Personality Traits and Ad-block Use A descriptive investigation of personality traits among ad-block users

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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 Arts

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Acknowledgements

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,

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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

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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

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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 ... 29

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Table 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

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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 21^st 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

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2 al., 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).

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3 The 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

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4 importance 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-

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5 block 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.

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2.0 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

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7 prompting 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).

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8 The 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

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9 for 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).

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10 2.4.4 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:

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• 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.

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12 Females 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).

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2.8 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.

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2.9 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 11^th 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

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15 ill-suited when developing new products. They are, however, established as suitable for developing advertising strategies and affect consumer’s feelings for brands (Yankelovich &

Meer, 2006). The process of segmentation could, potentially, be further improved upon.

Connecting ad-block behaviour with personality traits could improve the segmentation. It allows the advertisers to get a clearer understanding of their consumers, how they could improve their targeted ads while, possibly, simultaneously improve customer’s brand feelings.

This thesis will look at differences in personality traits amongst ad-block users as a possibility to further improve upon the segmentation step within marketing. Segments can be assessed through the big five personality traits, to give advertisers a better understanding of the consumers and the expected ad-block use. A segment could essentially be divided further into two groups, with one being more likely to be ad-block users and the other group being less likely to be ad-block users. With an expanded understanding of available segments, advertisers can adapt their digital strategy accordingly and improve their probability to reach said segment.

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3.0 Methodology

This chapter will recount the purpose of the research, describe how the purpose will be answered and describe what the key factors are going into the data analysis. The purpose of this research is therefore to look deeper into the behaviour of people who use ad-block

3.1 Purpose and Research questions

Marketing in digital media allows advertisers to tailor and customize the message towards a specific group or individual consumer. The purpose is therefore to create insight into the behaviour of ad-block users by answering the research questions.

- What personality traits are visible amongst ad-block users?

With the removal of third-party cookies on Google’s domain, the industry requires advertisers to adapt their strategies and be less reliant on the data of individual consumers. By answering the first question, advertisers could hopefully get a better understanding of group behaviour.

How certain personality traits are stronger or weaker amongst ad-block users. Previously mentioned researchers have proven how some traits significantly affect certain behavioural aspects in a large group. It could therefore be useful to understand if certain traits stand out as a possible influence on ad-block use.

- How can sex, age and personality traits affect the use of ad-blockers?

The second question will separate the sexes and the different age groups to search for a notable difference in their scores for each of the personality traits, to see if certain traits have different implications for the sexes. Such as, if certain traits are stronger within ad-block users amongst men, and women in different age groups, or just for one sex while the scores are lower for the other. This will also highlight the potential for identifying ad-block amongst a smaller consumer group. This is done as gender and age are seen as factors that affect personality traits and could therefore be factors that influence the decision to use an ad-blocker.

Previous research (Brinson et al., 2018; Söllner & Dost, 2019) states the reasoning and drivers behind the support of ad-blockers. However, there could be noticeable differences in between the personality traits on their reasoning on the use of ad-block. That is information that would help advertisers adapt their strategy to better accommodate their customers and improve their customer relationships with a better ad judgement. The intention of the research is therefore to understand which personality, if any, might impact people’s decision to use ad-blockers and to create a foundation for future research.

3.2 Research approach and design

3.2.1 Descriptive approach

The research questions will be answered through a descriptive approach since the aim is to create a better understanding of ad-block behaviour and its correlation with personality traits.

The research will be approached in a descriptive manner as the purpose is to increase the

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17 knowledge about ad-block behaviour. Secondary data is gathered to create an understanding of the subject. A quantitative method will then be utilized to gather primary data to answer the research questions.

Previous studies, reports and data are collected mainly from studies found in Google scholar.

Some of the keywords that were used consisted of “Ad-block”, “ad avoidance”, “ineffective digital marketing”, “personality traits”, “relationship marketing” and “targeted advertisements”. The literature review will inspire the questions set for participants. The previous research on ad-blockers showcases the expected response ratio amongst ad-block users, their age and sex. It allows for control of the data set and to check if the respondents reflect larger populations.

3.2.2 Target group & Sampling

The research is targeted towards English speakers that consume digital media. It is meant to roughly target the age range of 18-35 as they are seen as the most probable ad-block users. The distribution channels are chosen thereafter. The questionnaire will be published in forums targeted at students at Luleå Tekniska Universitet and other young adults. However, the questionnaire will be left open for anyone willing to partake, as it is problematic to target a small group of individuals online (Matthew & Sutton, 2017). That means that people of all ages have the possibility to participate. The limited timeframe and resources also make it all too troublesome to gather sufficient data from specific groups. The questionnaire is therefore sent out through e-mails, posted on Facebook, Linkedin and Reddit to create widespread participation. This method for distribution is chosen because ad-block users are found in digital places, and the offline method would reach the wrong target group and be unrepresentative.

This means that convenience sampling will be utilized, as the chosen participants are random and participate on their own terms.

3.2.3 Response and nonresponse bias

There is the possibility of response bias, that participants skew the truth and does not answer truthfully. An issue that can be best avoided with the phrasing of questions (Moore et al., 2017).

To avoid this issue, the chosen questions are inspired from previous research questionnaires and from using an already validated line of question, the BFI-10 (Rammstedt et al., 2007) for the personality test. There is also the issue of nonresponse bias that appears in online questionnaires. That is, the possibility that potential participants feel like they are too distant with their opinions and behaviour, and therefore choose to not respond (Moore et al., 2017).

This issue is difficult to address and exists in all online surveys. It is however good to have in mind if the data produce heavily skewed results (Moore et al., 2017).

3.2.4 Online Questionnaire

A questionnaire was published online on various social media channels and sent out to students at Luleå Tekniska Universitet. The survey is divided into five different parts, which are all explained below and can be seen in its entirety in Appendix A.

• Part 1 contains questions regarding the participant’s age and sex. A gentle introduction into the questionnaire that produces information that can be compared to target groups for previously referenced studies. Other factors, such as geographical location, was left

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18 out to keep the questionnaire as short as possible. It was also left out as the expected responses would generally come from students in Luleå and participants from other locations would be too scattered or too few to be able to compare them.

• Part 2 contains the BFI-10 personality test that is tried and validated (Rammstedt &

John, 2007). It contains 11 questions that will predict the personality traits of the participants. As agreeableness and extroversion are two traits that explain significant importance to all relationship marketing practices (Caliskan, 2019), the 11th item for agreeableness will be included in the BFI-10. These questions are seen in Appendix A and remain unaltered. They are on a Likert scale and participants answer to what degree they agree to the different statements.

• Part 3 is a short question asking participants if they use an ad-blocker or have one installed. This allows the participants to be divided into their sample groups and only the people that use ad-block would then move to part 4. The other participants would be sent straight to part 5 as part 4 is irrelevant for them.

• Part 4 consists of questions about which platforms users have ad-blockers installed and the reasoning as to why they installed them. This is meant to get an understanding of potential differences in how ad-blockers are used. For example, if certain traits are stronger for people that use ad-block on a specific platform. Ad-blockers are generally seen on computers, so it would therefore be of interest to see if users on certain platforms stand out.

• Part 5 contains two open-ended questions regarding participants attitudes towards digital advertisements. They were-open ended to allow participants to voice their actual opinions on advertisements. They were also meant to highlight potential trends seen for ad-block users and non-ad-block users. These questions were not required to be answered, as they do not help to answer any of the research questions.

The questionnaire is entirely in English, as a Swedish version of the BFI-10 has not been tried and validated. This is not expected to create any problems as the target group is considered to have a very high proficiency and fully comprehend the language (EF EPI, 2020). A long survey containing a BFI-44 test or other similar tests for personality traits could easily become too long and deter potential answers (Matthew & Sutton, 2017). Therefore, the survey is kept short and condensed to generate responses. One major reason as to why people use ad-blockers is their privacy concerns (Pujol et al., 2015). Some participants could find a survey about their personality trait to be intrusive. It will therefore be made explained how answers will be entirely anonymous and that the data set will not be published.

3.3 How the results were analysed

The collected data is analysed in a series of steps. Initially, it was analysed to check that the collected data looks like that of previous studies. Initially, the participants will be compared to

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19 that of previous research and reports mentioned in the literature review. Ad-block users are generally young adults, but not exclusively. The age of participant ad-block users and those without ad-block should not be that different from one another. Most researchers (Brinson et al., 2018; Iqbal et al, 2017; Pujol et al., 2015) and reports (AudienceProject, 2020; Kemp, 2021;

Pagefair, 2017) found that ad-blockers were roughly 18-30 years old. Ad-block usage ranged from 60% (AudienceProject, 2020) and 42,7% (Kemp, 2021) to a global usage of 11%

(Pagefair, 2017). Given the chosen distribution channels, the expected result of ad-block use should be comparable to reports with the larger number of ad-block users.

Half of the BFI scores had to be reversed, as questions 1,3,4,5 and 7 in the BFI-10 are reversed coded. Once they are reversed, they can be added together with the other question for the specific trait. Each trait has two questions each (except for agreeableness that has three questions). These two questions (three) are added together giving a maximum score of 10 (15) and a minimum score of 2 (3). The median value, 6 (9) indicates a neutral score. A neutral score on a personality trait means that the individual is neither positively nor negatively leaning in said trait. A strong score above the median indicates that the trait is more of a defining trait for that individual. For example, a score of ten in extraversion would indicate a highly sociable individual that gains energy from social situations, is open and sharing. A score below the median would indicate that the trait is a negatively defining trait for the individual. A low score in extraversion implies that the person is introverted, they dislike social situations and is a more private individual.

All responses were first divided into two separate groups depending on their use of ad-blockers.

The first group contains ad-block users and the second one contains all participants that do not use or have an ad-blocker installed. These groups were also divided by sex to get a better understanding of how differences in behaviour are different between the sexes. These groups were initially analysed and searched for outliers and normal distribution. Afterwards, t-Tests were run with both groups for each personality trait. These t-Tests could then prove if the difference seen in mean scores is significant or insignificant. The t-tests are performed using Excel and the ToolPak extension. The tests are set up with the null hypothesis that there are no difference between the two groups, so the null hypothesis for these t-Tests was that there are no differences of mean score, and the alternative hypothesis would then be that there are differences in mean score between ad-block users and non-ad-block users.

3.4 Credibility of the data

The gathered data is independently collected through primary sources and with no monetary interest to affect the data. The convenience sampling ensures somewhat random participation and therefore a random data set. Each participant partook with their own free will and had no incentive to produce false data. Secondary data used in the research only aimed to guide questions and possible conclusions. They were however of no effect on the collected primary data set. The re-test possibility for the questionnaire is fully possible. The re-test possibility for the sample for this research is however impossible. Given that participants were random and anonymous.

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20 The personality test (BFI-10) is validated and tried in multiple scenarios and should produce reliable results. An issue that arises is the credibility of the survey answers. Previous researchers (AudienceProject, 2020) found that there is a difference in between the actual number of ad- blockers in use, and the participants that stated that they used the software. They measured ad- block use both through a technical aspect, by detecting whether an ad-blocker was present, and by asking the participant whether they used one or not. More people said that they used ad- blockers than the researcher was able to see in their technical detection. That could provide an issue as this analysis will rely simply on participants answers.

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4.0 Data analysis

This chapter will be used to present the collected data and show the results of the analysis of the data. It will also discuss the answers from the open-ended questions.

4.1 Gender, age, and ad-block usage

The questionnaire resulted in a sample with a total of 159 responses. Most individuals were male, 60% of them. 37% were female and 3% chose not to disclose their sex. These respondents were divided into two separate groups depending on their answer to the questions of “Do you use an ad-blocker or have one installed?” as seen in table 1 below. The groups are of different sizes but given the large sample (n>40), t-tests are still viable (Moore et al., 2017).

Male Female Other Total Ad-block users: 73 34 5 112

(70,44%

Non-Ad-block users:

22 25 0 47

(29,56%)

Total 95

(59,75%

59

(37,11%) 5

(3,14%) 159 (100%)

Table 1: Respondent’s ad-block usage divided by sex

The group consisting of ad-blockers have a total of 112 observations, which is 70% of all observations. The group without ad-blockers consists of a total of 47 observations, which is roughly 30% of the observations. The group of ad-blockers were mostly males. While the group without ad-blockers consists of more females, by a small margin. These two groups were also divided by sex, to be able to see the potential differences between the sexes. The group of people that chose not to disclose their sex were too few to analyse and compare to men and women.

Ad-block users: Non-Ad-block users: Total Male 73 (76,4%) 22 (23,2%) 95 (100%)

Table 2: Male respondents

Male respondents were predominantly ad-block users, 76.4% of male participants to be exact.

Only 23,2% of male respondents did not use ad-blockers. The number of observations is heavily skewed amongst males. Given that n1+n2>40, meaning that t-Tests are still a viable option (Moore et al., 2017).

Ad-block users: Non-Ad-block users: Total Female 34 (57,6%) 25 (42,4%) 59 (100%)

Table 3: Female respondents

Female participants were more evenly divided with 57,6% of them using ad-blockers while 42,4% of them do not. The total number of females observation (59) is also larger than 40, which means that a t-Test would be acceptable even with a heavily skewed result. The 5 respondents that chose not to disclose their age will not be analysed in the comparisons between

Personality Traits and Ad-block Use A descriptive investigation of personality traits among ad-block users