PERSONALITY TRAITS AND AD-BLOCK USE - A DESCRIPTIVE INVESTIGATION OF PERSONALITY TRAITS AMONG AD-BLOCK USERS - DIVA PORTAL
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Personality Traits and Ad-block Use
A descriptive investigation of personality traits among ad-block users
August Bergsten
Business and Economics, master's level
2021
Luleå University of Technology
Department of Social Sciences, Technology and ArtsAcknowledgements Today marks the end of a long journey, the plenty of night spent on studies, plenty of evenings spent on writing. Today is the day that the final thesis is submitted. I would like to start off by expressing my gratitude to my supervisor, Jeandri Robertson, for her quick, qualitative responses, inspiration, and great guidance throughout this entire process. This process has been genuinely interesting and educational. I would also like to thank friends and opponents who helped me to shape my thesis. To friends and family that shared my survey. This thesis is a result of all your support, countless hours of music streamed and a supportive girlfriend willing to discuss articles and concepts she never cared for. You all assisted me greatly, and for that, I am truly thankful. June 2021 August Bergsten, __________
Abstract Advertisements have been in digital media for most of its lifetime. They have, however, been increasing with the years and more people are finding the number of advertisements to be excessive. Online users have therefore taken to avoiding advertisements by installing ad- blockers. There have been multiple studies on how ad-blockers work and why people use them. There have also been studies on which demographics mostly use ad-blockers. Younger men are generally seen as the typical ad-block user. However, none have seen if certain personality traits are more common amongst ad-block users. The purpose of this research is therefore to investigate if there are any differences in personality traits amongst ad-block users and non-ad-block users. The Big Five Inventory with 10- questions (BFI-10) personality test is used in an online survey to get an understanding of participants ad-block usage and personality traits. The personality traits that stood out were among females ad-block users with the personality traits of extraversion and neuroticism. The purpose of the study is to indicate a possible way for advertisers to prohibit their message to fall on uninterest and ad-avoiding recipients, and to provide some insights regarding ad-block user segmentation among Swedish ad-block users. Keywords: Personality trait, Ad-block, Ad avoidance, Big Five Inventory, Ineffective Marketing
Sammanfattning Reklamer har funnits i digitala medier i stort sett hela dess livstid. De har däremot ökat med åren och fler finner att antalet reklamer är överdrivet. Online användare har därför börjat undvika reklam genom att installera ad-blockers. Flertal studier har gjort på hur ad-blockers fungerar och varför de används. Det har även gjorts studier på vilken demografi som mestadels använder ad-block. Unga män är generellt det som ses som den typiska ad-block användaren. Ingen har däremot forskat på om något särskilt personlighetsdrag är vanligare bland ad- blockanvändare. Syftet med denna forskning är därför att utforska om det finns några skillnader i personlighetsdrag mellan ad-blockanvändare och användare utan ad-block. Personlighets testet, The Big Five Inventory bestående av 10 frågor (BFI-10), används i en online undersökning för att få en förståelse om deltagares ad-block användande och deras personlighetsdrag. Personlighetsdragen som stod ut var bland kvinnliga ad-blockanvändare extraversion och neurotisicm. Syftet med studien är att ange ett möjligt sätt för annonsörer att undvika att deras meddelande faller på ointresserade och reklamundvikande mottagare, och att ge insikt gällande segmentering av ad-blockanvändare bland Svenska ad-blockanvändare. Nyckelord: Personality trait, Ad-block, Ad avoidance, Big Five Inventory, Ineffective Marketing
Table of contents
1.0 Introduction .................................................................................................................... 1
1.1 Background ....................................................................................................................... 1
1.2 Ad-blocking software and users ....................................................................................... 2
1.3 Problem discussion ........................................................................................................... 3
1.4 Purpose specification ........................................................................................................ 4
1.5 Research questions ........................................................................................................... 5
1.6 Delimitations .................................................................................................................... 5
2.0 Literature framework ............................................................................................................ 6
2.1 Ad-blockers: A technical overview .................................................................................. 6
2.2 The driving force behind the growth of ad-blockers ........................................................ 6
2.3 Ad-block users and their observed behaviours ................................................................. 7
2.4 The industry’s methods to combat the use of ad-blocking software ................................ 8
2.4.1 Banner appeal ............................................................................................................. 8
2.4.2 Anti-ad-block scripts .................................................................................................. 9
2.4.3 Acceptable ads ........................................................................................................... 9
2.4.4 Disabling third-party cookies ................................................................................... 10
2.5 Personality traits ............................................................................................................. 10
2.6 Personality traits and their influence on privacy ............................................................ 11
2.7 Personality and relationship marketing .......................................................................... 12
2.8 Predicting the big five personality traits ......................................................................... 13
2.9 The Big Five Inventory (BFI-10) ................................................................................... 14
2.10 Theoretical framework ................................................................................................. 14
3.0 Methodology ...................................................................................................................... 16
3.1 Purpose and Research questions ..................................................................................... 16
3.2 Research approach and design ........................................................................................ 16
3.2.1 Descriptive approach................................................................................................ 16
3.2.2 Target group & Sampling ........................................................................................ 17
3.2.3 Response and nonresponse bias ............................................................................... 17
3.2.4 Online Questionnaire ............................................................................................... 17
3.3 How the results were analysed ....................................................................................... 18
3.4 Credibility of the data ..................................................................................................... 19
4.0 Data analysis ...................................................................................................................... 21
4.1 Gender, age, and ad-block usage .................................................................................... 21
4.2 Two sample t-Tests ......................................................................................................... 234.2.1 Openness t-Test ........................................................................................................ 23
4.2.2 Conscientiousness t-Test .......................................................................................... 25
4.2.3 Extraversion t-Test ................................................................................................... 26
4.2.4 Agreeableness t-Test ................................................................................................ 28
4.2.5 Neuroticism t-Test ................................................................................................... 29
4.3 Ad preferences ................................................................................................................ 31
5.0 Discussion .......................................................................................................................... 32
5.1 Minimal differences ........................................................................................................ 32
5.2 Extraversion and Neuroticism separating the samples ................................................... 33
5.3 The absence of difference between males ...................................................................... 33
5.4 Troublesome outlook for digital marketing .................................................................... 33
6.0 Conclusions ........................................................................................................................ 34
6.1 RQ 1: What traits are visible amongst ad-block users? .................................................. 34
6.2 RQ 2: How can sex, age and personality traits affect the use of ad-blockers? ............... 34
6.3 Practical implications ..................................................................................................... 35
6.4 Recommendations for future research ............................................................................ 35
6.4.1 Increase the length of the personality test ................................................................ 35
6.4.2 Personality traits and ad-block disabling ................................................................. 35
6.5 Limitations ...................................................................................................................... 35
7.0 References .......................................................................................................................... 37
Appendix A: The questionnaire ............................................................................................... 41
List of Tables
Table 1: Respondent’s ad-block usage divided by sex............................................................. 21
Table 2: Male respondents ....................................................................................................... 21
Table 3: Female respondents .................................................................................................... 21
Table 4: Openness: two sample t-Test ..................................................................................... 24
Table 5: Openness t-Tests divided by sex ................................................................................ 25
Table 6: Conscientiousness t-Test ............................................................................................ 25
Table 7: Conscientiousness t-Test divided by sex ................................................................... 26
Table 8: Extraversion t-Test ..................................................................................................... 27
Table 9: Extraversion t-Tests divided by sex ........................................................................... 28
Table 10: Agreeableness t-Test ................................................................................................ 28
Table 11: Agreeableness t-Tests divided by sex ...................................................................... 29Table 12: Neuroticism t-Test.................................................................................................... 30 Table 13: Neuroticism t-Tests divided by sex .......................................................................... 31 List of Figures Figure 1: Age distribution of participants graph with a data table. .......................................... 22
1.0 Introduction
The introduction contains a brief background of the area and leads to the problem discussion.
The purpose will be explained, and the research questions will be formulated alongside stated
limitations.
1.1 Background
Digital marketing strategies are crucial for businesses in the 21st century. It has been important
for a few decades and it will only increase to be more relevant in the future (Parment, 2015).
Marketing on digital platforms has the possibility to be targeted to the precise target, as users
are quickly identified when they enter a website and some of their information is known to the
website host. Advertisers have access to information about visitor’s interests, geographic
location, digital behaviour, and more. This access allows them to create more detailed
segmentations, form a strategy and directly target the correct audience. The industry has
however for the last few years been dealing with the challenge of ad-blockers (Söllner & Dost,
2019).
Ad-blockers are software installed on computers, tablets, or smartphones with the intention to
block intrusive advertisements, improve load times, and to protect the user’s privacy (Iqbal et
al., 2017). The extent of ad-blocker usage varies in different reports and articles. Google
estimated in 2016 that 11% of the entire internet population used an ad-blocker (Shellhammer,
2017). Kemp (2021) found through a global survey that 42,7% of internet users worldwide use
ad-blockers. Around 32% of Swedes say that they have an ad-blocker installed on either their
computer, tablet, or smartphone (AudienceProject, 2020). It is difficult to accurately say how
many current ad-block users that there actually are, but what is noticeable is that the usage of
ad-blockers has increased worldwide over the last century (Söllner & Dost, 2019). This is
troublesome for digital marketers as ad-blockers can lead to wasteful spending of resources, it
is also bad for website hosts as their visitors are not viewing advertisements which for some are
their source of income (Brinson et al., 2018).
Previous researchers have explained the implications that continuous increase in ad-block users
could have on digital marketing, how it possibly could alter the digital experience that we are
used to (Soltysik-Piorunkiewicz et al., 2019). The reasons why ad-blockers are so widely used
have also been established. Multiple studies (Pujol et al., 2015; Soltysik-Piorunkiewicz et al.,
2019; Tudoran, 2019) and reports (AudienceProject, 2020; Kemp, 2021) have found that
common arguments given as to why ad-blockers are used usually consists of a general dislike
of advertisements, a dislike of advertisements that are too personal and intrusive or that it
contains explicit content. A market research report made by NOVUS (2020) found that Swedes
have a general strong dislike of digital advertisements. Only 4% responded that they enjoy
advertisements online, while 69% said they dislike advertisements online (NOVUS, 2020).
That reveals an issue within itself, that people generally do not wish to be exposed to
advertisements when online. It also highlights the importance of understanding where
improvements can be made with marketing towards certain customers on online platforms.
Another reason why people use ad-blockers is that it enhances their digital experience, as less
data is required when loading new web pages since the advertisements do not load (Brinson et
1al., 2018). While the reasoning for using ad-blockers vary amongst users, they ultimately pose
a threat to the future of digital marketing.
Google (2021) issued a statement that they will disable third-party cookies, which have been
used by advertisers to gather data on consumers, trends, and activities of website visitors. The
removal of third-party cookies is meant to diminish the incentives for ad-block usage for people
that dislike tracking, personal ads and value their privacy (Brinson et al., 2018). Instead, Google
aims to supply advertisers with data, not on individuals, but on groups of consumers. That
allows users to stay somewhat private, while simultaneously providing necessary information
to advertisers (Temkin, 2021). The decreased amount of available data for advertisers on
individual consumers highlights the importance of understanding how certain groups of
consumers can be more or less likely to use ad-blockers.
Ad-block users are often described by demographic variables such as gender and age (Soltysik-
Piorunkiewicz et al., 2019). Previously ad-block users have been mostly men, but the divide
between men and women has disappeared with the years and women use ad-block to similar
degrees as men today (Kemp, 2021). Ad-block was commonly seen amongst younger users, but
older people are also observed to increase their ad-block use (Kemp, 2021). Variables such as
gender and age can define users but say very little about their behaviour (Junglas et al., 2008).
The theory of personality traits states that an individual’s personality is what affects behaviour
and how people see the world around them (McCrae & Costa, 1999). Personality traits have
been conceptualized in various forms, one commonly used model is the five-factor model by
McCrae and Costa (Srivastava, 1995). The model states that an individual’s personality can be
described by five separate traits (Openness, Conscientiousness, Extraversion, Agreeableness
and Neuroticism) that amongst other things affect how we behave (McCrae & Costa, 1999).
These traits are commonly investigated in research as they are believed to be rather consistent
throughout our lives and explain differences in behaviour (Soto et al., 2011). Segmentations
with personality traits in the mix allows for more effective marketing, as advertisements can be
even more personally suited for each individual within the chosen segment (Hirsh et al., 2012).
Personality traits have however never been investigated amongst ad-block users.
1.2 Ad-blocking software and users
The market is flooded with various alternatives of ad-blocking software, but they all have the
same main purpose, which is to shield people from advertisements on digital platforms to
different degrees (Pujol et al., 2015). There are free versions that fulfil the basic requirements
for most and some more advanced options that require payments (Brinson et al., 2018). Website
hosts and digital marketing agencies use mainly two different methods to combat ad-blockers
(Iqbal et al., 2017). Many advertisers become part of the acceptable ads program. A program
that contains certain guidelines that advertisers must follow if they want their advertisements
to be incorporated on their websites. These guidelines make sure that the advertisements follow
certain rules to make them less vulgar, refrain from irritating pop-ups and generally make them
more organic to the visitor’s digital experience. This is a free program for small businesses, but
the larger advertisers will pay an additional fee to be included (Iqbal et al., 2017; Zhao, et al.,
2017).
2The other option for advertisers is to instead adopt anti-ad-blocking scripts for their websites.
These scripts will detect when a visitor uses an ad-blocker and give them a banner to ask them
to turn it off. This banner can either force users to disable the ad-blocker or leave the site. It can
also try to plea with the visitors and try to convince them to turn it off as it would benefit the
website and allow them an income (Iqbal et al., 2017). The scripts are, however, sometimes
unable to detect ad-blockers. Users could have customized their settings, or the script is simply
not good at detecting certain ad-blockers (Pujol et al., 2015).
The typical individual to use an ad-blocking program can be identified through previous
research. People who use the software are generally young adults between the ages of 15-35
years old and most are male (Soltysik-Piorunkiewicz et al., 2019). However, more recent
research is showing that the knowledge and interest in ad-blockers is spreading to older
generations as well, as they see the positive effects it can have on their digital experience
(AudienceProject, 2020). Ad-blockers are more common amongst male consumers, but the
number of female users has risen over the years. The gap between male and female users is
closing in, especially seen in younger generations where more females are observed to use ad-
block (Kemp, 2021). Meanwhile, the difference in the number of users is larger with the older
generations, but it is closing in there as well at a slower pace (Kemp, 2021). The age and sex of
ad-block users is important for digital marketers to consider, as the number of individuals who
will consider blocking their content is increasing.
1.3 Problem discussion
The awareness of ad-blockers is increasing, and the number of users has been growing
throughout the years as more and more understand the benefits of using the software. A
troublesome trend as it means that digital marketing will lose effectiveness over time (Brinson
et al., 2018). Advertisers and programmers are constantly attempting to find ways to outsmart
one another, a race where the ad-blocker programmers have the upper hand (Iqbal et al., 2017).
The race is certainly an inconvenience for users and advertisers. However, the issue at hand is
the revenue loss at hand for advertisers. Ad-blockers will prohibit the marketers to reach their
recipient, meaning that their investment in their advertisement campaign has less of an impact
than projected. Some ad-blockers act in a more aggressive way and even fabricate views on
online advertisements, to waste money for the advertiser (Gordon, et al., 2021). This indicates
the benefits of understanding which consumers use ad-blockers since it would help advertisers
improve their use of resources.
Previous studies on the subject of ad-blockers (Alrizah et al., 2019; Brackebush, 2016; Brinson
et al., 2018; Iqbal et al., 2017; Pujol et al., 2015; Soltysik-Piorunkiewicz et al.,2019) have
mainly been focused on technical aspects and defined what ad-blockers are capable of and their
implications to digital advertisements. The reasoning as to why people use ad-blockers has been
established. The question of who ad-block users are, is rather well explained. Researchers have
identified who these users are and how they could perhaps change their behaviour and whitelist
websites or turn off their ad-blocker (Brinson et al., 2018). There is a lack of knowledge of
which personality traits are common amongst ad-block users, and how it might be an important
factor that separates the behaviour amongst users. The basics of marketing theory state the
3importance of understanding the customer’s wants and needs in order to successfully create a
marketing strategy. It is the first step in the marketing process (Parment, 2015). Therefore, the
consumer’s behaviour will be analysed as it is important to understand their purchasing
behaviour.
Differentiating customers is an important part of marketing, but it is not exclusively about
differencing between types of customers, but also the personality traits of a customer when it
comes to relationship marketing (Caliskan, 2019). Individuals with different personality traits
react differently to the same approaches. It would therefore be beneficial for digital marketers
to understand the connection between ad-block users, their personality traits, and their
reasoning for using ad-blockers (Hirsh et al., 2012). Personality traits are seen as relatively
constant throughout a person’s adult lifetime. They are more stable indicators of our inherent
traits and how we interact with our surroundings (McCrae & Costa Jr, The Stability of
Personality: Observations and Evaluations, 1994). It means that they are what characterize
individuals, not certain predictors of our habits (McCrae & Costa Jr, The Stability of
Personality: Observations and Evaluations, 1994). Certain interests, opinions and behaviour
usually change in shorter timespans. There are however findings that age, sex and geographical
locations are factors that affect personality traits and how people react to the world around them
(Soto et al., 2011). It affects how people feel about privacy (Junglas et al., 2008), brand affection
(Mulyanegara et al., 2009), social situations (Azucar et al., 2018) and even future factors such
as professional ambition (Salgado, 1997).
Understanding the customers and their attitudes towards digital advertisements is key when
creating a digital marketing strategy and making sure that the resources at hand are properly
allocated (Gordon, et al., 2021). Comprehending aspects that separate ad-block users and non-
ad-block users could allow for more effective advertisements. Identifying key factors, such as
personality traits and customer behaviour that identifies potential ad-block users could allow
businesses to assess their marketing strategy. Certain ad-block users could potentially be better
targeted outside conventional digital media outlets, through methods approved by ad-block
creators (Iqbal et al., 2017) or through channels that are not directly affected by ad-blocking
software. Such as guerrilla marketing (Castronovo & Huang, 2012) using eye-catching
messages that engage viewers and are unaffected by ad-block use (Jones et al., 2011). The key
is being able to separate ad-block users among themselves and from non-ad-block using
customers so that they can be reached through means that are beneficial to both parties.
Personality traits are commonly seen as factors that separate humans in their behaviour (Azucar
et al., 2018), but they have never been analysed with ad-block use.
1.4 Purpose specification
The purpose of this research is to dive further into the behaviour of ad-block users and
ultimately discover whether certain personality traits are more prone to using ad-blocking
software. It would enable marketers to effectively utilize their resources when forming a
marketing strategy, by making sure that their message will not be sent through channels where
there are few or no receivers. Previous research on the topic is too insufficient to make certain
conclusions. The general dislike of advertisements is stated in multiple studies and how ad-
4block use is a consequence of that. Behaviour and personality traits of ad-block users is however
an area that is unexplored by academic writers. There is a potential to improve the segmentation
process by finding key aspects that separate ad-block users and non-ad-block users. Therefore,
this study will aim to create further insight into ad-block use behaviour by looking at personality
traits as a potential factor that separates consumers that use ad-block and those who do not. The
findings could assist future researchers to better identify ad-block users using personality traits,
and that way get a better understanding of the users behaviour and improve upon digital
marketing.
1.5 Research questions
There are two research questions that this thesis is going to answer. Those two questions are
the following:
-What personality traits are visible amongst ad-block users?
-How do sex, age and personality traits affect the use of ad-blockers?
1.6 Delimitations
With the limitations in time, resources and previous research on the subject, the results will be
limited to exploring the potential differences. That means that the results can only indicate the
possibility for a difference in behaviour. The questionnaire is predominantly sent out through
channels populated with Swedish people. It should therefore be taken into consideration that
the results will mostly project English-speaking Swedes. The language barrier should be of
minimal impact on overall responses considering Swedes are considered highly proficient in
the English language (EF, 2020). The research will look at ad-block usage overall and not
limited to particular instances. The focus lies within exploring if a certain personality trait could
be more prominent amongst individuals with ad-blockers installed. It will also be looked at if
certain traits are more inclined to use ad-block on certain devices. Their behaviour, as to which
degrees the ad-block is used, will not be explored. That is, the difference in consumers
probability to disable their ad-block or whitelist specific websites, will not be looked at.
52.0 Literature framework
This chapter will establish what previous researchers have written on the relevant subjects. It
will build a foundation for the forthcoming chapters.
2.1 Ad-blockers: A technical overview
The rising popularity of ad-blockers has created a large number of alternatives. The most
popular ad-blockers in use are free-to-use and open-sourced software. These require no
payments whatsoever from their users. Their purpose is mainly to let people block
advertisements online. However, they also protect the users from malicious malware and
security threats. Since they block certain attempts by malware to infiltrate the user’s system
(Alrizah et al., 2019).
Ad-blockers operate with different methods. Some use filter lists. These lists are maintained
manually by users or publishers. They contain information about known locations of ads, that
is their domains and websites. When a website sends a request to the user’s device, the ad-block
will match it with the filter list. If there is a match, the ad will be hidden from the user (Alrizah
et al., 2019). Some ad-blockers work in a more unethical manner. They allow the website’s ad
request, and the script will click on all ads that are shown, while simultaneously hiding them
from the ad-block user. This will create false data and ensure that the advertisers must pay for
the advertisement. These ad-blockers are less frequently used, but they exist and are actively
used by some (Gordon, et al., 2021).
Ad-blockers that are free-to-use profit from advertising deals with larger publishers. Advertisers
and publishers will pay to have their domain be included in a whitelist, essentially allowing
their advertisements to be shown to ad-block users (Gritchkevich et al., 2020). For this to work
the publishers need to adapt to requirements set by the community of users, which generally
means that it will be less intrusive and more organic (Storey et al., 2017).
2.2 The driving force behind the growth of ad-blockers
The reasoning for adblocking software usage is well established, as there are multiple studies
on the “why” question on ad-block users and their reasoning. These studies disclose a good idea
of the typical adblocking software user. Most people use the software as they dislike excess
advertisements, do it for privacy reasons, want a more fluent experience or and some perceive
the targeting ads as too intrusive (Brinson et al., 2018).
The general dislike of digital advertisements is one major reason for the popularity of ad-
blockers. Digital advertising is a massive industry and by 2021 it is forecasted to have amounted
to more than half of all media ad spending worldwide (PubMatic, 2020). Global spending on
digital advertising has increased steadily which means that consumers will be exposed to an
increase of advertisements in digital media. Digital advertisements do have benefits, they are
good for digital markets, as they help websites stay afloat and it greatly benefits individual
content creators. Correctly targeted ads also benefit people. The sheer abundance is, however
6prompting users to solve the issue at hand by avoiding the advertisements with the help of ad-
blocking software (Brinson et al., 2018).
While some simply dislike advertisements, some find them all too annoying or intrusive. Pop-
up ads were prominent in the early days of the internet. They were advertisements that would
pop up and many users found them to be severely decreasing their enjoyment online. This
created an early demand for ad-blockers. Pop-up advertisements are less frequent in today’s
digital environment, but there are many other kinds of advertisements that users find to diminish
their experience (Söllner & Dost, 2019). These are for example advertisements containing
offensive or irrelevant messages. These advertisements maintain the demand for ad-blockers
for these consumers (Brinson et al., 2018).
Privacy is thoroughly mentioned within the subject of ad-blockers. Many feel that digital ads
are too targeted and intrusive to their privacy. Making them feel followed and exposed. The
usual problem with these advertisements comes from situations where the user is unfamiliar
with either the advertised brand or is new to the platform. Where the targeted advertisements
rely on information that is gathered through a third-party source (Brinson et al., 2018). Another
reason why people use ad-block software is the reduced load time for websites (Soltysik-
Piorunkiewicz et al., 2019). Many ad-blockers prohibit advertisements from loading when the
users enter the website, which requires less data for the user. Decreasing the load time and limits
the amount of data used when surfing the web (primarily used by smartphone users), which
many find to improve their experience with the decreased visual clutter (Brinson et al., 2018).
This issue can be solved through advertisements that are simpler, such as banners without
moving images or sound. Simply put, advertisements that stand out less and are more organic
in comparison with the website (Söllner & Dost, 2019).
2.3 Ad-block users and their observed behaviours
The people that use ad-block are thoroughly describe throughout previous research and reports.
They are generally young males between the ages of 15-35 years old and found in all
geographical locations around the world. They have also been observed to mostly be higher
educated individuals (Soltysik-Piorunkiewicz et al., 2019). These individuals use ad-blockers
mainly on computers, as the effectiveness of ad-blockers on smartphones is much lower
(Brinson et al., 2018).
Most of the younger generations know what ad-blockers are, knowledge about the software is
increasingly familiar to older generations as well. Due to their sheer popularity and their
potential positive effect on their digital experience. People learn about ad-blockers mainly
through word of mouth or from various places on the internet (Soltysik-Piorunkiewicz et al.,
2019). There are no indications that ad-blocking software has any effects on consumer’s
purchasing behaviour. They purchase products within similar price ranges and similar brands.
The time spent searching for products online is slightly longer for ad-block users, but to no
major significance. Therefore, ad-blocking software does not seem to alter the behaviour of
consumers, it only negatively affects advertisers (Frik et al., 2020).
7The whitelisting and deactivation behaviour portrayed by ad-block users differs in between
studies. Sołtysik-Piorunkiewicz, et al., (2019) found that a majority of users in Poland would
disable their ad-block to access blocked content and turn it on afterwards. Contradicting
findings from Söllner & Dost (2019) that proved only minor percentages would alter their
behaviour and disable their ad-block software and most would bounce away from the website.
It is important to consider that these two studies were carried out within two different countries,
Germany, and Poland. Both countries are among the largest when it comes to the number of
advertisements blocked in comparison to the population (Brackebush, 2016). Interestingly, the
younger generations are observed to be more concerned than older generations about their own
privacy and the threat of malicious advertisements containing malware (Soltysik-Piorunkiewicz
et al., 2019). Younger people are showing a growing concern over their privacy and their buying
behaviour is negatively affected by privacy concerns. Meanwhile, the older generations are less
likely to avoid a purchase with privacy concerns (Barbonaba-Juste et al., 2019).
Ad-blockers are most commonly seen amongst men, they are however also seen amongst
women in large numbers. According to Kemp (2021), 43,2% of women between the ages of
16-24 used ad-block, compared to 49,2% of males. The differences seen in the desire to avoid
ads amongst men, women of different ages have been known to exist for a long time. According
to Heeter and Cohen (1985, referenced in Speck & Elliot, 1997), younger radio listeners were
more likely to switch away and avoid radio commercials than older people. Similarly, younger
people and men overall were more likely to skip advertisements in printed media. They were
more likely to gloss over advertisements and pay less attention to them. The demographic that
has shown the biggest effect on consumer behaviour overall is gender and age. Younger people
and men, in general, are more likely to avoid advertising (Speck & Elliot, 1997). More recent
findings prove that gender and age are factors that affect the way ads are perceived and
consumed. They are, however, somewhat more nuanced than previously shown. Gender and
age show differences in behaviour, but the geographical location is also an important factor
(Rojas-Méndez, Davies, & Madran, 2009). There is therefore important to understand how
gender, age, and geographical location can affect behaviour. These same factors are also
observed to influence personality traits (Soto et al., 2011).
2.4 The industry’s methods to combat the use of ad-blocking software
2.4.1 Banner appeal
Previous researchers have explored the drivers behind ad-block user’s behaviour and how they
are affected in different scenarios. Through an exploratory study by Söllner and Dost (2019)
they observed the effect that different methods had on people who used ad-blockers. Users were
met with a banner when entering the website that asked if the users could whitelist them or turn
off their ad-blockers. The banner either tried to appeal to users in a fun or serious way. They
concluded that the differences were small between the two banners, as users in both groups
showed small inclinations to adjust or change their behaviour. Söllner and Dost observed
changes in around 1-2% of visitors. It could also be noted that people actually were deterred
away from the site with the appearance of the banner and showed increased avoidance
behaviour (Söllner & Dost, 2019). A minor percentage choose to deactivate their ad-blockers
8for select websites, but it will then require advertisers to comply with industry lenient standards
to make sure that they are less intrusive (Söllner & Dost, 2019).
Consumers who repeatedly visit the webpage are more likely to adapt their behaviour and act
on the appealing banner. While new visitors are more likely to bounce away. This indicates that
the method could be useful for websites with reoccurring visitors but is less useful when trying
to attract new consumers (Söllner & Dost, 2019). These numbers indicate that adopting the
simple banner has minor results but at low costs. It will not work as an ultimate solution to the
problem. As the avoidance behaviour from the website could ultimately create a greater loss
(Söllner & Dost, 2019).
2.4.2 Anti-ad-block scripts
These scripts detect ad-blockers that try to access the website and send them a notification that
they will be shut out unless they disable their ad-blocking software or whitelist the website.
This approach is meant to completely remove the issue of ad-blockers (Storey et al., 2017).
However, it instead creates new issues. Some users will simply avoid the website and bounce
away. Prioritizing their privacy and freedom to decide for themselves whether to disable it or
not. According to a survey by Pagefair (2017), 74% of Americans say that they would leave the
website when confronted with an anti-ad-block script. Indicating that this approach is less
favourable with the decrease of visitors.
The other effect that these anti-ad-block scripts have had is the creation of anti-ad-block script
filters (Iqbal et al., 2017). These are crowd.sourced filter lists that collect data through varying
methods of websites that use anti-ad-block scripts. They then use collected data to circumvent
the anti-ad-block script, through different methods, and ultimately allow the ad-block user
access to the website without disabling their software. These filter lists contain information
about anti-ad-block script vendors and deny these vendors to send HTTP requests and HTML
elements to avoid them from detecting the ad-block. This creates a race between the two sides
as they simultaneously try to combat each other’s methods and circumvent the other solutions.
An arms race that the advertising industry is losing (Iqbal et al., 2017).
2.4.3 Acceptable ads
The approach that many now are taking is to become a part of the acceptable ads programme,
a programme with guidelines that Google advises advertisers to follow regardless
(Shellhammer, 2017). It is a programme that is made to create a middle ground for both the
advertising industry and ad-block users. The way it works is a committee consisting of both
sides set out rules of how advertisements should be made. The set standard makes sure that
advertisements are respectful, nonintrusive, and relevant. Ad-block creators then whitelist them
and allow them to advertise to the ad-block users. This solution is free for most participants,
but large entities must pay a fee for the services to be included (Gordon, et al., 2021).
While this solution seems like a good option to combat the issue of ad-blockers, questions arise
about the effectiveness of the ads abiding to the standard. As they are less noticeable and
consumers might lose brand recognition (Weidenmark, 2020).
92.4.4 Disabling third-party cookies
David Temkin (2021), director of product management, ads privacy and trust at Google, put
forward a statement in a blog post that Google will remove support for third-party cookies.
First-party cookies are placed and controlled by the website owner themselves and are mainly
used to remember your selected preferences and to gather data to improve the website. These
will remain in use (Parsad, 2021). Third-party cookies are set by a third-party on the website
domain and collect data on users on all the websites under that domain. These cookies are small
bits of information that companies can place on individual visitor’s devices to gather data over
time across different websites. The collected data can then be used to get an understanding of
the consumer’s identity and behaviour. These are mainly used by advertisers and used to create
targeted advertisements (Parsad, 2021).
The removal of third-party cookies is combating the issues of consumers feeling tracked, which
is a popular reason for installing ad-blockers (Brinson et al., 2018). This means that Google
will not be tracking individual users’ data across multiple websites, they will instead provide
data of groups of consumers. Showing trends and behaviour of larger like-minded groups of
individuals. Enabling consumers to stay somewhat more private and simultaneously providing
necessary information to advertisers (Temkin, 2021). This could decrease the number of ad-
block users, but the actual effect of the change is difficult to forecast. This change will change
the industry, as large parts of the advertisement industry heavily rely on third-party cookies to
track and target consumers (Iqbal et al., 2017). Individual targeting will not be available for
marketers. Instead, they will have to target groups of consumers. It could therefore be important
to understand the target groups behaviour online.
2.5 Personality traits
A cornerstone of marketing is segmentation, which is the practice of dividing the market into
separate smaller segments based on variables such as geographics, demographics and
behavioural or psychographic aspects (Parment, 2015). It requires businesses to attain
information and create an understanding of the potential customers to know which segment the
most profitable target market is to focus on (Parment, 2015). A good way to target these
customers is to understand their personality traits to better engage with them and better predict
their reactance to a message. Tailoring the messages to the customers’ personality traits
improves the effectiveness and is better received by the end consumers (Hirsh et al., 2012).
One method to identify personality traits is through a model called the Five Factor Model (FFM)
or OCEAN, which is an acronym for the items it measures, the five big personality traits
(McCrae & Costa, 1985). These traits are Openness, Conscientiousness, Extraversion,
Agreeableness and Neuroticism. The model implies that an individual can through relatively
enduring patterns of thoughts, feelings and actions be characterized. That their traits can be
assessed not in a binary number, but in a spectrum (McCrae & Costa, 1999).
The Five personality traits that are measured explain the personality and behaviour of
individuals. They measure and conclude the following traits:
10• Openness to which degree an individual seeks new experiences and creatively thinks
about the future. A curious person would score highly. They tend to be creative and
open-minded. Low scores on the spectrum indicate a predictable and traditional
individual. One that dislikes changes (Hitt et al., 2015).
• Conscientiousness checks to which degree the individual will focus on tasks and works
towards them in a careful manner. High scores in this trait indicate an organized and
disciplined individual. The other end of the spectrum indicates a disorganized individual
that is impulsive and careless (Hitt et al., 2015).
• Extraversion will measure to what degree an individual will derive energy from
socialising and engaging people. A high score for this trait indicates an individual that
gains energy by socialising. While a low score means the individual prefers solitude,
privacy and might dislike attention (Hitt et al., 2015).
• Agreeableness is to what degree an individual will be easy-going and tolerant of their
surroundings. A high scoring individual is easy-going, trusting, and forgiving. Could
portray altruistic behaviour. A low score indicates some sceptical of their environment.
Difficulty to work with others and interacts poorly with others (Hitt et al., 2015).
• Neuroticism measures the individual’s emotional stability. Whether their self-esteem is
low, and they are anxious or calm and worry-free. High scores are portrayed by an
anxious and shyness. Lower scores indicate a calmer personality, one more emotional
stable and worry-free individual (Hitt et al., 2015).
These traits are measured on a spectrum, which means that a person can fit a trait to a certain
degree while not fully conforming to the basic tendencies of the said trait (McCrae & Costa,
1999). These personality traits are generally seen as static and consistent over long periods of
time, while behaviour, interests and feelings are more likely to change with time (Salgado,
1997).
2.6 Personality traits and their influence on privacy
Personality research has been a central part of studies concerning behaviour for multiple years
(Mulyanegara et al., 2009). Soto and Gosling (2011) found support for the belief that age and
sex are determining factors as to what personality trait define us. How age and maturity can
change the way we, as a group, change our personality. Openness and conscientiousness are
observed with a positive correlation to age with men and women. The older people grow, the
higher the score, on average, with these two traits. Agreeableness and extraversion are rather
consistent and independent of age. Men are seen scoring, on average, lower than females in
these two traits. Neuroticism has a negative correlation with both sexes with age, as younger
people have a higher average than older people. Young people tend to be more neurotic
personalities, they are less able to control their emotion, something adults tend to be better at.
11Females tend to score on the higher end in neuroticism in their younger ages, while men are
observed to have a mean value below the median value (Soto et al., 2011). As people grow
older, they learn more about the world around them, they mature and are able to look at the
world through a different lens. Personality traits are generally seen as constants throughout an
individual’s lifetime (Barbonaba-Juste et al., 2019), however, Soto et al. (2011) prove that
minor changes do happen.
Privacy concerns, which is a driving force behind the rise of popularity in ad-blockers (Brinson
et al., 2018), is observed to be influenced by individuals personality traits. Agreeableness,
conscientiousness, and openness all proved to affect privacy concerns, while emotional stability
(neuroticism) and extraversion would not influence it (Junglas et al., 2008). These findings
indicate that agreeableness, conscientiousness and openness could be factors that influence an
individual’s decision to install ad-blockers. While extraversion and neuroticism would be
factors that do not affect an individual’s decision to use ad-blockers, at least with privacy being
their reasoning. Korzaan & Boswell (2008) also found individual privacy is affected by the
agreeableness trait. They also found that neuroticism significantly affected computer anxiety in
individuals, that they are more fearful of the interaction with computers. People with neurotic
traits are prone to being anxious, nervous, and worrying, it is therefore not surprising that they
are more anxious towards using computers and digital communications (Korzaan & Boswell,
2008). Individuals with high scores on agreeableness are more trusting of their environment,
and therefore tends to have a lower concern for privacy. People with low agreeableness are the
opposite and are more likely to care for privacy. Individuals with high conscientiousness, that
are organized and disciplined, have higher concerns for privacy. Less conscientious individuals
tend to have lower privacy cares. High scoring in openness was also seen combined with
concern for privacy, while low scores in openness were the opposite (Junglas et al., 2008).
2.7 Personality and relationship marketing
Irrelevant advertisements are causing irritation and unnecessary visual clutter for people online
(Hirsh et al., 2012). These advertisements are either targeting the wrong recipient or contain
irrelevant information. Tailoring an advertisement towards the target consumer increases its
effectiveness (Hirsh et al., 2012). Creating advertisements towards consumers with certain
personality traits can further improve the effectiveness as people respond better towards
advertisements that are communicated in a way that corresponds with their stronger personality
traits (Caliskan, 2019). Therefore, to create a greater value for the customer relationship a
marketeer must factor in more variables towards their segment.
All personality traits show a greater appreciation towards tailored communication for them, but
people with high values in agreeableness and openness are more likely to positively respond to
relationship marketing in its entirety (Caliskan, 2019). Targeting individuals with higher scores
in neuroticism might have less of a positive effect. They do respond positively towards tailored
relationship marketing but to a lower degree. This implies that these individuals might be less
lucrative to target through these means (Hirsh et al., 2012).
122.8 Predicting the big five personality traits
Personality tests have long been the preferred way to discover and understand peoples
personalities. The interest in finding alternative ways to understand human behaviour and
psychological factors through alternative ways has increased within the last few years (de
Oliveira et al., 2011). An individual that actively spends time online often portray their
personality traits without even knowing it themselves. The way that they interact with various
touchpoints online says a lot about the individual and can help advertisers understand who the
person really is (Azucar et al., 2018). The digital footprint that users leave behind can be used
to predict their prominent personality traits to a certain degree. Social media channels contain
large amounts of information that is adequate for algorithms to create predictions about the
personality traits of the user (Azucar et al., 2018). Publicly available data on Facebook is
enough to predict user’s personality traits (Golbeck et al., 2011). Golbeck et al. (2011) proved
this by gathering information on individual’s gender, education, geographical location, religion,
interest and more of their Facebook profiles, while they would take a personality test. The
collected data could be compared to actual personality tests made by test subjects and the
predictability power could be proven (Golbeck et al., 2011).
Multiple studies (Chittaranjan et al., 2011; de Oliveira et al., 2011; LiKamWu et al., 2011) have
observed the possibility to predict personality traits from smart-phone usage. Through various
patterns, they all find that the ways humans interact with smartphones say something about their
personalities. Gathering information about consumers through simply behavioural patterns in
their smartphone can be an easier approach than the ordinary personality tests (de Oliveira et
al., 2011). Sensing personality traits through the way we use phones reaches beyond the
observable physical behaviours that we can witness (LiKamWu et al., 2011). The big five traits
can be observed through these methods.
A Danish study (Mønsted et al., 2018) does, however, question the degree to which the five
traits can be predicted through these methods. The study concluded that all traits could to some
degree be predicted, but extraversion stands out as the trait that is significantly better predicted
through smartphone usage patterns (Mønsted et al., 2018). Their data came from a study on
Danish students, meaning all have a specific geographical location and are within a specific age
range. Two variables that affect an individual’s personality traits, which could be a reason for
their results. The results can therefore not be generalized for the entire population. It is,
however, clear that personality traits can be predicted through variables such as usage patterns
and public information. Proving that the tools for marketers exist to identify their customers, to
predict how they would want to consume and react to advertisements. There is a clear benefit
to tailoring advertisements to target an individual’s personality traits. It increases the perceived
effectiveness and is better received by the consumer. With the ability to gather sufficient data
from various parts of individuals digital footprints, the effectiveness of marketing strategies
could be improved further upon and hopefully reduce the number of unwanted advertisements.
132.9 The Big Five Inventory (BFI-10)
Psychologists have thoroughly proven the consistency of the FFMs ability to repeatedly predict
personality traits in various contexts (Hitt et al., 2015; McCrae & Costa, 1999; Oh et al., 2011;
Salgado, 1977), but generally do so through a lengthy process with a long series of questions.
These tests can be time-consuming and draining for researchers which have created a necessity
for shorter versions of which the Big Five Inventory (BFI) is a result (Pervin & John, 1999).
BFI was initially a test compromising of 44 questions to test for an individual’s personality
traits. Even the BFI-44 is in today’s standard somewhat long. Requirements for faster testing
created the BFI-10. A test designed for surveys where individuals time is limited (Rammstedt
& John, 2007).
BFI-10 contains 10 items with an additional 11th item to use if agreeableness is the sought after
or the deciding trait. BFI-10 has previously been validated by Rammstedt and John (2007).
They tried the test’s reliance compared to results from BFI-44 tests which indicated that 70%
of the variances could be predicted by the BFI-10. The validity of the test has continuously been
proven and the test’s accuracy at predicting results from the BFI-44 with the BFI-10. It is proven
that it is a viable option in research that is short on time and resources (Rammstedt & John,
2007). The short test should therefore not be seen as a perfect substitute to the longer personality
tests that are available, but as a possibility to test the sample for potential differences. The one
weakness of the scale is the measurement of agreeableness, where the correlation to the BFI-44
is too low. Therefore an 11th item is available for research where agreeableness is considered
the crucial trait (Rammstedt & John, 2007).
One thing to keep in mind when analysing results from a personality test, such as the BFI, is
that there are multiple different tests used in modern research and they do not all test for the
exact same characteristics (Srivastava, 1995). They all contain different questions, designed to
predict the personalities of individuals in different ways. Therefore, they can give varying
results. Srivastava (1995) mentions that they are usually minor differences. They must however
be kept in mind when using a short test such as the BFI-10. The test can, potentially, test
variables that are not influential on ad-block usage.
2.10 Theoretical framework
The previous research creates the base, upon which the framework is built. The theoretical
framework describes the relationship between the key variables of the thesis and the gap that is
meant to be observed and analysed. An important step in the marketing processes is the step of
segmentation (Parment, 2015) where the customers are divided into separate segments
according to varying factors, where the most attractive segment will be chosen as the target
customers. The targeted customers are the ones that are deemed most profitable to engage and
prioritize (Parment, 2015).
Segmentation can generally be divided into four categories, geographic, demographic,
psychographic and behavioural (Parment, 2015). These four segmentations look at different
factors that set consumers apart and are all important for different reasons (Keegan & Green,
2017). Psychographic segmentation, segments based around psychological traits, are generally
14You can also read
