Tác động của việc nghiện điện thoại thông minh đến hành vi chấp nhận sử dụng thanh toán bằng ví di động: Một nghiên cứu thực nghiệm trong bối cảnh Việt Nam

Nội dung text: Tác động của việc nghiện điện thoại thông minh đến hành vi chấp nhận sử dụng thanh toán bằng ví di động: Một nghiên cứu thực nghiệm trong bối cảnh Việt Nam

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 THE IMPACT OF SMARTPHONE ADDICTION ON MOBILE WALLET PAYMENT ADOPTION: AN EMPIRICAL STUDY IN VIETNAMESE CONTEXT TÁC ĐỘNG CỦA VIỆC NGHIỆN ĐIỆN THOẠI THÔNG MINH ĐẾN HÀNH VI CHẤP NHẬN SỬ DỤNG THANH TOÁN BẰNG VÍ DI ĐỘNG: MỘT NGHIÊN CỨU THỰC NGHIỆM TRONG BỐI CẢNH VIỆT NAM Nhan Tran-Danh Ha Tran-Thi-Phuong Van Le-Thi-Thanh University of Economics – The University of Danang nhan.trandanh@due.edu.vn Abstract Mobile wallet payment has grown significantly recently along with enormous potential to explore. This study aims to identify the moderate role of smartphone addiction in driving mobile wallet payment adoption behavior as well as investigate the impacts of various factors on behavior intention of mobile wallet payment based on credibility-extended model of UTAUT2. The research model was empirically tested using 254 responses conducted from Viet - namese young consumers. The findings of research confirmed that perceived credibility, per - formance expectancy, facilitating conditions, price value and habit altogether have direct effects on the behavioral intention to adopt mobile wallet payment. The research results also revealed that the moderating effect of smartphone addiction played an important role in the adoption of mobile wallet payments. Keywords: Mobile wallet payment, Smartphone addiction, Adoption, Perceived credibility, UTAUT2 Tóm tắt Thanh toán bằng ví di động đã phát triển rất đáng chú ý gần đây cùng với nhiều tiềm năng to lớn cần khám phá. Nghiên cứu này nhằm xác định vai trò điều tiết của việc nghiện điện thoại thông minh trong việc thúc đẩy hành vi chấp nhận sử dụng thanh toán bằng ví di động cũng như khảo sát tác động của các yếu tố khác nhau đến ý định hành vi thanh toán bằng ví di động dựa trên mô hình UTAUT2 mở rộng với khía cạnh uy tín nhận thức. Mô hình nghiên cứu được khảo sát thực nghiệm với 254 phản hồi từ người tiêu dùng trẻ Việt Nam. Kết quả nghiên cứu khẳng định rằng uy tín cảm nhận, hiệu suất kỳ vọng, điều kiện hỗ trợ, giá trị tài chính và thói quen cùng có ảnh hưởng trực tiếp đến ý định hành vi chấp nhận sử dụng thanh toán bằng ví di động. Kết quả nghiên cứu cũng cho thấy tác động điều tiết của của việc nghiện điện thoại thông minh đóng một vai trò quan trọng ảnh hưởng đến hành vi chấp nhận sử dụng thanh toán bằng ví di động. Từ khóa: Thanh toán bằng ví di động, nghiện điện thoại thông minh, hành vi chấp nhận sử dụng, uy tín cảm nhận, mô hình UTAUT2 808
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 1. Introduction To make life easier, humans have constantly been trying to find and discover sophisticated and superior technologies. The struggle to transcend the limits of people’s thought and observation has filled the modern world with fascinating and useful technologies. Mobile phones have emerged as a breakthrough in this discoveries and it has supplemented the way that people are communicated (Matemba & Li, 2018). Nowadays, mobile phones are not only used for calling and texting messages but they also provide many considerable and convenient functions. The mobile payment systems that exist inside smartphone are not only help to implement payment transactions but they also plays an important role in driving force the socio-economic growth of regional and national (Karsen, Chandra, & Juwitasary, 2019). The mobile payment service can be called mobile payment since 2000 (Karsen et al., 2019). Mobile payment is the most advanced form of monetary instrument (Gao & Waechter, 2017). Currently, in the world, human payment needs such as the payments for purchase of products and services online as well as offline; pay - ment for invoices have been met by hundreds of mobile payment system (Dahlberg, Mallat, On - drus, & Zmijewska, 2008; Karsen et al., 2019). Traditional physical wallets has transformed into mobile wallets due to the integration of mobile technologies and payment methods, information and communication technology (Leong, Hew, Ooi, & Wei, 2020; Sharma, Mangla, Luthra, & Al-Salti, 2018) . The mobile wallet is a form of mobile payment system. Mobile wallet is considered as a big revolution in the digital world, which allows customers to pay for transaction online by using a mobile device. It means paying without cash (Sharma et al., 2018). Paying for transactions through mobile devices let consumers get rid of the requirement of cash using, bring ease and convenience, enable to perform a fast transaction without limits of location or time, and the ability to make secure information trans - ferring between devices with various transaction categories ranged from individual to high scale of payment volume (Pham & Ho, 2015; B. Shaw & Kesharwani, 2019; Talwar, Dhir, Khalil, Mohan, & Islam, 2020). According to Statista (2019), the world-wide returns for mobile payment is estimated to exceed 1 trillion U.S. dollars in 2019 due to the spread of mobile devices like smartphones and tablets. However, mobile wallet payment is still considered as a comparatively new research area in comparison with other technology adoption research fields such as e-com - merce, Internet banking or mobile banking, especially in Vietnamese context. Therefore, a holistic research that adapts universal technology acceptance model like UTAUT2 of Venkatesh, Thong, and Xu (2012) is needed. Furthermore, in researching toward mobile payment wallet adoption, based on the nature of e-payment, a credibility consideration for UTAUT2 is also required. In this studies, we examined all factors that could affect to consumer’s intention toward using mobile payment based on an integration between the UTAUT2 model and perceived cred - ibility extension. Besides, the moderating effect of smartphone addiction on mobile wallet pay - ment adoption was also investigated in this research. The paper is structured as follows. In the next sections we will discuss the conceptual model development with proposed hypothesis, methodology, results, and conclusion. 809
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 2. Literature Review 2.1. Mobile Wallet Payment Mobile wallets are the digital tantamount of a physical wallet. Whenever payment for goods and services needs to be made, the user just takes money out of their wallet and makes a payment. Likewise, in the case of a mobile wallet, one can preload a certain amount of money through a credit card, a debit card, or internet banking that can be used to make online and offline payments (Chawla & Joshi, 2019). Simply put, a mobile wallet is the replacement of a person’s wallet with a mobile phone equipped with the functions of a bank card, credit card, house key, company ac - cess control ID, subway tickets, membership card, and so on (Shin, 2009). A mobile wallet can be considered as a repository of all information related to a user that is required for mobile trans - actions (Chawla & Joshi, 2019). The mobile wallet is the latest mode of m-commerce that enables users to make transactions, share content, online shopping, accessing as well as bookings available services and so on. For using mobile wallet services, a smartphone with internet connection is needed (Grover & Kar, 2018). According to Chawla and Joshi (2019), mobile banking and mobile money are the precursor concepts of mobile wallets. Accordingly, mobile wallet can be seen as an extension of mobile banking and mobile money wherein customers can store their personal information along with details of different payment methods. Mobile money mentions of a range of services that can be offered through a mobile phone like mobile money transfer, mobile payments and mobile banking. Mobile banking mentions of a system that allows user to conduct financial transactions in his or her bank account through a mobile device. Secure mobile wallet includes four functions: (1) gen - eration of user identity and verification for authenticity, (2) various options for making financial transactions, (3) provision of for making m-commerce transactions, and (4) security provisions (Sharma et al., 2018). In general, mobile wallet transactions are very secure and convenient (Amoroso & Ackaradejruangsri, 2017; X. Chen & Li, 2017; C. Kim, Mirusmonov, & Lee, 2010; N. Shaw, 2014). Mobile wallet services are considered as a growing part of the digital economy. Since its inception, the mobile wallet has had exponential business growth with the introduction of its mo - bile commerce technology and its unique marketing business plan, and through the successful recruitment of a group of an enterprising and strong marketing force (L.-D. Chen, 2008). Various payment methods are integrated into one application system, which it is called mobile wallet (Ma & Yi, 2012). The widespread use of mobile wallets may lead to cashless societies in the future. Karsen et al. (2019) found that there are advantages and disadvantages to using the mobile pay - ment method. Accordingly, several advantages are easily accessible anywhere, an independent payment and the ability to queue in a long line because of cash payments can be avoided. In ad - dition to the benefits, some issues such as premium prices of the payment system, perceived se - curity risks, incompatibility with large payments and immunity of mobile payments can prevent the use of mobile payment methods (Karsen et al., 2019). Mobile wallets can be used to facilitate transactions through multi-channels like consumer to consumer, consumer to business, consumer to machine (i.e., paying for small-value transactions at a device such as a parking meter) and consumer to online (Shin, 2009). With the rapid devel - 810
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 opment of mobile technology as well as the ever-expanding mobile user base, the mobile wallet has been considered as having growth potential in the mobile commerce industry (Au & Kauff - man, 2008). The industry struggles to develop and build sound mobile commerce applications and simultaneously provide an environment for secure, cost saving, convenient and efficient busi - ness transactions (Shin, 2009). Consumers have better flexibility to process point-of-sale trans - actions with mobile payments. The mobile wallet supports faster processing at the point of sale and increased opportunity for impulse buying, reduces the need for cash on hand. Merchants can more directly engage consumers by sending discount coupons to their mobile handsets. In addition, mobile payments offer carriers the opportunity to establish a stronger relationship with customers by becoming their payment service providers (Shin, 2009) 2.2. The Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) Many theoretical models developed from theories related to psychology and sociology are being used to explain the acceptance and use of technology (Kwateng, Atiemo, & Appiah, 2019). In order to understand the underlying reason behind the intention to accept consumer behavior, researchers recommend the use of UTAUT theory, presenting the most comprehensive theoretical model to assess intent (Tai & Ku, 2013). The UTAUT model is used as a base model to explain an individual’s intention to adopt new technologies across various organizational and societal contexts (Celik, 2016). On the basis of review of previous technology acceptance research works, the UTAUT model was developed (Venkatesh, Morris, Davis, & Davis, 2003) based on eight competing mod - els – namely, theory of reasoned action, TAM/TAM2, motivation model, theory of planned be - havior, combined TAM and TPB, model of PC utilization, innovation diffusion theory and social cognitive theory (Venkatesh et al., 2003). Accordingly, this theory integrates both psychological and behavioral theories to take up the drawbacks of each; it combines variables from each of the aforementioned theories and adjusts them to offer an empirically supported model that allows researchers to investigate all the core determinants of technology adoption intention (Farah, Hasni, & Abbas, 2018). The original UTAUT includes four latent variables: performance expectancy, effort expectancy, social influence and facilitating conditions, which used to make predictions on customer’s behavioral intention to adopt and use a technology in an organization is moderated by their experience, gender, voluntariness of use and age (Venkatesh et al., 2012). Previous studies have suggested that the constructs of performance expectancy, social influence and effort ex - pectancy determine the behavioral intention toward technology use, while behavioral intention and facilitating conditions influence the actual use of a technology (Venkatesh et al., 2003). In previous studies, the model that has been the most widely used for determining the factors that affect technology acceptance and use to be the TAM formulated by (Davis, 1989) and (Davis, Bagozzi, & Warshaw, 1989). However, Yun, Han, and Lee (2013) shows that although TAM the - ory is more widely used, it only explains 40 percent of usage intention while UTAUT theory pre - dicts more than 70 percent of adoption intention. Therefore, the UTAUT model holds greater statistical and explanatory power. However, Stofega and Llamas (2009) suggested that the number of technological applica - tions, devices as well as services that are targeted at users recently, it became more necessary to 811
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 study the influential variables that driving force users into accepting and using new technologies. This led to the introduction of the UTAUT2 model by (Venkatesh et al., 2012). UTAUT2 inherited the four key constructs (facilitating conditions, performance expectancy, social influence and ef - fort expectancy) from the UTAUT model with a customer perspective (Kwateng et al., 2019). In addition, Venkatesh et al. (2012) improved the UTAUT2 theory by adding three new variables - namely: habit, hedonic motivation, and price value and included only three moderating variables (age, experience and gender) to make the model applicable to the consumer use context (Farah et al., 2018). Therefore, the advantage and correctness of this theory proceed from the fact that it incorporates individual, technological and environmental constructs to understand the drivers behind individual adoption intention (Nwagwu & Akeem, 2013). Compared to UTAUT, the ex - tensions proposed in UTAUT2 produced a substantial improvement in variance explained in be - havioral intention (56% to 74%) and technology use (40% to 52%) (Tandon, Kiran, & Sah, 2016). UTAUT2 (Extended Unified Theory of Acceptance and Use of Technology) have been empirically tested and proved important to measure user’s intention to use a technology (Alal - wan, Dwivedi, & Rana, 2017; Liébana-Cabanillas & Lara-Rubio, 2017; Madan & Yadav, 2018; Shin, 2009; Xu & Du, 2018; Zhou, 2014) . Alalwan et al. (2017) applied UTAUT2 to mobile banking and (Slade, Dwivedi, Piercy, & Williams, 2015) applied UTAUT2 to mobile payments, extending the model with perceived risk and trust. There are several important studies in which UTAUT2 is used to measure intention to use m-wallet technology (Sharma et al., 2018; Si - vathanu, 2019; Tandon et al., 2016). The results showed that the expansion of UTAUT2 brought a significant improvement in measuring user behavioral intention. UTAUT2 model as a theo - retical foundation was conceptually and practically more effective and useful. (Singh, Sinha, & Liébana-Cabanillas, 2020). The Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) is defi - nitely proposed to clarify the technology acceptance from the customer perspective. Consequently, in quest of selecting an appropriate model covering almost all constructs determining consumer adoption MWP, the UTAUT2 has been found as a theoretical foundation for proposing the con - ceptual model used in this study (Alalwan et al., 2017). Besides, this study combines UTAUT2 and perceived credibility to increase its predictive power and allow for a more rounded under - standing of customer’s behavioral intention. This study will therefore assess the impact of each of these eight variables on behavioral intention to adopt mobile wallet payment. 2.3. Smartphone Addiction The innovation and development of digital technology has transformed the mobile phone from a simple communication tool into a popular portable smart device (Nie, Wang, & Lei, 2020). Today, Smartphones have become globally popular (Lee, Chang, Lin, & Cheng, 2014). Millions of users have been attracted and switched from regular cell phones to smartphone due to its various functions (C. Chen et al., 2017; Salehan & Negahban, 2013). Besides being used for the necessary functions of a traditional phone about calling and texting others, a smartphone with internet access offers users more advanced activities for informational, interactional and recreational purposes such as using internet surfing, social connecting, sending digital photos, ordering groceries, booking tickets, carrying out online transactions, accessing news fast, and 812
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 so on (Hawi & Samaha, 2017; B. Shaw & Kesharwani, 2019). According to Nie et al. (2020) compilation, two prerequisites for individuals to engage in smartphone activities are digital mo - bility and connectivity. The portability of a smartphone hugely increases the availability of mo - bile activities. Concurrently, the convenience of Internet connectivity dramatically expands the richness of mobile activities. Nowadays, smartphones have become a principal part of daily ac - tivities to the extent of transforming individuals’ lives. Societies are being transformed as well, with the number of smartphone users possibly reaching over 2 billion by the end of 2016 and a rapid increase of smartphone ownership around the world because using smartphone is becoming indispensable (Taylor & Silver, 2019), specifically in Asia (Mak et al., 2014), and a high preva - lence of smartphone addiction among adolescents (Buctot, Kim, & Park, 2018; Haug et al., 2015). In India, smartphone addiction among adolescents ranged from 39% to 44% (Davey & Davey, 2014). In Spain, the estimated prevalence of cell phone dependence among adolescents was 20% (26.1% in females, 13% in males) (Sánchez-Martínez & Otero, 2009). In the UK, the prevalence of problematic mobile phone users among British students was 10% (Lopez-Fernan - dez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014), and in South Korea, 30.9% of middle school students were classified as a risk group for smartphone addiction (Cha & Seo, 2018). The smartphone power and the magnitude of its pervasiveness have made some researchers shift their focus from Internet addiction and problematic mobile phone use to smartphone addiction (Hawi & Samaha, 2017). Technology addiction is a specific form of behavioral addiction and is defined as “a be - havioral addiction that involves human-machine interaction and is non-chemical in nature” (Grif - fiths & Renwick, 2003). Smartphone addiction is defined as a behavior characterized by excessive utilize of smartphones for the objective of finding solace, relaxation or stimulation which leads to continuous coveting when it is out of reach and sight (Buctot et al., 2018). It is a phenomenon that individuals cannot take their eyes off the use of the phone, which leads to neglect of other aspects of their lives (Goswami & Singh, 2016). With the convenient and attractive features of the smartphone as well as its versatile functions, human are becoming addicted and dependent on it such that for them a day without using a smartphone seems to be an incomplete day. Five symptoms of smartphone addiction: Disregard for harmful consequences, preoccupation, inability to control craving, productivity loss, and feeling anxious and lost (Bian & Leung, 2015). Several researches have observed that smartphone addicts cannot control their smartphone use despite adversely affecting their constitution, moral and social well-being (Cha & Seo, 2018; Chun, 2018; Samaha & Hawi, 2016). Users can form different smartphone habits and behaviors while using their smartphones or participating in mobile activities. In terms of some good smartphone habits, users can take ad - vantage of the smartphone’s instrumental value, to improve the productivity of the individuals and increase the efficiency of working teams such as checking emails by smartphones anytime and anywhere, use mobile calendars, reminders, and informational search engines smartphone users can install some social applications (e.g., Facebook and WeChat) to communicate with oth - ers, as well as view videos, listen to music, play online games for interaction and entertainment (Nie et al., 2020). Aside from the benefits, the fact that people start to overuse their smartphones regardless of time or place, may result in unwanted consequences such as stress, and low satis - 813
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 faction with life (Lee et al., 2014; Samaha & Hawi, 2016), detrimental to health (Buctot et al., 2018), sleep disturbances and depression (Lemola, Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015) as well as anxiety and stress among youths (Haug et al., 2015). 3. Conceptual model development 3.1. Perceived Credibility According to Wang, Lin, and Luarn (2006), perceived credibility has been considered as the security and privacy concerns of users as well as the authenticity of service providers. Erdem and Swait (2004) have defined perceived credibility as “the belief that a partner is trustworthy and has the required expertise to carry out transactions”. Perceived credibility construct have been used in order to measure the individual security, privacy, risk and trust issues (Karjaluoto, Koenig Lewis, Palmer, & Moll, 2010; Luarn & Lin, 2005; Williams, Rana, Dwivedi, & Lal, 2011). The result reveals this relationship was strongly supported in the context of mobile banking services. Damghanian, Zarei, and Siahsarani Kojuri (2016) researched the impact of security on accepta‐nce of online banking mediating through risk and trust. Risk had a significant negative influence on trust in online banking, whereas security and trust had an significant influence on online banking. Internet and financial transactions via mobile phones are vulnerable to fraud, [due to the high uncertainty, intangibility, heterogeneity and vague outcomes of using this channel (e.g. (Perkins & Annan, 2013; Yuen, Yeow, Lim, & Saylani, 2010)) and the trustworthiness and in - tegrity of mobile wallet payment service providers therefore play an important role in motivating consumers to use it (B. Shaw & Kesharwani, 2019). The rationale behind including perceived credibility as a determinant of behavioral intention is due to the fact that customers cannot eval - uate the transactional situation in online banking services as in a face-to-face interaction with the physical bank personnel (Tarhini, El-Masri, Ali, & Serrano, 2016). In the context of mobile banking, because of security issues and lack of perceived credi - bility and trust, consumer is worries that their personal information and/or money might be trans - ferred to someone else without their knowledge. Therefore, many consumers will refuse to use the technology voluntary as they have to provide sensitive information on the net by which they do not have control over their own behavior (Gupta, Manrai, & Goel, 2019). According to Perkins and Annan (2013), consumers will only take part in the interaction if the perceived rewards exceed their fears from using the technology. Because Tarhini et al. (2016) argued that integrating perceived credibility into UTAUT will offer better prediction of customers’ behavioral intention and UTAUT2 is the result of the ad - justment and extension process from UTAUT, I believe that perceived credibility will be one of the most influential factors that may affect the adoption of mobile wallet payment. Therefore, the following hypothesis is proposed: H1: There is a positive relationship between consumers’ perceived credibility of mobile wallet payment and their intentions to adopt the technology. 3.2. Components of UTAUT2 model Based on the UTAUT2 model, performance expectancy, from the user’s perspective, is de - 814
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 fined as “the degree to which using a technology will provide benefits to consumers in performing certain activities” (Venkatesh et al., 2012). In the context of mobile wallets, performance ex - pectancy could be describe as the degree to which users perceive that using it as an alternative technology for making payments will speed up and advance their performance while implement - ing their daily sales and purchasing transactions (Yadav, 2016). Customer may have behavioral intention of mobile payment adoption if their perception that utilizing mobile payment would support them to achieve benefits in executing payment tasks. Effort expectancy is “the degree of ease associated with consumers’ use of technology” (Venkatesh et al., 2012). From the consumers’ perspectives, effort expectancy is an individual’s estimation of the effort required to accomplish a task utilizing a given technology. In the context of mobile wallet, effort expectancy is the extent to which users expect mobile wallet technology to be effortless and easy enough to understand, so as to be adopted in their daily lives (Yadav, 2016). If the users found the mobile wallet payment services easy to use and do not require much effort then they are more likely to adopt it (Tarhini et al., 2016). Social influence (SI) is characterized as “the extent to which an individual perceives that important others believe he or she should apply the new system” (Venkatesh et al., 2003). According to Kwateng et al. (2019), social influence is the importance users attach to the per - ception of close relations to use a particular innovation. In other words, people surrounding cus - tomers (family members, colleagues, friends, superiors, ) accommodate them the information and encouragements, which could play an important role in contributing to the customers’ aware - ness as well as the intention toward adopting technology (Alalwan et al., 2017; Oliveira, Thomas, Baptista, & Campos, 2016; Zhou, Lu, & Wang, 2010). Facilitating conditions mention about con - sumers’ assurance of the availability of facilities and support systems to use an innovation (Venkatesh et al., 2003). In the context of mobile wallets, FC like affordability and availability of smartphones and internet connection require knowledge about mobile phones, privacy laws and security to determine the adoption of mobile wallets (Chawla & Joshi, 2019). Hedonic mo - tivation is defined as the degree to which individual believe that using a technology could provide fun or pleasure (Amoroso & Magnier-Watanabe, 2012). Hedonic motivation was added into UTAUT2 to apprehend the emotion of enjoyment, arguing that for voluntary systems, hedonic motivation will be more influential in predict of consumers’ behavioral intention to use a tech - nology (Venkatesh et al., 2012). In extending UTAUT to the consumer context, Venkatesh et al. (2012) added the construct of ‘price value’ to UTAUT2 to represent the degree to which an indi - vidual believes that utilizing a technology could make him or her face a cognitive trade-off be - tween perceived benefits and monetary cost of using the technology. In the context of smartphone usage, price is considered as not appropriate when the smartphone has already been purchased and its costs have been absorbed. Y. H. Kim, Kim, and Wachter (2013) revealed that users have driving force to use their smartphone due to the hedonic motivation. Habit is as the extent to which an individual tend to perform behaviors automatically as a result of learning from prior experiences (Limayem, Hirt, & Cheung, 2007). According to Amoroso and Lim (2017), habit is positively interrelated with continued use of mobile phones because many smartphone apps being designed in a alike manner. Customer participation in mobile commerce will develop some ha - bitual behaviors. Therefore, the following hypotheses are proposed: 815
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 H2: There is a positive relationship between consumers’ performance expectancy of mobile wallet payment and their intentions to adopt the technology. H3: There is a positive relationship between consumers’ effort expectancy of mobile wallet payment and their intentions to adopt the technology. H4: There is a positive relationship between consumers’ social influences regarding mobile wallet payment and their intentions to adopt the technology. H5: There is a positive relationship between consumers’ facilitating conditions regarding mobile wallet payment and their intentions to adopt the technology. H6: There is a positive relationship between consumers’ hedonic motivation of mobile wal - let payment and their intentions to adopt the technology. H7: There is a positive relationship between consumers’ price value of mobile wallet pay - ment and their intentions to adopt the technology. H8: There is a positive relationship between consumers’ habit of mobile wallet payment and their intentions to adopt the technology. 3.3. Smartphone Addiction Nowadays, smartphones have become popular globally. Millions of users have been at - tracted and switched from regular mobile phones to smartphones. The benefits of smartphones and its popularity have led some researchers to shift their focus from Internet addiction and prob - lematic mobile phone use to smartphone addiction. Smartphone addiction occurs when individuals spend too much of their time using their smartphones, leading to distractions and neglect of other jobs. At the same time, when not using the phone, they feel uncomfortable and craving. Smart - phone, like all other devices, if used properly and under good control, it will benefit the user. Ha, Chin, Park, Ryu, and Yu (2008) assessed the psychological issues of excessive mobile phone use. The results shown that young people are more attracted to smartphones and display certain ad - dictive behavioral symptoms (Hawi & Samaha, 2017). It was also found that social groups influ - ence users to build technology awareness. In a study of social media connection overload and its impact on psychological health, high connection habits can be considered a positive sign when they find to reduce reduce bad moods and improve negative behavior outcomes (LaRose, Con - nolly, Lee, Li, & Hales, 2014). The credibility of mobile wallet payment will be higher for high addicts compared to those with low addiction because smartphone addicts would have a greater knowledge of service providers, processes and ways to handle all vulnerable situations. Thus, smartphone addiction has been hypothesized to moderate the relationships between the predictors of behavioral intention to use mobile wallet payment and its behavioral intention to use. There - fore, the following hypothesis is proposed: H9a: The relationship between perceived credibility and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. H9b: The relationship between performance expectancy and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. 816
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 H9c: The relationship between effort expectancy and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. H9d: The relationship between social influence and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. H9e : The relationship between facilitating conditions and behavioral intention to use mo - bile wallet payment will be moderated by smart phone addiction. H9f: The relationship between hedonic and behavioral intention to use mobile wallet pay - ment will be moderated by smart phone addiction. H9g: The relationship between price value and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. H9h: The relationship between habit and behavioral intention to use mobile wallet payment will be moderated by smart phone addiction. Based on the aforementioned arguments, we suggest the research framework and hypothe - ses in Figure 1 that includes: Figure 1. Proposed Research Model 4. Methodology and Data 4.1. Measurement instruments A questionnaire-based survey was developed in order to test the theoretical constructs. Constructs and measurement items were adapted with slight modifications from technology ac - ceptance literature to build the questionnaire. Measurement items for constructs of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, and behavioral intention are adapted from (Venkatesh et al., 2012). Constructs of 817
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 perceived credibility was operationalized by items adapted from (B. Shaw & Kesharwani, 2019) and (Gupta et al., 2019). Finally, (B. Shaw & Kesharwani, 2019) was adapted to operationalize the smartphone addiction construct. All main measurement items were measured on a five-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). Two demographic variables related to age and gender were also included in the questionnaire. Age was measured in years and gender was coded using a 0 or 1 dummy variable where 1 represented women. The questionnaire was primarily developed in English, based on the literature with re - viewing for content validity experts from a university. Because the data collection procedure was operated in Vietnamese context, then later all English instruments was translated into Viet - namese language by a professional translator. The questionnaire was built online with Google Form service. 4.2. Data collection and processing Based on the research objectives, the subjects selected in the study are those who have used mobile wallet payment at least once a year and excluded those who have not used mobile wallet payments to ensure guarantee the ability to answer evaluation questions for mobile pay - ments. Data were collected using a convenience non-probability sampling method with the help of an online questionnaire. Five hundred and forty eight (548) students and alumni from univer - sities in Vietnam were contacted by e-mail and social network account in May of 2020. A hyper - link to the online survey was included in the messages. Two hundred and fifty four (254) valid responses were received. This data level passed the minimum sample size of 5: 1 standards pro - posed by (Bollen, Keppens, & Stalmans, 1998) and (Hair, Black, Babin, Anderson, & Tatham, 1998) (in which, the study has 44 main observed variables in the proposal model, the sample needs to be a minimum size of 220). The overall response rate was 46%, which is reasonable for studies of this scale. 75% of the subjects were females. Because of our convenience sampling, this gender distribution in the sample could be results of that fact that women are have more in - terest on mobile shopping and mobile wallet payment than men and more willing to answer the questionnaire. The age ranged from 18 to 47 years old. Individuals which are university students accounted for 59% of the data. The sample is an indicative group to test the instrument because university students have high potential to adopt new mobile technologies such as mobile wallet payment (Sohn & Kim, 2008). The descriptive results show that, the frequency of regular use from 1 time per month or more accounted for the majority (accounting for 75.2%) and the two most popular mobile wallet providers are Momo (54.3%,) and Airpay (20.5%). This study uses partial least square (PLS) path modeling to test the theoretical model due to its most prominent justifications: nonnormal data, small sample sizes and formatively measured constructs (Joe F Hair Jr, Sarstedt, Hopkins, & Kuppelwieser, 2014). PLS- was executed via a two-stage data analysis: measurement model and structural model. The structural model estimates are not examined until the reliability and validity of the constructs have been established. Ac - cording to Joseph F Hair Jr, Hult, Ringle, and Sarstedt (2017) and Henseler, Ringle, and Sinkovics (2009), for assessing measurement model, researchers need to determine outer loadings, com - posite reliability, cronbach’s Alpha, average variance extracted (), and discriminant validity. 818
12. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 5. Results and Discussions 5.1. Measurement model In order to evaluate the constructs’ reliability, Cronbach’s Alpha reliability test was utilized. Cronbach’s Alpha of all constructs are above the expected threshold of 0.6 and all their indicators of item-total correlation are above the expected threshold of 0.3, showing evidence of internal consistency and would be utilized in further analysis. In the next step, in order to identify the di - mensionality of measurement scales, principle components factor analysis with varimax rotation could be adopted to examine the underlying patterns for the vast numbers of variables. The results of the second run of exploratory factor analysis (EFA) with with orthogonal rotation (varimax) showed that 32 initial measurement variables of constructs that could affect to behavioral intention were reduced to 30 variables and divided into 8 factors (KMO = 0.878; χ² ( 435) = 4446.143, p <0.001). Besides, the results of scale reliability test with Cronbach’s Alpha also showed that these 8 factors meet the requirements of reliability coefficient of 0.6 or higher and all item-total corre - lation of each measurement variables are greater than 0.3. In the last step of measurement model verification, all 33 main observed variables of 9 latent variables were used to conduct the partial least squares structural equation modeling (PLS-SEM). The analysis results show that the outer loadings for each of the latent variable of the present study were sufficiently greater than 0.7. Therefore, the present successfully met individual item reliability criterion. The results of Cron - bach’s alpha for all variables were greater than 0.7 and the composite reliability (CR) coefficients for each of the latent variable ranged from 0.860 to 0.926, this indicating strong reliability of the measures. The average variance extracted (AVE) have sufficiently greater than 0.6, thus the study demonstrated adequate convergent validity. The shared variance between factors was below the square root of the AVE of the individual factors, ratifying the discriminant validity. The results of cross loading show that all individual items are loaded higher on their respective constructs than on the other constructs. The square root of AVE was higher than the correlations among the latent variables. The heterotrait-monotrait ratio of correlations (HTMT) values, which lie above the di - agonal in Table 2, are below the threshold of 0.85. Therefore, the discriminant validity of the measurement model in this study is acceptable. VIF of all observed variables are below 3, thus, multicollinearity is not a concern in this study. The details are presented in Table 1 and Table 2. Table 1: Convergent Validity Composite Reli - Average Vari - Variance In - Cronbach’s Factors ability (CR) ance Extracted flation Factor Alpha (a) (AVE) (VIF) Perceived credibility 0.926 0.643 1.54 0.91 Performance expectancy 0.906 0.762 1.60 0.84 Effort expectancy 0.914 0.728 1.81 0.88 Social influence 0.862 0.677 1.36 0.76 Facilitating conditions 0.860 0.674 1.69 0.76 Hedonic motivation 0.879 0.708 1.48 0.80 Price value 0.916 0.783 1.99 0.86 Habit 0.880 0.647 1.66 0.82 Behavioral intention 0.875 0.701 0.79 819
13. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Table 2: Discriminant Validity BI EE FC HB HM PC PE PV SI BI 0,837 a 0,571 c 0,628 c 0,643 c 0,333 c 0,593 c 0,586 c 0,607 c 0,497 c EE 0,479 b 0,853 a 0,732 c 0,268 c 0,255 c 0,383 c 0,477 c 0,581 c 0,357 c FC 0,489 b 0,596 b 0,821 a 0,362 c 0,352 c 0,390 c 0,482 c 0,553 c 0,326 c HB 0,536 b 0,240 b 0,289 b 0,804 a 0,640 c 0,412 c 0,437 c 0,529 c 0,554 c HM 0,270 b 0,216 b 0,277 b 0,491 b 0,841 a 0,405 c 0,230 c 0,468 c 0,495 c PC 0,509 b 0,348 b 0,330 b 0,360 b 0,340 b 0,802 a 0,514 c 0,580 c 0,408 c PE 0,479 b 0,416 b 0,392 b 0,384 b 0,192 b 0,452 b 0,873 a 0,603 c 0,345 c PV 0,503 b 0,512 b 0,455 b 0,461 b 0,394 b 0,512 b 0,514 b 0,885 a 0,404 c SI 0,386 b 0,282 b 0,239 b 0,434 b 0,375 b 0,338 b 0,279 b 0,333 b 0,823 a a Square root of the average variance extracted (AVE) of each latent variable b Correlation between latent variables c HTMT value 5.2. Hypotheses Testing The R 2 value of the model was 0.52 signifying that means 52 percent of the variance in be - havioral intention to adopt mobile wallet can be explained by all exogenous variables. The cross- validated redundancy value (Q 2) in this study is 0.34 (greater than zero). This suggests that the model has predictive relevance. Five paths out of the eight relationships tested were significant at the significance level of 95 percent. Specifically, perceived credibility showed significant pos - itive impact on customer’s behavioral intention to use mobile wallet ( =0.22, t-value=3.35, p 0.05). Thus, H2 is not supported. Effor𝛽t expectancy indicated significant positive impact on customer’s behavioral intention to use mobile wallet ( =0.15, t- value=2.55, p 0.05). Hence, the posited H4 is not reinforced by the data and is rejected. 𝛽 On the other hand, the standardized β coefficients pointed out that the facilitating condi - tions, as hypothesized in H5, also showed significant positive effect on c𝛽ustomer’s behavioral intention to use mobile wallet ( =0.19, t-value=2.47, p<0.05), inferring that H5 is endorsed. Like - 820 𝛽
14. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 wise, as proposed in H6, hedonic motivation showed significant effect ( =-0.12, t-value=2.28, p 0.05). Thus, H7 is not supported. The standardized β coefficients pointed out that the habit, as hypothesized in H8, also showed significant effect on consumer’s behavioral inten - tion to use mobile wallet ( =0.33, t-value=5.65, p<0.05), inferring that H8 is endorse𝛽d. Indeed, this factor is anticipated and believed to be the strongest determinant of consumer’s behavioral intention to adopt mobile wallet payment. The Table 3 and Figure 2 illustrate the results of struc - tural model in detail. 𝛽 Table 3: Statistical Results of the Structural Model Constructs Beta SE t-value p-value Confirmed / H1: PC à BI 0.22 0.07 3.35 0.00 Confirmed H2: PE à BI 0.09 0.07 1.30 0.19 Rejected H3: EE à BI 0.15 0.06 2.55 0.01 Confirmed H4: SI à BI 0.08 0.06 1.37 0.17 Rejected H5: FC à BI 0.19 0.08 2.47 0.01 Confirmed H6: HM à BI -0.12 0.05 2.28 0.02 Confirmed H7: PV à BI 0.05 0.07 0.64 0.52 Rejected H8: HB à BI 0.33 0.06 5.65 0.00 Confirmed Figure 2. The Structural Model (PLS-SEM) 821
15. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 5.3. Moderating Role of Smartphone Addiction The moderating effect of smartphone addiction on the relationship between perceived cred - ibility, performance expectancy, effort expectancy, social influence, facilitating conditions, he - donic motivation, price value, habit and customer’ behavioral intention to adopt mobile wallet, as proposed in H9a-H9h, respectively, was tested using multi-group analysis (MGA). Mean-split was performed to create two groups. Out of the 254 respondents, 113 of them were classified into high smartphone addiction, while the remaining 141 respondents were grouped into low smartphone addiction. The PLS-SEM results in Table 4 depicted that consumer’s smartphone addiction signifi - cantly moderated the influence of perceived credibility on customer’s behavioral intention to use mobile wallet (D = -0.12, p <0.05), effort expectancy on customer’s behavioral intention to use mobile wallet (D = 0.09, p <0.05), facilitating conditions on customer’s behavioral in - tention to use mobile wallet (D = -0.18, p <0.05), habits on customer’s behavioral intention to use mobile wallet (D 𝛽 = -0.27, p <0.05). Therefore, H9a, H9c, H9e and H9h were supported. The relative effect of effo𝛽rt expectancy on customer’s behavioral intention to adopt mobile wallet was higher in the group with hi𝛽gh level smartphone addiction compared to the group with low level of smartphone a𝛽ddiction. However, the relative influence of perceived credibility, facili - tating conditions and habit on customer’s behavioral intention to adopt mobile wallet was higher in the group with low level of smartphone addiction compared to the group with high level of smartphone addiction. Table 4: Moderating Effect Testing Relations High smartphone addiction Low smartphone addiction Relations Path coefficients p-value Path coefficients p-value H1a: PC g BI 0.14 0.33 0.26 0.00 H2b: PE g BI 0.22 0.15 0.03 0.71 H3c: EE g BI 0.20 0.04 0.11 0.16 H4d: SI g BI 0.20 0.11 0.00 1.00 H5e: FC g BI 0.11 0.11 0.43 0.00 H6f: HM g BI -0.11 0.34 -0.11 0.09 H7g: PV g BI 0.01 0.90 0.01 0.90 H8h: HB g BI 0.17 0.11 0.44 0.00 6. Discussion From the results of PLS-SEM, the study investigated the influence of perceived credibility, effort expectancy, facilitating conditions, hedonic m otivation and habit on the behavioral intention of customers to adopt mobile wallet payment services. By including perceived credibility along 822
16. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 with the UTAUT2 constructs, the model is able to explain 52% of the variance in behavioral in - tention which supports the inclusion of perceived credibility as external factor in the conceptual model. The study has found that habit is the strongest determinant within the proposed model with a coefficient value of 0.33. The reason could be the transition from electronic payment to mobile wallet payment is not too hard. Perceived credibility was also approved to have a signif - icant impact on the behavioral intention to adopt mobile payment services with a coefficient value of 0.22. This confirms the important role of perceived credibility in motivating the under banked and unbanked customers to adopt a new service such as mobile wallet. An individual is more willing to adopt a new financial service when there is credibility in the service provider and as - surance against security and privacy risks. This could be due to the sensitive nature of financial transactions that are conducted by electronic channels. Such results are in the line with what has been approved by prior mobile banking and mobile payment studies regarding the role of per - ceived credibility (Gupta et al., 2019; Tarhini et al., 2016). Hence the role of perceived credibility is of great importance in defining the customers’ intention to adopt mobile financial services. Likewise, effort expectancy was also approved to have a positive significant impact on the behavioral intention to adopt mobile wallet services. This indicates that, if the customers perceive that using mobile wallet payments services needs less effort and is not difficult, they will be more likely to adopt the same. The findings also reveal that facilitating conditions is significant for the behavioral intention to adopt mobile wallet payment. This demonstrates that respondents pay a particular interest in the existence of facilities, resources, skills and assistance that are required to use mobile wallet payment successfully. The findings also reveal that hedonic motivation has a negative influence for the behavioral intention to adopt mobile wallet. The result of this study is inconsistent with the positive effect of hedonic motivation with previous research of (Gupta et al., 2019; Venkatesh et al., 2012). This could be for a transaction related to financial issues, user will not concern too much about joy, entertaining, pleasure or enjoyment, what mobile wallet providers need to do is to help users feel secure when making the transaction. Especially when mobile wallets are still not popular in the context of Vietnam On the other hand, the impact of performance expectancy, social influence and price value on behavioral intention was insignificant. The reason could be in Vietnam market, there are many mobile payment services for consumers to choose, especially internet banking services and the habit of using cash in transactions for Vietnamese people is still considerable. The payment needed to meet daily needs still depends heavily on cash. Thus, their focus is more on the advan - tages that mobile wallet provides and they do not concern too much about performance ex - pectancy. Likewise, there are many mobile payment services in Vietnam, mobile wallet providers are competing on the quality and price of services to attract users. Therefore, when compared to other forms of payment, researchers do not value the reason for accepting mobile wallets. Multi-group analysis results suggest the differential impact of perceived credibility on be - havioral intention to use for high versus low smartphone-addicted users. The impact was signif - icantly stronger for less addicted individuals compared to more addicted ones. The rationale may be that highly addicted individuals are already using smartphone applications for their payment. 823
17. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 Highly addicted individuals perceive mobile transactions as less credibility as they are aware of the financial risks, which may be the reason for the negative result. In the case of low smartphone addiction, the vulnerability of Internet and financial transactions still remains, but individuals are less aware of its negative impact. Likewise, the results also show that there is a differential impact of facilitating conditions on behavioral intention to use for high versus low smartphone-addicted users. The impact was significantly stronger for less addicted individuals compared to more ad - dicted ones The effect of smartphone addiction on the relationship between habit and behavioral in - tention was less negative for individuals with high smartphone addiction in comparison to those with low smartphone addiction. Highly addicted individuals are already using smartphone appli - cations that fulfill the basic requirement of payment. Therefore they do not evaluate mobile wallet as a habit of their use. The relationship between effort expectancy and behavioral intention was supported for highly addicted individuals. Since such individuals have had a lot of experience interacting with phone applications than individuals with lower addiction. Thus, the impact of effort expectancy on behavioral intention was positively moderated by smartphone addiction in favor of highly ad - dicted individuals. 7. Conclusion With the tremendous development of mobile technology as well as the ever-expanding mo - bile user base, the mobile wallet has been considered as having growth potential in the mobile commerce industry. Moreover, the digital economy is leading to cashless societies in the future, therefore research about mobile wallet is really necessary. There have been many studies on mo - bile payments, but there are fewer studies on a specific type of mobile payment such mobile wal - let, especially the moderation role of smartphone addiction on mobile wallet payment adoption. The results of this study complement the lack of empirical research on factors affecting con - sumer’s behavioral intention to adopt mobile wallet in the world and especially in Vietnam con - text. The study also contributed to an increase in the literature examining the role of perceived credibility as well as other factors in the UTAUT2 model on behavioral intention to use mobile wallet payment. This is also one of the few pioneering researches that introduces smartphone ad - diction while studying young people’s intention to use smartphone applications as a means of enhancing smartphone app usage. Beside of our study’s main contribution that adds into the existing body of knowledge, we also recognize its limitations, mostly regarding the sampling with typically young, highly edu - cated people as responders. The respondents’ behavioral patterns might diverge to some extent in comparison with the population average. With the behaviors that are mostly more pioneering and rapider to adopt new technologies, this sampling may have biased the effects. Future research can be constructed based on this study by examining the proposed model in different age groups or applying this model to other countries and also other contexts. Moreover, future research should make a longitudinal study to yield better results than a cross-sectional one because the relationship among factors that affecting on behavioral intention to use in different phases of adoption would provide more meaningful insights. 824
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