The role of intrusiveness and relevance in the relationship
between the interaction of online and mobile advertising and
purchase intention
by
MILOU STOKER
University of Groningen Faculty of Economics and Business
MSc Marketing Management June 2015 De Reest 19 8051 JP Hattem 06 45 53 66 17 m.c.stoker.1@student.rug.nl Academic year: 2014-2015 Student number: 2586975
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Abstract
The advancement of the internet technology enables manufacturers and retailers to use online and mobile as a growth area for advertising. The use of mobile phones have become commonplace in our everyday lives. However, for mobile advertising to be successful, business models need to capture the interaction between different advertising tools. This study focuses on the interaction effect of online and mobile advertising. Feelings of intrusiveness of the mobile message are conceptualized as key attitudinal reactions triggered by a mobile advertising message and therefore investigated in this research as mediator. Besides this, the role of relevance of the ad is investigated through a moderated mediation effect to determine if relevance of the ad can overcome feelings of intrusiveness and consequently increase purchase intentions. In order to answer the hypotheses, experimental research is done.
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Acknowledgement
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Management Summary
Over recent years, the role of online advertising continues to evolve. The adoption of the internet has grown significantly and is expected to grow even more. The internet has been a testing ground for different marketing approaches to online advertising and became an integral part of marketing plans of managers. More than 50% of the US consumers performs an online search for products before purchasing it. This active search for product information emphasize the importance of online advertising.
Nowadays, the adoption of digital mobile telecommunications is also growing very fast. The use of mobile phones have become commonplace in our everyday lives. It is expected that consumers are more likely to go online via their mobile phones than via their computer. Through the integration of the internet and mobile phones, a smartphone makes it possible to generate and use social context, in addition to individual context. Using social-context information, a company can offer more personalized advertisement and reach smaller consumer segments. There are opportunities in advertising spending in the online and mobile industry since the time spent in media by users is larger than the advertising spending by companies. However, for mobile advertising to be successful, business models need to capture the interaction between different advertising tools. The current problem is the research gap in the field of the interaction effects of internet and mobile advertising for people who perceive feelings of intrusiveness. It is expected that due to the clutter effect and the use of mobile phones (which is very personal) people experience feelings of intrusiveness. Since mobile advertising effectiveness increases with relevance of the ad, this could be a moderator that weakens the effect of the interaction between online and mobile advertising on feelings of intrusiveness so that the purchase intentions will increase. Summarized, this research gives answer on the following research question: ‘Does the relationship between the interaction of online and mobile advertising and purchase intention provoke feelings of intrusiveness and can these feelings be reduced by relevance of the ad?’.
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Table of content
1. Introduction ... 8
2. Literature review ... 12
2.1 Internet advertising effectiveness ... 12
2.2 Mobile advertising effectiveness ... 13
2.3 Interaction effect between online advertising and mobile advertising ... 14
2.4 Interaction Effect Example ... 15
2.5 Purchase intention ... 16
2.6 Consumers’ feelings of intrusiveness ... 17
2.7 Relevance of the Advertisement ... 19
2.8 Conceptual Model ... 20
3. Methodology ... 21
3.1 Measures ... 21
3.1.1 Dependent variable: Purchase intention ... 21
3.1.2 Independent variable: interaction online and mobile advertising ... 21
3.1.3 Independent variable: Intrusiveness ... 22
3.1.4 Independent variable: Relevance ... 22
3.1.5 Confounding Variables ... 23 3.2 Procedure ... 23 3.3 Sample ... 23 3.4 Model ... 24 4. Results ... 25 4.1 Descriptive statistics ... 25 4.2 Pre-analyses ... 28 4.2.1 Purchase intention ... 28 4.2.2 Intrusiveness ... 28 4.2.3 Relevance of the ad ... 29 4.3 Model estimation ... 29
4.3.1 Interaction effect on purchase intention ... 29
4.3.2 Intrusiveness ... 29
4.3.3 Relevance of the ad ... 30
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1. Introduction
Will your mobile phone be used as an advertising tool in the near future? Over recent years, the role of online advertising continues to evolve. The adoption of the internet has grown significantly and is expected to grow even more (Shankar and Hollinger, 2007) with a worldwide digital media ad spending from $104.57 billion in 2012 to $213.89 billion in 2018 (eMarketer, 2015). Online advertising gives marketers the opportunity to interact with customers more at the individual level. The internet has been a testing ground for different marketing approaches to online advertising and became an integral part of the marketing plans of managers. Online advertising differs from mass media like TV campaigns and paper advertisement in that it is more personalized and make use of more consumer information. The advancement of the internet technology enables manufacturers and retailers to use online, mobile, and social media as a growth area for advertising (Ailawadi, et al. 2009). According to Shankar et al. (2007) more than 50% of the US consumers performs an online search for products before purchasing it. This active search for product information emphasize the importance of online advertising. If we look at the marketing budget and how it is allocated, much budget is spend on paid search. With paid search advertising companies are able to address consumers directly during their online search for products or services. During their online search the consumer enters search terms, which are known as keywords, at an internet search engine (e.g. Google) (Rutz, Bucklin and Sonnier, 2012). Companies can target the consumers by the used search keywords by showing them an advertisement that fits with the keywords the consumer was searching for. Another common form of internet advertising are the display ads which are based on previous internet behavior and has evolved beyond traditional banner ads to include many visual and audio features (Goldfarb and Tucker, 2011). Nowadays, the adoption of digital mobile telecommunications is also growing very fast (Perlado and Barwis 2015; Meeker 2014). The use of mobile phones have become commonplace in our everyday lives (Balasubramanian, Peterson and Järvenpää, 2002). In the early years, the use of mobile phones and internet advertising were highly separated (Rice and Katz, 2003). The smartphone changed this separation by integrating mobile and internet. Consequently, mobile technologies have the potential to change the competitive landscape of business and to create new markets (Stewart & Pavlou, 2002). According to Chon and Cha, P. 58 (2011) “a smartphone is an appropriate device to infer user context because data on the frequent interactions between users and their devices can be easily collected using various kinds of embedded sensors”.
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via their computer. Worldwide, the mobile ad spending as a percentage of the total digital ad spending will increase from 8.4% in 2012 to 50.9% in 2018. According to the Pew Research Center (2015) 58% of the American adults owned a smartphone in 2014. This indicates that this business is growing fast and therefore is very interesting for mobile advertising. According to the Mobile Marketing Association (2010) mobile advertising can be defined as follows; “mobile advertising is the one or two-way exchange of value facilitated by a mobile consumer electronic device which is enabled by wireless technologies and communication networks”. From a more consumer-based perspective, mobile advertising is the process of promotion, pricing and distribution of products and services through the mobile channel (Innovative Interactive Mobile Advertising Platform, 2002). Through the integration of the internet and mobile phones, a smartphone makes it possible to generate and use social context, in addition to individual context. Using social-context information, a company can offer more personalized advertisement (Chon et al. 2011) and reach smaller consumer segments (Vatanparast and Butt, 2011). Meeker (2014) stated that there are opportunities in advertising spending in the online and mobile industry since the time spent in media by users is larger than the advertising spending by companies. However, for mobile advertising to be successful, business models need to capture the interaction between different advertising tools (Vatanparast et al. 2011). An interaction effect occurs when the combined effect of two media is stronger than their individual effects on the outcome variable (e.g. purchase intention) (Naik 2007). Prior research already investigated the synergy effect between different telecommunication tools online and offline (e.g. radio, television, magazines, newspaper and internet) (Naik and Raman 2003; Naik and Peters 2009; Prasad and Sethi 2009; Assael 2011; Varan, Murphy, Hofacker, Robinson, Potter, and Bellman 2013). However, the interaction effect between online and mobile advertising is not well established (Varan et al. 2013; Naik et al. 2009). The current research focuses on the effectiveness of the interaction effect of online and mobile advertising on purchase behavior.
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using different advertising channels (e.g. get exposed to an ad very often), can also cause feelings of intrusiveness (Al-Allak and Alnawas, 2010).
Our contribution to the literature is as follows: the mobile advertising market is under heavy development. The internet advertisement expenditures are also increasing. For marketers this are opportunities to target the consumer via different channels and personalize the messages. We know that advertisement via different channels can have an interaction effect, however, research on the interaction effect of mobile and online advertising is limited. According to Varnali, Yilmaz and Toker (2012) and Wehmeyer (2007) feelings of intrusiveness of the mobile message are conceptualized as key attitudinal reactions triggered by a mobile advertising message. Since mobile advertising effectiveness increases with relevance of the ad, this could be a moderator that weakens the effect of the interaction between online and mobile advertising on feelings of intrusiveness so that the purchase intentions will increase (Merisavo et al 2007; Leppaniemi, and Karjaluoto 2005; Vatanparast et al. 2010; Wang, Yang, Chen, Zhang, 2014; Barwise & Strong, 2002; Nasco & Bruner, 2008). Therefore, the following research question is formulated:
RQ: Does the relationship between the interaction of online and mobile advertising and purchase intention provoke feelings of intrusiveness and can these feelings be reduced by relevance of the ad?
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interaction effects within the online media and to what extent intrusiveness and relevance play
a role in this.
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2. Literature review
We review the literature on internet advertising, mobile advertising, their interaction effect and the role of intrusiveness and relevance in the effectiveness of this interaction on purchase intention. To explain the success of interaction effect between mobile and internet advertising it is important to highlight the place of both advertisement (ad) tools in the marketing mix.
To make a clear distinction between internet advertising and mobile advertising we refer to the article of Zhang, Chen and Lee (2013) who did research in the area of M-commerce (e.g. mobile advertising). Traditional E-M-commerce has been defined as the sharing of business information, conducting of business transactions by means of telecommunications networks and maintaining of a relationships between company and customer (Zwass, 2003; Zwass, 1996). M-commerce is an extension of E-commerce to wireless media, and can be seen as another channel through which values can be added to E-commerce processes. This extension does not represent a complete distinction of M-commerce from E-commerce. Therefore, Zhang et al. (2013) refer to the unique features of M-commerce such as location tracking and location-based services that are not possible in traditional desktop-based E-commerce. This also mean that M-commerce allow more personalized information such as location and social relationships to be collected. Since the growth of online advertising spending and the adoption of mobile telecommunications it becomes more important to capture the interaction effect of different advertisement tools for successful business models. These interaction effects will be discussed later.
2.1 Internet advertising effectiveness
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web page. Therefore advertisers view them as useful tools to reach their target audience. There is much research on banner ads and their effectiveness (Baltas, 2003; Cho, 2003; Robinson, Wysocka, & Hand, 2007). Until now, several trends have emerged in banner ads. One of these trends is the use of rich media which refers to online media containing animation or sound. However, such ads can be obnoxious which can drive the consumers to go to other webpages to escape from sensory overload (Shankar et al. 2007). According to Park (2013) there are four main types of banner advertisements. We distinguish expression method (a button advertisement), technology method (an interactive banner), position method (box and scroll banner), and figure method (standard banner). This last one will be applied in this research.
Although this popularity among advertisers, banner ads have been criticized from advertisers and industry experts. Their main argument originate from the inability to convert viewers to clickers. Although many experts cite that banner ads have little value for companies interested in driving sales, banner ads are a very useful tool in driving website traffic (Shankar et al. 2007). Hence, we include display advertising (namely, user exposure to a banner-type advertisement) as the independent variable in our model .
2.2 Mobile advertising effectiveness
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perspective, mobile advertising is the process of promotion, pricing and distribution of products and services through the mobile channel (Innovative Interactive Mobile Advertising Platform, 2002). Due to the modern smartphones capability companies can infer user activities and context such as location and transportation mode from various sensors (Hardt and Nath, 2012). For example, a user who likes American restaurants (inferred from past browsing history) can be shown ads of popular (inferred from other users’ past locations) American restaurants within walking distance of the user’s current location (inferred from the GPS). This is highly targeted advertising and can significantly increase the success of an ad by purchases (Hardt et al. 2012). One of the most important benefits of mobile advertising is that it is a kind of interactive network ad, which is carried by mobile communication network, and can make users receive information anytime anywhere (He, Chen and LV, 2013). This a pull-type technology where the mobile phone can be used as a user-driven media device to enhance the dynamics of business-to-consumer relationships (Tsang and Liang, 2004; Tripathi and Nair, 2006). More push-type technologies were pro-actively driven by the company (Barwise and Strong, 2002).
2.3 Interaction effect between online advertising and mobile advertising
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order. They found that exposure to content on the mobile phone first and the PC second resulted independently. Their combined effect in this order was not significant compared to repeat exposure on either device (PC-PC and mobile phone-mobile phone). An explanation for this can be the retrieval practice theory which suggests that retrieval difficulty can enhance memory for certain orders of exposure (Appleton-Knapp, Bjorik, and Wickens, 2005). Therefore, in this current research we will only focus on the sequential effect of exposure on the PC first, in this research referred to as “online advertising”, followed by mobile phone for the interaction effect.
With the use of interaction effect of internet and mobile advertising, marketers can strengthen the effect by combining these different advertising methods. In this research we measure if the combined effect of first exposure to an online ad and secondly to a mobile ad is larger than their individual effects. This means that there are two situations; (1) an advertising is delivered on the PC when the consumer is surfing on the web (2) an advertisement is delivered on a mobile phone.
2.4 Interaction Effect Example
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phones second and ignore the feelings of intrusiveness because the ad is personal relevant to them, finally have intentions to purchase the product.
2.5 Purchase intention
In this research, the dependent variable is a psychological measure of advertising effectiveness: consumers’ intentions to purchase. Although other measures of advertising effectiveness exist (e.g. brand attitude, brand awareness, brand recall, actual purchases), purchase intentions are common campaign objectives in practice and frequently used in advertising research (Grewal, Kavanoor, Costley, and Barnes, 1997; Vakratsas and Ambler, 1999). In this research we do not measure effectiveness in terms of actual behaviors like real purchases because in the mobile context, it is still overly ambitious to expect that mobile display advertising triggers behaviors that are traceable and directly referred to the mobile advertising exposure because most consumers do not yet purchase products through mobile devices (Bart, Stephen and Savary, 2014). According to Davis (1989) purchase intention is the likelihood that a potential customer will purchase from an online seller at given a point in time. Previous research has found that attitude toward advertising in general is a good measurement of advertising effectiveness. Haley an Baldinger (1991) found that when people like an advertisement it is a good predictor of sales effects. It has also been found that attitude toward advertising can be used as a construct that contributes to the effects of advertising exposure on purchase intentions (Lee and Miller, 2006). Based on this knowledge, and the above definitions of interaction effects of online and mobile advertising, we can argue that a positive attitude towards the ad stimulates the purchase intention. It is therefore interesting to investigate the difference in purchase intention if consumers see an advertisement on their PC, on their mobile phone, or on both for sequential interaction effects. It is expected that people that are exposed to an ad on the PC first and to a mobile ad second have a higher purchase intention compared to the other way around because people that are reminded to a product on different devices are more likely to purchase the product due to superior recall effects (Varan et al. 2013). Now we have conducted a literature review study on the interaction effect of online and mobile advertising and purchase intention, we can develop our first hypothesis: H1: There is a positive interaction between online and mobile advertising on purchase intention.
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advertising on purchase intentions. They demonstrated that consumers who are subjected to extensive advertising are more likely to react in a negative way towards advertising because of feelings of intrusiveness (Al-Allak et al. 2010). Besides, according to Wehmeyer (2007) feelings of intrusiveness are more likely to occur with mobile advertising because it is more personal. In the current research consumers get exposed more times to an ad and at a mobile device. It could be that this is too extensive and personal which can evoke feelings of intrusiveness. Therefore we introduce this topic in this research. First we will give an explanation of intrusiveness in the context of advertising.
2.6 Consumers’ feelings of intrusiveness
As stated earlier, advertising extensive advertising can evoke feelings of intrusiveness. Consumers’ unfavorable attitudes toward such advertisements are formed because of the negative attitude such as excessive advertising. To understand the effect of intrusiveness, we will first clearly define intrusiveness. Lavy Mikulincer, Shaver and Gillath (2009, p. 990)
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can influence the advertising effectiveness in our research. To investigate whether there are and what causes the feelings of intrusiveness, the following hypotheses are formulated:
H2a: Serving both online and mobile advertisements increases people’s feelings of intrusiveness relative to serving advertisements only online or via mobile.
H2b: Mobile advertising has a positive influence on people’s feelings of intrusiveness relative to advertising on PC or no advertising.
Li et al. (2002) belief that feelings of intrusiveness are related to both irritation and avoidance of ads. These are examples of negative attitudes and behaviors toward advertising. Because we already know that negative attitudes have a negative influence on advertising effectiveness, it is expected that feelings of intrusiveness decrease the interaction advertising effects on purchase intention. To test this assumption, we formulated the following hypothesis:
H3: People’s feelings of intrusiveness mediates the effect of interaction between online and mobile advertising on purchase intentions
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2.7 Relevance of the Ad
One of the most important success factors of mobile advertising to be effective is personalization of the mobile ad (Robins 2003; Wehmeyer and Müller-Lankenay 2005). According to Varnali et al. (2012) personal relevance of advertising message is often related to the literature of consumer involvement where involvement is defined as the subjective experience of personal relevance towards the subject of a mobile message. Several studies demonstrate that involvement, and thus ad relevance, significantly affects consumers’ response to advertisements and consequently purchase intentions (Petty, Cacioppo and Schumann, 1983; Al-Allak et al. 2010; Varnali et al. 2012; Drossos and Giaglis, 2004). According to Wehmeyer (2007) ad relevance can be seen as the result of successful targeting and/or personalization.
To describe the product relevance to the needs and values of a customer, the construct of product involvement is established in marketing. In the area of advertising, involvement determines whether a recipient is personally attracted by the ad and motivated to respond to it (Zaichkowsky 1985; Phelps and Thorson 1991). Advertising, which promote products of interest to a consumer, can be considered relevant if the consumer-product fit is the result of marketers’ targeting efforts. Product involvement can be measured by scales whereby the relevance of a product or object based on inherent needs, values and interests’ can be assessed (Zaichkowsky, 1985).
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online and mobile advertising. This effect will be investigated by answering the following hypothesis:
H4: Relevance of the ad weakens the mediation effect of intrusiveness between the interaction effect of online and mobile advertising and purchase intention.
2.8 Conceptual Model
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3. Methodology
In order to get interesting findings in our research we carried out an experiment where the independent variable and moderator are manipulated and the mediator and dependent variable are measured. For the experiment we needed 7 different respondent groups, so 7 different questionnaires were distributed. The questionnaire is spread through the internet and applicable for Dutch people. In the next section we will explain the measures and procedure.
3.1 Measures
3.1.1 Dependent variable: Purchase intention
For this study we used the dependent variable purchase intention to measure the advertising effectiveness. Purchase intention is a frequently used variable in prior research. Therefore, we used an existing scale to measure our dependent variable. For this research we applied the scale that is also used in the article by Grewal, Monroe and Krishnan (1998). This purchase intention construct contains three questions on a 7-point Likert scale and can be found in appendix 6.
3.1.2 Independent variable: interaction online and mobile advertising
In order to measure the interaction effect of online and mobile advertising we used different advertisements on different devices (e.g. PC and mobile). The different respondent groups are shown in table 3.1. Group 1 to 4 are exposed to an ad two times on the same device to get every respondent exposed an equal number of times to an ad and therefore we take away a possible frequency effect.
First of all we want to know if the advertising effect of combined media is larger than their individual effects. In order to do so we made different advertising groups. Next, we want to know if relevance plays a role in the effect of combined media advertising. Therefore, some people are randomly allocated to a ‘relevant’ group and others to a ‘not relevant’ group. Group 7 represents the baseline/reference group where no ad will be shown to be able to compare the other groups to a ‘normal’ situation when nothing occurs.
Exposed to.. Relevant Not relevant
Ad on the mobile phone (2x) Group 1 Group 2
Ad on the PC (2x) Group 3 Group 4
Ad on first PC than mobile phone Group 5 Group 6
No ad Group 7
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3.1.3 Mediator: Intrusiveness
Because we want to investigate the combined effect of online and mobile advertising, people get exposed to an ad two times. Therefore there is a risk that people find the ads intrusive. In order to measure people’s intrusive feelings we included 8 questions on a 7-point Likert scale that measures the intrusiveness construct. These 8 questions were previously used in the research by van Doorn et al. (2013) and will be used for every respondent group. The questions are included in appendix 8.
3.1.4 Moderator: Relevance
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3.1.5 Confounding Variables
In this subsection we pay attention to possible confounding variables that could explain extra variation in our dependent variable. The first possible confounding variable is that differences in advertising format let people persuade the ad in different ways. Empirical evidence for this comes from Varan et al. (2013). They showed that format matters in advertising effectiveness. Therefore we held the ad format for the mobile and PC as constant as possible. A second possible confounding variable could be that the interaction effect has a larger effect on intrusiveness due to frequency effects. Therefore, all respondents are exposed to an ad two times.
3.2 Procedure
The data for this study is obtained by conducting an online experiment. We conducted a 3×2 + 1 full factorial, between subjects, research design. Seven different questionnaires are used to create seven different cases. In this experiment, the ads and their relevance are manipulated. To create the different questionnaires for our experiment we used Qualtrics as survey builder platform. First, the questionnaire is distributed via Facebook. Secondly, a personal network on LinkedIn is used to generate respondents. Finally, the survey has been distributed through an online platform on Facebook.
Each respondent was asked to fill in the survey. The respondents were randomly assigned through one of the seven groups. This means that this research uses a between subject design. The survey started with a priming story where respondents had to imagine a certain situation on Monday evening and then were exposed to an ad. Next, a couple of general questions were asked about age, gender, education, and mobile phone and PC usage. These general questions were asked after the first ad exposure to distract people from what they previously saw. When people get exposed to an ad in quick succession without any distraction between the two exposure, the effect of two times ad exposure in different situations cannot be measured. In the second primed situation the respondents had to imagine that it was Wednesday evening when respondents were exposed to an ad so that they are aware that it are two different situations.
3.3 Sample
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of the respondents, we removed all the cases that did not meet requirements for (1) showing variation in answers, (e.g. respondents that filled in ‘1’ for every question are removed) indicating automatic pilot behavior and (2) no missing values in the response. These missing variables were not the same for each respondent with missing variables. We could not find any explanation why these people did not fill in some scores. It could be that they simply forgot to fill in this question. After these respondent analyses we removed another 8 cases. Finally 188 of the cases were useful for analysis. A more specific analysis of the sample description can be found in the results part of this paper.
3.4 Model
To test our hypotheses we made use of different analyses. Our independent variable, type of ad (e.g. mobile, PC, PC mobile), is dummy-coded into different groups. Where no ad is the reference group. For our moderator ‘relevance of the ad’ we used the six questions on a 7-point Likert scale that measure relevance. Besides, purchase intention and intrusiveness are also measured on a 7-point Likert scale. To answer our hypotheses we conducted a moderated mediation analysis. Therefore, we needed a regression analysis with the following structure to estimate the mediation effect of intrusiveness and the moderated mediation effect of relevance of the ad. The regression models are as follows:
Purchase intention = Constant + c’×Advertising + β×Intrusiveness + (D×Advertising×Relevance) + ε.
Me = Constant + α×Advertising + D×Relevance + (D×Advertising×Relevance) + ε
25 5% 41% 46% 1% 1% 1% 5%
Use of Smartphone Brand
HTC Samsung Iphone Nokia Blackberry SONY Anders
4. Results
In this chapter we will first show some descriptive statistics to describe the sample in more detail. A factor analysis is done to determine the validity of the underlying constructs for our analysis. To test the internal consistency of our variables, we did some Cronbach’s Alpha tests. The results section has to make clear whether or not there is found evidence and support for the hypotheses.
4.1 Descriptive statistics
The sample for analysis consists of 46 (24,5%) men and 142 (75,5%) women. The men/women ratio between the
different groups is shown in table 4.1. 186 respondents owned a PC and 182 owned a smartphone. This means that it is very likely that people get exposed to an ad on their mobile phone and/or PC. The frequencies of different smartphone
brands used by the respondents are Figure 4.1 Smartphone brands used by respondents
shown in figure 4.1.
Relevant Not relevant
Men Women Men Women
Mobile ad 10 20 4 22
PC ad 4 22 7 20
Interaction ad 9 20 3 22
No ad 9 16
Table 4.1 Distribution of men and women across groups
26 Figure 4.2 Level of Education
Figure 4.3 Age in years
The questions asked in this research are based on the type of device where the ad is displayed on and the relevance of the ad for the respondent. Table 4.2 shows the randomly assignment of the respondents to the different groups. Based on this table we can say that there is a good distribution of respondents across the seven groups.
Exposed to.. Relevant Not relevant Total
Ad on the Mobile phone (2x)
30 26 56
Ad on the PC (2x) 26 27 53
Ad on first PC than mobile phone
29 25 54
No ad 25 25
Total 85 103 188
Table 4.2 distribution of respondents across groups
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When we looked at the control question at the end of the survey where respondents were asked to confirm if they had to imagine that they were searching for a new laptop yes or no, we noticed that not everyone answered this question correctly. The percentage of correctly answered is shown in table 4.3. We can already say that this is a shortcoming in our research. We will discuss this later in the limitations part.
Exposed to.. Relevant Not relevant
Ad on the Mobile phone (2x) 86,7% 53,8%
Ad on the PC (2x) 88,5% 66,7%
Ad on first PC than mobile phone 89,7% 60%
No ad 44%
Table 4.3. Percentage of cases answered correctly
To see whether or not the percentages in table 4.3 are significantly different from each other we conducted a post-hoc test. The results are shown in table 4.4. The results are not significantly different within groups but are significantly different between groups (relevant vs not relevant).
Sig.
Relevant Not relevant
Mobile PC Interaction Mobile PC Interaction No ad
Relevant Mobile 0.876 0.790 0.005** 0.081 0.023* 0.000** PC 0.876 0.918 0.004** 0.067 0.019* 0.000** Interaction 0.790 0.918 0.002** 0.047* 0.012* 0.000** Not relevant Mobile 0.005** 0.004** 0.002** 0.279 0.610 0.414 PC 0.081 0.067 0.047* 0.279 0.577 0.059 Interaction 0.023* 0.019* 0.012* 0.610 0.577 0.190 No ad 0.000** 0.000** 0.000** 0.414 0.059 0.190 Table 4.4 Post-hoc test between and within groups that answered the control question correct.
*Significant at α = < 5% ** Significant at α = <1%
It is not possible to remove all the respondents that answered the control question incorrect because the number of remaining respondents is too low for a valid analysis.
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ANOVA analysis between groups. The results show to be highly significant. Therefore, we can say that the validity of our relevance construct is good. The results are shown in table 4.5.
Sum of Squares F Sig.
Between groups 24.704 14.167 0.000*
Table 4.5 Results ANOVA analysis for validity relevance group. *Significant at α = 1%
4.2 Pre-analyses
For our research we made use of different kind of scales to measure purchase intention and intrusiveness. To make the variables useful for further analysis we looked at if the questions all measure the same underlying construct. With the factor analysis we can increase the validity of our research and make sure that the items measure the same underlying construct. After the factor analysis we carried out a Cronbach’s alpha for the reliability to make sure that the questions are internally consistent. We did this for the items intrusiveness, relevance of the ad and purchase intention.
4.2.1 Purchase intention
Although we only have three questions for the purchase intention construct, we carried out a factor analysis for the validity of our model. For the combination of these three questions the total eigenvalue is 2.195 with a % of variance of 73.154%. The KMO statistic is 0.623 and Barlett’s test of Sphericity shows a p-value of 0.000. This mean that the model is highly significant with enough variance explained (i.e. >60%). The reliability analysis shows a Cronbach’s alpha of 0.817. Therefore we can say that the internal consistency of the three questions is good. They all measure purchase intention. The questions were measured on a 7-point Likert scale and will all be used in our analyses.
4.2.2 Intrusiveness
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4.2.3 Relevance of the ad
In order to check the reliability and validity of our relevance construct, we asked a combination of six questions on a 7-point Likert scale with the relevance as underlying dimension. The results of the factor analysis for the combination of these six questions show a total eigenvalue of 4.653. The % of variance explained is 77.549%. The KMO statistic is 0.875 and Barlett’s Test of Sphericity is highly significant with a p-value of 0.000. After the factor analysis we also checked the reliability. Results show a Cronbach’s alpha of 0.943 which is >0.7. Therefore we can conclude that the variables measure the same construct and can be used for further analysis.
4.3 Model estimation
In order to answer our hypotheses we carried out a moderated mediation analysis. The independent variables mobile ad, PC ad and interaction effect are dummy coded as 0/1. Where 1 represents an ad and 0 represents no ad, so that no ad will be the reference group. The variables in the analysis are the different groups that were exposed to a mobile ad, PC ad, and PC + mobile ad. There are no multicollinearity issues since all VIF scores are below 4. The results are shown in table 4.6.
4.3.1 Interaction effect on purchase intention
The first hypothesis that needs to be answered is if there is a direct effect of the interaction between online and mobile advertising on purchase intention. The results show that the interaction effect is not significant. The results are shown in table 4.6. Based on our analysis we cannot say that there is an interaction effect between online and mobile advertising. Thus, people’s purchase intention when first get exposed to an ad on the PC followed by an ad on their mobile phone is not significantly higher than people that get exposed to an ad on their mobile phone or PC twice. Therefore, we cannot accept our hypothesis 1.
4.3.2 Intrusiveness
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The results in table 4.6 show that there is no direct effect of the different advertisement groups on intrusiveness. It is therefore not the type of media and the interaction of different types of media that give people feelings of intrusiveness. Besides, advertisement in itself does not lead to higher purchase intentions. However, there is a direct effect of intrusiveness on purchase intention. Thus, we can accept hypothesis 3 and reject hypothesis 2. In section 4.4 we will discuss some additional analyses on this.
4.3.3 Relevance of the ad
Our conceptual model suggests a moderated mediation effect through relevance of the ad. We want to know if there is a significant difference on purchase intention between groups that find the ad relevant or not.
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4.4 Additional analyses
Since our moderated mediation analysis does not show significant results for all the hypotheses we carried out some additional analyses to find interesting results. For this additional analysis we conducted a mediation analysis with relevance of the ad as independent variable, intrusiveness as mediator and purchase intention as dependent variable. The results of this additional mediation analysis show that there is a direct effect of relevance of the ad on purchase intention and intrusiveness. The findings of these analyses are shown in table 4.7.
Table 4.7 Results for mediation analysis (Model 4 Hayes, 2013)
Predictors
Outcome variable: Intrusiveness
Outcome variable: Purchase intention
β t-statistic p-value β t-statistic p-value
Relevance -0.3153 -4.2907 0.000* 0.7076 10.8296 0.000*
Intrusiveness 0.0279 0.4479 0.6547
Indirect effect relevance -0.0088 -0.4340 0.6643
Table 4.7 Mediation effect with Relevance as IV * = significant at α = 5%
Early results suggest that feelings of intrusiveness do not significantly differ between different advertising groups. However, we do find differences in intrusiveness between respondents in the relevant group and respondents in the irrelevant group. This suggest that people who find the ad relevant to them, experience less feelings of intrusiveness. However, relevance is not mediated by intrusiveness in the effect on purchase intention.
Another finding in our additional analyses is a relationship between people who purchased products via their mobile phone in the past and their purchase intentions. The result of the ANOVA analysis is significant with p = 0.048, f = 3.083. Based on this we can suggest that people that have prior experience with mobile purchases are more willing to buy in the future. Finally, we also checked if there are significantly differences in experienced intrusiveness and relevance of the ad between people with prior experiences with mobile purchases and people who never purchased products via their mobile phone. For these analyses no significant relationships are found.
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5. General discussion
This study provides no support for the proposition that an interaction between online and mobile advertising increases purchase intention. When consumers are exposed to ads multiple times on different devices, they are not more/less willing to purchase. It is important to know how different types of media can strengthen each other. This research focuses on the interaction of online and mobile advertising. Following our research, we can say that advertising on different devices with the same format does not lead to higher purchase
intentions.
Intrusiveness as mediating variable showed in this study to be not statistically significant. A cause of this could be that people find ads in general equally intrusive and that this not differ between different media types. Further research should clarify this. Furthermore, the baseline group is exposed to only a brand logo of Wehkamp.nl. It could be that the differences between the groups are not primed enough so that the experience in the survey is not significantly different between the groups. However, based on our analyses, we can say that intrusiveness has a significantly direct negative effect on purchase intention. This indicates that people who experience feelings of intrusiveness are less willing to purchase. Although there is no significant difference in intrusiveness between the different advertisement groups, relevance of the ad does have a significant influence on intrusiveness.
The most common dimensions of intrusiveness in the context of advertising include (1) intrusion into consumer privacy (Milne and Rohm, 2004), (2) intrusion on media clutter (Elliot and Speck 1998), and (3) cognitive processing and task performance (Li, et al. 2002). In our research we investigated the first two dimensions. Further research should determine if the third dimension, cognitive processing and task performance plays a role in the mediation effect of intrusiveness.
34
the ad and motivated to respond to it (Zaichkowsky 1985; Phelps and Thorson 1991). Another explanation for the direct effect of relevance on purchase intention could come from Lin and Wang (2006). They found a significant effect of perceived value (e.g. relevance) on customer loyalty (which stimulate purchase intention) in a mobile commerce setting.
What we also find in our study is that the purchase intentions of people who previously purchased products via their mobile phone were significantly higher than those who never purchased products via their mobile phone. An explanation for this could come from the study by Lin et al., (2006) that found that trust and habit (which received in prior mobile purchase experience) are significant factors in determining customer loyalty and repurchase behavioral intentions.
In this research, one of the factors that determined intrusiveness was relevance of the ad. This is in line with our previously reviewed literature that stated that if a consumer is exposed to an ad that is personal relevant, customer’s experience may not feel as intrusive (Godin, 1999; Huber, 2012).
5.1 Limitations
In this study we made use of priming stories to manipulate the relevance variable. This priming took place before the ad via a story in which respondents were asked to imagine that he/she needs a new laptop, or not. It seems that respondents did not read the prime carefully or interpreted the control question right. The control question was asked after the questions for measuring purchase intention, so it could be that respondents were be a bit confused about the intention of the control question. There is need for another (more observational) research that measures this effect. Since we only had two weeks for data collection, we distributed a survey to get our results for the experiment. A study that investigates purchase intention where people first get exposed to an ad and where relevance has to be measured, may prefer a more experimental setting, instead of an experimental questionnaire. Besides, not many consumers are willing to admit that they are often influenced by advertising and thus, scales designed to measure the persuasive influence of advertising often reveal a sceptic and resistant consumer. More implicit measures are needed to reveal the impact of advertising on consumer perceptions and behavior (Fennis et al. 2010).
35
that people respond differently in real life than via a questionnaire. An observational research may show different results.
A laptop product ad is used for the different ads. It could be that other product categories show other results because product involvement is very important for relevance of the ad. Laptops can be more relevant for students than for 50+ people for instance. Besides, there could be a lot more factors involved that increases people purchase intention when buying a laptop (e.g. price, color, memory) because a laptop is a product that people do not buy on a daily basis. buying will be considered well. Products that are purchased more frequently and are less expensive could have shown different results in this research. Furthermore, next to attitudes, social norms and environmental factors could also have an influence on buying behavior intentions. People do what others want them to do (e.g. social norms) and people need to have control of time, money and resources (Fennis et al. 2010). However, these determinants are not captured in our research.
This research focused on online and mobile advertising. Six people in our research do not own a smartphone. Therefore, for these respondents it is hard to imagine that he/she get exposed to a mobile ad. Next, more than 75% of our respondents were 35 years or younger. Therefore, our age variable was not normally distributed. With 25% men and 75% women also the men/women ratio was not equally distributed. Further research may take this into account and can investigate if there are differences between men and women in responses to interactive advertising and consequently purchase intentions.
Finally, although we had at least 25 respondents per group, further research should collect more data and find more respondents to fill in the questionnaire. Due to incorrect answers on the control question, a lot of respondents dropped out. With at least 400 respondents, the results will be more reliable and useful.
5.2 Managerial implications
36
avoid intrusive ads.
As shown in our additional analyses, people who ever purchased products via their mobile phones in the past have a higher purchase intention than people who never purchased products via their mobile phone. Therefore, business can promote online purchases by showing the benefits of purchasing online and mobile in a way that people can purchase anytime anywhere and that it is easy to process. Once the person purchased online/mobile, he/she will continue to purchase online/mobile. Therefore it is important for retailers to target new online customers and to maintain existing customers by making the ads relevant and not intrusive.
37
they are searching for with what types of search terms (DBK, 2013). Based on that, advertisers and retailers can make their ads more relevant and less intrusive. SEO will become an important element of the marketing mix for online retailers. Finally, for offline stores it will be important as well because people are often searching online for product information.
5.3 Conclusion
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Appendix
Appendix 1A – Mobile + Relevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is maandagavond en je zit thuis op de bank met je mobiele telefoon. Al enige tijd denk je na over het kopen van een nieuwe laptop. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken.
Appendix 1B – Mobile + irrelevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is maandagavond en je zit thuis op de bank met je mobiele telefoon. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken.. Appendix 1C – PC + relevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is maandagavond en je zit thuis achter de laptop te surfen op internet. Al enige tijd denk je na over het kopen van een nieuwe laptop. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken. Appendix 1D – PC + irrelevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is maandagavond en je zit thuis achter de laptop te surfen op internet. Opeens komt er een advertentie in beeld die je direct bekijkt.
43
44
Appendix 2B – PC ad
45 Appendix 3 Q2 Wat is je geslacht? O Man O Vrouw Q3 Wat is je leeftijd? ………..
Q4 Wat is je hoogst afgeronde opleiding? O Basis O VMBO O HAVO O VWO O MBO O HBO O Universiteit
Q5 Heb jij wel eens een product gekocht via je mobiele telefoon? O Ja
O Nee
Q6 Ben je in het bezit van een laptop/PC? O Ja
O Nee
Q7 Ben je in het bezit van een smartphone? (een smartphone is een mobiele telefoon met internet mogelijkheden)
O Ja, ik heb een HTC O Ja, ik heb een Samsung O Ja, ik heb een i-phone O Ja, ik heb een Nokia O Ja, ik heb een Blackberry O Ja, ik heb een Sony
O Ja, maar ik heb geen van bovenstaande merken O Nee, ik heb geen smartphone
Appendix 5a – Mobile + relevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is woensdagavond en je zit thuis op de bank met je mobiele telefoon. Je denkt nog steeds na over het kopen van een nieuwe laptop. Opeens komt er een advertentie in beeld die je direct bekijkt.
46
Appendix 5B – Mobile + irrelevant
LEES DE VOLGENDE TEKST GOED DOOR
Stel jezelf de volgende situatie voor: Het is woensdagavond en je zit thuis op de bank met je mobiele telefoon. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken.
Appendix 5C – PC + relevant
LEES DE VOLGENDE TEKST GOED DOOR:
Stel jezelf de volgende situatie voor: Het is woensdagavond en je zit thuis achter de laptop te surfen op internet. Je denkt nog steeds na over het kopen van een nieuwe laptop. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken.
Appendix 5D – PC + irrelevant
LEES DE VOLGENDE TEKST GOED DOOR!
Stel jezelf de volgende situatie voor: Het is woensdagavond en je zit thuis achter de laptop te surfen op internet. Opeens komt er een advertentie in beeld die je direct bekijkt.
Klik op Verder om de advertentie te bekijken.
<ADVERTISEMENT>
Appendix 6- purchase intention
Geef nu antwoord op de volgende stellingen op een schaal van 1 helemaal mee oneens tot en met 7 helemaal mee eens:
1 Helemaal
mee oneens 2 3 4 5 6
7 Helemaal mee eens De kans dat ik een nieuwe laptop ga
kopen is groot
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Appendix 7 – relevance
Je hebt in deze enquête twee maal een advertentie voorbij zien komen. Beantwoord nu de volgende vragen en geef aan in hoeverre je het oneens of eens bent op een schaal van 1-7:
1 Helemaal mee oneens
2 3 4 5 6 7 Helemaal
mee eens De advertenties zijn belangrijk voor
me
De advertenties zijn waardevol voor me
De advertenties sluiten aan op wat ik nodig heb
De advertenties zijn bruikbaar voor me
De advertenties zijn de moeite waard om aandacht aan te geven
De advertenties zijn interessant
Appendix 8 - intrusiveness
Geef nu antwoord op de volgende stellingen: 1 Helemaal mee oneens 2 3 4 5 6 7 Helemaal mee eens Ik vind de advertenties verontrustend
Ik vind de advertenties opdringerig Ik vind de advertenties irritant Ik vind de advertenties vervelend Ik vind de advertenties oncomfortabel De leverancier weet veel over mij Deze advertenties geven mij een ongemakkelijk gevoel
Deze advertenties geven mij een onveilig gevoel
Appendix 9 – control question
1. Werd in de enquête gevraagd je in te beelden of je op zoek was naar een nieuwe laptop? O Ja
O Nee
48
