An empirical study on consumer adoption of the mobile wallet : adding innovation diffusion theory to the extended technology acceptance model with consumer trust.

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MASTER THESIS

A

n Empirical Study on Consumer Adoption of the Mobile Wallet:

Adding Innovation Diffusion Theory to the Extended Technology Acceptance Model

with Consumer Trust

University of Amsterdam Faculty of Economics and Business

MSc. In Business Administration Track: Digital Business Under supervision of: F. Javier Sese

By:

Student: Wai Lon Tai Student Number: 10001311

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Statement of Originality

This document is written by Student Wai Lon Tai who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and no sources other than those mentioned in the test and the references have been used.

The faculty of Economics and Business is only responsible for the supervision of completion of this work, not for the content.

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Acknowledgement

T

his thesis is the final step of my master’s degree curriculum in Digital Business at the Amsterdam Business School. The process of writing this thesis served as a valuable lesson as well as providing precious insights about myself. By conducting this study at such scale, I have discovered strengths and weaknesses in various areas of my life. Strengths such as balancing private and working time, discipline and hard work may receive my pride. However, weaknesses such as time-underestimation and several research skills areas could still be improved. Generally, it has been an interesting and pleasant experience in which I have gained more than in any course of this master’s program.

Taking this opportunity, I would like to thank my supervisor Javier Sese who guided me through this process with his passion for research, expertise, constructive criticism, support, and effort in writing this master thesis. Also, I would like to thank my friends and family for helping me to collect data as well as supporting me during this period.

I hope you will enjoy reading this thesis, and I hope my efforts will help the mobile wallet to move a small step ahead.

Kind regards,

Wai Lon Tai 22nd_{of June 2018}

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Abstract

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he mobile payments market is expected to proliferate in the very near future. Acting upon this development, technology giants such as Apple, Samsung and Google invested a significant amount of resources and developed the mobile wallet to reap the benefits of the market’s growing revenue potential. However, the mobile wallet adoption process has still been laborious in the Western countries in which it is introduced. Most existing studies investigated the factors of traditional mobile payments adoption. Due to the mobile wallet’s unique context, these insights cannot be applied with certainty as well as accuracy. Therefore, it is still not clear which specific factors and at what impact influence mobile wallet adoption. Moreover, in the era of data breaches, consumer trust in new technologies is more affected than ever before. Therefore, due to this topical relevance, it is a necessity to understand this aspect in more detail in the initial stage of adoption of the mobile wallet. More importantly, intergroup behaviors in technology adoption based on consumers’ innovativeness is highly discussed in academia. However, little studies in the traditional mobile payments domain have actually researched this phenomenon on group-specific factors.

Targeting on this gap, this study examined the intention of adopting the mobile wallet with an extended technology acceptance model including consumer trust and investigated the effects of four potential factors (perceived privacy and security, brand reputation, prior experience and social influence) on consumer trust. Furthermore, this study classified consumers into two categories (high and low innovative individuals) and examined the group-specific effects of personal innovativeness on consumers’ intention of adopting the mobile wallet.

Survey data from 264 consumers have been obtained and processed through preliminary analyses and structural equation modeling tests. Test results revealed that positive attitude towards the mobile wallet has the largest effect to increase usage intentions. This effect appeared to be more prevalent for low innovative individuals than high innovative individuals. Next, perceived ease of use appeared to increase perceived usefulness. Subsequently, perceived usefulness increased a positive attitude and usage intention. No evidence was found for the effect of perceived ease of use on attitude. With respect to the extended variables, consumer trust revealed only to increase a positive attitude and not usage intention, which appeared to have a larger effect for highly innovative individuals. Regarding the drivers of trust, perceived privacy and security, and prior experience appeared to be positively affecting to consumer trust for both low and high innovative individuals. However, evidence for brand reputation and social influence was found to increase high innovative individuals’ trust, and not for their counterpart. Based on these findings, academical and managerial implications are presented as well as several suggestions for future directions are proposed.

Keywords: Technology acceptance model (TAM), innovation diffusion theory (IDT), perceived privacy and risk, consumer trust, social influence, prior experience, mobile wallet, digital wallet, smartphone users, point of sale (POS) / P2P payment, mobile payments.

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List of Tables

§ Table 1. Key differences between Mobile Wallet and Traditional Mobile Payments………. 16

§ Table 2. Measurement Items……… 39

§ Table 3. Demographic profile of respondents………... 43

§ Table 4. Descriptive and correlation between the variables……… 43

§ Table 5. Measures of model fitness……….… 45

§ Table 6. Hypothesis test results………...… 46

§ Table 7. Hypothesis test results by high and low innovative individuals……… 48

§ Table 8. Hypotheses summary of main study and highly innovative individuals sub-study... 50

§ Table 9. Hypotheses summary of low innovative individuals sub-study……… 51

List of Figures

• Figure 1. Conceptual Framework……….………... 24

• Figure 2. Hypothesis test results………...………... 46

• Figure 3. Test results for highly innovative individuals………..… 47

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1. INTRODUCTION

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ess than four years ago, the first providers of the “new mobile payments system” appeared in the Western mobile payments market. Apple launched Apple Pay into the U.S. market on the 20th October 2014. For the first time, iPhone users could use their smartphone to conduct point-of-sale (POS) payments in-store as a replacement for the traditional cash, debit- and credit cards (Apple, 2018). Furthermore, monetary peer-to-peer (P2P) transactions between smartphones without any financial intermediaries such as a bank was a fact. Also, remembering complicated passcodes was no longer necessary, instead, using solely your finger, eye, voice or face for payment-authentications is required. Most importantly, conducting online payments was profoundly guaranteed to be the fastest, easiest and safest among any existing mobile payments solutions (Apple, 2018). Soon, technological giants such as Samsung and Google followed and launched their mobile payments system—Samsung Pay and Android Pay (renamed to Google Pay in 2017)—into the world.

However, using a smartphone as payment system was first introduced in Asia. Initially, China is the forerunner of such technology with introducing AliPay in 2009 and WeChat Pay in 2013. Forbes (2017) states that these systems are highly successful in China and living without would be unthinkable as it is already accustomed to Chinese consumers’ daily life. For example, these mobile wallets are used for any payments ranging from paying for breakfast to booking a holiday. This development led technology-savvy neighboring countries following up this success with Japan introducing Line Pay and South Korea with Kakao Pay in 2014. Later, Apple- Samsung- and Google Pay joined these fiercely competitive markets.

Surprisingly, the U.S., where the first mobile wallet is introduced, is still experiencing a remarkable low adoption rate compared to their highly successful Asian neighbors. Goldman Sachs (Yahoo Finance, 2017) reported that only 8%, 6% and 3% of the U.S. consumers use Apple Pay, Samsung Pay, and Google Pay respectively. In contrast, Chinese consumers conducted nearly eleven times more mobile wallet payments, and the total value of this market was relatively fifty times greater (Financial Times, 2017; Business Insider, 2018). Statistics show that POS-smartphone payments cover an adoption rate of above 90%, against 32% for traditional cash and debit- credit cards in the Chinese market (Business Insider, 2018). This substantial difference in mobile wallet adoption between the two largest world economies raises, subsequently, important questions.

The U.S. mobile payments industry is expected to proliferate (Statista, 2018). The current value of POS-smartphone payments is forecasted to increase from 16.24 to 34.16 billion

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US dollars in 2019—an increase of over 100%. However, current Western international availability of the mobile wallet is still relatively low. On the European continent, currently, Apple Pay, Samsung Pay or Google Pay are only available in U.K., Switzerland, France, Spain, Ireland, Italy, Sweden, Denmark, and Finland. It is announced that these systems will be very soon and gradually released in more European countries. In the Netherlands, some banking institutions offer similar services such as the ING-Wallet and ABN AMRO-Wallet. However, to date and equally to the U.S., these systems are experiencing a laborious adoption process (Betaalvereniging Nederland, 2017). Despite this reality, the Dutch POS-smartphone payments market is forecasted to also double its total value from 2018 to 2022 (Statista, 2018).

Thus, forecasts suggest that smartphone payments are becoming increasingly important in our society. In particular, the manner of conducting payments, both online and offline, will change significantly. The Asian-Pacific are already leading the way of such systems, whereas the Western is still in their cradle. Therefore, in understanding this development, insights regarding Western-specific factors influencing the mobile wallet adoption process must be acquired for both academical and managerial perspectives.

Academically, as prior technology acceptance studies in mobile payments are often outdated and inaccurate concerning the contextual-specific factors of the mobile wallet. First, the rapidly changing technological advancements profoundly impact the current practice. Studies have shown that when people undergo significant technological changes, simultaneously, their technological self-efficacy increases; which is the belief in one’s ability to successfully perform a technologically sophisticated new task (McDonald & Siegall, 1992). This development means that the rapid increase of smartphone possessions and advancements in the fast-changing technological context could have strongly increased consumers’ technological self-efficacy over the past decade; which, subsequently, impacts their general and contemporary beliefs in mobile payments.

Second, besides the core product of traditional mobile payments which is enabling online payments, the mobile wallet has added complementary products into its system such as P2P-feature and offline payments. Katz & Shapiro (1985) state that various direct and indirect network effects occur when the number of users increases due to complementary products. These effects, subsequently, influences the adoption process. Therefore, the results from prior mobile payment studies cannot be accurately applied due to the mobile wallet’s interdependent features and, thus, need to be studied in its unique context.

Third, the current traditional mobile payment literature is predominantly focused on the traditional features of mobile payment. In various studies, mobile devices are considered as a

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generic system without differences (Dahlberg et al., 2008; Schierz et al., 2010; Teo et al., 2015). However, it should be emphasized that mobile devices possess significant differential features which affect the purpose of usage, security risks and state of technology (Econsultancy, 2013). According to Bearden & Etzel (1982), consumers beliefs are influenced by specific reference-product groups. Therefore, based on the findings in various mobile payments studies, specific suggestions regarding smartphone-payments cannot be, accurately, made.

Lastly, the beliefs measured in prior mobile payments studies were predominantly linked with banking institutions. Studies have shown that risk reduction often relies on the brand image (Sheth & Venkatesan, 1968). Apple, Samsung and Google Pay are offered by prominent technology companies with the general reputation of offering high-quality products. In addition, statistics show that these three brands are considered as world’s most valuable with Apple 2nd, Google 3rd, and Samsung 4th (World Trademark Review, 2018). According to Forbes (2015), the most trusted providers of financial products are technology firms, instead banking institutions.

Importantly, in the era of data breaches, various risks are faced by users where personal and financial data is involved. Privacy and security issues are often topics in news headlines, and the number of data breaches evoked public unrest and distrust in using technologies over the past decade (Statista, 2018). For example, cases of Facebook, Uber, Cambridge Analytica, Yahoo and the continuing threats of smartphone’s security leaks (Fortune, 2018). Statista (2018) reports that data breaches with an exposure of 66.9 million records in 2005 increased to 179 million records in 2017. These significant developments have led the European Union to adapt the EU law framework on data protection and privacy with implementing the General Data Protection Regulation (GDPR) (European Commission, 2018). Despite these developments, Edelman’s Trust Barometer in technologies shows that the technology sector is still the most trusted industry (Edelman, 2018). However, globally, trust in technology has collectively declined among the informed public over the past years. Edelman (2018), argues that emerging technologies are facing increasingly more obstacles in gaining trust and are far less trusted than before. Telegraph (2017) published in an article that major obstacles for the U.S. mobile wallet adoption are trust and security issues. Consequently, the lack of confidence in privacy and security controls of payment systems is a major obstacle to e-commerce continuing growth (Malhotra, 2004). Therefore, a deep understanding of how trust affects mobile wallet adoption in the contemporary context is crucial.

In addition, innovation adoption in a social system does not happen simultaneously to everyone due to their personal innovativeness (Rogers, 1995). The mobile payment literature

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has predominantly considered the population as a generic group. Inter-group level studies in this domain are, therefore, scarce (Kim et al., 2010). Especially, understanding is important when innovativeness differences were found in study domains such as marketing, education and various information systems (Rogers, 1971; Mahajan, Muller & Bass, 1991; Karahanna, Straub & Chervany, 1999). Therefore, this study conducts an inter-group level study to increase the understanding in mobile wallet adoption by identifying adopters-specific factors concerning their degree of personal innovativeness.

These insights are of crucial managerial relevance; as mobile wallet providers have invested a significant amount of resources into the development of the system. As the mobile payments market is proliferating, the aim is to convince consumers to adopt their technology. When this objective is reached, the revenue potential is almost infinite by charging a transaction fee for every payment made in the online and offline commerce. Therefore, it is of high importance to gain an understanding of the contextual-specific factors influencing consumers’ choice of mobile wallet adoption. Consequently, evidence-based and trust-building strategies can be crafted and implemented into business decisions to accelerate the adoption process.

Therefore, this study attempts to identify contextual-specific insights for the academic field by developing an extended framework of the technology acceptance model with the incorporation of trust. To gain a deeper understanding of trust, this study will also attempt to identify potential drivers of trust to provide actionable trust-building insights. Moreover, inter-group behaviors among high and low innovative individuals are researched to identify inter- group-specific factors and degree of impact. To fill in this academic gap and to support strategic decisions of mobile wallet providers, the following research question is formulated:

“To what extent do the technology acceptance model and trust explain mobile wallet adoption in the mobile payments market, and how does this differ between high and low innovative consumers?”

To examine the research question, this study is structured as follows. First, a comprehensive literature review is provided. Second, the conceptual framework is developed, and research hypotheses are proposed. Third, the study’s methodology is explained. Fourth, data analyses and results are described. Fifth, discussion of the results including limitations, implications, and suggestions for future studies are elaborated. Lastly, the conclusion of this study is presented.

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2. LITERATURE REVIEW

The literature review starts with an explanation of the development and importance of the mobile wallet. Next, the key differences with traditional mobile payments are highlighted. Making this distinction clear is essential for this study as it focuses on the mobile wallet’s unique functionalities as well as its context which are elaborated in detail. Then, the incorporation of foundation models from prominent studies is explained. First, the technology acceptance model is discussed. Second, the importance of the innovation diffusion theory is included. Finally, this section ends with a summary.

2.1 DEVELOPMENT OF THE MOBILE WALLET

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owadays, consumers have various options to conduct online and offline payments. When purchasing goods and services, offline payments are commonly done by cash or a debit- credit card. In contrast, online payments require a device to conduct payments such as a laptop, tablet or a mobile phone. These mobile personal devices are convenient means for authorizing electronic transactions (Herzberg, 2003). Over the past decade, the global electronic commerce (e-commerce) has proliferated (Turban et al., 2017). Consequently, mobile devices are turning into crucial means to feed the expanding online monetary transactions need of e-commerce (Shao Yeh, 2009). The consumer requires safer and more secure online payment solutions and demand the banking industry to act on these concerns (Kim et al., 2010). Therefore, to sustain and maintain the growing e-commerce, a well-developed online payment system is an essential factor to convince consumers to shop online.

Engaging with e-commerce on a mobile device is defined as mobile commerce (m-commerce). According to Au & Kaufmann (2008), m-commerce is an extension of e-commerce which refers to all monetary transactions conducted via wireless mobile devices such as mobile phones or tablets. These transactions occur via a mobile website or application downloaded on the device. Formerly, banking institutions were the leading players to provide these financial services through their website or application for money transfers. While the growth of m-commerce is inextricably linked to e-commerce, mobile devices are becoming indispensable to conduct electronic payments. The value of m-commerce has been growing exponentially over the past decade, and without the end in sight yet (Statista, 2018).

Prior mobile payment solutions; mainly conducted via websites or applications, are often not user-friendly nor safe due to significant risks of data breaches in e-commerce (Business, 2017). These traditional systems could often be time-consuming or even exclude certain consumers. For example, m-commerce retailers often required a credit card as payment method. Besides, an extended form of personal and financial questions on the website or application was required to be filled in to proceed the payment. Various studies have shown

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the importance of the mobile payment system’s ease of use to predict actual system’s usage by consumers (Kim, Mirusmonov & Lee, 2010; Jeong & Yoon, 2013; Thakur & Srivastava, 2014). Significant risks and inconveniences of these traditional systems could, therefore, negatively affect the overall mobile payment experience, and subsequently, obstruct m-commerce growth. Prominent high-technology companies recognized the immense revenue opportunities in the growing importance of mobile devices to serve as a payment system. Therefore, firms such as Google, Apple, and Samsung have developed their advanced payment system to replace the traditional means and serve the mobile payment market. They introduced the mobile wallet which conducts not only online payments, but also offline payments. Now, consumers can make payments in-store with their smartphone—replacing cash and debit- credit cards. This advanced system is regarded as a replacement for traditional mobile payment systems (Gupta, 2013). However, existing laws and regulations governing mobile payments do not apply to the mobile wallet. For example, if fraud is committed due to vulnerabilities of the mobile wallet; banking institutions are reliable for the damage (Lowry, 2016).

The mobile wallet is a built-in application in smartphones and works through technologies such as the near field communication (NFC) or QR-code to make offline payments. QR-code payments occurred through scanning the code on the screen of smartphones (Lee, Cho & Jun, 2011). Later, when Apple, Samsung, and Google introduced their mobile wallets; Apple Pay, Samsung Pay and Google Pay respectively, offline payments using the NFC-chip of the smartphone made its entry into the world (Pal, Vanijja & Papasratom, 2015). Except for Samsung, Samsung Pay worked both with NFC, and the magnetic secure transmission (MST) enabled payment terminals in-stores. NFC-enabled devices allow two devices placed within a few centimeters of each other to exchange information—in this case enabling monetary transactions between a smartphone and an NFC-enabled payment terminal (Pal et al., 2015; Pham & Ho, 2015). Retailers without this particular terminal, but only a terminal for debit- credit cards payment are often using an MST-enabled terminal. MST is a technology which sends a magnetic signal from a compatible device to the payment terminal’s reader—in essence, it simulates the swiping movement of a debit- credit card (Choi & Lee, 2016). The advantage of Samsung Pay is that an upgrade to an NFC-enabled terminal is not required which is often a constraint for small retailers. The limitations are that it requires a financial investment, and most consumers are still using cash and debit- credit cards. Therefore, the need of such terminals is not recognized yet (Choi & Lee, 2016). However, major retailers and an increasing number globally have started to adopt NFC-enabled terminals (Statista, 2018).

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The online payment functionalities have improved significantly. The mobile wallet enables a faster and more convenient way to make payments in a few steps. First, the user is required to connect their debit- or credit card with the mobile wallet application. Then, users can make purchases without refilling their personal and financial details over again. Another advantage of this system is that consumers without a credit card are not excluded anymore (Apple, 2018; Samsung, 2018; Google, 2018). Thus, filling a form with sensitive details is not required anymore. Also, online payments are conducted without conveying customers’ actual payment card number. Instead, the mobile wallet creates and utilizes a unique digital card number for every payment. This unique construct is, therefore, more private and secured than ever before (Apple, 2018; Samsung, 2018; Google, 2018).

Another unique feature is peer-to-peer (P2P) transactions. This functionality enables monetary transactions between smartphones without the interference of banking institutions as mediator. Traditionally, people had to use their online bank account to make transactions to friends and family. This method could be time-consuming and inconvenient. For example, forgetting the bank account’s password, asking banking information of the receiver and filling in these details in for each transaction, and often the receipt of the money could be several days later. The P2P-functionality as a complementary product of the mobile wallet allows users to transfer money in real-time with each other. The only requirement is to transfer sufficient money from a bank account to the mobile wallet’s account (Apple, 2018; Samsung, 2018; Google, 2018). Therefore, the P2P-feature eliminates all steps in the traditional method of monetary transfers to peers and enables faster and safer ways to, for example, split bills and sending money to each other without revealing both actual financial details.

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2.2 TRADITIONAL MOBILE PAYMENTS AND THE MOBILE WALLET

The current mobile payments literature is predominantly focused on the traditional features of mobile payment. Various studies defined that mobile payment is distinguishable by its key characteristic using a mobile device to conduct electronic payments (Schierz et al., 2010). Others adopted a broad definition of mobile payment stating that all mobile internet-enabled mobile devices allowing payments for goods, services and bills authorization are classified as mobile payment (Dahlberg et al., 2008; Schierz et al., 2010; Teo et al., 2015). Specifically, Jeong & Yoon (2013) defined mobile payments as financial transactions solely enabled by banking institutions through cell phones, PDA, and smartphones.

In fact, is it reasonable to assume that each mobile device contains distinctive features, such as usage purpose, and degree of personality, security risks and technology sophistication (Econsultancy, 2013). Often, marketing strategies are crafted upon the specific usage purpose and features of the targeted mobile device. For example, it is forecasted that mobile payments with a tablet will decrease in the share of retail in m-commerce from 33.2% to 17.1% between 2017-2021. In contrast, sales generated through a smartphone will increase to 80.5% in that domain (Statista, 2018). Therefore, it is essential to recognize these distinctions in mobile payments studies which have often neglected these inter-group differences between mobile devices. Especially, when consumer beliefs are influenced by reference-product groups that directly impact their perceptions of a specific brand or product (Bearden & Etzel, 1982). Therefore, findings from prior mobile payments studies strongly lack in accuracy to be fully applicable to the mobile wallet's context.

The mobile wallet has extended its core product with several sub-products. Besides enabling online payments, offline- and P2P payments are also possible. It is reasonable to assume that with the inclusion of these products that one is likely to influence the other area of commerce due to existing network effects. According to Katz & Shapiro (1985), network effects occur mainly by the number of users and contain direct- indirect network effects. Direct network effect refers to the increase in product value simultaneously with the increase of users and new users derived from the demand-side network effect. The P2P-functionality requires a significant base of users to become successful. Therefore, a positive direct network effect could profoundly occur when an additional user of the P2P-functionality adds on the value of this specific feature to other individuals.

Indirect network effect refers to the product value derived from the number of complementary products offered. This effect will be substantial when various complementary products are offered along the core product. Consequently, the consumption of the core product

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and the value of other products will increase simultaneously (Katz and Shapiro, 1985). Therefore, it is essential to recognize these effects on consumers’ belief to find unique insights regarding the mobile wallet’s context. Prior findings on mobile payments are, therefore, not fully applicable and accurate.

Over the past decade, technological advancements have been taking a rapid pace. Increase in smartphone possessions and becoming accustomed and sophisticated its technology is reasonable to assume. Prior studies state that when people undergo significant technological changes, simultaneously, their technological self-efficacy will grow. This belief regards one’s ability to successfully perform a technological sophisticated new task (McDonald & Siegall, 1992). Therefore, it is likely that consumers possess higher technological self-efficacy regarding smartphone use than before. Consequently, this growth could have likely influenced individuals’ general criteria for forming a belief system on innovations. Thus, findings from prior studies on the belief system regarding mobile payments could be significantly different from nowadays. Bandura (1977) states that altering the level of strength of self-efficacy directly impacts the intention and behavior leading to significant psychological changes. Prior findings on mobile payments are, therefore, outdated and inaccurate to apply to the mobile wallet’s context.

Finally, it was unprecedented for high-technology firms such as Apple, Google, and Samsung to offer mobile payment solutions. Formerly, banking institutions were the only providers in the mobile payment market. While the technology giants have entered this industry, it is reasonable to assume that they generally possess higher quality technologies than the predecessors. This higher quality is also strongly emphasized by the premise that the mobile wallet is faster and safer than any mobile payment system. Therefore, it is likely that differences in perception towards both providers may cause different effects on consumers’ belief system regarding mobile payments. The provider’s brand image may strongly influence consumers’ evaluations of the product and risk reduction (Sheth & Venkatesan, 1968). The brand Google, Samsung, and Apple can be considered as prominent and could, therefore, influence the mobile wallet’s adoption. Prior findings on mobile payments are predominantly based on banking institutions as providers. Therefore, little can be said about the applicability of these results to the mobile wallet’s context. In section 2.2, the crucial distinction between banking institutions and high-technology companies as financial service providers regarding consumer trust is explained.

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In Table 1, an overview is provided of the fundamental differences between the mobile wallet and traditional mobile payments.

Table 1. Key differences between Mobile Wallet and Traditional Mobile Payment

Mobile Wallet

Traditional Mobile Payments

Near Field Communication (NFC): In-Store Payments Yes No

Peer-to-Peer payments (P2P): Send and Receive Money

to friends and family Yes No

Tokenization (encryption of financial & personal data) (merchants never receives your credentials)

Yes No

Passcode Authentication: Touch ID, Face ID, Iris Scanner, QR-Code

Yes No

In-App & Webstore Transaction Authorization with One Tap

Yes No

Provider: High-Technology Firms Yes No

Additional: Real-Time Returns Processing Yes No

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2.3 TECHNOLOY ACCEPTANCE MODEL

Davis (1989) introduced a widely used model by scholars to study individual intentions for technology adoption—the technology acceptance model (TAM). TAM’s theoretical foundation is initially based on the theory of reasoned action (TRA) by Fishbein & Ajzen in 1975. TRA suggests that subjective norms and attitude towards an action are likely affecting behavioral intention (BIU) and, subsequently, influencing how the individual acts (Fishbein & Ajzen, 1975). The initial attempt at developing TAM was to link psychological constructs with information systems and computers adoption to predict user acceptance. Therefore, TAM’s theoretical framework often serves in the technology acceptance literature as the foundation model to identify individual’s beliefs regarding specific technology usage which would ultimately predict actual usage (Davis, 1989).

Davis (1989) theoretical framework proposes two constructs predicting attitude. These variables are perceived usefulness (PU) and perceived ease of use (PEU). Perceived usefulness is defined as the degree a person believes that using the technology would increase work performance. Perceived ease of use is explained by the degree a person believes that using the technology is free of mental and physical efforts. When both variables are positively perceived, an increased positive attitude towards using (ATU) that technology will occur. Consequently, a stronger positive attitude would increase the intention to use the particular technology which, subsequently, results in a higher likelihood of actual system’s usage (Davis, 1989). TAM is not enclosed and, therefore, the advantage is the possibility for incorporation of external factors regarding the specific context as strongly suggested by the author (Davis, 1989).

Various studies included TAM to examine specific technology adoption. For example, in the traditional mobile payment literature, TAM is predominantly used to uncover individual beliefs about mobile payments. These studies revealed that PU and PEU are key determinants of traditional mobile payment adoption (Wei et al., 2009; Kim, Mirusmonov & Lee, 2010; Thakur & Srivastava, 2014; Dastan, 2016). Also, the online banking domain found same results (Koenig-Lewis, Palmer & Moll, 2010). In the e-commerce domain, similarly, Groß (2015) found PU and PEU along with mobile vendor factors such as perceived trust and enjoyment to be significant for mobile shopping adoption. The findings for TAM’s applicability equally apply to online shopping with similar results (Gefen et al., 2003). The high PU and PEU of mobile phones have profoundly contributed to its success (Mao et al., 2005). Hence, various studies in relevant domains of the mobile wallet demonstrated consistent results of TAM’s applicability and validity to predict technology adoption (Lai & Li, 2005; AI-Somali & Gholami, 2009; Schierz, Schilke & Wirtz, 2010; Kim, Mirusmonov & Lee, 2010). TAM is,

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therefore, also the most widely used model in the technology acceptance literature (Benbasat and Barki, 2007; Wei et al., 2011).

However, although TAM is a prominent model in technology acceptance studies, the model only explains a part of technology adoption. As mentioned above, Davis (1989) suggest expanding TAM with specific contextual factors to increase understanding of the technology in question. In alignment with this suggestion, various information system studies incorporated external factors in TAM to predict BIU more accurately (Pavlou, 2003; Roca, Chiu & Martinez, 2006; Schierz, Schilke & Wirtz, 2010; Jose Liebana-Cabanillas, 2014). Doing so, these studies agree that ATU act as co-determinant of BIU and not the main as TAM suggests. Another reason is TAM’s limited prediction ability to explain the total variance of technology adoption as social factors are excluded while technologies operate in a social setting (Malhotra & Galletta, 1999). Legris et al. (2003) concluded in a critical review within a longitudinal study of TAM that only 40% variance of system’s use is explained. The main critique is that the model is inconsistent and unclear. Importantly, critical factors for system’s adoption were not included and advised future studies to integrate TAM into a broader context including factors related to human and social change processes (Legris et al., 2003). Moreover, TAM was initially developed to manage business information systems in a workplace context to assess its quality (Davis, 1989). Therefore, influences from a workplace setting are profoundly different from what consumers are actually facing. Specific in this study, the mobile wallet is used in a social setting where consumers are faced with risks of exposing sensitive data. Therefore, to increase TAM’s predictive capability, it is crucial to put the model into a broader perspective with relevant contextual factors of the mobile wallet.

Nowadays, various risks and challenges are faced by users of traditional mobile payments. Privacy and security risks are often the main topics in news headlines. In the past decade, a growing number of privacy scandals and online data breaches by high-technology companies evoked public unrest and distrust in using various technologies (Statista, 2018). For example, cases of Facebook, Uber, Cambridge Analytica, Yahoo and the continuing threats of smartphone’s security leaks (Fortune, 2018). These significant developments have led the European Union to adapt the EU law framework on data protection and privacy with implementing the General Data Protection Regulation (GDPR) (European Commission, 2018). The fundamental principle is of the regulation is giving consumers more control of their data. Also, businesses are required to report data breaches within 72 hours.

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Despite these developments, Edelman’s Trust Barometer in technologies (2018) reports that trust in technologies is consistently measured around 75% making the technology industry the most trusted sector. However, consumers are still often not convinced of how transparent and authentic this sector operates and deals with data protection (Edelman, 2018). Technologies which are well-trusted by consumers, such as telecommunications and smartphones are currently paying off. In contrast, recent innovations still need to earn this trust (Edelman, 2018). According to Forbes (2015), the most trusted providers of financial products are technology firms compared with financial institutions.

Against these developments, consumer trust is an essential area to gain more understanding of technology acceptance. According to information system studies, trust is one of the main determinants contributing to the predictive ability of technology usage (Mukherjee & Nath 2003; Srivastava et al., 2010; Shaw, 2014; Jose Liebana-Cabanillas et al., 2014; Duane et al., 2014). Prior findings on trust in the traditional mobile payment literature studied solely banking institutions as mobile payment providers (Lu et al., 2011; Srivastava et al., 2010; Zhou, 2011). However, the contextual factors of the mobile wallet require an understanding towards the influence of technology companies. Moreover, due to the contemporary relevance and increasing importance, this study will incorporate trust in TAM’s framework as well as attempting to identify crucial factors explaining trust in the contemporary mobile wallet context. Identification of the trust drivers delivers a more in-depth and richer understanding of trust. Crucially, it enables mobile wallet providers to take actionable and concrete steps to increase system’s trust and, ultimately, the system’s usage.

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2.4 INNOVATION DIFFUSSION THEORY

Adopting a new idea or technology does not happen at one moment but over time. Everett Rogers (1962) introduced a classical theory trying to explain how new ideas and technologies spread through the population over time—the innovation diffusion theory (IDT). IDT explains the primary processes where diffusion of innovations occurs and classifies individuals in different process stages. The theory explains that corresponding personal traits at each stage are predisposed individual characteristics which are invariant across various technologies. These characteristics are continuously influencing technology adoption decisions and behavior (Rogers, 1995).

IDT literature has been widely applied in broad study domains such as education, sociology, marketing and information systems (Rogers, 1971; Mahajan, Muller & Bass, 1991; Karahanna, Straub & Chervany, 1999). Rogers (1995) applied IDT in his research at that time to innovative cases in telecommunications such as faxes, internet, mobile phones and email.

An innovation is “an idea, practice or object perceived as new by an individual or another unit of adoption” (Rogers, 1995, p. 37). Diffusion, on the other hand, is “the process by which an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 1995, p. 37). The author states that diffusion is a special type of communication spreading messages that are perceived as new. Communication, alternately, is defined as “a process in which participants create and share information with one another to reach a mutual understanding” (Rogers, 1995, p. 37). The author suggests that diffusion is different compared to solely a process of ordinary communication. Instead, diffusion is related to the message’s newness involving a certain degree of risk and uncertainty for the individual. Therefore, the individual receives information to reduce these concerns of innovation (Rogers, 1995). Thus, actual diffusion patterns differ among innovations where some contain a substantial adoption rate among a specific group, and others need a decade. In essence, the individual forms beliefs based on the received information and then make decisions to adopt or reject the innovation (Agarwal, 2000).

IDT classifies individuals into adopter-groups based on their initial use and the speed of adopting the innovation. This behavior is defined as the individual’s innovativeness which describes, “the degree to which an individual is relatively earlier in adopting an innovation than other members of his/her social system” (Rogers & Shoemaker, 1971, p. 27). Rogers (1995) classifies individuals into five adopter-groups based on their innovativeness: 1) innovators (2.5%), 2) early adopters (13.5%), 3) early majority (34%), 4) late majority (34%) and 5) laggards (16%). The interactions of these groups in a social system enable a domino effect.

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Diffusion occurs through a bell-shaped-curve indicating the number of individuals adopting the innovation at each stage. Besides, the velocity of diffusion is explained through an S-shaped curve indicating adoption’s tipping point at 50%. This curve states that the exponential growth occurs in the first half where innovators, early adopter, and early majority belongs. The next half states that diffusion is stagnating; indicating the adoption rate of the late majority and laggards (Rogers, 1995).

The adoption velocity of individuals is related to their trait of technological innovativeness. Thus, personal innovativeness is related to the degree of this trait which continuously influences individual adoption decisions and behavior (Rogers, 1995). Agarwal & Prasad (1999) defines the trait of personal innovativeness as a predisposed consumer trait invariant to various technologies and, subsequently, influencing adoption behavior as an internal motivation stimulus. In essence, these personal traits systematically influence consumers’ earliness of technology adoption compared to others. In turn, for that particular innovation, the individual ends up into one category of Rogers’ five classifications. Personal innovativeness is; therefore, a trait explains which explains their innovation adoption behaviors. A highly innovative individual is likely to end up in categories related to the exponential growth phase of technology diffusions. In contrast, low innovative individuals are likely to end up in categories related to the stagnation phase (Rogers, 1995).

Categorizing consumers based on their initial experience with the mobile wallet is unreachable as the system is relatively new. Personal innovativeness is an enduring trait possessed by everyone (Rogers, 1995; Agarwal & Prasad, 1999; Jackson et al., 2013). Therefore, personal innovativeness is measured as it highly predicts the likelihood to which part of Rogers’ adopter’s classifications they belong. As mentioned above, high innovative individuals are likely to belong to the early adopters (exponential growth stage). In contrast, low innovative individuals belong to the late adopters (stagnation phase). The advantage of personal innovativeness is it independence regarding the timing of adoption and its high predictability of innovations initial use velocity.

In the technology acceptance literature, user-group studies are often neglected (Kim et al., 2010). While individuals significantly differ in personal innovativeness which profoundly affects their behavior of innovation adoption, it is highly relevant to include this distinction in this study. Gaining an accurate understanding of mobile wallet adoption in the context individual differences concerning innovativeness is essential for academia; to build on relevant factors per group, and managers; to target effectively and create specific marketing campaigns for each target group.

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2.5 CHAPTER’S SUMMARY

Nowadays, consumers have various ways to conduct online and offline payments for products and services. A sophisticated mobile payment system is crucial to feed the growing e-commerce. Traditional mobile payment systems are often inconvenient and time-consuming, and users were faced with risks of security issues. The mobile wallet, introduced by high-technology companies, tackles these problems. Banking institutions are not the only providers anymore in the mobile payment market. The revolutionary mobile wallet allows users to conduct online, offline and P2P-payments with a faster, more convenient, secure and seamless experience than traditional mobile payment systems.

The mobile wallet is exposed to specific contextual factors, such as wherein consumers possess a higher technological self-efficacy, direct and indirect network effects, specific mobile device influences, and brand image influences from prominent technology firms. Prior findings from the traditional mobile payment literature, therefore, do not fully and accurately explain the mobile wallet’s adoption process. These studies have often neglected inter-group differences between mobile devices and were conducted in a context which does not apply to the unique context of the mobile wallet. As a result, factors influencing mobile wallet adoption cannot be adequately interpreted. Thus, specific questions regarding the mobile wallet could not be answered with utmost accuracy.

Various studies in the technology acceptance literature have shown the predictive ability of TAM. Therefore, TAM is included to serve as the foundation model explaining mobile wallet adoption. However, TAM is known regarding its limited predictive ability and, suggested by TAM’s author, various studies have incorporated relevant contextual factors in their studies to increase the predictive ability of actual usage of the studied technology. With the recent data breaches in major news headlines and the significant security risks of sensitive data, the contemporary relevance and importance of consumer trust in technologies are growing. Therefore, this study tries to understand the influence of this factor by incorporating it into TAM’s framework. Moreover, attempts are made to identify essential factors influencing trust in the mobile wallet.

Finally, not everyone adopts technologies simultaneously. In fact, the predisposed individual characteristic “personal innovativeness” continuously influences individual adoption decisions and behavior concerning technologies. This study, therefore, incorporates IDT into TAM by classifying consumers into two groups; high innovative individuals versus low innovative individuals. Doing so, user-group differences in behaviors are identified which furthers the understanding of specific-related factors to each group.

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3. CONCEPTUAL FRAMEWORK

This chapter starts with an explanation and visualization of the research model developed for this study. First, the primary constructs of TAM are discussed. Second, consumer trust is elaborated in detail. Third, four potential determinants of consumer trust are identified and discussed. Fourth, based on the theoretical background encompassing earlier findings in relevant fields of each construct, several research hypotheses are developed accordingly. Finally, IDT which forms the basis of the inter-group level study including its exploratory character in this research is explained.

3.1 CONCEPTUAL FRAMEWORK

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n Figure 1, the research model is depicted. In this model, TAM serves as the foundation framework to predict mobile wallet adoption. This framework contains behavioral intention to use (BIU), attitude towards using (ATU), perceived usefulness (PU) and perceived ease of use (PEU). However, TAM’s predictive ability is limited and, therefore, it is suggested to extend the model with relevant contextual factors to predict BIU more accurately (Davis, 1989). Various information system studies have followed this suggestion (Pavlou, 2003; Roca, Chiu & Martinez, 2006; Schierz, Schilke & Wirtz, 2010; Jose Liebana-Cabanillas, 2014). Due to the highly topical relevance of consumer trust (CT) as described in the previous chapter, this construct is added to TAM. Moreover, to further the understanding of the effect and establishment of trust in the mobile wallet context, four potential determinants are identified which are perceived privacy and security (PPS), brand reputation (BR), prior experience (PE) and social influences (SI). Finally, this study attempts to conduct an inter-group level study where the research population is categorized into high or low innovative individuals as this distinction is relevant from both academic and managerial aspect as described in the previous chapter. An elaboration of the constructs in more detail is provided below.

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Figure 1. Conceptual Framework

3.1.1 FACTORS OF TAM

Relevance of Behavioral Intention to Use

TAM’s objective is to predict actual technology usage which is strongly determined by the degree of BIU. BIU is, therefore, proposed as the primary determinant of actual usage in this framework (Davis, 1989). BIU is defined as an individual’s intention to perform particular behaviors concerning the subject, which theoretical foundation is derived from the reason action theory (TRA) by Fishbein & Ajzen (1975).

In the technology acceptance literature, BIU predominantly serves as the primary predictor of an individual’s actual behavior. When actual usage of a specific technology cannot be measured, BIU adopts its position as the dependent variable. In various domains such as information systems and, specifically, traditional mobile payments, substantial evidence has demonstrated the consistency and validity for this relationship (Yiu, Grant, & Edgar, 2007; Schiertz, 2010; Venkatesh, Thong & Xu, 2012; Barnett et al., 2015). Building on these findings, it can be suggested that an individual’s intention concerning a specific technology usage is the most influential predictor of actual behavior. Drawing upon this theoretical understanding, BIU

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serves as the dependent variable in this study’s framework. Doing so, an appropriate indication can be drawn upon the degree of future mobile wallet usage. As results, contemporary insights concerning mobile wallet’s usage intention can serve as the basis of future studies in the mobile wallet domain. Moreover, these findings will actively support mobile wallet developers to implement mobile wallet’s improvements as well as taking actionable steps to enhance the adoption process.

Relevance of Attitude towards Using

TAM suggests that ATU is one of the two predictors of BIU towards using a specific technology and is, therefore, proposed as having a direct influence on BIU in the framework (Davis, 1989). Derived from TRA, this theory suggests that a more positive attitude increases the likelihood of BIU to perform that behavior (Fishbein & Ajzen, 1975). In essence, attitude is a reflection of salient behavioral, normative and control beliefs which are the primary determinants of human behavior (Ajzen & Driver, 1991). Prior findings in the technology acceptance literature equally highly support the validity and constancy for this relationship concerning both end user- and organizational perspective (Venkatesh et al., 2003; Amaoko-Gyampah & Salam, 2004; Venkatesh & Bala, 2008). Specifically, various studies in traditional mobile payments and mobile banking support this linkage (Lai & Li, 2005; AI-Somali & Gholami, 2009; Schierz, Schilke & Wirtz, 2010; Kim, Mirusmonov & Lee, 2010).

Relevance of Perceived Usefulness

In TAM’s framework, PU serves as one of the two co-determinants of BIU as well as ATU (Davis, 1989). These effects, therefore, states the position of ATU as a partial mediator between PU and BIU. The construct is defined as “the degree to which a person believes that using a particular system will enhance his or her job performance (Davis, 1989, p. 320).” Thus, higher perceptions of usefulness increase the likelihood of a more positive BIU and ATU (Davis, 1989).

Relevance of Perceived Ease of Use

One of the two co-determinant of ATU is PEU as well as serving as the predictor of PU (Davis, 1989). These effects, therefore, states the position of PU as a partial mediator between PEU and ATU (Davis, 1989; Venkatesh, 2000; Venkatesh & Davis, 2000; Venkatesh et al., 2002). The construct is defined as “the degree to which a person believes that using a particular system would be free of effort (Davis, 1989, p. 320).” The effort is defined as the availability

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of limited amount of resource which is allocated to various activities (Radner & Rothschild, 1975). TAM suggests that when a system is easier to use compared with alternatives, the perception of usefulness, as well as a more positive ATU, also increase (Davis, 1989).

3.1.2 ROLE OF TRUST

Relevance of Consumer Trust

As described in the previous chapter, the topical relevance, and importance of CT is increasing in the contemporary technology industry. Data breaches have increased exponentially such as cases of Facebook and Uber highlighted media headlines (Fortune, 2018). Malhotra (2004) states that lack of confidence in privacy security of payment systems is a significant obstacle to e-commerce growth. Despite these developments, Edelman’s Trust Barometer in technologies shows that the technology sector is still the most trusted one (Edelman, 2018). However, globally, trust in technology has collectively declined among the informed public over the past years. Edelman (2018), argues that emerging technologies are facing increasingly more obstacles in gaining trust and are far less trusted than before.

Therefore, it is crucial to understand this factor in more detail concerning technology adoption in the era of data breaches. Moreover, when technical aspects of technology are perceived well; high PU and PEU, which these perceptions do not eliminate other essential concerns of consumers (Agarwal et al., 2009). For example, customers are increasingly concerned about privacy and security issues when engaging online with technologies (Cranor et al., 1999; Malhotra et al., 2004; Linck et al., 2006). To overcome these concerns of risk and uncertainties, CT plays a crucial role according to the information system domain (Gefen et al., 2003; Pavlou & Gefen, 2004). In various technology domains where personal and financial data are involved, CT is considered to have a crucial impact on individual’s attitude as well as adoption intentions (Mukherjee & Nath 2003; Srivastava et al., 2010; Shaw, 2014; Jose Liebana-Cabanillas et al., 2014; Duane et al., 2014). More importantly, understanding how initial trust is created is crucial in the product lifecycle’s early phase to enhance its adoption process (Li et al., 2008).

CT encompasses two dimensions which are trust regarding enabling infrastructure (technologies), and technology providers (Srivastava et al., 2010). CT towards technology providers is defined as “the belief of the trustor that the trustee will fulfill the trustor’s expectations without taking advantage of the trustor’s vulnerabilities” (Batiz-Lazo & Efthymiou, 2016, p. 286), in which the trustee is a human being or organization. CT is crucial

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for online vendors where monetary transactions are involved (Brynjolfsson & Smith, 2000). CT regarding technologies is defined as where the trustee is a technological artifact such as information systems, and the willingness of the trustor to behaviorally depend on a piece of software to do a task according to expectations (McKnight, 2005; Srivastava., 2010). In this study, CT is defined by the latter one; that is, dependence on the mobile wallet to act and perform according to consumers’ expectations.

3.1.3 FACTORS OF TRUST

Relevance of Perceived Privacy and Security

As data breaches are inextricably linked with sensitive personal data, perceived privacy and security is crucial in trust-building in various technology domains. The rapid growth of mobile commerce allowed marketers to increasingly collect and disseminate data of any consumer engaging in online retailing (Miyazaki & Fernandez, 2001). Subsequently, these developments raise increasing concerns about online privacy and security (Cranor et al., 1999; Malhotra et al., 2004; Linck et al., 2006). As a result, any technology in the online environment is likely to deal with privacy and security concerns. Specifically, when personal and financial data are involved, consumers experience an increased risk vulnerability due to the lack of control of third party’s access to their data (Chen & Dibb, 2010). When these concerns are relieved, PPS will increase, resulting in a higher trust in this technology aspect.

PPS is context-specific, which means that any technology contains its specific vulnerabilities. In support of this study, PPS is defined as “the degree to which a customer believes that using a particular mobile payment procedure will be secure” (Shin, 2009, p. 1364). Especially, when online monetary transactions are involved, high-security controls are a crucial prerequisite (Link et al., 2006). This author studied security perceptions concerning traditional mobile payments, and categorized security into two dimensions; objective and subjective security. Objective security is related to technical features of the technology which is categorized into five sub-goals of security: confidentially, authorization, non-repudiation, and authentication (Merz, 2002; Link et al., 2006). In contrast, subjective security is the mental belief about the degree of the overall security according to the user. In traditional mobile payment, the real security issue often lies within subjective security, and not so much the objective security (Link et al., 2006).

Online privacy concerns the degree that the acquired confidential information of internet users are protected by privacy agreements concerning the distribution of consumer data

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(Metzger, 2004). Personal data shared with third parties without the consent of the customer is, therefore, a violation of privacy. In fact, consumers often cannot control the terms of how their data is used. This inability of control, therefore, raises major privacy concerns (Kim et al., 2008). Concerning this study, consumers’ belief regarding the subjective security of privacy concerns are taken into account.

Relevance of Brand Reputation

The current study focuses on prominent technology firms such as Apple, Samsung, and Google as newcomers in the mobile payment market. A highly notable aspect derived from this unprecedented development is their, globally, highly respected brand reputation. In support of this statement, these three high-technology brands are ranked among the highest in the world’s most valuable brand ranking with Apple 2nd, Google 3rd and Samsung 4th (World Trademark Review, 2018). The marketing literature states that in service-relationships between firms and consumers, CT in vendors strongly impacts sales outcomes (Johnson & Grayson, 2005). To create CT in this aspect, the reputation of an actor is considered as a crucial factor to create trust towards an organization (Doney & Cannon, 1997).

The reputation of the brand is defined as a kind of public information revealing the “merit of trust” (trustworthiness) of an actor (Doney & Cannon, 1997). Specifically, a brand is defined as “a distinguishing name and/or symbol (such as a logo, trademark, or package design) intended to identify the goods or services of either one seller or a group of sellers, and to differentiate these goods from those of competitors (Aaker, 1991, p. 7).” Selnes (1993) defined brand reputation as an attitude towards the brand and a long-run evaluation of the brand. Essentially, it is the perception of quality associated with the brand name (Selnes, 1993).

Moreover, Aaker (1991) states that the function of a strong brand facilitates a reduction in uncertainty when intrinsic cues or attributes of a product or service are difficult to assess. Intrinsic cues involve physical and technical features whereas extrinsic cues are not related to the product or service itself but the brand. The brand delivers a perception of overall quality and not necessarily based on knowledge of the detailed specifications of the product or service (Aaker, 1991). Furthermore, brand reputation is a critical element of trust-building (Ganesan, 1994; Lau & Lee, 1999; Afzal et al., 2009). Thus, in essence, BR serves as a trusted external indicator of quality measurement of its products and services. That is, consumers trust that a strong and positive BR delivers greater product/service quality. Therefore, this study proposes BR with trust as the full mediator for ATU and BIU.

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Relevance of Prior Experience

Over the past decade, the possession of smartphones and m-commerce have increased exponentially as described in the previous chapter. This development also indicates that the number of consumers who have engaged in traditional mobile payment, at least once, has increased significantly. Whether they used online banking, filling in payment-forms, or more traditionally, sending SMS-payments on their smartphone, this experience has contributed to a particular belief concerning mobile payments. Various studies suggest that prior experience supports the prediction of a particular response to judgmental task and behavior (Helson, 1964; Fishbein & Ajzen, 1977; Ajzen & Fishbein, 1980). Specifically, TRA argues that beliefs determine an individual’s attitude towards a behavior due to the knowledge of the consequences of this behavioral action (Fishbein & Ajzen, 1975). The evaluation of consequences occurs when experienced individuals employ knowledge gained from their PE to form their intentions (Fishbein & Ajzen, 1975). Specifically, regarding technologies, these individuals possess a stronger intention to use the technology again, than inexperienced individuals (Taylor & Rodd, 1995). Thus, insights from PE are involved in this evaluation process where trusted consequences are considered before proceeding the actual action.

In the marketing domain, prior online purchase experiences influence the likelihood of online purchase intention (Ling et al., 2010). Similarly, Shim et al. (2001) argued that satisfactory online purchases lead to continual behavior in online purchases. This linkage is explained as trust is created found in a study by Gefen et al. (2003). This study demonstrated that satisfied experienced consumers compared to new customers, the degree of trust in the e-retailer is lower for the latter one. The experienced customers were likely to purchase from the website again as trust was established due to positive PE.

This is explained since PE reduces uncertainties and simplify relationships with others (Gefen, 2000). PE with systems leads to familiarity which is described as increased understanding based on previous interaction, experiences, and learning of what, why, where and when others or objects do. It deals with the understanding of the current actions and complements the knowledge of how to use the specific system (Gefen, 2000). Luhmann (2018, p. 19), states “without familiarity with the context, trust cannot be adequately anchored to specific desirable behaviors and thus cannot be as strongly conferred. Familiarity creates this background, and is, therefore, the precondition for trust.” As trust is naturally context-dependent, understanding the given context is often a critical antecedent (Luhmann, 2018). Also, familiarity builds trust because it provides a framework of future expectations as well as letting people create concrete ideas of what to expect based on prior experience (Blau, 2017).

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Drawing upon these theoretical understanding and TRA, it is highly plausible to assume that PE has a significant influence on CT as prior knowledge is activated with strong confidence to reduce uncertainties and to predict consequences accurately before proceeding to action. Relevance of Social Influence

Besides brand reputation serving as an external cue for overall quality measurement, the consumers’ social environment also plays a significant role. For example, family and friends could influence behaviors, as well as social media influencers. These are individuals possessing the power to influence many people and shape audience attitudes through blogs, tweets and other social media or traditional media (Freberg et al., 2011). Moreover, opinion leaders, due to their social status and education, can informally influence attitudes and behaviors in a desired way with relatively high frequency (Li & Du, 2011).

The term SI was initially introduced in the book “Social Influence” (Turner, 1991). The book suggests that shared social identity serves the basis of SI. The social identity theory explains that individuals’ self-conception is related to the group wherein they perceive themselves as group member. Consequently, this perception leads to motivation and active striving to reach agreement and behavioral coordination according to that identity (Tajfel & Turner, 2004). Essentially, this theory explains intergroup behaviors including the associated cognitive processes and beliefs in group processes and intergroup relations (Hogg, 2016). This author states that social identity processes exhibit social comparisons, positive distinctiveness, social- self categorization and depersonalization. Specifically, the latter two plays a crucial role on the reduction of subjective uncertainty as a powerful human motive. Therefore, uncertainty reduction is the core motivational component of the theory (Hogg, 2016).

The process occurs as individuals need certainty about the world and their place within it. As “certainty renders existence meaningful and gives one confidence in how to behave and what to expect from one’s physical and social environment, as uncertainty about one’s attitudes, beliefs, feelings, and perceptions, as well as about oneself and other people is aversive (Hogg, 2000, p. 227).” By perceiving themselves as a group member, working theories about the world are developed to form the basis of (in)appropriate (social norms) and to act together to realize this shared vision (Tuner, 1991).

In the technology acceptance literature, SI fills an essential role in technology adoption in predicting BIU (Taylor & Todd, 1995; Kulviwat et al., 2009; Zhou et al., 2010; Yang et al., 2012; Thakur & Srivastava, 2014). The social identity theory states that SI serves as one of the essential bases of trust in an individual’s physical and social environment. Since, this domain

An empirical study on consumer adoption of the mobile wallet : adding innovation diffusion theory to the extended technology acceptance model with consumer trust.

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n Empirical Study on Consumer Adoption of the Mobile Wallet:

Statement of Originality

Acknowledgement

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Abstract

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Table of Contents

List of Tables

List of Figures

1. INTRODUCTION

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2. LITERATURE REVIEW

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3. CONCEPTUAL FRAMEWORK

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