Design of Reliability Applications Online Transportation in the

Medan City Community with Perspective as Users

Budi Anshari Nasution

Rahima Br. Purba

, Iskandar Muda

Student Postgraduate, Faculty Economic and Business, Universitas Sumatera Utara, Medan

Indonesia

Lecture Faculty Economic and Business, Universitas Sumatera Utara, Medan Indonesia

Keywords: Information Technology, Information Systems, Behavior Aspects.

Abstract: Along with the development of information technology, growing developers who try to provide applications

that can provide ease in meeting the needs of convenient transportation, comfortable and affordable prices.

Information System Success Model using the Delone and MacLane Model can be used to see the reliability

and success of online transport applications. The unsuccessful hypothesis proposed proves that other

determinants become factors that affect the reliability and success of its particular information system online

transport even if the model used has been valid. Lack of some respondents in this study is suspected to cause

the research data obtained is not sufficient for decision making in the hypothesis. Aspects of Behavior

(Behavior Aspects) both users and applications are presumed also influence the intensity of use (Intent to use)

and user satisfaction (User Satisfaction).

1 INTRODUCTION

The complexity of human needs causes a change in

society. This paper is shown in the rapid development

of information technology. Information technology

itself can be described as a technology that combines

computer networks with lines of communication that

carry data, voice or video. One form of application of

visible information technology development is in the

transport sector. Along with the development of

information technology, growing developers who try

to provide applications that can provide ease in

meeting the needs of convenient transportation,

comfortable and affordable prices. Especially in the

city of Medan which has a level of congestion that has

increased throughout the years.

Table 1: Number of registered motor vehicles (Units) 2009

– 2013

Years Cars Bus Pickup MotoCycle

2009 297.922 29.498 194.946 3.091.510

2010 327.467 29.978 203.452 3.478.230

2011 356.931 71.112 217.254 3.924.007

2012*) 386.144 71.590 231.750 4.292.933

2013 416.405 71.900 242.445 4.584.431

Sources: Polda North Sumatera Direktorat Lalu

Lintas Province North Sumatera.

The number of vehicles each year is increasing,

visible from the data above. The increase in the

number of vehicles is not balanced with the city's

highway capacity. According to data from the

Department of Transportation of Medan City (2016),

the number of motor vehicles reaches 2.7 million

units with a length of 3,191.5 km and a speed ratio of

23.4 km / h and a Capacity Ratio of 0.76. Private

vehicle 97.8%, general vehicle 2.2%, two-wheel

vehicles 75.95%, and four-wheel 24.05 %. This

condition that led to the creation of applications to

facilitate the search for means of transportation.

Various applications appear to solve the

problem. The claim that appeared in 2010 is Gojek,

in 2012 is Grab. And in the Year 2017 is Uber. Each

transport application implements a different tariff

system. It also offers ease of payment, i.e., in cash,

auto debit and wallet balance like Go-pay and Grab-

pay.

These applications offer new things in the means

of transportation in Medan City. The higher the

sophistication of technology, the higher the problem

can be caused by the technology itself. The success of

an information system can be explained from various

things, including quality system, the quality of

information provided, and user satisfaction using the

information system.

Nasution, B., Purba, R. and Muda, I.

Design of Reliability Applications Online Transportation in the Medan City Community with Perspective as Users.

DOI: 10.5220/0010067409510956

In Proceedings of the International Conference of Science, Technology, Engineering, Environmental and Ramiﬁcation Researches (ICOSTEERR 2018) - Research in Industry 4.0, pages

951-956

ISBN: 978-989-758-449-7

951

In analyzing the determinants of reliability in the

online transport applications used by drivers, this

study used the Delone & Mclean model. The Delone

& Mclean model reflects the dependence of the six

measurements of information system success. The six

elements are: (1) Information quality, (2) System

Quality, (3) Service Quality, (4) Intention To Use, (5)

User Satisfaction, and (6) Net Benefits (Delone and

Mclean, 2003).

2 LITERATURE REVIEW

This Information data processed into a useful form for

the wearer receives the data source as input and

processes it into information product as output. The

information system consists of several sub-systems or

components of hardware, software, and brainware, as

well as procedures for running inputs, processes,

outputs, storage, and controls that transform data

sources into information (8). Information systems can

also be interpreted as a system in an organization that

brings daily transaction processing needs that support

organizational operational functions that are

managerial with the strategic activities of an

organization to be able to provide to certain parties

with information needed to make decisions.

Uber Technologies Inc. is an American

multinational company engaged in the field of online

transportation services. This company is a developer

with the name of Uber application. This application

provides transportation on demand facility. This

application has a function as an intermediary between

the driver and the consumer who need transportation

means this application is available in the Google

Store, Apps Store and Microsoft. So that consumer of

this uber is party that use smartphone. From the driver

side, this application makes it easy for drivers to use

private vehicles, so they are not tied to any party

(Rosyadi, 2017), (Utomo, et al. 2017).

Grab is a transport company by using mobile

device taxi online application to make a booking

passenger shuttle from the place that has been

determined user and delivered in accordance with the

purpose of the order on the application. This app is

also controlled with GPS as a tool map (Utomo, et al.

2017), (Chan, et al. 2017).

Go-Jek is also a transportation service

company using applications that make it easier for

people in transportation services (Chan, et al. 2017).

Various choices of these applications make their

existence increasingly coloring the universe of online

transportation services competition, in this

competition conditions these three companies seeks

in improving service facilities to customers.

The Delone and McClean Information Model

Success Model is a model that illustrates how far the

contribution of a product generated by an information

system to an organization (Delone and Mclean,

2003).

The success of the online transport company is

seen through the performance of its employees. If the

employees are good then it will affect the company

profit. Performance optimization requires a

motivation in the employees. Basically, both the same

reward and punishment are needed to stimulate

someone to improve their quality. Reward is raised to

motivate a person to be active in carrying out

responsibilities because there is the assumption that

by giving rewards for the results of his work,

employees will be more work maximally[5]. While

punishment is raised for a person who commits

mistakes and offenses to be motivated to stop deviant

behavior and lead to positive behavior (Tarigan et al.

2018)

Figure 1. DeLone and McLean Model of Information

System Success (Delone and Mclean, 2003)

3 METHOD OF RESEARCH

This study was designed using quantitative research.

[9] Quantitative analysis is research whose data are

expressed in numbers and analyzed by statistical

techniques. The research is often in the form of

experiments and surveys. This research was

conducted in Medan City with a schedule of

questionnaire distribution from May 14, 2018, until

June 02, 2018. The type of research data used is

primary data. Primary data is data obtained by

researchers by way of spreading form to respondents

and interviews with some respondents who met to be

backup data. Primary data in this study include the

results of filling the questionnaire distributed to the

respondents.

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952

Population in this research is all user driver of

transportation application (go-jek, Uber, and Grab).

The sample of this study is the user drivers of

transport applications (go-jek, Uber, and Grab) which

fill out the questionnaire dated May 14, 2018, to June

02, 2018. Data collection using survey methods

conducted online.

4 RESULT AND ANALYSIS

4.1 Testing Validitas

Table 2: Value Average Variance Extranced.

Average Variance

Extracted (AVE)

IN_X4 0,638

IQ_X1 0,636

IU_Z1 0,564

NB_Y 0,622

SQ_X3 0,565

SYQ_X2 0,592

US_Z2 0,650

From the output of the analysis can be seen

that the Average Variance Extraced (AVE) value

above 0.50. This means it qualifies for validity.

4.2 Testing Reliabilitas

Table 3: Cronbach’s Alpha.

Cronbach's Alpha

IN_X4 0,813

IQ_X1 0,936

IU_Z1 0,692

NB_Y 0,916

SQ_X3 0,887

SYQ_X2 0,868

US_Z2 0,921

From the results of the analysis output can be seen

that the value of Cronbach's Alpha above 0.60. This

means qualifying reliability.

Figure 2. Structure Path Coefficient Values.

From the result of the path coefficient above can

be seen that all variables did not affect the value of t-

statistics generated for all variables <1.96. This

means that all alternative hypotheses are rejected

1. Influence of Information Quality on Intention of

use

From the table above we can see that the impact

of Information Quality on Intention of use has

an at-statistic value of 0.184 and p-value value

of 0.854. Because the amount of t <1.96 then

Information Quality does not affect the Intention

of Use.

2. Influence of Information Quality to User

Satisfaction

From the above table, the impact of Information

Quality on User Satisfaction has a t-statistic

value of 0.008 and p-value of 0.993. Because the

amount of t <1.96 then Information Quality does

not affect User Satisfaction.

3. Effect of System Quality on Intention of use

From the table above the impact of System

Quality on Intention of Use has a t-statistic value

of 0.119 and p-value value of 0.905. Because the

amount of t <1.96 then the System quality does

not affect the Intention of Use

4. Effect of System Quality on User Satisfaction

From the table above the influence of System

quality on User, Satisfaction has a t-statistic

value of 0.003 and p-value value of 0.997.

Because the amount of t <1.96 then System

Quality does not affect User Satisfaction.

Design of Reliability Applications Online Transportation in the Medan City Community with Perspective as Users

953

5. Effect of Service Quality on Intention of use

From the above table, the influence of Service

Quality on Intention of Use has a t-statistic value

of 0.376 and p-value of 0.707. Because the

amount of t <1.96 then Service Quality does not

affect the Intention of Use.

6. Effect of Service Quality on User Satisfaction

From the above table, the influence of Service

Quality to User Satisfaction has a t-statistic

value of 0.067 and p-value value of 0.946.

Because the amount of t <1.96 then Service

quality does not affect User Satisfaction.

7. Effect of User Satisfaction on Intention of use

From the table above the influence of User

Satisfaction on Intention of Use has a t-statistic

value of 0.372 and the value of p-value of 0.710.

Because the amount of t <1.96 then the

information quality does not affect the Intention

of Use.

8. Effect of Incentives on Intention of use

From the table above the influence of Incentives

to the intention of Use has a t-statistic value of

0.380 and the value of p-value of 0.704. Because

the amount of t <1.96 then Incentives does not

affect the Intention of Use.

9. Effect of Incentives on User Satisfaction

From the above table, the impact of incentives

on User Satisfaction has a t-statistic value of

0.041 and p-value value of 0.967. Because the

value of t <1.96 then Incentives no effect on

User Satisfaction.

10. Impact of Intention of use on Net Benefits

(Individual Impact)

From the table above the influence of intention

of use on Net Benefits (Individual Impact) hast-

statistic value of 0.123 and p-value value of

0.902. Because the amount of t <1.96 the

Intention of Use does not affect the Net Benefits

(Individual Impact).

11. Effect of User Satisfaction on Net Benefits

(Individual Impact)

From the above table, the influence of User

Satisfaction on Net Benefits (Individual Impact)

has an at-statistic value of 0.009 and p-value

value of 0.993. Because the amount of t <1.96

then User Satisfaction does not affect the Net

Benefits (Individual Impact).

Next, we see the Indirect Effect of the variable; we

can see the following table.

Table. 4: Specific Indirect Effect.

Specific

Indirect

Effects

IN_X4 -> US_Z2 -> IU_Z1 0,098

IQ_X1 -> US_Z2 -> IU_Z1 0,000

SQ_X3 -> US_Z2 -> IU_Z1 0,152

SYQ_X2 -> US_Z2 -> IU_Z1 0,020

IN_X4 -> IU_Z1 -> NB_Y 0,009

IQ_X1 -> IU_Z1 -> NB_Y 0,001

SQ_X3 -> IU_Z1 -> NB_Y 0,005

SYQ_X2 -> IU_Z1 -> NB_Y -0,003

IN_X4 -> US_Z2 -> IU_Z1 -> NB_Y 0,002

IQ_X1 -> US_Z2 -> IU_Z1 -> NB_Y 0,000

SQ_X3 -> US_Z2 -> IU_Z1 -> NB_Y 0,003

SYQ_X2 -> US_Z2 -> IU_Z1 -> NB_Y 0,000

IN_X4 -> US_Z2 -> NB_Y 0,078

IQ_X1 -> US_Z2 -> NB_Y 0,000

SQ_X3 -> US_Z2 -> NB_Y 0,121

SYQ_X2 -> US_Z2 -> NB_Y 0,016

From the table above can be concluded there is an

indirect influence that is:

1. Effect of Incentives on the intention of use

through User satisfaction of 0, 098 with

significant 5%.

2. Effect of Information Quality on Intention of Use

through User Satisfaction of 0,000 with

significant 5%

3. Effect of Service Quality on Intention of Use

through User Satisfaction of 0.152 with

significant

4. 5%.

5. Effect of System Quality on Intention of Use

through User Satisfaction of 0,020 with

significant 5%.

6. Effect of Incentives on Net Benefit through

Intention of Use of 0,009 with significant 5%.

7. Effect of Information Quality on Net Benefit

through Intention of Use of 0.001 with solid 5%.

8. Impact of Service Quality on Net Benefit through

Intention of Use of 0,005 with solid 5%.

9. Effect of System Quality on Net Benefit through

Intention of Use of -0,003 with solid 5%.

10. Incentive Effect on Net Benefit through User

satisfaction and intention of Use of 0,002 with

solid 5%.

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11. Effect of Information Quality on net benefit

through User satisfaction and purpose of use of

0.000 with solid 5%.

12. The influence of Quality Services on Net benefit

through User Satisfaction and plan of use is

0,003 with solid 5%.

13. Effect of System Quality on Net gain through

User Satisfaction and purpose of use of 0.000

with solid 5%.

14. Incentives influence net benefit through User

satisfaction of 0.078 with solid 5%.

15. Effect of Information Quality on net gain through

User satisfaction of 0,000 with solid 5%.

16. Impact of Service Quality on net benefit through

User satisfaction of 0.121 with solid 5%.

17. Effect of System Quality on net gain through

User satisfaction of 0.016 with solid 5%.

18. Considering the result of the research, it is known

that the model compiled related to Information

System Success Model proposed shows that the

variables used in the model are strongly

categorized, but all hypotheses are not

significant, this is contrary to Rosyadi

research[2]. Several factors among them can

cause the difference between the results of this

study with previous research has not fulfilled the

number of respondents who followed the survey.

But interviews and open-ended questions to the

respondents found that there are some indications

of new findings regarding the determinants of

successful online transport applications that are

related to Behavior User Issues about incentives

(punishment & rewards) that directly affect

income that will accept by them every day. Not

equal the position of application users and

application providers when a dispute over

incentive calculation also affects the usage level

(intense to use) and even user satisfaction. Thus

the reliability and success of an information

system application is not only determined by the

safety and success of the application itself but

also influenced by behavioral factors of both

application users and application providers.

19. The study also found that in general the

reliability of available online transport

applications for both motorcycles and cars is

relatively similar and we found that more than

30% of users use more than one online transport

application, indicating that other factors affect

the success of the transport application system

online. From the results of interviews and open

questions put forward, it is known that the

incentive factors affect the level of use of the

applications they use. This factor becomes the

driver of user behavior in using the available

online transportation. On the other hand, the

form of this incentive also has not satisfied the

user because there are still many weaknesses of

the application provider in detecting the fraud

happened by the users and or other user groups

such as fictitious travel order and false user.

Changed incentive schemes with unscheduled

also considered the user only benefits the

application provider. Unequal positions at

different calculations occur in the

implementation of incentives affecting the

drivers of online transport as users in

determining which transport applications will be

used. This is by the reward and punishment

theory[5]

5 CONCLUSION

The conclusion of this paper described are:

a. Information System Success Model developed by

Delone and Mclane can be used to see the

reliability and success of online transport

applications

b. The unsuccessful hypothesis proposed proves that

other determinants become factors that affect the

reliability and success of its particular information

system online transport even if the model used has

been valid.

c. Lack of some respondents in this study is

suspected to cause the research data obtained is

not sufficient for decision making in the

hypothesis.

d. Behavior Aspects (both Behavior Aspects) both

users and applications are also suspected to affect

the intensity of use (Intent to use) and user

satisfaction (User Satisfaction).

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