Technology acceptance model for internet banking: an invariance analysis
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Information & Management 42 (2005) 373–386
Technology acceptance model for internet banking:
an invariance analysis
Vincent S. Lai*, Honglei Li
Faculty of Business Administration, The Chinese University of Hong Kong, Shatin, Hong Kong
Received 20 May 2003; received in revised form 15 January 2004; accepted 21 January 2004
Available online 8 April 2004
Abstract
The technology acceptance model (TAM) has been applied in different contexts to investigate a wide range of information
technologies (IT), and a cumulative tradition has already been developed in this stream of research. Most TAM studies have been
empirical investigations, using the survey approach with great success. TAM is a mature model and has been validated in
different contexts. However, it still needs to be empirically investigated for its invariance across different respondent subgroups
in order to make sure that different sample profiles would not have a negative effect on the findings. Unfortunately, this has not
happened in most TAM research. Here, we applied different levels of invariance analysis on the TAM construct in the context of
Internet banking acceptance. We concluded that the TAM construct was invariant for our sample across different gender, age,
and IT competence subgroups. These findings suggested that male and female, old and young, IT expert and novice,
conceptualized the TAM construct in very similar ways. These findings allowed us to understand TAMs validity in technology
acceptance research.
# 2004 Elsevier B.V. All rights reserved.
Keywords: Internet banking; Invariance analysis; Technology acceptance model
1. Introduction still need to be extended to incorporate different
technologies, users, and organizational contexts
IT acceptance has been the subject of much research [15]. This is especially true when studying e-banking
in the past two decades. Several theories have emerged system, where the technology settings and transaction
that offer new insights into acceptance and use, at both environments are drastically different from conven-
the individual and organizational levels. Among these tional environment.
theories, the technology acceptance model (TAM) has In addition, a few researchers (e.g., [1,13,16]) have
received more attention [22]. A cumulative tradition empirically validated the TAM with demographic
has already been established in its research, especially variables, such as gender and age. Although their
in management and IS disciplines. However, the the- findings suggested that these variables would have
oretical validity and empirical applicability of TAM varied effects on decision processes, we believe that
these effects were caused by the instrument itself.
*
Corresponding author. Tel.: þ852-2609-7811;
Thus the TAM instrument should go through an
fax: þ852-2603-5104. invariance test across such variables prior to its being
E-mail address: vslai@cuhk.edu.hk (V.S. Lai). used in a survey. Researchers need to be sure that their
0378-7206/$ – see front matter # 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.im.2004.01.007
374 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386
instrument is invariant across different subgroups and parameter coefficients are not significantly different
that their sample profiles do not have affect survey from each other in group comparisons. In the past,
findings. For example, if the TAM is applied to several methods have been proposed for testing fac-
investigate the effects of age, gender, and competency torial invariance. Van de Vijver and Harsveld [26]
of IT technology use and adoption, it would then be proposed the examination of the factor parameters of
important for the TAM instrument to be validated for the unconstrained model and identified those with the
its invariance to them. It is of utmost importance that largest between-group differences as being non-invar-
researchers, when defending their findings, state iant. Marsh and Hocevar [19] suggested examining the
whether they are constituted by hypothesized research modification indexes in the fully constrained model
or are artifacts of non-invariance. and interpreting large modification indexes of the
The objective of this study was to validate the TAM associated items as indications of non-invariance.
instrument in the context of Internet banking, with a However, of all the proposed methodologies, Byrne
focus on its non-invariance to age, gender, and IT et al. [2] approach has been more widely accepted and
competence. These demographic variables were applied [5–7], due to the justifiability and rigidity of its
singled out because they may have significant effects approach. This approach applies confirmatory factor
on an individual’s adoption decision. Prior literature analysis (CFA) to derive and compare the chi-square
has already suggested that such variables are critical (w2) and fit statistics of an unconstrained and a series of
factors. Thus, the instrument must be carefully vali- constrained measurement models. The unconstrained
dated for its invariance to these demographic variables model is estimated without any conditions, while the
to ensure the hypothesized effects. constrained models are estimated with the conditions
that one or more specified factor parameters would
have the same value for both groups.
2. Invariance analysis Specifically, configural and factorial invariance
analyses, based on Byrne et al. approach, start with
Empirical study has been a dominant research the unconstrained model. If the fit statistics derived
methodology in the IS field. Researchers following from the model were unsatisfactory, it would then be
this approach adopt surveys and questionnaires to unnecessary to continue with the invariance analysis
investigate the correlations of the variables of their for the subgroups. Otherwise, the invariance analysis
proposed models. However, a central concern is moves on to estimate a fully constrained model. The w2
whether or not all survey respondents have the same and fit statistics of this fully constrained model are
meanings for the survey items. Hence, measurement compared for any difference with the unconstrained
equivalence (invariance) across a sample population is model. If the difference is significant, then the con-
an important issue [9,17]. Researchers (e.g., [23] have struct of at least one of the models would have at least
repeatedly stressed the importance of invariance ana- one non-invariant item. It is then necessary to find the
lysis, with a particular focus on the construct’s form, non-invariant item by devising a series of partially
factorial, and intercept invariance, and urged the devel- constrained models and testing the changes in the chi-
opment of constructs that are operationalized in an square (Dw2) statistics between the models’ constructs
unambiguous way to achieve measurement equiva- for significance. If Dw2 for a partially constrained
lence. If survey items do not display a form of invar- model, when compared to the fully constrained model,
iance, researchers will find it difficult to decide whether is significant, then the constraint associated with
the observed difference arises from the hypothesized this partially constrained model is a source of non-
difference [10]. invariance. Once the invariant items are identified, a
Invariance, or measurement equivalence, exists at researcher has several options for dealing with them,
different levels, with factorial invariance being a including eliminating them from the study, retaining
prerequisite for higher levels of equivalence. A con- them if legitimate arguments can justify their partial
struct is said to have it if item responses of different factorial invariance on results, or treating the variance
groups (e.g., subgroups of age, gender, or country) are as a meaningful source of data concerning differences
associated with the same construct and their factor between groups [11].V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 375
Subsequent to factorial invariance analysis, a 3. Research model
researcher can perform higher levels of measurement
equivalence or invariance by checking the construct’s Our research model is shown in Fig. 1. It is a simple
covariance matrices, error terms, and latent variable TAM, without any external variables, testing perceived
correlations. The performance of these between-groups usefulness (PU) and perceived ease of use (PEOU).
tests is similar to factorial invariance analysis, except As the objective of this study is TAM’s measurement
that the data would be based on covariance matrices, equivalence, the focus of the model is shifted to
error terms, or variable correlations. The source of demonstrate whether gender, age, and IT competency
invariance, based on these methods, could be summar- affect a response to TAM.
ized into two main categories, conceptual disagree-
ment and psychometrical disagreement.
Conceptual disagreement occurs when different 4. Research methodology
groups adopt different concepts or different references
when considering the same construct. Even though 4.1. Instrument development and pre-test
people in different groups see the construct in the same
way, disagreement may still exist as they may consider A survey technique was used to collected data. To
the weight or the loading of the same item on the same ensure the validity and reliability of the questionnaire,
construct differently. Such differences are deeply a three-stage validation was conducted. First, when-
rooted in our brain, primarily because of our training ever possible, previously validated questions and
or experience. generally accepted instrument construction guidelines
Psychometrical disagreement deals mainly with [3,12,24] were followed. Second, the survey was pre-
the invariance of metrical indexes between each tested by three business professors with expertise in
group. This exists when people in different groups survey research, IS, and banking and by fourteen bank
agree conceptually on the same construct but measure customers with Internet banking experience. The feed-
it in different ways. Differences may be manifested as back from this phase resulted in some restructuring
source-specific biases, such as differences in random and refinement of the survey to improve its quality
errors, in the variability of latent factors, and in the and content validity. Third, a pre-test of the question-
correlations among hidden factors. naire was administered to 32 MBA students taking a
Perceived
Usefulness
Attitude Intention to
Towards Use Use
Perceived
Ease of Use
Gender
Age
IT Competency
Fig. 1. Research Model.376 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386
graduate-level class in electronic commerce. Cron- 4.3. Invariance analysis procedure
bach’s alpha values for all question items from this
pre-test were above 0.80, suggesting adequate reli- Confirmatory factor analyses were performed to
ability of the questionnaire [21]. The final version of evaluate the factorial invariance of our measurement
the questionnaire, edited for a few minor changes, is model. The objective of these tests was to check
provided in Appendix A. whether our measurement model had achieved mea-
surement equivalence across different gender, age, and
4.2. Variable operationalizations IT competency groups and find the sources of
between-group-differences that were meaningful to
Studies on PEOU, PU, attitude towards use (ATT), different groups. The sequence of these invariance
and intention to use (ITO) have been well researched, tests is summarized in the flowchart of Fig. 2. As
especially in the context of the TAM application illustrated, a total of six invariance tests were per-
[4,8,18,20]. They have also been developed, validated, formed. The first two on the configural pattern and
and adopted in IT adoption and diffusion research. In factorial loadings were to determine whether the
our study, the items used to measure PEOU, PU, ATT, model had suffered from any invariance due to con-
and ITO were adapted from Davis, Moon and Kim, ceptual disagreement. The last four on measurement
and Teo et al. [25]. errors, latent factor variability, latent factor mean, and
Conceptual Disagreement Test
Configural
Invariance
Factorial
Invariance Fails
Conduct Item Level Invariance Invariance of Factor
Test and Find Solutions Loadings
Factorial
Invariance Exists
Invariance of Random Invariance of Variability of Invariance of Intercept then Invariance of Path
Measurement Error Latent Variables of Latent Factor Mean Coefficients
Variance
Explanation for Source of Differences
Psychometric Disagreement Test
Fig. 2. Flowchart of measurement invariance tests.V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 377
Table 1
Tests for measurement invariance
Test Null hypothesis Test statistic(s) If test statistic significant If test statistic
(H0): (reject H0), then n.s. (fail to
reject H0), then
(1) Invariance of For two groups: w2uncon , CFI, TLI, Stop. Inadequate baseline model Go to test 2
ð1Þ ð2Þ
configurational Lform ¼ Lform other fit indices
loadings
(2) Invariance of For all i, j in the Dw2 ¼ w2con w2uncon , Factorial invariance fails, Go to test 3, 4, 5, 6
factorial loadings model of two changes in other conduct construct and item
ð1Þ ð2Þ
groups lij ¼ lij or fit indices level test and find out solutions.
ð1Þ ð2Þ
Lx ¼ Lx
(3) Invariance of For all i items in the Dw2 ¼ w2con w2uncon , Theta–delta invariance fails. Invariance exists
random model of two groups changes in other Conduct item level test to
ð1Þ ð2Þ
measurement dii ¼ dii fit indices find out significant items and
errors explain the sources of difference
(4) Invariance of For all the latent fators Dw2 ¼ w2con w2uncon , Latent fact, invariance fails.
variability of i in each of the two changes in other Conduct factor level test to find
ð1Þ ð2Þ
latent variables groups fii ¼ fii fit indices out significant factor and explain
the sources of difference
(5) Invariance of For each latent fator Dw2 ¼ w2con w2uncon , Latent factor mean invariance
latent mean of i in each of the two changes in other fails. Explain the sources
ð1Þ ð2Þ
latent variables groups ki ¼ ki fit indices of difference
(6) Invariance of For each the existing Dw2 ¼ w2con w2uncon , Path coefficients invariance
path coefficients latent factors changes in other fails. Explain significant
relationship i, j fit indices relationships
ð1Þ ð2Þ ð1Þ ð2Þ
bij ¼ bij or gij ¼ gij
path coefficients were tests on psychometric disagree- either agreed or strongly agreed that they were IT
ment. Details of these six invariance tests are elabo- competent; whereas only 24.6% disagreed or strongly
rated in Table 1. disagreed.
5.2. Measurement model analysis
5. Results
A CFA using LISREL 8.5 was conducted to test our
5.1. Respondent’s profile measurement model. The overall model fit was
assessed using eight goodness-of-fit indices: w2/degree
Questionnaires were distributed to 312 business of freedom, normalized fit index (NFI), non-normal-
graduate students at a major university in Hong Kong; ized fit index (NNFI), comparative fit index (CFI),
247 were returned. Of these returned questionnaires, goodness of fit index (GFI), adjusted goodness of fit
six were only partially completed and therefore index (AGFI), root mean square residual (RMSR), and
excluded from the data analysis, resulting in an effec- root mean square error of approximation (RMSEA).
tive response rate of 77.2%. These 241 respondents The w2 statistic was not used because of its sensitivity
ranged in age from 21 to over 45, but most (78.4%) to sample size [14]. The results of these indices, along
were between 25 and 40. Using 35 years of age as a with their recommended values for the common model
demarcation line, 134 (55.8%) respondents were cate- fit, are shown in Table 2. Although the GFI index
gorized as ‘young’ while the remaining 44.2% were failed to meet the recommended minimum values, its
‘old’. The distribution of gender was quite balanced, value discrepancy of 0.01 led us to believe that the
with 122 (50.6%) of the respondents being female. On model fit was reasonably adequate to assess the result
a Likert scale of one to five, 51.6% of our respondents for the structural model.378 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386
Table 2
Fit indices for measurement and structural model
Fit indices Recommended value Measurement model Structural model
Chi square/degree of freedom 3.00 2.01 1.95
Normalized fit index (NFI) 0.90 0.95 0.96
Non-normalized fit index (NNFI) 0.90 0.96 0.97
Comparative fit index (CFI) 0.90 0.96 0.98
Goodness of fit index (GFI) 0.90 0.89 0.92
Adjusted goodness of fit (AGFI) 0.80 0.84 0.88
Root mean square residual (RMSR) 0.10 0.06 0.06
Root mean square error of approximation (RMSEA) 0.08 0.08 0.06
Construct reliability was initially evaluated using coefficients indicated that items intended to measure
Cronbach’s alpha reliability test. As indicated in the same construct converged as originally envisaged,
Table 3, the values of all our variables exceed 0.90, suggesting the adequacy of the discriminant validity of
which was significantly above the 0.7 level suggested our measurement model.
for exploratory research, justifying the reliability of
our measurements for model testing. Additionally, a 5.3. Invariance analyses
discriminant validity test was performed using factor
analysis. A varimax-rotated principal component With the validation of our model’s applicability,
factor analysis was conducted and the results are invariance analyses were then performed to determine
given in Table 3. As shown, a total of four factors the effect of gender, age and IT competence on the
were extracted; these matched the number of con- construct of our model. As a first step, a configural
structs in our research model. A review of the loading invariance test was conducted to determine if males
Table 3
Summary of measurement scales
Construct Mean S.D. Cronbach’s alpha Factor loading
1 2 3 4
Perceived usefulness
PU1 5.02 1.27 0.801
PU2 5.01 1.22 0.822
PU3 5.05 1.19 0.95 0.834
PU4 5.04 1.22 0.842
PU5 4.71 1.29 0.786
PU6 5.13 1.29 0.747
Perceived ease of use
EOU1 5.50 1.15 0.850
EOU2 5.30 1.18 0.90 0.868
EOU3 5.36 1.22 0.798
Attitude towards use
ATT1 5.00 1.26 0.763
ATT2 5.04 1.27 0.95 0.783
ATT3 4.99 1.31 0.806
Intention to use
ITO1 4.82 1.34 0.826
ITO2 4.78 1.35 0.94 0.767
ITO3 4.61 1.37 0.811V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 379
Table 4
Results of configural invariance analysis for gender, age and IT competence
w2 df IFI CFI NNFI RMSEA
Gender Male 114.1 84 0.97 0.97 0.96 0.06
Female 109.9 84 0.98 0.98 0.97 0.05
Stacked model 223.9 168 0.97 0.97 0.97 0.06
Age Old 127.3 84 0.96 0.96 0.95 0.07
Young 120.9 84 0.97 0.97 0.96 0.06
Stacked model 248.3 168 0.96 0.96 0.95 0.06
IT competence Expert 107.1 84 0.98 0.98 0.97 0.06
Novice 142.9 84 0.95 0.95 0.93 0.08
Stacked model 250.8 168 0.96 0.96 0.95 0.07
and females would use the same pattern in measuring justifying the invariance of the unconstrained and con-
the items; if this occured, the data of each group fits the strained models.
model well but if different genders used a different Following this comparison, models that constrained
pattern of items for the same construct, configural non- individual constructs (PEOU, PU, ATT, and ITO) were
invariance would exist and further invariance analyses set up for further factorial invariance analysis. The
would be unnecessary. The results of our configural results, shown in Table 5, suggest that all Dw2 with
invariance analysis, shown in Table 4, suggest that the Ddf, and fit statistics are not significantly different
w2 and fit indices for each gender group are good between the models compared. Through these tests, it
enough, providing evidence of the configural invar- is concluded that our model fits the construct very well
iance of the construct. Similar results, also shown in and that the factor loadings for the three factors do
Table 4, were obtained when conducting configural not have any non-invariance, justifying the factorial
analysis for age and IT competence, supporting that invariance of our construct.
configural invariance exists for the gender, age and IT Following the validation of our construct’s factorial
competence groups. invariance, a theta–delta invariance test was carried
In the second step, factorial analysis was performed out to ensure that the error terms of our three sub-
to determine if males and females, old and young, groups were non-invariant. Since theta–delta is related
people with high and low IT competence conceptua- to reliability issues, this invariance test could be
lize our Internet banking construct in the same way. If considered as validating the reliability equivalence
gender has an effect on the measurement equivalence of the three pairs of groups. If theta–delta non-invar-
of the construct, observed scores from the groups iance existed in our construct, it could be caused by a
would be on a different scale and therefore would different understanding of the surveyed items between
not be directly comparable. In such a scenario, we the three subgroups in the PEOU, PU, ITO, and ATT of
would then need to identify the observed items that Internet banking.
caused such non-invariance. The situation is similar As shown in Table 6, the gender Dw2 was found to be
for age and IT competence. significantly different between the unconstrained and
In performing such factorial invariance analysis, an fully constrained models, despite their comparable
unconstrained baseline model was initially established, NNFI, CFI and RMSEA statistics. The source of
followed by a fully constrained model. The Dw2 and non-invariance, which was tested using the partially
Ddf and fit statistics (in our case, NNFI, CFI, and constrained models, was clearly ITO and ATT. These
RMSEA) of the two models were then calculated for findings suggest that their error terms between
comparison purposes. According to the results in males and females are different. Similar invariance
Table 5, the changes in Dw2 with Ddf for gender, tests were conducted for age and IT competency. As
age, and IT competency are not significant; and the indicated, the error variances of PU were found to
fit statistics of the two models are also quite comparable, have significant difference between the young and380 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386
Table 5
Results of factorial invariance analysis for gender, age and IT competence
Test Model w2 df Dw2 Ddf NNFI CFI RMSEA
Gender 1 Unconstrained baseline model 223.9 168 0.97 0.97 0.06
2 Fully constrained model 236.9 179 13.0 11 0.97 0.97 0.06
2.1 Loadings on PEOU 227.7 170 3.8 2 0.97 0.97 0.06
2.2 Loadings on PU 229.7 173 5.7 5 0.97 0.97 0.06
2.3 Loadings on ATT 224.8 170 0.9 2 0.97 0.97 0.06
2.4 Loadings on ITO 226.6 170 2.7 2 0.97 0.97 0.06
Age 1 Unconstrained baseline model 248.3 168 0.95 0.96 0.06
2 Fully constrained model 256.8 179 8.5 11 0.96 0.97 0.06
2.1 Loadings on PEOU 248.5 170 0.2 2 0.96 0.96 0.06
2.2 Loadings on PU 249.1 173 0.8 5 0.96 0.97 0.06
2.3 Loadings on ATT 250.2 170 1.9 2 0.96 0.96 0.06
2.4 Loadings on ITO 253.9 170 5.6 2 0.95 0.96 0.06
IT Competence 1 Unconstrained baseline model 250.8 168 0.95 0.96 0.07
2 Fully constrained model 263.8 179 12.9 11 0.96 0.96 0.07
2.1 Loadings on PEOU 251.9 170 1.1 2 0.96 0.96 0.07
2.2 Loadings on PU 255.4 173 4.6 5 0.96 0.96 0.07
2.3 Loadings on ATT 255.5 170 4.7 2 0.95 0.96 0.07
2.4 Loadings on ITO 253.4 170 2.6 2 0.95 0.96 0.07
old groups, which implied that the error terms of understanding the questionnaire, or measurement
PU between old and young groups was significantly error, etc. For the groups of people with high and
different. A possible source for the latent factor low IT competence, the results showed that the error
error terms may have been their different ability in terms of PU and ATT are non-invariant. This result is
Table 6
Results of theta–delta analysis for gender, age and IT competence
Test Model w2 df Dw2 Ddf NNFI CFI RMSEA
Gender 2 Factorial invariance model 236.9 179 0.97 0.97 0.06
3 Fully constrained model 278.2 194 41.3** 15 0.96 0.96 0.05
3.1 Delta of PEO 239.1 182 2.2 3 0.97 0.97 0.06
3.2 Delta of PU 248.2 185 11.2 6 0.97 0.97 0.06
3.3 Delta of ATT 255.3 182 18.3** 3 0.96 0.97 0.07
3.4 Delta of ITO 246.3 182 9.3* 3 0.97 0.97 0.06
Age 2 Factorial invariance model 256.8 179 0.96 0.97 0.06
3 Fully constrained model 277.1 194 20.3 15 0.96 0.96 0.06
3.1 Delta of PEO 257.4 182 0.6 3 0.96 0.97 0.06
3.2 Delta of PU 271.4 185 14.5* 6 0.96 0.96 0.06
3.3 Delta of ATT 261.8 182 4.9 3 0.96 0.96 0.06
3.4 Delta of ITO 257.0 182 0.2 3 0.96 0.97 0.06
IT Competence 2 Factorial invariance model 263.8 179 0.96 0.96 0.07
3 Fully constrained model 321.5 194 57.6** 15 0.94 0.95 0.07
3.1 Delta of PEO 268.4 182 4.6 3 0.96 0.96 0.07
3.2 Delta of PU 290.8 185 27.0** 6 0.95 0.95 0.08
3.3 Delta of ATT 275.8 182 11.9** 3 0.95 0.96 0.07
3.4 Delta of ITO 269.0 182 5.2 3 0.96 0.96 0.07
*
P < 0:05.
**
P < 0:01.V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 381
Table 7
Results of the covariance of latent variables invariance analysis for gender, age and IT competence
Test Model w2 df Dw2 Ddf TLI CFI RMSEA
Gender 2 Factorial invariance model 236.9 179 0.97 0.97 0.06
4.1 COV (PEOU) 238.3 180 1.3 1 0.97 0.97 0.06
4.2 COV (PU)) 237.3 180 0.3 1 0.97 0.97 0.06
4.3 COV (ATT) 237.2 180 0.3 1 0.97 0.97 0.06
4.4 COV (ITO) 236.9 180 0.0 1 0.97 0.97 0.06
Age 2 Factorial invariance model 256.8 179 0.96 0.97 0.06
4.1 COV (PEOU) 256.8 180 0.0 1 0.96 0.97 0.06
4.2 COV (PU)) 257.1 180 0.3 1 0.96 0.97 0.06
4.3 COV (ATT) 258.6 180 1.8 1 0.96 0.96 0.06
4.4 COV (ITO) 258.5 180 1.7 1 0.96 0.96 0.06
IT competence 2 Factorial invariance model 263.8 179 0.96 0.96 0.07
4.1 COV (PEOU) 264.1 180 0.3 1 0.96 0.96 0.07
4.2 COV (PU)) 263.9 180 0.1 1 0.96 0.96 0.07
4.3 COV (ATT) 266.1 180 2.4 1 0.96 0.96 0.07
4.4 COV (ITO) 265.1 180 1.3 1 0.96 0.96 0.07
* **
P < 0:05; P < 0:01.
similar to the gender effect. The error term between PU ! ATT. The difference in the behavior of people
PU and ATT are very different for the people with high with high and low IT competence of the TAM, in
and low IT competence. the context of Internet banking, resulted from the
Subsequent to the theta–delta invariance test, a differences in the coefficients of PU ! ITO and
latent factor variables invariance test was performed ATT ! ITO.
to determine if the variance of the construct between
the latent variables was the same for our three sub- 5.4. Model testing
groups. The results, in Table 7, do not suggest any
significant difference, thereby, implying that the var- LISREL 8.5 was used to test our research model with
iations of each construct for the three pairs of groups the sample covariance matrix shown in Appendix B
are not significantly different. Similar conclusions as input. The results, as listed in Table 2, show that all
were drawn on the invariance test on the latent factor eight fit indices for our testing model (w2/df ¼ 1:95,
mean. The findings, given in Table 8, suggest that only NFI ¼ 0:96, NNFI ¼ 0:97, CFI ¼ 0:98, GFI ¼ 0:92,
gender was found to have latent mean non-invariance AGFI ¼ 0:88, RMSR ¼ 0:06, and RMSEA ¼ 0:06)
for its subgroups. This difference between the evalua- have clearly exceeded the minimum recommended
tion of the TAM by males and females, in the context values suggested for a good model fit, implying the
of Internet banking, resulted from the differences in adequacy of our model for further statistical analysis,
the latent mean of attitude. including its causal link evaluation. Subsequently, the
The final invariance test on coefficient invariance Internet banking TAM was run separately for adopters,
was performed to determine if the gender, age, and IT non-adopters, and a combination of both. The results
competency subgroups had a different relationship of these three runs, given in Table 10, show that the
with some variables in our TAM model. The findings, TAM is an appropriate model for studying Internet
shown in Table 9, suggest that only IT competence banking acceptance. Of the three runs, the adopters
was found to have coefficient invariance for its sub- group provided the best support for the TAM, with
groups. The age and IT competence groups were non- all variables significant at P < 0:01. Interestingly,
invariant in a certain relational path. The difference although all the TAM variables of the non-adopters
between the old and the young people’s behavior in group were significant from P < 0:05 to382 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386
Table 8
Results of latent mean invariance analysis for gender, age and IT competence
Test Model w2 df Dw2 Ddf TLI CFI RMSEA
Gender 2 Factorial invariance model 236.9 179 0.97 0.97 0.06
5 Intercept invariance model 251.8 190 14.9 11 0.97 0.97 0.06
6.1 Latent mean of PEOU 254.9 191 3.1 1 0.97 0.97 0.06
6.2 Latent mean of PU 255.2 191 3.4 1 0.97 0.97 0.06
6.3 Latent mean of ATT 256.5 191 4.7** 1 0.97 0.97 0.06
6.4 Latent mean of ITO 252.3 191 0.5 1 0.97 0.97 0.06
Age 2 Factorial invariance model 256.8 179 0.96 0.97 0.06
5 Intercept invariance model 265.7 190 8.9 11 0.96 0.97 0.05
6.1 Latent mean of PEOU 266.1 191 0.4 1 0.96 0.97 0.05
6.2 Latent mean of PU 267.1 191 1.4 1 0.96 0.97 0.05
6.3 Latent mean of ATT 265.8 191 0.1 1 0.96 0.97 0.05
6.4 Latent mean of ITO 265.7 191 0.0 1 0.96 0.96 0.05
IT Competence 2 Factorial invariance model 263.8 179 0.96 0.96 0.07
5 Intercept invariance model 268.2 190 4.4 11 0.96 0.97 0.06
6.1 Latent mean of PEOU 268.4 191 0.2 1 0.96 0.97 0.06
6.2 Latent mean of PU 268.2 191 0.0 1 0.96 0.97 0.06
6.3 Latent mean of ATT 268.8 191 0.6 1 0.96 0.97 0.06
6.4 Latent mean of ITO 268.3 191 0.2 1 0.96 0.97 0.06
*
P < 0:01.
**
P < 0:05.
Table 9
Results of path coefficient invariance analysis for gender, age and IT competence
Test Model w2 df Dw2 Ddf TLI CFI RMSEA
Gender 2 Factorial invariance model 237.0 181 0.97 0.97 0.06
6.1 Equal b(2,1) PU ! ATT 243.7 182 6.7** 1 0.97 0.97 0.06
6.2 Equal b(3,1) PU ! ITO 237.1 182 0.1 1 0.97 0.97 0.06
6.3 Equal b(3,2) ATT ! ITO 237.0 182 0.0 1 0.97 0.97 0.06
6.4 Equal g(1,1) PEOU ! PU 237.3 182 0.3 1 0.97 0.97 0.06
6.5 Equal g(2,1) PEOU ! ATT 241.0 182 3.9* 1 0.97 0.97 0.06
Age 2 Factorial invariance model 260.4 181 0.96 0.96 0.06
6.1 Equal b(2,1) PU ! ATT 263.6 182 3.3 1 0.96 0.96 0.06
6.2 Equal b(3,1) PU ! ITO 265.7 182 5.3* 1 0.96 0.96 0.06
6.3 Equal b(3,2) ATT ! ITO 265.7 182 5.4* 1 0.96 0.96 0.05
6.4 Equal g(1,1) PEOU ! PU 261.0 182 0.6 1 0.96 0.96 0.06
6.5 Equal g(2,1) PEOU ! ATT 261.4 182 1.0 1 0.96 0.96 0.06
IT Competence 2 Factorial invariance model 269.1 181 0.95 0.96 0.07
6.1 Equal b(2,1) PU ! ATT 270.0 182 0.9 1 0.95 0.96 0.07
6.2 Equal b(3,1) PU ! ITO 269.5 182 0.5 0.96 0.96 0.07
6.3 Equal b(3,2) ATT ! TO 269.8 182 0.8 1 0.96 0.96 0.07
6.4 Equal g(1,1) PEOU ! PU 269.6 182 0.6 1 0.96 0.96 0.07
6.5 Equal g(2,1) PEOU ! ATT 271.3 182 2.2 1 0.95 0.96 0.07
*
P < 0:05.
**
P < 0:01.V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 383
Table 10
Results classified by adoption types
Research paths Adopters (t-value) Non-adopters (t-value) Overall (t-value)
*** ***
PEOU ! PU 7.23 5.54 11.02***
PEOU ! attitude 2.96*** 3.51*** 4.30***
PU ! attitude 5.86*** 2.03* 6.67***
PU ! intention 3.09** 2.29* 0.88
Attitude ! intention 7.47*** 8.45*** 11.89***
*
P < 0:05.
**
P < 0:01.
***
P < 0:001.
group, the overall effect of the PU on the ITO was error terms of PU and ATT for individuals with higher
found not to be significant. and lower IT competency; and (3) between males and
females in their coefficient paths of PEOU ! ATT.
Thus, invariance was found in our TAM construct in
6. Discussion the areas of measurement errors and path coefficients.
The implications of these non-invariance findings to
Our invariance analyses provided better under- researchers are straight forward. They need to go back
standing of TAMs applicability in Internet banking to the questionable items and evaluate the wordings,
adoption and its construct invariance validity. From a semantics, and structures of the questions for improve-
series of invariance analyses, we concluded that our ments. However, researchers must be aware of the fact
TAM construct was invariance of configural loadings, that developing a questionnaire free of misconceptions
factorial loadings, covariance of latent variables, and for all different sample subgroups is almost impossi-
latent mean across gender, age, and IT competence. Of ble: researchers should consider and validate measure-
the six invariance tests conducted, four were found ment invariance across a sample population when
invariant, including the most important configural and designing their survey instrument.
factorial loadings. This conclusion suggested that
males and females, old and young, and IT novices
and experts, in general, conceptualize the TAMs con- 7. Conclusion
struct, including its variables PEOU, PU, ITO, and
ATT, in the same way and with the same factor The reliability and invariance analyses supported the
loading. This conclusion also suggested that our find- validity of our TAM instrument for evaluating Internet
ings do not suffer from biases of gender, age, and IT banking acceptance. The LISREL results on path coef-
competency of the respondents’ demographic profiles. ficients also indicate the significance of our model and
With such a confirmation, we are confident that our the correlations of almost all the research variables. As
research confirmed the applicability of the TAM in expected, the relationships between PEOU, PU, ATT,
Internet banking research. We are also confident that and ITO were positive and highly significant, which
this research concludes that PEOU and PU, free from was consistent with prior TAM research. These find-
possible gender, age, and IT competency biases, were ings support prior research showing that TAM is a good
critical in influencing the ATT and ITO of Internet model for evaluating intention and actual use of IT.
banking usage. The only unexpected finding was the path of
Although our invariance analyses suggested that our PU ! ITO, which was not supported in our study.
TAM construct, to a large extent, was invariant to More interestingly, this non-significance was due to a
gender, age, and IT competency, we detected some conflicting significant positive perspective of adopters
sources of non-invariance among these variable sub- and a significant negative perspective of non-adopters
groups. There were differences: (1) between males and of the correlation. Obviously, the adopters were con-
females in the error terms for ATT and ITO; (2) in the vinced of the usefulness of Internet banking, which384 V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 consequently affected their intention to use the tech- 8. Limitations nology. However, the non-adopters found Internet banking less useful. In fact, the current support and Our research could have been improved if a random capacity of Internet banking was rather limited, as it sample of Internet banking users had been selected. only provided fundamental bank account management The use of student subjects in this research was due and online stock trading services. These services, mainly to the convenience of the sample and the unfortunately, could be easily replaced by other tech- refusal of local banks to reveal their customer infor- nologies, such as phone banking, and other services, mation. Although the profiles of our graduate students such as traditional brokerage and online brokers’ are similar to the current Internet banking users’ Internet trading services. When substitutes for Internet profiles, it would have been better if a random sample banking were so plentiful and their replacement costs of Internet banking users had been provided by local were minimal, non-adopters may not have had a banks. positive intention to use Internet banking even though they found it useful. As invariance analyses were performed prior to Acknowledgements running our research model, we can be sure of our research findings, free from gender, age and IT com- The authors would like to acknowledge the financial petency biases, in the Internet banking context. To support (Direct Research Grant #4450004) of the date, few TAM research studies have validated their Chinese University of Hong Kong in the conduct of constructs with invariance analysis. this research. Appendix A. Survey questionnaire Perceived usefulness PU1 I can accomplish my banking tasks more quickly using Internet Banking. PU2 I can accomplish my banking tasks more easily using Internet Banking. PU3 Internet Banking enhances my effectiveness in utilizing banking services. PU4 Internet Banking enhances my efficiency in utilizing banking services. PU5 Internet Banking enables me to make better decisions in utilizing banking services. PU6 Overall, I find Internet Banking useful. Perceived ease of use PEOU1 Learning to use Internet Banking is easy for me. PEOU2 It is easy to use Internet Banking to accomplish my banking tasks. PEOU3 Overall, I believe Internet Banking is easy to use. Attitude ATT1 In my opinion, it is desirable to use Internet Banking. ATT2 I think it is good for me to use Internet Banking. ATT3 Overall, my attitude towards Internet Banking is favorable. Intention to use ITO1 I will use Internet banking on a regular basis in the future. ITO2 I will frequently use Internet banking in the future. ITO3 I will strongly recommend others to use Internet banking.
V.S. Lai, H. Li / Information & Management 42 (2005) 373–386 385
Appendix B. Covariance matrix
PEOU1 PEOU2 PEOU3 PU1 PU2 PU3 PU4 PU5 PU6 ATT1 ATT2 ATT3 ITO1 ITO2 ITO3
PEOU1 1.33
PEOU2 0.96 1.35
PEOU3 1.06 1.09 1.49
PU1 0.78 0.86 0.92 1.61
PU2 0.70 0.83 0.91 1.36 1.48
PU3 0.69 0.83 0.88 1.24 1.20 1.41
PU4 0.69 0.88 0.87 1.25 1.24 1.32 1.48
PU5 0.57 0.68 0.71 1.02 1.04 1.09 1.13 1.65
PU6 0.80 0.92 0.90 1.26 1.21 1.22 1.29 1.07 1.66
ATT1 0.82 0.84 0.85 0.95 0.89 0.96 0.97 0.77 1.08 1.58
ATT2 0.79 0.83 0.81 0.95 0.90 0.93 0.92 0.74 1.05 1.38 1.58
ATT3 0.85 0.82 0.87 1.05 1.01 0.99 1.04 0.77 1.13 1.37 1.41 1.72
ITO1 0.81 0.85 0.86 0.93 0.93 0.90 0.95 0.71 0.97 1.22 1.22 1.39 1.80
ITO2 0.76 0.85 0.80 0.87 0.90 0.93 0.96 0.70 0.97 1.23 1.23 1.38 1.67 1.83
ITO3 0.83 0.87 0.82 0.89 0.88 0.92 0.96 0.82 0.97 1.21 1.22 1.33 1.45 1.48 1.87
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