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Cronbach'S Alpha Coefficient of Deposit Intention Component


Results of the fourth factor analysis: (Table 20, Appendix 5)

The Bartlett test results in the KMO and Bartlett's test tables (table 3.4) with sig = 0.000 and KMO index = 0.806 > 0.5 both meet the requirements.


Table 3.4: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.806

Bartlett's Test of Sphericity

Approx. Chi-Square

3.483E3

df

378

Sig.

.000

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CronbachS Alpha Coefficient of Deposit Intention Component


At the Eigenvalues ​​level = 1.146, factor analysis extracted 8 factors and with a total extracted variance of 74.538% (greater than 50%) meeting the requirements (Table 20b, Appendix 5). The results in Table 3.5 (see details in Table 20c, Appendix 5) show that the factor loading coefficients of these variables are all greater than 0.5 meeting the requirements.


Table 3.5: Factor rotation matrix (4th time)




Component


1

2

3

4

5

6

7

8

SAT2

.889








SAT6

.885

SAT4

.816

SAT1

.801

SAT5

.745

STT7


.889







STT9

.862

STT1

.829

STT5

.809

STT4

.659

DNNV1



.889






DNNV2

.872

DNNV3

.832


SDC1




.751





SDC5

.701

SDC2

.688

SDC4

.598

AHXH4





.928




AHXH2

.897

AHXH1

.764

NBTH2






.809



NBTH1

.804

NBTH4

.765

PTHH5







.850


PTHH1

.827

PTHH2

.819

LITC2








.885

LITC1

.815


Based on the analysis of the factor rotation matrix (Table 3.4), the scale results have a total of 8 factors extracted from 28 observed variables. The first factor consists of 5 observed variables (SAT1, SAT2, SAT4, SAT5, SAT6) grouped by the mean command and named the Safety component, denoted as SAT. The second factor consists of 5 observed variables (STT1, STT4, STT5, STT7, STT9) grouped by the mean command and named the Convenience component, denoted as STT. The third factor consists of 3 observed variables (DNNV1, DNNV2, DNNV3) grouped by the mean command and named the Staff component, denoted as DNNV. The fourth factor consists of 4 observed variables (SDC1, SDC2, SDC4, SDC5) grouped by the mean command and named the Empathy component, denoted as SDC. The fifth factor consists of 3 observed variables (AHXH1, AHXH2, AHXH4) grouped by the mean order and named the Social Influence component, denoted as AHXH. The sixth factor consists of 3 observed variables (NBTH1, NBTH2, NBTH4) grouped by the mean order and named the Brand Awareness component, denoted as NBTH. The seventh factor consists of 3 observed variables (PTHH1, PTHH2, PTHH5) grouped by the mean order and named the Tangibles component.



denoted as PTHH. The last factor consists of 2 observed variables (LITC1, LITC2) grouped by the mean command and named the Financial Benefits component denoted as LITC. The Transform/Compute Variable command in SPSS software groups the above variables.

3.7.2. Evaluation of the savings intention scale

3.7.2.1. Cronbach's alpha coefficient analysis

The Deposit Intention Component Scale consists of 3 observed variables (YD1, YD2, YD3) with a Cronbach's alpha coefficient of 0.825. The total item correlation coefficients of the observed variables measuring this component all meet the standard (greater than 0.3). Therefore, this component scale meets the requirements and the observed variables of this component are used for EFA exploratory analysis.

Table 3.6: Cronbach's alpha coefficient of the Deposit Intention component


Observation variable

Scale mean if variable excluded

Scale variance if variable is excluded

Total variable correlation

Cronbach's alpha

if variable type

Deposit Intention Factor: Alpha = 0.825

YDGT1

7.4806

2,553

.697

.745

YDGT2

7.5874

2,390

.686

.754

YDGT3

7.3301

2,447

.663

.777


3.7.2.2. Exploratory factor analysis (EFA)

The results of Bartlett's test of sphericity in the KMO and Bartlett's test tables (table 21a, appendix 5) with sig = 0.000 and KMO index = 0.720 both meet the requirements.

At the Eigenvalues ​​level = 2.226 (Table 21b, Appendix 5), factor analysis extracted 1 factor from 3 observed variables with the extracted variance of 74.201% (> 50%) meeting the requirements. Based on the analysis of the factor rotation matrix table (Table 21c, Appendix 5), the Transform/Compute Variable command was used to



Group 3 variables meet the requirements (YDGT1, YDGT2, YDGT3) with factor loading coefficient > 0.5

named Deposit Intent component denoted as YDGT

Table 3.7: Interpretation of components after factor rotation


STT

Encryption

Interpretation

Safety Component (SAT)

Factor 1

SAT1

Customer information security

SAT2

The financial foundation of banking

SAT4

Security conditions of the transaction point

SAT5

The guidance and advice of the staff makes you feel secure.

SAT6

Hotline available for after-hours troubleshooting

Convenience Component (STT)

Factor 2

STT1

Large network of transaction points/ATMs

STT4

Reasonable opening and closing trading hours

STT5

Simple and quick procedures for depositing savings at the bank

STT7

Can be transacted via electronic banking

STT9

Has the function of transferring interest to ATM account when it is time to receive interest

Staff composition (SME)

Factor 3

DNNV1

Nice and neat staff uniform

DNNV2

The staff's attitude is friendly and polite when welcoming customers.

DNNV3

Good staff consulting skills

The Empathy Component (SDC)

Factor 4

SDC1

The bank has a good customer care program (calling to notify you of the maturity date of your savings book, wishing you a happy birthday)

SDC2

Bank savings products to suit your needs

SDC4

The bank has attractive promotions to attract you.

savings

SDC5

Banks always bring the best benefits to customers.

Social Influence (SI) component

Factor 5

AHXH1

Family members encourage you to save money in the bank.

AHXH2

Friends and colleagues encourage you to save money in the bank.

AHXH4

Bank staff advise you to save money when you have spare money.




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Brand Awareness Components (NBTH)

Factor 6

NBTH1

Recognize the bank's brand name, logo, image, and signature jingle

NBTH2

Frequency of appearance in advertising media

NBTH4

Attractive and unique advertising program suitable for Vietnamese culture

Tangible Media Components (PTHH)

Factor 7

PTHH1

Modern banking equipment

PTHH2

Modern facilities

PTHH5

Attractive bank advertisements.

Financial Benefit Component (LITC)

Factor 8

LITC1

High savings interest rate

LITC2

Reasonable service fee

Deposit Intent (YDGT) Component

YDGT factor

YDGT1

Saving at bank X is my intention.

YDGT2

Saving at bank X is my best choice.

YDGT3

I definitely choose to save at Bank X when I have idle money.


3.7.3. Adjustment research model

The results of the EFA exploratory factor analysis show that the variables measuring the factors affecting the intention to choose a bank to deposit money are grouped into 8 factors. Although there were 45 variables removed from the initial input through the Cronbach's alpha reliability coefficient analysis and the EFA exploratory factor left only 28 variables, the remaining variables of these 8 factors did not change the nature of each component in the theoretical research model. The factor of intention to choose a bank to deposit savings, including 3 observed variables, remained the same after factor analysis. Therefore, the theoretical research model remained the same.



Eight factors: Safety (SAT), Convenience (STT), Staff (DNNV), Empathy (SDC), Social Influence (AHXH), Brand Awareness (NBTH), Tangibles (PTHH), Financial Benefits (LITC) are considered as independent variables and Intention to Deposit (YDGT) is the dependent variable included in correlation analysis and multiple linear regression.

3.7.4. Correlation and multiple linear regression

3.7.4.1. Correlation analysis

Pearson correlation analysis results (Table 22, Appendix 5), we see that the correlation coefficient between the Intention to Deposit component (YDGT) with 6 independent variables: SAT, STT, DNNV, SDC, NBTH, LITC is in the same direction and the Sig value is very small (<0.05), the correlation between the Intention to Deposit component with the two variables AHXH and PTHH has a slightly high Sig value and a negative correlation. Therefore, it is necessary to check and evaluate in detail in the multiple linear regression analysis.

3.7.4.2. Multiple regression analysis

3.7.4.2.1. Testing the significance of variables in the model

To test the consistency between the eight components affecting the Intention to deposit money and the Intention to deposit savings component, the multiple linear regression function with the single-entry method (Enter) is used. That is, SPSS 16.0 software processes all the input variables at once and gives statistical parameters related to the variables. The larger the standardized partial regression coefficient of a component, the higher the level of influence of that component on the choice of a bank for customers to deposit savings. If the signs are the same, the level of influence is positive and vice versa.

Results of the first multiple regression analysis

The results of the first multiple regression analysis in Table 3.7 (see details in Table 23, Appendix 5), the Sig. values ​​for the components SAT, STT, DNNV, SDC, NBTH, LITC are all very small (less than 0.05).

The two variables AHXH and PTHH have Sig values ​​greater than 0.05. Therefore, the two variables AHXH and PTHH are eliminated because they are not statistically significant. It can be affirmed that the components SAT, STT, DNNV, SDC, NBTH, LITC are significant in the model.



Therefore, the second multiple regression was performed between the six variables SAT, STT, DNNV, SDC, NBTH, LITC and the dependent variable YDGT.

Table 3.8: Summary of first regression coefficients



Model


Unstandardized Coefficients


Standardize d Coefficients


t


Sig.


Collinearity Statistics


B

Std. Error


Beta


Tolerance


VIF

1 (Constan t)

SAT STT DNNV

SDC

.700

.149

.177

.146

.391

.629

.051

.075

.053

.065


.171

.135

.141

.347

1.112

2,923

2,360

2,777

6.031

.268

.004

.019

.006

.000


.664

.691

.879

.684


1,505

1,448

1,138

1,461


Social Security

-.139

.090

-.074

-1.536

.126

.981

1,019

NBTH

.167

.078

.123

2,126

.035

.673

1,487


PTHH

-.147

.085

-.085

-1.737

.084

.956

1,046

LITC

.103

.044

.131

2,318

.021

.707

1,414

a. Dependent Variable: YDGT


Results of the second multiple regression analysis

With the results of the second regression analysis in Table 3.9, the Sig. values ​​corresponding to the variables SAT, STT, DNNV, SDC, NBTH, LITC are all less than 0.05. Therefore, it can be confirmed again that these variables are significant in the model.


Table 3.9: Summary of the second regression coefficients



Model


Unstandardized Coefficients

Standard and Coefficient

ts


t


Sig.


Collinearity Statistics


B


Std. Error


Beta

tolerance


VIF


1

(Constantan)

-.567

.310


-1.828

.069




SAT

.150

.051

.172

2,920

.004

.667

1,499


STT

.166

.075

.127

2.222

.027

.706

1,417


DNNV

.141

.053

.136

2,662

.008

.880

1,136


SDC

.409

.065

.363

6,337

.000

.703

1,423


NBTH

.171

.079

.127

2,164

.032

.674

1,483


LITC

.105

.045

.134

2,342

.020

.708

1,413

a. Dependent Variable: YDGT


3.7.4.2.2 Regression equation

With the data set obtained within the scope of the study and based on the multiple linear regression results table (Table 3.9), the multiple linear regression equation shows the factors affecting the Intention to deposit money as follows:

YDGT = - 0.567 + 0.150*SAT + 0.166*STT + 0.141*DNNV + 0.409*SDC + 0.171*NBTH + 0.105*LITC

Independent variables (X): Safety (SAT), Convenience (STT), Staff (DNNV), Empathy (SDC), Brand awareness (NBTH), Financial benefits (LITC)

Dependent variable (Y): Intention to deposit component (YDGT).

3.7.4.2.3 Checking regression assumptions

Regression analysis is not only a description of observed data but also an inference of the relationship between variables in the population from the observed results in the sample. The results of the sample extrapolated to the value of the population must satisfy the following necessary assumptions:

Linear relationship assumption: This assumption will be checked by scatter plotting the standardized residual and standardized predicted value. The result (Figure 3.1) shows that the residuals are randomly scattered around the straight line through zero, without forming any particular shape. Thus, the linear relationship assumption is met.

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