an extension of trust and tam model with tpb in the initial adoption of on-line tax: an empirical...

ARTICLE IN PRESS

Int. J. Human-Computer Studies 62 (2005) 784–808

1071-5819/$ -

doi:10.1016/j

�Correspo

E-mail ad

www.elsevier.com/locate/ijhcs

An extension of Trust and TAM model withTPB in the initial adoption of on-line tax:

An empirical study

Ing-Long Wu�, Jian-Liang Chen

Department 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), 51–90) 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 model

with 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

see front matter r 2005 Elsevier Ltd. All rights reserved.

.ijhcs.2005.03.003

nding author. Tel.: +886 5 2720411x34620; fax: +8865 2721501.

dress: [email protected] (I.-L. Wu).

www.elsevier.com/locater/ijhcs

ARTICLE IN PRESS

I.-L. Wu, J.-L. Chen / Int. J. Human-Computer Studies 62 (2005) 784–808 785

1. Introduction

Customer service is a series of activities designed for resolving purchasingproblems 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). Ingeneral, e-service could include customer service as part of on-line shopping andpure-play service offered in e-commerce. Initially, on-line consumers did not demandhigh 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 theInternet bubble burst and the profit gained from e-commerce far away frommarketer 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 attributedto the ability of providing customers with better service to attract and retaincustomers, and eventually, building a long-term relationship with customers.

In contrast, while the functions of government is mainly to provide informationand delivery service to citizens and business partners, government with its customerssuch as citizens and business organizations, in essence, can be considered as a specialtype of service industry. This consideration drives us to impose e-commerce featureson supporting the operation of government. This is called e-government and a typeof pure-play service offered in e-government. In particular, on-line tax declaration isan 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 usagerate is still quite low regardless the constantly promotional effort. Among theinfluential factors of the low usage rate, the key fundamental can be attributed to theinitial adoption (acceptance) of the innovative service by s since the initial adoptionof an e-service is the important driving force to further influence continued use of theservice (Kwon and Zmud, 1987).

For advocating users’ behavior toward the initial adoption of on-line tax, systemdevelopers thus require first understanding their real needs and expectations in orderto offer more favorable services. In fact, an understanding of the users’ behaviorwould 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 ortransaction is offered without physical face-to-face contact and simultaneousexchange of services and money. The spatial and temporal separation of e-commercebetween customers and e-vendors as well as the unpredictability of the Internetinfrastructure generate an implicit uncertainty around the initial adoption of on-lineservice (Pavlou, 2003). Accordingly, the initial adoption of on-line tax basicallyinvolves the acceptance of both the Internet technology and on-line serviceproviders. As technology acceptance model (TAM) is mainly proposed fortechnology-based perspective through two system features of perceived usefulness(PU) and perceived ease of use (PEOU) (Davis et al., 1989), it is incomplete in thecontext of on-line services.

ARTICLE IN PRESS

I.-L. Wu, J.-L. Chen / Int. J. Human-Computer Studies 62 (2005) 784–808786

A model, named Trust and TAM, has been previously presented in exploring theacceptance of on-line shopping setting (Gefen et al., 2003a). This model integrativelyplaced use of on-line system into both system features such as ease of use andusefulness and trust in e-vendors. This result indicated that these variables are goodpredictors for behavior intention to use on-line shopping. However, a diffusion ofinnovative 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 andexplaining human behavior while also considering the roles of individualorganizational members and social system in this process (Ajzen, 1991). Accordingly,the three influencers in this theory, i.e. attitude, subjective norm and perceivedbehavioral control, can be interpreted as attitude for technology role, subjectivenorm for organizational members and social system roles, and perceived behavioralcontrol for individual role.

As the focus of this study is on the on-line tax setting, which is considered as a typeof innovative technology, organizational and social systems such as peer or superiorinfluence and self-efficacy in computer or external resource constraint should playthe 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 includingsubjective 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 isplaced as an important antecedent of attitude, subjective norm, and perceivedbehavioral 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 businessorganization and public institutions and governments are indeed a special type ofservice industry, its applications in public agencies or e-government in Taiwan hasbeen greatly driven by current and previous administrations for providing citizensand organizations with more convenient access to government information andbetter services. Among them, on-line tax declaration is one of the top priorities in theconstruction of e-government and begins for trial and experimental use around 2years ago and is going for the third-year period. Taxpayers are still allowed todeclare their tax for the choice of either paper form or e-form. In order words, it is avoluntary-based context for use of emerging technology. Until now, on-line tax isstill in the initial stage of its usage and the usage rate is still relatively low for keepingin the interval of 10–15% while it was initially launched in the year 2000. There is noindication 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 ofcommunicating with on-line tax in an efficient, effective, and safe manner. This study

ARTICLE IN PRESS


tries to understand, analyse, and solve this problem from the perspective of the initialadoption of virtual service. This may explain some of the major reasons for a lowrate in system usage.

2.2. Relevant models in IT adoption

TAM is an adaptation of the theory of reasoned action (TRA) by Fishbein andAjzen (1975) and mainly designed for modeling user acceptance of informationtechnology (Davis et al., 1989). This model hypothesizes that system use is directlydetermined by behavioral intention to use, which is in turn influenced by user’sattitude toward using the system and PU of the system. Attitude and PU are alsoaffected by PEOU. PU, reflecting a person’s salient belief in using the technology,will be helpful in improving performance. PEOU, explaining a person’s salientbeliefs 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 anindication of high prediction power of technology usage. These determinants are alsoeasy to understand for system developers and can be specifically considered duringsystem requirement analysis and other system development stages. These factors arecommon in technology-usage settings and can be applied widely to solve theacceptance problem (Taylor and Todd, 1995).

TPB underlying the effort of TRA has been proven successful in predicting andexplaining human behavior across various information technologies (Ajzen, 1991,2002). According to TPB, a person’s actual behavior in performing certain action isdirectly influenced by his or her behavioral intention and in turn, jointly determinedby attitude, subjective norm and perceived behavioral control toward performing thebehavior. Behavioral intention is a measure of the strength of one’s willingness to tryand exert while performing certain behavior. Attitude (A) explains the feeling of aperson’s favorable or unfavorable assessment regarding the behavior in question.Furthermore, a favorable or unfavorable attitude is a direct influence to the strengthof behavioral beliefs about the likely salient consequences. Accordingly, attitude (A)is equated with attitudinal belief (abi) linking the behavior to a certain outcomeweighted by an evaluation of the desirability of that outcome (ei) in question, i.e.A ¼ Sabiei. Subjective norm (SN) expresses the perceived organizational or socialpressure of a person while intending to perform the behavior in question. In otherword, subjective norm is relative to normative beliefs about the expectations of otherpersons. It can be depicted as individual’s normative belief (nbi) concerning aparticular referent weighted by motivation to comply with that referent (mci) inquestion, i.e. SN ¼ Snbimci.

Perceived behavioral control (PBC) reflects a person’s perception of ease ordifficulty toward implementing the behavior in interest. It concerns the beliefs aboutpresence of control factors that may facilitate or hinder to perform the behavior.Thus, control beliefs about resources and opportunities are the underlyingdeterminant 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 ¼ Scbipi. In sum, grounded on the effort of TRA, TPB is proposed to eliminate

ARTICLE IN PRESS


the limitations of the original model in dealing with the behavior over which peoplehave incomplete volitional control (Ajzen, 1991). In essence, TPB differs from TRAin 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, subjectivenorm, perceived behavior control) of intention. TPB simply combines each of thebelief structures into one unidimensional belief construct and as a result, the beliefstructures, in fact, representing a variety of underlying dimensions, may not beconsistently related to the antecedents of intention. Moreover, the underlyingdimensions of the beliefs structures are, in essence, different for various applicationsettings and this combination makes TPB difficult to be generalizable across varioussettings. By decomposing the belief structures of TPB (Decomposed TPB), theirrelationships 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 threedimensions: individual self-efficacy, resource facilitating conditions, and technologyfacilitating conditions. After that, while comparing Decomposed TPB with TAM,TAM is, in fact, a part of Decomposed TPB and consequently, Decomposed TPBshould provide a more complete understanding of IT adoption relative to the moreparsimonious TAM (Taylor and Todd, 1995). Based on the above logic, it is betteroff 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 intentiontoward the initial adoption of on-line tax.

2.3. Trust

The functionality and contribution of trust can be apparently identified from theeconomic framework of social exchange (Kelley and Thibaut, 1978; Kelley, 1979).Within social exchange, business transactions are usually carried out without explicitcontract or control mechanism against opportunistic behavior so that the partiesinvolved in these activities are not able to attain complete legal protection andexpose themselves in a complicated social environment with mass uncertainty. Toinsure better rewards from the economic activities, people make efforts to reduce thissocial complexity and avoid risk from being exploited (Wrightsman, 1972). Trust isbasically seen as a common mechanism for reducing social complexity and perceivedrisk of transaction through increasing the expectation of a positive outcome andperceived certainty regarding the expected behavior of trustee (Luhmann, 1979;Grabner-Kraeuter, 2002; Gefen, 2004). In particular for on-line business, withoutreducing social complexity and risk resulting from the undesirable opportunisticbehavior 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.

ARTICLE IN PRESS


Moreover, trust was further explained more clearly in terms of a number of trustantecedents: 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 ofwhat is happening in the present (Luhmann, 1979). Cognition-based trust examineshow trust is developed from first impression rather than through experience ofpersonal interactions. According to this research stream, cognition-based trust isformed through categorization process and illusion of control (Brewer and Silver,1978; Meyerson et al., 1996). Calculative-based trust can be developed by people’srational assessment of the costs and benefits of another party while cheating orcooperating in the relationship. Trust in this view is derived from an economicanalysis occurring in ongoing relationship, namely that it is not worthy for the otherparty to engage in opportunistic behavior (Coleman, 1990; Lewicki and Bunker,1995; Doney et al., 1998). Institution-based trust refers to an individual’s perceptionof an institutional context, which mainly concerns security from guarantees, safetynets, or other impersonal structures inherent in the specific context (Shapiro, 1987;McKnight et al., 1998). Personality-based trust or propensity trust explains thetendency to believe or not to believe in others and further trust them. This type oftrust 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-basedtrusts are more relevant to the formation of the initial trust, since people inherentlyhas cognitive resource limitation for often recognizing subjects by the firstimpression and personality is an important determinant in the initial stage of arelationship building. Initial trust refers to trust in an unfamiliar trustee while theactors do not yet have credible, meaningful information about or affective boundswith each other. While people gain experience and familiarity with the trustee in thelater stage, continued trust by people will be more influenced by experiential personalinteraction (McKnight et al., 1998). In sum, as on-line tax is a type of e-servicebetween government agency and citizens, and their transactions are primarilythrough virtual channel without face-to-face contact, perceived uncertainty and riskassociated 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 initialadoption of on-line tax.

2.4. Trust and TAM relationship

The connections between trust and TAM have been widely discussed in literaturein 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-lineshopping setting (Gefen et al., 2003a). This model explicitly indicated theirrelationship as trust is an antecedent of PU, PEOU is an antecedent of trust, andtrust has a direct influence on behavioral intention to use. Trust is one of the

ARTICLE IN PRESS


determinants of PU, especially in an on-line environment, because part of theguarantee that consumers will sense the expected usefulness from the web site isbased on the sellers behind the web site. Moreover, trust is recognized to havepositive 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 serviceis useful (Gefen et al., 2003a).

On the other hand, PEOU is hypothesized to have positive influence on trustbecause PEOU can help promote customers’ favorable impression on e-vendors inthe initial adoption of on-line service and further, cause customers to be willing tomade investment and commitment in buyer-seller relationship (Ganesan, 1994;Gefen et al., 2003a). In general, while following the definition of social cognitivetheory, PEOU can be argued to positively influence a person’s favorable outcomeexpectation toward the acceptance of an innovative technology (Bandura, 1986).This is because cognition-based trust, as discussed previously, is mainly built on thefirst impression of a person toward certain behavior and extensively, PEOU in termsof on-line service can be considered the first feeling or expectation established forfurther continued on-line transaction. In sum, while on-line tax is considered aspecial type of e-service, the Trust and TAM model is partly fitted to this on-line taxsetting while there are additional variables, as discussed below, to be included in theparticular context.

2.5. Trust and TPB relationship

The relationship between trust and TPB can be examined in a variety of aspects inwhich trust is hypothesized as the common antecedent of attitude, perceivebehavioral control, and subjective norm. For attitude construct, trust in e-vendoris viewed as a salient behavioral belief that directly affects customer’s attitude towardthe purchase behavior. While an e-vendor is trustworthy, it is more possible that theconsumer will gain benefits and avoid possible risks from adopting on-line service(McKnight and Chervany 2002; Pavlou, 2003). As cost-benefit paradigm greatlyinfluences people’s attitudinal beliefs and outcome judgments, trust can be a directinfluencer that determines people’s attitude toward behavior (Bandura, 1986; Daviset al., 1989). Besides, research has shown that trust definitely increases theconfidentiality of business relationship and determines the quality of transactionbetween buyers and sellers as well as people’s outcome expectation on manycommerce activities (Luhmann, 1979; Lewis and Weigert, 1985; Hosmer, 1995).According to social cognitive theory, outcome expectation refers to people’sestimation of a given behavior yielding a particular outcome, which is closely relatedto people’s attitude toward behavior (Bandura, 1986). Therefore, trust is apparentlyan 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 betweencustomers and e-vendors become more expectable (Pavlou, 2002). Explicitly, trust

ARTICLE IN PRESS


influences perceived behavioral control through control factors of self-efficacy andfacilitating favorable conditions. According to the psychological reports, self-efficacyin personal relationships is constructed from self-confidence and mutual trust infriendships (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 perceptualresource that facilitates customers to gain control over on-line transactions. Whilecustomers trust an e-vendor that behaves in accordance with their expectation, thetrust beliefs are likely to increase customer’s perceived behavioral control over on-line transactions (Pavlou, 2002).

For subjective norm construct, researchers have found that mutual trust andmutual influence between users and IS units are highly correlated to each other basedon a study concerning the performance of information system group (Nelson andCooprider, 1996). Furthermore, Decomposed TPB revealed that there are peer andsuperior influences on users for determining subjective norm toward IS usage(Taylor and Todd, 1995). Derivatively, it can be predicted that trust in peers andsuperiors about their beliefs of IS usage should play a role in determining subjectivenorm. Similarly, trust in e-vendors about their reputation, brand name, and servicemay positively influence subjective norm over the behavior of on-line transactions.Besides, they may indicate certain relationship between trust in peers and superiorsand trust in vendors. As the opinions from the referents of peers and superiors arepositive for certain e-vendors in the market, trust in peers and superiors in thissituation can enhance user beliefs in trusting these e-vendors and in turn, subjectivenorm toward the behavior of on-line transactions. Therefore, whatever types of trustare with direct and indirect influences on subjective norm, they are all the importantantecedents 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 inon-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) hasbeen well studied in on-line shopping setting and showed that understanding boththe Internet technology and trust issue is critical in determining behavioral intentionto use on-line shopping, as discussed in Section 2.3. Besides, the diffusion of on-linetax could also be influenced by the potential antecedents such as individuals,organizational members, and social system while the issue for innovative technologyis well discussed in Rogers (1995). An extension of Trust and TAM model with TPBwould be in more comprehensive manner to understand the acceptance behaviortoward on-line tax and hopefully, this extension would provide us with higherexplanatory power to examine this problem and effectively improve the low usagerate. This extension model in on-line tax is indicated in Fig. 1. Accordingly, thehypotheses are presented as below.

ARTICLE IN PRESS


Hypotheses 1, 2, 5, 6, and 10 are proposed based on TAM as discussed in Section2.1 while Hypotheses 3 and 4 are initiated underlying TPB as described in Section2.1. More importantly, Hypotheses 7–9 are the unique features from Trust and TAMmodel, which are derived from the detailed discussion in the first, second, and thirdparagraphs of Section 2.4, respectively. Hypotheses 11 and 12 are mainly developedbased on Trust and TAM model in Section 2.3, i.e. PEOU indicated as a directprediction to trust and trust to PU. Furthermore, these hypotheses were furtherverified 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-linetax.

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.

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.

ARTICLE IN PRESS


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 testthis 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 inAppendix A. The first part is nominal scales and the remainders are seven-pointLikert scales.

4.1.1. Basic information

This part of questionnaire is used to collect basic information about respondentcharacteristics including gender, age, education, occupation, and experience (one-time users for the first year, or continued users for more than 1-year experience) inon-line income tax declaration.

4.1.2. TAM

This part of questionnaire is constructed based on the constructs of PU andPEOU in TAM model and is adapted from the measurement defined by Venkateshand 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 isadapted from the measurement defined by Bhattacherjee (2000), including fouritems. 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 adaptedby the measurement defined by Venkatesh and Davis (1996, 2000), includingthree 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 customer–seller relationship, therefore, a couple of measuringitems concerning market, opportunistic, and honest issues, which are irrelevant to

ARTICLE IN PRESS


the on-line tax setting, are dropped from the list. After the screen and shorteningprocess, this part comprises three items.

4.2. Sample organizations and respondents

In order to collect on-line tax declaration users’ information, researchers firstrequired getting permission from the Tax Bureau to express the need for academicresearch purpose. Basically, the personal information of the users in on-line incometax declaration is confidential under the law of privacy right and forbidden todistribute it. However, under certain circumstances, the Tax Bureau can permit toprovide certain types of the personal information for academic research purposewhile at the same time without violating the law of privacy right. The applicationprocedure for this service is described as below.

While the application gets approval, the Tax Bureau will help e-mail invitationletters to the users in the e-service with an elicitation message for the purpose ofunderstanding their experience in the initial adoption of on-line income taxdeclaration. The invitation letter also indicates a web site for the users to instantlyhyperlink to an on-line questionnaire. The users are free to participate in thisinvitation. After that, 8000 users were randomly selected from the population sampleand accordingly, invitation letters were sent out by e-mail. Furthermore, in order toimprove survey return, follow-up procedure was carried out with another invitationletter for non-responding users after 3 weeks.

4.3. Sample demographics

Of the 8000 on-line questionnaires distributed, 1383 users were replied, withincomplete 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%. Sampledemographics are depicted in Table 1. The seemingly low response rate raises theconcern about non-response bias. A test for non-response bias was conducted usingtwo responding subsamples: early and late respondents. These two groups werecorrelated on the sample characteristics of gender, age, education, occupation, andexperience. The result indicates that there is no significant systematic non-response

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% 20–29 10.9% College 60.4% Institution 22.9%

30–39 44.8% Graduate 26.5% Information 20.3%

40–49 30.2% Doctorate 4.2% Service 15.2%

450 13.8% Manufacturing 11.9%

Others 22.2%

ARTICLE IN PRESS


bias in the respondent sample, suggesting that the respondent sample was a randomsubset 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 ofquestionnaire were all adapted from the literature and have been reviewed carefullyby practitioners. Next, confirmatory factor analysis in AMOS software was used toanalyse construct validities, basically the analytical procedure including threestages 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 meansquare 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 andsignificant, composite construct reliability is a minimum of 0.8, and average varianceextracted (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 eachconstruct should be larger than its square correlation with other constructs (Fornelland Larcker, 1981).

The indices for the measurement model indicate a good fit with w2=df (991.1/231 ¼ 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 forthis model are reported in Table 2. The item loading (l) for these constructsranges from 0.78 to 0.98 and is also significant at 0.01 level, construct reliability

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 0.80–0.90 0.92 0.75 —

PEOU 0.93–0.97 0.96 0.87 0.54 —

INT 0.97–0.98 0.98 0.95 0.82 0.50 —

PBC 0.92–0.94 0.95 0.85 0.75 0.65 0.73 —

PU 0.84–0.92 0.93 0.77 0.67 0.48 0.59 0.52 —

SN 0.78–0.98 0.86 0.67 0.24 0.16 0.24 0.17 0.22 —

TST 0.84–0.98 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).

ARTICLE IN PRESS


ranges from 0.86 to 0.98, and AVE ranges from 0.67 to 0.95. Appendix B alsoreports the covariance matrix generated by AMOS. Moreover, the AVE of eachconstruct is all above its square correlation with other constructs. Thus, thismeasurement 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 causalstructure of the proposed model in this study. The evaluation of this research modelcan be carried out in three steps. First, a GFI for the structural model was examinedas the same GFIs applied in assessing the measurement model. Second, thestandardized path coefficients and their statistical significance for the hypotheses inthis model were estimated. Finally, as a measure of the entire structural equation, anoverall coefficient of determination ðR2Þ was calculated, similar to that found inmultiple regression analysis. The testing results of GFIs are all under the acceptablelevels with, w2=df (1049.2/236 ¼ 4.45), AGFI (0.90), GFI (0.92), NFI (0.97), CFI(0.97), and RMSE (0.06). Furthermore, the standardized path coefficients are allsignificant at 0.01 level except for the paths from PU to intention and subjectivenorm to intention. As a result, Hypothesis 1 and 4 are not supported while the otherhypotheses are all supported. In general, trust indicates important relationships withthe three antecedents of intention to use in TPB while the relationships in Trust andTAM 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, perceivedbehavioral control, and subjective norm as well as the relationships among trust,PEOU, and PU in Trust and TAM model (Fig. 2).

PEOU

TrustR2 = 0.19

PUR2 = 0.31

AttitudeR2 = 0.59

IntentionR2 = 0.69

SNR2 = 0.08

PBCR2 = 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.

ARTICLE IN PRESS


Intention to use on-line tax in this research is jointly predicted by PU (b ¼ 0:08,Standardized path coefficient), attitude (b ¼ 0:55), perceived behavior control(b ¼ 0:27), and subjective norm (b ¼ 0:05) and these variables totally explain 69%of the variance on intention to use (R2 ¼ 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 useis higher than Taylor and Todd (1995) with R2 ¼ 0:60, Bhattacherjee (2000) withR2 ¼ 0:52, and Chau and Hu (2001) with R2 ¼ 0:42. Among these relationships,attitude toward the behavior and perceived behavior control are two majorinfluencers on individual’s behavioral intention to use on-line tax. Moreover,attitude indicates more importance than perceived behavior control in determiningbehavioral intention to use on-line tax. The result quite conforms to the findingsreported with business-based setting in prior research. Nevertheless, PU andsubjective norm do not produce significant impacts on behavioral intention to use inthis research.

For the result in PU, previous empirical studies on TAM and extended TAM haveshown inconsistence for either with significant influence (Moore and Benbasat, 1991;Chau, 1996) or with insignificant influence on behavioral intention to use (Chenet al., 2002). Indeed, it, in essence, implies an indirect influence of PU on behavioralintention to use via the mediator, attitude toward using on-line tax. A plausiblereason for this may be explained as below. The on-line tax context in this study isfocused 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 abehavioral intention to use, rather than firstly form a favorable attitude/belief to useon-line tax. The favorable attitude/belief to use on-line tax is just like a time cushionbefore directly taking behavioral intention to use on-line tax. This implies thatpotential users would need to take a period of time to carefully change theirpsychological state to adopting on-line tax. Consequently, the attitude towardadopting on-line tax demonstrates a larger influential power on behavioral intentionto use (b ¼ 0:55).

For subjective norm, the result is similar to the finding reported in Taylor andTodd (1995) and Chau and Hu (2001), but differs from the conclusion inBhattacherjee (2000) for exploring the adoption of e-service with the case ofelectronic brokerage. The latter one indicated that subjective norm could influenceintention to use as strong as attitude does. However, Venkatesh and Davis (2000)gave a more complete report in that subjective norm could significantly determineintention to use in a mandatory-usage context, but its impact would become lesssignificant while users are in a voluntary-usage context as the case of on-line tax inthis study. In particular, while on-line tax in this study is placing at the initialadoption stage, there are lack of enough references from prior adopters such asfriends, peers and superiors (perceived social pressure). From the perspective, on-linetax in this study quite differs from the case of e-service in Bhattacherjee’s study.Accordingly, it is reasonable to expect that the effect of subjective norm on intentionto use on-line tax should indicate insignificance.

ARTICLE IN PRESS


Attitude is predicted by PU (b ¼ 0:34), PEOU (b ¼ 0:21), and trust (b ¼ 0:40)with jointly 59% of the total variance explained. In that, the effect of trust onattitude is greater than PU and PEOU. This implies an important fact for researchersthat traditional TAM may not completely explain the attitude/behavior toward theacceptance of on-line tax. The result also partially validates the conclusion of Trustand TAM model by Gefen et al. (2003a) since the influential relationship is in termsof trust and behavioral intention to use in the Trust and TAM model. In general,trust should be necessarily included in TAM for effectively understanding theacceptance of e-service. Moreover, trust (b ¼ 0:33) explains 27% of the totalvariance in determining perceived behavioral control and is considered as animportant 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 incomputer or external resource constraint such as the Internet infrastructure forcitizens, the trust beliefs will be able to increase citizen’s perceived behavioral controlin performing the behavior.

On the other hand, trust (b ¼ 0:24) significantly influences subjective norm whileexplaining only 8% of the total variance in subjective norm. The reason for this istwo-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 referentssuch as friends, peers, and superiors who concern the initial adoption of the on-linetax. The connection between user’s trust and perceived social pressure to performon-line tax behavior seems to be expectable as the underlying definition in thismodel. Next, the reason for 8% of the total variance explained might be becausethere are a number of potential influencers to subjective norm remaining to beidentified 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 inTPB in the on-line tax setting. This validates the necessity to extend Trust and TAMmodel with TPB in this study in order to have larger explanatory power in the initialadoption of on-line tax (R2 ¼ 0:69 as indicated above).

Finally, trust (b ¼ 0:30) and PEOU (b ¼ 0:35) both significantly influence PU andjointly explain 31% of the total variance in PU. The former is similar to the findingsreported 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 ¼ 0:44), as discussed earlier in the literature,significantly affects trust and explains 19% of the total variance in trust. This resultalso conforms to Trust and TAM model in Gefen et al. (2003a) in on-line shoppingsetting.

6. General discussions

There are many issues influencing user’s decision in the initial adoption of on-lineservice. While considering both the Internet and e-vendor issues in the acceptance ofon-line service, Trust and TAM model, as discussed in Gefen et al. (2003a), is well

ARTICLE IN PRESS


defined for its validity in exploring on-line service setting. Extensively, on-line tax isconsidered as a special type of e-service and the diffusion of this servicemight concern the roles of individual state, organizational members, and socialsystem except for the factors in TAM. An extension of Trust and TAM modelwith TPB aims at increasing the predictive power of behavioral intention to use on-line tax. Empirical data show that trust is considered as an important antecedentof the three determinants of intention to use, attitude with b ¼ 0:40, perceivedbehavioral control with b ¼ 0:33, and subjective norm with b ¼ 0:24, and inturn, jointly contributes a high explanatory power with R2 ¼ 0:69 to behavioralintention to use on-line tax. While compared to other models with trust and TAM inthe literature, this extension with TPB empirically demonstrates substantialimprovement in the explanatory power of behavioral intention to use on-line tax.This result indeed provides more insight for understanding the low usage rate in on-line tax.

Next, although PU (b ¼ 0:34) and PEOU (b ¼ 0:21) in TAM more likelyrepresenting technology-based antecedents both significantly influence attitudetoward the behavior; however, trust (b ¼ 0:40) indicating trust-based antecedentdemonstrates more positive impact on the attitude. The results indicate a fact thatinitial users tend to rely more on trust in non-technology features than on PEOU andusefulness in technology-based features to form their attitude toward the behavior.As a result, they jointly determine 59% of the total variance in the attitude.Moreover, trust (b ¼ 0:30) and PEOU (b ¼ 0:35) both have positive impact on PU.As we knew from previous research, PU always showed it as an importantdeterminant of attitude in TAM and PEOU may often indicate its influence onattitude through the mediator of PU. The reason can be explained by that PEOU hasbeen well recognized as a basic requirement for system design and deductively, itsimpact on attitude toward adopting information technology has increasingly becomeof less importance (Davis et al., 1989; Chau, 1996). This can also be found in thisstudy, b ¼ 0:35 for PEOU linking to PU versus b ¼ 0:21 for PEOU linking toattitude. In addition, this study indicates that trust almost plays an equallyinfluencing role on PU as PEOU.

7. Conclusions

The purpose of this research is to propose an extension of Trust and TAM modelwith TPB in a more comprehensive manner that jointly predicts user acceptance(initial adoption) in on-line tax. A large sample survey from users of on-line tax wasemployed to empirically examine this research model. There are several new findingsregarding the roles of Trust, TAM, and TPB in on-line tax as discussed previously.These findings have important implications for both practitioners and researchers.

For practitioners, although on-line tax is mainly presented for usage by thefeatures of the Internet and communication technologies, however, this studyshows that recognizing both technological and trust-based issues are importantin increasing citizen’s behavioral intention to use this service. The TAM beliefs

ARTICLE IN PRESS


(PU and PEOU) and trust are shown to be two sets of underlying antecedents indetermining behavioral intention to use, each contributing its significant influenceon 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 carefullypay attention to both aspects. Besides, as discussed previously, novice users tend torely more on trust in non-technology features than on PEOU and usefulnessin technology-based features to develop their attitude toward the behavior. Inother words, trust is more important in determining user’s attitude than PEOUand usefulness in on-line tax. The major trust-based concerns may includeprivacy protection, accuracy to declaration, and unauthorized access andso on.

Fundamentally, while trust is empirically identified as an antecedent of PU and inturn, an antecedent of attitude, this has some practical implications in enhancing theattitude 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-linetax. Examples of the mechanisms include statements of guarantees, increasedfamiliarity through advertising, long-term customer service, and offeringincentives to use. After that, PU of on-line tax emerges as an important issue inattracting new users and should be carefully designed in terms of users’ requirementsto reflect PU of this service. Without an original consideration from trust aspect, awell-designed on-line tax with significant PU will not well perform in attractingnovice users.

For researchers, past research on technology acceptance implicitly assumed thatthe success of system use is mainly dependent on technological aspect and does notconsider the notion of uncertainty. However, the advent of the Internet hasintroduced uncertainty and risk in system acceptance and use because people oftenneed to use the Internet to communicate, collaborate, and transact with individualsand organizations without physical face-to-face interaction. Thus, uncertainty isincreasingly becoming the underlying determinant of the Internet-base system usage.Traditionally, TAM mainly focuses on the aspect of system features and thus, isinsufficient in capturing the roles of individuals, organizational members, andsocial system in the Internet-based system usage, in particular, on-line tax. TPBwith the antecedents of attitude, perceived behavioral control, and subjective normwill 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 andfurther, empirically demonstrates relatively satisfactory results for providingmore insight to this problem. This approach may be as a basis for similar researchin the area.

Furthermore, subsequent research can be founded on this work. This study hasfocused on users who are inexperienced or the initial adoption in e-service. However,prior research has suggested that determinants of behavioral intention change interms of users’ level of experience (McKnight et al., 1998; Karahanna et al., 1999).Additional research, both longitudinal and cross-sectional, is needed to examine thedifferences of this framework as users evolving from being aware of the e-vendor, to

ARTICLE IN PRESS


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% oftotal variance explained in subjective norm. Thus, it is possible to identifypotential factors that could influence subjective norm to some extent. Futureresearch could be explored on the matter to better predict subjective norm and inturn, behavioral intention to use. Other possible beliefs have been suggested in themanagement and psychological areas, including loyalty, reliability, and openness(Hosmer, 1995). More research with the alternative conceptualization of trust wouldbe useful in more understanding the role of trust in the initial adoption of on-lineservice.

Finally, although this study has produced some interesting results, it may still havesome limitations. First, approximately 80% of the respondents are male in thisempirical study. Much research has shown that gender difference could causediscrepancies in the effects of attitude, perceived behavioral control, and subjectivenorm on user’s behavioral intention (Venkatesh and Morris, 2000; Armitage et al.,2002). Although gender does not produce statistical significance on systematicnon-response bias in the sample respondents, the empirical findings may be littlebiased for not reflecting the population distribution of gender. Next, there areapproximately 10–15% of taxpayers in adopting on-line tax. Obviously, the on-linetax is still at the early stage of adoption. Definitely, this research is greatly necessaryfor us to gain more insight on further promoting its widespread usage. This imposesa limitation of generalizability to the population. However, the same respondents arerandomly 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 forthe population problem and can be widely generalized for its practical use.

ARTIC

LEIN

PRES

S

Appendix A. Questionnaire

Part 1. Basic information

1. Gender: &Female &Male2. Age: &Less than 20 years old &20–30 years old &30–40 years old

&40–50 years old &Larger than 50 years old3. Education: &High school &College &Graduate school &Doctorate4. Occupation: &Finance &Institution &Information &Service &Manufacturing

&Other5. Experience in using on-line income tax declaration: &One-time user &Continued user

Part 2–4. Constituent constructs in hypothetic research modelScale design for the following questionnaire:

1: Strongly disagree (SD) 2: Moderately disagree 3: Somewhat disagree4: Neutral (N) 5: Somewhat agree 6: Moderately agree7: Strongly agree (SA)

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

I.-L.

Wu

,J

.-L.

Ch

en/

Int.

J.

Hu

ma

n-C

om

pu

terS

tud

ies6

2(

20

05

)7

84

–8

08

802

ARTIC

LEIN

PRES

S

SD N SAPerceived usefulness (adapted from Venkatesh and Davis, 1996, 2000)PU1 Using the OITD would improve my performance in income tax

declaration.1 2 3 4 5 6 7

PU2 Using the OITD would improve my productivity in income taxdeclaration.

1 2 3 4 5 6 7

PU3 Using the OITD would enhance my effectiveness in income taxdeclaration.

1 2 3 4 5 6 7

PU4 I find the OITD to be useful in income tax declaration. 1 2 3 4 5 6 7

Ease of use (adapted from Venkatesh and Davis, 1996, 2000)EOU1 My interaction with the OITD is clear and understandable. 1 2 3 4 5 6 7EOU2 Interaction with the OITD does not require a lot of mental effort. 1 2 3 4 5 6 7EOU3 It is easy to get the OITD to do what I want it to do. 1 2 3 4 5 6 7EOU4 It is easy to use the OITD. 1 2 3 4 5 6 7

Attitude (adapted from Bhattacherjee, 2000)ATT1 Using OITD for income tax declaration would be a good idea. 1 2 3 4 5 6 7ATT2 Using OITD for income tax declaration would be a wise idea. 1 2 3 4 5 6 7ATT3 I like the idea of using OITD for income tax declaration. 1 2 3 4 5 6 7ATT4 Using OITD for income tax declaration would be a pleasant

experience.1 2 3 4 5 6 7

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 3 4 5 6 7

SN2 People who influence me would think that I should use OITD. 1 2 3 4 5 6 7SN3 People whose opinions are valued to me would prefer that I should

use OITD.1 2 3 4 5 6 7

I.-L.

Wu

,J

.-L.

Ch

en/

Int.

J.

Hu

ma

n-C

om

pu

terS

tud

ies6

2(

20

05

)7

84

–8

08

803

ARTIC

LEIN

PRES

S

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 3 4 5 6 7PBC2 Using OITD was entirely within my control. 1 2 3 4 5 6 7PBC3 I had the resources, knowledge, and ability to use OITD. 1 2 3 4 5 6 7

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 3 4 5 6 7INT2 Given that I have access to the OITD, I predict that I would use it. 1 2 3 4 5 6 7INT3 If I have access to the OITD, I want to use it as much as possible. 1 2 3 4 5 6 7

Trust (adapted from Gefen et al., 2003a)TST1 Based on my perception with OITD, I know it is predictable for the

service.1 2 3 4 5 6 7

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

1 2 3 4 5 6 7

TST3 Based on my perception with OITD, I believe it helps or cares citizensin tax declaration.

1 2 3 4 5 6 7

I.-L.

Wu

,J

.-L.

Ch

en/

Int.

J.

Hu

ma

n-C

om

pu

terS

tud

ies6

2(

20

05

)7

84

–8

08

804

ARTIC

LEIN

PRES

S

Appendix B. Covariance matrix

PU1 PU2 PU3 PU4 EOU1 EOU2 EOU3 EOU4 ATT1 ATT2 ATT3 ATT4 SN1 SN2 SN3 PBC1 PBC2 PBC3 INT1 INT2 INT3 TST1 TST2 TST3

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 1.189

SN1 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 0.969

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 0.679 1.046

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 0.659 1.002 1.05

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 0.653 0.977 0.997 1.029

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 0.514 0.621 0.632 0.612 1.764

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 0.547 0.641 0.65 0.634 1.286 1.366

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 0.554 0.645 0.66 0.643 1.243 1.291 1.378

I.-L.

Wu

,J

.-L.

Ch

en/

Int.

J.

Hu

ma

n-C

om

pu

terS

tud

ies6

2(

20

05

)7

84

–8

08

805

ARTICLE IN PRESS


References

Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Process

50, 179–211.

Ajzen, I., 2002. Perceived behavior control, self-efficacy, locus of control, and the theory of planned

behavior. Journal of Applied Social Psychology 32, 1–20.

Armitage, C.J., Norman, P., Conner, M., 2002. Can the theory of planned behavior mediate the effects of age,

gender and multidimensional health locus of control? British Journal of Health Psychology 7, 299–316.

Bandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall,

Englewood Cliffs, NJ.

Bhattacherjee, A., 2000. Acceptance of e-commerce services: the case of electronic brokerages. IEEE

Transactions on System, Man, and Cybernetics—Part A: Systems and Humans 20 (4), 411–420.

Brewer, M.B., Silver, M., 1978. In-group bias as a function of task characteristics. European Journal of

Social Psychology 8, 393–400.

Chau, Y.K., 1996. An empirical assessment of a modified technology acceptance model. Journal of

Management Information Systems 13 (2), 185–204.

Chau, Y.K., Hu, J.H., 2001. Information technology acceptance by individual professionals: a model

comparison approach. Decision Sciences 32 (4), 699–719.

Chen, L.-D., Gillenson, M.L., Sherrell, D.L., 2002. Enticing online consumers: an extended technology

acceptance perspective. Information & Management 39, 705–719.

Coleman, J.S., 1990. Foundation of Social Theory. Harvard University Press, Cambridge, MA.

Davis, F.D., Bagozzi, R.P., Warshaw, P.R., 1989. User acceptance of computer technology: a comparison

of two theoretical models. Management Science 35, 982–1002.

Doney, P.M., Cannon, J.P., Mullen, M.R., 1998. Understanding the influence of national culture on the

development of trust. Academy of Management Review 23 (3), 601–620.

Fishbein, M., Ajzen, I., 1975. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and

Research. Addison-Wesley, Reading, MA.

Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and

measurement errors. Journal of Marketing Research 18, 39–50.

Ganesan, S., 1994. Determinants of long-Term orientation in buyer-seller relationships. Journal of

Marketing 58, 1–19.

Gefen, D., 2004. What makes an ERP implementation relationship worthwhile: linking trust mechanisms

and ERP usefulness. Journal of Management Information Systems 21 (1), 263–288.

Gefen, D., Karahanna, E., Straub, D., 2003a. Trust and TAM in online shopping: an integrated model.

MIS Quarterly 27 (1), 51–90.

Gefen, D., Karahanna, E., Straub, D., 2003b. Inexperience and experience with online stores: the

importance of TAM and Trust. IEEE Transactions on Engineering Management 50 (3), 307–321.

Grabner-Kraeuter, S., 2002. The role of consumers’ trust in online-shopping. Journal of Business Ethics

39, 43–50.

Henry, J.W., Stone, R.W., 1994. A structural equation model of end-user satisfaction with a computer-

based medical information systems. Information Resources Management Journal 7 (3), 21–33.

Hosmer, L.T., 1995. Trust: the connecting link between organizational theory and philosophical ethics.

Academy of Management Review 20 (2), 379–403.

Karahanna, E., Straub, D.W., Chervany, N.L., 1999. Information technology adoption across time: a

cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly 23 (2), 183–213.

Kelley, H.H., 1979. Personal Relationships: Their Structure and Processes. Lawrence Erlbaum Associates,

Mahwah, NJ.

Kelley, H.H., Thibaut, J.W., 1978. Interpersonal Relations: A Theory of Interdependence. Wiley, New York.

Kim, H.-W., Xu, Y., Koh, J., 2004. A comparison of online trust building factors between potential

customers and repeat customers. Journal of the Association for Information Systems 5 (10), 392–420.

Kwon, T.H., Zmud, R.W., 1987. Unifying the fragmented models of information systems implementation.

In: Boland, R.J., Hirschheim, R.A. (Eds.), Critical Issues in Information Systems Research. Wiley,

New York, pp. 227–251.

ARTICLE IN PRESS


Lewicki, R.J., Bunker, B.B., 1995. Trust in relationships: a model of trust development and decline.

In: Bunkers, B.B., Rubin, J.Z. (Eds.), Conflict, Cooperation and Justice. Jossey Bass, San Francisco,

pp. 133–173.

Lewis, J.D., Weigert, A., 1985. Trust as a social reality. Social Forces 63 (4), 967–985.

Luhmann, N., 1979. Trust and Power. Wiley, Chichester, England.

Matsushima, R., Shiomi, K., 2003. Developing a scale of self-efficacy in personal relationships for

adolescents. Psychological Reports 92 (1).

McKnight, D.H., Chervany, N.L., 2002. What trust means in e-commerce customer relationships:

an interdisciplinary conceptual typology. International Journal of Electronic Commerce 6 (2),

35–72.

McKnight, D.H., Cummings, L.L., Chervany, N.L., 1998. Initial trust formation in new organizational

relationships. Academy of Management Review 23 (3), 472–490.

McKnight, D.H., Choudhury, V., Kacmar, C., 2002. Developing and validating trust measures for e-

Commerce: an integrative typology. Information Systems Research 13 (3), 334–359.

Meyerson, D., Weick, K.E., Kramer, R.M., 1996. Swift trust and temporary groups. In: Kramer, R.M.,

Tyler, T.R. (Eds.), Trust in Organizations: Frontiers of Theory and Research. Sage Publications,

Thousand Oaks, pp. 166–195.

Moore, G.C., Benbasat, I., 1991. Development of an instrument to measure the perception of adopting an

information technology innovation. Information System Research 2 (3), 192–222.

Nelson, K.M., Cooprider, J.G., 1996. The contribution of shared knowledge to IS group performance.

MIS Quarterly 20 (4), 409–432.

Pavlou, P.A., 2002. What drives electronic commerce? A theory of planned behavior perspective. Best

Paper Proceedings of the Academy of Management Conference, Denver, CO, pp. 9–14.

Pavlou, P.A., 2003. Consumer acceptance of electronic commerce—integrating trust and risk with the

technology acceptance model. International Journal of Electronic Commerce 7 (3), 69–103.

Pavlou, P.A., Gefen, D., 2004. Building effective online marketplaces with institution-based trust.

Information Systems Research 15 (1), 37–59.

Rogers, E.M., 1995. The Diffusion of Innovation, fourth ed. Free Press, New York.

Saeed, K.A., Hwang, Y., Yi, M.Y., 2003. Toward an integrative framework for online consumer behavior

research: a meta-analysis approach. Journal of End User Computing 15 (4), 1–26.

Shapiro, S.P., 1987. The social control of impersonal trust. American Journal of Sociology 93, 623–658.

Taylor, S., Todd, P.A., 1995. Understanding information technology usage: a test of competing models.

Information System Research 6 (2), 144–176.

Turban, E., King, D., Lee, J., Warkentin, M., Chung, H.M., 2002. Electronic Commerce: A Managerial

Perspective. Prentice-Hall, Upper Saddle River, NJ.

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., Davis, F.D., 2000. A theoretical extension of the technology acceptance model: four

longitudinal field studies. Management Science 46 (2), 186–204.

Venkatesh, V., Morris, M.G., 2000. A longitudinal field investigation of gender difference in individual

technology adoption decision-making process. Organizational Behavior and Human Decision Process

83 (1), 33–60.

Wrightsman, L.S., 1972. Interpersonal trust and attitudes toward human nature. In: Zand, D.E. (Ed.),

Trust and Managerial Problem Solving. Administrative Science Quarterly 17, 229–239.

Zucker, L.G., 1986. Production of trust: institutional source of economic structure, 1840–1920. In: Staw,

B.M., Cummings, L.L. (Eds.), Research in Organizational Behavior. JAI Press, Greenwich, CT,

pp. 53–111.

Further reading

Teo, T.S.H., 2002. Attitudes toward online shopping and the Internet. Behaviour & Information

Technology 21 (4), 259–271.

ARTICLE IN PRESS


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.

an extension of trust and tam model with tpb in the initial adoption of on-line tax: an empirical...

Documents