M-BANKING IN METROPOLITAN BANGKOK AND A COMPARISON WITH OTHER COUNTRIES

ABSTRACT

Early days, the prevalence of Internet technology generated e-commerce, e-business, and innovative products and services to market. At present, the diffusion of mobile technology creates m-commerce. Mobile banking is a banking service which helps customers in easily making online transactions anywhere, anytime. It has been adopted extensively in developed countries. However, for Thailand, this acceptance rate is still low. Thus, this paper is aimed at to gather factors affecting m-banking acceptance, both on adoption side and barrier side, to explore the effects of those factors, to guide banks and financial firms to attract more customers, and to compare the differences and similarities of m-banking key success factors from different countries. The quantitative approach using questionnaire survey along with the qualitative approach using interviews are used to test the model. The result shows that the positive factors have more influence on an intention to use m-banking than the negative factors.

Keywords: M-banking, m-commerce, acceptance factors, barriers

INTRODUCTION

Highly competitive environments force banks to seek strategies to achieve competitive advantages. Mobile banking is a kind of electronic banking that applies SMS and WAP services to facilitate customers in making online transactions. Significant reasons which are appealing trendy customers, reducing costs per transactions, gaining revenue from service fees, enabling new service channels, and supporting future customers compel financial firms to provide mobile banking services. According to the Sybase survey, more than sixty percent of banks worldwide have planned to offer mobile banking services by 2010 [I]. American bankers also predicted that U.S. households using mobile banking would reach 11 million households by 2009 [2]. Nevertheless, the KPGM survey indicates that less than ten percent of U.S. consumers had tried mobile banking. The slow growth rate of mobile banking usage shows lack of publicity and marketing on mobile service security and benefits which customers would gain [3].

Some researches have been conducted to identify determinants of mobile banking success. Laforet and Li investigated consumers' attitudes towards online and mobile banking in China [4]. Luarn and Lin identified factors influencing users' acceptance of mobile banking in Taiwan by extending the technology acceptance model (TAM) with one trust-based construct - perceived credibility, and two resource-based constructs - perceived self-efficacy and perceived financial cost [5]. Lee and Chung explored factors affecting trust in and satisfaction with mobile banking by applying three quality factors based on DeLone and McLean's model - system quality, information quaUty, and interface design quality [6]. Gu et.al vaUdated determinants of intention to use mobile banking by unifying the extended TAM and the trust-based TAM [7]. Suoranta investigated factors affecting the adoption of mobile banking in Finland by employing five innovation attributes - relative advantage, complexity, compatibility, trialability, observability, with a perceived risk, and external factors - social system, time, and communication channels [8]. However, none of these researches completely consider and evaluate acceptance factors against obstacle factors.

The objectives of this paper are to combine positive factors - constructs from TAM and the tiieory of planned behavior (TPB), with negative factors - the consumer resistance factors, to compare effects of those positive and negative factors on mobile banking, and to discover differences or similarities in the results of other countries and Thailand. Qualitative data through in-depth interviews are collected to primarily support the research model. Quantitative data through surveys are also gathered to test the hypotheses.

RESEARCH MODEL AND HYPOTHESES

The research model in Figure 1 is firstly developed according to various theories and researches. Due to cultural differences between the researched countries and Thailand, quatitative data by in-depth interviews are also gathered to preliminary support the model. Finally, quantitative data are collected to reveal the results.

Resistance Factors

Device Barrier. Functional barriers, particularly usage barriers, are pointed to be a factor influencing customers to resist innovations [10-12]. Siau et al. believe that the mobile device technologies that are user interface of mobile devices, ease of input and navigation, and readability of mobile device displays have effects on trust in m-commerce [13]. Vlachos and Vrechopoulos specify that device quality has a strong influence on service quaUty perceptions which directly affect mobile internet adoption and use [14]. Serenko and Bontis confirm that, to increase user satisfaction with mobile services such as mobile portals, the content of mobile services has to be optimized to support the device limitations which are screen size, memory, CPU, and communication bandwidth [15]. Sivanard and Geeta also support that too small screen size significantly affects the mobile ATM service adoption in Malaysia [16]. Thus, this research proposes the following hypothesis:

H1 : Device barrier has the negative effect on the behavioral intention.

Perceived Risk. Perceived risk is a personal perception of potential damage or loss [10]. Commonly, consumers are more concerned by risks from using financial services such as fraud transactions or identity theft than risks from buying products online, risks from data security such as data manipulation, unauthorized data access, and unwanted usage patterns tracking, and risks from privacy violations [8, 18]. Risk barrier is specified to be a factor influencing consumer resistance to innovations [12]. Perceived risk is also a factor that determines mobile payments and mobile commerce acceptance [19, 17]. Bauer et al. also support that Perceived Risk has a negative effect on the attitude toward mobile marketing [18] Thus, this research proposes the following hypothesis:

H2: Perceived risk has the negative effect on the behavioral intention.

Lack of Information. Sufficient information guides consumers to make better decisions. Information such as details of products or services, their benefits, usage guidelines, etc. should be provided to the consumers. Amin et al. point that normally consumers have to realize benefits of new products or services before accepting them [20]. Pikkarainen et al. also specify that information provided on the web site is one of main factors influencing onUne-banking acceptance [21]. Adequate information is a factor affecting users' attitudes about web-ATM services as well [22]. Moreover, lack of information such as awareness about a service and its benefits obstructs the adoption of Internet banking [23, 24]. Thus, this research proposes the following hypothesis:

H3: Lack of information has the negative effect on the behavioral intention.

Perceived Financial Cost. Perceived financial resource affects behavioral intention to use m-service. Perceived financial resource is defined as the extent to which a person believes that he or she has enough money to pay for the system [25]. Lower product costs or service fees can lead to higher perceived financial resource. Moreover, conformingly with Luarn and Lin [5], Wu and Wang sum up that the cost has the negative effect on intention to use. They specify that the reasonable price or low adoption cost is important for mobile commerce [19]. MaUat points that premium pricing impedes new customers from applying mobile payments [17]. Fenech also specifies that price consciousness has an influence on WAP shoppers' intentions to use [26]. Perceived financial cost is defined as the extent to which a person beUeves that using mobile banking will cost money [5]. Thus, this research proposes the following hypothesis:

H4: Perceived financial cost has the negative effect on the behavioral intention.

Adoption Factors

Subjective Norm. Subjective norm is an individual's perception to perform the recommended behavior according to pressures from his/her peers, family, society, and culture. The theory of planned behavior (TPB) specifies that subjective norm is one of key determinants to explain behavior intention [9]. Empirical study also indicates that subjective norm positively affect the usage behavior of mobile banking, internet banking, wireless internet services and E-payment services [7, 27-29]. Thus, this research proposes the following hypothesis:

H5: Subjective norm has the positive effect on the behavioral intention.

Perceived Usefulness. TAM is the most extensively used theory in the field of information systems. It specifies two main factors influencing user's decision to accept and use a technology: perceived usefulness and perceived ease of use [31]. Luarn and Lin [5] found that perceived usefulness is a vital factor determining the mobile consumer usage. Wang et.al also agree that most customers choosing mobile services because they see their benefits [25]. On another side, Suoranta support that lack of awareness and benefits realization are important factors which impede m-banking acceptance [8]. Moreover, perceived usefulness is a factor contributing to the adoption of wireless finance such as trading stocks and the acceptance of 3G mobile value-added services as well [27, 32]. Thus, this research proposes the following hypothesis:

H6: Perceived usefulness has the positive effect on the behavioral intention.

Perceived Ease of Use. TAM points that perceived ease of use influence the innovation acceptance. It decrease the effort paid in learning and applying new technologies. Many researches give support to TAM that perceived ease of use has the positive impact on perceived usefulness and mobile services intention to use [5, 25, 27, 32]. Thus, this research proposes the following hypotheses:

H7: Perceived ease of use has the positive effect on perceived usefulness.

H8: Perceived ease of use has the positive effect on the behavioral intention.

Self-Efficacy. According to Wood and Bandura, self efficacy refers to beliefs in one's capabiUties to mobiUze the motivation, cognitive resources, and courses of action needed to meet given situational demands [33]. The theory points that if one believes one-self having self-efficacy, he or she tends to perform behavior [34]. Ellen et al. indicate that persons perceiving low self-efficacy with a new technology will be more resistant it than persons perceiving high self-efficacy [35]. Like Luarn and Lin, Wang et al. also support that self-efficacy has the positive influence on perceived ease of use and the intention to use mobile services [5, 25]. Thus, this research proposes the following hypotheses:

H9: Self-efficacy has the positive effect on perceived ease of use.

H10: Self-efficacy has the positive effect on the behavioral intention.

Behavioral Intention. Behavioral Intention is readiness of individuals to act expected behavior. According to TAM and TPB, it is the main construct that drives a person to perform behavior or to actual use innovations [9, 31]. Thus, this research proposes the following hypotheses:

H11 : Behavioral intention has the positive effect on actual use.

Qualitative Analysis of Adoption and Resistance Factors

Since the reviewed literature is based on dissimilar environments and cultores, this research add in-depth interview to derive interpretive qualitative data from people with Thai cultural background. The respondents' profiles are described in Table 1. The questions such as "what are factors you consider in applying m-banking?", "what are your opinion on m-banking?", "what are your concerns about m-banking?" were asked from 30 people, both m-banking users and non-m-banking users. The following statements highlight some interesting points:

"I use mobile banking because of its convenient, especially being able to perform transactions everywhere every time outside."

"I use mobile banking because my friends use it. They seem to have no problems. But some said that he faced system 's malfunction problem, such as repeatedly charged. However, the bank refund system makes them feel better. Moreover, since mobile banking is more convenient than other channels. I continually use it."

"I think people will adopt mobile banking services more if its program interface is very simple, just like ATMs to which you require no one to teach using them."

"I do not use mobile banking because it is uncomfortable; particularly, I have to press on the button one by one for each character. I think using internet banking on a laptop is better."

"I feel afraid to use mobile banking. I fear that my personal information would leak because I didn't do transactions directly with the bank. However, Ithinkinternet banking is safer. But if I want to perform a transaction with high amount of money, I prefer to go to the bank branch."

"I never thought to use mobile banking because I don 't want to be charged anything additional. The internet banking is currently free of charges. I will change to apply mobile banking services only if there are no service fees."

"I see ads of mobile banking on television. But I do not want to use it because it has no ads which give me information about its security."

"Frankly speaking, I do not know much about technology, but I saw some mobile banking from news or the media. It can steal or cheat us. I don 't want to take risks, so I think that it would be better not to use."

"I do not know whether mobile banking charges the service fees or not, but I think it charges. I'm lazy to find information on how much it will charge. So, I think it's the duty of the service providers to provide clear information. If they did not give information, people would probably not use because they are still afraid that the service would be expensive. The m-banking users should be only those who want to try something new."

The above answers expose some interesting information. In summary, the qualitative results reveal 6 factors that are device barrier, perceived risk, lack of information, perceived financial cost, subjective norm, perceived usefulness, perceived ease of use, and self-efficacy, conforming to the literature reviews. Overall all factors were supported by subjects (participants) responses.

RESEARCH DESIGN AND METHOD

Participants

Quantitative data collected through survey questionnaires are adapted to confirm the proposed research model. Participants of this research are bank customers and mobile users in the Bangkok metropolitan area in which is fully supported by wireless infrastructures. Both information from m-banking users and nonusers are gathered to support different sides of factors. Opinions from non-users also reveal the attitudes of non-familiar users toward m-banking. At first, data was collected from 30 people to be as a pre-test. Then, 200 questionnaires were dispersed through the online system.

Measures

A questionnaire consists of four main sections . An introduction section describes research purposes and the m-banking definition to help respondents understand the questionnaire clearly. Questions in the first section ask about details of their mobile phones and the mobile phones' usage behavior in nominal scales. The second section surveys their behavior in performing transactions; for example, the frequency of doing transactions through each channel, in ratio scales. The third section asks respondents' opinions and their m-banking usage behavior using 5 points Likert scale. The last section collects demographic data of the respondents in nominal scales. Details of the questionnaire are extracted in Appendix. Table 2 shows the literature review for developing the survey instrument.

DATA ANALYSIS AND RESULTS

Of the 200 questionnaires along with e-mail asking for respondents" cooperation, 195 surveys was returned. Seventyfour survey respondents reported using m-banking as a preferred channel, while the other 121 respondents preferred other means. Firstly, the research instrument was evaluated its reliability and validity. Then, the respondents' demographic data were analyzed using descriptive statistics. Thirdly, factor analysis was used to analyze variability among items and to group a multitude of items into common basic constructs. Lastly, the hypotheses were tested using the multiple linear regression analysis.

Reliability Assessment

To assess the internal consistency of each construct, the Cronbach's alpha coefficients were computed. Table 3 shows calculation results which reveal the adequate reUability because all values were greater than 0.80 and more than half were greater than 0.9.

Descriptive Statistics

Table 4 and Table 5 show descriptive statistics analysis using frequency and percentage which is applied to summarize the respondents' demographic profiles and their behavior in performing financial transactions.

Sixty percent of respondents were female. Major respondents are in the working age and the average income level. Many respondents apply more than one channel for doing transactions. Due to their conveniences and the technology reUability, the most preferred channel is ATM or deposit machines. However, comparing to the ATM channel, the number of other electronic channel preferences is still low.

Factor Analysis

Considering the Kaiser-Meyer-Olkin (KMO) and Bartlett's test, the KMO metric for measuring sampling adequacy is 0.856 that means it appropriates to use factor analysis on data. The Bartlett's Test of Sphericity is significant at probability level 0.05 which means the correlation matrix is not an identity matrix. The principal component analysis with the varimax rotation was used to investigate the factor structure. The criteria to accept items are factor loadings greater than 0.5. Finally, eight factors with eigenvalues greater than 1.0 were formed. Those factors can explain 77.5% of the total variance as shown in Table 6 and Table 7.

Hypotheses Testing

Eleven hypotheses were tested using multiple linear regression analysis. AU hypotheses are accepted as summarized in Table 8. These factors accounted for 68.5% of the variance in the behavioral intention to use m-banking. Eight of eleven hypotheses are significant at probability level 0.01 (hypotheses with asterisks) and the rest are significant at probability level 0.05. The results reveal that every positive factor has more influence on the mbanking acceptance than all negative factors. These obviously point that, for both m-banking users and non-users, the main drives of m-banking usage are the support factors.

Considering the barrier side, device barrier is the most important factor impeding the acceptance. Then, the effect levels of negative factors are lack of information, perceived financial cost, and perceived risk respectively. Considering me support side, subjective norm and perceived usefulness have greater influence on the intention to use m-banking. Perceived ease of use also affect the intention in the same level as selfefficacy, but in the lower level than subjective norm and perceived usefulness.

DISCUSSION

Practical Adaptations for Banks

As shown in the results, all factors conforming to the Uterature review are supported. Subjective norm is the most influential factor in m-banking adoption. It can be described that for some Asian countries, mobile phones are also used to present the owners' social statuses. Thus, m-banking can become fashion and can make users feel that they are in trends. Social currents have a strong impact on the way people decide as well; for example, in Thailand, the popularity of Blackberry phones comes from friends' influence and trends. So, banks should build m-banking trends by conducting more campaigns using celebrities. The secondly influential factor is perceived usefulness. Many users select m-banking channel as their first choice because of its comfort. So, banks should promote the benefits in terms of doing transactions conveniently anywhere, anytime, especially during rush hour. Self-efficacy has the third order effect on m-banking adoption. It affects perceived ease of use of m-banking users and non-users too. Therefore, banks should set their target to the customers who tend to have high self-efficacy such as people who are familiar with mobile technology or teenagers. Ease of use is the fourth important factor. Hence, banks should set simple steps for m-banking which let customers easily process transactions on their own or take a little effort to learn the procedures.

For obstacle factors, mobile phones still have limitations, such as screen sizes or processing powers, making them unable to be comparable to computers. So, the device constraint becomes the first supporting factor to refuse m-banking. Although mobile technology has been developed everyday to overcome the problems, financial firms should design interfaces to eliminate those limitations. Information is very important in the world today, so lack of information is the secondly obstacle factor that blocks non-users from trying m-banking. Hence, banks should provide sufficient information for customers including the mbanking existence, benefits comparing to other channels, usage methods, its security, and so on. The thirdly obstructive factor is perceived financial cost. Many users and non-users perceive that the costs of m-banking are quite high due to its application cost and the network connection cost. So, banks should not charge the application fees as a promotion tool for new users. Moreover, because the always-on connection is the basic requirement for some phones such as iPhone, banks should tell their customers that the service does not generate any incremental costs from network connections. On the contrary, it helps in reducing the cost of going to bank branches or charges for connecting internet via computers. Perceived risk is the lastly impediment factor. Although many studies point out diat perceived risk is an important factor, this research found an interesting argument that, for users who are used to perform onUne transactions such as online banking, online shopping, etc., they tend to have low concern about the service risks. Therefore, banks should find mbanking customers from online banking customers first. Then, after the customer base is expanded, the existing customers will convince new customers to try the services by their word-ofmouths.

COMPARING RESULTS OF DIFFERENT COUNTRIES

Many researches about m-banking and m-payments have been conducted in various countries. This section draws a comparison between results of Thailand and other countries. In conclusion, the results show that factors influencing m-banking and m-payments adoption vary according to different countries and environments as shown in Table 9 and Table 10.

M-Banking in Malaysia and United Arab Emirates

Comparing to Malaysia, Sivanand et al. explore obstacles for the mobile Internet banking services adoption by bank account holderss in Klang Valley of Malaysia. The study findings point that developing a cost effective, technically supportive and reliable mobile Internet banking system is important [42]. Amin et al. examine the undergraduate students' attitudes on adopting mobile banking, focusing on Islamic banking in FT, Labuan. The research aims to analyze the affects of attitudes, expectation, and demographics on perception of latest banking channel, then mobile banking adoption. The attitudes are significantly related to race, religion, age and field of study. Moreover, the results indicated that more than 80 percent of university students tend to be potential customers for mobile banking in the future [43].

In conclusion, lack of information, perceived risk, perceived financial cost, device barrier, self-efficacy, and perceived usefulness in terms of the ease of access to relevant information or services, the level of security in mobile devices and the level of security in conducting financial transactions in networks, cost of mobile devices and cost of subscription, display screen size, and details of respondents' attitudes are also confirmed. More supported factors are complete range of financial transaction services. But value-added services, and the connection speed between the mobile device and the Internet factors are rejected [42, 43].

Investigating United Arab Emirates, Khalifa et al. expanded the theory of planned behavior to explore drivers of mobile commerce by augmenting the TPB with the perceived consequences construct. The results identify that potential mcommerce adopters are sensitive to the cost and privacy. In addition, their findings also specify that an individual may not use m-commerce due to some perceived negative consequences, e.g., security or privacy threats. In summation, subjective norms and perceived usefulness in terms of subjective norm and perceived consequences are supported. Additional confirmed factor is attitude (in a broader sense). Nevertheless, self-efficacy in terms of perceived behavioral control is rejected [44].

M-Banking in Korea, China, and Taiwan

Examining Korea, Gu et al. collect and validate determinants of behavioral intention of consumers to mobile banking. Their study results found that self-efficiency, perceived ease-of-use, and perceived usefulness directly and indirectly have influences on users' intention to mobile banking. Structural assurances are also the antecedent of trust which affects behavioral intention of mobile banking [7]. Kim et al. aim at understanding the effects of trust-inducing forces on the initial trust of people in mobUe banking. Four types of forces are institutional offering (structural assurances), cognition (perceived benefits), personality (personal propensity) and firm characteristics (firm reputation). The analysis results show that perceived benefits, propensity to trust and structural assurances have significant effects on the initial trust [45]. Lee and Chung also investigate factors influencing trust in and satisfaction with mobile banking, focusing on the three quality factors that are system quality, information quaUty, and interface design quality. The results reveal that only system quality and information quality affect customers' trust and satisfaction significantly [6].

In conclusion, similar to Thailand, self-efficacy, subjective norm, perceived usefulness, perceived ease of use, and perceived risk in terms of self efficacy, social influence, perceived benefits, perceived ease of use, and trust/ structural assurance/ personal propensity to trust are confirmed. Differently, interface design quality which helps in correcting the device barrier factor is rejected. Additional explored factors are faciUtating conditions, system quality, information quality, familiarity with bank (rejected), and firm reputation (rejected) [6, 7, 45].

Considering China and Taiwan, Laforet and Li explore the market status of China's online and mobile banking. Their findings identify the different result from the West which specifies that online and mobile banking Chinese users are males, not necessarily young and highly educated. The security issue is found to be the most important factor, but the main barriers to mobile banking in China are lack of awareness and understanding of the perceived benefits of mobile banking [4], Luarn and Lin extend the technology acceptance model in the context of mobile banking, by adding perceived credibility, perceived self-efficacy, and perceived financial cost to the model. Their results are consistent with previous researches in terms of perceived usefulness and perceived ease-of-use. Other factors are also verified to be antecedents of behavioral intention to mobile banking [5]. Wu and Wang examine the determinants of mobile commerce user acceptance by merging innovation diffusion theory, perceived risk and cost into the TAM2. The results show that users' intentions are significantly affected by perceived risk, cost, compatibility, and perceived usefulness. In addition, compatibility has the strongest influence on behavioral intention [19].

In summation, the same accepted factors are lack of information, perceived risk, perceived usefulness, self-efficacy, and perceived financial cost, in terms of awareness, reference group's influence, perceived credibility, perceived usefulness, perceived self-efficacy, and cost. Factors such as past. experiences with computer/new technology and compatibility, are also investigated. UnUke ThaUand, the subjective norm in terms of reference group's influence and perceived ease of use are rejected in the Taiwanese's environment [4, 5, 19].

M-Banking in South Africa and Kenya

Comparing to South Africa and Kenya, Brown et al. examines factors affecting ceU phone banking adoption in South Africa. Factors include perceived relative advantage, the ability to try and experiment with the innovation first (trialability), and the diversity of banking needs of a potential user were found to be drivers for the adoption. On the contrary, the perceived sense of risk is a major factor impeding the adoption [36]. Njenga explains the current state of mobile banking along with a review of emerging service provider and customer traits in Kenya. He points that demands mobile banking implementations would be increased due to improved network coverage, quality connections, and reduced costs to enhance affordability to all prospective consumers [46],

In summary, the same supported factors are perceived usefulness, perceived risk, lack of information, and device barrier in terms of relative advantage/ diversity of bank services and real time cash alternative, perceived risk, resistance from banks, and availabihty of capable handsets. Factors for m-banking success in Kenya are user demand, increase in banking basket size, first-time access to m-banking, diversification of mobile operator business, increase in local and international money transfers, increased diffusion of mobile phones, lack of clear business models, resistance from banks, lack of global technology standards, financial regulations and legislation, support issues, consumer rights concerns. More studied factors are trialability, cell phone/ user experience (rejected), and facilitating conditions (rejected) [36, 46].

M-Banking in Europe: Finland, Netherlands, and German

Considering Finland, Souranta seeks to determine factors driving mobile banking service adoption in Finland. The findings reveal that mobile banking innovation explaining usage behavior, especially relative advantages, compatibility, communication and trialability. Conversely, complexity and risk yield no confirmation as adoption obstacles [8]. Mallat performs the quaUtative study using focus group research to examine customer adoption of mobile payments. The mobile payment adoption is found to depend on factors that are a lack of other payment methods or urgency. Other barriers include premium pricing, complexity, a lack of critical mass, and perceived risks [17],

In conclusion, perceived usefulness, lack of information, subjective norm, and perceived financial cost in terms of relative advantage and impact of use situations, observability, network externalities, and costs are supported. On the contrary, perceived ease of use and perceived risk in terms of complexity and perceived risks and trust in mobile payment service providers are accepted in one paper [17], but are rejected in another paper [8].

Exploring Netherlands, Kleijnen et al. study factors contributing to wireless finance adoption. Perceived cost, system quality and social influence were explored while the latter two show significant relevance to consumer acceptance of wireless finance. Besides, age, computer skills, mobile technology readiness, as moderating factors, are proven to associate with the context. In summation, perceived usefulness are accepted along with perceived system quality, but perceived ease of use, perceived financial cost, and subjective norm in terms of perceived ease of use, perceived costs, and social influence are rejected [27].

Comparing to German, Bauer and Barns investigates factors inducing customers to accept the mobile phone in promotional contents. The results provide the evidence of positive relationships between innovativeness, information seeker-behavior, and attitude toward advertising with mobile marketing. Entertainment and information value are also concluded to be good mobile marketing drivers. In summary, perceived usefulness, subjective norm, perceive risk, and lack of information in terms of perceived utility/perceived maintenance utility/ perceived information utility/perceived social utility, social norms, perceived risk, and knowledge about mobile communications are confirmed. Additional accepted factors are attitude toward advertising, information seeker-behavior, and innovativeness [18].

CONCLUSIONS

Because of the slow growth rate of m-banking in Thailand, this paper explores the adoption and resistant factors for m-banking. The adoption factors are mainly developed conforming to TAM and TPB theories. The resistant factors are combined from literature reviews. Those factors are proved their relevance to mbanking acceptance using qualitative interviews and quantitative surveys. Eight factors that are subjective norm, perceived usefulness, self-efficacy, perceived ease of use, device barrier, lack of information, perceived financial cost, and perceived risk along with eleven hypotheses are accepted. The result can be appUed to banks to improve their services and to attract more users to apply the services. Moreover, this research compares the results of affecting factors for m-banking in Thailand and others. The similar key factors for m-banking adoption or rejection in many countries are perceived usefulness and lack of information. Nevertheless, subjective norm, self-efficacy, perceived ease of use, device barrier, perceived financial cost, and perceived risk yield different influences for different countries.

For further research, more factors such as attractiveness of alternative, convenience, context, compatibility, expressiveness, mobility, privacy, speed of transaction, system quality, technology anxiety, and trialability [47] should be explored. Samples from the countryside should also be collected since the mobile technology can be beneficial for rural areas which have difficulties in accessing bank branches and internet infrastructure but are supported by the mobile phone network infrastructure. Finally, differences between the results from the city and the up-country should be compared as well.

[Sidebar]

Received: August 5, 2010 Revised: October 12, 2010 Accepted: November 8, 2010

[Reference]

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[Author Affiliation]

JIRAPORN SRIPALAWAT

Thammasat University

Bangkok, Thailand 10200

MATHUPAYAS THONGMAK

Thammasat University

Bangkok, Thailand 10200

ATCHARAWAN NGRAMYARN

Thammasat University

Bangkok, Thailand 10200