Willingness to be Targeted byPersonalized Online Advertising : A Cross-Cultural Study on Privacy Calculus

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School of Business, Society and Engineering Master Thesis in International Marketing Spring 2020

Willingness to be Targeted by

Personalized Online Advertising

A Cross-Cultural Study on Privacy Calculus

AUTHORS:

ANAS DAHAN, SHAGHAYEGH HOSSEINI

Supervisor: Konstantin Lampou

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Abstract

Date: 09/06/2020 Level: Master thesis in International Marketing, 15 credit

Institution: School of Business, Society and Engineering, Mälardalen University Authors: Anas Dahan Shaghayegh Hosseini

(92/01/14) (80 /01/20)

Title: Willingness to be Targeted by Personalized Online Advertising Tutor: Konstantin Lampou

Keywords: Personalized advertising, Privacy concern, Willingness,

Individualism/Collectivism, Uncertainty avoidance Questions: -What is the impact of privacy concern and perceived risk versusperceived benefits on willingness to be targeted by personalized online marketing?

-How culture influences privacy concerns and risk perception?

-To what extent does culture impact willingness to be targeted by personalized advertising? How does privacy and risk mediate this effect?

Purpose: Investigate the effect of national culture on consumer intention towards being targeted by personalized marketing, and if culture has an effect on consumer decision making process.

Method: Quantitative research method

Conclusion: Culture showed as a vital phenomenon concerning consumers’ willingness towards being targeted by personalized advertising. Besides, consumers decision making process is also affected by culture. Concerning consumers decision making process privacy and risk showed a negative effect on consumers’ willingness to be targeted by personalized advertising Simultaneously, the perceived benefit positively affects willingness.

Furthermore, privacy and risk found out to be culturally sensitive. In relation to calculus theory, the majority of consumers value benefits more than risk.

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Acknowledgments

We would like to thank those who supported us in completing our Master dissertation. We wish to express our sincere appreciation to our supervisors Konstantin Lampou for his valuable support.

Lastly, we want to thank our family and friends, and all the research respondents who participated in the survey questionnaires and provided the information for this research.

Västerås, 9th of June 2020

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List of Figures

Figure 1: Culture Dimensions ... 10

Figure 2: Proposed framework of factors in willingness to be targeted in online advertising Inspired by (Burster et al., 2017; Dinev et al., 2006; Xu et al., 2011) ... 19

Figure 3: Participants from different countries ... 32

Figure 4: participants’age and gender ... 33

Figure 5: Cultural dimensions ... 34

Figure 6: IND and UA level among three different cultures ... 35

List of Tables

Table 1: Summary of hypotheses ... 20

Table 2: Operationalization for Research Questions. ... 26

Table 3: Correlation between variables. ... 37

Table 4: Mediating effect of privacy concern ... 41

Table 5: Mediating effect of perceived risk ... 41

Table 6: Hypotheses Results ... 42

Table A 1: Gender ... viii

Table A 2: Age ... viii

Table A 3: Cultural Dimensions Demographic ... viii

Table A 4: Descriptive Statistics (Mean and SD) ... ix

Table A 5: Correlation (PC, PER, BEN) - (WIL) ... ix

Table A 6: Correlation (HUA, COL) - (PC, PER, WIL) ... x

Table A 7: Regression ... x

Table A 8: Regression ... xi

Table A 9: Comparing the results in different countries ... xii

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1 Introduction

This section gives background information about personalized marketing and introduces the reader to factors that affect willingness in general. Furthermore, the problem will be formulated in this dissertation, followed by the purpose and the research questions.

1.1 Background

Marketing is a vital process in any business organization as it links the producers to the consumers (McDonald & Wilson, 2016). The development of Internet technology has changed the way of marketing; traditional marketing turned into digital marketing in a variety of market segments (Lamberton & Stephen, 2016). With the development of technology, the marketing industry discovered personalized marketing as a new phenomenon. Personalized marketing (PM) targets consumers based on their activity background and preferences (Lambrecht & Tucker, 2013). Users' activities could be tracked frequently to collect information and establish a more personalized advertisement (Estrada- Jiménez et al., 2017).

Despite the increase in internationalization, PM strategies adoption in different countries was concerned, and generalization in marketing became a warning in internationalization (Samaha et al., 2014). Culture is mentioned as a significant factor in explaining different behavior in individuals. It influences individuals’ attitude, intention, and their decision-making process (Xu-Priour et al., 2014; Zheng, 2017).

Estrada-Jiménez et al. (2017) defined personalized advertisement as the most profitable marketing strategy. Tucker (2014) suggested a positive attitude towards the personalized advertisement. He argued based on the benefits of PM and mentioned consumers' advantages by receiving more connected information to their interests and preferences. Besides, the potential negative consequences of violating consumers' privacy were also mentioned in some studies (Aguirre et al.,

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2015; Tucker, 2014;). However, online privacy concerns are confirmed to differ among people in different cultures (Krasnova et al., 2012; Lowry et al., 2011; Zheng, 2017).

Information privacy has been extensively investigated in previous studies. Raising consumers' concerns regarding the collection and utilization of personal information online without their permission was reported frequently (Burster et al., 2017; Dinev et al., 2013; Lee & Rha, 2016; Ziesak, 2013). Online users are concerned about the inapt of collection, storage, and utility of their personal information for different purposes without their permission (Keith et al., 2013). The privacy calculus model has been adopted in previous researches and infers that individuals employ a risk-benefit analysis when it comes to their information disclosure (Evens & Damme, 2016; Dinev et al., 2006; Xu et al., 2011). Personal information disclosure in personalized marketing presents both benefits and risks to users. The concept of risk is related to privacy. The perceived risk identified as a significant factor that is directly connected to information privacy concerns. Risk has been introduced separately from privacy and described as the perceived potential risk that occurs when personal information is shared (Dinev et al., 2006). The disclosure of personal information increases the risk of misusing personal information (Awad & Krishnan, 2006) and the threats to information unauthorized utilization (Xu et al., 2011). Besides, the effect of perceived risk in consumers' online behavior was also mentioned in different studies (Atorough & Donaldson, 2012; Noort et al., 2008). Willingness to share personal information is based on the trade-off between the value of personalization and privacy concerns, as long as the benefits of personalized offerings are higher than the risk of information sharing, consumers are willing to interact online (Dinev et al., 2006; Evens & Damme, 2016; Xu et al., 2011). In the face of such a paradox, the businesses require to show their consumers that they will receive more benefits than the potential risk caused by information misuse (Wang et al., 2016).

Hofstede's cultural dimensions are chosen as a theoretical foundation in this study to understand the effect of cultural differences as a demographic background on consumers' attitudes and intentions toward personalized marketing. Two cultural dimensions (individualism-collectivism and uncertainty avoidance), among five, are chosen to measure the cultural background of individuals in this study. The two dimensions were widely used in previous cross-cultural studies concerning privacy and risk in different areas (Ko et al., 2004; Lowry et al., 2011; Zheng, 2017).

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1.2 Problem Formulation

Companies must become aware of factors that are affected by consumer attitudes, values, and opinions in different settings, such as privacy and risk. Besides, national culture is broadly mentioned as a vital framework that influences many dimensions of human intention, behavior, and the decision-making process (Al Kailani & Kumar, 2011; Soares et al., 2007). Thus, organizations are facing a challenge to enter the new global markets; managing cross-cultural differences is needed to understand consumers’ attitudes regarding their online information privacy and their intention to partake in online interactions. Concerning the internationalization phenomenon among businesses and the rise in personalized advertising utilization, there are calls in marketing literature for more cross-cultural research in connection with personalization. The comparisons of consumers’ perceived information privacy-related risk and intention to interact online among different cultures are needed to avoid complications in personalized advertising.

The information privacy concern is rising among online users concerning the extreme growth in digital marketing. The negative aspects of personal information disclosure, such as risk and privacy issues, have been mentioned in various studies (De Keyzer et al., 2015; Estrada-Jiménez et al., 2017; Karwatzki et al., 2017; Jung, 2017). Estrada-Jiménez et al. (2017), Lambrecht & Tucker (2013), and Tucker (2014) mentioned businesses and consumers’ benefits from personalized advertisement, as well as the consumers’ experience regarding their privacy. Besides, Doorn and Hoekstra (2013) suggested the advantages of personalized advertising. However, they argued that privacy issues might be higher than benefits and raise insecure feelings of personal information disclosure.

Awad & Krishnan (2006) suggested that marketers are facing a paradox concerning personalization and privacy. They argued that privacy concern is more significant in consumers who value information transparency, and they are unwilling to participate in personalized offerings. Researchers have studied different aspects of the growing field of personalization in various fields. A vast majority of previous research has focused on the outcomes of personalization regarding consumers’ online attitudes and behavior (Al Kailani & Kumar, 2011; Lee & Rha, 2016;

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Shiau & Luo, 2012; Zhang, 2011; Ziesak, 2013). Many have found a positive impact in diverse areas, such as online buying behavior (Dodoo & Wu, 2019; Pappas et al., 2014; Setyani et al., 2019).

Previous studies have been attempting to understand how the influence of personalization leads to greater online marketing effectiveness. Nevertheless, the utilization of personalized marketing requires a perspective on the determinants of users’ willingness to be targeted by personalized advertising. A vast majority of researchers have studied a single-country perspective and, therefore, cannot consider the cross-cultural aspect in which online users are influenced by their information privacy invasion and how this will affect their intention to interact online.

There is a need for understanding consumers’ intentions concerning their information privacy issues. It helps the marketers to understand consumers’ expectations concerning their privacy, overcome possible adverse consumer reactions from personalization, and get better results from their marketing strategies. Consumers’ intention towards personalized marketing cited as the willingness to be targeted by personalized advertising in this paper. Privacy calculus theory is conceptualized to understand the evaluation of risk and benefits among consumers and have a better understanding of their decision making regarding their information privacy towards personalization.

Culture as a background is studied in this paper to understand whether deferring Hofstede’s cultural dimensions among consumers, having an impact on their attitudes toward online information privacy concerns, perceived risk, and their intention to be targeted. The results would be useful to understand how marketers can govern these factors to be successful in their personalized online marketing strategies. An understanding of cultural differences and its implications on consumer willingness to be targeted by personalized marketing is essential to understand whether cultural diversity and national characteristics should be acknowledged when businesses are planning their online marketing strategies.

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1.3 Purpose

The purpose of this research is to test a model that posits national culture affects consumers’ intention to receive personalized advertising. Based on the privacy calculus theory, consumers consider a trade-off between risk and benefits in their decision-making process (Smith et al., 2011), the model assumes that information privacy-related risk and benefits are factors that affect the intention to receive personalized advertising. likewise, national culture has an impact on privacy and risk. A quantitative research method applied to investigate the effect of these factors on consumers' willingness to be targeted by personalized advertising.

The framework in this study is based on cultural dimensions theory by Hofstede (1991) to measure cultural differentiation and privacy calculus theory to understand consumers’ decision- making processes. Perceived risk and benefits are the main variables mentioned in the privacy calculus theory relating to consumers' behavior interaction in social exchanges (Dinev et al.,2013; Xu et al., 2009). Besides, privacy concerns argued as one of the most important variables affecting behavioral intention and reactions (Awad & Krishnan,2006; Burster et al., 2017; Lankton et al., 2017). Three different cultures from (Iran, Saudi Arabia, and Sweden) were included in this research to investigate the national culture impact on personalized advertising.

1.4 Research Questions

This thesis investigates consumers’ intention to be targeted by personalized advertising and examines the factors that influence this intention. Furthermore, the paper examines the effect of culture on online privacy issues. Thus, our study aims at answering the following questions:

1-What is the impact of privacy concerns and perceived risk versus perceived benefits on willingness to be targeted by personalized online marketing?

2-How culture influences privacy concerns and risk perception?

3-To what extent does culture impact willingness to be targeted by personalized advertising? How do privacy and risk mediate this effect?

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2 Literature Review and Conceptual Framework

Consumer willingness to be targeted by personalized online advertising depends on a variety of factors. Privacy concerns, perceived risk, and benefits are the essential aspects considered in this study. In addition, the impact of cultural background on privacy concerns and perceived risk is studied. This section will focus on the concepts of personalized advertising and the factors affecting it from a broader perspective and relate the culture as a background to privacy perception. This chapter contains the development of a conceptual research framework and the hypotheses formulation.

2.1 Online Marketing

Digitalization and the Internet changed the marketing tools and changed the way of advertising in companies. It led advertisements methods from traditional to digital (Lamberton & Stephen, 2016). Online advertising had more advantages than conventional for organizations; it allowed companies to learn about their customers, inform them and create engagement to provide better products and services to their customers (Lamberton & Stephen, 2016). In online advertising, information is delivered to consumers faster, more efficient, and creates new opportunities for companies that can never be reached in traditional marketing (Shukla, 2010). Business organizations are massively moving into online marketing with the increased efficiency of the Internet (Chaffey & Chadwick, 2019).

2.2 Personalized Online Advertising

Personalized online marketing became an interesting area for researchers in the last few years; it changed the idea of mass marketing to target each customer individually (Furey, 2016). It mentioned as a particular design to offer the best to the customers’ specific needs (Estrada-Jiménez et al., 2017), provides the right type of offers to the consumers at the right time (Jung, 2017), and

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mentioned as the most effective marketing method and strategy in recent years (Estrada-Jiménez et al., 2017).

The personalized marketing goal is to target and engage the customers through individual communication (Jung, 2017). Delivering the individualized content got possible by using artificially intelligent technology through collecting and analyzing online consumers’ data (Jung, 2017). Customers’ personal information gets collected and saved in the databases by companies; the information will be used to target the users with the best advertisement match to their profiles. The advertisements appear on web pages or social media platforms as banner or sidebar ads (Lambrecht & Tucker, 2013).

In recent years social media websites and apps such as Facebook, Instagram, and Twitter also provide space for advertisement. These spaces are mainly used to display personalized advertisements. The space for displaying ads offers advertisements based on the user’s interaction on the social media platform (Lambrecht & Tucker, 2013; Okazaki & Taylor, 2013). Advertisers can collect the data related to users, such as likes, comments, search, and any interaction or action users have online (Lambrecht & Tucker, 2013).

Personalized online marketing poses a threat to consumers’ information as much as it enhances efficiency in the marketing environment. As personalized online marketing became more challenging to regulate, the awareness of personal data privacy and risks is raising increasingly among users (Deuker, 2009; Ham, 2017; Vemou et al., 2014), and the circumstances use of the data was continuously questioned (Aguirre et al., 2016; Kokolakis, 2017; Jung, 2017).

2.3 Willingness to be Targeted by Personalized Advertising

Attitude explained as individuals' established way of thinking, feelings, expectations. It shows the general evaluation of an individual regarding a subject (Linehan, 2014). Attitudes are substantial as they influence consumers' beliefs and behavior (Linehan, 2014). Consumers' intention is explained as a decision to accomplish an action and mostly lead to behavior (Ajzen, 2005). The understanding of individuals' attitudes and intentions helps the researcher to follow consumers' decision-making processes of a particular behavior (Ajzen, 2015).

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The collection and utilization of online users' information raised information privacy concerns among individuals (Burster et al., 2017; Dinev et al., 2013). Consumers' attitude and intention were studied in various researches to understand the outcome of online information utilization in diverse topics, such as online advertising, mobile marketing, consumer decision-making in e-commerce, online interaction, and buying behavior (Tucker, 2014; Wang et al., 2016; Xu et al., 2011; Yang Kang, 2015).

The relationship between personalization in online marketing and information privacy demonstrated as a paradox in previous studies (Aguirre et al., 2015; Karwatzki et al., 2017; Lee & Rha, 2016). In this paper, consumers' attitudes toward information privacy and risk were measured to understand the intention to receive personalized marketing. The consumers' intention is mentioned as the willingness to be targeted by personalized advertising. A multiplicity of factors influences the consumers' desire to share their personal information and willingness to be targeted by online personalized advertising. In connection with the purpose of this study, it would be imperative to investigate the impact of information privacy concerns, perceived risk, and benefit on the willingness of consumers to be targeted by personalized advertising.

2.4 Hofstede Cultural Dimensions

Culture as a whole refers to the collection of beliefs, norms, and values of people in a society. In general, the way people live (Thompson et al., 2018). Culture is a complex concept and affects an individual's attitude and behavior (Al Kailani & Kumar, 2011; Soares et al., 2007). To be able to examine cultural differences, Hofstede's theory of cultural dimensions was chosen by the authors in this study.

Hofstede's cultural model was chosen in this study as it is widely used in cross-cultural studies in various topics (Al Kailani & Kumar, 2011; Guo, 2008; Lowry et al., 2011; Gupta & Shukla, 2019; Tsoi & Chen, 2011; Zheng, 2013), in comparative, cross-cultural studies as a framework to formulate hypotheses. Likewise, applied as a model in cross-cultural studies in marketing and advertising researches (De Mooij, 2015; Soares et al., 2007; Yang & Kang, 2015).

Hofstede's cultural dimensions were measured in this study, to understand whether the online users' attitudes toward information privacy concerns, perceived risk and willingness to be targeted by

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personalized advertising differ among various cultures. Hofstede (1991) defines culture as the mind program, which distinguishes the members of one group from the other. This programming starts in peoples' childhood and will be difficult to change or erase it later on. This mental programming is based on the social environment in which people grew up and is a collection of life experiences. Collective programming starts with data collecting from family life and continues in the society like school, workplace, and different communities' people are a member of (Hofstede, 1991).

The cultural dimension is described as an aspect of the culture, and it can be measured in comparison to other cultures (Hofstede, 2001). Between 1967 and 1973, Hofstede developed four dimensions of his theory. The theory ranks cultures on four spectrums: Power Distance (PDI), Individualism (IDV), Masculinity (MAS), Uncertainty Avoidance (UAI) (Hofstede, 1991). In 1988, his new studies led to identifying the fifth dimension: Long - Term Orientation (LTO) (Hofstede, 1991).

As it was mentioned in the introduction, two dimensions, Individualism-Collectivism, and Uncertainty Avoidance were used in different studies concerning privacy issues and appeared to be the most relevant dimensions to this study (Atorough & Donaldson, 2012; Ko et al., 2004; Lowry et al., 201; Noort et al., 2008; Zheng, 2017 ).

2.4.1 Individualism (IDV) vs Collectivism (COL)

Individualism is based on being self-oriented and centered. In individualistic cultures, people prefer to differentiate their values from a group. Decisions are made more individually; personal freedom and adventure are more important than group achievements. (Hofstede, 2001). Individualistic societies enhance and support individual success. People within this society work hard towards establishing their goals at a personal level (Miltgen & Peyrat, 2014).

Vice versa, in collectivistic societies, people are more integrated into groups, bound more to each other, and follow the group goals and the best for the community. Collectivism focuses on group values and relationships and intends to give away some personal benefits instead of group loyalty, while individualism is centralized on individual interests (Hofstede, 2001).

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2.4.2 Uncertainty Avoidance (UAI)

According to Hofstede (2011), Uncertainty avoidance is mentioned as one of the national cultural dimensions and describes how people in a particular culture feel endangered in unknown situations. It expresses the level of tolerance in the society facing a novel, unusual event. The cultures with high uncertainty avoidance (HUA), members are mostly resistant to change and uncomfortable in unknown situations. In countries with low uncertainty avoidance (LUA), members are assumed to take more risk, tolerate the changes, and adjust to the novel situations easier (Hofstede, 2001).

2.4.3 Cultural Differentiation

Figure 1 mentions the various levels of individualism/ collectivism and uncertainty avoidance among three distinct cultures (Iran, Saudi Arabia and Sweden) chosen for this study.

Figure 1: Culture Dimensions

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2.5 Privacy Calculus

2.5.1 Information Privacy Concerns

Definitions of privacy vary in different fields. In this study, information privacy is defined as the ability to control the collection and utilization (Culnan, 1993; Westin, 1968). The data collection becomes consumers’ concern when the personal data and information are collected and added to databases (Dinev et al., 2013). Information privacy concern defines individuals' attitudes toward the collection and use of their information by organizations (Dinev et al., 2013).

Online information privacy concern is conceptualized as consumers’ concern in relation to their online personal information collection and the use of their data for different purposes. Studies showed a rise of privacy concerns among online users regarding unauthorized secondary use of their data, losing control over their personal information and tracking their online activities (Evens & Damme, 2016; Malhotra et al., 2004; Yang, 2012). As technology accelerates, companies gather more information about their online users (Koohikamali et al., 2017). In addition, privacy concerns in e-commerce seem to be one of the most critical discussions in today’s society (Liao et al., 2011).

2.5.1.1 Privacy Concerns and Willingness to be Targeted by Personalized Marketing

Liao et al. (2011) examined the impact of privacy concerns on individuals’ behavior and intentions. The results showed that the level of privacy concern affects consumers’ online intention (Liao et al., 2011). According to Smith et al. (2011), there is a direct link between individuals’ privacy concerns and online behavior.

Evens & Damme (2016) suggested the relationship between control over personal data and the willingness to share information. Consumers with a higher ability to protect their data and privacy were up to 52% more willing to share their information than others (Evens & Damme, 2016). Awad & Krishnan (2006) mentioned a majority of previous studies that investigated the individuals’ willingness to share their information. They aimed to examine users’ willingness to partake in online personalization. Awad & Krishnan (2006) concluded that the more value the

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information privacy has for the users, the less they are willing to be profiled online. Concerning the previous studies, we aim to investigate the impact of online information privacy concerns on consumers’ willingness to be targeted by personalized online advertising. Thus, we hypothesize that:

H1: Information privacy concern has a negative impact on consumer willingness to be targeted by personalized online advertising.

level of privacy concerns in individuals has been shown to impact their decision making (Awad & Krishnan, 2006; Culnan & Armstrong, 1999; Dinev & Hart, 2006; Dinev & Hart, 2013; Malhotra et al., 2004). This determination considers the view of privacy as a complex. The notion of privacy risk mentioned being an essential factor of privacy: Furthermore, the calculus framework of privacy was developed based on the risk related to privacy (e.g., Burster et al., 2017; Dinev et al., 2006; Xu et al., 2011). Indivituals tend to manage their privacy while communicating, the perceived risks and benefits of information disclosure play a significant role in conducting the privacy-related behavior in individuals (Masur & Scharkow, 2016). The “privacy calculus” model indicates that online users’ information disclosures and intention to interact are consequences of evaluating the risks of their disclosures with the satisfaction Benefits (Culnan & Armstrong, 1999).

2.5.2 Privacy Calculus Theory

The Privacy Calculus theory was founded by Laufer & Wolfe (1977) and defined as behavior calculation, which assumes that individuals are thinking about their behavior consequences (Smith et al., 2011). The theory measures the related cost and benefits of a behavior and does a trade-off between cost and benefit. Culnan and Armstrong (1999), as well as Dinev and Hart (2006) applied the calculus model and built a framework to study online interactions on the Internet. They investigated the acceptance of personal data utilization for targeted advertising (Culnan & Armstrong, 1999) and online shopping (Dinev & Hart, 2006). The privacy calculus has been applied to understand consumers’ decision-making process different contexts (Chang & Heo, 2014; Chao et al., 2013; Krasnova & Veltri, 2010; Min & Kim, 2015; Shiau & Luo, 2012; Shibchurn & Yan, 2015; Wang et al., 2016).

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In this paper, risk and benefit are mentioned as disadvantages and advantages the consumer receives by being targeted by online personalized advertising. However, privacy calculus is popular because of its simple cost-and-benefit evaluation; the concept has been criticized because of failure in rationality (Chang & Heo, 2014; Min & Kim, 201); Individuals may not be able to estimate all the risks and benefits. To avoid such a failure, we specified risk and benefits in the context of our study:

2.5.2.1 Perceived Risks of Information Disclosure

Risk has been shown to operate as privacy-related beliefs to the potential consequences of information disclosure. Thus, based on the literature, we identify the perceived risk as a significant factor that directly connected to information privacy concerns. Perceived risk affects a decision-making process when individuals sense uncertainty (Dinev et al., 2006). Risk defined as the uncertain and unwanted outcomes of behavior (Kesharwani et al., 2012), which may cause a loss in different situations (Crespo et al., 2009). In General, perceived risk is described based on two elements, uncertainty and consequences (Zheng, 2017). In this study, we look at these uncertainties and implications concerning the utilization of online users' personal information in personalized marketing. Perceived risk, in this paper, is defined with three dimensions; the threats individuals face related to the use of their personal information online, the threat of unauthorized access to personal data, and sharing information with third parties. (Xu et al., 2011).

2.5.2.1.1 Perceived Risk and Willingness to be Targeted by Personalized Marketing

Online users experience threats in their online interaction because of their information utilization on the Internet (Kesharwani et al., 2012). Since individuals mostly show the motivation to minimize risk consequences, failure, and loss in Internet interaction, the perceived risk is mentioned as one of the indicators in explaining consumer online behavior (Zheng, 2017). It affects behavior aspects such as decision-making, evaluation, and response to online marketing messages (Atorough & Donaldson, 2012). The level of the perceived risk by consumers influences their decision-making processes (Steinhart et al., 2013), and plays an essential role in online transactions (Kesharwani et al., 2012).

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Steinhart et al. (2013) confirmed the influence of perceived risk on consumers' attitudes and intentions. Kesharwani et al. (2012) argued that the users' fears based on their information risks, negatively affect their online transaction intentions. Besides, the reduction of perceived risk positively influences the willingness to interact (Kesharwani et al., 2012). According to these shreds of evidence, the following hypothesis is developed:

H2: The perceived risk of information disclosure has a negative impact on consumer willingness to be targeted by personalized advertising.

2.5.2.1.2 Perceived Benefits of Information Disclosure

Perceived benefits of personalization are expected to have a positive influence on the intention to disclose personal information (Wang et al., 2016). Personalized advertising offers benefits by sending relevant information or services to users based on their activity background and preferences (Lambrecht & Tucker, 2013). These advertisements are designed based on users’ data (Lambrecht & Tucker, 2013), and planned to send the offers to consumers at the right time when it is needed (Xu et al., 2011). Receiving relevant information is a crucial advantage to convince consumers to exchange their personal information for personalized advertisement (Zhu et al., 2017).

2.5.2.1.3 Perceived Benefit and Willingness to be Targeted by Personalized Marketing As it was mentioned, personalized advertisement is tailored to consumers’ activity background, needs, and interests (Lambrecht & Tucker, 2013). Therefore, it may motivate consumers to disclose their personal information in exchange for access to personalized ads (Chen et al., 2018). Indeed, personalization was mentioned as a benefit by other studies (Awad & Krishnan, 2006; Xu et al., 2011; Wang et al., 2016; Chen et al., 2018). In this study, customers’ perceived benefits are defined as the advantages individuals gain by receiving more relevant information, offering the right product/ service at the right time, and searching time-saving for consumers, as it was mentioned by Doorn & Hoekstra (2013).

To summarize, personalization is based on information disclosure (Wang et al., 2016). Individuals are likely to give up a degree of privacy in return for potential benefits concerning personalization (Xu et al., 2011). Benefits are expected to be positively correlated to intention and provide

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direction through motivating users to engage in the target advertisement (Awad & Krishnan, 2006). Thus, we hypothesize:

H3: Perceived benefits are positively related to consumers’ willingness to be targeted by personalized advertising

2.6 The Cross-Cultural Perspective on Privacy Calculus

The right to privacy is considered as a core value for individuals, and the need for privacy is universal; however, it needs to be understood in the context of culture and may differ among cultures (Neuliep, 2017). Nevertheless, privacy seems to have a vital role in social behavior acceptance. Understanding privacy preferences in various cultures helps to understand when and where personalized marketing can be useful as a relationship marketing strategy (Neuliep, 2017). Hence, Hofstede’s (1991) defined that culture is a critical issue to consider for analyzing privacy-related behavior.

As it was mentioned above, Hofstede’s dimensions have been widely used in multicultural studies. We focus on two dimensions of individualism/ collectivism and uncertainty avoidance. These dimensions are particularly relevant to online interactions and the privacy calculus (Krasnova & Veltri, 2010; Lowry et al., 2011).

2.6.1 Individualism/Collectivism and Privacy Concerns

The relationship between individualism and privacy concerns was studied in different researches. However, the results were conflicting across studies. Both negative and positive correlations between collectivism and privacy concern were concluded in two various studies mentioned by Lowry et al. (2011). Gheorghiu et al. (2009) also found a negative relationship between individualism and privacy concerns. In contrast, Krasnova & Veltri (2010) suggest a positive relationship, and a nonsignificant result was found by Cao & Everar (2008).

According to Yang & Kang (2015), individualism had more tendency to control personal information. Xu-Priour et al. (2014) argued that collectivists are more interested in sharing information; the stronger desire for sharing information in collectivism was also confirmed by Madupu & Cooley (2010). Based on the aforementioned, privacy concern in relation to IND-COL

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differs among cultures, as collectivist cultures are more intend to share information and individualism intend to have more control over their information, we developed hypothesis 4 as:

H4: Collectivism is negatively related to information privacy concerns in online consumers.

2.6.2 Individualism/Collectivism and Perceived Risk

According to Hofstede (1984), risk-related issues are culturally sensitive. Cultural dimensions present a valid theoretical foundation to understand perceived risk because culture not only has an effect on an individual's reaction to risk but also how they perceive and estimate it (Zheng, 2017). Krasnova and Veltri (2010) argued that people in individualistic cultures are more likely to avoid the risks related to their information than those from collectivists. Zheng (2017) concluded that individualism cultures perceived a higher level of online risk in comparison to collectivism. Due to what was mentioned above, we hypothesize that:

H5: Collectivism is negatively related to consumers’ perceived risk of online information disclosure.

2.6.3 Uncertainty Avoidance and Privacy Concerns

In societies with a LUA, trust develops among members more quickly. Consumers from HUA cultures experience more fear of mishandling their privacy in online interactions (Hwang & Lee, 2012). HUA cultures demand more security in their lives, whereas LUA cultures search for more adventure (Lowry et al., 2011). Lowry et al. (2011) mentioned two different studies that hypothesized the positive effect of uncertainty avoidance on information privacy concerns. Negative affect was concluded by one of the studies, and the result in the other one was nonsignificant. Furthermore, Cao & Everar (2008) and Al Kailani & Kumar (2011) found a positive effect of uncertainty avoidance on information privacy concerns. We agree with Cao & Everard (2008) and Lowry et al. (2011), and thus, we propose that higher uncertainty avoidance scores will positively increase information privacy concerns.

H6: High level of uncertainty avoidance positively influences consumers’ information privacy concerns.

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2.6.4 Uncertainty Avoidance and Perceived Risk

De Mooij (2015) argued that uncertainty avoidance was hypothesized as risk avoidance in some researches, and the result did not support the hypothesis. Risk avoidance is not the same as the uncertainty avoidance dimension. However, the result in some studies confirmed the relation between risk perception and high uncertainty avoidance (De Mooij, 2015). Al Kailani & Kumar (2011) illustrate the desire in HUA members to minimize risk in online activities, vice versa, LUA online consumers have a lower degree of perceived risk (Al Kailani & Kumar, 2011).

Hypothesis 7 was developed to look specifically into consumers’ attitude toward online perceived risk:

H7: High level of uncertainty avoidance positively influences consumers’ perceived risk concerning the utilize of their information.

2.7 Culture and willingness

Zheng (2017) argued that the impact of perceived risk on online buying intention differs in individualistic and collectivistic cultures. The results of the study support that individuals who face a lower level of perceived risk have a higher intention for online purchasing.

Zheng (2017) argued the effect of culture on online communication. The impact of national culture on individuals’ intentions, attitudes, and behavior was also considered by Hofstede (2001). He stated different values, attitudes, and preferences in individuals from different cultures.

In collectivistic societies, members maintain strong interdependence; collectivists are more open to sharing their information. Vice versa, individualists intend more to control their personal data. As it was mentioned Hofstede (2001), in cultures with a high level of uncertainty avoidance, members usually feel intimidated by unknown situations and try to avoid uncertainty.

To study the impact of culture on consumers’ willingness to be targeted by personalized advertising and if privacy concerns and perceived risk has any mediating effect on this relationship, we developed hypotheses 8,9 10 and 11 as:

H 8: Collectivism has a positive impact on consumers willing to be targeted by personalized advertising.

H 9: Privacy concerns moderating the effect of collectivism on willingness. H 10: Perceived risk moderating the effect of collectivism on willingness.

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H 11: High level of uncertainty avoidance negatively affects consumers willing to be targeted by personalized advertising.

H12: Privacy concerns moderating the impact of high level of uncertainty avoidance on willingness.

H 13: Perceived risk moderating the impact of high level of uncertainty avoidance on willingness.

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2.8 Framework for this Project

Direct effect Indirect effect (mediator)

Figure 2: Proposed framework of factors in willingness to be targeted in online advertising Inspired by (Burster et al., 2017; Dinev et al., 2006; Xu et al., 2011)

The proposed model presents thirteen hypotheses (H1 to H13). The hypotheses describe the impact of two intervening variables (privacy concern, perceived risk), independent variable (benefit) on the dependent variable (willingness to be targeted by personalized advertising), and two independent variables on the mediating variables (Privacy concern and perceived risk). The two independent variables, individualism and uncertainty avoidance, represent cultural dimensions (Hofstede, 2011) of online consumer.

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Table 1: Summary of hypotheses

RQ1

H1: Information privacy concern has a negative impact on consumer willingness to be targeted by personalized online advertising.

H2: The perceived risk of information disclosure has a negative impact in consumer willingness to be targeted by personalized advertising.

H3: Perceived benefits are positively related to consumers’ willingness to be targeted by personalized advertising

H4: Collectivism is negatively related to information privacy concerns in online consumers.

H5: Collectivist is negatively related to consumers’ perceived risk of online information disclosure.

RQ2 H6: High level of uncertainty avoidance positively influences consumers’ information privacy concern.

H7: High level of uncertainty avoidance positively influences consumers’ perceived risk concerning the utilize of their information.

RQ3

H 8: Collectivism has a positive impact on consumers willing to be targeted by personalized advertising.

H 9: Privacy concerns moderating the effect of collectivism on willingness.

H10: Perceived risk moderating the effect of collectivism on willingness.

H 11: High level of uncertainty avoidance negatively affects consumers willing to be targeted by personalized advertising.

H12: Privacy concerns moderating the impact of high level of uncertainty avoidance on willingness.

H13: Perceived risk moderating the impact of high level of uncertainty avoidance on willingness.

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3. Methodology

This chapter presents the research method and approach. It renders the general structured guideline to how the research is approached in different parts such as the operational definition, data collection, sampling population, quality, and ethical considerations.

3.1 Research Method and Approach

One of the essential points in the business dissertation design is choosing the procedure of the research. Whether it's qualitative, quantitative, or mixed-method, it will have an impact on the choice of data collection and analysis (Bryman & Bell, 2015). This study aims to test existing hypotheses regarding the relationship between different variables. Therefore, the quantitative method will be the most applicable approach of this study (Bryman & Bell, 2015). The quantitative method requires measuring the concept from current theories and understanding the relationship between variables in order to confirm or refuse suggested hypotheses based on findings (Bryman & Bell, 2015).

There are two different research strategies, inductive and deductive. The deductive approach is an explanation from a more general to a more specific perspective (Bryman & Bell, 2015). In this study, the deductive approach is used due to understanding the relationship between online users' willingness to be targeted by personalized advertising, online information privacy concerns, perceived risk, and benefits. The purpose of using this approach is to gather quantitative data and analyze the relationship between the variables (Saunders et al., 2009). The previous work of other researchers in a specific related area was studied, and existing theories gathered. The research questions and hypotheses developed based on the literature review and selected theories by the authors (Saunders et al., 2009).

Furthermore, a descriptive study has been conducted to investigate the relationship between cultural dimension independent variables (Individualism/ collectivism, uncertainty avoidance) and the dependent variables (privacy concerns and perceived risk) concerning Hofstede's cultural dimensions theory. Furthermore, to examine the relationship between perceived benefit, privacy

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concerns, and perceived risk as intertwining variables and willingness as the dependent variable. The exchange and calculus theory were also applied in this study to understand and conduct a value comparison between the related hypotheses.

3.2 Data Collection and Questionnaire Design

The primary data is collected from an online survey questionnaire, which was designed to examine the online user's opinion, attitude, and intention related to several factors that are assumed to have a significant impact on the willingness to be targeted by personalized advertising. Furthermore, the literature review was collected from published scientific articles, journals, and books in Google scholars, Mälardalern's online library, and print form to support the study by providing relevant information. The specific search terms and phrases were used by the authors to collect relevant information; personalized advertising, privacy concerns, willingness to be targeted by online advertisement, perceived risk, perceived benefits, collectivism- individualism, uncertainty avoidance, and calculus theory.

A questionnaire is developed based on the relevant literature review to collect data and test the hypotheses of this study. It was constructed in Google Form and written in English language. The questions contained simple words and phrases to give a better understanding of the meaning to respondents. The survey contains an introduction to ensure the participants have a clear picture of personalized online advertising. During the two weeks, from May first to fifteen, the survey was sent out to people in personal messages via mobile phone, emails, and messenger. Besides, it was shared consistently on social media platforms (WhatsApp, Facebook, LinkedIn, Instagram). Digital communication was chosen to collect data for this study due to the current situation of Covid19 and the importance of social distancing. Social media platforms made it easy for us to get access to people and gather higher numbers of data in such a short period.

The questionnaire consists of two parts; the first part provides general information about the respondents. Questions related to participants' demographics such as country of birth, current country of residence, and age. The second part of the questionnaire designed to measure the dependent and independent variables. The questionnaires are designed to evaluate participants' opinions about the variables, (PartI) Individualism/collectivism, (PartII) Uncertainty avoidance,

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(Part III) Privacy concerns, (PartIV) Perceived risk, (PartV) Perceived benefits, and (PartVI) Willingness to be targeted by personalized advertising (see table A10).

The survey contained a total of 22 questions; 3 to 4 questions measure each variable. A 5-point Likert scale is preferred over 7-point, as the 5-point scale is considered more relevant to improve response rates and quality by decreasing the difficulty level of the respondents (Bryman & Bell, 2015). The questions and scale items were developed based on previous studies. Firstly, 5- point scale used to estimate two opposite opinions (individualism versus collectivism, and high and low level of uncertainty avoidance); this scale helps to identify the respondent culture

dimensions. The scale and questions for measuring the cultural variables were adopted from Leonidou et al., 2013; Lowry et al., 2011; Minkov, 2013; Thien et al., 2014. Secondly, a five-point Likert scale form of (1=strongly disagree, 2=disagree, 3=neither disagree nor agree, 4=agree, 5=strongly agree) is used to empower the participants to express their level of agreeing or disagreeing with each particular variable concerning privacy, risk, benefits, and willingness. All the questions and Likert scale in this part were inspired by relevant previous studies and adopted from their survey questionnaires (Awad& Krishnan, 2006; Dinev et al., 2006; Dinev et al., 2013; Xu et al., 2009; Xu et al., 2011) (see table 2).

3.3 Sample Approach

In this study, the target population is defined as online users from three different countries (Iran, Saudi Arabia, Sweden). According to Hofstede (2011), cultural dimensions occur in each community. As this study performs a quantitative cross-cultural approach and Hofstede's cultural dimensions are valid on a national level (Hofstede, 2011), the data was collected from these three different cultures. Based on collected data, three distinct cultures will be categorized into various chosen cultural dimensions (Individualism/collectivism and uncertainty avoidance) to recommend analysis, develop measurements through numbers and statistics, and test the hypotheses (see Figure 2). Iran, Saudi Arabia were chosen as they are the authors' countries of birth, and Sweden is their current country of residence. Being connected to the selected countries makes it easier to collect data for this study. Besides, the different levels of cultural dimensions among the three countries make it possible to do cross-cultural research among participants.

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Non-probability sampling is defined as a technique in which the samples are selected based on the personal evaluation of the researcher rather than random selection (Saunders et al., 2009). In this study, data collected with the non-probability method, participants are not chosen randomly, and there is not equal opportunity for all the people in the target population to participate in the data collection process. Non-probability sampling is used in this paper, as it is quantitative research, and due to time limitation, it was impossible to include a random possibility for the target population (Saunders et al., 2009).

Furthermore, the convenience sampling method, as a non-probability sampling technique, was chosen. The data was collected from the population who were easily available to participate in the data collecting process (Bryman & Bell, 2015). The data is more satisfying if it is collected from the entire sample population. However, this research measures the variables at a national level, and the culture is too large to consider the whole population (Saunders et al., 2009).

The survey questionnaire was shared across several social media platforms. Participants included friends, family, colleges, and other connections, who were available to participate and answer the questions. However, there are no clear instructions regarding what makes a sample statistically justified. Thus, the greater the sample size is, the more it may generalize the population (Bryman & Bell, 2015). During the analysis, all the participants who did not answer all the questions or were other cultures than selected ones will be rejected for the study.

3.4 Operationalization of Research Questions

Operationalization notes to the process of determining variables into measurable factors. The concepts are described and measured empirically and quantitatively in the process (Biggeri & Libanora, 2011). Based on the literature review, a theoretical framework was identified, and the relevant relationships between variables were considered for this research. Correlations between variables are stated as hypotheses in the conceptual framework (see figure 2). The variables were adopted from previous studies, as choosing the measurable variables from prior relevant research increases the level of reliability (Bryman & Bell, 2015). The survey questions are adopted from previous studies that examined the same variables in various topics. The intertwining variables in

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this research are privacy concerns, perceived risk, and perceived benefit. The primary dependent variable is the willingness to be targeted by online personalized advertising. Two of Hofstede’s cultural dimensions, individualism/collectivism, and uncertainty avoidance are included in this research, as the independent variables related to privacy concerns and perceived risk. All the information is provided in Table 2.

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Table 2: Operationalization for Research Questions.

Literature review

Research Question Research Objective Theory Survey questions

References

Willingness to be targeted online

1.To what extent does culture impact willingness to be targeted by personalized advertising?

To determine the impact of culture on willingness to be targeted by personalized online advertising. Privacy Calculus And Hofstede Cultural Dimension s Q (16,1718) Adopted from: Awad& Krishnan, 2006) Information privacy concern

1. What is the impact of privacy concern and perceived risk versus perceived benefits on willingness to be targeted by

personalized online marketing?

To determine the impact of privacy concern on willingness to be targeted by personalized online advertising. Privacy Calculus Q (8,9,10,11) Adopted from: (Dinev et al., 2013) (Dinev et al., 2006) Awad& Krishnan, 2006) Risk perception

To determine the impact of perceived risk on willingness to be targeted by personalized online advertising. Q (12,13,14,15 ) Adopted from: (Xu et al., 2009) (Xu et al., 2011) (Dinev et al., 2013) (Dinev et al., 2006)

Benefits To determine the impact

of perceived benefits on willingness to be targeted by personalized online advertising. Q (19,20,21,2 2) Adopted from: (Xu et al., 2011) (Xu et al., 2009) (Dinev et al., 2013) (Dinev et al., 2006 Individualism vs

Collectivism How culture

influences privacy concerns and risk perception?

To determine whether culture has an effect on consumer’s privacy concern and risk perception. Hofstede Cultural Dimension s Q (4,5,6,7) Adopted from: (Leonidou et al, 2013). (Lowry et al., 2011) (Thien et al., 2014) (Minkov, 2013) Uncertainty

Avoidance How does privacy and

risk mediating this effect? Q (1,2,3) Adopted from: (Lowry et al., 2011) (Thien et al., 2014) (Minkov, 2013)

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3.5 Data Analysis Method

To examine the relationship between the variables, the collected data were analyzed with SPSS statistical software. Statistical analysis is used to measure and analyze the variables' impacts on each other. The collected data is converted and numerically coded into a data file in the SPSS software. The responses were coded as (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree (Saunders et al., 2009) for answers related to privacy, risk, benefit, and willingness.

Concerning cultural dimensions, questions related to collectivism/ individualism were coded as (1&2) collectivist, (3) neutral, (4&5) individualist, and uncertainty avoidance (1&2) low level of uncertainty avoidance, (3) neutral, and (4&5) high level of uncertainty avoidance. Spearman correlation analysis is used to test and analyze the hypotheses (H1 to H6).

The analysis is categorized into two stages. The first stage estimates the impact of the independent variables, individualism/collectivism, and uncertainty avoidance on information privacy concerns and perceived risk, in this case, privacy concerns and perceived risk are mentioned as the dependent variables. In the second stage, the effect of information privacy concerns, perceived risk, and benefit as intertwining variables on the dependent variable willingness are studied.

3.5.1 Descriptive Statistics and Correlation Analysis

A descriptive statistic is used to explain the essential data found in this study (Bryman & Bell, 2015). A simple data summary in the form of tables or graphic analysis is provided to describe the general data. The respondents were categorized based on the demographic findings: age, gender, and nationality to observe the numbers of respondents belonging to different generations and countries.

The results of the respondents' opinions about the dependent and independent variables were utilized to find the relationship between variables: (Individualism-Privacy concerns (PC), Individualism-Perceived risk (PER)), (Uncertainty avoidance-Privacy concerns, Uncertainty

avoidance-Perceived risk), (Privacy concern-Willingness), (Perceived risk-Willingness), (Perceived benefits (BEN)- Willingness (WIL)). The correlation coefficient applied to measure

the strength of the relationship between the variables. Spearman Correlations is considered in this research because it helps to investigate and measure the strength and direction between two ranked

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variables, to find out whether there is a relationship between the variables or not. If the correlation value is - 1.0 ≤ rs ≤ 1.0, the validity of the result is confirmed, (–1 and 1) in this domain means an absolute correlation; the higher the correlation between the variables, the greater the predictive validity is (Bryman & Bell, 2015). A correlation of less than rs ≤ 0.3 is stated as small, between 0.3 ≤ rs ≤ 0.5 medium and one above 0.5 is reported large. The findings greater than 1.0 or less than -1.0 shows an error in the measurement (Saunders et al., 2009).

3.5.2 Regression Analysis

Following on correlation, regression analysis was performed in this study in order to achieve more details in connection to interrelationships' results between the variables. Regression is a numerical value that describes relationships between variables and how an independent variable impacts a dependent variable (Bryman & Bell, 2015). Multiple linear regression analysis is used in this study to estimate the effect and calculate the dependent variable changes with the changes in the independent variable (Saunders et al., 2009).

The multiple linear regression analysis was utilized to run a significant test, in the significance levels equal to 0.01, 0.05. If the p-value is less than the significant level, then there is a relationship between the variables. This regression provides a correlation between the independent variables and the variance in the dependent variable. (Saunders et al., 2009). The multiple linear regression examined the nature of the relationship among different variables in order to argue the conceptual model. There are seven hypotheses in the conceptual model. With multiple linear regression, we examine the variance in the dependent variable willingness to be targeted by personalized advertising, when the three independent variables (PC, PER, BEN) change.

3.6 Quality Criteria

Validity and reliability are essential assessment principles for proving the findings in quantitative research (Bryman & Bell, 2015). Where the validity “is concerned with whether the findings are really about what they appear to be about” (Saunders et al., 2009, p. 157), and reliability is “The degree to which an instrument will produce similar results at a different period” (Gray, 2017, p. 780).

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3.6.1 Validity

Validity refers to the results of the study being valid and accurate; It measures if the results of the study are truthful. Without it, the results could be erroneous (Bryman & Bell, 2015). It means whether the results meet all that required in a scientific research method, and it is mandatory for all kinds of studies (Saunders et al., 2009). Malhotra and Grover (1998, p. 408) stated that “validated instruments of both independent and dependent variables can alleviate confounding effects in determining the true relationship among variables”. It is a compulsory requirement for all types of studies (Bryman & Bell, 2015).

Predictive validity is often used to evaluate research, and it is suitable for testing behaviors. It was chosen for this study to measure different dimensions among individuals, which affect their reference to a future behavioral decision (Agresti, 2013). To test what mostly affects individuals’ willingness to be targeted by personal advertising.

3.6.2 Reliability

The reliability measures the stability, consistency, and trustworthiness of the obtained result in a research study (Bryman & Bell, 2015; Gray, 2017). It measures the consistency of the relationship between the variables in the research (Saunders et al., 2009). Cronbach’s alpha test was applied in this study to measure the scale of reliability. Cronbach’s alpha measures internal consistency and shows how close the variables are related to each other (Bryman & Bell, 2015). To support the internal consistency in this study, explicitly, the seven hypotheses in the conceptual model, acceptable α value is required. Acceptable reliability value (α) is 0.70 or above, which means if α ≥ 0.7, the results are reliable (Saunders et al., 2009).

3.7 Research Limitations

The quantitative method, which is utilized in this study, includes limitations. Understanding the individual’s emotional reactions to the questions is not possible in a quantitative approach (Agresti, 2013). In this topic, individuals’ previous experience can affect the level of awareness regarding the use of their data disclosure and willingness to be targeted online. Relying exclusively on the answers in the survey could be a limitation in this study. In addition, quantitative studies that

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collect data through an online-questionnaires may contain more errors as respondents have full control over and can decide not to answer some questions. Besides, respondents do not have the possibility to ask questions, and this may affect their comprehensive understanding of the questions (Saunders et al., 2009). Furthermore, by using snowball sampling, there is a potential for a high similarity between respondents as people are sharing the questionnaires with their network (Bryman & Bell, 2015).

A probable limitation within this study is its singular perspective; the research has specifically assessed advertising and privacy concerns within the confines of a virtual environment. As such, key findings herein have narrower application-only to online users. Furthermore, due to the time constraint, some dimensions mentioned in the literature review, such as three of the Hofstede culture dimensions, were not included in the study, consequently limiting the content scope. Additionally, other studies have shown that sensitivity to privacy concerns is inclined to brand strength (Myerscough et al., 2006.) The quantitative method is chosen for this study regarding the current situation with COVID 19 virus and social distancing regulation. In a normal situation, a mixed-method could be applied to have more reliable results for this topic.

3.8 Ethical Considerations

Over the past decades, the importance of ethics in research grew importantly (Saunders et al., 2009). Since this research includes human participation, it is important to mention ethical considerations, mostly concerning participants' information (Saunders et al., 2009).

3.8.1 Ethical Expectations

The increasing accountability required of research activity has been overwhelming (Haggerty, 2004). In particular, some learning institutions do not guarantee research related permission without proper ethical compliance (to collect personal information). Human Research Ethics Regulations are spelled out by the Committee of Publishing Ethics (CoPE). These guidelines emphasize the importance of the ethical dimension of research. Moreover, the literature of Lune (2017) and Denzin & Lincoln (2011) were duly considered in designing a comprehensive ethical model for this research.