1
By Annejet van der Vegte
A study exploring the effects of the use of locational data
for sending personalized messages, and the influences of
customer loyalty, transparency and previous privacy
invasions
The effect of using locational data for
personalization on customers’ feeling of
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The effect of using locational data for
personalization on customers’ feeling of
privacy and their attitude towards the
personalized message
By Annejet van der Vegte
University of Groningen
Faculty of Economics and Business
Master’s thesis MSc Marketing
Management
Completion date: 13-01-2019
Van Speijkstraat 153-2
1057GX Amsterdam
+316 144 222 07
annejetvdvegte@hotmail.com
Student number: 2484927
Supervisor: dr. M. (Martijn) Keizer
Second supervisor: dr. J. (Jelle) Bouma
A study exploring the effects of the use of locational data
for sending personalized messages, and the influences of
3 Abstract
4 Preface
Writing this master’s thesis is my final project of the study Marketing Management, and therefore of my time at the University of Groningen. Being able to choose a topic of my own interest, having a high amount of independency and being able to combine writing my thesis with an internship, all made that it was definitely one of the highlights of my study.
I want to thank dr. Martijn Keizer for being my supervisor, always providing me with useful feedback and always willing to help. It was great to be trusted with so much freedom, which made this study much more fun to do. Next, I want to thank dr. Jelle Bouma for being my second supervisor, and for helping me find an internship at VodafoneZiggo, a company that fits me and matches my interests.
Also, I want to thank Celesta Lücker-Swart for guiding me through my internship at VodafoneZiggo, always challenging me to get the best out of my study and helping me with all my questions. Lastly, I want to thank all members of the Market & Customer Insights team at VodafoneZiggo: due to them I had an amazing time doing my internship, with lots of jokes and long-during lunches. They definitely made writing my thesis much more fun to do!
Finally, I hope that you enjoy reading this study, and that it might give you useful insights.
5 Managerial summary
6 Table of contents
1. Introduction 7
2. Literature review 10
2.1 Personalization 10
2.2 Locational data of customers 13
2.3 Personalization and feelings of privacy 14
2.4 Moderating factors 15
2.4.1 Transparency about use of data 16
2.4.1.1 Which data is used 16
2.4.1.2 Why data is used 17
2.5 Loyalty of customers 17
2.6 Previous privacy invasion 18
2.7 Conceptual model 20
3. Methodology 21
3.1 Participants and design 21
3.2 Procedure and stimuli 21
3.3 Analysis plan 27
3.4 Sample demographics 28
4. Results 29
4.1 Descriptives 29
4.2 Results of testing the hypotheses 29
5. Discussion 36
5.1 Summary of findings 36
5.2 Theoretical contributions and practical implications 37
5.3 Limitations and future research directions 39
References 41
Appendix 1 48
7 1. Introduction
Nowadays, data is at the heart of most companies. And because of the excessive availability of data, personalization in the way of dealing with customers is becoming almost the standard. Customers are not surprised anymore by discounts based on their personal shopping history, and expect relevant advertisements and personalized emails in which they are greeted by their first name. Companies have more information about their customers than ever before, and it is almost impossible for a customer to properly use the internet without data being collected every minute. Next to this, the majority of people carries a mobile phone everywhere and is constantly connected to a mobile network and the internet: therefore, targeted advertisement can be delivered to the customer at anytime, anywhere, based on personal customer data (Cleff, 2007).
For companies, this new way of dealing with customers, using data to personalize marketing and service, is mainly a positive development. Previous empirical research already suggests many advantages for companies: the advertisement effectiveness of firms improves when the amount of data used increases (Goldfarb and Tucker, 2011; Tucker, 2012), targeted advertisements increase the chance that advertising also truly leads to the consumer purchasing the product (Kox et al., 2015), and personalized advertisements are twice as effective as similar, not personalized versions (Tucker, 2014). However, finding the right balance between using personal customer data for commercial targets, and respecting the privacy of customers, is often complicated. Not having this balance creates privacy concerns, which may lead to resistance to the advertisement’s appeal by customers (White et al., 2008). Because of this fear for customers’ resistance, companies might limit the tailoring of their advertisements (Lohr, 2010).
8 (DDMA, 2016). Also, although customers often provide personal data to companies themselves, 58% of the consumers still considers their personal data as their own property, and opine that they should be able to trade their data for free services, discounts or other advantages (DDMA, 2016). These two sides of personalization, both the advantages and the disadvantages, are called the Personalization Paradox: ‘Personalization can be both an effective and an ineffective marketing strategy, depending on the context’ (Aguirre et al., 2015).
There are several ways in which companies personalize their marketing and service. Examples of these are a personalized URL that gathers information about customer preferences to create a personalized website, communicating with customers based on their location, or simply using a customer’s first name in the header of your company’s email (Arora et al., 2008; Xu et al., 2011). But is personalizing the customer journey by using this kind of data techniques always a clever idea? Will your brand image improve when using these techniques, or will your customers feel like being followed, and being violated in privacy?
Because of intelligent technology in physical products, companies are now able to collect new kinds of data about their customers (Morey et al., 2015). An example of this is collecting customers’ locational data via their mobile device. The average mobile user uses its device frequently during the day, and almost never leaves the house without taking it (Cleff, 2007). This makes that consumers can stay in touch with other people constantly, but also makes that due to GPS signals and several Wi-Fi networks, they can be followed constantly. Lots of downloaded applications ask if they can use the GPS data of the mobile phone used to improve their services, and therefore constantly know where their customers are. However, this data is most times used without being visible to the customer, or to sell to third parties. Therefore, in this research I hope to find out what the effect is of using locational data for personalization while the customer could be easily aware of it.
The goal of this research is to show companies what the effect is of personalization based on locational data on the feeling of privacy of the customer, and therefore their attitude towards the personalized message. Earlier research is done on personalization based on previous purchasing behavior, or the ability of consumers to customize their own offer, but the effect of the use of locational data on the feeling of privacy of the customer is never researched before. Therefore, in this research I will focus on personalization using locational kind of data, and its possible consequences.
9 marketing purposes or for service purposes on the attitude of the customer towards the personalized message?’ Two different message purposes will be tested: messages with a marketing purpose, and messages with a service purpose, both with or without the use of locational data. Also, several other factors that might influence this relationship are included in this research. At first, feeling of privacy of the customer is expected to mediate this relationship. This mediating relationship is expected to be moderated by three factors: loyalty of customers, previous experience with privacy invasion and transparency about the use of data, split in why data is used and which data is used. These moderating factors are analyzed in order to find out if on beforehand set conditions (loyalty, previous experience) and variable factors controlled by the company (transparency) have an effect on the relationship between the use of locational data or not and the feeling of privacy of the customer.
This study will be done among customers in the telecom sector. This sector differs from other sectors in a way that most products sold are subscriptions. When buying these subscriptions, customers provide companies with a considerable amount of of personal data, and enter in a relationship that often lasts for one or two years. By entering this customer-company relationship, customers agree with a privacy statement in which often is stated that the customers’ personal data can be used for marketing and service purposes. This makes that companies in this sector often have much more opportunities in using their customers’ data, comparing to companies in other sectors.
10 2. Literature Review
2.1 Personalization
In this study, the key term is personalization. Personalization could be defined as ‘the ability to provide content and services tailored to individuals based on knowledge about their preferences and behavior’ (Adomavicius and Tuzhilin, 2005). Often, by using personalization, companies hope to build a meaningful one-to-one relationship with their customer, and with that increase their customers’ loyalty. When successfully applying personalization, based on knowledge preferences and behaviors, customer communication could be customized, and an individual’s need could be understood and satisfied to a great extent. In literature, many definitions of personalization are mentioned. Whereas Simonson and Itamar (2005) in their definition focus on tailored content and services based on rich knowledge, Riecken (2000) in his definition focuses more on building customer loyalty and a one-to-one relationship, and in the Personalization Consortium (2003) the main focus is on the tailoring of electronic commerce interactions, using both technology and customer information. A definition of personalization that comprises most of this, and that therefore is used in this research, is the definition of Adomavicius and Tuzhilin. According to Adomavicius and Tuzhilin (2005), ‘personalization tailors certain offerings (such as content, services, product recommendations, communications, and e-commerce interactions) by providers (such as e-e-commerce Web sites) to consumers (such as customers and visitors) based on knowledge about them, with certain goal(s) in mind.’
11 and therefore individual customer data (Cleff, 2007). So, 1-to-1 personalization is in this research the necessary kind of personalization to focus on.
Designing a personalization process could be done using two methods. First is the data-driven method, which is most popular among firms. In this method, customer profiles are built from earlier collected data, and are used in matchmaking algorithms. After this, the impact of the personalization is measured. The second method is called the goal-driven method. This method starts with a predefined set of goals, followed by the types of personalized offers that match this goal, and therefore should be delivered to consumers. To make it possible to deliver this kind of personalization, the profiling and matchmaking technologies are determined, and the customer profiles are stored with the right types of information. In the end, the right data is collected to build these consumer profiles (Adomavicius and Tuzhilin, 2005). Using the goal-driven method, companies often have to gather ‘opt-ins’ of customers first: customers should actively permit the company that their data can be used for marketing or service purposes. However, when a customer’s opt-in is requested, companies are often not very explicit about what data will be used and for what purposes, which makes that customers often don’t know what exactly they’re giving permission for (Beldad et al., 2010). Therefore, privacy concerns may still be an issue in this method. In this research, the focus will be on the goal-driven method, since the goal of collecting the data is known on beforehand: consumers are actively asked if their locational data can be used for marketing or service purposes by asking them to opt-in, so that personalized offers based on consumers’ location can be made.
12 However, there are also some downsides to the use of personalization. A big concern, mainly of customers, is violation of privacy. Since personalization is done using lots of data, for companies it becomes more and more interesting to collect excessive amounts of data about their customers. Doing this, they could use personalization to a maximum extent, and they could benefit of the advantages of targeting (Arora et al., 2008; Kox et al., 2015). This leads to companies constantly looking for ways to gather data about their customers, and customers becoming more and more frightened of what is done with this data and by who: for them, it becomes more and more complicated what kind of personal information about them is collected, and how this data is used (Kox et al., 2015). Companies might not only attract customers with their personalization, and with that increase their profits; they might also scare off people by over asking, and becoming too close. According to a research done by Turow, Henessy and Draper, 55% of the Americans says it’s incorrect if a store creates profiles of their customers based on the information they gather to increase the services they provide, and that they won’t agree with it (Turow et al., 2015).
In this research, a difference will be made between personalization for marketing purposes and personalization for service purposes. Whereas in marketing the main purpose is obtaining new customers and increasing the sales among new and existing customers (Webster, 1992), service is mainly about helping your customers and keeping them satisfied, so that they won’t churn (Hart, Heskett and Sasser, 1990). Since a company’s marketing strategy is in the end mostly in the interest of the company, whereas a company’s service strategy is much more in the interest of the customer, one can assume that customers get much more discontented when receiving a marketing message in comparison to receiving a service message. Therefore, the distinction between marketing and service purposes of a message could be interesting to make, which is done in this study.
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2.2 Locational data of customers
Due to new developments in mobile communication technologies, a new kind of marketing is created: location-aware marketing (LAM) (Xu et al., 2011). LAM can be defined as ‘targeted advertising initiatives delivered to a mobile device from an identified sponsor that is specific to the location of the consumer’ (Unni and Harmon, 2007). With 4.57 billion mobile phone users worldwide (Statista, 2018), almost all GPS-enabled, marketers who own this data could align their personalized marketing messages based on the specific geographical location of their customer, and with this predict their needs. They could target geographically, and if their consumers reach this target place, reach their mobile customers through their mobile devices (Xu et al., 2011). Companies are not automatically authorized to use the locational data of their customers, but can ask permission to their customers to do so. When obtaining this locational data, detailed user profiles can be assembled, and users can be easily identified. This information could be used to create customized and personalized advertising messages (Cleff, 2007). Personalization using LAM could for example be executed by sending a personalized text or e-mail to a customer based on his or her location, which might be at home or close to a company’s shop. Using this way of personalized marketing, companies could remind customers of their possible needs while at a relevant place, so they might be able to take action quickly. Next to this, since nowadays people are constantly focused on their mobile device, a message will almost all times immediately grab the attention of the customer (Cleff, 2007).
LAM could be done following two different approaches: the cbased approach and the overt-based approach. When using the covert-overt-based approach, marketers track the physical locations of the mobile devices of customers, and based on this data, location-sensitive content is automatically sent to them. When using the overt-based approach, customers themselves initiate requests, and ask for information and services based on their GPS location (Unni and Harmon, 2007; Xu et al., 2009). So, in the cbased approach the initiative of using personal data lies at the company, while at the overt-based approach the initiative of using personal data lies at the consumer. For example, using the covert approach, a customer could receive a message because of entering a specific area, without requesting this message. In the overt approach, a consumer could share his or her location and ask for a list of nearby bars or restaurants.
14 without explicit efforts by the consumer (Kendall and Kendall, 1999). Therefore, in this research the focus will be on the covert approach of LAM. However, this approach also raises a lot of privacy questions: customers might get the feeling that they lose control about what companies do with their personal data and why (Milne et al., 2008), which might lead to higher privacy concerns. Next to this, the use of locational data might give an intrusive feeling to customers, when not carefully monitored (Cleff, 2007). Therefore, in this research a closer look will be taken on the effect of using locational data on the attitude of people towards the personalized message, possibly influenced by their feeling of privacy. Since in this research we will test both the effect of personalization for marketing purposes as well as the effect of personalization for service purposes, we formulate the following hypotheses:
H1: The use of locational data by companies for the purpose of personalization of marketing or service
has a negative effect on the attitude of customers towards the personalized message
2.3 Personalization and feelings of privacy
One of the main discomforts of customers regarding personalization is their privacy. When looking into existing literature, privacy can be defined as ‘the ability of the individual to control the terms under which personal information is acquired and used’ (Westin, 1967). Looking at information privacy, this can be defined as ‘the ability of the individual to personally control information about one’s self’ (Stone et al., 1983). From this definition, one could say that in order to decrease the perceived privacy risk of consumers, companies should increase the perceived level of control over personal information, and previous research already found out that issues of informational control are key in creating favorable consumer reactions (Stewart and Segars, 2002).
15 information, or possibly give false information. This might lead to incomplete and false data sets and customer profiles, which might lead to wasted effort and inaccurate targeting (Cleff, 2007).
When consumers release their personal data to a company, they expect that only the company might use their information for profiling and personalized transactions, and that their personal data will not be indiscriminately shared with third parties (Chellappa and Sin, 2005). Out of fear for invasion of privacy, although they might like to receive personalized services, customers want to share as little information as possible (Xu et al., 2011). Especially when the data-driven method of collecting data for personalization is used, and consumers are not sure yet how their data is used, customers might be more alert on their privacy. Therefore, I expect that the use of locational data has a negative effect on the feeling of privacy of the customer, and the following hypothesis is formulated:
H2: The feeling of privacy of customers mediates the relationship between use of personalization and
the attitude of people towards the personalized message.
2.4 Moderating factors
There are several factors that could influence the relationship between personalization and the feeling of privacy of the customer. To optimize the personalization, firms are constantly trying to obtain more and more data about their customers. On the other hand, although customers might want personalized offers and services, they want to provide the company with a minimum amount of data. To stimulate consumers to expose their personal information and preferences to companies, firms should provide incentives to the customer to share their data (Murthi and Sarkar, 2003). These incentives could be given to customers in return for them providing data, but could also be something indirectly related to the moment of data gathering.
16 transparency about the use of data, the loyalty of the customer towards the company and the previous experience of customers with privacy invasions.
2.4.1 Transparency about use of data
In this world where companies gather data in all possible ways, customers often barely have a view anymore on what companies know about them. Therefore, a certain level of trust of the customer in the company is needed, trusting them to respect their privacy. Transparency about the use of data helps building this level of trust and therefore the feeling of privacy of customers (O’Hara, 2012). Transparency about collecting data and aggregating datasets could be defined as ‘providing information regarding the kinds and forms of data and datasets used’ (Zarsky, 2013). Since transparency helps building trust, I expect transparency about the use of data by companies to have a moderated effect on the relationship between the use of locational data or not and the feeling of privacy of the customer. In this research, I will split transparency about the use of data in two sub-categories: transparency about which data is used, and transparency about why data is used. A split between these two ways of being transparent is made to see how far a company should go when being transparent: if only mentioning the kind of data used already has effect, or if the company also has to explain why the data is used. Telling customers why data is used might explain the benefits for the customer, which might increase the tolerance of data use, and therefore increases the feeling of privacy of the customer. To see whether there is indeed a difference between these kinds of transparency, and to see the possible different effects on the feeling of privacy of customers, two different hypotheses are set.
2.4.1.1 Which data is used
17 and which decreases their level of trust (Turner et al., 2003)’. Applying this to a business situation like collecting personal data, this might also lead to lower levels of trust and feelings of emotional violation (Martin et al., 2017). According to the gossip theory, there are two factors that might decrease the negative effects of the collection of data without customers’ permission: transparency and control. When transparency is given, the customer is aware of which information is shared, and knows the potential harm this might give, and therefore can think about how to counter the possible negative effects (Martin et al., 2017). Therefore, I expect that when sending personalized messages based on locational data, being transparent about which data is used increases the trust of customers towards the company, and therefore there feeling of privacy when receiving the personalized message, and I formulate the following hypothesis:
H3: Being transparent about which data is used for personalization moderates the relationship
between use of personalization and feelings of privacy of the customer
2.4.1.2 Why data is used
Customers often are aware that companies collect data about them, and therefore want to be updated on what kind of data is collected and for what purposes this collected data is used (Stoyanovich et al., 2016). So, when the goal is to get customers to trust personalized technologies, an important action point is to provide an intuitive explanation to the customer on why a particular personalized offering was made. This not only makes it clear to the consumer what the company is doing with their personal data, but also helps customers to decide, for example, which recommendations that are based on this data are trustworthy and which are not (Adomavicius and Tuzhilin, 2005). Being transparent about why data is used to make personalized offerings therefore might increase the feeling of privacy of customers, since in this way they know the gathering of data is done with a clear visible purpose. Therefore, I formulate the following hypothesis:
H4: Being transparent about why data is used for personalization moderates the relationship between
use of personalization and feelings of privacy of the customer
2.4.2 Loyalty of customers
18 product or service has to be bought or used frequently (Dick and Basu, 1994). Loyal customers are attractive to companies in two ways: first, they raise the purchase and usage levels, and therefore the sales, and second, a close relationship is built between the brand and the customer, which makes that the customer will retain much longer than a usual customer (Uncles et al., 2002). According to Shaffer and Zhang (2000), one-to-one promotions lead to higher profits in equilibrium for the firm that has more loyal customers.
When a customer is truly loyal to a brand, he keeps having a positive attitude towards the brand despite situational influences (Oliver, 1997). So, despite personalization might be seen by the customer as a violence of privacy, brand loyalty might make that the customer still buys the product and keeps having favorable attitudes towards the brand. The higher customer loyalty, the higher the acceptance of information or actions that are viewed as negative by the customer (Uncles et al., 2002). On the other hand, when marketers are able to provide superior value to the customer using personalization, they will be rewarded for it with higher customer loyalty, which will create a big barrier to competition (Peppers and Rogers, 1997). So, customer loyalty could possibly decrease the negative effects of personalization. For example, when customers are brand loyal, they might be less suspicious towards the brand and trust the brand with their personal data. This leads to diminished privacy concerns, and therefore will give a more positive attitude towards personalization. Therefore, I expect customer loyalty to have a positive effect on the feeling of privacy of customers, and I formulate the following hypothesis:
H5: The level of loyalty of a customer moderates the relationship between use of personalization and
feelings of privacy of the customer
2.4.3 Previous privacy invasion
19 Awad and Krishnan (2006) tried to find a link between previous online privacy invasions and the willingness to partake in online personalization, and found that there is a link between pervious online privacy invasion and attitude towards personalized advertising, but no link between previous online privacy invasion and attitude towards personalized service. So, literature is still divided about this topic. Since in the media more and more attention is given to privacy violations and its negative consequences for consumers (NOS, 2018; Noort and Paauwe, 2018; Weijer, 2018; Dance, 2018; Kasteleijn, 2018), much more people are aware that they might have been a victim of a privacy violation, and read about the negative consequences it could give. This might make them more careful in sharing data, and more suspicious when their information is used for personalization. Therefore, in this research I expect previous privacy invasions to have a negative effect on the feeling of privacy of the customer, and therefore I formulate the following hypothesis:
H6: Former negative experience with privacy invasions of a customer moderates the relationship
20
2.5 Conceptual model
The conceptual model shows the relationship between the independent variable, the personalization based on locational data, and the dependent variable, the attitude towards the personalized message. Two different purposes for the use of locational data are researched: the use of personalization for marketing purposes and the use of personalization for service purposes. I expect that this relationship is mediated by feeling of privacy of the customer, and that this mediation is moderated by three different variables: customer loyalty, previous privacy invasions and transparency.
The expected mediating variable in the model is feeling of privacy of the customer. This mediator is expected to have a positive effect on the willingness to accept personalized marketing: the higher the feeling of privacy of the customer, the more the customer is probably willing to accept the personalized offer.
Next to this, I adopted three moderators in this research: loyalty of customers, previous experience with privacy invasion and the transparency about the use of data. Both loyalty of customers and transparency about use of data are expected to have a positive effect on the relationship between personalization and feeling of privacy; previous experience with privacy invasion is expected to have a negative effect on this relationship.
Figure 1: Conceptual Model.
Personalization based on locational data For marketing purposes
For service purposes
Attitude towards the personalized marketing/service
Loyalty of customers Previous experience with privacy invasion
-
Transparency about use of data
- +
+
Feeling of privacy of the customer +
21 3. Methodology
3.1 Participants and design
Participants
A questionnaire was sent by e-mail using Qualtrics to 17.400 people, of which 724 voluntarily participated in this research. E-mail addresses of these people were gathered from the Vodafone customer base, and customers were randomly selected. A between-subjects design was used, and customers were randomly divided into 8 groups. Since the questionnaire was sent in name of Vodafone, which is a commercial telecom company, the response rate of 4,2% is not remarkable. All people to which the questionnaire was sent are Vodafone customers in the age group of 25 till 54 years old, and are customers with a postpaid subscription. Qualtrics was used to collect the data, using an online survey. All potential respondents received an e-mail with an invitation to participate in the research, without on beforehand specifying what the research was about. An overview of the questionnaire can be found in appendix 1.
Design
To test the hypothesis, two conditions (marketing vs. service) were used. The dependent variable in this model is attitude towards the personalized marketing/service. Feeling of privacy is expected to be a mediating variable, which is expected to be moderated by the loyalty of customers, their previous experience with privacy invasion, and the transparency about the use of data of the company. In total, this leads to 8 conditions, which can be found in table 1. On overview of this conceptual model can be found in figure 1.
3.2 Procedure and stimuli
Procedure
22 the customer makes use of internet via mobile data / the 4G network. These two questions were asked to make sure the topic of this survey speaks to the customer’s mind, and questions about the feeling of privacy can be answered based on real feelings instead of imagined feelings.
Before the respondent answered questions about one of the conditions, some additional questions were asked to test the loyalty of the customer towards the Vodafone brand. Privacy related questions were asked after the marketing or service message is shown, to make sure people were not manipulated when answering questions about their attitude towards the personalized message.
Following this, respondents were reading a small text, either linked to the marketing or the service condition. In the marketing condition, people read a text in which they were asked to imagine that they are looking for a new telephone subscription, eventually at a new provider, but that they didn’t find the best deal yet. In the service condition, people read a text in which they were asked to imagine that they were walking in the city center, looking for the shortest way to walk to an appointment. However, for some reason they could not connect to mobile data. After reading this, each respondent got to see an image of a telephone showing a message related to one of the eight conditions. An overview of the specific details of each condition can be found in table 1. An example of the situations given to the respondents and the messages sent, both of the marketing and service condition, can be found in appendix 1.
1. In the first condition, the respondent got to see a message with a marketing purpose in which locational data is used to personalize the message. There is transparency about which data is used through a small note in the end of the message mentioning the specific data categories.
2. In the second condition, the respondent got to see a message with a marketing purpose in which locational data is used to personalize the message. There is transparency about why data in that message is used through a small note in the end mentioning what the purpose is of the data in that message.
3. In the third condition, the respondent got to see a message with a marketing purpose in which locational data is used to personalize the message. There is no transparency about which data in that message is used and why this data is used.
23 purpose in which no data is used to personalize the message. Since no data is used, there is also no transparency.
5. In the fifth condition, the respondent got to see a message with a service purpose in which locational data is used to personalize the message. There is transparency about which data in that message is used through a small note in the end of the message mentioning the specific data categories.
6. In the sixth condition, the respondent got to see message with a service purpose in which locational data is used to personalize the message. There is transparency about why data in that message is used through a small note in the end mentioning what the purpose is of the data in that message.
7. In the seventh condition, the respondent got to see message with a service purpose in which locational data is used to personalize the message. There is no transparency about which data in that message is used and why this data is used.
8. The eight condition is a control condition; the respondent got to see a message with a service purpose in which no data is used to personalize the message. Since no data is used, there is also no transparency.
Table 1.
Condition Type of data Transparency Purpose # Respondents
1 Locational Which data Marketing 87
2 Locational Why data Marketing 98
3 Locational None Marketing 87
4 None None Marketing 86
5 Locational Which data Service 82
6 Locational Why data Service 100
7 Locational None Service 87
8 None None Service 97
24 related to Vodafone/the telecom sector). In the end, the respondent answered some final questions about age, gender and level of education, and there was an open space in which the respondent could, if wanted, give any final remarks on the survey or on the topic. After this, the respondent was thanked for participating, and the survey ended.
Pretest
To ensure construct validity, a pretest was done. In this pretest, the survey was sent to 10 people with knowledge of both doing research and the telecom sector. Of these people, 4 filled in the survey and gave their feedback. These people all filled in the questionnaire of the first condition. In the pretest, the routing of the survey was tested, as well as how clearly the questions were formulated. After this first pretest, small changes were made in formulations of some questions. After this, the questionnaire of the first condition was sent to 2000 people, and the answers of the respondents were again checked thoroughly, also looking at the answers in the open space at the end, to see if respondents filled in any remarkable answers. Since the pretest gave no remarkable results and all questions were filled in by the respondents, the questionnaire was sent to another 14.000 people.
Manipulating transparency
Transparency was manipulated through the different messages, by adding notes at the end of the message to show people which or why personal data was used to send the message. In condition 1 and 5, it is specified which specific personal data is used to create the message: that the locational data of customers is used to send that specific message. In condition 2 and 6, it is specified why specific personal data is used to create the message: that personal location data is used to send relevant messages. In condition 3 and 7, no notification about transparency is added. Condition 4 and 8 are control conditions: in these conditions, no locational data is used, and therefore also no transparency about use of data is added to these messages. The exact text of the manipulations, both in Dutch (the language used in the questionnaire) and the English translations can be found in table 2.
Table 2
Condition Manipulation (English) Manipulation (Dutch) 1 and 5 This message is sent based on your
locational data
Dit bericht is gestuurd op basis van je locatiedata
2 and 6 To be able to send you relevant messages, we used locational data for this message
25 Manipulation check
To check whether the manipulation of type of data used was done successfully, the pretest was used to see whether the questionnaire was clear and could be totally understood by customers. Next to that, a manipulation check was added to the questionnaire. This manipulation check was conducted by asking the respondents whether it was clear to them which personal data was used for creating the message, using the question ‘Onder aan het bericht wat je net te zien kreeg, stond een schuingedrukte tekst, waarin uitgelegd werd welke data gebruikt is voor het samenstellen van dit bericht. Welk soort data werd er voor dit bericht gebruikt?’ (In the end of the message you just saw was explained in italics what kind of data was used for creating the message. What kind of data was used for this message?). In the conditions in which locational data was used, of the people could tell at the end of the survey that locational data was used. This percentage is rather low; an explanation for this could be that the message was shown to people in the beginning of the questionnaire, while the manipulation check was shown to people at the end of the questionnaire. This could have led to people noticing the manipulation, but not being able to recall the kind of data used after many questions about other topics were asked.
Measurement loyalty of customers
To measure the respondents’ loyalty towards the company, in this case Vodafone, two questions were asked. The first question used was gathered from the research of Bradu and Ciobanu, measuring affective loyalty. Respondents were asked whether they think the Vodafone brand is better than other telecom brands (Bradu and Ciobanu, 2012), using the question ‘In hoeverre vind je Vodafone een beter merk dan de andere telecommerken die er zijn (bijvoorbeeld KPN, Tele2 of T-Mobile?’ (To what extent do you think Vodafone is a better telecom brand than the other telecom brands you know (for example KPN, Tele2 or T-Mobile)?) For measuring affective loyalty, in line with Bradu and Ciobanu, a 7-point Likert scale was used, ranging from 1 (total disagreement) to 7 (total agreement). For measuring action loyalty, the Net Promotor Score (NPS), a KPI often used by companies, was used, using a 11-point Likert scale ranging from 0 to 10 (Reichheld, 2001). The average score people gave using this scale was a, resulting in a Net Promotor Score of. The Cronbach’s Alpha of these two items together is, and this value will only decrease when an item is deleted, so both items were put together in one scale.
Measurement attitude towards personalized marketing or service
26 had to tell how much they agreed/disagreed with these statements, based on a 7-point Likert scale. These statements measured how happy people were to receive this message, how positive their attitude was towards personally receiving this information, how this message improves their image of Vodafone, how annoying they think the message was (recoded), and how pushing they think the message was (recoded). An example of a question used to measure the attitude of customers towards the personalized message is ‘Ik ben blij om een berichtje te ontvangen met informatie over deze aanbieding’ (I am happy to receive a message about this discount). Together, these 5 questions have a Cronbach’s Alpha value of which is highly over .6, and won’t increase significantly when an item is deleted, so all items are combined into one factor.
Measurement feeling of privacy of the customer
In line with the research of Sheehan and Hoy (2000), to measure the feeling of privacy of the consumer after seeing the advertisement, respondents were asked how much they concern about their privacy, using a seven-point Likert scale, going from ‘not at all concerned’ to ‘extremely concerned’. Next to this first privacy question, other questions about privacy are asked using researches of Hoekstra & van Doorn (2013) and Kim and Agarwal (2004) about how important it is for people to know where their personal data is used for, if they think Vodafone owns too much personal information about them, and if they were less happy to receive this message because of their privacy concerns. An example of a question used to measure a customer’s feeling of privacy is ‘Na het zien van dit berichtje ben ik bezorgd dat Vodafone te veel persoonlijke informatie over mij heeft’ (After seeing this message, I’m worried that Vodafone owns too much personal information about me). For all these questions, a 7-point Likert scale was used. These questions together have a Cronbach’s Alpha of. The Cronbach’s Alpha would be higher when deleting a question, but since the Cronbach’s Alpha when not doing this is still highly over .6, all questions are combined into one item.
Measurement previous experience with privacy invasion
27 used. As an overall score, the mean of these items was taken. The Cronbach’s alpha of these two items together is, and this value would only decrease when deleting an item, so both variables are combined into one factor.
3.3 Analysis plan
After the data was collected using Qualtrics, the raw dataset was downloaded from Qualtrics and uploaded into the statistical analysis program SPSS. In total, out of 17.400 people, 724 people started the questionnaire. First, all incomplete questionnaires were deleted from the dataset, which made that 621 questionnaires remained. Second, all surveys of people who said not to be a Vodafone Customer (0.13%) and not to use mobile data on their phone (0.42%) were deleted. Third, two questions about the attitude of customers towards the personalized message were recoded to be able to combine them into one scale, and outliers were detected using Mahalanobis distance, computing the p-values and deleting all items with p 0.01 or p < 0.01. After, this, 541 questionnaires remained. After this, Cronbach’s Alpha was executed to account for internal validity, reliability and consistency. Four items were created: Loyalty, Privacy, Attitude and Previous Privacy Invasion. Several One-way ANOVA’s were done to test the general effects and direct effects in the model, and PROCESS Macro (model 1, 4, 7, and 9, 5000 bootstraps) was used to test mediating and moderating effects.
3.4 Sample demographics
Among the 541 remaining respondents, the average age is 43 years old. 55,2% of the people were male, 40,3% of the people were female and 4,5% of the people are other/rather do not want to say. The most frequent education of people who filled in the questionnaire was ‘Middelbaar Beroepsonderwijs’ (31,2%), followed by ‘Hoger Beroepsonderwijs’ (29,9%) and University (11,3%). In table 3, the number of respondents per condition are showed after deleting the outliers and incomplete questionnaires.
Table 3.
Condition Type of data Transparency Purpose # Respondents after deleting
1 Locational Which data Marketing 65
2 Locational Why data Marketing 76
28
4 None None Marketing 73
5 Locational Which data Service 60
6 Locational Why data Service 74
7 Locational None Service 60
29 4. Results
4.1 Descriptives
4.2 Results of testing the hypotheses
H1: The use of locational data by companies for the purpose of personalization of marketing or service
has a negative effect on the attitude of customers towards the personalized message
H2: The feeling of privacy of customers mediates the relationship between use of personalization and
the attitude of people towards the personalized message.
H3: Being transparent about which data is used for personalization has a positive moderating effect on
the mediated effect of feeling of privacy of the customer.
H4: Being transparent about why data is used for personalization has a positive moderating effect on
the mediated effect of feeling of privacy of the customer.
H5: Level of loyalty of a customer has a positive moderating effect on the mediated effect of feeling of
privacy of the customer.
H6: Former negative experience with privacy invasion of a customer has a negative moderating effect
30 5. Discussion
5.1 Summary of findings
5.2 Theoretical contributions and practical implications
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38 Appendix 1
1. Questionnaire (in Dutch)
Category Topic Question (in Dutch)
Check Customer Vodafone Klopt het dat je klant bent van Vodafone?
Use of data Maak je wel eens gebruik van internet op je telefoon via je mobiele data / het 4G netwerk?
Loyalty NPS Op een schaal van 0 tot 10, hoe waarschijnlijk is het dat jij Vodafone zal aanbevelen aan vrienden of familie?
Better brand In hoeverre vind je Vodafone een beter merk dan de andere telecommerken die er zijn (zoals KPN, Tele2 of T-Mobile)?
Attitude Happiness Ik ben blij om een berichtje te ontvangen met deze informatie
Personal approach Het is goed dat ik persoonlijk op de hoogte gebracht wordt van deze informatie
Image of Vodafone Het ontvangen van dit berichtje verandert mijn beeld van Vodafone op een positieve manier
Annoying Dit berichtje is vervelend Pushing Dit berichtje is opdringerig
Privacy Worries Na dit berichtje maak ik mij zorgen over mijn privacy Personal data Na dit berichtje ben ik bezorgd dat Vodafone te veel
persoonlijke informatie over mij heeft
Usage of data Het is heel belangrijk voor mij dat ik op de hoogte ben van hoe mijn persoonlijke data gebruikt wordt
Privacy – happiness Omdat ik mij na het zien van dit berichtje zorgen maak over mijn privacy, ben ik minder blij om dit berichtje te
ontvangen
Use of data Was het voor jou duidelijk om uit dit berichtje af te leiden of er persoonlijke data gebruikt is?
Privacy violation
Check Heb je wel eens het gevoel gehad dat je privacy is geschonden?
If yes – alertness In hoeverre ben jij, door deze eerdere privacy schending, alerter geworden op je privacy in andere situaties? If yes – cautiousness In hoeverre ben jij, door deze eerdere privacy schending,
voorzichtiger met het delen van persoonlijke informatie? Manipulation
check
Onder aan het bericht wat je net te zien kreeg, stond een schuingedrukte tekst, waarin uitgelegd werd welke data gebruikt is voor het samenstellen van dit bericht. Welk soort data is er gebruikt voor het samenstellen van dit bericht?
Background Age Wat is je leeftijd?
Gender Wat is je geslacht?
39 2. Situations (in Dutch)
(Marketing) Je mobiele telefoonabonnement is bijna verlopen, dus je moet op zoek naar een nieuw abonnement, eventueel bij een nieuwe provider. Je bent op zoek naar een goede deal, maar hebt nog geen specifieke telefoon of provider op het oog.
Een dag later loop je in de stad langs een Vodafone winkel, en ontvang je het volgende bericht:
- Message 1 -
(Service) Je loopt in de stad, op weg naar een afspraak. Om de kortste route naar je locatie te bepalen, pak je je telefoon erbij om het op te zoeken. Als je je telefoon in handen hebt, zie je dat je geen internet hebt: je krijgt op geen enkele manier verbinding met het netwerk. Je stopt je telefoon weer in je tas, en vraagt een voorbijganger naar de kortste weg.
Tien minuten nadat je door hebt dat je geen bereik meer hebt, ontvang je het volgende bericht:
- Message 2 -
