SEM Analysis in Internet Banking and Education Report

Universidade Nova de Lisboa

Information Management School

Degree in Marketing Research and CRM

Quantitative Methods for Marketing Explanatory methods

Professor: Tiago Oliveira Martins2nd July 2016

Internet Banking 1Impact of Education on Adoption and Use

Internet BankingImpact of Education on adoption and use

Paper Prepared By:Kamran Huseynov M2015370

António Pires M2015340

Agshin Guleddinoglu M2015371

Table of Contents1. Executive Summary2. Introduction3. Model, Methodology & Sample Characteristics4. Results

4.1.Internet Banking Whole Database 4.1.1. Evaluation of Measurement Model 4.1.2. Evaluation of Structural Model

4.2.Internet Banking Elementary and High School Education4.2.1. Evaluation of Measurement Model 4.2.2. Evaluation of Structural Model

4.3.Internet Banking Undergraduate and Graduate Education 4.3.1. Evaluation of Measurement Model 4.3.2. Evaluation of Structural Model

5. Discussion and conclusions6. References7. Annex – Constructs and Questionnaire Questions


1. Executive Summary

As internet banking evolved during the last decade and most banks implemented internet banking systems to reduce costs, while improving customer service, several research studies investigated the main determinants of Internet banking adoption.

One of this studies, by Martins, Oliveira & Popovič (2014), developed an integrated model to explain customers’ intention to adopt and use of Internet banking, combining the unified theory of acceptance and use of technology (UTAUT) with perceived risk theory to explain behaviour intention and usage behaviour of Internet banking.

Based on that model and the data collected for that study, this paper analyzes the impact on the model of the level of education on internet banking adoption and use. Two main groups where analyzed (1) individuals with elementary and high school education and (2) individuals with undergraduate and graduate degrees.

The main findings (differences) were:

The most educated (2) have a higher concern about Performance, Financial and Privacy risks for internet adoption and the less educated (1) have a higher concern about Performance and Time risks;

The negative influence of Perceived Risk on Performance Expectancy and on Behavioural Intention is higher among the most educated (2);

The negative influence of Effort Expectancy (the degree of ease associated with the use of internet banking) on Perceived Risk is significantly higher in (2) and the positive influence of Social Influence (the opinions of user’s friends, relatives and superiors on Behavioural Intention) is slightly higher among group (2);

The effect of Behavioural Intention on Usage Behaviour Intention is higher in group (1), meaning that less educated people are slightly more likely to use internet banking if they have the intention to use it than more educated people.


2. IntroductionInternet banking evolved during the last decade to become one of the most profitable e-commerce applications (Lee, 2009) and most banks implemented internet banking systems to reduce costs while improving customer service (Xue, Hitt, & Chen, 2011).

Considering that Internet banking services are perceived to be riskier than purchasing traditional banking services (Cunningham, Gerlach, Harper, & Young, 2005), to understand the internet banking adoption Martins, Oliveira & Popovič (2014) held a study to determine what drives customers to adopt internet banking. The result was the development of a research model that couples the perceived risk theory (Featherman & Pavlou, 2003) with the unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003) and proposed an integrated model to explain customers’ intention to adopt and use of Internet banking.

This project is based on that study and the integrated model developed. The goal is to analyze the impact of Education in Internet banking adoption. The original database (sample) was divided in two separate subsamples for the analysis – (1) respondents (117) with elementary or high school education and (2) respondents (132) with undergraduate or graduate degrees.

In the next sections of this paper we will present the research model, the methodology and characteristics of the (sub)sample(s) used, the results for the global sample (whole database) and for each subsample, (1) & (2), and conclusions.


3. Model, Methodology & Sample Characteristics

For the project we used the integrated research model, based on UTAUT + Perceived risk, developed by Martins, Oliveira & Popovič (2014) without interaction effects (gender and age), represented in Figure 1.

Figure 1

The statistical technique used was Structural Equation Modelling Partial Least Squares (PLS-SEM) and to compute the model we used the professional statistical software package SmartPLS 2.0.

The sample for the study was supplied by Prof. Tiago Oliveira with 249 answers to a questionnaire whose questions (constructs and indicators) are outlined in the Annex.

This sample was divided into two subsamples, based on the level of education of the respondent, identified throughout this paper as ‘Elementary or High School Education’ and ‘Undergraduate and Graduate Education’.


4. Results4.1. Internet Banking Whole Database

4.1.1. Evaluation of Measurement Model 4.1.1.1. Indicator Reliability

In the case of checking indicator reliability of our reflective measurement model we opened Outer loadings of all variables that we used. We have several indicators (on yellow in Table 1) that are below threshold value (0.708), like FR2 (PCR) – 0.69, PR3 (PCR) – 0.67, PSR1 (PCR) – 0.63, PSR2 (PCR) – 0.67, SR1 (PCR) – 0.6, SR2 (PCR) – 0.62, TR1 (PCR) – 0.52, TR4 (PCR) –0.703, SI5-0.67. As we see the issue is only with indicators of PCR construct (Perceived Risk) and just one indicator of SI construct, but we will not remove these variables until we check composite and convergent reliability.

Table 1


4.1.1.2. Internal ConsistencyFrom Composite Reliability column (Table 2), we can see that all reflective constructs have high level of internal consistency reliability. As we know that the minimum value for this criterion is 0.702 in conclusive research, so the minimum value shown in this table is variable SI which is showing the lowest value (0.8916). We can also see that composite reliability of single item variable UB is 1.

Table 2

4.1.1.3. Convergent ReliabilityFor checking the convergent reliability of the model, we need to see the AVE column (Table 2). This value should be higher than 0.50, so we can confirm that all latent variables have good convergent reliability with the minimum value of 0.5649 (PCR) and 0.6258 (SI), so we will not delete any variable from our model based on the indicator reliability.

4.1.1.4. Discriminant Validity In this case we first checked the cross loadings of indicators (Table 3). The only issue here was the variables that were used both for PCR and to their original latent variables. Especially the indicator loadings of PCR variables are less than cross loadings, but just with this information we cannot delete any variable. So we checked the variables according to Fornell-Larcker criteria, that proposes that the square root of the AVE of each construct should be higher than the


construct’s highest correlation with any other constructs in the model. We estimated the square root of AVE for all variables and the only problem (on yellow Table 4) was with the constructs PCR and PFR. Square root of AVE in PCR is less than the correlation of FR-PCR, OR-PCR, PFR-PCR, PR-PCR and square root of AVE in PFR is less than the correlation of PFR-PCR.

Table 3


Table 4

4.1.2. Evaluation of Structural Model4.1.2.1. Collinearity assessment

For assessment of this issue we exported the latent variables data from SmartPLS and ran the linear regression analysis based on the relationships between these latent variables, in order to get VIF for each of them. This procedure shows if the exogenous variables have collinearity problems or not. First we ran the analysis for BI (PCR, SI, EE, PE) and then for UB (BI, FC). Results were significant, as all the variables have VIF below value 5. So we decided that there is not collinearity issues among exogenous variables.

Exogenous variables

of BIVIF

Exogenous variables

of UBVIF2

PCR 1.20102 BI 1.75121SI 1.21457 FC 1.75121EE 2.70107 PE 2.56868

Table 5

4.1.2.2. Size and Significance of Path coefficientsIn order to assess significance of each path coefficient we ran bootstrapping tool of SmartPLS with 249 cases and 5000 samples. Then compared the coefficient values of the original sample with bootstrapping values and calculate the t value for each path coefficient. We got results both for path coefficients and for total effects. We take in consideration 3 levels of significance: p<0.10 -> Z>1.645, p<0.05 -> Z>1.960 and p<0.01 -> Z>2.32. All the coefficients are significant at 0.01 level of significance (Table 6), except FC>UB (not significant) and SI>BI (0.05 significance level). In total effects (Table 7), results are almost similar, FC>UB not significant, EE >PE significant in p<0.10, SI>BI and SI>UB significant in level p<0.05, while all others are significant in level 0.01.


Table 6

Table 7


4.1.2.3. Assessment of Coefficient Determination (R²)R² values range from >=0.75 (substantial), 0.50-0.75 (moderate) to <0.50 (weak). Results (Table 2) were: OR, PFR, UB are substantial, BI, FR, PR, TR are moderate, PSR (0.45), SR (0.38) weak, and PCR (0.09), PE (0.06) completely weak.

4.1.2.4. f² effect Size In order to conduct f² effect size we ran the model several times without exogenous variables, calculated several R² values and based on f² effect formula estimated the values for this measurement. Rule thumb for this measurement is 0.02, 0.15 and 0.35 for small, medium and large effects in endogenous variables. Based on the results we can see that in each case the value of this measurement is below 0.10. So we decided in this model that f² effect size is small on endogenous latent variables.

Excluded

variablePE EE SI PCR FC

f² assesment

f² for BI 0.108

0.0808

0.0159 0.084 small medium high

f² for UB 0.0021 0.02 0.15 0.35

Table 8

4.1.2.5. Assessment of the predictive relevance (Q²) and q² effect size

For measurement of q² effect size, first we ran blindfolding calculation of SmartPLS and we choose omission distance as 7, as the division of the number of observations (249) by 7 is not an integer. We ran the model for UB, then for BI endogenous latent variables, for getting Q² predictive relevance. In the end, by omitting exogenous variables one by one, we got different Q² values for estimating q² effect size. The q² effect size has the same rules of thumb as the f² effect test > 0.2, 0.15 and 0.35, meaning small, medium and large effects in endogenous variables. As all the values are below 0.08, we decided q² effect size is small in this model.

Excluded


q² assesmentq² for BI 0.097 0.071 0.0136 0.0743 small medium high q² forUB 0.0846 0.02 0.15 0.35

Table 9


4.2. Internet Banking Elementary and High School Education (1)4.2.1. Evaluation of Measurement Model

4.2.1.1. Indicator ReliabilityBased on Outer loadings of all variables we find out that FR2 (PCR)-0.57, PR3 (PCR)-0.68, PSR1 (PCR)-0.69, PSR2 (PCR)-0.68, SR1 (PCR)-0.65, SR2 (PCR)-0.68, TR1 (PCR)-0.61, SI4-0.702, SI5-0.68 (on yellow in Table 10) are the indicators that are below the threshold value (0.708). The differences for the whole database model are in TR4’s value (PCR) that increased, but there is not any problem with it, and in SI4, that decreased to 0.702, and now there is a problem with this indicator but again we have to analyze composite and convergent reliability of these variables.

Table 10


4.2.1.2. Internal Consistency

As we did in the whole database, we checked again the composite reliability column (Table 11) and got reliable results with lowest value of 0.9018 (SI). This give us significant evidence that in this model there is internal consistency of reflective measurement.

Table 11

4.2.1.3. Convergent Reliability

This criteria requires a minimum value of 0.50 in the AVE column (Table 11). From the results we can see that there is not any problem in the AVEs of the latent variables, as the minimum values are 0.58 (PCR) and 0.65 (SI). We didn’t find significant differences in AVEs between the whole database model and the elementary and high school model. The differences are in the range 0.02-0.05 more or less. Of course UB value is again 1, because it is a single item variable.

4.2.1.4. Discriminant Reliability

The same problem that appeared in the whole database model appeared also in the elementary and high school model (Table 12), so the issue is with variables that we used both for the PCR construct and the original constructs, like OR, PFR, PR, PSR, SR, TR. They have almost the same values as in the whole database model. Then we estimated square root of AVE for each variable, in order to check variables according to Fornell-Larcker criterion. These results are also the same as with the whole database model; the issue (on yellow Table 13) is only with PCR and PFR constructs, where the square root of AVE in PCR is less than the correlation of FR-PCR, OR-PCR, PFR-PCR, PR-PCR and the square root of AVE in PFR is less than the correlation of PFR-PCR.


Table 12

Table 13



This assessment was done the same way as for the whole database sample. Results were significant for BI (PCR, SI, EE, PE) and UB (BI, FC) latent variables. Highest value was for EE (3.02), exogenous variable of BI latent variable.

Exogenous variables

of BIVIF

Exogenous variables

of UBVIF2

EE 3.02867 BI 1.93621PE 2.76267 FC 1.93621Si 1.37954

PCR 1.113

Table 14

4.2.2.2. Size and Significance of Path coefficientsWe used the same significance level as for the whole database sample and we checked the significance for both path coefficients and total effects in this subsample. In the path coefficients (Table 15), the only difference is that PCR>PE changed significance level from 0.01 to 0.05, all others remained the same: FC>UB not significant, SI>BI is significant at 0.05 level while others are significant at 0.01 level. The total effects results (Table 16) are not as similar as in the whole database sample. EE>PE also became not significant (1.12), EE>PSR changed from 0.01 to 0.05 significance level, PCR>PE changed from 0.01 to 0.05, SI>BI and SI>UB changed from 0.05 to 0.01 level while others like FC>UB are not significant and other coefficients are significant in level 0.01, like in the whole database sample.


Table 15

Table 16

4.2.2.3. Assessment of Coefficient Determination (R²)Internet Banking 16Impact of Education on Adoption and Use

The result of this assessment is almost the same as the one of the whole database sample and the only differences are small changes in the values. OR, PFR, UB are substantial, BI, FR, PR, TR are moderate, PSR (0.48), SR (0.45) weak, and PCR (0.03), PE (0.03) completely weak (Table 11).

4.2.2.4. f² effect Size The measurement was done as in the whole database sample, and results are almost the same as in the previous model; the only difference is that some values changed in the range 0.01-0.02 and this model based on the elementary and high school education sample also has smaller f² effect size in endogenous variables.

Excluded


f² assesment

f² for BI 0.082

0.0815 0.025

0.0855 small medium high

f² for UB 0.0005 0.02 0.15 0.35

Table 17


Table 18 shows that all the values of q² effect size are small as in the whole database sample. The only difference is in FC>UB, which has a negative value (-0.019).

Excluded


q² assesment

q² for BI 0.068

0.0703

0.0197

0.0728 small medium high

q² forUB -0.019 0.02 0.15 0.35

Table 18


4.3. Internet Banking Undergraduate and Graduate Education (2) 4.3.1. Evaluation of Measurment Model

4.3.1.1. Indicator ReliabilityBased on Outer loadings of all variables we find out that PR3 (PCR) -0.68, PSR1 (PCR) -0.56, PSR2 (PCR) -0.67, SR1 (PCR) -0.54, SR2 (PCR) -0.54, TR1 (PCR) -0.49, TR4 (PCR) -0.61, SI1 -0.63, SI2 -0.64, PE2-0.7 have issues related to the indicator reliability, which are almost the same as in the whole database, but the differences are in FR2 and SI5 that increased and there is not any problem with them, as well as with SI1, SI2, PE2 that decreased and there is a problem now with indicator reliability. As we mentioned above, we cannot delete variables based only on indicator reliability, we have to check the results for composite and convergent reliability.

Table 19


4.3.1.2. Internal Consistency There is not any issue related to composite reliability (Table 20) which requires the minimum value of 0.702 and the lowest values are 0.8834 (SI) and 0.8898 (PE). The results of this criteria are almost the same as for the whole database.

Table 20

4.3.1.3. Convergent ReliabilityTo evaluate this criteria we have to check the AVE column (Table 20) to see if there is any value below 0.50. In this case the minimum value is 0.5516.

4.3.1.4. Discriminant ReliabilityThe loadings of all variables (Table 21) are greater than cross loadings, except the variables related with PCR because we used them 2 times, for their original latent variables and for the perceived risk latent variable, thus it gives an error in this criteria. Then we estimated the root square of AVE of each variable (Table 22) and got results almost the same than in the previous models which had problems only in PCR and PFR.


Table 21

Table 22



All exogenous variables of BI (PCR, SI, EE, PE) and UB (BI, FC) got values of VIF below 2, so we cocluded that this sample also has not any collinearity problem, as the maximum value for VIF acceptable is 5.

Exogenous variables

of BIVIF

Exogenous variables

of UBVIF3

EE 1.92759 BI 1.23533PCR 1.32021 FC 1.23533

SI 1.09022 PE 1.80331

Table 23

4.3.2.2. Size and Significance of Path coefficientsSame analysis was done for subsample of Undergraduate and Graduate Education, the only difference (Table 24) from Elementary and High School is that EE>BI is not significant rather than being significant in level of 0.01 from path coefficients, while EE>PE is significant in level of 0.05 rather than being not significant like in subsample of Elementary and High School. Other differences are SI>BI and SI>UB (Table 25) that are statistically significant in level 0.05, while it was at 0.01 level in first subsample.

Table 24


Table 25

4.3.2.3. Assessment of Coefficient Determination (R²)In this subsample the results are different than those in the previous one and those of the whole database sample, as some variables changed according to the rules of thumb. BI changed from moderate to weak (0.52>0.44), OR changed from substantial to moderate (0.85>0.73) and PCR (0.18) and PE (0.11) remained completely weak (Table 20).

4.3.2.4. f² effect Size Results of f² effect size are similar with the previous sample and it is small.

Excluded


f² assesment

f² for BI 0.10.036

80.037

70.105

7 small medium high

f² for UB 0.0337 0.02 0.15 0.35

Table 26



The only different value is FC>UB which has a negative value (-0.0091). Other values are similar to previous measurements in the whole database and in the elementary and high school education samples.

Excluded variable

PE EE SI PCR FCq² assesment

q² for BI 0.084 0.029 0.032 0.0869 small medium high q² forUB -0.0091 0.02 0.15 0.35

Table 27


5. Discussion and conclusionsIn the next graphs we present the structural model with path coefficients and r-squares for the ‘Internet Banking Whole Database’ (Figure 2), ‘Internet Banking Elementary and High School Education’ (Figure 3) and ‘Internet Banking Undergraduate and Graduate Education’ (Figure 4).

Figure 2 - Internet Banking Whole Database

Figure 3 - Internet Banking Elementary and High School Education


Figure 4 - Internet Banking Undergraduate and Graduate Education

The first conclusion for Whole Database is that results are the same as the ones published in Martins, Oliveira & Popovič (2014), pag. 8 Table 5 (UTAUT+PCR-D).

Comparing (1) Internet Banking Elementary and High School Education results with (2) Internet Banking Undergraduate and Graduate Education:

Perceived Risk (PCR)

Main differences in the 7 type of risks built in the model are that perceived Performance, Financial and Privacy risks are higher in (2) and Time, Psychological, Social and Overall risks are higher in (1). Performance and Time risks are the ones of more concern in (1) and Performance, Financial Privacy the ones in (2). Social and Psychological risks are the less relevant in both samples, namely Social Risk in sample (2) (0.54);

The negative influence of Perceived Risk on Performance Expectancy is higher in (2), -0.34 vs -0.17;

The negative influence of Perceived Risk on Behavioural Intention is higher in (2), -0.40 vs -0.26.

Performance Expectancy (PE)

The impact of Performance Expectancy (the user perception of performance improvements by using internet banking) on Behavioural Intention is about the same in both samples.


Effort Expectancy (EE)

The negative influence of Effort Expectancy (the degree of ease associated with the use of internet banking) on Perceived Risk is significantly higher in (2), -0.43 vs -0.19;

The impact of Effort Expectancy on Behavioural Intention is about the same in both samples.

Social Influence (SI)

The positive influence of Social Influence, the opinions of user’s friends and relatives and superiors on Behavioural Intention, is slightly higher in (2), 0.15 vs -0.12. Influence rather low compared with PE and EE.

Facilitating Conditions (FC)

Some difference between (2) and (1), 0.09 vs -0.02, in the impact of Facilitating Conditions (the level of organizational and technical infrastructures available to support internet banking) on Usage Behaviour. However FC was found not to be statistically significant to explain UB.

Behavioural Intention (BI)

The effect of Behavioural Intention on Usage Behaviour Intention is higher in (1), 0.90 vs 0.84. This means that less educated people are slightly more likely to use internet banking if they have the intention to use it than more educated people.

In terms of explanatory power the UTAUT and PCR models coupled explained 55% of the variance of Behaviour Intention in the case of the ‘Internet Banking Elementary and High School Education’ sample and only 45% in the case of the ‘Internet Banking Undergraduate and Graduate Education’ sample.

Regarding Usage Behaviour, the model explains about the same variance in both cases, 78.24% in the case of the ‘Internet Banking Elementary and High School Education’ sample and 78.93% in the case of the ‘Internet Banking Undergraduate and Graduate Education’ sample.


6. References

Cunningham, L. F., Gerlach, J. H., Harper, M. D., & Young, C. E. (2005). Perceived risk and the consumer buying process: internet airline reservations. International Journal of Service Industry Management, 16(4), 357–372.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451–474.

Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.

Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13. http://doi.org/10.1016/j.ijinfomgt.2013.06.002

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. http://doi.org/10.2307/30036540

Xue, M., Hitt, L. M., & Chen, P. (2011). Determinants and Outcomes of Internet Banking Adoption. Management Science, 57(2), 291–307. Retrieved from http://pubsonline.informs.org/doi/abs/10.1287/mnsc.1100.1187


7. ANNEXConstructs and questionnaire questions (indicators)

Performance expectancy (PE) Internet banking is useful to carry out my tasks - PE1 I think that using Internet banking would enable me to conduct tasks more quickly -

PE2 I think that using Internet banking would increase my productivity - PE3 I think that using Internet banking would improve my performance - PE4

Effort expectancy (EE) My interaction with Internet banking would be clear and understandable - EE1 It would be easy for me to become skilful at using Internet banking - EE2 I would find Internet banking easy to use - EE3 I think that learning to operate Internet banking would be easy for me - EE4

Social influence (SI) People who influence my behaviour think that I should use Internet banking - SI1 People who are important to me think that I should use Internet banking - SI2 People in my environment who use Internet banking services have more prestige

than those who do not - SI3 People in my environment who use Internet banking services have a high profile -

SI4 Having Internet banking services is a status symbol in my environment - SI5

Facilitating conditions (FC) I have the resources necessary to use Internet banking - FC1 I have the knowledge necessary to use Internet banking - FC2 Internet banking is not compatible with other systems I use - FC3

Performance risk (PFR) Internet banking might not perform well and create problems with my credit - PFR1 The security systems built into the Internet banking system are not strong enough to

protect my checking account - PFR2 The probability that something’s wrong with the performance of Internet banking is

high - PFR3 Considering the expected level of service performance of Internet banking, for me to

sign up and use, it would be risky - PFR4 Internet banking servers may not perform well and thus process payments

incorrectly - PFR5

Financial risk (FR) The chances of losing money if I use Internet banking are high - FR1 Using an Internet-bill-payment service subjects my checking account to potential

fraud - FR2 My signing up for and using an Internet banking service would lead to a financial

loss for me - FR3 Using an Internet-bill-payment service subjects my checking account to financial

risk- FR4


Time risk (TR) I think that if I use Internet banking then I will lose time due to having to switch to a

different payment method - TR1 Using (2003) Internet banking would lead to a loss of convenience for me because I

would have to waste a lot of time fixing payments errors - TR2 Considering the investment of my time involved to switch to (and set up) Internet

banking, it would be risky - TR3 The possible time loss from having to set up and learn how to use e-bill payment is

high - TR4

Psychological risk (PSR) I think that Internet banking will not fit in well with my self-image or self-concept -

PSR1 If I use Internet banking services, it would lead me to a psychological loss because it

would not fit in well with my self-image or self-concept - PSR2

Social risk (SR) If I use Internet banking, it will negatively affect the way others think of me - SR1 My signing up for and using Internet banking would lead to a social loss for me

because my friends and relatives would think less highly of me - SR2

Privacy risk (PR) The chances of using the Internet banking and losing control over the privacy of my

payment information is high - PR1 My signing up and using of Internet banking would lead me to a loss of privacy

because my personal information would be used without my knowledge - PR2 Internet hackers (criminals) might take control of my checking account if I use

Internet banking services - PR3

Overall risk (OR) On the whole, considering all sorts of factors combined, it would be risky if I use

Internet banking - OR1 Using Internet banking to pay my bills would be risky - OR2 Internet banking is dangerous to use - OR3 I think that using Internet banking would add great uncertainty to my bill paying -

OR4 Using Internet banking exposes me to an overall risk - OR5

Behavioural intention (BI) I intend to use the system in the next months - BI1 I predict I would use Internet banking in the next months - BI2 I plan to use the system in the next months - BI3 I intend to consult the balance of my account on the platform of Internet banking -

BI4 I intend to perform a transfer on the platform of Internet banking - BI5

Usage behaviour (UB) What is your actual frequency of use of Internet banking services? (i) Have not used; (ii) once a year; (iii) once in six months; (iv) once in three months; (v) once a month; (vi) once a week; (vii) once in 4–5 days; (viii) once in 2–3 days; (ix) almost every day.


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SEM Analysis in Internet Banking and Education Report

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