Technology Adoption Models

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

THE LITERATURE REVIEW OF TECHNOLOGY ADOPTION MODELS

AND THEORIES FOR THE NOVELTY TECHNOLOGY

PC Lai

Help University, Malaysia

ABSTRACT

This paper contributes to the existing literature by comprehensively reviewing the concepts,

applications and development of technology adoption models and theories based on the literature

review with the focus on potential application for the novelty technology of single platform E-

payment. These included, but were not restricted to, the Theory of Diffusion of Innovations (DIT)

(Rogers, 1995), the Theory of Reasonable Action (TRA) (Fishbein and Ajzen, 1975), Theory of

Planned Behavior (TPB) (Ajzen, 1985, 1991), Decomposed Theory of Planned Behaviour,

(Taylor and Todd, 1995), the Technology Acceptance Model (TAM) (Davis, Bogozzi and

Warshaw, 1989, Technology Acceptance Model 2 (TAM2) Venkatesh and Davis (2000) and

Technology Acceptance Model 3 (TAM3) Venkatesh and Bala (2008). These reviews will shed

some light and potential applications for technology applications for future researchers to

conceptualize, distinguish and comprehend the underlying technology models and theories that

may affect the previous, current and future application of technology adoption.

Keywords: TAM, TRA, TBP, DIT, TTF, technology adoption, single platform E-payment

1. INTRODUCTION

Constant technological change simultaneously creates threats to established business

models, while also offering opportunities for novel service offerings (Lai, 2006; 2007; 2010;

2016). Leading firms often seek to shape the evolution of technological applications to their own

advantage (Lovelock, 2001; Lai, 2007). With the advanced and dynamic growth of technologies,

how fast the consumers are accepting these technologies depends on a number of factors such as

availability of technology, convenience, consumers’ need, security etc. There have been a

number of researchers addressing the consumers’ adoption of new technologies (Meuter,

Ostrom, Roundtree, and Bitner, 2000; Dapp, Stobbe, and Wruuck. 2012; Lai and Zainal, 2014,

2015; Lai, 2016). Therefore, this paper presents the literature review of the technology

acceptance models and theories leading to the development of the novel technology single

platform E-payment theoretical framework.

Manuscript first received/Recebido em: 2017/02/19 Manuscript accepted/Aprovado em: 2017/04/19

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

This paper analyzed the technology adoption models and theories leading to the

theoretical framework for an integrated E-payment system known as the “single platform E-

payment System” of the technology acceptance for Card, Internet and Mobile. These included,

but were not restricted to, the Theory of Diffusion of Innovations (DIT) (Rogers, 1995) that

started in 1960, the Theory of Task-technology fit (TTF) (Goodhue, and Thompson, 1995), the

Theory of Reasonable Action (TRA) (Fishbein and Ajzen, 1975), Theory of Planned Behavior

(TPB) (Ajzen, 1985, 1991), Decomposed Theory of Planned Behaviour, (Taylor and Todd,

1995), the Technology Acceptance Model (TAM) (Davis, Bogozzi and Warshaw, 1989), Final

version of Technology Acceptance Model (TAM) Venkatesh and Davis (1996), Technology

Acceptance Model 2 (TAM2) Venkatesh and Davis (2000), Unified Theory of Acceptance and

Use of Technology (UTAUT), Venkatesh, Morris, Davis and Davis (2003) and Technology

Acceptance Model 3 (TAM3) Venkatesh and Bala (2008). This review could shed some light

and potential applications for technology applications for future researchers to conceptualize,

distinguish and comprehend the underlying technology models and theories that might affect the

previous, current and future application of technology adoption.

2. TECHNOLOGY ADOPTION MODELS AND THEORIES

Hoenig (1995) as well as Lai (2016) noted that the rate at which payment systems

develop depends largely on a struggle between rapid technological change and natural barriers to

new product or service acceptance. A number of theories have proposed to explain consumers’

acceptance of new technologies and their intention to use. These included, but were not restricted

to, the Theory of Diffusion of Innovations (DIT) (Rogers, 1995) that started in 1960, the Theory

of Task-technology fit (TTF) (Goodhue, and Thompson, 1995), the Theory of Reasonable Action

(TRA) (Fishbein and Ajzen, 1975), Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991),

Decomposed Theory of Planned Behaviour, (Taylor and Todd, 1995), the Technology

Acceptance Model (TAM) (Davis, Bogozzi and Warshaw, 1989), Final version of Technology

Acceptance Model (TAM) Venkatesh and Davis (1996), Technology Acceptance Model 2

(TAM2) Venkatesh and Davis (2000), Unified Theory of Acceptance and Use of Technology

(UTAUT), Venkatesh, Morris, Davis and Davis (2003) and Technology Acceptance Model 3

(TAM3) Venkatesh and Bala (2008).

Rogers (1995) proposed that the theory of ‘diffusion of innovation’ was to establish the

foundation for conducting research on innovation acceptance and adoption. Rogers synthesized

research from over 508 diffusion studies and came out with the ‘diffusion of innovation’ theory

for the adoption of innovations among individuals and organization. The theory explicates “the

process by which an innovation is communicated through certain channels over time among the

members of a social system” (Rogers, 1995, p. 5).

Basically, it’s the process of the members of a social system communicated an innovation

through certain channels over time known as diffusion. The Rogers’ (1995) diffusion of

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

innovation theory explained that the innovation and adoption happened after going through

several stages including understanding, persuasion, decision, implementation, and confirmation

that led to the development of Rogers (1995) S-shaped adoption curve of innovators, early

adopters, early majority, late majority and laggards as shown in Figure 1.

Figure 1. Innovation Adoption Curve (Roger, 1995)

Technology readiness (TR) refers to people’s propensity to embrace and use of new

technologies for accomplishing goals in home life and at work (Parasuraman and Colby, 2001).

Based on individual’s technology readiness score and the technology readiness, Parasuraman and

Colby (2001) further classified technology consumers into five technology readiness segments of

explorers, pioneers, skeptics, paranoids, and laggards. This is similar to Rogers (1995) S-shaped

adoption curve of innovators, early adopters, early majority, late majority and laggards. The

Diffusion of innovation or Technology readiness is vital for organization implementation success

because it is market focus.

According to Goodhue et al. (1995), Task-technology Fit (TTF) emphasizes individual

impact. Individual impact refers to improved efficiency, effectiveness, and/or higher quality.

Goodhue et al. (1995) assumed that the good fit between task and technology is to increase the

likelihood of utilization and also to increase the performance impact since the technology meets

the task needs and wants of users more closely. As shown in Figure 2, this model is suitable for

investigating the actual usage of the technology especially testing of new technology to get

feedback. The task-technology fit is good for measuring the technology applications already

release in the marketplace like in the google play store or apple store app (iTunes) etc.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Figure 2. Task-technology fit (Goodhue and Thompson, 1995)

The Theory of Reasonable Action (Fishbein and Ajzen, 1975) is one of the most popular

theories used and is about one factor that determines behavioural intention of the person’s

attitudes toward that behaviour as shown in Figure 3. Fishbien and Ajzen (1975) defined

“attitude” as the individual’s evaluation of an object and defined “belief” as a link between an

object and some attribute, and defined “behaviour” as a result or intention. Attitudes are affective

and based upon a set of beliefs about the object of behaviour (e.g: Credit card is convenient). A

second factor is the person’s subjective norms of what they perceive their immediate

community’s attitude to certain behaviour (e.g: my peers are using credit card and it’s a status to

have one).

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Figure 3. The Theory of Reasonable Action (Fishbein and Ajzen, 1975)

Ajzen (1991) developed Theory of Planned Behavior which is about one factor that

determines behavioural intention of the person’s attitudes toward that behaviour as shown in

Figure 4. The first two factors are the same as Theory of Reasonable Action (Fishbein and Ajzen,

1975). The third factor that is known as the perceived control behaviour is the control which

users perceive that may limit their behaviour (e.g: Can I apply for the credit card and what are

the requirements?).

Figure 4. The Theory of Planned Behavior (Ajzen, 1991)

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Decomposed Theory of Planned Behaviour (Decomposed TPB) was introduced by

Taylor and Todd (1995). The Decomposed TPB consists of three main factors influencing

behavior intention and actual behavior adoption which are attitude, subjective norms and

perceived behavior control. Shih and Fang (2004) examined the adoption of internet banking by

means of the TPB as well as Decomposed TPB.

There has been a great deal of research on the Theory of Reasoned Action (Ajzen &

Fishbein, 1980; Sheppard, Hartwick, and Warshaw, 1988) Theory of Planned Behavior (Ajzen,

1991) and Decomposed Theory of Planned Behaviour, (Taylor and Todd, 1995) but mostly used

for products already in the marketplace and included the view of society (Subjective norm).

Technology Acceptance Model (TAM) was introduced by Fred Davis in 1986 for his

doctorate proposal as shown in Figure 5. An adaptation of Theory of Reasonable Action, TAM is

specifically tailored for modeling users’ acceptance of information systems or technologies.

Figure 5. Original Technology Acceptance Model (Davis, 1986).

In 1989, Davis used TAM to explain computer usage behaviour as shown in Figure 6.

The goal of Davis’ (1989) TAM is to explain the general determinants of computer acceptance

that lead to explaining users’ behaviour across a broad range of end-user computing technologies

and user populations. The basic TAM model included and tested two specific beliefs: Perceived

Usefulness (PU) and Perceived Ease of Use (PEU). Perceived Usefulness is defined as the

potential user’s subjective likelihood that the use of a certain system (e.g: single platform E-

payment System) will improve his/her action and Perceived Ease of Use refers to the degree to

which the potential user expects the target system to be effortless (Davis, 1989). The belief of the

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

person towards a system may be influenced by other factors referred to as external variables in

TAM.

Figure 6. First modified version of Technology Acceptance Model (TAM) (Davis, Bogozzi and Warshaw, 1989).

The final version of Technology Acceptance Model was formed by Venkatesh and Davis

(1996) as shown in Figure 7 after the main finding of both perceived usefulness and perceived

ease of use were found to have a direct influence on behaviour intention, thus eliminating the

need for the attitude construct.

Figure 7. Final version of Technology Acceptance Model (TAM) (Venkatesh and Davis, 1996).

Venkatesh and Davis (2000) proposed the TAM 2 as shown in Figure 8. This study

provided more detail explanations for the reasons users found a given system useful at three (3)

points in time: pre-implementation, one month post-implementation and three month post-

implementation. TAM2 theorizes that users’ mental assessment of the match between important

goals at work and the consequences of performing job tasks using the system serves as a basis

for forming perceptions regarding the usefulness of the system (Venkatesh and Davis, 2000).

The results revealed that TAM 2 performed well in both voluntary and mandatory environment.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Figure 8. Technology Acceptance Model (TAM 2) (Venkatesh and Davis, 2000).

Venkatesh and Bala (2008) combined TAM2 (Venkatesh & Davis, 2000) and the model

of the determinants of perceived ease of use (Venkatesh, 2000), and developed an integrated

model of technology acceptance known as TAM3 shown in Figure 9. The authors developed the

TAM3 using the four different types including the individual differences, system characteristics,

social influence, and facilitating conditions which are determinants of perceived usefulness and

perceived ease of use. In TAM3 research model, the perceived ease of use to perceived

usefulness, computer anxiety to perceived ease of use and perceived ease of use to behavioral

intention were moderated by experiences. The TAM3 research model was tested in real-world

settings of IT implementations.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Figure 9. Technology Acceptance Model (TAM 3) (Venkatesh and Bala, 2008).

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Figure 10. Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis and Davis,

2003).

Venkatesh, Morris, Davis and Davis (2003) studied from the previous models/theories

and formed Unified Theory of Acceptance and Use of Technology (UTAUT) shown in Figure

10. The UTAUT has four predictors of users’ behavioral intention and there are performance

expectancy, effort expectancy, social influence and facilitating conditions. The five similar

constructs including perceived usefulness, extrinsic motivation, job-fit, relative advantage and

outcome expectations form the performance expectancy in the UTAUT model while effort

expectancy captures the notions of perceived ease of use and complexity. As for the social

context, Venkatesh et al. (2003) validation tests found that social influence was not significant in

voluntary contexts.

2.1 COMPARING THE MODELS

The TAM, TRA, TPB, TAM2, TAM3 and UTAUT have been used over the years by

various researchers to explain the adoption technology systems. This section will briefly discuss

the comparisons of these theories and lead to why TAM is selected for the novel technology of

single platform E-payment.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

These studies provide different context and methodology measuring different variables

using different models in different settings. After reviewing all the technology adoption models,

this paper will discuss the three most likely technology adoption models by comparing the

Technology Acceptance Models (TAM), Theory of Reasoned Action (TRA) and Theory of

Planned Behavior (TPB). In addition, this paper will discuss the extension TAM models TAM2,

TAM3, UTAUT as well and then discuss the TAM as framework for the novel technology of

single platform E-payment.

2.2 COMPARING TECHNOLOGY ACCEPTANCE MODELS (TAM), THEORY OF

REASONED ACTION (TRA) AND THEORY OF PHANNED BEHAVIOR (TPB)

Davis, Bagozzi and Warshaw’s (1989) study compared the Technology Acceptance

Model (TAM) with Theory of Reasoned Action (TRA) and resulted in the convergence of TAM

and TRA. This led to a model based on the three theoretical determinants which are the

perceived usefulness, perceived ease of use and behaviour intention. The study found social

norms (SN) as an important determinant of behavior intention to be weak. TAM does not include

social norms (SN) as a determinant of behavior intention (BI), which is an important

determinant, theorized by Theory of Reasoned Action TRA and Theory of Planned Behavior

(TPB).

Mathieson (1991) and Yi, Jackson, Park, and Probst (2006) argued that human and social

factors could play a role in the adoption of technology using TPB model. Therefore, the TAM

could be extended with constructs from the TPB to incorporate the social factors that could

explain technology adoption. Nevertheless, the TPB in Chau and Hu (2002) noted that social

norm and behavior intention to use finding was negative and did not support that social norm

would influence behavior intention. Shih and Fang (2004) also examined the adoption of internet

banking by means of the TPB as well as Decomposed TPB and found that it was in line with the

findings of Venkatesh and Davis (2000) that subjective norm was likely to have a significant

influence on behavioural intention to use in a mandatory environment, whilst the effect could be

insignificant in a voluntary environment. Since, this study is voluntary; therefore the Shih and

Fang (2004) study will not apply in the novel technology of single platform E-payment System.

Davis, Bagozzi and Warshaw (1989) explained that social norms scales had a very poor

psychometric standpoint, and might not exert any influence on consumers’ behavior intention,

especially when the information system application like single platform E-payment System was

fairly personal while individual usage was voluntary. TAM was also specifically designed to

address the factors of users’ system technology acceptance (Chau and Hu 2002). Thus, the

comparisons of the study confirmed that Technology Acceptance Model was easy to apply

across different research settings. Han (2003) as well as Lai and Zainal (2014; 2015) noted that

using TAM capability was favorable compared with TRA and TPB.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

2.3 COMPARING TECHNOLOGY ACCEPTANCE MODELS (TAM), TAM2, TAM3 AND

UTAUT

TAM2, an extension of the TAM was developed by Venkatesh and Davis (2000) due to

the limitations of the TAM in terms of explanatory power (R²). The aspiration for the TAM2 was

to keep the original TAM constructs intact and “include additional key determinants of TAM’s

perceived usefulness and usage intention constructs, and to understand how the effect of these

determinants changed with increasing users’ experience over time with the target system”

(Venkatesh & Davis, 2000, p.187). Because TAM2 only focused on the determinants of TAM’s

perceived usefulness and usage intention constructs, TAM3 by Venkatesh and Bala (2008) added

the determinants of TAM’s perceived ease of use and usage intention constructs for robustness.

Therefore, TAM3 presented a complete nomological network of the determinants of users’

Information Technology System adoption (Venkatesh and Bala, 2008).

Venkatesh et al. (2003) incorporated four key determinants in the UTAUT model and

there were performance expectancy, effort expectancy, social influence and facilitation

conditions as well as four key moderators like gender, age, voluntariness and experience.

According to Bagozzi (2007), UTAUT might be a powerful model due to its parsimonious

structure and higher explanatory power (R²) but the model did not examine direct effects which

might reveal new relationships as well as important factors from the study which were left out by

subsuming under the existing predictors only. TAM2 and TAM3 also did not measure and

examine direct effects which might reveal new relationships as well as important factors from the

study.

Technology Acceptance Model (TAM2) by Venkatesh and Davis (2000), TAM3 by

Venkatesh and Bala (2008) and UTAUT by Venkatesh, Morris, Davis and Davis (2003) were not

selected since the situation was for products to be implemented in the marketplace and taken into

consideration of subjective norm that included society not required for this study involving the

novelty technology of single platform E-payment System. Davis, Bagozzi and Warshaw (1989)

explained that social norms scales had a very poor psychometric standpoint, and might not exert

any influence on consumers’ behavior intention, especially when information system application

like single platform E-payment System was fairly personal while individual usage was voluntary.

UTAUT is an extension from TAM2 and TAM3 is an extension of TAM2 that includes social

influence, therefore they will not be used in this study based on social norm. TAM2, TAM3 and

UTAUT use moderators but the present study only focuses on the factors and consumers’

intention to use single platform E-payment System. Furthermore, TAM2, TAM3 and UTAUT

did not include direct relations studies. Therefore, TAM2, TAM3 and UTAUT were not

favorable to study the novelty technology of single platform E-payment System.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

2.4 EXTENSION FROM TAM FOR THE NOVEL TECHNOLOGY OF SINGLE

PLATFORM E-PAYMENT SYSTEM

A novel technology discussed here will be the single platform E-payment System.

According to (Lai and Zainal, 2015), there is a lack of empirical investigations combining the

factors of the three E-payments (Card, Internet and Mobile) in one study which encourages the

researcher to study the single platform E-Payment system since previous researches only focused

on the three systems separately (Card, Internet, Mobile). As the future integrated E-payment

instruments, single platform E-payment system is a novel system as previous researches only

focused on the three systems separately and individually (Card, Internet, Mobile) Lai, (2016).

TAM model developed by Davis is the most used framework in predicting information

technology adoption (Paul, John and Pierre, 2003). Lee and Jun (2007) argued that TAM should

be able to analyze factors affecting adoption intentions beyond perceptions of convenience and

usefulness. Though TAM had received much support (Yang, 2005), it focused on the effects of

perceptions of the technology's usefulness and convenience on adoption intentions (Luarn and

Lin, 2005; Lai and Zainal, 2015). Thus, it is favorable for the use of determining the novelty

technology like the single platform E-payment System.

In fact, TAM has become so popular that it has been cited in most of the research that

deals with users’ acceptance of technology (Lee, Kozar and Larsen, 2013). TAM attempts to

help researchers and practitioners to distinguish why a particular technology or system may be

acceptable or unacceptable and take up suitable measures by explanation besides providing

prediction. Even though TAM has been tested widely with different samples in different

situations and proved to be valid and reliable model explaining information system acceptance

and use (Mathieson, 1991; Davis and Venkatesh, 1996,), many extensions to the TAM have been

proposed and tested (e.g. Venkatesh and Davis, 2000; Venkatesh, Speier and Morris 2002;

Henderson and Divett, 2003; Lu, Yu, Liu, and Yao, 2003; Lai and Zainal, 2014; 2015; Lai,

2016).

Davis (1986) mentioned that behavior intention to use was being mediated by attitude.

Nevertheless, attitude was excluded as its mediator in Venkatesh and Davis (2000) TAM2 and

theorized a direct relationship between the constructs and intention to use. TAM initially

included attitude, but this was later dropped due to its weak role as a mediator between the

constructs and intention to use (Mun, Joyce, Jae & Janice, 2006). Thus, in this paper the study

has adapted the Venkatesh and Davis’ (1996) version of TAM to measure consumers’ behavior

intention to use instead of Davis’ (1986) version by omitting the attitude towards use and actual

usage for the novel technology of single platform E-payment (Lai and Zainal, 2015). In addition,

the study will extend the 1996 version of Technology Acceptance Model by including security

factors and use the mediator with the direct and indirect relationship of the factors and

consumers’ intention to use the single platform E-payment System (Lai, 2016). Therefore, in

figure 11 showed the Stimulus Theoretical Framework for the novelty technology of the single

platform E-payment System. According to Lai (2016), the design and security are the stimulus

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

that represent the system and features capabilities while, the perceived ease of use and perceived

usefulness are the organism that represents the motivation to use the system that leads to

consumers’ respond to use the system. The Stimulus Theoretical Framework provides the

structure for the research hypothesis. Thus, the figure 11 also showed the “Design and Security

Stimulus Research Theoretical Hypothesis” (Lai, 2016).

Figure 11. Stimulus Theoretical Framework (Design and Security Stimulus Research Theoretical Hypothesis) (Lai,

2016).

3. CONCLUSION

The above discussion, concepts, applications and development of technology adoption

models and theories based on the literature review encompass different views and interpretations.

The literature reviews share the difference of technology adoption models and theories with

different theoretical insights, research problems, variables, and measurements. The development

of the new theoretical research framework will depend on a number of factors but not limited to

the following: the research problems and objectives, gap analysis, the target market (users or

developers, etc), the organizations’ goals and the understanding of technology adoption models

and theories based on the available materials and others. Such understanding is vital to enable the

interested parties (e,g: students, academics, researchers, government, organizations) to relate

with both the theory and practical aspects of the technology adoption models and theories. These

reviews will shed some light and potential applications for technology applications for future

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

researchers to conceptualize, distinguish and comprehend the underlying technology models and

theories that may affect the previous, current and future application of technology adoption.

REFERENCES

Ajzen, I. (1991). The Theory of Planned Behavior. Organization Behavior and Human Decision

Processes, Academic Press, Inc. 179-211.

Bagozzi, R.P. (2007) The Legacy of the Technology Acceptance Model and a Proposal for a

Paradigm Shift. Journal of the Association for Information Systems, 8, 244-254.

Chau, P. Y. K., and Hu, P., J. (2002). Examining a model of information technology acceptance

by individual professionals: An exploratory study. Journal of Management Information Systems,

18 (4), 191-229.

Davis, F.D. (1986). A technology acceptance model for empirically testing new end-user

information systems: Theory and results. Massachusetts, United States: Sloan School of

Management, Massachusetts Institute of Technology.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of

information technology. MIS Quarterly, 13(3), 319-340.

Davis, F. D., Bogozzi, R., P., & Warshaw, P., R. (1989). User acceptance of computer

technology: A comparison of two theoretical models. Management Science, 35, 982-1003.

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user

perceptions and behavioral impacts. International Journal of Man-Machine Studies. 38, (3), 475–

487

Davis, F. D., and V. Venkatesh. (1996). A critical assessment of potential measurement biases in

the technology acceptance model: Three experiments Internet. J. Human-Comput. Stud. 45 19–

45.

Dapp. T, Stobbe, A., and Wruuck P. 2012. The future of (mobile) payments - New (online)

players competing with banks, Deutsche Bank Research, 20th December 2012, pp.1-31.

Dewan & Chen. (2005). Mobile payment adoption in the US: A cross-industry, cross platform

solution. Journal of Information Privacy and Security. 1 (2), 4 – 25.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to

theory and research. Reading, Mass; Don Mills, Ontario: Addison-Wesley Pub. Co.

Goodhue, D. L., & Thompson, R. L. (1995). Task technology fit and individual performance.

MIS Quarterly, 19, 213-236.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Han, S. (2003). Individual adoption of information systems in organisations: a literature review

of technology acceptance model TUCS Technical Report 540; TUCS.

Henderson, R. & Divett., M., J. (2003). Perceived usefulness, ease of use and electronic

supermarket use. International Journal of Human-Computer Studies, 59, 383-395.

Lai P. C (2006). The significant of E-business and knowledge-based Customer Relationship in

the E-market Place Environment. INTI Journal, 2 (1) 552-559.

Lai, P. C. (2007). The Chip Technology Management Implication in the Era of Globalization:

Malaysian Consumers’ Perspective, Asia Pacific Business Review, 3(1), 91-96

Lai P. C. (2010). E-business and E-banking. Japan Society for Software Science and

Technology, Itech research group.

Lai, P. C. (2014) “Factors influencing consumers’ intention to use a single platform E-payment

System.” UNITEN.

Lai, P. C. (2013) “Cashless, Cardless, Contactless and Convenience of MySIM™.” GlobalCLAS

Technology..

Lai P. C. & Ahmad, Z. A. (2014). Perceived Enjoyment of Malaysian consumers’ intention to

use a single platform E-payment. Paper presented at International Conference on Liberal Arts &

Social Sciences., 25th - 29th April, 2014, Hanoi, Vietnam

Lai P. C. & Zainal A. A., (2015). Consumers’ Intention to Use a Single Platform E-Payment

System: A Study among Malaysian Internet and Mobile Banking Users. Journal of Internet

Banking and Commerce. (20) (1) 1-13

Lai P. C. & Zainal A.A, (2015). Perceived Risk as an Extension to TAM Model: Consumers’

Intention To Use A Single Platform E-Payment. Australia Journal Basic and Applied Science,

9(2): 323-330.

Lai, P. C. (2016) Design and Security impact on consumers’ intention to use single platform E-

payment, Interdisciplinary Information Sciences, 22 (1), 111-122

Lee, T.M., & Jun, J.K. (2007). The role of contextual marketing offer in Mobile commerce

acceptance: comparison between Mobile Commerce users and nonusers. International Journal of

Mobile Communications, 5(3), 339-356.

Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model; past,

present and future. Communication of AIS, 12 (50), 752-780.

Luarn, P., & Lin, H., H. (2005). “Toward an understanding of the behavioral intention to use

mobile banking.” Computers I Human Behavior, 21, 873-891.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Lu, J., Yu, C. S., Liu, C., & Yao, J. (2003). Technology acceptance model for wireless internet.

Journal of Internet Research, 13(2), 206-222.

Lovelock, C. (2001). Services Marketing, People, Technology, Strategy, Prentice Hall, New

Jersey.

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model

with the theory of planned behavior. Information Systems Research, 2(3), 173-191.

Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies:

Understanding customer satisfaction with technology-based service encounters. Journal of

Marketing, 64(3), 50-65.

Mun Y. Yi, Joyce D. J., Jae S. P. & Janice C. P. (2006). Understanding information technology

acceptance by individual professionals: Toward an integrative view. Information &

Management, 43, 350-363

Parasuraman, A., & Colby L. C. (2001). Techno-Ready Marketing, The Free Press.

Paul L., John I., & Pierre, C. (2003). Why do people use information technology? A critical

review of the technology acceptance model. Information & Management, 40, 191–204.

Rogers, E.M. (1995). Diffusion of Innovations. 4th ed., New York: The Free Press

Sheppard, B. H., Hartwick, J., Warshaw. P. R., (1998) The Theory of Reasoned Action: A Meta-

Analysis of Past Research with Recommendations for Modifications and Future Research. The

Journal of Consumer Research, 15, (3) 325-343

Shih, Y.Y. & Fang, K. (2004). The use of a Decomposed Theory of Planned Behavior to study

Internet banking in Taiwan. Internet Research, 14 (3), 213-223.

Taylor, S. and Todd, P. A. (1995). Understanding Information Technology Usage: A Test of

Competing Models. Information Systems Research, 6, 144-176.

Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use:

Development and test. Decision Sciences, 27(3), 451-481.

Venkatesh,V. (2000). Determinants of perceived ease of use: integrating control, intrinsic

motivation, and emotion into the technology acceptance model. Information Systems Research,

11(4), 342-365.

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance

Model: Four Longitudinal Field Studies. Management Science, 46 (2), 186-204.

Venkatesh, V., and Morris, M. G. (2000). Why Don’t Men Ever Stop to Ask For Directions?

Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS

Quarterly (24:1), 115-139.

JISTEM - Journal of Information Systems and Technology Management

Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38

ISSN online: 1807-1775

DOI: 10.4301/S1807-17752017000100002

JISTEM, Brazil Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38 www.jistem.fea.usp.br

Venkatesh, V., Morris, M.G., Davis, F.D., & Davis, G.B. (2003). User Acceptance of

Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478.

Venkatesh, V. and Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on

Interventions. Decision Science, 39 (2), 273-312.

Yang, K.C.C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore.

Telematics and Informatics. 22, 257-277

Yi, M.Y., Jackson, J.D., Park, J.S. & Probst, J.C. (2006). Understanding information technology

acceptance by individual professionals: Toward an integrative view. Information &

Management, 43 (3), 350-363.