The impacts of social influence, perceived risks on consumers’ intention to use mobile payment in Vietnam

KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” The Impacts of Social Influence, Perceived Risks on Consumers’ Intention to Use Mobile Payment in Vietnam Nguyen Thi Khanh My Vietnam – Korea University of Information and Communication Technology ntkmy@vku.udn.vn Abstract. With the liberalization and globalization, the Internet has been used as a medium of transac- tion in almost all aspects of human living. This study aims to discover the important factors influencing consumers’ intention of using mobile payment. The survey questionnaire was developed and distributed to 250 respondents, out of which 201 valid responses were used for further statistical analysis. Results suggest that social influence and perceived risk are significant factors that contribute to the intention of consumers on mobile payment. The findings of the research are valuable for mobile payment providers to develop and extend the number of users in Vietnam. Keywords: Social Influence, Perceived risks, Consumers’ Intention, Mobile payment 1 Introduction The explosive growth of electronic payment requirements, especially mentions the contributions of the Internet is the key trade channel and extremely exciting potential power but it has not yet been fully exploited. Consumers previously are unfamiliar with the Internet and only treat it as a means of gathering information. But now, con- sumers are gradually accepting this channel for purchasing decisions and their transactions. The increase of cus- tomer’s needs in mobility when payment for the transaction has created a requirement for a new payment tool allowing the transactions more feasible and convenient [14]. Nowadays, payment via mobile phones is a no longer strange problem because the mobile phone has become a necessary tool for each individual. According to statistics Ministry of Information and Communications, the number of mobile users in Vietnam reaches 135 million in June of 2019. Therefore, Vietnam is a huge potential market to harness the mobile payment market. In recent years, many kinds of researchers have been described as the factors that affect consumers’ intention in mobile payment [9, 15, 2s]. In terms of perceived risk, Mitchell [13] suggests that risk percep- tion is more powerful at interpreting consumers’ behavior because they prefer reducing risks than maxim- izing utility in purchasing. Besides, Oye et al. [15] highlighted that social influence is the predictor of the acceptance and use of technology. Moreover, some studies reveal that social influence reduces risk percep- tion [10, 18]. Therefore, we examine the impact of social influence and perceived risks as antecedents of customers’ intention in mobile payment in this study. 2 Literature Review 2.1 Mobile payment Mobile payment refers to a payment method that is utilized as an alternative for financial transactions. It uses mobile devices (such as mobile phones, smart-phone, or Personal Digital Assistant) and wireless com- munication technologies (such as mobile telecommunications networks, or proximity technologies). Mobile devices can be utilized in a variety of payments, such as payments for digital content, concert or flight tickets, parking fees, bus/train/taxi fares. In addition, bills and invoices are used to pay by mobile devices. Therefore, mobile devices allow the users to connect to a server, perform authentication and authorization, make mobile payments, and subsequently confirm the completed transaction [1]. 96 Nguyen Thi Khanh My 2.2 Perceived Risks Perceived risk has been used to explain online shoppers’ behavior. It has been de-fined as “a combination of uncertainly plus seriousness of outcome involved” [2] and “the expectation of losses associated with the purchase and act as an inhibitor to purchase behavior” [16]. However, the size of the perceived risk may change by the product (or service) class [3]. Lee [11] defined “perceived risk in online payment as the subjectively determined expectation of loss by an online transaction”. In this study, perceived risk is posited as a prominent barrier to the consumer in the intention of mobile payment. 2.3 Social Influence Social influence is defined in this study as individuals’ perceived pressure from social networks on adop- tion. In the adoption literature, social influence has long been considered as a critical element in explaining adoption behavior [8]. Social influence on technology acceptance behavior has been widely acknowledged. Venkatesh and David [21] implied that social influence has only a significant impact on technology adop- tion under mandatory settings, and also that its effect moderates as users begin to have direct experiences with the target system. On the other hand, several researchers have disagreed that the construct has limited conceptualization because it emphasizes only the normative part of societal beliefs as opposed to wider societal contexts [20]. Therefore, it is necessary to research the link between social influence and technol- ogy acceptance [7]. 3 Methodology 3.1 Research Model Figure 1. presents the research model showing the proposed hypotheses. Our study proposes that social influence may have direct and indirect (through perceived risks) influences on behavior intention. Besides, consumers’ perceived risks have a direct influence on consumers’ intentions on mobile payment. More specifically, perceived risk stands between social influence and consumers’ intention to use mobile pay- ment - the perceived risk is a mediator variable. We suggest that the importance of our hypothesis model that perceived risk mediate the relationship between social influence and behavior intention. 3.2 Research Hypotheses The relationship between social influence and behavioral intention has been investigated by many previous studies. Yang et al. [22] found that social influence has a significant and direct influence on the adoption intention of mobile payment services. On the other hand, social influence also tends to reduce the perceived risk because they provide strong evidence indicating the appropriateness of the adoption decision [15]. Hence, we hypothesize that: Hypothesis 1: Social influence affect consumers’ intention to use mobile payment. 97 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” Hypothesis 2: Social influence affect consumers’ perceived risk. Previous online shoppers’ behavior and information systems research have high-lighted perceived risk as an inhibitor to customers’ adoption of online transactions. In predicting e-service adoption, facets of risk (e.g., time risk, privacy risk, and financial risk) were identified as antecedents [4]. The relationship between perceived risk and transaction intentions can be explained by the notion of perceived behavioral control [3]. Perceived risk among consumers translates into their reluctance to use electronic payment (e.g., debit and/or credit card) over the Internet, which further results in their disengagement from electronic transactions [17]. Thus, the present study hypothesizes: Hypothesis 3: Perceived risk affects consumers’ intention to use mobile payment. 3.3 Sample To test the proposed relationships, the present study administered online and paper questionnaires. Subjects for the study were suitable selected people that were approached in different places (supermarkets, coffee shops, companies) to complete the self-administered survey. Besides, we sent the link of the online survey to the groups buying online on Facebook to invite participants to join our survey. Finally, 201 usable ques- tionnaires were collected. For the multiple variables, the maximum sample size for the research is given by n = 50 + 8*3 = 74 [19]. Therefore, the number of collected questionnaires is relatively sufficient for the research. In addition, these respondents were balanced in terms of gender (45.7% of them were male), relatively young (45% of them were between 24 and 30 years old), generally highly educated (62.3% of them had completed their university studies). 4 Data analysis 4.1 Measures The questionnaire was first developed in English and then translated to Vietnamese. The questionnaire used seven-point Likert scales, in which participants chose from one (strongly disagree) to seven (strongly agree). The measurement instrument contained the scales from previous studies. Four items scale for behavior intention is adapted from Kim et al. [9] and measured the intention to adopt mobile payment service. Five items on social influence were adapted from Lu et al. [12] to measure the impact of subjective norms and social image on mobile payment adoption. Besides, the three items scale relating to perceived risks is taken from Yang et al [22]. Table 1. Measurement instruments and internal reliability. Construct Number of items Cronbach’s alpha References Social Influence 5 0.92 Lu et al. [12] Perceived Risks 3 0.88 Yang et al. [22] Behavior Intention 4 Kim et al. [9] The research model includes three constructs. Cronbach’s alpha was used to assess internal consistency reliability and construct validity. As shown in Table 2, Cronbach’s alpha for social influence, perceived risks, and behavioral intention were 0.92, 0.88, and 0.89 respectively were higher than 0.7. Therefore, there is the internal reliability of the scales used in this study. 4.2 Results Table 2. contains the analyses necessary to examine the mediational hypothesis. Following the steps outlined earlier for testing mediation, we first examined that social influence was related to intention to use mobile pay- 98 Nguyen Thi Khanh My ment by regressing behavior intention on social influence. The result showed that social influence was signifi- cantly associated with intention on mobile payment (B = 0.324, b = 0.251, p < 0.001). Hypothesis 1 was sup- ported and the requirement for mediation in Step 1was met. Next, to establish that social influence was related to the hypothesized mediator, perceived risks, we regressed perceived risks on social influence. Social influence was significantly associated with perceived risks (B = -0.388, b = -0.367, p < 0.001), and thus hypothesis 2 was supported and the requirement for mediation in Step 2 was met. To test whether the hypothesized mediator – perceived risks – was related to intention on mobile payment, we regressed intention in mobile payment simul- taneously on both social influence and perceived risks. Perceived risk was significantly associated with behavior intention while controlling for social influence (B = -0.778, b = -0.69, p < 0.001). The third regression equation also provided an estimate of the relation between social influence and intention on mobile payment, controlling for perceived risks. The regression coefficients in Step 3 indicated that the influence of social influence on be- havior intention on mobile payment was strengthened (b from 0.251 to 0.347). Therefore, perceived risks func- tioned as a mediator in this model, explaining the causal link between social influence and behavior intention to use mobile payment. All steps have variance inflation factors (VIF) measure less than 10, which, therefore, we suggested that there was no apparent multicollinearity issue in this model. The results in Table 2 demonstrated that the relationship between social influence and intention to use mobile payment was increased when perceived risks were added to the model by using multiple regression. Table 2. Testing for perceived risks as a mediator Steps in testing for mediator B Std. Error VIF Step 1: SI ® BI 0.324 0.075 0.251 1 Step 2: SI ® PR -0.388 0.09 -0.367 1 Step 3: SI, PR ® BI SI 0.452 0.076 0.347 1 PR -0.778 0.072 -0.69 1 *SI: social influence; PR: Perceived Risk; BI: behavior intention Table 3. Results of hypothesis testing Hypothesis content Result H1: Social influence affect consumers’ intention to use mobile payment Supported H2: Social influence affect consumers’ perceived risk Supported H3: Perceived risk affects consumers’ intention to use mobile payment Supported 5 Conclusion This study aimed to develop a theoretical model of mobile payment that incorporates social influence and perceived risks to empirically test whether the constructs affect consumers’ intention in mobile payment. The results of this study indicate that there are significant relations among the constructs, offering findings to the current literature with the validated research model. The following section will review our findings and offer explanations for our observations regarding each finding. First, we found that positive social influence will increase the intention of customers to pay through mobile. This may be true because social influence played an affecting role in online shoppers’ positive behavioral responses (Wang et al., 2004). Besides, social influence enhances not only consumers’ trust but also consumers’ expectations to use mobile payment. Therefore, based on trust from social, consumers have more confidence at all levels from purchasing to finishing payment. Second, we found the social influence reduced customers’ perceived risks. The negative relationship between social influence on risk perceptions is consistent with academic research [15, 22], which suggests that enhanced social influence can be by reducing perceived risks. Users tend to rely more on information 99 KỶ YẾU HỘI THẢO KHOA HỌC QUỐC GIA CITA 2020 “CNTT VÀ ỨNG DỤNG TRONG CÁC LĨNH VỰC” from social relationships to decrease their perceived risks. Thus, we suggest that mobile payment providers need to build trustworthiness cues from social to customers and thereby increase consumers’ intention to use mobile payment. Findings of this exploratory suggested that perceived risks significantly influence intention in mobile payment. Because mobile payment has more risk than traditional payment methods, online shoppers in high perceived risks will increase their reluctance to use mobile payment. In other words, if consumers’ subjec- tive expectation of suffering a loss associated with paying online, customers’ acceptance of mobile payment will be more difficult. Especially, the results also indicated that perceived risk was a significant mediator of associations between social influence and consumers’ intention in mobile payment. Previous research has found that both perceived risk and social influence affect behavior intention [6], and propensity to social influence reduces perceived risk beliefs [22]. Perceived risk, as a significant mediator, can explain that positive influences from friends, col- leagues, and social circles are critical determinant for perceiving fewer risks and further have a greater intention to pay through mobile. Hence, the findings of this research which are valuable for mobile payment providers. They should encourage sharing experiences via social networks and word of mouth (both offline and online) will help convince young consumer who are easily to accept new technology. Besides, mobile payment providers need to address consum- ers’ concerns through sffective advertising and handle bad information that is spread on social networking. This is especially the case among highly collective-culture countries such as Vietnam where individuals are more easily influenced by others’ opinions than those living in low collective-culture countries. As with any research, there were a number of limitations in this study. Firstly, this study does not examine mediator effect based on Kerry and Baron method [2], we are focusing here on the use of multiple regression of Frazier et al. [5]. Secondly, it is worth noting that the profile of the respondents might influence the results of study. Finally, futures studies will include potential determining or modifying factors with similar payment de- vices or new payment technologies. References 1. Antovski, L., & Gusev, M. (2003). “M-Payments. Paper presented at the 25th International Conference of Information Technology Interfaces”, Cavtat, Croatia. 2. Baron, R. M, & Kerry, D. A. (1986). “The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations”. Journal of Personality and Social Psychology, 51(6), 1173-1182. 3. Bauer, R.A. “Customer behaviors as risk taking, in: Cox D.F. (Ed), Risk Taking and Infor-mation Handling in Customer Behavior”, Boston University Press, Boston. 4. Featherman, M.S. & Pavlou, P.A. (2003). “Predicting e-services adoption: A perceived risk facets perspective”, International journal of human-computer studies, 59(4), 451-474. 5. Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). “Testing moderator and mediator effects in counseling psychology research”. Journal of Counseling Psychology, 51, 115-134. 6. Hong, S.J., & Tam, K. Y. (2006). “Understanding the adoption of multipurpose information appliances: The case of mobile data services”, Information Systems Research, 17(2), 162-179. 7. Karahanna, E. and Limayem, M. (2000). “E-mail and v-mail usage: generalizing across tech- nologies”, Journal of Organizational Computing and Electronic Commerce, 10(1), 49-66. 8. Karahanna, E. Straub, D. W., & Chervany, N. L. (1999). “Information Technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs”, MIS Quarterly, 23(2), 183-213. 9. Kim, C., Mirusmonov, M. & Lee, I. (2010). “An empirical examination of factors influenc-ing the intention to use mobile payment”, Computers in Human Behavior, 26, 310-322. 10. Koenig Lewis, N., Morgan, M., Palmer, A. and Zhao, A. (2015). “Enjoyment and social influence: Predicting mobile payment adoption”. The Service Industries Journal, 35(10), 537-554. 100 Nguyen Thi Khanh My 11. Lee, M.C (2009). “Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit”, Electronic commerce research and ap-plications, 8(3), 130-141. 12. Lu, J., Yao, J., & Yu, C. (2005). “Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology”, The Journal of Strategic Information Systems, 14(3), 245- 268. 13. Mitchell, V. W. (1999). “Consumer perceived risk: Conceptualization and models”, Euro-pean Journal of Marketing, 33(1/2), 163-195. 14. Ondrus, J., & Pigneur, Y. “Towards a holistic analysis of mobile payments: A multiple perspectives approach”, Electronic Commerce Research and Applications, 5(3), 246-257. 15. Oye, N.A. Iahad, N.A. Rahim (2014). “The history of UTAUT model and its impact on ICT acceptance and usage by academicians”. Education and Information Technologies, 19(2014), 251- 270. 16. Peter, J.P., & Ryan, M. J. (1976). “An investigation of perceived risk at the brand level”, Journal of marketing research, 184-188. 17. Salam, A., F., Rao, H. R., & Pegels, C. C. (2003). “Consumer-perceived risk in e-commerce transactions: Communications of the ACM”, 46(12), 325-331. 18. Shin, D., H.(2010). “Modelling the interation of users and mobile payment system: Conceptual framework”. International Journal of Human Computer Interaction, 26(10), 917-940. 19. Tabachnick, B. G., & Fidell, L. S. (1996). “Using multivariate statistics (3rd ed.)”. New York: HarperCollins. 20. Terry, D.J. and Hogg, M.A. (2002). “Social contextual influences on attitude behavior cor- respondence, attitude change, and persuasion”, Attitude Behavior, and Social Context: The Role of Norms and Group Membership, Erlbaum, Mahwah, NJ, 1-9. 21. Venkatesh, V. and Bala, H. (2008). “Technology acceptance model 3 and a research agenda on development and test”. Decision Science, 27(3), 451-481. 22. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). “Mobile payment services adop-tion across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits.” Computers in Human Behavior, 28(1), 129-142. 101