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Int. J. Human-Computer Studies 62 (2005) 784808

An extension of Trust and TAM model with

TPB in the initial adoption of on-line tax:

An empirical study

Ing-Long Wu

, Jian-Liang ChenDepartment of Information Management, National Chung Chen University, 160, San-Hsing,

Ming-Hsiung, Chia-Yi, Taiwan

Received 23 August 2004; received in revised form 7 March 2005; accepted 22 March 2005

Communicated by P. Zhang

Abstract

While on-line tax is considered as a special type of e-service, the adoption rate of this service

in Taiwan is still relatively low. The initial adoption of on-line tax is the important driving

force to further influence the use and continued use of this service. The model of Trust and

technology acceptance model (TAM) in Gefen et al. (2003a, MIS Quarterly 27(1), 5190) has

been well studied in on-line shopping and showed that understanding both the Internet

technology and trust issue is important in determining behavioral intention to use. Besides, the

diffusion of on-line tax could also be influenced by the potential antecedents such as

individuals, organizational members, and social system while the issue for innovative

technology is well discussed in Rogers (1995, The Diffusion of Innovation, fourth ed. Free

Press, New York). Theory of planned behavior (TPB) is the model widely used to discuss the

effect of these antecedents in behavioral intention. An extension of Trust and TAM modelwith TPB would be in more comprehensive manner to understand behavioral intention to use

on-line tax. Furthermore, a large sample survey is used to empirically examine this framework.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: On-line tax; Trust and TAM model; Trust; TPB

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1071-5819/$ - see front matter r 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijhcs.2005.03.003

Corresponding author. Tel.: +8865 2720411x34620; fax: +8865 2721501.

E-mail address: [email protected] (I.-L. Wu).
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1. Introduction

Customer service is a series of activities designed for resolving purchasing

problems that customers encounter throughout the product life cycle to enhancecustomer satisfaction. When customer service is supplied over the Internet,

sometimes automatically, it is referred to as e-service (Turban et al., 2002). In

general, e-service could include customer service as part of on-line shopping and

pure-play service offered in e-commerce. Initially, on-line consumers did not demand

high levels of customer services and the Internet service was fairly basic such as on-

line catalogue, on-line transaction, and order fulfillment. However, on noticing the

Internet bubble burst and the profit gained from e-commerce far away from

marketer expectations, business managers began to search the new potency of e-

commerce. They found that the key to success in the Internet era is mainly attributed

to the ability of providing customers with better service to attract and retain

customers, and eventually, building a long-term relationship with customers.

In contrast, while the functions of government is mainly to provide information

and delivery service to citizens and business partners, government with its customers

such as citizens and business organizations, in essence, can be considered as a special

type of service industry. This consideration drives us to impose e-commerce features

on supporting the operation of government. This is called e-government and a type

of pure-play service offered in e-government. In particular, on-line tax declaration is

an important function of e-government since it is highly related to the life of citizens.

Thus, the government in Taiwan is aggressively encouraging citizens to use this e-service for their tax declaration. Currently, the survey data indicates that the usage

rate is still quite low regardless the constantly promotional effort. Among the

influential factors of the low usage rate, the key fundamental can be attributed to the

initial adoption (acceptance) of the innovative service by s since the initial adoption

of an e-service is the important driving force to further influence continued use of the

service (Kwon and Zmud, 1987).

For advocating users behavior toward the initial adoption of on-line tax, system

developers thus require first understanding their real needs and expectations in order

to offer more favorable services. In fact, an understanding of the users behavior

would be fundamentally beneficial to system design of an e-service since it couldeffectively identify the barriers for designing reference in advance. However, e-

commerce is a less verifiable and controllable environment in which on-line service or

transaction is offered without physical face-to-face contact and simultaneous

exchange of services and money. The spatial and temporal separation of e-commerce

between customers and e-vendors as well as the unpredictability of the Internet

infrastructure generate an implicit uncertainty around the initial adoption of on-line

service (Pavlou, 2003). Accordingly, the initial adoption of on-line tax basically

involves the acceptance of both the Internet technology and on-line service

providers. As technology acceptance model (TAM) is mainly proposed for

technology-based perspective through two system features of perceived usefulness(PU) and perceived ease of use (PEOU) (Davis et al., 1989), it is incomplete in the

context of on-line services.

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A model, named Trust and TAM, has been previously presented in exploring the

acceptance of on-line shopping setting (Gefen et al., 2003a). This model integratively

placed use of on-line system into both system features such as ease of use and

usefulness and trust in e-vendors. This result indicated that these variables are goodpredictors for behavior intention to use on-line shopping. However, a diffusion of

innovative technology is highly related to communication channels, individuals,

organizational members, and social system except for the technology itself ( Rogers,

1995). Theory of planned behavior (TPB) is the model widely used in predicting and

explaining human behavior while also considering the roles of individual

organizational members and social system in this process (Ajzen, 1991). Accordingly,

the three influencers in this theory, i.e. attitude, subjective norm and perceived

behavioral control, can be interpreted as attitude for technology role, subjective

norm for organizational members and social system roles, and perceived behavioral

control for individual role.

As the focus of this study is on the on-line tax setting, which is considered as a type

of innovative technology, organizational and social systems such as peer or superior

influence and self-efficacy in computer or external resource constraint should play

the important role in determining the acceptance of on-line tax (Taylor and Todd,

1995). As a result, an extension of Trust and TAM model with TPB including

subjective norm and perceived behavioral control should be in a more comprehen-

sive manner to examine the acceptance of on-line tax. In this extension, trust is

placed as an important antecedent of attitude, subjective norm, and perceived

behavioral control. Hopefully, this will provide us more information to solve thisproblem of low usage rate in using on-line tax.

2. Literature review

2.1. On-line tax declaration

As the Internet and its applications are increasingly becoming popular in business

organization and public institutions and governments are indeed a special type of

service industry, its applications in public agencies or e-government in Taiwan hasbeen greatly driven by current and previous administrations for providing citizens

and organizations with more convenient access to government information and

better services. Among them, on-line tax declaration is one of the top priorities in the

construction of e-government and begins for trial and experimental use around 2

years ago and is going for the third-year period. Taxpayers are still allowed to

declare their tax for the choice of either paper form or e-form. In order words, it is a

voluntary-based context for use of emerging technology. Until now, on-line tax is

still in the initial stage of its usage and the usage rate is still relatively low for keeping

in the interval of 1015% while it was initially launched in the year 2000. There is no

indication in a stable growth of its usage in the near future. On the basis of thedilemma in the use of on-line tax, the challenges may lie in convincing taxpayers of

communicating with on-line tax in an efficient, effective, and safe manner. This study

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tries to understand, analyse, and solve this problem from the perspective of the initial

adoption of virtual service. This may explain some of the major reasons for a low

rate in system usage.

2.2. Relevant models in IT adoption

TAM is an adaptation of the theory of reasoned action (TRA) by Fishbein and

Ajzen (1975) and mainly designed for modeling user acceptance of information

technology (Davis et al., 1989). This model hypothesizes that system use is directly

determined by behavioral intention to use, which is in turn influenced by users

attitude toward using the system and PU of the system. Attitude and PU are also

affected by PEOU. PU, reflecting a persons salient belief in using the technology,

will be helpful in improving performance. PEOU, explaining a persons salient

beliefs in using the technology, will be free of any effort (Taylor and Todd, 1995).

The appeal of this model lies in both specific and parsimonious as well as an

indication of high prediction power of technology usage. These determinants are also

easy to understand for system developers and can be specifically considered during

system requirement analysis and other system development stages. These factors are

common in technology-usage settings and can be applied widely to solve the

acceptance problem (Taylor and Todd, 1995).

TPB underlying the effort of TRA has been proven successful in predicting and

explaining human behavior across various information technologies (Ajzen, 1991,

2002). According to TPB, a persons actual behavior in performing certain action isdirectly influenced by his or her behavioral intention and in turn, jointly determined

by attitude, subjective norm and perceived behavioral control toward performing the

behavior. Behavioral intention is a measure of the strength of ones willingness to try

and exert while performing certain behavior. Attitude (A) explains the feeling of a

persons favorable or unfavorable assessment regarding the behavior in question.

Furthermore, a favorable or unfavorable attitude is a direct influence to the strength

of behavioral beliefs about the likely salient consequences. Accordingly, attitude (A)

is equated with attitudinal belief (abi) linking the behavior to a certain outcome

weighted by an evaluation of the desirability of that outcome (ei) in question, i.e.

A Sabiei. Subjective norm (SN) expresses the perceived organizational or socialpressure of a person while intending to perform the behavior in question. In other

word, subjective norm is relative to normative beliefs about the expectations of other

persons. It can be depicted as individuals normative belief (nbi) concerning a

particular referent weighted by motivation to comply with that referent (mci) in

question, i.e. SN Snbimci.

Perceived behavioral control (PBC) reflects a persons perception of ease or

difficulty toward implementing the behavior in interest. It concerns the beliefs about

presence of control factors that may facilitate or hinder to perform the behavior.

Thus, control beliefs about resources and opportunities are the underlying

determinant of perceived behavioral control and it can be depicted as controlbeliefs (cbi) weighted by perceived power of the control factor (pi) in question, i.e.

PBC Scbipi. In sum, grounded on the effort of TRA, TPB is proposed to eliminate

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the limitations of the original model in dealing with the behavior over which people

have incomplete volitional control (Ajzen, 1991). In essence, TPB differs from TRA

in its addition of the component of perceived behavior control.

However, TPB does not further elaborate the relationship between the beliefstructures (i.e. Sabiei, Snbimci, Scbipi) and the antecedents (attitude, subjective

norm, perceived behavior control) of intention. TPB simply combines each of the

belief structures into one unidimensional belief construct and as a result, the belief

structures, in fact, representing a variety of underlying dimensions, may not be

consistently related to the antecedents of intention. Moreover, the underlying

dimensions of the beliefs structures are, in essence, different for various application

settings and this combination makes TPB difficult to be generalizable across various

settings. By decomposing the belief structures of TPB (Decomposed TPB), their

relationships should become clearer, more understandable for practical purpose

(Taylor and Todd, 1995).

Attitudinal belief structure is decomposed into three dimensions: ease of use, PU,

and compatibility. Normative belief structure is decomposed into two dimensions:

peer and superior influences. Control belief structure is decomposed into three

dimensions: individual self-efficacy, resource facilitating conditions, and technology

facilitating conditions. After that, while comparing Decomposed TPB with TAM,

TAM is, in fact, a part of Decomposed TPB and consequently, Decomposed TPB

should provide a more complete understanding of IT adoption relative to the more

parsimonious TAM (Taylor and Todd, 1995). Based on the above logic, it is better

off to extend Trust and TAM model with TPB or Decomposed TPB to widelyconsider the potential underlying determinants, system features, individuals,

organizational members and social system, for better predicting the intention

toward the initial adoption of on-line tax.

2.3. Trust

The functionality and contribution of trust can be apparently identified from the

economic framework of social exchange (Kelley and Thibaut, 1978; Kelley, 1979).

Within social exchange, business transactions are usually carried out without explicit

contract or control mechanism against opportunistic behavior so that the partiesinvolved in these activities are not able to attain complete legal protection and

expose themselves in a complicated social environment with mass uncertainty. To

insure better rewards from the economic activities, people make efforts to reduce this

social complexity and avoid risk from being exploited (Wrightsman, 1972). Trust is

basically seen as a common mechanism for reducing social complexity and perceived

risk of transaction through increasing the expectation of a positive outcome and

perceived certainty regarding the expected behavior of trustee (Luhmann, 1979;

Grabner-Kraeuter, 2002; Gefen, 2004). In particular for on-line business, without

reducing social complexity and risk resulting from the undesirable opportunistic

behavior of e-vendor, only short-term transactions would be possible (Kim et al.,2004; Pavlou and Gefen, 2004). Accordingly, trust is an important determinant in e-

commerce including public services.

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Moreover, trust was further explained more clearly in terms of a number of trust

antecedents: knowledge-based trust, cognition-based trust, calculative-based trust,

institution-based trust, and personality-based trust (Zucker, 1986; Gefen et al.,

2003a). Knowledge-based trust is built on familiarity with other parties. Familiaritybuilds trust because it reduces social uncertainty through increased understanding of

what is happening in the present (Luhmann, 1979). Cognition-based trust examines

how trust is developed from first impression rather than through experience of

personal interactions. According to this research stream, cognition-based trust is

formed through categorization process and illusion of control (Brewer and Silver,

1978; Meyerson et al., 1996). Calculative-based trust can be developed by peoples

rational assessment of the costs and benefits of another party while cheating or

cooperating in the relationship. Trust in this view is derived from an economic

analysis occurring in ongoing relationship, namely that it is not worthy for the other

party to engage in opportunistic behavior (Coleman, 1990; Lewicki and Bunker,

1995; Doney et al., 1998). Institution-based trust refers to an individuals perception

of an institutional context, which mainly concerns security from guarantees, safety

nets, or other impersonal structures inherent in the specific context (Shapiro, 1987;

McKnight et al., 1998). Personality-based trust or propensity trust explains the

tendency to believe or not to believe in others and further trust them. This type of

trust is based on a belief that the others are typically well meaning and reliable

(Wrightsman, 1972; McKnight et al., 2002).

Among the five types of trust antecedents, cognition-based and personality-based

trusts are more relevant to the formation of the initial trust, since people inherentlyhas cognitive resource limitation for often recognizing subjects by the first

impression and personality is an important determinant in the initial stage of a

relationship building. Initial trust refers to trust in an unfamiliar trustee while the

actors do not yet have credible, meaningful information about or affective bounds

with each other. While people gain experience and familiarity with the trustee in the

later stage, continued trust by people will be more influenced by experiential personal

interaction (McKnight et al., 1998). In sum, as on-line tax is a type of e-service

between government agency and citizens, and their transactions are primarily

through virtual channel without face-to-face contact, perceived uncertainty and risk

associated with on-line tax are the major concern of the citizens in using this newtechnology. Trust will be the important potential influencer to examine the initial

adoption of on-line tax.

2.4. Trust and TAM relationship

The connections between trust and TAM have been widely discussed in literature

in that the relationships between PU, PEOU, and trust are hypothesized in many on-

line-based business settings (Gefen et al., 2003a, b; Pavlou, 2003; Saeed et al., 2003;

Gefen, 2004). In particular, a model of Trust and TAM was well defined in on-line

shopping setting (Gefen et al., 2003a). This model explicitly indicated theirrelationship as trust is an antecedent of PU, PEOU is an antecedent of trust, and

trust has a direct influence on behavioral intention to use. Trust is one of the

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determinants of PU, especially in an on-line environment, because part of the

guarantee that consumers will sense the expected usefulness from the web site is

based on the sellers behind the web site. Moreover, trust is recognized to have

positive effect on PU since trust allows consumers to become vulnerable to e-vendorto ensure that they gain the expected useful interaction and service (Pavlou, 2003).

While consumers initially trust their e-vendors and have an idea that adopting on-

line service is beneficial to their job performance, they will believe the on-line service

is useful (Gefen et al., 2003a).

On the other hand, PEOU is hypothesized to have positive influence on trust

because PEOU can help promote customers favorable impression on e-vendors in

the initial adoption of on-line service and further, cause customers to be willing to

made investment and commitment in buyer-seller relationship (Ganesan, 1994;

Gefen et al., 2003a). In general, while following the definition of social cognitive

theory, PEOU can be argued to positively influence a persons favorable outcome

expectation toward the acceptance of an innovative technology (Bandura, 1986).

This is because cognition-based trust, as discussed previously, is mainly built on the

first impression of a person toward certain behavior and extensively, PEOU in terms

of on-line service can be considered the first feeling or expectation established for

further continued on-line transaction. In sum, while on-line tax is considered a

special type of e-service, the Trust and TAM model is partly fitted to this on-line tax

setting while there are additional variables, as discussed below, to be included in the

particular context.

2.5. Trust and TPB relationship

The relationship between trust and TPB can be examined in a variety of aspects in

which trust is hypothesized as the common antecedent of attitude, perceive

behavioral control, and subjective norm. For attitude construct, trust in e-vendor

is viewed as a salient behavioral belief that directly affects customers attitude toward

the purchase behavior. While an e-vendor is trustworthy, it is more possible that the

consumer will gain benefits and avoid possible risks from adopting on-line service

(McKnight and Chervany 2002; Pavlou, 2003). As cost-benefit paradigm greatly

influences peoples attitudinal beliefs and outcome judgments, trust can be a directinfluencer that determines peoples attitude toward behavior (Bandura, 1986; Davis

et al., 1989). Besides, research has shown that trust definitely increases the

confidentiality of business relationship and determines the quality of transaction

between buyers and sellers as well as peoples outcome expectation on many

commerce activities (Luhmann, 1979; Lewis and Weigert, 1985; Hosmer, 1995).

According to social cognitive theory, outcome expectation refers to peoples

estimation of a given behavior yielding a particular outcome, which is closely related

to peoples attitude toward behavior (Bandura, 1986). Therefore, trust is apparently

an important antecedent of attitude toward the on-line transaction behavior.

For perceived behavioral control construct, trust can increase perceivedbehavioral control over on-line transactions since the virtual interactions between

customers and e-vendors become more expectable (Pavlou, 2002). Explicitly, trust

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influences perceived behavioral control through control factors of self-efficacy and

facilitating favorable conditions. According to the psychological reports, self-efficacy

in personal relationships is constructed from self-confidence and mutual trust in

friendships (Matsushima and Shiomi, 2003). Hence, mutual trust in the relationshipbetween customers and e-vendors should increase customer self-efficacy and in turn,

increase perceived behavioral control. On the other hand, trust can be a perceptual

resource that facilitates customers to gain control over on-line transactions. While

customers trust an e-vendor that behaves in accordance with their expectation, the

trust beliefs are likely to increase customers perceived behavioral control over on-

line transactions (Pavlou, 2002).

For subjective norm construct, researchers have found that mutual trust and

mutual influence between users and IS units are highly correlated to each other based

on a study concerning the performance of information system group (Nelson and

Cooprider, 1996). Furthermore, Decomposed TPB revealed that there are peer and

superior influences on users for determining subjective norm toward IS usage

(Taylor and Todd, 1995). Derivatively, it can be predicted that trust in peers and

superiors about their beliefs of IS usage should play a role in determining subjective

norm. Similarly, trust in e-vendors about their reputation, brand name, and service

may positively influence subjective norm over the behavior of on-line transactions.

Besides, they may indicate certain relationship between trust in peers and superiors

and trust in vendors. As the opinions from the referents of peers and superiors are

positive for certain e-vendors in the market, trust in peers and superiors in this

situation can enhance user beliefs in trusting these e-vendors and in turn, subjectivenorm toward the behavior of on-line transactions. Therefore, whatever types of trust

are with direct and indirect influences on subjective norm, they are all the important

antecedents of subjective norm in on-line service.

3. Research model

While on-line tax is considered as a special type of e-service, the initial adoption in

on-line tax, in essence, concerns both the roles of the Internet technology and e-vendor in providing service. The Trust and TAM model in Gefen et al. (2003a) has

been well studied in on-line shopping setting and showed that understanding both

the Internet technology and trust issue is critical in determining behavioral intention

to use on-line shopping, as discussed in Section 2.3. Besides, the diffusion of on-line

tax could also be influenced by the potential antecedents such as individuals,

organizational members, and social system while the issue for innovative technology

is well discussed in Rogers (1995). An extension of Trust and TAM model with TPB

would be in more comprehensive manner to understand the acceptance behavior

toward on-line tax and hopefully, this extension would provide us with higher

explanatory power to examine this problem and effectively improve the low usagerate. This extension model in on-line tax is indicated in Fig. 1. Accordingly, the

hypotheses are presented as below.

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Hypotheses 1, 2, 5, 6, and 10 are proposed based on TAM as discussed in Section

2.1 while Hypotheses 3 and 4 are initiated underlying TPB as described in Section

2.1. More importantly, Hypotheses 79 are the unique features from Trust and TAM

model, which are derived from the detailed discussion in the first, second, and thirdparagraphs of Section 2.4, respectively. Hypotheses 11 and 12 are mainly developed

based on Trust and TAM model in Section 2.3, i.e. PEOU indicated as a direct

prediction to trust and trust to PU. Furthermore, these hypotheses were further

verified for their validity by empirical data.

Hypothesis 1. PU has positive effect on intention to use on-line tax.

Hypothesis 2. Attitude has positive impact on intention to use on-line tax.

Hypothesis 3. Perceived behavior control positively influences intention to use on-

line tax.

Hypothesis 4. Subjective norm has positive effect on intention to use on-line tax.

Hypothesis 5. PU has positive impact on attitude to use on-line tax.

Hypothesis 6. PEOU positively influences attitude to use on-line tax.

Hypothesis 7. Trust has positive effect on attitude to use on-line tax.

Hypothesis 8. Trust has positive impact on perceived behavior control to use on-line

tax.Hypothesis 9. Trust positively influences subjective norm to use on-line tax.

Hypothesis 10. PEOU has positive impact on PU to use on-line tax.

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PEOU

Trust

PU

Attitude Intention

SN

PBC

H1

H2

H3

H4

H5

H6

H7

H8

H9

H10

H11

H12

TAM

TPB

Fig. 1. Research model.


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Hypothesis 11. Trust has positive effect on PU to use on-line tax.

Hypothesis 12. PEOU positively influences trust in using on-line tax.

4. Research design

A large sample survey of on-line tax declaration was employed to empirically test

this research model. The instrument and respondent sample are designed as below.

4.1. Instrument development

The instrument is designed to include a four-part questionnaire as presented in

Appendix A. The first part is nominal scales and the remainders are seven-point

Likert scales.

4.1.1. Basic information

This part of questionnaire is used to collect basic information about respondent

characteristics including gender, age, education, occupation, and experience (one-

time users for the first year, or continued users for more than 1-year experience) in

on-line income tax declaration.

4.1.2. TAM

This part of questionnaire is constructed based on the constructs of PU and

PEOU in TAM model and is adapted from the measurement defined by Venkatesh

and Davis (1996, 2000), containing four items for both constructs.

4.1.3. TPB

This part of questionnaire is developed based on the constructs of attitude,

perceived behavior control, subjective norm, and intention to use. Attitude is

adapted from the measurement defined by Bhattacherjee (2000), including four

items. Perceived behavior control was adapted from the measurement definedby Taylor and Todd (1995) and Bhattacherjee (2000), including three items.

Subjective norm is adapted from the measurement defined by Taylor and Todd

(1995) and Bhattacherjee (2000), including three items. Intention to use is adapted

by the measurement defined by Venkatesh and Davis (1996, 2000), including

three items.

4.1.4. Trust

Trust items are composed to reflect trust beliefs of citizens in using on-line tax.

This part of questionnaire is thus adapted from the study of Gefen et al. (2003a).

Because the measurement in Gefen et al. is originally developed for on-line businessand its focus is on customerseller relationship, therefore, a couple of measuring

items concerning market, opportunistic, and honest issues, which are irrelevant to

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the on-line tax setting, are dropped from the list. After the screen and shortening

process, this part comprises three items.

4.2. Sample organizations and respondents

In order to collect on-line tax declaration users information, researchers first

required getting permission from the Tax Bureau to express the need for academic

research purpose. Basically, the personal information of the users in on-line income

tax declaration is confidential under the law of privacy right and forbidden to

distribute it. However, under certain circumstances, the Tax Bureau can permit to

provide certain types of the personal information for academic research purpose

while at the same time without violating the law of privacy right. The application

procedure for this service is described as below.

While the application gets approval, the Tax Bureau will help e-mail invitation

letters to the users in the e-service with an elicitation message for the purpose of

understanding their experience in the initial adoption of on-line income tax

declaration. The invitation letter also indicates a web site for the users to instantly

hyperlink to an on-line questionnaire. The users are free to participate in this

invitation. After that, 8000 users were randomly selected from the population sample

and accordingly, invitation letters were sent out by e-mail. Furthermore, in order to

improve survey return, follow-up procedure was carried out with another invitation

letter for non-responding users after 3 weeks.

4.3. Sample demographics

Of the 8000 on-line questionnaires distributed, 1383 users were replied, with

incomplete response and not the one-time users (the continued users) deleted,

resulting in a sample size of 1032 users for an overall response rate of 12.9%. Sample

demographics are depicted in Table 1. The seemingly low response rate raises the

concern about non-response bias. A test for non-response bias was conducted using

two responding subsamples: early and late respondents. These two groups were

correlated on the sample characteristics of gender, age, education, occupation, and

experience. The result indicates that there is no significant systematic non-response

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Table 1

Sample demographics

Gender Age Education level Occupation

Female 20.1% o20 0.3% High school 8.9% Finance 7.5%

Male 79.9% 2029 10.9% College 60.4% Institution 22.9%

3039 44.8% Graduate 26.5% Information 20.3%

4049 30.2% Doctorate 4.2% Service 15.2%450 13.8% Manufacturing 11.9%

Others 22.2%


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bias in the respondent sample, suggesting that the respondent sample was a random

subset of the sample frame.

5. Analysis and findings

5.1. Analysis of the measurement model

First, content validities should be relatively acceptable since the various parts of

questionnaire were all adapted from the literature and have been reviewed carefully

by practitioners. Next, confirmatory factor analysis in AMOS software was used to

analyse construct validities, basically the analytical procedure including three

stages as described below. First, a measurement model should be assessed forgoodness-of-fit. The literature suggested that, for a good model fit, chi-square/

degrees of freedom (w2=df) should be less than 3, adjusted goodness-of-fit index(AGFI) should be larger then 0.8, goodness-of-fit index (GFI), normed fit index

(NFI), and comparative fit index (CFI) should all be greater than 0.9, and root mean

square error (RMSE) should be less than 0.10 (Henry and Stone, 1994). Second,

convergent validity is assessed by three criteria. Item loading (l) is at least 0.7 and

significant, composite construct reliability is a minimum of 0.8, and average variance

extracted (AVE) for a construct is larger than 0.5 (Fornell and Larcker, 1981).

Finally, discriminant validity is assessed by the measure that the AVE of each

construct should be larger than its square correlation with other constructs (Fornell

and Larcker, 1981).

The indices for the measurement model indicate a good fit with w2=df (991.1/231 4.29), AGFI (0.90), GFI (0.93), NFI (0.97), CFI (0.98), and RMSE (0.056).

The results of reliability as well as convergent and discriminant validities for

this model are reported in Table 2. The item loading (l) for these constructs

ranges from 0.78 to 0.98 and is also significant at 0.01 level, construct reliability

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Table 2

Construct reliability, convergent validity and discriminant validity

Construct Item loading Construct reliability Factor correlations

AVE ATT PEOU INT PBC PU SN TST

ATT 0.800.90 0.92 0.75

PEOU 0.930.97 0.96 0.87 0.54

INT 0.970.98 0.98 0.95 0.82 0.50

PBC 0.920.94 0.95 0.85 0.75 0.65 0.73

PU 0.840.92 0.93 0.77 0.67 0.48 0.59 0.52

SN 0.780.98 0.86 0.67 0.24 0.16 0.24 0.17 0.22

TST 0.840.98 0.92 0.79 0.63 0.44 0.57 0.55 0.45 0.24

Attitude (ATT), Perceived ease of use (PEOU), Intention (INT), Perceived belief control (PBC), Perceived

usefulness (PU), Subjective norm (SN), Trust (TST).


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ranges from 0.86 to 0.98, and AVE ranges from 0.67 to 0.95. Appendix B also

reports the covariance matrix generated by AMOS. Moreover, the AVE of each

construct is all above its square correlation with other constructs. Thus, this

measurement model indicates a high degree of reliability as well as convergent anddiscriminant validities.

5.2. Analysis of the structural model

The technique of structured equation modeling was used to examine the causal

structure of the proposed model in this study. The evaluation of this research model

can be carried out in three steps. First, a GFI for the structural model was examined

as the same GFIs applied in assessing the measurement model. Second, the

standardized path coefficients and their statistical significance for the hypotheses inthis model were estimated. Finally, as a measure of the entire structural equation, an

overall coefficient of determination R2 was calculated, similar to that found in

multiple regression analysis. The testing results of GFIs are all under the acceptable

levels with, w2=df (1049.2/236 4.45), AGFI (0.90), GFI (0.92), NFI (0.97), CFI(0.97), and RMSE (0.06). Furthermore, the standardized path coefficients are all

significant at 0.01 level except for the paths from PU to intention and subjective

norm to intention. As a result, Hypothesis 1 and 4 are not supported while the other

hypotheses are all supported. In general, trust indicates important relationships with

the three antecedents of intention to use in TPB while the relationships in Trust and

TAM model are maintained in on-line tax. The detailed discussion of the results willbe presented by the order of the antecedents of intention to use, attitude, perceived

behavioral control, and subjective norm as well as the relationships among trust,

PEOU, and PU in Trust and TAM model (Fig. 2).

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PEOU

Trust

R2

= 0.19

PU

R2

= 0.31

Attitude

R2

= 0.59

Intention

R2

= 0.69

SN

R2

= 0.08

PBC

R2 = 0.27

0.08

0.55*

0.27*

0.08

0.34*

0.21*

0.40*

0.33*

0.24*

0.35*

0.30*

0.44*

Fig. 2. Standardized solution of the structural model. Number on path: standardized coefficient, R2:

coefficient of determination, *: po0:01.


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Intention to use on-line tax in this research is jointly predicted by PU (b 0:08,Standardized path coefficient), attitude (b 0:55), perceived behavior control(b 0:27), and subjective norm (b 0:05) and these variables totally explain 69%

of the variance on intention to use (R2 0:69, Coefficient of determination). Whilecomparing the presented results with previous TPB-based studies in IS acceptance,

the explanatory power of the current research model for behavioral intention to use

is higher than Taylor and Todd (1995) with R2 0:60, Bhattacherjee (2000) withR2 0:52, and Chau and Hu (2001) with R2 0:42. Among these relationships,attitude toward the behavior and perceived behavior control are two major

influencers on individuals behavioral intention to use on-line tax. Moreover,

attitude indicates more importance than perceived behavior control in determining

behavioral intention to use on-line tax. The result quite conforms to the findings

reported with business-based setting in prior research. Nevertheless, PU and

subjective norm do not produce significant impacts on behavioral intention to use in

this research.

For the result in PU, previous empirical studies on TAM and extended TAM have

shown inconsistence for either with significant influence (Moore and Benbasat, 1991;

Chau, 1996) or with insignificant influence on behavioral intention to use (Chen

et al., 2002). Indeed, it, in essence, implies an indirect influence of PU on behavioral

intention to use via the mediator, attitude toward using on-line tax. A plausible

reason for this may be explained as below. The on-line tax context in this study is

focused on the stage of the initial adoption and voluntary use in tax declaration.

In other words, users in the on-line tax are still in a trial and experimentalmanner. Users positive PU in using on-line tax may not immediately lead to a

behavioral intention to use, rather than firstly form a favorable attitude/belief to use

on-line tax. The favorable attitude/belief to use on-line tax is just like a time cushion

before directly taking behavioral intention to use on-line tax. This implies that

potential users would need to take a period of time to carefully change their

psychological state to adopting on-line tax. Consequently, the attitude toward

adopting on-line tax demonstrates a larger influential power on behavioral intention

to use (b 0:55).For subjective norm, the result is similar to the finding reported in Taylor and

Todd (1995) and Chau and Hu (2001), but differs from the conclusion inBhattacherjee (2000) for exploring the adoption of e-service with the case of

electronic brokerage. The latter one indicated that subjective norm could influence

intention to use as strong as attitude does. However, Venkatesh and Davis (2000)

gave a more complete report in that subjective norm could significantly determine

intention to use in a mandatory-usage context, but its impact would become less

significant while users are in a voluntary-usage context as the case of on-line tax in

this study. In particular, while on-line tax in this study is placing at the initial

adoption stage, there are lack of enough references from prior adopters such as

friends, peers and superiors (perceived social pressure). From the perspective, on-line

tax in this study quite differs from the case of e-service in Bhattacherjees study.Accordingly, it is reasonable to expect that the effect of subjective norm on intention

to use on-line tax should indicate insignificance.

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Attitude is predicted by PU (b 0:34), PEOU (b 0:21), and trust (b 0:40)with jointly 59% of the total variance explained. In that, the effect of trust on

attitude is greater than PU and PEOU. This implies an important fact for researchers

that traditional TAM may not completely explain the attitude/behavior toward theacceptance of on-line tax. The result also partially validates the conclusion of Trust

and TAM model by Gefen et al. (2003a) since the influential relationship is in terms

of trust and behavioral intention to use in the Trust and TAM model. In general,

trust should be necessarily included in TAM for effectively understanding the

acceptance of e-service. Moreover, trust (b 0:33) explains 27% of the totalvariance in determining perceived behavioral control and is considered as an

important antecedent of perceived behavioral control in on-line tax. In other words,

while citizens trust the on-line tax provider that behaves to improve self-efficacy in

computer or external resource constraint such as the Internet infrastructure for

citizens, the trust beliefs will be able to increase citizens perceived behavioral control

in performing the behavior.

On the other hand, trust (b 0:24) significantly influences subjective norm whileexplaining only 8% of the total variance in subjective norm. The reason for this is

two-fold. First, this indicates that while users establish the initial trust in on-line tax,

it will help enhance the users normative beliefs about the expectations of referents

such as friends, peers, and superiors who concern the initial adoption of the on-line

tax. The connection between users trust and perceived social pressure to perform

on-line tax behavior seems to be expectable as the underlying definition in this

model. Next, the reason for 8% of the total variance explained might be becausethere are a number of potential influencers to subjective norm remaining to be

identified for accounting for the rest of the total variance explained. In sum, trust,

generally, is closely linked to the three antecedents of behavioral intention to use in

TPB in the on-line tax setting. This validates the necessity to extend Trust and TAM

model with TPB in this study in order to have larger explanatory power in the initial

adoption of on-line tax (R2 0:69 as indicated above).Finally, trust (b 0:30) and PEOU (b 0:35) both significantly influence PU and

jointly explain 31% of the total variance in PU. The former is similar to the findings

reported in the literature such as Trust and TAM model in Gefen et al. (2003a)

and this model discussed in Pavlou (2003). The latter regularly corroboratesmost prior research on TAM in both on-line and general information techno-

logies. Furthermore, PEOU (b 0:44), as discussed earlier in the literature,significantly affects trust and explains 19% of the total variance in trust. This result

also conforms to Trust and TAM model in Gefen et al. (2003a) in on-line shopping

setting.

6. General discussions

There are many issues influencing users decision in the initial adoption of on-lineservice. While considering both the Internet and e-vendor issues in the acceptance of

on-line service, Trust and TAM model, as discussed in Gefen et al. (2003a), is well

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(PU and PEOU) and trust are shown to be two sets of underlying antecedents in

determining behavioral intention to use, each contributing its significant influence

on behavioral intention to use through a number of mediators such as attitude,

perceived behavioral control, and subjective norm. This means that to effectivelyattract citizens to use on-line tax, the design of on-line tax needs to carefully

pay attention to both aspects. Besides, as discussed previously, novice users tend to

rely more on trust in non-technology features than on PEOU and usefulness

in technology-based features to develop their attitude toward the behavior. In

other words, trust is more important in determining users attitude than PEOU

and usefulness in on-line tax. The major trust-based concerns may include

privacy protection, accuracy to declaration, and unauthorized access and

so on.

Fundamentally, while trust is empirically identified as an antecedent of PU and in

turn, an antecedent of attitude, this has some practical implications in enhancing the

attitude toward using on-line tax. On-line tax provider should first develop trust-

building mechanisms for citizens in order to attract novice users to accept on-line

tax. Examples of the mechanisms include statements of guarantees, increased

familiarity through advertising, long-term customer service, and offering

incentives to use. After that, PU of on-line tax emerges as an important issue in

attracting new users and should be carefully designed in terms of users requirements

to reflect PU of this service. Without an original consideration from trust aspect, a

well-designed on-line tax with significant PU will not well perform in attracting

novice users.For researchers, past research on technology acceptance implicitly assumed that

the success of system use is mainly dependent on technological aspect and does not

consider the notion of uncertainty. However, the advent of the Internet has

introduced uncertainty and risk in system acceptance and use because people often

need to use the Internet to communicate, collaborate, and transact with individuals

and organizations without physical face-to-face interaction. Thus, uncertainty is

increasingly becoming the underlying determinant of the Internet-base system usage.

Traditionally, TAM mainly focuses on the aspect of system features and thus, is

insufficient in capturing the roles of individuals, organizational members, and

social system in the Internet-based system usage, in particular, on-line tax. TPBwith the antecedents of attitude, perceived behavioral control, and subjective norm

will be in a complementary manner to enhance the prediction capability of TAM.

This study extends Trust and TAM model with TPB in exploring on-line tax and

further, empirically demonstrates relatively satisfactory results for providing

more insight to this problem. This approach may be as a basis for similar research

in the area.

Furthermore, subsequent research can be founded on this work. This study has

focused on users who are inexperienced or the initial adoption in e-service. However,

prior research has suggested that determinants of behavioral intention change in

terms of users level of experience (McKnight et al., 1998; Karahanna et al., 1999).Additional research, both longitudinal and cross-sectional, is needed to examine the

differences of this framework as users evolving from being aware of the e-vendor, to

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having experience with the e-vendor, to being continued use of the e-vendor.

Despite the significant influence of trust on subjective norm, there is only 8% of

total variance explained in subjective norm. Thus, it is possible to identify

potential factors that could influence subjective norm to some extent. Futureresearch could be explored on the matter to better predict subjective norm and in

turn, behavioral intention to use. Other possible beliefs have been suggested in the

management and psychological areas, including loyalty, reliability, and openness

(Hosmer, 1995). More research with the alternative conceptualization of trust would

be useful in more understanding the role of trust in the initial adoption of on-line

service.

Finally, although this study has produced some interesting results, it may still have

some limitations. First, approximately 80% of the respondents are male in this

empirical study. Much research has shown that gender difference could cause

discrepancies in the effects of attitude, perceived behavioral control, and subjective

norm on users behavioral intention (Venkatesh and Morris, 2000; Armitage et al.,

2002). Although gender does not produce statistical significance on systematic

non-response bias in the sample respondents, the empirical findings may be little

biased for not reflecting the population distribution of gender. Next, there are

approximately 1015% of taxpayers in adopting on-line tax. Obviously, the on-line

tax is still at the early stage of adoption. Definitely, this research is greatly necessary

for us to gain more insight on further promoting its widespread usage. This imposes

a limitation of generalizability to the population. However, the same respondents are

randomly selected from the sample frame and thus, in a position to be wellrepresentative of the population. As a result, the empirical findings should be free for

the population problem and can be widely generalized for its practical use.

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Appendix A. Questionnaire

Part 1. Basic information

1. Gender: &Female &Male

2. Age: &Less than 20 years old &2030 years

&4050 years old &Larger than 50 yea

3. Education: &High school &College &Graduate s

4. Occupation: &Finance &Institution &Information

&Other

5. Experience in using on-line income tax declaration: &One-time user &Continued user

Part 24. Constituent constructs in hypothetic research model

Scale design for the following questionnaire:

1: Strongly disagree (SD) 2: Moderately disagree 4: Neutral (N) 5: Somewhat agree

7: Strongly agree (SA)

Note: OITD: abbreviation of on-line income tax declaration.

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SD

Perceived usefulness (adapted from Venkatesh and Davis, 1996, 2000)

PU1 Using the OITD would improve my performance in income tax

declaration.

1 2

PU2 Using the OITD would improve my productivity in income tax

declaration.

1 2

PU3 Using the OITD would enhance my effectiveness in income tax

declaration.

1 2

PU4 I find the OITD to be useful in income tax declaration. 1 2

Ease of use (adapted from Venkatesh and Davis, 1996, 2000)

EOU1 My interaction with the OITD is clear and understandable. 1 2

EOU2 Interaction with the OITD does not require a lot of mental effort. 1 2

EOU3 It is easy to get the OITD to do what I want it to do. 1 2

EOU4 It is easy to use the OITD. 1 2

Attitude (adapted from Bhattacherjee, 2000)

ATT1 Using OITD for income tax declaration would be a good idea. 1 2

ATT2 Using OITD for income tax declaration would be a wise idea. 1 2

ATT3 I like the idea of using OITD for income tax declaration. 1 2

ATT4 Using OITD for income tax declaration would be a pleasant

experience.

1 2

Subjective norm (adapted from Taylor and Todd, 1995; Bhattacherjee, 2000)SN1 People who are important to me would think that I should use

OITD.

1 2

SN2 People who influence me would think that I should use OITD. 1 2

SN3 People whose opinions are valued to me would prefer that I should

use OITD.

1 2

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Behavioral control (adapted from Taylor and Todd, 1995; Bhattacherjee, 2000)

PBC1 I would be able to use the OITD well for income tax declaration. 1 2

PBC2 Using OITD was entirely within my control. 1 2

PBC3 I had the resources, knowledge, and ability to use OITD. 1 2

Intention to use (adapted from Venkatesh and Davis, 1996, 2000)

INT1 Assuming I have access to the OITD, I intend to use it. 1 2

INT2 Given that I have access to the OITD, I predict that I would use it. 1 2

INT3 If I have access to the OITD, I want to use it as much as possible. 1 2

Trust (adapted from Gefen et al., 2003a)

TST1 Based on my perception with OITD, I know it is predictable for the

service.

1 2

TST2 Based on my perception with OITD, I believe it provides good

service.

1 2

TST3 Based on my perception with OITD, I believe it helps or cares citizens

in tax declaration.

1 2

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Appendix B. Covariance matrix

PU1 PU2 PU3 PU4 EOU1 EOU2 EOU3 EOU4 ATT1 ATT2 ATT3 ATT4 SN1 SN2 SN3 PBC1 PBC2 PB

PU1 0.943

PU2 0.892 1.392

PU3 0.819 0.962 1.056

PU4 0.701 0.73 0.748 0.804

EOU1 0.412 0.476 0.456 0.461 1.314

EOU2 0.451 0.516 0.49 0.488 1.213 1.332

EOU3 0.44 0.527 0.49 0.474 1.204 1.228 1.339

EOU4 0.469 0.523 0.507 0.49 1.169 1.229 1.228 1.397

ATT1 0.483 0.516 0.519 0.489 0.432 0.469 0.472 0.487 0.845

ATT2 0.408 0.413 0.438 0.428 0.402 0.415 0.425 0.436 0.679 0.892

ATT3 0.449 0.472 0.487 0.468 0.417 0.441 0.463 0.475 0.662 0.611 0.805

ATT4 0.535 0.595 0.578 0.564 0.667 0.711 0.698 0.766 0.737 0.627 0.731 1.189SN1 0.32 0.326 0.354 0.305 0.298 0.319 0.305 0.33 0.401 0.302 0.38 0.576 1.747

SN2 0.217 0.243 0.26 0.201 0.192 0.204 0.19 0.245 0.243 0.115 0.204 0.392 1.359 2.152

SN3 0.253 0.281 0.294 0.221 0.211 0.22 0.215 0.271 0.262 0.148 0.218 0.404 1.386 1.799 1.909

PBC1 0.416 0.46 0.446 0.44 0.629 0.648 0.683 0.666 0.562 0.511 0.557 0.745 0.338 0.202 0.217 0.959

PBC2 0.434 0.489 0.471 0.457 0.717 0.723 0.757 0.763 0.582 0.531 0.577 0.827 0.388 0.255 0.256 0.937 1.228

PBC3 0.383 0.431 0.417 0.416 0.635 0.619 0.663 0.641 0.534 0.497 0.541 0.689 0.269 0.111 0.13 0.836 0.929 0.9

INT1 0.513 0.556 0.546 0.521 0.535 0.561 0.578 0.582 0.656 0.582 0.655 0.794 0.438 0.288 0.323 0.701 0.734 0.6

INT2 0.506 0.548 0.546 0.513 0.514 0.547 0.572 0.569 0.666 0.581 0.659 0.791 0.442 0.293 0.322 0.696 0.718 0.6

INT3 0.512 0.554 0.545 0.525 0.513 0.551 0.568 0.56 0.672 0.582 0.663 0.789 0.427 0.288 0.309 0.69 0.703 0.6

TST1 0.439 0.503 0.497 0.431 0.542 0.552 0.559 0.604 0.549 0.467 0.51 0.73 0.454 0.414 0.407 0.539 0.671 0.5

TST2 0.441 0.495 0.484 0.445 0.527 0.543 0.555 0.584 0.593 0.529 0.551 0.745 0.433 0.354 0.354 0.571 0.679 0.5

TST3 0.439 0.494 0.476 0.434 0.509 0.535 0.544 0.573 0.601 0.525 0.552 0.754 0.442 0.346 0.353 0.578 0.678 0.5

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Ing-Long Wu is a professor and chair in the Department of Information Management at National Chung

Cheng University. He gained a Bachelor in Industrial Management from National Cheng-Kung

University, an M.S. in Computer Science from Montclair State University, and a Ph.D. in Management

from Rutgers, the State University of New Jersey. He has published a number of papers in Information &Management, Decision Support Systems, Behavior and Information Technology, Psychometrika, Applied

Psychological Measurement, and Journal of Educational and Behavioral Statistics. His current research

interests are in the areas of e-commerce, customer relationship management, supply chain management,

strategic information systems, and business process reengineering.

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