994). As such, we will support our reliability
analysis for the “Violation of Expectations” variable in the composite Reliability value and
the Average Variance Extracted, as we will see below.
Composite Reliability is a measure of internal consistency in scale items, much
like Cronbach’s alpha (Netemeyer, Bearden et al. 2003). It is an indicator of the shared
variance among the observed variables used as an indicator of a latent (Bagozzi 1981). Values
for Composite Reliability are acceptable when they are above 0.7 (F. Hair, Black et al. 2010).
24
All values meet this requirement, even the problematic variable, “Violation of Expectations”,
which scores a very high value of 0.974, validating this variable for reliability.
That said, we will also consider Average Variance Extracted. AVE is the average amount
of variance in indicator variables that a construct is managed to explain (Bagozzi 1981). The
values for this coefficient must be greater than 0.5, where AVE indicates the percentage of
total variance that is explained by the latent variable. When a factor scores a value of 0.8, this
means that the referred variable explains 80% of the variance of the indicators. (F. Hair,
Black et al. 2010). All variables under analysis presented values above the 0.5 cutoff, ranging
from 52.1% to 97.1%.
Table 8 – Cronbach’s Alpha, Composite Reliability and Average Variance Extracted Analysis
Cronbach's
Alpha Composite Reliability
Average Variance Extracted
(AVE)
Brand Hate 0,903 0,906 0,764
Brand Love 0,918 0,888 0,636
Consumer Complaining
0,924 0,922 0,798
Corporate Wrongdoings
0,751 0,76 0,527
Negative Word of Mouth
0,806 0,811 0,603
Patronage Recuction/Cessation
0,893 0,918 0,799
Violation of Expectations
0,58 0,974 0,971
After testing the quality of the extracted factors, we moved to the discriminant validity test.
Discriminant validity shows that two measures that are not supposed to be related are, in
fact, unrelated. If correlations between factors do not exceed the value of .85 (Bagozzi and
Yi 1989) and the AVE of each construct is greater than the correlations between them
(Bagozzi 1981), discriminant validity is supported (Anderson and Gerbing 1988). Analyzing
25
Table 9, the discriminant validity is confirmed because both criteria are met, i.e. the variables
do not overlap.
Table 9 – Discriminant validity Test
Brand Hate
Consumer Complaining
Corporate Wrongdoings
Negative Word of Mouth
Patronage Reduction/ Cessation
Violation of Expectations
Brand Hate 0,874
Consumer Complaining
0,183 0,894
Corporate Wrongdoings
0,501 0,243 0,726
Negative Word of Mouth
0,431 0,279 0,396 0,777
Patronage Reduction/Cessation
0,221 -0,002 0,153 0,102 0,894
Violation of Expectations
0,145 0,145 0,199 0,214 0,104 0,985
Next, in order to gauge the quality of the analysis, we will consider the values of the factor
weights (Loadings), the Bartlett Sphericity Test and the Kaiser-Meyer-Olkin Sampling
Adequacy Test (KMO). Those tests were performed for each construct on SPSS, with a
Varimax rotation. This rotation type is adequate to our study given that the Varimax method
aims to obtain a factorial structure in which, one and only one of the original variables is
strongly associated with a single factor and little associated with the rest (Marôco 2014).
The factor weights must be greater than 0.7 for the structure to be considered well defined,
where values above 0.6 are already considered acceptable (F. Hair, Black et al. 2010).
The Bartlett Sphericity Test compares an observed correlation matrix to the identity matrix.
Essentially, it checks to see if there is a certain redundancy between the variables that we can
summarize with a few numbers of factors. The null hypothesis of the test states that the
variables are not correlated; the alternative hypothesis states that the variables are correlated
enough to where, the correlation matrix diverges significantly from the identity matrix
(Marôco 2014). We are looking for a p-value < 0.05, so we can reject the null hypothesis
(Marôco 2014).
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The Kaiser-Meyer-Olkin Measure of Sampling Adequacy is a statistic that indicates the
proportion of variance in your variables that might be caused by underlying factors. If the
value is less than 0.50, the results of the factor analysis probably won't be very useful (Marôco
2014).
Moving on to factor extraction, we resorted to Eigenvalue. This indicator should have values
greater than 1. If so, we can conclude that one (or more) variables explain the total variance
of the original variables (Marôco 2014).
We also took into consideration the communalities value. Communalities indicate the
amount of variance in each variable that is accounted for. Initial communalities are estimates
of the variance in each variable accounted for by all components or factors. For principal
components extraction, this is always equal to 1.0 for correlation analyses. Said that, we are
interested in the values of the extraction communalities. This value is an estimate of the
variance in each variable accounted for by the components. The bigger the values, the better
the extracted components represent the variables. According to literature, a value over 0.5 is
a good enough indicator (Marôco 2014).
27
Table 10 – Factor Analysis
Constructs Questions
KMO Measure of Sampling Adequacy
Bartlett’s Test of
Sphericity
Factorial weight
Eigenvalues Communalities
Requirements > 0.5 < 0.001 > 0.7 > 1 > 0.5
Brand Hate
BH1: I don’t like this brand.
.521 .000
0.865
2.522
0.748
BH2: I hate this brand. 0.886 0.786
BH3: I would like to get revenge on this brand.
0.994 0.989
Brand Love
BL1: Using products/services from this brand used to show something of me as a person.
.841 .000
0.813
5.110
0.86
BL2: Using products/services from this brand used to make me feel good.
0.801 0.723
BL3: Using products/services of this brand used to give my life meaning.
0.864 0.868
BL4: Without noticing, I caught myself daydreaming about this brand.
0.726 0.722
BL5: I was willing to spend more money than what should be reasonable to be able to use this brand over another.
0.721 0.553
BL6: I used to wish to use this brand’s products/services.
0.867 0.894
BL7: The products/services of this brand used to be a perfect fit for my taste and preferences.
0.81 0.875
BL8: I used to feel emotionally connected to this brand.
0.777 0.643
Corporate Wrongdoings
CWD1: The products/services of this brand are produced/provided in a reprehensible manner.
.635 .000 0.818 2.010 0.669
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CWD2: This brand had improper conduct regarding the preservation of the environment and its sustainability.
0.748 0.559
CWD3: This brand had improper conduct regarding social issues.
0.884 0.781
Violation of Expectations
VEX1: Given the brand in question, I expected better of the products/services they featured.
.628 .000
0.729
1.631
0.531
VEX2: The products / services of this brand had an inappropriate price for their quality.
0.768 0.59
VEX3: Given what I knew from the competition, I expected more from this brand’s produtcts/services.
0.714 0.509
Negative Word of Mouth
NW1: I told my family and friends how bad I feel this brand is.
.676 .000
0.898
2.166
0.806
NW2: I did negative reviews of this brand on on-line platforms.
0.825 0.681
NW3: When I noticed my friends or family were about to buy products/services from this brand I tried to change their mind.
0.824 0.679
Patronage Reduction/Cessation
PR1: I don’t want to spend any more money on this brand.
.658 .000
0.958
2.482
0.919
PR2: I try to have no connection of any kind with this brand (publicity, for example).
0.946 0.894
PR3: If I must user products/services from this brand, I’ll use them as little as possible.
0.818 0.669
Consumer Complaining
CP1: I felt the need to make the brand representatives have a hard time.
.688 .000
0.967
2.609
0.935
CP2: I felt the need to be unpleasant to the brand representatives.
0.958 0.918
CP3: I felt the need to make someone of the brand pay for their bad performance.
0.87 0.757
29
To get a better visualization of the values presented for the various items of the analysis
described above, this table was compiled with all the relevant values. Outputs extracted from
SPSS can be found on attachments, from annex 4 through 10.
Starting with the quality analysis, we can see that all the necessary requirements have been
met, and it can be stated that: the definition of the structure is good, there is a good
correlation between the variables and also a good homogeneity.
Regarding data extraction and the respective Eigenvalue, values greater than 1 were verified
in all constructs, validating that the extracted values justify a considerable proportion of the
total variable of the original variables.
Finally, the values of communalities are all greater than 0.5 and as such, all extracted
components represent well the variables to which they refer.
Finally, we are clear to move forward with the analysis of the proposed model.
4.10. Structural Model Validation
In this chapter we tested the study hypotheses previously presented. For this, we used the
SPSS plug-in Hayes’ Process. This plug-in includes 76 different models for moderation and
moderation analysis (Bolin 2014). This said, we ran our hypothesis tests via model 1, given
that the relationships we are trying to explain are all of the same kind: an independent variable
impacting directly on the outcome variable with a third one (the moderation variable)
moderating this relationship (as can be seen in the Figure 4).
Figure 4 – Moderation Model 1
Source: Bolin (2014)
30
There are three previous requirements in order to run this plug-in in good terms:
• Construct Mean – In order to correlate the variables, we will use the mean of all
items of each construct and correlate them that way.
• Mean Centering – we will be using mean centering for all the variables that define
products. This will enhance the interpretability of data and allow us to get accurate
values over the direct effects of the independent on the outcome variable (Bolin
2014). Mean centering would not be necessary if we were only concerned over the
interaction effects but, given that we are also interested in the direct effects, this will
allow us to test more than one hypothesis in one test.
• Huber-White Heteroscedasticity- Consistent Inference – this option allows to
fit the model in the eventual case it does contain heteroscedastic residuals (F Hayes
2003).
4.11. Hypothesis Test Results and Discussion
The Table 11 shows the main results of the present study.
Table 11 – Hypothesis Results
Hypothesis Results
H1: Corporate Wrongdoings has a positive effect on Brand Hate. Supported
H2: Violation of Expectations has a positive effect on Brand Hate. Supported
H3: Brand Hate has a positive effect on nWOM. Supported
H4: Brand Hate has a positive effect on Consumer Complaining. Supported
H5: Brand Hate has a positive effect on Patronage Reduction/Cessation. Supported
H6: Experienced Brand Love has a moderating effect on Corporate Wrongdoings.
Supported
H7: Experienced Brand Love has a moderating effect on Violation of Expectations.
Supported
H8: Experienced Brand Love has a moderating effect on nWOM. Not
Supported
31
H9: Experienced Brand Love has a moderating effect on Consumer Complaining.
Not Supported
H10: Experienced Brand Love has a moderating effect on Patronage Reduction/Cessation.
Not Supported
The model referring to hypotheses 1 to 5, besides being a model adapted from (Zarantonello,
Romani et al. 2016) and with interactions similar to those presented by (Hegner, Fetscherin
et al. 2017), is important in that it allows to assess whether the base model, where the
moderating effect of Brand Love were tested, is robust enough to draw valid conclusions
about it. That said, all assumptions regarding the relationships of the Brand Hate antecedents
with Brand Hate and the Brand Hate outcomes with Brand Hate were validated, proving
that the base model is robust enough to insert the moderating effect under analysis study.
• H1 and H6
Table 12 – Model Summary for H1 and H6 *significant for p < 0.05
Model Summary
R R-sq MSE F df1 df2 p
.4380 .1919 1.7002 16.0653 3 203 .000*
In Table 12, we are able to find a summary for the interactions we are testing. Given that the
p-value is < 0.05, we can affirm that the overall model is significant.
Overall model: F (3, 203) = 16.07, p < 0.01, R² = .1919 Table 13 – Direct effect coefficient for H1 *significant for p < 0.05
b se t p LLCI ULCI
Constant 4.8365 0.0906 53.3622 .000* 4.6578 5.0152
CWD .3077 .0474 6.4868 .000* 0.2142 0.4013
BL -.0544 .0606 -0.8976 .370 -0.1739 0.0651
Interaction -.0676 .0324 -2.0877 .038* -0.1314 -0.0038
In Table 13 it is possible to extract two important information:
1- If the direct effect of Corporate Wrongdoings has impact over Brand Hate.
2- If the Brand Love variable has a moderating effect of the relationship between
Corporate Wrongdoings and Brand Hate.
Corporate Wrongdoings has a significant effect over Brand Hate, given that is p-value is <
0.05. Given its b value, it is possible to state that, for every unit increase of Corporate
Wrongdoings value, Brand Hate increases 0.31 units.
32
Corporate Wrongdoings: b = .31, t (203) = 6.49, p < 0.05.
This validates H1: Corporate Wrongdoings has a positive effect on Brand Hate.
Hypothesis 1 “Corporate Wrongdoings has a positive effect on Brand Hate” was
validated with a coefficient value of .31. The value of this coefficient is quite considerable
and serves to show how moral misconducts, deceptive communication or inconsistency with
the values promoted by the brand, become quite expressive and lead the consumer to be
highly displeased. This “ideological incompatibility” impact on brand has been also proven
in researches like (Hegner, Fetscherin et al. 2017, Zarantonello, Romani et al. 2018).
On the line respective to interaction, it is possible to verify if the variable Brand Love has a
moderating effect over the relationship established between Corporate Wrongdoings and
Brand Hate. Given that it’s p-value < 0.05, we can attest that the interaction is statistically
significant.
Interaction: b = -.0676, t (203 )= -2.09, p = . < 0.05.
Table 14 – Moderation coefficient analysis for H6 *significant for p < 0.05
BL Effect (b) se t p LLCI ULCI
(-) 1 SD .4096 .0667 61.434 .000* .2782 .5411
Mean .3077 .0474 64.868 .000* .2142 .4013
(+) 1 SD .2059 .0694 29.652 .003* .0690 .3427
Table 14 allows the understanding of the way the moderating effect affects the relationship
between Corporate Wrongdoings and Brand Hate at – 1 Standard Deviation, at the value of
mean and at + 1 Standard Deviation. With this, we can create 3 different levels of Brand
Love: the lower one (at -1 SD), the average one (at the mean) and the high one (at +1 SD).
For Low Brand Love Values: b = .41, t (203) = 6.14, p < 0.05 (Significant) – Regarding
Low Brand Love values, for every unit increase in 1 unit of Corporate Wrongdoings, Brand
Hate increases .41 units.
For Average Brand Love Values: b = .31, t (203) = 6.5, p < 0.05 (Significant) - Regarding
Average Brand Love values, for every unit increase in 1 unit of Corporate Wrongdoings,
Brand Hate increases .31 units.
For High Brand Love Values b = .21, t (203) = 3, p = < 0.05 - (Significant) - Regarding
High Brand Love values, for every unit increase in 1 unit of Corporate Wrongdoings, Brand
Hate increases .21 units.
33
We can conclude that, the higher the Brand Love Value, the least impact Corporate
Wrongdoings has over Brand Hate.
This validates H6: Experienced Brand Love has a moderating effect on Corporate
Wrongdoings.
• H2 and H7
Table 15 – Model Summary for H2 and H7 *significant for p < 0.05
Model Summary
R R-sq MSE F df1 df2 p
.3206 .1028 1.8876 7.8037 3 203 .000*
In Table 15, we are able to find a summary for the interactions tested. Given that, the p-value
is < 0.05, we can affirm that the overall model is statistically significant.
Overall model: F (3, 203) = 7.8037, p < 0.05, R² = .1028 Table 16 – Direct effect coefficient for H2 *significativo para p < 0.05
b se t p LLCI ULCI
Constant 4,8254 .0950 50,7810 .000* 4,6381 5,0128
VEX 0,0491 .0638 3,2974 .001* 0,0651 0,2587
BL -0,0102 .0645 -0,1585 .874 -0,1375 0,117
Interaction -0,105 .0312 -3,3645 .001* -0,1666 -0,0435
As can be seen, Violation of Expectations has a significant effect over Brand Hate, given
that is p-value is < 0.05. Given its b value, it is possible to state that, for every unit increase of
Violation of Expectations value, Brand Hate increases 0.05 units.
Violation of Expectations: b = .05, t (203) = 3.3, p = < 0.05
This validates H2: Violation of Expectations has a positive effect on Brand Hate.
Hypothesis 2 “Violation of Expectations has a positive effect on Brand Hate” was
equally validated with a coefficient value of .05. This value is relatively low compared to
other research done on similar matter (Zarantonello, Romani et al. 2016, Hegner, Fetscherin
et al. 2017). This breach of expectations is a variable assembled, mostly, of bad past
experiences. The impact of these bad past experiences is highly influenced by the duration
and intensity that a consumer's relationship had pre-established with the brand, i.e. a long-
standing brand consumer who sees his expectations defrauded will be more affected than a
consumer with a more superficial relationship (Langner, Bruns et al. 2016). In the
34
questionnaire presented, part of the episodes of labeled as violation expectations, in
respondents with low brand love levels, are episodes of one or two interactions with the
brand. Therefore, one cannot consider those relationships as long-term ones, and as such,
reducing the possible impact of this variable on the constitution of the Brand Hate.
Regarding the interaction column, we can check if the variable Brand Love has a moderating
effect over the relationship established between Violation of Expectations and Brand Hate.
Given that it’s p-value < 0.05, we can afirm that the interaction is statistically significant.
Interaction: b = -.105, t (203) = -3.36, p = < 0.05.
Table 17 – Moderation coefficient for H7 *significant for p < 0.05
BL Effect
(b) se t p LLCI ULCI
(-) 1 SD .3203 .0685 4.6731 .000* .1851 .4554
Mean .1619 .0491 3.2974 .001* .0651 .2587
(+) 1 SD .0035 .0675 0.0524 .9583 -.1295 .1366
Again, we’ll use Table 17 to understand the way the moderating effect affects the relationship
between Violation of Expectations and Brand Hate at – 1 Standard Deviation, at the value
of mean and at + 1 Standard Deviation.
For Low Brand Love Values: b = .32, t (203) = 4.67, p < 0.05 (Significant) – Regarding
Low Brand Love values, for every unit increase in 1 unit of Violation of Expectations, Brand
Hate increases .32 units.
For Average Brand Love Values: b = .16, t (203) = 3.3, p < 0.05 (Significant) - Regarding
Average Brand Love values, for every unit increase in 1 unit of Violation of Expectations,
Brand Hate increases .16 units.
For High Brand Love Values b = .21, t (203) = 3, p < 0.05- (Not Significant)
We can conclude that, the higher the Brand Love Value, the least impact Violation of
Expectations has over Brand Hate.
This validates H7: Experienced Brand Love has a moderating effect on Violation of
Expectations.
Hypothesis 6 and 7, “Experienced Brand Love has a moderating effect on Corporate
Wrongdoings” and “Experienced Brand Love has a moderating effect on Violation
of Expectations” respectively, refers to the moderating effect of Brand Love on the
35
relationship between the Brand Love antecedents and Brand Love. Both hypotheses were
validated, with values that vouch that, the higher the Brand Love value experienced prior
to the deterioration of the relationship, the lower the impact recorded in the antecedent.
Note that, Hegner, Fetscherin et al. (2017) states that Corporate Wrondoings is the largest
brand hate propeller, outweighing Violation of Expectations. In this situation, we are able to
see that, the largest propeller according to literature, is the one with the highest moderating
effect out of the two, as if brand love could “soften” the Corporate Wrongdoings effect.
• H3 and H8
Table 18 – Model summary for H3 and H8 *significant for p < 0.05
Model Summary
R R-sq MSE F df1 df2 p
.4004 .1603 3.3282 12.9819 3 203 .000*
In Table 18, we are able to find a summary for the interactions we are testing. Given that the
p-value is < 0.05, we can affirm that the overall model is significant.
Overall model: F (3, 203) = 12.98, p < 0.01, R² = .1603 Table 19 – Direct effect coefficient for H3 *significant for p < 0.05
b se t p LLCI ULCI
Constant 3.7966 .1259 30.1667 .000* 3.5485 4.0448
BH 0.1154 .0880 5.4384 .000* .3051 .6521
BL -0.2358 .0898 -2.6249 .009* -.413 -.0587
Interaction -0.0048 .0668 -0.0714 .9431 -.1364 .1269
Contrarily to what happened on the first two hypothesis we tested, here, only the direct
effect of Brand Hate on Negative word of Mouth is statistically significant. The
interaction is not statistically significant, therefore Brand Love does not have a moderating
effect over the relationship between Brand Hate and Negative Word of Mouth.
Regarding the direct effect, it is possible to state that, for every unit increase of Brand Hate
value, Negative Word of Mouth increases 0.12 units.
Brand Hate: b = .12, t (203) = 5.44, p < 0.01.
This validates H3: Brand Hate has a positive effect on Negative Word of Mouth.
Hypothesis 3 “Brand Hate has a positive effect on nWOM” was validated with a
coefficient of .12. Again, the value of this coefficient is lower than that shown in similar
36
researches (Zarantonello, Romani et al. 2016, Hegner, Fetscherin et al. 2017, Zarantonello,
Romani et al. 2018). As in the study by Hegner, Fetscherin et al. (2017), instead of splitting
the negative word of mouth construct into 2: “public complaining” and “private
complaining”, we included items that tested both strands, in building our construct. When
we look at the items separately, we see that, the mode value of the private complaining item
gets the highest mode value in the Likert scale, while the item that corresponds to public
complaining gets the minimum mode value. Moreover, the average difference of these two
items is almost one point out of seven, allowing us to conclude that, for our sample, even if
the Brand Hate effect is positive on the negative word of mouth and brand hate interaction,
it would have a higher coefficient if the construct was only contemplating private
complaining.
Interaction: b = -.0048, t (203) = -.0714, p = .9431
Unfortunately, the interaction is not statistically significant, which invalidates H8:
Experienced Brand Love has a moderating effect on nWOM.
• H4 and H9
Table 20 – Model summary for H4 and H9 *significant for p < 0.05
Model Summary
R R-sq MSE F df1 df2 p
.2071 .0429 2.5790 2.6754 3 203 .0483
According to Table 20, we are able to find a summary for the interactions we are testing.
Given that the p-value is < 0.05, we can attest that the overall model is significant.
Overall model: F (3, 203) = 2.68, p < 0.05, R² = .0429 Table 21 – Direct effect coefficient for H4 *significant for p < 0.05
b se t p LLCI ULCI
Constant 1.9886 .1097 18.1352 .000* 1.7724 2.2048
BH 0.1801 .0730 2.4684 .014* .0362 .324
BL -.1229 .0852 -1.4431 .1505 -.2909 .045
Interaction -.0321 .0474 -.6775 .4989 -.1257 .0614
Here, the direct effect of Brand Hate over Consumer Complaining is statistically
significant, given that p-value = .0144. For every unit increase in Brand Hate value, there’s
an increase of .19 units of Consumer Complaining.
37
Brand Hate: b = .18, t (203) = 2.47, p < 0.05.
This validates H4: Brand Hate has a positive effect on Consumer Complaining.
Hypothesis 4 “Brand Hate has a positive effect on Consumer Complaining” was also
proven with a coefficient of .18. This variable is related to the most aggressive behaviors and
is therefore associated with attack strategies to deal with hate. This is a value that, despite
being lower than what is recorded in similar researches (Zarantonello, Romani et al. 2016,
Hegner, Fetscherin et al. 2017), it is still expressive and, looking at the brands cited by
respondents (large corporations with much larger resources than a mere disgruntled
consumer), it takes a very brave and bold behavior, driven by a need for revenge to make
that stand, a position that ends up often being repressed due to the poor chances of the
consumer getting the justice that he deserves (Grégoire, Tripp et al. 2009).
Due to the low p-value for interaction, we can say that the moderator effect of Brand Love
over the Brand Hate and the Consumer Complaining relationship is not statistically
significant (p-value = .4989).
Interaction: b = -.0321, t (203) = -.6775, p = .4989
That said, H9: Experienced Brand Love has a moderating effect on Consumer Complaining,
is invalid.
• H5 and H10
Table 22 – Model summary for H5 and H10 *significant for p < 0.05
Model Summary
R R-sq MSE F df1 df2 p
.2001 .0401 2.8233 12.9819 3 203 .039*
As we’ve seen in the previous tests, in Table 22, we are able to find a summary for the
interactions we are testing. Given that the p-value is < 0.05, we can affirm that the overall
model is significant.
Overall model: F (3, 203) = 12.98, p < 0.05, R² = .0401
38
Table 23 – Direct effect coefficient for H5 *significant for p < 0.05
b se t p LLCI ULCI
Constant 5.9334 .0975 60.8467 .000* 5.7411 6.1257
BH 0.1859 .0682 2.7253 .007* .0514 .3204
BL -.0382 .0649 -.5875 .5575 -.1662 .0899
Interaction -.0207 .0430 -.4821 .6302 -.1055 .064
Once again, we can see that the direct effect is statistically significant but the interaction
is not. Regarding the direct effect, it is possible to state that, for every unit increase of
Brand Hate value, Patronage Reduction value increases 0.19 units.
Brand Hate: b = .19, t (203) = 2.73, p < 0.05.
This validates H5: Brand Hate has a positive effect on Patronage Reduction/Cessation.
Hypothesis 5 “Brand Hate has a positive effect on Patronage Reduction /
Cessation.”, also valid with a coefficient value of .19. The value of this coefficient is quite
similar to those presented in researches with similar purposes (Zarantonello, Romani et al.
2016, Hegner, Fetscherin et al. 2017, Zarantonello, Romani et al. 2018). Considering the
results presented for the other two outcomes, this one presents a more expressive value. It
can be justified with an argument, similar to the one presented in the previous hypothesis, in
that, having most of the respondents mentioned brands with a large commercial presence
and with access to large quantities of money and man power, may have driven the need for
revenge away, ending this episode with the unsatisfied customer detaching himself from the
brand, as last resort (Grégoire, Tripp et al. 2009).
Interaction: b = -.0207, t (203) = .0430, p = .6302
However, the interaction is once again not statistically significant, invalidating H10:
Experienced Brand Love has a moderating effect on Patronage Reduction/Cessation.
The last 3 hypotheses, H8: “Experienced Brand Love has a moderating effect on
nWOM”, H9: “Experienced Brand Love has a moderating effect on Consumer
Complaining” and H10: “Experienced Brand Love has a moderating effect on
Patronage Reduction/Cessation”, were not supported. Although several studies have
advocated in favor of the premise that, with the brand love outcomes in consideration, some
types of relationship should be influenced by the feeling of love for the brands(Carroll and
Ahuvia 2006, Roy, Eshghi et al. 2013, Bagozzi, Batra et al. 2015), others disagree with this
perspective. Grégoire and Fisher (2006) conceptualizes relationship quality as a higher-order
39
that is reflected in trust, satisfaction, commitment and identification. In the same article, the
authors analyze the impact of a bad experience with a product/service and the connection
with relationship quality, for customers with high relationship quality and for customers with
low relationship quality. They say that, under circumstances where the customers feels like
the bad experience they are receiving is for the brand to blame, there are no noticeable
differences between the desire for retaliation of high versus low relationship quality
customers. This goes on line with our results.
40
5. General Considerations This investigation focuses on Brand Hate relationships, specifically those that were
previously considered Brand Love relationships. As such, this research has two major
objectives: to verify whether love of the previously felt brand softens the creation of the
relationship around hatred and, when this hate relationship is constituted, if the previously
felt love influences the hate-dealing behavior of consumers.
Using a quantitative methodology based on a model that mirrors the brand hate catalysts and
their repercussions, IBM SPSS Statistic 26 software was used to address the 207 valid
responses.
Regarding the impact of Brand Love on Brand Hate's antecedents, the results indicated that
its effect is effectively felt, showing that the higher the Brand Love value, the lower the
impact of Brand Hate's antecedents on building this type of relationship. As for the outcomes
of Brand Hate and the impact that the previous love relationship could have, the results were
negative. This is because when a customer of a brand starts to hate it, their behaviors are
very similar to those displayed by consumers who have never felt any prior affinity for the
brand.
It should be borne in mind that forming a hate relationship with a brand is a much more
individualistic process than the repercussions that the brand may later suffer at the hands of
its consumers. While for a consumer to classify a brand as a target of hatred is an internal
process that only involves himself, two of the three outcomes discussed here imply that the
consumer expresses his inner voice and externalizes his feelings (in this case, negative word
of mouth and consumer complaining). Most brands listed as hate targets by respondents are
large corporations with impactful worldwide expression. These brands have such a large
dimension that it is difficult for the individual to have a clear definition as to who is to blame
for what happened and often ends up feeling powerless over the weapons at his disposal.
While there is plenty of literature regarding good practices on how to mantain a customer
happy and connected to the brand, there is some lack of research into what can be done if
these loyalty approaches are not well performed.
41
6. Contributions, Limitations and Future Research Suggestions As with any research, this study has limitations, which will be listed below, as well as some
suggestions for future research.
The first limitation concerns the fact that, the present study uses a convenience sample, due
to the limited time to carry out this study, which implies some caution in generalizing the
results. Future studies may seek to apply this questionnaire to a more representative and
geographically spread sample. Consumers belonging to different societies, societies that are
characterized by different cultural traits, weigh the same indicators and the same episodes in
different ways, which may lead to different conclusions than the ones presented here (Lee,
Motion et al. 2009). This study could also be done taking into consideration
sociodemographic variables such as gender, age and educational attainment.
Another aspect to underline is the study of only two Brand Hate antecedents, when there
are others considered in the literature, which other investigations may include, such as
symbolic incongruity (Hegner, Fetscherin et al. 2017).
In the survey that constituted the representative sample for this study, the measurement of
Brand Hate and Brand Love values was made using a Likert scale from 1 to 7, where, due to
the small sample size, all values were considered. It would be interesting to carry out the
same study, having a bipartite sample of: approximately half of respondents with notorious
Brand Hate values (average of all constituent items of this construct greater than 4.5
(Zarantonello, Romani et al. 2016)) and lacking significant Brand Love values; the other half
would be constituted by respondents with notorious Brand Hate values (same cutoff as
before) and with noticeable Brand Love values previously experienced (average of all
constituent items of this construct greater than 4.5 (Batra, Ahuvia et al. 2012)). Thus, the
results could be as conclusive as possible given that the sample is close to ideal.
Finally, it is suggested to conduct qualitative studies that, being a methodology with a finer
exploratory aspect and presenting even greater flexibility than quantitative ones, allow to
simplify the complexity of this type of relationships and possibly add some kind of
antecedent or consequence that is not being considered in the existing literature.
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8. Attachments
8.1. Annex 1 - Questionnaire
Objective Question Authors
A) Verify the existence of hate for the said brand
I don’t like this brand. (Zarantonello,
Romani et al. 2016) I hate this brand.
I would like to get revenge on this brand.
B) Verify if, in the past, the respondent nurtured love for
the said brand
Using products/services from this brand used to show something of me as a person.
(Bagozzi, Batra et al. 2015)
Using products/services from this brand used to make me feel good.
Using products/services of this brand used to give my life meaning.
Without noticing, I caught myself daydreaming about this brand.
I was willing to spend more money than what should be reasonable to be able to use this brand over another.
I used to wish to use this brand’s products/services.
The products/services of this brand used to be a perfect fit for my taste and preferences.
I used to feel emotionally connected to this brand.
C) Antecedents of Brand Hate
The products/services of this brand are produced/provided in a reprehensible manner.
Corporate Wrongdoings - (Zarantonello,
Romani et al. 2016)
This brand had improper conduct regarding the preservation of the environment and its sustainability.
This brand had improper conduct regarding social issues.
Given the brand in question, I expected better of the products/services they featured.
Violation of Expectations - (Zarantonello,
Romani et al. 2016)
The products / services of this brand had an inappropriate price for their quality.
Given what I knew from the competition, I expected more from this brand’s products/services.
49
E) Outcomes of Brand Hate
I told my family and friends how bad I feel this brand is.
Negative Word of Mouth -(Grégoire and Fisher 2006)
I did negative reviews of this brand on on-line platforms.
When I noticed my friends or family were about to buy products/services from this brand I tried to change their mind.
I don’t want to spend any more money on this brand. Patronage
Reduction/Cessation - (Grégoire and
Fisher 2006)
I try to have no connection of any kind with this brand (publicity, for example).
If I must user products/services from this brand, I’ll use them as little as possible.
I felt the need to make the brand representatives have a hard time. Consumer
Complaining - (Grégoire and Fisher
2006)
I felt the need to be unpleasant to the brand representatives.
I felt the need to make someone of the brand pay for their bad performance.
F) Sociodemographic data
Age
Sex
School Level
8.2. Annex 2 – Hate target brands
50
Chosen Brand Quantity
Apple 19
McDonald’s 10
zara 10
Nestlé 7
NOS 7
Nokia 6
Adidas 5
MEO 5
Nutella 5
Primark 5
Bershka 4
Nike 4
Samsung 4
Electronic Arts 3
LG 3
New Balance 3
Pantene 3
Pepsi 3
Pull&Bear 3
Vodafone 3
Espírito Santo – Autocarros de Gaia
2
Herbalife 2
Huawei 2
L’Oréal 2
Puma 2
Ryanair 2
Skechers 2
Toshiba 2
Worten 2 Chosen Brand Quantity
Apple 19
McDonald’s 10
zara 10
Nestlé 7
NOS 7
Nokia 6
Adidas 5
MEO 5
Nutella 5
Primark 5
Bershka 4
Nike 4
Samsung 4
Electronic Arts 3
Chosen Brand Quantity
LG 3
New Balance 3
Pantene 3
Pepsi 3
Pull&Bear 3
Vodafone 3
Espírito Santo – Autocarros de Gaia
2
Herbalife 2
Huawei 2
L’Oréal 2
Puma 2
Ryanair 2
Skechers 2
Toshiba 2
Worten 2 Chosen Brand Quantity
Apple 19
McDonald’s 10
zara 10
Nestlé 7
NOS 7
Nokia 6
Adidas 5
MEO 5
Nutella 5
Primark 5
Bershka 4
Nike 4
Samsung 4
Electronic Arts 3
LG 3
New Balance 3
Pantene 3
Pepsi 3
Pull&Bear 3
Vodafone 3
Espírito Santo – Autocarros de Gaia
2
Herbalife 2
Huawei 2
L’Oréal 2
Puma 2
Ryanair 2
Skechers 2
51
Chosen Brand Quantity
Toshiba 2
Worten 2 Chosen Brand Quantity
Apple 19
McDonald’s 10
zara 10
Nestlé 7
NOS 7
Nokia 6
Adidas 5
Chosen Brand Quantity
MEO 5
Nutella 5
Primark 5
Bershka 4
Nike 4
Samsung 4
Electronic Arts 3
LG 3
New Balance 3
8.3. Hated brands – Word Cloud
52
8.4. Annex 4 – Brand Hate SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,521
Bartlett's sphericity test
Approx Chi-Squared
800,26
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 2,522 84,081 84,081 2,522 84,081 84,081
2 0,46 15,35 99,43
3 0,017 0,57 100
Communalities
Initial Extraction
BH1 1 0,748
BH2 1 0,786
BH3 1 0,989
Component Matrix
Component
1
BH1 0,865
BH2 0,886
BH3 0,994
53
8.5. Annex 5 – Brand Love SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,841
Bartlett's sphericity test
Approx Chi-Squared
1379,03
gl 28
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 5,11 63,87 63,87 5,11 63,87 63,87
2 1,029 12,86 76,73 1,029 12,86 76,73
3 0,539 6,737 83,467
4 0,459 5,74 89,207
5 0,382 4,78 93,987
6 0,303 3,792 97,779
7 0,113 1,414 99,192
8 0,065 0,808 100
Communalities
Initial Extraction
BL1 1 0,86
BL2 1 0,723
BL3 1 0,868
BL4 1 0,722
BL5 1 0,553
BL6 1 0,894
BL7 1 0,875
BL8 1 0,643
Component Matrix
Component
1
BL1 0,813
BL2 0,801
BL3 0,864
55
8.6. Annex 6 – Corporate Wrongdoings SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,635
Bartlett's sphericity test
Approx Chi-Squared
163,28
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 2,01 66,984 66,984 2,01 66,984 66,984
2 0,642 21,408 88,392
3 0,348 11,608 100
Communalities
Initial Extraction
CW1 1 0,669
CW2 1 0,559
CW3 1 0,781
Component Matrix
Component
1
CW1 0,818
CW2 0,748
CW3 0,884
56
8.7. Annex 7 – Violation of Expectations SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,628
Bartlett's sphericity test
Approx Chi-Squared
55,666
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 1,631 54,353 54,353 1,631 54,353 54,353
2 0,728 24,277 78,629
3 0,641 21,371 100
Communalities
Initial Extraction
VEX1 1 0,531
VEX2 1 0,59
VEX3 1 0,509
Component Matrix
Component
1
VEX1 0,729
VEX2 0,768
VEX3 0,714
57
8.8. Annex 8 – Negative Word of Mouth SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,676
Bartlett's sphericity test
Approx Chi-Squared
213,28
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 2,166 72,189 72,189 2,166 72,189 72,189
2 0,525 17,491 89,681
3 0,31 10,319 100
Communalities
Initial Extraction
NWOM1 1 0,806
NWOM2 1 0,681
NWOM3 1 0,679
Component Matrix
Component
1
NWOM1 0,898
NWOM2 0,825
NWOM3 0,824
58
8.9. Annex 9 – Patronage Reduction/Cessation SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,658
Bartlett's sphericity test
Approx Chi-Squared
538,3
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 2,482 82,723 82,723 2,482 82,723 82,723
2 0,455 15,162 97,886
3 0,063 2,114 100
Communalities
Initial Extraction
PR1 1 0,919
PR2 1 0,894
PR3 1 0,669
Component Matrix
Component
1
PR1 0,958
PR2 0,946
PR3 0,818
59
8.10. Annex 10 – Consumer Complaining SPSS Output
KMO and Bartlett Test
Kaiser-Meyer-Olkin measure of sampling adequacy.
0,688
Bartlett's sphericity test
Approx Chi-Squared
646,78
gl 3
Sig. ,000
Total variance explained
Component
Initial eigenvalues Sums of Squared Loads
Extraction
Total %
variance %
Cumulative Total
% variance
% Cumulative
1 2,609 86,979 86,979 2,609 86,979 86,979
2 0,344 11,456 98,435
3 0,047 1,565 100
Communalities
Initial Extraction
CP1 1 0,935
CP2 1 0,918
CP3 1 0,757
Component Matrix
Component
1
CP1 0,967
CP2 0,958
CP3 0,87
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