Master’s Thesis
Data-Driven Advertising: Progressive or Ominous? Studying the Determinants of Online Behavioural Advertising Avoidance and Scepticism on Facebook
Robyn Johnston Student I.D. 11190019
Graduate School of Communication
Master’s programme of Communication Science Supervisor: Dr. Young-shin Lim
Contents
Introduction
Chapter I - Theoretical framework Chapter II - Methodology
Chapter III - Results Chapter IV - Discussion
Abstract
This paper explores the determinants of online behavioural advertising avoidance on Facebook. Specifically, it examines the strength of specific determinants; persuasion knowledge, perceived personalization, ad irritation and privacy concerns on ad avoidance with a mediating effect of ad scepticism. A survey was conducted across a sample of 192 European participants. Regression and mediation analyses using PROCESS macro were performed in order to test the effects of the four potential determinants on advertising avoidance and scepticism. Findings from the analyses show that advertising irritation and perceived privacy concerns had significant effects on ad scepticism ad avoidance. Neither perceived personalization nor persuasion knowledge was found to be significant predictors of ad scepticism or ad avoidance. There were no mediating effects of ad scepticism between the determinants and advertising avoidance. Marketers can benefit from these findings by creating online campaigns which illicit minimal irritation that may trigger negative affective responses from individuals. In order to reduce privacy concerns it would be beneficial for policy makers to introduced more stringent laws that protect the online data of individuals.
Keywords: online behavioural advertising, Facebook, ad scepticism, persuasion knowledge, ad irritation, privacy concern, personalization.
Introduction
Today, the advancement of information technology has allowed advertisers to shift focus from traditional mass advertising to personalized advertising based on individual preferences (Baek & Morimoto, 2012). Information about consumers browsing history and personal
preferences is often collected, for the purpose of developing tailored advertising (Wong, 2015). The adjustment of online advertisements to online surfing behaviour is called online behavioural advertising (OBA). With nearly all advertisers and advertising platforms now using this
technique to serve online ads (Smit,Van Noort, & Voorveld, 2014). While this can be beneficial for driving web innovation, for brands and consumers alike, it also raises problematic security concerns around the issue of privacy. The Guardian’s Juliette Garside states that OBA is “fraught with ethical and reputational risk” and even goes as far as to suggest that big data, digital
advertising and consumer trust are about to collide (Garside, 2015, p.7). Both companies and advertisers must recognize the fine line between what their consumers would like to use and what they would consider to be overly intrusive (Wong, 2015). Therefore it is important for society, advertisers and policy makers alike to discern what are the significant factors that lead to advertising avoidance. Upon comprehension of this, advertisers can make suitable amendments in order to curve the rapid advancement of advertising avoidance.
The leading social networking site, Facebook, has heavily implemented this new form of advertising. A recent report on media use in the UK claimed that 73% of adults have a social networking profile, with 95% of these adults having a Facebook profile (Ofcom, 2016; Young, Kuss, Griffiths, & Howard, 2017). With the next most popular SNSs after Facebook – Whatsapp and Twitter – are used by 28% and 26% respectively, emphasizing Facebook’s significance (Ofcom, 2016; Young et al., 2017). Furthermore, worldwide there are over 1.94 billion active
Facebook users for March 2017, which is an 18 per cent increase year on year (“Top 20 valuable Facebook statistics”, 2017). In the beginning, Facebook was criticized by advertising analysts as just a network with little commercial power or potential for consumer insight (Kim & Huh, 2017). This changed in early 2013, when Facebook forged partnerships with data brokers including Epsilon, Acxiom and Datalogix, (Delo, 2013). These data brokers add around 500 behavioural and interest-based categories and subcategories that can be combined with Facebook’s existing targeting capabilities (Sterling, 2013). This rise of Facebook behavioural advertising happened to coincide with a privacy fight across Europe. A report commissioned by the Belgian data protection authority claimed that Facebook used long-term cookies to track users, as well as non-users of Facebook, when browsing the open web, using its social plugins such as the Like button, which is placed on 13 million sites, including health and government sites (Gibbs, 2015). One of the ways Facebook set out to counteract this negativity is to give users the opportunity to “opt-out” of behavioural advertising in their settings but the controversy surrounding Facebook’s advertising techniques remains a contentious issue.
In recent months the media interest in the power of data harvesting and Facebook advertisements has been gaining momentum. Yet there is still confusion towards this new method of advertising. On one hand, almost half of U.S. consumers surveyed regarding OBA said that: “Advertising that is tailored to my needs is helpful because I can find the right products and services more quickly” (Marshall, 2014, p.2). On the other hand, many feel as though this level of personalization is an intrusion into their privacy. Okazaki and Hiroze (2009) stated that many people have voiced their concerns about the potential harm of the use of their personal and private information. Jarvis (2016) states that too much online advertisements only leads one way: avoidance, with people installing ad blockers, which significantly harms the advertising business
and other businesses. On top of these conflicting views, as many as 92% of consumers do not understand how their data is used, according to a Chartered Institute of Marketing report (Ghosh, 2016). This leaves consumers with a confusing and paradoxical stance on the nature of online behavioural advertising with many seeing the benefit in tailored ads, yet most are not willing to have their data used for such means.
In a climate where negativity surrounding online behavioural advertising is gaining momentum, the purpose of this study is to examine the determinants that influence OBA
avoidance and scepticism, specifically on Facebook. It is crucial to understand the antecedents of ad scepticism and avoidance in order to sustain the advertising industry and thus maintain
reputable content in today’s society. A study carried out by Malloy, McNamara, Cahn, and Barford (2016) found that the estimated monthly revenue lost due to ad blockers on 10 large publisher sites varies between $3.9M and $120K. This then leads to a rise of advertorial content; leading to the depletion in quality and truth of online news and media. eMarketer estimated that brands in the U.S. will spend $4.3 billion on native ads this year, up 34% from what they spent last year (Peterson & Fishman, 2015). This is due to ad blockers finding it difficult to recognize that a piece of content has been paid for by a brand and is therefore an ad (Peterson & Fishman, 2015), Additionally OBA is one of the most innovative business opportunities
available to advertisers today, as it enables them to send contextually relevant messages to consumers (Shin & Lin, 2016). Therefore, it is highly relevant to understand why people avoid this form of advertising as potentially hundreds of millions of dollars are at stake for media companies, advertisers, and advertising platforms (Horstman, 2015).
Another reason for investigating OBA avoidance is the lack of existing literature on the topic. A significant portion of previous research focuses on how advertising works once it has
engaged consumer’s attention. Yet it could be argued that it is equally, if not more important, to understand the vast majority of advertising which is intentionally ignored (Duff & Faber, 2011). In addition, extant literature on advertising avoidance tends to focus on traditional media such as television, radio, newspapers, and magazines with little attention on Internet ad-avoidance (Cho & Cheon, 2004). Scholars have identified and analyzed many predictors of advertising both in traditional and online media (Baek & Morimoto, 2012). However, studies into ad avoidance in online behavioural advertising on Facebook context remains limited. This study aims to fill this gap in the literature, paying particular attention to persuasion knowledge, ad irritation, privacy concerns and ad scepticism regarding online behavioural advertising avoidance on Facebook. Developing an understanding of what drives OBA avoidance has the propensity to provide a tripartite solution to advertisers, lawmakers, and consumers. Firstly, an understanding will enable advertisers to develop a comprehensive theoretical framework of ad avoidance that goes beyond traditional advertising. It will also fine-tune their direct marketing communication strategies in an effort to decrease consumer avoidance of OBA. Secondly, it will answer as to what Internet-users do in order to avoid OBA on Facebook and what are their main reasons in doing so. Finally, it will give law and policy-makers some insight into the mechanisms and outcomes of OBA on Facebook and whether it is timely to introduce more up-to-date laws regarding OBA on Facebook’s advertising practices. This study chose to answer these questions amongst a
European sample. This is due to European Parliament having the power to grant laws over members of the European Union regarding privacy and data collection.
Thus, the following research question was formulated:
What are the determinants of ad avoidance and ad scepticism in the context of online behavioural advertising on Facebook? Specifically, this study seeks to determine whether
persuasion knowledge, ad irritation, privacy concern and perceived personalization have a significant effect on scepticism and avoidance. Whilst also testing to see if ad scepticism plays a mediating role between ad avoidance and the determinants.
Chapter 1
Theoretical Framework Online behavioural advertising (OBA) and Facebook
Online behavioural advertising (OBA) is the adjustment of advertisements to previous online surfing behaviour via the collection of data, usually by the installation of ‘cookies’(Smit et al., 2014). These targeted advertisements can appear in the form of banner ads or sponsored content in the individual’s Facebook newsfeed. OBA uses technology to integrate traditional
demographic qualities with real time intents of users (Tomarchio, Bellacci, & Privitera, 2010). Since the rapid advancement of big data science and dynamic retargeting, behavioural
advertising and personalization is predicted to be the future of online advertising (Liu & Mattila, 2017). In many instances, free web services run their business model around the concept of OBA, which includes monetizing personal information via internet advertising and e-commerce (Carrascosa, Mikians, Cuevas, Erramilli, & Laoutaris, 2015).
As aforementioned there is ambivalence towards whether OBA is considered a positive or negative implementation. If advertisers implement OBA too much it could cause consumers to employ blocking software for cookies and advertisements or lead to strict regulatory
interventions (Carrascosa et al., 2015). On the other hand, Tomarchio et al., (2010) found that behaviourally targeting already interested users will results in a higher click through rate and therefore increase the ROI of their campaigns.
OBA can be implemented by a variety of online platforms including social networking sites, online news websites, Youtube, and many more. In this research the focus will be on OBA on Facebook. Facebook provides a substantial source of consumer information such as
2014). In addition to this data, Facebook also tracks online browsing outside of Facebook in order to use in targeted advertising. Historically, Facebook based their advertising on the individual’s interests on their profiles and pages they had ‘liked’ (Peterson, 2015). Now
Facebook is also using passive data—when users go on their PCs and phones—to make its own ads more personalized than ever (Peterson, 2015). Advertisers who want to target Facebook users who are interested in a specific activity or product will be able to reach that audience with greater accuracy. Facebook uses this data and complex targeting algorithms in order to display personalized advertisements in the user’s news feed to enhance advertising effectiveness (Aguirre, Mahr, Grewal, de Ruyter, & Wetzels, 2015).
Advertising avoidance
Advertising avoidance is considered to be one of the biggest obstacles for the future of advertising and it has been a focus for researchers to find out the determinants of this avoidance (Li & Huang, 2016). Moreover, since the Internet is a tool or task-performing medium rather than a solely entertainment medium, such as other traditional forms of media, users may avoid Internet ads more vigorously (Cho & Cheon, 2004). Speck and Elliot (1998, p.61) define the concept as “all actions by media users that differentially reduce their exposure to ad content”. Cho and Cheon (2004) breakdown advertising avoidance into three components: cognitive (belief about an object), affect (feeling or emotional reaction to an object), and behaviour (actions to avoid an object) responses.
In the context of online behavioural advertising on Facebook, cognitive avoidance occurs when the user tunes out of shifts focus (Nam, Kwon, & Lee, 2010). This is an automatic process that involves the visual screening of a stimuli embedded within the ad and does not require any conscious decision or behavioural action. It is manifested through “memory without perception”,
that is, the presence of implicit memory but the absence of explicit memory (Chatterjee, 2008). Another form of OBA avoidance on Facebook is down to user’s emotional responses towards the advertisement, which could be either positive or negative. Consumers who intensely dislike Internet ads are likely to increase their negative attitude towards the ad and therefore avoid the source of their displeasure (Cho & Cheon, 2004).
Users can also employ behavioural avoidance processes such as installing ad-blockers. Facebook and ad blockers have been at contest for years: as soon as Facebook writes code that renders ad blockers useless then the ad-blocking community will likely find another workaround (Hardawar , 2016). Currently, existing blockers scour a webpage’s source code for signs they are ads but can be easily disguised by anti-ad blocking sites (Sheehan, 2016). Another way users can stop online behavioural advertising without installing ad-blockers is to adjust their settings. For the purpose of this study all three forms of avoidance will be taken into account.
Ad scepticism
Obermiller and Spangenberg (1998) describe scepticism towards advertising as a
tendency to disbelieve the informational claims of advertising. In this instance, ad scepticism is a stable characteristic of consumers that plays a role in responses to advertisements (Obermiller & Spangenberg 1998). This phenomenon can be explained by Brehm’s (1989) Reactance Theory. As a rule, people desire the freedom to think, feel, and act as they choose and when they realise that they are subject to a commercial attempt that is trying to persuade them, they will perceive it as a threat to their autonomy thus triggering reactance (Brehm, 1989). White, Zahay,
Thorbjørnsen, and Shavitt (2008) extended research on Brehm’s (1989) theory which usually focused on privacy concerns to highly personalized messages that convey distinctive knowledge of their characteristics. This implies that when advertisers target behavioural advertising at an
unaware consumer, people may experience this an invasion of their online privacy and therefore feel deceived (Zarouali, Ponnet, Walrave, & Poels, 2017). Because of this privacy intrusion and thus feelings of deception, people may be more likely to react to the advertising by criticizing the method and process the advertising with more scepticism (Boerman, van Reijmersdal, &
Neijens, 2014).
Consumers who are high sceptics are more likely to discount the credibility of ad claims, which guards the user against potentially harmful consequences across ad contents and contexts (Xie, 2016). Next to reactance, Knowles and Linn (2004) distinguish another prominent factor of resistance to persuasion: a distrust of commercial stimuli. This form of resistance induces consumers to be guarded when faced with a commercial message, which becomes heightened in the online behavioural context, especially when people have a lack of information regarding the methods used (Zarouali et al., 2017). Extant literature has provided empirical evidence
supporting the extent of scepticism as a determinant of reactance responses to persuasive advertising attempts (Baek & Morimoto, 2012). Indeed, Obermiller and Spangenberg (1998) found that more sceptical consumers evaluated advertised offers more negatively than did less sceptical consumers. Taken altogether, it is expected that those high in ad scepticism will be more inclined to avoid advertising, since consumer scepticism towards online behavioural advertising reflects a general distrust of advertiser tactics, including using previous online data. Therefore the following hypothesis has been determined:
H1: Advertising scepticism will be positively related to advertising avoidance Persuasion knowledge
Friestad and Wright’s (1994) Persuasion Knowledge Model focuses on how people use their knowledge of persuasion motives to interpret, evaluate and respond to influence attempts.
This accumulation of knowledge, which the model assumes is developmentally, historically and culturally contingent, influences their subsequent reactions to the persuasive attempt (Friestad & Wright, 1994). When individuals are exposed to these persuasive messages, they will activate and carry out strategies designed to defend against the persuasive message (Evans & Park, 2015). The main assumption is that the more persuasion knowledge you have, the less susceptible you will be to the persuasive message therefore the better you are able to resist commercial
persuasion attempts (Friestad & Wright, 1994; Tutaj & van Reijmersdal 2012). With the assumption being widely backed up by a number of empirical studies (Livingstone & Helsper 2006; Tutaj & van Reijmersdal 2012).
Generally, previous research has tended to focus on more traditional forms of advertising (Evans & Park 2015; Boerman et al., 2012), significantly less information exists as to whether covert forms of advertising such as online behavioural advertising has on the resulting
persuasion knowledge activation (Tutaj and Van Reijmersdal, 2012). According to Flanagin and Metzger (2000), it is more difficult for consumers to evaluate the information on the Internet, compared to traditional media, due to the constant evolving nature of the Internet. In this case Internet literacy skills, or more specifically Facebook skills, can also be said to be an integral part of OBA persuasion knowledge of the individual. Scholars have provided various definitions of Internet literacy but in general they have defined it as the ability of access, understand,
analyse, evaluate and produce (Kim & Yang 2016). Ultimately, if a user has little Internet literacy it is highly likely that their persuasion knowledge of OBA would also be low. Therefore in this study the persuasion knowledge measurement scales will be adapted to online behavioural advertising in the Facebook context in order to correctly measure subsequent persuasion
One of the aims of this study is to examine how persuasion knowledge plays a role in the effects of online advertising format on ad scepticism and ad avoidance. After becoming aware of a persuasive message on Facebook, they may realize their autonomy is being threatened and as a reaction they may encounter scepticism, which then leads to reactance, or ad avoidance (Brehm, 1989; Van Reijmersdal, Tutaj, & Boerman, 2013). It can be assumed that persuasion knowledge leads to ad scepticism and ad avoidance, with ad scepticism mediating the effect between the two. Indeed, Wei, Fischer, and Main, (2008) suggest that covert marketing tactics negatively affect the way consumers respond to brands when persuasion knowledge is manipulated. Therefore we have come to the following hypotheses:
H2a: Persuasion knowledge will be positively related to ad scepticism H2b: Persuasion knowledge will be positively related to ad avoidance
H2c: The relationship between persuasion knowledge and ad avoidance will be mediated by ad scepticism
Privacy concern
Privacy concern is a natural drive for the individual in which they seek to control and limit physical, interactional, psychological and information access to the self or their group (Burgoon, Parrot, Le Poire, Kelley, Walther, & Perry, 1989). The legal definition refers to “the right to be left alone” (Warren and Brandeis, 1890, p. 205). Concerns over privacy become elevated when users recognize a lack of control over their information (Sieber, 1998). Wu, Huang, Yen, and Popova (2012) suggested anonymity was a crucial aspect of privacy, with Internet use rendering the achievement of such impossible. Furthermore, consumers do not have control over the secondary use of the personal information they provide during their Internet activity (Wu et al., 2012). Considering the plethora of definitions, the current study defines
privacy concern in the context of OBA on Facebook as the degree to which a consumer is worried about the potential misuse of their online activity data collected by advertisers.
Since OBA is based on previous site visits, it involves the concepts of personal space and privacy. Central to privacy is the issue of privacy concerns (Baek & Morimoto, 2012; Smit et al., 2013). Advertisers use this data to persuade or change attitudes, which could be construed as a threat to autonomy and freedom (Brehm, 1966). Several public opinion polls and surveys have highlighted a concern about what companies know about them and whether this information is used ethically (Smit et al., 2013). Zogby International (reported in Sipior, Ward, & Mendoza, 2011) found that 80% were either ‘‘somewhat’’ or ‘‘very’’ concerned about online tracking for the purpose of advertising. This online privacy concern often leads to a rejection of e-commerce or even unwillingness to use the Internet (Wu et al., 2012). Dolnicar and Jordaan (2007) show that the vast majority of consumers intend to take action when they suspect that their personal information is not protected, such as avoiding ads. Additionally, individuals with higher privacy concerns also tend to show lower tolerance to personalized messages (Bleier & Eisebeiss, 2015). Given the covert and personalized way information technology intrudes a consumer’s private domain, OBA could evoke an elevated level of resistance (Baek & Morimoto, 2012). Ultimately high privacy concerns lead to negative attitude and scepticism towards the ad ( Phelps, D’Souza, & Nowak, 2001) thus leading to ad avoidance (Baek & Morimoto, 2012).
H3a: Privacy concerns will be positively related to ad scepticism H3b: Privacy concerns will be positively related to ad avoidance
H3c: The relationship between ad avoidance and privacy concern will be mediated by ad scepticism
Advertising irritation may be defined as “provoking, annoying, causing displeasure, and momentary impatience’’(Aaker and Bruzzone, 1985, p. 48) . There have been many studies identifying aspects of advertising that lead to these negative feelings. Indeed, Wells, Leavitt, and McConville (1971) identified 6 basic dimensions of personal reactions to advertising: humour, power, warmth, uniqueness, personal relevance and irritation. In a survey of U.S. consumers Bauer and Greyser (1968) identify annoyance or irritation as the main reasons people criticize advertising. Baek and Morimoto (2012) suggested there are several factors that may trigger ad irritation, such as: ad content and execution; when an ad is untruthful; an abundance of ads or displayed too frequently. Park and Salvendy (2012) identified ad irritation as one of the three factors that shapes attitudes towards ads.
These definitions of ad irritation are attributed to more traditional forms of advertising but can also cross over to OBA on Facebook. For example, consumers may also feel over stimulated when viewing many ads in a short time or seeing a single ad too frequently (Bauer & Greyser, 1968). It is a common tenet of OBA that if you viewed or browsed for an item whilst shopping online it will appear constantly as a banner ad. Additionally, in Baek and Morimoto’s (2012) study into personalized offline advertising they suggested when consumers feel lack of control over their personal information they are likely to have irritating experiences that could contribute to ad scepticism. Indeed, Morimoto and Chang (2012) found that unsolicited
commercial email leads to increased ad scepticism. These examples can be attributed to the data collection which is necessary for the functioning of OBA. Thus when they have found out that their data has been used, the likely result is a retreat away from the source of irritation, or ad avoidance (Kennedy 1971; Krugman 1983; Park and McClung 1986; Soldow and Principe 1981).
Therefore it can be assumed that ad irritation leads to ad scepticism and avoidance but that the latter is also mediated by the former. Indeed, ad irritation results in negative attitudes toward advertising (Morimoto & Chang, 2006) and thus negatively affects the value of
advertising (Ducoffe, 1996). Ultimately, if consumers feel as though they lack control over their personal information posed by behavioural advertising, they are likely to have irritating
experiences that could contribute to cognitive or behavioural components of resistance including ad scepticism and avoidance (Baek & Morimoto, 2012). Therefore we propose the following hypotheses:
H4a: Ad irritation will be positively related to ad scepticism H4b: Ad irritation will be positively related to ad avoidance
H4c: The relationship between ad avoidance and ad irritation will be mediated by ad scepticism Perceived personalization
Pepper and Rogers (1997) defined perceived personalization as the process of using a customer’s information to deliver a targeted solution to that customer. Personalization has evolved from unsolicited commercial e-mail, postal direct mail, telemarketing, and text messaging to banner advertising and advertising on social network sites (Baek & Morimoto, 2011; Maslowska, Smit, & van den Putte, 2016). One of the reasons behind this ever increasing use of targeted advertising is the availability and amount of consumers personal data, which marketers can use to make their offers personalized to a great extent (Maslowska et al., 2016). Furthermore, personalized advertising may include added incentives such a special offers and discounts.
According to Brehm’s (1966) psychological reactance theory it would be presumed that when a user finds out that the personalization is a persuasive attempt, reactance would
occur. Baek and Morimoto (2016) stated that degree of reactance also depended on perceived utility of the advertised products. The primary tenet of OBA on Facebook is that ads showing a product or brand the user has already browsed will then follow them around. As the user has already shown interest in what is being advertised the perceived utility will be high, reducing reactance. Indeed, empirical evidence suggests that when ads are perceived as useful and valuable, they elicit lower avoidance responses from consumers (Baek and Morimoto, 2016; Pasadeos, 1990). Another factor as to why reactance may not occur is due to the unobtrusiveness of the data collection; which may benefit the consumer by not interrupting their surfing
experience (Aguirre et al., 2015). Additionally, extent research shows that ad information value is diminished to the extent that consumers are sceptical of advertising (Baek and Morimoto, 2016; Obermiller & Spangenberg, 1998). Xu (2006) also support these sentiments, suggesting that the personalization of content is the most effective way to prevent mobile advertising from being perceived as intrusive and irritating. Whilst studies suggest that privacy concern is a major issue amongst Internet users, a recent study indicates a majority of respondents are willing to share their personal data with advertisers when presented with the opportunity to receive incentives (Pew, 2016). When users perceive these ads as useful, the elicit lower avoidance responses from consumers (Pasadeos,1990; Baek & Morimoto, 2012).
H5a: Perceived personalization will be negatively related to ad scepticism H5b: Perceived personalization will be negatively related to ad avoidance
H5c: The relationship between ad avoidance and perceived personalization will be mediated by the effects of ad scepticism
Ad scepticism Privacy concern Ad avoidance Persuasion knowledge Ad irritation Perceived personalization H2a H3b H4b H5b H1 Figure 1: Conceptual model
H2c: Persuasion knowledge → Ad scepticism → Ad avoidance H3c: Privacy concern → Ad scepticism → Ad avoidance H4c: Ad irritation → Ad scepticism → Ad avoidance
H5c: Perceived personalization → Ad scepticism → Ad avoidance H2b
H3a
H4a
Chapter II Method Participants
A survey was conducted with 192 European Facebook users. Participants mainly
comprised of the researchers social connections, Facebook groups, and via snowball method 129 (67.2 %) were female and 63 (32.8%) were male. The participants ranged between the age of 18 and 63 (M = 27.51, SD= 6.93). 81 (42.2%) of the participants were British nationals, 64 (33.3%) were Dutch nationals and 9 (4.2%) were German nationals. The sample was relatively well educated with the majority, 102 participants (53.1%), being educated to bachelor level, 66 (34.4%) educated to masters level, 11 (5.7%) having some college but no degree and 6 (3.1%) were a high school graduate.
Procedure
Responses were then collected via an online survey using Qualtrics after being posted onto the researcher’s Facebook profile and student survey Facebook groups. After giving their informed consent, the participants were asked whether they lived in Europe and whether they had their own Facebook profile: the two prerequisites for taking the survey. Then participants were questioned regarding the IVs in the model in the following order: perceived
personalization; objective persuasion knowledge; ad irritation, persuasion knowledge; ad scepticism; privacy concern; ad avoidance and then some demographic questions. Measurements
Advertising Avoidance
To measure the dependent variable, advertising avoidance, a six-item scale was adapted from Baek and Morimoto (2014), Cho and Cheon (2004), and Elliot and Speck (1998).
Participants were asked to rate on a 7-point Likert scale (1=Strongly disagree, 7 = Strongly agree) to what extent they agreed with the following statements such as: “I intentionally ignore any Online Behavioural Advertising on Facebook” and “I hate any Online Behavioural
Advertising on Facebook”. The six items were computed into a scale of advertising avoidance and upon analysis, a good reliability (Cronbach’s α =.84) was reported.
Persuasion knowledge
Persuasion knowledge was measured with 7-point Likert scales merged from two previous studies (Bearden, Hardesty, & Rose, 2001; Ham & Nelsen, 2016) and also adapted to the online behavioural advertising on Facebook context such as: “I know how Facebook displays personalized ads to me” and “I can tell Facebook advertising has strings attached, requiring my online behavioural information”. The six items were then averaged to compute a reliable persuasion knowledge scale (Cronbach’s α =.79).
Ad scepticism
In order to measure the mediating variable in this study, ad scepticism, an eight-item scale was taken from Obermiller and Spangenberg (1998) and altered to measure the OBA on Facebook environment. Measurement included items such as: “Facebook advertising is trustworthy” and “Facebook advertising’s aim is to inform the consumer”. Participants were asked to rate on a 7-point Likert scale (1=Strongly disagree, 7 = Strongly agree) to what extent they agreed with the eight items. After recoding was completed the eight items were then averaged to compute a highly reliable ad scepticism scale (Cronbach’s α =.92)
Privacy concern
To measure the participants perceived privacy concerns regarding online behavioural advertising on Facebook, a scale derived from Dolnicar and Jordaan (2007) and was adapted to
the relevant environment. Measurement included items such as: “I feel uncomfortable when information is shared without permission” and “I am concerned about misuse of personal information. Participants were asked to rate on a 7-point Likert scale (1=Strongly disagree, 7 = Strongly agree) to what extent they agreed with the 6 statements. A high reliability was reported and the eight items were computed into an average (Cronbach’s α =.89)
Ad irritation
In order to measure participants perceived ad irritation, an eight-item scale was
implemented in the online behavioural advertising context on Facebook used by Fritz (1979) and adapted to the online behavioural advertising context. Participants were asked to rate on a 7-point Likert scale (1=Strongly disagree, 7 = Strongly agree) how irritating they found OBA with definitions such as: negative, irritating, pointless, unappealing regressive, unattractive, vulgar, awful. Upon analysis, a very high reliability was reported and all items were averaged into one scale (Cronbach’s α =.94)
Perceived personalization
In order to measure how users perceived OBA to be personalized to them it was measured using a six-item scale derived from Dolnicar and Jordaan (2007) and adapted to the online behavioural advertising context. Measurement included items such as: “Advertising on Facebook makes purchase recommendations that match my needs” and “I think that advertising on Facebook enables me to order products that are tailor-made for me”. Participants were asked to rate on a 7-point Likert scale (1=Strongly disagree, 7 = Strongly agree) to what extent they agreed with the five items. Upon analysis, a very high reliability was reported and all items were averaged into one scale (Cronbach’s α =.89). Complete scales for all measurements can be found in Appendix
Table 1
Correlations, means, and standard deviations for model variables
Measure M SD 1 2 3 4 5 6 1. Ad avoidance 4.72 1.24 1 2. Perceived personalization 3.87 1.26 .07 1 3. Ad irritation 4.74 1.30 .72** -.03 1 4. Ad scepticism 5.24 1.03 .53** -.08 .66 1 5. Privacy concerns 5.63 1.12 .49** .34 .43** .38** 1 6. Persuasion knowledge 5.13 0.88 .12 .18 .13 .09 .12 1 ** p < .001
Chapter III Results
All the direct hypotheses were tested using regression analyses and the mediation hypotheses were tested using (2013) PROCESS macro (with 5000 bootstrap samples) as
suggested by Hayes (2013). The PROCESS macro generates estimates of mediation, or indirect effects, based on bootstrap samples of the data. In this study, one analysis – model 4 – was run several times to determine the indirect effects of the different independent variables in this analysis
Firstly, a regression analysis was run in order to test the first hypothesis, which suggested that ad scepticism would positively influence ad avoidance. In this study our results found that this was not the case, ad scepticism did not significantly predict ad avoidance thus rejecting H1 (β = 0.10, SE = 0.08, t = 1.26, p = .208)
Regression analysis was used in order to test the hypothesis 2a which predicted that persuasion knowledge would be positively related to ad scepticism. Results showed that persuasion knowledge was not a significant predictor of ad scepticism (β = 0.01, SE = 0.07, t = 0.11, p = .915). Another regression analysis was used in order to test hypothesis 2b which predicted that persuasion knowledge would be positively related to ad avoidance. Results showed that persuasion knowledge was not found to be a significant predictor of advertising avoidance (β = 0.00, SE = 0.07 t = 0.01, p = .994). An analysis with Hayes’ macro PROCESS was used to investigate the relationship between persuasion knowledge and advertising
avoidance and as to whether ad scepticism mediates the effect between the two. The results found that the indirect effect of persuasion knowledge on advertising avoidance through ad
scepticism was not significant (point estimate = 0.00, SE = .01, 95% bootstrap CI = [-0.0133, 0.0243]). Thus, all three hypotheses regarding persuasion knowledge can be rejected.
Regression analysis was used in order to test hypothesis 3a, which predicted that privacy concerns would have a positive effect on ad scepticism. Results indicated that privacy concern was indeed a significant predictor of ad scepticism (β = 0.12, SE = 0.56 t = 2.11, p = .036). Another regression analysis was used in order to test hypothesis 3b, which predicted that privacy concerns would have a positive effect on ad avoidance. Results indicated that privacy concern was indeed a significant predictor of ad avoidance (β = 0.23, SE =0.06, t = 3.72, p < .001). The next analysis was used to test the mediation effect of ad scepticism on the relationship between privacy concern and ad avoidance. The outcome was a non-significant result (point estimate = 0.01, SE = .02, 95% bootstrap CI = [-0.0045, 0.0581]). Thus, hypothesis 3a and 3b can be confirmed but hypothesis 3c is rejected.
Regression analysis was used in order to test hypothesis 4a, which predicted that ad irritation would have a positive effect on ad scepticism. Results indicated that ad irritation was indeed a significant predictor of advertising scepticism (β = 0.10, SE =0.05, t = 1.26, p < .001). Regression analysis was used again in order to test hypothesis 4b, which predicted that ad irritation would have a positive effect on ad avoidance. Results indicated that ad irritation was indeed a significant predictor of advertising avoidance (β = 0.55, SE = 0.06 t = 8.64, p < .001). The next analysis was used to test the mediation effect of ad scepticism on the relationship between ad irritation and ad avoidance. The outcome was a non-significant result (point estimate = 0.05, SE = 0.04, 95% bootstrap CI = [-0.0328, 0.1305]). Thus, hypothesis 4a and 4b can be confirmed but hypothesis 4c is rejected.
Regression analysis was used in order to test hypothesis 5a, which predicted that perceived personalization would be negatively related to ad scepticism. Results showed that perceived personalization was not found to be a significant predictor of advertising scepticism (β
= -0.06, SE = 0.05, t = -1.22, p = .222). Again, regression analysis was used in order to test hypothesis 5b, which predicted that perceived personalization would be negatively related to ad avoidance. Results showed that perceived personalization was not found to be a significant predictor of advertising avoidance (β = 0.08, SE = 0.05, t = -1.67, p = .967). An analysis with Hayes’ macro PROCESS was used to investigate the relationship between perceived
personalization and advertising avoidance and as to whether ad scepticism mediates the effect between the two. The results found that the indirect effect of perceived personalization on
advertising avoidance through ad scepticism was not significant (point estimate = -0.01 SE = .01, 95% bootstrap CI = [-0.0321, 0.0031]). Thus, all the hypotheses regarding perceived
personalization can be rejected.
Overall, the results of this study indicate that ad scepticism does not significantly mediate the relationship between persuasion knowledge, perceived privacy concerns, ad irritation and perceived personalization. The findings did find that perceived privacy concerns and advertising irritation were significant predictors of ad scepticism and ad avoidance. Whilst previous research has indicated that perceived personalization and persuasion knowledge regarding OBA will have an effect of subsequent advertising scepticism and avoidance, no significant results were found in this study. Therefore hypothesis 2a, 2b, 3a and 3b were confirmed with no mediating effect of ad scepticism.
Ad scepticism Privacy concern Ad avoidance Persuasion knowledge Ad irritation Perceived personalization .01 .23* .55* .08 .10 Figure 2: Model testing
H2c: Persuasion knowledge → Ad scepticism → Ad avoidance (.00) H3c: Privacy concern → Ad scepticism → Ad avoidance (.01) H4c: Ad irritation → Ad scepticism → Ad avoidance (.05)
H5c: Perceived personalization → Ad scepticism → Ad avoidance (-.01) * p < .05
.00
.12*
.10*
Chapter IV Discussion
The present study aimed to examine the potential determinants of online behavioural advertising on Facebook and whether ad scepticism played a mediating role. As more time is spent online, personalizing ads to individual preferences will become more widespread. Therefore assessing the effect of personalization on ad avoidance is of paramount concern to developing effective communication strategies. This study seeks to build on previous studies into personalized advertising online and offline (Baek & Morimoto, 2012; Speck and Elliott 1997; Cho & Cheon, 2004) this time in the context of OBA on Facebook.
Comparable to Baek and Morimoto’s (2013) study, perceived privacy concern and advertising irritation was a significant predictor of advertising scepticism and avoidance. Whilst previous research has indicated that perceived personalization and persuasion knowledge regarding OBA will have an effect of subsequent advertising scepticism and avoidance, no significant results were found in this study. The same can be said for the mediation effect. Therefore hypothesis 3a, 3b, 4a and 4b were confirmed with no mediating effect of ad scepticism.
This study revealed that ad irritation acts as a significant predictor of ad scepticism and ad avoidance. It can therefore be assumed that the affective responses cause by OBA will lead to elevated levels of ad avoidance. Key elements of OBA on Facebook, such as the same ads appearing repeatedly and the content of the ads, incite irritation thus urging the user to avoid the ads and have elevated levels of scepticism towards them. This finding was in accordance with Baek and Morimoto (2012) who also found a link between ad irritation and ad avoidance. Emphasizing the significance of advertising irritation, Baek and Morimoto (2013) also found the
effect of ad attitude on attitudinal variables was magnified in digital media - such as unsolicited commercial emails - compared to traditional media, which could suggest as to why advertising irritation was an influential predictor in the OBA context. Furthering this notion, the context of Facebook could have influenced this finding as users may feel more inclined to avoid advertising in a space they feel is social and should not be invaded by the “uninvited brand’’ (Fournier & Avery, 2011). Indeed, Fournier and Avery (2011) found that marketers were confronted with the realization that social media was made for people, not for brands. On top of this, users are likely to have irritating experiences when they find out they have a lack of control over their personal information. Thus with the current media spotlight being on Facebook and privacy, people may have elevated irritation when it comes to advertising on Facebook, compared with other
contexts.
This study reveals that privacy concern regarding behavioural advertising individuals receive on Facebook indeed has a direct effect on their subsequent ad scepticism and avoidance. With the rise of OBA itself the media has been highlighting the sometimes ominous ways in which these ads are created. What is notable is that previous studies have found that privacy concerns tend to be elevated in older generations compared to younger generations (Leist, 2013). Taking into account that average age of the sample was relatively young this finding shows that privacy concerns are also applicable to the younger generation. This again may be explained by the elevated computer and Internet literacy skills attributed to this demographic which means they are able to turn of behavioural advertising or install ad blockers unlike their older counterparts.
Results also concluded that persuasion knowledge regarding OBA in the Facebook context were not significant predictors of advertising scepticism nor advertising avoidance. This
may be in part due to the individual differences within participants and whether they take the beneficial stance (relevant messages) or see the risks (privacy infringements) (Ham,
2017). Ham’s (2017) study also found that persuasion knowledge was positively associated with perceived benefits and risks from OBA, or risk perception. Therefore the mixed reactions of the participants in this study may be accounted for by this explanation. Regardless of their levels of persuasion knowledge, it may be due to another latent construct which is construing OBA as beneficial or harmful, thus impacting on their levels of ad scepticism and ad
avoidance. Additionally Smit, Van Noort, Voorveld’s (2013) survey which found that respondents were concerned about the misuse of their personal data despite their often lack of understanding. This assumption could explain how privacy concern was a predictor for ad avoidance yet persuasion knowledge was not. This could be due to the technical nature of OBA on Facebook: people can more easily understand the privacy concerns than the actual
mechanisms themselves.
In accordance with persuasion knowledge; perceived personalization was not a
significant predictor of ad avoidance. This is in stark comparison to Baek and Morimoto’s (2012) study which found it the most significant predictor of ad avoidance. It could be assumed that whilst some participants may feel a sense of familiarity with the brand that is being advertised to them it is likely that there is also an equal portion of participants who may have feelings of reactance (Brehm, 1966) resulting in a non-significant result. In addition the “personalization paradox” could come into play in this instance (Aguirre, et al., 2015). Despite evidence that response rates (accepting ads rather than avoiding them) improve with greater personalization, such efforts also could increase consumer discomfort thus leading to advertising avoidance. Aguirre et al (2015), suggests the success or failure of personalization depends on context. Thus
in the context of OBA on Facebook it would suggest that knowledge of the underpinnings of OBA creates the ideal environment for the personalization paradox to occur.
However, this study does have its limitations. In this study we did not distinguish
between different specific types of OBA on Facebook. This study simply analyzed the term as a broader concept, but perhaps it would have been insightful to separate both sponsored content within the user’s newsfeed and banner ads. Tutaj and van Reijmersdal (2012) found that ad scepticism differed for subtle and prominent online ad formats with users being significantly more sceptical towards banners than sponsored content. This creates an opening for future research. Moreover, due to the young age and the participants residing in mainly two of the European countries, the sample is not representative of the general European population. Another pitfalls of the sample due to the sampling method was occupation – a significant number were students. In order to be able to apply the findings to the greater population a more representative sample would be required. Suggestions for future research would recommend a sample beyond Europe.
The present study aimed to examine the motivations behind online behavioural advertising avoidance in the Facebook context. With the rapid advancement with Internet technologies and online advertising, a thorough comprehension of the effect of behavioural tracking on advertising is crucial in developing effective marketing strategies, understanding advertising avoidance strategies and providing information to policy makers. Ad scepticism was not found to be a significant predictor of avoidance yet privacy concerns and ad irritation were. This offers some practical implications for marketers because it shows them that they should aim to avoid irritating ads where possible, either by altering content or the frequency at which the user view the ads. Another suggestion is to implement practices that will promote positive
affective reactions such as humour via viral or interactive campaigns rather than negative affective reactions so often yielded by low-budget banner ads. Furthermore marketers and European law-makers alike must notice that privacy concern was also a significant driver of ad avoidance. This can be combatted by policy makers creating laws Facebook must adhere to when it comes to OBA thus keeping the privacy concerns of the European population at bay. It should also be acknowledged that perceived personalization, a fundamental element of OBA, did not incite ad scepticism or avoidance. Which may be welcome news to advertisers knowing they can carry on matching individual’s data with the advertising they receive on Facebook. Whilst persuasion knowledge was not a significant predictor of ad avoidance this does not mean marketers should ignore this potential antecedent of avoidance. Perhaps in the near future when people become more technologically aware the influence of this factor may change. Therefore future research into OBA is needed as society and the underpinnings of online advertising changes.
Overall, this study’s findings can contribute to marketers on their quest for fine-tuning the online behavioural advertising on Facebook in order for them to avoid advertising scepticism avoidance. In this digital age, this study has underlined the importance of targeting an audience without inciting irritation. Furthermore, with the relentless media not shying away from
revealing the potential worrying practices marketers will go to to harvest personal data it is important to put the public’s privacy concerns at ease thus not exasperating advertising
scepticism and avoidance. It also highlights that perceived personalization still hangs in a state of flux due to the outlined pros and cons exhibited by the practice. This is likely to remain on a fluctuating spectrum controlled by whether technological advances cross the ethical line and what the media decides to report upon. It is hoped that practitioners will take note of the
antecedents towards OBA outlined in this study, and also provide some impetus for academics to further delve into the rapidly advancing subject that is OBA on Facebook.
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Appendix A Measurements
Advertising avoidance. Please answer the last statements regarding online behavioural advertising. How much do you agree with the following statements? (1= strongly disagree, 7= strongly agree)
I intentionally ignore any Online Behavioural Advertising on Facebook I hate any Online Behavioural Advertising on Facebook
It would be better if there were no Online Behavioural Advertising on Facebook Online behavioural advertising on Facebook makes me feel uneasy
I have adjusted my Facebook settings in order not to receive Online Behavioural Advertising I download the latest ad blockers to stop Facebook ads
Advertising scepticism. Please answer the following statements about your views towards online behavioural advertising on Facebook. How much do you agree with the following statements? (1= strongly disagree, 7= strongly agree)
Facebook advertising is trustworthy
Facebook advertising’s aim is to inform the consumer.
I believe behavioural advertising on Facebook is informative. Online behavioural advertising on Facebook is generally truthful.
Online behavioural advertising on Facebook is a reliable source of information about the quality and performance of products.
In general, online behavioural advertising on Facebook presents a true picture of the product being advertised.
I feel I have been accurately informed after viewing (reading, listening to) most online behavioural advertising on Facebook
Most OBA on Facebook provides consumers with essential information
Persuasion knowledge. The following statements are about advertising practices on Facebook, how much do you agree with the following statements? (1= strongly disagree, 7= strongly agree)
I know how Facebook displays personalized ads to me
I can tell Facebook advertising has strings attached, requiring my online behavioural information
I understand how a marketer shows the tailored ads to me using behaviour tracking I know how online marketers offer the tailored information to me
I can see through behavioural advertising technology used to get me to buy in the online advertising
I can separate benefit and harm for the persuasion tactic of online behavioural advertising
Privacy concern. How much do you agree with the following statements?
When I receive online behavioural advertising on Facebook... (1= strongly disagree, 7= strongly agree)
I feel uncomfortable when information is shared without permission. I am concerned about misuse of personal information.
It bothers me to receive too much advertising material of no interest. I feel fear that information may not be safe while stored.
I think companies share information without permission.
Ad irritation. How much do you agree with the following statements?
When I receive online behavioural advertising on Facebook, I think it is . . . (1= strongly disagree, 7= strongly agree)
Negative. Irritating. Pointless. Unappealing. Regressive. Unattractive. Vulgar.
Perceived personalization. Please answer the following statements regarding Facebook advertising. How much do you agree with the following statements? (1= strongly disagree, 7= strongly agree)
Advertising on Facebook makes purchase recommendations that match my needs.
I think that advertising on Facebook enables me to order products that are tailor-made for me. Overall, this advertising on Facebook is tailored to my situation.
Advertising on Facebook makes me feel that I am a unique customer. I believe that advertising on Facebook is customized to my needs.
