Thesis
MSc Business Studies
Specialization Marketing
The influence of privacy sensitivity, privacy awareness and
personalization benefits on the willingness to provide certain types of
information in a m-commerce environment.
Nadine Hendriks, 10524371
Supervisor: Frank Slisser
Table of contents Abstract 2 Introduction 3 Literature review 9 Conceptual model 22 Research design 23 Data Analysis 27 Discussion 37 Conclusion 47 References 49 Appendix 1 54 2
Abstract
This thesis addresses the privacy-personalization paradox within m-commerce. Previous research indicated that companies can derive advantages through knowledge of customers’ needs and behavior. However, the growing attention for mobile commerce and the personal character of mobile devices, turned information privacy within mobile environments into an important subject. The aim of this research is to discover about privacy awareness of consumers and privacy sensitiveness within an m-commerce environment. Also, how consumers’ willingness to provide data to companies can be influenced by personalization benefits.
Through a qualitative research at both the side of companies and customers, an answer to this question was formed. Privacy awareness within m-commerce environments seems lower in comparison to ‘general’ online environments, mainly because users are not aware of which information can be gathered and how to protect information. A part of the customers is more privacy sensitive and therefore cautious with specific types of data like social networks and GPS-locations. Financial benefits could have an impact on the willingness to share personal
information, but in most situations this only counted for information which was relevant for the specific company. Often, personal recommendations didn’t have an impact, as consumers prefer to make their own independent choices, without being influenced. Finally, customers attach value to convenience within m-commerce environments and therefore understand that more personal information is needed.
Chapter 1: introduction
Nowadays internet is closely embodied into everything we do, at work but also during our private lives. More than 1.6 billion people in the world use a phone regularly and the total number of phone subscriptions raised to over four billion (Karaatli et al, 2010). The rapid increase in the usage of smartphones and the more advanced capabilities and connectivity led to a new era, in which people are always online, anywhere at any time. The online information streams that flow from these activities on the internet offer companies opportunities to increase their knowledge about customers. As a consequence, companies can discover about consumer’ demographics, interests and (buying) behavior. Within the marketing field segmenting and profiling of
customers based on personal data is an essential point of interest (Hung, 2012). Afterwards, these profiles are used to target customers individually and make them personalized offers.
Results show that customers react positively on online product recommendation which are based on previous purchases (Lun-Ping Hung, 2012). Besides, Thongpapanl & Ashraf (2011) show that information targeted to individual preferences increases customers satisfaction and serves as a driver for online sales performance. Until recently, customers didn’t always consider the
intentions of a company for collecting personal data and didn’t have a clear view of the purposes for which data was collected. Consumers highly appreciate benefits like convenience and utility, therefore they were willing to lose some of their privacy to companies. (Acquisti & Loewenstein, 2012).
However, gradually society is discovering the downside of the fact that companies possess and have control over this customer data. Smith et al. (2011) mentioned that privacy was one of the biggest concerns of customers in 2011. The large amount of possibilities for companies to collect, process, distribute and use personal information seems to worry customers. On the other hand, companies see growing opportunities in the availability of personal customer data. Within the next years additional customer information becomes available because of the digitalization of our society. This will have an impact on the growing field of mobile commerce, as mobile features like location tracking and location-based service, are even more personal than features used within e-commerce.
An example could be found in the Dutch news recently, and was about how companies use the information from mobile phones to increase their knowledge about customers. Some companies use Wi-Fi tracking within their stores as a method to discover the duration of a customer’s visit and whether they came to the store before. In the United States, several stores already use these methods are used for a longer period time. The customers of the companies in question caused commotion, after the New York Times posted an article about this topic, customers felt that their privacy was abused (Trouw, 2014).
Although companies use similar methods online, it causes less disturbance compared to the implementation in offline environments. The previous example made people aware of how visible their behavior to companies actually is. Therefore, the emphasis in this paper lies on the personalization-privacy paradox and whether customers are aware of what is happening with
their personal data. Also, with due observance of the fact that prolonged personalization might scare off customers instead of making them satisfied.
Scholars conducted research on the privacy-personalization paradox within online environments and the consumers’ view towards sharing personal data with companies. However, less light has been shed on mobile environments and it has been acknowledged that more research is this area is needed. According to Zhang et al (2013), privacy awareness within mobile environments has not yet been investigated. Besides, Sutanto et al. (2013) mentioned that no clear view exists on which types of information consumers see as too privacy sensitive. Furthermore, it is important to take into account the benefits a company offers, such as discount or convenience, as it influences consumers’ willingness to provide personal information. In combination with a developed view on customers’ awareness and sensitiveness of different types of personal information, this research offers a better understanding of how companies should design their online offerings. The central construct of this paper is understanding customers’ willingness to provide personal information to companies. Bélanger & Crosser (2011) mentioned that more knowledge is needed on how companies can leverage their understanding of customers’ information privacy, this research aims to contribute to this view.
Taking this into account, the research question of this paper is:
How do privacy awareness, privacy sensitiveness and personalization benefits influence customers’ willingness to provide certain types of personal information within a m-commerce environment?
To be able to conduct this research successfully, the main question will be divided into multiple sub questions to get more clarity on the subject:
1. How is personal information collected for online and mobile personalization? 2. Which benefits and risks are associated with sharing personal information with
companies for online and mobile personalization?
3. To what extent are consumers aware of the personal information gathered by companies in m-commerce?
4. To which extent do consumers view these types information as privacy sensitive? 5. How do personalization benefits influence customers’ willingness to provide personal
information?
The aim of this research is to give companies insights in the considerations consumers make while sharing personal data within mobile environments. Especially, because the subject of mobile personalization is relatively new and still at an experimental stage (Ho & Chau, 2013). Moreover, a clear view on which types of information companies use for mobile personalization and the benefits created from this data is lacking.
This research will be conducted through a descriptive and exploratory study. Firstly, through a descriptive study the different types of information companies require for personalization and the benefits are created with it, are analyzed. Afterwards, the exploratory research will explain more about privacy awareness and privacy sensitiveness of these types of personal information. Also, it should be investigated whether consumers are more likely to give up sensitive personal data when benefits are offered in return. To receive more in-depth knowledge about the opinion of
consumers towards mobile personalization, interviews will be conducted. Besides, interviews with several companies which are specialized in this topic will contribute to an enhanced view of online personalization.
The next part chapter discusses several theoretical contributions which are made within the field of information privacy and will discuss the main concepts of this study. Afterwards, the focus lies on the research design, followed by the results of the study. Finally, the discussion will show how these results can be interpreted and in the conclusion the main remarks are summarized.
Chapter 2: literature overview
In the literature review, the theory that has been developed in the area of privacy and
personalization research will be discussed to provide the reader with a proper background. First of all, the concept of privacy is discussed. Thereafter, the focus lies on m-commerce and the differences between m-commerce and e-commerce. Furthermore, the privacy-personalization paradox is discussed through highlighting essential papers within the field. It is important to distinguish privacy issues in online and m-commerce, therefore the differences will also be underlined. Finally, several types personal information used for personalization are addressed.
Privacy
This paper only focuses on information privacy, therefore definitions of privacy with another scope aren’t discussed. An early definition of information privacy comes from Westin (1967), who stated that privacy can be defined as: the control over when, how, and to what extent information about an individual is communicated to others.
More recently, researchers (Dillon, 2010; Milberg, 2000; O’Neil, 2001; Phelps et al, 2000) adapted this definition by adding new concepts as: trust, expectations, ability and results of privacy dynamics. Focusing less on the actual term of privacy, emphasizing that privacy is not a stand-alone concept, but is influenced by other constructs. It is important to take these constructs this into account while conducting research upon privacy sensitive data, as it is often dependent of a specific situation.
Besides, privacy can apply to: behavior, attitude, process or a goal. Also, overlapping terms exist like: confidentiality, secrecy and anonymity (Heng, 2011). Privacy is examined within political, individual and societal contexts (Smith et al. 2011). Nevertheless, because of the qualitative scope of this paper, the focus lies on individual information privacy in terms of attitudes, actual behavior will not be measured.
An example of a definition of privacy which is focused on attitudes is the one of Clarke (1999), who defined privacy as: “The interest people have in controlling, or at least significantly
influencing, the handling of information about themselves. However, as this paper focuses on individuals, the definition of Bélanger & Crosser (2011) is more applicable. After reviewing multiple articles about information privacy they came to the following definition: “Information privacy refers to the desire of individuals to control or have influence over data about
themselves”. This paper assumes that individuals want to be able to decide what happens with their personal data, as it is their own property. However, it is important to take into account that differences between attitude, intentions and behavior exist (Ajzen, 1985) which can have an influence on the reliability of the research, for example through perceived behavioral control. According to Ajzen (1985), a lack of resources, like time or skills can also keep people from performing the planned behavior. Therefore, examples of behavior that corresponding to customers’ attitudes should be described during this research, in this way insights of how individuals control or influence data about themselves can be taken into account. This shows whether customers have a real interest in controlling or influencing the information about themselves.
Sometimes, it is argued that privacy is non-existent within the digital age. Companies like
Facebook and Google are would mistreat privacy of its users by secretly collecting personal data. However, both companies are extremely successful as users rapidly embraced the programs (Thierer, 2013). Therefore, it seems that users increasingly accept to share personal information with companies, when this information is needed to get access to a specific service. As a
consequence, consumers lose control over this data and accept a loss of privacy. Hence, this paper is written around the view that customers’ personal data is their property, and they should be able to make decisions about it. To be able to do so, they need to have a clear view of which data is gathered and for which purposes. When consumers want to make a well-weighted choice in providing companies with their personal data they need to understand what happens with the information. The next part of this paper emphasizes on personalization through customer data of customers in m-commerce.
M-commerce
Tiwari et al (2006) refer to m-commerce as: ‘any transaction, involving the transfer of ownership or rights to use goods and services, which is indicated and/or completed by using mobile access to computer-mediated networks with the help of an electronic device.’. However, the definition of Tiwari (2006) as this research aims to include all the activities that are undertaken on a mobile device, including mobile browsing.
Therefore, the following definition will be referred to in this paper: ‘m-commerce is about using mobile devices or smartphones to access mobile websites or use mobile apps for mobile
services’. Different types of services exist, like; mobile banking, mobile payment, mobile news, 11
mobile gaming and mobile retailing and purchases (Zhang et al, 2013). Within mobile banking, sensitive personal information is needed in order to provide the customer with a complete service, therefore banks have access to for example transaction data. Within this form of mobile
commerce, consumers don’t have the choice to decide whether they want to share this information. Besides, banks are already dealing with customer data for a longer time.
As this research aims to describe the the trade-off consumers make, it focuses on companies that gather new customer insights to be able to build profiles for personal targeting, for example within retail environments. It is also important to take into account that core functionalities of popular mobile applications are often based on personal customer information, like location or interests (Zhang et al, 2013). In the ideal situation, customers should be able to make the choice whether or not these services deliver enough value to share their personal information with the provider.
The popularity of m-commerce has increased in the recent years, however privacy concerns may negatively influence the willingness of customers to adopt m-commerce. As described by Shilton (2009), a mobile phone is like a big brother in your pocket’, as all types of data are shared
everywhere you go. Some scholars argue that the nature of m-commerce doesn’t differ
significantly from e-commerce and that it should rather be seen as an extension of e-commerce. The defenders of this view mention that m-commerce should be seen as just another channel of the e-commerce process which can be used to add value. Namely, similarities can been
discovered in the way data is collected, through registration, capturing Internet Protocol (IP)
addresses, using ‘cookies’ or getting information from social networks such as Facebook and Twitter (Zhang et al, 2013).
Boritz et al (2011) specify that during the registration process companies often request name, e-mail address and credit card number, sometimes complementary data like preferences and income is collected. Through IP (Internet Protocol) addresses companies can discover which web pages a customer visited. Through cookies ( a file which is stored in a computer or mobile, which contain information like customers’ traits, preferences and behavioral information) customers can be identified. Social networks give companies the opportunity to provide more detailed personal information about their customers.
Zhang et al (2013) also state that m-commerce does have some unique features which are not present in the context of e-commerce:
- Ubiquity: mobile commerce can be virtually conducted from ‘anywhere’. - Immediacy: m-commerce can be conducted at ‘anytime’.
- Localization: location-based services are present.
- Instant connectivity: smartphone users are constantly online. - Proactive marketing: allowance of push marketing.
- Augmented identity tracking down: the owner of a smartphone can easily be identified.
Because of these specific features, m-commerce is seen as more critical when it comes to information privacy compared to e-commerce. Personal information can be collected at anytime
and anywhere. Moreover, as customers’ can be reached by companies at almost every moment, the risk of privacy disruption is much higher. Besides, the security of smartphones is often worse in comparison to desktops or laptops (Zhang et al, 2013).
The context is important to take into account while looking at mobile environments, as Yang et al (2012) stated. As an example, it was discussed that mobile devices may be of less value at home, as substitute devices are available. This is corresponding to the view of Zhang et al (2013) that a difference with e-commerce is that mobile commerce can be conducted from ‘anywhere’.
Also, new mobile applications appear on which value is created with personal data on a large scale, like Biketastic and AndWelness. Finally, these application can help customers to make decisions in their everyday life, regarding their cycle route or health. Often, it is difficult to assess for customers what is collected, and whether it will be reused with permission by other parties. (Shilton, 2009). This is an example of how m-commerce differs from e-commerce and what kind of personal mobile services are developed.
Privacy-Personalization Trade-Off
The privacy-personalization trade-off was addressed by multiple scholars. Personalization is defined as: the means by which online content can be tailored on a website for every visitor. Personalization can also be achieved without using personal information, like comparisons to other customers that bought similar products (Awad & Krishnan, 2006). Personalization is seen as an important driver for future success for companies, as it leads for instance to: increased customer loyalty, brand awareness and customer satisfaction. To implement this strategy
effectively, companies utilize and analyze customers’ data, so that a customized value
proposition can be delivered (Jackson, 2007). So, a firm requires personal information from an individual, which customers’ may not be willing to share automatically.
According to Sutanto et al (2013), the privacy-personalization paradox is even more present in the mobile industry, because the phone is closely tied to the specific user. The phone is taken anywhere, which means customers can be tracked at any time. On the other hand, personalization offers advantages as the phone process becomes easier for the user.
Vlasic & Kesic (2007) conducted an extensive research on relationship personalization, whether customers were interested in a relationship with a business and were trusting them with their personal information. Customers’ attitudes towards relationship personalization were measured among several industries. They discovered that political parties and credit card companies were negatively related with customers’ willingness for relationship personalization. This was explained by the possibility of a ‘big brother psychosis’. Other companies like: health organizations, the amusement industry, banks, research agencies, the cosmetics industry, the tourism industry, producers of durable goods, producers of industrial goods, producers of consumer goods and telecoms, were more positively related to willingness of customers for relationship personalization. Vlasic & Kesic (2007) also acknowledge the impact of trust on the readiness of individuals to provide personal information, within certain industries this level of trust may be higher than in others. In this research the main focus lies on personalization by commercial organizations.
Privacy Concerns
Fletcher (2003) emphasized that customers have growing concerns about the demands of organizations to uncover private information. Sutanto et al (2013) show that users of mobile applications have raised concerns about the privacy of their personal information. Privacy concerns are comprehensive, extensive research is conducted on this topic as various elements are related. According to Heng et al (2011), privacy concerns can be defined as: customers’ concerns about possible loss of privacy as a result of information disclosure to a specific external agent (e.g., a specific website).
Companies that are motivated to implement personalization should take privacy concerns into consideration as several researchers have indicated that privacy concerns are negatively influencing customers’ willingness to provide personal information (Culnan and Armstrong 1999; Dinev and Hart 2006; Malhotra et al. 2004). Besides, Lee et al (2012) have made a distinction between different types of customers, namely: the unconcerned, the pragmatists and the fundamentalists. The first group has no concerns and trusts the companies with the
information and believes that it will not be misused. Fundamentalists find themselves at the other end and do not share any information. In the middle, there is the group pragmatists who only share information with companies that protect their privacy. It is important to take into account that certain types of customers may be more willing to share information than others. Lee et al (2012) show how companies can manage customers privacy concerns and how it can influence their competitive position. Lee et al (2012) only focused on decreasing customers’ privacy concerns in terms of trust and guaranteed safety of data, and leaves out of account the influence of other benefits.
Il-Horn et al (2002) however, refer to two possible ways for companies to overcome the online information privacy concerns are described: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. One should note that this study used the expectancy based theory of motivation instead of actual behavior. The expectancy theory measures the intention of customers by asking a customer’s opinion about a certain situation. In contrast to intention or attitudes, behavior gives a better representation of a real-life situation, but is also more difficult to measure, as was
discussed by Fishbein & Ajzen (1975).
Furthermore, it should be considered that differences exist in the importance of types of personal information to an individual. For example, information about the bad conditions of somebody’s health is seen as more risky than types of other personal information, such as age (Sutanto et al, 2013). Besides, personal information used in m-commerce differs from the information which is used in e-commerce. Therefore, the level of privacy concerns might differ between m-commerce and e-commerce as well.
Willingness of customers to provide personal information
The willingness of customers to provide these types personal information to companies depends on their assessment of related costs, risks and benefits (Premazzi et al, 2010). The risks are presented by the privacy concerns a customer may have about the type of information one shares with the company. Furthermore, trust, knowledge and privacy awareness are important elements to take into account while conducting research on privacy concerns. (Fletcher, 2003).
Scholars found that increased trust of customers in certain websites or online channels could increase the willingness to be profiled online of share personal information. Also, Eastlick et al (2006) mention that there is a strong link between trust and privacy concerns. Companies can take measures to increase trust or decrease privacy concerns when aiming to use customers data. For example, Sutanto et al (2013) found that implementing a privacy safe feature on smartphones leads to a higher process gratification of personalization. Because of this feature users showed a higher application user behavior (process gratification) and besides, adverts were saved more frequently. This is a way for companies to reduce privacy concerns and motivate customers to engage in personalization. It is important for online retailers to consider various ways to take away privacy concerns because of its direct impact on purchase intentions (Eastlick et al, 2006).
Il-Horn et al (2002) state that people may be willing to hand in privacy in return for advantages they value . These advantages can relate to the financial incentives a company offers, or the convenience personalization brings to the customer. Premazzi et al. (2010) found that the presence of incentives does not always have a positive effect and sometimes caused people to have less willingness to provide information. Especially, when the company fails to establish a certain level of trust, financial incentives could have a negative effect.
More users access information with their mobile phone, therefore convenience might play an important role. The mobile device has a small screen size, limited memory and processing power and therefore might face more challenges when it comes to effective browsing (Zhang & Lai, 2011). This can be one of the reasons why users might value personalization on within
m-commerce more compared to online environments. According to Zhang et al (2011) convenience can be represented by a reduction of complexity caused by excessive information. Also,
peng et al (2006) mentioned that the value of personalized content can be mainly explained by the theory of information overload. Besides, personalization can contribute to reducing customers’ search efforts and transaction time. For example, in order to make the registration easier and more convenient, some providers also offer users the possibility to log in through the Facebook-connect button, so that there is no need to create a new account. Until recently all the personal data on Facebook were automatically shared with an application when this option was used. Facebook was already criticized for sharing this information with third-parties, as
consumers were often not aware (Van der Kolk, 2014).
Besides, customers enjoy the use of free services from companies like Whatsapp and Google, and simultaneously share large amounts of personal data. As Acquisti (2013) mentioned, Google claimed that users of a Gmail account have 'no reasonable expectation' that the e-mails that are sent should be private. In this way, data is the currency that users pay with in exchange for the usage of a certain service.
Privacy Awareness
Zhang et al (2013) studied privacy concerns in the context of the APCO model (Antecedents Privacy Concerns Outcomes), which includes multiple of the previous discussed elements (Zhang et al, 2013). Antecedents are: privacy experiences, privacy awareness, personality differences, demographic differences and culture climate. Zhang et al (2013) focused on the influence of demographic differences on privacy concerns. They mentioned that other variables, such as privacy awareness are not yet investigated in the context of m-commerce, therefore this paper aims to give more insights into this subject.
The article of Sim et al (2012) looked at disclosure behavior of individuals in specific situations and therefore refers to privacy awareness in terms of situation awareness. Which can be defined as: ‘the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.’ This
definition will be taken into account during this research. It is important to consider that there can be a lack of human decision making, as the situation can cause customers to unintentionally disclose information.
Graeff & Harmon (2002) did conduct research on the collection and use of personal data in terms of consumers’ awareness. However, the focus of this research lies on data collection by
companies through discount (loyalty) cards and not online data collection. Graeff & Harmon (2002), mention that even though consumers have increasing concerns about their privacy, they are not always aware of the fact that these discount cards give companies the opportunity to track their buying behavior. Awareness is measured as the degree to which consumers actually know why companies make use of discount cards. Only a limited percentage of consumers (16.5%) mentioned that companies introduced loyalty cards for the purposes of database marketing or/of collecting information about customer buying habits. However, a limitation of this study is that it was conducted among a limited group of people within a specific demographic area.
Similar to the previous research, most studies in the area of information privacy are focused on customer discount cards or the Web instead of mobile. Premazzi et al (2010) emphasized that future research should look at disclosure behavior on a mobile medium. Andrade et al (2002) conducted research at self-disclosure on the web, by looking at concerns for self-disclosure.
However, the actual disclosure behavior might differ from the attitudes, this was not taken into account during the research of Andrade.
Bélanger & Crosser (2011) mentioned that there is a need of information about how organizations can leverage understanding of customers’ information privacy concerns in
designing their online offerings, is required. Besides, Sutanto et al (2013) discussed that research was never conducted on which information aspects users consider to be too private or sensitive. By combining these elements, it is possible to conduct an interesting and relevant research within the field of m-commerce which addresses a gap in the literature.
The literature that has been discussed gives a better view on what the concepts of privacy and personalization entail. Also, it gives insight in the types of research which have been conducted within the area of online information privacy. Privacy concerns might impede the growth of m-commerce and online personalization. However, consumers may not have concerns about all types of personal information. There is no clear view which types are more privacy sensitive, furthermore insights on privacy awareness within m-commerce are lacking. Finally, companies can influence customers willingness to provide information by offering benefits like convenience or financial incentives.
Conceptual model
References
Chapter 3: Research Design
Level of information privacy concerns Willingness to provide certain types of information Degree of trust in the organization Attitude towards financial incentive Attitude towards convenience Degree of personalization Customer satisfaction Privacy awareness
RISKS
BENEFITS
Privacy sensitivity of industry Privacy sensitivity of information Attitude towards personalized recommendations 22Chapter 3: research design
This research was conducted through a descriptive and exploratory study. The goal is to further build and refine the existing literature through highlighting new subjects from multiple
perspectives. Firstly, a descriptive study aimed to understand how companies gather personal information and which types of information are required for personalization. Also, it is discussed how companies create personalization benefits for users. These interviews were held at multiple companies which are specialized in online personalization and have much knowledge on the topic. Besides, to understand the risks for consumers of sharing their personal information and discover how consumers can protect their personal information online, I held interviews with companies which are trying to increase privacy awareness among the Dutch population.
The following topics were discussed during the interviews: which personal data companies can obtain from customers and what is required to create a personalized environment, how this data is gathered and which benefits are offered in return. Also, taking into account the differences
between online and mobile personalization, with a focus on the unique specifications of
m-commerce (Zhang, 2013). Besides, the scope of the interview is to understand how companies try to make customers aware of the information that is needed for personalization and how customers are convinced to share personal data. In total 10 interviews were conducted in order to develop a complete view of the subject and to ensure the construct validity.
These face-to-face interviews were recorded and transcribed in order to properly analyze the data through the use of NVivo. The interviews lasted between 30 and 50 minutes. The interviewees
received a framework of the questions and themes that will be discussed. At first, open questions were asked, when these questions didn’t provoke elaborated answers, examples are given so that the interviewee has a better idea of the objective of the question.
The second part of this research aims to discover customer insights on customers awareness and sensitiveness of different types of personal information. Besides, the aim is to discuss whether consumers are more likely to give up sensitive personal data when benefits are offered in return. Also, the focus lies on the consideration that consumers make when it comes to their personal data. To receive more in-depth knowledge about the opinion of consumers towards mobile personalization, interviews were conducted. I tested the questions several times among friends and family to discover how improvements could be made and whether questions were understood correctly. As the interviewees had to have patience to answer all the questions, the location was important, namely a place where people were waiting or resting. Finally, I choose to conduct the interviews in the intercity train from Amsterdam Zuid to Groningen. During the process I tried to approach people from different backgrounds from various age groups, to have a broader view on the subject. In total, I conducted 16 interviews which lasted around 10 minutes, existed of a combination of open and closed questions and were recorded. At that moment I noticed that I collected enough data to answer the main question and that answers from the respondents were starting to be repeated by others. Afterwards, the interviews with consumers were processed and transcribed with NVivo.
The start of the interview indicated how customers are aware of their privacy and the data that is gathered by companies, and how it influences their behavior. Furthermore, I asked for specific
examples of how they are trying to control their privacy. This includes how consumers are adapting their privacy settings and if they check the privacy policies of firms when using a mobile application (Sim et al, 2012). Besides, I asked which information types they are willing to share with companies, and whether there are certain types of information or situations in which they are more careful (Sutanto et al, 2013).
Afterwards, it was important to identify how customers value personalization benefits while using a mobile application, like discount and efficiency, and how this influenced the willingness to provide personal information to companies. This questions were based on the method of Awad & Krishnan (2006), asking whether they would be willing to share personal information when a personalized service or discount is offered in return based on the previous activities.
The advantage of a qualitative approach is that I could ask the interviewees what their motives were for specific situations and I can ask them for examples. However, I will also include some closed questions in case the questions don’t provoke specific answers. Moreover, to be able to give a broader image of the population and better select the next respondents, variables like: age, income, gender, education and postal code will be asked.
Strengths and limitations
The different constructs of the research are interdependent, which means that the interviews with the companies first have to be conducted before the consumers’ side of the problem can be highlighted. Also, the different constructs of the research made it more difficult to provide a well-structured research plan, as the interviews with the consumers could not be conducted before the
interviews with the companies were finished. However, this approach enabled me to provide a complete view on the trade-off consumers make while providing sensitive data to companies. Also, the qualitative approach of this research also gives the opportunity for a broader view on personalization from both the consumer side as the company side. On the other hand, it will lead to a more specific research from which the ability to generalize will be lower. Because interviews are more time consuming, there is a limitation to the amount of respondents.
Chapter 4: Data Analysis
For the analysis of the data, a combination of a deductive and inductive approach is used. By using NVivo, multiple codes could be diverted from the sub-questions and themes that were discussed during the interviews. Besides, several codes were developed during the coding process were separated into multiple categories, to get a better insight. Furthermore, through a text search query it was possible to further investigate important subjects, and see under which
circumstances they were relevant. Also, the word frequency query helped to see which answers were often given. The text query assisted to see how certain topics were related. Afterwards, the data was organized and displayed. Several questions were answered in the interviews, like which information was gathered and how this information was gathered. Also, as the subject is
comprehensive, related subjects were discussed and included.
As the interviews with consumers were more structured compared to the interviews with companies it was easier to indicate the different themes within the answers and to structure the data. The data and knowledge from the first set of interviews was taken into consideration while developing the questionnaire for the second set of interviews, which enabled me to ask more specific questions.
The tables below show the outcomes in a clear manner. The first three tables contain results from the interviews with companies and tables 4-6 are focused on the results of the interviews with customers.
Table 1 – Types of personal information gathered by companies for personalization
Type Description Example
Tracking of behavior within websites or mobile applications Data is collected through behavior of online users
‘Heel veel consumenten zijn zich eigenlijk nog niet bewust dat door alles wat je doet data wordt afgegeven.’
‘Die cookies worden op je computer geplaatst en eigenlijk kan je zeggen dat het al een achterhaalde technologie is, die zijn ook uitgevonden als een soort van handig dingetje. Maar er zijn vele geavanceerdere technieken, browser fingerprinting. Dan wordt die informatie niet lokaal op jouw computer opgeslagen, maar op de server van een bedrijf. En daar kan je niks aan doen.
Social Networks
Data on social networks are used to create consumer profiles
‘Of je nou een pagina liked of zoiets dergelijks, dan weet je wel wat, want op basis van likes kun je ook al profielen maken van mensen, dat gebeurt ook. Op basis alle dingen die jij liked in Facebook, weet ik eigenlijk wie jij bent.
Registration Ask consumers to fill in forms with personal information
‘Dus ga je activatiecampagnes doen die ervoor zorgen dat mensen zich met een e-mailadres aanmelden. ‘ ‘Mensen vulden dat braaf in en stuurden dat weer terug. Dat gaat heel ver, over dagelijkse
boodschappen, waar doen ze boodschappen, wat kopen ze, gezondheid, financiële informatie, koophuis ja of nee. Telefoons, tablet, wat lees je, waar kijk je naar, wat voor programma’s, ja of nee, op welke manier, algemene belangstelling, waar hebben ze bepaalde interesse in, vakantievoorkeur, hoeveel auto’s, lease auto of eigen auto, vervoer, hoe woon je, in een flat, heb je een hypotheek ja of nee, al die informatie wordt gevraagd. ‘
Location-based Asking to use location, to provide specific services or information
‘Contextinformatie wordt steeds belangrijker, dat is location-based, waar jij bent op een website, waar jij bent in een winkel. ‘
As presented in Table 1, companies use several techniques for the collection of data, from which some are difficult to avoid as a user. With the behavior somebody shows online, data is gathered and a complete profile can be build. Companies like Facebook and Google have much knowledge about their users. Especially when different types of data are combined and elaborated analysis take place, it is possible to discover almost anything about a specific person. Therefore it is difficult to assess the privacy sensitiveness of particular individual types of data, as combinations result in a complete digital identity and uncover sensitive details. This is also taken into account during the interview with customers by asking a more general questions and looking at privacy sensitive behavior. Besides, most companies target on group level, certain preferences are
compared with a large group with similar characteristics. (‘Een bedrijf kijkt altijd naar een groep, en kijkt altijd naar gezamenlijke kenmerken van een bepaalde groep’). Also, compared to the United States, it is not allowed to connect IP-addresses of computers to physical names and addresses in the Netherlands (Persson, 2014). Personal data are defined as: all information relating to an identified or indefinable individual (CBP, 2014). However, this definition is not completely clear as companies can discover more about an individual through analysis, combination and comparisons of several types of data.
Looking at the differences between online and mobile gathering of data it is also difficult to draw a line as companies don’t think in different channels, as they are all used to deliver the same message, which means that there are similar ways of gathering data. However, because of mobile devices more information becomes available and location-based services are also growing.
Table 2 – Comparison between general internet and mobile internet data collection
Concept Description Example
Data gathering through mobile browsing.
The data from mobile browsing and online browsing can be tracked by for example cookies and can be deleted.
‘Ja, in principe het mechanisme is precies hetzelfde. Het hangt er vanaf wat voor telefoon je hebt. Je hebt sowieso de private browser op je telefoon, dus dat er geen cookies worden bewaard. Je kan gegevens wissen net als op je laptop.’
Behavior within mobile
applications.
Behavior within mobile applications can’t be deleted easily.
‘Een mobiele applicatie heeft een applicatienummer en zolang dat geïnstalleerd wordt mijn gedrag vastgehouden en kan ik het niet wissen. Er zitten vaak ook nog accounts aan, dat je je moet
registreren met een e-mailadres, dan zit je helemaal vast’
GPS-tracking on mobile.
GPS-tracking makes it more personal, as companies can follow you.
‘Het grootste verschil is dat je op mobiel niet anoniem kan zijn en je komt er niet meer vanaf, en ook GPS, er wordt toch wel wat meer, er kan meer worden vastgelegd. Je kan letterlijk gevolgd door het land.’
Besides, it depends which company is the owner of a mobile application and the country of origin, as the rules between the European Union and the United States differ. (‘Wat het hier extra uitdagend maakt, is dat diegene die zeg maar, technisch voor jou inregelen hoe jij toestemming kan vragen, zijn Google en Apple.’) Another difference was noticeable within mobile
applications, as it isn’t always possible to delete specific behavior.
The results from Table 2 were included in the questionnaire for consumers, and therefore
questions were related to these concepts, as this specifies the difference between general internet and mobile internet data collection.
Table 3 –risks and benefits associated with sharing personal information with companies for online and mobile personalization.
Benefit/risk Description Example
Free services Personal data is often used as a form of payment for a service
‘Dat mensen snappen, de klik maken, dat als iets gratis is, dat op dat moment data het ruilmiddel is.’ Relevant or
additional offers
Suitable, interesting offers, based on specific needs. No spam.
‘Je moet ze bedanken voor wat ze kopen, doe af en toe korting te geven.’ ‘Dit zijn voor mij goede aanbiedingen’.
No overload of information
Helps and assists you with choices
‘Het is belangrijk dat jij snel vind dat je zoekt’. ‘Het gaat erom dat je het gevoel hebt om beter te worden geholpen’
Convenience Helps you save time and makes the activities easier.
‘Omdat mensen niet telkens opnieuw een wachtwoord in willen vullen .’
Loss of privacy Being able to decide not to share certain types of information.
‘Anders ben je niet vrij, als iemand de hele dag met je mee zou lopen, dan zou je je anders gedragen en zou je andere dingen doen. Daarnaast zou je permanent, je extreem ongemakkelijk voelen.’ ‘Privacy is ook het recht om dingen te mogen verbergen’
Loss of control over personal data
Difficult to assess what happens with
information which is gathered
‘Vervolgens is er wel de vraag wat er met die informatie wordt gedaan, dat is heel moeilijk om te controleren, want dat gebeurt achter te schermen’ ‘Het risico is altijd dat er beslissingen over jou worden genomen die je niet wil, die je niet kan voorspellen.’
Table 3 presents the outcomes of personalization, the benefits and risks which are involved, which were also related to during the interviews with consumers. This questionnaire was mainly focused on m-commerce, therefore it is important to first focus on the mobile behavior of the interviewees as it gives a better image of the way mobile applications and mobile browsing are used. The applications used by almost all the interviewees were Whatsapp and Facebook, also
applications from NS, Albert Heijn and 9292ov are popular. Besides, consumers read their e-mail on their mobile phone and use services from Google like Gmail, Google Maps and Google Search, both at home and out of home.
During interviews with customers several topics were discussed and the tables below show the results per theme, privacy awareness, privacy sensitiveness and personalization benefits.
Table 4 – privacy awareness in m-commerce
Construct Description Example
Attitude towards and concerns about privacy issues.
Most consumers are concerned about the data that they provide on mobile phones and within mobile application. Consumers think about it, but it is not on the top of their mind.
‘Ik denk er wel over na, maar ik pas mijn gedrag er niet echt op aan.’ ‘Ik hou me eigenlijk te weinig bezig met de veiligheid, ik zou dat meer moeten doen. Ik hou er achter mijn computer er meer rekening mee dan op applicaties, ik ben met internet bekender.’ Knowledge about intention of data gathering
Consumers are more aware of the data that is gathered on computers compared to m-commerce. They realize that companies look at online browsing behavior for targeted advertisements. Quite often they understand that cookies are a way to gather this information, but think about it more during computer browsing compared to mobile internet use.
‘Voor de adverteerders is het interessant om te weten wat het koopgedrag is van mensen, zodat de marketing daarop afgestemd kan worden, kan je weer je voordeel mee doen.’
‘Ik heb geen idee welke
persoonlijke gegevens verzameld worden door mobiele applicaties.’ Behavior to
protect personal data
Consumers try to protect personal data to some extent by deleting cookies, or not accepting them. However, on their computer they have more knowledge
‘Het privacy beleid lees ik niet bewust. Je denkt meer, oh dat is een leuke applicatie dat is handig.’
about these techniques than on their mobile phone. Besides, they indicate that it is easier.
‘Nee, ik weet eigenlijk niet welke privacy instellingen ik heb ingesteld op mijn mobiel.’
‘Ik wis de cookies op mijn computer wel en op mijn mobiel niet, ik weet niet waarom, ik weet ook niet hoe dat moet.’
‘Ik hou er achter mijn computer er meer rekening mee dan op
applicaties, ik ben met gewoon internet bekender.
Table 4 shows the extent to which consumers are aware of the issues around information privacy and whether they think and act about it differently within m-commerce compared to regular internet use.
Table 5 – privacy sensitiveness of information
Information on Facebook
Nobody is using Facebook connect, afraid that they might share too much information, as the data is only meant for friends and not for companies.
‘Ik maak geen gebruik van
Facebook login, als ik dat zie staan, dan komen ze automatisch op mijn gegevens en dan weten ze wel heel veel.’
‘Eigenlijk maak ik nooit gebruik van Facebook-connect, omdat ik denk dat ze allemaal dingen op Facebook posten en dat het dan allemaal met elkaar verbonden is.’
Location-based services
GPS-location is preferred to keep private, consumers don’t like the idea that they can be followed everywhere.
‘GPS staat altijd uit, ik wil niet dat mensen zien waar ik ben.’
‘GPS-locatie sta ik niet altijd toe, ik zet deze altijd bewust uit.’
Consumers are being more careful
All customers are paying attention to the personal information they share online to some extent. By being more careful, they try to control their digital identity, and try to limit the risks.
‘Ik ben überhaupt vrij voorzichtig met informatie op internet,
waardoor er weinig informatie is die niet gezien mag worden door
anderen.’
‘Ik deel bepaalde dingen niet op Facebook, of bepaalde uitspraken zou ik niet zo snel doen. ‘
Customers experience drawbacks of being privacy sensitive
Certain customers are more privacy sensitive and don’t want to share personal information, like their online behavior but they have no other option than accepting cookies.
Ik kan vaak niet op ‘nee’ klikken, omdat ik anders niet verder kom, dus ik moet het wel accepteren. Als ik wel op ‘nee’ zou kunnen klikken dan zou ik dat wel doen.
Table 5 summarizes the results of the questions which were focused on privacy sensitiveness of information. It was already indicated that this subject is difficult to measure as it is situation and person dependent. Also, as combinations of information and comparisons with larger groups can reveal a rather complete picture of a person, individuals don’t have a clear understanding of the consequences of sharing certain types of personal information. However, the results did indicate that on mobile devices customers are cautious with their GPS-location. Besides, customers take care when it comes to the personal data on Facebook and mentioned they have less trust in the company. Besides, customers are more aware that their data on Facebook is used for other purposes, as advertisments. Also, there were indicators that certain customers are more privacy sensitive and are not willing to share much information online, but it is not always possible to control it as websites make it difficult to avoid cookies for example.
Table 6 – Personalization benefits Perceived value of personalized recommend- dations is limited.
Most customers don’t see the added value of personalized
recommendations and content. They believe it is limiting their choices and therefore less surprising. Besides, they believe it is difficult for a computer to decide what they want.
Also, it depends of the store, personalized offer in store which is visited frequently (Albert Heijn) are more preferable. In this case, customer perceive more benefits and higher relevance.
‘Ze hoeven niet per se hun aanbod op mij af te stemmen, ik kan het zelf ook wel vinden. Ik vind het vooral eng dat je niet weet wat er over je verzameld wordt en dat je niet weet wat er mee gebeurd.’
‘Bijvoorbeeld bij de Albert Heijn, kan ik me wel voorstellen dat als je
aanbiedingen ontvang voor producten die ik vaak koop, bijvoorbeeld
brood,… Ik bezoek deze winkel vaak, waardoor ik het idee heb dat ik er meer voordeel uit haal.
Financial benefits (discount or free products) can increase the likelihood of sharing information.
It depends on the kind of information, a discount can’t always make a change. However, a part said that it is necessary to offer the discount, otherwise they wouldn’t give it. Therefore, discounts might have a positive influence.
‘Ik zou wel persoonlijke informatie afstaan, die ik überhaupt bereid zou zijn om te delen in ruil voor een korting of product, zoals interesses.’
Convenience can have a positive impact.
Customers understand that the users process becomes more convenient when websites remember information and often appreciate it.
‘Het zijn sites die ik wil bekijken als het makkelijker gaat met cookies, dan vind ik dat prima.’
‘Soms is het buitengewoon handig als gegevens worden onthouden, scheelt erg veel tijd.’
Part of customers is willing to pay for the security of their data
Customers are willing to pay a small amount to protect their data, but also argued that the free applications were valuable enough to agree with a loss of control. Besides, it was noticed that it is difficult to have complete trust in another application.
‘Ik zou zeker wel betalen voor een applicatie die dit niet doet, als je kijkt naar hoeveel kosten je bespaart met Whatsapp, dan vind ik het
bijvoorbeeld niet raar om €2,00 per maand te betalen’
‘Ik zou niet bereid zijn om te betalen, ik weet dan nog niet of ik het wel kan vertrouwen.’
Table 6 indicates the opinion of customers about personalization benefits and how it influences them to share more data. Convenience has the most positive impact, customers feel that some steps can be time consuming and they accept that certain data is needed to optimize. However, personalized recommendations were not always positively related with willingness to provide data, as customers didn’t see the added value. For many customers it was necessary to receive a financial benefit, this was the only way for them to be convinced share more data, but it would only be the data that they would normally be willing to give like interests.
The more privacy sensitive customers would be willing to pay for an application from which they are certain that their privacy is not compromised. Especially when it comes to a service like Whatsapp, which is giving enough value to be willing to pay a small amount per month.
However, with mobile communication applications it depends on the behavior of the mass, as it is important that a large group is using the same service to be able to communicate effectively.
Chapter 5: Discussion
In this chapter the results of the study will be discussed and compared to previous finding in the field of information privacy. Afterwards, the managerial implications of this paper and the limitations are presented.
General discussion
First of all, the interviews with companies provided more knowledge on the theme of
personalization and showed how data is gathered, profiles are build and personalization benefits are created. The gathering of personal data happens in multiple ways and was similar to the description of (Zhang et al, 2013), who indicated that data is collected by registration, capturing Internet Protocol (IP) addresses, using ‘cookies’ or gathering information from social networks such as Facebook and Twitter. Moreover, several interviews indicated that companies look at combinations of different types of data and apply analyses that give new insights and can reveal other aspects of a person that they were not keen on sharing directly. However, there is a lack of knowledge of measurements of meta-analyses, which bring together a large amounts of data. This makes it difficult to assess for customers which measurements are used and what type of profile of them exists within an organization.
Premazzi et al (2010) argued that the willingness of customers to provide personal information to companies depends on their assessment of related costs, risks and benefit. However, it is often difficult for customers to avoid being tracked or accept cookies, as companies optimize their websites and direct the customer to certain choices. Customers intentions are influenced by the
design of the website, for example because certain buttons are prominently present and therefore can’t be ignored. Customers can’t always make the consideration whether they want to share personal data, which is not completely customer friendly. Moreover, clear information on the websites concerning the reason of collecting this information is often missing. Because of a lack of transparency, it is difficult for customers to assess the added value of for example cookies. Especially, companies that work from a customer centric approach might reconsider whether customers should be able to make their own choices. The example of Albert Heijn, who gives customers the possibility to choose between a personalized customer card or an anonymous customer card, is a way to leave the decision for personalization to the customer that could also be used online.
This paper is mainly focused on m-commerce, in relation to using mobile devices or smartphones to access mobile websites or use mobile apps for mobile services’ (Zhang et al, 2013). The interviews were partly aimed at differentiating the information which is gathered for
personalization within mobile environments from ‘general’ online personalization. However, the result was that it is difficult to evaluate as the country of origin of the owner of the application (United States or Europe) has to be taken into account, in the United States the privacy rules are less strict compared to Europe. Which means that companies from the United States can gather more information in compared to European companies, so it isn’t possible to specify the exact information that companies use for personalization.
However, the most important differences between mobile and online are GPS-tracking and location based services, which Zhang (2013) also identified. Furthermore, data on mobile devices
are partly gathered through mobile browsing and within mobile applications. The data that is shared through mobile browsing is very comparable to ‘general’ use of internet. The data that are shared within mobile applications is connected with a username and therefore this can’t be deleted.
Il-Horn et al (2002) mentioned that companies can offer financial gains or convenience in exchange for personal information. This could also be recognized during the interviews,
companies offer customers reduction on favorite products, give customers special treatments or free products/services. Companies make sure that customers can easily browse through the website, that certain information is remembered and assist customers to find the right product. In this way companies contribute to the convenience of the process, like stated by Il-Horn et al (2002). This view is also in line with the theory of information overload from Ting-peng et al (2006). Compared to online environments, within mobile environments it is important to note that free applications can gather and process personal information from which users might not always be aware. This was also taken into account during the interviews with customers.
As (Dillon, 2010; Milberg, 2000; O’Neil, 2001; Phelps et al, 2000) already specified, privacy is not a stand-alone concept. Therefore, the questions in the interview took into account specific situations, which helped to understand the considerations of consumers. As was mentioned by these researchers, trust is an important influencer of privacy concerns. During this research there was no specific emphasis on trust, but some interviewees did mention that it plays an important role. Besides, consumers had a higher willingness to share information with organizations that they visit often. This was explained by the fact that the recommendations would have more
relevance, which was not taken into account by Vlasic & Kesic (2007) in their research about relationship personalization. Besides, some groups didn’t have an interest into establishing a personal relationship with companies and share personal data, ‘ik zie het als een zakelijke relatie, ik koop een product en hoef verder niks te delen’.
Customers showed concerns about data they provided within online and mobile environments, however they have less knowledge about which information is exactly gathered through mobile internet use. Besides, they are not completely aware with which activities on mobile internet data is shared. Zhang et al (2013) indicated that mobile commerce is conducted anywhere and at any time, this fact could make it more difficult to assess for customers which information is shared . Customers are not totally familiar with their mobile devices and take less time to consider the details.
Similar to the research of Graeff & Harmon (2002), this research asked whether customers knew which information was gathered and what was the reason why companies gather information. Customers acknowledged that their online activities, for example browsing history, could be remembered and shared with third parties for advertisements. In the research of Graeff & Harmon (2002) customers were not always aware of the fact that their buying behavior was tracked. However, this research showed that customers were more informed and concerned: ‘Ik weet dat de gegevens allemaal naar de NSA enzo gaan, naar andere mensen, andere bedrijven, zodat ze kunnen zien wat je leuk vindt.’ This could be explained by the fact that the theme of information privacy is more discussed by the media nowadays and through this knowledge their privacy concerns and awareness had increased, as Fletcher (2003) indicated.
Therefore, it can be argued that customers are aware that their data is gathered and used for different purposes by companies. However, compared to ‘general’ online environments, consumers have less knowledge of which data is gathered by mobile applications and during mobile browsing. Moreover, customers mentioned that they don’t take many specific actions on protecting their information, for example cookies are almost never disabled on mobile devices. It was noticeable that consumers take more actions to secure their privacy on computers compared to mobile devices. Il-Horn et al (2002) mentioned that privacy concerns could be tackled by companies through presenting the privacy statements. However, customers indicate that these statements are never read due to the lack of transparency.
Sutanto et al (2013) mentioned that research on privacy sensitiveness of information was never conducted before, this is the reason why it is difficult to assess the privacy sensitiveness of certain types of information, as customers can often not predict what the consequences are. However, this research did indicate that customers are more cautious with certain types of information than with others, especially with their personal information on Facebook and GPS-location. Customers were not willing to share their location with all applications, in this case m-commerce can be seen as more critical and corresponds to the view of Zhang et al (2013), who indicated that localization is an important unique feature to consider. All customers were to some extent paying attention to information they share online and with mobile applications. This indicates that customers have concerns for self-disclosure (Andrade et al, 2002) on mobile devices.
However, a certain group of customers is more privacy sensitive and therefore less willing to share information. This was also indicated by Lee et al (2012) , and could be similar to the group described as fundamentalists, who are not willing to share information. This group is more aware of what is happening with the information and therefore and tries to undertake specific actions. But, as mentioned before customers realized that it isn’t always possible to protect personal data. This group did indicate that they were willing to pay a small amount of money for a mobile application that keeps their personal data private and ensures that information is not used by third parties.
Customers were more willing to share personal data when a benefit was offered in return,
especially financial benefits like discount or a free product. In contrast to the view of Premazzi et al. (2010), who stated that financial benefits can also have a negative impact on customers’ willingness to provide data, this was explained by a lack of trust into the organization. However, during this research customers mentioned that a financial benefit was absolutely necessary to convince them to share data. Moreover, customers only want to share data like interests or activities and only when this information is relevant for the company because value can be created with it.
Some customers indicated that they receive a large part of offers already, for example by e-mail, therefore they aren’t willing to receive personalized offers.’ Ik denk dat het vooral in het volume zit waarin je dingen krijgt, ik merk dat als ik heel veel aanbiedingen toegestuurd krijg dat ik er dan helemaal niet meer naar kijk.’ Therefore, it is important for companies to consider whether their message has real added value to consumers, especially within m-commerce. As Zhang et al
(2013) indicated, users are instantly connected and the allowance of push marketing makes the chance of disturbance higher.
Besides, interviewees stated that they were not that familiar with buying products on mobile devices. Mobile devices were mainly used for communication through Whatsapp or Facebook, games and information (9292 and NS). Consumers mentioned that the main reason was a lack of convenience because of the small screen of the mobile phone, which is corresponding to the view of (Zhang & Lai, 2011). Therefore, this might be one of the success factors of a further adoption of m-commerce under customers.
Managerial implications
As was discussed by Bélanger & Crosser (2011), more knowledge on how organizations can leverage understanding of customers’ information privacy concerns in designing their online offerings, is needed. The implications of this research for organizations will be discussed in the following part of this paper.
Customers stated that they are to some extent cautious with the information they share online and within mobile environments, and therefore are not willing to share all types of personal
information with companies. Especially, customers consider the information which is shared on Facebook and they aren’t prepared to give their e-mail addresses as this can result in a large amounts of unwanted messages. Customers are aware of the fact that companies can track a large part of their behavior online, but often don’t consider the possibility that companies combine this data to find new insights. Therefore, companies might consider to give customers more influence 43
on which personal information is shared. Besides, the profile that companies created of specific customers could be communicated or verified by the customer, so that a higher level of trust can be build.
Customers are more aware of what companies are doing with the information but there is a lack of transparency which information is gathered and what the exact purposes are. There is a group of customers which is less willing to share information and is trying to protect their personal data online. Even financial benefits or personalized recommendations will not influence their opinion. However, customers notice that companies aren’t providing them with the tools to make their own decisions. Customers have an interest in protecting their personal data, however they don’t want to put effort into it. Therefore, even though customers are aware of the personal data that is gathered and the consequences, they still share information with companies because of
difficulties they experience while trying to avoid tracking. Companies could aim to be more transparent about information privacy towards customers, but choosing the right transparency strategy can be difficult, as was explained by Granados & Gupta (2013). For example, because information should be hidden from competitors, in order to maintain competitive advantages. Companies could see information privacy transparency as an unique selling point, but
transparency strategy is a comprehensive subject on which more research is needed.
More privacy sensitive customers are less willing to share information and are more protective. However, for a financial benefit like a discount or free product customers would consider to sharing more information, this is important to take into account for companies. Moreover, it was indicated that this would only relate to information which is relevant for the core services of the
