Technology acceptance model for internet banking ... - Semantic Scholar

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 IT acceptance has been the subject of much research in the past two decades. Several theories have emerged that offer new insights into acceptance and use, at both the individual and organizational levels. Among these theories, the technology acceptance model (TAM) has received more attention [22]. A cumulative tradition has already been established in its research, especially in management and IS disciplines. However, the theoretical validity and empirical applicability of TAM *

Corresponding author. Tel.: þ852-2609-7811; fax: þ852-2603-5104. E-mail address: [email protected] (V.S. Lai).

still need to be extended to incorporate different technologies, users, and organizational contexts [15]. This is especially true when studying e-banking system, where the technology settings and transaction environments are drastically different from conventional environment. In addition, a few researchers (e.g., [1,13,16]) have empirically validated the TAM with demographic variables, such as gender and age. Although their findings suggested that these variables would have varied effects on decision processes, we believe that these effects were caused by the instrument itself. Thus the TAM instrument should go through an invariance test across such variables prior to its being 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

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V.S. Lai, H. Li / Information & Management 42 (2005) 373–386

instrument is invariant across different subgroups and that their sample profiles do not have affect survey findings. For example, if the TAM is applied to investigate the effects of age, gender, and competency of IT technology use and adoption, it would then be important for the TAM instrument to be validated for its invariance to them. It is of utmost importance that researchers, when defending their findings, state whether they are constituted by hypothesized research or are artifacts of non-invariance. The objective of this study was to validate the TAM instrument in the context of Internet banking, with a focus on its non-invariance to age, gender, and IT competence. These demographic variables were singled out because they may have significant effects on an individual’s adoption decision. Prior literature has already suggested that such variables are critical factors. Thus, the instrument must be carefully validated for its invariance to these demographic variables to ensure the hypothesized effects.

2. Invariance analysis Empirical study has been a dominant research methodology in the IS field. Researchers following this approach adopt surveys and questionnaires to investigate the correlations of the variables of their proposed models. However, a central concern is whether or not all survey respondents have the same meanings for the survey items. Hence, measurement equivalence (invariance) across a sample population is an important issue [9,17]. Researchers (e.g., [23] have repeatedly stressed the importance of invariance analysis, with a particular focus on the construct’s form, factorial, and intercept invariance, and urged the development of constructs that are operationalized in an unambiguous way to achieve measurement equivalence. If survey items do not display a form of invariance, researchers will find it difficult to decide whether the observed difference arises from the hypothesized difference [10]. Invariance, or measurement equivalence, exists at different levels, with factorial invariance being a prerequisite for higher levels of equivalence. A construct is said to have it if item responses of different groups (e.g., subgroups of age, gender, or country) are associated with the same construct and their factor

parameter coefficients are not significantly different from each other in group comparisons. In the past, several methods have been proposed for testing factorial invariance. Van de Vijver and Harsveld [26] proposed the examination of the factor parameters of the unconstrained model and identified those with the largest between-group differences as being non-invariant. Marsh and Hocevar [19] suggested examining the modification indexes in the fully constrained model and interpreting large modification indexes of the associated items as indications of non-invariance. However, of all the proposed methodologies, Byrne et al. [2] approach has been more widely accepted and applied [5–7], due to the justifiability and rigidity of its approach. This approach applies confirmatory factor analysis (CFA) to derive and compare the chi-square (w2) and fit statistics of an unconstrained and a series of constrained measurement models. The unconstrained model is estimated without any conditions, while the constrained models are estimated with the conditions that one or more specified factor parameters would have the same value for both groups. Specifically, configural and factorial invariance analyses, based on Byrne et al. approach, start with the unconstrained model. If the fit statistics derived from the model were unsatisfactory, it would then be unnecessary to continue with the invariance analysis for the subgroups. Otherwise, the invariance analysis moves on to estimate a fully constrained model. The w2 and fit statistics of this fully constrained model are compared for any difference with the unconstrained model. If the difference is significant, then the construct of at least one of the models would have at least one non-invariant item. It is then necessary to find the non-invariant item by devising a series of partially constrained models and testing the changes in the chisquare (Dw2) statistics between the models’ constructs for significance. If Dw2 for a partially constrained model, when compared to the fully constrained model, is significant, then the constraint associated with this partially constrained model is a source of noninvariance. Once the invariant items are identified, a researcher has several options for dealing with them, including eliminating them from the study, retaining them if legitimate arguments can justify their partial factorial invariance on results, or treating the variance as a meaningful source of data concerning differences between groups [11].


Subsequent to factorial invariance analysis, a researcher can perform higher levels of measurement equivalence or invariance by checking the construct’s covariance matrices, error terms, and latent variable correlations. The performance of these between-groups tests is similar to factorial invariance analysis, except that the data would be based on covariance matrices, error terms, or variable correlations. The source of invariance, based on these methods, could be summarized into two main categories, conceptual disagreement and psychometrical disagreement. Conceptual disagreement occurs when different groups adopt different concepts or different references when considering the same construct. Even though people in different groups see the construct in the same way, disagreement may still exist as they may consider the weight or the loading of the same item on the same construct differently. Such differences are deeply rooted in our brain, primarily because of our training or experience. Psychometrical disagreement deals mainly with the invariance of metrical indexes between each group. This exists when people in different groups agree conceptually on the same construct but measure it in different ways. Differences may be manifested as source-specific biases, such as differences in random errors, in the variability of latent factors, and in the correlations among hidden factors.

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3. Research model Our research model is shown in Fig. 1. It is a simple TAM, without any external variables, testing perceived usefulness (PU) and perceived ease of use (PEOU). As the objective of this study is TAM’s measurement equivalence, the focus of the model is shifted to demonstrate whether gender, age, and IT competency affect a response to TAM.

4. Research methodology 4.1. Instrument development and pre-test A survey technique was used to collected data. To ensure the validity and reliability of the questionnaire, a three-stage validation was conducted. First, whenever possible, previously validated questions and generally accepted instrument construction guidelines [3,12,24] were followed. Second, the survey was pretested by three business professors with expertise in survey research, IS, and banking and by fourteen bank customers with Internet banking experience. The feedback from this phase resulted in some restructuring and refinement of the survey to improve its quality and content validity. Third, a pre-test of the questionnaire was administered to 32 MBA students taking a

Perceived Usefulness

Attitude Towards Use Perceived Ease of Use

Gender Age IT Competency

Fig. 1. Research Model.

Intention to Use

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graduate-level class in electronic commerce. Cronbach’s alpha values for all question items from this pre-test were above 0.80, suggesting adequate reliability of the questionnaire [21]. The final version of the questionnaire, edited for a few minor changes, is provided in Appendix A. 4.2. Variable operationalizations Studies on PEOU, PU, attitude towards use (ATT), and intention to use (ITO) have been well researched, especially in the context of the TAM application [4,8,18,20]. They have also been developed, validated, and adopted in IT adoption and diffusion research. In our study, the items used to measure PEOU, PU, ATT, and ITO were adapted from Davis, Moon and Kim, and Teo et al. [25].

4.3. Invariance analysis procedure Confirmatory factor analyses were performed to evaluate the factorial invariance of our measurement model. The objective of these tests was to check whether our measurement model had achieved measurement equivalence across different gender, age, and IT competency groups and find the sources of between-group-differences that were meaningful to different groups. The sequence of these invariance tests is summarized in the flowchart of Fig. 2. As illustrated, a total of six invariance tests were performed. The first two on the configural pattern and factorial loadings were to determine whether the model had suffered from any invariance due to conceptual disagreement. The last four on measurement errors, latent factor variability, latent factor mean, and

Conceptual Disagreement Test

Configural Invariance

Factorial Invariance Fails Conduct Item Level Invariance Test and Find Solutions

Invariance of Factor Loadings

Factorial Invariance Exists

Invariance of Random Measurement Error Variance

Invariance of Variability of Latent Variables

Invariance of Intercept then of Latent Factor Mean

Explanation for Source of Differences

Psychometric Disagreement Test

Fig. 2. Flowchart of measurement invariance tests.

Invariance of Path Coefficients


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Table 1 Tests for measurement invariance

(1)

(2)

(3)

(4)

(5)

(6)

Test

Null hypothesis (H0):

Test statistic(s)

If test statistic significant (reject H0), then

If test statistic n.s. (fail to reject H0), then

Invariance of configurational loadings Invariance of factorial loadings

For two groups: ð1Þ ð2Þ Lform ¼ Lform

w2uncon , CFI, TLI, other fit indices

Stop. Inadequate baseline model

Go to test 2

For all i, j in the model of two ð1Þ ð2Þ groups lij ¼ lij or ð1Þ ð2Þ Lx ¼ Lx For all i items in the model of two groups ð1Þ ð2Þ dii ¼ dii

Dw2 ¼ w2con w2uncon , changes in other fit indices

Factorial invariance fails, conduct construct and item level test and find out solutions.

Go to test 3, 4, 5, 6


Invariance exists

For all the latent fators i in each of the two ð1Þ ð2Þ groups fii ¼ fii


For each latent fator i in each of the two ð1Þ ð2Þ groups ki ¼ ki For each the existing latent factors relationship i, j ð1Þ ð2Þ ð1Þ ð2Þ bij ¼ bij or gij ¼ gij

Dw2 ¼ w2con w2uncon , changes in other fit indices Dw2 ¼ w2con w2uncon , changes in other fit indices

Theta–delta invariance fails. Conduct item level test to find out significant items and explain the sources of difference Latent fact, invariance fails. Conduct factor level test to find out significant factor and explain the sources of difference Latent factor mean invariance fails. Explain the sources of difference Path coefficients invariance fails. Explain significant relationships

Invariance of random measurement errors Invariance of variability of latent variables Invariance of latent mean of latent variables Invariance of path coefficients

path coefficients were tests on psychometric disagreement. Details of these six invariance tests are elaborated in Table 1.

either agreed or strongly agreed that they were IT competent; whereas only 24.6% disagreed or strongly disagreed. 5.2. Measurement model analysis

5. Results 5.1. Respondent’s profile Questionnaires were distributed to 312 business graduate students at a major university in Hong Kong; 247 were returned. Of these returned questionnaires, six were only partially completed and therefore excluded from the data analysis, resulting in an effective response rate of 77.2%. These 241 respondents ranged in age from 21 to over 45, but most (78.4%) were between 25 and 40. Using 35 years of age as a demarcation line, 134 (55.8%) respondents were categorized as ‘young’ while the remaining 44.2% were ‘old’. The distribution of gender was quite balanced, with 122 (50.6%) of the respondents being female. On a Likert scale of one to five, 51.6% of our respondents

A CFA using LISREL 8.5 was conducted to test our measurement model. The overall model fit was assessed using eight goodness-of-fit indices: w2/degree of freedom, normalized fit index (NFI), non-normalized fit index (NNFI), comparative fit index (CFI), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), root mean square residual (RMSR), and root mean square error of approximation (RMSEA). The w2 statistic was not used because of its sensitivity to sample size [14]. The results of these indices, along with their recommended values for the common model fit, are shown in Table 2. Although the GFI index failed to meet the recommended minimum values, its value discrepancy of 0.01 led us to believe that the model fit was reasonably adequate to assess the result for the structural model.

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Table 2 Fit indices for measurement and structural model Fit indices

Recommended value

Measurement model

Structural model

Chi square/degree of freedom Normalized fit index (NFI) Non-normalized fit index (NNFI) Comparative fit index (CFI) Goodness of fit index (GFI) Adjusted goodness of fit (AGFI) Root mean square residual (RMSR) Root mean square error of approximation (RMSEA)

3.00 0.90 0.90 0.90 0.90 0.80 0.10 0.08

2.01 0.95 0.96 0.96 0.89 0.84 0.06 0.08

1.95 0.96 0.97 0.98 0.92 0.88 0.06 0.06

Construct reliability was initially evaluated using Cronbach’s alpha reliability test. As indicated in Table 3, the values of all our variables exceed 0.90, which was significantly above the 0.7 level suggested for exploratory research, justifying the reliability of our measurements for model testing. Additionally, a discriminant validity test was performed using factor analysis. A varimax-rotated principal component factor analysis was conducted and the results are given in Table 3. As shown, a total of four factors were extracted; these matched the number of constructs in our research model. A review of the loading

coefficients indicated that items intended to measure the same construct converged as originally envisaged, suggesting the adequacy of the discriminant validity of our measurement model. 5.3. Invariance analyses With the validation of our model’s applicability, invariance analyses were then performed to determine the effect of gender, age and IT competence on the construct of our model. As a first step, a configural invariance test was conducted to determine if males

Table 3 Summary of measurement scales Construct

Mean

S.D.

Cronbach’s alpha

Factor loading 1

Perceived usefulness PU1 5.02 PU2 5.01 PU3 5.05 PU4 5.04 PU5 4.71 PU6 5.13

1.27 1.22 1.19 1.22 1.29 1.29

Perceived ease of use EOU1 5.50 EOU2 5.30 EOU3 5.36

1.15 1.18 1.22

0.90

Attitude towards use ATT1 5.00 ATT2 5.04 ATT3 4.99

1.26 1.27 1.31

0.95

Intention to use ITO1 ITO2 ITO3

1.34 1.35 1.37

0.94

4.82 4.78 4.61

0.95

2

3

4

0.801 0.822 0.834 0.842 0.786 0.747 0.850 0.868 0.798 0.763 0.783 0.806 0.826 0.767 0.811


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Table 4 Results of configural invariance analysis for gender, age and IT competence w2

df

IFI

CFI

NNFI

RMSEA

Gender

Male Female Stacked model

114.1 109.9 223.9

84 84 168

0.97 0.98 0.97

0.97 0.98 0.97

0.96 0.97 0.97

0.06 0.05 0.06

Age

Old Young Stacked model

127.3 120.9 248.3

84 84 168

0.96 0.97 0.96

0.96 0.97 0.96

0.95 0.96 0.95

0.07 0.06 0.06

IT competence

Expert Novice Stacked model

107.1 142.9 250.8

84 84 168

0.98 0.95 0.96

0.98 0.95 0.96

0.97 0.93 0.95

0.06 0.08 0.07

and females would use the same pattern in measuring the items; if this occured, the data of each group fits the model well but if different genders used a different pattern of items for the same construct, configural noninvariance would exist and further invariance analyses would be unnecessary. The results of our configural invariance analysis, shown in Table 4, suggest that the w2 and fit indices for each gender group are good enough, providing evidence of the configural invariance of the construct. Similar results, also shown in Table 4, were obtained when conducting configural analysis for age and IT competence, supporting that configural invariance exists for the gender, age and IT competence groups. In the second step, factorial analysis was performed to determine if males and females, old and young, people with high and low IT competence conceptualize our Internet banking construct in the same way. If gender has an effect on the measurement equivalence of the construct, observed scores from the groups would be on a different scale and therefore would not be directly comparable. In such a scenario, we would then need to identify the observed items that caused such non-invariance. The situation is similar for age and IT competence. In performing such factorial invariance analysis, an unconstrained baseline model was initially established, followed by a fully constrained model. The Dw2 and Ddf and fit statistics (in our case, NNFI, CFI, and RMSEA) of the two models were then calculated for comparison purposes. According to the results in Table 5, the changes in Dw2 with Ddf for gender, age, and IT competency are not significant; and the fit statistics of the two models are also quite comparable,

justifying the invariance of the unconstrained and constrained models. Following this comparison, models that constrained individual constructs (PEOU, PU, ATT, and ITO) were set up for further factorial invariance analysis. The results, shown in Table 5, suggest that all Dw2 with Ddf, and fit statistics are not significantly different between the models compared. Through these tests, it is concluded that our model fits the construct very well and that the factor loadings for the three factors do not have any non-invariance, justifying the factorial invariance of our construct. Following the validation of our construct’s factorial invariance, a theta–delta invariance test was carried out to ensure that the error terms of our three subgroups were non-invariant. Since theta–delta is related to reliability issues, this invariance test could be considered as validating the reliability equivalence of the three pairs of groups. If theta–delta non-invariance existed in our construct, it could be caused by a different understanding of the surveyed items between the three subgroups in the PEOU, PU, ITO, and ATT of Internet banking. As shown in Table 6, the gender Dw2 was found to be significantly different between the unconstrained and fully constrained models, despite their comparable NNFI, CFI and RMSEA statistics. The source of non-invariance, which was tested using the partially constrained models, was clearly ITO and ATT. These findings suggest that their error terms between males and females are different. Similar invariance tests were conducted for age and IT competency. As indicated, the error variances of PU were found to have significant difference between the young and

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Table 5 Results of factorial invariance analysis for gender, age and IT competence

Gender

Age

IT Competence

Test

Model

w2

df

Dw2

Ddf

NNFI

CFI

RMSEA

1 2 2.1 2.2 2.3 2.4

Unconstrained baseline model Fully constrained model Loadings on PEOU Loadings on PU Loadings on ATT Loadings on ITO

223.9 236.9 227.7 229.7 224.8 226.6

168 179 170 173 170 170

13.0 3.8 5.7 0.9 2.7

11 2 5 2 2

0.97 0.97 0.97 0.97 0.97 0.97

0.97 0.97 0.97 0.97 0.97 0.97

0.06 0.06 0.06 0.06 0.06 0.06

1 2 2.1 2.2 2.3 2.4


248.3 256.8 248.5 249.1 250.2 253.9

168 179 170 173 170 170

8.5 0.2 0.8 1.9 5.6

11 2 5 2 2

0.95 0.96 0.96 0.96 0.96 0.95

0.96 0.97 0.96 0.97 0.96 0.96

0.06 0.06 0.06 0.06 0.06 0.06

1 2 2.1 2.2 2.3 2.4


250.8 263.8 251.9 255.4 255.5 253.4

168 179 170 173 170 170

12.9 1.1 4.6 4.7 2.6

11 2 5 2 2

0.95 0.96 0.96 0.96 0.95 0.95

0.96 0.96 0.96 0.96 0.96 0.96

0.07 0.07 0.07 0.07 0.07 0.07

old groups, which implied that the error terms of PU between old and young groups was significantly different. A possible source for the latent factor error terms may have been their different ability in

understanding the questionnaire, or measurement error, etc. For the groups of people with high and low IT competence, the results showed that the error terms of PU and ATT are non-invariant. This result is

Table 6 Results of theta–delta analysis for gender, age and IT competence

Gender

Age

IT Competence

*

P < 0:05. P < 0:01.

**

Test

Model

w2

df

Dw2

Ddf

NNFI

CFI

RMSEA

2 3 3.1 3.2 3.3 3.4

Factorial invariance model Fully constrained model Delta of PEO Delta of PU Delta of ATT Delta of ITO

236.9 278.2 239.1 248.2 255.3 246.3

179 194 182 185 182 182

41.3** 2.2 11.2 18.3** 9.3*

15 3 6 3 3

0.97 0.96 0.97 0.97 0.96 0.97

0.97 0.96 0.97 0.97 0.97 0.97

0.06 0.05 0.06 0.06 0.07 0.06

2 3 3.1 3.2 3.3 3.4


256.8 277.1 257.4 271.4 261.8 257.0

179 194 182 185 182 182

20.3 0.6 14.5* 4.9 0.2

15 3 6 3 3

0.96 0.96 0.96 0.96 0.96 0.96

0.97 0.96 0.97 0.96 0.96 0.97

0.06 0.06 0.06 0.06 0.06 0.06

2 3 3.1 3.2 3.3 3.4


263.8 321.5 268.4 290.8 275.8 269.0

179 194 182 185 182 182

57.6** 4.6 27.0** 11.9** 5.2

15 3 6 3 3

0.96 0.94 0.96 0.95 0.95 0.96

0.96 0.95 0.96 0.95 0.96 0.96

0.07 0.07 0.07 0.08 0.07 0.07


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Table 7 Results of the covariance of latent variables invariance analysis for gender, age and IT competence

Gender

Age

IT competence

*

P < 0:05;

**

Test

Model

w2

df

Dw2

Ddf

TLI

CFI

RMSEA

2 4.1 4.2 4.3 4.4

Factorial invariance model COV (PEOU) COV (PU)) COV (ATT) COV (ITO)

236.9 238.3 237.3 237.2 236.9

179 180 180 180 180

1.3 0.3 0.3 0.0

1 1 1 1

0.97 0.97 0.97 0.97 0.97

0.97 0.97 0.97 0.97 0.97

0.06 0.06 0.06 0.06 0.06

2 4.1 4.2 4.3 4.4


256.8 256.8 257.1 258.6 258.5

179 180 180 180 180

0.0 0.3 1.8 1.7

1 1 1 1

0.96 0.96 0.96 0.96 0.96

0.97 0.97 0.97 0.96 0.96

0.06 0.06 0.06 0.06 0.06

2 4.1 4.2 4.3 4.4


263.8 264.1 263.9 266.1 265.1

179 180 180 180 180

0.3 0.1 2.4 1.3

1 1 1 1

0.96 0.96 0.96 0.96 0.96

0.96 0.96 0.96 0.96 0.96

0.07 0.07 0.07 0.07 0.07

P < 0:01.

similar to the gender effect. The error term between PU and ATT are very different for the people with high and low IT competence. Subsequent to the theta–delta invariance test, a latent factor variables invariance test was performed to determine if the variance of the construct between the latent variables was the same for our three subgroups. The results, in Table 7, do not suggest any significant difference, thereby, implying that the variations of each construct for the three pairs of groups are not significantly different. Similar conclusions were drawn on the invariance test on the latent factor mean. The findings, given in Table 8, suggest that only gender was found to have latent mean non-invariance for its subgroups. This difference between the evaluation of the TAM by males and females, in the context of Internet banking, resulted from the differences in the latent mean of attitude. The final invariance test on coefficient invariance was performed to determine if the gender, age, and IT competency subgroups had a different relationship with some variables in our TAM model. The findings, shown in Table 9, suggest that only IT competence was found to have coefficient invariance for its subgroups. The age and IT competence groups were noninvariant in a certain relational path. The difference between the old and the young people’s behavior in the context of Internet banking, resulted from the differences in the coefficients of PEOU ! ATT and

PU ! ATT. The difference in the behavior of people with high and low IT competence of the TAM, in the context of Internet banking, resulted from the differences in the coefficients of PU ! ITO and ATT ! ITO. 5.4. Model testing LISREL 8.5 was used to test our research model with the sample covariance matrix shown in Appendix B as input. The results, as listed in Table 2, show that all eight fit indices for our testing model (w2/df ¼ 1:95, NFI ¼ 0:96, NNFI ¼ 0:97, CFI ¼ 0:98, GFI ¼ 0:92, AGFI ¼ 0:88, RMSR ¼ 0:06, and RMSEA ¼ 0:06) have clearly exceeded the minimum recommended values suggested for a good model fit, implying the adequacy of our model for further statistical analysis, including its causal link evaluation. Subsequently, the Internet banking TAM was run separately for adopters, non-adopters, and a combination of both. The results of these three runs, given in Table 10, show that the TAM is an appropriate model for studying Internet banking acceptance. Of the three runs, the adopters group provided the best support for the TAM, with all variables significant at P < 0:01. Interestingly, although all the TAM variables of the non-adopters group were significant from P < 0:05 to