Successful Implementation …… (Hermawan) 19

SUCCESSFUL IMPLEMENTATION

OF ENTERPRISE RESOURCE PLANNING

Hermawan

Information System Management Depatment,

BINUS Graduate Program – Master of Information Systems Management, Bina Nusantara University

Jl. Kebon Jeruk Raya No. 27, Kebon Jeruk, Jakarta 11530, Indonesia

hermawantjhia@yahoo.co.id

ABSTRACT

The purpose of this research was to analyze factors that influence a successful implementation of

Enterprise Resource Planning (ERP) at PT Agung Sedayu Group. Measuring the success of ERP implementation

was needed to find out what success factors influence the implementation process. The DeLone and McLean

success model was used as a measurement tool with employees who actively used the ERP system as

respondents. The research method used was a descriptive quantitative method with questionnaires as data

collection techniques with the indicators on DeLone and McLean model. Data taken from the questionnaire were

187 samples from 350 active user population systems at PT Agung Sedayu Group. Questionnaire data were

processed using the SPSS application to test the validity and reliability; then linear regression analysis was

performed to test the hypothesis. From the results of the research, it is known that all variables of system quality,

information quality, service quality, usage, user satisfaction, and net benefits have an influence on the successful

implementation of ERP at PT Agung Sedayu Group. The conclusion presents that the successful implementation

of ERP is more influenced by user satisfaction supported by the level of use, system quality, information quality,

and service quality.

Keywords: Enterprise Resource Planning, DeLone and McLean model, system quality, information quality,

service quality

INTRODUCTION

Enterprise Resource Planning (ERP) is an investment that has high value and is not seen in a

company (Simamora et al., 2015). ERP is a system that helps to organize business processes in an

integrated unit such as marketing, production, purchasing and accounting, and storing all transactions

in a database used by the company and providing management reporting tools (Brady, Monk, &

Wagner, 2001). With the existence of an ERP system, the company’s business processes will be more

effective, mutually integrated into each division, improve company performance, and can assist

management in managerial decision making (Wicaksono, Mulyo, & Riantono, 2015).

In Indonesia, many large companies have not integrated each company’s business processes

into computerized systems due to large costs and a long time. Moreover, in small companies, ERP

implementation is difficult to implement even the thought of implementing an integrated system like

that as if it is still new. ERP implementation also provides major changes in the company, therefore it

is necessary to know the important aspects that must be considered such as user, commitment,

software and hardware, training, and change in order to provide knowledge and assist stakeholders in

developing relevant strategic steps and policies (Irfani, 2015). In addition, after the implementation of

the ERP system, many questions arise mainly from management, such as how much influence the ERP

implementation has on the performance and productivity of all employees towards the company.

Another question arises; how much influence the ERP implementation has to make business processes

20 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

more effective, efficient, and support the company’s performance to make decisions and increase

profits.

Wijaya and Alianto (2012) have stated that an ERP system is an integration program

application, with cross-functional from an organization and the main purpose of using an ERP system

is to improve the more effective and efficient work methods of an organization. According to Alianto

and Wijaya (2013), ERP systems will help related business units to share data and information, reduce

costs, and improve business processes, which will have an impact on improving work outcomes

(productivity) to be more efficient and effective. Anardani and Putera (2018) have also argued that an

ERP system is a software package that is assigned to coordinate the company’s internal strength so as

to produce a management system that is more effective and efficient in processing data. The company

performance growth can be seen in Figure 1.

500

1000

1500

2000

2500

2013 2014 2015 2016

In
b

il
li

o
n

(
ID

R
)

Company Performance Growth

Revenue

Expense

Net Profit

Figure 1 Company Performance Growth

(Source: PT Agung Sedayu Group)

Based on data in Figure 1 on the growth of company performance, since the ERP

implementation in 2015, the company’s revenue increases due to time efficiency that increases

employee productivity and could reduce operational costs such as paperless which has an impact on

increasing the company’s net profit. Therefore, questions arise from the management of how much

influence the ERP implementation has on employee productivity towards the company and how much

influence the ERP implementation has on making business processes more effective and efficient.

Research to analyze the success of ERP implementation has been carried out by many

researchers. Wisudiawan (2015) has analyzed the success factors of information systems using the

DeLone and McLean model using parametric statistical methods that use interval ratio data. It finds

that user satisfaction is determined by three factors, namely system quality, information quality, and

service quality. Setyo and Rahmawati (2015) also have examined the problem of the influence of

information quality and information system quality on the satisfaction and performance of users of

information systems whose results find that information quality affects intention to use and user

satisfaction. Saputro, Budiyanto, and Santoso (2016) have also analyzed the success of e-government

using DeLone and McLean’s success model case study in Pekalongan city government with the Slovin

sampling method. It finds that information quality and system quality influence the intention user

while information quality, system quality, and service quality have a positive effect on user

satisfaction.

Successful Implementation …… (Hermawan) 21

Based on the results of previous research studies, it is known that there are many researches on

the success of ERP implementation. However, to analyze the success of ERP implementation in the

implementation process which is only three years old, there is still no differences in population use,

samples, time periods, and different methods as reasons for the authors to analyze the success of ERP

implementation. In addition, this research is also asked directly by the ERP implementation manager

to help evaluate the implementation process in order to focus on important factors so that it can save

time and costs of implementing an ERP system. Regarding these problems, the purpose of this

research is to analyze the factors that influence the success of ERP implementation and assist

management in evaluating the implementation process.

From this research, the company will get benefits, namely; (1) It is expected that the

management of the company can find out what variables can affect the success of the system being

implemented. (2) It is expected that the management department can improve the quality of the

system, the quality of information, and the quality of services that can affect the level of use and the

level of user satisfaction so that it can ultimately increase the net benefits of the company.

METHODS

This research is descriptive and explorative research with a quantitative approach. Explorative

research is research conducted to obtain very general information about research problems, namely

information about the success of enterprise resource planning implementation. The population of this

research is Agung Sedayu Group employees who have used an enterprise resource planning business

system of 350 people. Respondents are the operational, purchasing, warehouse, and finance

accounting parts of the staff level, supervisor, manager, general manager, and director.

Based on the Slovin formula, the total sampling of the population of 350 users of the ERP

information system is:

N = N / (1+ (N x e2))

N = 350 / (1+ (350 x 0,052))

N = 350 / (1+ (350 x 0,0025))

N = 350 / (1+ 0,875)

N = 350 / 1,875

N = 186,67

The value of N shows the number of samples of 186,67 rounded to 187. Therefore, the number

of sampling required for this research is 187 sampling. This research uses one sampling technique,

namely random sampling. This sampling technique can provide the most biased generalizations as

investigations and randomly distribute in the population. Primary data in this research comes from

questionnaires distributed to respondents either directly or via the internet. In this research, researchers

obtained secondary data based on data obtained from supporting libraries and pieces of literature in

libraries, journals, previous research, and websites or the internet.

The researcher is part of the ERP implementation system team so that it can make direct

observations by observing the object of research in the company. To get the appropriate data, the

researcher will make a question in the form of a questionnaire based on defined variables.

Questionnaires are given to users of information systems within the company to get information and

feedback on the use of information systems that have been implemented. So it is known to what extent

the information system is in accordance with the wishes of the user, the duration of the use of

information systems, the extent to which information systems help and facilitate the user in carrying

22 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

out daily work, and an analysis of the benefits obtained by the organization on system implementation.

The data on the questionnaire regarding the evaluation of the success of the ERP implementation will

be shared with random users, and the researcher receives the questionnaire again if it has been filled in

by the respondent. Questionnaires are used to measure accurately the extent of the influence of

variables.

Before the hypothesis test is carried out, the results of the questionnaire data will be tested for

validity and reliability testing using the SPSS version 20 application. Validity test is used to measure

whether the questionnaire questions have represented a picture of the intended variable. The Pearson

correlation method is used to test the validity of question items with a significance value of 0,05%.

This means that the level of trust in decision making to accept or reject the hypothesis is 95% with a

margin of error of 5%. With the number of the sample as many as 187, then the R-Table obtained from

the R product moment with a significance value of 0,05 is 0,148. Reliability test is used to measure

whether the questionnaire questions are reliable for research. Reliability testing is done using

Cronbach’s alpha. Where if the Cronbach’s alpha value >= 0,6 then the question is declared reliable.

Conversely, if the Cronbach’s alpha value is <0,6, the question is declared unreliable. After the quality

of the data is declared valid and reliable, the researcher conducts linear regression analysis to test the

hypothesis which aims to measure what factors influence the success of enterprise resource planning

implementation. Variable measurements for the DeLone & McLean model will use a Likert scale from

strongly disagree to strongly agree. This scale is indicated by the criteria; number 1 means strongly

disagree, 2 means disagree, 3 means enough, 4 means agree, 5 means strongly agree (Sekaran &

Bougie, 2009).

DeLone & McLean model is a framework used for measuring the success of IS (Information

Systems). There are six dimensions for measuring the success of the IS model (Petter, DeLone, &

McLean, 2008); (1) system quality is the desired characteristics of an information system, for

examples easy to use, flexible, system reliability, easy to learn, and intuitive system features,

sophistication, flexibility, functionality, data quality, portability, integration, importance, and response

times. (2) Information quality is desired characteristics of system output, namely management reports

and web pages, for example relevant, easy to understand, accurate, concise, complete, security,

currency, timeliness, and usability. (3) Service quality is a service provided by the system to the user

both directly and indirectly, for examples responsiveness, accuracy, reliability, technical competence,

and empathy from staff members. (4) Intention to use; intention to use is an attitude (attitude) while

use is a behavior (behavior) in the use of the system by the user. (5) User satisfaction is the level of

user satisfaction with reports, web sites, and support services, for example, user behavior. (6) The net

benefit is the expansion of IS in contributing to the success of individuals, groups, organizations,

industries, and nations, for examples improving decision making, increasing productivity, increasing

sales, reducing costs, increasing profits, market efficiency, user welfare, and economic development.

Table 1 shows the definition of each variable.

Table 1 Definition Variabel

No Variabel Definition Indicator

1 System Quality (SQ) Desired characteristics of information systems are

reliable for everyday use.

- Easy to Use

- Easy to Learn

- Secure

- Response Time

2 Information Quality

(IQ)

Characteristics generated from the output system

for data processing are able to provide precise and

complete information.

- Accuracy

- Relevance

- Reliable

- Completeness

Successful Implementation …… (Hermawan) 23

Table 1 Definition Variabel (Continued)

No Variabel Definition Indicator

3 Service Quality (LQ) In the form of services provided by the system to

users of the system either directly or indirectly.

- Responsiveness

- Reliability

- Competence

4 Use (U) A behavior that the user shows for using the

system.

- Dependency

- Frequency

- Intention to reuse

5 User Satisfaction

(US)

User satisfaction level for system usage. - Recommendation

- Expectation

6 Net Benefits (NB) Benefits provided by the system to all users in all

related parts.

- Effectiveness

- Efficiency

- Decision Making

To show the relationship between variables with one another, the hypothesis used (H1)

System Quality has a positive influence on Use. (H2) System Quality has a positive influence on User

Satisfaction. (H3) Information Quality has a positive influence on Use. (H4) Information Quality has a

positive influence on User Satisfaction. (H5) Service Quality has a positive influence on Use. (H6)

Service Quality has a positive influence on User Satisfaction. (H7) Use has a positive influence on

User Satisfaction. (H8) Use has a positive influence on Net Benefit. (H9) User Satisfaction has a

positive influence on Net Benefit. The Research Hypothesis can be seen in Figure 2.

Figure 2 Research Hypothesis

RESULTS AND DISCUSSIONS

The questionnaire is distributed randomly to the operational, purchasing, warehouse, and

financial accounting departments that use ERP system by using Google form media with a domain

address: https://goo.gl/forms/ESbrNj5hcr34uxsn2. Respondents have a diverse age range, but the

largest users are in the age range 26-35. Respondents on average have undergraduate education levels.

This shows that respondents are people who are quite educated and young. The profile distribution of

the respondents can be seen in Table 2.

24 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

Table 2 Profile Respondent

Based on Sex Based on Age Based on Education

Male (54.5%) 26-35 (57,2%) S1 (87,1%)

36-45 (17,1%) SMA (6,4%)

Female (45.5%) 18-25 (13,9%) <SMA (4,3%)

46-55 (9,1%) S2 (2,2%)

>=55 (2,7%)

(Source: Questionnaire Data)

After distributing the questionnaire, the following are the results of each question asked.

Based on the results of the system quality variable questionnaire data consisting of four indicators,

most of the respondents agree on all indicators. This shows that employees have a perception that the

system is easy to use and the information system is quite easy to learn but still needs program

improvement so that the system is easier to learn by new system users. Table 3 is a description of the

questionnaire data of system quality variable.

Table 3 Descriptive Questionnaire Data of System Quality Variable

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 System Quality (SQ1) Easy To Use 0% 0% 5,35% 71,12% 23,53% 4,18

2 System Quality (SQ2) Easy To Learn 0% 0% 3,74% 72,73% 23,53% 4,20

3 System Quality (SQ3) Secure 0% 0% 4,81% 70,59% 24,60% 4,20

4 System Quality (SQ4) Response Time 0% 0% 3,21% 72,19% 24,60% 4,21

(Source: Questionnaire Data)

Based on the results of the questionnaire of variable data of information quality which consists

of four indicators, most of the respondents agree on all indicators. This shows that employees have a

perception that the system has produced complete information to help their daily work and the

information system is quite easy to understand but still needs improvement to improve the information

generated from the system. Table 4 is a description of the questionnaire data of information quality

variables.

Table 4 Descriptive Data Questionnaire of Information Quality Variable

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 Information Quality

(IQ1)

Accuracy 0% 1,07% 10,16% 66,84% 21,93% 4,10

2 Information Quality

(IQ2)

Relevance 0% 0,53% 6,42% 68,45% 24,60% 4,17

3 Information Quality

(IQ3)

Reliable 0% 1,07% 8,02% 70,05% 20,86% 4,11

4 Information Quality

(IQ4)

Completeness 0% 0% 6,42% 65,78% 27,81% 4,21

(Source: Questionnaire Data)

Based on the results of the questionnaire variable data service quality which consists of three

indicators, most of the respondents agree on all indicators. This shows that employees have the

Successful Implementation …… (Hermawan) 25

perception that they will easily get information from the system and the system is able to provide

responses to system users, but the response from the system still needs to be improved to provide

better service to users. Table 5 is a description of the questionnaire data of service quality variables.

Table 5 Descriptive Data Questionnaire of Service Quality Variables

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 Service Quality (LQ1) Responsiveness 0% 0% 4,81% 69,52% 25,67% 4,21

2 Service Quality (LQ2) Reliability 0% 0% 4,81% 70,59% 24,60% 4,20

3 Service Quality (LQ3) Competence 0% 0% 9,09% 66,31% 24,60% 4,16

(Source: Questionnaire Data)

Based on the results of the questionnaire of data use variable which consists of three

indicators, most of the respondents agree on all indicators. This shows that employees often use the

system to conduct analysis to help their daily work and the system is quite often used to download data

from the system. Table 6 is a description of the questionnaire data of use variables.

Table 6 Descriptive Data Questionnaire of Use Variable

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 Use (U1) Dependency 0% 0,53% 8,56% 52,41% 38,50% 4,29

2 Use (U2) Frequency 0% 0% 8,02% 52,94% 39,04% 4,31

3 Use (U3) Intention to Reuse 0% 0% 11,23% 52,41% 36,36% 4,25

(Source: Questionnaire Data)

Based on the results of the questionnaire data of user satisfaction variable which consists of

two indicators, most of the respondents agree on all indicators. This shows that employees are satisfied

with the current system and will continue to use the existing system for the future and the system has

produced informative information. Table 7 is a description of the questionnaire data of user

satisfaction variables.

Table 7 Descriptive Questionnaire Data of User Satisfaction Variable

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 User Satisfaction (US1) Recommendation 0% 0% 12,30% 61,50% 26,20% 4,14

2 User Satisfaction (US2) Expectation 0% 0% 11,23% 59,36% 29,41% 4,18

(Source: Questionnaire Data)

Based on the results of the questionnaire data of net benefit variable which consists of three

indicators, most of the respondents agree on all indicators. This shows that the use of the system is

able to provide high enough support to make decisions by decision-makers and the system is quite

easy to use but still needs improvement in the future. Table 8 shows the description of the

questionnaire data of net benefit variables.

26 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

Table 8 Descriptive Data Questionnaire of Net Benefit Variable

No Variable Indicator STS

(1)

(2)

(3)

(4)

(5)

Average

1 Net Benefit (NB1) Effectiveness 0% 0% 6,95% 58,82% 34,22% 4,27

2 Net Benefit (NB2) Efficiency 0% 0.53% 6,95% 57,75% 34,76% 4,27

3 Net Benefit (NB3) Decision Making 0% 0% 5,35% 48,13% 46,52% 4,41

(Source: Questionnaire Data)

Researchers will test the validity and reliability of whether all questionnaire questions can be

declared valid and reliable or not. Based on the validity test in Table 9, it appears that the value of R-

calculate all indicators of each variable for the total indicators of each variable is >0,148 (R-table). It

is decided that the whole question of all indicators of each variable is declared valid. The Cronbach

alpha value for all indicator variables is >= 0,6. It is decided that all indicators of each variable are

reliable. Validity and reliability test results can be seen in Table 9.

Table 9 Validity and Reliability Test Results

Variable R-Calculate Validity Test Results Cronbach’s Alpha Reliability Test Results

SQ1 0,729 Valid 0,662 Reliable

SQ2 0,705 Valid
SQ3 0,773 Valid

SQ4 0,608 Valid

IQ1 0,691 Valid 0,685 Reliable

IQ2 0,759 Valid
IQ3 0,697 Valid

IQ4 0,725 Valid

LQ1 0,908 Valid 0,839 Reliable

LQ2 0,828 Valid

LQ3 0,876 Valid

U1 0,795 Valid 0,720 Reliable

U2 0,794 Valid

U3 0,813 Valid

US1 0,851 Valid 0,623 Reliable

US2 0,854 Valid

NB1 0,831 Valid 0,766 Reliable

NB2 0,845 Valid

NB3 0,800 Valid

(Source: Data Processing)

After all data is declared valid and reliable (based on validity and reliability tests in Table 9),

it can be said that the data is feasible to be processed in the next stage, namely testing the nine

hypotheses using linear regression analysis in SPSS version 20 software.

Based on the model summary in Figure 3, the value of R (Correlation) between system quality

and use is 0,224. While the value of R2 (Determination Coefficient) is 0,050 which means the

influence of system quality with use is 5%. Based on the research of Saputro, Budiyanto, and Santoso

(2016), it is found a positive relationship between system quality and information quality to use. Users

will use the system by paying attention to the quality of the system. This can be due to the high

concern of the user for the quality of the existing system. Users also feel that the system has helped

them in working on their daily work processes. The training provided about the system is good and the

user views the quality of the system as important as a reason for use. In this research, the relationship

Successful Implementation …… (Hermawan) 27

between system quality and use gets positive results so that it is in accordance with previous research.

The results of hypothesis testing (H1) can be seen in Figure 3.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,224a 0,050 0,045 0,41489

a. Predictors: (Constant), SystemQuality

Figure 3 Hypothesis Test Result (H1)

(Source: Data Processing)

Based on the model summary in Figure 4, the value of R (Correlation) between system quality

and user satisfaction is 0,298. While the value of R2 (Determination Coefficient) is 0,089 which

means the influence of system quality with user satisfaction is 8,9%. Based on the research of

Wisudiawan (2015), it is found a positive relationship between system quality, information quality,

and service quality to user satisfaction. System quality affects user satisfaction. The better quality of

the system, user satisfaction will increase. Users still feel satisfied even though the quality of the

system is only categorized as good. This can be because the services provided by the IT team are good

and the system helps users in completing operational work. In this research, the relationship between

system quality and user satisfaction receive positive results in accordance with previous research. The

results of hypothesis testing (H2) can be seen in Figure 4.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,298a 0,089 0,084 0,39312

a. Predictors: (Constant), SystemQuality

Figure 4 Hypothesis Test Result (H2)

(Source: Data Processing)

Based on the model summary in Figure 5, the value of R (Correlation) between information

quality and use is 0,260. While the value of R2 (Determination Coefficient) is 0,068, which means the

influence of information quality with use is 6,8%. Based on the research of Saputro, Budiyanto, and

Santoso (2016), it is found a positive relationship between system quality and information quality to

use. Users use information systems by paying attention to the quality of information produced. This

can be caused by the awareness of the user regarding the quality of the information generated from the

system. In this research, the relationship between information quality and use gets positive results so

that it is in accordance with previous research. The results of hypothesis testing (H3) can be seen in

Figure 5.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,260a 0,068 0,063 0,41105

a. Predictors: (Constant), InformationQuality

Figure 5 Hypothesis Test Result (H3)

(Source: Data Processing)

28 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

Based on the model summary in Figure 6, the R-value (Correlation) between information

quality and user satisfaction is 0,229. While, the value of R2 (Determination Coefficient) is 0,053,

which means the influence of information quality with user satisfaction of 5,3%. Based on the research

of Wisudiawan (2015), it is found a positive relationship between system quality, information quality,

and service quality to user satisfaction. The quality of information affects user satisfaction. The better

the quality of information generated from the system, user satisfaction will increase. IT must provide

good quality information, namely availability, up to date, reliable, and according to user needs, so that

the information generated is useful in the management decision-making process and as input for

management in determining the next strategic step. In this research, the relationship between

information quality and user satisfaction gets positive results so that it is in accordance with previous

research. The results of hypothesis testing (H4) can be seen in Figure 6.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,229a 0,053 0,047 0,40082

a. Predictors: (Constant), InformationQuality

Figure 6 Hypothesis Test Result (H4)

(Source: Data Processing)

Based on the model summary in Figure 7, the value of R (Correlation) between service quality

and use is 0,365. While, the value of R2 (Determination Coefficient) is 0,133, which means the

influence of service quality and use is 13,3%. Based on the research of Caldeira & Ward (2002), it is

found a positive relationship between service quality and usage. The IT team has provided satisfactory

service and the quality of services provided by IT in accordance with what is expected by the user,

causing this to affect the use of the system. Another thing that affects the use of the system is the

obligation to use the system, and the system can help, accelerate, and simplify the completion of daily

work so that the quality of service influences. In this research, the relationship between service quality

and use gets positive results so that it is in accordance with previous research. The results of

hypothesis testing (H5) can be seen in Figure 7.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,365a 0,133 0,128 0,39637

a. Predictors: (Constant), ServiceQuality

Figure 7 Hypothesis Test Result (H5)

(Source: Data Processing)

Based on the model summary in Figure 8, the value of R (Correlation) between service quality

and user satisfaction is 0,208. While the value of R2 (Determination Coefficient) is 0,043 which

means the influence of service quality with user satisfaction of 4,3%. Based on the research of

Wisudiawan (2015), it is found a positive relationship between system quality, information quality,

and service quality to user satisfaction. The IT team has provided maximum service, this has a great

effect on user satisfaction. Other things need to be improved to increase user satisfaction such as the

quality of the system and the quality of the information provided by the system. Information or

instructions and training are clear in order to help users complete their daily work. In this research, the

Successful Implementation …… (Hermawan) 29

relationship between service quality and user satisfaction receive positive results in accordance with

previous research. The results of hypothesis testing (H6) can be seen in Figure 8.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,208a 0,043 0,038 0,40279

a. Predictors: (Constant), ServiceQuality

Figure 8 Hypothesis Test Result (H6)

(Source: Data Processing)

Based on the model summary in Figure 9, the R (Correlation) value between use and user

satisfaction is 0,482. While the value of R2 (Determination Coefficient) is 0,233, which means that the

use effect with user satisfaction is 23,3%. Based on the research of Petter, DeLone, and McLean

(2008), it is found a positive relationship between use and user satisfaction. The high use of the system

can ensure user satisfaction with the existing system. Users feel satisfied because there are many other

factors that influence it such as services provided by a maximum IT team, good system quality, and a

system that can generate information to support work. In this research, the relationship between use

and user satisfaction gets positive results so that it is in accordance with previous research. The results

of hypothesis testing (H7) can be seen in Figure 9.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,482a 0,233 0,228 0,36074

a. Predictors: (Constant), Use

Figure 9 Hypothesis Test Result (H7)

(Source: Data Processing)

Based on the model summary in Figure 10, the value of R (Correlation) between use and net

benefit is 0,426. While the value of R2 (Determination Coefficient) is 0,181 which means the use

effect with net benefit is 18,1%. Based on the research of Petter, DeLone, and McLean (2008), it is

found a positive relationship between system use and net benefits. The benefits perceived by users are

obtained because it uses a system that can help, accelerate, and facilitate the user in completing day-to-

day operational work. In this research, the relationship between use and net benefit received positive

results so that it is in accordance with previous research. The results of hypothesis testing (H8) can be

seen in Figure 10.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,426a 0,181 0,177 0,47123

a. Predictors: (Constant), Use

Figure 10 Hypothesis Test Result (H8)

(Source: Data Processing)

30 Journal The WINNERS, Vol. 20 No. 1, March 2019: 19-31

Based on the model summary in Figure 11, the value of R (Correlation) between user

satisfaction and net benefit is 0,435. While the value of R2 (Determination Coefficient) is 0,189 which

means the influence of user satisfaction with net benefit is 18,9%. Based on the research of Setyo and

Rahmawati (2015), it is found a positive relationship between user satisfaction and net benefits. Users

feel satisfied with the existing information system that can guarantee users will use it continuously to

support their work. This can be due to user expectations according to the desired system usability. In

this research, the relationship between user satisfaction and net benefit receive positive results so that

it is in accordance with previous research. The results of hypothesis testing (H9) can be seen in Figure

11.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0,435a 0,189 0,185 0,46890

a. Predictors: (Constant), UserSatisfaction

Figure 11 Hypothesis Test Result (H9)

(Source: Data Processing)

CONCLUSIONS

Based on the results of research on the success of ERP system implementation, it can be

concluded that in general, ERP systems have a positive influence on the effectiveness, efficiency, and

decision making of users. Based on data processing results, ERP implementation has a positive effect

on usage variables (how often users use the system), user satisfaction (level of user satisfaction in

using the system), and net benefits (net system benefits to users and companies). The above

components as a whole are affected positively by the implementation of ERP systems on the company.

Then for system quality variables, information quality and service quality function some of the

variables that must be improved in order to influence user usage and satisfaction.

Based on the conclusions, in the implementation of an ERP system, there are several very

important things which are the keys to the success of implementing an ERP system and should be

improved by companies and ERP system developers, namely management commitment and training in

the use of ERP systems. Management commitment is needed so that the ERP system has been built

and applied in the company to be used maximally as a whole so that the function of the ERP system

can run properly. Then the training factor is important because users get information on how to use all

functions in an existing ERP system to the maximum, so the purpose of implementing an ERP system

is to improve the efficiency and effectiveness of existing business processes in the company.

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