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“Brand Loyalty” Factor Scale


CL2. Products/services

Bank X's assets are of good quality.


11.89


12,633


.621


.854

CL3. Bank X's products/services are very valuable.

trust


11.76


12,083


.676


.841

CL4. Bank X provides

provide great product/service features


11.87


12,079


.675


.841

CL5. Bank services

Product X is very effective.

11.84

12,252

.649

.848

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“Brand Loyalty” Factor Scale

(Source: SPSS analysis results)


We see that Cronbach's Alpha total = 0.865 > 0.7; the variable-total correlation coefficients are all greater than 0.3. In which, no variable is removed to increase Cronbach's Alpha, we keep the original scale to put into exploratory factor analysis (EFA)

Table 4.4 Scale of factor “Brand loyalty”



Cronbach's Alpha 0.902

Medium

scale if variable type

Variance

scale if variable type

Total variable correlation

Cronbach's

Alpha when removing variables

TT1. I am very loyal.

with bank X

9.97

10,252

.898

.833

TT2. I have always been interested in learning more about banking in practice.

X


10.09


10,310


.762


.880


TT3. I will also recommend bank X's services to others.

other


10.06


10,070


.764


.880

TT4. In the future, I

will use services from bank X more


10.11


11,053


.711


.897

(Source: SPSS analysis results)


We see that Cronbach's Alpha total = 0.902 > 0.7; the variable-total correlation coefficients are all greater than 0.3. In which, no variable is removed to increase Cronbach's Alpha, we keep the original scale to put into exploratory factor analysis (EFA)

Table 4.5 Scale of the “Decision” factor



Cronbach's Alpha 0.778

Average scale if

variable type

Scale variance if

variable type

Total variable correlation

Cronbach's Alpha when eliminated

variable

QD1. I prefer to use the products/services of bank X over those of other banks, even though the products/services of these banks are

alike


8.13


1,983


.554


.762

QD2. If another bank has the same features as bank X, I will still use the service there.

bank X


7.43


1,481


.749


.536


QD3. If there is another bank as good as bank X, I will also choose it.

bank X


6.93


1,902


.555


.763

(Source: SPSS analysis results)


We see that Cronbach's Alpha total = 0.778 > 0.7; the variable-total correlation coefficients are all greater than 0.3. In which, no variable is removed to increase Cronbach's Alpha, we keep the original scale to put into exploratory factor analysis (EFA).

Thus , after analyzing the Cronbach's Alpha reliability coefficient of the scales, the following conclusion can be drawn: no variables were eliminated, the variables will be included in the next factor analysis step to ensure statistical reliability.


4.3 Exploratory factor analysis EFA

The factor analysis in this topic is carried out using the Principal Component Analysis method and Varimax rotation to group the factors. The first step is to consider the extraction coefficient of the variables. If any variable has this coefficient less than 0.5, it will be eliminated. The next step is to consider 2 criteria:

+ KMO coefficient (Kaise – Mayer – Olkin) must satisfy the condition 0.5 ≤ KMO ≤ 1


+ Bartlett test considers the hypothesis H 0 : the correlation between observed variables is 0 in the population. If this test is significant (sig < 0.05), the observed variables are correlated with each other in the population (Hoang Trong & Chu Nguyen Mong Ngoc, Statistics Publishing House, 2008).

The results of the factor groups are shown in the Rotated Component matrix table and the factor loading in this table must have a value greater than 0.5 to ensure reliability.


convergence between variables in a factor (Hoang Trong & Chu Nguyen Mong Ngoc, Statistics Publishing House, 2008). Stop point when extracting factors with Eigenvalue greater than 1 (SPSS default, factors with Eigenvalue less than 1 will not have the effect of summarizing information better than an original variable, because after each standardization, each original variable has a variance of 1). The scale is accepted with the total extracted variance equal to or greater than 50% (Gerbing & Anderson, 1998).

4.3.1 Factor Analysis - Independent Variables


Kaiser – Meyer – Olkin coefficient : KMO = 0.793 > 0.5 (0.5 ≤ KMO ≤ 1) shows that factor analysis is appropriate.

Bartlett's test has a sig value = 0.00 < 0.05, proving that the observed variables are correlated with each other in the population. From there, we reject the hypothesis H0, and conclude that: "The variables included in the factor analysis are correlated in the population".

All factor loadings of observed variables are greater than 0.5, which ensures that EFA analysis is practically meaningful.

Table 4.6 Variance explained



Factor

Initial Eigenvalues

Sum of squares of the system

number of downloaded

Sum of squares of the system

number of rotated loads

Total

Percent of the method

wrong (%)

Cumulative Percentage (%)

Total

Percent of the method

wrong (%)

Cumulative Percentage (%)

Total

Percent of the method

wrong (%)

Cumulative Percentage (%)

1

3,737

21,980

21,980

3,737

21,980

21,980

3,382

19,892

19,892

2

3,394

19,964

41,944

3,394

19,964

41,944

3.318

19,519

39,411


3

3,007

17,686

59,630

3,007

17,686

59,630

3.133

18,428

57,839

4

2,725

16,028

75,658

2,725

16,028

75,658

3,029

17,819

75,658

5

.562

3.305

78,963







6

.505

2,973

81,936







7

.494

2,904

84,840







8

.463

2,723

87,563







9

.382

2,248

89,811







10

.359

2.112

91,923







11

.297

1,747

93,670







12

.291

1,713

95,383







13

.252

1,485

96,868







14

.167

.984

97,852







15

.145

.851

98,704







16

.115

.677

99,381







17

.105

.619

100,000







(Source: SPSS analysis results)

The results of table 4.6 show that according to the Eigenvalue >1 criterion, 4 factors are extracted and these 4 factors will explain 75.66% of the variation in the data.

The Eigenvalue (representing the portion of variation explained by each factor) is greater than 1, so the extracted factor is meaningful in summarizing the information best.

Table 4.7 Factor rotation results



Observation variable

Factor

1

2

3

4

CL1. Bank X applies modern technology in services.

mine

.941





CL4. What product/service features does Bank X offer?

Great

.805




CL3. Bank X's products/services are very reliable.

.802




CL5. Bank X's service is very efficient.

.779




CL2. Bank X's products/services are of good quality.

.761




LT1. Bank X makes me feel accepted by everyone.

receive


.954



LT2. People want to use banking services.

row X


.896



LT4. Bank X creates a special image in the mind

client


.893



LT3. I really like bank X.


.879



TT1. I am very loyal to bank X.



.945


TT2. I am always interested in learning more about the reality of

bank X



.870


TT3. I will also introduce the services of bank X to others.

other people



.866


TT4. In the future, I will use services from bank X.

more



.831


NB1. I recognize the products/services of bank X.




.945

NB2. Bank X is easily recognizable compared to other banks.




.852

NB3. Employees of bank X understand more about the bank




.838

NB4. When it comes to the name of a bank X, I can remember the logo

its




.833

(Source: SPSS analysis results)


The result of factor rotation shows that 17 variables are grouped into 4 factors. Based on the result of factor rotation, theoretical basis and nature of specific variables in each factor, the author names the factors as follows:

Factor 1 – “Perceived quality” includes the following variables: CL1. Bank X applies modern technology in its services CL2. Bank X’s products/services are of good quality

CL3. Bank X's products/services are very reliable.

CL4. Bank X offers excellent product/service features CL5. Bank X's services are efficient

Because these variables all belong to the component "Brand quality", factor 1 is still named "Brand quality", the new variable code is CL

Factor 2 – “Brand association” includes the following variables: LT1. Bank X makes me feel accepted by everyone LT2. People want to use the services of bank X LT3. I really like bank X

LT4. Bank X creates a special image in the minds of customers.

Because these variables all belong to the "Brand Association" component, factor 2 is still named "Brand Association", the new variable code is LT.

Factor 3 – “Brand loyalty” includes the following variables: TT1. I am very loyal to bank X

TT2. I am always interested in learning more about bank X in practice TT3. I will also recommend bank X's services to others TT4. In the future, I will use bank X's services more

Because these variables all belong to the component "Brand Loyalty", factor 3 is still named "Brand Loyalty", the new variable code is TT.

Factor 4 – “Brand awareness” includes the following variables:


NB1. I recognize the products/services of bank X NB2. Bank X is easier to recognize than other banks NB3. Bank X employees understand the bank better

NB4. When I think of the name of a bank X, I can remember its logo.

Because these variables all belong to the “Brand Awareness” component, factor 4 is still named “Brand Awareness”, the new variable code is NB.

Table 4.8 Summary of factor group results



New variable code

Old variable code

Interpretation

Variable name

new

CL

CL1

Bank X applies modern technology

great in its service

Brand quality

CL2

Products/services of bank X have

good quality

CL3

Bank X's products/services are very

reliable

CL4

Bank X offers the following features:

great product/service

CL5

Bank X's service is very efficient.

LT

LT1

Bank X gives me the feeling

accepted by everyone

Brand association

LT2

People want to use

services of bank X

LT3

I really like bank X

LT4

Bank X creates a special image

different in the mind of the customer

TT

TT1

I am very loyal to bank X.

Brand loyalty

TT2

I am always interested in finding

understand more about bank X

TT3

I will also recommend your services.

bank X to others

TT4

In the future, I will use the service

from bank X more

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