Các yếu tố về ý định tiêu dùng trong việc sử dụng thanh toán di động tại Việt Nam

Nội dung text: Các yếu tố về ý định tiêu dùng trong việc sử dụng thanh toán di động tại Việt Nam

1. THE FACTORS OF CONSUMER INTENSION OF USING MOBILE PAYMENT IN IN VIETNAM CÁC YẾU TỐ VỀ Ý ĐỊNH TIÊU DÙNG TRONG VIỆC SỬ DỤNG THANH TOÁN DI ĐỘNG TẠI VIỆT NAM Ming-Kun Lin Lunghwa University of Science and Technology Abstract Within the context of emerging mobile technologies, Mobile payment or M-payment has been introduced as the new trend for payment method bringing more value and convenience to consumers. However, the development of M-payment services is still quite timid in some South East Asia countries including Vietnam. This is also the reason and motivation for the author to do this research. The goal of this study is to spot out some factors that affect the use behavior (UB) of M-payment consumers in five major metropolises where most influential on economic in Vietnam. To investigate the factors affecting UB of Vietnamese M-payment consumers, researcher proposed a research model which analyzes the impact of various variables extracted from system quality (ST), service quality (SV), security (SC), social influence (SI) on behavioral intention (BI) to use and BI on M-payment UB. A quantitative questionnaire was used to measure responses of participants and Partial Least Squares (PLS) method was employed to analysis the collection data as well as test all hypotheses. The results indicated that SI has been the important factors leading to the BI to use M-payment following by SV and SC issues and BI also had strong influence on UB of consumers. Since M-payment is still at the infancy stage and is one of the most exciting mobile applications for the next few years in Vietnam, the identification of important factors concerning M-payments in this study will assist merchants and software developers to design and improve the systems and service to ensure the full acceptance and continuous use of the systems. Finally, a set of suggestion for the subsequent research works also was listed at the end of this study. Keywords:Mobile Payment, Unified Theory of Acceptance and Use of Technology (UTAUT), Vietnam. Tóm tắt Trong bối cảnh các công nghệ di động mới phát triển, thanh toán di động hoặc M- payment được coi là xu hướng mới trong phương thức thanh toán, mang lại nhiều giá trị và tiện ích hơn cho người tiêu dùng. Tuy nhiên, sự phát triển của dịch vụ M-payment vẫn còn khá khiêm tốn tại một số quốc gia Đông Nam Á, trong đó có Việt Nam. Đây cũng là lý do và động lực để tác giả thực hiện nghiên cứu này. Mục tiêu của nghiên cứu này là phát hiện ra một số yếu tố ảnh hưởng đến hành vi sử dụng (UB) M-payment của người tiêu dùng tại năm đô thị lớn, nơi có tầm ảnh hưởng nhất đến nền kinh tế Việt Nam. Nhằm điều tra các yếu tố ảnh hưởng đến UB của người sử dụng M-payment tại Việt Nam, nhà nghiên cứu đã đề xuất một mô hình nghiên cứu phân tích tác động của các biến khác nhau được 889
2. tóm lược từ chất lượng hệ thống (ST), chất lượng dịch vụ (SV), bảo mật (SC), tác động xã hội (SI) đối với ý định hành vi (BI) sử dụng và BI đối với hành vi sử dụng M-Payment. Bảng câu hỏi định lượng được sử dụng để đo lường phản ứng của người tham gia và phương pháp Bình phương tối thiểu (PLS) được sử dụng để phân tích dữ liệu thu thập cũng như kiểm định tất cả các giả thuyết. Kết quả cho thấy rằng SI là yếu tố quan trọng dẫn đến BI sử dụng M-payment, tiếp theo là các vấn đề liên quan tới SV và SC, và BI cũng tác động đáng kể đến UB của người tiêu dùng. Vì M-payment vẫn đang ở giai đoạn sơ khai và là một trong những ứng dụng di động hiệu quả nhất trong vài năm tới tại Việt Nam, việc xác định các yếu tố quan trọng liên quan đến M-payment trong nghiên cứu này sẽ hỗ trợ các doanh nghiệp và nhà phát triển phần mềm thiết kế và cải thiện hệ thống và dịch vụ để đảm bảo các hệ thống được chấp nhận đầy đủ và sử dụng liên tục. Cuối cùng, phần cuối nghiên cứu này cũng đưa ra một số đề xuất cho những nghiên cứu sâu hơn trong tương lai Từ khóa: Thanh toán di động, Lý thuyết thống nhất về chấp nhận và sử dụng công nghệ (UTAUT), Việt Nam. 1. Introduction Over the last few years, payment systems used in business activities have been altered by the advancement of Information and Communication Technologies (ICT) such as ubiquitous Internet access and innovative mobile devices - smartphones (Liébana- Cabanillas, 2014). Nowadays, more and more consumers use their mobile phones to make purchases. The growth in the number of mobile devices particularly smartphones supported the potential opportunities presented by mobile commerce (m-commerce). As m-commerce increases in popularity, it creates requirements for new payment instruments to enable feasible and more convenient transactions (Ondrus & Pigneur, 2006). Therefore, M- payment is expected to become an important and essential channel for conducting financial transactions (Adebiyi et al., 2013). The Organisation for Economic Cooperation and Development (OECD) (2012) defines m-payment as: “M-payments are payments for which payment data and instruction are made via mobile phones or other mobile devices. Such payments would include Internet payments using a mobile device, as well as payments made through mobile network operators (MNOs). Note that the location of the payer and supporting infrastructure is not important: the payer may be on the move (remote payments) or at a point of sale (POS)”. A report from Juniper Research has found that the value of global M-payments transaction reached approximately $507 billion in 2014, increasing nearly 40% year-on-year (M-payment Strategies: Remote, Contactless & Money Transfer 2014-2018). Besides, according to the data of Capgemini Analysis in M-payment (2014), the number of transactions in Global M-payment will reach 46.9 billion in 2015, up from just only 7 billion in 2011. In Vietnam M-payment users are using this new type of payment now primarily for mcommerce, P2P (peer-to-peer) value transfers, POS. Vietnamese users are average in 890
3. usage among these three types of M-payments. There are some well-known M-payment players in Vietnam namely Mobivi, Soha Pay, Mpay, PatNet, Payoo, 1Pay, VinaPay, NganLuong (Teachinasia, 2013). Nevertheless, Vietnam is still a Cash-based society. Cash is “king” since the bulk of personal consumption is done through the medium of cash. It is also a barrier for consumers to transition from Cash on Delivery (COD) to making online payments. According to Vice Prime Minister Vu Duc Dam, M-payment in Vietnam is developing very slowly compared to other countries in the region and around the world. Currently, only about 10% of the payment transaction is using Mobile Banking (Speaking at the ceremony honoring Electronic Banking favorite in Vietnam - My eBank 2014 by electronic newspaper VnExpress held in Hanoi at 19.11.2014). Considering the low adoption rate of M-payment, it is essential to identify the factors affecting consumer UB of M-payment. The next section, chapter 2 will be dedicated to a literature review relevant to this research. Chapter 3 contains the conceptual framework, hypotheses, measurement items, research design, data collection procedures and data analysis techniques that will be used in this study. Chapter 4 includes the descriptive analysis of the respondents and the analysis results. Finally, chapter 5 consists of the discussion of the findings of the study, the limitations and suggestions of this study. 2. Literature Review 2.1. Mobile Payment M-payment is defined as a financial business transaction activities conducted through a mobile device like mobile phone, smartphone, tablet based on mobile network (Zhao & Kurnia, 2014). According to Xin et al. (2013) and Li et al. (2014), M-payments fall broadly into two categories: POS contactless payments (or proximity M-payment) and mobile remote payments. From the targets of transaction, M-payment can be classified into P2P payment, C2B payment (consumer-to-business) and B2B payment (business-to- business) (Deloitte 2012). Moreover, from the provider’s perspectives, M-payment can be classified into three types: mobile network operator centric, financial institution centric and third-party operator centric (Lu et al. 2011). In terms of M-payment adoption, the topic on consumer in the domain of M- payment raised the interests of many scholars. Understanding consumer preferences and the reasons to use or not use a specific technology-enabled service is important for designing a viable service that create conducive value to consumers, merchants, and the other stakeholders. A summary of current research in term of M-payment adoption is present in the table below: 891
4. Table 1. Review of M-payment research after 2013 Authors Topic & Theor Core Key Findings Location e-tical Constructs Model ST, SV, Zhou An empirical Information The main factor effect on trust (2013) Quality examination of is SV. SV also is the main continuance (IQ), Trust, factor influence on satisfaction. Satisfaction, intention of Flow is affected by SV and IQ. Flow, D&M Continuance intention of M- mobile Continuance payment is determined by trust, IS payment success Intention flow and satisfaction. Service Model providers have to offer quality services of services, system, and information in order to facilitate consumer continues to use Mpayment services. China Perceived Ease PU is a significant factor in of predicting the intention to use Use (PEOU), MCC. PEOU is a significant construct in predicting MCC NFC mobile Perceived adoption and has a positive credit card Usefulness (PU), relationship with PU with turn (MCC): The Perceived Risk in affect MCC acceptance. PR Tan et next frontier of (PR), Perceived and PFC is an insignificant al. TAM construct in this research. The mobile Finance Cost (2014) intention of both the genders payment? (PFC), SI, followed the same patterns Personal equally, therefore gender was Innovativeness found to have moderating Malaysia in insignificant effect on the paths Information of the structural model. Technology (PIIT) 892
5. Factors influencing Near Field SC, SI, Communicatio Technology Trust, SI and technology n (NFC) availability effect positively Dutot adoption: An Available, Trust, PU. SC issues affect PEOU. TAM (2015) extended PEOU, PU, The results are showing a TAM Intention of Use strong support for the extended TAM model proposed. approach (IU), Usage France Examining PU, PEOU, ubiquity, and Mobile structure assurance have PEOU, PU, significantly positive influence Payment User on trust, which will turn to Yan & Adoption from Structural affect usage intention of Yang the assurance, consumer. Merchants and TAM Ubiquity, providers in mobile service (2015) Perspective of Trust, usage context must to concern about Trust intention trust to make a good condition for adoption and usage of M- China payment services. The Integrated Attitude towards The intention to use a new Di Model on mobile services, technology is affected by the Compatibility, Ease of use, Usefulness, and the Pietro et Mobile UTAU al. Ease of Use, SC of that technology. The Payment T, Usefulness, SC, Usefulness is simultaneously (2015) IU, Behavioral affected by Ease of Acceptance TAM (IMMPA): An use, use, Compatibility, and Attitude empirical towards mobile services. The application to model confirms that IU has a public significantly direct effect on M- transport payment actual usage. Italy (Europe) 2.2. Related Theory: The Unified Theory of Acceptance and Use of Technology (UTAUT) Drawing The UTAUT was developed by Venkatesh et al. (2003) which is an extension of Technology acceptance model (TAM), representing a shift from technology acceptance to unified view (Wong et al., 2015). UTAUT has two endogenous variables 893
6. consist of BI to use and UB of technology; Four exogenous variables including facilitating conditions (FCs), SI, performance expectancy (PE), and effort expectancy (EE); Four moderators which are voluntariness, experience, age, and gender. UTAUT was built not only to predict and explain the adoption of technological innovations in organizations (Venkatesh et al., 2003), but also it can be employed to investigate the adoption of information systems of consumers and private users. For instance, UTAUT is frequently adopted and used of information systems such as M- payment acceptance research (Leong et al. 2013; Di Pietro et al., 2015), Mobile Banking (Oliveira et al., 2014), Internet Banking (Martins et al., 2014), Electronic payment (Junadi, 2015), Mobile Advertising (Wong et al., 2015), Technology - Based Service (Tsourela & Roumeliotis, 2015), 3G Mobile Communications (Mardikyan et al., 2012), Education (chang, 2013), and so on. 2.3. Relevant research and relationship between research constructs The following table provides the definition of the constructs relating to this study and shows some previous researches that have demonstrated the relationship between all of the constructs. Table 2. Definition and relationship of the constructs Construct Definition Relationship between research construct ST represents the quality of the information system processing Many studies have found that ST and itself, which includes software BI to and data components (Lee and Yu, 2012). ST measures Use have positive relationships. System following aspects: ease of use, (Cheng, 2012; Islam, 2012; Li et al., Quality ease of learning, compatibility, 2012; Ramayah et al., 2010; Wang & function ability, reliability, availability, user requirements, Chiu, 2011; Zhou (2013)). flexibility, system features, (Garcia-Smith& Effken (2013); Balaban et al., (2013)). 894
7. SV was defined as the overall A number of researchers examined support delivered by the M- the relationship between SV and payment service provider, and it consumer BI to use. Wang and Chiu applies regardless of whether this (2011) discovered SV as a significant support is delivered by the factor in determining users’ intentions Service banking service provider, an towards e-learning system use. Quality outsourcing software merchant, or Similarly, Cheng, 2012; Li et al., 2012; an Internet service provider Balaban (2012); Zhou (2013) also (Delone & McLean, 2003; Lee & reported a significant positive effect of Yu, 2012). SV on intention to use. Huang and Cheng (2012) referred SC is a set of procedures, Some researchers believe that current mechanisms and computer SC standards and rules will allow programs to authenticate the consumers to perform all operations in source of information and ensure Security a safety way. They demonstrated that the integrity and privacy to avoid SC and intention to use have a positive the problems of the data and the relationship (Nasri & Charfeddine, network. Some SC standards and 2012; Junadiª, 2015; Di Pietro et al., rules will allow consumers to 2015). perform all operations in a safety way. SI is defined as the degree to The relationship between SI and BI which an individual perceives that has been empirically investigated by important others believe he or she should use the new system many previous studies (Kwong & Park, Social (Venkatesh et al., 2003; Chong et 2008; Tsu Wei et al., 2009; Gu, Lee, & Influence al., 2010; Thakur, 2013; Dutot, Suh, 2009; Kim et al., 2011; Chong et 2015). al., 2010; Chong et al., 2012). Many studies have found the significantly positive relationship between SI and Intention to Use (Nikou & Bouwman, 2014; Lu et al., 2011; Yang et al., 2012; Tan et al., 2014; Liébana- Cabanillas, 2014). 895
8. Mohammadi (2015) In the technology acceptance Behavioral defined dimension, many studies are conducted to exploring the relationship between Intention Intention as the likelihood that an to this two construct: BI and UB. Nikou individual will use an Information & Bouwman (2014) Mohammadi, Use System. 2015; Di Pietro et al., 2015 reported that Intention to Use has a significant relationship influence on Use. 3. Research Design and Methodology 3.1. Research Framework System Quality H1 S ervice Quality H2 Behavioral H5 Use Behavior Intention Security H3 H4 Social Influence Figure 1. Conceptual Framework 3.2. Research Hypotheses From the Literature Review, independent factors such as ST, SV, SC, SI have an indirect effect on M-payment acceptance through its impact on consumer’ BI to use M- payment. Thus, the following five hypotheses will be tested: H1: ST will affect positively Consumer’ BI to use in the context of M-payment. H2: SV will affect positively Consumer’ BI to use in the context of M-payment. H3: SC will affect positively Consumer’ BI to use in the context of M-payment. H4: SI will affect positively Consumer’ BI to Use in the context of M-payment. H5: BI will affect positively Consumer’ UB in the context of M-payment. 3.3. Research Design A quantitative research technique, the self- administered was developed in English version and then translated into Vietnamese. Survey questionnaires were sent to the target population from 5 big cities in Viet Nam: Hanoi, Ho chi minh, Can tho, Da nang, Hai Phong. All of the items in questionnaire are generated from previous studies, then modified to fit the context of M-payments and written in the form of statements with which M-payment users 896
9. are to agree or disagree on a five-point Liker-type scale. After dispensing the links of questionnaires through Facebook accounts to 400 target populations, a total 245 valid survey responses were collected. All data collected was back-translated into English. SmartPLS (Smart Partial Least Squares) were used to analysis the collected data to examine the relationship between dependent and independent constructs in the research model. 4. Analysis and Resuts 4.1. Validity and reliability measures (PLS measurement model results) According to our result, the measurement model is completely satisfactory. Firstly, all standardized loading are greater than 0.740 (table 4). Furthermore, PLS does not directly provide significance tests. Significance levels for loadings, weights, and paths were calculated through bootstrapping. We used bootstrapping (N=1000) to perform significance testing for the loadings. Factor loading of each item was highly significant (P <0.001) as illustrated by the t- value (T-statistics) of the outer loadings in smartPLS output. These values ranged from a low value of 16.5 to a high of 141 (table 5). Consequently, the individual item reliability is adequate. Table 4. Factor loadings (both) and cross loadings System Service Social Behavioral Use Quality Quality Security Influence Intention Behavior STQ1 0.793 0.322 0.338 0.350 0.266 0.254 STQ2 0.883 0.315 0.370 0.347 0.257 0.254 STQ3 0.827 0.310 0.339 0.315 0.296 0.248 STQ4 0.752 0.314 0.354 0.303 0.236 0.202 STQ5 0.740 0.347 0.354 0.289 0.341 0.295 STQ6 0.840 0.315 0.343 0.289 0.230 0.255 SVQ1 0.314 0.802 0.679 0.592 0.625 0.476 SVQ2 0.387 0.759 0.648 0.639 0.585 0.479 SVQ3 0.366 0.889 0.708 0.657 0.641 0.572 SVQ4 0.274 0.775 0.638 0.595 0.591 0.464 SVQ5 0.302 0.845 0.660 0.660 0.676 0.550 SVQ6 0.340 0.866 0.686 0.616 0.599 0.512 SC1 0.412 0.749 0.905 0.754 0.691 0.609 SC2 0.391 0.717 0.845 0.740 0.661 0.556 SC3 0.431 0.711 0.854 0.747 0.714 0.595 897
10. SC4 0.359 0.648 0.837 0.672 0.594 0.516 SC5 0.232 0.619 0.807 0.578 0.570 0.508 SI1 0.408 0.703 0.722 0.912 0.669 0.490 SI2 0.289 0.634 0.760 0.845 0.591 0.525 SI3 0.349 0.639 0.728 0.851 0.675 0.544 SI4 0.337 0.717 0.745 0.929 0.743 0.571 SI5 0.343 0.660 0.697 0.869 0.700 0.567 BI1 0.314 0.687 0.723 0.672 0.905 0.653 BI2 0.313 0.661 0.650 0.693 0.890 0.668 BI3 0.291 0.665 0.666 0.690 0.875 0.568 UB1 0.334 0.614 0.684 0.640 0.697 0.939 UB2 0.250 0.535 0.532 0.491 0.619 0.922 Table 5. T-Statistics and P-Values of outer model loading Constructs Items T Statistics P Values STQ1 21.214 0.000 STQ2 35.414 0.000 System Quality α : STQ3 25.695 0.000 0.893 STQ4 18.407 0.000 STQ5 16.498 0.000 STQ6 26.015 0.000 SVQ1 34.943 0.000 SVQ2 23.957 0.000 Service Quality α : SVQ3 60.257 0.000 0.912 SVQ4 28.191 0.000 SVQ5 46.255 0.000 SVQ6 46.545 0.000 SC1 69.043 0.000 SC2 44.082 0.000 Security α : SC3 47.157 0.000 0.901 898
11. SC4 37.909 0.000 SC5 33.905 0.000 SI1 65.129 0.000 SI2 31.226 0.000 Social Influence α : SI3 34.364 0.000 0.948 SI4 100.997 0.000 SI5 40.000 0.000 BI1 72.211 0.000 Intention to Use α : BI2 64.969 0.000 0.869 BI3 49.883 0.000 Actual Usage α : UB1 140.989 0.000 0.846 Ub2 77.432 0.000 Second, six constructs meet the requirement of composite reliabilities are greater than 0.7, demonstrated in table 13. In addition, such latent variables achieve convergent validity because their average variance extracted (AVE) was above the recommended value of 0.5 (table 6). Thus we concluded that all our constructs had satisfactory convergent validity. Table 6. Convergent validity and discriminant validity CR AVE ST SV SC SI BI UB 0.918 0.652 STQ 0.807 0.927 0.679 SVQ 0.401 0.824 0.929 0.723 SC 0.435 0.813 0.850 0.946 0.778 SI 0.392 0.761 0.826 0.882 0.919 0.792 BI 0.344 0.754 0.764 0.769 0.890 0.928 0.866 UB 0.316 0.619 0.658 0.612 0.709 0.931 CR : (Construct / composite reliabilities) AVE : (Average variance extracted) Matrix diagonals (both): The square roots of the AVEs Finally, we can observe that the six constructs demonstrate adequate discriminant validity. This is achieved both via the comparison of the square root of AVE vs correlations (table 6) and the cross-loadings table (table 5). In addition, most constructs have good distribution because the skewness is < 2 and kurtosis < 5 as shown in table 7. 899
12. Table 7. Kurtosis, Skewness, mean, and Standard deviation Standard Mean deviation Kurtosis Skewness ST1 4.135 0.690 -0.058 -0.409 ST2 4.159 0.719 -0.309 -0.447 ST3 4.176 0.776 -0.534 -0.527 ST4 4.131 0.722 -0.606 -0.334 ST5 4.155 0.740 -0.706 -0.378 ST6 4.106 0.737 -0.534 -0.355 SV1 4.245 0.704 -0.027 -0.598 SV2 4.037 0.784 -0.240 -0.474 SV3 4.000 0.952 -0.632 -0.543 SV4 4.147 0.805 -0.024 -0.699 SV5 4.069 0.857 -0.471 -0.565 SV6 4.041 0.903 -0.407 -0.583 SC1 4.020 0.941 -0.821 -0.514 SC2 4.053 0.940 -0.525 -0.671 SC3 4.012 0.887 -0.259 -0.588 SC4 4.090 0.871 -0.443 -0.623 SC5 4.122 0.751 -0.818 -0.322 SI1 4.069 0.782 -0.750 -0.329 SI2 4.057 0.846 -0.621 -0.476 SI3 4.061 0.867 -0.199 -0.611 SI4 4.086 0.831 0.106 -0.592 SI5 4.037 0.905 0.489 -0.771 BI1 4.184 0.795 0.277 -0.735 BI2 4.024 0.908 0.198 -0.773 BI3 4.151 0.851 -0.133 -0.695 UB1 3.649 1.121 -0.236 -0.709 UB2 3.131 1.128 -0.738 -0.054 900
13. 4.2. Hypotheses results (PLS structure model results) The SmartPLS result for Beta value of all path coefficients and the R2 are indicated in Figure 1, significant paths are represented with bold arrows. Table 8 summarized the hypotheses and outcome 901
14. Table 8. Hypotheses Testing Results Hypothesis Suggested Path T-value P-value Support effect coefficients (bootstrap) H1: ST BI + -0.017 0.416 0.677 No H2: SV BI + 0.301 3.743 0.000 Yes H3: SC BI + 0.236* 2.362 0.018 Yes H4: SI BI + 0.351 3.954 0.000 Yes H5: BI UB + 0.709 22.891 0.000 Yes *P 0.05) hence hypothesis 1 was rejected. In contrast, SV had a positive influence on intention since the path between SV and BI was highly significant (beta = 0.301; t = 3.743; p 0.05), upholding hypothesis 3. Besides, the path between SI and BI was also highly significant (beta = 0.351; t = 3.954; p > 0.001), SI thus had a significantly positive influence on BI, confirming hypothesis 4. Finally, BI is concluded that had positively affected UB, this two construct yielded a significant path (beta =0.709; t = 22.891; p > 0.001), hypothesis 5 was thus accepted. It is important to note that we used R2 to measure the model’s explanatory power, interpreted in the same way as for regression analysis. The coefficient of determination, R2 is 0.672 for the BI endogenous latent variable. This reveals the latent variables ST, SV, SC, and SI explain about 67% (R2 = 0.672) of the variance in BI. UB, similarly, the coefficient of determination, R2 is 0.503, this reveals the latent variables BI explain about 50% (R2 = 0.503) of the variance in UB. 5. Conclusion and Dicussion 5.1. Theoretical Implications This study extends the UTAUT to explain consumer acceptance of M-payments and promises an understanding of the factors that influence the acceptance of M-payments. The results of this study indicated that SV, SC issues, SI had an indirect influence on the M-payment adoption of Vietnamese consumers through consumers’ BI to use. Frist at all, SI had the strongest effect on BI to use M-payment, followed by SV and SC issues. In addition, BI also had strong influence on UB of consumers. Thus we conclude that this study contributes to a better understanding of the factors that influence the acceptance of M-payments in Vietnam. 902
15. M-payments system is one of the most exciting mobile applications for the next few years in Vietnam, so that the results of the research in this thesis should be of interest to the business communities. The results of this study provides valuable information for mobile phone manufacturers, merchants, banking system, software developers, and practitioners as well as governments when developing their communication and business strategies regarding to Mpayment adoption. The identification of important factors concerning M-payments in this study will assist them to develop and improve their systems and service to ensure the full acceptance and continuous use of the systems. 5.2. Practical Implications The results obtained from this research suggest a few areas the M-payments industry should consider in order to develop and establish the industry. To increase the adoption of Mpayments, it is important that M-payment provider should think a way to build a good reputation or having a good company image in order to attract more M-payments customers. These approaches will be associated with ST. Service providers and system designers should ameliorate and improve the quality of the system in the early stage of the development. Furthermore, Good quality of service has always proved important to consumers’ acceptance of M-payment. To attracting and retaining customers, M-payment provider should maintain and enhance the quality of their service. Last but not least, SC issue is one of the determinants that influence consumer acceptance of M-payments. Providers should concern about selecting an appropriate and secure technology solution, therefore with the advantage of new technologies, service providers can increase the SC of the M-payment environment. 5.3. Limitations Firstly, this study was conducted in only 5 major metropolises in Vietnam with data collection were geographically constrained, therefore the results may not be applicable to the whole country or other countries. Secondly, the sample method used in this study is convenience sampling and snowballing sampling, this method makes the randomness of sampling is in insufficient and may lead to the deviation of a sample. Thirdly, the research framework only takes into consideration the consumers’ perspective and was focused only on ST, SV, SI and SI dimensions. It might not fully reflect overarching situation of M- payment services in Vietnam. 5.3. Future research The results of our study offer insights into several issues that deserve further investigation. First at all, researcher should gather the viewpoints of merchants in subsequent research works. Besides, future research can extend UTAUT theoretical model to investigate the acceptance of M-payment system in particular, and generally technology applications in Vietnam or other countries. Finally, in view that M-payment is still at the infancy stage in Vietnam, it call for research in different dimension such as research can be extended to proposing ways to increase M-payment adoption rate, studying of factors affect continuous use of M-payment, or exploring the satisfaction of M-payment consumers, etc. 903
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