Electronic Service Quality and Perceived Value in Mobile based

Services

Doddy Ridwandono, Tri Lathif Mardi Suryanto and Gita Islamiwaty Suherlan

Department of Information Systems, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, Indonesia

Keywords: Service Value, Perceived Value, Mobile Services.

Abstract: Indonesia is one of the countries with the highest number of mobile user growth in the world. To support its

services, telecommunications companies provide mobile-based services. This paper aims to determine the

service quality attributes of mobile services, and reveal their relationship with another variable, Perceived

Value. The survey was conducted electronically on users of the mobile services application (My-Telkomsel)

in Surabaya, Indonesia. 523 and 115 respondents were collected for E-S-Qual and E-Recs-Qual scale,

respectively. The collected data was further tested using SmartPLS software. The result was: E-S-Qual

which consists of Efficiency, System Availability, Fulfilment, and Privacy has a significant effect on

Perceived Value. As for the E-RecS-Qual, only the Responsiveness variable has a significant effect on

Perceived Value. Two other variables, Compensation and Contact, have no significant effect. This research

could encourage service providers to put emphasize on certain quality attributes. In addition, this study

provides insight regarding the effect of service quality on Perceived Value.

1 INTRODUCTION

There is a need to assess service quality (Batagan

2013). The growth of various types of services

encourages the creation of new ways of delivering

services and increases the interest of researchers to

study the field (Furrer et al. 2020). One of them is a

study in the change in customer preferences (Patten

et al. 2020) due to device adoption in both the

desktop and mobile contexts (Kaatz 2020). This

paper focuses on one particular aspect, Service

Quality in the mobile services context.

The level of urgency to conduct research on

service quality in a mobile context is high.

According to statista.com and datareportal.com, it is

close to 60% of the world’s total population use the

internet. Around 91% of internet users use mobile

devices in which Indonesia is ranked 4

in the

number of the internet user (Pengguna and Indonesia

2020). The increase in internet users has encouraged

many companies to develop mobile-based services,

and therefore the quality of their services needs to be

measured (Tharanikaran et al. 2017), (Rita et al.

2019), (Furrer et al. 2020). These facts were the

motivation to identify the mobile-based services

quality dimension in Indonesia.

The object of this research is an Indonesian

telecommunications company mobile-based service,

MyTelkomsel (my.telkomsel.com). The application

is intended to provide convenience for customers in

managing accounts and accessing services using a

smartphone. Services that can be fulfilled include

purchasing data packages, as well as providing

information needed by a customer. This object was

chosen considering its large number of users and

transactions (Kusdinar and Ariyanti 2020). While

the model chosen to identify the variables of service

quality is ServQual (Parasuraman et al. 2005)

(Parasuraman et al. 2005) is one of the studies

that many referenced regarding Service Quality.

Many researchers use this research as the basis for

model development. As an example, (Tharanikaran

et al. 2017), (Mujinga 2020), examined the effect of

service quality on customer satisfaction in the

context of e-banking and online shoping (Rita et al.

2019). There are also researchers who adopt

question items from (Parasuraman et al. 2005) for

hotel services domain (Le et al. 2020). Moreover,

Service quality could not only predict customer

satisfaction, but also predict the impact on

relationship quality (Rahahleh et al. 2020) and

perceived value (Mendoza et al. 2020), (Li and

592

Ridwandono, D., Mardi Suryanto, T. and Suherlan, G.

Electronic Service Quality and Perceived Value in Mobile based Services.

DOI: 10.5220/0010369100003051

In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 592-598

ISBN: 978-989-758-501-2

Shang 2020), (ÇETİNSÖZ 2015), (Akter et al.

2013).

(Parasuraman et al. 2005), suggested that the

model should be tested in the context of pure

service. (Akinci et al. 2010) argued that E-S-QUAL

and E-RecS-QUAL (Component of ServQual) are

scales that can be used to measure service quality in

the context of internet banking. Their research was

later adopted by (Tharanikaran et al. 2017), which

was also done on the pure service object. Based on

the background, the purpose of this paper is to

answer whether the ServQual dimension can be used

to measure the quality of mobile-based services

(MyTelkomsel). The second, is Perceived Value

influenced by electronic service quality.

2 PREVIOUS RE SEARCH

Figure 1: E-S-Qual Conceptual Model/Result

Service quality or SERVQUAL designed to

measure the gap between expectations and customer

perceptions (Parasuraman et al. 1988). (Parasuraman

et al. 2005) argued, that there are seven dimensions

of electronic service quality: efficiency system

availability, fulfilment, privacy (grouped into ES-

QUAL - all stages of customer interaction with

service); Responsiveness, Compensation, and

Contact (grouped into E-RecS-QUAL - to measure

the level of recovery in the event of a service

failure). (Akinci et al. 2010) and (Tharanikaran et

al. 2017) applied the scale in a non-retail context

while (Parasuraman et al. 2005) applied the model

on online retail companies (i.e., Amazon and

Walmart).

This paper adopts the scale for online financial

services, such as internet banking which has less

tangible elements which was developed by

(Tharanikaran et al. 2017). It was hoped that the

scale is appropriate for the characteristics of the

object chosen (myTelkomsel). To test the

nomological validity of ServQual, this paper also

adopts the variable which were used in

(Parasuraman et al. 2005), Perceived Value.

Perceived Value is defined as an evaluation of the

total benefits of a product/services by the customer

(ÇETİNSÖZ 2015). Details regarding the

measurement technique are presented in the

methodology section.

3 METHODOLOGY

The approach used to validate the E-Service Quality

variable on the My Telkomsel application service

refers to (Akinci et al. 2010). The ServQual

dimension was grouped into E-S-Qual and E-Recs-

Qual variables. E-S-Qual variables consist of:

Efficiency, System Availability, Fulfilment, and

Privacy. While E-Recs-Qual variables consist of:

Responsiveness, Compensation, Contact.

For the E-S-Qual scale, all collected respondents

were used. Meanwhile, for the E-Recs-Qual scale,

only uses a number of respondents who has a

specific condition (i.e. who have experienced

problems and were seeking for help from service

provider to solve these problems). The conceptual

model of this research can be seen in Figure 1 and

Figure 2. Figure 1 is the conceptual model of E-S-

Qual while Figure 2 is the conceptual model of E-

Recs-Qual. Due to limited space, the conceptual

model shown was also the output / test result of the

SmartPLS tools.

Based on (Akinci et al. 2010) and (Parasuraman

et al. 2005) the ServQual scale was grouped into 2

(i.e. E-S-Qual Scale and E-Recs-Qual). Since this

study also intends to conduct a Nomological test

using the Perceived Value variable, the hypothesis

of this study is:

E-S-Qual Scale

H1: Efficiency is considered to have an influence

on Perceived Value

H2: System Availability is considered to have an

influence on Perceived Value

H3: Fulfilment is considered to have an influence

on Perceived Value

H4: Privacy is considered to have an influence on

Perceived Value

E-Recs-Qual scale:

H5: Responsiveness is considered to have an

influence on Perceived Value

Electronic Service Quality and Perceived Value in Mobile based Services

593

H6: Compensation is considered to have an

influence on Perceived Value

H7: Contact is considered to have an influence on

perceived value.

Figure 2. E-Recs-Qual Conceptual Model/Result

4 RESULTS

4.1 Respondent

Total respondent in this study were 523 people, the

sample profile can be seen in table 1.

Table 1: Sample Profile

Age <25 429

–

40 32

41-55 10

>55 2

Sex Male 204

Female 319

Occupation Student 71%

Employee 16%

etc 13%

For the E-S-Qual Scale, all of the respondents,

523 people, were used. While for the E-Recs-Qual

scale, the respondents were 115 people (i.e. who

have experienced problems and were seeking for

help to solve these problems).

4.2 E-S-Qual Loading Factor, AVE,

Discriminant Validity.

Section 4.2 describes the results of the validity and

reliability tests of E-S-Qual variables (i.e.

Efficiency, Fulfilment, Privacy, System

Availability).

Table 2: E-S-Qual Loading Factor

Item Loadin

Facto

Efficienc

EF01 0.773

Efficienc

EF02 0.767

Efficiency EF03 0.732

Efficiency EF04 0.777

Efficiency EF06 0.812

Efficienc

EF08 0.805

Fulfilment FU01 0.805

Fulfilment FU02 0.816

Fulfilment FU03 0.806

Fulfilment FU04 0.728

PerceivedValue PE01 0.733

PerceivedValue PE02 0.834

PerceivedValue PE03 0.623

PerceivedValue PE04 0.847

Privacy PR01 0.884

Privacy PR02 0.874

Privac

PR03 0.869

stemAvailabilit

SA01 0.806

stemAvailabilit

SA02 0.762

SystemAvailabilitySA03 0.852

SystemAvailabilitySA04 0.757

All question items were adopted from

(Tharanikaran et al. 2017). Based on the validity

test, items EF05 and EF05 were dropped, then the

data was retested. As can be seen in Table 2, all

items from the Efficiency, Fulfilment, Privacy,

System Availability variables have a value above

0.707, therefore they were considered valid.

Table 3: E-S-Qual AVE

Average

Variance

Extracted

(

AVE

)

EFFICIENCY 0.606

FULFILLMENT 0.623

PERCEIVED VALUE 0.584

PRIVACY 0.767

SYSTEM AVAILABILITY 0.632

Table 3 shows the AVE value of each variable. It can

be seen that all AVE values are above 0.5, thus this result

supports the validity.

Table 4: E-S-Qual Discriminant Validity

EF FU PE PR SA

EF 0.778

FU 0.655 0.789

PE 0.544 0.561 0.764

PR 0.458 0.51 0.419 0.876

SA 0.762 0.644 0.56 0.508 0.795

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Table 4 shows the value of discriminant validity.

The value of the discriminant validity for each

variable must be above 0.70 and there is no

discriminant validity value from other variables

which were larger. Referring to table 4, all of the

data have met these criteria.

Table 5: E-S-Reliability Test Value

Cronbach’s

Alpha

Composite

Reliabilit

Efficienc

0.870 0.902

Fulfilment 0.798 0.868

Perceived Value 0.765 0.847

Privac

0.849 0.908

System

Availibilit

0.806 0.873

Table 5 shows the results of the reliability test.

Almost all values were above 0.8, except for

Fulfilment. This shows that the level of reliability is

considerably good.

4.3 E-Recs-Qual Loading Factor

Section 4.3 specifically addresses E-Req-Qual. The

variables tested were: Compensation, Contact and

Responsiveness. As is the case with E-S-Qual, what

will be tested is the validity and reliability of the

variables.

Table 6: E-Recs-Qual Loading Factor

Ite

Loadin

Facto

Com

ensation CO01 0.925

Compensation CO02 0.955

Contact CT01 0.819

Contact CT02 0.921

Contact CT03 0.886

Perceived Value PE01 0.79

Perceived Value PE02 0.859

Perceived Value PE03 0.65

Perceived Value PE04 0.841

Res

onsiveness RE01 0.87

Res

onsiveness RE02 0.875

Res

onsiveness RE03 0.901

Based on table 6, it can be seen that all values

were above 0.707, it can be concluded that all

question items were valid.

Table 7: E-Recs-Qual AVE

Average Variance

Extracted (AVE)

EFFICIENCY 0.884

FULFILLMENT 0.778

PERCEIVED VALUE 0.768

PRIVACY 0.623

SYSTEM AVAILABILITY 0.632

The results of calculating the AVE value are

shown in table 7. In which, all values were above 0.5

so that it can be concluded, that all variables are

valid.

Table 8: E-Recs-Qual Discriminant Validity

CO CT PE RE

CO 0.94

CT 0.334 0.877

PE 0.432 0.36 0.789

RE 0.569 0.602 0.631 0.882

(Note: Co: Compensation, CT: Contact, PE: Perceived

Value, RE: Responsiveness)

Table 8 shows the value of discriminant validity.

The value of the discriminant validity for each

variable must be above 0.70, and there is no

discriminant validity value from other variables that

were larger. Referring to table 8, all data have met

these criteria.

Table 9: E-Recs-Qual Reliability Test Value

Cronbach’s

Composite

Reliabilit

Com

ensation 0.87 0.938

Res

onsiveness 0.858 0.913

Contact 0.85 0.908

Perceived Value 0.795 0.867

Table 9 shows the results of the reliability test.

All values (Compensation, Responsiveness and

Contact) were above 0.8. This shows that the level

of reliability is good. After testing the validity and

reliability, the next step is to examine the inner

model.

4.4 Inner Model

The results of the calculation in the form of image /

conceptual models can be seen in Figure 1 and

Figure 2. In the inner model, there are at least two

important things that need to be considered, the R

value and hypothesis testing.

Electronic Service Quality and Perceived Value in Mobile based Services

595

Table 10: E-S-Qual R

R Square R Square

Adjuste

Perceived

Value

0.399 0.395

Based on table 10, E-S-Qual (Efficiency,

Fulfilment, Privacy, System Availability) can

account for about 40% of the Perceived Value. Thus

these results show moderate results, since there are

still 60% of other variables that can explain

Perceived Value.

Table 11: E-Recs-Qual R

R Square R Square

uste

Perceived

Value

0.407 0.391

Similar results were also given by E-Recs-Qual,

E-Recs-Qual can explain about 40% of Perceived

Value. The results can be referred to in table 11

Table 12: E-S-Qual Hypothesis Test

P Values

Efficienc

 Perceived Value 0.008

Fulfilment  Perceived Value 0.000

Privacy  Perceived Value 0.0028

System Availability  Perceived

Value

0.000

With regard to hypothesis testing, all variables E-

S-Qual (Efficiency, Fulfilment, Privacy, System

Availability) influencing the perceived value. See

table 12. Different results were shown in Table 13,

that there was only one variable, namely

Responsiveness which affects the perceived value.

Table 13. E-Recs-Qual Hypothesis Test

P Values

Compensation 

Perceived Value

0.235

Contact  Perceived

Value

0.789

Responsiveness 

Perceived Value

0.000

5 DISCUSSIONS

This paper contributes to the research conducted by

(Parasuraman et al. 2005), especially with regard to

E-Recs-Qual. Although there was a challenge to get

respondents for the E-Recs-Qual variable. The

proportion of qualified users to qualify as E-Recs-

Qual respondents (Individual who have experienced

problems and were seeking for help to solve these

problems) is approximately 1 in 5. Therefore, to

obtain a sufficient number of respondents requires

distributing a lot of questionnaires.

All of Parasuraman's E-S-Qual constructs

(efficiency, system availability, fulfillment and

privacy) meet the psychometric levels / values.

However there were two question items that must be

dropped: EF5 and EF5. Regarding the E-Recs-Qual,

all existing constructs / variables are valid and

reliable. These findings can help managers to

allocate existing resources to improve aspects

related to Electronic Service Quality.

The test results also show that the dimensions of

the E-S-Qual (efficiency, system availability,

fulfillment and privacy correlates with Perceived

Value). Whereas, for E-Recs-Qual dimensions, only

the responsiveness dimension was correlated with

Perceived Value. The dimensions of contact and

compensation do not correlate with perceived value.

Discussion regarding the findings will be presented

in the following sub section.

5.1 Efficiency–Perceived Value

Correlation

Efficiency relates to interface design, which allows

customers to easily find what they need. Efficiency

is one of the four variables from the E-S-Qual scale

that has the strongest influence on Perceived Value.

(Parasuraman et al. 2005) argued that the companies

need to give emphasis to this variable. The same

result is also shown in (Akinci et al. 2010) that the

Efficiency and Fulfillment variables show a stronger

direct effect on Perceived Value.

5.2 System Availability–Perceived

Value Correlation

The finding that Availability has a positive effect on

should encourage organizations to pay more

attention to these factors. Since there is a close

relationship between service quality and customer

satisfaction (Ma 2012), (Chavosh et al. 2011), (Ma

and Zhao 2012).

5.3 Fulfilment–Perceived Value

Correlation

Apart from Efficiency, Fulfillment is the second of

the 4 variables from the E-S-Qual scale that has the

strongest influence on Perceived Value. The findings

on the correlation between Fulfilment and Perceived

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Value are the same as the research results in (Akinci

et al. 2010) and (Parasuraman et al. 2005).

5.4 Privacy–Perceived Value

Correlation

Referring to (Parasuraman et al. 2005), previous

research has argued that Web site privacy may not

be important for more frequent users (Wolfinbarger

and Gilly 2003). However, this study has the same

results as the research conducted by (Parasuraman et

al. 2005), that the perception of privacy does affect

Perceived Value. This result also confirms the need

for companies to increase customer data security and

assure customers that the company can guarantee the

confidentiality of their data.

5.5 E-Recs-Qual Dimensions-Perceived

Value Correlation

The discussion regarding the test results related to

the E-S-Qual dimensions (efficiency, system

availability, fulfilment, privacy) have already been

discussed in subsections 5.1 to 5.4. Section 5.5

describes the test results regarding the dimensions of

the E-Recs-Qual (i.e. responsiveness, contact,

compensation).

This study examined the E-Recs-Qual, by

following the dimensions described in the

(Parasuraman et al. 2005). In his research,

Parasuraman was unable to test these variables since

the number of respondents was inadequate.

In this study, the number of respondents for the E-

Recs-Qual scale was 115 people. In other word, only

115 people out of 523 total respondents had

experienced problems when using the application

and reported the incident.

The responsiveness dimension shows a positive

correlation on perceived value. Thus it shows that

the higher the responsiveness value, the positive

impact it will have on perceived value. However, for

two other dimensions, contact and compensation,

have no correlation with perceived value. (Akinci et

al. 2010) suggested, that mobile-based service users

do not prefer to use the telephone / face-to-face

assistance channel when they face a problem. With

regard to compensation, there is no evidence that it

is correlated with Perceived Value. Based on this,

with regard to customer complaints, mobile-based

service providers must prioritize one main thing,

Responsiveness.

6 CONCLUSIONS

1. The test results, based on questionnaire data

from 523 respondents for E-S-Qual, and 115

respondents for E-Req-Qual show:

Almost all question, except EF05 and EF07,

which were adopted from (Tharanikaran et al.

2017) are valid and reliable. These items of

questions can be used to measure the quality

of mobile-based services.

2. All variables from E-S-Qual (Efficiency,

Fulfilment, Privacy, System Availability) have

an effect on Perceived Value. This could have

the impact that, in order for a customer to

have a good perceived value, there is a need to

taking these variables into account.

3. With regard to E-Rec-Qual (level of recovery

in the event of a service failure), only the

Responsiveness variable affects the perceived

value.

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