Ý định sử dụng dịch vụ thanh toán di động của thế hệ millennials: Nghiên cứu tại Thành phố Hồ Chí Minh

Nội dung text: Ý định sử dụng dịch vụ thanh toán di động của thế hệ millennials: Nghiên cứu tại Thành phố Hồ Chí Minh

1. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 INTENTION OF MILLENNIALS TOWARDS USING MOBILE PAYMENT SERVICES: AN EMPIRICAL IN HO CHI MINH CITY, VIETNAM Ý ĐỊNH SỬ DỤNG DỊCH VỤ THANH TOÁN DI ĐỘNG CỦA THẾ HỆ MILLENNIALS: NGHIÊN CỨU TẠI THÀNH PHỐ HỒ CHÍ MINH Nguyen Tuan Duong, Nguyen Thi Quynh Nga, Bui Nhat Tien, Nguyen Thi Hong Diem, Trinh Gia Han Foreign Trade University, Ho Chi Minh City campus nguyentuanduong.cs2@ftu.edu.vn ABSTRACT The primary objective of this study is to identify factors affecting customer behavior intention toward using mobile payment services of millennials in Ho Chi Minh City, Vietnam. The proposed research model is based on the unified theory of acceptance and use of technology combining the perceived risk and trust. Data collected through field research by structured questionnaire with 369 valuable samples were analyzed with the implementation of Structural Equation Modeling (SEM) in order to indicate the factors that influence the intention of using the mobile payment of millennials in Ho Chi Minh City, Vietnam. The result showed that the revised model had a good fit to the data. The positive influence of 04 factors including Performance expectancy, Effort expectancy, Social influence, and Trust on millennials behavior intention towards using mobile payment services was confirmed. Besides, the indirect impact Effort expectancy and trust on customers’ behavior intention through Performance expectancy also indicated. However, the influence of Hedonic motivation on behavior intention and Perceived risk on behavior intention was not supported. These findings of the research are expected to be beneficial for both theoretical and managerial implications related to the intention to use mobile payment services. Keywords: Customer behavior intention, financial technology, millennials, mobile payment. TÓM TẮT Nghiên cứu này với mục tiêu chính là xác định các yếu tố và mức độ ảnh hưởng của các yếu tố đó đến ý định sử dụng dịch vụ thanh toán di động của thế hệ millennials tại thành phố Hồ Chí Minh. Mô hình nghiên cứu đề xuất được xây dựng dựa trên lý thuyết thống nhất về chấp nhận và sử dụng công nghệ mở rộng (UTAUT2) kết hợp biến cảm nhận về rủi ro và sự tín nhiệm. Dữ liệu được thu thập thông qua khảo sát bằng bảng hỏi khảo sát với 369 mẫu có giá trị được đưa vào phân tích theo mô hình cấu trúc tuyến tính (SEM). Kết quả chỉ ra rằng, mô hình phù hợp với dữ liệu thị trường. Có 04 yếu tố tác động thuận chiều lên ý định sử dụng dịch vụ thanh toán di động bao gồm: hiệu quả mong đợi, nỗ lực kỳ vọng, ảnh hưởng của xã hội, sự tín nhiệm. Trong đó, yếu tố sự tín nhiệm, hiệu quả mong đợi có mức tác động thuận chiều cao nhất. Bên cạnh đó, tác động gián tiếp nỗ lực kỳ vọng và sự tín nhiệm đối với ý định sử dụng dịch vụ thanh toán di động thông qua hiệu quả mong cũng được chỉ ra. Tuy nhiên, ảnh hưởng của động lực hưởng thụ đối với ý định hành vi và sự cảm nhận rủi ro đối với ý định sự dụng không được khẳng đinh. Nghiên cứu đóng góp ý nghĩa về lý thuyết và ý nghĩa thực tiễn cho cả tổ chức và cá nhân liên quan đến ý định sử dụng các dịch vụ thanh toán di động nhằm làm tác động đến định sử dụng dịch vụ thanh toán di động của khách hàng. Từ khóa: Ý định sử dụng, công nghệ tài chính, millennials, thanh toán di động. 1. Introduction In recent years, the Fourth Industrial Revolution (Industry 4.0) is growing at a strong pace, especially with the development of Financial Technology (Fintech). This trend is bringing a lot of positive changes to Vietnamese economy in recent years and it strongly influence development strategies and business practices of traditional financial service providers. In this field, mobile payment service witnessed the largest number of startups with more than 20 companies (Vietnam Fintech Startups 2016, 2017). The above data and the government direction “Towards a cashless society” by the Deputy Governor of the State Bank of Vietnam have proven the importance and potential of mobile payment service development. However, it is evitable to see that deploying mobile payment in Vietnam still encounters 202
2. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 great difficulties and that the number of customers accepting this service is very limited. One of the reasons is that the Vietnamese’s cash payment habit is still popular among the majority of people. Moreover, the lack of understanding towards this new technology also leads to concerns about risks (Dao My Hang et al, 2018). Therefore, Fintech startups must acknowledge the concerns of customers when using mobile payment services and factors affecting their intention to adopt this kind of service. Especially, the target customers of mobile payment services in particular and fintech services in general mostly are the youngers, tech-savy millennials and wealthier customers. This group of target customer need to be explored and understood clearly. Therefore, this study will facilitate the business managers to understand the key factors affecting millennial customers’ intention to use mobile payment services. First and foremost, the study will briefly introduce the theoretical basis of behavioral intention, thereby proposing the research model and hypotheses regarding the relationship between these factors. Secondly, the factors influencing the millennial customers’ intention to use mobile payment services will be examined with the data collected from target respondents in Ho Chi Minh City, Vietnam. Finally, based on the analyzed results, the discussion and implications will be discussed. Besides, further research directions to overcome the limitations of this study will also be mentioned. 2. Literature background and methodology 2.1. Literature background 2.1.1. Mobile payment service (MPS) Mobile payment service is defined as “a type of payment transaction processing, in which the payee uses mobile communication techniques in conjunction with mobile devices for initiation, authorization, or completion of payment” (Pousttchi & G Wiedemann, 2019). Similarly, according to Dahlberg, Guo, and Ondrus (2015), mobile payment is a modern payment service which is based on wireless communication technology of the mobile phone. Specifically, customers can purchase goods, enjoy services and pay bills via a mobile device. 2.1.2. Millennial generation The term Millennials often refers to individuals reaching adulthood around the beginning of the 21st century. The first generation to grow up and integrate intimately with digital devices and the Internet (Thompson & Gregory, 2012). In this study, researchers have used different birth-year boundaries to determine Millennial generation. In general, the earliest to be identified as Millennials are those born in 1976 and the latest being born in 2004. This generation contains youngers, tech-savy customers. Vietnam has approximately 58% young population born and live in the internet era, 44% of total population is Internet User, and 69% people penetrating mobile phone and smartphone. Vietnam is a promising market online businesses, digital agencies and application developers. Besides, with only 31% of 15 years old and older had an account at a financial institution in 2014 (World Bank, 2015) and most of consumers experienced their first online services via mobile instead of other devices, clearly, it brings a lot of opportunities for mobile payment services. 2.1.3. Conceptual model An individual’s behavioral intention is defined as the magnitude of one’s intention to use the product or service in the future. In the self-service technology, behavioral intention is considered as the extent to which the customer tend to use this kind of services. Behavioral intention has been studied continuously and was identified to be the strongest factor affecting technology adoption behavior (Ajzen, 1985; Venkatesh, G Morris, B Davis, & Davis, 2003; Venkatesh, Thong, & Xu, 2012). Numerous models have been used to investigate customer behavior intention towards technology acceptance and adoption. This study used the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) as the model is proposed a theoretical foundation for proposing a conceptual model. The UTAUT2 is developed to explain technology acceptance from the customer perspective (Venkatesh et al., 2012). This is an 203
3. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 extended version of the Unified Acceptance and Technology Utilization Theory (UTAUT), where three new constructs (hedonic motivation, price value, habit) were added to the original model (including performance expectancy, effort expectancy, social influence, facilitating conditions). The UTAUT2 model has been proven to be a better model compared to others as it includes a full range of factors that explain customer’s behavioral intention, as well as provide a more accurate prediction (C.-Y. Huang & Kao, 2015). Moreover, the UTAUT model is theorized to study the usage behavior from an organization’s perspective, whereas the UTAUT2 model is applied in customer-focused contexts. As the purpose of the study is to identify factors affecting the customer’s intention to use mobile payment services, the UTAUT2 model might provide more insights. However, the UTAUT2 model doesn’t include two factors: perceived risk and trust, which are both important factors affecting customer’s behavior intention to use mobile payment services. Specifically, perceived risk and trust have been studied extensively in the field of mobile payments. The relationship between perceived risk has been proven by many studies studies (Koenig-Lewis, Palmer, & Moll, 2010; Lu, Yang, Y. K. Chau, & Cao, 2011; Luo, Li, Zhang, & Shim, 2010, Shin, 2010). In addition, the effect of trust has been shown to have a profound impact on customers’ intention in several studies (Y. Huang & Liu, 2012; Lu et al., 2011; Shin, 2010; Zhou, 2011). For reasons above, in this study perceived risk and trust have been included as an extension to the UTAUT2 model to broaden the theoretical horizon of UTAUT2. Because this study is expected to investigate the intention of millennials toward mobile payment services. Thus, the authors decided to not include two factors, habit and price value, in the research model, because these factor reflect the evaluation of customer adaptions that is used for the customer after using the services. 2.1.3.1. Performance expectancy (PE) Performance expectancy is defined as the benefits and utilities that could be attained from using such innovative channels (Venkatesh et al., 2003). Performance expectancy has been discovered to be one of the most influential factors driving behavior intention to adopt and use information systems and information technology (Dwivedi & Lal, 2007). Venkatesh et al. (2012) argued that customers have a tendency to compare the benefits and utilities attained in relation to the money cost paid to use technology. Thus, further benefits and utilities perceived when using technology in general and mobile payment service in particular could contribute to the value of the technology. Therefore, this study proposes the following hypothesis: H1: Performance expectancy has a positive influence on customer’s behavior intention to adopt mobile payment services. 2.1.3.2. Effort expectancy (EE) Effort expectation can be conceptualized as the “extent of ease connected with the use of system” (Venkatesh et al., 2003). There have been several empirical studies which have proven the important role of effort expectancy (Martins, Oliveira, & Popovič, 2014; Riffai, Grant, & Edgar, 2012) or captured the factors such as perceived ease of use (Alalwan, Dwivedi, Rana, & Simintiras, 2016; Kesharwani & Bisht, 2012; Rodrigues, Oliveira, & Costa, 2016) in shaping customers’ behavior intention toward mobile payment services. According to (Davis et al., 1989), individuals could be involved in the cognitive trade- off process between the efforts required to successfully apply the technology in front of the benefits and advantages attained by using technology. Thus, the research proposed that perceived ease of use could contribute to the behavioral intention to use technology directly or indirectly by facilitating the role of perceived usefulness. Therefore, this study formulates the following hypotheses: H2: Effort expectancy has a positive influence on customer’s behavior intention to adopt mobile payment services. H3: Effort expectancy has a positive influence on Performance expectancy. 204
4. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 2.1.3.3. Social influence (SI) The definition of Social influence can be described as the “extent to which an individual perceives that important others believe he or she should apply the new system” (Venkatesh et al., 2003). The role of Social influence in enhancing behavior intention toward technology has been emphasized in previous studies (Abu-Shanab, Pearson, & Setterstrom, 2010; Martins et al., 2014). Most prominently, according to (Nysveen, E. Pedersen, & Thorbjørnsen, 2005), Social influence has a positive impact on customers’ intention to use mobile services such as messaging, communication and payment. Thus, the study postulates the next hypothesis: H4: Social influence has a positive influence on customer’s behavior intention to adopt mobile payment services. 2.1.3.4. Facilitating conditions (FC) Facilitating conditions is defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system” (Venkatesh et al., 2003). Several studies on factors that affect behavior intention toward using mobile payment services have pointed out that Facilitating conditions have a positive effect on customers’ behavior intention (Ming Lang Yeh & Yin Li Tseng, 2017; Titus Tossy, 2016; Lo Ka Foon, 2014). This can be attributed to the fact that using such technology usually require a particular set of skill, resources and technical infrastructure (Alalwan, Dwivedi, Rana, Lal, & Williams, 2015; Alalwan, Dwivedi, & Williams, 2016; Zhou, Lu, & Wang, 2010). Thus, the more favorable the conditions are, the safer and easier it is for customers to use the service. Consequently, the study proposes that: H5: Facilitating conditions has a positive influence on customer’s behavior intention to adopt mobile payment services. 2.1.3.5. Hedonic motivation (HM) According to (Venkatesh et al., 2012), Hedonic motivation is conceptualized as the feeling of cheerfulness, joy or enjoyment, which is stimulated by applying technology. They also pointed out that humans are not only concerned about the performance of the technology, but also the feeling brought about by the technology. It has been discovered that Hedonic motivation is among the strongest factors affecting behavior intention toward the use of technology. The study by (Nguyen, Khanh Cao, Linh Dang, & Anh Nguyen, 2016) mentioned perceived enjoyment is among the predictors of customers’ behavior intention toward the use of mobile payment services in Vietnam. Therefore, it can be seen that the greater the enjoyment of using mobile payment services, the higher the behavior intention toward the use of mobile payment services. Thus, the study articulates the following hypothesis: H6: Hedonic motivation has a positive influence on customer’s behavior intention to adopt mobile payment services. 2.1.3.6. Trust (TR) The definition of Trust can be described as the faith that the other party will act following the proper behavior of generosity, integrity, and ability (Gefen, 2000; Zhou, 2011). Customers with a high level of confidence in mobile payment services will feel the integrity and accountability of the service provider, and at the same time, increase their intention to use the service (Gefen, Karahanna, & Straub, 2003). Despite not included in the UTAUT2 model, Trust plays an important role to the behavior intention toward the use of mobile payment services because transactions made through mobile networks are vulnerable and less reliable compared to traditional methods. In the context of Vietnam, Trust has been proven to pose a positive impact on customers’ behavior intention (Nguyen et al., 2016). Besides, Trust was empirically proven to significantly influence not only customers’ intention but also on Performance expectancy (Luo et al., 2010). In the study by (Gefen et al., 2003), Trust is argued to have a 205
5. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 direct effect on behavior intention to use mobile payment services or indirectly influence behavior intention by facilitating the role of Performance expectancy. Consequently, the study proposes that: H7: Trust has a positive influence on customer’s behavior intention to adopt mobile payment services. H8: Trust has a positive influence on Performance expectancy. 2.1.3.7. Perceived risk (PR) Perceived risk can be defined as the likelihood of a customer suffering a loss in pursuit of the favored consequences of using a service (Featherman & Pavlou, 2003). Perceived risk is an important factor, affecting negatively customers’ behavior intention toward the use of technology (Baabdullah, Nasseef, & Alalwan, 2016; Gan, Clemes, Limsombunchai, & Weng, 2006; Gerrard, Cunningham, & Devlin, 2006). The particular interest in this factor can be attributed to the high uncertainty, intangibility, heterogeneity, vagueness characteristics as well as the lack of human interaction in the field of online payment (Al-Gahtani, 2011; Featherman & Pavlou, 2003; Kesharwani & Bisht, 2012; M. Curran & L. Meuter, 2005; Martins et al., 2014). Thus, the following hypothesis is formulated: H9: Perceived risk has a negative influence on customer’s behavior intention to adopt mobile payment services. The proposed research model is shown in the below Figure. Figure 1: The proposed research model Source: Venkatesh et al. (2012), Featherman & Pavlou (2003) and Gefen et al. (2003) 2.2. Methodology To achieve the research objectives, a quantitative research approach will be employed. Because the quantitative research approach is most widely employed in an empirical study, in which connections between variables is explored and examined (Bell, Bryman, & Harley, 2018). The population of this research contains millennials in Ho Chi Minh city. The researcher will attempt to select a set of samples that could have the presence of respondents with difference in experience of using smartphones and the familiarity with mobile payment service. Besides, the respondents’ characteristics are expected various in terms of gender, age, income. Under the constraints of time and budget, a sample will be selected from the target population by non-probability sampling technique which does not allow the population with the same probability to be selected. This study will be conducted in two main phases including preliminary research and formal research. In the preliminary research phase, from research objectives, theoretical background, previous relevant studies, a research framework will be proposed. In addition, a draft of structured-questionnaire 206
6. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 will be prepared in English with measurement scales of the elements in UTAUT2 model and behavioral intention from the adoption from previous relevant studies (Venkatesh et al. 2012, Featherman & Pavlou 2003, Gefen et al.,2003). The questionnaire will be translated into Vietnamese by two lecturers who have professional knowledge of marketing domain. To ensure the accuracy and clarity of the used terminologies in the questionnaire, 15 people including 5 officers, 5 lectures and 5 students were invited to review the questionnaire before releasing the final version that will be used in the formal survey. In the second phase, formal research and data analysis process are implemented. A mass survey will be conducted with a final questionnaire that has been adjusted after the pretest step in order to obtain the empirical data. The final questionnaire is presented in the appendix 1. 2.2.1. Sample A sample of 369 millennials in Ho Chi Minh City was surveyed and included in this study. This sample size is satisfied the requirement of Regression analysis is n 8m + 50 (in which: n is the sample size; and m is the number of independent variable) according to Tabachnick and Fidell (2001). In this study, with m = 7, the minimum sample size of the study is 106. Besides, according to Bollen (1989), in order to confirm the reliability of the sample size as well as EFA analysis, at least 5 samples are needed for each observed variable. Here in the proposed model and scale development, with the total of 29 independent variables, the sample size must be at least 29x5 = 145. The data were collected by two forms of the survey including hardcopy and online surveys. In particular, the hardcopy forms of the questionnaire were distributed to the target respondents in some classes at universities in Ho Chi Minh City and in some few convenience stores such as Circle K, B's Mart, where attracts a lot of young people. Besides, the link of online survey was sent to email of target respondents to collect data. 2.2.2. Measurement The questionnaire was designed based on the previous measurement, translated into Vietnamese, and pretested. The observed variables were measured by the Likert scale with anchors ranging from 1- totally disagree to 5- totally agree to measure the observed variables. Besides, there are some close-ended questions to collect the respondent’s information in terms of the experience in using smart phone, the similarity of mobile payment service, respondents’ demographic elements. The measurement of the research factors adopted from the research of Venkatesh et al. (2012), Featherman & Pavlou (2003) and Gefen et al. (2003). In which, there are 33 items measured 8 variables included in the final questionnaire including Behavior intention (BI) - 4 items (ex. I plan to use mobile payment services in future), Performance expectancy (PE) – 4 items, Effort expectancy (EE) – 4 items, Social influence (SI) – 3 items, Facilitating conditions (FC) – 4 items, Hedonic motivation – 3 items, Trust (TR) – 4 items, Perceived risk (PR) – 7 items. 2.2.3. Data analysis In order to analyze the statistical data after cleaning, the study will apply the following process. Firstly, descriptive statistics analysis of demographic variables will be deployed in order to demonstrate reliability and ability to represent for the target population of the sample. The results of descriptive statistics analysis will provide general information of respondents. The validity and reliability of the research constructs and the measurement items will be demonstrated through a preliminary scale testing (Cronbach’ Alpha testing, Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) analysis with the support of SPSS software packages and AMOS software with the whole collected samples. To test the reliability and internal consistency of measurement scales, the value of Cronbach’ alpha reliability of all items must be more than 0.70 and the Corrected Item-Total Correlation must be equal or lager than 0.5. Concerning EFA, in order to demonstrate the suitability of factor analysis with this set of data, the result of EFA analysis must satisfy these elements: (1) Sig value. Bartlett's test 50%. Convergent validity and discriminant validity will be deployed with the set of retaining items after eliminating the 207
7. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 items that are not satisfied with the reliability requirement. In particular, the convergent validity assessment needs to satisfy two requirements, in which each construct must have the standardized regression loading value of each construct greater than 0.5 and the average variance extracted (AVE) over 0.5. The discriminant validity assessment is required to show the greater value in the comparison between the AVE of each pair of the research constructs and the corresponding squared inter-constructs correlation. In addition, the Confirmatory factor analysis (CFA) in order to examine the fitness of the model with the set of data need the have the indexes satisfied these criteria: 1 0.9; GFI- Goodness of Fit Index>0,8; CFI-Comparative Fit Index>0.9; RMSEA-Root Mean Square Error of Approximation < 0,08 (Hair, Black, Babin, Anderson, & Tatham, 2006b). Finally, the model fitness and the relationships among the independent variables and behavior intention will be examined with the implementation of Structural Equation Modeling (SEM) analysis with the support of AMOS software. 3. Results and Discussion 3.1. Result 3.1.1. Respondents’ profile and characteristics Of the 369 valid samples after having been screened, respondents’ characteristics such as experience of using smartphones and the familiarity with mobile payment service. Besides, the respondents’ characteristics are expected various in terms of gender, age, income were aggregated. There were 71.3% of the respondents were female compared to 28.7% of the total respondents were male. Regarding the age of respondents, it was noticed that the majority felt into the group of 18 to 25, accounting for 78.3%. The age group of 25 to 30 constituted 12.2%. Only 9.5% of respondents were above 35 years old. Concerning the income, most of the respondents have income less than 10 million VND per month and this group captured 76.5%. There were 13.5% of respondents having monthly income more than 10 million VND. This result is accordant with the age of respondent in Vietnam context where the GDP per capita was around 2,500 USD (equal to 60 million VND) per year. Notably, 63.5% of respondents have been using smart phone more than 5 years, 25.2% of them having 3 to 5 years of using smart phone, whereas only 9.5% of samples using smart phone less than 3 years. To answer for the similarity of mobile payment service, 87.8% of surveyed people chose options of knowing and knowing but not using. Only 12.8% of respondents haven’t heard about this service. 3.1.2. Constructs reliability testing by Cronbach’s Alpha The Cronbach's Alpha coefficient testing result showed that most of the measurement scales had Cronbach's Alpha coefficient ≥0.7. Particularly, among independent variables, there were 06 out of 08 independent variables having Cronbach's Alpha greater than 0.8 naming Performance expectancy (PE, α=0.856), Effort expectancy (EE, α=0.896), Social influence (SI, α=0.883), Hedonic motivation (HM, α=0.847), Price value (PV, α=0.818), and Perceived risk (PR, α=0.891). The variables Facilitating conditions (FC) and Trust (TR) achieved an alpha reliability of 0.784 and 0.795 respectively. However, in order to improve the value of Cronbach's Alpha, two observed variables were eliminated including FC4 (I can get help from others when I have difficulties using mobile payment and PR5 (Using mobile payment will not fit well with my self-image.). Because these items have the Corrected Item-Total Correlation less than 0.4 the and after eliminating these items, the Cronbach’s alpha values of Facilitating conditions and Perceived risk have been improved The dependent variable, Behavioral intention (BI) had the Cronbach's Alpha coefficient of 0.867. This result is a consequence of a well-designed, clear questionnaire, well- grouped, and satisfied samples (Hair, Black, Babin, Anderson, & Tatham, 1998). 3.1.3. Exploratory Factor Analysis (EFA) The exploratory factor analysis (EFA) was conducted to test the validity of the measurement of independent variables that met the requirements of Cronbach's Alpha reliability testing. By using SPSS version 22.0, the exploratory factor analysis produced the results as presented in Table 1 below. The results of EFA satisfied these elements: (1) Sig value. Bartlett's test = 0.000 <0.05; (2) 0.5 <KMO 208
8. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 coefficient = 0.884 50%. This result demonstrated for the suitability of factor analysis with this set of data. At the same time, the factorization results in seven factors. There were there items eliminated including FC3, FC1, and TR4 because they have the factor loading less than 0.5. Furthermore, the observation of the scale of Effort expectancy (EE) and FC2 (I have the knowledge necessary to use mobile payment) converge into a new factor of 5 items. This result is reasonable because this observed variable also mention the extent of ease connected with the use of mobile payment from the academic background of respondents. Table 1: Factor rotation matrix result Variable Component PR2 1 2 3 4 5 PR7 .760 PR3 .757 PR6 .749 PR4 .745 PR1 .739 EE4 .862 EE3 .849 EE2 .809 EE1 .806 FC2 .599 BI3 .889 BI4 .865 BI1 .781 BI2 .528 PE2 .902 PE3 .789 PE1 .693 PE4 .646 SI2 .868 SI1 .864 SI3 .772 HM2 .880 HM1 .844 HM3 .704 TR2 .912 TR3 .685 TR1 .674 Source: The result of data analysis by SPSS, 2019 209
9. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 The model after revision is presented in the figure 2 below. Figure 2: The revised research model 3.1.4. Confirmatory factor analysis (CFA) After modifying the model, the Confirmatory factor analysis (CFA) was deployed to analyze the measurement scale, testing the fitness of the model with the set of data (Hair, Black, Babin, Anderson, & Tatham, 2006a). CFA by AMOS 22.0 showed that model’s indexes have results as follows: CMIN = 654.856; 1 0.9; GFI= 0,814 > 0,8; CFI= 0.944 > 0.9; RMSEA= 0.052 0.9; GFI= 0,877 > 0,8; CFI= 0.932 > 0.9; RMSEA= 0.057 < 0,08; p=0.000. Besides, 06 out of 08 hypotheses were supported at the significant level of 5%. Two hypotheses were not supported including H5: Hedonic motivation has a positive influence on customer’s behavior intention to adopt mobile payment services and H8: Perceived risk has a negative influence on customer’s behavior intention to adopt mobile payment services. The result of testing hypotheses is presented in the table 2 below. Table 2: The result of testing hypotheses Hypothesis Estimate S.E. C.R. P H1: Performance expectancy has a positive influence on customer’s behavior intention to adopt mobile BI < PE .251 .076 3.302 payment services. H2: Effort expectancy has a positive influence on customer’s behavior intention to adopt mobile BI < EE .151 .065 2.322 .020 payment services. H3: Effort expectancy has a positive influence on PE < EE .390 .055 7.095 Performance expectancy 210
10. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 H4: Social influence has a positive influence on customer’s behavior intention to adopt mobile BI < SI .123 .055 2.240 .025 payment services. H5: Hedonic motivation has a positive influence on customer’s behavior intention to adopt mobile BI < HM .100 .065 1.551 .121 payment services. H7: Trust has a positive influence on Performance expectancy. BI < TR .263 .077 3.408 H7: Trust has a positive influence on Performance expectancy. PE < TR .293 .063 4.672 H8: Perceived risk has a negative influence on customer’s behavior intention to adopt mobile BI < PR -.046 .047 -.966 .334 payment services. Source: The result of data analysis by SPSS, 2019 3.2. Discussion As presented, 06 hypotheses received empirical supported. The findings revealed that 06 key factors such as performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV) and trust (TR) affect customer behavior intention to adopt mobile payment service. These results are consistent with existing literature in the information system area (e.g. Venkatesh et al., 2003, 2012), in the mobile payment context (e.g. A.A.Alalwan et al., 2018) and in Vietnam as well (e.g. Dao My Hang et al., 2018). Moreover, the empirical results have supported the two significant relationships of effort expectancy versus performance expectancy (EE) and trust (TR) versus performance expectancy. These are in line with prior studies (Davis et al., 1989; Luo et al. 2010). However, as seen in Table 3, the path coefficient between hedonic motivation and behavior intention is found to be non-significant, which means the respondents are not concerned about the aspects associated with hedonic factors informing their intention to adopt mobile payment. Arguably, the important and natural role of hedonic motivation in shaping behavior intention to use mobile payment has been observed varying over the prior literature of online banking technologies. For instance, the findings from 79 UTAUT2 empirical studies revealed that only 46 studies (58%) utilized hedonic motivation while the remaining 33 studies (42%) omitted the construct. Unlike UTAUT2, moderator’s association of hedonic motivation were non-significant in determining consumer intention to use technology (Kuttimani Tamilman et al., 2019). Finally, it can be explained that Millennials focus on convenience, not the feeling of cheerfulness, joy or enjoyment, which is stimulated by applying technology. The empirical results of this study also indicated that perceived risk did not have a negative influence on behavior intention. In this context, the abandonment value of Millennials is not high and Vietnamese mobile payment service provider is confident that they can find a way to manage the money flowing through the mobile network in order to ensure the interests of both consumers and regulators. Results of the current study have profoundly contributed to the area of the information system and mobile payment by extending the current understanding regarding such important phenomena of interest as well as providing valuable insights for academic perspective. Firstly, this study reviews and evaluates the most updated models and theories conducted in the technology field. Significantly, this study creates concern regarding the applicability of these theories over customer contexts. Thus, the UTAUT2 was extended by including the trust and perceived risk. Indeed, trust is one of the most frequently used and predictive factors that was proposed along with UTAUT2 factors in the same conceptual model which demonstrates a significant contribution to the expansion of the theoretical horizon of the UTAUT2. Besides, this study provides new trends via examining the impact of effort expectancy on performance 211
11. INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2019 ICYREB 2019 expectancy and the impact of trust on performance expectancy. As discussed above, the current study will facilitate understanding of customers’ behavior intention towards using mobile payment. 4. Conclusion This study was conducted with the aim to clarify and identify the main factors influencing Millennials customers’ intention to use mobile payment services. In order to explain the behavioral intention of using such technology from the customer’s perspective, the proposed conceptual model was built based on the UTAUT2, which was also extended by including the factors perceived risk and trust. Data for the study was collected from 369 Millennials living in Ho Chi Minh City and some other provinces. The factors namely performance expectancy, effort expectancy, social influence, and trust were able to significantly predict the customer behavioral intention. By doing so, this study was able to provide both academics and practitioners with significant contributions. Firstly, in academic terms, the study helped to introduce the system of scales for measuring the factors shaping behavioral intention towards the use of mobile payment services in Vietnam, a concept which has earned much attention in the world as well as Vietnam. The study has also tested the use of UTAUT2 in the context of studying about mobile payment services and proposed a new approach in integrating new factors into the model. this can be considered as a reference model in developing future research directions. Secondly, the study result provided researchers in the field of behavioral science with a better overview of the factors affecting the behavioral intention towards the use of mobile payment services in Vietnam. Thirdly, the study also helped companies, businesses, and organizations operating in the field of mobile payment services to exploit the factors directly influencing the intention to use mobile payment services so that they can devise suitable strategies to enhance the behavioral intention towards using mobile payment services of the Vietnamese, thus boost their business. A number of research directions could enrich the study stream. The data in the study was mainly collected from Ho Chi Minh City and surrounding provinces, with research subjects mostly belonging to the 18-34 age group. The penetration of mobile payment services may vary among countries due to their different economic, cultural, social, technological, and demographic features, so it is necessary to expand the scope of data collection and sampling. The study was conducted under the quantitative approach, without focused group interviews. This, in turn, could have constrained the ability of the current study to have a closer look by clarifying more of the issues related to customers’ intention towards mobile payment services. Therefore, conducting a mixed-method approach (quantitative and qualitative) could provide a more detailed explanation of the current study's results particularly regarding those non-significant relationships. The study only showed a number of factors affecting the intention to use mobile payment services, but not the actual adoption of customers towards this type of service. Therefore, studying the relationship between the intention to use and the actual adoption behavior of customers might reveal more details into the usage behavior of customers for mobile payment services. In addition, it is necessary to analyze in- depth the influence of demographic factors such as gender, age, income, and technology experience to understand the different levels of impact on the behavior of different target groups. Furthermore, moderator variables could be included in the model to show the factors that can increase or decrease the relationship between the factors and customer intention to use mobile payment services. Further, the study has not looked at the problem from the service providers’ perspective, but fully on the customers’ perspective. Therefore, this could be a limitation for not providing a full picture of clarifying the main aspects related to the successful implementation and adoption of mobile payment services from both sides i.e., customers and service providers. REFERENCES [1] Abu-Shanab, E., Pearson, J., & Setterstrom, A. (2010). Internet Banking and Customers’ Acceptance in Jordan: The Unified Model’s Perspective. Communications of the Association for Information Systems (CAIS), 26, 493-525. 212
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