WEB-BASED INFORMATION SYSTEMS SATISFACTION

Theoretical Development and Testing of Competing Models

Christy M. K. Cheung

Department of Finance & Decision Sciences, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong

Matthew K. O. Lee

Department of Information Systems, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong

Keywords: User Satisfaction, Web-base Information System, Information Quality, System Quality.

Abstract: User satisfaction has been widely used in evaluating the performance of web-based information systems

(WIS) since the growth of the World Wide Web. This study aims at investigating the structure and

dimensionality of the WIS satisfaction construct. We tested the competing models built upon the web

satisfaction model and assessed the psychometric properties of the factors and measuring items using

confirmatory factor analysis. Our findings suggested that WIS satisfaction can be explained by a higher-

order factor model with six first-order factors (i.e., understandability, reliability, usefulness, access,

usability, and navigation) and two correlated second-order factors (i.e., web information satisfaction and

web system satisfaction). The model provides a good-fit to the data and is theoretically valid, reflecting the

logical or formal consistency. Implications of the current investigation for practice and research are

provided.

1 INTRODUCTION

User satisfaction is one of the most important

measures of information systems success (Rai et al.,

2002; DeLone and McLean, 1992; 2003; Zviran and

Erlich, 2003). It has become a particularly important

evaluation measurement of web-based information

systems (WIS) since the rapid growth of the World

Wide Web. Despite the fact that there is a rich

literature of end-user information satisfaction

conducted in traditional information system (IS)

environment, very little is known about user

satisfaction in the web-based environment due to the

different natures of these two kinds of IS (Isakowitz

et al., 1998, Kaschek et al., 2004). The users of

traditional information system are mainly

professionals in organizations while those of WIS

comprise of both professional and non-professional

users. Besides, these systems perform different

functions to fulfill the needs of these two types of

end-users. Furthermore, the richness of information

and the nature of unstructured and highly

individually customizable interactions typically

exhibited by WIS redefine the standard of user

satisfaction in the web environment. As a result,

findings from prior studies on user satisfaction may

not be valid in the context of WIS. There is a need to

investigate the concept of user satisfaction under the

new context of WIS.

The purpose of this study is thus to investigate

the multi-faceted structure and dimensionality of the

web-based information systems satisfaction

construct through an examination of several

competing theoretical measurement models. The

results are anticipated to increase our understanding

of WIS satisfaction, thereby laying a concrete

foundation for the development of a validated and

robust instrument for measuring WIS satisfaction,

which may serve as a practical evaluation tool for

evaluating web-based information systems.

2 THEORETICAL

BACKGROUND

Satisfaction has been extensively studied from

diverse theoretical perspectives. The discipline of

information systems has a long history of research in

M. K. Cheung C. and K. O. Lee M. (2007).

WEB-BASED INFORMATION SYSTEMS SATISFACTION - Theoretical Development and Testing of Competing Models.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Society, e-Business and e-Government /

e-Learning, pages 46-53

DOI: 10.5220/0001275800460053

 SciTePress

End-User Computing (EUC) satisfaction, which is

also a widely adopted indicator of IS success

(DeLone and McLean, 1992; 2003; Rai et al., 2002).

EUC satisfaction is generally defined as an overall

affective evaluation an end-user has regarding his or

her experience related to the information system.

Doll and Torkzadeh (1988) developed a 12-item

instrument that measures the five components of

EUC satisfaction, namely, content, accuracy, format,

ease of use, and timeliness. This instrument was one

of the best known and frequently employed

measurements of end-user computing satisfaction

(e.g., McHaney et al., 2002). Doll et al. (1994)

performed a confirmatory factor analysis of the EUC

satisfaction instrument, so as to test the alternative

factor structures of EUC satisfaction and to assess

the psychometric properties of the factors and items.

Their results provide strong support for their EUC

satisfaction instrument.

One of the differences between WIS and

traditional IS is that the former involves more end-

users direct information consumption and interaction

than the latter. Hence, information provided by the

system and the quality of the systems are decisive in

determining the level of web-based information

system satisfaction. In this regard, McKinney et al.

(2002) proposed a theoretical model of web

satisfaction, which argues that web satisfaction

should be analyzed at information level and system

level. In other words, web satisfaction can be

analyzed in terms of web information quality

satisfaction (Web-IQ satisfaction) and web system

quality satisfaction (Web-SQ satisfaction). Building

upon expectation confirmation theory, Web-IQ

satisfaction and Web-SQ satisfaction is determined

by Web IQ disconfirmation and Web SQ

disconfirmation respectively, and these

disconfirmations are based on the evaluations of the

expectation and perceived performance on the

quality constructs.

3 FACTOR STRUCTURE FOR

WIS SATISFACTION

McKinney et al. (2002) conceptualized web-based

information system satisfaction as a

multidimensional concept that was made up of Web-

IQ satisfaction and Web-SQ satisfaction, which, in

turn, was comprised of three dimensions

respectively. This hierarchy implies that users

evaluate WIS performance at multiple levels with

multiple dimensions, and ultimately combine these

evaluations to arrive at an overall WIS satisfaction

perception. WIS satisfaction is therefore the overall

affective evaluation a user has regarding his or her

experience related to the web-based information

system. In McKinney et al.’s (2002) web

satisfaction model, understandability, reliability, and

usefulness of information were the three key

dimensions related to information quality. They also

empirically determined three dimensions of system

quality for web customer satisfaction, including

access, usability, and navigation (See Table 1).

Table 1: Dimensions of Web Information Satisfaction.

Dimensions Definition Manifest

Variables

Under-

standability

Concerned with

such issues as

clearness and

goodness of the

information

 Clear in

meaning

 Easy to

understandin

 Easy to read

Reliability Concerned with

the degree of

accuracy,

dependability, and

consistency of the

information

 Trustworthy

 Accurate

 Credible

Usefulness Users’ assessment

of the likelihood

that the

information will

enhance their

decision

 Informative

 Valuable

Access Refers to the speed

of access and

availability of the

web site at all

times

 Responsive

 Quick loads

Usability Concerned with

the extent to which

the web site is

visually appealing,

consistent, fun and

easy to use

 Simple layout

 Easy to use

 Well

organized

Navigation Evaluates the links

to needed

information

 Easy to go

back and

forth

 A few clicks

4 COMPETING MODELS FOR

WIS SATISFACTION

In this study, we followed Doll et al.’s (1994)

approach to test the five alternative factor structures

of WIS satisfaction with 21 observable items.

WEB-BASED INFORMATION SYSTEMS SATISFACTION - Theoretical Development and Testing of Competing

Models

Models 1 to 3 represent the non-hierarchical

structure with only first-order factor, and Model 4

and 5 represent the hierarchical structure with more

than one level of abstraction.

Model 1 is a first-order factor model. One factor

(WIS satisfaction) is hypothesized to account for all

the common variance among the 21 items. This is

consistent with the idea used in the end user

computing satisfaction literature, adding the item

scores to obtain a total satisfaction score.

Model 2 hypothesizes six orthogonal or

uncorrelated first-order factors (i.e.,

understandability, reliability, usefulness, access,

usability, and navigation). McKinney et al. (2002)

performed an exploratory factor analysis resulting in

six factors. Thus, Model 2 is considered a plausible

alternative model of underlying data structure.

Model 3 is a first-order factor model with the six

factors correlated with each others to represent

different dimensions of the concept of WIS

satisfaction. Assuming the six factors are correlated

allows us to capture the common variance in the

model.

Model 4 hypothesizes six first-order factors and

two second-order factors (web information

satisfaction and web system satisfaction). Based on

McKinney et al.’s (2002) model, understandability,

reliability, and usefulness are dimensions of

information quality. We believe these three first-

order factors are highly correlated, and their

covariations can be captured by a second-order

factor (Web information satisfaction). Similarly,

access, usability, and navigation are closely related

to system quality, and a second-order factor (Web

system satisfaction) is proposed to capture their

covariations.

Model 5 assumes that the two second-order

factors in Model 4 are correlated. Similar to Model 3,

we assume the correlations between the two second-

order factors, so that the common variation in the

model can be captured.

5 RESEARCH METHOD

The sections below describe the details of data

collection procedure, measurement, data analytical

approach, and model competing criteria.

5.1 Data Collection

The web-based information system in question is

known as “Blackboard Learning System

(http://www.blackboard.com)”, an Internet-based

learning portal for students in campus-based

education institutions. Through this portal, students

can access to course materials, course

announcements, and other relevant documents of

each course they are enrolled in. The portal also

contains communication facilities (e.g., discussion

forums, group pages, and virtual classrooms) for

students to exchange ideas and opinions.

The web-based portal was introduced to the first-

year undergraduate students at the beginning of the

semester. After six-week’s usage, students were

invited to voluntarily complete an online

questionnaire that covered all the measures of the

constructs in this study. A total of 515 usable

questionnaires were collected. The respondent rate

was 64.4%. Among the respondents, 54.8% were

female and 45.2% were male.

5.2 Measurement

Table 2 lists the measures used in this study.

Basically, we borrowed the measures from

McKinney et al. (2002) but modified the wordings

so as to fit them to this particular context of web-

based information systems user satisfaction. The

measurements employed a seven-point Likert scale,

from “1=never” to “7=always”.

5.3 Data Analytical Approach

The proposed factor structures were examined

through the LISREL VIII framework. LISREL is

one of the most widely used Structural Equation

Modeling (SEM) techniques in IS. According to

Chin (1998), if SEM is accurately applied, it can

surpass the first-generation techniques such as

principle components analysis, factor analysis,

discriminant analysis, or multiple regression.

Specifically, SEM provides a greater flexibility in

estimating relationships among multiple predictors

and criterion variables. It allows modeling with

unobservable latent variables, and it estimates the

model uncontaminated with measurement errors.

As suggested by Doll et al. (1994), competing

models should be specified based on logic, theory,

and prior studies. The LISREL framework offers us

a systematic approach to statistically compare the

theoretical models using the goodness-of-fit indexes.

The best model is then selected as representing the

factor structure and dimensionality of WIS

satisfaction in the sample data. Further, the

psychometric properties (i.e., reliability and validity)

of the selected model are examined.

WEBIST 2007 - International Conference on Web Information Systems and Technologies

5.4 Criteria for Comparing

Model-Data Fit

The determination of model fit in structural equation

modeling is not as straightforward as it is in other

statistical approaches in multivariate procedures.

Chi-square test is the only statistical test that

identifies a correct model given the sample data. In

contrast to traditional significance testing, the

researcher is interested in obtaining a non-significant

chi-square. Such a finding indicates that the

predicted model is congruent with the observed data.

Another alternative is the ratio of the chi-square to

the degrees of freedom. Researchers have

recommended using Normed Chi-Square as low as 2

or as high as 5 to indicate a reasonable fit (Hair et al.

1992). However, Chi-square test is highly sensitive

to the sample size and the departures from

multivariate normality of the observed variables

(Bollen, 1989). Given its sensitivity to many factors,

researchers are encouraged to complement the chi-

square measure with other fit indexes (Hair et al.,

1998).

In IS research, absolute fit indexes and

incremental fit indexes are the two most widely used

measures to determine how well the data fits the

proposed model. For instance, Doll et al. (1994)

used absolute fit indexes, including the Goodness-

of-Fit Index (GFI) and the Root Mean Square

Residual (RMSR), to evaluate individual models.

They also used incremental fit indexes, including the

Normed Fit Index (NFI) and the Adjusted Goodness-

of-Fit Index (AGFI), to reflect the improvement in

fit of one model over an alternative. Some

researchers (Joreskog and Sorbom, 1996; Hair et al.,

1998) provided the criteria and interpretation of

these measures.

6 RESULTS

The models are analyzed using confirmatory

maximum likelihood estimation.

6.1 Checking for Multivariate

Normality

Multivariate normality is an important assumption of

confirmatory factor analysis. To check if our

observations are independently and identically

distributed, we examined the skewness and kurtosis

for each scale. Skewness refers to the lack of

symmetry of a data distribution, while kurtosis refers

to whether the data distribution is peaked or flat

relative to a normal distribution. Skewness for scale

items ranged from 0.014 to 0.336 and kurtosis

ranged between 0.015 and 1.041 were well within

the robustness thresholds for normality.

Table 2: Lists of the Measures used in this Study.

Dimensions Items

UND1

The information on

Blackboard is clear in meaning

UND2

The information on

Blackboard is easy to

comprehend

UND3

The information on

Blackboard is easy to read

Under-

standability

UND4

In general, information on

Blackboard is understandable

for you to use

REL1 The information on

Blackboard is trustworthy

REL2 The information on

Blackboard is accurate

REL3 The information on

Blackboard is credible

Reliability

REL4 In general, information on

Blackboard is reliable for you

to use

USE1

The information on

Blackboard is informative to

your usage

USE2 The information on

Blackboard is valuable to your

usage

Usefulness

USE3 In general, information on

Blackboard is useful for you to

use

ACC1 Blackboard is responsive to

your request

ACC2 Blackboard is quickly loading

all the text and graphic

Access

ACC3 In general, Blackboard is

providing good access for you

to use

USA1 Blackboard is having a simple

layout for its contents

USA2 Blackboard is easy to use

USA3 Blackboard is of a clear design

Usability

USA4 In general, Blackboard is user-

friendly

NAV1 Blackboard is being easy to go

back and forth between pages

NAV2 Blackboard is providing a few

clicks to locate information

Navigation

NAV3 In general, Blackboard is easy

to navigate

WEB-BASED INFORMATION SYSTEMS SATISFACTION - Theoretical Development and Testing of Competing

Models

6.2 Model Estimation

Specification of the models included fixing one of

the paths from each of the six primary factors at 1.0,

and the factor variance for the higher-order factor at

1.0. These are important for model identification.

Model 1 fixes the factor variance for the single first-

order factor (WIS satisfaction) at 1.0 and allows the

21 observable variables to be free. For Models 2 and

3, the first path for each of the six first-order factors

(i.e., understandability, reliability, usefulness, access,

usability, and navigation) is fixed to 1.0. For Model

2, the covariances among the six first-order factors

are fixed to zero. For Models 4 and 5, the first path

for each of the six first-order factors (i.e.,

understandability, reliability, usefulness, access,

usability, and navigation) is fixed to 1.0. The first

path for each of the two second-order factors (i.e.,

Web-IQ satisfaction and Web-SQ satisfaction) is

fixed to 1.0. For Model 4, the covariances between

Web-IQ satisfaction and Web-SQ satisfaction is

fixed to zero. For all five models, the number of

available data point is p(p+1)/2 = 21 × 22 / 2 = 231.

For Model 1, there are 42 free parameters that

include 21 error variances for the measured variables

and 21 factor loadings. This leaves (231-42) = 189

degrees of freedom for Model 1. There are 42 free

parameters for Model 2 which include 21 error

variables, a total of (21-6) = 15 factor loadings, and

6 first-order factor variances. This results in 189

degrees of freedom for Model 2. The free parameters

for Model 3 include 21 error variables, 15 factor

loadings, 15 covariances among the first-order

factors, and 6 first-order factor variances. Thus,

Model 3 has 174 degrees of freedom. For Model 4,

there are 46 free parameters that include 21 error

variances for measured variables, 15 first-order

factor loadings, 4 second-order factor loadings, 6

primary factor disturbances. This leaves 185 degrees

of freedom. Finally, there are 47 free parameters for

Model 5, including 21 error variances for measured

variables, 15 first-order factor loadings, 4 second-

order factor loadings, 6 primary factor disturbances,

and 2 second-order factor variances, and 1

covariance between second-order factors. This

provides 182 degrees of freedom.

6.3 Goodness-of-Fit

Table 3 summarizes the goodness-of-fit indexes for

the five competing models. As expected, the large

sample size causes the chi-square statistics of all

models statistically significant with p-value < 0.0001.

Models 1 to 3 are the first-order factor models. Both

Models 1 and 2 provide poor fit to the data, where

their fit indexes do not fulfill the recommended

acceptance levels. Model 3 provides a good fit to the

data with desirable goodness-of-fit indexes, and

demonstrates a significant improvement over Model

2. The NFI index increases significantly from 0.66

(Model 2) to 0.94 (Model 3), and the AGFI index

improves from 0.43 (Model 2) to 0.87 (Model 3).

Model 4 and Model 5 represent the second-order

factor models. The two models provide reasonable

model-data fit, and their fit indexes are close to the

recommended levels. Comparing Model 4 and

Model 5, Model 5 performs slightly better than

Model 4, with a lower value of normed chi-square

(Model 4: 7.67, Model 5: 3.46) and a higher value of

GFI (Model 4: 0.86, Model 5: 0.89). Like Model 3,

Model 5 also provides substantial improvement over

Model 4. The NFI index increases significantly from

0.86 (Model 4) to 0.94 (Model 5) and the AGFI

index improves from 0.82 (Model 4) to 0.87 (Model

5).

Table 3: Goodness of Fit Indexes for Competing Models

(n=515).

In comparing the goodness-of-fit among all

competing models, we notice that the first-order

model (Model 3) performs the best. As suggested by

Marsh and Hocevar (1985), the purpose of higher-

order model is to explain the covariation among the

lower-order factors in a more parsimonious way. In

fact, even the higher-order model can explain the

factor covariation effectively, its goodness-of-fit can

Absolute Fit

Measures

Incre-

mental

Fit

Measures

Model

Chi-

square

(df)

Normed

Chi-square

(Chi-

square/ df)

GFI

1539.80

(189) 8.15 0.74 0.05 0.68

3429.03

(189) 18.14 0.53 0.55 0.43

592.05

(174)

3.40 0.90 0.03 0.87

1418.96

(185)

7.67 0.86 0.47 0.82

630.29

(182)

3.46 0.89 0.04 0.87

GFI – Goodness of Fit Index

RMSR – Root Mean Square Residual

AGFI – Adjusted Goodness of Fit Index

NFI – Normed Fit Index

WEBIST 2007 - International Conference on Web Information Systems and Technologies

never be better than the corresponding first-order

model. Harlow and Newcomb (1990) further

suggested four guidelines for model selection,

including (a) logical or formal consistency, (b)

empirical adequacy, (c) the ability to capture most of

the essential relations among the variables, and (d)

simplicity. Based on these criteria, only Model 3 and

Model 5 could be retained. Among the two models,

Model 5 is more theoretically valid, reflecting the

logical or formal consistency. Similar to the case in

Harlow and Newcomb (1990), Model 5 presents the

relationships in the data in an organized and

conceptually descriptive manner. In sum, Model 5 is

the most appropriate model to capture the structure

of WIS satisfaction. Figure 1 depicts the hierarchical

structure of Model 5 with their respective factor

loadings and residual variances. Each of the factor

loadings is large and highly significant with

correspondingly low residence variances, offering

further support for Model 5.

Indeed, we believe that a third-order factor

model with six first-order factors (i.e.,

understandability, reliability, usefulness, access,

usability, navigation), two second-order factors

(Web-IQ satisfaction and Web-SQ satisfaction), and

one third-order factor (WIS satisfaction) may

provide a richer explanation of the underlying

structure of WIS satisfaction. However, this model

cannot be uniquely determined and hence cannot be

estimated. According to Rindskopf and Rose (1988),

there must be at least three second-order factors (for

the third-order factor model) if the model is to be

identified

6.4 Psychometric Properties

After examining the overall model fit, we turn to

examine the parameters estimates for Model 5.

Table 4 presents the statistical significance of the

estimated loadings, their corresponding t values, and

R-square values for the 21 observed variables. All

items present significant factor loadings, each with a

t-value higher than 2.00, on their underlying latent

factor. Fornell and Larcker (1987) stated that a

loading of 0.70 to latent variable is considered to be

a high loading since the item explains almost 50

percent of the variance in a particular construct. In

our study, all items have high loadings (0.71 or

above) to its respective construct.

Composite reliability (CR) and average variance

extracted (AVE) are also computed to assess the

construct validity. A composite reliability of 0.70 or

above and an average variance extracted of more

than 0.50 are deemed acceptable (Hair et al., 1998).

As shown in Table 4, all the measures fulfill the

recommended levels, with the composite reliability

ranges from 0.87 to 0.93 and the average variance

extracted ranges from 0.68 to 0.79. Overall, the

measures of the selected model have desirable

psychometric properties.

Figure 1: Factor Loadings and Residence Variances in

Model 5.

Web-IQ

Satisfactio

Web-SQ

Satisfactio

Under-

standabilit

Reliability

Usefulness

Access

Usability

Navigation

UN1

UN2

UN3

UN4

RE1

RE2

RE3

RE4

USE1

USE2

USE3

ACC1

ACC2

ACC3

USA1

USA2

USA3

USA4

NAV1

NAV2

NAV3

0.88 (14.31)

0.88 (14.24)

0.80 (13.94)

0.86 (13.98)

0.86 (14.13)

0.83

(

14.01

)

0.85

(

14.10

)

0.89 (14.38)

0.91 (14.47)

0.87 (14.26)

0.71 (8.79)

0.88 (9.02)

0.88 (9.08)

0.83 (8.35)

0.90 (8.43)

0.89 (8.40)

0.89 (8.41)

0.88 (8.99)

0.83

(

8.99

)

0.84 (9.03)

0.92 (12.17)

0.91 (12.18)

0.91 (12.20)

0.94 (8.44)

0.96 (7.88)

0.95 (8.39)

0.48 (11.27)

Key: 0.84 (9.03) = Factor Loading (Residence Variances)

0.83 (14.21)

WEB-BASED INFORMATION SYSTEMS SATISFACTION - Theoretical Development and Testing of Competing

Models

Table 4: Parameter Estimates For Model 5 (Six first-order

factors and two second-order factors).

7 DISCUSSION AND

CONCLUSION

The results suggested that WIS satisfaction can be

assessed by a large number of highly related factors.

The second-order factor model (Model 5) with six

first-order factors (i.e., understandability, reliability,

usefulness, access, usability, and navigation) and

two correlated second-order factors (i.e., web

information satisfaction and web system satisfaction)

provides a good-fit to the data and is more

theoretically valid, reflecting the logical or formal

consistency.

7.1 Managerial Implications

Understanding WIS satisfaction is particularly

important because a high level of WIS satisfaction is

associated with several key outcomes, including

enhanced IS continuance usage (Bhattacherjee,

2001), the realization of IS success (DeLone and

McLean, 1992; 2003), and improved user

performance (Gelderman, 1998). In the current study,

our higher-order factor model can greatly assist web

designers in understanding how users assess web-

based information systems satisfaction. Essentially,

the model can help explain three basic issues: (1)

what defines WIS satisfaction, (2) how WIS

satisfaction is formed, and (3) which attributes are

relatively important to the formation of WIS

satisfaction. These three factors require managerial

attention in efforts to improve user satisfaction with

the web-based information systems. Thus, we

believe our hierarchical structure model can

substantially enhance web designers’

conceptualization and understanding of WIS

satisfaction.

In addition, the multilevel conceptualization of

WIS satisfaction allows for analysis at different

levels of abstraction. Web designers can use the

complete scale to determine an overall WIS

satisfaction, or they can focus on specific area that is

in need of attention.

7.2 Research Implications

This study also has significant implications for

academics. In response to the call for developing

standardized instruments and completing a research

cycle (Doll et al., 1994), the current study performed

a confirmatory factor analysis on the McKinney et al.

(2002) satisfaction instrument to test the alternative

factor structures of WIS satisfaction and to assess

the psychometric properties of the factors and their

measuring items. Our results provide a strong

support for McKinney et al.’s instrument. An

obvious extension of this research is to conduct

replication studies for other web-based information

systems, and to explore the adaptation of this scale

in other online environment.

Researchers in social sciences argued that the use

of hierarchical factor structure can enhance the

conceptualization and the estimation of human

judgment models. Similarly, we believe our higher-

order factor model can capture users’ overall

evaluation of WIS satisfaction through the

underlying commonality among dimensions in the

second-order factor.

Finally, this study demonstrates the advantages

of using confirmatory factor analysis (CFA) for

comparing alternative factor structures. CFA

facilitates researchers to define alternative models

for the testing of competing models and to generate

parameter estimates of the models. Also, researchers

can easily perform model comparisons using

subjective indicators. However, indeterminacy of

hierarchical models is common when sufficient

restrictions are not imposed. This work has been

restricted to estimating only second-order

Latent

Variable

Observed

Variable

Factor

value

R-

squares

UN1 0.83 14.21 0.76

UN2 0.88 14.32 0.78

UN3 0.88 14.24 0.77

Under-

standability

CR = 0.91

AVE = 0.72

UN4 0.80 13.94 0.70

RE1 0.86 13.17 0.70

RE2 0.86 12.70 0.73

RE3 0.83 13.07 0.70

Reliability

CR = 0.91

AVE = 0.71

RE4 0.85 12.82 0.72

USE1 0.89 11.89 0.75

USE2 0.91 11.30 0.77

Usefulness

CR = 0.92

AVE = 0.79

USE3 0.87 12.45 0.72

ACC1 0.71 14.26 0.50

ACC2 0.88 13.14 0.61

Accountability

CR = 0.87

AVE = 0.68

ACC3 0.88 11.39 0.70

USA1 0.83 13.88 0.68

USA2 0.90 12.85 0.76

USA3 0.89 13.37 0.73

Usability

CR = 0.93

AVE = 0.77

USA4 0.89 13.16 0.74

NAV1 0.88 12.95 0.70

NAV2 0.83 13.03 0.69

Navigation

CR = 0.89

AVE = 0.72

NAV3 0.84 12.14 0.74

WEBIST 2007 - International Conference on Web Information Systems and Technologies

hierarchical models. Future research must attempt to

find means to estimate higher-order structures.

ACKNOWLEDGEMENTS

The work described in this paper was substantially

supported by a grant from the Research Grants

Council of the Hong Kong Special Administrative

Region, China [Project No. CityU 1361/04H].

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WEB-BASED INFORMATION SYSTEMS SATISFACTION - Theoretical Development and Testing of Competing

Models