the influences of system usability and user satisfaction on continued internet banking services...

Electron Commer ResDOI 10.1007/s10660-014-9136-5

The influences of system usability and user satisfactionon continued Internet banking services usage intention:empirical evidence from Taiwan

Huei-Ting Tsai · Jui-Lin Chien · Ming-Tien Tsai

© Springer Science+Business Media New York 2014

Abstract This study proposes an extended technology acceptance model to inves-tigate the effects of system usability and satisfaction on users’ intention to continueusing Internet banking services. Based on a survey data from 304 respondents, struc-tural equation modeling technique was employed to validate the model. The empiricalresults found that users’ continuance usage intention is jointly determined by per-ceived usefulness, perceived compatibility and satisfaction level. The hypothesizedmodel explains 48.2 % of the variance in continuous usage intention. Results of multi-group analysis reveal that there are different concerns and priorities between skilledand less skilled users. Given that the sample of this study is collected from a particularindustry in Taiwan, the generalizability of the findings may be limited. However, thecomprehensiveness and representativeness of the research sample is a major strengthof this study.

Keywords Internet banking services · Technology acceptance model ·System usability · User satisfaction · Continuous usage intention · Taiwan

H.-T. Tsai · J.-L. Chien (B)Department of Business Administration and Institute of International Business,National Cheng Kung University, No. 1 University Road, Tainan City 70101, Taiwan ROCe-mail: [email protected]

H.-T. Tsaie-mail: [email protected]

M.-T. TsaiBusiness School of Wuyi University,No. 16, Wuyi Avenue, Wuyishan City, Fujian Province,People’s Republic of Chinae-mail: [email protected]

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1 Introduction

The emergence of the Web has effected a radical change in the way of which anindividual lives. Owing to the ubiquity of the Internet and the rapid advancement ofWeb-based technologies, Web-based e-finance service has created a whole new wayfor financial institutions in delivering financial products/services to their authorizedcustomers in many developed and developing countries [57]. The pervasiveness ofthe Internet enables individuals to perform various financial activities regardless thespatial and temporal limitations through Internet banking services (IBSs) system [6].Furthermore, the establishment of IBSs system also enables banking practitioners torelieve the pressure on the increasing of the branch offices and thus significantly loweroperating costs.

Cognitive dissonance theory (CDT) [27] suggests that there are different concernsand priorities between initial adopters and continued users of a product/service. Com-pared with continued use, initial adoption and acceptance-discontinuance anomaly(i.e. users discontinue purchasing/using a given product/service after having initiallyaccepted it) often contributes to an inefficient and ineffective use of resources forsubscription-based e-commerce systems [65]. It can be suggested, therefore, that thecontinuous users (or repeat buyers) would more likely to generate profits for the com-pany than initial adopters.

Usability is a more inclusive concept than functionality and characteristic in termsof denotation and connotation [83]. System usability can be referred to the extent towhich a particular system can be utilized with efficiency and effectiveness in varietyof environmental conditions [14]. Nielsen [58] asserts that system usability can beconceptualized as system’s effectiveness and efficiency which are closely similar tousefulness and ease to use. Hong et al. [41] also suggest that the concept of effectivenessand simplicity are similar to the definition of usefulness and ease to use.

Parthasarathy and Bhattacherjee [65] proposed usefulness and compatibility as twoimportant service attributes and found evidence of the significant effects of these twoattributes on subscribers’ initial adoption and post-adoption behaviors. Tornatzky andKlein [81] also claim that relative advantage (PU), complexity (PEOU), and perceivedcompatibility are found to be consistently associated with IT/IS adoption and use.Based on the TAM model and referred to the arguments of Parthasarathy and Bhat-tacherjee [65] and Tornatzky and Klein [81], perceived system usability is being spec-ified to include three manifest variables, namely perceived usefulness (PU), perceivedease of use (PEOU), and perceived compatibility. Referring to the definition given byDavis [21], we define PU as the extent to which a person perceives that implementingvarious financial transactions via IBSs system would enhance his/her job and/or lifeperformance, while PEOU as the extent to which a person perceives that interactingwith the IBSs system would be free of physical and mental effort. Further, perceivedcompatibility is defined as the extent to which a person perceives the use of IBSs asbeing consistent with his/her lifestyle, existing values, current needs, and prior expe-riences. The perceived innovation attributes of observability and trialability are notattempted to be addressed here, primarily because previous studies have confirmedempirically that the correlations between these two attributes and user’s continuedIT/IS usage intention is consistently non-significant or only marginally significant [69].

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The influences of system usability and user satisfaction

IBSs practitioners profoundly recognized the importance of customers’ continuousloyalty and usage in sustaining firms’ competitive advantage and achieving superiorfinancial performance [6]. IBSs practitioners have allocated considerable resourcesto building a highly loyal customer base [53] and thereby seek to secure a stableprofit stream over the past years. However, it does frequently not achieve desiredeffect. Arguably, this can be explained by the fact that there may sometimes have beennegative discrepancies between users’ pre-adoption and post-adoption experiencesof the services [10]. Whilst intensive studies have been conducted concerning thequest of the initial adoption of IBSs (e.g. [37,47,75]), only few is known so far aboutthe continued IBSs usage, especially in Taiwan is scarce. This study intends to fill theresearch gap by proposing a “continuance” model with an attempt to clarify the role andincidence of users’ post-adoption perceptions of system usability and post-adoptionsatisfaction in continuance intention to use IBSs.

The primarily objective of this study is to identify the factors influencing user’scontinued IBSs usage intention. A series of statistical analyses were carried out toanswer the following research questions: (1) what are the salient factors influencingusers’ intention to continue using IBSs after initially accepting the services? (2) howdo the factors influence users’ intention to continue use of the IBSs? (3) whether theanswers of above two research questions are varying between different user groupswith different level of experience and frequency of using IBSs? The statistical resultssupport eight of the nine specific hypotheses. The results of multi-group analysis revealthat there are some variations between different user groups which are distinguishedby usage experience and frequency of the IBSs.

The remainder of the paper is organized as follows. The research background andtheoretical foundation of this paper are outlined in Sect. 2. Section 3 presents theresearch model upon which the specific hypotheses to be examined are developed. Adetailed description toward the proposed causal relationships among the constructs ofinterest is also presented in this section. Section 4 describes the research methodologyused for the present study. Section 5 begins with the establishment of the reliability andvalidity of the constructs and their corresponding measures. In the following of thissection, we report the results of the hypotheses testing and draw out the implicationsderived from the findings. The empirical evidence of the multi-group analysis is dis-played in the last part of this section. In the Sect. 6, we specify some limitations of thisstudy and suggest some interesting avenues for further research. Section 7 concludeswith a discussion of the important contributions of this study.

2 Research background and theoretical basis

The expectation confirmation theory (ECT) [63] suggests that consumers’ satisfac-tion with a given service is determined by the disconfirmation between customers’pre-adoption expectations and their post-adoption perception of the service perfor-mances. Individuals’ intention to continue using a technology is influenced by theirpost-adoption perceptions of technology performance [45]. Accordingly, it can beconjectured that users’ would more likely to continue using a service if their pre-adoption expectations of service performance has been confirmed or surpassed. Cur-

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ran and Meuter [20] propose that further research on self-service technologies (SSTs)should be shifted from pre-adoption expectations to post-adoption perceptions. There-fore being motivated by above-mentioned trends, identify the effects of customers’post-adoption perceptions of system usability and post-adoption satisfaction on theirintention to continue using the IBSs is the research issue that we seek to addressin this study. Users’ post-adoption perception about the system usability is a vitalfactor for the system success [26]. Post-adoption satisfaction is based on one’s cogni-tive evaluation-disconfirmation of the performances of a product/service [78]. Conse-quently, the present research model can be regarded as an appropriate model to predictusers’ continuance intention of using IBSs.

As background to this study, we first provide a brief review of the theoreticalfoundation and relevant literatures, which acts as the basis for the conceptualizationof the research model and for the operationalization of variables. This study definesthe IBSs system as an online transaction platform that empowers authorized usersto perform various financial activities via wireless Internet-enabled devices withoutany help from bank’s staffs. Continuous usage intention is defined as an individual’ssubjective evaluation on the likelihood of continuing use of the IBSs in the future,following a period of use.

2.1 The differences between initial adoption and continued usage

According to Bhattacherjee’s [10] suggestions, there exists at least two significantdifferences between initial adoption and continued use for a subscription-based IT/IS.Firstly, users’ initial adoption is a crucial first step towards the realization of thesubscription-based IT/IS success, while its eventual success is determined primar-ily by users’ continued use. Secondly, customers’ infrequent usage and discontinuedusage often leads to the failure of subscription-based IT/IS. The importance of contin-ued usage, as compared with initial adoption, is evident from the fact that the cost foracquiring a new customer may cost as much as five times more than that for retain-ing or expanding the services to an existing customer [10,65]. From a relationshipmarketing management perspective, encouraging customers’ continued use is a nec-essary strategic initiative for developing and maintaining firm’s market assets [10,25].Customers’ continued use is considered to be a better measurement for evaluating theimplementation of a certain technology [90].

2.2 Technology acceptance model

Intention-based models, such as theory of reasoned action (TRA) [28] and theory ofplanned behavior (TPB) [3], and TAM, provide a valuable theoretical underpinningfor behavioral research. Nevertheless, both TRA and TPB is a general model andhence can not used to delineate precisely individuals’ IT usage intention and behavior[23]. The TAM was adapted from the TRA which stipulated that an individual’s ITusage behavior is influenced by his/her intentions to use, which in turn, are governedby his/her attitude toward IT usage. The attitude toward IT usage is expected to be

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determined jointly by two beliefs dealing with (1) the PU of using the IT and (2) thePEOU of the IT. Besides, PEOU is assumed to be the predictor of PU.

Essentially, adoption intention and continuance intention can be treated as equiv-alent constructs but measured at different time points, after the user has sufficientexperience to form his/her cognitive beliefs and attitudes [40]. Previous studies haveimplicitly assumed that continued usage is an extension of adoption and used TAMto explain post-adoption phenomena [10], although it was not included in the originalTAM model. Legris et al. [49] claim that the predictive power of TAM may be improvedsignificantly by incorporating other situation or technology-specific constructs.

We choose TAM model as the theoretical basis for this study was motivated by thefollowing two reasons: (1) TAM is generally conceived as one of the most parsimo-nious, but robust, theoretical models and has been applied extensively to investigatecontinued e-services usage, such as e-Government website [84], e-learning [74], andmobile banking [73]. (2) The general applicability of TAM has been consistentlyvalidated in different time spans, application technologies, and use contexts [40,82]through the integration of various domain-specific constructs.

TAM is well suited for measuring general levels of satisfaction across diverse userpopulations, contexts, and technologies since it provides a quick and inexpensiveapproach to gather general information about an individual’s PU and PEOU of usinga specific system [54]. Nevertheless, the original TAM model still has some defects,although this model has been validated by previous studies. For instance, firstly, TAMmodel has limitation to capture accurately IT/IS usage behaviors and to explain fullythe consequences of IT/IS usage, principally caused by its over-simplistic view ontechnology usability [49]. Secondly, the TAM model can only account for a fractionof variance of system usage without other external variables integrated into the model[49]. Thirdly, there is no absolute measure for PU and PEOU exist across varyingtechnological and organizational contexts.

3 Research model and hypotheses

For the research purpose, this study proposes a “continuance” model of IBSs basedon TAM model. Referring to the arguments of Parthasarathy and Bhattacherjee [65]and Tornatzky and Klein [81], this study conceptualizes PU, PEOU, and perceivedcompatibility as three components of the system usability of IBSs in order to enhancethe predictive ability of the present model. Moreover, the proposed model also incor-porates a quasi-attitudinal construct, use satisfaction, to obtain a more holistic pictureof the continuance intention of using IBSs. Figure 1 depicts graphically the researchmodel and the hypothesized causal relationships among the constructs under study.

3.1 The hypothesized effect of user satisfaction on continuous usage intention

Hong et al. [40] suggest that managing users’ satisfaction levels is critical to encouragecontinued IT products/services usage. Researchers have found that there is a positiveassociation between users’ post-adoption satisfaction and continuous usage intention(e.g. [25,73]). Research in the American Customer Satisfaction Index (ACSI) also pro-

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Continuous Usage

Intention

PerceivedEase of Use

PerceivedUsefulness

PerceivedCompatibility

UserSatisfaction

H1(+)

H5(+)

H6(+)H7(+)

H2(+)

H3(+)

H4(+)

H8(+)

H9(+)

Perceived System Usability

Fig. 1 Research model and hypotheses

vides empirical evidence validating the positive correlation between customer satis-faction and customer loyalty [31]. Based on these arguments, the following hypothesisis postulated:

H1 User satisfaction will positively affect continuous usage intention of IBSs.

3.2 The hypothesized effects of perceived system usability on continuous usageintention

PU has been found to be consistently correlated with user usage intention and/or behav-ior during both initial adoption and post-adoption phase [21,65,82]. Customers’ loy-alty intention toward a technology is dependent as much upon one’s personal cognitiveappraisal about whether using this technology can enhance his/her task performance[23]. Customers will likely to show their loyalty to a certain technology if they per-ceived this technology as being beneficial in accomplishing their specific tasks at hand[8].

A customer will evaluate the costs and efforts involved for using an IT, even thoughhe/she is satisfied with the IT and intends to become a full adopter. The salient effectof the PEOU on continuous usage intention has been supported by several empiricalstudies (e.g. [84,86]). In an empirical investigation on mobile Internet continuanceintention, Hong et al. [40] also demonstrate the significant effect of PEOU on users’continued usage intention.

To obtain a comprehensive picture of customers’ continuance intention of using e-services, the effect of perceived compatibility should be taken into account cautiously[47]. In a research that conducted by Liao and Lu [51], shows that perceived compat-ibility is a major determinant of continued e-learning websites usage intention. The

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greater compatibility of the IBSs with users’ values system, prior experiences, andbanking needs, the more likely the users will actually use the services [79]. Based onthe suggestions mentioned above, it may reasonably assumed that the more compat-ible the use of IBSs is with users’ lifestyle, existing values, current needs, and priorexperiences, the greater the possibility of the services will be used continuously bythem. The preceding discussions lead to the following hypotheses:

H2 PU will positively affect continuous usage intention of IBSs.

H3 PEOU will positively affect continuous usage intention of IBSs.

H4 Perceived compatibility will positively affect continuous usage

3.3 The hypothesized effects of PU and PEOU on user satisfaction

Customers will be satisfied with a specific mobile service if they perceive the servicequality and performance are fit with their expectations [89]. As defined originally inthe TAM model, PU is considered as a post-adoption perception and has been provedto be associated positively with users’ satisfaction level [10]. Customers are morelikely to have higher satisfaction level with an e-service when the service is perceivedto be useful to them [67].

In the TAM model, PEOU is considered to be an important cognitive belief and hasa positive impact on individual’s attitude toward IT usage. Given that the satisfactionis recognized broadly as a quasi-attitudinal construct and often considered fully as anattitude [32], we can thus anticipate that the PEOU has a positive influence on usersatisfaction. Lee et al. [48] conducted a comparison study among Taiwan, Korea, andHong Kong, and reported that PEOU was consistently a significant determinant ofuser satisfaction with mobile Internet services. Based on the foregoing observationsand arguments, the following hypotheses are proposed:

H5 PU will positively affect user satisfaction with the IBSs.

H6 PEOU will positively affect user satisfaction with the IBSs.

3.4 The proposed interaction relationships among the components of systemusability

Thong et al. [80] contend that a system that is easy to use will facilitate the system’sown ability in achieving users’ desired performance. As theorized in the TAM model, asystem will perceived to be useful if it is perceived as easy to learn and use. Conversely,customers will perceive a system as being only marginally useful to them if the systemis perceived as difficult to understand and use [24]. Several previous investigations havedemonstrated the significant effect of PEOU on PU (e.g. [40,46,88]).

The theoretical linkage from perceived compatibility to PU and PEOU has beenwidely explored by researchers in the IT/IS field (e.g. [1,62]), even though the per-ceived compatibility often being viewed as an independent antecedent of attitude. An

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innovation will be perceived to be useful if it is consistent with users’ existing values,needs, and past experiences [19]. Based upon the arguments above, we presume thatindividuals will perceive IBSs as useful in managing their personal banking affairs ifthe services are perceived to be compatible with their lifestyle, existing values, currentneeds, and prior experiences.

An innovation will be perceived as easy to use if it is perceived to be compatiblewith users’ existing knowledge and experience [19]. An individual will perceive thatusing a technology is effortless if it is perceived to be compatible with his/her previousexperience on similar or related technologies [44]. Thus, we can infer that customerswill perceive the IBSs as easy to use if the services are perceived to be compatiblewith their own existing knowledge and previous experience on other e-finance services.Based on the above arguments, the following hypotheses are posited:

H7 PEOU will have a positive effect on PU of the IBSs.

H8 Perceived compatibility will positively affect PU of the IBSs.

H9 Perceived compatibility will positively affect PEOU of the IBSs.

4 Research methodology

The intent of this research is twofold: (1) to examine the hypothesized associationsamong selected variables, and (2) to further examine the same hypothesis set throughmulti-group analysis to examine whether there exists differences between differentuser groups. Structural equation modeling (SEM) was used as the statistical techniquefor data analysis. The analysis unit of this research is current IBSs individual users.The instrument development process, sampling procedures, and the scale validationprocess will be elaborated next.

4.1 Instrument development

A self-administered questionnaire was developed based on the operational definitionsof the constructs of interest. By reviewing academic literature on relevant topic, wedevelop a comprehensive set of measures to capture properly the essence of the researchquestions addressed in this study. All constructs were measured using pre-validatedmultiple-item scales (e.g. [56,64,75,77]), using a five-point Likert-type scale for eachitem, anchored between “completely disagree” (coded as 1) and “completely agree”(coded as 5), with the midpoint “neither disagree nor agree” (coded as 3). Because thesubjects of this study were confined to the native Taiwanese population, the question-naire hence was written in traditional Chinese.

4.2 Pre-test and pilot test

The draft questionnaire was firstly validated by a panel of researchers and industryexperts. Then, a pre-test with 15 current IBSs individual users was performed to ensure

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the instrument’s clarity, readability and validity. Subsequently, the revised question-naire was pilot tested with 115 current IBSs individual users to establish its validity,reliability, and applicability before distribution. The pilot test results indicated thatthe revised questionnaire was appropriate for the data collection requirements of thisstudy.

The final version of questionnaire consists of 17 fixed-alternative items and eachconstruct was measured by at least three items. Following the recommendations ofDavis and Venkatesh [22], items were grouped under their respective construct inthe questionnaire for ensuring that the survey participants follow the logical flowof ideas between the items. An identification code was printed on the front page ofeach questionnaire in order to identify the survey source. The English version of thequestionnaire is presented in Appendix 1.

4.3 Sampling and data collection

A purposive convenience sampling approach was performed in attempting to ensurethat the sample characteristics are approximately the same amongst the populationcharacteristics. In addition, the geographical area (i.e. eastern, western, southern,northern, and midland Taiwan) and the geographical location (i.e. city, suburb, andrural area) of the sample have also been taken into account for enhancing the samplerepresentativeness and the generalizability of the findings. In this research, the par-ticipants were selected randomly from five local branches which are subordinate todifferent financial holdings (FHs) in Taiwan.

A survey packet containing 250 cover letters, survey questionnaires, self-addressedpre-paid reply envelopes, and reminder postcards, was mailed to each sampledbranches. During the survey administration period, the Public Administration Division(PAD) of each sampled branch was authorized to deal with all of the survey administra-tion activities involved, in order to protect the participants’ privacy and confidentiality.The questionnaire was sent attached to the participants’ regular monthly statements.One month after the initial questionnaire was mailed, a thankyou/reminder postcardwas mailed to the participants to remind those who had not yet returned the question-naire to do so immediately, and to acknowledge those who had already taken part.

The survey was carried out during June and August 2011. Out of 1,250 distributedquestionnaires, 304 usable questionnaires were returned, yielding an effective responserate of 24.32 %. Over 56 % (n = 171) of the effective respondents were male and three-fourths (n = 228) ranged in age from 20 to 40 years, with a mean of 36.24 years. Thedistribution of the respondents’ gender and age reflected the characteristics of the IBSsusers population in Taiwan. In addition, nearly 60 % of the respondents (n = 175) hadmore than two years experience with the IBSs, with a mean of 2.82 years, and morethan 70 % (n = 215) had usage frequency of at least twice a month, indicating that allrespondents have had sufficient experience and ability to appropriately express theirpost-adoption perceptions of IBSs.

In postal survey research, non-response is a potential source of selection bias thatwould jeopardize the internal validity of the study. To evaluate the non-response bias,a comparison of early and late respondents on sample source and respondents’ gender

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Table 1 Descriptive statistics

Construct Mean Std. residual Std. deviation Skewness Kurtosis

CUI 3.831 0.062 1.077 −1.102 0.504

USAT 3.564 0.049 0.861 −0.849 0.388

PEOU 3.657 0.051 0.884 −0.884 0.398

PU 3.655 0.047 0.819 −0.963 0.443

PCO 3.704 0.043 0.742 −1.194 1.603

CUI continuous usage intention, USAT user satisfaction, PU perceived usefulness, PEOU perceived easeof use, PCO perceived compatibility

was undertaken by using t tests, as recommended by Armstrong and Overton [7].This comparative analysis is imperative in increasing the convergence of the samplerepresentativeness although the threat of non-response bias could not be ruled outcompletely.

Those participants responded within a month of receiving the survey are termed asearly respondents (n1= 199) while those who responded after receiving a follow-upreminder postcard are termed as late respondents (n2= 105). The results of t testsshow that there are no significant differences in sample source and respondents’ gen-der between early and late respondents (Wilk’s λ = 0.987; p = 0.148). It is reasonable,therefore, to conclude that non-response bias is not present [7]. A one-way analysisof variance (ANOVA) [59] was performed to estimate whether the mean responsevalue of continuous usage intention was significantly different across different sam-pled branches. Analytical results revealed that continuous usage intention was notsignificantly associated with sample source (F = 1.011; p = 0.402), thereby indicatingthe generalizability of the findings across different IBSs systems.

5 Data analysis and results

In this section, we begin with the descriptive statistics for the data which contributeto realize the characteristics of the data collected. The descriptive statistics of eachconstruct are shown in Table 1. The mean score of each construct is above the midpointvalue of three on the five-point scale, signifying that the response portrayed a definitelypositive perception of the construct in question. Andreassen et al. [5] point out thatthe standard error of the path coefficient between latent variables for the test statisticsmay be distorted if the assumption of multivariate normal distribution does not exist.As reported in Table 1, values of both skewness and kurtosis are well within theacceptable threshold of ±2 [11] and the standardized residual of mean value and itsstandard deviation was closer to zero and one respectively, indicating normality of thedata distribution [36].

5.1 Measurement validation

In order to determine the appropriateness of explanatory factor analysis (EFA), theKaiser–Meyer–Olkin (KMO) measure of sampling adequacy (MSA) and the Bartlett’s

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test of sphericity were performed. The EFA results show a KMO value of 0.907, whichis considerably greater than the critical value of 0.50, and a Bartlett’s Test of Sphericityvalue of 3299.240 (p < 0.001), indicating suitable for factor analysis of the datacollected [36].

After the empirical data was collected, EFA with principal components analysis withvarimax rotation was conducted to purify the measurement items for each constructand to evaluate the instrument’s reliability and validity. As displayed in Appendix 2,the EFA produced a five-factor solution consisting of 17 items with eigenvalue greaterthan one, explained 77.283 % of the cumulative variance. The explained variance ofeach of the five extracted factors ranged from 13.099 to 18.240 %. Besides, all the itemsloaded highly on their designated factors (0.745 or better), with minimal cross-loadings(only one item loaded over 0.30 on another factor), confirming the unidimensionalityof the constructs and providing strong evidence to support the instrument’s reliabilityand validity [85].

Most researchers have recognized that common method variance (CMV) is a majorvalidity threat to research findings in behavioral research, when data were obtainedfrom a single-informant [71]. Since the measures for all variables are part of a self-report questionnaire, a Harman’s one-factor test proposed by Podsakoff and Organ[70] was carried out on the data and results shown that there was no single generalfactor that best represented the data, providing some evidence that CMV is not aserious issue in our study [70]. Further, as exhibited in Appendix 2, the Cronbach’salpha value of each factor was found to be ranging from 0.812 to 0.935, suggesting ahigh internal consistency [61].

5.2 Measurement model assessment

A two-stage analytical procedure was performed on the data for ensuring the findingsare derived from a well-constructed instrument possessing sound psychometric prop-erties [4,36]. Stage one involved the assessment of both convergent and discriminantvalidity for the measurement model. In stage two, the structural model was analyzed toinvestigate the hypothesized associations among the constructs and their significance.The data were analyzed by SEM technique using LISREL 8.52 [43] with maximumlikelihood estimation (MLE) approach. For SEM analysis, Chin [16] states that thecorrelation matrix is best suited to and has been widely used to analyze the data whichwere collected from single population samples. The correlation matrix, as presentedin Appendix 3, was chosen as the input matrix because the data used for this studywere collected from a particular industry and targeted a specific user group in Taiwan.

The fit of the measurement model must be taken into consideration before thestructural model was tested [55]. As reported in Table 2, all the fit indices of con-firmation factor analysis (CFA) model do not violate their corresponding thresholdvalues, demonstrating evidently the measurement model has an excellent fit with thedata. Specifically, the p value was not statistically significant at α = 0.05, certifyingthe consistency of the proposed model with real data. Accordingly, we can concludeconfidently that the present data can be explained properly by the proposed model [43].

Construct validity is an important criterion in evaluating the instrument’s validity.As indicated in Table 3, the completely standardized factor loadings all exceeded the

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Table 2 Measurement model fitindices of CFA

Model fit indices Recommendedvalue [References]

Goodness-of-fitstatistics

χ2/df ≤3.00 [17] 116.129/109 =1.065

P value �0.05 [36] 0.302

Goodness-of-fit index (GFI) >0.90 [33] 0.957

adjusted goodness-of-fitindex (AGFI)

>0.80 [33] 0.939

Normed fit index (NFI) >0.90 [9] 0.984

Non-normed fit index (NNFI) >0.90 [38] 0.998

Non-normed fit index (CFI) >0.90 [9] 0.999

Root mean square error ofapproximation (RMSEA)

<0.06 [42] 0.0147

Table 3 Psychometricproperties of measures

t values are all significant atp < 0.001 level; the factorloadings of all measurementitems are completelystandardized solutionFL factor loadings

Items FL (>0.70) t value

Perceived ease of use

PEOU1 0.867 17.623

PEOU2 0.799 15.747

PEOU3 0.755 14.575

Perceived usefulness

PU1 0.743 13.900

PU2 0.782 14.852

PU3 0.782 14.853

Continuance usage intention

CUI1 0.900 19.932

CUI2 0.926 20.900

CUI3 0.901 19.959

User satisfaction

USAT1 0.884 19.088

USAT2 0.852 18.007

USAT3 0.792 16.128

USAT4 0.829 17.246

Perceived compatibility

PCO1 0.828 16.804

PCO2 0.852 17.522

PCO3 0.732 14.115

PCO4 0.708 13.483

benchmark value of 0.70 [61] and reached significant level (p < 0.001), suggestingthat all indicators are significantly correlated with their respective constructs, hencethe construct validity of the instrument was assured [16].

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Table 4 Measurement model fit indices for convergent validity and construct correlation matrix

Items Item reliability(SMCs)(>0.5)

CR (>0.7) AVE (>0.5) CUI USAT PEOU PU PCO

CUI1 0.810 0.935 0.826 0.909

CUI 0.858

CUI2 0.812

USAT1 0.781 0.905 0.706 0.551 0.840

USAT2 0.726

USAT3 0.628

USAT4 0.686

PEOU1 0.751 0.849 0.653 0.504 0.577 0.808

PEOU2 0.639

PEOU3 0.570

PU1 0.552 0.813 0.592 0.603 0.583 0.478 0.769

PU2 0.611

PU3 0.612

PCO1 0.685 0.863 0.612 0.529 0.508 0.522 0.431 0.782

PCO2 0.725

PCO3 0.536

PCO4 0.501

The leading diagonal elements display the square root of AVE for each construct; the off-diagonal elementsshow the squared correlation between construct pairs; all correlations are significant at p < 0.001 levelCR composite reliability, AVE average variance extracted, SMCs squared multiple correlations equivalentto R2 in regression

The acceptability of the measurement model can also be confirmed by assessingthe model’s convergent and discriminant validity. The convergent validity of the mea-surement scales was examined by checking the item reliability, composite reliability(CR), and average variance extracted (AVE) [15]. As shown in the second columnof Table 4, the item reliability ranged from 0.501 and 0.858, indicating acceptableitem reliability [36]. The CR was computed to establish the internal consistency ofthe constructs under study. As illustrated in the third column of Table 4, the value ofthe CR coefficients ranged from 0.813 to 0.935, the CR was established [60] and theinternal consistency of the constructs was thus achieved. The AVE reflects the over-all amount of variance that is attributed to the construct in relation to the amount ofvariance attributable to measurement error [30]. The higher the AVE value, the morerepresentative the items are of the underlying construct. As displayed in the fourthcolumn of Table 4, the AVE value for respective construct ranging between 0.592 and0.826, the AVE was acceptable [36]. The results above provide strong evidence forsupporting the convergent validity of the measures.

Discriminant validity can be used to ascertain whether the constructs are clearly dis-tinct from one another. Discriminant validity was assessed by comparing the squaredroot of the AVE of individual construct with the squared correlation between con-struct pairs [30]. As the right-hand side of Table 4 shows, the squared root of the AVE

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of individual construct is significantly greater the bivariate correlations between theconstruct and all other constructs. Hence, the discriminant validity was confirmed.

Discriminant validity can also be demonstrated through a series of χ2 differencetests between the original CFA model and each constrained model [4]. In the con-strained model, the covariance between each pair of constructs is fixed at one andwhere all constructs in the CFA model are allowed to co-vary freely with constrainedmodels [12]. As shown in Appendix 4, the χ2 differences ranged from 193.357 to546.055. All these χ2 differences are higher than the critical value of χ2 with onedegree of freedom (i.e. χ2 (1, 0.001) = 10.827), further supporting the discriminantvalidity of the constructs [33].

Multicollinearity may reduce the reliability of the coefficient estimates of magnitudeand direction [35]. Accordingly, it is necessary to evaluate the impact of multicollinear-ity on the estimation of the regression coefficients and their associated statistics byassessing the variance inflation factor (VIF) values. Multicollinearity was consideredsevere when VIF value was higher than the threshold level of 10 [36]. In this study,calculations of VIF ranged from a low of 1.201 to a high of 1.663, implying thatthe multicollinearity issue does not arise and the “independence” of the independentvariables can be ensured.

5.3 Structural model assessment and hypotheses testing

Given that the measurement model provides an excellent fit to the data, an “assessmentof nomological validity” will be provided by the assessment of the structural model [4].The standardized results of the structural model assessment are illustrated graphicallyin Fig. 2. As illustrated, eight out of the hypothesized relationships are supported inthe estimated structural model, except for Hypothesis 3: PEOU → continuous usageintention. In addition, the structural model was also found to have an excellent overallfit to the empirical data, as given in the upper right corner of Fig. 2.

Hypothesis 1 aims to test the effect of user satisfaction on continuous usage inten-tion. Results show that user satisfaction has a significant but small positive effect oncontinuous usage intention (β = 0.157, p < 0.05). Thus, Hypothesis 1 was accepted.Hypothesis 2–4 concentrated on the connections between user’s perceptions of systemusability (PU, PEOU, and perceived compatibility) and continuous usage intention. Itis observed that PU (β = 0.355, p < 0.001) and perceived compatibility (γ = 0.226,p < 0.001) both have significant positive effects on continuous usage intention. How-ever, contrary our expectation, the effect of PEOU on continuance usage intention wasnot significant (β = 0.127, p > 0.05). Over 48 % of the variance in continuous usageintention was explained collectively by user satisfaction, PU, and perceived compat-ibility. Hypothesis 2 and 4 were confirmed, but Hypothesis 3 was not. Hypothesis 5and 6 are designed to address the effects of PU and PEOU on user satisfaction. Resultsshow that both PU (β = 0.402, p < 0.001) and PEOU (β = 0.395, p < 0.001) have asignificant and nearly equal impact on user satisfaction and 47.1 % of the variance wasexplained, therefore supporting Hypothesis 5 and 6. Hypothesis 7 aimed to investi-gate whether there is a significant correlation between PU and PEOU. As anticipated,PEOU showed a positive relationship towards PU ( β = 0.333, p < 0.001). Hypothesis

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Continuous Usage

Intention

PerceivedUsefulness

UserSatisfaction



H20.355***

(t = 4.756)

H10.157*

(t = 2.188)

H50.402***

(t = 5.936)

H70.333***

(t = 4.210)

H60.395***

(t = 6.152)

H30.127n.s.

(t = 1.750)

H80.271***

(t = 3.515)

H90.535***

(t = 8.419)

H40.226***

(t = 3.532)

R2 = 0.471 R2 = 0.482

R2 = 0.282

R2 = 0.286

* p < 0.05; *** p < 0.001; n.s. p > 0.05.

2/df =122.738/110 = 1.226; P value = 0.192; GFI = 0.955; AGFI = 0.937; NFI = 0.983; NNFI = 0.997; CFI = 0.998; RMSEA = 0.0195

Fig. 2 Standardized LISREL solution

7 was therefore substantiated. Finally, in Hypothesis 8 and 9, the effects of perceivedcompatibility on PU and PEOU were investigated. As expected, perceived compat-ibility does affect significantly both PU (γ = 0.271, p < 0.001) and PEOU (γ =0.535, p < 0.001), providing evidence to support these two Hypotheses. A moderateamount of variance (28.2 %) in PU was accounted for jointly by PEOU and perceivedcompatibility. Moreover, perceived compatibility explained 28.6 % of the variance inPEOU.

Given that TAM was not originally developed to be used to predict users’ continuedIT usage intention, previous TAM-based studies on e-services consistently explainedabout 43–57 % of the variance in continuance intention (e.g. [18,51,84]). It thereforecan be suggested that this model had an acceptable explanatory power with regardto continued IBSs usage intention. In comparison with earlier related studies (e.g.[40,52]), moreover, the explanatory power of user satisfaction can also be regarded assatisfactory.

An additional examination was undertaken to estimate the standardized direct andindirect effects for obtaining a more complete understanding of the total effects ofeach of the variables on another [66]. The estimate of direct effect is the same as thepath coefficient associated with each path, whereas indirect effect represents the sumof the product of two or more path coefficients. The total effect refers to the sum ofdirect and indirect effect.

As shown in Table 5, all variables have significant and positive total effect oncontinuous usage intention. Of particular note, PEOU appears to significantly affectcontinuous usage intention mediating by PU and user satisfaction, although PEOUdoes not impact directly on continuous usage intention. Results also reveal that per-ceived compatibility has the highest total effect on continuous usage intention. All

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Table 5 Standardized direct, indirect, and total effect

Effect path Direct effect Indirect effect Total effect

PU on

USAT 0.402*** – 0.402***

CUI 0.355*** 0.063* 0.418***

PEOU on

PU 0.333*** – 0.333***

USAT 0.395*** 0.134*** 0.530***

CUI 0.127n.s. 0.202*** 0.329***

USAT on

CUI 0.157* – 0.157*

PCO on

PU 0.271*** 0.178*** 0.450***

PEOU 0.535*** – 0.535***

USAT – 0.393*** 0.393***

CUI 0.226*** 0.290*** 0.515***

* p < 0.05; *** p < 0.001; n.s.p > 0.05

these above findings demonstrated obviously that the usability of IBSs system is quiteimportant in achieving customer loyalty.

5.4 Discussion of the implications of the research findings

This research indicates that user satisfaction exerts a significant positive impact oncontinuous usage intention, denoting that the more satisfied the users are with IBSs,the more likely the users will continue to use the services. This observation is con-sistent with the findings of previous studies (e.g. [10,25,73]). The level of user’spost-adoption satisfaction with a service experience was determined primarily by indi-vidual’s subjective evaluation about the service quality based on his/her own first-handusage experience. As opposed to the pre-adoption satisfaction, users’ post-adoptionsatisfaction is hence more likely to result in their loyalty intention. Customers’ loy-alty intention and continuance trade relations have long been considered as the key tomaintain that firms’ competitive advantage and to ensure that firms can obtain attrac-tive returns on their investments [10]. It can hence be concluded that user satisfactionis a primary driver leading to the success of the IBSs system.

Of all the proposed antecedents of continuous usage intention, PU is the most pre-dominant factor in determining users’ continued IBSs usage intention. Extending theperspective of task-technology fit (TTF) theory [34], PU also can be considered asthe “fit” between system’s performance and user’s requirements. In this vein, cur-rent users will continue using the IBSs if they perceive the services as beneficial inachieving individuals’ desired performance goal on life- and/or work-related tasks.Following the suggestions of Parthasarathy and Bhattacherjee [65], we recommendthat the IBSs practitioners should attend to the manifestation of the full capability

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of IBSs for helping users to better understand the services’ benefits through inten-sive marketing communications, effective word-of-mouth marketing campaign, andaggressive advertising strategy.

Contrary to our expectation, PEOU displayed less association with continuoususage intention, meaning explicitly that the “ease of use” may no longer be the cus-tomers’ primary concern in making their decisions about continued or discontinuedIBSs usage. This observation can be attributed to the characteristics of the study sub-jects. Almost 60 % of the respondents had more than two years experience with theIBSs, and over 70 % had usage frequency of at least twice a month. According to thedescriptive statistics of the PEOU (M = 3.657, with the mean of each item between3.62 and 3.70), we can presume that the vast majority of the respondents are famil-iar with the use of IBSs. Venkatesh et al. [82] argue that the impact of PEOU on anindividual’s IT usage intentions tends to be weakened gradually with accumulatingusage experience. As shown in Table 5, however, PEOU has a significant total effecton continuous usage intention (p < 0.001). This signifies that the role of PEOU infacilitating users’ continued IBSs usage intention should not be underestimated oreven neglected. Conversely, the IBSs providers must educate their customers throughthe appearance of section “learning centre” on the homepage of Web sites, regulartraining courses, and arranged one-on-one meetings in order to teach them how to useeffectively the services so as to raise their continuance usage intention.

The study found that perceived compatibility exhibits a significant but moderateinfluence on continuous usage intention. This result specifies that individuals willdevelop a habitual usage pattern if they perceive the using of IBSs as compatible withtheir lifestyle routines, prior experiences, and banking needs. Here we report evidencefor the important of the compatibility of IBSs in determining users’ continued usage ofthe services. Moore and Benbasat [56] found that PU and perceived compatibility loadon the same factor. Karahanna et al. [44] also state that PU and perceived compatibilityare often not distinguished explicitly. In addition, the usefulness of a system can be alsodescribed as a function of the fit between the system and user’s preferred life/work style[2]. We hence recommended that the IBSs practitioners should devote to developingmulti-functional and integrative services that emphasized the compatibility with user’sdesired life style or existing work practices for motivating current users to continueusing the services.

As predicted in Hypothesis 5 and 6, the more useful and easy-to-use the IBSs isperceived to be, the more likely the users are to be satisfied with the services. Inthe context of post-adoption behavior, users’ satisfaction level with a technology willbe decreased, in turn undermining their loyalty intention, if the technology does notfulfill their expected performance threshold [10]. Hence, the IBSs practitioners shouldendeavor to promote customers’ satisfaction and thereby encourage their continued useof the services through the enhancement of the system’s efficiency and effectiveness(i.e. usefulness and ease of use).

The empirical results also found that the effect of PU on user satisfaction is slightlyhigher than, but fairly close to, that of PEOU, suggesting that the customers are likelyto place equal weight upon the efficiency and effectiveness of the IBSs system in theirsatisfaction judgment. This phenomenon can be attributed to the characteristics of IBSssystem and common IBSs users’ use habit. On the one hand, the IBSs are focused on

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providing utilitarian values to promote customers’ loyalty accordingly, the PU-usagerelationship has emerged as an important factor influencing customers’ satisfactionlevel with IBSs. On the other hand, the most common IBSs users may only performsome simple banking activities in general situation, the PEOU-usage relationshiptherefore appears not to be a primary concern in their satisfaction judgment.

Human–computer interaction (HCI) studies have traditionally considered the “easeof use” as the primarily approach in enhancing system efficiency. The significant effectof PEOU on PU has been confirmed by previous research on the field of e-service (e.g.[50,68]). In the present investigation, we also found evidence to support the significanteffect of PEOU on PU. Moreover, as presented in Fig. 2, PEOU has a significant indirecteffect (0.333 × 0.355 = 0.118) on continuous usage intention via the mediation of PU.Accordingly, in order to enhance users’ perceptions of usefulness of IBSs, practitionerscould make efforts to construct a well-designed and user-friendly operation interfaceconfiguration, and which in turn encourages current users to continue using the servicesoffered by them.

This study found that both PU and PEOU were influenced significantly by per-ceived compatibility. This result further demonstrates the necessity of consideringthe perceived compatibility in the research of users’ continued IBSs usage intention.Again, perceived compatibility also displayed a significant indirect effect of 0.227 oncontinuous usage intention through the mediating role of both PU and PEOU. Theresult connotes that users will perceive the IBSs as being useful and easy-to-use, inturn, increase the likelihood of continued usage if they perceive the services to becompatible with their requirements. Based on the above findings, IBSs practitionersmay provide more add-valued services that concern the compatibility of the servicewith the users’ needs and expectations through customization and personalization,instead of deploying resources to enhance system functionality and interface charac-teristics. After all, function and system innovation may involve higher uncertaintiesand potential technological bottlenecks.

5.5 Multi-group analysis

Individuals’ continued e-service usage intention was influenced significantly by theirown experiences and expertise [29]. In seeking to provide a more thorough under-standing of users’ continuance intention toward IBSs, a multi-group analysis on twosubsamples according to their IBSs usage experience and frequency was made toascertain whether there are different concerns and priorities between skilled and lessskilled users by utilizing LISREL procedures.

In order to collect the required data for multi-group analysis, a second samplingprocedure using same instrument as above was performed on ten bank branches dif-ferent from those of first sampling. The conduction of the second sampling procedurewas with the goal of minimizing the self-report bias in usage experience and frequency,of enhancing the sample representativeness, and of maximizing the generalizabilityof the multi-group analysis findings to the wider study population.

As presented in Table 6, the survey data from the second sampling trip actuallycomprised two subsamples, namely subsample A and subsample B, that were indi-vidually collected from five different FHs in Taiwan. The skilled users were defined

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Table 6 The structure of the survey data obtained from the second sampling trip

Subsample Data source (geographical area) Skilled users Less skilled uses

Subsample A Hua Nan FHs (eastern Taiwan) 16 9

Yuanta FHs (western Taiwan) 29 16

Mega FHs (southern Taiwan) 30 21

Taishin FHs (northern Taiwan) 42 15

Shin Kong FHs (midland Taiwan) 25 10

Subtotal 142 71

Subsample B Waterland FHs (eastern Taiwan) 26 22

SinoPac FHs (western Taiwan) 32 28

E.SUN FHs (southern Taiwan) 21 36

China development FHs (northern Taiwan) 27 16

JihSun FHs (midland Taiwan) 16 23

Subtotal 122 125

Table 7 The model-data fit indices for measurement model of the sample for multi-group analysis

Sample χ2/df P value GFI AGFI NFI NNFI CFI RMSEA

Entire sample (n = 267) 1.339 0.0104 0.939 0.915 0.978 0.992 0.994 0.0357

Skilled user (n = 142) 1.127 0.173 0.907 0.870 0.960 0.993 0.994 0.0300

Less skilled users (n = 125) 1.265 0.032 0.884 0.838 0.961 0.986 0.988 0.0462

here as the users who had more than two years experience with IBSs and employ theservices at least twice a month against the less skilled users were those who had upto two years experience with IBSs and employ the services less than twice a month.Notably, individuals’ intention to continue using a specific IBSs system is the primaryresearch issue addressed in the present study. Nevertheless, one may employ simulta-neously two or even more alternative but different IBSs systems which may produceself-report bias towards an individual’s usage experience and frequency of IBSs. Moti-vated by mitigating the influence of self-report bias, the final research sample used formulti-group analysis was comprised exclusively of the skilled users of subsample Aand the less skilled users of subsample B.

Compared with the analysis of covariance (ANCOVA), the multi-group analysis ofstructural invariance (MASI) provides a rather rigorous statistical technique for testingthe measurement invariance (i.e. equivalence) across different population subgroups[76]. The model-data fit among the entire sample, skilled users sample, and less skilledusers sample must be estimated before performing the MASI. The values presented inthe second row of Table 7 reveal that the entire sample used for multi-group analysishas a satisfactory model-data fit. Besides, the values displayed in the third and fourthrow of Table 7 indicate that both skilled and less skilled users sample are all well fitwith the proposed model, it is well to proceed with the subsequent MASI. The result ofMASI indicated that there was a nonstatistically significant difference in χ2 value (p =0.242) between skilled and less skilled users, confirming the measurement invarianceacross different user groups.

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skilled user group (n1 = 142)

less skilled user group (n2 = 125)

Continuous Usage

Intention

PerceivedUsefulness

UserSatisfaction



H50.269*

(t=2.456)

H10.212*

(t=2.268)

H20.210*

(t=1.992)

H40.211*

(t=1.975)

H30.351**

(t=3.068)

H60.035n.s.

(t=0.294)

H80.503***(t=4.744)

H90.623***(t=5.899)

H70.247*

(t=2.055)

R2=0.244

R2=0.373

R2=0.430

R2=0.388


n.s. indicates insignificant relationship and represented by dotted line.

Continuous Usage

Intention

PerceivedUsefulness

UserSatisfaction



H50.236*

(t=2.340)

H10.181*

(t=2.066)

H20.384***(t=3.622)

H40.344***(t=3.395)

H30.249*

(t=2.495)

H60.075n.s.

(t=0.848)

H80.300**

(t=3.046)

H90.386***(t=4.144)

H70.422***(t=4.920)

R2=0.298

R2=0.494

R2=0.288

R2=0.149


Fig. 3 Results of multi-group analysis

The Fig. 3 presents graphically the results of multi-group analysis. As illustrated,the results of multi-group analysis are similar to the results of hypotheses testing butwith different path coefficients and significance levels. As seen, the PEOU exerted

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an insignificant effect on the continuous usage intention for both skilled and lessskilled users. This means that “easy-to-use” is no longer a major concern in theircontinued/discontinued IBSs usage decision for these two user groups. That is, a user-friendly operation interface configuration alone can not be used to attract customersto become loyal to the services. This finding can be explained by the following twoobservation: (1) in general situation, most common users of IBSs merely performsome regular and simple pattern of banking activities, such as bill payment, accounttransfers, and checking balances alike; (2) a complete operation specification anda simple, convenient and user-friendly operation interface configuration have beenregarded as a necessary equipment for operating the IBSs system.

For less skilled users, the effect of user satisfaction on continuous usage intentionis higher than that for skilled users. In general, customers’ demands will be morediverse when they were experienced. In this condition, if the service providers can notfulfill completely customers’ requirements (e.g. lower-than-expected), user satisfac-tion will be reduced accordingly, negative emotions of dissatisfaction, frustration, andcomplaint will be brought subsequently which may reduce their intention to continueusing the service they provide.

Of worthy note, the results of both hypotheses testing and multi-group analysishave all shown that user satisfaction only exerted a modest effect on continuous usageintention. This observation can be explained by two potential causes. Firstly, Rai et al.[72] pointed that the effect size of user satisfaction on IS usage will be diminished,while there was a strong correlation between PU and IS usage. Secondly, the itemsused to measure users’ satisfaction do not define explicitly the scope and boundariesbetween users’ post-adoption perceptions of overall satisfaction and online channelsatisfaction with the IBSs.

For these two different user groups, perceived compatibility appears to have a strongpositive impact on users’ continuous usage intention, meaning that perceived compat-ibility is consistently the most significant contributor to users’ continued IBSs usageintention across different user groups. Given that the IBSs have now become a ubiq-uitous channel for distributing banking products and financial services in Taiwan, it istherefore conceivable that the perceived compatibility has been considered as the mostcritical factor for motivating customers to continue using the services. Of particularnote, perceived compatibility has emerged as a most critical factor influencing con-tinued IBSs usage intention for skilled users. According to this observation, we canconclude that the perceived compatibility may be a major consideration in determiningwhether an individual will use the services over a long-term period of time.

6 Limitations and avenues for future research

Whilst the results of statistical analyses indicated several implications, there are someresearch limitations that should be acknowledged. Additionally, this paper also pointsthe way toward interesting avenues for future research. In this section, we begin bydiscussing the generalizability of the present research. Firstly, the method of purposivesampling was used in the selection of research respondents. However, the main disad-vantage of this sampling method is that it lacks representativeness due to the sample’s

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homogeneous composition and thus limited the generalizability of the findings [36].Secondly, the instrument used to measure the users’ perceptions toward IBSs may bebiased because the research subjects are confined to the current and continued users,rather than discontinued users. Thirdly, the results of this study may be affected bythe sampling bias, because of the fact that the majority of respondents are experiencedand active IBSs users. Fourthly, this data is skewed by age and is concentrated in theyounger age groups, which may limit the generalizability of the findings to broaderpopulations. It is reasonable to expect that the elderly age groups will hold differentperceptions regarding the ease of use of the IBSs. Fifthly, this research has providedempirical evidence toward the capability of the proposed extended TAM model inreflecting the effects of system usability and user satisfaction on continued IBSs usageintention. However, our empirical data was collected from a particular industry andtargeted a specific user group in Taiwan, hence may not be generalizable to the broaderpopulation. It can be anticipated that replicate the proposed model to other countriesmay obtain different findings. Yoo and Donthu [87] maintain that the generalizabil-ity of the TAM-based model can be significantly improved by doing a comparativeanalysis with cross-cultural or cross-national data. Another future research possibilitywould be to replicate the present model with diverse sample groups in order to furtherverify the application of the present model in predicting the continued usage of IBSs.

Subsequently, we also discuss other research limitations and possible directionsfor future research. Firstly, this study only considers the influences of users’ sub-jective perceptions of system usability (i.e. PU, PEOU, and perceived compatibility)and users’ subjective emotional appraisal (i.e. user satisfaction) on continued IBSsusage intention. Notice, however, that the technology acceptance-related factors andindividuals’ emotional experiences and responses can not fully explain users’ contin-uance intention to use a particular technology. Many previous TAM-based studies onusers’ adoption and usage of mobile services were conducted from the perspectiveof objective features (e.g. [13,86]). Future research should, therefore, concentrate onidentifying the effects of objective features (such as usage volume and frequency andetc.) on users’ continued IBSs usage intention.

Secondly, continuous usage intention is the primary research issue addressed inthe present study, rather than actual continued usage. Moreover, intention does notnecessarily lead to actual behavior [3]. It is argued that future studies should go beyondcontinuous usage intention to continuous usage behavior.

It can be expected that the association between users’ post-adoption experience andcontinuous usage intention may be changed once the learning curve has been achievedand sufficient usage experience has been collected through frequent usage. As a con-sequence, the third limitation of this study is that the data used to validate the proposedmodel was collected at one particular time point. Studies with longitudinal design canbe employed to capture faithfully the complex and dynamic interrelationship betweenpost-adoption experiences and continuous usage intention.

Fourthly, as suggested by Hirschman [39], the more one’s own experiences withan innovation, the more easily the benefits of the innovation can be recognized andperceived by him/her. The real benefits of an IT can be appreciated easily, while the IThas been used by users as a routine method [25]. Extending these above suggestionsfurther, we can extrapolate that continuous usage intention has some extent effects

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on users’ post-adoption perceptions of system usability. However, this was not theresearch question of interest to our research, and hence this issue should be addressedempirically by future research.

Finally, the explained variance of the continuous usage intention in this study wasenhanced significantly from 0.385 to 0.482 because of the incorporation of the per-ceived compatibility construct. To broaden the research lens, the perceived compati-bility construct should be incorporated in future TAM-based models as well.

7 Conclusion and contributions

The objective of this paper is to identify and elucidate the effects of users’ post-adoptionperceptions of system usability and post-adoption satisfaction on their intention tocontinue using IBSs. For this research objective, we develop the IBSs continuancemodel that goes beyond previous IBSs adoption models to consider PU, PEOU, per-ceived compatibility, and user satisfaction as the determinants of continued IBSs usageintention. The IBSs continuance model was validated empirically by a field surveyof current IBSs individual users. The results confirmed the majority of the formu-lated hypotheses. Further, the multi-group analysis also provides empirical evidenceto demonstrate that there are different concerns and priorities in continued IBSs usagedecision-making between skilled and less skilled users.

Even with the limitations stated above, this research still made several importantcontributions to the existing body of knowledge in the field of IBSs research. Comparedwith previous IBSs adoption research, the most main contribution of this work is thatthe post-adoption perspective we adopted can be used to explain clearly the possibleorigin of the acceptance-discontinuance anomaly phenomenon. We believe this workrepresented an important step in forwarding the research in the field of IBSs. Othernoteworthy contributions of this study would be discussed as following.

The first important contribution of this study, apart from other similar research thataccounted for multi-dimensional variables, is concentrated on investigating the effectsof user’ post-adoption perceptions of system usability and satisfaction on continuanceintention of using IBSs. We hope this research can inspire the research community tomove the research field forward.

The second important contribution of this paper emerges from the incorporation ofthe perceived compatibility into the TAM model and examines empirically the effecton users’ continued IBSs usage intention. Currently, the role of perceived compatibil-ity in continued e-services usage intention remains ambiguous. Conceptualizing andincorporating the perceived compatibility into TAM model can be treated as a novelresearch avenue in the research of users’ continued IBSs usage intention. Our analy-sis confirms that the perceived compatibility is indeed a crucial driver of customers’loyalty and retention.

The third important contribution of this paper lies in performing a multi-groupanalysis that can be applied to access the proposed causal relationships across differ-ent user groups. We hope that the results of multi-group analysis will help the IBSspractitioners to develop specific customer service and marketing strategies for eachdifferent customer segment.

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The fourth important contribution of this paper is that our empirical findings canprovide a valuable reference to functional/utilitarian e-service practitioners for makingcompetitive strategy decisions to obtain customers’ loyalty and thereby increase thefirms’ profits.

The present study has successfully integrated the perceived compatibility into TAMmodel, so we that can obtain a more complete picture of users’ continued IBSs usageintention. Besides, the salient role of perceived compatibility in influencing users’continued IBSs usage intention has also been validated empirically. In contrast toprevious related studies, therefore, the academic originality of the research presentedin this work lies in the theoretical perspective and the specific theoretical implica-tions derived from the research findings. Further, the originality of the methodologyemployed in this study arises from the samples used for hypotheses testing and thoseused for multi-group analysis were collected respectively from different sample pop-ulations (i.e. different bank branches of different FHs). In multi-group analysis, fur-thermore, the samples of the skilled user group and those of the less skilled user groupalso were gathered from different subsample aimed at minimizing the self-report biasin usage experience and frequency. In addition to this, the comprehensiveness and rep-resentativeness of the research sample can also be considered as a major strength ofthis study that may enhance significantly the generalizability of the research findings.

Appendix 1: Questionnaire measures for research model constructs

Perceived usefulness [77]

PU1 Internet banking services enhance the productivity of my banking activities.PU2 Internet banking services enable me to accomplish banking activities more easily

and quickly.PU3 Overall, I find Internet banking is useful for my banking activities.

Perceived ease of use [77]

PEOU1 It is easy for me to learn how to utilize the Internet banking services.PEOU2 My interaction with the Internet banking services is clear and understandable.PEOU3 I find the Internet banking services are easy to use.

Users’ satisfaction [64]

USAT1 I am satisfied with the performance of Internet banking services.USAT2 I am pleased with the experience of using the Internet banking services.USAT3 My decision to use the Internet banking services was a wise one.USAT4 My feeling with using the Internet banking services was better than traditional

“brick and mortar” bank branch.

Perceived compatibility [56]

PCO1 Internet banking is compatible with my lifestyle.PCO2 Using Internet banking fits well with the way I like to manage my finances.PCO3 Using Internet banking is compatible with all aspects of banking.PCO4 Using Internet banking is compatible with my prior experiences.

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Continuous usage intention [75]

CUI1 I would continue to use Internet banking for my banking needs.CUI2 Continuing to use Internet banking for handing my banking transactions is

something I would do in the future.CUI3 I would continue to see myself using Internet banking for handing my banking

transactions

Appendix 2: Results of explanatory factor analysis

USAT PCO CUI PEOU PU

USAT1 0.846 0.169 0.142 0.180 0.181

USAT4 0.819 0.156 0.176 0.216 0.104

USAT2 0.785 0.185 0.208 0.198 0.209

USAT3 0.775 0.180 0.172 0.143 0.217

PCO2 0.137 0.811 0.174 0.188 0.161

PCO3 0.120 0.791 0.165 0.127 0.029

PCO1 0.223 0.786 0.181 0.150 0.123

PCO4 0.143 0.776 0.083 0.127 0.096

CUI1 0.223 0.204 0.851 0.141 0.209

CUI3 0.226 0.197 0.850 0.145 0.210

CUI2 0.200 0.219 0.841 0.214 0.225

PEOU1 0.230 0.180 0.154 0.838 0.089

PEOU3 0.188 0.190 0.094 0.792 0.162

PEOU2 0.196 0.179 0.197 0.786 0.154

PU3 0.200 0.151 0.135 0.147 0.812

PU2 0.272 0.039 0.152 0.152 0.795

PU1 0.113 0.168 0.314 0.101 0.745

Eignvalue 7.549 1.746 1.503 1.246 1.095

Cronbachs’ alpha 0.904 0.861 0.935 0.846 0.812

Percent of variance 18.240 17.098 15.214 13.632 13.099

Cumulative percentage of variance 18.240 35.338 50.552 64.184 77.283

Used SPSS principal components analysis with varimax rotation methodNumbers in bold indicate factor loadings

Appendix 3: Correlation matrix of total sample

PEOU1 PEOU2 PEOU3 PCO1 PCO2 PCO3 PCO4 USAT1 USAT2

PEOU1 0.997

PEOU2 0.747 1.046

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PEOU3 0.714 0.620 0.991

PCO1 0.373 0.343 0.383 0.899

PCO2 0.350 0.416 0.372 0.770 0.873

PCO3 0.336 0.298 0.291 0.618 0.676 0.876

PCO4 0.305 0.294 0.296 0.622 0.628 0.558 0.886

USAT1 0.455 0.405 0.383 0.401 0.327 0.303 0.319 0.987

USAT2 0.424 0.433 0.454 0.410 0.398 0.315 0.330 0.811 0.945

USAT3 0.434 0.386 0.363 0.406 0.363 0.260 0.306 0.723 0.709

USAT4 0.449 0.450 0.388 0.416 0.346 0.290 0.240 0.786 0.720

PU1 0.282 0.317 0.243 0.367 0.374 0.244 0.171 0.338 0.341

PU2 0.296 0.354 0.292 0.234 0.238 0.121 0.202 0.466 0.482

PU3 0.296 0.323 0.325 0.302 0.350 0.233 0.221 0.383 0.444

CUI1 0.325 0.377 0.299 0.438 0.437 0.347 0.278 0.424 0.453

CUI2 0.416 0.429 0.340 0.431 0.442 0.321 0.316 0.388 0.463

CUI3 0.372 0.364 0.302 0.390 0.417 0.367 0.278 0.421 0.467

Mean 3.65 3.70 3.62 3.65 3.71 3.74 3.72 3.55 3.57

USAT3 USAT4 PU1 PU2 PU3 CUI1 CUI2 CUI3

USAT3 1.005

USAT4 0.724 0.970

PU1 0.362 0.308 0.875

PU2 0.402 0.380 0.600 0.983

PU3 0.443 0.317 0.597 0.665 1.021

CUI1 0.418 0.424 0.497 0.409 0.420 1.123

CUI2 0.439 0.377 0.520 0.419 0.430 0.870 1.170

CUI3 0.412 0.393 0.492 0.410 0.412 0.862 0.863 1.141

Mean 3.58 3.55 3.61 3.65 3.70 3.83 3.82 3.84

Leading diagonal elements display the standard deviation of each item

Appendix 4: Pair-wise discriminant validity analysis of constructs

Construct pair-wise χ2 df χ2 difference

Original CFA model 116.129 109 –

CUI and USAT 849.025 110 732.896

CUI and PU 309.486 110 193.357

CUI and PEOU 413.468 110 297.339

CUI and PCO 628.658 110 512.529

USAT and PU 324.258 110 208.129

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Construct pair-wise χ2 df χ2 difference

USAT and PEOU 389.040 110 272.911

USAT and PCO 662.184 110 546.055

PU and PEOU 363.192 110 247.063

PU and PCO 370.521 110 254.392

PEOU and PCO 414.388 110 298.259

All χ2 differences were significant at p = 0.001 level

Appendix 5: Correlation matrix of the sample for multi-group analysis

(a) skilled users


PEOU1 0.924

PEOU2 0.810 1.001

PEOU3 0.641 0.602 0.889

PCO1 0.262 0.251 0.212 1.036

PCO2 0.307 0.283 0.253 0.768 0.972

PCO3 0.280 0.255 0.254 0.687 0.672 1.062

PCO4 0.287 0.215 0.236 0.724 0.693 0.693 0.992

USAT1 0.270 0.255 0.238 0.361 0.341 0.441 0.393 0.924

USAT2 0.299 0.312 0.253 0.274 0.288 0.376 0.369 0.782 0.990

USAT3 0.407 0.368 0.380 0.227 0.229 0.215 0.222 0.644 0.747

USAT4 0.321 0.387 0.330 0.331 0.362 0.438 0.295 0.724 0.751

PU1 0.421 0.355 0.272 0.223 0.420 0.301 0.270 0.362 0.388

PU2 0.284 0.370 0.128 0.259 0.290 0.170 0.284 0.259 0.366

PU3 0.373 0.314 0.078 0.216 0.263 0.246 0.306 0.302 0.364

CUI1 0.396 0.328 0.199 0.473 0.491 0.520 0.432 0.495 0.427

CUI2 0.276 0.292 0.212 0.326 0.414 0.449 0.342 0.391 0.404

CUI3 0.333 0.375 0.174 0.426 0.424 0.492 0.436 0.514 0.438

Mean 3.42 3.55 3.54 3.51 3.54 3.44 3.58 3.59 3.65


USAT4 0.709 1.017

PU1 0.316 0.369 0.993

PU2 0.279 0.308 0.638 0.959

PU3 0.327 0.236 0.681 0.628 0.991

CUI1 0.331 0.338 0.464 0.340 0.448 1.161

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CUI2 0.221 0.355 0.340 0.363 0.330 0.785 1.174

CUI3 0.308 0.334 0.383 0.347 0.354 0.861 0.825 1.157

Mean 3.72 3.73 3.44 3.34 3.35 3.70 3.65 3.72


(b) less skilled users


PEOU1 0.912

PEOU2 0.669 1.039

PEOU3 0.589 0.770 1.117

PCO1 0.385 0.521 0.481 0.967

PCO2 0.395 0.506 0.526 0.932 0.931

PCO3 0.386 0.527 0.566 0.858 0.815 1.028

PCO4 0.388 0.483 0.450 0.886 0.828 0.839 1.009

USAT1 0.355 0.359 0.474 0.525 0.509 0.494 0.530 0.992

USAT2 0.344 0.293 0.371 0.379 0.433 0.376 0.414 0.912 0.842

USAT3 0.277 0.199 0.323 0.396 0.447 0.401 0.275 0.731 0.772

USAT4 0.208 0.263 0.364 0.402 0.404 0.356 0.329 0.763 0.680

PU1 0.324 0.402 0.409 0.534 0.580 0.549 0.595 0.293 0.307

PU2 0.355 0.461 0.395 0.509 0.593 0.480 0.561 0.267 0.289

PU3 0.358 0.481 0.428 0.623 0.698 0.626 0.682 0.412 0.406

CUI1 0.349 0.231 0.250 0.420 0.457 0.480 0.527 0.379 0.348

CUI2 0.418 0.326 0.398 0.482 0.526 0.561 0.515 0.373 0.347

CUI3 0.345 0.198 0.300 0.368 0.420 0.376 0.436 0.298 0.311

Mean 3.50 3.66 3.64 3.60 3.62 3.63 3.59 3.62 3.60


USAT3 0.858

USAT4 0.776 0.909

PU1 0.291 0.258 1.185

PU2 0.276 0.261 0.908 1.232

PU3 0.331 0.318 0.850 0.783 1.110

CUI1 0.342 0.349 0.400 0.326 0.335 1.139

CUI2 0.354 0.328 0.531 0.428 0.428 0.835 1.207

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CUI3 0.284 0.252 0.444 0.347 0.341 0.879 0.878 1.141

Mean 3.68 3.58 3.66 3.66 3.56 3.76 3.79 3.75


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Huei-Ting Tsai is an assistant professor of Department of Busi-ness Administration and Institute of International Business, NationalCheng Kung University. She received her Ph.D. from the Univer-sity of Cambridge and masters from Oxford University and LondonSchool of Economics. Her research centers on internationalizationstrategies of firms from emerging markets and has appeared in Jour-nal of International Marketing, Journal of Business Research andCalifornia Management Review.

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Jui-Lin Chien is a Ph.D. candidate at Department of BusinessAdministration and Institute of International Business, NationalCheng Kung University. He received his master degree from the Col-lege of Engineering, National Cheng Kung University. His researchinterests lie in areas of consumer psychology and behavior, market-ing research, and Internet marketing. He has published in the Quality& Quantity.

Ming-Tien Tsai is Dean of Business School at the Wuyi Univer-sity of China. He received his Ph.D. degree from the Columbia Uni-versity. His research focuses on marketing research. His articles havebeen published by journals such as the Journal of Business Research,International Journal of Commerce & Management, and Quality &Quantity.

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