The effect of location-based services on store visit intention moderated by privacy and scarcity

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Michiel Kanis


The effect of location-based services on store visit

intention moderated by privacy and scarcity

Student: Michiel Kanis, S2384620

Address: V.Dijksweg 16, 8276BH, Zalk, Netherlands Contact details:, +31612987383

University: University of Groningen, Faculty of Economics and Business Course: Master Thesis, MSc Marketing Management, 2013-2014.

Completion date: 23-06-2014

Research theme: Living in a multiscreen world. Seminar supervisor: Lara Lobschat.



Internet advertising is one of the fastest-growing fields in advertising. One of the major drivers that make internet advertising a success are the technological developments. These developments make it possible to for example target consumers not on an aggregate level but more individually. This study focuses on the effect of targeting consumers, based on their geographical location. Consumer’s privacy need is expected to play a huge role in the adoption of location-based service and the success of location-based targeting. Besides this, also the effect of scarcity elements in the advertisements, on the level of persuasion of the advertisement itself, is studied. Finally, this study investigate a three-way interaction effect to show that when consumers are targeted based on their location, they ignore their privacy need, if the offer is interesting by the use of scarcity elements. It is expected that this will develop a higher store visit intention. Experimental research is done in order to answer the research questions and the study focuses on the Dutch market.


Table of Contents





2.6 SUMMARY ... 20


3.1 MEASURES ... 21





3.2 PROCEDURE ... 23

3.3 SAMPLE ... 24

3.4 MODEL ... 24

4. RESULTS ... 26




4.2.2 NEED FOR PRIVACY ... 28



4.3.2 SCARCITY ... 30

4.3.3 PRIVACY ... 31


4.3.5 RESULTS SUMMARY ... 32




1. Introduction

Will your smartphone be used as a direct marketing device? Over the last years, the field of marketing has changed a lot. In the beginning of 1991, internet adoption increased quickly in all the high-developed countries (Maitland, 1998). This new online world offers marketers the opportunity to get in contact with their customers on an individual level. It differs from mass marketing like TV and newspapers because it is more accurate and make use of more consumer information (e.g. direct mailings). Internet advertising can be seen as more personal advertising channel. This new kind of advertising became a fast growing part of the company’s marketing mix, expected to grow from a total of $23.4 billion in 2008 to $34 billion in 2014 (Hallerman, 2009). If we look at marketing budgets, and how this is allocated to the different marketing tools, a crucial part of this budget is spend on paid search campaigns (Rutz, Bucklin & Sonnier, 2010). Paid search campaigns are basically done via search machines like Google, Bing and Yahoo. Consumers can be targeted by using their search keywords and show them an advertisement that fits with their search question. Also targeting consumers based on previous internet behavior, in for example display networks, are a common form of internet advertising. (Goldfarb & Tucker, 2011)


phone that incorporates a public general-purpose operating system, to which users can freely add applications, extend functionality, or customize (Shiraishi et al, 2011).


There is much unknown in the field of location-based targeting in combination with mobile marketing, while the adoption of smartphones is quite high. This study focuses on creating store visit intention via the use of Location Based Services (LBS). Previous research only focused on online metrics (conversion rate, click trough rate etc.) and consumer attitudes towards mobile marketing (Sultan, Rohm & Gao, 2009; Shankar et al, 2010; Scharl, Dickinger & Murphy, 2004). But these studies are undervaluing the location-based aspect and only focusing on targeting and the adoption criteria for LBS. This study zooms in on the effects of a mobile advertisement on store visit intention, whether location-based targeting is used or not. This is an offline metric, which is interesting to investigate with the increase of M-Commerce. Mobile commerce stimulates consumers to buy products via their smartphone. That means that they will not visit the store anymore. For retailers it is interesting to find ways that attract customers to their local stores via the use of mobile advertising. The current problem is the literature gap in the field of using LBS for people who really attach value to their privacy. This is relevant because it is know that LBS uses private (geographical) data, which might be a problem for self-conscious, privacy sensitive consumers (Xu, 2009).


of using LBS on offline measurable variables like store visit intention (Sultan, Rohm & Gao, 2009). Our first research question focuses therefore on the following:

RQ1: Is there a difference in store visit intention for people who are targeted at home,

or in the shopping center, with location-based advertising via their mobile phone?

Previous research shows that privacy concerns of consumers affect the acceptance of LBS (Shankar et al, 2010). The question is; does privacy concerns also influence behavior when people have accepted that their privacy is being used? Our second research question focuses therefore on the following:

RQ2: Does the need for privacy influences the relationship between the use of

location-based services and store visit intention?


RQ3: Does scarcity, indicated in the provided ad, influence the relationship between

the use of location-based services and store visit intention?

Also interesting is the three-way interaction effect that will be investigated. Until now it is unclear whether consumers have the power to overcome their privacy concerns toward an advertisement if the offer is scarce. In sum we can say that from a scientific point of view we zoom in on a new variables that are not studied in relationship to location-based services before. All previous research has a clear focus on the adoption and use of the service itself. Our last research question therefore focuses on the following:

RQ4: Does the three-way interaction effect of privacy, scarcity and location-based

services influence the store visit intention of consumers?


situation; you have visited some webpages about shoes on H& Your H&M account is

liked to you Facebook account. When you are in the shopping center nearby an H&M store, and take a look at your Facebook timeline, you see an H&M advertisement. The advertisement contains the following message; “Just five pairs of these shoes in stock. When you buy this product within 60 minutes in our store 100 meters ahead of your current location, you will receive 20% discount!

Store visit intentions are expected to increase. Our research will show, clarify and provide reasoning for this effect, and also take into account that scarcity of the product offer even can stimulate people at home to directly go to the shopping center, and visit the store.


2. Literature Review

To explain the success of location-based services, it is important to highlight the place of LBS in the marketing mix. Nowadays there are, compared with ten years ago, many different online marketing advertising tools. An advantage of internet marketing is the measurability, particularly focused the on costs and benefits relationship. Online advertising can be defined as; “deliberate messages placed on third-party websites including search engines and directories available through internet access. Deliberate messages mean that the advertiser intends to place the message on the online medium” (Ha, 2008). For years, this is mainly done via standard banners. But, standard banner advertising is not so effective anymore. Benway (1999) explained bad performing banners by ‘consumer banner blindness’. People now what a banner is and does, and have developed skill to avoid them. Marketers need to look at new ways of making their online ads more standout and interesting, and increase the ad-involvement. Targeted advertising is one of these solutions.


The Mobile Marketing Association (MMA) defines mobile marketing as “the use of wireless media as an integrated content delivery and direct response vehicle within a cross-media or standalone marketing communications program” (MMA, 2006). It refers to the pairing of mobile devices with commercial transactions, giving customer services anywhere and anytime through a wireless, internet-enabled device and without use of a computer (Clarke, 2008; Xu, 2007). According to Advertising age (2006) mobile marketing is defined as “the use of wireless media as an integrated content delivery and direct-response vehicle within a cross-media marketing communications program”. This last definition also includes the cross-media aspect. This means my definition an integration between online and offline.

Although, one of the major purposes of Mobile Marketing is M-Commerce, also the combination with offline integration is interesting. M-commerce is a channel extension of E-commerce. E-commerce has been defined as; “the sharing of business information, maintaining of business relationships, and conducting of business transactions by means of telecommunications networks” (Zhang, Chen & Lee, 2013). M-Commerce just simply adds mobile phones and tablets as network devices to this definition. An interesting development in Mobile Marketing is the use of smartphone data for geographical targeting. The geographical location of specific individual consumers can be used to send messages when they are on a certain location. In the next subsection we will explain LBS and their current implications, by using previous research.


about discounts. This research is using the location-based services variable in a way that consumers are targeted in the shopping center or at home. When talking about company’s using location-based services, we mean this is similar the use of location-based targeting. LBS are defined as ‘mobile computing applications that provide information and functionality to users based on their geographical location” (Shek, 2010). Barnes (2003) describes location-based services as network-based services that integrate a derived estimate of a mobile device’s location with other information to provide value-added to users. Based on the literature in the previous subsections, we define location-based services in the marketing mix as; an online advertising tool, which targets consumers by the use of their geographical location, and pushes the advertisement on the consumers’ mobile device. So, location-based services contain elements of all the three advertising subsets discussed earlier. To conclude we can argue that location-based services consist of two different aspects. We distinguish the targeting aspect and the location aspect.

2.1 LBS Services/Requirements

Previous research focuses on the success factors of LBS and distinguishes different types within LBS.There are four different types of location-based services that all pursue their own goals (Dhar & Varshney, 2011). There are information/ directory services, tracking and navigation services, emergency services and location-based advertising. This last one is most interesting for this research. This service provides marketing promotions alerts by for example sending coupons via targeted and customized ads.


customers can easily navigate to the store. One of the key insights of previous research done by Dhar and Varshney (2011) is that location-based mobile advertising has potential to generate significant revenues leading to successful business models. But there are key technical and business challenges (e.g. privacy concerns). The question is; why these concerns? To maintain the success of location-based services, a good developed customer-relationship management needs to be implemented within company’s to get access and apply consumer data (Dow, 2013). These systems make use of consumer’s private data. By law, location-based services must be permission-based. That means that the end user must opt-in to the service in order to use it (Zhao et al, 2012). This condition of location-based services will be investigated further in this research.

With the use of LBS, marketers can create location-based hyper personal marketing campaigns. In this research we measure LBS by distinguish if consumers are location-based targeted (LBT) or not. As mentioned before, location based targeting is an aspect of location-based services. That means there are two situations; (1) a mobile advertisement is delivered on a smartphone when the consumer is in the shopping center (with LBT) and (2) a mobile advertisement is delivered on a smartphone when the consumer is at home (without LBT).

2.2 LBS Example


Based on the previous situational explanation of LBS we can define four propositions that are interesting to investigate. (1) Consumers like the advertisement more because they get it on a specific location, and developed a higher intention to visit the store (proposition 1), (2) Consumers may feel their privacy is being violated and therefore develop a lower intention to visit the store (proposition 2) and (3) Consumers experience feelings of scarcity of the product and therefore want to visit the store even more (proposition 3). At the end (4) we also investigate a three-way interaction effect to make clear that when consumers are targeted based on location ignore their privacy need, if the offer is interesting, and finally will visit the store (proposition 4). To answer these questions, we first need to define the variables store visit intention, consumers privacy need, and the scarcity principle, in order to explain their relationship with location-based services.

2.3 Store Visit Intention

Store traffic is important because it can stimulate intended and unintended sales. The prevailing assertion is that feature promotions can have a positive impact on store traffic and sales. A feature promotion is a product specific discount or promotional message on individual product level (Gijsbrechts, Campo & Goossens, 2003). Specifically, feature promotions tend to draw customers into the store, which should on itself impact sales. Once in the store, shopper’s rarely only purchases featured items, but also spend a substantial amount on non-promoted products (Gijsbrechts, Campo & Goossens, 2003).


Assael (1998) explained that purchase intention is a behavior that emerges in response to an object. Consumers tend to make purchases of an item if the customer has obtained the expected level of satisfaction. Store visit intention is a variable that show a lot of similarities compared to purchase intention. It indicate the level of attractiveness a consumer experience to actual visit the store. This can be clarified by the use of the theory of planned behavior (TPB). “TPB is a theory that indicates subjective norm, perceived behavioral control, and attitude, all of which influence intention, which in turn influences behavior” (Ajzen, 1985). Intention indicates the probability of a person behaving in a certain way (Fishbein & Ajzen, 1975). Store visit intention than can be defined as; “The behavioral intention that a consumer has to visit a store. Consumers tend to visit a store if they have obtained the expected level of satisfaction.

There are a lot different variables that affect the store visit intention. We distinguish four different attributes that influence the attractiveness of an offline retail store; merchandise, service, promotion, and convenience. Merchandise variables measure product selection, assortment, quality, guarantees, and pricing. Service variables examine general service in the store and sales clerk service for merchandise return, credit policies, etc. Promotion variables record sales, advertising, and appetizer features that attract customers (e.g. a “What's new” section). Convenience variables include store layout and organization features (Arnold, Tae & Tigert, 1983)


are walking in the shopping center are more likely to visit the store directly because they are nearby and the barrier for an actual visit becomes lower. Now we have conducted a literature review study on both location-based targeting and store visit intention, we can develop our first hypothesis:

H1: There is a positive relationship between the use of location-based targeting,

relative to non location-based advertising, and the store visit intention.

It is expected that consumers who are targeted when located in the shopping center are more likely to visit the store. Previous research has shown effects of mobile marketing as a communication tool on generating consumers' purchase intention. Privacy concerns were introduced in this research, but the direct effect of consumers’ privacy need on an intention was not investigated (Al-alak & Alnawas, 2010). We therefore used research done by Goldfarb and Tucker (2011), which explain general privacy issues in online advertisements. Consumers may have the feeling of privacy violation if they see that their geographical location is used in an advertisement. To explain this effect, we first need explain privacy need in the context of location-based targeting.

2.4 Consumer’s Privacy Need


sense that it varies with life experience (Altman, 1977). According to much of the research in location-based targeting, privacy need is an essential, crucial and underestimated issue (Snekkenes, 2001; Bisdikian et al, 2001)

While LBS offer great connectivity and personalization for consumers, they also threaten users’ information privacy through tracking of their preferences, behaviors, and identity. The study ‘The Role of Push–Pull Technology in Privacy Calculus: The Case of Location-Based Services’ done by Xu et al (2009), shows an privacy calculus model to explore the role of information delivery in influencing individual privacy decision making. Individual privacy decision making is often described in terms of a calculus, where personal information is given in return for certain benefits (Laufer & Wolfe, 1977). In the sense of LBS this might means that, if the discount of a product is high enough, consumers will give up a piece of their privacy in order to get this discount.


adoption. These studies showed that location is seen as valuable and a real privacy sensitive good for consumers. Location-based services also use cookies and other tracking information to target consumers based on their location (and other variables). Therefore consumers might feel limited in their privacy and respond different to the advertisement message, or LBS in general. It is expected that when consumers feel limited in their privacy, they respond more negative to the advertisement than people who do not attach so much value to their online privacy. To test this assumption, we have developed the following hypothesis.

H2: The need for privacy has a weakening effect on the relationship between

location-based targeting and store visit intention.

It is expected that people who attach high value to their privacy have a lower store visit intention when location-based targeting is used. Besides the consumer characteristic privacy, also elements of the advertisement message itself can influence the relationship between LBS and store visit intention. This research focuses on scarcity of the offer in specific. The scarcity principle seems to be one of the most powerful influence principles that affect consumers’ implicit attitudes, which influence directly consumer behavior (Fennis & Stroebe, 2010). Previous search done by Cialdini (2009) shows that when a product or service is scarce, people are more likely to buy or move in the desired direction. To elucidate scarcity we first explain what the scarcity principle is and why it is so effective.

2.5 Scarcity in Advertising


overcome privacy concerns, which take mostly place in the cognitive explicit memory, the implicit memory needs to be activated / stimulated. This can be done via multiple heuristic advertisement influence principles (Langer, 1992; Langer, Blank & Chanowitz, 1978) that stimulate simple, automatic, stereotype and non-conscious thinking. One of these principles that will be used in this study is scarcity.

The scarcity effect is a powerful social-influence principle used by marketers to increase the subjective desirability of products. The scarcity principle means that consumers may prefer products that are scarce due to excess demand if they are pursuing a goal of conformity, whereas they should be more likely to diverge from the majority in domains that others use to infer identity (Berger & Heath, 2007; Herpen, Pieters & Zeelenberg, 2009). Scarcity is used frequently in what is known as the deadline technique in advertising (Cialdini, 2009) when promoting now or never discounts, limited offers that suggest exclusiveness and special editions of various products (Inman, Anil & Raghubir, 1997). Products that are scarce are being perceived as more valuable (Lynn, 1991).

But what makes this principle so effective? There is a theoretical approach that explains this via the commodity theory. This theory claims that any commodity will be valued to the extent that it is unavailable (Brock, 1968,). The underlying mechanism for this effect is caused by people’s desire for uniqueness and distinctiveness (Snyder & Fromkin 1980). Based on this commodity theory we can argue that there is interesting link between scarcity and LBS that needs to be investigated.


action. Wu at al (2012) conclude in their study that scarcity increase purchase intention significantly. This is why we can argue that scarcity will increase store visit intention also when used in a location-based targeted product / service offer. To measure this effect we developed hypothesis three. This hypothesis measures the direct effect of scarcity on the relationship between location-based targeting and store visit intention.

H3: The use of scarcity has a strengthening effect on the relationship between

location-based targeting and store visit intention.

It is expected that ads containing scarcity elements are creating higher store visit intentions when location-based targeting is used. The hypothesis above isolate the effect of privacy need, and only take the direct effect of scarcity into account. For our research it is even more interesting to see if there is a three-way interaction effect. This effect means that the two variables combined with our LBS variable give an answer on hypothesis four.

H4: The three way interaction effect of scarcity, privacy and location-based targeting

has a strengthening effect, which realizes higher consumer store visit intentions.

2.6 Summary


3. Empirical Evidence

We carry out an analysis via experimental research. This is done by the use of four questionnaires/cases. Only Dutch people, who own a smartphone, are allowed to take part in the experiment, which is done and spread via the internet. This section of the paper explains all the measures, procedures, the sample and the model used for this research.

3.1 Measures

This subsection discusses the measures for the dependent variable store visit intention and the three independent variables; Location-based services, Scarcity and Privacy need.

3.1.1 Dependent variable: Store visit intention

This study is using the variable store visit intention as dependent variable. This variable is a not common variable in previous literature and studies. To come up with a good and reliable measure for this variable, there is made us of a combination of different papers. With the use of research by (Venkatesh et al, 2003) and (Azjen & Fishbein, 1980) the scales for intention are developed. Besides these papers, also research by Spears & Singh (2004) is used to develop good scales for this experiment. All these researches combined provided a list of 7-points likert scaled questions that give a clear view on the respondents store visit intention.

3.1.2 Independent variable: location-based-services (LBS)


variable is done via a text prime. After the experiment we asked the respondent to answer the following question; “According to the study, at what place did you get the advertisement on your mobile phone?” With the use of this question we can check whether the manipulation have worked or not. Appendix A1 and A2 show the priming stories used in the experiment.

3.1.3 Independent variable: Privacy need

The second variable is privacy need. Privacy is used as moderating variable in this research. We will measure consumers privacy need with questions like; How often have you personally experienced incidents whereby your personal information was used / abused by some company or e-commerce web site without your authorization? Or, How often have you personally been victim of what you felt was an improper invasion of privacy? Not only backward looking questions are used. Also questions that focus on their current behavior are implemented (e.g. Do you read company’s privacy terms? Do you accept automatically all terms and conditions while signing up for a website or buying a product? Do you want to have more control of your privacy?) These questions come from research of Smith, Milberg & Burke (1996). But also research done by Buchanan at al (2007) will be used to test privacy in online environments in specific. The questions about privacy are likert scaled and uses a 7-points scale. Privacy as moderating variable will not be manipulated because we treat is as individual consumer variable.

3.1.4 Independent variable: Scarcity


One group respondents was exposed to an ad with scarcity elements, the other group saw a non-scarce product offer. Appendix B1 and B2 show the used ads and scarcity elements.

3.2 Procedure

The data for this study is acquired by the use of an online experiment. We have a 2x2 full factorial, between subjects, research design. Four different questionnaires are used to create four different cases. As discussed, the location-based targeting and scarcity variables are manipulated. The following table 1 visualizes the four experimental cases.

Qualtrics is used as survey platform for data collection. To give the experiment more trust and body, the website is launched. This website maintained that we could easily spread the surveys throughout this internet. Basically, the start of the survey spread is done via Facebook. Furthermore, some LinkedIn groups that discuss themes covered in this research are approached. At the end, a lot of online blogs and forums are contacted to place links to our survey website. To higher the response rate, and improve the attention / involvement of people while filling in the survey, we implemented a prize. Respondents could win a gift card if they filled in all the cases in a correct way and provided their email address.

Each respondent was asked to fill in one of the cases. This means that this research uses a between subject design. The survey started with a couple of general questions and a prime. The respondent was told on what location he got an advertisement on his Smartphone.

Table 1 (0) Home (1) On-the go (0) Non-Scarce (1) Scarce

Case / Questionnaire 1 1 1

Case / Questionnaire 2 1 0

Case / Questionnaire 3 0 1


After that the store visit intention is measured. Finally, the survey asked the respondent to fill in questions and statements about their privacy need, and ended with some control questions to test the manipulations. An overview of the questionnaire can be found in appendix C.

3.3 Sample

When the experiment stopped, 355 respondents started the questionnaire. Of these responders, 290 finally filled in the whole questionnaire. We found that 270 finally meet al the requirement of (1) owning a smartphone, and (2) lived in the Netherlands. After some further respondent’s analyses, we removed all the cases that did not meet requirements for (1) questionnaire fill in time > 3 minutes and (2) showed no variation in answers, indicating automatic pilot behavior. Summarized we can say that this second analysis removed another 21 cases. Finally, 249 of the cases were usable for analysis. A more deeper statistical sample description could be found in the result part of this paper.

3.4 Model


Store visit intention = 𝛽0 + 𝛽1 × LBS + 𝛽2 × Privacy + 𝛽3 × Scarcity +  (  𝛽4 ×

LBS × Privacy ) +  (  𝛽5 × LBS × Scarcity ) +  (  𝛽6 × Privacy × Scarcity ) + (  𝛽7 ×

LBS Privacy × Scarcity )

Stores visit intention is a scaled variable lying in a grade range from 1 to 7. Both scarcity and location-based targeting are dummy data, coded with a 0 or 1. Because privacy is a scaled question with a range of 1-7, we could not make a sufficient dummy variable of it. The reason for doing a regression analysis has to do with this scale issue, and also because the variable is not manipulated. The regression analysis gives us a clear answer on the direct and indirect effect of privacy, and gives us the ability to test a three-way interaction effect. The next conceptual model visualizes the relationship of the different variables, and also indicates the expected effects.


4. Results

This chapter starts with showing descriptive statistics of the respondents and some survey questions to describe the sample. Second, the results of the analysis done in order to test and the hypotheses and answer the research questions are given. There is explained what survey questions are used to come up with factored variables. The Cronbach’s alpha is given to clarify the internal consistency of these variables. The result part provides insights whether or not there is found support for the hypotheses, and discusses the statistical evidence. Finally this will develop the base for the conclusion and discussions section of this research.

4.1 Descriptive statistics

The sample for analysis consists of 94 men (37.8) and 155 woman (62.2%). All the respondents are from Dutch origin and personally own a Smartphone. The proportion of respondents coming from high school is 32.1%. Further, 20.9% is MBO, 37.8% is HBO and 9.2% is graduated on university level. 90 of the respondents (36.1%) indicated that they have recently bought products via their mobile phone, 159 people (63.9%) stated they did not. 96.8% of the respondents answer that they take their smartphone to the shopping center. This means that the sample have experience in using a smartphone on the go. If we analyze the sample in terms of smartphone ownership, than we can say that 33.7% of the respondents own an iPhone, 30.9% a Samsung device, 13.7% a HTC and the other 21.7% own another brand.


51.8% of the respondent filled in a case with scarcity elements, 48.2% of the respondent a non-scarce case. Furthermore, 49.4% filled in a case with location-based targeting, 50.6% of them got a case with no targeting. The average privacy score is 5.125 with a standard deviation of 1.088. The groups are not significantly different from each other since we found a good distribution over the different cases. What already can be noticed is the shortcoming of the scarcity prime. The foundation and limitations of this finding will be discussed later.

4.2 Preparing for analysis

To measure store visit intention and consumer privacy need, we made use of different kind of questions, with different kind of scales. For further analysis, we factored the questions into one variable by the use of a factor analysis. By the use of this method we maintain that the grouping of these questions used for this measure have good validity. After that, we carried out a reliability analysis (Cronbach’s alpha) to prove that these questions are internally consistent. Because there was low variation in the privacy need variable, the regression analysis showed some issues regarding multicollinearity. To solve this problem, there is done a statistical standardization for the privacy variable. In order to do this, we used the technique of mean centering (Aiken & West, 1991). This new standardized privacy index is used in the regression model to test hypotheses two and four.

Table 2 Case 1 Case 2 Case 3 Case 4 Total

Q1 Product 100% 100% 95.2% 96.7% 98%

Q2 Location 100% 100% 95.3% 100% 99%

Q3 Scarcity 92.9% 66.8% 89.3% 63.9% 78%


4.2.1 Store visit intention

The factor analysis explained, for a combination of ten questions, a total eigenvalue of 7.368, and a variance % of 73.680%. We also made use of a dendogram to check the outcome. The KMP statistic is 0.927 and the Bartlett’s test of Sphercity showed a P value of 0.000, which means that this model is highly significant. After factoring, the reliability analysis showed a Cronbach’s alpha of 0,958 for the combination of the ten questions. Based on this information we can argue that the measure is of good quality. For both ANCOVA and regression analysis we use the store visit intention variable as scaled variable lying in a range between 1-7.

4.2.2 Need for Privacy

The factor analysis explained for a combination of 6 questions a total eigenvalue of 3.522, and a variance % of 58.702%. We also made use of a dendogram to check the outcome. The KMP statistic is 0.850 and the Bartlett’s test of Sphercity shows a P value of 0.000, which means that this analysis model is also highly significant. After the factor analysis, the reliability showed a Cronbach’s alpha of 0.843 for the combination of the six questions. Based on this information we can also argue for the variable privacy need that we have a well-founded measurement set of questions. For the regression analysis we used privacy as a scaled variable lying in a range between 1-7.

4.3 Model Estimations


4.3.1 Location and Gender

The first research question that needs to be answered is if there is a direct effect of using location-based targeting on the store visit intention of consumers. The first ANCOVA analysis showed that location is significant with p = 0.000 and f = 52.868. This means that there is a significant effect. The question is if there is also a difference between groups. Therefore we used a contract analysis. The Levene statistic of 4.165 shows that this model is significant with p = 0.042. The mean store visit intention for non-targeted consumers is 3.69 and the mean for the targeted people is 4.92. This difference between the two groups is highly significant with p = 0.000 and f = 52.463.

Based on this analysis we can say that there are differences between the groups, and that this difference is statistically significant. Because the ANCOVA shows also a significant effect for location we can say that; When a consumer get an advertisement when he is in the shopping center, he is more likely to visit the store, compared to somebody who receives this messages when he is at home. We may therefore

accept our hypothesis and find support to answer research question one.

Furthermore, this location-based effect is even stronger when the consumer is a woman. Men react less strong on an advertisement when they are located either in the shopping center or at home. The ANCOVA output found support that this is statically significant with p = 0.018 and f = 5.664. We conducted a contract analysis for this variable to check the significance of this difference between the two groups. Our test displayed a Levene statistic of 13.162 and a p value of 0.000.


The mean store visit intention for woman is 4.49 and the mean for men is 4.01. Based on the contrast analysis we can argue that this difference between the two groups is significant with p = 0.013 and f= 6.228. Despite the fact that there was no research question for the effect of gender, we can say that there is support for concluding that there is a significant difference in store visit intention between men and woman.

4.3.2 Scarcity

The second research question focuses on the moderation effect of the use of scarcity on the relationship between location-based targeting and store visit intention. The first ANCOVA analysis showed that the effect of scarcity is not significant with p = 0.177 and f = 1.833. This means that the effect of scarcity itself has no direct significant effect on store visit

intention. The contrast analysis may show that there is a difference between groups. Our test shows a Levene statistic of 2.084 with a p value of 0.150. The contrast analysis therefore is not statically significant. The mean store visit intention of respondents who were exposed to a scarce advertisement is 4.41. The mean store visit intention of respondents who were exposed to a non-scarce advertisement is 4.22. Despite the fact that the output show an f value of 1.030 and p = 0.311, we did not find support that this is significantly different.

Figure 3


Summarized we can argue that we did not found support for research questions three that scarcity has a moderating effect. Therefore, the 0 hypothesis could not be rejected. We did not find support that scarcity elements used in the advertisement have a positive effect on the relationship between location-based targeting and store visit intention, or that there is a difference between the two groups.

4.3.3 Privacy

Research question two focuses on the moderation effect of privacy on the relationship between location-based targeting and store visit intention. Table 3 shows the output of the regression analysis. Our total model is highly significant with p = 0.000 and f = 7.991. The total model explains 19% of the variance for our dependent variable (R2 = 0,188). To find an answer for the interaction effect of privacy, the variable Location * Privacy is important. This interaction effect has a p value of 0.412 and t = 0.822. The beta seems to be positive, which was not expected. But because the effect of this variable is not statistical significant we cannot make sufficient conclusion about it. Furthermore, none of the other variables in the model are significant. The moderation effect of scarcity, which is studied in the previous research question, shows a p value of 0.232 and t = 1.199. The beta for this variable is 2.091, which is quite high. When this effect seemed to be significant, we could argue that using scarcity in advertisements have a positive effect on store visit intention.


4.3.4 Three-way interaction effect

For research question four, the same linear regression analysis is done, and we based this answer on the same data for table 4. We have stated that our regression model itself is sufficient, but none of the independent variables itself are significant. The variable used to answer the last research question is Location * Privacy * Scarcity, which visualizes a three-way interaction. With a p value of 0.245 and a t value of -1.165 this interaction effect is not statistically significant and therefore we could not reject our 0 hypothesis. To conclude this last research question, we cannot say that people with high privacy need, that got a scarce offer, have a higher store visit intention.

4.3.5 Results summary


4.4 Solved Manipulation Check, Study 2

Because the manipulation for scarcity showed to be not very reliable, an extra analysis is done. All the respondents were checked in terms of correct answering the manipulation questions. If the question was filled in wrong, the respondent’s case was removed. To summarize the new sample, we provided a short description below.

After filtering we seem to have 181 correct responders left. 60.2% of the respondents are woman, 39,8% of them are men. 55.8% of the respondent filled in a case with scarcity, 44.2% of the respondent a non-scarce case. Furthermore, 49.7% filled in a case with location-based targeting on the go, 50.3% of them got a case with location-location-based targeting at home. The average privacy score is 5.112 with a standard deviation of 1.112. The new respondent group contains the following distribution over cases, displayed in table 6. We did not mention the manipulation check statistics again, because the issues regarding these statistics are solved by the uses of this new analysis.

Based on this information we can say that this equals the respondents group of the first analyses that is done without filtering. All the analyses are done again with this new group of respondents. The ANCOVA and contrast analyses showed no significant different results compared to the output of the first group respondent. Therefore we did not analyze the research questions one and three again, and do not display models and outputs of this analysis. However, for research question two and four there seemed to be remarkable changes in beta’s and significance. Table 5 shows the new regression analysis. The new total regression model is highly significant with p = 0.000 and f = 6.213, and the total model explains 20% of the variance for our dependent variable (R2 = 0.199).

Table 6 Case 1 Case 2 Case 3 Case 4 Total


To start analyzing this model, the interaction variables Location * Privacy and Location * Privacy * Scarcity are interesting for research question two and four. The regression analysis still shows a p > 0.05 for the moderation effect of privacy on the relationship between location-based targeting and store visit intention. This variable has a p value of 0.102, and a t value of 1.646. Based on this analysis we might still not accept the hypothesis for research question 2. But, the data shows significant output for our three-way interaction effect. Our variable Location * Privacy * Scarcity scores a p value of 0.027 with t = -1.131. The beta is negative, that means that this effect is negative and therefore lower the effect of location-based targeting on store visit intention.


4.5 Results Table Overview

Table 3

ANCOVA Analysis Type III sum of


Mean Square F Sig.

Corrected Model 109.069 36.356 20.485 0.000* Intercept 1809.786 1809.786 1019.729 0.000* Gender 10.052 10.052 5.664 0.018* Scarcity 3.253 3.253 1.833 0.117 Location 93.829 93.829 52.868 0.000* Error 434.819 1.775 Total 5173.631 Corrected Total 543.888

(ANCOVA analysis, * = significant at α=5%). ANCOVA Model Sig. 0.106, f = 1.926

Table 4

Regression analysis (Study 1) Unstandardized Beta Standardized Beta T Sig. Constant 4.301 4.325 0.000* Location 0.069 0.023 0.050 0.960 Scarcity -1.299 -0.439 -1.029 0.304 Privacy -0.130 -0.096 -0.730 0.466 Location x Scarcity 2.091 0.598 1.199 0.232 Location x Privacy 0.213 0.376 0.822 0.412 Privacy x Scarcity 0.287 0.507 1.226 0.222

Location x Privacy x Scarcity -0.286 -0.566 -1.165 0.245

(Linear regression analysis, * = significant at α=5%). R2 = 0.188, Regression Model Sig. 0.000, f = 7.991

Table 5

Regression analysis (Study 2) Unstandardized Beta Standardized Beta T Sig. Constant 5.383 4.300 0.000* Location -1.487 -0.509 -0.861 0.391 Scarcity -3.099 -1.055 -2.032 0.044* Privacy -0.324 -0.245 -1.442 0.151 Location x Scarcity 4.264 1.310 2.057 0.041* Location x Privacy 0.529 0.952 1.646 0.102 Privacy x Scarcity 0.655 1.180 2.330 0.021*

Location x Privacy x Scarcity -0.873 -1.382 -2.231 0.027*


5. General Discussion

This study provides consistent support for the proposition that using location-based services increase the store visit intention of consumers. When consumers are targeted based on their location, the likelihood that they will visit the company displayed in the advertisement increases significantly. It is important to understand that location-based services using targeting and locations. This research focuses on the location aspect on mobile. So, we can say the location aspect is isolated. The targeting itself can be done in much more different ways (e.g devices, in-app vs. web based, screen size, timing, etc.)

A surprising finding of this research is that women react significantly stronger on location-based services. Their store visit intention showed to be higher compared to men. An explanation for this fact may be that the ad attention for woman in general, is higher than form men (Camphorn, 2011). The effect of this finding by Camphorn may be that the interpretation of the banners used in this research is watched with more attention and care by women compared to men.


behavior. This finding can indicate that this kind of persuasion needs to be measured with for example implicit memory/association tests (Greenwald, McGhee & Schwartz, 1998; Nosek, Greenwald & Banaji, 2007).

The other moderator, privacy need, seems to be not significant in both studies. Our experiment did not manipulate this variable, what could indicate this outcome. Priming respondents in a situation that they are very privacy sensitive or not, can give a broader view on this variable. Currently the distributions of the privacy need level in this study is not very well distributed. With more variance, the result might be different. Anyway, we can say something about privacy, because our three-way interaction effect seemed to be significant in study 2, but with a negative beta. This means that the combination of moderators did not affect the relationship between location-based targeting and consumers store visit intention positively as expected. This conclusion is based on the second regression model with the filtered respondent sample. Our first study did not find support for it.

It could be that privacy need is something context dependent, or more implicit. Meaning; you only care when things are going wrong and otherwise decisions are made on the automatic pilot. Therefore, people might not know about themselves if they really attach value to their privacy. These day’s, people who say privacy is important for them, are putting tons of gigabits data online via social media and other websites, that are easily assessable for businesses and other people, not consciously knowing that this information is ready to be used in/for advertisement purpose (Norberg, Horne & Horne, 2007). This is specially the case for the younger generation (Viser, 2005).

5.1 Limitations


the advertisement elements. There need to come research that primes this effect in another way, to isolate the scarcity effect for this study. Besides this, there is made use of a ‘laptop’ advertisement. It could be that other product categories show other results because product involvement may affect the relationship between location-based targeting and store visit intention. When a respondent experience laptop issues in real life, he will respond more positive to this research than responds that are satisfied with their current laptop product.

To prime the respondents there is made us of the brand Mediamarkt. It could be that respondents who experience an aversion to that brand, or standardly buy everything there, respond differently. After all, respondents who are more online focused may also show different outcomes in research that uses offline metrics, like store visit intention, as dependent variable. Other research may focuses more on an online dependent variable metric to isolate this effect. Finally, a study that investigates store visit intention as variable may prefer a more experimental setting of the research, instead of experiments via questionnaires. An observational research, in a real life setting, may increase the reliability of this research. We conclude this based on the knowledge that scarcity and privacy effects occur implicitly.

This research focused on people who own a smartphone. 75% of the responders were 40 years or younger. Our age variable was therefore not normal distributed. Also the spread of women and men was not quite evenly distributed. Further research may take this into account that there can be a difference in the way younger people perceive and process this kind of communication compared to elderly people. Variables as, experience with smartphones or preferences regarding online / offline shopping may show moderation effects for the consumers store visit intention.

5.2 Managerial implications


to their offline shops, this might cause a change. There is a lot unknown about targeting consumers. Targeting based on location is one of the aspects that is currently experiencing much attention from advertising agencies. With the knowledge of this study we can argue that campaign efficiency can increase by targeting the consumers when they are on the go, and not at home. Retails may therefore optimize their campaigns very concrete by targeting shopping center locations in specific.

It seems that using scarcity elements in a banner does have an effect on the consumer store visit intentions. Therefore, businesses can experiment with implementing this variable in their banner sets. This can work out differently per product category. Previous research has shown that this powerful variable influence the consumers’ implicit memory. Although we did not find support in our study that this effect the store visits intention between the groups significantly, we may assume that this has an effect based on findings in study 2.

When retails implement locational-based targeting, as a common form of advertising, the privacy discussion may grow amongst consumers and politicians. There is still no clear legislation for this type of advertisement. Especially, since the technological developments for targeting are not stagnating at the moment. Retailers know that targeting consumers based on as much information that is available, also CRM data, will improve their campaign efficiency and effectiveness. It is expected that in the near future new privacy issues will be put on the social political agenda. Most important keeping consumers informed about the fact that their data is being used for advertisement purposes.


brand image. It is worth, and important, to take into account that these effects take place because those will higher the potential value of location-based services. It increases the accountability of this for of advertising, and justified the use of implementing location-based services in the marketing mix.

5.3 Conclusion

Our theoretical framework identifies the effect on store visit intention by targeting consumers by the use of their location. The insights provided by this positive relationship between the variables could help retails to successful implement location-based services into their marketing mix. Scientifically this opens new ways to further analyze other variable that might have a strengthening or weakening effect on this relationship. As investigated in this study, scarcity and privacy were variables that could influence the relationship. Although we did not found statistical proof for the effects of our moderating variables, we can argue that there are signs that these variables indeed influence the relationship between location-based targeting and the consumers store visit intention.


6. References

1. Advertising Age. (2006). Special advertising section of Advertising Age. December 4, p.20. 2. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J.

Beckman (Eds.), Action control: From cognition to behavior. Heidelberg: Springer. 3. Ajzen, I., M. Fishbein (1980). Understanding Attitudes and Predicting Social Behavior,

Prentice- Hall, Englewood Cliffs, NJ.

4. Aiken, L., West, S.G. (1991). Multiple Regression: Testing and Interpreting Interactions, Newbury Park, CA: Sage Publications.

5. Al-alak, B.A.M., Alnawas, I.A.M. (2010) Mobile Marketing: Examining the Impact of Trust, Privacy Concern and Consumers' Attitudes on Intention to Purchase. International journal of business and management. 5(3).

6. Altman, I. (1977). Privacy regulation: Culturally universal or culturally specific? Journal of Social Issues. 33(3): 66-84.

7. Arnold, S. J., Tae, H. O., &Tigert, D. J. (1983).Determinant attributes in retail patronage: Seasonal, temporal, regional, and international comparisons. Journal of Marketing Research. 20(1): 149–157.

8. Assael, H. 1998. Consumer Behavior and Marketing Action. Boston, M.A.

9. Babakus, E., Beinstock, C.,Van Scotter, J. (2004). Linking Perceived Quality and Customer Satisfaction to Store Traffic and Revenue Growth. Decision Sciences., 35(4): 713-737. 10. Barnes, J. S. (2003). Known by the network: The emergence of location-based mobile

commerce. In E.-P. Lim & K. Siau (Eds.), Advances in mobile commerce technologies. p.171–189.

11. Barton, J. (2011). Details On Groupon Privacy Practices. Telecommunications Reports. 77(15): 23

12. Benway, Jan P. (1999). Banner Blindness: What Searching Users Notice and Do Not Notice on the World Wide Web, Ph.D. dissertation. Rice University.

13. Berger, J., Heath, C. (2007). Where consumers diverge from others: identity signaling and product domains. Journal of Consumer Research, 34(2): 121-134.

14. Bisdikian, C. et al. (2001). Enabling location-based applications, in Mobile Commerce, p.38. 15. Bowie, N. E., Jamal, K. (2006). Privacy rights on the internet: Self-regulation or government

regulation? Business Ethics Quarterly, 16(3): 323-342.

16. Brannon, L.A., Brock, T.C. (2001). Limited time for respondents enhances behavior

corresponding to the merits of compliance: Refutations of heuristic-cue theory in service and consumer setting. Journal of consumer psychology. 10(1): 135-146

17. Brock, Timothy C. (1968). Implications of Commodity Theory for Value Change, in Psychological Foundations of Attitudes, Anthony G. Greenwald, Timothy C. Brock, and Thomas M. Ostrom, eds., New York: Academic Press, p.243–275.

18. Buchanan, T., Paine, C., Joinson, A. N., & Reips, U. D. (2007). Development of measures of online privacy concern and protection for use on the Internet.Journal of the American Society for Information Science and Technology, 58(2), 157-165.

19. Camphorn, M.F. (2011) Gender effects in advertising. International journal of Market Research. 53(2): 147-170

20. Cialdini, R.B., Sagarin, B.J. (2005). Principles of interpersonal influence. Persuation; Psychological insights and perspectives. 143-171. Thousand Oaks, CA:Sage.

21. Cialdini, R.M. (2009). Influence, Science and Practice (5th edition). Boston; Allyn and Bacon 22. Cialdini, Robert B. (2009). Influence, ISBN 978-90-5261-715-2, Pearson Education.

23. Clarke Irvine (2008), "Emerging Value Propositions for M-commerce", Joumai of Business Strategies. 25(2): 41-57.


25. ComScore. comScore Reports $47.5 Billion in Q3 2013 Desktop-Based U.S. Retail E-Commerce Spending. Assessed 10 march 2014, lion_Dollar_in_Q3_2013_Desktop_Based_US_Retail_ECommerce_Spending_Up_13_Percen t_vs_Year_Ago

26. Cunningham, P.J. 2002. Are cookies hazardous to privacy? Information management journal, 36(3): 52-54.

27. Davis, F.D,. Warshaw, P.R,. (1991). Choice Sets and Choice intentions. Journal of Social Psychology. 131(6): p823-830.

28. Definition Smartphone. Oxford Dictionary. Assed march 10, 2014, Available at

29. Dhar, S., Varshney, U. (2011). Challenges and Business models for mobile Location-based services and advertising communications of the ACM. 54(5).

30. Dow, C. (2013). Mobile marketing and the value of customer analytics. International Journal of Mobile Marketing. 8(1).

31. Dreze, Xavier and F. Hussherr (2003), Internet Advertising: Is Anybody Watching? Journal of Interactive Marketing, 17(4): 8–23.

32. Emarketer. Smartphone Users Worldwide Will Total 1.75 Billion in 2014. Assessed 10 march 2014,

33. Evans, Benedict (2013), Smartphones: High Prices, Huge Market. Technology Review. 116(3): 72-73.

34. Fennis, M.F., Stroebe, W. (2010). The psychology of advertising (first edition). Psychology press.

35. Fishbein, M., Ajzen, J. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison Wesley.

36. Gabler, C.B., Reynolds, K.E. (2013). Buy Now or Buy Later: The Effects of Scarcity and Discounts on Purchase Decisions. Journal of Marketing Theory & Practice. 21(4): 441-456 37. Gibson, Bruce (2006). Entertaining Mobile, excerpted from Mobile Entertainment Markets

Opportunities & Forecasts, 2006–2011, Juniper Research.

38. Gijsbrechts, E., Campo, K., Goossens, T., (2003). The impact of store flyers on store traffic and store sales: a geo-marketing approach. Journal of Retailing. 79(1): 1–16.

39. Goldfarb, A., Tucker, C. (2011). Online Display Advertising: Targeting and Obtrusiveness. Marketing Science 30(3): 389

40. Goldfarb, A., Tucker, C. (2011). Privacy Regulation and Online Advertising. Management Science. 57(1): 57-71

41. Greenwald, A.G., McGhee, D.E., Schwartz, J.L.K. (1998). Measuring individual differences in implicit cognition: the implicit association test. Journal of personality and social

psychology. 74(1): 1464-1480

42. Ha, L. (2008). Online Advertising Research in Advertising Journals: A Review. Journal of Current Issues & Research in Advertising. 30(1): 31-48.

43. Hallerman, David (2009). US Online Ad Spend Turns the Corner, eMarketer, (December 11), (accessed September 13, 2011), available at 44. Herpen, E., Pieters, R. and Zeelenberg, M. (2009), When demand accelerates demand: trailing

the bandwagon. Journal of Consumer Psychology. 19(3): 302-312.

45. Inman, J.J., Anil, C.P., & Raghubir, P. (1997). Framing the deal: The role of restrictions in accentuating deal value. Journal of consumer research. 24(1): 68-79.

46. Jung, J.M., Kellaris, J.J. (2004). Cross-national Differences in Proneness to Scarcity Effects: The Moderating Roles of Familiarity, Uncertainty Avoidance, and Need for Cognitive Closure. Psychology & Marketing. 21(9): 739-753.

47. Lambrecht, A., Tucker, C. (2013). When Does Retargeting Work? Information Specificity in Online Advertising. Journal of Marketing Research. 55(5): 561-576


49. Langer, E.J., Blank, A., Chanowitz, B. (1978). The mindlessness of ostensibly thoughtful action: The role of ‘placebic’ information in interpersonal interaction. Journal of personality and social psychology. 36(1): 635-642.

50. Laufer, R.S., and Wolfe. (1977). M. Privacy as a concept and a social issue: a multidimensional developmental theory. Journal of Social Issues. 33(3): 22–41.

51. Lynn, Michael (1991). Scarcity Effects on Value: A Quantitative Review of the Commodity Theory Literature. Psychology and Marketing. 8(1): 43–57.

52. Maitland, C. (1998). Global Diffusion of Interactive Networks: The Impact of Culture.

Department of Telecommunication; 409 Comm Arts and Sciences; Michigan State University. p.268-286.

53. MMA (2006a), Mobile Marketing Industry Glossary, available at, accessed 10 march, 2014.

54. Myles., Ginger., Friday., Adrian., Davies., Nigel. (2003). Preserving privacy in environments with location-based applications. IEEE Pervasive Computing, 2(1): 56-64

55. Norberg, P.A., Horne, D.R., Horne D.A. (2007). The privacy paradox: Personal information disclosure intentions versus behaviors. Journal of Consumer Affairs. 41(1): 100-126

56. Nosek, B.A., Greenwald, A.G., Banaji, M.R. (2007). The implicit association test at age 7: A methodological and conceptual review. Social psychology and the unconscious: the

automaticity of higher metal processes. New York Psychology press: 265-292.

57. Pan, X., Xu, J., Meng, X. (2012). Protecting Location Privacy against Location-Dependent Attacks in Mobile Services. IEEE Transactions on Knowledge & Data Engineering. 24(8): 1506-1519

58. Rice, R.E., Katz, J.E. (2003). Comparing internet and mobile phone usage: digital divides of usage, adoption, and dropouts. Telecommunications Policy. 27(8): 597

59. Rutz, O., Bucklin, J., Randolp, E., Sonnier, G.P. (2010). A Latent Instrumental Variables Approach to Modeling Keyword Conversion in Paid Search Advertising. Journal of Marketing Research. 49(3): 306-319.

60. Schacter, D.L., Chiu, C.Y.P., Ochsner, K.N. (1993). Implicit memory: a selective review. Annual review of psychology. 16(1): 159-182

61. Scharl, A., Dickinger, A., Murphy, J. (2004). Diffusion and success factors of mobile marketing. Electronic Commerce Research & Applications. 4(2): 159-173

62. Schmidt, F., Haberkamp, A., Schmidt, T. (2011) Dos and don’ts in response priming research. Advances in cognitive Psychology. 7(1): 120-131

63. Schuinanii, JH., von Wangenheim, F., Groene N. (2014). Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance Among Users of Free Web Services. Journal of Marketing. 78(1): 59-75.

64. Shankar, V., Venkatesh, A., Hofacker, C., Naik, P. (2010). Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues. Journal of Interactive

Marketing. 24(2): 111-120.

65. Shek, S. (2010). Next generation location based services for mobile devices. Leading Edge Forum, Computer Science Corporation. p.1–66.

66. Shiraishi, Y., Ishikawa, D., Sano, S., Sakurai, K (2011) Smartphone Trend and Evolution in Japan. Mobile Computing Promotion Consortium. Smart Phone Promotion Committee. 67. Smith, A. Pew Research Center’s (2014) Smartphone Ownership 2013 Update. (Assessed at

10 march 2014) 68. Smith, H. J., S. J. Milberg, and S. J. Burke (1996). Information Privacy: Measuring

Individuals’ Concerns about Organizational Practices, MIS Quarterly. 20(2): 167-196. 69. Snekkenes, E. (2001), Concepts for personal location privacy policies, in Proceedings of

Electronic Commerce. p.48-57.

70. Snyder, Charles R., and Howard L. Fromkin (1980), Uniqueness: The Human Pursuit of Difference, New York: Plenum Press.


72. Sultan, F., Rohm, A.J., Gao, T. (2009). Factors Influencing Consumer Acceptance of Mobile Marketing: A Two Country Study of Youth Markets. Journal of Interactive Marketing. 23(4): 308-320

73. Uglow, Sue (2007). The Race for Mobile Content Revenues, excerpted from Business Models for Mobile Content Providers: Strategic Options and Scenarios, 2007–2012, Juniper Research. 74. Venkatesh, V., M. G. Morris, G. B. Davis, and D. F. Davis (2003). “User Acceptance of

Information Technology: Toward A Unified View,” MIS Quarterly 27(3): 425-478.

75. Viser, M. (2005). Website’s power to overexpose teens stirs a warning, Boston Globe.

Accessible via: pose_teens_stirs_a_warning/

76. Wicker, S.B. (2012). The Loss of Location Privacy in the Cellular Age. Communications of the ACM. 55(8): 60-68

77. Worchel, S., Lee, J., Adewole, A. (1975). Effects of supply and demand on ratings of object value. Journal of personality and social Psychology. 31(1): 906-914

78. Wu, W-Y., Lu, H-Y., Wu, Y-Y., Fu, C-S. (2012) The effects of product scarcity and consumers' need for uniqueness on purchase intention. International Journal of Consumer Studies. 36(3): 263-274

79. Xu , J. D. (2007). The influence of personalization in affecting consumer attitudes toward mobile advertising in China, Journal of Computer Information Systems. 47(2): 9–19. 80. Xu, H., Teo, H.H., Tan, B.C.Y., Agarwal, R. (2009). The Role of Push--Pull Technology in

Privacy Calculus: The Case of Location-Based Services. Journal of Management Information Systems. 26(3): 135-173.

81. Zhang, R., Chen, J.Q., Lee, C. (2013). Mobile commerce and consumer privacy concerns. Journal of Computer Information Systems. 53(4): 31-38.



Appendix A1


Stel jezelf de volgende situatie voor: Het is woensdagmiddag en je zit thuis op de bank. Je bent op

zoek naar een nieuwe laptop want die van jou is net kapot gegaan. Op tafel ligt je mobiele telefoon. Je merkt dat er een advertentie op het beeldscherm van je telefoon verschijnt, en je bekijkt deze direct.

! Klik op Bekijk de advertentie om de advertentie te bekijken.

Appendix A2


Stel jezelf de volgende situatie voor: Het is woensdagmiddag en je loopt in het winkelcentrum van

de stad. Je bent op zoek naar een nieuwe laptop want die van jou is net kapot gegaan. In je broekzak zit een mobiele telefoon. Tijdens het winkelen pak je de telefoon en merk je dat er een advertentie op het scherm staat. Je bekijkt de advertentie direct.


Appendix B1 Appendix B2

A scarce Banner

A non-scarce banner

This banner contains the following elements that emphasizes a feeling of scarcity: 1. Alleen vandaag. This means: only

available today. A offer limitation in terms of time

2. Beperkte voorraad. This means: not much of these products in stock. A offer limitation in terms of availability

This banner contains the following elements that emphasizes a non-scarce feeling: 1. De hele maand. This means: the deal

is available for 30 days. A offer surplus in terms of time


Appendix C (Questionnaire in Dutch, Qualtrics output) Q1 Wat is je geslacht? " Man " Vrouw Q2 Wat is je leeftijd? ………..

Q3 Wat is je hoogst afgeronde opleiding? " VMBO " HAVO " VWO " MBO " HBO " Universitair

Q4 Ben je in het bezit van een smartphone? (Een smartphone is een mobiele telefoon met internet mogelijkheden)

" Ja, ik heb een iPhone " Ja, ik heb een Samsung " Ja, ik heb een HTC " Ja, ik heb een Nokia " Ja, ik heb een Blackberry " Ja, ik heb een Sony

" Ja, maar ik heb geen van bovenstaande merken " Nee, ik heb geen smartphone

Q5 Heb jij wel eens een product gekocht via je mobiele telefoon? " Ja

" Nee

Q6 Neem jij je telefoon mee als je gaat winkelen in de stad? " Ja

" Nee


Q7 Stel jezelf de volgende situatie voor: Je hebt echt een nieuwe laptop nodig, want jouw huidige laptop is kapot gegaan. Hoe groot is nu de kans dat je, na het zien van de advertentie, direct naar de Mediamarkt gaat om even rond te kijken?




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