Donny Novianto, Master Information System Management,...

International Journal of xxxxxx

Vol. x, No. x, (20xx), pp. xx-xx

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ANALISYS INTENSITY OF USE AND THE LEVEL OF EMPLOYEE

SATISFACTION TO INFORMATION SYSTEM PERFORMANCE

MANAGEMENT SYSTEM (MOS5200) USING DELONE AND MC LEAN

MODEL

1Donny Novianto,

2Sfenrianto

1,2 Master Information System Management, Bina Nusantara University,

[email protected], [email protected]

Abstract

The purpose of this case study is to analyze the quality of the work system MOS5200

applications that affect the level of user satisfaction, intensity of use, and the net benefit to

the PT. Inspurs. The evaluation method used is DeLone & McLean framework consisting

of six variables, namely: Quality Systems, Information Quality, Service Quality, Intensity

Users, User Experience, and Net Benefits. Methods of analysis include validity,

reliability, normality test, and test hypotheses. The results of the case study is an

evaluation and recommendations that can be a reference for the PT. Inspurs in innovating

continuously in adopting technologies that fit the needs of employees and the times. The

conclusion of this evaluation is of twelve hypotheses tested, eleven of which are quite

good and the related negative.

Keywords: DeLone & Mclean, Evaluation, SPSS for Windows Ver. 21

1. Introduction

The company's success is influenced by several factors, including the level of

employee satisfaction. The level of employee satisfaction can affect the productivity of

employees so that the company's attention focused on the level of employee satisfaction.

To meet the need for good information system required a system of good design, and

good programming system and capable of allowing users to deliver and develop a wide

range of facilities to access information provided.

MOS5200 is one of Inspur software that integrates multi-vendor multi-technology

networks, which can collect raw data (counter) to then generate KPI (Key Performance

Indicator) in accordance with a specified schedule. MOS5200 real time data showing the

trend of the network and also monitor the performance of the alarm on the network.

MOS5200 help the team analyzes the network O & M in the right way and fast.

Model DeLone & Mclean (D & M) has been widely used to measure the success of the

system. From time to time the model D & M always modified to find the needs or

requirements that correspond to various types of information systems, and users from

different viewpoints. Masrek said, the user aspect and the aspect of user satisfaction better

information quality, system quality, and quality service on each dimension or a

combination of all three, methods of D & M prove able to offer the development of the

information systems success model. (Masrek, 2007).

Based on the above problems, it can be seen in the gap or gaps in the quality of

applications expected, needed a case study to evaluate the problems that occur in the

application. Analysis and evaluation will be conducted using a model Mclean and DeLone

about the success of information systems. Thus, the results of this evaluation are expected

to be known the circumstances that exist at the moment so do repairs on parts that are

needed or improved as well as maintain existing services.


Vol. x, No. x,(20xx)

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2. Theoretical Framework

2.1 Information Technology

Definition of information technology proposed by Maharsi (2000) explains that the

information technology as a combination of computer technology and

telecommunications with other technologies, such as: hardware, software, database,

network technology, and other telecommunications equipment. Furthermore, information

technology is used in the organization's information systems to provide information for

the users in the decision making process. Information technology will continue to evolve.

A strong information technology will be a competitive advantage for the company.

Information technology is a major source of strategic advantage, with a focus on how far

the application of information technology will improve efficiency and effectiveness. The

presence of information technology capable of supporting significantly better service,

related to the availability of information, information accuracy, completeness, and

timeliness. Implementation of information technology can be said to be successful if it

can be utilized by the user to the fullest and useful for the efficiency and effectiveness of

its work. After work, do developments in the form of adjustments according to the latest

needs of users in the enterprise.

2.2 Information System

The information system is a basic requirement that must be met by an organization to

sustain its survival. According to Hall (2001) information system is a series of formal

procedures where data is collected, processed into information, and distributed to users.

Criteria of information systems, among others, flexible, effective and efficient. With the

application of the information system of the organization will be more competitive

because it will get a lot of benefit from the sophistication of information systems.

Technology Acceptance Model (TAM) developed by Davis (1989) offers a foundation to

gain a better understanding of the behavior of users in the acceptance and use of

information systems (Davis 1989). TAM believes that the use of information systems will

enhance the performance or effectiveness of individuals or organizations, in addition to

the use of information systems is easy and requires no effort from users.

2.3 User Satisfaction

2.3.1 Meaning of User Satisfaction

Doll and Torkzadeh (1988) define user satisfaction as affective attitude towards a

particular computer application by someone who interact with the application directly.

Doll and Torkzadeh (1988) use traditional survey of 618 respondents in studying user

satisfaction by modifying the instrument and factor analysis. His research resulted in

twelve-item instrument measuring user satisfaction with the quality of systems and

information, obtained from end users of information systems. Twelve items generated is

divided into five components, namely: content, accuracy, format, ease of use, and

timeliness. Somers research results, Nelson, and Karimi (2003) showed that all items

contained in the instrument user satisfaction has validity and reliability are assured to

measure the success of an information system.

2.3.2 Quality of Service Information System

The concept of service quality to meet expectations if the expected service equal to

that felt satisfying means for users on the quality of services provided by the software

application provider information systems. Similarly, said the perception does not meet

expectations if the expected services outweigh the perceived service quality means the

service does. The quality applied to service quality information system should be able to



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identify a list of quality indicators by Parasuraman translated by Sutanto (2001: 32)

among other tangibles, reliability, responsiveness, assurance, and empathy.

2.3.3 User Satisfaction and Effectiveness of Information Systems

Research Basuki and Abdurachman (2001) on the role of computer software is Open

Source (Linux) for the efficiency and effectiveness of the use of information technology

to prove that five variables user satisfaction on the effectiveness of the use of information

technology is the variable stability/reliability, flexibility, punctuality, productivity and

support suppliers (vendors), while seven more user satisfaction variables did not affect the

effectiveness of the use of information technology, such as: completeness of

functionality/features, accuracy, safety (security), innovation, education / training, ease of

use and documentation.

3. Research Methodology

3.1 Research Model

Based on the theories and the results of previous studies that have been studied,

DeLone and McLean in 1992 and then developed a parsimonious model of what they call

the success model of information system DeLone & McLean (D&M IS Success Model).

The model reflects the dependence of the measuring six factors (variables) the success of

information systems, six of those factors is the quality system, the quality of information,

usage, user satisfaction, individual impact and organizational impact. By examining more

than 100 articles published in journals such as the well-known information systems

Information Systems Research, and Journal of Management Information Systems,

DeLone and McLean (2003) to improve and update the model. Here is a model of success

of information systems D&M (2003).

Figure 1 Research Model

The model developed by DeLone and McLean quickly got a response because the

model is simple, valid, and indeed was required to become a reference in creating

information systems that can be applied successfully (Jogiyanto, 2007), in addition to the

results of research Nils Urbach, Stefan Smolnik and Gerold Riempp the title A

methodological Examination of Empirical Research on information systems success: 2003

to 2007, show that the success model of information system developed by DeLone and

McLean is a model that is most widely used as a measure of success of information

systems.

The information system will be discussed in this study are MOS5200 used to help

make it easier to process the data and statistics and also petrified to create a report that is

useful for optimization teams to analyze data as appropriate. MOS5200 application is

required by a team reporting to process data quickly to generate the report. Portal inidapat

all employees accessed via the internet at page



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http://10.13.57.5:15200/mos5200/index1.jsp

Figure 2 MOS5200 System

MOS5200 system built by PT Inspur and was first tested in PT Inspur Jabodetabek area

in early 2012. As the system data provider, MOS5200 system into a device that allows

rapid data processing, data daily, weekly or monthly. In addition, the performance of data

can easily be sent directly via email or through short messages (sms).

3.2 Hypotesis

Analysis of the data used in this study is a quantitative analysis. According to Malhotra

quantitative analysis is the analysis of the data using the data in the form figures obtained

as a result of the measurement or sum (Maholtra, 2010: 163). There are 12 hypotheses

were performed in this study. The number of hypotheses is the same with the number of

purchase intention Discussed above factors. The hypothesis proposed to be concluded by

calculation of the value and significance path coefficients for each stream studied. The

hypothesis are as below:

H1: positive direct influence on the intensity of use of the Quality System

H2: positive direct influence on the intensity of use of Information Quality

H3: positive direct influence on the intensity of use of Quality of Service

H4: positive direct influence on the intensity of the use of User Satisfaction

H5: net Benefits positive direct effect on Intensity of Use

H6: positive direct influence on the intensity of use of the Quality System

H7: positive direct effect on the Information Quality User Satisfaction

H8: direct positive effect on the Quality of Service User Satisfaction

H9: intensity of use of a positive direct effect on User Satisfaction

H10: positive direct effect on User Satisfaction Net Benefit

H11: positive direct effect intensity of use of the Net Benefits

H12: positive direct effect Satisfaction Using the Net Benefits

4. Research Result

4.1 Overview of Respondents MOS52000

This study has 200 respondents from active user of MOS5200, consist of 187 male

respondents and 13 female respondents.

Tabel 4.1 Gender User Application MOS5200



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Gender Male Female Total

Amount 187 13 200

Respondents: Gender; shows that respondents in this study there is a distribution where

the number of male respondents more than female respondents

Figure 3. 1 Respondents: Gender

Characteristics of respondents in the form of long working respondents in PT Inspur, the

largest is between 1-5 years, there are 117 people. Then the work of 10-15 years and

above 15 years: respectively 21 and 17 people. Furthermore, respondents who work over

5-10 years, there were 25 new people and working respondents 20 people.

Tabel 4. 1 Length of work

Length of

Work

(years)

< 1 1-5 5-10 10-15 >15 Total

Amount 20 117 25 21 17 200

From the following diagram, it appears that the largest number of respondents have

worked between 1-5 years (58.5%). Medium respondents who work for less than 1 year

only 8.5%. Overall long working respondents are relatively evenly spread

Figure 4. Grafik Length of Work

The characteristic of other respondents are from department. The questionnaires returned

found most respondents of Performance Radio 75 people, 45 respondents came from Core

Performance, 32 each from transmission Performance, Performance Capacity of 24

people, 19 people from Performance IPBB, and 5 people from Performance Vas.

Tabel 4. 2 List the number of respondents per department



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Department Amount

Performance Radio 75

Performance Core 45

Performance Capacity 24

Performance IPBB 19

Performance transmission 32

Performance Vas 5

Total 200

Generally from 4.3 diagram encountered Radio Performance largest number of

respondents (37%), and the smallest 3% of Performance Vas

Figure 5. Grafik Length of Work

The characteristics of the respondent next educational level. Of the 200 respondents, the

most common is a graduate of S1 (86), which is the fewest D3 (24 votes).

Tabel 4. 4 Education Level Of Respondents

Education

Level D3 S1 S2 Total

Amount 35 162 3 200

From the above table looks the respondents' education level S1 which is the largest at

81%, and the fewest is S2 which is 3%. The percentage of each level of education can be

seen by the graph below.

Figure 6. Figure Percentage Of Total Respondents By Education



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4.2.1 Results Descriptive Analysis

Respondents to the questions related to the Quality System

According to the table below, the condition of the Quality System at PT. Inspur included

in either category. With 74.3% of employees answered agree, meaning that most of the

employees of PT. Inspur feel the quality system generated by the application MOS5200

has been fairly consistent. While 25.7% answered disagree or mediocre quality related

systems

Tabel 4. 5 Respondents to the questions related to the Quality System

Simbol T.STS T.TS T.B T.S T.SS Bobot Persentase(%)

KS01 15 40 171 216 270 712

71.2%

KS02 14 36 171 304 175 700

70.0%

KS03 10 28 81 348 310 777 77.7%

KS04 5 20 144 332 270 771 77.1%

KS05 8 30 114 232 405 789 78.9%

KS06 6 40 165 380 120 711 71.1%

Average 74.3%

Respondents to the questions related Quality of Information Tabel 4. 6 Respondents to the questions related Quality of Information


KI01 25 86 150 264 80 605 60.5%

KI02 5 28 258 272 135 698 69.8%

KI03 43 92 99 240 90 564 56.4%

KI04 21 82 78 240 260 681 68.1%

KI05 5 24 186 312 215 742 74.2%

KI06 9 46 180 308 155 698 69.8%

Average 66.5%

According to the table, the conditions of Information Quality at PT. Inspur included in

either category. With 66.5% of employees answered agree, meaning that most of the

employees of PT. Inspur feel the quality of the information generated by the application

MOS5200 own good. While 33.5% answered disagree or mediocre quality related

information.

Respondents to the questions related to Quality of Service Tabel 4. 7 Respondents to the questions related to Quality of Service


KL01 0 36 105 464 155 760 76.0%

KL02 3 42 147 344 205 741 74.1%

KL03 3 24 102 280 405 814 81.4%

KL04 51 64 84 236 150 585 58.5%

KL05 24 76 126 200 230 656 65.6%

KL06 18 60 153 260 180 671 67.1%

Rata-Rata 70.5%



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According to the table, the conditions Quality of Service at PT. Inspur included in either

category. With 70.5% of employees answered agree, meaning that most of the employees

of PT. Inspur feel Quality of Service using MOS5200 application is good enough. While

29.5% answered disagree or mediocre Related Quality of Service.

Respondents to the questions related to usage intensity Tabel 4. 8 Respondents to the questions related to usage intensity


IP01 13 78 153 236 190 670 67.0%

IP02 9 40 195 236 235 715 71.5%

IP03 2 38 147 300 275 762 76.2%

IP04 8 74 168 276 150 676 67.6%

IP05 9 50 120 444 75 698 69.8%

IP06 9 32 198 308 160 707 70.7%

Rata-Rata 70.5%

According to the table, the conditions on the intensity of use of PT. Inspur included in

either category. With 70.5% of employees answered agree, meaning that most of the

employees of PT. Inspur feel the intensity of the users in using MOS5200sudah quite

intensive applications. While 29.5% answered disagree or mediocre associated Intensity

of Use.

4.2.2 Validity Test

Test used to determine the validity of a decent (valid) and whether or not the question.

Criteria for the decision is to compare the value of Corrected Item - Total Correlation

compared with the value of r table level (α) of 0.05 is equal to 0.202. If the value of Corrected Item - Total Correlation bigger than r table then a decent indicator (valid) and

vice versa (Imam Ghozali, 2011). Following the presentation of each questionnaire

compared with r-table.

Table 4.1 Validity test result

Variabel Butir Pertanyaan Total Correlation r-table N=200 Keterangan

System Quality KS01 0.721 0.138 Valid

KS02 0.797 0.138 Valid

KS03 0.697 0.138 Valid

KS04 0.715 0.138 Valid

KS05 0.701 0.138 Valid

KS06 0.743 0.138 Valid

Quality

Information KI01 0.748 0.138 Valid

KI02 0.738 0.138 Valid

KI03 0.745 0.138 Valid

KI04 0.765 0.138 Valid

KI05 0.773 0.138 Valid

KI06 0.743 0.138 Valid

Quality of

Service KL01 0.747 0.138 Valid

KL02 0.734 0.138 Valid

KL03 0.736 0.138 Valid

KL04 0.702 0.138 Valid

KL05 0.737 0.138 Valid



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KL06 0.744 0.138 Valid

Intensity of

Use IP01 0.787 0.138 Valid

IP02 0.723 0.138 Valid

IP03 0.772 0.138 Valid

IP04 0.727 0.138 Valid

IP05 0.779 0.138 Valid

IP06 0.760 0.138 Valid

User

Satisfactions KP01 0.819 0.138 Valid

KP02 0.738 0.138 Valid

KP03 0.812 0.138 Valid

KP04 0.742 0.138 Valid

KP05 0.802 0.138 Valid

KP06 0.735 0.138 Valid

Net Benefits MB01 0.824 0.138 Valid

MB02 0.827 0.138 Valid

MB03 0.768 0.138 Valid

MB04 0.673 0.138 Valid

MB05 0.710 0.138 Valid

MB06 0.728 0.138 Valid

4.2.3 Reliability Test

Reliability is the degree of how much of a measuring gauge with a stable and

consistent (Jogiyanto, 2007). Research reliability testing to determine the consistency of a

measuring device for measuring the same symptoms. Each gauge should have the ability

to provide consistent measurement results. Test the reliability of the data that has been

collected using Cronbach's Alpha (Usman, 1995), namely :

• rα (positif) > 0.6 0.6 then the variable is declared reliable. • rα (negatif) ≤ 0.6 t hen the variable is declared unreliable.

The following calculation reliability test is performed to determine whether the

questionnaire used was appropriate and reliable

Table 4.4 ReliabilityTest

No Variabel Scores Reliability Declare

1 System Quality 0.899 Reliable

2 Quality Information 0.906 Reliable

3 Quality of Service 0.892 Reliable

4 Intensity of Use 0.913 Reliable

5 User Satisfactions 0.917 Reliable

6 Net Benefits 0.910 Reliable

The coefficient alpha (Cronbach alpha) has a value above 0,60.Sehingga can be concluded

that the variables - variables of the study is reliable or has a high reliability, so as to have

a high accuracy to be used as variables (constructs) in a study.



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4.2.4 Normality Test with Z-Z-Skewness and Kurtosis

According to Agus Purwoto (2007: 96) normality test serves to test whether the

regression model, the dependent variable and independent variables both have normal

distribution or not. Normality Test is done by analyzing Graph Normal P-P of

Standardized residual cumulative probability. Normality can be seen by looking at the

spread of the data (points) on the diagonal axis of the graph Normal P-P Plot.

Basis for decision-making.

1. If the data is spread around the normal line and follow the direction of the normal

line, the regression model to meet the assumption of normality

2. If the data is spread away from the normal line and did not follow the direction of

the normal line, the regression model did not meet the assumptions of normality Figure 7

Normality Test Model 1

Figure 8


Figure 9




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From Figure 7, 8 and 9 above is known that the pattern shows the distribution of points

around the diagonal line and follow the direction of the diagonal line, it indicates a

regression model to meet the assumptions of normality

4.2.5 Hypothesis Test

Based on result of multi linier regression test, this study compare it with the

hypothesis.

a. H1: Positive direct influence on the intensity of use of the Quality System. From the calculation results obtained lane path coefficient direct influence on the

intensity of the use of quality systems y₁a = 0.060. The score> 0.05 then Ho is rejected and H1 accepted. Thus it can be concluded that the quality system is a direct positive

effect on the intensity of use.

b. H2: positive direct influence on the intensity of use of Information Quality From the calculation results obtained path coefficient lines directly influence the

quality of information on the intensity of use y₁a = - 0.285. The score 0.05 then Ho is rejected and H1 accepted. Thus it can be concluded that the quality of service a positive direct impact on

the intensity of use

d. H4: positive direct influence on the intensity of the use of User Satisfaction From the calculation results obtained lane path coefficient direct influence on the intensity

of use of user satisfaction y5a = 0.421. The score> 0.05 then Ho is rejected and H1 accepted. It can be concluded that user satisfaction with positive direct effect on the

intensity of use

e. H5: net Benefits positive direct effect on Intensity of Use From the calculation of the path coefficients obtained a net benefit lane direct influence

on the intensity of use y6a = 0.492. The score> 0.05 then Ho is rejected and H1 accepted. It can be concluded that the net benefits of a positive direct impact on the

intensity of use

f. H6: positive direct influence on the intensity of use of the Quality System From the calculation results obtained path coefficient lines directly influence the quality

of the system to user satisfaction y1b = 0.194. The score> 0.05 then Ho is rejected and



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H1 accepted. Thus it can be concluded that the quality system is a positive direct impact

on user satisfaction

g. H7: positive direct effect on the Information Quality User Satisfaction From the calculation results obtained path coefficient lines directly influence the quality

of information on user satisfaction y2b = 0.312. The score> 0.05 then Ho is rejected and H1 accepted. Thus it can be concluded that the quality of information a positive direct

impact on user satisfaction

h. H8: direct positive effect on the Quality of Service User Satisfaction From the calculation results obtained lane path coefficient direct influence on the quality

of service user satisfaction y3b = - 0.074. The score 0.05 then Ho is rejected and H1 accepted. It can be concluded that the intensity of the use of a positive direct impact on

user satisfaction.

j. H10: positive direct effect on User Satisfaction Net Benefit From the calculation results obtained path coefficient lines directly influence net benefits

to the user satisfaction y6b = 0.358. The score> 0.05 then Ho is rejected and H1 accepted. It can be concluded that the net benefits of a positive direct impact on user

satisfaction.

k. H11: positive direct effect intensity of use of the Net Benefits From the calculation results obtained path coefficient lines directly influence the intensity

of use of the net benefits y4b = 0.533. The score> 0.05 then Ho is rejected and H1 accepted. It can be concluded that the intensity of the use of direct positive effect on net

benefits.

l. H12: positive direct effect Satisfaction Using the Net Benefits From the calculation of the path coefficients obtained directly influence user satisfaction

path towards the net benefits y4b = 0.420. The score> 0.05 then Ho is rejected and H1 accepted. It can be concluded that the user satisfaction on the positive direct net benefits.

5. Conclusion Based on the research result, this study can has several conclusions.

1. The quality system of direct positive effect on the intensity of use. This is evidenced

by a score y₁a = 0.060> 0.05. These results indicate that the quality of the resulting system is still little influence on the intensity of use. It is necessary for improvement

of the quality management system.

2. Quality information direct impact negatively on the intensity of use. This is evidenced

by a score y₁a = - 0.285 0.05. These results indicate that the quality of service that is produced can increase the intensity of use of the application MS5200.

4. User satisfaction a positive direct impact on the intensity of use. This is evidenced by

a score y5a = 0.421> 0.05. These results showed that user satisfaction is able to increase the intensity of use of the application MS5200.

5. The net benefits of a direct positive effect on the intensity of use. This is evidenced by

a score y6a = 0.492> 0.05. These results indicate that the perceived net manffat employees are able to increase the intensity of use application MS5200.



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6. Quality system a positive direct impact on user satisfaction. This is evidenced by a

score y1b = 0.194> 0.05. These results indicate that the quality of the resulting system is less able to provide user satisfaction.

7. Quality of positive information direct effect on user satisfaction. This is evidenced by

a score y2b = 0.312> 0.05. These results indicate that the quality of information produced is able to provide user satisfaction.

8. Quality of service is a direct positive impact on user satisfaction. This is evidenced by

a score y3b = 0.074> 0.05. These results indicate that the quality of service is not yet able to provide user satisfaction.

9. The intensity of the use of a positive direct impact on user satisfaction. This is

evidenced by a score y4a = 0.310> 0.05. These results indicate that high intensity of usage is able to provide user satisfaction.

10. The net benefits of a positive direct impact on user satisfaction. This is evidenced by a

score y4a = 0.358> 0.05. These results indicate that the net benefits perceived by the user is able to provide user satisfaction.

11. The intensity of the use of direct positive effect on net benefits. This is evidenced by a

score y4a = 0.533> 0.05. These results indicate that high intensity of usage is able to provide great benefits for the user.

12. User satisfaction a positive direct impact on the net benefits. This is evidenced by a

score y4a = 0.420> 0.05. These results showed that user satisfaction is able to provide great benefits for the user.

Recommendation for future research, Jumlah responden terkait aplikasi MOS5200

diharapkan lebih banyak lagi pegawai MOS5200 yang ikut berpartisipasi agar hasil

penelitian semakin valid dan Penelitian ini masih dibutuhkan studi literatur yang lebih

banyak dan mendalam sehubungan dengan e-government dan metode evaluasi guna

memperkuat pemaparan hasil evaluasi dengan teori-teori yang mendukung.

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Authors

Donny Novianto has received his 3rd diploma degree from Politeknik Telkom

Bandung, Department of Information System, in 2011 and continue to finish his

bachelor degree at Bina Nusantara University Jakarta in 2014. He is a student at

Information System Management, Binus Graduate Programs, Bina Nusantara

University, Jakarta-Indonesia..

Sfenrianto has received his Ph.D. from Faculty of Computer Science, University of

Indonesia, in 2014. Currently, he is a faculty member at Binus Graduate Programs,

Bina Nusantara University, Jakarta-Indonesia. His research interests include e-

Learning, e-Business, Knowledge Management, Business Intelligence, Data Mining

and Information System.



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Abstract2.1 Information Technology2.2 Information System2.3 User Satisfaction2.3.1 Meaning of User Satisfaction2.3.2 Quality of Service Information System2.3.3 User Satisfaction and Effectiveness of Information Systems

3. Research Methodology3.1 Research Model3.2 Hypotesis

4. Research Result4.1 Overview of Respondents MOS520004.2.1 Results Descriptive Analysis4.2.2 Validity Test4.2.3 Reliability Test4.2.4 Normality Test with Z-Z-Skewness and Kurtosis4.2.5 Hypothesis Test

5. Conclusion

Donny Novianto, Master Information System Management,...

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