This chapter consists of the literature review for this study. It starts with a description on smart cards e-payment, followed by the explanation of various models on adoption of a new system. The next two sections will explain the comparisons of the models and the past research done on the extensions of the technology adoption model. The review of literature leads to the development of the theoretical framework and hypotheses for this study.
Smart cards technology has an outstanding growth and among the fastest growing use of current technologies. In fact smart cards greatly improve the comfort and security of any transaction. It is important to note that consumer acceptance and confidence are vital for the further development of smart card technology or we can say that acceptance has been viewed as a function of user involvement in smart card systems development. Understanding the factors that influence user acceptance of information technology is of interest both to researchers in a variety of fields as well as procurers of technology for large organizations. Adoption of smart card technologies should not be made simply, knowing the customers perception of and behavioural intention to use technology should be key in the decision-making process. The purpose of this study is to develop a model for user adoption of using smart cards technology.
It has been almost three decades since Roland Moreno invented and patented the smart card technology in 1974 (Quisquater, 1997). It gained widespread acceptance in European and Asian regions (McElroy & Turban, 1998). Schlumberger, one of the world leaders in smart cards forecasted that more than 3100 million smartcards would be consumed worldwide by the year 2003 (Anonymous, 2000).
There are many applications to smart cards and usage is becoming more and more pervasive within our society (McElroy & Turban, 1998). The use of smart card can be found in transits, electronic payments, banking, access control, telecommunications, healthcare, education and more. The power, intelligence, enhanced capacity and the reduced cost provided by the technology attracted acknowledgements from users and organisations worldwide (Hibbert 2000, McElroy & Turban, 1998, Szmigin & Bourne 1999, Puri 1997).
Smart cards normally appear in the same shape as credit cards and embedded with a chip or microprocessor that can handle and store up to 10 to 100 times more information than traditional magnetic-stripe cards (Fancher, 1997). They can be found in four different categories: they are contact, contactless, hybrid and combi (Hibbert, 2000).
2.2 Smart Card E-Payments and Payment stakeholders
Electronic payment systems (EPS) have attracted much attention of practitioners and researchers due to their importance for the completion of consumer oriented electronic commerce transactions. This has led to a rapid growth in the development of various electronic payment systems. Early research on electronic payment systems mainly focused on the technological aspects of the system, particularly those that are related to the functionality and implementation issues [Camenisch and Stadler, 1996 & Herzberg, 2003]. More recently, the focus of study in this area has shifted to the managerial/business aspects of electronic payment systems, as many electronic payment systems failed to be diffused within the community. These more recent studies explore such issues as reasons for use and non-use of the system, the process of adoption by the users, as well as strengths and weaknesses of the system based on various dimensions, including transaction costs, risks, size of payments, and actual payment time (Chau and Poon, 2003, Liao and Wong, 2004, Yu and Kuo, 2006). Thus, these studies take into account the consumer's point of view.
The Stakeholders are any parties that have a vested interest in the success of the system and are affected by the system and, therefore, play a critical role in ensuring the success of the system. Typically, an electronic payment system is not provided by one organization only, but by various parties (stakeholders) that are systematically arranged in a planned manner by some pre-determined rules. Customers and merchants are also considered stakeholders since they are affected by the payment system. Stakeholders have different roles, interests and hidden agendas which all affect the success of the electronic payment systems. Thus, by studying electronic payment systems from the stakeholder perspective, a better understanding of the diffusion process of the systems can be obtained (Au and Kauffman, 2003).
2.3 Smart Card E-Payments and Payment models
Smart cards based Electronic Payment System: Smart cards are receiving renewed attention as a mode of online payment. They are essentially credit card sized plastic cards with the memory chips and in some cases, with microprocessors embedded in them so as to serve as storage devices for much greater information than credit cards34 with inbuilt transaction processing capability (Chakrabarti and Kardile, 2002).
This card also contains some kinds of an encrypted key that is compared to a secret key contained on the user's processor. Some smart cards have provision to allow users to enter a personal identification number (PIN) code. Smart cards have been in use for well over the two decades now and have been widespread mostly in Europe and Asian Countries. Owing to their considerable flexibility, they have been used for a wide range of functions like highway toll payment, as prepaid telephone cards and as stored value debit cards.
Compared with traditional electronic cash system, smart cards based electronic payment systems do not need to maintain a large real time database. They also have advantages, such as anonymity, transfer payment between individual parties, and low transactional handling cost of files. Smart cards are also better protected from misuse35 than, say conventional credit cards, because the smart card information is encrypted. Currently, the two smart cards based electronic payment system- Mondex36 and Visa Cash are incompatible in the smart cards and card reader specification. Not knowing which smart card system will become market leader; banks around the world are unwilling to adopt either system, let alone other smart card system.
Therefore, establishing a standard smart card system, or making different system interoperable with one another is critical success factors for smart card based payment system. Kalakota and Whinston (1996), classified smart cards based electronic payment system as (1) relationship based smart cards and electronic purses. Electronic purses, which may replace money, are also known as debit card37. Further Diwan and Singh (2000) and Sharma and Diwan (2000), classified38 smart cards into four categories. These are: (1) memory cards: this card can be used to store password or pin number. Many telephone cards use these memory cards (2) shared key cards: it can store a private key such as those used in the public key cryptosystems. In this way, the user can plug in the card to a workstation and workstation can read the private key for encryption or decryption (3) signature carrying card: this card contains a set of pre-generated random numbers. These numbers can be used to generate electronic cash (4) signature carrying cards: these cards carry a co-processor that can be used to generate large random numbers. These random numbers can then be used for the assignment as serial numbers for the electronic cash.
2.4 Models and Theories on Adoption of New Technologies
There are various theories and models used in the research of adoption of new technologies, to investigate the determinants of acceptance and use of new technology. These models are based on the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975), the Theory of Planned Behavior (TPB) (Ajzen, 1985) and Technology Acceptance Model (TAM) (Adams et al., 1992; Davis, 1989; Davis et al., 1989). The determinants for the adoption of technology based on these models comes from the individual beliefs, attitudes, subjective norm,
perceptions of behavioral control, perceived usefulness and its perceived ease of use. Another model that is frequently used in information technology to explain user adoption of new technologies is Rogers' (1983) Diffusion of Innovation (DOI) theory. This model also uses behavioral intention or behavior itself as the dependent variable but the determinants are usually established according to the characteristics of the new technology such as relative advantage, complexity and compatibility.
2.4.1 Theory of Reasoned Action (TRA)
The Theory of Reasoned Action (TRA), proposed by Fishbein and Ajzen (1975) stated that a behavioral intention can be shaped by the attitude towards behavior and subjective norm.
According to this theory, attitude towards behavior is defined as an individual's positive or negative feelings associated with a particular behavior (Fishbein and Ajzen ,1975). Fishbein and Ajzen (1975) explained that subjective norm refers to perception that most people who really matter to the individual think that he either should or should not perform the behavior in question.
2.4.2 Theory of Planned Behavior (TPB)
The Theory of Planned Behavior (TPB) was proposed by Ajzen (1985) as an extension of the TRA (Fishbein and Ajzen, 1975) to account for situations where individuals do not have full control over their behavior by adding another construct, perceived behavioral control (PBC) for the TPB model. This construct reflects how individuals perceive the internal and external limitations to their behavior. Basically, this refers to how easy or difficult people believe it would be to perform certain behaviors (Ajzen, 1985). In TPB, behavioral intention is influenced by the attitude towards behavior, the subjective norm and the perceived behavioral control.
Attitude toward the Behavior
Behavior
Intention
Subjective
Norm
Davis et al (1989)
Perceived
Behavioral
Control
Figure 1 Theory of Planned Behavior (Ajzen, 1985).
2.4.3 Decomposed Theory of Planned Behavior (DTPB)
Taylor and Todd (1995) proposed a model called the Decomposed Theory of Planned Behavior (DTPB) by bringing together concepts from two distinct theories which are Diffusion of Innovation Theory (DOI) and Theory of Planned Behavior (TPB). DTPB takes three characteristics from DOI which are relative advantage, complexity and compatibility combined with perceived behavioral control from TPB.
According to Taylor and Todd (1995), DTPB offers a number of advantages because it is clearer and easier to understand the relations among beliefs, attitudes and intentions. It also enables application of the model to a variety of situations and it is more relevant in managerial conditions because it helps to determine specific factors that lead to adoption and use of new technology.
2.4.4 Technology Acceptance Model (TAM)
Applying the Theory of Reasoned Action (TRA), Davis (1989) developed the TAM for modeling of user acceptance of information technology (IT) by showing that beliefs influence attitudes about information technology, which lead to intentions and subsequently behaviors of actual technology usage. Davis (1989) has shown that perceived usefulness and perceived ease of use of the technology influenced the beliefs that lead to system usage. Based on TAM, the greater the perceived usefulness and the perceived ease of use, the better is the individual interests towards the new technology and the higher the intention to adopt it.
Perceived Usefulness
Actual System Use
Attitude toward Use
Intention to Use
External Variables
Perceived Ease of Use
Figure 2 Technology Acceptance Model (Davis et al., 1989).
Many studies have used TAM to evaluate user adoption of various information technologies like e-commerce (Lee et al., 2006; McKechnie et al., 2006), e-government (Schaupp and Carter, 2005; Belanger and Carter, 2008), Internet banking (Wang et al., 2003; Pikkarainen et al., 2004, Guriting and Ndubisi, 2006; Yiu et al., 2007), mobile banking (Luarn and Lin, 2004), mobile commerce (Yang, 2005; Wu and Wang, 2004), e-learning (Liao and Lu, 2008; Saade and Bahli, 2005; Ong et al, 2004), open source software (Gallego et al, 2007), mobile credit card (Amin, 2007) and internet tax-filing systems (Chang et al, 2005; Wang, 2002).
Chan et al. (2005) claimed that TAM proves to be a valid model to explain the taxpayers' acceptance of the Internet tax-filers' system. Guriting and Ndubisi (2006) found that perceived usefulness and perceived ease of use are strong determinants of behavioral intention to adopt online banking. It is similar with the findings by Yiu et al. (2007) on their study on Internet banking in Hong Kong. A study by Lee et al. (2006) found that perceived usefulness, perceived ease of use and perceived enjoyment, significantly enhanced consumer attitude and behavioral intention towards an online retailer.
2.4.5 Diffusion of Innovation (DOI)
The Diffusion of Innovation theory is useful to explain the process of innovation adoption. The individual's decision on whether to use the technology is based on the following five characteristics that consistently proved to be determinants of the diffusion rate of an innovation (Rogers, 1983). They are:
(1) Relative advantage and it refers to the extent to which the innovation is perceived as superior to all other options.
(2) Compatibility and it refers to the extent to which the innovation is perceived as being in line with the values, needs and experiences of prospective adopters.
(3) Complexity and it refers to the extent which the innovation is perceived as difficult to understand or use.
(4) Observability and it refers to the extent to which the benefits or attributes of the innovation can be observed, pictured or described to prospective adopters.
(5) Trialability and it refers to the extent which the innovation can be experienced before its actual adoption.
The relationship between each of these characteristics and the intention to adopt the new technology is positive, except the complexity attribute, which shows a negative relationship to the intention to adopt.
2.4.6 Unified Theory of Acceptance and Use of Technology (UTAUT)
Theory of Acceptance and Use of Technology aims to explain user intentions to use an information system and subsequent usage behaviour. It is widely used in the field of information and communication technology acceptance modelling. It consists of four key constructs namely performance expectancy, effort expectancy, social influence, and facilitating conditions which are direct determinants of usage intention and behavior (Venkatesh et. al., 2003). Gender, age, experience, and voluntariness of use are posited to mediate the impact of the four key constructs on usage intention and behavior (Venkatesh et. al., 2003; Masrom and Hussein, 2008).
2.5 Comparisons of Models and Theories
All the models and theories (TRA, TPB, DTPB, TAM, DOI and UTAUT) contain the same independent variable, which is the intention to use. TRA dependent variables are attitude towards behavior and subjective norm. By adding perceived behavioral control to TRA, we have TPB. TAM dependent variables are on perceived usefulness and the perceived ease of use. DOI dependent variables are somewhat overlapping with TAM dependent variables. They are perceived usefulness in TAM and relative advantage in DOI, and perceived ease of use in TAM and complexity in DOI. Based on that, by adding compatibility, observability and trialability to TAM, we will have DOI. By combining three characteristics of DOI (relative advantage, complexity and compatibility) with perceived behavioral control from TPB, we have DTPB. UTAUT is aimed to explain user intentions to use an information system and subsequent usage behaviour.
2.6 Extension of the Existing Technology Adoption Models
As technology has continued to transform continuously especially on the Internet applications, many extensions of the models, especially from TAM have been proposed. Studies have been done by introducing other dependent variables such as perceived risk (Lu et al, 2005; Curran and Meuter, 2005; Walker and Johnson, 2006; Cunningham et al, 2005; Lee and Allaway, 2002), trust (Lanseng and Andreassen, 2007; Schaupp and Carter, 2005; Wu and Chang, 2005), experience with technology (McKechnie et al, 2006; Cheong and Park,2005) and computer self-efficacy (Ndubisi and Jantan, 2003; Wang et al, 2003; Thompson et al, 2006).
2.7 Theoretical Framework
This theoretical framework for this study is described in this section.
2.7.1 Gap in the Literature
Although there are extensive studies of various technologies like Internet banking, online purchase and government e-services employing various technology adoption models like TAM and DOI, there is very little literature concerning the adoption of smart card e-payment (Touch 'n Go & Rapidpass) system. Thus, the research here will focus on the adoption of this new technology.
2.7.2 Research Model
The dependent variable for this research is the intention to use smart card e-payment (Touch 'n Go & Rapidpass) system. The actual usage is not used as the dependent variable since there is little of this technology that is in use in Malaysia. Similar in many other studies of TAM with the extended model (e.g., Adams et al., 1992; Wang et al., 2003; Fusilier and Durlabhji, 2005; Luarn and Lin, 2005), the attitudes construct has been removed to simplify the model. Based on the literature review, TAM is able to explain and offer a better prediction on the users' intention to use a new technology. As such, this research uses selected constructs from TAM (Davis et al., 1989) which are perceived ease of use (PEOU) and perceived usefulness (PU) as the independent variables. Since TAM was created to explain the factors of Information System (IS) acceptance, other independent variables are included in this study of adoption of smart card e-payment (Touch 'n Go & Rapidpass) system. The other independent variables are Previous experience (Ajzen and Fishbein, 1980), Compatibility (Rogers, 2003; Karahanna et al, 1999), Social Influence (Ajzen, 1985 ; Venkatesh and Davis, 1996, 2000), Support (Bailey and Pearson, 1983; Al-Gahtani et al., 2007) and security (Vijayasarathy, 2004) . Hence, the following model as shown in Figure 3 is developed for this research.
Perceived Usefulness
H1
H3 H4
Perceived Ease of Use H2
Prior Experience H5
Intention to Use Smart Card E-Payment (Touch 'n Go & Rapidpass) system
Compatibility
H6
Support
H7
Social Influence
H8
Security H9
Figure 3 : Research Model for Intention to Use Smart Card E-Payment (Touch 'n Go & Rapidpass) System
There are 2 groups of Variable being studied in Research.
Independent variables that consist of perceived usefulness, perceived ease of uses, prior experience, compatibility, support, social Influence and security.
Dependent variable intention to use smart card e-payment (Touch n' Go & Rapidpass) system.
Hypotheses Development
This section describes the hypotheses development based on the research model.
2.9 Hypothesis Statement
H1 : Perceive usefulness has a direct positive effect on the intention to use smart card e-payment (Touch n' Go & Rapidpass)
H2 : Perceived ease of use has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H3 : Perceived ease of use has a direct positive relationship (via usefulness) on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H4 : Perceive ease of use has a direct positive effect on perceived usefulness
H5 : Prior experience has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H6 : Compatibility has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H7 : Support has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H8 : Social Influence has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H9 : Security has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.1 Perceived usefulness (PU)
Perceived usefulness is defined as ''the degree to which a person believes that using a particular system would enhance his or her job performance'' (Venkatesh and Davis, 2000). Individuals who believed that using smart card systems could lead to positive outcomes also tended to have a more favorable attitude towards it. In addition, there is an empirical support for the relationship between perceived usefulness and attitude towards use (Agarwal and Prasad, 1999; Moon and Kim, 2001).
There is considerable research in the information system (IS) area that provides evidence of the significant effect of perceived usefulness on usage intentions on various internet applications (Lu et al., 2005; Pikkarainen et al., 2004; Schaupp and Carter, 2005). It was found that perceived usefulness positively influences the acceptance of online antivirus applications (Lu et al., 2005) and online ranking (Pikkarainen et al., 2004). These results are consistent with the findings on government online applications by Schaupp and Carter (2005) who found that perceived usefulness has a positive effect on the usage intentions of e-voting.
The inclusion of this variable in this study is that Malaysian users take advantage of the smart cards and online applications because it is useful to make payment easier and more quickly. Thus, perceived usefulness is eligible to be applied in this research. The following hypothesis is hence developed:
H1 : Perceive usefulness has a direct positive effect on the intention to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.2 Perceived ease of use (PEU)
Users like and plan to use the system more frequently as the system becomes an easy one to use. Perceived ease of use is "the degree to which a person believes that using a particular system is free of effort" (Saade and Bahli, 2005). A broader view of ease of use includes elements such as ease of learning, ease of control, and understandability (Davis 1989).
The study done by Amin (2007) conducted in Labuan and Sabah, Malaysia has shown a significant impact of perceived ease of use on the intention to use mobile credit card. The results generally support the findings of the positive effect on perceived ease of use on the intentions to use other technologies like electronic healthcare (Lanseng and Andreassen, 2007), online retailing of financial services (McKechnie et al., 2006) and biometric devices (James et al., 2006). Based on these findings, it is likely that the general relationship found in TAM is also applicable to smart card e-payment system. Thus, based on these findings, the following hypothesis is developed:
H2 : Perceived ease of use has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.3 Perceived ease of use (PEU) via Perceived usefulness (PU)
The effects of perceived usefulness and perceived ease of use on the intention to use new technology have been proposed and tested by many studies of TAM. Perceived usefulness and perceive ease of use influence the level of intention toward the usage of technology (Davis et al 1989), Adam et al (1992) and Davis et al (1989) reported that usefulness has a strong relationship with the system usage.
Davis et al (1989) identified ease of use as an important determinant of system usage through perceived usefulness. The findings supported to other studies like Mathieson(1991) reported that perceived ease of use explains the significant amount of variance in perceive of usefulness and Jantan et al (2001) found that perceived usefulness and perceived ease of use have a direct effect on the system usage. Ndubisi et al (2001) and Ramayah et al (2002) identified that there is an indirect positive relationship between perceived ease of use and usage via perceived usefulness.
Based on these findings, it is likely that the general relationship found in TAM is also applicable to smart card e-payment system. Thus, based on these findings, the following hypothesis is developed:
H3 : Perceived ease of use has a direct positive relationship (via usefulness) on intention to use smart card e-payment (Touch n' Go & Rapidpass)
H4 : Perceive ease of use has a direct positive effect on perceived usefulness
2.9.4 Prior Experience (PE)
Previous experience is a determinant of behavior (Ajzen and Fishbein, 1980). We predict that those who have used smart card before have more favorable attitudes towards its use than those who have not used. Other studies have demonstrated an impact of type of experience on the attitude-behavior relation, with direct experience resulting in stronger relations than indirect experience (Fazio & Zanna. 1978; Regan & Fazio, 1977).
Taylor and Todd (1995) discovered that users experienced with similar system will have greater intention to use the system. Therefore it is believed that an individual prior experience in computer and internet usage has a positive impact on perceived usefulness and perceived ease of use of smart card e-payment. They also found significant differences between experience and inexperienced users of an information system. Individuals who used the system earlier will have higher usage than no via users.
Lucas and Spitter (1999) reported that prior use is an important determinant of technology acceptance, Black et al (2001) found that previous experience with the computer or internet is one of the strongest influencing factors that affect internet banking adoption. Jiang Hsu Kein and Lin (2001) state that the more experienced an internet user is, he more experienced an internet user is, he more likely they are to adopt new internet technology. Thus, the following hypothesis will be tested :
H5 : Prior experience has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.5 Compatibility (COMP)
Compatibility is the degree to which the innovation is perceived to be consistent with the potential users' existing values, previous experiences and needs (Sonnenwald, et al., 2001). In other word, it is quality of an innovation that fits easily into the values and routine of an individual. Rogers (2003) discussed the compatibility of the innovation with the values, culture and practices of individuals. Therefore, compatibility will have a positive effect on usefulness.
The researchers working with the theory base of innovation diffusion have discovered a similar relationship between the two constructs, namely relative advantage and compatibility, on the one hand, and IT adoption, on the other. Relative advantage is the incremental benefit to be gained by the use of one innovation over its alternatives, whereas compatibility is the extent to which an innovation is compatible with the user's prior experiences (Rogers, 2003). The linkages among these variables, clearly reminiscent of the constructs PU and PEU and relationships in technology acceptance theory, have been empirically verified in the IS literature.
Brancheau, 1987 and Brancheau and Wetherbe,1990 for example, discovered a strong linkage between relative advantage and compatibility and adoption of spreadsheet software across a variety of industries while Hoffer and Alexander,1992 found them in the diffusion of database machines. Moore,1989 and Moore and Benbasat, 1991 discovered these same causal connections in the domain of PC adoption. Recently, Karahanna, 1993 has also found support for the influence of relative advantage and compatibility on the intentions of users to adopt Windows software. This research stream has been important in explaining first-order effects for how such beliefs about systems lead users to positive attitudes toward systems; intentions to use these systems and system use. As such, the following hypothesis is proposed to be tested :
H6 : Compatibility has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.6 Support (SUP)
User support have similar representation to the facilitating condition of UTAUT model which is defined as "the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system" ( Al-Gahtani, et al., 2007).
Besides, the degree of support from managers to ensure sufficient allocation of resources and act as a change agent to create a more conductive environment for Information System success (Igbaria et al.,1997 and Liao and Landry, 2000). Management support is able to ensure sufficient allocation of resources and act as a change agent to create a more conducive environment for information technology success. Therefore, management support is associated with information technology success and a lack of it is considered a critical barrier to the effective utilization of information technology (Abdul-Gader, 1992). Thus, based on these findings, the following hypothesis is developed:
H7 : Support has a direct positive effect on intention to to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.7 Social influence (SI)
Social influence is defined as "the degree to which an individual perceives that it is important others believe he or she use the new system". In some studies it is named as "Subjective norm" and it is described as the "person's perception that most people who are important to him think he should or should not perform the behavior in question" (Ajzen, 1985). In other word, social influence suggests that behavior is instigated by one's desire to act as how others act or think one should act (Chun Der Chen. et al, 2003).
In Taylor and Todd's study (1995), social influences were equivalent to subjective norm and defined as other people's opinion, superior influence, and peer influence. Venkatesh and Davis (2000) later expanded social influences to include subjective norm and image as well. Image is derived from the research on diffusion of innovations. Moore and Benbasat (1991) defined it as the extent to which use of an innovation is perceived as enhancement of one's status in a social system. Davis and his colleagues (1989) believed that in some cases people might use a system to comply with others' mandates rather than their own feelings and beliefs. Empirical support for the relationship between social norms and behavior can be found in many studies (e.g., Tornatsky and Klein, 1982; Venkatesh and Davis, 2000).
According to social identity theory, individuals tend to form self-concepts that consist of personal and social identities. The former encompasses idiosyncratic characteristics such as abilities and interests, and the latter encompasses salient group classifications (Tajfel and Turner 1979). Based on the self-concepts formed, individuals tend to classify themselves and others into various social groups based on the prototypical characteristics, enabling them to order the environment and locate themselves and others within the same group (Turner 1985). Thus, based on these findings, the following hypothesis is developed:
H8 : Social Influence has a direct positive effect on intention to to use smart card e-payment (Touch n' Go & Rapidpass)
2.9.8 Security (SEC)
Many researchers have reported that users' concern about security has increased and it has been known as one of the most significant factor for technology acceptance. In this study security is defined as "the degree to which a person feels that security is important to them and believes that using smart card is secure" (Vijayasarathy, 2004). By protecting the integrity, availability and confidentiality of the content in the system, security controls could help to protect the overall content quality of the system (Whitman & Mattord, 2003). Content quality is a major determinant of overall information system quality (Liaw & HUANG, 2003), which has a positive effect on individual's perceived ease of use of information systems. Therefore, we hypothesize that security has positive influence on satisfaction and attitude toward use. As such, the following hypothesis is proposed to tested :
H9 : Security has a direct positive effect on intention to use smart card e-payment (Touch n' Go & Rapidpass)
