What drives mobile shopping app usage : the impact of privacy and security perceptions

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What Drives Mobile Shopping App Usage:

The Impact of Privacy and Security Perceptions

MSc in Business Administration – Digital Business Track

Written by Secil Egen - 11779780

Supervised by Dr. Umut Konus

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Statement of Originality

This document is written by Student Secil Egen who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Acknowledgement

I would like to express my deepest gratitude to Dr. Umut Konus, who has been inspiring me with his enthusiasm and knowledge. It has been a great pleasure to work with him throughout the writing process. He provided me with his guidance and support and motivated me to keep on track.

Moreover, I would like to thank my mother who has done everything she could do to support me in having my Master’s degree in University of Amsterdam. She is my true hero. Lastly, I would like to thank my friends who were there for me no matter how far they were. I hope that this thesis can have your interest with its subject focus and you will enjoy reading it.

Kind regards, Secil Egen

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Abstract

Business environment has experienced a tremendous change with the emergence of mobile technologies. Mobile commerce has become a regular practice for companies with commerce activities and beyond that, the rise of an app culture presented a new platform for these activities. There are several reasons that plays a role in customer’s decision making process for preferring mobile shopping applications over browsers, desktop and brick-and-mortar stores. This study aims to shed light on these factors by placing privacy and security perceptions of customers in the center. An online survey will be conducted amongst customers with online shopping experience. The study differs from the existing literature in that it examines possible differences amongst the general and brand related perceptions of privacy and security. As the amount of data produced by various sources continues to grow exponentially in the era of big data and the understanding of customers shift, managers can benefit from this research by optimizing their relationships with customers in terms of their privacy and security stance accordingly. Moreover, the recent change of General Data Protection Regulation that European Union put in practice will result in a higher attention on the subject given, therefore it is crucial for the literature to provide a deep understanding on individual’s privacy preferences and linked behaviors.

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Rapid developments regarding the internet access and progress in device technology have been playing an important role in consumers’ daily preferences. Mobile devices and the use of their technology have affected a large scope of business practices such as e-commerce and marketing strategy. Recent trend reports in mobile technologies indicate that the significance of the subject will continue to grow. Almost every retailer (98%) is aware of the fact that the way consumers search and buy products will be an important issue to understand in the upcoming years (“2017 Digital Trends”, 2017).

The interest in mobile shopping grows as businesses starts to recognize its noteworthy potential. Mobile devices can be leveraged by companies to satisfy the needs of particular customer segments as they can be personalized (Al Dmour et al.,2014). In addition, when mobile applications are used as a platform of commerce, companies can provide their customers with features such as order tracking, loyalty points, rewards, personalized coupons, product comparison and accessing reviews (Natarajan, 2017). As the usage rate of mobile shopping applications is growing, the customers start making use of these applications for various purposes. According to a consumer survey by Apptentive (2015), 55% of the people who use mobile apps for shopping claims to use apps while visiting the retailer’s offline store. The reasons behind the in-store usage of mobile applications are to use discounts, to compare prices and to have an overview of user ratings and reviews. Therefore, it is crucial to have a vast knowledge on the factors affecting the adoption of mobile shopping applications in order to utilize them while engaging with the customers and also building revenue for the company.

Delivering value with mobile devices by offering e-commerce service is becoming critical for retail companies which are having challenges to understand consumer behavior on these platforms (Barnes, 2002). There is a great amount of literature about the shopping adoption

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main factors on a customer’s mobile commerce adoption. Meola (2016) suggests that the main barriers to keep customers from adopting mobile commerce are smaller screen size, speed of wireless networks and the fact that customers feeling unsecure while giving their personal information via a mobile device than a desktop device.

Trust, privacy and security issues are also investigated in regard to their effects on adoption and usage of mobile commerce. On one hand, companies are struggling with the data they gather from consumer interactions and how to use them. On the other hand, customers face with privacy and security risks that may unveil their personal information, taste and other data to third parties. As the data produced increase exponentially, it is important to have the knowledge of how consumers perceive the companies’ stand on processing their data and how they reflect this to their usage of mobile commerce platforms. Furthermore, European General Data Protection Regulation (GDPR) which is effective as of May 25th, 2018 is another dimension that proves privacy and security issues regarding mobile shopping should be investigated. GDPR can be explained simply as the law that set how the data of European citizens are governed by the companies. The discussion around this subject itself has gained the attention of customers making them more aware of how their data are collected and handled by the companies. GDPR contains regulations on how companies should provide transparency on their treatment of data along with enabling customers to access their personal data to limit or delete as they see the necessity. The practice of managing the privacy of customers may be seen by companies as an additional cost and effort, yet, when applied effectively, can lead to strategic benefits and and a sincere relationship with customers (Goldfarb and Tucker, 2013).

In this study, my main objective is to research to what extent individuals’ perception of privacy and security concerns affect the mobile shopping adoption. Besides the insight on privacy and security perceptions, the study will help to understand the role of brand loyalty, online experience and shopping enjoyment. It is important to know if privacy and security

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concerns interact with other factors such as shopping enjoyment, or age of the individual. An online survey is conducted to understand customer perception and mobile shopping application usage for three different types of industries.

Surprisingly, only little of the research to this day mobile commerce focus in the area of shopping behavior via a mobile application although these applications are very promising. Therefore, I aim to contribute to the existing literature on mobile shopping adoption in several ways. First of all, the data will allow us to understand whether or not there is a relationship between privacy and security perceptions and mobile shopping adoption. Second, by investigating individuals’ perspective on privacy and security issues before and after branded setting, it will be possible to know if there is such an effect of brand as well as if there is any change in mobile shopping application usage. Finally, by conducting the research for three different type of industries having mobile shopping applications, we will see how the relation between privacy and security concerns in mobile shopping adoption differentiate for such products.

From a managerial perspective, this study aims to shed light on the drivers and barriers of mobile shopping applications usage from a perspective of customer personalities and privacy and security perceptions. As suggested by Ackerman (1999) et al., when privacy is the issue, ‘one size fits all’ is a wrong approach since individuals have different views on this subject. In the study, the authors distinguish between different clusters depending on the level of their privacy concern. Those clusters, which are named as privacy fundamentalists, pragmatic majority and marginally concerned, are differentiated from each other by the way they choose to disclose their personal data. Moreover, this study will create a reference point for companies to manage their mobile shopping applications in a more customer-oriented way. According to a recent study by Forbes, every 1 of 2 millennials have downloaded a mobile shopping

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website. Millennials, who are described as people that were born between 1983 and 2000 can be classified as the purchasing power of society today. Therefore, it is extremely important for companies to have a deeper knowledge about what drives their customers towards mobile shopping application usage if they want to build sustainable businesses.

This research is continued with a detailed literature review on mobile commerce, mobile shopping applications and possible factors that drive the usage of mobile shopping applications. Second, the conceptual framework is presented, followed with hypothesis development. Then, research methodology is discussed. Lastly, analysis of the results is discussed and implications are given.

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2. Literature Review

In this section, relevant literature is presented to provide a basis for the empirical work that will follow. First, the emergence of mobile commerce and the application culture as its consequence will be discussed. Then, possible factors that may play a role in the use of mobile application are listed by extending the literature on adoption of new technologies, electronic commerce, mobile commerce, mobile applications and online privacy and security.

2.1. Rise of Internet and Mobile Commerce

The use of wireless networks and adoption of internet by individuals started a revolution in daily lives. Mobile devices established a presence in the center of our lives in a short period of time, users regularly check their devices and take them everywhere they go (Persaud and Azhar, 2012). The growth in the mobile market verifies the increasing significance of the subject matter. According to a recent report on the mobile economy, the number of unique mobile subscribers is expected to reach 5.7 billion in 2020 with a 4.2% compound annual growth rate (“The Mobile Economy”, 2017). Moreover, with the invention of smartphones and tablets, mobile devices have rapidly changed the way we connect with the world. An article by Zenith Media states that the smartphone penetration has reached to 63% in 2017 in the countries forming 65% of the world’s population (2017). Mobile devices consisting of both tablets and smartphones are the main platform for accessing the internet and consumers are expected to spend 73% of their internet usage in 2018 via these devices.

Taylor and Levin (2014) define smartphone as “a mobile phone with an operating system (e.g. Apple iOS, Android, Windows Mobile, Palm or Blackberry) that offers internet connectivity and allows the user to install apps, or small-sized applications”. The emergence of the fast penetration of the smartphones globally has offered the opportunity for firms to utilize

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(M-Commerce). M-Commerce is defined by Barnes (2002) as “any transaction with a monetary value -either direct or indirect- that is conducted over a wireless telecommunication network”. Bang et al. emphasizes that m-commerce is different than the traditional commerce with two main characteristics (2013). First, prevalence of the mobile internet enables m-commerce to occur at anytime and anywhere. Second, having smaller size screens give rise to a simpler navigation and decrease the length of page visit. Yet, the number of consumers that utilizing their mobile devices for shopping is still low whereas potential consumers seem to refrain due to several reasons (Lu and Yu-Jen Su, 2009).

There are a lot of studies on the area of m-commerce regarding the adoption of and behavior on usage. For example, Nysveen et al. (2005) studied the driving forces on the adoption of mobile services. They found significant support on motivational and attitudinal influences as well as normative pressure and perceived control on consumer motives while some of them are also affected by the nature of the process depending on whether it is goal-oriented or not. Kim et al. (2007) study the value based adoption of Mobile Internet (M-Internet) as it is fundamental to understand m-internet adoption to have a clear knowledge about m-commerce as a first step. M-Internet is used as the enabler technology for m-commerce, which is different from other e-commerce types due to its mobility (i.e. having the real-time connection) and reach (i.e. being able to be contacted at any time) aspects.

Majority of the recent literature on m-commerce adoption reflects the antecedents of theories that were developed to explain the adoption of a specific technology such as technology acceptance model (Davis, 1989), theory of diffusion of innovations (Rogers, 1983), and theory of planned behavior (Ajzen, 1991). For example, Groß (2015) extends the technology acceptance model (TAM) with perceived enjoyment and trust factors to explain the mobile shopping behavior and finds that trust in the firm and perceived enjoyment are other important factors affecting a customer’s mobile shopping experience as well as the known TAM factors.

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Lu and Yu-Jen Su (2009) investigate the different factors that affects the purchase intention on mobile shopping websites where they distinguish between intrinsic and extrinsic motivations. They show that anxiety, usefulness, enjoyment and compatibility are influencers on consumers’ intentions.

2.2. Mobile Shopping Applications

In order to attract more consumers and have an extended share on the market, many companies go beyond using marketing tools via mobile devices and introduce their own shopping apps which help companies to have a competitive edge over their rivals (Musa et al., 2016). In comparison with traditional web pages which are accessed by using mobile browsers, mobile applications offer marketers certain benefits such as more secured features and ability to prevent customers from getting exposed to competitor’s marketing activities (Taylor and Levin, 2014).

Industry reports on application stores confirms the growing market trend as well. As of March 2017, number of available applications has reached to 2,800,000 in Google Play Store whereas Apple App Store offers 2,200,000 applications (Statista, 2018).

Nowadays, it is easy to find mobile shopping applications for various industries such as travel, entertainment, fashion and education. Mobile shopping applications provide a platform for both customers and companies where they can leverage extra features beyond a simple shopping transaction like loyalty programs, rewards, tracking purchase information and etc. For example, Amazon, one of biggest retailers on global level distinguishes its mobile shopping application from company’s other platforms with several aspects as notifying customers with better deals, faster and better search capability, tracking the package and managing the shopping list easier.

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Also, a recent analysis by Criteo shows that conversion rates through applications are three times higher than mobile browsers and 66% of the mobile sales in 2017 Q4 are carried out on applications when the retailers with both an application and a mobile-friendly website are considered in the United States.

The rise of an ‘app culture’ in parallel with the pervasion of omni-channel practices enable the existence of a prolific area to study consumer desires and behaviors (Taylor and Levin, 2014).

While the most of the research on mobile shopping applications focus on pre-existing theories such as TAM and theory of planned behavior (TPB), Ahuja and Khazanchi (2016) propose a new model on adoption of mobile shopping applications where convenience, collaboration, hedonic motivation and habit are the main constructs in adoption of mobile app usage. Natarajan et al. (2017) studies the intention to use mobile shopping applications with a model combining both TAM and theory of diffusion of innovations (TDI) and finds that highly innovative consumers with a higher level of intention on using mobile shopping applications are less concerned about the price they are paying.

2.3. Possible Factors Affecting Mobile Shopping Application Usage 2.3.1 Personal Innovativeness

The use of mobile applications in commerce activities is a relatively new concept for the customers, therefore its adoption may be affected by the extent to which customer is ready to try new technologies. Rogers (1983) categorizes the adoption behavior of individuals under five categories according to their level of innovativeness which are innovators, early adopters, early majority, late majority and laggards. Several researchers adopted Rogers’s classification while investigating the role of personal innovativeness regarding the intention to use new technologies. Aldás-Manzano et al. (2009) investigated the personality variables along with

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TAM factors and concluded that affinity, compatibility and innovativeness have a direct influence on behavioral adoption of mobile shopping. Moreover, Madan and Yadav (2018) also supported the findings related to personal innovativeness and found strong relationship with a person’s perceived innovativeness and behavioral intention to use mobile shopping.

2.3.2 Shopping Enjoyment

Shopping enjoyment, or as mentioned in previous literature, perceived enjoyment or hedonic motivation is another dimension that may affect the adoption of new technologies. After TAM is developed by Davis (1989), Davis et al. (1992) extended this model with perceived enjoyment. According to Davis et al. (1992), perceived enjoyment means “the extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated”. Perceived enjoyment is determined as an antecedent in the literature related with the adoption of mobile technologies and m-commerce. Verkasalo et al. (2009) finds that perceived enjoyment is a determinant factor in intention to use smartphone applications for both groups of users and non-users of these applications. Kim et al. (2007) recommends developers to include fun features to mobile services since customers have an intention to adopt pleasing services and finds that perceived enjoyment is a significant contributor for the value of a service and therefore determines the intended adoption. In the mobile shopping context, Al Dmour et al. (2014) finds in their study that commerce through a mobile device is related to be interesting and exciting by their sample. They suggest that marketers should also emphasize on the hedonic aspect of mobile shopping. Therefore, the perception of customers on how the mobile shopping application bringing joy and pleasure to them is an important factor to analyze.

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2.3.3 Mobile Experience

Past experience of a customer is an influential driver when they make a decision on possible purchases (Mubarak Alharbi et al., 2013). Kim et al. find that the digital experience of an individual which is a dependent on both mobile and online experiences is positively associated with the amount of mobile applications that individual uses and also related with the intention to buy online (2017). Also, more experienced online customers are expected to use mobile shopping apps more than inexperienced ones since the application interfaces look like company webpages (Bang et al., 2013). Therefore, the familiarity of a consumer with using the mobile shopping application can be a factor in using the application.

2.3.4 Loyalty

Srinivasan et al. (2002) refer to the customer loyalty occurring in e-commerce environment with ‘e-loyalty’ and define as “as a customer’s favorable attitude toward the e-retailer that results in repeat buying behavior”. Shankar et al. (2003) question the role of loyalty and satisfaction in both online and offline shopping environments as well as the relation between loyalty and satisfaction. It is found that when an online channel is used for the purchase, customers express a greater loyalty to the firm which is also supported by the fact that online environment enables companies to share more information with the customer. When considered the mobile environment, 67% of tablet and smartphone shoppers indicate that they only use shopping apps from their favorite stores according to a recent report by Adobe (2013). On the other hand, in order to establish customer loyalty programs, companies require customers to hand in personal information such as demographic characteristics or consumption habits. Loyalty should be tested as a possible factor affecting the usage of mobile shopping application as well as it might be beneficial to know its relationship with privacy and security perceptions in mobile environment.

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2.3.5 Demographic Characteristics

Demographic characteristics refer to the information of individuals on age, gender, nationality and education level. In the literature, the are several findings that these characteristics may play a role in differentiation of the groups. This section will present the studies where there occurred differences amongst these groups related to mobile usage, internet adoption and privacy and security perceptions.

Age

Age is the most related factor with the adoption of new technologies such as the usage of mobile shopping applications. Older people are found to be less skilled in internet related technologies (Reisenwitz et al., 2007). Also, in another study conducted for young consumers, Knezevic and Delic (2017) finds that majority of young consumers are experienced in using mobile devices and mobile shopping applications.

Gender

In the context of online shopping, women tend to prefer their mobile devices for search purposes and also purchasing in comparison with men. In a recent research, when US consumers are questioned that realized a transaction within the last month, it is seen that 67% of these consumers are female. When compared with 2013 data, the increase in women proportion also stands out (Smith, 2015).

2.3.6 Privacy and Security Perceptions

The revolution in the business and marketing practices established new and fascinating occasions to transform customer relationships to a greater depth while depending profoundly on the personal data collected from individuals (Khatibloo and Spiliotes, 2018). The way the

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as well as customers. GDPR, which is legally binding for all of the companies that are linked with a European customer, is also another reason that gathered attention to this subject. GDPR has been in practice as of May 25th, 2018 and regulated how the personal information are treated and protected by the companies. From a customer point of view, Goldfarb and Tucker (2013) explain that customers are usually kept in the dark in terms of privacy practices of companies. Nonetheless, they became more skeptical on whether the companies abuse their data or expose to third parties over time, even when the data is managed properly in fact. Hence, Goldfarb and Tucker make a suggestion for companies to reorganize their privacy practices by putting customers in the center (2013).

The new practices which emerged with the innovative use of data, also come with major risks for both companies and customers. From a customer point of view, perceived risk of a particular financial situation is defined as having three dimensions; security, privacy and monetary risks (Natarajan et al., 2018). As addressed in the literature, there may be certain problems affiliated with online commerce such as credit card security, loss of personal information, product quality, delivery, returning policy et cetera (Knežević and Delić, 2017). These problems can be linked to either security, privacy and monetary risks that are mentioned above and may also be applied to mobile commerce.

While engaging with customers in a mobile environment, it is important to recognize trust, which consists of privacy and security parameters, as an enabler in addition to the ease of use and other utilities (Shankar et al., 2010). Along with numerous advantages of mobile commerce, it should also be considered that m-internet services form a basis for a shopping experience unlike existing ones as enabling retailers to address personalized information and use of real-time location services (Yang, 2010). In many cases, it may also be possible for potential customers to have a perception of privacy or security invasion. According to a recent study by Placecast on customer attitude towards companies using online search and purchase data to

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customize advertising activities, 32% of smartphone owners indicate that they are concerned about the accessibility of the data shared and 23% do not want their data to be used under any circumstances (as cited in “Omnichannel Trends 2015”, 2014).

Because of the increasing role of mobile devices in daily life, it is more likely to encounter threats of data loss considering the fact that mobile devices are more open to incidences of accidental loss due to their nature (Kurkovsky and Syta, 2010). In their research conducted among people aged between 18 and 25 who are known as ‘digital natives’, majority of participants stated that they’re concerned with privacy and security associated with mobile devices and are aware of the likely outcomes of such information loss. Moreover, in another related research where mobile banking acceptance is studied, it is found that initial trust is a dominant factor when people consider using mobile services (Kim et al., 2009).

Most studies that are made in the area of mobile commerce or mobile services adoption handles privacy and security as a combined subject of risk (Miyazaki and Fernandez, 2001; Pavlou, 2003; Nepomuceno et al., 2014; Chang et al., 2005; Mubarak Alharbi et al., 2013; Madan and Yadav, 2018) and some of them prefer to investigate it as only trust (Hillman and Neustaedter, 2017; Siau and Shen, 2003). On the contrary, as some of the literature also did (Duh et al., 2002; Keith et al., 2013; Ghosh and Swaminatha, 2001; Gurung and Raja, 2016; Nepomuceno et al., 2012), it is essential to have a distinct look at these subjects since they demonstrate separate concepts. Gurung and Raja (2016) treat them as different dimensions and found that security and privacy concerns along with trust beliefs have an effect on a user’s attitude regarding their initial use of e-commerce.

Security, as defined by Oxford English Dictionary, can be ‘the state of being protected from unauthorized access; freedom from the risk of being intercepted, decoded or tapped’ with a reference to telecommunication and computer systems. The concerns on collecting data for

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customized practices. Yet, as this information about real people is very advantageous for organizations, it may also be an attractive instrument for criminals, too (Ransbotham, 2017). Ghosh and Swaminatha pointed out that the physical disadvantages of mobile devices such as capacity and power limitations may drive developers to favor online performance features over security countermeasures (2001). Customers state that they give a major attention to having a secure connection while making purchases online and are very sensible when it comes to deciding on the service provider that protects their personal information (Mubarak Alharbi et al., 2013). Accordingly, security concerns of customers in this study will be addresses as the unauthorized access to their information without the knowledge of the company or company’s action on not fulfilling the deal.

In addition to the concerns about how well the companies establish a secure online experience for customers, m-commerce applications incorporate consequential privacy risks for users. Many people feel more comfortable entering their personal information to their own mobile device. Most of the companies use this personal data to offer value-added services by asking permission to access user’s location, favorite meal, date of birth etc. Yet, some customers consider such a detailed service as an invasion to their privacy (Ghosh and Swaminatha, 2001).

Ackerman et al. (1999) conducts a research to have a better understanding of privacy preferences of consumers and found that reactions of individuals differ considerably for various scenarios. In the end of the research three groups are defined: privacy fundamentalists, pragmatic majority, and marginally concerned. Therefore, it is substantial for companies to know their customer’s stand in detail for privacy issues and tailor their policies accordingly. With the help of reviewed literature above, in this study, privacy matter will be approached as the way companies handle data provided by customers and customers’ concerns regarding

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being tracked by means of habit, being contacted without consent or share of their data to third parties.

2.4 Research Question

When compared with the importance of mobile shopping area, very little research studies have been conducted to reveal consumers’ acceptance. Taking into consideration the literature reviewed above, it is inevitable that more research is needed to have a better understanding of the broad area of mobile shopping applications which remains unclear by means of the drivers and barriers of adoption. To be more specific, it is important to understand the relationships between the possible drivers and barriers in the context of current business era where information and data management is an important pillar to designate a company’s success. When addressing the data management, it is also essential to have an understanding of privacy and security perceptions of customers.

Even though privacy and security perceptions are investigated in relation to mobile devices, less attention is paid to their role in adoption of mobile shopping applications. Therefore, the main focus of my research will be understanding the factors affecting usage of mobile shopping applications where privacy and security perceptions of individuals will be in the center. While questioning privacy and security, it is also crucial to investigate the underlying personal characteristics associated with these concerns since it is expected to receive a different reaction with different individuals. Also, people’s experience of mobile devices and the services these devices offer may be an important factor while observing their privacy and security perceptions. High amount of experience could lead to less security concerns driven by the familiarity of the situation. On the other hand, more privacy concerns could occur for an experienced user because of the extended knowledge of the information processed in such transactions.

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the general and brand associated privacy and security concerns on the usage of mobile shopping application. In short, this research will try to answer the following question by filling the existing gap in the literature:

“What is the role of privacy and security perception of individuals in the possession and usage of mobile shopping applications?

It is self-evident that general and brand related privacy and security concerns cannot explain the adoption and usage of mobile shopping apps. Having a knowledge of their interactions with other factors would be useful to modify their impact. Therefore, a follow up question on top of the main research question rises as:

“How the privacy and security concerns of individuals interact with other factors in driving customers’ adoption and usage of the mobile shopping apps?”

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3. Conceptual Framework and Hypothesis Development

Figure 1. Conceptual Framework

Figure 1 visualizes the conceptual framework for the study. As mentioned earlier, the main purpose of this study is to investigate the factors affecting mobile shopping application usage of customers while the privacy and security concerns and the relationship between them are placed as the main focus.

Security and privacy concerns are determined as a significant factor in relation with the adoption of new technologies. In the context of commerce activities, security and privacy concerns increases an individual’s risk perception which results in the negative outcome of intention. Therefore, following hypotheses are developed:

H1a: Mobile shopping application possession is negatively influenced by general privacy and security concerns.

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H1b: Mobile shopping application possession is negatively influenced by brand related privacy and security concerns.

H2a: Mobile shopping application usage is negatively influenced by general privacy and security concerns.

H2b: Mobile shopping application usage is negatively influenced by brand related privacy and security concerns.

When a user is more experienced in using the internet technology, it is likely that the security concerns of this individual may disappear in time since the experience will give that person the confidence to use this technology properly. Gurung and Raja (2016) classifies security concerns as evolutionary beliefs in contrast to privacy concerns. It is possible for these concerns to change completely over time in the case of more consciousness and experience is gained. Yet, it is more likely that the person will be the subject of marketing activities of companies like retargeting as the mobile and online existence of this person increase. When the privacy beliefs are considered, this situation would lead to the person feeling more exposed with his/her habits or information. Therefore, following hypotheses are developed:

H3a: The negative relationship between general privacy and security concerns and mobile shopping application possession is moderated by mobile experience, where this relationship is stronger for users with a high level of mobile experience.

H3b: The negative relationship between general privacy and security concerns and mobile shopping application usage is moderated by mobile experience, where this relationship is stronger for users with a high level of mobile experience.

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There are several studies that associating loyalty for a brand with a high level of trust. Also, being familiar with a friend and having a knowledge about the product are also elements that reduce the level of perceived risk (Nepomuceno, 2014). Therefore, when a customer is a regular for a brand, it is expected for the customer to build trust in all aspects. In the light of this, following hypotheses are developed:

H4a: The negative relationship between brand related privacy and security concerns and mobile shopping application possession is moderated by brand loyalty, where this relationship is stronger for users with lower brand loyalty.

H4b: The negative relationship between brand related privacy and security concerns and mobile shopping application usage is moderated by brand loyalty, where this relationship is stronger for users with lower brand loyalty.

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4. Research Design

In order to investigate the effects of general and branded privacy and security concerns on mobile shopping applications, an online survey is conducted to gather cross-sectional data building a base for the quantitative study. The online survey is published via Qualtrics tool and conducted only in English. The data is gathered on three different kinds of industries in order to understand whether or not there is any difference on behavior with regard to different products. The questionnaire started with the questions addressed to all respondents such as general privacy & security concerns, mobile experience etc. After first part is completed, he respondents are randomly assigned to one type of industry by the Qualtrics tool: online retail, fashion and transportation. In the beginning of the second part, the respondents revealed their preference for a specific brand in the given industry and responded brand related questions accordingly. At the last part of the survey, participants were asked about their demographic information. A pilot study is conducted in order to make sure that the survey is clear and can be answered in the given time which is 7 minutes on average.

4.1 Sample

The target population for this study is individuals aged eighteen and older with mobile and online experience. Respondents are approached via online channels such as Facebook, LinkedIn and personal e-mail. While conducting the research, a convenience sampling technique is used. During the data collection period which was a 4 weeks of period, the questionnaire was attempted to spread to as many respondents as possible.

4.2 Measures

The respondents were first asked about their online presence, mobile experience and general privacy and security concerns, personal innovativeness and shopping enjoyment. Therefore,

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these variables are common for all respondents participated in the survey. Second part of the survey questioned the brand related variables; brand loyalty, brand related privacy and security concerns regarding a specific brand of their choice in the industry they’re assigned to. Since these three industries were asked to different respondents, they will be analyzed separately. In the last section, demographics of the respondents are asked. Table 1 summarizes the overview of the variables, measures and the measure levels used in the research.

Table 1: Overview of Measures and Measure Levels of Variables

Variable Measure Level

General Privacy Concerns 7-points Likert Scale Interval General Security Concerns 7-points Likert Scale Interval Brand Related Privacy Concerns 7-points Likert Scale Interval Brand Related Security Concerns 7-points Likert Scale Interval Brand Loyalty 7-points Likert Scale Interval

Mobile Experience Numeric Ratio

Personal Innovativeness 7-points Likert Scale Interval Shopping Enjoyment 7-points Likert Scale Interval

Gender Female = 0, Male = 1 Nominal

Age Numeric Ratio

Online Presence (Per Day) Numeric Ratio

Transactions in Past Year Numeric Ratio

Purchases in Past Year (in EUR) Numeric Ratio Mobile Shopping App Possession No = 0, Yes = 1 Nominal Mobile Shopping App Usage No = 0, Yes = 1 Nominal

For online presence, respondents were asked the amount of the hours they spend online in a day. For mobile experience, respondents stated their years of experience with a mobile device which is defined at the beginning of the survey with a detailed explanation (smartphone and tablet).

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et al.’s (2012) 3-item which has a Cronbach’s alpha of 0,96. Brand related versions of these general privacy and security concerns are used exactly the same but asked the respondents to answer for a specific brand. Mobile shopping app possession and usage are asked to the respondents as whether they possess or use the shopping application of the brand they stated and coded either 1 (yes) or 0 (no).

Personal innovativeness is measured by using Kuo and Yen’s (2009) 3-item which has a Cronbach’s alpha of 0,89. Shopping enjoyment is measured by using Konus et al.’s (2008) 2-item which has a Cronbach’s alpha of 0,91. Brand loyalty is measured by using Yi and Jeon’s (2003) 4-item which has a Cronbach’s alpha of 0,93.

A seven-point Likert scale is used from strongly agree to disagree, the measures which are taken from other studies as explained above are adapted accordingly.

4.3 Analysis

In order to test hypotheses given above, IBM SPSS Statistics version 24.0.0.0 was used to perform the analysis. A separate analysis was conducted for 3 different industries (online retail, fashion, transportation) x 2 different dependent variables (possession and usage) x 2 effects (main and moderating effect). Since the dependent variables are coded as 0 or 1, binary logistics regression was performed to see the effects of explanatory and control variables on the possession or usage of mobile shopping apps.

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5. Results

This section provides the result of the analysis conducted with the binary logistics regression. In total, 323 respondents are participated in the survey. Yet, 119 of these surveys had missing values which were related to main measures, therefore taken out of the data. Also, during the analysis I realized that 2 of the respondents answered numerical questions with irrational responses which resulted in being outliers. These answers are also taken out. As a consequence, there were 202 respondents that were qualified to be analyzed for a total of 3 industries.

The age of the respondents varied from 20 to 59, and the mean for industries were 27 for online retail (M= 27,49; SD=3,85), 29 for fashion (M= 28,72; SD=6,41) and 28 for transportation (M= 27,75; SD=5,68). Gender distribution of the included population was 67% female and 33% male, where the ratio differed for three industries (Online Retail: F: 70%, M: 30%; Fashion: F: 62%, M: 38%; Transportation: F: 71%, M: 29%). Table 2 represents an overview of baseline demographics.

Table 2: Baseline Demographics

M SD Age OR 27,49 3,85 FS 28,72 6,41 TR 27,75 5,68 Male Female Gender OR 70% 30% FS 62% 38% TR 71% 29%

Note: OR=Online Retail; FS=Fashion; TR=Transportation

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Transportation is the industry with most mobile application possession (M=0,58, SD=0,50) followed by online retail (M=0,57, SD=0,50) and fashion industry (M=0,37, SD= 0,49). When using the mobile shopping applications for a given industry, online retail is the first one with a mean of 0,45 (SD=0,50), followed by transportation with a mean of 0,37 (SD=0,49) and fashion with a mean of 0,29 (SD=0,46).

The mean amount spent in the past year for online retail industry was 479 Euros (SD=580,57) with a mean purchase frequency of 12 (SD=11,85). The mean amount spent in the past year for fashion industry was 531 Euros (SD=571,36) with a mean purchase frequency of 11 (SD=9,81). The mean amount spent in the past year for transportation industry was 1062 Euros (SD= 1617,77) with a mean frequency of 9 (SD=10,35). An overview can be seen in Table 3. Also, correlation tables of three industries with all variables included in the study and descriptive statistics can be found in Appendix A.

Table 3: Overview of Mobile Shopping App Usage Behaviour

Industry Variable Min Max M SD

Online Retail Mobile App Possession 0 1 0,57 0,50

Mobile App Usage 0 1 0,45 0,50

Purchases in Past Year (EUR) 0 3500 478,77 580,57

Transactions in Past Year 0 50 11,71 11,85

Fashion Mobile App Possession 0 1 0,37 0,49

Transportation Mobile App Possession 0 1 0,58 0,50

5.1. Validity and Reliability

For the variables which were questioned with more than one item, reliability check was performed in order to see if the multiple items are qualified to form a reliable scale. The variables with a Cronbach’s alpha of 0,6 or above are then computed into one single item to prepare for

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the regression analysis. In the study, the items that belonged to general privacy concerns, personal innovativeness and brand related privacy concerns in fashion and transportation industry were not found to have a Cronbach’s alpha od 0,60 or higher. Therefore, one item from each variable is deleted t while computing into a single item and α> 0,60 is assured in the final

check. On the other hand, it was not possible to increase the α for brand related privacy concerns in online retail industry, Instead, a single item was chosen among three items. Table 3 shows an overview for the initial and final Cronbach’s alpha results.

Table 4: Reliability Analysis

Variables Cronbach's Alpha

_Initial _Final

General Privacy Concerns 0,54 0,62

General Security Concerns 0,84 0,84

Personal Innovativeness 0,69 0,73

Shopping Enjoyment 0,78 0,78

Brand Related Privacy Concerns

OR 0,50 -

FS 0,78 0,80

TR 0,6 0,71

Brand Related Security Concerns

OR 0,93 0,93 FS 0,91 0,91 TR 0,92 0,92 Brand Loyalty OR 0,89 0,89 FS 0,68 0,68 TR 0,90 0,90

Note: OR=Online Retail, FS=Fashion, TR=Transportation

5.2. Online Retail

The results in Table 4 represents the outcome of the binary logistics regression for both main and moderating effects for mobile shopping app possession in the online retail industry. The first model with the main effects of explanatory and control variables has managed to predict 32% of the variation (p=0,14) in mobile shopping app possession, while the model with moderating effects included explained 42% of the variance with a marginally higher

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significant effect on increasing or decreasing the possession or the usage of the mobile shopping apps. Also, when the moderating effect of brand loyalty on brand related privacy and security concerns and the moderating effect of mobile experience on general privacy and security concerns are checked, the model did not show a significant result (p=0,60; 0,09; 0,22 and 0,10 respectively.

Table 5 shows the results of the binary logistics regression conducted to explore variation in the usage rate of mobile shopping apps. The model with the moderating effects included can be used to explain 41% of the variance in mobile shopping app usage with a p value of 0,08. General privacy and security concerns did not have significant negative impact

Table 4: Binary Logistics Regression for Mobile Shopping App Possession in Online Retail

Variables B S.E. Wald Sig. Exp(B) B S.E. Wald Sig. Exp(B)

General Privacy Concerns -0,45 0,31 2,11 0,15 0,64 -2,23 1,41 2,51 0,11 0,11

General Security Concerns 0,36 0,36 0,99 0,32 1,43 2,17 1,15 3,58 0,06# 8,73

BRPC 0,12 0,23 0,25 0,61 1,13 -0,03 0,65 0,00 0,97 0,97 BRSC -0,37 0,33 1,24 0,27 0,69 0,63 0,70 0,82 0,37 1,89 Brand Loyalty -0,26 0,31 0,70 0,40 0,77 1,37 1,08 1,61 0,21 3,95 Mobile Experience -0,01 0,09 0,01 0,94 0,99 0,01 0,39 0,00 0,98 1,01 Personal Innovativeness -0,04 0,31 0,02 0,89 0,96 -0,01 0,35 0,00 0,98 0,99 Shopping Enjoyment 0,18 0,22 0,64 0,42 1,20 0,22 0,25 0,77 0,38 1,25 Gender -0,60 0,70 0,73 0,39 0,55 -0,65 0,77 0,72 0,40 0,52 Age -0,11 0,10 1,09 0,30 0,90 -0,17 0,12 2,12 0,15 0,84

Online Presence (Per Day) -0,06 0,12 0,22 0,64 0,95 -0,16 0,14 1,43 0,23 0,85

Transactions in PY 0,11 0,05 5,29 0,02* 1,12 0,11 0,05 4,11 0,04* 1,11

Purchases in PY (in EUR) 0,00 0,00 2,00 0,16 1,00 0,00 0,00 2,10 0,15 1,00

Brand Loyalty*BRPC - - - 0,15 0,28 0,27 0,60 1,16 Brand Loyalty*BRSC - - - -0,51 0,30 2,97 0,09# 0,60 Mobile Experience*GPC - - - 0,17 0,14 1,50 0,22 1,18 Mobile Experience*GSC - - - -0,17 0,10 2,71 0,10# 0,85 Constant 3,99 3,62 1,22 0,27 54,17 1,72 5,44 0,10 0,75 5,61 N=69

Main Effect With Moderators

Note: PY=Past Year; BRPC=Brand Related Privacy Concerns; BRSC= Brand Related Security Concerns; GPC=General Privacy Concerns; GSC=General Security Concerns; Variable 'Gender': Female (0), Male (1); Significance levels: #p<0,10; *p<0,05; **p<0,01.

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on the mobile shopping app usage (p=0,14 and 0,15). Brand related privacy concerns are also found to have no significant impact on the increasing level of mobile shopping app usage (p=0,93), On the other hand, higher level of brand related security concerns are found to have a weak negative impact on the usage of mobile shopping apps (p=0,08).

The results also show that the negative impact of brand related security concerns on the mobile shopping app usage rate is significantly less pronounced with a higher level of brand loyalty (Exp(B)=0,54 and p=0,05).

Table 5: Binary Logistics Regression for Mobile Shopping App Usage in Online Retail

General Privacy Concerns -0,33 0,29 1,30 0,26 0,72 -2,15 1,45 2,18 0,14 0,12 General Security Concerns -0,06 0,33 0,04 0,85 0,94 1,62 1,12 2,11 0,15 5,07

BRPC -0,01 0,23 0,00 0,95 0,99 0,06 0,65 0,01 0,93 1,06 BRSC 0,07 0,31 0,05 0,83 1,07 1,25 0,71 3,16 0,08# 3,50 Brand Loyalty -0,18 0,30 0,34 0,56 0,84 2,20 1,32 2,80 0,09# 9,06 Mobile Experience 0,03 0,08 0,11 0,75 1,03 0,01 0,40 0,00 0,99 1,01 Personal Innovativeness -0,25 0,30 0,72 0,40 0,78 -0,21 0,35 0,36 0,55 0,81 Shopping Enjoyment 0,07 0,22 0,09 0,76 1,07 0,11 0,24 0,22 0,64 1,12 Gender 0,07 0,68 0,01 0,92 1,08 0,09 0,76 0,02 0,90 1,10 Age -0,06 0,10 0,35 0,56 0,94 -0,13 0,12 1,16 0,28 0,88

Online Presence (Per Day) -0,01 0,12 0,01 0,94 0,99 -0,15 0,14 1,10 0,29 0,86 Transactions in PY 0,06 0,03 3,13 0,08# 1,06 0,06 0,04 2,58 0,11 1,07 Purchases in PY (in EUR) 0,00 0,00 1,55 0,21 1,00 0,00 0,00 1,94 0,16 1,00

Brand Loyalty*BRPC 0,06 0,29 0,05 0,83 1,07 Brand Loyalty*BRSC - - - -0,63 0,31 4,02 0,05* 0,54 Mobile Experience*GPC - - - 0,16 0,14 1,34 0,25 1,18 Mobile Experience*GSC - - - -0,15 0,10 2,23 0,14 0,86 Constant 2,28 3,38 0,46 0,50 9,81 -0,77 5,75 0,02 0,89 0,46 N=69

Note: PY=Past Year; BRPC=Brand Related Privacy Concerns; BRSC= Brand Related Security Concerns; GPC=General Privacy Concerns; GSC=General Security Concerns; Variable 'Gender': Female (0), Male (1); Significance levels:

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5.3. Fashion

The results on Table 6 shows the binary logistics regression for mobile shopping app possession in the fashion industry. The model with the moderating effects can explain the variances in the independent variable with 70% (p<0,05). Higher level of brand related security concerns are found to have a significant negative impact on the usage of mobile shopping apps (p=0,01). On the contrary, higher level of brand related privacy concerns are found to have a significant positive impact on the usage of mobile shopping apps (p=0,01). General privacy and security concern are not found to have a significant impact on the possession of mobile shopping apps for fashion industry (p=0,18; p=0,07). The results also show that the negative impact of brand related security concerns on the mobile shopping app possession rate is

Table 6: Binary Logistics Regression for Mobile Shopping App Possession in Fashion Industry

Variables B S.E. Wald Sig. Exp(B) B S.E. Wald Sig. Exp(B) General Privacy Concerns 0,41 0,32 1,70 0,19 1,51 1,46 1,09 1,78 0,18 4,29 General Security Concerns -0,17 0,34 0,26 0,61 0,84 2,09 1,14 3,38 0,07# 8,10 BRPC 0,09 0,39 0,06 0,81 1,10 -6,10 2,29 7,08 0,01** 0,00 BRSC 0,46 0,39 1,38 0,24 1,58 5,53 2,08 7,09 0,01** 251,15 Brand Loyalty 0,07 0,47 0,02 0,88 1,07 -1,11 2,57 0,19 0,67 0,33 Mobile Experience 0,00 0,09 0,00 0,99 1,00 0,89 0,50 3,17 0,08# 2,44 Personal Innovativeness 0,33 0,43 0,56 0,45 1,39 0,86 0,65 1,77 0,18 2,37 Shopping Enjoyment -0,55 0,33 2,74 0,10# 0,58 -1,65 0,71 5,44 0,02* 0,19 Gender -1,17 0,85 1,90 0,17 0,31 -3,22 1,49 4,69 0,03* 0,04 Age -0,04 0,07 0,33 0,57 0,96 0,10 0,10 1,02 0,31 1,11

Online Presence (Per Day) -0,39 0,15 6,57 0,01** 0,68 -0,60 0,24 6,45 0,01* 0,55 Transactions in PY 0,02 0,04 0,26 0,61 1,02 0,06 0,06 1,13 0,29 1,07 Purchases in PY (in EUR) 0,00 0,00 5,34 0,02* 1,00 0,00 0,00 7,08 0,01** 1,00

Brand Loyalty*BRPC 2,45 0,93 6,90 0,01** 11,59 Brand Loyalty*BRSC - - - -1,91 0,83 5,26 0,02* 0,15 Mobile Experience*GPC - - - -0,07 0,10 0,44 0,51 0,94 Mobile Experience*GSC - - - -0,18 0,11 2,76 0,10# 0,84 Constant -1,18 3,15 0,14 0,71 0,31 -13,93 9,99 1,94 0,16 0,00 N=68

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significantly less pronounced with a higher level of brand loyalty (p=0,02). Also, positive impact of the brand related privacy concerns are less pronounced with a higher level of brand loyalty (p=0,01). Moderating effect of mobile experience on general privacy and security concerns are not found to be significant (p=0,51 and p=0,10).

The results of the binary logistics regression for mobile shopping app usage in fashion industry summarized in table 7 shows that the negative impact of general security concerns on the usage is significant with a p value of 0,04, while general security concerns have the opposite impact yet not significant (p=0,85). Brand related privacy concerns reflects a positive impact

Table 7: Binary Logistics Regression for Mobile Shopping App Usage in Fashion Industry

General Privacy Concerns 0,16 0,34 0,22 0,64 1,18 -0,25 1,32 0,04 0,85 0,78 General Security Concerns 0,59 0,41 2,12 0,15 1,81 3,02 1,47 4,24 0,04* 20,56 BRPC -0,34 0,39 0,73 0,39 0,71 -7,63 2,96 6,66 0,01** 0,00 BRSC 0,12 0,44 0,07 0,79 1,13 6,89 2,61 6,98 0,01** 980,49 Brand Loyalty -1,02 0,58 3,07 0,08 0,36 0,63 2,07 0,09 0,76 1,88 Mobile Experience 0,02 0,09 0,05 0,83 1,02 0,35 0,43 0,64 0,42 1,42 Personal Innovativeness 0,33 0,45 0,52 0,47 1,39 0,73 0,61 1,44 0,23 2,08 Shopping Enjoyment -0,36 0,36 1,00 0,32 0,70 -0,99 0,62 2,54 0,11 0,37 Gender -1,12 0,89 1,59 0,21 0,33 -4,51 2,09 4,64 0,03* 0,01 Age -0,03 0,07 0,24 0,62 0,97 0,14 0,11 1,53 0,22 1,15

Online Presence (Per Day) -0,41 0,16 6,79 0,01 0,66 -0,65 0,25 6,76 0,01** 0,52 Transactions in PY 0,00 0,04 0,01 0,93 1,00 0,06 0,06 1,00 0,32 1,06 Purchases in PY (in EUR) 0,00 0,00 2,54 0,11 1,00 0,00 0,00 4,83 0,02* 1,00

Brand Loyalty*BRPC 2,67 1,08 6,16 0,01* 14,41 Brand Loyalty*BRSC - - - -2,80 1,08 6,72 0,01** 0,06 Mobile Experience*GPC - - - 0,09 0,13 0,47 0,49 1,10 Mobile Experience*GSC - - - -0,16 0,12 1,80 0,18 0,86 Constant 2,24 3,16 0,50 0,48 9,35 -10,79 9,22 1,37 0,24 0,00 N=68

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impact (p=0,01). The model with the moderating variables shows a significant result with a p value less than 0,05 (p=0,00) and explains 70% of the variations. The negative impact of brand related security concerns on the mobile shopping application possession is less pronounced with a higher level of brand loyalty (p=0,01). Moderating impact of mobile experience on general privacy and security concerns are not found to be significant (p=0,49 and p=0,18).

5.4. Transportation

For transportation industry, the results of the binary logistics regression for mobile shopping app possession can be seen on Table 8. The model with the moderators can explain 51% of the variations with a p value less than 0,05 (p=0,02). General privacy and security

Table 8: Binary Logistics Regression for Mobile Shopping App Possession in Transportation Industry

General Privacy Concerns -0,30 0,35 0,76 0,38 0,74 0,59 1,18 0,25 0,62 1,81

General Security Concerns 0,93 0,39 5,62 0,02* 2,53 0,59 1,55 0,15 0,70 1,80

BRPC 0,45 0,48 0,85 0,36 1,56 -3,21 1,68 3,66 0,06# 0,04 BRSC -0,34 0,38 0,77 0,38 0,72 3,10 1,53 4,10 0,04* 22,19 Brand Loyalty -0,16 0,26 0,41 0,52 0,85 -0,43 0,92 0,22 0,64 0,65 Mobile Experience 0,11 0,11 1,10 0,29 1,12 0,25 0,37 0,46 0,50 1,28 Personal Innovativeness -0,61 0,35 2,97 0,09# 0,55 -0,80 0,45 3,18 0,07# 0,45 Shopping Enjoyment 0,09 0,29 0,10 0,75 1,10 0,31 0,35 0,78 0,38 1,37 Gender 0,62 0,78 0,63 0,43 1,86 0,59 0,89 0,44 0,51 1,81 Age 0,05 0,08 0,31 0,58 1,05 -0,01 0,09 0,02 0,88 0,99

Online Presence (Per Day) 0,14 0,14 1,10 0,30 1,15 0,15 0,16 0,94 0,33 1,16

Transactions in PY 0,06 0,04 2,15 0,14 1,06 0,06 0,05 1,59 0,21 1,06

Brand Loyalty*BRPC - - - 1,07 0,49 4,78 0,03* 2,91 Brand Loyalty*BRSC - - - -1,04 0,46 5,05 0,03* 0,35 Mobile Experience*GPC - - - -0,08 0,11 0,58 0,45 0,92 Mobile Experience*GSC - - - 0,05 0,13 0,17 0,68 1,06 Constant -3,74 3,21 1,36 0,24 0,02 -3,13 5,06 0,38 0,54 0,04 N=68

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concerns show a negative impact on mobile shopping app possession, yet there is no significant impact (p=0,62 and p=0,70). Brand related security concerns are found to have a negative impact on mobile shopping app possession with significance (p=0,04). Yet, the impact of brand related privacy concerns are not significant (p=0,06). Mobile experience of the individuals are not found to have a significant moderating impact on general privacy and security concerns (p=0,45 and p=0,68). The negative impact of high level of brand related security concern on mobile shopping app possession is less pronounced with a higher level of brand loyalty (p=0,03).

The results of the binary logistics regression performed to understand factor affecting

Table 9: Binary Logistics Regression for Mobile Shopping App Usage in Transportation Industry

General Privacy Concerns 0,07 0,37 0,03 0,86 1,07 0,12 1,07 0,01 0,91 1,13

General Security Concerns 0,19 0,31 0,37 0,55 1,20 -0,05 1,25 0,00 0,97 0,96

BRPC 0,01 0,46 0,00 0,98 1,01 1,24 1,51 0,68 0,41 3,47 BRSC 0,28 0,39 0,50 0,48 1,32 -1,13 1,45 0,61 0,43 0,32 Brand Loyalty -0,50 0,29 3,00 0,08# 0,61 -0,95 1,10 0,74 0,39 0,39 Mobile Experience 0,14 0,09 2,11 0,15 1,15 0,10 0,26 0,15 0,70 1,11 Personal Innovativeness -0,13 0,37 0,13 0,72 0,88 -0,12 0,39 0,09 0,76 0,89 Shopping Enjoyment 0,12 0,26 0,22 0,64 1,13 0,10 0,26 0,15 0,70 1,11 Gender 1,09 0,76 2,07 0,15 2,97 1,09 0,77 2,03 0,15 2,99 Age 0,09 0,08 1,31 0,25 1,09 0,09 0,08 1,32 0,25 1,10

Online Presence (Per Day) -0,12 0,12 0,91 0,34 0,89 -0,12 0,13 0,78 0,38 0,89

Transactions in PY 0,03 0,03 1,15 0,28 1,03 0,04 0,03 1,39 0,24 1,04

Brand Loyalty*BRPC -0,38 0,47 0,65 0,42 0,68 Brand Loyalty*BRSC - - - 0,45 0,45 1,01 0,32 1,57 Mobile Experience*GPC - - - 0,00 0,09 0,00 0,99 1,00 Mobile Experience*GSC - - - 0,02 0,10 0,02 0,88 1,02 Constant -5,16 3,07 2,83 0,09 0,01 -3,74 4,82 0,60 0,44 0,02 N=68

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is not found to be significant with a p value more than 0.05 (p=0,32) and explained 31% of the variations in usage. General privacy concerns performed a negative impact on the usage while general security concerns showed a positive impact, yet none of the relations were found to be significant (p=0,91 and p=0,97). Likewise, the negative impact of brand related privacy concerns and positive impact of brand related security concerns on mobile shopping app usage for transportation industry are also not found significant (p=0,41 and p=0,43). Also, the moderating effect of mobile experience on general privacy and security concerns and moderating effect of brand loyalty on brand related privacy and security concerns are not found significant (p=0,42; p=0,32; p=0,99; p=0,88).

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6. Conclusion 6.1. Discussion

Table 10 provides an overview of the results on the formulated hypotheses with this study. Although some of the hypotheses were partially supported, the results were not sufficient to accept any of the hypotheses therefore all of them are rejected.

This study aimed to relate brand related and general privacy and security concerns with user’s adoption and usage of the mobile shopping applications separately, in the light of previous literature where trust, a combination of privacy and security, is found to affect new technology adoption. However, it is not significant for any of the mentioned industries to form such an effect general privacy and security concerns. This may be a sign of brand related privacy and security concerns being more influential on one’s choice of adopting or using such technology and is worth for further testing. It is important to explore as companies may come

Dependent Explanatory Moderator Online Retail Fashion Transportation

H1a GPC n.s. n.s. n.s. GSC n.s. n.s. n.s. H1b BRPC n.s. (-) n.s. BRSC n.s. (+) (+) H2a GPC n.s. n.s. n.s. GSC n.s. (+) n.s. H2b BRPC n.s. (-) n.s. BRSC n.s. (+) n.s. GPC Mobile Experience n.s. n.s. n.s. GSC Mobile Experience n.s. n.s. n.s. GPC Mobile Experience n.s. n.s. n.s. GSC Mobile Experience n.s. n.s. n.s. BRPC Brand Loyalty n.s. (-) (-) BRSC Brand Loyalty n.s. (-) (-) BRPC Brand Loyalty n.s. (-) n.s. BRSC Brand Loyalty (-) (-) n.s. Possession

Notes: BRPC=Brand Related Privacy Concerns; BRSC= Brand Related Security Concerns; GPC=General Privacy Concerns; GSC=General Security Concerns

(+): Hypothesis is significantly supported. (-): Hypothesis is significantly not supported.

Usage Possession Usage Usage Usage Possession Industry H3a H3b H4a H4b M ode ra ti ng E ffe ct s M ai n E ffe ct s

Table 10: Overview of Results

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The only industry that brand related security concerns are observed to have a negative impact on both the possession and usage of the mobile shopping apps is fashion industry. Also, in the transportation industry, their effect is observed for only mobile shopping app possession. Therefore, H1b and H2b are partially accepted. This may result from online retail being the industry that has the largest amount of mobile shopping app usage. Since the security concern levels are not very high for that industry, the effect may be observed in only other two industries.

Moderating effect of brand loyalty and mobile experience are not found to be inline with the hypothesis or significant, there fore H3 and H4 are fully rejected. This result may be driven by the fact that mobile experience was not a differentiating factor for most of the participants. As the penetration of smart devices are growing, most of the users adapt this technology more or less at the same time, therefore one’s general privacy and security concerns are not dependent on experience. Also, moderating effect of brand loyalty had some contrary results with hypothesis, which indicates that being loyal to a brand does not necessarily mean having full trust on the same brand. People may still argue how the companies treat their information, even they keep purchasing the company’s products and form a loyal customer base.

The results show that the companies still have opportunity gap to grow their business in the mobile channel. Mobile shopping apps usage rates varied 29% to 45% for all three industries. Apart from tested factors, there may be several reasons to take into account to explain the low level of adoption and usage. Some of them are pronounced as convenience of the device, screen size, the companies that still not supporting mobile devices, convenience of the payment methods used on mobile devices etc.

Control variables, being shopping enjoyment, personal innovativeness, age and gender are also tested. Yet, they are only found to have significant results for fashion industry. For the

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possession of mobile shopping applications, a low level of shopping enjoyment resulted in a low level of app possession. This result may be explained as this industry having more intangible motivation in its nature, users enjoy the time they spend browsing the app tend to possess it. This result is not valid for the usage of mobile shopping apps in the fashion industry and should remind us that people may use application for various users. It is possible for a person that enjoys shopping to use the app for search and completed the purchase process in a brick-and-mortar store since trying out can be found to be more fun. In fashion industry, it is also found that the female participants are slightly more likely to possess and use a company’s mobile shopping app. Such a difference is not found in the other industries.

The partially unexpected results of this study can also be linked to the privacy paradox. As the results reveal the statements of the participants, it is hard to link them fully with the adoption and usage behavior. Privacy paradox also explains that even though people reveal high level of concerns, this may not reflect in a negative way in their behavior (Norberg et al., 2007; Kokolakis, 2017). To conclude, even though the results did not bring any significant findings, they formed a base for the literature for privacy and security perceptions in relation with the growing field of mobile shopping applications and therefore can be benefited from by further researchers.

6.2. Managerial Implications

The business professionals in online retail, fashion and transportation industry should carefully plan and put in practice their strategies to expand the business on mobile channels. Even though this study did not find sufficient proof that general and brand related privacy and security concerns are influential on one’s preference in mobile shopping, privacy and security is a growing point in question. With the General Data Protection Regulation for EU Citizens

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overview of their user’s privacy preferences. From a practice point of view, this may result in a increasing awareness regarding privacy. In conclusion, even though we were not able to find significant results of privacy and security being effective on possession and usage, it may change in the future as it it not only a concern of European companies but also companies worldwide.

The results also suggested some significant results regarding the mobile shopping app possession for transportation industry and possession and usage for fashion industry for brand related privacy concerns, yet, no evidence for general privacy concerns. This finding should be tested with further research by the companies as well, as it indicates an important outcome. Having heavily privacy concerned customers does not necessarily mean that they are concerned when it comes to a specific brand. The marketers can accomplish creating this difference applying different marketing strategies such as building brand trust and providing a transparent communication with their customers. It is important for the companies to understand this difference and have their business practices with not a general perspective but with a more tailor-made approach by focusing on the need or perception of each customer group.

6.3. Limitations and Further Research

In the population included in the research there was a skewness regarding the female population which may have resulted in bias in the results of the study. Therefore, getting an even ratio for gender should be aimed by the future research. Also, the population sample was quite limited since there was no financial support for this project being a Master’s thesis. Future studies with bigger sample sizes may achieve more significant results in terms of different industries as well as explanatory variables.

What drives mobile shopping app usage : the impact of privacy and security perceptions

What Drives Mobile Shopping App Usage:

The Impact of Privacy and Security Perceptions

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