Htet Khine Soe
Student of Graduate School of Business, Assumption University, Thailand
Rawin Vongurai, Ph.D.
Lecturer of Graduate School of Business, Assumption University, Thailand
Mobile banking is studied the most value-adding and necessary mobile commerce application (Baptista and Oliveira, 2015; Malaquias and Hwang, 2016; Chaouali, W., Souiden, N. and Ladhari, R. (2017)). Laukkanen and Kiviniemi (2010) defined mobile banking as “an interaction in which a customer is connected to a bank via a mobile device such as a cell phone, smartphone, or personal digital assistant”.
Mobile banking services admit the customers to check account balances, transfer funds between account to account, and make mobile top-up bill and others payments. They have a huge potential market because of their always-on functionality and the option for customers can open their own mobile wallet accounts at anywhere of without needing to pay a visit to the bank.
Perceived ease of use (PEOU)
Davis (1989) described the perceived ease of use that “the degree to which a person believes that using a particular system would be free of effort”. It is the terms which a customer believes that a system is easy to learn or use. This system is similar to the complexity system used in innovation diffusion theory (IDT) (Rogers, 1995).
Mobile banking technology should be simple and easy for the customer to understand in order to enhance acceptance (Chitungo and Munongo, 2013; Mortimer, G., Neale, L., Hasan, S.F.E. and Dunphy, B. (2015); Koksal, 2016). The factors affects the complexity in mobile banking system such as navigation problems, a small screen size, and transaction issues. Venkatesh (2000) found the perceived ease of use by integrating internal control (computer self-efficacy) and external control (facilitating condition) into technology acceptance model (TAM).
The other studies (Davis, 1986, 1989) also pointed that perceived ease of use can impact perceived usefulness because other item being equal the easier the technology is to use the more useful it can be. The research in mobile banking system shows that perceived ease of use has significant effect on perceived usefulness.
Social influence (SI)
The theory of reasoned action (TRA) and its additions (Fishbein and Ajzen, 1975) require that human behavior is followed by intentions, which are designed based on an individual’s attitude towards the behavior and perceived subjective norms. Venkatesh et al. (2003) represented subjective norms as social influence, which is derived from theories such as theory of reasoned action (TRA), theory of planned behavior (TPB), decomposed theory of planned behavior DTPB, TAM2, C-TAM-TPB, the model of PC utilization (MPCU), and image in IDT.
Social influence mentioned an individual’s perception of other people’s opinions if he or she should perform a particular behavior. The studies of mobile banking adoption have shown a relationship between social influence and intention to use mobile banking (Laukkanen et al., 2007; Amin et al., 2008; Riquelme and Rios, 2010; Puschel et al., 2010; Sripalawat et al., 2011; Dasgupta et al., 2011; Tan and Lau, 2016).
Computer self-efficacy (CSE)
The derivation of self-efficacy is social cognitive theory (SCT) (Bandura, 1986). Self-efficacy expectation is the “conviction that one can successfully execute the behavior required to produce the outcomes” (Bandura, 1977). Additional, “expectations of self-efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences” (Bandura, 1977).
Self-efficacy belief is termed computer self-efficacy, which is termed as one’s perception of his or her ability to use a computer (Compeau and Higgins, 1995). In the mobile banking, if the customer believes that he or she has the required knowledge, skill, or ability to operate mobile banking, there is a higher chance of trying to usage the service. Through this hypothesis, the study explores whether a customer has the self-confidence to use mobile banking. Previous studies have exposed empirical evidence of a causal link between perceived ease of use and self-efficacy (Luarn and Lin, 2005; Wang, Y.-S., Lin, H.-H. and Luarn, P. (2006); Sripalawat et al., 2011; Jeong and Yoon, 2013).
Perceived financial cost (PFC)
The cost incurred in conducting mobile banking could slow its adoption. In the mobile banking, the cost has been found to be a major barrier to adoption (Yu, 2012; Hanafizadeh, P., Behboudi, M., Koshksaray, A.A. and Tabar, M.J.S. (2014); Alalwan, A.A., Dwivedi, Y.K. and Rana, N.P. (2017)). The cost incurred consist of the initial purchase price, equipment cost, subscription charges, and transaction cost. Perceived financial cost is the extent to which a person believes that using mobile banking would be costlier than other options (Luarn and Lin, 2005).
Security is a serious concern when conducting financial transactions through electronic channels. Hence, this could be one of the main barriers to the adoption of mobile banking, as personal or financial information could be exposed and used for fraudulent activities. Kalakota and Whinston (1997) defined security as “a threat which creates circumstances, condition, or event with the potential to cause economic hardship to data or network resources in the form of destruction, disclosure, modification of data, denial of service and/or fraud, waste, and abuse”.
Mobile banking contains more uncertainty and risk to the customer. In the mobile/wireless environment, security can be considered as the mobile payment-enabling application security, network security, and device security. The security mechanism of mobile banking has a positive effect on intention to use.
Trust can be defined as the willingness to make one vulnerable to actions taken by a trusted party based on the feeling of confidence or assurance (Gefen, 2000). Masrek et al. (2012) defined trust in mobile banking as “the belief that allows individuals to willingly become vulnerable to the bank, the telecommunication provider, and the mobile technology after having the banks, and the telecommunication provider’s characteristic embedded in the technology artefact”.
Trust shows a significant role in the adoption of mobile banking, helping customers overcome the fears of security/privacy risks and fraudulent activities in the mobile channels (Gu et al., 2009; Zhou, 2011; Afshan and Sharif, 2016). Trust is improved by the security mechanisms provided by mobile banking services. Customers are more likely to trust the new service if adequate security is provided for their transaction data.
The researchers such as Komiak and Benbasat (2004) have noticed trust from the emotional point of view and defined as the extent to which an individual feels secure and confident about relying on the trustee. Ennew and Sekhon (2007) have defined the trust as “individual’s willingness to accept vulnerability on the grounds of positive expectations about the intentions or behavior of another in a situation characterized by interdependence and risk.” This definition combines both the emotional as well as cognitive dimensions of trust. Therefore, consumer trust could be described as a function of the degree of risk involved in the situation and it is basically needful only in uncertain situations.
Behavioral intentions (BI)
Intention is defined as a prediction of actual behavior in socio-psychological studies (Bagozzi, 1989). The studies evidenced that intention is a prediction of actual behavior. Bae (2014) point out that intentions are powered by a person’s attitude, norms and self-control. This study is founded Ajzen’s Theory of Planned Behavior. The theory is used for behavioral intentions. It emphasize that a person’s behavior is intentional is the result of attitude, subjunctive norms and self-control.
Behavioral intention is also described as the extent to which users are willing to use a technology (Carlsson, Carlsson, Hyvonen, Puhakainen ; Walden, 2006). The subjective norm construct for behavioral intention is the most supreme antecedent (Ajzen, 1991). The theory of planned behavior (TPB) explains the purchase intention (Ajzen ; Madden, 1986). The theory of reasoned action (TRA) describes that performance of behavior is presented by the intention to carry out the behavior itself (Warshaw, 1980). The theoretical studies point out that intentions predicts a person’s behavior. This view align with a context of BI to use customer intention of mobile banking system for this system.
Research Framework and Methodology
This study proposed to identify the factors influencing acceptance and adoption of mobile banking systems in Myanmar and develop the behavioral intention to use the mobile banking in the Myanmar banking sector.
The conceptual framework of the study is adopted from the theoretical framework of Intention to use mobile banking in India (Sindhu Singh and R.K. Srivastava, 2018). The framework using in this research to find the customer intention to use the mobile banking system in Myanmar. To these study the factors consists of self-efficiency, perceived ease of use and social influence and intention to use.
The other factors included security, Trust, and perceived financial cost, which are recognized to influence mobile banking acceptance(Luarn and Lin, 2005; Lee et al.,2007; Zhou, 2011; Yu, 2012; Hanafizadeh et al., 2014; Afshan and Sharif, 2016). The bank customer has many digital payment system to use than mobile banking where these six constructs play an important role.
The study aimed that if the mobile banking system is easy to use, customers have the self-confidence to use and it is secure, trustworthy system, and cheaper than other digital payment system, more customers will be willing to use mobile banking system. Thus, the conceptual framework is developed to study the factors of influencing to use mobile banking in Myanmar as shown in Figure 1.
The hypotheses of this research based on the conceptual framework to find the relationship between Self-Efficacy, perceived ease of use, Social Influence, Security, Trust, perceived financial cost that influence the customer intention to use the mobile banking in Myanmar. There are four hypotheses in this study are as follow;
H1:Self-efficacy has significant influence on perceived ease of value of mobile banking system.
H2: Self-Efficacy (H2a), perceived ease of use (H2b), Social Influence (H2c), Security (H2d), Trust (H2e), perceived financial cost (H2f) have significant influence on intention to use mobile banking system.
H3: Security has significant influence on Trust of mobile banking system.
H4: There is a significant mean difference in monthly income level on intention to use mobile banking system.
This research was conducted by performing the qualitative analysis for the adoption of mobile banking systems in Myanmar through a survey method. The survey was carried on in form of online and offline questionnaire to collect all required data. The convenience and snowball sampling techniques were used as non-probability sampling for the data collection. There are three parts of in questionnaire which are screening question, Likert scale and demographic.
Measurement of Conceptual Framework and Variables
The target respondent of this research were people who live in Myanmar and have used mobile banking system. The literature review was conducted to ensure that the model were appropriate for developing the conceptual framework and to understand all variables incorporated in this study. A five-point Likert scale was applied to test all hypotheses by ranking from strongly disagree (1) to strongly agree (5) throughout this study to measure the hypotheses.
Population and sample
The research questionnaire was distributed through the online and offline based survey with 200 respondents answered to the survey. The questionnaires have been distributed using sampling techniques of the convenience and snowball methods in order to obtain the data. The people who live in Myanmar continuously 6 months and have used the mobile banking system were selected as target respondents for this study.
The reliability test and validity of the questionnaire was established the pilot test by distributing 30 respondents. Cronbach’s Alpha Coefficient was considered to examine the reliability level of each group of items included in the questionnaire. The test result of independent variable is consistent the requirement standard with Cronbach’s Alpha Coefficient higher than 0.6 (Cronbach, 1951).
The Cronbach’s Alpha Coefficient result in a range between 0.733 and 0.899 which is greater than 0.6. Therefore, the questionnaire developed for this study is fully achieved the standard required for reliability test. The result is shown in Table 1.
Consistency of the scales test (N=30)
Variables Number of items Cronbach’s Alpha
Perceived ease of use (PEOU) 2 0.752
Social Influence (SI) 3 0.733
Computer self-efficacy (CSE) 2 0.789
Security (S) 3 0.842
Perceived financial cost (PFC) 3 0.748
Trust (T) 4 0.836
Behavioral Intention (BI) 4 0.899
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