Syntax Literate: Jurnal Ilmiah Indonesia p�ISSN: 2541-0849 e-ISSN: 2548-1398

Vol. 7, No. 11, November 2022

 

SERVQUAL INFLUENCE ON CUSTOMER SATISFACTION, COMPLAINTS, ENGAGEMENT, AND LOYALTY IN INDONESIA�S BIGGEST INTERNET PROVIDER

 

Malinda Puteri Kusaeni, Sri Rahayu Hijrah Hati

Universitas Indonesia, Indonesia

Email: [email protected]

 

Abstract

Since the epidemic, digitization has advanced more quickly than anybody could have anticipated. From 2019 to 2021, IndiHome's product surpasses its rivals by controlling more than 80% of the market share. On the other hand, it is paradoxical that IndiHome would lose close to 10% of its customers in 2022 while also gaining more. This study aims to examine the influence of service quality and lead to customer satisfaction on IndiHome by PT. Telkom Indonesia. In addition,� the� researcher� also� explores� the �relationship� between� customer satisfaction, customer complaints, customer engagement, and customer loyalty in the telecommunication industry. With information gathered from users in the Jabodetabek area, SEM PLS will be utilized to examine the association between the dependent and independent variables individually. Results indicated that CS is crucial, particularly for internet service providers where SQ is important. �The results also found that customer satisfaction and customer engagement have a positive effect on customer� loyalty. But the relationship of customer satisfaction toward customer complaints, then toward loyalty, is shown to be unsignificant. The findings give marketers in the same business a scientifically validated example from Indonesia's largest internet service provider to sharpen competitiveness in the industry's constantly expanding market. This� research� was� conducted� only� focused� on revealing the relationship between customer satisfaction and customer complaints then to loyalty, without analyzing what factors lead to customers filing a complaint. Therefore, future� research� is� expected to be able to test the determinant factors of the complaints.

 

Keywords: service quality; customer satisfaction; customer engagement; customer complaints; customer loyalty

 

Introduction

The pace of digitalization has increased significantly after the COVID-19 pandemic's emergence. By investing in and developing in response to the requirements of society for digital services in the present and the future, the state-owned company PT Telkom Indonesia (Persero) Tbk continues to solidify its position as a supplier of internet services in the telecommunications industry. Telkom bases its business organization around customer segments, or Customer Facing Units (CFU), in order to develop and enhance value for customers. The Consumer CFU, which offers fixed voice, fixed broadband, IP-TV, and digital services, includes the IndiHome product.

IndiHome product surpasses its rivals by controlling more than 80% of the market share from 2019 to 2021. Due to the COVID-19 pandemic's increased demand for internet access, new rivals have entered the telecoms sector, which has reduced IndiHome's market share. With 9.2 million subscribers and a 75.2% market share, Telkom remains to be Indonesia's largest fixed broadband business provider under the IndiHome brand until the end of 2022 (Telkom Indonesia, 2023). Despite being the best among its competitors, IndiHome still faces several difficulties. According to its 2022 annual report, the biggest difficulty is lowering churn (Telkom Indonesia, 2023). In 2022, IndiHome loses roughly 10% of its national customer base, or 145.000 subscribers. This is paradoxical because IndiHome also gains a significant number of new customers in 2022, up 611.000 from the previous year. This come to question, why IndiHome fail to keep their existing customers but seems easier to gain new customers?

According to a study, compared to new consumers, loyal customers and those who have previously had positive experiences may have higher expectations, be more sensitive to flaws that increase uncertainty, and be less forgiving (Chandrashekaran, Rotte, Tax, & Grewal, 2007). This demonstrates that clients of IndiHome are not devoted to the business. Customer loyalty is the steadfast determination to repeatedly repurchase a good or service in the future, even when there are better options on the market. The price of acquiring new clients is higher than the price of keeping existing ones. Customers that are loyal to a brand have an emotional connection to the good or service, and they won't be drawn to another brand even if it has a greater offer (Lenka, Suar, & Mohapatra, 2009).

The findings of an empirical study conducted on another service provider show how customer satisfaction and trust levels influence loyalty.�� In turn, service quality has an impact on satisfaction (Set�-Pamies, 2012). It has become difficult for the telecoms sector to increase their competitiveness for customers given the market dominance of fixed broadband internet services. Companies need to increase service quality to compete in the market since it affects customer satisfaction and loyalty. The business must also put a strong emphasis on customer satisfaction since it can draw in new clients in addition to those who are currently using the service.

This study intends to investigate how customer satisfaction influence service quality in IndiHome by PT. Telkom Indonesia. Additionally, the researcher looks at the connections between consumer satisfaction, customer complaints, customer engagement, and customer loyalty in the telecommunications business. The results of this study might be used by marketers to enhance their marketing efforts, especially given the fierce rivalry among internet service provider providers. This study is anticipated to show the impact of the aforementioned factors, making it beneficial for businesses, especially new entrants in the market, to sharpen competition by investigating the case of IndiHome, Indonesia's largest internet service provider.

 

Research Methods

Customers of the product IndiHome from PT. Telkom Indonesia served as the study's participants and samples. Customers of IndiHome who utilize at least one service internet alone, internet with telephone, internet with TV, or all services internet, telephone, and TV were chosen as the study's population. By the end of 2022, this population consist of 2,356,166 IndiHome users. The SEM criteria are used to establish the sample size for the investigation, with a minimum sample size of 200 (Dash & Paul, 2021).

Respondent information was collected using questionnaire techniques. The findings of the data gathered from IndiHome customers will be determined by analysis of the obtained data using the SmartPLS software. Linear Regression and Descriptive Statistics were the analysis techniques employed in this study. The author's theory on the relationship between service quality and customer satisfaction, as well as the relationship between customer satisfaction and customer engagement, customer complaints, and customer loyalty, is put to the test using the linear regression analysis approach. The characteristics of IndiHome customers are ascertained and explained in general using the descriptive statistics analysis approach.

The link between the dependent and independent variables will then be tested individually using SEM PLS. For evaluating distinct multiple regression equations, SEM PLS is a suitable and effective testing approach. The use of SEM PLS allows for the testing of distinct correlations or models with moderating factors. Additionally, this study uses SEM PLS to estimate associated multipliers and linkages and to verify whether the proposed model well captures the observed occurrence (Hair, Black, Babin, & Anderson, 2010).

 

Research Model

Adapted from previous studies, this study focuses on examining the influence of service quality on customer satisfaction, loyalty, engagement, and complaints on IndiHome by PT. Telkom Indonesia. Within the market framework that supplies fixed broadband internet, there is fierce rivalry in the telecoms sector. Each supplier must work hard to build client loyalty through better service quality, which consequently increases customer satisfaction, in order to compete and retain its existence. In contrast to other research, this one also looked at customer complaints to learn more about how customer loyalty could be influenced by how satisfied customers are with the services they receive.

According to published research, a high degree of customer satisfaction boosts a company's reputation, protects its market share, fosters customer loyalty, and reduces customer complaints (Hallencreutz & Parmler, 2019). Because of this, PT. Telkom Indonesia, with its IndiHome product, can maintain market share in the telecom sector by reducing customer complaints that damage the company's reputation.

 

Figure 1

Research Model

Diagram Description automatically generated

 

Influence Of Service Quality On Customer Satisfaction

Customer satisfaction is essential for an organization or corporation to succeed, according to earlier research. It has been demonstrated that customer satisfaction and service quality are strongly correlated. Customer satisfaction may be increased by raising service standards (Wattoo & Iqbal, 2022).

In order to increase customer satisfaction, service providers in the context of PT. Telkom Indonesia's IndiHome product must make sure that they deliver superior quality services by taking into account the five dimensions: tangibility, reliability, responsiveness, assurance, and empathy. According to the foregoing description, the following formulation serves as the initial research hypothesis:

H1: Service quality has a positive influence on customer satisfaction.

 

The influence of Customer Satisfaction on Customer Complaints

According to study conducted to service company customers, customer satisfaction has a negative effect customer complaints (Yilmaz & Ari, 2016). Numerous research came to the same conclusion as the study's findings: customer complaints are a result of dissatisfaction , and a rise in customer satisfaction reduces the number of complaints.

According to another research, a service company's platform will receive less complaints from customers when they are more satisfied (Wattoo & Iqbal, 2022). As a result, the study also supported the idea that there is a bad correlation between customer service and complaints. According to the foregoing description, the following formulation serves as the initial research hypothesis:

H2: Customer satisfaction has a negative influence on customer complaints.

 

The influence of Customer Satisfaction on Customer Loyalty

The results of a prior study showed a significant positive association between customer satisfaction and loyalty. Customers' loyalty to the company providing the service is therefore increased by their satisfaction with the service�(Yilmaz & Ari, 2016). The results of this study are supported by several other studies, which show that there is a substantial and positive correlation between passenger contentment and loyalty, and that as satisfaction rises, loyalty follows.

According to the findings of the hypothesis test, customer loyalty is directly impacted by customer satisfaction�(Zaid & Patwayati, 2021). These findings confirm numerous earlier studies' findings that customer loyalty is significantly influenced by customer satisfaction.� The findings suggest that customer satisfaction, which develops through customer involvement, has an effect on elevating consumer loyalty. According to the foregoing description, the following formulation serves as the initial research hypothesis:

H3: Customer satisfaction has a positive influence on customer loyalty.

 

The influence of Customer Satisfaction on Customer Engagement

In a study conducted by (Yilmaz & Ari, 2016), believe that offering great products and services and displaying positive attitudes are the two most effective strategies to ensure client involvement.

Another analysis shows, customer satisfaction is an important construct affecting customer engagement. High-satisfied customers are more likely to interact with the brand, as shown by the positive and significant relationship between customer satisfaction and customer engagement (Hapsari, Clemes, & Dean, 2017). According to the foregoing description, the following formulation serves as the initial research hypothesis:

H4: Customer satisfaction has a positive influence on customer engagement.

 

The influence of Customer Complaints on Customer Loyalty

A� complaint� expresses� dissatisfaction� with� the� service� provider� by� the customer/consumer� when� the� service� fails. When customers receive that service performance is lower than their expectations; they will complain and feel disappointed. As a result, dissatisfied consumers are more prone to complain than satisfied ones.

According to the literature, high customer satisfaction results in a better company reputation, greater customer loyalty, and fewer customer complaints (Hallencreutz & Parmler, 2019). According to the foregoing description, the following formulation serves as the initial research hypothesis:

H5: Customer complaints has a negative influence on customer loyalty

 

The influence of Customer Engagement on Customer Loyalty

This� study (Zaid & Patwayati, 2021), also discovered that customer engagement has a favorable and significant influence on customer loyalty.� This discovery is in conformity with the findings of numerous earlier research. This suggests that the engagement shaped by customer satisfaction and loyalty to a service provider. According to the foregoing description, the following formulation serves as the first research hypothesis:

H6: Customer engagement has a positive influence on customer loyalty.

 

Results and Discussion

Result

1. Respondent Demography

To test the developed hypothesis, a survey was conducted with a total of 308 respondents. The respondents' profiles in this study are presented in Table 1.

 

Table 1

Respondent Profile

Variable

Frequency

Percentage

Gender

Male

163

52,92%

Female

145

47,08%

Age

17-20

61

19,81%

21-30

10

3,25%

31-40

148

48,05%

41-50

65

21,10%

>50

24

7,79%

Education

SMA / SMK

78

25,32%

D3 (Diploma Degree)

20

6,49%

S1 (Bachelor's Degree)

173

56,17%

S2 (Master's Degree)

36

11,69%

S3 (Doctorate Degree)

1

0,32%

Occupation

Freelancer

2

0,65%

Stay at Home Parent

18

5,84%

Private Enterprise Employee

85

27,60%

Student

30

9,74%

Researcher

1

0,32%

Government / State-Owned Enterprise Employee

145

47,08%

Unemployed

1

0,32%

Entrepeneur

22

7,14%

Retiree

2

0,65%

Educator (Teacher / Lecturer)

2

0,65%

Service Used

Internet & Landline Phone

36

11,69%

Internet & TV

102

33,12%

Internet only

74

24,03%

Internet, Landline Phone, and TV

96

31,17%

Location Type

Apartment

9

2,92%

Office

12

3,90%

Landed House

287

93,18%

Length of Usage

<1 years

35

11,36%

1-3 years

96

31,17%

3-5 years

59

19,16%

>5 years

118

38,31%

Location

Bekasi

43

13,96%

Bogor

22

7,14%

Depok

20

6,49%

West Jakarta

9

2,92%

Central Jakarta

10

3,25%

South Jakarta

34

11,04%

East Jakarta

37

12,01%

North Jakarta

86

27,92%

Tangerang

47

15,26%

 

2. Outer Model Analysis

In other words, the outer model defines how each indicator relates to its corresponding latent variable. The outer model analysis is used to ascertain the relationship between latent variables and their indicators. The model is evaluated using the SmartPLS data analysis technique using three measurement criteria: convergent validity, discriminant validity, and reliability testing (Composite Reliability and Chronbach Alpha).

 

a. Validity Test

According to the common rule of thumb, indicator factor loadings ≥ 0.7 are considered valid. However, factor loadings between 0.5 and 0.6 can still be accepted during the development of a new model or indicator (Haryono, 2017).

 

Table 2

1st Validity Test

 

AS

CC

CE

CL

CS

EM

REL

RES

TA

AS1

0.930

 

 

 

 

 

 

 

 

AS2

0.896

 

 

 

 

 

 

 

 

AS3

0.908

 

 

 

 

 

 

 

 

AS4

0.906

 

 

 

 

 

 

 

 

CC1

 

0.756

 

 

 

 

 

 

 

CC2

 

0.966

 

 

 

 

 

 

 

CC3

 

0.463

 

 

 

 

 

 

 

CC4

 

0.723

 

 

 

 

 

 

 

CE1

 

 

0.874

 

 

 

 

 

 

CE2

 

 

0.920

 

 

 

 

 

 

CE3

 

 

0.926

 

 

 

 

 

 

CE4

 

 

0.898

 

 

 

 

 

 

CE5

 

 

0.851

 

 

 

 

 

 

CE6

 

 

0.914

 

 

 

 

 

 

CE7

 

 

0.920

 

 

 

 

 

 

CE8

 

 

0.866

 

 

 

 

 

 

CL1

 

 

 

0.896

 

 

 

 

 

CL2

 

 

 

0.926

 

 

 

 

 

CL3

 

 

 

0.955

 

 

 

 

 

CL4

 

 

 

0.922

 

 

 

 

 

CS1

 

 

 

 

0.951

 

 

 

 

CS2

 

 

 

 

0.950

 

 

 

 

CS3

 

 

 

 

0.944

 

 

 

 

EM1

 

 

 

 

 

0.919

 

 

 

EM2

 

 

 

 

 

0.902

 

 

 

EM3

 

 

 

 

 

0.901

 

 

 

EM4

 

 

 

 

 

0.872

 

 

 

REL1

 

 

 

 

 

 

0.868

 

 

REL2

 

 

 

 

 

 

0.903

 

 

REL3

 

 

 

 

 

 

0.888

 

 

REL4

 

 

 

 

 

 

0.898

 

 

REL5

 

 

 

 

 

 

0.809

 

 

RES1

 

 

 

 

 

 

 

0.911

 

RES2

 

 

 

 

 

 

 

0.919

 

RES3

 

 

 

 

 

 

 

0.908

 

RES4

 

 

 

 

 

 

 

0.910

 

TA1

 

 

 

 

 

 

 

 

0.438

TA2

 

 

 

 

 

 

 

 

0.931

TA3

 

 

 

 

 

 

 

 

0.863

TA4

 

 

 

 

 

 

 

 

0.956

 

The table above displays the results of the estimation computation of the outer loading test using SmartPLS. The output indicates that two items, CC3 and TA1, are invalid because their factor loadings are less than 0.7. As a result, these items will be eliminated, and another validity test will be carried out.

 

Table 2

2nd Validity Test

 

AS

CC

CE

CL

CS

EM

REL

RES

TA

AS1

0.930

 

 

 

 

 

 

 

 

AS2

0.896

 

 

 

 

 

 

 

 

AS3

0.908

 

 

 

 

 

 

 

 

AS4

0.906

 

 

 

 

 

 

 

 

CC1

 

0.756

 

 

 

 

 

 

 

CC2

 

0.969

 

 

 

 

 

 

 

CC4

 

0.721

 

 

 

 

 

 

 

CE1

 

 

0.874

 

 

 

 

 

 

CE2

 

 

0.920

 

 

 

 

 

 

CE3

 

 

0.926

 

 

 

 

 

 

CE4

 

 

0.898

 

 

 

 

 

 

CE5

 

 

0.851

 

 

 

 

 

 

CE6

 

 

0.914

 

 

 

 

 

 

CE7

 

 

0.920

 

 

 

 

 

 

CE8

 

 

0.866

 

 

 

 

 

 

CL1

 

 

 

0.896

 

 

 

 

 

CL2

 

 

 

0.926

 

 

 

 

 

CL3

 

 

 

0.955

 

 

 

 

 

CL4

 

 

 

0.922

 

 

 

 

 

CS1

 

 

 

 

0.951

 

 

 

 

CS2

 

 

 

 

0.950

 

 

 

 

CS3

 

 

 

 

0.944

 

 

 

 

EM1

 

 

 

 

 

0.919

 

 

 

EM2

 

 

 

 

 

0.902

 

 

 

EM3

 

 

 

 

 

0.901

 

 

 

EM4

 

 

 

 

 

0.872

 

 

 

REL1

 

 

 

 

 

 

0.868

 

 

REL2

 

 

 

 

 

 

0.903

 

 

REL3

 

 

 

 

 

 

0.889

 

 

REL4

 

 

 

 

 

 

0.898

 

 

REL5

 

 

 

 

 

 

0.809

 

 

RES1

 

 

 

 

 

 

 

0.911

 

RES2

 

 

 

 

 

 

 

0.919

 

RES3

 

 

 

 

 

 

 

0.908

 

RES4

 

 

 

 

 

 

 

0.910

 

TA2

 

 

 

 

 

 

 

 

0.938

TA3

 

 

 

 

 

 

 

 

0.878

TA4

 

 

 

 

 

 

 

 

0.963

 

The results of the outer loading test (2nd validity test) estimation calculation are displayed in the table above. All item factor loadings have values over 0.7, as seen in the output. These items are therefore regarded as legitimate.

 

b. Reability Test

A tool used to assess a questionnaire's consistency as an indicator of a variable or construct is reliability testing. If a measuring tool such as a questionnaire is trustworthy, it will be able to produce measurements that are steady or repeatable. Utilizing composite reliability and the Cronbach's Alpha coefficient, the research instrument's dependability is evaluated in this work.

According to Chin (1998) in (Gozali & Latan, 2015), Cronbach's Alpha and Composite Reliability values should be better than 0.70 for confirmatory research, while values between 0.60 and 0.70 are still acceptable for exploratory research, according to the standards used to evaluate reliability. The analysis of the data from the testing of composite reliability and Cronbach's Alpha yielded the following results:

 

Table 3

Reability Test

 

Cronbach's Alpha

rho_A

Composite

Reliability

Average Variance Extracted

(AVE)

AS

0.931

0.933

0.951

0.828

CC

0.808

1.748

0.861

0.677

CE

0.965

0.968

0.970

0.804

CL

0.943

0.945

0.959

0.855

CS

0.944

0.945

0.964

0.900

EM

0.920

0.922

0.944

0.807

REL

0.922

0.924

0.942

0.764

RES

0.933

0.933

0.952

0.832

SQ

0.961

0.962

0.964

0.576

TA

0.918

0.919

0.948

0.860

 

c. Discriminant Validity

By analyzing the correlation values between constructs in cross-loadings, discriminant validity determines whether a latent construct predicts its own values more accurately than the values of other constructs. There are several methods for evaluating discriminant validity, including:

 

Examining Cross-loading Values

Cross-loading values can be used to evaluate discriminant validity. If all indicators have correlation coefficients that are greater with their own construct compared to the correlation coefficients of the indicators in other construct blocks, it may be stated that each indicator within the block contributes to the construct in that column (Haryono, 2017).

 

Table 4

Cross-Loading

AS

CC

CE

CL

CS

EM

REL

RES

TA

AS1

0.930

0.102

0.514

0.604

0.638

0.582

0.627

0.674

0.526

AS1

0.930

0.102

0.514

0.604

0.638

0.582

0.627

0.674

0.526

AS2

0.896

0.110

0.484

0.548

0.588

0.524

0.587

0.607

0.451

AS2

0.896

0.110

0.484

0.548

0.588

0.524

0.587

0.607

0.451

AS3

0.908

0.109

0.422

0.457

0.526

0.543

0.512

0.610

0.516

AS3

0.908

0.109

0.422

0.457

0.526

0.543

0.512

0.610

0.516

AS4

0.906

0.121

0.413

0.442

0.529

0.549

0.473

0.609

0.488

AS4

0.906

0.121

0.413

0.442

0.529

0.549

0.473

0.609

0.488

CC1

0.045

0.756

0.069

0.044

-0.041

0.022

-0.002

0.000

0.085

CC2

0.127

0.969

0.122

0.153

0.058

0.133

0.126

0.131

0.161

CC4

0.086

0.721

0.095

0.051

0.007

0.072

0.023

0.021

0.086

CE1

0.529

0.120

0.874

0.808

0.749

0.618

0.629

0.568

0.491

CE2

0.467

0.065

0.920

0.765

0.699

0.566

0.579

0.480

0.488

CE3

0.461

0.080

0.926

0.811

0.728

0.605

0.578

0.510

0.492

CE4

0.516

0.157

0.898

0.737

0.657

0.578

0.591

0.537

0.543

CE5

0.382

0.103

0.851

0.627

0.569

0.434

0.522

0.443

0.477

CE6

0.404

0.125

0.914

0.711

0.617

0.531

0.542

0.460

0.519

CE7

0.423

0.101

0.920

0.706

0.643

0.542

0.555

0.511

0.543

CE8

0.417

0.138

0.866

0.664

0.580

0.496

0.545

0.471

0.499

CL1

0.499

0.159

0.757

0.896

0.698

0.631

0.571

0.558

0.441

CL2

0.497

0.148

0.712

0.926

0.748

0.619

0.630

0.600

0.481

CL3

0.531

0.087

0.810

0.955

0.778

0.654

0.635

0.614

0.481

CL4

0.566

0.101

0.744

0.922

0.785

0.652

0.654

0.652

0.512

CS1

0.594

0.041

0.723

0.819

0.951

0.706

0.706

0.712

0.490

CS2

0.591

0.031

0.694

0.746

0.950

0.659

0.664

0.689

0.508

CS3

0.603

0.039

0.677

0.749

0.944

0.677

0.680

0.731

0.538

EM1

0.605

0.109

0.530

0.624

0.669

0.919

0.568

0.669

0.540

EM1

0.605

0.109

0.530

0.624

0.669

0.919

0.568

0.669

0.540

EM2

0.524

0.129

0.522

0.597

0.609

0.902

0.544

0.624

0.514

EM2

0.524

0.129

0.522

0.597

0.609

0.902

0.544

0.624

0.514

EM3

0.554

0.129

0.604

0.659

0.674

0.901

0.632

0.630

0.562

EM3

0.554

0.129

0.604

0.659

0.674

0.901

0.632

0.630

0.562

EM4

0.484

0.060

0.549

0.602

0.627

0.872

0.539

0.565

0.532

EM4

0.484

0.060

0.549

0.602

0.627

0.872

0.539

0.565

0.532

REL1

0.472

0.104

0.562

0.565

0.594

0.534

0.868

0.573

0.570

REL1

0.472

0.104

0.562

0.565

0.594

0.534

0.868

0.573

0.570

REL2

0.521

0.112

0.555

0.595

0.615

0.539

0.903

0.631

0.467

REL2

0.521

0.112

0.555

0.595

0.615

0.539

0.903

0.631

0.467

REL3

0.570

0.063

0.596

0.662

0.707

0.582

0.889

0.654

0.522

REL3

0.570

0.063

0.596

0.662

0.707

0.582

0.889

0.654

0.522

REL4

0.581

0.093

0.533

0.574

0.663

0.586

0.898

0.662

0.518

REL4

0.581

0.093

0.533

0.574

0.663

0.586

0.898

0.662

0.518

REL5

0.500

0.062

0.531

0.545

0.564

0.538

0.809

0.565

0.518

REL5

0.500

0.062

0.531

0.545

0.564

0.538

0.809

0.565

0.518

RES1

0.596

0.110

0.500

0.614

0.693

0.673

0.679

0.911

0.551

RES1

0.596

0.110

0.500

0.614

0.693

0.673

0.679

0.911

0.551

RES2

0.637

0.079

0.572

0.628

0.739

0.648

0.719

0.919

0.535

RES2

0.637

0.079

0.572

0.628

0.739

0.648

0.719

0.919

0.535

RES3

0.627

0.107

0.459

0.564

0.635

0.606

0.580

0.908

0.584

RES3

0.627

0.107

0.459

0.564

0.635

0.606

0.580

0.908

0.584

RES4

0.649

0.083

0.498

0.584

0.663

0.601

0.597

0.910

0.536

RES4

0.649

0.083

0.498

0.584

0.663

0.601

0.597

0.910

0.536

TA2

0.463

0.139

0.507

0.453

0.477

0.533

0.532

0.518

0.938

TA2

0.463

0.139

0.507

0.453

0.477

0.533

0.532

0.518

0.938

TA3

0.529

0.135

0.487

0.476

0.483

0.563

0.531

0.586

0.878

TA3

0.529

0.135

0.487

0.476

0.483

0.563

0.531

0.586

0.878

TA4

0.521

0.150

0.572

0.509

0.537

0.566

0.583

0.573

0.963

TA4

0.521

0.150

0.572

0.509

0.537

0.566

0.583

0.573

0.963

 

From the above output, it can be observed that all indicators have correlation coefficients that are higher with their respective variables compared to the correlation coefficients of the indicators with other variables. Therefore, it may be stated that each indication inside the block contributes to the variable or construct in that column.

 

Comparing the Square Root of AVE Values

By comparing the square root of AVE (Average Variance Extracted) values for each construct with the correlations between the constructs and other constructs in the model, discriminant validity is further evaluated. If the square root of AVE for each construct is greater than the correlation value between that construct and other constructs in the model, it indicates good discriminant validity.

 

Table 5

AVE Values

 

AS

CC

CE

CL

CS

EM

REL

RES

TA

AS

0.910

 

 

 

 

 

 

 

 

CC

0.121

0.823

 

 

 

 

 

 

 

CE

0.505

0.123

0.897

 

 

 

 

 

 

CL

0.566

0.133

0.818

0.925

 

 

 

 

 

CS

0.628

0.040

0.736

0.814

0.949

 

 

 

 

EM

0.604

0.120

0.614

0.691

0.718

0.899

 

 

 

REL

0.606

0.099

0.635

0.674

0.721

0.637

0.874

 

 

RES

0.688

0.104

0.557

0.656

0.749

0.693

0.707

0.912

 

TA

0.545

0.153

0.564

0.518

0.539

0.598

0.593

0.604

0.927

 

Based on the aforementioned findings, it can be seen that each variable's square root of AVE values is larger than its correlation values with the other variables in the model. As a result, it may be said that the model has excellent discriminant validity according to the AVE criteria.

 

 

 

 

3. Inner Model Analysis

 

Figure 2

SEM Results

 

Hypotheses Testing

The analysis will be done during this phase of hypothesis testing to see if there is a significant relationship between the independent variables and the dependent variable. The path coefficients, which represent the parameter coefficients and the significance levels of the t-statistics, are examined during the hypothesis testing.

The significance of the estimated parameters reveals details about the connections between the variables under study. The probability of 0.05 is the cutoff point for rejecting or accepting the given hypothesis. The estimation results for the structural model's testing are shown in the table below:

 

Table 6

Hypotheses Testing

 

Original Sample (O)

Sample Mean (M)

Standard Deviation (STDEV)

T Statistics (|O/STDEV|)

P Values

CC -> CL

0.057

0.051

0.048

1.180

0.239

CE -> CL

0.465

0.470

0.055

8.420

0.000

CS -> CC

0.040

0.039

0.091

0.434

0.664

CS -> CE

0.736

0.738

0.030

24.679

0.000

CS -> CL

0.469

0.463

0.057

8.171

0.000

SQ -> CS

0.811

0.812

0.024

33.285

0.000

 

Hypothesis formulation:

H1: SQ influences CS.

H2: CS influences CC.

H3: CS influences CL.

H4: CS influences CE.

H5: CC influences CL.

H6: CE influences CL.

 

Basis for decision-making (based on T-statistics with a significance level of 0.05):

1.     Ho is accepted if T-statistics < 1.65 (No influence).

2.     Ho is rejected if T-statistics ≥ 1.65 (Significant influence).

 

Basis for decision-making (based on significance value):

1.     If the P-value > 0.05, then H0 is accepted (No influence).

2.     If the P-value ≤ 0.05, then H0 is rejected (Significant influence).

Conclusion:

1.     SQ influences CS. This is observed from the output of the Path Coefficient, where the calculated t-value is greater than the tabulated t-value (33.285 > 1.65) and the P-value is less than 0.05 (0.000 < 0.05), indicating the rejection of Ho and acceptance of Ha. The coefficient value (Original sample column) is positive, indicating a positive influence, meaning that an increase in SQ leads to an increase in CS.

2.     CS does not influence CC. This is observed from the output of the Path Coefficient, where the calculated t-value is less than the tabulated t-value (0.434 < 1.65) and the P-value is greater than 0.05 (0.664 > 0.05), indicating the acceptance of Ho and the rejection of Ha.

3.     CS influences CL. This is observed from the output of the Path Coefficient, where the calculated t-value is greater than the tabulated t-value (8.171 > 1.65) and the P-value is less than 0.05 (0.000 < 0.05), indicating the rejection of Ho and acceptance of Ha. The coefficient value (Original sample column) is positive, indicating a positive influence, meaning that an increase in CS leads to an increase in CL.

4.     CS influences CE. This is observed from the output of the Path Coefficient, where the calculated t-value is greater than the tabulated t-value (24.679 > 1.65) and the P-value is less than 0.05 (0.000 < 0.05), indicating the rejection of Ho and acceptance of Ha. The coefficient value (Original sample column) is positive, indicating a positive influence, meaning that an increase in CS leads to an increase in CE.

5.     CC does not influence CL. This is observed from the output of the Path Coefficient, where the calculated t-value is less than the tabulated t-value (1.180 < 1.65) and the P-value is greater than 0.05 (0.239 > 0.05), indicating the acceptance of Ho and the rejection of Ha.

6.     CE influences CL. This is observed from the output of the Path Coefficient, where the calculated t-value is greater than the tabulated t-value (8.420 > 1.65) and the P-value is less than 0.05 (0.000 < 0.05), indicating the rejection of Ho and acceptance of Ha. The coefficient value (Original sample column) is positive, indicating a positive influence, meaning that an increase in CE leads to an increase in CL.

 

Discussion

The results in Table 6 show how closely related the chosen constructions are to one another. Figure 2 demonstrates that all statistically significant dimensions correspond to the Service Quality variable. Responsiveness (26.466) has the most impact, followed by Assurance (21.886) and Reliability (21.452).

Users of IndiHome are more worried about the service provider's responsiveness when using the internet. Customer assistance and prompt service are examples of responsiveness in the provision of high-quality services (Parasuraman, Zeithaml, & Berry, 1988). Users might believe that internet service providers ought to prioritize quickly providing assistance to their clients, which would raise the perceived SQ.

Assurance came in second as the factor that had the most impact on SQ, behind responsiveness. These findings are in line with the initial study on SQ dimensions (Parasuraman, Zeithaml, & Berry, 1988). It implies that the service provider must be knowledgeable, respectful, and able to instill confidence in their workforce. As a result, the service provider not only provides assistance promptly and responsively, but also gives people assurance in the service.

As demonstrated in this study, higher CS will result from improved SQ. The results are likewise comparable to the past investigations of (Wattoo & Iqbal, 2022). The finding that SQ has a significant positive impact on consumer satisfaction supports earlier research findings (Hussain, Al Nasser, & Hussain, 2015). The correlation coefficient between SQ and CS was calculated to be 0.811 with a significance level of 0. According to the research, an improvement in SQ would result in better CS for IndiHome consumers in Jabodetabek. Hence, H1 (SQ →CS) is approved.

The study, however, does not provide evidence for hypothesis 2. That is, at a significance value of 0.664, there was no correlation between customer satisfaction and customer complaints (CS →CC). Supported by a prior research (Wirtz & Lovelock, 2016), according to statistics, just 5�10% of customers who are dissatisfied with a service really complain. The proportion can occasionally be much lower. Although only a minority of dissatisfied customers complain, there�s evidence that consumers across the world are becoming better informed, more self-confident, and more assertive about seeking satisfactory outcomes for their complaints (Wirtz & Lovelock, 2016).

����������� The positive, significant effect between customer satisfaction and loyalty (CS →CL) demonstrates that satisfied consumers are more likely to intend to use a product or service for an extended length of time. The results of earlier research, which say (Hapsari, Clemes, & Dean, 2017), when a consumer is happy with the service received, their requirements and wishes have been met by the purchased service. Customers who are pleased with their interactions and are pleased with their choice of service provider are more inclined to recommend the service provider to others.

Customers that are satisfied are more inclined to interact with the company, as shown by the positive and substantial association between customer satisfaction and customer engagement (CS →CE). Satisfied internet usage experiences may result in an increase in engaged customers. This outcome is consistent with other research on the topic of customer satisfaction and engagement, in (Hapsari, Clemes, & Dean, 2017) it is proven that a customer who is satisfied with the overall services provided, is more likely to be engaged with the service provider than customers who have an unfavourable outcome.

The findings indicate that there is no significant correlation between customer complaints and customer loyalty (CC →CL). As a result, the study does not support hypothesis 5. In line with the findings of the current stud�(Yilmaz & Ari, 2016), investigating the loyalty of another service providers, determined that the relationship between customer complaints and customer loyalty is not significant.

This outcome also demonstrates how crucial it is to consider the causes of customer complaints. According to research, there are four primary motivations for consumer complaints. These are generally to: (1) Obtain restitution or compensation, (2) Vent their anger, (3) Help to improve the service, (4) For altruistic reasons (Wirtz & Lovelock, 2016). When a consumer complains to a service provider, it doesn't always signify that they intend to stop using the service. On the other side, customers could choose to accept the service as it is and not voice any complaints at all, even if they are dissatisfied.

Customer loyalty is significantly impacted by customer engagement. IndiHome, an internet service provider, should therefore take advantage of the beneficial effect that customer satisfaction has on customer engagement. Additionally, this study discovered that customer loyalty is positively and significantly impacted by customer engagement. (CE →CL).� This observation is consistent with the outcomes of numerous earlier research (Hapsari, Clemes, & Dean, 2017) (Zaid & Patwayati, 2021). This research suggests that consumer involvement has an effect on raising loyalty, proving that engagement creates customer loyalty.

 

Conclusions

The success of the majority of successful organizations depends on CS, particularly for IndiHome as an internet service provider where the SQ is crucial. As a result, this study aims to pinpoint the factors that influence the SQ of IndiHome, Indonesia's largest internet service provider, which ultimately results in CS. Additionally, it investigates how CS affects customer complaints, customer engagement, and customer loyalty.

Based on a review of the literature, this study identifies five SQ dimensions in service: tangibility, reliability, responsiveness, assurance, and empathy. According to the study's findings, customer satisfaction and service quality have a positive and significant relationship.

The studies also indicated that customer satisfaction and customer engagement have a favorable and significant influence on customer loyalty. But the link of customer satisfaction toward customer complaints, then customer complaints toward loyalty, is demonstrated to be unsignificant in the instance of IndiHome.

According to the insignificance of customer complaints variable, IndiHome fails to comprehend why customers are leaving, which is why it keeps losing customers while also gaining more new ones. IndiHome, the largest internet service provider with numerous platforms, also falls short of educating an effective customer complaints channel, preventing the satisfaction from being expressed as a complaint.

Aside from the contribution, this study has some limitations that can be addressed in a subsequent study. This research was conducted only focused on revealing the relationship between customer satisfaction and customer complaints then to loyalty, without analyzing what factors lead to customers filing a complaint. Future studies should therefore be able to test the factors that influence customer complaints to internet service providers.

Future studies may be conducted to test this model in various country locations, such as rural areas, in order to improve generalizability since this study was conducted in the major cities of the Jabodetabek area.

 

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Copyright holder:

Malinda Puteri Kusaeni, Sri Rahayu Hijrah Hati (2022)

 

First publication right:

Syntax Literate: Jurnal Ilmiah Indonesia

 

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