A call for mobile advertising : examining attitudinal responses to personalised mobile display advertising on smartphones

Graduate School of Communication

A Call for Mobile Advertising

Examining attitudinal responses to personalised mobile display advertising on

smartphones

Master Thesis

Faculty of Social and Behavioural Science Persuasive Communication track

Supervisor: dr. Ester de Waal

Date of completion: January 31, 2018 Student: Romeijn Sadée

Abstract

Smartphones present advertisers with state-of-the-art opportunities to reach consumers individually. While effectiveness of mobile advertising is measured by analysing click-through rates, both practitioners and academics lack proficient understanding of factors influencing mobile advertising effectiveness. This study addresses the most common form of mobile advertising – mobile display advertising (MDA) – and analyses attitudinal responses to and click-through intentions of personalised MDA on smartphones. The main questions of this research are: What is the effect of personalised versus non-personalised mobile display advertising on attitudes toward the ad and subsequently on intentions to click on the ad? And to what extent do privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone influence the effect of personalised versus non-personalised MDA on the attitude toward the ad? An online between-subjects survey-embedded experiment (N = 157) was conducted among European smartphone users between 18 and 60. The results indicate that attitudes toward the ad mediate the effect of personalised MDA on intentions to click on the ad. While privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone do not influence the latter effect, additional analyses revealed that perceived distraction of the ad does significantly influence the effect of personalised MDA on attitudes toward the ad. Higher levels of distraction result in less positive attitudes toward the ad. The results alert that follow-up research is necessary to examine what other factors influence mobile advertising effectiveness, as the importance of mobile advertising – for academics and marketers – is growing rapidly.

Keywords: mobile advertising, mobile display advertising, personalisation paradox, attitudes

Introduction

Do you recall the time when owning a mobile phone was extraordinary? The time when mobile phones could hardly do anything else than calling your mom and texting with your friends? Although it may seem like ages ago, those days are roughly fifteen years behind us. Oh, how times have changed.

Mobile phones dominate modern-day life. Approximately 60% of the entire global population uses mobile phones. This rate is expected to reach 62% by 2019 (eMarketer, 2016). The devices have captured prominent roles in people’s day-to-day lives: mobile phones are always on, close-by, checked regularly, and allow continuous connectivity with others (Grewal, Bart, Spann, and Zubcsek, 2016). They have become personal devices that function as an extension of one’s personality and individuality (Grant and O’Donohoe, 2007; Sultan, Rohm, and Gao, 2009; Vincent, 2005). As a result, users become emotionally attached to these devices (Gao, Rohm, Sultan, and Pagani, 2013). Today, smartphones provide enduring connectedness through Wi-Fi, 3G, and 4G-networks, and offer a variety of communication channels, applications, and storage possibilities. For most consumers, smartphones have surpassed personal computers as the principal gateway to the Internet (O’Kane, 2013). The widespread prominence of smartphones presents advertisers with unique opportunities to reach consumers anytime and anywhere (Aguirre, Roggeveen, Grewal, and Wetzels, 2016; Grant and O’Donohoe, 2007; Grewal, Bart, Spann, and Zubcsek, 2016; Hameed, Shaf, Ahsan, and Yang, 2010; Liu, Sinkovics, Pezderka, and Haghirian, 2012).

Mobile marketing is understood as “the set of actions that enables firms to communicate and relate to their audiences in a relevant, interactive way through any mobile device or network” (Mobile Marketing Association, 2010, p. 7). Mobile advertising can be understood similarly as mobile advertising, which refers to practices that organisations employ to communicate and engage with consumers to enable them to access information,

download content, or purchase products on mobile devices (Mobile Marketing Association, 2008). The popularity of smartphones among consumers is fuelling the exorbitant amounts of mobile ad spending (Nadeema, Rodríguez, and Pérez-Vegaa, 2015), as mobile advertising has increasingly become an important feature of firms’ advertising efforts (Grewal et al., 2016; Liu et al., 2012; Kim, Heo, and Chan-Olmsted, 2010; Rohm, Gao, Sultan, and Pagani, 2012; Saadeghvaziri and Seyedjavadain, 2011). In 2015, mobile advertising accounted for an estimated $69 billion in spending worldwide (eMarketer, 2015a). Predictions are that it is likely to surpass computer-based advertising soon (eMarketer, 2015b). The most common form of mobile advertising is Mobile Display Advertising (MDA) (Bart, Stephen, and Savary, 2014). MDA takes the form of banner images in mobile applications and on mobile web pages (Bart et al., 2014). The effectiveness of MDA is measured by analysing click-through rates (Idemudia, 2014a, 2014b; Nakamura and Abe, 2005). To positively influence such rates, marketers collect data about users’ demographics, preferences, online behaviours, and search histories. With this information, marketers aim to construct tailor-made mobile adverts for individual consumers (Liu, 2017; Liu and Simpson, 2016; Nyheim, Xu, Zhang, and Mattila, 2015; Tucker, 2014; Xu, Liao, and Li, 2008). However, research has shown that personalising advertisements can both be an effective and ineffective communication strategy (Aguirre, Mahr, Grewal, De Ruyter, and Wetzels, 2015).

This notion is known as the personalisation paradox. It entails that personalised advertising messages can both increase and decrease consumer engagement with an organisation (Aguirre et al., 2016; Aguirre et al., 2015). Personalisation is understood as the ability to construct tailored messages based on knowledge about one’s preferences and behaviour (Adomavicius and Tuzhilin, 2005; Nyheim et al, 2015). As an advertising strategy, personalising content poses several advantages to companies. By collecting data about each consumer, firms can deliver the right content to the right person at the right time (Liu and

Simpson, 2016), enhancing advertising effectiveness and boosting business opportunities (Lee and Cranage, 2011; Liu and Simpson, 2016; Murthi and Sarkar, 2003; Song and Zinkhan, 2008; Zhang and Wedel, 2009). From a consumer’s perspective, personalisation offers benefits such as convenience and efficiency, increasing their attitudes toward the personalised content (Kim and Han, 2014; Okoe and Boateng, 2015; Sheng, Nah, and Siau, 2008) and enhancing their intentions to purchase from firms who adopt personalisation (Lee and Cranage, 2011). Paradoxically, when personalised content triggers one’s privacy concerns or perceptions of intrusion, it is likely to diminish a consumer’s engagement with a firm (Aguirre et al., 2016), and negatively influences consumers’ attitudes toward personalised messages (Joinson and Paine, 2007). Attitude toward advertising is described as a learned predisposition to react in a positive or negative manner toward advertising (MacKenzie and Lutz, 1989). In marketing academia, attitudes are understood as reliable measures of advertising effectiveness and form an understanding of subsequent consumer behaviour (Nadeema et al., 2015). This thought correlates to the Theory of Reasoned Action (TRA), which posits that attitude is a determinant of intention, which subsequently predicts behaviour (Ajzen and Fishbein, 1977). In this respect, measuring the effectiveness of personalised advertisements requires examining one’s attitude toward the ad, and one’s intention to click on the advert. As personalisation is an important factor in constructing consumers’ attitudes toward advertising (Kim and Han, 2014; Okoe and Boateng, 2015; Xu, 2006), attitudinal responses to personalised messages are incremental factors in analysing the effectiveness of personalised MDA (Bart et al., 2014).

The present study analyses the effect of users’ attitudes toward personalised adverts on smartphones and assesses the effects of personalised (versus non-personalised) MDA on users’ intention to click on the ad. As attitude is a determinant of intention (Ajzen and Fishbein, 1977), the effect of personalised MDA on the intention to click on the ad is assumed

to be explained by the attitude towards the ad. From previous studies, we know that the effect of personalised advertising on attitude toward the ad, is influenced by users’ privacy concerns, perceived relevance of the ad, and their emotional attachment to their smartphones. Whereas privacy concerns negatively affect the relationship between personalisation and attitude toward the ad (Baek and Morimoto, 2012; Malhotra, Kim, and Agarwal, 2004; Pesaud and Azhar, 2012), perceived relevance of the ad positively affects personalisation’s effect on attitudinal responses (De Keyzer, Dens, and De Pelsmacker, 2015; Kim, 2013; Petty, Barden and Wheeler, 2002). Consensus on the effect of emotional attachment to the smartphone on the relationship between personalisation and attitudes toward the ad has yet to be reached. On the one hand, emotionally attached users want to control their mobile territory (Verkasalo, López-Nicolás, Molina-Castillo, and Bouwman, 2010), which implies a negative effect of emotional attachment. On the other hand, emotionally attached users want to customise their phones with unique content (Gao et al., 2013; Kolsaker and Drakatos, 2009), attributing to the belief that they might embrace personalised MDA. Proficient understanding of effective MDA on smartphones and influences of the variables described above is scarce among academics and marketers. This study contributes to overcoming this scarcity of knowledge on MDA effectiveness on smartphones.

Given the widespread prominence of smartphones and marketers’ interest in mobile advertising (Berman, 2016), a better understanding of factors influencing mobile advertising campaigns is necessary (Bart et al., 2014). To effectively advertise on smartphones, understanding users’ perceptions, attitudes, and intentions is critical (Kim and Han, 2014; Noor, Sreenivasan, and Ismail, 2013). This study provides such an understanding. The main research questions of this study are: What is the effect of personalised versus non-personalised mobile display advertising (MDA) on the attitude toward the ad and subsequently on the intention to click on the ad? And to what extent do privacy concerns,

perceived relevance of the ad, and emotional attachment to the smartphone influence the effect of personalised versus non-personalised MDA on the attitude toward the ad?

To date, no prior research investigated the effects of privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone, when analysing MDA effectiveness on smartphones. The importance of mobile advertising is growing rapidly. This study adds to the current understanding of advertising on this medium, and provides new insights into users’ receptiveness of personalised MDA on smartphones.

Theoretical framework

Personalisation

Consumers use their smartphones for a variety of purposes beyond talking or texting, which gives advertisers opportunities to collect data and personalise their advertisements (Aguirre et al., 2016; Grewal et al., 2016). By gathering and using consumer data, companies want to meet consumers’ needs and demands through personalised offers (Aguirre et al., 2015; Berman, 2016; Montgomery and Smith, 2009). In relation to general ads, personalised ads allow firms to create deep connections with consumers (Urban, Liberali, MacDonald, Bordley, and Hauser, 2014), as personalised messages lead to higher response rates of advertisements than non-personalised messages (Aguirre et al., 2015; Bleier and Eisenbeiss, 2015; Köster, Rüth, Hamborg, and Kasper, 2015). Ultimately, organisations aim to influence purchase decisions with personalised ads (Hawkins, 2012). personalisation is an important factor in constructing consumers’ attitudes toward advertising (Kim and Han, 2014; Okoe and Boateng, 2015; Xu, 2006). Building on the Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1977), positive attitudes toward the ad will lead to higher intentions to click on the ad. The concepts of attitude, intention, and behaviour form the foundation of TRA (Ajzen and Fishbein, 1977). Ajzen and Fishbein (1977) described that attitude affects behaviour through

intention. This study follows TRAs line of reasoning and assumes that the effect of personalised MDA on one’s intention to click on the ad is explained through the attitude toward the ad. Prior studies described that personalisation leads to higher click-through intentions (Aguirre et al., 2015; Köster et al., 2015; Tam and Ho, 2006). Yet, these studies were conducted in a classic mobile phone context, lowering their generalisability to today’s status-quo. Today, smartphones offer significantly more features and applications than classic mobile phones. This study examines whether the findings of Aguirre et al., (2015), Köster et al., (2015), and Tam and Ho, (2006) still hold. Therefore, the first hypothesis is:

H1: Personalised mobile display advertising leads to a more positive attitude toward the ad, which in turn increases one’s intention to click on the ad, whereas non-personalised mobile display advertising leads to a more negative attitude toward the ad, decreasing one’s intention to click on the ad.

Privacy concerns

Privacy concerns refer to the degree in which consumers are worried about the potential invasion of the right to prevent the disclosure of personal data to others (Baek and Morimoto, 2012, p. 63). Privacy concerns exist among consumers as they are afraid of intrusion and invasion of their privacy (Awad and Krishnan, 2006; Gao et al., 2013; Persaud and Azhar, 2012; Smit, Van Noort, and Voorveld, 2014), or fear problems stemming from online identity theft (Bandyopadhyay, 2009). Consumers are reluctant to receive marketing messages that they may not want and lack the control to determine when, where, and from whom to receive advertisements (Persaud and Azhar, 2012). Smit et al. (2014) found that consumers in the EU are worried about the misuse of their data and are especially negative about the idea of privacy violations. Consumers have little control over which companies collect their data and

with whom they share this information (Angwin, 2010). Such feelings contribute to privacy concerns.

When advertisements are based on one’s personal data, privacy concerns may arise (Persaud and Azhar, 2012). Such concerns have contradicting effects for companies since they heighten risk perceptions of consumers and decrease levels of trust and the willingness to engage with the brand (Van Slyke, Shim, Johnson, and Jiang, 2006). Moreover, privacy concerns potentially increase scepticism toward the ad, leading to complete avoidance of advertising (Baek and Morimoto, 2012), and can provoke negative consumer attitudes (Persaud and Azhar, 2012). Malhotra et al. (2004) described that privacy concerns in online settings negatively influence attitudes toward online marketing efforts. Translated to this study, privacy concerns, thus, negatively influence the effect of personalised MDA on attitude toward the ad. Therefore, the second hypothesis is:

H2: The positive effect of personalised mobile display advertising on the attitude toward the ad is less positive for people with higher levels of privacy concerns.

Perceived relevance of the ad

To overcome privacy concerns, marketers must construct messages that benefit consumers in a meaningful way (De Keyzer et al., 2015). When a message is personalised, consumers tend to see the content as more self-relevant because it contains information about themselves (Tam and Ho, 2005). Tam and Ho (2006) explained that content relevance refers to whether the message is relevant to the consumer’s processing goals. The Elaboration Likelihood Model of Persuasion (ELM) of Petty and Cacioppo (1986) assists in explaining how consumers can perceive the relevance of the ad.

ELM describes two types of elaboration resulting from two routes of persuasion. When

consumers are motivated and able to process a message, an advertisement is likely to be processed via the central route. Processing via the peripheral route happens when consumers are not motivated nor able to process a message (De Keyzer et al., 2015). Central processing leads to higher elaboration, whereas peripheral processing results in a lower elaboration of the message (Petty and Cacioppo, 1986). Considering ELM, personalisation should benefit attitudes both under high and low elaboration (Petty et al., 2002). For high elaboration, personalisation leads to biased message processing: arguments in personalised messages are perceived as stronger than arguments in non-personalised content (De Keyzer et al., 2015). For low elaboration, personalisation serves as a heuristic cue that prompts positive attitude change (De Keyzer et al., 2015).

Self-referencing is important in both types of elaboration. It refers to the extent to which a consumer relates information to him or herself (Tam and Ho, 2006). Hence, self-referencing relates to perceived relevance of the ad. It has a positive effect on attitude in both high and low elaboration (Hawkins, Kreuter, Resnicow, Fishbein, and Dijkstra, 2008).

Kim (2013) and De Keyzer et al. (2015) explained that relevance of the ad positively influences consumer responses. De Keyzer et al. (2015) described that personalisation leads to higher perceived relevance of the ad, which subsequently spurs positive attitudes toward the ad. Yet, consumers weigh potential benefits to possible losses related to personalised ads (Sultan et al., 2009). Therefore, it can be argued that perceived relevance of the ad has a moderating, instead of mediating, influence in the context of this study. Petty et al. (2002) described that personalised advertising increases message processing and attitudes when the perceived relevance of the ad is high. When consumers perceive content as relevant, the message receives more attention, leading to greater elaboration, message processing, and

ultimately persuasion (Tam and Ho, 2005). In this sense, perceived relevance of the ad strengthens the effect of personalised (versus non-personalised) MDA on attitude towards the ad. It is critical, however, that consumers perceive the message as tailored to their needs and preferences (Kramer, 2007). Therefore, the third hypothesis states:

H3: The effect of personalised (versus non-personalised) mobile display advertising on the attitude toward the ad is more positive for people who perceive the ad as more relevant to their needs.

Emotional attachment to smartphone

Mobile phones characterise more than just a communication device: they are also used to represent the self through personalised features (Du, 2012; Jung, 2014; Sultan et al., 2009; Tossell, Kortum, Shepard, Rahmati, and Zhong, 2012; Verkasalo et al., 2010). Personal attachment to the smartphone refers to the level in which a user seeks to personalise his or her phone with unique content related to one’s identity (Du, 2012; Sultan et al., 2009). Personal attachment and emotional attachment can be understood similarly, as emotional attachment refers to the extent to which a user uses the phone continuously, considers him or herself addicted to the device, and considers it to be an integral part of life (Gao et al., 2013). Emotional attachment also refers to the value that users attain to personal information (photos, videos, texts, etc.) stored on mobile phones (Du, 2012; Sultan et al., 2009). Mobile phones have a prominent role in sustaining a connection with friends and family (Bacile, Ye, and Swilley, 2014; Jung, 2014; Wehmeyer, 2007) and keeping the device on provides a sense of being easily contactable and easily able to contact others (Vincent, 2005). The enthusiasm for mobile phones and their social embedding make mobile technologies valuable channels for advertisers (Kolsaker and Drakatos, 2009).

Vincent (2005) predicted that emotional attachment to mobile devices would be a strong influencer in future adoptions of new mobile services. In the case of mobile advertising, contradicting evidence exists considering the effect of emotional attachment to the smartphone. On the one hand, Gao et al. (2013) found that emotional attachment to the mobile phone positively influences attitudes toward mobile marketing. These authors found that emotional attachment to the mobile phone had a significant effect on attitudes toward mobile marketing messages. Thus, highly attached users were found to be more positive toward mobile advertising (Gao et al., 2013).

On the other hand, Verkasalo et al. (2010) found that emotionally attached users could be more demanding, thus, more critical toward mobile advertising. They explained that emotional attachment negatively influences the relationship between personalisation and attitudes toward the ad, as emotionally attached users want to have control over their mobile territory.

There is a general lack of enthusiasm for mobile advertising among consumers (Kolsaker and Drakatos, 2009). Kolsaker and Drakatos (2009) proposed that mobile advertisers must understand what information users would be interested in and willing to receive. Differentiated and well-targeted strategies are critical for mobile advertising effectiveness (Kolsaker and Drakatos, 2009). In this respect, Kolsaker and Drakatos stated that personalised advertisements could overcome the general lack of enthusiasm for mobile advertising. Considering the lack of consensus on the effect of emotional attachment to the smartphone on attitudes toward mobile advertising, the following research question was formulated:

RQ1: How does emotional attachment to the smartphone influence the effect of personalised (versus non-personalised) mobile display advertising on the attitude toward the ad?

In sum, the analyses of this study revolve around the effect of personalised versus non-personalised MDA on users’ intention to click on the ad, hypothesising a mediating role for attitude toward the ad. Considering the personalisation paradox, this research examines moderating effects of privacy concerns and perceived relevance of the ad on the relationship above. This effect is also moderated by one’s emotional attachment to the smartphone. Figure 1 outlines the conceptual model of this study.

Figure 1: Conceptual model of the current study.

Method

Sample

The research was conducted among European smartphone users between 18 and 60. Convenient sampling via Facebook, e-mail, and personal contact was used to find participants for this study. A total of 192 respondents participated, but 166 participants completed the survey entirely. Participants from non-European Union countries (n = 5) were not considered in the analyses. In this sense, all participants were from countries with the same regulations on the collection and usage of online consumer data, formulated by the European Union. Only 3 respondents reported that they did not have a smartphone, and 1 respondent reported that she did not use her smartphone to surf the Internet. This resulted in a final sample of 157

respondents. 54.1% (n = 85) of the final sample were males. 82.2% (n = 129) of the final sample followed or completed a university bachelor. 15.9% (n = 25) of the final sample followed or completed a university of applied science bachelor. Just 1.9% (n = 3) of the final sample only completed their high school education. The mean age of final sample was 28.22 years (SD = 8.90). 89.8% (n = 141) of the participants reported that they had made at least one purchase via their smartphone. A majority (94.2%; n = 145) of the respondents in the final sample lived in the Netherlands at the time they participated in this study. Appendix B outlines the frequencies of the demographics of the respondents in the final sample. Participants did not receive compensation for their efforts.

Design

An online between-subjects survey-embedded experiment was conducted in Qualtrics. Participants were randomly assigned to one of the two conditions of this study: personalised mobile advertising (n = 75), or non-personalised mobile advertising (n = 82).

Materials

The stimuli were screenshots of personalised or non-personalised mobile display advertising (MDA). Bart et al. (2014) explained that MDA campaigns that promote utilitarian (versus hedonic) and higher (versus lower) involvement products – such as clothing – increase favourable attitudes of consumers, increasing their purchase intentions. For the current study, it was important that the advertised products in the stimuli correspond to users’ search behaviours on their smartphones. To test this an exploratory pre-test among 18 European smartphone users was conducted. 72.2% (n = 13) of the respondents were males. The sample of this pre-test consisted of smartphone users between 18 and 60 (M = 24.28, SD = 2.42). Participants were asked what type of product information they search via their smartphones.

This pre-test found that, when searching product information for potential purchases, smartphone users predominantly look for clothing products. See Table 1 (Appendix A) for the results of the exploratory pre-test.

In line with Bart et al. (2014) and the results from the exploratory pre-test, the stimuli of this study were personalised and non-personalised clothing mobile display advertisements. Personalised MDA were constructed conform a respondent’s gender and type of clothing. A cover story was used to personalise advertisements on type of clothing (see Appendix C). The story informed respondents that they had been looking for winter coats on their smartphones, because they needed a new one. Personalisation occurred when a respondent was exposed to MDA promoting men’s/women’s winter coats. When personalisation did not occur, respondents were exposed to general men’s and women’s clothing.

The stimuli were two winter coats mobile display ads (tailored to gender) and general mobile display ads of men’s or women’s clothing (see Appendix D). Each stimulus consisted of three standing models, wearing clothing from Asos.com, a well-known clothing website in Europe. The personalised MDA for male respondents contained three male models wearing winter coats and the text “shop now men’s winter coats at www.asos.com”. The personalised MDA for female respondents contained three female models wearing winter coats and the text “shop now women’s winter coats at www.asos.com”. The non-personalised MDA contained two male models and one female model wearing t-shirts from Asos and the text “shop now at www.asos.com”. The brand name (Asos) was placed on the right bottom of each stimulus.

Procedure

By sharing a link to the survey through Facebook and e-mail, and through personal contact participants were contacted. Data was collected between 11-12-2017 and 18-12-2017, and completing the survey took approximately 10 minutes. Before entering the survey,

participants completed a consent form. Respondents received written instructions during the survey. During the pre-questionnaire of the research, respondents received demographic questions, and questions regarding their private smartphone usage. Afterwards, respondents were provided with a cover story of which respondents had to imagine that this scenario applied to themselves. The cover story described that participants had to imagine that they needed a winter coat, and used their smartphone to search for a new one. They were informed that they did not find the right winter coat yet. After a quick search for a winter coat, participants had to imagine that they wanted to check the news on the mobile application of the Guardian (a newspaper in Great-Britain). They were forwarded to a screenshot of this mobile application. Male respondents were randomly assigned to either personalised MDA about men’s winter coats, or non-personalised MDA about clothing in general. Female respondents either received personalised MDA about women’s winter coats, or non-personalised and non-gender specific MDA. After exposure to the stimuli, respondents received a post-questionnaire in which they were asked to answer the questions conform the cover story that they read earlier. The post-questionnaire contained questions regarding respondents’ intentions to click on the ad, their attitude toward the ad, the perceived relevance of the ad, their levels of privacy concerns, and their emotional attachment to their own smartphones. Finally, all respondents were debriefed about the true nature of the study, thanked for their participation, and informed about the opportunity to ask questions to the researcher.

Measures

Intention to click on the ad

The dependent variable of this study, intention to click on the ad, was measured on a single item (‘It is likely that I will click on this advertisement’) seven-point Likert-scale, 1 (strongly

disagree) to 7 (strongly agree), based on Chen, Clifford, and Wells (2002) and De Keyzer et al. (2015) (M = 2.64, SD = 1.68).

Attitude toward the ad

Attitude toward the ad was measured on a 4-item (‘I find this advertisement convincing’, ‘The advertisement shows me important product qualities’, ‘The advertisement is pleasant to look at’, ‘The advertisement is informative’) seven-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree), based on Lichtlé (2007) (Eigenvalue = 2.32,  = .76, M = 2.89, SD = 1.14).

Perceived relevance of the ad

Participants were asked to report on the extent to which they perceived the MDA as relevant to their situation (as described in the scenario). Perceived relevance of the ad was measured on a 3-item (‘For me, the advertisement was relevant’, ‘For me, the advertisement was useful’, ‘For me, the advertisement was indicative of my interests’) seven-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree), based on Ahluwalia, Unnava, and Burnkrant (2001) (Eigenvalue = 2.38,  = .87, M = 3.67, SD = 1.60).

Privacy concerns

Moreover, participants were asked to the extent in which they are concerned about their online privacy. Privacy concerns were measured on a 4-item (‘I believe that personal data have been misused too often’, ‘I am concerned about the potential misuse of personal data’, I fear that information has not been stored safely’, ‘I feel uncomfortable when data are shared without my permission’) seven-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree), designed by Baek and Morimoto (2012) (Eigenvalue = 2.39,  = .77, M = 5.47, SD = 1.02).

Emotional attachment to the smartphone

Emotional attachment to the smartphone was measured on a 6-item (‘I regard my smartphone as personal device’, ‘When I am awake, my phone is an integral part of my life’, ‘The way my smartphone looks is important to me’, ‘I consider myself addicted to my smartphone’, ‘I fear losing my smartphone’, ‘I cannot live without my smartphone’) seven-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree), based on Sultan et al. (2009), Kolsaker and Drakatos (2009), and Persuad and Azhar (2012) (Eigenvalue = 2.56,  = .72, M = 4.92, SD = .94).

Attitude toward the brand

As one’s attitude toward the brand is likely to influence one’s attitude toward the ad, this study also measured respondents’ attitudes toward the brand Asos. Attitude toward the brand was measured on a 5-item (‘I think the brand in the advertisement is appealing’, ‘I think the brand in the advertisement is good’, ‘I think the brand in the advertisement is pleasant’, ‘I think the brand in the advertisement is favourable’, ‘I think the brand in the advertisement is likeable’) seven-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree), based on Spears and Singh (2004) (Eigenvalue = 3.96,  = .93, M = 4.11, SD = 1.23).

Demographics

Respondents were asked to report their age in years, gender, and level of education. Moreover, respondents needed to report the country in which they were living at the time they filled in the survey. All respondents were asked if they possessed a smartphone, and if they had used their smartphone to surf the Internet. Respondents also needed to report if they had

ever purchased a product via their smartphones. If not, they were asked to the likelihood that they would make a purchase via their smartphones in the future.

Additional variables

Hindrance is an important factor in causing negative attitudes toward mobile advertisements (Bhave, Jain, and Roy, 2013; Nadeema et al., 2015). The limited screen size of mobile phones reduces the amount of information that can be displayed in a mobile advertisement (Aguirre et al., 2016; Berman, 2016). Therefore, it is imperative that the information displayed in the ad meets the needs and demands of smartphone users. Consequently, additional variables – irritation toward the ad, and perceived distraction of the ad – were constructed to analyse their influence on the effect of personalisation on the intention to click, through the attitude toward the ad.

Irritation toward the ad was measured on 3-item (‘The advertisement appearing while checking the news annoys me.’, ‘Within the context of the news app, the size of the advertisement is too big.’, and ‘The advertisement appearing while checking the news is a welcome reminder of what I am looking for.’) 7-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree) (Eigenvalue = 1.78,  = .65, M = 5.65, SD = 1.05).

Perceived distraction of the ad (M = 4.53, SD = 1.81) was measured on a single-item (‘The advertisement appearing while checking the news distracts me from reading the new’) 7-point Likert-scale, 1 (strongly disagree) to 7 (strongly agree).

The construction of all variables, including the factor loadings of the items related to the latent constructs are described in Appendix E.

Randomisation

To examine if there was an equal distribution of respondents among the two conditions (personalised versus non-personalised) one-way ANOVAs and Chi-square tests were conducted. There was no significant difference between the mean age in the personalised condition (M = 28.49, SD = 9.02) and the mean age in the non-personalised condition (M = 27.96, SD = 8.83), F(1, 155) = .138, p = .710. Moreover, there were no significant differences between the two conditions regarding the gender of respondents, χ2(1) = .70, p = .404. The two conditions did neither significantly differ from one another based on the education level – high school, university of applied science, or university – of respondents F(1, 155) = .618, p = .433. A majority of the convenient sample consisted out of people living in the Netherlands. Therefore, a new variable was constructed in which respondents were coded as living in the Netherlands, or not. No significant differences between were found, χ2(1) = 1.86, p =.173. The findings above confirm that the randomisation in this study was successful.

The two conditions did significantly differ from one another in terms of respondents’ attitudes toward the brand. A one-way ANOVA indicated that the attitude toward the brand in the personalised condition (M = 4.34, SD = 1.29) and the non-personalised condition (M = 3.89, SD = 1.14) significantly differed from one another, F(1, 155) = 5.30, p = .023. This means that respondents were not equally distributed among the two conditions based on their attitude toward the brand (Asos). However, as the attitude toward the brand was measured after ad-exposure, personalisation slightly influenced one’s attitude toward the brand, r (157) = .18, p < .05.

Manipulation checks

To check whether the manipulations were successful, Chi-square tests and independents samples t-tests were conducted. Significant differences between the conditions were found

regarding respondents’ answers to the item ‘The advertisement displayed:’, χ2(3) = 125.01, p < .001. As expected, 72.7% of the respondents who were in the control condition reported that the screenshot presented general clothing for men and women. Correspondingly, 98.6% of the respondents in the personalised condition reported to have been exposed to an advertisement containing winter coats. No significant differences were found for male and female respondents in their answers to the item ‘The advertisement displayed:’, χ2(1) = 1.13, p = .288. This means that within the personalisation condition, males and females answered comparably to the item on what type of products the advertisement displayed. However, significant differences were expected. This means that the manipulation based on gender was not successful. Since the two conditions in general (not taking gender into account), did significantly differ from one another, there was sufficient ground to continue analysing.

The cover story was meant to create personalisation based on the advertised product. Respondents in the personalised condition (M = 5.81, SD = 1.03) perceived the advertisement in the screenshot more related to their intended search behaviour than respondents in the control condition (M = 2.82, SD = 1.70). This difference was significant, t(124.5) = -13, p <.001, 95% CI [-3.45, -2.54].

In addition, respondents in the personalised condition (M = 5.28, SD = 1.42) perceived the products in the more related to their current needs than respondents in the control condition (M = 2.59, SD = 1.57). This difference was significant, t(146) = -10.92, p <.001, 95% CI [-3.17, -2.20].

Main analyses

All the analyses used PROCESS (Hayes, 2013) as method, with 1,000 bootstrap samples to estimate the bias corrected bootstrap confidence intervals (BCBCI). In every analysis, the independent variable was personalisation (versus non-personalisation), and the number of analysed respondents was 157. Age was used as control variable since younger people are

expected to be slightly more positive to personalised advertisements than older people. A significant correlation between age and attitude toward the ad was found, r (155) = -.18, p < .05. Moreover, attitude toward the brand was also used as control variable since people with more positive attitudes toward the brand were expected to be more positive toward the ad than people who were less positive toward the brand. A significant correlation between attitude toward the brand and attitude toward the ad was found, r (155) = .49, p < .001. By using these control variables, the relationships examined in this research can be understood more adequately. In Figure 2, the results of the analyses are integrated in the study’s conceptual model.

Mediation effect

To test if personalised (versus non-personalised) mobile display advertising leads to a more positive attitude toward the ad which, in turn, increases one’s intention to click on the ad, PROCESS model 4 was used. The mediation model was found to be significant, b = .17, SE = .09, 95% BCBCI [.03, .40]. This implies that the attitude toward the ad partly explains the effect of personalisation on one’s intention to click on the ad. The first hypothesis of this study can be accepted, but a small effect of personalisation on the intention to click, through the attitude toward the ad was found.

Moderation effects

With the use of PROCESS model 1, the remaining hypotheses of this study were analysed. In addition, PROCESS model 7 was used to assess a potential moderated mediation effects by considering the influence of privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone, on the mediation effect described above.

Privacy concerns

It was tested whether the positive effect of personalised mobile display advertising on attitude toward the ad was less positive for people with higher levels of privacy concerns. A non-significant negative interaction effect between personalisation and privacy concerns on the attitude toward the ad was found, b = -.02, SE = .16, t(151) = .12, p = .906, 95% BCBCI [-.30, .34]. This implies that higher levels of privacy concerns do no significantly influence the effect of personalisation on the attitude toward the ad. These findings reject the second hypothesis of this study.

Perceived relevance of the ad

The third hypothesis questioned whether the positive effect of personalised mobile display advertising on attitude toward the ad differs for higher levels of perceived relevance of the ad. A non-significant positive interaction effect between personalisation and perceived relevance of the ad on the attitude toward the ad was found, b = .05, SE = .12, t(151) = .46, p = .646, 95% BCBCI [-.18, .29]. This means that the perceived relevance of the ad does not significantly influence the effect of personalisation on the attitude toward the ad. These findings reject the third hypothesis of this study.

Emotional attachment to smartphone

The question was posed: how does emotional attachment to the smartphone influence the effect of personalised (versus non-personalised) mobile display advertising on the attitude toward the ad? A non-significant positive interaction effect between personalisation and emotional attachment to the smartphone on the attitude toward the ad was found, b = .07, SE = .17, t(151) = .42, p = .677, 95% BCBCI [-.27, .41]. This implies that one’s emotional

attachment to the smartphone does not significantly influence the effect of personalisation on the attitude toward the ad.

Note: N = 157, the striped lines indicate non-significant effects. Figure 2: Findings of the study integrated in the conceptual model.

Additional analyses

Mobile advertising is subject to unique circumstances that might affect consumers’ perceptions of and attitudes toward advertising messages. Therefore, additional analyses were conducted, with irritation toward the ad and perceived distraction of the ad as moderating variables.

Additional analyses made use of PROCESS model 7 and examined if the mediating effect of the first hypothesis was moderated by irritation toward the ad and perceived distraction of the ad. These analyses also controlled for age and the attitude toward the brand.

Irritation toward the ad

A non-significant moderated mediation effect was found, b = .08, SE = .10, 95% BCBCI [-.11, .29]. This means that irritation toward the ad does not significantly influence the mediation effect of personalisation on the intention to click on the ad through the attitude toward the ad.

Perceived distraction of the ad

A significant moderated mediation effect was found, b = .10, SE = .05, 95% BCBCI [.24, -.02]. This represents a small and negative effect. This implies that perceived distraction of the ad does significantly influence the effect of personalisation on the intention to click on the ad through the attitude toward the ad. The effect of personalisation on the attitude toward the ad is less positive for higher levels of perceived distraction of the ad, resulting in a lower intention to click on the ad.

Discussion

This study examined the effect of personalised versus non-personalised MDA on smartphones on attitudes toward the ad and, subsequently, intentions to click on the ad. In line with previous studies (Kim and Han, 2014; Okoe and Boateng, 2015; Xu, 2006), the results indicate that users are more positive toward personalised mobile ads than non-personalised mobile ads. Positive attitudes toward the ad, in turn, lead to higher intentions to click on the ad than negative attitudes toward the ad. This corresponds to the Theory of Reasoned Action (Ajzen and Fishbein, 1977), which states that attitude is an important determinant of intention. Delivering personalised content enhances positive attitudes toward advertising and increases intentions to click on the ad (Lee and Cranage, 2011; Liu and Simpson, 2016; Murthi and Sarkar, 2003; Song and Zinkhan, 2008; Zhang and Wedel, 2009). This study shows that this reasoning also holds for personalised MDA on smartphones.

Furthermore, the extent to which privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone influence the effect of personalised versus non-personalised MDA on the attitude toward the ad was examined. First, privacy concerns do not influence the latter relationship, as people with higher levels of privacy concerns are not less

positive toward personalised MDA than people with lower levels of privacy concerns. Based on Awad and Krishnan (2006), Gao et al. (2013), Persaud and Azhar (2012), and Smit et al. (2014) it was expected that higher levels of privacy concerns would negatively impact the effect of personalised MDA on attitudes toward the ad. This discrepancy potentially stems from the unnatural research setting and the cover story used in this study. The unnatural setting might have caused disparity between reported levels of privacy concerns in this study and sincere feelings of privacy concerns that occur in natural settings. Moreover, the cover story did not entail actual online behavioural data of individual respondents, which could be another explanation why respondents in the personalised condition were not less positive toward the ad than those in the non-personalised condition.

Second, the results indicate that people with higher levels of perceived relevance of the ad, did not significantly have more positive attitudes toward the ad than people with lower levels of perceived relevance. This contradicts to the studies of Kim (2013), Sultan et al. (2009) and Petty et al. (2002). The artificial personalisation of ads might have been the reason why perceived relevance of the ad did not influence the relationship between personalised MDA and attitude toward the ad. Personalised ads might not have been genuinely perceived as relevant by respondents, due to disparity between the needs described in the cover story and their actual needs. When future studies seek ways to personalise ads more to individual respondents, influences of perceived relevance could be different.

Third, the question arose whether emotionally attached smartphone users would react differently to personalised MDA than smartphone users who were less emotionally attached to their devices. The results show a non-significant influence of emotional attachment to the smartphone on attitudes toward the ad. Conflicting evidence on the influence of emotional attachment exists, attributing either a negative (Verkasalo et al., 2010), or positive (Gao et al., 2013) influence of emotional attachment on attitudes toward personalised mobile ads. In this

study, emotional attachment to the smartphone was measured quite broadly, focussing predominantly on the device itself rather than the content on it. Possibly, users are attached to their smartphones’ content and functionalities instead of the actual device. Future studies should investigate whether emotional attachment to the content on smartphones influences the effect of personalised MDA on attitudes toward the ad.

Interestingly, an additional analysis indicated that perceived distraction of the ad significantly influences the effect of personalised MDA on attitudes toward the ad. People who perceived the ad as more distracting were less positive toward the ad than people who perceived the ad as less distracting. Plausibly, the ads used in this study were too big in comparison to screen sizes of mobile phones, causing perceptions of distraction among respondents. Even more, perceived distraction could potentially occur when the looks of the ad do not fit the context in which the ad appears. To better understand the dimensions that cause perceptions of distraction of mobile advertising, a multiple item construct to measure perceived distraction of the ad should be used in follow-up studies.

Admittedly, some remarks need to be made concerning the method and generalisability of this study. Since convenient sampling was used to find participants, the study’s sample is not entirely representative for European smartphone users between 18 and 60. The sample mostly consisted of people between age 20 and 30. Next, 92.4% of the respondents lived in the Netherlands, and 82.2% was highly educated. This could have influenced the results. Therefore, generalising the findings to the larger population is problematic. Moreover, while contemporary practices of personalising advertisements on smartphones are rich and advanced, this study was only able to personalise the stimulus materials based on gender and product type by constructing a cover story. The results could have been influenced by the fact that the cover story did not correspond to respondents’ real-life behaviours and preferences. Also, the artificial research setting could explain why no

significant moderating influences were found for privacy concerns, perceived relevance of the ad, and emotional attachment to the smartphone, as respondents might answer differently in a more natural research setting.

Notwithstanding the limitations, this study responds to the developing trends in mobile advertising: delivering personalised ads on smartphones. In this respect, this study builds a foundation for future studies on mobile advertising and factors influencing its effectiveness. Most studies to date focus on mobile advertising on classic mobiles phones. However, today’s smartphones present rich and diverse advertising opportunities compared to classic mobile phones, alerting that prior studies on mobile advertising demand revisions. Considering that this topic is gaining importance at a rapid pace, this study provides avenues for future research on this matter.

To conclude, the popularity of smartphones is driving the billion-dollar industry of mobile advertising. Designing personalised mobile ads for individual smartphone users is becoming the new status-quo. Yet, both practitioners and academics lack sufficient understanding of factors influencing personalised mobile advertising effectiveness. This study shows that effect of personalised MDA leads to more positive attitudes toward the ad, which in turn positively influences intentions to click on the ad. Moreover, the extent to which a mobile ad is perceived as distracting influences the effect of personalised MDA on attitudes toward the ad. Still, there is a large gap to be filled for academic research on mobile advertising, and accurate knowledge about mobile advertising effectiveness is scarce among practitioners. It shows that there is a call for mobile advertising, and the phone is ringing off the hook.

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APPENDIX A

Table 1: Frequencies of Pre-test item ‘When looking up product information for potential

purchases on your smartphone, what type of products are you interested in?’ (multiple-option answer scale).

Product type Frequency Percentage

Clothing

Holiday destinations Technological products Products for groceries Books

Shoes

Tickets to music events Other products Luxury watches Cars 11 10 10 9 8 8 8 5 4 2 61.1 55.6 55.6 50.0 44.4 44.4 44.4 27.8 22.2 11.1

APPENDIX B

Demographic information of respondents in final sample

Table 2: Frequencies of Education level of respondents in the final sample. Education level Frequency Percentage

Low Moderate High 3 25 129 1.9 15.9 82.2

Note: ‘Low’ indicates completion of high school education, ‘Moderate’ indicates following or completion of a university of applied science bachelor, and ‘High’ indicates following or completion of a university bachelor.

Table 3: Frequencies of the Age (in groups) of respondents in the final sample.

Age group Frequency Percentage

18 – 20 21 – 30 31 – 40 41 – 50 51 – 60 10 109 22 9 7 6.4 69.4 14.0 5.7 4.5

Table 4: Frequencies of the country in which respondents were living during participation.

Country Frequency Percentage

Netherlands Germany Belgium Italy Spain 145 4 3 3 2 92.4 2.5 1.9 1.9 1.3

APPENDIX C

Cover Story

A cover story was designed to create a similar mind set among all respondents of this survey. With the use of this story, the aim was to create a realistic scenario to which all respondents could relate to. All respondents were asked to read the cover story carefully, and to answer the questions that followed afterwards in correspondence to the scenario in the cover story. The cover story is displayed in Figure 3.

APPENDIX D