International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 13, No. 9, 2019 Paper—Decision Making in Selecting Mobile Payment Systems Decision Making in Selecting Mobile Payment Systems https://doi.org/10.3991/ijim.v13i09.10834 Wornchanok Chaiyasoonthorn King Mongkut's Institute of Technology, Ladkrabang, Thailand Wornchanok.ch@kmitl.ac.th Abstract—Thailand is transforming its economy into a digital economy. Mobile payment (m-payment) is a core technology that helps the country phases from the manufacturing-based economy into the digital economy. However, a question remains what factors influencing people to adopt mobile payment. Lit- tle literature focuses on users in Thailand. This study aims to determine factors associating with the decision-making process in selecting m-payment systems of respondents in Bangkok. The study addresses a research question. What do factors segregate m-payment adoption? 820 respondents were asked by using a questionnaire. Employment of confirmatory factor analysis (CFA) developed the measurement showing acceptable validity and reliability. The study uses multinomial logistic regression to classify Technology Choices (TCs). The re- sults show low values of Pseudo R-Square, indicating that there is a lack of practical variables. Discussions and suggestions are addressed in this research. Keywords—Decision making, Technology adoption, Consumer behaviour, End-user behaviour, Intention to use, Mobile payment, M-payment, Thailand. 1 Introduction As a member of the Association of Southeast Asian Nations (ASEAN), Thailand is attempting to connect its e-payment systems with other members. Thailand used a digital push strategy to boost the usage of e-payment and m-payment systems. As a plan to reduce banknotes, the government promotes the use of mobile and internet banking increasing more than 140 % from 2012 to 2016 [1]. M-payment systems benefit both government and citizen. The Thai government saves money from printing paper money, whereas the citizens are convenient to pay anywhere and anytime. Moreover, Thai merchants can be global traders using an m- payment system to receive cash from shoppers internationally. The goal of the Thai government is to develop a cashless payment system as well as to boost electronic commerce (e-commerce) transactions [2]. Electronic Transactions Development Agency [3] estimated that in 2017 the e-commerce transactions would rise to 2,812 billion baht (around $ 85.2 billion: 33 Baht per USD) or almost 10 percent increase when compared with 2016. This increase in e-commerce transaction would lead to the rise in m-payment transactions. For example, the Bank of Thailand shows that the 126 http://www.i-jim.org https://doi.org/10.3991/ijim.v13i09.10834 https://doi.org/10.3991/ijim.v13i09.10834 https://doi.org/10.3991/ijim.v13i09.10834 Paper—Decision Making in Selecting Mobile Payment Systems adoption of internet banking, mobile banking, electronic money has increased by 983, 553, and 506 percent respectively between 2010 and 2016 [4]. However, to promote the effective use of m-payment systems, the understandings of perceived trust, privacy concern, and perceived risk are crucial. These factors sig- nificantly determine the use of m-payment systems [5]–[11]. In Information Systems, UTAUT2 is one of the most modern theory, explaining the use of consumer technolo- gy [12]. UTAUT2 does not incorporate trust, risk, and privacy concern into the model. Users do not need to use a single technology to perform all tasks. Instead, they can select a wide range of Technology Choices based on the context of their use. Each technology channel has different levels of trust, privacy concern, and risk. These con- ditions potentially determine the use of consumer technologies such as m-payment systems. Therefore, this research aims to determine factors associating with the decision- making process in selecting an m-payment system of respondents in Bangkok, Thai- land. The study addresses a research question: What do factors segregate m-payment adoption? Our research contribution is a statistical model for explaining the selection of different technology adoption. 2 Literature Review Little research has been done to understand how users select different technologies depending on their contexts. Hernandez and Mazzon [13] showed a possibility to use behavioral theories to understand technologies selections. They used the diffusion of innovation (DoI) [14], the theory of planned behavior (TPB) [15], and the technology acceptance model (TAM) [16]. However, these theories are old. Besides, Information Systems researchers have developed Unified Theory of Acceptance and Use of Tech- nology 1&2 (UTAUT 1&2) [11], [12], [17], which are more contemporary theories. Therefore, I decided to apply UTAUT2 for testing the applicability of a new theory. Unified Theory of Acceptance and Use of Technology (UTAUT) aims to fix the problem of TAM because TAM has a lack of other possible factors associated with the phenomenon being explained. Besides, TAM constructs are too similar to other theories. For example, the relative advantage of DoI is similar to perceived usefulness. Hence, applying only TAM can misguide developers in the wrong direction [18, p. 217]. New constructs are added in UTAUT2; these constructs are habit, facilitating conditions, and hedonic motivation. Furthermore, moderating factors are added in UTAUT2. These moderators are gender, age, and experience [12], [17]. 2.1 Prior studies Table 1 shows research conducted in the past and relationships associated with TAM. However, few studies have used UTAUT2 as the research framework. iJIM ‒ Vol. 13, No. 9, 2019 127 Paper—Decision Making in Selecting Mobile Payment Systems Table 1. The Summary of Relationships between Independent and Dependent Variables IV DV Reference Usefulness Use behavior [19] Ease of Use Usefulness [18], [20] Ease of Use Intention [21] Relative Advantage Intention [18] Compatibility Intention [18], [21] Trial-ability Intention [18] Voluntariness Intention [18] Innovativeness Intention [8] Innovativeness Usefulness [22] Innovativeness Ease of use [22] Perceived value Intention [9], [10] Risk Intention [7], [8], [10], [23] Trust Intention [9]–[11], [21], [23], [24] Usefulness Trust [7] Ease of use Trust [7] Security Trust [6], [9], [25], [26] Privacy concern Trust [6], [9] Innovativeness Risk [27] Innovativeness Trust [28] Trust Value [9] The conceptual framework consists of Behavioral Intention (BI), Performance Ex- pectancy (PE), Effort Expectancy (EE), Facilitating Conditions (FC), Social Influence (SI), Price value (PV), Hedonic Motivation (HM), and Habit (Ha). In addition to UTAUT2’ constructs, I decided to add Personal Innovativeness (PI), Perceived Trust (PT), Perceived Risk (PR), and Privacy Concern (PC) based on prior studies. Figure 1 shows the conceptual framework adapted from Venkatesh et al. [12] and Hernandez and Mazzon [13]. TC refers to modes of technology that users use for paying e-money. TC was first used in adoption research by K. K. Kim & Prabhakar [29], referring to different modes of technology usage. BI refers to the degree to which people intend to perform a technology [30]. BI ex- plains UB [12]. BI is an intermediate construct connecting UB and other attitudinal constructs [15]. PE represents the perception of users; they think that using a particular technology brings benefits to them [12]. PE can be an attitudinal construct, especially cognitive information [15], [31]. Besides, PE can be viewed as extrinsic motivation [32], [33]. EE refers to the extent to which customers view that a particular m-payment sys- tem is easy to use [17]. Successful technologies should not create confusion for their users when the users want to use. FC refers to the degree to which a user of an m-payment system thinks that he or she has technological infrastructure supports the use of m-payment system [17]. This construct is similar to compatibility and perceived behavioral control [14], [15], [17], [34]. 128 http://www.i-jim.org Paper—Decision Making in Selecting Mobile Payment Systems Fig. 1. The conceptual framework SI means the degree to which the use of an m-payment system believes that im- portant people think that he or she should or should not use such an m-payment sys- tem [17]. Society can help to expedite the rate of technology adoption. PV is a construct introduced to explain adoption behavior. Consumers weight the benefits that they obtain to cost that they pay [12]. Like PE, price value is used to describe BI. M-payment can have a transaction cost. Hence, this construct is appropri- ate for this study. HM refers to the degree of fun, enjoyment, happiness when the user uses a particu- lar technology [12]. HM significantly positively affects BI in many technologies such as learning management software [35], social media [36], and e-commerce [37]. iJIM ‒ Vol. 13, No. 9, 2019 129 Paper—Decision Making in Selecting Mobile Payment Systems Ha refers to the degree that the user of an m-payment system think that he or she uses the technology as their habit. Ha influences the UB and BI of users [12]. A study shows that Ha affects both BI and UB regarding mobile banking [38]. PI is a construct that has been introduced by Rogers [14]. People who have innova- tiveness adopt new technology rapidly than people who have less innovativeness [14]. PI has a significant impact on BI to use information technology [8]. PT can be viewed as confidentiality, integrity, authentication, of the m-payment system [6]. PT enhances the attitudes of users to become confident with the m- payment system which in turn declines the uncertainty [7], [11]. PT may be related to technological parts of the m-payment systems. PR shows that people decline the rate of adoption regarding risk technology such as e-commerce and m-payment. PR can be viewed as the costs of technology adoption [5]. Rakhi and Mala [8] include PR as the same construct as privacy risk and financial risk. Studies show that PR negatively influence BI [8], [10]. PC shows how much users trust a system. Customers feel that their information perhaps is misused by electronic services such as e-commerce and m-payment com- panies and systems [6], [9]. 3 Methodology The author employed a self-reported paper-based questionnaire. The attitudinal measurement in this study consisted of a seven-point bipolar semantic differential scale from strongly disagree (1) to strongly agree (7) since this scale can be assumed as a numerical scale, unlike a Likert scale. The respondents were asked what the most recently used technology channel was. Table 2 shows the measurement of attitudinal constructs. Table 2. The Measurement of Attitudinal Constructs Constructs Items Description Reference BI BI1 I intend to use this mobile payment continuously in the future. (Fishbein and Ajzen, 2010; Venkatesh et al., 2003, 2012) BI2 I attempt to use this mobile payment in everyday life. BI3 I plan to use this mobile payment often. BI4 I expect to use this mobile payment continuously. PE PE1 I find that this mobile payment is useful in my life. (Venkatesh et al., 2003, 2012) PE2 Using this mobile payment makes my work accomplishes quickly. PE3 Using this mobile payment increases the efficiency of my work. PE4 Using this mobile payment makes me work faster and save my costs. EE EE1 Learning how to use this mobile payment is easy for me. (Venkatesh et al., 2003, 2012) EE2 Using this mobile payment is clear and understandable. EE3 I find that using this mobile payment is easy. EE4 I find that it is easy to be an expert in using this mobile payment. SI SI1 People who are important to me think that I should use this mobile payment. (Fishbein and Ajzen, 2010; Venkatesh et al., 2003, 2012) SI2 People who influence my behaviour think that I should use this mo- bile payment. SI3 People whose opinions I like think that I should use this mobile payment. 130 http://www.i-jim.org Paper—Decision Making in Selecting Mobile Payment Systems Constructs Items Description Reference SI4 People whom I respect and admire encourage me to use this mobile payment. FC FC1 I have enough resources to use this mobile payment. (Venkatesh et al., 2003, 2012) FC2 I have enough knowledge to use this mobile payment. FC3 This mobile payment is compatible with other technologies I use. FC4 I often get support from other people when I have a problem using this mobile payment. HM HM1 Using this mobile payment is fun. (Venkatesh et al., 2012) HM2 Using this mobile payment makes me happy. HM3 Using this mobile payment is entertaining. HM4 I feel happy when I use this mobile payment. Ha Ha1 Using this mobile payment is my habit. (Venkatesh et al., 2012) Ha2 I feel addicted to using this mobile payment. Ha3 I must use this mobile payment often. Ha4 Using this mobile payment becomes my normal routine. PV PV01 Expenses occurring from this mobile payment are reasonable. (Venkatesh, Thong and Xu, 2012) PV02 Using this mobile payment is worthy when compared with costs. PV03 When compared with costs, this mobile payment creates value. PR PR01 Using this mobile payment brings risk to me. (Chellappa and Pavlou, 2002; Rakhi and Mala, 2014; Yang et al., 2015) PR02 Using this mobile payment tends to make me lose. PR03 Using this mobile payment is uncertain. PR04 Using this mobile payment has the potential to bring financial loss. TR TR01 This mobile payment is trustworthy. (Flavián and Guinalíu, 2006; Roca, García and de la Vega, 2009) TR02 The company that provides this mobile payment is trustful. TR03 This mobile payment is faithful. TR04 I trust this mobile payment. PC PC01 I worry that my personal information can be misused. (Flavián and Guinalíu, 2006; Bonsón Ponte, Carvajal-Trujillo and Escobar- Rodríguez, 2015). PC02 I worry that my personal information can be sold and exchanged. PC03 I worry that my personal information can be used without my permis- sion. PC04 I worry that my personal information can be collected, tracked, and analyzed. PI PI01 If I hear the news about new technology, I will try quickly. (Rogers, 1983) PI02 I am the first person who tries new technology. PI03 I like to try new technology. PI04 I like to exploit new ideas. In addition to the attitudinal constructs, I measured experience (EXP) of users in the number of years. The natural logarithm was used to transform experience into a linear scale (ln (EXP)). Age is in the number of years. Education is also the number of years. Gender is 0 for males and 1 for female. Income is 0 for people who earn 25,000 Baht (about $ 808.45) a month or lower and 1 for people who earn more than 25,000 Bath a month. All attitudinal constructs here were tested by using confirmatory factor analysis (CFA). The reliability was measured by using Cronbach’s Alpha and composite relia- bility. The acceptable value is more than 0.70 [39]. Regarding construct validity, the standardized factor loading should be higher than 0.70 and the average variance ex- tracted (AVE) should be higher than 0.50 [39]. To satisfy the discriminant validity, iJIM ‒ Vol. 13, No. 9, 2019 131 Paper—Decision Making in Selecting Mobile Payment Systems the comparison between AVEs and the squared correlation between two constructs is used to investigate whether or not the constructs are different [39]. SPSS and Amos were analytical tools in this research. 4 Results The sample size is 820 respondents. 397 (48.4 percent) are males, and 423 (51.6 percent) are female. Table 3 shows the distribution of cases of Technology Choices. There are six classes: 1) (using) m-wallets in physical stores, 2) m-wallets on the Internet, 3) m-banking in physical stores, 4) Mobile banking, and 5) other choices. The ‘others' class is used as a reference. These classes reflect the most recently used technology choices (TC). Table 3. The Group Information Technology Choices Number Percentage 1) m-wallets in physical stores 232 28.3% 2) m-wallets on the internet 106 12.9% 3) m-banking in physical stores 100 12.2% 4) m-banking on the internet 80 9.8% 5) Mobile banking 111 13.5% 6) others 191 23.3% Our initial findings show indices of Pseudo R-Square: Cox and Snell (.355), Nagelkerke (.367), and McFadden (.128). Likelihood Ration Tests were performed. The variable that has the highest p-value was removed, and then Likelihood Ratio Tests were performed again. I removed the following variables: HM * Age, PV*Age, PV*Gender, FC*Gender, PR, FC, EE, SI, HB*Gender, BI, HM, EDU, PE, PI, HB, PV, TR, HM*ln (EXP), HB* Ln (EXP), HB* Age, HM*Gender, and Gender respec- tively. Table 4 shows the log-likelihood value, which is a measure of selecting inde- pendent variables identical to stepwise regression [39]. After I removed the non-significant variables, I obtained the values of Pseudo R- Square: Cox and Snell (.214), Nagelkerke (.224), and McFadden (.070). These values of Pseudo R-Square show the assessments of overall model fit. Of practical im- portance, these indices show low scores [39]. Table 4. The Likelihood Ratio Tests Effect -2 Log Likelihood of Reduced Model Chi-Square df Sig. Intercept 2604.757 16.209 5 0.006 Age 2614.094 25.547 5 0.000 Income 2633.563 45.015 5 0.000 BI*Ln (EXP) 2610.299 21.751 5 0.001 FC*Age 2653.459 64.911 5 0.000 FC*Ln (EXP) 2633.227 44.679 5 0.000 132 http://www.i-jim.org Paper—Decision Making in Selecting Mobile Payment Systems Table 4 shows the outcome after I terminated non-significant variables as men- tioned before. The results are age, income, and the interactions between BI and ln (EXP), between FC and age, and between FC and ln (EXP). Table 5. The Parameter Estimate Technology Choices B Std. Error Wald df Sig. Exp(B) M-wallets in stores Intercept -1.217 0.397 9.395 1 0.002 Age -0.021 0.015 1.888 1 0.169 0.979 Income 0.949 0.237 16.028 1 0.000 2.583 BI*Ln (EXP) 0.185 0.058 10.101 1 0.001 1.203 FC*Age 0.019 0.003 32.011 1 0.000 1.019 FC*ln (EXP) -0.320 0.070 21.103 1 0.000 0.726 M-wallets on the internet Intercept -0.870 0.488 3.186 1 0.074 Age -0.041 0.020 4.321 1 0.038 0.959 Income 0.723 0.287 6.338 1 0.012 2.062 BI*ln (EXP) 0.116 0.076 2.327 1 0.127 1.123 FC*Age 0.021 0.004 28.484 1 0.000 1.021 FC*ln (EXP) -0.364 0.087 17.705 1 0.000 0.695 M-banking in stores Intercept -0.310 0.509 0.370 1 0.543 Age -0.039 0.020 3.694 1 0.055 0.962 Income 0.506 0.298 2.888 1 0.089 1.658 BI*ln (EXP) 0.197 0.075 6.972 1 0.008 1.218 FC*Age 0.014 0.004 13.478 1 0.000 1.014 FC*ln (EXP) -0.406 0.088 21.254 1 0.000 0.666 M-banking on the internet Intercept 0.594 0.645 0.848 1 0.357 Age -0.088 0.027 10.800 1 0.001 0.916 Income 1.773 0.316 31.550 1 0.000 5.891 BI*Ln (EXP) -0.068 0.094 0.529 1 0.467 0.934 FC*Age 0.014 0.004 10.826 1 0.001 1.015 FC*ln (EXP) -0.209 0.101 4.271 1 0.039 0.812 Mobile banking Intercept -0.363 0.510 0.506 1 0.477 Age -0.091 0.023 14.961 1 0.000 0.913 Income 0.095 0.301 0.100 1 0.752 1.100 BI*ln (EXP) 0.238 0.077 9.668 1 0.002 1.269 FC*Age 0.029 0.004 47.491 1 0.000 1.029 FC*ln (EXP) -0.474 0.087 29.628 1 0.000 0.623 Note: The ‘Others’ class (Other types of m-payment systems) is the reference group. As I can see from table 5, customers who use m-wallets in stores are those who have a high income. The more income that they have, the more likely they use m- wallets in stores. Other factors are found in forms of interactions. The findings show that the interaction between BI and ln (EXP) and the interaction between FC and age help to classify consumers who use m-wallets in a physical store with positive direc- tions. The interaction between FC and ln (EXP) shows a negative direction, suggest- ing that consumers who have both high FC and ln (EXP) tend not to use m-wallets in stores. Considering using m-wallets on the Internet, customers who choose this channel tend to be young people rather than older people (negative relationship with age). iJIM ‒ Vol. 13, No. 9, 2019 133 Paper—Decision Making in Selecting Mobile Payment Systems Income is a positive classifier. Higher income consumers tend to use m-wallets on the Internet more than those who have lower income. The interaction between FC and age shows that people who have both high FC and age tend to use credit care on the inter- net. The interaction between FC and ln (EXP) shows a negative relationship with the using credit card on the Internet. Concerning using m-banking in stores, both age and income do not have capabili- ties to classify users who use m-banking in stores. However, three interaction effects can classify users who use m-banking in stores. Users who have high both BI and ln (EXP) and users who have high both FC and age tend to use m-banking in stores, while the opposite trend is the users who have high both FC and ln (EXP); these users do not tend to use m-banking in physical stores. In terms of using m-banking on the Internet, young users tend to use m-banking on the Internet more than older users. Income is a positive factor showing that the rich tend to use m-wallets on the Internet more than the poor do. The interaction effect between FC and age shows a positive relationship, suggesting that users who have high both FC and age tend to use m-banking on the Internet more than those who have low both FC and age. The interaction between FC and ln (EXP) shows a negative relationship. Users who have high both FC and ln (EXP) tend not to use m-banking on the Internet. Regarding internet banking, the finding suggests that age is a negative classifier. Older users tend not to use internet banking while young users manage to do so. The interaction effect between BI and ln (EXP) shows that users who have high both BI and ln (EXP) tend to use the internet banking more than those who have low both BI and ln (EXP). Likewise, the interaction between FC and age shows that users who have high both FC and age tend to use the internet baking more than those who have low both FC and age. The interaction between FC and ln (EXP) shows a negative relationship. Those who have high both FC and ln (EXP) tend not to use internet banking while those who have low both FC and ln (EXP) tend to use Internet Bank- ing. 5 Discussion The applicability of UTAUT2 [12] in technology classification is not apparent. Since UTAUT2 is a social science theory, it does not consider economic variables such as income. This study suggests that income is the most influential variable, for almost technologies except Internet banking, helping the decision making of users in selection m-payment systems. On the other hand, BI, which is the most utilized varia- ble in social science, shows little effects on TC. TC tends to rely on socio-economic statuses, such as age and income. Although UTAUT2 constructs able to improve the classification of TC are BI and FC, both constructs are forms of interactions, not di- rectly segregating TC. The evidence is the set of the values of Pseudo R-Square. However, UTAUT2 predicts two moderators correctly. The interaction between FC and ln (EXP). Ln (EXP) moderates/interacts the path between FC and BI. Then I expected to find strong technology adoption of a technology channel for novice users 134 http://www.i-jim.org Paper—Decision Making in Selecting Mobile Payment Systems who have high FC. In addition to ln (EXP), age moderates the path between FC and BI. I expected to find strong technology adoption of a technology channel for older users who have high FC. Hence, the findings support Venkatesh et al.[12]. I did not find significant evidence of gender. The roles of PT, PR, and PC are not significant for users to change the modes of technology usage. This research has not found any support for PT, PR, and PC for technology classification. Studies have supported the uses of PR [7], [8], [10], [23], PT [9], [10], [21], [23], and [6], [9] in adoption research. However, these constructs are not capable of classifying different types of technology usage. This finding is consistent with that of Hernandez and Mazzon [13], showing that security and privacy are not capable of classifying three classes of banking users: 1) Internet/non Internet banking users, 2) non-internet users/ non-internet banking users, and 3) internet bank- ing users. Unlike Hernandez and Mazzon [13] who showed that income was not a significant classifier in the context of Internet banking, our research shows that income is essen- tial for Thai m-payment users. Another inconsistency issue with Hernandez and Maz- zon [13] is that their study showed a significance of education while our research has not found education important. This might be based on the purchasing power that might be different between the respondents of this study and Hernandez and Mazzon (2007). This research has limitations. The sampling is a quota sampling, balancing between male and female. This sampling cannot be the representation of the entire population. Therefore, statistical generalization is not a strength of this research. Additionally, I have imbalanced classes. The percentages of technology choices are not well distrib- uted. However, this research can generalize to theory (theoretical generalization). Alt- hough the findings are not comprehensive, they serve as a starting point for theoretical development for technology selection theory. This research calls for a different theory for technology adoption from mainstream behavioral paradigms such as TPB, TAM, UTAUT1, and UTAUT2. 6 Conclusion Thailand is transforming its economy into a digital economy. M-payment is a core technology that helps Thailand move from the real economy to the digital economy. However, researchers are curious about what factors influencing people to adopt mo- bile payment. Little literature focuses on users in Thailand. This study determines factors associating with the decision-making process in selecting an m-payment sys- tem of respondents: What do factors segregate m-payment adoption? 820 respondents were asked by using a questionnaire. SEM was employed to develop the measurement showing acceptable validity and reliability. I used multinomial logistic regression to classify technology choices. The results show low values of Pseudo R-Square, indicat- ing that there is a lack of significant information from possible variables. The signifi- cant classifiers are age, income and the interactions between BI and ln (EXP), be- iJIM ‒ Vol. 13, No. 9, 2019 135 Paper—Decision Making in Selecting Mobile Payment Systems tween FC and age, and between FC and ln (EXP). Our research contribution is a sta- tistical model for explaining the selection of different technology adoption. Predicting the use of completing technology adoption is different from traditional technology adoption research. Therefore, our study calls for a better theory in understanding the selection of TC. 7 References [1] “Singapore, Thailand Weigh E-Payment Alliance in Digital Push,” Bloom- berg.com, 04-Oct-2017. [2] Business Insider, “Thailand’s national e-payment system should bolster e- commerce,” Business Insider, 2016. http://www.businessinsider.com/thailands-nation al-e-payment-system-should-bolster-e-commerce-2016-7. [Accessed: 03-Mar-2018]. [3] Electronic Transactions Development Agency, “ETDA organizes Thailand e- Commerce Week 2017; Push the Growth of Thais by e-Commerce, Maintaining ASEAN Championship – Toward to World Market,” 2017. https://www.etda.or.th/ content/thailand-e-commerce-week-2017-press-conference.html. [Accessed: 03-Mar- 2018]. [4] Bank of Thailand, “Overview of payment system transactions,” 2018. https://www. bot.or.th/Thai/Statistics/PaymentSystems/Pages/StatPaymentTransactions.aspx.[Accessed : 03-Mar-2018]. [5] A. S. Alhakami and P. Slovic, “A Psychological Study of the Inverse Relation- ship Between Perceived Risk and Perceived Benefit,” Risk Anal., vol. 14, no. 6, pp. 1085–1096, Dec. 1994. https://doi.org/10.1111/j.1539-6924.1994.tb00080.x [6] C. Flavián and M. Guinalíu, “Consumer trust, perceived security and privacy pol- icy,” Ind. Manag. Data Syst., vol. 106, no. 5, pp. 601–620, Jun. 2006. https://doi.org/10.1108/02635570610666403 [7] J. C. Roca, J. J. García, and J. J. de la Vega, “The importance of perceived trust, security and privacy in online trading systems,” Inf. Manag. Comput. Secur., vol. 17, no. 2, pp. 96–113, Jun. 2009. https://doi.org/10.1108/09685220910963983 [8] T. Rakhi and S. Mala, “Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India,” Internet Res., vol. 24, no. 3, pp. 369–392, May 2014. https://doi.org/10. 1108/intr-12-2012-0244 [9] E. Bonsón Ponte, E. Carvajal-Trujillo, and T. Escobar-Rodríguez, “Influence of trust and perceived value on the intention to purchase travel online: Integrating the effects of assurance on trust antecedents,” Tour. Manag., vol. 47, pp. 286– 302, Apr. 2015. https://doi.org/10.1016/j.tourman.2014.10.009 [10] Y. Yang, Y. Liu, H. Li, and B. Yu, “Understanding perceived risks in mobile payment acceptance,” Ind. Manag. Data Syst., vol. 115, no. 2, 2015. [11] Malik Khlaif Gharaibeh and Muhammad Rafie Mohd Arshad, “Using the UTAUT2 Model to Determine Factors Affecting Adoption of Mobile Banking Services: A Qualitative Approach,” International Journal of Interactive Mobile Technologies (iJIM), vol. 12, no. 4, 2018. https://doi.org/10.3991/ijim.v12i4.8525 136 http://www.i-jim.org http://www.businessinsider.com/thailands-national-e-payment-system-should-bolster-e-commerce-2016-7. http://www.businessinsider.com/thailands-national-e-payment-system-should-bolster-e-commerce-2016-7. http://www.businessinsider.com/thailands-national-e-payment-system-should-bolster-e-commerce-2016-7. https://www.etda.or.th/content/thailand-e-commerce-week-2017-press-conference.html. https://www.etda.or.th/content/thailand-e-commerce-week-2017-press-conference.html. https://www.etda.or.th/content/thailand-e-commerce-week-2017-press-conference.html. https://www.bot.or.th/Thai/Statistics/PaymentSystems/Pages/StatPaymentTransactions.aspx. https://www.bot.or.th/Thai/Statistics/PaymentSystems/Pages/StatPaymentTransactions.aspx. https://www.bot.or.th/Thai/Statistics/PaymentSystems/Pages/StatPaymentTransactions.aspx. https://doi.org/10.1111/j.1539-6924.1994.tb00080.x https://doi.org/10.1111/j.1539-6924.1994.tb00080.x https://doi.org/10.1108/02635570610666403 https://doi.org/10.1108/02635570610666403 https://doi.org/10.1108/09685220910963983 https://doi.org/10.1108/09685220910963983 https://doi.org/10.1108/intr-12-2012-0244 https://doi.org/10.1108/intr-12-2012-0244 https://doi.org/10.1016/j.tourman.2014.10.009 https://doi.org/10.1016/j.tourman.2014.10.009 https://doi.org/10.3991/ijim.v12i4.8525 Paper—Decision Making in Selecting Mobile Payment Systems [12] V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer Acceptance and Use of In- formation Technology: Extending the Unified Theory of Acceptance and Use of Technology,” MIS Q., vol. 36, pp. 157–178, 2012. https://doi.org/10.2307/41410412 [13] J. M. C. Hernandez and J. A. Mazzon, “Adoption of internet banking: proposition and implementation of an integrated methodology approach,” Int. J. Bank Mark., vol. 25, no. 2, pp. 72–88, Mar. 2007. https://doi.org/10.1108/02652320710728410 [14] E. M. Rogers, Diffusion of Innovations, 3rd ed. New York: The Free Press, 1983. [15] M. Fishbein and I. Ajzen, Predicting And Changing Behavior: The Reasoned Ac- tion Approach. New York: Psychology Press, 2010. [16] F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Manag. Sci., vol. 35, pp. 982–1003, 1989. https://doi.org/10.1287/mnsc.35.8.982 [17] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance of Information Technology: Toward a Unified View,” MIS Q., vol. 27, no. 3, pp. 425–478, 2003. https://doi.org/10.2307/30036540 [18] C. R. Plouffe, M. Vandenbosch, and J. Hulland, “Intermediating technologies and multi-group adoption: A comparison of consumer and merchant adoption inten- tions toward a new electronic payment system,” J. Prod. Innov. Manag., vol. 18, no. 2, pp. 65–81, 2001. https://doi.org/10.1016/s0737-6782(00)00072-2 [19] T. Pikkarainen, K. Pikkarainen, H. Karjaluoto, and S. Pahnila, “Consumer ac- ceptance of online banking: an extension of the technology acceptance model,” Internet Res., vol. 14, no. 3, pp. 224–235, Jul. 2004. https://doi.org/10.1108/106622 40410542652 [20] Khaled Aldiabat, Anwar Al-Gasaymeh, and Ameer Sardar Kwekha Rashid, “The Effect of Mobile Banking Application on Customer Interaction in the Jordanian Banking Industry,” International Journal of Interactive Mobile Technologies (iJIM), vol. 13, no. 2, 2019. https://doi.org/10.3991/ijim.v13i02.9262 [21] L. Carter and F. Bélanger, “The utilization of e-government services: citizen trust, innovation and acceptance factors*,” Inf. Syst. J., vol. 15, no. 1, pp. 5–25, 2005. https://doi.org/10.1111/j.1365-2575.2005.00183.x [22] K. C. C. Yang, “Exploring factors affecting the adoption of mobile commerce in Singapore,” Telemat. Inform., vol. 22, no. 3, pp. 257–277, Aug. 2005. https://doi. org/10.1016/j.tele.2004.11.003 [23] Sevgi Özkan, Gayani Bindusara, and Ray Hackney, “Facilitating the adoption of e-payment systems: theoretical constructs and empirical analysis,” J. Enterp. Inf. Manag., vol. 23, no. 3, pp. 305–325, Apr. 2010. https://doi.org/10.1108/1741039101 1036085 [24] Satwinderjit Singh, Izzal Asnira Zolkepli, and Cheah Wen Kit, “New Wave in Mobile Commerce Adoption via Mobile Applications in Malaysian Market: In- vestigating the Relationship Between Consumer Acceptance, Trust, and Self Effi- cacy,” International Journal of Interactive Mobile Technologies (iJIM), vol. 12, no. 7, 2018. https://doi.org/10.3991/ijim.v12i7.8964 [25] R. K. Chellappa and P. A. Pavlou, “Perceived information security, financial lia- bility and consumer trust in electronic commerce transactions,” Logist. Inf. Manag., vol. 15, no. 5/6, pp. 358–368, Dec. 2002. https://doi.org/10.1108/095760 50210447046 iJIM ‒ Vol. 13, No. 9, 2019 137 https://doi.org/10.3991/ijim.v12i4.8525 https://doi.org/10.2307/41410412 https://doi.org/10.2307/41410412 https://doi.org/10.1108/02652320710728410 https://doi.org/10.1108/02652320710728410 https://doi.org/10.1287/mnsc.35.8.982 https://doi.org/10.1287/mnsc.35.8.982 https://doi.org/10.2307/30036540 https://doi.org/10.2307/30036540 https://doi.org/10.1016/s0737-6782(00)00072-2 https://doi.org/10.1016/s0737-6782(00)00072-2 https://doi.org/10.1108/10662240410542652 https://doi.org/10.1108/10662240410542652 https://doi.org/10.3991/ijim.v13i02.9262 https://doi.org/10.3991/ijim.v13i02.9262 https://doi.org/10.1111/j.1365-2575.2005.00183.x https://doi.org/10.1111/j.1365-2575.2005.00183.x https://doi.org/10.1016/j.tele.2004.11.003 https://doi.org/10.1016/j.tele.2004.11.003 https://doi.org/10.1108/17410391011036085 https://doi.org/10.1108/17410391011036085 https://doi.org/10.3991/ijim.v12i7.8964 https://doi.org/10.3991/ijim.v12i7.8964 https://doi.org/10.1108/09576050210447046 Paper—Decision Making in Selecting Mobile Payment Systems [26] M.-J. Kim, N. Chung, and C.-K. Lee, “The effect of perceived trust on electronic commerce: Shopping online for tourism products and services in South Korea,” Tour. Manag., vol. 32, no. 2, pp. 256–265, Apr. 2011. https://doi.org/10.1016/j.tour man.2010.01.011 [27] H. Dai, X. (Robert) Luo, Q. Liao, and M. Cao, “Explaining consumer satisfaction of services: The role of innovativeness and emotion in an electronic mediated en- vironment,” Decis. Support Syst., vol. 70, pp. 97–106, Feb. 2015. https://doi.org/10. 1016/j.dss.2014.12.003 [28] W. D. Salisbury, R. A. Pearson, A. W. Pearson, and D. W. Miller, “Perceived se- curity and World Wide Web purchase intention,” Ind. Manag. Data Syst., vol. 101, no. 4, pp. 165–177, Jun. 2001. https://doi.org/10.1108/02635570110390071 [29] K. K. Kim and B. Prabhakar, “Initial Trust and the Adoption of B2C e- Commerce: The Case of Internet Banking,” SIGMIS Database, vol. 35, no. 2, pp. 50–64, Jun. 2004. https://doi.org/10.1145/1007965.1007970 [30] Y. Malhotra and D. F. Galletta, “Extending the Technology Acceptance Model to Account for Social Influence: Theoretical Base and Empirical Validation,” pre- sented at the 32nd Hawaii International Conference on System Sciences, 1999. https://doi.org/10.1109/hicss.1999.772658 [31] G. R. Maio and G. Haddock, The Psychology of Attitudes and Attitude Change. Thousand Oaks: SAGE Publications Inc., 2010. [32] K.-Y. Lin and H.-P. Lu, “Why people use social networking sites: An empirical study integrating network externalities and motivation theory,” Comput. Hum. Behav., vol. 27, pp. 1152–1161, 2011. https://doi.org/10.1016/j.chb.2010.12.009 [33] Y. Malhotra, D. F. Galletta, and L. J. Kirsch, “How Endogenous Motivations In- fluence User Intentions: Beyond the Dichotomy of Extrinsic and Intrinsic User Motivations,” J. Manag. Inf. Syst., vol. 25, pp. 267–299, 2008. https://doi.org/10. 2753/mis0742-1222250110 [34] S. Taylor and P. A. Todd, “Understanding Information Technology Usage: A Test of Competing Models,” Inf. Syst. Res., vol. 6, pp. 144–176, 1995. [35] A. Raman and Y. Don, “Preservice Teachers’ Acceptance of Learning Manage- ment Software: An Application of the UTAUT2 Model,” Int. Educ. Stud., vol. 6, no. 7, p. p157, Jun. 2013. https://doi.org/10.5539/ies.v6n7p157 [36] O. Oechslein, M. Fleischmann, and T. Hess, “An Application of UTAUT2 on So- cial Recommender Systems: Incorporating Social Information for Performance Expectancy,” in 2014 47th Hawaii International Conference on System Sciences (HICSS), 2014, pp. 3297–3306. https://doi.org/10.1109/hicss.2014.409 [37] F. J. Pascual-Miguel, Á. F. Agudo-Peregrina, and J. Chaparro-Peláez, “Influences of gender and product type on online purchasing,” J. Bus. Res., vol. 68, no. 7, pp. 1550–1556, Jul. 2015. https://doi.org/10.1016/j.jbusres.2015.01.050 [38] G. Baptista and T. Oliveira, “Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators,” Com- put. Hum. Behav., vol. 50, pp. 418–430, Sep. 2015. https://doi.org/10.1016/j.chb. 2015.04.024 [39] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Anal- ysis: A Global Perspective, 7th ed. Upper Saddle River, New Jersey: Pearson Prentice Hall, 2010. 138 http://www.i-jim.org https://doi.org/10.1108/09576050210447046 https://doi.org/10.1016/j.tourman.2010.01.011 https://doi.org/10.1016/j.tourman.2010.01.011 https://doi.org/10.1016/j.dss.2014.12.003 https://doi.org/10.1016/j.dss.2014.12.003 https://doi.org/10.1108/02635570110390071 https://doi.org/10.1108/02635570110390071 https://doi.org/10.1145/1007965.1007970 https://doi.org/10.1145/1007965.1007970 https://doi.org/10.1109/hicss.1999.772658 https://doi.org/10.1109/hicss.1999.772658 https://doi.org/10.1016/j.chb.2010.12.009 https://doi.org/10.1016/j.chb.2010.12.009 https://doi.org/10.2753/mis0742-1222250110 https://doi.org/10.2753/mis0742-1222250110 https://doi.org/10.5539/ies.v6n7p157 https://doi.org/10.5539/ies.v6n7p157 https://doi.org/10.1109/hicss.2014.409 https://doi.org/10.1109/hicss.2014.409 https://doi.org/10.1016/j.jbusres.2015.01.050 https://doi.org/10.1016/j.jbusres.2015.01.050 https://doi.org/10.1016/j.chb.2015.04.024 https://doi.org/10.1016/j.chb.2015.04.024 Paper—Decision Making in Selecting Mobile Payment Systems 8 Author Wornchanok Chaiyasoonthorn is an Assistant Professor at Faculty of Admin- istration and Management, King Mongkut’s Institute of Technology Ladkrabang. She has taught courses in management information systems and electronic commerce. She has researched areas of marketing, knowledge management, and technology adoption. Her current research interests are adoption behavior, organizational behavior, and technology management. Article submitted 2019-05-09. Resubmitted 2019-06-29. Final acceptance 2019-07-10. Final version published as submitted by the authors. iJIM ‒ Vol. 13, No. 9, 2019 139