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2 The reliability test: the Cronbach’s alpha test

2 The reliability test: the Cronbach’s alpha test

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Table 4.2: The results of Cronbach’ alpha test



Scale Mean if Scale Variance if Corrected

Item Deleted

Item Deleted

Item-Total

Correlation



Squared

Multiple

Correlation



Cronbach's

Alpha if Item

Deleted



Rewards

REWARD01

REWARD02



Cronbach's Alpha

7.4641

3.365



.721



0.823

.532



.723



7.7416



2.779



.710



.527



.729



REWARD03



7.3397



3.370



.620



.386



.812



Policy

POLICY04

POLICY05

POLICY06



Cronbach's Alpha

8.2871

2.744

8.3684

2.676

8.1866

3.028



.757

.710

.706



0.852

.573

.509

.506



.760

.808

.810



Information

IMFORM07

INFORM08

INFORM09



Cronbach's Alpha

6.4498

3.681

5.8517

4.310

6.6077

3.836



.644

.462

.614



0.744

.451

.215

.429



.571

.782

.609



Personalizatio

n

PERSON10

PERSON11

PERSON12



Cronbach's Alpha

7.2057

2.520

7.3206

2.277

7.1483

2.762



.598

.661

.516



0.759

.390

.446

.274



.668

.592

.758



Staff

STAFF13

STAFF14

STAFF15

STAFF16



Cronbach's Alpha

9.9139

10.1962

10.0526

10.2440



.777

.804

.832

.759



0.908

.646

.650

.705

.603



.887

.878

.868

.894



Tangibility

TANGI17

TANGI18



Cronbach's Alpha

3.4593

1.230

3.2249

1.435



.769

.769



0.868

.591

.591



Communicatio

n

COMMU19

COMMU20



Cronbach's Alpha

2.8373

1.185

3.2105

1.109



.663

.663



0.797

.440

.440



Reputation

REPU21

REPU22



Cronbach's Alpha

7.7990

2.979

7.7321

2.986



.711

.733



0.829

.538

.558



6.291

6.245

6.175

6.349



33



.741

.722



REPU23



7.9234



Customer

satisfaction

SATIS24

SATIS25

SATIS26



Cronbach's Alpha

7.7177

2.598

7.8660

2.578

7.8421

2.499



Customer

Loyalty

LOYAL27

LOYAL28

LOYAL29



Cronbach's Alpha

7.0191

4.028

7.0383

3.729

7.5981

3.155



2.869



.628



.395



.831



.774

.758

.764



0.879

.600

.576

.585



.821

.834

.830



.634

.649

.516



0.754

.465

.480

.266



.635

.604

.796



The result performed that 10 scales had the result of Cronbach’s alpha above

0.7, the highest was 0.908 (Staff in the Customer Loyalty Program) and the lowest

was 0.744 (Information in the Customer Loyalty Program). Moreover, the corrected

item-total correlation of each item is above 0.3. This indicates that all scales fit the

requirement for reliability. As a result, these measures were used in establishing the

main survey to test the study hypotheses.

Moreover, in the scale of Information, Cronbach's alpha would increase from

0.744 to 0.782 if item INFORM08 were deleted or not used for computing an overall

task value score. As the same situation, in the scale of Reputation, Cronbach's alpha

would increase from 0.829 to 0.831 if item REPU23 were deleted or not used for

computing an overall task value score. For the scale of Loyalty, Cronbach's alpha

would increase from 0.754 to 0.796 if item LOYAl29 were deleted or not used for

computing an overall task value score. The author decided that these items should be

removed from the scale. Note first that alpha increased by a large degree from deleting

these items. Second, these items did not correlates very well with the composite score

from others in the scales (the item-total correlation for item INFORM08 is 0.462,

REPU23 is 0.628 and LOYAL29 is 0.516).



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4.3 Exploratory factor analysis (EFA)

After analyzing the Cronbach’s alpha, the author evaluated the measurement

scales by conducting exploratory factor analysis. The purpose of EFA is to define

which set of items go together as a group or are answered similarly by respondents

(Leech et al., 2005). In this study, EFA was run through the Principal Axis Factoring

with Varimax rotation method. As the conceptual model that there are ten factors:

Rewards, Policy, Information, Personalization, Tangibility, Communication, Staff,

Reputation, Customer Satisfaction and Customer Loyalty. The author examined if the

items belonging to one concept actually are in the same group. Based on the test of

assumption, the KMO was 0.893 presenting sufficient items for each factor. KMO test

indicates one whether or not enough items are predicted by each factor. The Bartlett

was significant (0.000 less than 5%) means that the variable are correlated highly

enough to provide a reasonable basis for factor analysis.

Table 4.3: KMO and Bartlett's Test for all variables

KMO and Bartlett's Test

Kaiser-Meyer-Olkin

Measure

Sampling Adequacy.

Bartlett's Test Approx. Chi-Square

of Sphericity

df

Sig.



of .893

3184.526

253

0.000



By doing EFA (Principal Axis Factoring with Varimax rolation method), the

result showed that four factors were extracted from 10 items measuring: perceived

risk, social cost, consumers’ attitude toward purchasing counterfeit products and

purchase intention toward counterfeit products. Moreover, the cumulative of the first

six factors occupied for 74.3 percent of variance. This indicated that major percent of

variance could be explained by six initial items.



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Table 4.4 : Total Variance Explained for all variables

Total Variance Explained



Factor



Initial Eigenvalues



Total



Extraction Sums of Squared Rotation

Loadings

Sums of

Squared

Loadingsa

Total

%

of Cumulative Total

Variance %

8.619

39.179

39.179

6.415

2.137

9.713

48.893

6.952

1.345

6.113

55.006

6.031

.867

3.940

58.947

2.841

.745

3.387

62.333

3.649

.669

3.041

65.375

3.969



%

of Cumulative

Variance %

1

8.959

40.723

40.723

2

2.472

11.238

51.961

3

1.625

7.388

59.349

4

1.218

5.538

64.887

5

1.082

4.920

69.807

6

1.010

4.589

74.396

Extraction Method: Principal Axis Factoring.

a. When factors are correlated, sums of squared loadings cannot be added to obtain a total

variance.



Additional, the author has eliminated the items of REPU21 and REPU22 due to

the reason that the distance from the max value and the next value are under 0.25. The

Rotated Factor Matrix showed the items and factor loading for rotated factors with

loading higher than 0.5 are significant as requirement. The items clustered into six

groups that they belong to.

There are several changes in factors. This means the perception of interviewees

about some variables is different from the hypotheses of the study and some previous

theory. However, this change reflects the thought and perception of the respondents

about the factors actually influence their satisfaction as well as loyalty in shopping at

the supermarket. After EFA, Customer Satisfaction and Customer Loyalty turn into

the same group that can be name as Customer Outcome. For the same situation, Staff

and Communication are in the same group named Serving, which is same for Rewards

and Policy as belowed:

Customer Outcome (Customer Satisfaction, Customer Loyalty)

Serving (Staff, Communication)



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Regulation (Rewards, Policy)

These replacement variables including one dependent variable and five

independent variables were entered into the regression equation at the same time. And

the results are presented in the next section

Table 4.5: Rotated Component Matrix for all variables



Customer

Outcome

Serving



Regulation



Personalization

Information

Tangibility



SATIS24

SATIS25

SATIS26

LOYAL27

LOYAL28

STAFF13

STAFF14

STAFF15

STAFF16

COMMU19

COMMU20

REWARD01

REWARD02

REWARD03

POLICY04

POLICY05

POLICY06

PERSON11

PERSON10

IMFORM07

INFORM09

TANGI17

TANGI18



Component

1

2

.783

.787

.785

.709

.712

.794

.821

.824

.798

.644

.706



3



4



5



6



.664

.710

.753

.663

.681

.535

.727

.829

.814

.828

.839

.852



4.4 Multiple regression analysis

After testing the Cronbach’s Alpha Analysis and EFA, the author conducted the

multiple regression analysis in order to define the relationship between six factors

mentioned above. According to Hair et al. (2010), multiple regression analysis helps

the author to predict the level of impact of independent variable on dependent



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variable. The relationship between the independent variables (such as Serving,

Regulation, Personalization, Information, Tangibility) and Customer Outcome (which

in combined by Customer Satisfaction and Customer Loyalty) was tested by standard

multiple regression analysis. It is necessary for testing correlation between variables

by using Pearson correlations. The results present that the explanatory variables are

not correlated with each other.

Table 4.6: Correlations or all variables

Correlations

Customer

Outcome



Regula

tion



Tangibil

ity



Pearson

1

Correlation

Pearson

.515**

1

Correlation

Tangibility

Pearson

.377**

.170*

1

Correlation

Information Pearson

.503**

.428**

.483**

Correlation

Personalizati Pearson

.522**

.517**

.309**

on

Correlation

Serving

Pearson

.607**

.785**

.272**

Correlation

**. Correlation is significant at the 0.01 level (2-tailed).



Informat Personaliza

ion

tion



Serving



Customer

Outcome

Regulation



1

.424**



1



.446**



.609**



1



The result of running the Multiple Regression was reported to determine how well the

model fit:

Table 4.7: Model Summaryb

Model



R



R Square



Adjusted

Square



1



.768a



.590



.579



R Std. Error of the

Estimate

Durbin-Watson

.50770



1.479



a. Dependent Variable: Customer Outcome

b. Predictors: (Constant), Regulation, Serving, Personalization,

Tangibility, Information



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According to the Model Summary table, the multiple correlation coefficient (R)

was 0.768, R Square was equal 0.590 and adjusted R Square was 0.579, showing that

57% of the variance in consumers’ outcome could be predicted from five independent

variables.

Table 4.8: ANOVA alpha

ANOVAa

Model

1 Regression



Sum

Squares

75.146



of df



Mean

Square

15.029



5



F



Sig.



58.307



.000b



Residual

52.325

203

.258

Total

127.471

208

a. Dependent Variable: Customer Outcome

b. Predictors: (Constant), Regulation, Serving, Personalization, Tangibility, Information



The value of F was 58.307 and sig<0.05 indicates that the combination of these

variables significantly predicts the dependent variable.

Table 4.9: Coefficients alpha

Coefficientsa

Model



Unstandardized

Coefficients

B

Std. Error

1 (Constant)

.328 .214

Regulation

.196 .055

Serving

.559 .057

Tangibility

.045 .038

Information

.046 .046

Personalization

.110 .050

a. Dependent Variable: Customer Outcome



Standardized

Coefficients

Beta

.200

.537

.063

.054

.117



t



1.534

3.545

9.766

1.207

.984

2.226



Sig.



.127

.000

.000

.229

.326

.027



Collinearity Statistics

Tolerance



VIF



.635

.670

.743

.661

.729



1.574

1.492

1.346

1.513

1.372



Since there new group of scales that have been formed, the author have to revised

conceptual model before testing hypothesis base on the multiple regression analysis.

4.5 Revised research model



39



After checking the result of the reliability of each variable and validity of the

measurement scales testing, there is change of independent factors. The change here

can be understood that specific group of customer shopping at various supermarket in

Ho Chi Minh City did not clearly distinct some factors that were group after EFA

testing. Firstly, the concept of Satisfaction and Loyalty in others sectors are extremely

clear distinct. But most of the respondents suppose that these two factors are the

same. In the terms of loyalty have the satisfaction and vice versa. It can be explained

the same for Rewards and Policy, they assumed that these factors are the regulation of

the Customer Loyalty Program. And for the Staff and Communication factors, the

respondent assumes that they are just the Serving that they gain from the Customer

Loyalty Program. Therefore, the revised and finalized research model is describe in

the following figure.

Figure 4.1: The revised and finalized research model



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Table 4.10: Finalized hypotheses of the research

Hi

H1(a,b,c,d,e)

H1a

H1b

H1c

H1d

H1e

H2



Hypotheses

There is a positive impact of customer loyalty program service quality on the

customer outcome

There is a positive impact of regulation on the customer outcome

There is a positive impact of serving on the customer outcome

There is a positive impact of tangibility on the customer outcome

There is a positive impact of personalization on the customer outcome

There is a positive impact of information on the customer outcome

Higher the store reputation, higher will be the customer loyalty.



H1a: There is a positive impact of regulation on the customer outcome

As presented in above table, sig value of perceived risk was under 0.05. It

indicated that the factor of regulation have the positive impact on the customer

outcome. Moreover, the regulation has the highest beta coefficient with the value of

0.537 which reflects that it has the greatest contribution to customer outcome.

H1b: There is a positive impact of serving on the customer outcome

As presented in above table, sig value of perceived risk was under 0.05. It

indicated that the factor of serving have the positive impact on the customer outcome.

Moreover, the regulation has the second high beta coefficient with the value of 0.200

which reflects that it also has the great contribution to customer outcome.

H1c: There is a positive impact of tangibility on the customer outcome

With the sig value 0.326 (>0.05), the author could conclude that the factor of

tangibility did not have the relationship with the customer outcome on this research.

H1d: There is a positive impact of personalization on the customer outcome

As presented in above table, sig value of perceived risk was 0.27 (under 0.05).

It indicated that the factor of personalization have the positive impact on the customer

outcome.

H1e: There is a positive impact of information on the customer outcome



41



With the sig value 0.229 (>0.05), the author could conclude that the factor of

information did not have the relationship with the customer outcome on this research.

H2: Higher the store reputation, higher will be the customer loyalty.

Since the elements of factor reputation have been eliminated all throughout the

process of running Cronbach’s Alpha and EFA testing, so the author assumes that

store reputation did not have the relationship with the customer outcome on this

research.

Table 4.11: Summary of hypotheses testing results

No.

H1a

H1b

H1c

H1d

H1e

H2



Hypotheses

There is a positive impact of regulation on the customer outcome

There is a positive impact of serving on the customer outcome

There is a positive impact of tangibility on the customer outcome

There is a positive impact of personalization on the customer

outcome

There is a positive impact of information on the customer

outcome

Higher the store reputation, higher will be the customer loyalty.



Testing result

Supported

Supported

Not supported

Supported

Not supported

Not supported



4.6 Explanation for the finding results of the hypotheses

According to the consumer behavior literature and recent empirical research,

we defined seven variables (Rewards, Policy, Staff, Communication, Information,

Tangibility, Personalization) that impact on customer satisfaction, and then Customer

Satisfaction as well as Store Reputation that influence Customer Loyalty in the

supermarket retail sector. However, our study findings suggest that the anticipated

negative impact of tangibility and information on customer outcome, store reputation

on customer outcome and the most important findings that customer satisfaction and

customer loyalty are the same in the research.

This meant that consumers never pay attention to the store reputation to decide

their selection where to buy, and they do not care much about the differentiation

between satisfaction and loyalty for a retailer. These findings are different with the



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results from previous empirical. In the best of the effort, the author could not find the

empirical studies in other countries as well as the limited number of researches about

retail loyalty in Vietnam to be an evidence for this finding but the possible explanation

for this outcome might lie in the different context of conducting the research. Most of

the previous studies were studied at different countries which have a different culture,

standard of living, standard of moral, life style, legal…this could lead to different

concepts of consumers in retail loyalty.

Regarding to this finding, in order to explain and confirm this outcome in

practice, the author also conducted the in-depth interviews with 5 respondents about

the reasons why (1) customer satisfaction and customer loyalty are inclusive, (2) staff

and communication are the same, (3) rewards and policy are inclusive and (4) store

reputation does not affect the customer loyalty.

For the first matter of Customer Satisfaction and Customer Loyalty, the point

of customer satisfaction is that people feel comfortable, pleasure and happy when

shopping at the supermarket. From that pleasure, they will also choose that

supermarket again when have the needs to buy goods. The comfort in shopping

includes the tendency to buy again, that mean the satisfaction and the loyalty in this

in-depth interview are inclusive or can be said that they are the same.

For the second matter of Staff and Communication, all of five people in the indepth interview assume that these two factors are the place they get the information or

raise the suggestion about the Customer Loyalty Program Quality. All the promotions

within the program or the process of information feedback, the customer can easily get

through by the Staff and the Website (Communication) of the specific supermarket.

For the third matter of Rewards and Policy, all interviewees declared that they are

fully aware of these two factor but they decide that Rewards or Policy are all in

regulation of Customer Loyalty Program. For example, the gift that they will receive

or how they can get bonus in a period of time are exactly the regulation. For the last

matter of store reputation does not affect the customer loyalty, all interviewees



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