Reducing advertising avoidance in a world of clutter
A master thesis by Michael Funding Fisker
Name: Michael Funding Fisker
Title: Reducing advertising avoidance in a world of clutter
Course: CLM English IMK
Student number: MF89315
Student exam number: 401879
Academic supervisor and instructor: Poul Erik Flyvholm Jørgensen
Department: Institut for Erhvervskommunikation (Business Communication)
Size excluding blanks and summary: 163.550 characters, 74.34 standard pages
E-mail: michael.fisker@gmail.com
Year: 2012
1
Table of contents
Page(s)
Summary
3-4
1. Introduction
5-7
1.1 Limitations to the thesis
7
2. Theories and theoretical background
8
2.1 The structure of advertising clutter
8-9
2.2 Perceived advertising clutter
10-11
2.3 The functional approach (media attitude)
11-12
2.4 The informational processing approach (the ability & capacity to process a message) 12-16
2.5 The structural approach (the physical attributes of advertising clutter)
16-19
2.6 The overall impact of advertising clutter (advertising avoidance)
19-25
2.6.1 Components of advertising avoidance
25-31
3. Analysis
32
3.1 Quick summary of theories and frameworks
32-34
3.2 Analyses and surveys and their impact on the explained theories
34-38
3.2.1 Advertising avoidance in different media
38-48
3.2.2 Demographic regressions and their impact on advertising avoidance
48-53
3.2.3 Same sample but different results
53-55
3.2.4 Uniform in-depth analysis of attitudes towards advertising based on demographics 55-61
3.2.5 Shavitt et al., attitudes towards advertising based on demographics
4. Discussion
61-70
71
4.1 Three theories on the structure of advertising clutter and avoidance
71-80
4.2 Discussion of the hypothesis and the analytical results
81-84
5. Conclusion
85-88
Bibliography
89
Appendix
90
2
Summary
This is a master thesis on advertising avoidance. The thesis is divided into five chapters and
includes an introduction with a limitations section, a theory chapter, a chapter on analysis based on
surveys, a discussion chapter and a chapter for the conclusion.
In the first chapter (introduction), the topic and the justification for the thesis are explained. It tells
the reader that the issue of avoidance behaviour is one of the greatest challenges in advertising,
because the consumers avoid advertisements. The chapter defines advertising avoidance as all
actions taken by media users to reduce their exposure to advertising content. Advertising clutter is
explained as an aspect that leads to avoidance behaviour, and is defined as the amount of noneditorial content in an editorial media vehicle that when exceeding the tolerance of the consumer is
perceived as clutter. The research question (RQ) is introduced, which is how can advertising
avoidance be reduced without reducing the amount of advertisements? The hypothesis behind the
RQ is that I believe it is possible to reduce advertising avoidance via other means than reducing the
quantity of ads, which the classical view indicates as the only option. Three theories are introduced
that point to advertising avoidance and clutter being about more than just the quantity of ads. I
hypothesize that theories will help me to verify my hypothesis about reducing avoidance without a
quantity reduction of ads and that most aspects of these theories will have an impact. In the
limitations section I explain that time and space, and the limited available analytical material proved
to be hindrances for this thesis and that if these limitations were lifted, a more accurate and precise
answering of both RQ and verification of the hypothesis would have been possible.
In the second chapter (theory), I explain and treat the three theories and models that form the
background and support for this thesis. The model of Ha and McCann includes three approaches
that lead to advertising clutter, which in turn leads to advertising avoidance. The functional
approach has to do with the attitude of the consumer towards both advertising and the medium it is
channelled through. The informational approach has to do with information and messages given in
ads and the consumers’ ability and capacity to process them. The structural approach has to do with
the physical attributes of ads and includes the design, size, frequency, format and quantity of ads.
They lead to perceived ad clutter, which is the consumers’ perception of “clutteredness”. When the
3
consumer considers the media vehicle to be cluttered he/she will avoid the ads, which is called the
overall impact of ad clutter (avoidance). The model of Cho and Cheon has a different structure but
many similarities as well. It sees both prior negative experiences and perceived goal impediment as
direct triggers of avoidance in addition to perceived ad clutter. The model of Speck and Elliot has a
flat structure with four aspects leading to avoidance, which are demographic and media-related
variables, ad perceptions and communication problems.
In the third chapter (analysis), I treat the surveys of Speck and Elliot and of Shavitt et al.
Participants answer questions about their avoidance behaviour and attitudes towards ads in various
media. Based on the results I analyse that it is possible to reduce avoidance in other ways than
reducing the amount of ads, the results are not quite consistent, but they all show that most aspects
of each model and theory going beyond the classical view of ad clutter has an impact on consumer
attitude and avoidance behaviour. The demographic samples are not quite balanced and results
should be taken with reservations. I analyse that demographics and media choice are significant in
order to obtain a more positive attitude towards ads. I analyse that elements (sub-aspects) of the
influencing aspects have different impacts, but it is impossible based on the surveys to say which
elements are the most influential.
In the fourth chapter (discussion), the theories, analyses, limitations and obtained results throughout
the entire thesis are treated and discussed. The strengths and weaknesses of each theory and model
and accurateness of ditto are also discussed. Where one model lacks a depth in structure, another
lacks inclusion of important aspects that have been substantiated to have an impact on clutter and
avoidance. The analysed results of the surveys are discussed. In the fifth chapter (conclusion), the
whole thesis including theories, analyses, discussions, limitations, research question and hypotheses
are concluded upon. The conclusion is that the hypotheses are verified. Almost all aspects of each
theory had in impact on avoidance behaviour. It is possible to reduce ad avoidance without reducing
the amount of ads. The research question could not be answered in full. Future research is necessary
to reveal exactly which elements of influential aspects are the most significant and how avoidance
can be reduced in addition to choosing the right medium for the correct demographic group. It may
also reveal the correct structure of avoidance. (4393 characters ex. blanks)
4
1. Introduction
Title: Reducing advertising avoidance in a world of clutter.
Research question: How can advertising avoidance be reduced without reducing the amount of
advertisements?
One of the greatest challenges and nightmares of advertisers is advertising avoidance, a strategy
consciously and subconsciously employed by those exposed to advertisements to passively or
actively ignore or avoid being influenced by advertisements. In short, advertisng avoidance can be
defined as “…All actions [taken] by media users that differentially reduce their exposure to ad
content”1. A more detailed and elaborate definition of advertising avoidance will be given in the
next chapter concerning the theories and theoretical background of the thesis.
Advertising avoidance is a major part of and a result of advertising clutter, they are invariably
linked together and without advertising clutter, advertising avoidance would not exist either. This is
the classical definition of advertising clutter: “Advertising clutter is usually perceived as the
presence of a large amount of non-editorial content in an editorial medium…when the amount
exceeds a consumer’s acceptance level in an editorial media vehicle, it is viewed as clutter…”2.
When an editorial media vehicle is perceived to be cluttered by non-editorial content (mainly
advertisements), an avoidance strategy is triggered by the consumer/person exposed to the
advertisements as a defence mechanism to protect him-/herself from exposure and informational
overload. However, as it will be substantiated many times in this thesis, the classical definition of
advertising clutter is more than likely too simplistic and does not take many important aspects into
account.
It is the luck and good fortune of advertisers that these statements can be substantiated, for if
advertising clutter really is nothing but the quantity of advertisements in an editorial media vehicle,
then advertising avoidance would not only be an unsolvable problem, it would not even be treatable
1
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, p. 61, M.E. Sharp Inc.,
1997.
2
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal of
Advertising, p. 570, 2008.
5
outside of theory. Why not? If advertising theoreticians and researchers were to adhere to the
classical definition of advertising clutter and its’ linked aspect of advertising avoidance, this would
be the equation of reducing advertising avoidance:
Advertising clutter = quantity of advertisements in editorial media vehicle. High quantity of
advertisements = perceived advertisement clutter = advertisement avoidance. Subsequently, the
only manner of reducing advertising avoidance would be to reduce perceived advertising clutter, the
only manner of reducing perceived advertising clutter would be to reduce the quantity of
advertisements. Herein lays the problem. Who in the real world could convince his/her competitors
to lower their quantity of advertisements and find a consensus to the amount of the advertising each
advertiser would be allowed to “air”? Fortunately, it is the firm conviction of this researcher that the
problem is much less straight forward, and that solutions to the problem of advertising avoidance
can found be elsewhere, because I do not subscribe to the definition of advertising clutter that
results in the aforementioned equation of reducing advertising avoidance. Based on theories and
models proposed by other researchers, this thesis will substantiate the claim that advertising clutter
is far more than just the quantity of advertisements in an editorial media vehicle and that advertising
avoidance is not only the result of perceived advertising clutter, which leads me to believe and
hypothesize that advertising avoidance can be reduced, and not just by reducing the amount of
advertisements in an editorial media vehicle. The main theories and models upon which I build my
hypothesis and expectations are those of professors Ha & McCann3, those of professors Cho &
Cheon4 and those of professors Speck & Elliot5.
I hypothesize that through other aspects of advertising clutter, the collective advertising avoidance
can be reduced. I expect to be able to substantiate beyond reasonable doubt that advertising
avoidance can be reduced by reducing the perceived advertising clutter and that I can provide
substantiated evidence to this hypothesis not only through reasoning of theories and their
implications, but also through statistical surveys supporting this claim when analysed. I set out to
analyse and investigate which aspects (aside from quantity) that bear the greatest significance in
triggering ad avoidance, and through these analyses and assessments answer my research question
of how ad avoidance can be reduced without reducing advertisements, because that is no solution as
3
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal of
Advertising, 2008.
4
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, 2004.
5
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., 1997.
6
was explained earlier in this chapter. I expect that nearly all, if not all, aspects explained in
professor Ha’s integrated model of advertising clutter6 will bear an element of importance in
reducing advertising avoidance as they all impact perceived advertising clutter , however not all to
the same extent. With this thesis I hope to prove that assessing the quality of advertisements rather
than just the quantity will help in probably not solving, but at least treating the problem of
advertising avoidance.
1.1. Limitations to the thesis
Many conditions limit the work of this thesis. Above all else considering the importance and impact
that advertising clutter and advertising avoidance has on the marketing and advertising business
fields, it is quite remarkable how little research has taken place on this topic, and how much that
limited amount of material leans on previously carried out research. Despite the uncertainty of the
structure of both advertising clutter and avoidance, I was only able to find a single theoretical model
that dared to break with the classical view of advertising clutter (that of Ha & McCann7), research
on this topic made hereafter either carefully sidesteps to avoid entering into a discussion of the
structure, or leans to the research of Ha and McCann, if not supporting the classical view of
advertising clutter. As for advertising avoidance, I found two theories that elaborated or diverted
from the classical view (those of Cho & Cheon8 and of Speck & Elliot9). Aside from a limited
number of theoretical suggestions, I also found a limited amount of analytical material making it
difficult to investigate all theoretical aspects included in the theorised advertising clutter and
avoidance models. Thus I focused on the most central aspects that I could make use of in the
available material. As I wished to make an in-depth analysis of the aspects I treated, it also meant
that I had to limit my analytical work to a few central and important surveys and analyses in order
to avoid a superficial treatment of the material, which in the end would have made both my
discussion and conclusions very difficult and perhaps also less accurate than otherwise. In that
relation time and space also played key-roles as I did not have the time or the space to go into depth
with more analyses and surveys.
6
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal of
Advertising, p. 575, 2008.
7
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal of
Advertising, 2008.
8
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, 2004.
9
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., 1997.
7
2. Theories and theoretical background
This chapter will provide a theoretical background for the thesis and explain the theories that it is
based upon. It will treat the material upon which the theories, analyses and discussions are based,
and explain beforehand which material is to substantiate the claims, statements and conclusions of
this thesis. This chapter is to introduce the readers to the theories, surveys and discussions of the
materials used and does provide not a full analysis or in-depth explanation of the analytical
materials, as that task will undertaken throughout the chapter of analysis that follows this chapter.
2.1. The structure of advertising clutter
In order to understand advertising avoidance it is of the utmost importance to understand the
concept of which advertising avoidance is an integrated part, namely advertising clutter. As
explained in the introduction of this thesis, advertising avoidance is a result of advertising clutter.
Therefore the theories that will be explained in this thesis are those that in great detail explain many
of the different aspects that are contained within the structure of advertising clutter, and logically it
will begin with the main aspect, the structure of advertising clutter itself.
Advertising avoidance is not only a central aspect in advertising clutter, it is also the main negative
impact on/result of advertising clutter. Looking at professor Louisa Ha’s integrated model10 (see
next page) and framework for advertising clutter, advertising avoidance is found in the non-physical
attributes section of advertising clutter. Avoidance is a major part of the overall impact of
advertising clutter. Professor Ha’s view of advertising avoidance as part of the impact of advertising
clutter is more clearly seen in her conceptual framework models for offline11 and online12
advertising clutter, where she defines the other aspects of the overall impact of advertising clutter,
namely advertising memory reduction and lower perceived editorial quality.
As these models and conceptual frameworks show, if professor Ha is correct in her structuring of
advertising clutter, the classical view of advertising clutter is far too simplistic, only focussing on
10
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 575, 2008.
11
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 578, 2008.
12
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 579, 2008.
8
the physical attribute of quantity. Although Professor Ha quotes the traditional definition of
advertising clutter, she clearly distances herself from it and explains that her research is aimed at:
“[re-examining] the concept of ad clutter. Proposing a new analytical framework for online clutter...
[incorporating differences between online and offline media environments], and differentiate the
physical presence of ad clutter and perceived ad clutter”13. As no other researcher known to the
author of this thesis have attempted to re-evaluate the concept of advertising clutter, and since this
structure can be substantiated through the analytical and theoretical material applied to this thesis,
the concept and framework proposed by professor Ha will prior to the analytical work of this thesis
be considered the most accurate structure, although a 100% accurate model of advertising clutter
could prove to be task not yet accomplished, as this thesis will later explain why.
Figure 1: Ha’s integrated model of advertising clutter14
13
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 570, 2008.
14
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal of
Advertising, p. 575, 2008.
9
2.2. Perceived advertising clutter
Perceived advertising clutter is perhaps the most central aspect of all in understanding and
explaining both advertising clutter and advertising avoidance, because both clutter and avoidance
are strongly linked to the perception of advertisements. As it can be seen in professor Ha’s
integrated model of advertising clutter, perceived advertising clutter plays the biggest role of all
aspects in this concept, it is both the central aspect that sums up all three approaches to advertising
clutter (the aspect which all elements lead to), and it is also the aspect that represents all of the nonphysical attributes of advertising clutter. It is also the aspect that in its’ entity, summed up by the
three approaches, leads to the overall impact of advertising clutter, mainly advertising avoidance.
Although the name itself is a detailed description of the role that perceived advertising clutter plays,
it can hardly be considered a definition of this important aspect. Professors Speck and Elliot have
stated that perceived advertising clutter represents a consumer’s evaluation of the amount of
advertising rather than an objective measuring of the amount, and they thus arrive at this definition
of perceived advertising clutter: “Perceived ad clutter [is] one’s belief that the amount of advertising
in a medium is excessive”15.
The observant reader will notice here that this definition falls very well in line with the classical
definition of advertising clutter, namely that it is all about the quantity of advertising in an editorial
media vehicle. If comparing to professor Ha’s model of advertising clutter, this would mean a
removal of the non-physical approaches to advertising clutter and removal of the aspects of the
physical/structural approach to advertising clutter, leading to perceived advertising clutter.
However, this does not necessarily mean that professors Speck and Elliot have miscalculated their
definition or that professors Ha and McCann have made errors in their conceptual framework of
advertising clutter, because perception means subjectivity, a perceived state may not be the actual
“real world” state. A consumer may very well (as will be substantiated many times later in this
thesis) be influenced in his/her perception of advertising clutter by the messages of the
advertisement(s) (informational processing approach), by his/her attitude towards the used medium
15
Speck, P. & Elliot, M., “Consumer perceptions of advertising clutter and its impact across various media”, Journal of
advertising research, p. 3, 1998.
10
and advertising in general (functional approach) as well as the size, design, location etc. (structural
approach) of the advertisement(s) without realising it, and thus attribute it to the amount of
advertising, even if it in reality was not the quantity that triggered his/her perception of the
advertising clutter being excessive and ultimately initiating an avoidance strategy to avoid further
influence and information overload.
2.3. The functional approach (Media attitude)
After explaining the main aspects relating to advertising avoidance and advertising clutter, namely
the impact of advertising clutter (mainly advertising avoidance) and perceived advertising clutter,
which sums up advertising clutter and accumulate into advertising avoidance as a result, it is
difficult to state in advance prior to an analysis, which of the three approaches to advertising clutter
that is most central and significant to advertising avoidance, as they all appear equally dominant in
theory in professor Ha’s integrated model of advertising clutter. Thus no representation of
significance is intended by the order in which the three approaches are treated. Their respective
significances will be addressed during and mainly after the analysis chapter, in the discussion
chapter of this thesis.
The functional approach to advertising clutter has to do with consumer responses as to how the
media fulfil the needs of the consumer. It deals with the functionality of each medium that the
consumers use and the attitude that through this usage and the tasks completed arises towards these
media. In this chapter of this thesis concerning the theories and the next chapter concerning the
analysis I will explain how the functional approach works and analyse using surveys conducted how
different consumers view (their attitudes) the different media based on how they individually use
them. It is my hypothesis that these attitudes, being various will affect the perceived advertising
clutter in the same variation, thus leading to a different perception of “clutteredness”, dependent on
the individual consumer. I will test this hypothesis through analysis of the surveys conducted.
The overall attitude towards each medium is measured through the consumers’ perceived
harmfulness/offensiveness, usefulness/informativeness, trustworthiness (trust in the medium),
11
regulation (governmental interference) and price/cost of the medium (subscription, media license
fee etc.)16. According to professors Ha and McCann, because consumers use media differently to
perform certain functions and tasks, advertising clutter will be perceived differently depending on
the consumer’s reason(s) for using the particular medium17. Consequently, the reasons the
consumers have for using different media and the functions and tasks they want to perform using
those media will affect the likelihood of the consumer to apply an avoidance strategy, based on
whether or not the consumer is successful in his/her performance of a function and task. This in turn
will affect the consumer’s attitude towards the medium in which his/her task was either a success or
a failure.
This aspect is also highly related to the next approach this thesis will address, the informational
processing approach, as the information received is often the consumer’s measurement of success or
failure, e.g.: “did I find the information I needed/wanted” or “Was I gratified/satisfied by/with the
information I received”. If the information that was received did not in some way live up to the
consumer’s expectations or needs, it could very well trigger a feeling of excessive advertising i.e.
clutter, which subsequently leads to the triggering of an avoidance strategy on the part of the
consumer.18
2.4. The informational processing approach (The ability & capacity to process a message)
Unlike the two other approaches (functional and structural) the informational processing approach
is a multifaceted approach. Whereas the functional approach is almost exclusively about attitude
towards the media consumed, and the structural approach is solely about the physical attributes of
advertising clutter, the informational processing approach includes no less than four theoretical
aspects pertaining to the message and information sent and the ability and will of the consumer to
16
Shavitt, S. Et al., “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p.1021, 2004.
17
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 572, 2008.
18
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 573, 2008.
12
understand and accept that message/information, and react accordingly19. Thus it is perhaps the
most complex and demanding approach to understand, and (at the very least in theory) the
understanding of it is crucial to success for the advertisers. Any significant miscommunication will
lead to the rejection of the information and loss of credibility for the advertised brand and product
according to the ELM (Elaborative Likelihood Model) theory of Petty and Cacioppo20.
According to the ELM theory the advertiser must choose the correct informational or
transformational (emotional) message strategy to persuade the consumer to act and feel as the
advertiser desires (usually this means to purchase a service or product). To do so the advertisement
can follow a set of tools belonging to either the central route or the peripheral route, depending on
whether or not the decision to purchase the advertised product is a low or high involvement
decision. Ideally, the advertiser uses the right route, the right tools and effects along with a
convincing message, which favours the brand and the product advertised. Should the advertiser fail
to convince the consumer of the credibility of the message, it will lead to negative reactions by the
consumer and a feeling of his/her time being wasted, which in turn makes the consumer feel that the
medium vehicle is cluttered (which then again leads to advertising avoidance).
As explained briefly in the beginning of this section of the chapter, the informational processing
approach takes an internal look on the aspects related to advertising clutter, rather than a structural
or consumer-related functional approach, this approach deals with the aspects that lie within the
advertisement itself, namely the message and the way it affects the consumers and their ability to
process the message. One of the most well-known and applied informational processing approach
tools is that of the information overload theory, which (according to Ha and McCann) was
suggested by professor Miller as early as in 195621. As the name of the theory suggests this theory
has to do with the capacity that the human memory has for storing information (such as brand
names, products and other advertisement-related information). When the capacity is exceeded, the
person exposed to information experiences an overload of information, and the brain memory starts
19
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 573-574, 2008.
20
Percy, L. & Elliot, R., ”Strategic advertising management”, Oxford University Press, p. 208-209, 2009.
21
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 573-574, 2008.
13
to “delete” data to create room for new pieces of information. Professor Miller also indicates that it
is not necessarily the newest information that is kept and the oldest information that is deleted. This
theory goes well in hand with the classical definition of advertising clutter as it focuses solely on
the quantity of advertisements (more specifically on the information quantity), and it does not take
the quality or relevance of the message/information to the consumer into account. However, this
important aspect of the informational processing approach is dealt with in the selective attention
theory.
The selective attention theory22 is closely related to the core of this thesis, namely advertising
avoidance, because choosing to be selectively attentive is an avoidance strategy. Also the last
informational processing approach aspect, the reactance theory, is closely linked to advertising
avoidance. The selective attention theory differs from the information overload theory by taking the
human ability to sort information by interest and relevance into account. As Wickens (according to
Ha and McCann) explains it; consumers’ selective attention to information/messages is a protective
mechanism, which is used to allocate the consumers’ limited resources for attention according to
their needs and interests23.
Quoting professor Ha on this theory she says: ”consumers’ decisions not to pay attention to
advertising are the result of perceived lack of relevance of the ads to their lives because their
attention resources are reserved for editorial content”24. This statement is at the centre of my
research and could very well be a support for my hypothesis of advertising avoidance (and clutter)
being much more than just a quantity overload. If it can be substantiated and supported by professor
Ha’s statement in my analysis, it will strongly indicate if not prove that advertising avoidance and
clutter as well are a result of irrelevant or poorly messaged information in advertisements, rather
than a too high quantity of advertisements. If that is the case, it should be possible to reduce the
frequency and amount of advertising avoidance (because of lowered perceived advertising clutter)
22
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 574, 2008.
23
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 574, 2008.
24
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 574, 2008.
14
without necessarily lowering the amount of advertisements in a specific editorial media vehicle. The
selective attention theory could very well be a central aspect in solving/treating the problem of
advertising avoidance, if it is proven beyond reasonable doubt that consumers enforce the avoidance
strategy of selectively averting their attention away from advertisements due to perceived
irrelevance of the information given in advertisements, which in theory is also supported by the
ELM model as explained earlier. However, it seems unlikely that the informational processing
approach can stand alone as a decisive factor in “ridding” the advertising industry of the “disease”
called advertising avoidance. Factors such as media attitude (the functional approach) should have
an impact on the preconceived opinion of the media vehicle used to send the message, and thus
have a bearing in advance on the consumer’s perceived relevance, depending on how e.g.
trustworthy the consumer considers the medium. This theoretical condition and balance between
theoretical aspects will be tested and investigated further in the chapter of this thesis concerning the
analysis. This explanation is simply meant to clarify the considerations that must be taken into
account, when trying to understand the aspects of advertising avoidance, advertising clutter and
their relationship and the need to understand this relationship in order to make a proper and useful
analysis of the research question. Naturally, despite my arguments for considering the traditional
structural definition of advertising clutter too simplistic and inaccurate, the structural approach
pertaining to the physical aspects of advertising clutter, must be taken into account as well,
especially considering that the structural approach does not only concern the quantity of advertising,
but many other factors such as design (size, location, features etc.), time, frequency and so on. This
third approach will be explained and clarified in this chapter right after the fourth and final aspect in
professor Ha’s model concerning the informational processing approach, namely the reactance
theory, which is closely linked together with advertising avoidance, as avoidance is a reaction.
The reactance theory is only touched upon in the utmost briefness by professor Ha, but through the
sources used by the same the relevance for including it in the framework of advertising clutter and
ultimately advertising avoidance becomes more clear. It is a theory that aims to explain the
resistance to and avoidance of clutter (which is ultimately what this thesis seeks a treatment
for/solution to). However, it is a bit one-sided and weights heavily on a theory based on advertising
that is more or less forced upon the consumer. According to Ha and McCann, Professors Brehm and
15
Brehm25 define reactance as the negative response/reaction to advertising that is perceived as a
violation of the consumer’s free agency, and professors Clee and Wicklund26 add that coercion will
not result in compliance (most likely not a surprise to most researchers). Rather, they say, coercion
will lead to consumers being even more resistant to advertisements and their information/messages
by avoiding the advertisements all together. In alignment with the other theories of the
informational processing approach, this means (in theory atleast) that compliance cannot be forced
and attention to the advertisement messages must be earned through perceived relevance, interest
and non-intrusive advertising. Failure to comply with these requirements (among others) will not
only lead to the use of an advertising avoidance reaction/strategy, but in accordance with the
reactance theory, it should also reinforce and strengthen the avoidance behaviour and resistance to
future attempts of obtaining the attention of the consumer to advertisements. This conclusion or
analysis of theoretical implications will also be substantiated and supported later in this chapter by
advertising avoidance theories and models that show that prior negative experiences result in and
add to advertising avoidance behaviour strategies being executed, which again shows the close
relationship between the reactance theory and the concept and theory of advertising avoidance.
2.5. The structural approach (the physical attributes of advertising clutter)
The structural approach to advertising clutter is undoubtedly the most applied and incorporated
approach in researchers’ and advertisers’ strategy to avoid advertising clutter, although it is not
synonymous with the classical/traditional definition of advertising clutter, it holds said definition as
one of its’ key elements, namely quantity. Even when only considering the structural approach to
advertising clutter, professor Ha’s integrated model of advertising clutter reveals that the traditional
definition of advertising clutter is too simplistic and lacks an incorporation or understanding of key
physical elements. One does not necessarily have to subscribe to or completely concur with the
framework presented by professor Ha to see the strong likelihood of that statement being true.
Important physical aspects of advertisements such as size, location, frequency and design elements
cannot be overlooked, as they are almost certainly bound to play some role in the perception of
“clutteredness” in a media vehicle.
25
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 574, 2008.
26
Clee, M. & Wicklund, R., “Consumer behavior and psychological reactance”. Journal of Consumer Research, 6(4), p.
389–405, 1980.
16
All physical attributes and structural elements of advertising clutter fall under the category of the
structural approach, which includes quantity, frequency, design, size, location, format etc. That is
quite a mouthful to handle and to make it more tangible and manageable, professor Ha divides the
aspects into three main dimensions, which she defines as quantity, intrusiveness and
competitiveness27.
Quantity is the defined as the proportion of advertisements in the specific media vehicle. Here it is
certainly worth noting and understanding that quantity does not refer to a specific number of
advertisements, but the time and space consumed by advertisements compared to editorial content.
Therefore by that scale and formula one advertisement lasting 30 seconds count as much as two
advertisements lasting 15 seconds, and a decrease in editorial content (both time- and number-wise)
will result in a perceived increase in advertisement quantity without any change being actually
made to the time and space consumed by the advertisements. This is an often overlooked factor as
advertisers tend to count the number of advertisements (and sometimes the time consumed) without
considering the ratio of advertisement to editorial content.
Intrusiveness is defined as the degree to which the advertisements interrupt the “flow” of any
editorial content in a media vehicle. Among the strongest examples of this aspect are TV
commercials that incessantly interrupt TV programs and movies in TV. The strong feeling of
intrusiveness and increased clutter is nearly self-evident with the vast number of people buying the
right to skip or have advertisements removed from the editorial content they were consuming. They
buy machines that record their favourite programs and movies and skips advertising, or they buy
advertising free versions of programs for their PCs and smartphones. This behaviour alone points to
the strong likelihood of advertising clutter being much more than just quantity as intrusiveness is
related more strongly to degree and frequency than to the exact quantity of the advertisements
contained in each interruption. Clearly, many consumers would find it far more intrusiveness and
frustrating to be interrupted in their mindset (consuming editorial content) every 15 minutes, even if
just for three minutes each time, than just once every hour but for 12 minutes in one “wrap”, despite
both examples equalling 12 minutes of advertisements and 48 minutes of editorial content.
27
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 571-572, 2008.
17
Competitiveness is defined as the degree to which similar products or messages delivered by
competitors are advertised in the same advertising pod i.e. same commercial break on a radio or TV
station, same magazine issue, newspaper issue, same online site/sub-site etc. In short
competitiveness is mainly created by the presence of advertising messages of different brands and
product lines that belong to the same product category. To avoid this unwanted competitiveness
while advertising a company’s products and services, it has increasingly become common practice
to negotiate a product exclusivity clause in the terms and conditions of advertising contracts 28. This
practice means that competitors must “race” or outbid each other for the most attractive advertising
pods (e.g. primetime TV on a national network) as the exclusivity clause prevents those companies
advertising a similar product or service (in the same product category) from “airing” their brand in
the same pod. Exactly how dissimilar the advertised products of different brands must be to “beat”
the exclusivity clause will naturally be a matter of the exact wording and practice of the advertising
company, and most likely the bigger the influence that the selling company has and the price they
are willing to pay, the more strict and exclusive terms they can negotiate with the advertisers. This
practice could easily be seen as a breach of equal competition on any market, because it effectively
prevents small enterprises from advertising their products and services in the most attractive media
vehicles at the best rated hours.
The practice of exclusivity is mainly applied in media vehicles that offer the consumer little or no
control of the flow of content. This applies to media such as TV and radio, where the consumer is
“forced” to follow the flow of the content arranged by the program manager. This type of media is
called “captive” media, because the consumer is not in control. The competiveness (and exclusivity
practice) does not carry nearly the same influence on the consumer in media like magazines,
newspaper or online media vehicles, because the consumer is far more in control of the flow of
content in these media, they are in other words, “self-paced”. This type of media is thus called “selfpaced” media, because the consumer is in control. The consumer can flip through and/or skip pages,
go to other links etc. and thus avoid the advertisements that are unwanted, and avoid exposure to
advertisements containing competing brands and products. Therefore the aspect of competiveness
has little or no impact at all on consumers consuming content in a self-paced media vehicle, who
28
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 572, 2008.
18
avoid the confusion that conflicting/competing advertisement can lead to in captive media, i.e. if
both Colgate and Zendium advertise their toothpastes, the consumer must deduct in his/her mind on
a basis of many factors which toothpaste he/she should buy.
The elements of the structural approach that have not been dealt with in this section of the
theoretical background and theories chapter are those concerning frequency (advertising repetition)
and size, location and features (advertising design). These elements and aspects will be not dealt
with in this thesis, as the analytical material to substantiate and investigate the impact of these
elements could not be procured, and possibly do not exist as of yet (further information can be
found in the limitations section of the introduction chapter).
2.6. The overall impact of advertising clutter (advertising avoidance)
This is what it is all about, advertising avoidance, the curse of advertisers. In order to understand
and find an answer to my hypotheses regarding this problem, it is not enough to have an idea and
understanding of what leads to advertising avoidance, it is also necessary to understand the
construction of advertising avoidance itself. As this section of the chapter will show, it is not as
easy as it may sound, because so many aspects in advertising both internal and external influence
the structure and shape of advertising avoidance, and there are multiple suggestions for how
advertising avoidance should be understood, characterised and depicted.
Seeing as advertising avoidance is so closely linked to advertising clutter, and being the main
negative result of advertising clutter, it will come as little surprise to those deeply engaged in this
research field that advertising avoidance and its structure is like advertising clutter dependent on the
media vehicle that it is channelled through. Two major models and theories regarding the structure
and content of advertising avoidance can be found, one model structuring advertising avoidance on
the internet by professors Cho and Cheon29, and another model structuring advertising avoidance in
29
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 93, 2004.
19
print and broadcast (primarily TV and radio) media by professors Speck and Elliot30. In addition to
explaining what is contained in advertising avoidance, these models predict what triggers avoidance
strategies in a much more detailed and explicit way than the models of professors Ha and McCann.
This should not be too surprising as Ha and McCann attempt to explain advertising clutter, wherein
advertising avoidance is a part, rather than having advertising avoidance as their focus aspect.
Therefore, the reader will also be able to notice that perceived advertising clutter, which is the
centre and sole predecessor of the overall impact of advertising clutter in Ha and McCann’s
integrated advertising clutter model is only one of three major triggering aspects of advertising
avoidance in Cho & Cheon’s hypothesized advertising avoidance model.
Figure 2: Cho & Cheon’s hypothesized model of advertising avoidance31
30
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997.
31
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 93, 2004.
20
This richly detailed model of advertising avoidance gives the reader an alternative understanding
and entry point to advertising avoidance than the model of Ha and McCann. The main point of
commonality is that perceived advertising clutter is still at the centre of the theorised model, which
strengthens this thesis’ interpretation of that aspect being of immense importance to understanding
the concept of advertising avoidance. However, the sum that adds up to perceived advertising
clutter is different in this model. It originates from aspects that belong to the informational
processing approach if using the corresponding terminology of Ha and McCann’s integrated model
of advertising clutter. Cho and Cheon, whilst referring to the same theoretical ideas/basis, apply a
slightly different wording by saying: “The conceptual explication of ad avoidance as a function of
perceived goal impediment and perceived ad clutter stems from information theory...Advertising
avoidance is also theorized to be a function of prior negative experience”32.
Aside from both models (that of Ha/McCann and Cho/Cheon respectively) pointing to perceived
advertising clutter from that/those aspects, it is clear that the informational processing approach and
information theory are referring to the same ideas and concepts, with some lack of verification
though. Excessiveness is also mentioned in Ha and McCann’s model (information overload theory),
but it could also refer to the functional approach, in which non-editorial content (advertising) can be
considered excessive leading to a increase in negative attitude towards a specific media vehicle.
Whilst irritation, if compared to Ha and McCann’s model, more or less convincingly belong to the
reactance theory in the informational processing approach, exclusiveness is an aspect almost solely
mentioned in the structural approach in Ha and McCann’s model, in which Ha and McCann define
three dimensions for this approach, quantity, intrusiveness and competitiveness (under which aspect
exclusivity falls). It therefore almost seems to this researcher that Cho and Cheon pack central
aspects of all three approaches (functional, informational processing and structural) in one box
called information theory, which then corresponding to Ha and McCann’s model leads to perceived
advertising clutter. On the basis of these observations it is difficult to conclude otherwise, especially
considering that Cho and Cheon do not specify in any greater detail what exactly they mean by the
information theory, and without the background of knowing the works of Ha and McCann the
puzzle could have been even greater. This by no means make me conclude that the model or
theories of Cho and Cheon are without substance or relevance to this thesis, as their model and
32
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 90, 2004.
21
work finds its’ strength in the other aspects included in their model. In fact all other elements
included and their structural connections are highly useful to theorise and build upon as I will now
explain and demonstrate.
As explained earlier in this section, Cho and Cheon’s hypothesized model of advertising avoidance
has in addition to perceived advertising clutter two other aspects that trigger and result in
advertising avoidance. One of these is perceived goal impediment, which is occasioned by
advertising. This triggering aspect is specifically valid for the internet medium, as consumers are
more likely to be goal-directed and oriented, when using/consuming the internet medium than they
are using some of the other media vehicle types. Also internet advertisements are according to
professors Li, Edwards and Lee perceived to be more intrusiveness than advertisements channelled
through other media33. Because the internet is perhaps the most consumer controllable medium,
many consumers use it primarily to search for information or achieve a certain goal. When/if this
goal is impeded and the search is hindered (search hindrance) or interrupted (disruption) it will lead
to a negative attitude towards the media vehicle (see functional approach, section 2.3.) and it
ultimately leads to an avoidance strategy being triggered. In addition, if the consumer is distracted
during his/her search or goal-directed interaction online it will also lead to occurring avoidance
behaviour. Based on these theories and the deductions made by applying them, it can be
hypothesized that perceived goal impediment leads to advertising avoidance just as perceived
advertising clutter. The greater the goal impediment the greater the amount of advertising
avoidance, which is also what professors Cho and Cheon conclude34.
As this researcher has discovered through the treatment of the theories connected to this thesis, the
works of professors Cho and Cheon fall well in line with the works of professors Ha and McCann,
although the terminology and the approached angles differ to a certain extent. Thus the first
mentioned duo of professors reach an almost identical theoretical conclusion on the role of
perceived advertising clutter as that reached by the latter mentioned duo of professors, and therefore
there is little need to repeat the similarly sounding hypothesized role of the aspect of perceived
33
Li, H., Edwards, S. & Lee, J., "Measuring the Intrusiveness of Advertisements: Scale Development and Validation",
Journal of Advertising, 31 (summer), p.37-47, 2002.
34
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 90, 2004.
22
advertising clutter, except to say that a higher amount of perceived advertising clutter leads to a
higher amount of advertising avoidance.
The second aspect in addition to perceived advertising clutter that triggers advertising avoidance is
prior negative experiences. This is another aspect that Ha and McCann do not take into account as a
factor leading directly to advertising avoidance, they barely mention it as a part of advertising
clutter, although multiple theoreticians suggest that prior negative experiences have an impact on
perceived advertising clutter, which in turn leads to advertising avoidance. Based on their sources
Cho and Cheon, however, hypothesize that the greater the prior negative experiences are the greater
the advertising avoidance tendency becomes35. They state that the consumer’s prior knowledge and
experiences influence the type and degree of information processing, which includes systematic
organisation, comparisons, evaluation of brand and purchasing behaviour. Information obtained
from experience will have a strong and direct impact on attitude and behaviour. Based on the work
of professors Hoch and Deighton, Cho and Cheon conclude that consumers tend to rely primarily on
their own personal experiences that they value and rank higher than any outside coming knowledge
and information36.
This theory, if it holds true, strongly indicates that ensuring consumers good and positive
experiences to fall back on and to base their opinions on should significantly lower the amount of
advertising avoidance. Prior negative experiences will have multiple ways of increasing an
avoidance behaviour, as there can be many triggers and entry-points for negative experiences, and
what the consumer considers the cause of the negative experience. On one side it will often be the
brand, the product or the company behind the product/brand that takes the blame for the negative
experience, because so many triggers of advertising avoidance behaviour is related to the indirect
sender of the message. This is usually the case if the message or the product in some way is
offensive or untrustworthy in the eyes of the consumers. On the other side, it could also be the
medium vehicle that takes the blame if the medium is perceived to be the cause of unsuccessful
communication e.g. if the sound or subtitles fall out, if distortions or disturbances in the chosen
35
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 91, 2004.
Hoch, S., & Deighton, J., "Managing What Consumers Learn from Experience," Journal of Marketing, 53 (April), p. 34, 1989.
36
23
layout in the medium is frequent and a nuisance to the consumer, or if the layout is unprofessional.
In short, advertising avoidance is not just one thing. If the medium is the cause of the avoidance
behaviour, the advertiser may very well have massive success with the exact same advertisement
(within the restrictions on similarity of the respective medium) that failed miserable, if the media
vehicle chosen to convey the message is changed.
In their model of advertising avoidance, Cho and Cheon divide the aspect of prior negative
experiences into three main categories: Dissatisfaction, perceived lack of utility and perceived lack
of incentive. Dissatisfaction is probably the worst and strongest prior negative experience type that
a consumer can draw on seen from the perspective of an advertiser, because it is such an
emotionally difficult barrier to overcome. Once a consumer has a negative experience with a
product or service advertised, he/she is very unlikely to try products of that brand again, unless
absolutely forced to by important practical, financial or personal circumstances. Naturally there are
many ways of being dissatisfied and depending on what triggered the dissatisfaction, the “cure” will
also differ. Seen from the viewpoint of the advertising company probably the worst-case scenario is
that the consumer feels that he/she has been lied to, which will trigger a strong aversion towards to
the advertised brand and a massive loss of credibility. Once the consumer is dissatisfied for this
reason, he/she will be convinced that the company behind the brand is untrustworthy and most
likely lying about their other products as well, rendering it very nearly impossible to convince the
consumer of their honesty and integrity. The mere mentioning of a product or brand associated with
that company could instantly resolve in an avoidance strategy being triggered, which means the
advertiser no longer has the “ear” of the consumer and can attempt to convince him/her otherwise.
The consumer will then be willing almost guaranteed to try all the competitors’ products or services
in the same category before even considering trying that brand again. At this stage, one of the very
few likely ways of getting the consumer’s attention and interest back is if the advertiser can
convince someone that the consumer trusts to try and endorse their brand, and as research suggests
even this is not guaranteed to solve this problem. It could very likely take a long time of strong
endorsement and personal positive experiences shared by a trusted or admired community of
people, as one person’s endorsement will often fall short37.
37
Jobber, D., “Principles and practice of marketing”, McGraw-Hill International (UK) Ltd., p.814, 2004.
24
On the face of it and in theory, both perceived lack of utility and perceived lack of incentive appear
to be “milder” triggers of advertising avoidance, as they do not stir the same sense of negativity that
comes with direct dissatisfaction, but it depends on how the aspects are interpreted. Sadly, Cho and
Cheon do not specify their understanding of those terms in such detail that it can be concluded
whether they mean pre- or post-purchase perceived lack of utility and incentive. However, through
logic deduction I am compelled to conclude that the intended meaning of those terms is prepurchase perceived lack of utility or incentive. If the consumer has post-purchase perceived lack of
utility of a product or service, it should immediately trigger dissatisfaction, which is already
characterised in the first aspect. However, it makes sense to talk about pre-purchase perceived lack
of utility or incentive, as this will only lead to lack of interest rather than dissatisfaction, and
therefore it should be characterised separately as indicated in Cho and Cheon’s model of advertising
avoidance. That it does not lead to dissatisfaction does not mean that it will not trigger an avoidance
strategy, because if an advertised product is perceived to give no incentive for purchase and holds
no utility for the consumer, he/she will become inattentive as the advertisement is not of interest to
the consumer. The major difference is still that without dissatisfaction it will logically be a much
more manageable task to regain the attention of the consumer, which is why it was said that these
two aspects are “milder” prior negative experiences than dissatisfaction. An adaption of the
message to create perceived utility and incentive for the consumer could regain the attention of the
consumer by following the AIDA38 and/or ELM39 model to improve the message, whereas the
dissatisfaction aspect requires a middleman to endorse the company and reverse the negative
feelings that lead to a complete “block-out” of all advertisements made for that company, which in
turn means that a change of message or an internal change is most likely no longer enough to
prevent the avoidance strategy made by the consumer.
2.6.1. Components of advertising avoidance
Having defined and explained the three triggers of advertising avoidance (perceived advertising
clutter, perceived goal impediment and prior negative experiences), the turn has finally come to
explain the main term and aspect itself, advertising avoidance. As the intention and research of this
thesis is to investigate possibilities for lowering advertising avoidance without decreasing the
38
39
Jobber, D., “Principles and practice of marketing”, McGraw-Hill International (UK) Ltd., p.420-422, 2004.
Percy, L. & Elliot, R., ”Strategic advertising management”, Oxford University Press, p. 208-209, 2009.
25
amount of advertisements, it is perhaps the most vital theoretical background knowledge to have
that this research explains and goes into depth with, namely the concept of advertising avoidance
itself. The model and theories of Cho and Cheon define and divide this aspect into three main
strategies for passively or actively avoiding advertisements for reasons explained in theory thus far.
The professors divide the avoidance strategies into the three following types: Cognitive avoidance,
affective avoidance and behavioural avoidance40. Their views of this division are supported by
professors Speck and Elliot41, who use the term mechanical avoidance instead of affective
avoidance, but concur in most details of the avoidance strategies. They also present a model for
predicting avoidance behaviour, which will be explained after the views and theories of professors’
Cho and Cheon.
As mentioned there are three avoidance strategy types: Cognitive avoidance, affective avoidance
and behavioural avoidance. If explained shortly in plain English to give the reader an idea of the
division of avoidance strategies, these terms could be “translated” roughly into avoidance of the
brain/mind, emotional/feeling based avoidance and physical action avoidance.
The cognitive avoidance component consists of the consumer’s belief and evaluation of an object,
here meaning an advertisement. The more negative (based on the three aspects leading to
advertising avoidance) the evaluation and beliefs related to the advertisement are, the more
unfavourable the cognitive component is, leading to a higher risk of a cognitive avoidance strategy,
e.g. intentional ignoring of advertisement. Ignoring strategies are considered an act of the
brain/mind and not necessarily a physical action, which makes it cognitive rather than behavioural.
Switching focus to ignore advertisement is thus also a cognitive avoidance strategy.
The affective avoidance component consists of the consumer’s feelings and reaction to an object,
again meaning an advertisement. The more intensely (based on the three aspects leading to
advertising avoidance) the consumer dislikes the advertisement, the more unfavourable the affective
40
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 91, 2004.
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 61-62,
1997.
41
26
component is, leading to a higher risk of an affective avoidance strategy, which is considered to be
when the consumer avoids the source of their displeasure. This is also closely linked with media
attitude. Affective avoidance leads to increased negative attitude towards both advertising in
general and the media vehicle which conveyed the disliked message. So the affective avoidance is a
strategy that goes from an emotional reaction to a physical action of avoidance.
The behavioural avoidance component consists of all of the consumer’s physical avoidance actions
except lack of attention (which whether actually physical or not falls under the category of
cognitive avoidance). Cho and Cheon do not define what triggers behavioural avoidance and thus
what increases the risk of a behavioural avoidance strategy being executed by the consumer. The
reason for this may be that behavioural avoidance is neither an emotional nor intellectual response,
but simply a physical response that can be encouraged by any of the advertising avoidance triggers.
To those unfamiliar with these theories the behavioural avoidance will very likely be synonymous
with advertising avoidance, as it does not entail an evaluation of the mechanisms behind the
response in the same way as the cognitive and affective avoidance components, merely an
observation of physical actions taken.
Before ending theoretical chapter and the section about the main aspect itself, namely advertising
avoidance, I will explain professors Speck and Elliot’s model and theories for predicting advertising
avoidance. After all that is what all these theories are meant to lead to, an understanding of
advertising avoidance and how (if) it can be decreased without decreasing the amount of
advertisements, as that is a nearly impossible task, as explained in the introduction of this thesis.
27
Figure 3: Speck and Elliot’s model for predictors of advertising avoidance42
In their model, professors Speck and Elliot take the effect of four variable groups into account that
lead to advertising avoidance: Demographic variables, media-related variables, advertising
perceptions and communication problems43. As I have pointed out earlier, a rather large amount of
overlap exists in the different theories and models that I treat in this thesis to get an understanding
of the concept called advertising avoidance, but as with the other models this model also gives
variations not including or explained in the same way in other theories. The most significant novelty
brought to my theoretical work by this theoretical framework comes through the first variable
group, demographic variables. The other theories that otherwise go well into depth with many of the
advertising avoidance related aspects do not take demographics into account in any direct way as
this model, in fact professors Speck and Elliot comment on this context themselves saying:
“Demographics variables have long been used to assess the viewing habits of individuals. To a
lesser extent, they have also been linked to audience attrition and ad avoidance...”44. Demographic
42
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997
43
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63-65,
1997.
44
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997.
28
variables as the name suggests has to do with all the variables of the individual consumer that may
have an effect on their views on advertising and ultimately based on what and why they execute
avoidance strategies. Naturally, aspects that are affected by perception (advertising clutter, media
attitude etc.) also relate to demographics, but not as directly as this model that takes demographics
into account as a separate aspect. Considering the importance of demographics this is a surprise to
professors Speck and Elliot that argue that all surveys show that ignoring (cognitive avoidance)
increases with income, no matter how it is measured. They also state that taking eyes off screen
(behavioural avoidance) in addition to income also increases with education, household size and
employment. They arrive at many other conclusions about the demographics of the consumers’
avoidance behaviour, which will be analysed and discussed in the next chapters (analysis and
discussion). These statements relate to avoidance in the medium of television, and professors Speck
and Elliot point out that this is the medium where the most is known about the effects of
demographics, as television is perhaps the most in-depth researched medium in modern time, giving
basis for the broadest set of analytical comparisons.
Media-related variables in Speck and Elliot’s theories include three variables that apply to the four
media they deal with (television, radio, newspapers and magazines): Overall exposure to a medium,
attitude towards a medium, and breath of exposure within a medium. Overall exposure to a medium
is the time the consumer is exposed to a specific media vehicle seen overall in a constrained
timeframe (typically measured in a day, week or month). Attitude towards a medium means the
same here as in the many theories I have treated in this thesis, which is the consumer’s personal
perception of the medium, which in turn has an influence on how the medium is consumed. Breath
of exposure within a medium is an aspect I have not come across in the other theories treated in this
thesis, it refers to the relative amount of material the consumer samples within a medium45. Based
on a number of sources Speck and Elliot find that television research suggests that media exposure
is not connected to advertising avoidance with the exception of one source saying otherwise.
According to Speck and Elliot, professor Clancey46 found that heavy television users are most likely
to have their eyes on the screen during commercials. This might not necessarily mean that heavy
45
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 64,
1997.
46
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 64,
1997.
29
television users are less inclined to execute an avoidance strategy, they might execute a cognitive
rather than a behavioural or affective avoidance strategy by being selectively or non-attentive whilst
still having their eyes on the screen. However, what it does indicate is that media exposure and
advertising avoidance are related.
Advertising perceptions has to do with the consumer’s perceptions of advertising. Speck and Elliot
say that the consumer’s predisposition to avoid advertisements in a medium is likely to be related to
categorical beliefs and perceptions about them. From multiple sources Speck and Elliot deduce that
consumers respond categorically to advertisements, because they probably have general attitudes
about advertisements in a medium. In other words, responses to advertisements in a specific
medium are a learned process that consumers initiate reflexively based on their beliefs and attitudes
towards advertisements in that medium. For instance studies47 show that 75% of all television
zapping occur during the first advertisement in a sequence, this could very well be a reflexive
response to the beliefs and attitude of the consumer about advertisements in television, namely that
they are e.g. annoying interruptions of editorial content and not useful. Therefore before even
seeing what the advertisement is about and if any of advertisements in the sequence might be of
interest, the consumer reflexively zaps away out of sheer habit and a learned process that stimulates
this avoidance response to a television commercial break. As it is a reflexive response this
avoidance behaviour could prove immensely difficult to minimize, and very likely changing the
attitude about advertisements in a specific medium via some sort of “back way” might be the best
chance to change this reflexive response that translates directly into advertising avoidance.
The aspect of communication problems refer to communication problems that are related to
advertising. Speck and Elliot argue that advertising avoidance also reflects communication
problems that are related to the advertisements themselves and that the information theory48 may
support and account for this claim. The information theory investigates the role of “noise”
(interference) in communications, and when the theory is extended to include interactive processes,
47
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 64,
1997.
48
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 65,
1997.
30
noise includes all advertising related communication elements. Thus being a significant source of
this noise or interference advertising related communications may encounter three types of
problems: Advertisements obstruct/hinder the consumer’s search for media (editorial) content,
advertisements distract the consumer while processing media content and advertisements disrupt the
media processing all together.
Advertisements hinder the consumer’s search for media content with their numbers, size, location
etc. that makes it difficult for the consumer to locate and identify the content he/she was searching
for, and as explained in the previously treated theories and models, search hindrance triggers and
encourage avoidance behaviour, which is supported by the statements of Speck and Elliot in this
theory. To a broad extent the aspect of communication problems of this theory with its’ three types
of problems (aspects) resembles the aspect of goal impediment49 in the theory of Cho and Cheon
with the three types of impediment also being hindrance, disruption and distraction. On this basis, I
am once again compelled to state that although attacking the problem of advertising avoidance from
different angles there is a great deal of consensus among the leading researchers that publish works
on this issue, at the very least in theory. To make this situation clearer and to give the appropriate
amount of emphasis to the work of professors Speck and Elliot, I will finish this theoretical chapter
by explaining the last two types of communication problems as stated by the aforementioned
professors, after which the analysis of these theories can begin and be put to the test.
Advertisements are distracting, because they interfere with non-advertisement (editorial) content.
This is the case when e.g. promotional messages run at the bottom of the television screen during a
program, which then compete with the editorial content for the consumer’s attention, and thus
degrade the viewing experience. If this occurrence exceeds the distraction tolerance of the viewer,
he/she will very likely execute an avoidance strategy. Finally, advertisements totally disrupt the
media processing, because they (both directly and indirectly) compel the consumer to stop
processing (whether reading, viewing and/or listening to) the desired content. Commercial breaks
are according Speck and Elliot the clearest example of this communication problems type.
49
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 90, 2004.
31
3. Analysis
This chapter will provide an analysis of theorised aspects for the thesis. This thesis has treated three
of the major theories and frameworks on this topic that are published in reputable sites and thus
provide a good foundation and structure for the understanding of the aspects that pertain to the
hypothesis and topic I wish to treat and test in this thesis. However, without a separate analysis
testing these theories in practice, it remains theoretical and speculative. Thus this chapter will
through survey and studies conducted on potential consumers put these major theories to the test to
discover, which theories hold true, and hopefully it will ultimately lead to an answer to the main
hypothesis and reason for this thesis, namely if it is actually possible to lower advertising avoidance
without decreasing the amount of advertising. In the chapter following this chapter, the discussion, I
will discuss the results of the analytical figures and how they compare to the theoretical
assumptions and claims made by the theoreticians. I will discuss what these results mean to the
perceived structure and understanding of their theories, how likely it makes the theories come
across, and how accurate it means that the aspects are portrayed. Above all the discussion will be
used to discuss the verification or falsification of my hypothesis and expectations regarding
advertising avoidance and the aspects linked hereto.
3.1. Quick summary of the theories and frameworks
Before beginning analyzing the surveys conducted in connection with advertising avoidance and the
aspects related to advertising avoidance, I would like to give the reader a quick overview and
summary of the applied theories in this thesis that form the basis of said work to make the process
more manageable. Three theoretical frameworks are applied to this thesis in order to give an
understanding of the researchers’ different views and takes on the concept of advertising avoidance.
The first theory mentioned in this thesis is that of professors Ha and McCann50, who have formed
an integrated model of advertising clutter, which (the perception thereof) leads to an overall impact
50
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, 2008.
32
(mainly advertising avoidance). In short their model and theories show that advertising avoidance is
a product of perceived clutter, which is summed up by three approaches to advertising clutter:
1. The structural approach, which includes all physical attributes of advertising clutter.
Contained herein are the structural elements, advertising design (size, location, format etc.),
frequency (repetition) and quantity (amount of advertisements).
2. The informational processing approach, which includes four theories that all relate to how
much and in which manner is communicated to the consumer. Is the message credible,
interesting and desire-creating and does the advertiser avoid overloading the consumer with
information? Those are the main aspects of the informational processing approach theory.
3. The functional approach, which includes the functional purpose for using the medium (task
orientations, goals and search) and attitude of the consumer towards to the specific
advertising medium, and advertisements in general.
The second theory treated in this thesis is that of professors Cho and Cheon51, who have formed a
hypothesized model of advertising avoidance, which they see as three strategies, cognitive
(avoidance of the mind e.g. ignoring), affective (emotional avoidance, reaction to the
advertisement), and behavioural (physical avoidance action taken to avoid the influence of the
advertisement).
In their model advertising avoidance comes from (is triggered by) three aspects: perceived goal
impediment (which is comprised of search hindrance, disruption and distraction), perceived
advertising clutter (the sole triggering aspect in Ha and McCann’s theory), which is comprised of
excessiveness, exclusiveness and irritation (unlike the three approaches in Ha and McCann’s
model), and prior negative experiences (which is comprised of dissatisfaction, perceived lack of
utility and perceived lack of incentive). Despite the difference in terminology the structures and
aspects of these two theories bear resemblances as explained in the theoretical chapter.
51
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, 2004.
33
The third and final theory treated in this thesis is of that professors Speck and Elliot52, who have
formed a model of predictors for advertising avoidance. As Cho and Cheon, they too see advertising
avoidance as three strategies, cognitive and behavioural strategies they have in common. Instead of
affective avoidance they use the terminology of mechanical avoidance, as they do not in detail
explain this aspect it is difficult to find out how it differs from the aspect explained by Cho and
Cheon.
In their model, Speck and Elliot define four aspects that predict/trigger the execution of an
avoidance strategy (this is where their theory differs strongly from the other theories): demographic
variables (age, gender, income, education and other individual variables that impact the tendency to
execute an avoidance strategy), media-related variables (mainly attitude towards the specific
medium and the perception of advertisements in this medium), advertising perceptions (perceptions
and attitude to advertisements in general and their impact on tendency to execute an avoidance
strategy) and communication problems (all communicative problems that prevent the consumer
from achieving a desired goal, completing a task or finding information, which leads to irritation
and ultimately triggers an avoidance strategy).
With this short summary of theories upon which this thesis is based, the analysis of theoretical
claims and structure can be commenced.
3.2. Analyses and surveys and their impact on the explained theories
This section will include explanations and analyses of the conducted surveys and studies. Since the
theories treated in this thesis (as explained earlier) have so many similarities in aspects and
sometimes structure, it would require a very cumbersome effort and a vast amount of repetition to
go through each of the theories chronologically with all the analyses that relate to each of them,
seeing as many of the analyses (due to the similarities of the theories) relate to more or all of them.
To avoid repeating and treating the same analytical material two or three times over, I will instead
go through the analytical material based on the authors of these surveys, explain what they mean,
52
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., 1997.
34
how they relate to each of the theories and what it means to each theory respectively. Furthermore, I
will briefly explain the impact it may have on my main hypothesis and research, as it will be dealt
with more thoroughly in the next chapter concerning the discussion of this thesis.
As explained in the limitations section (see chapter 1) only a very limited breath and amount of
analysis/research surveys have been conducted in this field of advertising and marketing, and thus
the reader will notice that not all aspects of the theoretical models used to explain the structure of
advertising avoidance will be able to be analysed. However, many central aspects are covered to a
certain extent in this analysis and they should form a strong picture and indication of what
advertising avoidance is and provide answers to my hypothesis and expectations.
A good place to start could be the surveys conducted by professors Speck and Elliot, as they are
also the minds behind the theory and model for predicting advertising avoidance, also their analysis
keep very well in line with their theoretical work and is easily comparable to the other theories.
Here is the demographics sample table (see the next page) that makes up the consumers, who
participated in the surveys and thus play a vital role in what the analysis figures show.
35
Figure 4: Sample characteristics for Speck & Elliot’s surveys53
53
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 66,
1997.
36
Before moving on to the results and aspects investigated, I will go through the sample and explain
the demographic “balance sheet” so that the readers will know which demographic representation
the results are based on. Looking at age the survey is conducted on a fairly balanced group with
nearly an equal amount of consumers in their thirties and forties. The sample has a slightly higher
(than the average) representation of consumers in their sixties and a slightly lower representation of
consumers in their fifties. The only group that is not represented close to the average are the
consumers aged in their twenties. The average representation is 189 (946 (amount of participants)/5
(number of groups) = 189.2). With the eldest group (aged 61+) being represented the most and the
youngest group (aged 20-30) being represented the least, the analysis should to a certain degree
represent the views of the more senior consumers more than the views of the youth.
When it comes to gender (sex) the representation is strongly leaning towards the female consumers,
who are represented by nearly twice the numbers of males, or 2/3 of the participants. Thus the
results should to some extent represent the views of women more than men. Looking at income the
readers will find a reasonably balanced representative group, however, with a slight inclination
towards to the lower income groups than the higher income groups. This does not necessarily
indicate an imbalance, because one does not need to be a professor in political science to know that
the population of most countries is not on average completely in the middle of income groups, more
likely leaning towards to the lower income groups. So this representation could very well be a
balanced picture of the consumers.
Also the sample for education looks very balanced indeed with each educational level almost
completely evenly represented except for the middle group, which is more strongly represented, still
indicating a finely balanced sample, as the middle also represents the average. Looking at the
marital status sample it becomes clear that Speck and Elliot have tried to maintain a finely balanced
sheet for their survey, as this aspect is also fairly balanced, with only a slight overrepresentation of
married consumers. The employment status of the majority of consumers asked in this survey is
full-time employment, nearly half of the participants have full-time employment (45.56%) and only
a minority are not employed or chose not to respond (16.91%). Despite the major imbalance this
may very well be a representative selection of the consumers as few countries would struggle with a
37
30-40% unemployment rate. The last demographic characteristic is by far the most imbalanced,
whether speculating in representativeness or not. The race (ethnicity) sample shows that the
consumer group participating in this survey is completely dominated by Caucasians (White), they
account for no less than 82.98% of all participants.
To sum up the demographics sample of this survey, it could very well be argued that Speck and
Elliot have done well in gathering a representative and/or balanced group of consumers to
participate, except in two demographic categories, namely age and race. It is not so noticeable in the
age category, although a slightly more youthful group probably would have given a more balanced
and representative selection of the average consumer, leading to more accurate results in the survey.
The only truly critical area is that of the race (ethnicity), where the average consumer is certainly
not well represented. Speck and Elliot research at American universities and thus apply their
research and surveys to American conditions to a high degree, and being the melting pot of the
world many Americans would look in disbelief if they were told that the group selected for this
survey reflected that. This survey completely overhears the voice of the big Black, Hispanic and
Asian groups of the United States. This survey will almost exclusively reflect the actions, opinions
and thoughts of the Caucasians (White) with nearly an 83% representation. Whether this is a
conscious choice or not, I cannot say, but if so, Speck and Elliot “paint” a very one-sided picture of
the American consumers.
3.2.1. Advertising avoidance in different media
The first aspect this survey looks at is the overall advertising avoidance executed in the four most
dominant media (the survey is from 1997, hence the lacking representation of the internet medium,
other surveys will be used to treat the internet): Television, newspapers, magazines and radio. The
participants/respondents were asked on a one to seven scale how often they did the following (i.e.
executed an avoidance strategy) for each medium, with one being “never” and seven being
“always” (See the figure on the next page).
38
Figure 5: Summary of advertising avoidance in each medium54
As the figures show this summary does not go into details as to which types or strategies of
advertising avoidance the consumers execute, rather a short overall picture of the total advertising
avoidance in each medium. This makes for a fine way of starting the analysis by establishing
whether the problem of advertising avoidance is more outspoken in some media compared to other
media or if it is the same for all media. When looking at the figures and judging exclusively by
these, it is a reasonable conclusion to make that the problem of advertising avoidance appears to be
of a somewhat dissimilar size in the different major media. Advertising avoidance strategies are
executed much less often by those consuming the radio medium (3.9 of 7) than by those consuming
the television medium (4.9 of 7). Magazines appear to be the “victim” of a high amount of
avoidance (4.5 of 7) leaning slightly closer to television’s undesired first place, whereas the
newspaper medium nearly escape the avoidance strategies to the same extent that radio does (with
4.0 of 7). With 4.9 indicating a fairly frequent execution of avoidance strategies and 3.9 indicating a
slightly below average (4 being average between 1 and 7) frequency, this puts the four media into
two roughly defined categories with a print medium and a broadcast medium in each.
54
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 69,
1997.
39
Fairly high frequency of avoidance: Television 4.9, Magazines 4.5
Average or below frequency of avoidance: Newspapers 4.0, Radio 3.9
With this brief and very preliminary conclusion on the overall advertising avoidance in each
medium in mind this thesis will now go into a more detailed analysis of advertising avoidance
based on Speck and Elliot’s theories and model.
Figure 6: Speck & Elliot’s avoidance model’s aspects analysed55
55
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70,
1997.
40
As the reader will notice this figure does not include the first of the four aspects that professors
Speck and Elliot predict will trigger avoidance, namely demographic variables. The reason for this
is that demographic variables in themselves cannot trigger an avoidance strategy; they can only alter
the strength of the tendency towards avoidance, e.g. age cannot result in avoidance, but the age of
the consumer can affect how strongly the consumer feels encouraged to avoid advertisements. The
demographic variables will be treated in the regressive figures of this analysis, which will be treated
after these analytical figures.
The first aspect included in the descriptive statistics table is media-related variables, which contains
three measured parameters. The first parameter is media usage i.e. how much time is spent
consuming that medium. This is not an important factor as more popular media have a greater
chance of attracting more advertisers, which will have an impact on the attitude towards to the
medium and many other factors that affect advertising clutter and avoidance. Hardly surprising to
most readers, this nineties survey shows that television is by far the most consumed medium of all
with an average consumption per consumer of 1055 minutes per week, equivalent to 17 hours and
35 minutes, which is nearly 10.5% of the entire week. Radio comes in on a comfortable second
place (well ahead of newspapers in third place) with an average consumption of 600 minutes per
week, or ten hours. Although radio’s firm grip of the media “pole position” has long since been
surrendered to the television medium, it still enjoys a high consumption rate due to its’ employment
as background “noise” for a lot of activities. Consumers listen to the radio while working in
factories or as craftsmen out in the open, they listen to the news and weather report while driving to
and from their workplace and during the summer period radio-listening is a major pastime activity
at summerhouses or allotment garden houses just to mention a few examples. For the reasons that
keep radio alive as a major medium the breath of media usage is particularly low, because it forms a
background activity for most consumers, listening to the news or music while working. This is
reflected very well in the breadth of media usage, where all other major media are sampled almost
completely evenly broadly, whereas radio is sampled far more narrowly a full 2 points (on a 1 to 7
point scale) behind television.
41
Magazines and newspapers are consumed 162.8 minutes and 233.6 minutes per week on average
respectively, but with almost the same breadth of sample as television with 5.2 and 5.3 respectively
(television being at 5.4).
The third parameter included in the aspect of media-related variables is where this part of the survey
caught this researcher by surprise. I was astonished by the fact that the attitude towards each of the
four media was almost completely the same, ranging from 4.7 to 4.9, a miniscule range of 0.2
points. Attitude towards the medium was measured on the Likert scale of 1 to 7, where 7 is the most
positive. Although it had since the beginning of my research been my expectation and hypothesis
that the amount of advertisements alone would not give the full picture of the consumers’ attitude
towards the medium (when negative attitude leads to perceived clutter, which leads to avoidance), I
could not have foreseen how little impact the amount of advertisements really has if this survey
gives a truthful picture. Naturally, the amount of media use is not the same as the amount of
advertisements, but it is a fair assumption that the more consumed and attractive a medium is the
more it will attract the advertisers and their desire to channel their advertising messages through
that medium in the hope of reaching the majority of potential buyers. Thus there should be a certain
degree of comparability between the amount of media use and the amount of advertisements in the
medium. No advertiser would buy “air-time” or space in a medium that is consumed very little,
unless the advertiser knows that the small group of consumers using that medium belong to the core
of his/her target audience. With a hint of goodwill these results indicate that the amount of
advertisement have a small to no impact at all on the attitude of the consumers towards the different
media, which is good news for this researcher since I hope to substantiate that another way of
reducing advertising avoidance other than reducing the amount of advertisements is possible.
The second aspect included in the descriptive statistics table is perceptions of advertising in each
medium. This aspect is divided into six belonging parameters, three negative and three positive that
characterise the consumers’ perceptions of advertising in the different media. All
parameters/components are measured on the Likert scale of one to seven, with a higher value being
the best for the positive parameters and a lower value being the best for the negative parameters.
42
The first parameter is the positive component of useful (meaning do the consumers find the
advertising in the specified medium useful).
The consumers seem to find advertising in all the media almost equally useful, except for
newspaper advertisements. Only the newspaper medium is rated above average (neutral point
between negative and positive = 4.0 points) with a score of 4.3 points, whereas the advertisement of
the other media are rated slightly negative ranging from 3.9 down to 3.7 points. With all media
placed within 0.3 points of a neutral perception of the advertising, it is a natural presumption to
make that the consumers are fairly neutral in advance to advertising in each medium, neither being
prejudiced against nor in favour of the advertisements, with perhaps a minor advantage for the
newspaper medium. All in all, it does not seem to be usefulness of advertising in a medium that
bears a significant influence on the consumers executing an avoidance strategy.
The second parameter is also a positive adjective to the perceptions of advertising, namely
interesting. Here too the values are closely placed to the point of neutrality fluctuating from 3.8
points to 4.4 points. This time the small favouritism leans towards the magazine medium, which I
do not find particularly surprising, because magazines is in its’ nature a medium of hobbies and
professions/trades. Many consumers read magazines because they have a private or professional
interest in the topic(s) that the magazine is based on, and magazines will generally have a high
amount of trade- or hobby-related advertisements, which naturally have a higher chance of being
interesting for the consumer, because he/she reads the magazine exactly due to an interest in that
topic. Also in this parameter I do not find sufficient variation or fluctuation from neutrality to
conclude that it bears a significant impact on avoidance behaviour.
The third parameter is the first negative component of this aspect in this part of the survey and it is
related to the amount of advertisements in each medium, namely excessiveness. Unlike in the
previous two parameters a major fluctuation from neutrality is found here. Whereas the advertising
in the newspaper and radio media are considered only slightly excessive (4.3 and 4.6 points
respectively), advertising in magazines (5.3 points) and particularly television (6.0 points) is
43
considered very excessive. Whether or not the amount of advertisements in magazines and
television is considerably higher than in newspapers and radio, the consumers clearly perceive these
media to have a much more excessive amount of advertisements. These results indicate that
lowering the amount of advertisements in television and magazines should have a reducing effect
on advertising avoidance, but since the issue here is perceived excessiveness it could also mean that
a restructuring of advertisements in these media would mean a more manageable absorption of
advertisements and ultimately a less negative perception of advertising being excessive. As theories
have indicated the feeling of advertisements being excessive is also related to both the message and
the design of the advertisements (just to mention a few aspects).
The fourth parameter is “annoying”, a negative parameter that could be linked both to the message
and the physical appearance (structure and design) of the advertisements. The results indicate that
the consumers perceive advertisements in print media to be less annoying than advertisements in
broadcast media. Newspapers and magazines have below average ratings (3.6 and 3.7 points
respectively), whereas radio and television have above average ratings (4.2 and 4.5 points
respectively) indicating a somewhat higher level of negativity/annoyance with the advertisements in
the broadcast media than with that of the print media, as I explained. One of the reasons for this
could be found in the audience control of the media. Print media are self-paced and broadcast media
are captive and thus the consumer is in much less control and cannot skip annoying
advertisements56. Irrespective of the reasons for which the consumers find broadcast media
advertisements more annoying than print media ditto, these results certainly indicate a need for
increased care and precision when making advertisements for the broadcast media compared to
doing the same for print media. If the consumer is annoyed with an advertisement in a magazine,
he/she could simply turn the page and read on, whereas the consumer has to change channel or
frequency when consuming broadcast media if an advertisement annoys him/her, because he/she
cannot skip the advertisement.
The two last parameters seem to fall very well in line with that interpretation and analysis, because
they indicate a more positive look on advertising in print media in general. Looking at the last
56
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 578-579, 2008.
44
positive parameter, believable, only a small fluctuation is found for all media except television. It is,
however, noteworthy that the small fluctuation points to a deduction of the consumers finding
advertisements in broadcast media less believable than in print media. Least credible or believable
is advertising in television with the low 3.5 point rating. Also the last parameter, the negative aspect
of “wastes my time”, sees television in the undesirable most negative place. In fact, television
advertising is the only advertising perceived to be above average in the negative aspect of wasting
the consumers’ time with a 4.3 point rating. In this parameter the divide between print and
broadcast media is much clearer with both print media receiving a below average rating of 3.5
points, and radio landing nearly on average with a 3.9 point rating.
The overall analysis of the “perceptions of advertising in each medium” aspect is quite mixed with
the six parameters pointing to an unclear conclusion of the attitude towards advertising in each
medium. However, the three last parameters show results that favour print media advertising over
broadcast media advertising although not by any epoch-making margin. Still, one medium does
stand out, television. Seemingly, consumers do not like advertising in television as they rate the
medium the highest in all negative aspects. The consumers also consider television advertisements
the least credible/believable.
The last aspect of this figure is “communication problems related to advertising”. This aspect of
Speck and Elliot’s theoretical model57 (upon which the analysis is based) corresponds 100% to the
perceived goal impediment part of Cho and Cheon’s hypothesised advertising avoidance model58,
which incorporates the exact same three elements of search hindrance, disruption and distraction.
Related to Ha and McCann’s theories and model59 this aspect relates primarily to the informational
processing approach, in which communication problems are informational problems (hence the
placement), but it also relates to the functional approach (search hindrance is directly related to task
orientations). Since the three parameters are all negative, the higher the rating the worse the
57
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997.
58
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 93, 2004.
59
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 575, 2008.
45
communication problems are. Looking at search hindrance a marked difference between the media
is seen. Search hindrance seems to be a major problem in all media except for the newspaper
medium that fares very well and receives a far below average rating (average is 4.0, newspaper has
a rating of 2.8). The second placed medium is radio, which has a rating of 5.5, twice that of the
newspaper medium indicating that advertising hinders the consumers’ search for information or
achieving a goal to a very high degree in most media. Magazines and television fare even worse
with ratings of 5.8 and 6.3 respectively. The consumer responses are to some degree surprising,
especially the magazine rating is higher than anticipated. That advertising in captive media
interrupting editorial content (and being without audience control) can lead to a high degree of
search hindrance is not very surprising, thus this researcher understands the ratings of television and
radio. The rating of magazines is quite another story. Magazines belong to the self-paced group of
media and like newspapers they should be fairly well organized with a division of sections (and
often magazines even have a table of content) so their 5.8 rating is a surprise to me. The generally
very high ratings strongly suggest that here is an aspect worth doing something about. Treating this
aspect could certainly be a way of lowering advertising avoidance, because through better
organisation of advertisements consumers would be able to find their way around the information
they are looking for and be less annoyed with the advertisements, which means they are less prone
to execute an avoidance strategy.
For the element of disruption the picture is somewhat different and the fluctuations are exclusively
in a negative direction, still with the newspaper medium as the least negative though, rated at 4.4
points. The advertising of the radio medium is perceived to be slightly more disruptive with a rating
of 4.7 points. The undesired top spot in perceived disruption is shared by magazines and television
advertising, each rated at 5.5 points. As explained in the theoretical chapter, Speck and Elliot see
commercial breaks (television advertising) as the clearest example of disruption and advocate that
disruption should prove the most dominant in this medium60, but it seems that advertising in
magazines disrupt the media processing just as much and create as many communication problems
as advertising in television, atleast that is the consumers’ perception according to these figures.
60
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 65,
1997.
46
Distraction is a difficult element to measure, because both internal and external situational content
have an influence on the consumer being distracted, while consuming a medium. If it is, however,
considered that all external influences are the same for each medium or that only the internal
influences should be accounted for then the results can be used to make an analytical
documentation. As with the two other elements/parameters newspaper is the “winner” of this
analysis again being borderline neutral or average with a rating of 4.1, which makes it the least
distracting medium to consume while presented to advertisements. The “runner-up” is magazines
with a rating of 4.8 and it does make sense considering the consumers’ consumption patterns for
each medium that print media advertising is perceived as less distracting than advertising in
broadcast media. When the consumer sits down and reads the newspaper or a magazine he/she is: 1.
fully concentrated on reading and not paying attention to other things, and 2. in control because
print media are self-paced media where the consumer can skip advertisements by turning the page.
Thus television and radio receive much more negative ratings by the consumers, who perceive
advertising in these media to be far more distracting giving the television and radio media ratings of
5.3 and 6.1 respectively. Thus radio advertisements are perceived to be the most distractive by far
and create the most communication problems. Considering the way I have explained earlier how the
radio medium is consumed this is not too surprising either. Radio is the favourite background
medium and while going to and from work, as well as while working many consumers listen to the
radio in their “peripheral field of attention”, meaning they are listening, but not really paying
attention to the messages, because they are doing other things at the same time (unlike when
reading printed media). Thus when focusing on editorial content while doing other things, the
consumer will easily be disrupted by advertising because: 1. He/she cannot skip or control it,
because radio is a captive medium, and 2. He/she is doing something else (often work) and cannot
wait for the advertising segment to finish to hear that which caught his/her interest. As Speck and
Elliot cleverly deduct themselves, radio is the only medium where distraction helps to explain
advertising avoidance61. For the reasons mentioned above radio is the medium, where distraction is
by far the most likely to trigger an avoidance strategy, also because the consumer is most prone to
lose his/her patience since he/she is not at liberty to wait, while doing other things. The same does
not apply to the other captive medium, television, because the consumer is unlikely to use the
61
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70,
1997.
47
medium as a background activity, and few people are at liberty to watch television while at work, or
going to or from work.
3.2.2. Demographic regressions and their impact on advertising avoidance
Based on the results achieved in figure 6 (their table 3) Speck and Elliot assessed the data and
analysed the regressions for the four aspects of their model and theory on advertising avoidance,
and calculated the regression values62. As the results of the three already addressed aspects hang
tightly together with the analysis of the figures that I have already analysed, the interesting
regression values to look at are those of the fourth aspect that was not addressed in their not figure,
namely the demographic variables. Thus the other parts of table 4 will not be included in this
analysis.
Figure 7: Demographic regressions of advertising avoidance by medium63
Regressions refer to the impacted change to a figure by the aspect/parameter in question. As
demographics was the only aspect of Speck and Elliot’s model and theory not addressed in table 3 it
is highly interesting to see, how demographics change/impact the figures I’ve analysed in table 3.
As the reader will notice the highest regressions (changes) are marked with ** (10% and up) or *
62
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 68-69,
1997.
63
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 71,
1997.
48
(8-9%) to show which changes Speck and Elliot find significant. Also the asterisks indicate a higher
precision/certainty about the figures, *p. < .05 and **p< .01. P being the possible deviation from
stated figure. Thus higher figures also indicate a higher degree of certainty64. Looking at the figures
the demographic variables that need an indication of “higher or lower” to make sense, the indication
is higher e.g. for age the regression comes with higher age, the same for income, education,
household size and employment, the regression indicated is a factor of increase in that aspect. For
the last three aspects it would be nonsense to say higher or lower, as people cannot be more or less
married, either they are or they are not. In the aspect of race, the regression comes through a nonwhite race i.e. the change in figures that comes when the consumer is of a race other than
Caucasian. For gender the norm is set to male and the regression comes with female consumers.
Looking the parameter of age, it seems that the tendency to avoid advertisements increases strongly
for the newspaper medium the older the consumer is with a regression of 11%. Even more
significantly the older the consumer is, the less prone he/she becomes to avoid advertisements in
both broadcast media, with a regression of -20% to -22% for television and radio respectively.
Looking solely at this analytical result, it means that advertisers will probably do well in advertising
products for younger consumers in print media and products for more senior consumers in
broadcast media, in order to lower the amount of advertising avoidance without lowering the
amount of advertisements. The high percentages indicate that my hypothesis of lowering the
avoidance behaviour without decreasing the amount of advertisements could very well be verified.
The parameter of income generates less strong regressions, although still some of significance. The
bad news is, however that avoidance behaviour increases in all media with higher income, most
significantly for magazines and television advertisements. Since the regressions are all increases it
becomes a matter of choosing the lesser of two evils (in this case the least of four evils to be
precise). As the increases in avoidance behaviour are the weakest in newspapers and radio these are
by this measurement the best media to advertise for those of higher incomes, whereas it cannot be
said categorically for sure whether the other two media are the best for advertising for the lower
income groups or not, although it may be beneficial to use the television and magazine media for
64
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 68,
1997.
49
lower income groups, as the fluctuation may indicate a low starting point. This is however just a
speculation, it is not possible to substantiate this through the analysis.
The education parameter testifies to the sad news for advertisers targeting well-educated consumers,
namely that the more educated the consumers are the higher their tendency towards avoidance
behaviour will be in general. Only advertising in the radio medium shows an opposing tendency,
however, only with a regression of a mere 2%. If it can console the advertisers the bright side of the
bad news is that the avoidance tendency “only” increases by 3% and 4% among the highly educated
consumers for the media of television and magazines respectively. The medium to avoid when
addressing well-educated consumers is that of newspapers. Along with the first parameter of this
figure, this shows that consumers that are well-educated and of a senior age have a strong tendency
to avoid advertising in particularly newspapers.
The household size parameter clearly indicates a path for lowering advertising avoidance as did the
parameter of age. The more people the household contains the better it is to use print media as the
avoidance behaviour decreases by 8% and 6% for magazines and newspapers respectively.
Consequently it is better for the advertisers to use the broadcast to target the small households as
regressions show increases of 5% and 6% for bigger households within the television and radio
media respectively.
Very interestingly, the marital status of the consumers who get married is as much a blessing to the
advertisers as to the happy couples themselves. Marriage quite one-sidedly decreases the
consumers’ tendency to avoid advertisements all together. Whereas the married consumers show a
slightly increased tendency to tolerate advertisements in print media better, they avoid
advertisements in broadcast media far less than their single counterparts by a margin of 7% and 8%
in television and radio respectively. The survey does not indicate and take into account whether
widows and widowers should count as married or single consumers, nor those consumers who are
divorced. Information regarding these aspects may have had an impact and been of strong interest to
this thesis.
50
Observing the figures for the parameter of employment, it is hard to make sense of the underlying
connection between the parameters of income, education and employment. In the figures for
employment, it seems that increased employment decreases the tendency to avoid advertisements in
all four major media. Although only by a margin of 2% in all media except for television (by a
margin of 5%), I find these figures quite puzzling. It seems a fair assumption to say that consumers
with a high degree of education have the highest incomes, and equally fair to say that these
consumers are most likely to have full-time employment as well-educated employees are generally
more sought after than those without higher degrees of education.
This is where the puzzle begins, because the figures for both income and education clearly indicate
a strong general tendency for increased avoidance behaviour with an increased income and a higher
degree of education, so it is somewhat mysterious that increased employment leads to a decreased
tendency towards avoidance behaviour in all media, if the presupposed connection between the
three parameters is correct. Only two words come to my mind that will account for such a counterintuitive result, subjectivity and psychology. As the reader will be able to read, many of the
theorised aspects in the models of leading researchers in the field of advertising avoidance include
the vital term of “perceived”. Perceived advertising clutter, perceived lack of utility, perceived lack
of incentive, perceived goal impediment etc. the consumers’ psychological state plays a part in the
analysis and the “shape” of many aspects that are central to advertising avoidance. As this is not a
psychology thesis I can only speculate in the instability of the consumers’ perceptions of the aspects
that are dependent upon the feelings, thoughts and opinions of the consumers. Clearly, the
demographic changes that occur in the consumers’ lives alter their perceptions of these fragile
aspects (when they become married, pass a higher education, find full-time employment, receive a
raise in income etc.) so why not psychological factors that are not accounted for here? Does this
mean that an entire analysis that does not carry the psychological factors is wasted and without real
use? Of course not, but it does mean that the figures should only be considered guidelines and
tendencies rather than solid facts. As I will demonstrate before moving on to a another survey (and
after finishing this demographics analysis), it is entirely possible, due to these dependent variables,
to receive quite dissimilar results based on the exact same sample group of participants.
51
Looking at the parameter of race, it seems that it is “easier” to advertise products targeted at nonwhite consumers. Avoidance behaviour (when facing advertisements in all four media) decrease
when the consumers are not Caucasian. The biggest decreases take place in print media by a margin
of 4% and 6% for magazines and newspapers respectively, whereas broadcast media experience a
decrease of 2% for both media. As for the parameter of gender a division occur that is noteworthy
for the advertisers. It seems that advertisers would do well in advertising products targeted at
women in broadcast media and products that are targeted at men should be advertised in print
media. Women have an increased tendency to avoid advertising in print media (of 1% in magazines
and 4% in newspapers), and a decreased avoidance tendency in broadcast media by a margin of 2%
and 5% for radio and television respectively. Throughout the analytical survey I have given my
conclusions and analysis of the figures and what they indicate, but to make a comparison here are
the conclusions of professors Speck and Elliot. I agree with the majority of the points they make,
but I consider their conviction in the results and their conclusions too confident and self-assured.
Figure 8: Speck and Elliot’s conclusions and summary65
65
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 74,
1997.
52
I find that Speck and Elliot come up with many correct observations regarding this analysis,
however, they tend in some aspects to put more emphasis on certain aspects than the figures
warrant. Regarding newspaper advertising, Speck and Elliot conclude that people will likely avoid
advertisements if they find them to be un-useful or unbelievable (among other aspects), despite the
fact that the figures show a below average perception of those aspects both rated at 3.8 in 7 (4 being
average)66. I considered those aspects to be of little significance in the magazine medium for that
very reason, and I find Speck and Elliot’s statement to be slightly lavish on that background. As I
said earlier, I agree with many of the deduced tendencies that Speck and Elliot find, but I cannot
concur with their level of conviction and confidence in these conclusions based on the figures.
3.2.3. Same sample but different results
As I explained towards the end of the previous section it is entirely possible to obtain quite
dissimilar results for advertising avoidance behaviour from two surveys based on exactly the same
group of survey participants because of the consumer perception dependent variables that are
central to the theories and models of advertising avoidance and advertising clutter. In 1998, a year
after the survey that I have just methodologically analysed in most aspects, Speck and Elliot
revisited the topic and made a new analysis based on the exact same sample group. As the reader
will notice the sample characteristics for the first survey67 are the same as this survey68 a year after.
As the question-based survey of advertising avoidance is repeated simply in a slightly different
scale and with new added media, most researchers would expect to find rather similar results, but
this is where my idea of unaccounted psychological (or other unknown) factors finds its
substantiation as the results are quite far from the first survey’s ratings.
66
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70,
1997.
67
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 66,
1997.
68
Speck, P. & Elliot, M., “Consumer perceptions of advertising clutter and its impact across various media”, Journal of
advertising research, p. 6-7, 1998.
53
Figure 9: Avoidance behaviour based on second survey69
The newer survey asks the participants basically the same thing as the “old” one70, how often they
avoid advertising in each of the media. Aside from the phrasing, which is subordinate, as they both
refer to all actions that can be characterised as avoidance behaviour, the only difference between the
two survey parts is the scale used to measure the answers. They both use the Likert scale, but
whereas the first survey ranged from 1 to 7, the new survey ranges from 2 to 7, thus creating a
higher middle-point or average (4.5 instead of 4.0).
With this in mind, the expectation would be that all figures from the first survey are slightly higher
in this survey (as the scale interval has been moved up), but this is where the “x-factor” shows
itself. For some reason, despite that all (known) variables are the same, the numbers are all lower or
the same and this is not even including the difference the higher scale-value inevitably causes. For
the broadcast media, the television rating drops from 4.9 to 4.67 and the radio rating stays nearly the
same increasing from 3.9 to 3.94 (which is much less than the scale change can account for). For
69
Speck, P. & Elliot, M., “Consumer perceptions of advertising clutter and its impact across various media”, Journal of
advertising research, p. 9, 1998.
70
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 69,
1997.
54
the print media, the magazine rating stays nearly the same dropping from 4.5 to 4.49, and as the
only medium that shows a remotely understandable change the newspaper rating increases from 4.0
to 4.1, although it is still a fairly small increase considering the scale change. Whether these
observations put into a larger context mean that consumers forget their opinions and constantly reevaluate their views on advertising for each medium, or there is a more stable measurable factor
behind these unexpected results is difficult to say, but individuals do constantly evaluate their
opinions on many aspects, even though they know in what direction they are leaning e.g. although
mid-aged to older females know that they dislike advertising in newspapers and want to avoid them,
when continuously asked their opinion on a scale (rather than just “yes” or “no”), they will
invariably rate their opinions slightly different from time to time. So irrespective of what other
factors play a part that researchers are unfamiliar with, this variation in itself has the consequence
that numbers will always fluctuate and can only be seen as tendencies rather than static facts, and
continuous studies must be carried out to constantly monitor the consumers for major changes in
opinions and avoidance tendencies to advertise the most effective way.
3.2.4. Uniform in-depth analysis of attitudes towards advertising based on demographics
As I explained in the limitations section of the introductory chapter my focus in this analysis is due
to the circumstances (limited time, space and applicable material) centred on a few central aspects,
and it seems to me based on the theories that I have treated that consumer perceptions and attitudes
are among the most central aspects to analyse, when it comes to answering my main hypothesis on
the possibility of lowering advertising avoidance without reducing the amount of advertisements.
Also for this reason, I have chosen not to focus on the structural and physical aspects of advertising.
Although I do believe and have found some substantiation for the fact that the amount of
advertisements carries an impact on avoidance, I cannot solve my hypothetical issue with an
analysis of these aspects, because the solution to this aspect would in isolation be exactly to reduce
the amount of advertisements, which is what I am seeking an alternative to. Thus I will dedicate the
rest of this chapter to analysing an even more in-depth survey dealing with the perception
dependent variables that are so central to advertising avoidance. This material is formed by
professors, who have not made a theory of the structure of advertising avoidance or clutter, and thus
have no interest in shaping it to fit a specific theoretical framework.
55
The large in-depth survey that this thesis will deal with and analyse is the work of professors
Shavitt, Vargas and Lowrey (hence forth credited as Shavitt et al.)71. Their work has two main
focuses, to analyse the overall and general attitude of the consumers towards advertising in the
different media, and to analyse which demographic factors account for the most significant changes,
which is also a major indicator of which characteristics of the consumers that trigger avoidance
behaviour. Thus this thesis is primarily interested in this part of the survey, which also takes up the
most space and focus in their work. For two main reasons, I find it to be less important to analyse
the former part of the survey. First of all, the consumers’ attitude towards advertising in any
medium is generally negative unless directly sought after, and as such need little further
investigation to bring a conclusion that is of any use. This claim is substantiated in table 3 of Shavitt
et al.’s survey (see below) that shows a negative general attitude in all aspects and primarily a
positive attitude in the media, where advertising is actively sought after (catalogues and classifieds).
Secondly, Speck and Elliot’s analysis explained the consumer’s attitude towards/ perception of
advertising in each medium on a level of depth that unless contradicted in key figures renders an
analysis of the same aspect somewhat superfluous (see figure 6).
Figure 10: Overall attitude towards advertising72
71
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., 2004.
72
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1021, 2004.
56
Before moving on to the demographic analysis, which is of great interest and use to this thesis, I
would like to refer the attention of the reader to the methodology used to create the survey and
afterwards to explain the sample characteristics that form the background for the survey. This is
how the results for the survey’s analysis were obtained: “In each survey, respondents were asked
the same questions about their overall attitudes toward advertising in the given medium, their
specific perceptions about it, and their demographic classifications. Before these questions,
respondents were [to] read a statement defining the nature of the advertising medium being asked
about”73. As it can be read the figures that will be analysed are results of questionnaires like the
other analyses that I have treated in this thesis, and not a measurement of responses or the like as
some types of surveys would require. This is due to the fact that the theoretical background for
these analyses is that the professors attempt to measure the consumers’ perceptions, which are so
central in advertising avoidance, as mentioned. As I have also mentioned this will always lead to a
degree of subjectivity as perceptions are subjective in nature, and as such may vary for unknown
reasons.
This is how the demographics sample looks (see the next page), and as the reader can see the
participant group is not the same size for the different media. Compared to the surveys conducted
by Speck and Elliot this survey includes a different set of media, with television and radio being
repetitions and catalogues and classifieds being new. Catalogues have similarities in type and
structure to magazines, they look a lot alike and the advertisements therein are read for the interest
and desire of the topic, but naturally there are also differences as catalogues are more or less
definable as one big advertisement for the brand or company’s products. Evenly so, classifieds have
a comparability with newspapers, more specifically classifieds are advertisements usually found as
a section in a newspaper. Despite the connection between magazines and catalogues, and
newspapers and classifieds, they are not the exact same media and as such only partially
comparable, which means a “100%” comparison to Speck and Elliot’s survey is not possible for the
print media. Unlike Speck and Elliot, Shavitt et al. have made a general (an average) sample and
used it to obtain a general picture of attitudes towards advertising across the different media. As the
survey figures will later show, they have also produced a set of figures for the overall attitude that
73
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1018, 2004.
57
insures the reader an understanding of the collective attitudes towards advertising in all the media.
Lastly, the professors behind this work have included a category of media that Speck and Elliot did
not, the category that Shavitt et al. call out-of-home (OOH). OOH is not a single advertising
medium, it includes all media encountered outside of the home, such as posters, billboards, samples
of food in a store, advertising pamphlets or leaflets etc. Although it naturally has a very useful side
to it to be able to compare figures across surveys, it is not problematic that Shavitt et al.’s survey
includes different media from those of Speck and Elliot’s survey, as the main point of these surveys
is to support my hypothesis that it is possible to lower advertising avoidance without decreasing the
amount of advertisements in a medium. Thus in themselves the figures are the most essential key to
answering the question of how to lower avoidance behaviour without reducing advertisement
quantities, if possible.
Figure 11: Sample demographics for Shavitt et al.’s surveys74
Looking at the sample demographics for this survey, the demographic variables are represented on
the vertical line of the table and the media are placed horizontally with the number of participants
included in each media stated in parenthesis underneath the medium.
74
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1017, 2004.
58
The first demographic variable is the consumer’s gender. Looking across the media and the general
survey sample, it becomes clear that a strong representation of female consumers has been gathered
in this research. Women are in majority of all media with a representation of 50.9% to 66.5% of all
participants in the survey. Female consumers account for two thirds of the sample in the media of
catalogues and classifieds, but only account for a few percent more than the majority in the media
of radio and out-of-home advertising. Also in the general sample the female consumers account for
nearly two thirds of the sample, and thus the results achieved in the analysis will have a tendency to
reflect female consumers’ opinions and perceptions more than those of the male consumers, except
for the results pertaining to radio and out-of-home (OOH) advertising.
The second demographic variable that is included in the survey is the age of the consumer, and it is
divided into three age-groups, the 18-34 year-old, the 35-54 year-old and the 55-65 year-old
consumers. The mid-aged group of 35-54 year-old consumers is represented the strongest in all
media with a participation share of 41.5% to 50.5%. This means that aside from having the
strongest representation in all media, this group holds an absolute majority in the OOH (50.1%) and
the catalogue media (50.5%). The young adult group of 18-34 year-olds have a significantly higher
level of representation than the more senior group (55-65 year-olds) with a share of 32% to 39.2%
of the sample in each medium. Roughly, this is how the representation division looks between the
three age groups: 18-34 year-olds account for 37%, 35-54 year-olds account for 47%, and 55-65
year-olds account for 16%. Thus the results achieved in the analysis will have a tendency to reflect
the younger mid-aged consumers’ opinions and perceptions more than those of the more senior
consumers. The medium sample that should reflect the most balanced results is that of television,
with a near 40/40/20 percentage division between the age groups.
The third demographic variable in this survey is the level of education the consumers have, and it is
divided into two groups, those of the participants that have a college degree and those who do not
have a college degree. The overall representation of consumers who do not have a college degree is
much stronger than the group of college graduates, and they account for nearly two-thirds of the
samples on average (64.8% if each sample is considered of even value), with the college graduate
group accounting for slightly more than one-third (35.2% by the same measurement as above).
59
Exactly how many Americans hold a college degree is not part of this researcher’s knowledge, but
the figures may not be an imbalanced representation of the average American consumer. The most
evenly split sample of education is found in the medium of out-of-home advertising, where
consumers with a college degree account for 40.2% of the survey sample. The most uneven division
is found in the medium of classified advertising, where the college graduate participants account for
just over one-fourth (26.1%) of the sample. This means that the results of the analysis should reflect
the opinions and perceptions of the less well-educated consumers more than those of the consumers
with college degrees.
The fourth and final demographic variable included in this survey is that of income. As with the
other aspects (aside from age) this parameter is divided into two groups, consumers with an annual
income below $35.000 (US dollars) and consumers with an income exceeding $35.000. Looking at
the division and representation of each group, it seems quite clear that either Shavitt et al. or Speck
and Elliot have an imbalanced and unrepresentative sample of participants in this category of
aspects. In Speck and Elliot’s survey the groups that have an income of less than $35.000 account
for nearly 55% of the sample75, whereas that collective group in Shavitt et al.’s survey barely
account for more than 40% of the sample, a remarkably noticeable difference considering the fact
that a strived balanced representation of the average consumer should be a priority of the
researchers in the fields of advertising and marketing. I acknowledge the fact that the seven years
from 1997 (the year of Speck and Elliot’s survey) to 2004 (the year of Shavitt et al.’s survey) will
mean that more consumers will earn above $35.000, but the increased percentage still seems high,
and if the income had increased that much, perhaps the division should have been placed at a higher
income level. Looking at the figures for each medium, the division is fairly close to the same across
the media, with a near 60/40 split in all media, with the exceptions of classifieds and OOH (out-ofhome) that have splits of 47/53 and 36/64 respectively.
I have already touched upon the subject, and I will now look at the strengths and weaknesses of the
two surveys (Speck & Elliot and Shavitt et al.) compared to each other, before moving on to the
analytical figures.
75
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 66,
1997.
60
Strengths and weaknesses of the two surveys
Shavitt et al.’s survey has a weakness in its division of the participants in the professors’ survey,
because they make so few groups (two for all parameters except age (three groups for age). This
makes it hard to target a more narrowly specified group and see what triggers avoidance behaviour
for these consumers compared to Speck and Elliot’s survey, where the participants are divided into
five groups for age and education, and no less than seven groups for income. The problem or
weakness for Speck and Elliot’s work is then that they do not really make enough use of their data
sample. They simply measure if an increase in the parameter (increased income, being older or
having a higher level of education) triggers an increase or decrease in avoidance behaviour. An
analysis that is sufficient to show tendencies and estimates in many cases, but for many advertisers
it could prove much too unspecific to use in advertising targeting, as they would probably like to
see how a more finely isolated group of consumers react.
It is precisely the strength of Shavitt et al.’s survey that it analyses specifically how each group
react (avoidance-wise) compared to the others, rather than just whether an increase in the parameter
will also lead to an increase of avoidance behaviour. Ideally, a mix of the two survey processes
could have been golden to this thesis as the many finely divided groups of Speck and Elliot, and the
analytical layout of Shavitt et al. could very likely have given the advertisers and this thesis a much
“sharper” image of how the consumer respond to advertising, and what triggers advertising
avoidance strategies.
3.2.5. Shavitt et al., attitudes towards advertising based on demographics
As the final section of the analysis chapter in this thesis, I will now treat and analyse the survey
figures for the work of Shavitt et al. Looking at the analytical results that Shavitt, Vargas and
Lowrey arrive at, they have shown the impact of the four demographic variables included in the
survey on the consumers’ attitudes towards advertising in each medium (which is central to the
theories for triggering avoidance behaviour) in percentages, either with a minus indicating a
decrease in the factor based on the demographic group in the variable, or with a plus indicating an
increase on same terms. The figures that the demographic regression table 4 (see figures 12 and 13)
61
regulates and shows the demographic impact on are those of table 3 (see figure 10), which analyse
the overall attitude towards advertising in each medium, and the factors indicating these perceptions
and attitudes are thus the same in both tables (all three figures, 10, 12 and 13). To help fellow
researchers and other interested readers, the group of professors have marked significant differences
between the demographic groups by surrounding boxes. One box-marked figure means that the
figure is significantly different from the other figure(s) in the same factor and demographic
variable. Two-box marked figures mean that the figures are significantly different from each other,
but not necessarily from the third figure (only applicable for the age sample).
Exactly how Shavitt, Vargas and Lowrey determine what level of difference constitutes a
significant difference that warrants a box-marking in their tables is not easily discovered, they do
not clearly specify how they evaluate the differences and thus mark the figures that apply. One
thing is clear though, it is not the same for all figures, and/or it is not based solely on the percentage
difference between the figures of the same factor in the same demographic variable. As an example
of this situation the reader could look at figure 12 (see the next page), the overall collective figures
section (called all samples) that gathers all samples (top column of figure 12) and notice factor two:
Harmfulness/offensiveness in the demographic variable of gender. The two figures are both marked
with boxes, indicating that they are significantly different from each other. The difference amounts
to 8% (from +5% to -3%). In the same column (The all samples section), but in the overall attitude
towards advertising row in the demographic variable of income, a similar difference can be found,
but without any box-markings. The difference in these figures also amounts to 8% (from +8% to
0%).
For the reasons stated above and the transparency of the argumentation in this thesis, I will not base
any analytical conclusions on the box-markings as the system is not clearly defined, instead I will
go through the figures and comment on the impact of the demographic variables, their groups and
the factors that signify an important variation in relation to advertising avoidance, based on
percentage differences, and conclude on the possible changes in the media vehicles that could
possibly lead to reduced avoidance behaviour in accordance with the figures of table 4 (figures 12
and 13).
62
Figure 12: Analytical demographic results (part 1)76
76
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1024, 2004.
63
Figure 13: Analytical demographic results (part 2)77
77
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1025, 2004.
64
When comparing this demographic part of the survey to that of Speck and Elliot78 the aspects that
are investigated here are comparable to those in the “perceptions of advertising in each medium”
section, except this survey focuses solely on these aspects, but in return elaborates far more on them
and generate more data, which could lead to a more in-depth understanding of these aspects. This
also means that this survey contains far too many figures to elaborate on each of them without
taking up the space of an entire thesis on its’ own. Therefore, I will go through the figures of the
collective sum of samples, the all samples section in detail, and afterwards go through the overall
attitude toward advertising rows of each medium and demographic variable, which should give a
good collective image of “the state of the affairs” in this part of advertising avoidance.
The first two samples represent the collection of media, the first representing the collective sum of
all media and the second representing the average sum of all media. Hereafter the reader will find
the sample figures for each of the five included media.
Looking at the first sample that is the “all samples” collective sum of all media samples, it should
be possible to obtain a view of the overall “attitude towards advertising in the media” situation. The
figures in the overall attitude row for the “all samples” indicate that the gender of the consumer is
not very decisive in advertising avoidance on the basis of advertising attitudes, as they fluctuate by
only 2-3%, female consumers being slightly more positive (+2%) and male consumers slightly
more negative (-3%). The gender of the consumer seems to play an even smaller part in the factor
of usefulness with a fluctuation of only one percent for either gender, again females being a bit
more positive and males more negative. In the factor of harmfulness the fluctuation is slightly
bigger, again with male consumers being more negative (bear in mind that a positive percentage is
“negative” in factors that are negative, as they indicate increase in perception of that factor).
Looking down through all the factors (trustworthiness, government regulation and cost in addition
to the mentioned) the analysis must be the same. Male consumers have a more negative attitude
towards advertising overall than female consumers. The fluctuations would not be characterised by
78
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 71,
1997.
65
most researchers as enormous, but they are certainly noticeable. Especially for the factor of
government regulation in which male consumers have a 13% increased perception of too much
governmental regulation of advertisements, where female consumers have an 8% decreased
perception. In all factors and overall consumers have an increased negative perception of
advertising if they are male and a positive perception if they are female (compared to the average
prior to demographic influence).
Staying in the “all samples” section, it would appear that age is a much more influential
demographic variable, but unlike my prior research and analysis that indicated that older consumers
have a tendency to be negative in their attitude towards advertising in most media, this analysis
shows a rather mixed share of positivity and negativity between the youngest and oldest age groups.
All the figures (except for the government regulation factor) show an increased percentage in
perception of each factor for the youngest age group, going to a decrease for mid-aged group, and
highly decreased percentage for the oldest group. This means that the 18-34 year-old consumers
both see advertising in media overall as more useful and more harmful than do the more senior age
groups. The government regulation factor is the only factor showing a contrary tendency with the
two younger age groups having a slightly decreased perception of government regulation (by just
1%), whereas the oldest age group have a notably increased perception (by 7%). The factor of
advertising cost is probably the most surprising and possibly mind-boggling. It shows that the
youngest age group perceive (increased by 11%) the advertising cost to be too high, whereas the
mid-aged group perceive (decreased by 8%) the advertising cost to be too high to a far lesser extent.
The puzzling side to this tendency is however that the oldest age-group have an uninfluenced
perception (which means a much more positive perception than the youngest age group, but a much
more negative perception than the mid-aged group). What makes the consumers negative in
attitude, then positive and then back to neutral is certainly mysterious considering the more “oneway” tendencies previously analysed in this thesis for the influence on avoidance and attitude of the
age variable. The one thing that more or less all analytical material applied in this thesis suggests is
that age is a central and highly influential parameter/variable. This analysis is no different as
fluctuations of the age variable exceed the fluctuations of all other variables in the overall sample.
Most of all in factor 2, harmfulness that has a fluctuation of a massive 32% (from 19% to -13%).
66
The educational level (still for the “all samples”) of the consumer appears to play a role that is less
significant than that of the age variable, yet a more significant role than that of the gender variable.
As could be expected on the basis of the former analysis done in this thesis concerning the variable
of education, overall the well-educated consumers have a far more negative attitude toward
advertising in the media going from a 6% positive to a 10% negative fluctuation (when going from
non-graduate to college graduate). The well-educated segment of the consumers also find the
advertisements less useful and informative than the consumers with a less academic background,
which again goes well in line with the earlier achieved results. The well-educated consumers also
tend to consider government regulation slightly excessive, which is not the case for the nongraduates. Consumers with college degrees also consider the costs of advertising to be much too
high, and the fluctuation of this factor is nearly as high as is the overall attitude toward advertising
going from 5% to -10%. Despite the many results falling in line with the assumptions that could
easily be made based on the results thus far, two factors fall out of line and form a slightly
moderated image of the influence of the educational level of the consumers. Seemingly, the welleducated segment of the consumers in this survey find the advertisements less harmful and slightly
more trustworthy. Although the fluctuations in these two factors are lower than in most of the
factors pointing to the previously uncontested conclusion, they still account for enough to be made a
point of. That point is that although well-educated consumers tend to be more critical and possibly
negative towards advertising, it is possible to gain their trust and avoid offending these consumers.
These results lead me to believe that the message of the advertisements is essential as the theories
suggest in preventing avoidance behaviour. Offensiveness and trustworthiness are two keywords in
theories involving the message of advertisements such as the ELM model79 that clearly states the
importance of choosing the right message to achieve credibility and trustworthiness, and avoid
offending the target audience. The overall analytical observation must still be that it takes more to
prevent the avoidance behaviour of the well-educated consumers than it does to prevent the
avoidance behaviour of the less academic consumers.
The fourth and last demographic variable is that of income. The all samples figures for the
demographic variable of income indicate that income is not the most influential or significant
variable, when it comes to attitudes and avoidance behaviour. The fluctuations are somewhat on par
79
Percy, L. & Elliot, R., ”Strategic advertising management”, Oxford University Press, p. 208-209, 2009.
67
with the variable of gender, perhaps even slightly less marked. Overall the consumers earning more
than $35.000 seemingly have a neutral attitude toward advertising, whereas those consumers
earning less have slightly positive attitude (increase of 8%). A very similar result is seen in the
factor of usefulness/informativeness, the only difference being that the consumers earning less than
$35.000 have an increased positive attitude by 7% instead of 8%. The picture is nearly the same for
the factor of harmfulness/offensiveness, where the first slight indication of above average negativity
is seen, although only by 1% (belonging to the +$35.000 income group). Trustworthiness is the first
of only two factors that see a positive response from the higher income group with increase of 2%,
the lower income group lands on a 4% increase. The government regulation factor is the second
factor that produces a positive response from the higher income group, albeit with just a single
percent. This is also the only factor in the entire sample that marks completely even results between
the two groups, meaning that the demographic variable of income does not have any influence on
the consumers’ perception of government regulation on advertising overall, according to this
survey. The last factor, cost (of advertising) is the only factor with a fluctuation greater than 10%
between the two groups and it shows the same pattern as the whole column, but more distinctly.
That pattern is that negativity in attitude toward advertising increases with higher income, here the
difference is 15% (going from 10% to -5%).
I will analyse the results of the samples of each medium, but as explained earlier due to time and
space constraints I will not look at each factor and every figure, but focus on the overall attitude
toward advertising and thus the impact on avoidance behaviour for each medium and demographic
variable, as this is the gathering factor of the analytical figures, which should prove the best basis
for analysis.
I will start with the medium of television and proceed chronology in terms of the table (see figures
12 and 13). Starting with television the overall attitude toward advertising factor provides both
significant and insignificant differences between the demographic groups. Gender, unlike in the all
samples figures, seems to play a fairly significant role. Male consumers appear to be more negative
in their attitude (by a margin of 9%) than female consumers, although both genders are overall
negative. Age could easily be characterised as having even greater impact with a massively
68
increasing negativity with higher age, going from 1% positivity to 46% and 58% negativity for two
oldest groups. This is one of the biggest differences found in the entire survey. Education does not
appear to impact the consumers’ attitude toward advertising significantly, yet the well-educated
consumers are slightly more negative. The same pattern is seen in the variable of income, nearly the
same negativity with a slight increase for those earning more money.
Advertising in the catalogue medium is viewed upon more positively than in television, which is
not that surprising seeing as consumers of catalogues are browsing them for the exact reason of
seeing advertisements and products. Still, the differences in overall attitude of the demographic
group are interesting. Female consumers are more positive about catalogue advertisements than
their male counterparts. Mid-aged and young consumers are almost evenly positive, whereas the
senior age-group is even more positive about the advertisements. Consumers without a college
degree are much more positive than those who have graduated from college. Consumers with higher
incomes are also significantly more positive about catalogue advertising than the lower income
group consumers.
Classified advertising is also looked upon with kind eyes by the consumers. Looking at the overall
attitude toward advertising it seems that gender is not the most decisive demographic variable with
a fluctuation of just 5%, with male consumers being the slightly more positive group. The age of the
consumer appears to have a much more significant impact on the consumers’ attitude toward
advertising, the fluctuation is a very noteworthy 36%. The positive attitude toward advertising in
classifieds decrease with higher age, thus the risk of avoidance behaviour is the highest among the
oldest of the three age groups. Age is by far the most decisive demographic variable in the medium
of classifieds, as all the other variables display fluctuations that together do not even amount to half
that of the age variable. Education does not play an important role it seems, with both non-graduates
and college graduates being nearly evenly positive overall, the college graduates are just 2% more
positive than the non-graduates. Aside from the age variable, income seemingly plays the biggest
part (demographically speaking), as the fluctuation is 8% with the higher income group being more
positive than the lower income group.
69
Advertising in both broadcast media (television and radio) are in the overall aspect burdened with a
negative attitude by the consumers. The radio sample displays negative figures in every
demographic variable just as television did (with the minor exception of 18-34 year-olds). In radio
advertising the gender of the consumer does not appear to significantly impact the overall attitude,
with a minor fluctuation of just 2%, the female consumers being slightly more negative than the
male ditto. As with so many other media, age plays a part and the older the consumer becomes the
higher the tendency to be negative is. The negativity increases one-sidedly as the consumer gets
older, although with a margin of just 8%, it is not as extreme an increase as in many other media
(e.g. television). In the radio sample education plays a bigger role than age does, the fluctuation is a
full 18% of increased negativity, and shows (as many other analyses have) that the well-educated
group is quite a bit more critical and negative toward advertising. The income variable shows a
fluctuation of 9%, nearly the same as the age variable did. A 9% increase in negativity is found in
the higher income group, meaning that higher income leads to an increased negative attitude toward
radio advertising.
The out-of-home sample is the only sample, where gender plays no role at all. Both genders have a
positive attitude (8% increased from average). Age plays a major part in this medium and the
impact of age differences is massive. The fluctuation is a staggering 41%, going from 31% to -10%,
and again with the older group being the most negative, the mid-aged group second-most negative,
and the youngest group being the most positive (and here the only positive group). Clearly,
demographics is one of the most important and influential aspects of advertising avoidance in this
medium, as the fluctuation of the education variable is even bigger than that of the age variable,
amounting to 45%, with the well-educated consumers being strongly negatively (-19%) and the less
educated consumers being strongly positive (26%). The income variable also plays an important
part and shows off a highly noticeable fluctuation of 29% between the two income groups. The
higher income group is again the more negative group, thus making it difficult to advertise
expensive products to those who can afford it.
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4. Discussion
This chapter will go through the analysed results of the surveys, compare them and discuss the
achieved results and what they mean in relation to the main hypothesis of this thesis, namely
whether my expectations for the possibilities of lowering advertising avoidance without reducing
the amount of advertising actually hold up, to what degree, and what expectations advertisers could
and should have on this background, and perhaps most importantly (seen from an advertiser’s
viewpoint) exactly how it is then possible to lower advertising avoidance. Thus in short this chapter
will compare theories and analytical results and form a basis for the conclusion, which will follow
this chapter as the final chapter with remarks and an evaluation of this thesis. I will also in short
comment on the possible impact of the limitations of this thesis, and how they may have altered the
conclusions and results for this work.
4.1. Three theories on the structure of advertising clutter and avoidance
In my research I found three academically acceptable (originating from trustworthy sources)
theories including models for how advertising avoidance (and clutter) is structured and could be
perceived. These three theories are those of professors Ha and McCann80, who focus on an aspect
leading to advertising avoidance, namely advertising clutter, but advertising avoidance is still a
central aspect of their theory. The theories and model professors Cho and Cheon81, who have made
a hypothesized model of advertising avoidance based on their own analytical results82 that advocate
for their structure. Lastly, the theories and model of professors Speck and Elliot83 is part of my
theoretical framework, and they too base their structure of advertising avoidance on their own
analytical results that have been treated in detail in the analysis chapter of this thesis.
The reasoning behind selecting to treat all three theories on advertising avoidance is that the
concept that advertising avoidance is a result of, advertising clutter is not clearly defined. The
80
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 575, 2008.
81
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 93, 2004.
82
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 92, 2004.
83
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997.
71
structure of both advertising clutter and avoidance is debatable, and to illustrate the opaqueness of
these concepts that are essential to answering my hypothesis, I had to incorporate all theories and
models for comparative analysis. As I mentioned in the introduction of this thesis advertising clutter
is seen as a large amount of non-editorial content in a media vehicle, and when the amount exceeds
the tolerance of the receivers (consumers as described in the analysis) it is perceived as clutter84.
This definition belongs to the classical view that only embodies the structural aspect of quantity of
advertising, and as both the analytical results and the theoretical frameworks indicate and
substantiate, this definition is too one-sided, and lacks some aspects that this thesis has more than
amply demonstrated play an important part in leading to and being part of advertising clutter and
avoidance.
The pressing question then becomes, since it is now clear that advertising clutter leads to
advertising avoidance, and that much more than just the quantity of advertising play a part, which
theory is then accurate, because the theories (and the analyses) that portrayed the classical definition
as insufficient and possibly misleading are all different. The short and cryptic answer is all of them
and none of them. All of them, because they all incorporate aspects that can be proven (or certainly
strongly indicated) to have an impact on advertising avoidance. None of them, because they all lack
aspects that each of the others incorporate, so irrespective of which of them is the most accurate,
none of them are completely accurate as they each lack aspects that can be proven to have an
impact.
Analyses treated in this thesis have shown in several figures that demographic variables have a
major impact on attitude towards advertising and on tendencies to avoid advertising. In that regard
Speck and Elliot’s model has one of their four avoidance predicting aspects proven accurate, not
necessarily in the structural set-up, but in the fact that it is correctly included in an avoidance
model. The same reasoning leads to considering (atleast some of) the functional approach of Ha and
McCann’s model to be correct, again not necessarily in placement and structure of the elements, but
in the inclusion of the aspect seeing as attitude towards advertising both in general and as a media
dependent element belongs to the professors’ functional approach aspect.
84
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 570, 2008.
72
On the basis of both Shavitt et al.85 and Speck and Elliot’s86 surveys not only is demographic
variables substantiated to be of importance in relation to advertising avoidance, but also mediarelated variables, as the attitude of the same demographic group changes with the different media.
The same goes for the advertising perceptions aspect, which is also included in Speck and Elliot’s
model. This aspect corresponds along with media-related variables to some degree to the functional
approach aspect included in Ha and McCann’s model, which is thus also vouched for seen from this
perspective. As is seen in Speck and Elliot’s survey factors such as believability, interest and
“wastes my time” also have a say, and these three factors are closely related to the message of the
advertising, which if communicated poorly or inappropriately will lead to a negative attitude and
possibly avoidance behaviour. Thus communication problems in Speck and Elliot’s model, the
informational approach in Ha and McCann’s model and parts of the perceived goal impediment and
prior negative experiences aspects in Cho and Cheon’s model all have legitimacy in being
represented in a model of advertising avoidance, as they all have a certain impact on avoidance
behaviour. Shavitt et al.’s two mentioned aspects fall under this argument, because all six elements
of these two aspects could be the result of communication problems or ineffective messaging in the
advertisements.
The reason why I do not separate these elements in greater detail is that although they are clearly
affected by the survey results of my analysis, it is a near impossible task to split the results up and
say how much of the negative attitude is due to search hindrance, and how of the search hindrance
is due to poorly constructed messages in the advertisements, just to give an example. Even if a
researcher were ask these extremely detailed questions and make a massive analytical figure of it,
the consumers would likely not be able to answer for atleast two reasons: 1. They cannot remember
exactly what triggered their desire to avoid advertising or what prevented them from finding the
material they needed (goal impediment, subsequently search hindrance) in the long series of
experiences that amount to their perceptions of advertising in the specific medium and the specific
media vehicle. 2. Even if they could remember what they perceived as the problem(s) in each case
where they encountered them, they would most likely not link them with the concepts or
85
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1024-1025, 2004.
86
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70-71,
1997.
73
terminology that the researcher(s) behind the survey would present them to, and as this thesis have
shown multiple terms have been used for nearly the same concepts by the researchers. In other
words, not even the researchers are sure exactly how to define each aspect and what elements to
include in them, so how should “regular” consumers be aware of all these terms and know what
they contain even before being asked by the researcher of a survey?
With this situation in mind, it is not so surprising that this thesis is not able to present and analyse
surveys that take the more precise elements of each aspect into account, but rather stay on a more
general level representing the outlining aspects of advertising avoidance and analysing them, which
will still indicate which parts of the theoretical models that carry an importance and thus deserve to
be represented in a model of advertising avoidance.
Going back to the discussion of which theory and model is the most accurate, I’ll make a
clarification of which aspects of which theories I can certainly account for being justified through
the analysis conveyed in this thesis.
Starting with the model and theories of Speck and Elliot87, I can substantiate and point out the
importance directly or indirectly of all four aspects that in their model lead to advertising avoidance,
some of them I have made an example of, and some not. Demographic variables and media-related
variables were very central aspects of the surveys I have analysed, and it was throughout the
analysis possible to point out an influence of these variables on attitude towards the medium and
advertising in general, as well as the changes that occurred for the demographics when the medium
was switched and vice versa. Although in advance it would have been nearly self-evident to most
people that advertising perceptions have an influence on advertising avoidance, as in itself the way
consumers view advertising impact the desire to consume or avoid advertising, it is still an aspect I
could point out carried an impact if necessary to substantiate that claim. This claim was
substantiated when I analysed the impact of attitude toward advertising in general, and the
difference it made when other factors were changed on this aspect. That part of the analysis belongs
87
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 63,
1997.
74
to Speck and Elliot’s survey. The only aspect that requires a bit more (although not much) indirect
substantiation is that of communication problems. Despite not having figures that directly indicate
dissatisfaction on the part of the consumer based on communication problems, the surveys
displayed figures for interest, trustworthiness, believability, perception of time wasted etc. aspects
that all more or less directly could be linked to communicational mismatches or offensiveness,
whether the messages simply bored the consumer or prevented him/her from obtaining information
that he/she desired. The more specific separation and evaluation of the individual elements is not
possible, but showing that the aspect carries an impact on advertising avoidance and attitude toward
the medium and advertising is accomplished with the figures of the surveys.
Four aspects that all are validated as being correctly implemented and leading to advertising
avoidance, does that mean Speck and Elliot broke the “enigma code” and found the answer to the
structure of advertising avoidance? No, multiple problems could be argued for about this model.
First of all there is no indication as with the other models in what sequence they occur and what
elements more specifically are included in each aspect, they simply all point from the same position
simultaneously to advertising avoidance, and that is all things considered clearly not correct.
Naturally some aspects influence avoidance at the same time, such as demographic variables and
media-related variables, because the demographic group constellation of the consumer and the
medium being consumed is a prerequisite on the same level, before the tendency or desire for
avoidance can emerge in the first place, but what about prior negative experiences, what about
perception of “clutteredness”, aspects that clearly require a past input or future observation that
have an impact on the overall perception leading to the accumulated tendency to trigger an
avoidance behaviour? These central aspects that can also be substantiated are not even included,
and they indicate a need for a sequential model structure, as i.e. the perception of clutter cannot be
attained before consuming the advertisements in a medium, unlike the demographic variables that
are a prerequisite for that perception even before beginning consumption.
It is the major problem associated with the model of Speck and Elliot not only that important
aspects are lacking, but that the lack of these aspect indicating a sequential structure, leaves Speck
and Elliot with a somewhat unordered mass of effects and aspects all leading to avoidance
75
simultaneously. This does not necessarily make their model the least accurate as the other models
have their flaws and problematic sides too.
Looking at the theories and model of Ha and McCann88, it contains three aspects leading to
perceived advertising clutter the sole aspect that these professors see as leading to avoidance
behaviour, among other things, as they call it the overall impact of advertising clutter. Thus with
this circle-definition that advertising clutter leads to the overall impact of advertising clutter they
avoid treading on thin ice all together, as it is apparent that the impact of advertising clutter must be
the result of advertising clutter. It could be argued that the major problem of this model is that it is
overly cautious and avoids many “traps” by not treating the more uncertain aspects of advertising
clutter and avoidance at all. They do not include aspects such as perceived goal impediment and
prior negative experiences, which have been substantiated as being influential aspects. Search
hindrances or perceived lack of utility or incentive have been pointed out in my analysis to have an
effect on attitude toward advertising, which does impact perceived advertising clutter. The “wastes
my time” factor of Speck and Elliot’s survey is to a wide extent linked to the elements of negative
experiences called lack of utility and incentive. If the consumer perceives the advertisement as a
waste of time, he/she will see it as both a lack of utility (i.e. he/she has no use for it) and a lack of
incentive (i.e. “there’s nothing in it for me as a consumer”).
The three aspects included in their model that leads to perceived advertising clutter can all be
substantiated to be rightfully included (again, I am not saying the structure is accurate). The
functional approach of which the main element is attitude towards advertising in general and
towards the different media is an essential and central part of the survey of Shavitt et al. that deals
almost exclusively with advertising and media attitudes based on demographic variables. Thus the
majority of that survey and that part of my analysis connected to this survey will testify to the
importance of including such an aspect in a model of advertising avoidance. As I have explained on
multiple occasions earlier both in this chapter and the preceding chapter, the analysis also
substantiated the need for an aspect of informational processing regarding the messages in the
advertisements and their impact on the consumers’ attitudes towards advertising and the medium
88
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 575, 2008.
76
leading to avoidance behaviour when reaching a certain negative level. Thus the informational
processing approach should also be considered by most researchers as an appropriate aspect to
include in a model of avoidance and/or clutter.
As it would not very likely serve to answer my hypothesis of reducing advertising avoidance
without lowering the amount of advertising, I did not in my analysis include materials that could
point to the impact of structural elements, which includes the quantity of advertising. Thus I have
no substantiation in my own material for the need to include the structural approach in a model, but
as many surveys have shown that have been formed on the basis of the classical definition of
advertising clutter, the quantity of advertisements do have an impact on advertising clutter and
avoidance behaviour. An example of this could be the survey of Burst Media from 2004 that in
chart 2 shows that if a site is cluttered with advertisements (an excessive amount) the consumers
initiate an avoidance strategy of either leaving (behavioural) or paying less attention (cognitive).
77
Figure 14: Burst media survey of selective attention towards advertising on cluttered sites89
This means that just as with the model of Speck and Elliot the model of Ha and McCann can be
argued for in all its’ aspects, but as mentioned earlier, this model is in the opinion of this researcher
too withholding and lacking many important aspects that many would consider essential parts of
such a theory and model. The strength of this model, however, is then that it does portray a
sequential structure with the three approaches first leading to advertising clutter, which then in turn
leads to advertising avoidance and other impacts of advertising clutter. To discuss which of the two
models is the most accurate would be difficult as they both lack important aspects that the other
model includes, and the structure of advertising avoidance is difficult to conclude on, and at best
arguable.
89
http://www.burstmedia.com/pdf/2004_05_01.pdf
78
Looking at the last of the three theories and models those of Cho and Cheon90, their model is
perhaps the most detailed and well-structured model. It has a symmetrical structure much like that
of Ha and McCann, but it is probably more explicit about the contents of the aspects as Cho and
Cheon in their model visually separate the elements contained in the aspects from the aspect box
and connect them with arrows. Their model is the only model that explains the elements contained
in perceived advertising clutter, but this could be explained by Ha and McCann’s perception of all
three approaches/aspects leading to perceived advertising clutter, and as such the elements of each
approach could be considered to be the elements contained in advertising clutter itself. Like Speck
and Elliot they believe that multiple aspects lead directly to advertising avoidance, including
advertising clutter, which is the sole perceived entry-point to avoidance in Ha and McCann’s model.
With this in mind the model of Cho and Cheon is quite similar in this structural layout to that of Ha
and McCann. If the element boxes were not attached to perceived goal impediment and prior
negative experiences, but all led to perceived advertising clutter, their model would more than
halfway be the same. So although the models are different and that the aspects are given somewhat
dissimilar names, it is not a strange idea to think that the teams of professors had many of the same
ideas and probably concur with each other in much of the structure of such a model. On the other
hand the very thing that this model (that of Cho and Cheon) does have multiple entry-points
(triggering aspects) for advertising avoidance gives a high degree of similarity to the model of
Speck and Elliot, as mentioned earlier.
What makes me sympathise with this model is that it in many ways merges the two other models
into one and extends into what their analytical results made them believe on the basis of confirmed
or rejected hypotheses regarding advertising clutter. Theoretically and analytically it is a model that
is certainly based on a strong foundation, and it includes an explanation and explicitness of the
advertising avoidance aspect itself that is unparalleled by the other models with its definition of the
three avoidance strategy types. Regrettably, I still do not believe that a correct model of advertising
avoidance has been made, and it is certainly not this model, despite all good features. It could very
well be that this model too has its’ grave flaws.
90
Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising, p. 93, 2004.
79
Structurally, it could be substantiated that the elements belonging to the two other aspects leading to
advertising avoidance (perceived goal impediment and prior negative experiences) also belong to or
lead to perceived advertising clutter, as Ha and McCann would claim in accordance with their
model. Disruption, search hindrance and certainly distraction are linked to the perception of clutter.
If a media vehicle is perceived to be cluttered, it could also distract the consumer or hinder his/her
search. This is correctly seen as a goal impediment, but it could also be the result of clutter that
makes a search hindered. The same goes for prior negative experiences, dissatisfaction could very
well be a result of a media vehicle being too cluttered for the tolerance of the consumer. Based on
my research and findings, I would probably have considered this model more accurate if arrows had
led from perceived goal impediment and prior negative experiences to perceived advertising clutter
in addition to leading to advertising avoidance, as the aspects both independently and dependently
could lead to an avoidance strategy being triggered.
In addition to the aforementioned flaws, the model of Cho and Cheon has a lack of included
aspects. Although it has been time and time again substantiated that the structure of advertising,
primarily quantity, has an impact on perceived advertising clutter, no elements or aspects are
connected to the perceived advertising clutter aspect in their model to indicate this relation. Only
the element of excessiveness shows that the professors did consider the amount of advertising to a
valuable element to include. Whether it was the fear of indicating the importance of the classical
view of advertising clutter that stayed their hands or something else cannot be said here, but clearly
they have not emphasized the importance of the aspects belonging to advertising clutter to remotely
the same degree as have Ha and McCann.
In conclusion, whether it is possible or not to construct a perfect single completely accurate all
purpose model of advertising avoidance I do not know. It is certainly no easy task, as the structure
of this concept is highly debatable and very difficult to prove. Also the many aspects and
possibilities for yet unaccounted aspects make the work a harrowing one. None of the models are
perfect, yet neither of the three is without usability in the research of advertising avoidance, as they
each bring important aspects with them that can be substantiated.
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4.2. Discussion of the hypothesis and the analytical results
Before concluding on all my findings and gathering all the treads into the main thread in my
conclusion of this thesis, its aspects and my hypothesis, this section will be a discussion of the
analytical results and the surveys’ impact on my hypothesis that formed the basis for this thesis.
Given the size and nature of a master thesis compared to the vastness of the topic and field of
advertising avoidance, it was necessary to strictly limit the research to a few central aspects and
areas in which I believed that I could find an answer to my hypothesis and hopefully prove my
expectations to be accurate. I cannot say that I have substantiated that all my expectations are
accurate, as I could not include surveys for all aspects that I expected would be of significance and
properly analyse them and treat them in this thesis, as there was simply not the time nor the space to
do so. This means e.g. that I would not at this point claim that I have fully proven that all Ha and
McCann’s approaches91 in their model are of significance, as I had to prioritise aspects that could
possibly lead to help answering my hypothesis. Thus the more explicit and detailed significance of
primarily the structural approach has not been discovered and explained in this thesis. However, it
was pointed out that the structural approach did have an impact, as the survey of Speck and Elliot92
will testify to. Excessiveness, which belongs to the structural approach dealing with the quantity of
advertising, can be seen to have a noticeable impact, and especially in television the excessiveness
(i.e. the amount) of advertising seems to be a major problem rated at 6 in 7 possible points on the
Likert scale by the consumers.
Although I was able to establish that all three approaches (functional, informational processing and
structural) had an impact on advertising avoidance, as I explained earlier in this chapter, a more
specific division of importance between the three still seems difficult to figure out. It was my
expectation from the beginning of this thesis that the approaches would not impact advertising
avoidance to the same extent thus that one approach would be more important and impact
avoidance behaviour more than the others, despite my results, I cannot make a qualified estimate of
91
Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”, International Journal
of Advertising, p. 575, 2008.
92
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70,
1997.
81
which aspect that is, because some other aspects influenced the results at the same time. This means
that e.g. Speck and Elliot’s survey table 3 (see figure 6) show impacts for multiple approaches with
the same or nearly the same influence. An example could be the aspect of perceptions of advertising
in each medium (attitudes toward and perceptions of media and advertising belong to the functional
approach) where an element such as “believable” is also linked to the informational processing
approach, because believability or trustworthiness often has to do with the advertised message.
Thus irrespective of what the result is, it can and should be linked to both approaches, making it
difficult to assess the importance of either, as they are so closely related. If any conclusion had to be
made from this, it could very well be that all approaches individually and together have such an
impact that irrespective of which one is the most influential, all should be regarded with the same
caution and attention. This would be easier to substantiate as all approaches (with the slight
exception perhaps of the structural approach) have been tested in the surveys and show that they all
influence the perceptions and attitudes of the consumers, which in the end leads to advertising
avoidance.
The main hypothesis of this thesis itself should naturally be discussed, and in this aspect, which is
the main and most important aspect (as the foundation and reason for this thesis) it is my conviction
that I have been able to establish a strong possibility of advertising clutter and avoidance being far
more than just the result of the quantity of advertisements. The three theories of the professors Ha
and McCann, Speck and Elliot, and Cho and Cheon all lead to believe that advertising avoidance is
possible to reduce without reducing the amount of advertisements. Despite their strong differences,
they all have a structure that indicates that advertising avoidance is dependent on other variables
than the feeling the consumers have of information overload or excessiveness. The surveys based
on these theories, and those that are not, all indicate that the consumers’ attitudes towards
advertising in general and the medium that conveys the advertising messages can be affected and
influenced in a positive direction seen from the perspective of an advertiser. As the theories have all
suggested that the attitude and perception of the consumer is the key to whether he/she will use an
avoidance strategy, this thesis can conclude that if the attitude and perception of the consumer can
be made more positive, the chance of an avoidance behaviour decreases, which is exactly what my
hypothesis is about, proving that other factors than quantity can lead to reduced advertising
avoidance.
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Alone the results of the surveys regarding the demographic variables more than strongly suggest
that the consumers’ attitude is affected by his/her age, gender, income and education, and their
views based on these demographics changes with the medium the advertisements are channelled
through. Thus alone targeting the audience through the appropriate medium should lead to reduced
advertising avoidance. The survey of Shavitt, Vargas and Lowrey93 substantiates that claim. The
overall attitude towards advertising in the television sample shows that young female consumers
respond much more positively in general to television advertising than mid-aged or older male
consumers. Thus choosing this medium to reach a young female target audience should help to
reduce advertising avoidance. Other media display different possibilities, for instance, if targeting a
female senior audience, it appears that catalogues is a more suitable medium, as the figures show
older female consumers to be the least negative in their attitude. Yet again other media suit different
target audiences. Speck and Elliot have based on their survey results, as shown in the analytical
chapter, formed a summary94 of the avoidance behaviour in print and broadcast media, and it goes
well in line with what is discussed in this chapter regarding media attitudes and its impact on
avoidance behaviour.
The problem with attempting to reduce advertising avoidance this way is then that it does not fit all
target groups. It is fairly easy to find a demographic constellation that is more or less negative
towards advertising in all media and who finds advertising in all media to be untrustworthy,
unbelievable, uninteresting, a waste of time, excessive and so on. Another problem with this
otherwise interesting way of approaching the issue with avoidance behaviour is that the advertisers’
target audiences are most likely not consciously aware of their negative perceptions towards
advertising when not asked they simply react per reflex. This means that they may very well not be
reachable through the media vehicles in which they are less prone to avoid advertising, and this
gives the advertisers a problem. Their main goal is to effectively reach the majority of their
potential customers as inexpensively as possible, which means that in many cases only the national
mass-media channels will suffice in achieving that goal. If the target audience is demographically
spread, their media preferences differ and they are not reachable through the media vehicle in which
93
Shavitt, S. Et al. “Exploring the role of memory for self-selected ad experiences: Are some advertising media better
liked than others?”, Wiley periodicals Inc., p. 1024-1025, 2004.
94
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 74,
1997.
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they are potentially least likely to avoid advertising, the whole notion fails. The attempt to reduce
avoidance must be supported by other elements.
This made me reach the conclusion that advertising must look inward, it has a bad reputation
overall and as the theories also indicate advertisement must be changed from within to effectively
gain a more positive attitude and perception from the consumers. This change is possible, which is
substantiated through the results achieved by Speck and Elliot95. Advertising is found the most
useful in newspapers, the most interesting in magazines, the least excessive and annoying in
newspapers and magazines, the most believable in newspapers, and the least waste of time in
magazines and newspapers. Unless the advertisements are the same in all media and it is the
medium itself that makes the consumers more or less negative in their attitudes towards advertising,
a key must be hidden here on how to reduce avoidance. As explained earlier, obtaining more
specific results that could have shown more clearly why the consumers have the opinions
mentioned a few lines ago, was not possible and it is probably also a very difficult survey to make,
but it is certainly recommendable. Clearly, some internal features have a decisive influence on the
attitude of the consumer. Newspaper advertising is found the most useful, which could mean that
the advertisements are generally more informative or less misleading than that of other media.
Magazine advertising is considered the most interesting, which could mean that the message is
more captive, the visuals are more attractive and spectacular just to give a few examples. If the
medium alone is not the decisive factor, clearly the surveys then show that internal features (size,
design, message, length, interruption level etc.) are central in reducing advertising avoidance. Can
advertising avoidance be reduced without reducing the amount of advertisements? Based on the
surveys and theories behind them it seems almost beyond doubt that the answer is yes. How can it
be reduced? As mentioned above that answer is not certain, but based on the research of this thesis
and on the basis of other researchers’ works this thesis can offer some very qualified suggestions on
how to go about it, clearly the advertisers of each medium should look at the figures of these
surveys and see how they can be inspired by the advertisers of other media.
95
Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E. Sharp Inc., p. 70,
1997.
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5. Conclusion
This final chapter will conclude on the thesis in its entirety, all the theories, analyses, discussions,
limitations, the answer to my hypothesis and possibilities for future research.
The justification for and reasoning behind this thesis was to verify or falsify my research question,
the hypothesis that inspired me to formulate that question, and to research an important field that
has been researched to a certain extent, but in which much “terra incognita” is still encountered. My
research question is how can advertising avoidance be reduced without reducing the amount of
advertisements, and my hypothesis behind this question is that I believe it is possible to find other
ways of influencing the consumers consuming advertising in the different media so that their
attitudes towards advertising would be more positive (or less negative if seen that way). The
theories that I was able to find regarding this topic suggested to me that my hypothesis was not
impossible to verify. The classical view of advertising clutter, which seems to be the most decisive
factor in those theories leaning away from said view, has prevailed in a majority of the research
behind clutter and avoidance, and this thesis is intended to prove this view too one-sided and
lacklustre. I consider this part of the challenge to be accomplished, almost every figure of each
analytical part of the surveys included in this thesis indicated more than strongly that advertising
clutter perception, attitude towards advertising, the media and avoidance behaviour depend on many
more aspects than just the quantity of advertisements. Even without analysis to support my
hypothesis, if it is accepted that advertising clutter is a matter of perception (as suggested by the
theories of Ha & McCann and Cho & Cheon), then clearly unless all humans are that much alike,
demographics must play a role in avoidance behaviour and advertising clutter tolerance.
Although the analytical material that can be found from reliable scientific sources benchmarking
other aspects of advertising clutter and avoidance than quantity is fairly limited, a small number of
scientific articles that could further substantiate what has already been indicated in this thesis exist.
They relate to the impact of repetition and design, mostly structural aspects that I explained in the
discussion why I chose not to prioritise.
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The limitations to this thesis is largely due to the lack of research in aspects that could either have
substantiated in greater detail or changed the results of this thesis. As explained in the discussion
chapter more research in the field of advertising clutter and avoidance could help to further answer
the questions asked in this thesis. As the situation is, it is my belief that I have managed to prove the
classical view of advertising clutter (and thus the triggering of avoidance behaviour) to be
inadequate at best, and wrong at worst. Regrettably, the above stated situation also means that the
answer to my research question remains partly unanswered as I was not explicitly able to fully
substantiate how to reduce advertising avoidance without reducing the amount of advertising in the
media. I was able to locate areas within the complex structure of advertising avoidance that could
hold the key to treating the issue of avoidance of advertising, but although I could point out an
impact in attitude by demographics and media, I could not pinpoint all factors that made the
changes occur, whether it is the structure of the message, the contents of the message, the length of
a commercial, the placement on a website etc. Due to the lack of detailed surveys addressing these
elements of the aspects leading to advertising clutter and ultimately avoidance behaviour, it
remained partly speculative. I could substantiate that each main aspect of all three theories had an
impact, but not which elements were decisive and thus what to change in order to reduce
advertising avoidance. However, the basis is formed for future research that if addressing said
elements could use this research to find out where to start looking. A result is naturally not
guaranteed, but indications have been made by not only this researcher, but also by those behind the
works that were applied in this thesis that could lead to a more accurate answer of my research
question.
As for the discussed correctness of each theory, it is important to have the right theoretical
background to evaluate how advertising clutter and avoidance could be seen in the future, since the
classical view clearly falls short. The three models suggested by professors Ha and McCann, Cho
and Cheon, and Shavitt, Vargas and Lowrey each represent dissimilar yet closely related views of
this topic. The similarities suggest that a certain outer framework and consensus might be reached
among the researchers, and as explained in the discussion they all have their strengths and
weaknesses. Perhaps the method of discovery can also be reversed i.e. that the analytical work made
in the future could help establish a commonly acceptable model, if that analytical research e.g.
discovered via its results a previously unknown or unaccounted for factor. Imagining a perfect
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theory that explains these (advertising clutter and avoidance) somewhat elusive concepts can be
hard, as the aspects of all theories could be substantiated to have an influence on advertising
avoidance, but it was not possible to point out exactly which elements of these aspects accounted
precisely for which effect, and thus it cannot be said with certainty which elements should be
included in the model and which elements should not. As they structurally also contradict each
other in certain areas a merger of the models also seems a difficult challenge, and perhaps not a
challenge that will lead to the perfect model after all.
It seems rather clear that further analytical research with more specific questions going deeper into
the elements and separating them, rather than staying on the aspect level should help researchers to
gain a better knowledge on how to reduce advertising avoidance and what the structure of this
concept is more precisely. One of the main problems in undertaking this task is, as mentioned
earlier that consumers are very likely not aware of these subconscious actions to the extent that they
could truthfully and fully answer questions regarding the elements of the theorised aspects of
advertising avoidance. It is much easier to respond to a questionnaire asking about your behaviour
than about your reasoning in details for making that decision, and most likely they will have
forgotten such (to them otherwise) insignificant data. To exemplify, a consumer dislikes a media
vehicle because he thinks the site is cluttered and it prevents him from seeing the content he wishes
to see. He could most likely, as in the surveys treated in this thesis, respond to this reasoning fairly
accurately, but if asked if it was the design, placement, amount of advertisements, the messages, the
time consumption etc. that ultimately made him trigger an avoidance strategy and how he would
rate these elements’ importance in his decision, it would require a massive registration and memory
on the part of consumer to respond accurately and the question of out-coming distractions,
disruptions and so on had to be taken into account. If such a research could be successfully carried
out, based on my results and conclusions, it could quite likely lead to an unprecedentedly close
approximation of the answer to the research question and to the correct structure of advertising
avoidance.
In short what has this thesis taught this researcher and what I can conclude from these lessons? 1.
There is no theoretical model of advertising avoidance that completely accurately structures and
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explains the concept of advertising avoidance, and it is a difficult challenge to undertake the making
of such a model due to the research required in order to form said model, and it may or may not due
to its complexity actually be possible. 2. It is possible to reduce avoidance behaviour and the
perception of clutter without decreasing the amount of advertisements in a media vehicle; the
analyses clearly show that other factors play major roles as well, thus my hypothesis has been
verified, although with certain limitations. 3. The limitations include that it is not possible based on
this thesis to answer my research question in greater detail. I could establish which aspects had an
impact (which is the majority of aspects), but not which specific elements made the decisive
difference, nor which aspects played a bigger part than the others. Regulation of the elements and
aspects in order to reduce avoidance behaviour based on this thesis would therefore be a matter of
qualified guessing rather than absolute certainty.
One last important notion is that since so many aspects, elements and factors are dependent on the
individuals’ perceptions and with some surveys contradicting each other to a certain degree, it is
also the question if it is ever possible to make an analysis that will lead to more than “just” qualified
guesswork. Naturally with more study and research comes a higher degree of certainty for the
results, but it could prove hard to analyse them all and obtain a coherent enough image (not
contradictory) to answer the questions this thesis could only partly answer.
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Bibliography
1) Burst Media, http://www.burstmedia.com/
2) Cho, C-H. & Cheon, H-J., “Why do people avoid advertising on the internet?”, Journal of Advertising,
2004.
3) Clee, M. & Wicklund, R., “Consumer behavior and psychological reactance”. Journal of Consumer
Research, 6(4), 1980.
4) Ha, L. & McCann, K., “An integrated model of advertising clutter in offline and online media”,
International Journal of Advertising, 2008.
5) Hoch, S., & Deighton, J., "Managing What Consumers Learn from Experience," Journal of Marketing,
53 (April), 1989.
6) Jobber, D., “Principles and practice of marketing”, McGraw-Hill International (UK) Ltd., 2004.
7) Li, H., Edwards, S. & Lee, J., "Measuring the Intrusiveness of Advertisements: Scale Development
and Validation", Journal of Advertising, 31 (summer), 2002.
8) Percy, L. & Elliot, R., ”Strategic advertising management”, Oxford University Press, 2009.
9) Shavitt, S. Et al., “Exploring the role of memory for self-selected ad experiences: Are some
advertising media better liked than others?”, Wiley periodicals Inc., 2004.
10) Speck, P. & Elliot, M., “Predicators of advertising avoidance in print and broadcast media”, M.E.
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media”, Journal of advertising research, 1998.
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Appendix
All materials referred to in this thesis are taken from stable/static sources and are able to be
procured from university libraries and library databases. This is valid for all scientific articles and
books referred to in this thesis. The only exception to this is the online and non-static source of
Burst Media, the article procured from their online site is thus included in this folder following this
page.
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