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Customer Loyalty Factor Scale Results Table 13: Customer Loyalty Factor Scale Results


Always compare between banks before using card services.


0.883



There is a conversion cost when converting.


0.745



When converting it takes time to convert.


0.734



The decision to use a card service is important.


0.683



Use card service because you are used to dealing with banks.



0.890


Using card service because someone else opened an account

section



0.852





Use card service because transaction location is near.



0.768


Low error rate during use.




0.896

ATM machine is of good quality.




0.849

Enthusiastic staff.




0.542

Eigenvalues

5,480

2,789

2,045

1,367

Extracted variance %

24,617

47,069

62,840

77,873

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Customer Loyalty Factor Scale Results Table 13: Customer Loyalty Factor Scale Results

(Source: data processing by SPSS)


After rotating the factors for the first time, we see that the concentration of observations in each factor is quite clear. The analysis results table shows that there are a total of 15 observations that create 4 factors with Eigenvalues ​​greater than 1. We also see that these 4 factors will explain 77.873% of the variation in the data. This ratio is relatively high in factor analysis.


2.2.3.3. Naming and explaining factors


Based on the results of the factor matrix after rotation, we have the following 4 factors:


Factor 1: Includes variables: Feeling comfortable using card services, Using card services is an enjoyable experience, Service meets expectations, Good customer and staff relationships. This factor is named: Satisfaction. The average value of the member factors will give us the new variable value used for regression analysis later.

Factor 2: Includes variables: Consider many factors before using card services, Always compare between banks before using card services, When switching, there is a switching cost, When switching, there is a switching time, The decision to use card services is important . This factor is named : Switching barriers and the decision to choose the average value of the member factors will give us the new variable value used for regression analysis later.

Factor 3: Includes variables: Using card services because of familiarity with bank transactions, Using card services because others have opened accounts, Using card services because the transaction location is nearby . This factor is named : Habit of choosing the average value of member factors will give us the new variable value used for regression analysis later.

Factor 4: Includes variables: Low error rate during use, Good quality ATM, Enthusiastic staff . This factor is named : Service quality . The average value of the member factors will give us the new variable value used for regression analysis later.

Based on the results of the factor matrix after rotation (Mean Factor Analysis Table)

service) we have the following factors:


The factor includes the variables: The bank understands the needs so does not want to switch to another bank, Prioritizes using Vietinbank cards over other banks, Always considers the bank as the first choice, Encourages using Vietinbank card services . This factor is named:


loyalty, the average value of the member factors will give us the variable value

used for later regression analysis.


After factor analysis, we selected 4 factors affecting loyalty including Satisfaction, Switching and selection barriers, Selection habits, Service quality . These are the factors that will be used in the regression analysis in the next section.



Component Matrix a

The bank understands the needs so I don't want to change to another bank.

0.864

Prioritize using Vietinbank cards over other banks

0.837

Always consider NH as the first choice

0.805

Encourage the use of Vietinbank card services

0.784

KMO = 0.774

Eigenvalues ​​= 2.709

Extraction Sums of Squared Loadings Cumulative % = 67,715

Cronbach's Alpha = 0.838

2.2.3.4. Results of customer loyalty factor scale Table 13: Results of customer loyalty factor scale




(Source: data processing by SPSS)


The results are as follows:


+ KMO coefficient = 0.774>0.5 so it is acceptable.


+ Bartlett's Test of Sphericity result has Sig = 0.00 using multiplicative analysis

element is appropriate.


+ Eigenvalues ​​criterion > 1


+ Total extracted variance is 67,715% > 50,000%, meeting the requirements.


+ All variables have factor loading coefficient > 0.5


The factor analysis results for this scale show us that the variables have cohesion and reflect the same category, which is loyalty to the E-Partner domestic debit card of Vietnam Joint Stock Commercial Bank for Industry and Trade - Nam Thua Thien Hue branch.


The scale of these variables satisfies the requirements of factor analysis and has a Cronbach Alpha coefficient of 0.838, which satisfies the conditions, so the scale of these factors is reliable and is used in the next analysis.

2.2.3.5. Test for normal distribution


Testing for normal distribution is a condition to ensure the satisfaction of the variables for factor analysis. According to Master Dao Hoai Nam, Ho Chi Minh City University of Economics, the Skewness coefficient and Kurtosis concentration coefficient are used to test the normal distribution of factors. A Skewness distribution is not considered a normal distribution when its Statdard error is less than -2 or greater than 2. Similarly, a Kurtosis distribution is not considered a normal distribution when its Standard is less than -2 or greater than 2.

Through the analysis table, we see that the Std. Error of Skewness and Std. Error of Kurtosis values ​​of the factors are all less than 2. Thus, it can be concluded that the above factors are normally distributed.

Table 14: Normal distribution test




Satisfaction

satisfied

Barriers to Transfer

change and decide


Choice habits

Quality

service

Heart

loyal

N

Valid

150

150

150

150

150

Missing

0

0

0

0

0

Skewness

0.064

-1,728

0.416

-0.280

-0.188

Std. Error of Skewness

0.198

0.198

0.198

0.198

0.198

Kurtosis

-0.682

3,106

-1,399

-1,496

0.970

Std. Error of Kurtosis

0.394

0.394

0.394

0.394

0.394


(Source: data processing by SPSS)


2.2.4. Testing hypotheses and research models through regression analysis


2.2.4.1. Adjustment model


After evaluating the scale using Cronbach Alpha coefficient and factor analysis, we have determined that there are 4 factors affecting loyalty. These are loyalty due to service quality, switching barriers and choice decisions, choice habits, and satisfaction . The new model adjusted in this study is as follows:

Satisfaction (F1)

Switching barriers and choice decisions (F2)

Loyalty (LTT)

Choice habits

(F3)

Quality of service

(F4)


Figure 13: Adjusted research model We have the following linear regression equation:


Y i = β 0 + β 1 X 1i + β 2 X 2i + β 3 X 3i + β 4 X 4i +e i In which:

Y i : loyalty value of the i-th observation. X pi : independent variable p for the i-th observation. β k : partial regression coefficient of the k-th variable. e i : error term of the regression equation.

2.2.4.2. Adjustment hypothesis :


H 01 : Satisfaction does not affect loyalty to E-Partner domestic debit card service of Vietinbank - Nam Thua Thien Hue branch

H 02 : Switching barriers and choice decisions do not affect loyalty to E-Partner domestic debit card service of Vietinbank - Nam Thua Thien Hue branch

H 03 : Choice habits do not affect loyalty to E-Partner domestic debit card service of Vietinbank - Nam Thua Thien Hue branch

H 04 : Service quality does not affect loyalty to E-Partner domestic debit card service of Vietinbank - Nam Thua Thien Hue branch.

2.2.4.3. Correlation coefficient matrix between variables :


The correlation coefficient test is to check the linear relationship between independent variables and dependent variables. If the variables are strongly correlated, attention must be paid to the problem of multicollinearity after regression analysis. According to the correlation coefficient matrix, the dependent variable has a linear correlation with 4 independent variables. In which, the correlation coefficient between Loyalty and Switching Barriers and Choice Decision is the highest at 0.462, the correlation coefficient between Loyalty and Service Quality is the lowest at 0.383 and the correlation coefficient between Loyalty and Satisfaction is 0.443; the coefficient


The correlation between Loyalty and Choice Habit is 0.390. Thus, it can be said that these independent variables can be included in the model to explain service loyalty.




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