p–ISSN: 2723 - 6609 e-ISSN: 2745-5254

Vol. 5, No. 4 April 2024 http://jist.publikasiindonesia.id/

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1694

Assessment of the Success of Digital Signature and Stamp

Implementation in the National Economic Rescue (PEN)

Application System at Credit Insurance Companies in

Indonesia

Nur Syamsuhadi

, Sfenrianto

Universitas Bina Nusantara Jakarta, Indonesia

Email: [email protected]

, [email protected]

*Correspondence

ABSTRACT

Keywords:

DeLone and McLean,

PLS, PEN, Success

System, Credit Guarantee.

Utilization technology in sector industry non-bank finance

in Indonesia is increasingly widespread, including in the

guarantee credit of state-owned companies. The company

uses System Application Guarantee for transaction

guarantee credit with produce Certificate Guarantee as proof

of approval. The request can be active in the context of the

COVID-19 pandemic and the Indonesian government's

efforts to implement the company's National Economic

Recovery (PEN) program guarantee. Implementation

feature sign hand and digital stamp on the System

Application PEN underwriting allows provision of

document Guarantee Certificate in the form of legal and

supportive softcopy restrictions contact physique during a

pandemic. Study This aim is to measure the successful

implementation features of the success models by Delone

and McLean and analyze the connection between variables

in research. The benefits cover assessment, evaluation, and

strategy-based results evaluation using feature sign hand and

digital stamp. Delone and McLean's original model was

modified to eliminate variable intention to use. The research

was conducted by analyzing the results of questionnaires

from 108 officials' user systems throughout Indonesia.

Process of data analysis using the method of Partial Least

Square with device SMARTPLS software. Research results

prove that quality systems will influence the satisfaction of

future users who will be influential to benefit clean from the

feature sign hand and digital stamp on the System

Application PEN Guarante.

Introduction

The use of technology in the non-bank financial sector is expanding, especially in

credit guarantee companies in Indonesia (Fidhayanti, 2020). They implement a Guarantee

Application System to manage credit guarantee transactions, which generate Guarantee

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1695

Certificates as valid proof. During the COVID-19 pandemic, the government has taken

extraordinary measures to support the economy, including engaging credit guarantee

companies (Kurniawan & Solihin, 2022). Rules such as PP RI Number 43 of 2020 were

issued to allow the use of digital signatures and stamps. Indonesia Electronic Certification

Providers (PSrE) collaborate with credit guarantee companies to verify electronic

signatures. Implementation of this feature needs to be measured to evaluate its impact and

help companies improve guarantee services (Amang, 2015). This study evaluates the

implementation of digital signature and stamp features in the PEN Product Guarantee

Application System as part of the National Economic Recovery (PEN) program. The

research questions focus on the success rate of implementation and the relationship

between the variables in the model (Nurhidayah, 2020). The main objective is to measure

implementation success by utilizing the DeLone and McLean models and to analyze the

correlation of variables in the research context. The advantages of this research include

evaluating the use of digital signature and stamp features, developing improvement

strategies, and evaluating cooperation with electronic certification providers. Although

this research is limited to credit guarantee companies that involve active users of

application systems, the focus is on evaluating and measuring the implementation of

digital signature and stamp features on the system application. Lastly, this research aims

to support companies in improving the quality of underwriting services within the

framework of the PEN program run by the Indonesian government.

Digital Signature

Digital signatures have existed since the beginning of information and

communication technology development. The article "New Directions in Cryptography"

(Firmanesa, 2016) introduced the concept of digital signatures to maintain document

confidentiality. The concept of a digital signature involves a hash function, a one-way

mathematical function that generates a unique value for each input data. The electronic

document signing process involves processing the document as input in a hash function,

generating a unique value based on the data. In the running, PDF documents use standard

DSA (Digital Signature Algorithm) hash signatures and secure X.509 format, associating

public keys with identities such as websites or organizations (Kara, Arifin, & IIswahyudi,

2021). Digital signatures are essential in ensuring the integrity and authentication of

electronic documents in the modern technological era.

Digital Stamp

Although stamps and seals share similar meanings, their usage differs. "Stamp"

refers to marking using a stamp, and a stamp is the result of an image or writing printed

on an object. Stamps, just like postage stamps, are attached to important documents like

agreements and receipts. All three are symbols of individual identity, officials, or groups

in human history. Stamps function as identification and provide more substantial

assurance of approval in documents. In the context of this study, credit guarantee

companies in Indonesia replace manual stamps with digital stamps on Guarantee

Certificate documents. Creating digital stamps is a modern, practical, and secure method

involving digital technology. Credit guarantee companies comply with the X.509 format

Nur Syamsuhadi, Sfenrianto

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1696

standard for digital stamps through a recognized Electronic Certification Agency. Digital

stamps are part of a legitimate digital signature solution, supporting the National

Economic Recovery program by using documents in softcopy form.

Digital Documents

Digital documents are physical records scanned or inputted into a computer and

then saved in an electronic format that can be modified. Another term is an electronic

document. In Indonesia, digital documents must comply with various regulations,

including Law Number 11 of 2008 concerning Information & Electronic Transactions

and Government Regulation Number 71 of 2019, which regulates the use of Electronic

Certificates issued by Indonesian Electronic Certification Operators (PSrE). Article 42,

paragraph 1, and articles 51, 55, and 57 of these regulations also regulate procedures for

using and administering Electronic Certification in Indonesia.

Models of DeLone and McLean

According to the DeLone and McLean model (1992), the quality of information and

systems affects user satisfaction, intended use, and organizational and individual impact,

influencing information systems' success.

Guarantee Certificate

According to the Regulation of the Minister of Finance of the Republic of Indonesia

Number 99/PMK. 010/2011, which amended the Regulation of the Minister of Finance

Number 222/PMK. 010/2008, Guarantee Certificate is a sign of approval given by a credit

guarantee company in Indonesia to Guarantee Recipients related to Guaranteed

obligations. This rule regulates the roles and procedures related to Guarantee Certificates.

PEN (National Economic Recovery) Guarantee Application System

According to (Frisdayanti, 2019), an application system is a computer-based

platform that processes data and provides information to support an organization's

decision-making, coordination, and control. Meanwhile, according to (Surya & Apriyanti,

2021), an application system is a set of connected components to collect, process, store,

and distribute information to achieve organizational goals. In this context, the PEN

Guarantee Application system is a digital solution used by a credit guarantee company in

Indonesia to support the National Economic Recovery (PEN) program. The primary

function of this system is to simplify the credit guarantee process by producing a valid

Certificate of Guarantee as proof of approval (Khoer & Atnawi, 2022).

Partial Least Square - Structural Equation Modeling (PLS-SEM)

The PLS-SEM (Partial Least Squares - Structural Equation Modeling) method is an

approach to modeling complex causal relationships in path models with latent variables

[10]. In SEM (Structural Equation Model) analysis (LUBIS & Shara, 2021), this

technique combines factor analysis and regression analysis to examine the relationships

between variables in the model. PLS (Partial Least Squares) is a structural equation model

focusing on a component or variant-based approach (Maghfiroh, 2018). Compared to the

covariance-based SEM approach, PLS is more prediction-oriented. Recommendations for

using PLS-SEM include testing theoretical frameworks from a prediction perspective

when structural models are complex or when the research objective is to understand

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1697

increasing complexity or extend existing theory, especially with financial ratio data or

similar.

SmartPLS

SmartPLS is software with a graphical interface for modeling structural equations

based on variance with the partial least squares (PLS) path modeling method. It is

prevalent in management, marketing, and information systems. "SmartPLS" stands for

"Partial Least Squares Structural Equation Modeling." The Partial Least Squares (PLS)

method is a statistical approach to analyzing complex data models. With a user-friendly

graphical interface, SmartPLS allows researchers to define models and analyze results

quickly. SmartPLS is also affordable, making it suitable for researchers on a budget.

Likert Scale

The Likert scale is a psychometric method used in social and psychological research

to measure agreement or disagreement with certain statements or statements. Called after

the Rensis Likert, this scale allows respondents to rank statements with answer choices

in a range of five to seven choices. Typically, this scale consists of options such as

"Strongly Agree," "Agree," "Neutral," "Disagree," and "Strongly Disagree." The Likert

scale helps describe the attitudes or opinions of respondents to the topic under study, and

the results can be analyzed statistically to identify trends or patterns in the data.

Research Methods

This research method uses a quantitative approach. Research begins by referring to

the theories that then form a framework. The researchers collect data through

questionnaires about the system's quality, information, use, user satisfaction, and net

profit. The questionnaire uses a five-point Likert scale to measure respondents' responses.

This study analyzes the suitability of developing digital signatures and stamps in the

Credit Guarantee Application System at a credit guarantee company in Indonesia. In this

context, the researchers use the DeLone and McLean Information Success model to

evaluate the implementation and recommend improvement. We will analyze data that

meet the requirements using the Partial Least Square (PLS) method in Structural Equation

Modeling (SEM), and we will process the data using SmartPLS version 3 software.

Theoretical Framework

The research conducted by the researchers has several stages, as shown in Figure 3

below:

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Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1698

Figure 3. Stage Study

1) Determination of the IS (Information System) Success Model

This study applies the DeLone and McLean methods in measuring the success of

information systems, mainly through customer satisfaction.

2) Questionnaire Preparation

Before compiling the questionnaire, we identified variables from the DeLone and

McLean models. The questions within the questionnaire serve the purpose of assessing

the information system's success.

3) Questionnaire Distribution

Questionnaires were distributed via digital forms to users of digital signature and

stamp features on credit guarantee applications in all company branch offices in

Indonesia.

4) Collection of Questionnaire Results

Researchers collect complete questionnaires within a predetermined time limit.

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1699

5) Validity and Reliability Test

Questionnaire result data were tested for validity and reliability using SmartPLS software.

6) Data Analysis

After validating and ensuring the reliability of the data, the researchers analyzed it

to identify the variables that influence the success of the application's signature and digital

stamp features.

7) Conclusions and Recommendations

The final stage of this research is to draw conclusions and provide recommendations

for further research or the practical application of the results.

Model

This research employs (DeLone & McLean, 2003) information system success

model, but previous studies have shown a relationship between the dimensions in the

model. Therefore, a formation hypothesis is needed to explain this linkage. Based on

previous research, the most significant relationship in this model is between system

quality and end-user satisfaction, as well as other relationships such as information quality

with user satisfaction, system use with user satisfaction, and quality system with system

usage.

Figure 4. DeLone & McLane Information Success Model (2003)

Variables

This study applies an information system success model, which involves six main

variables: System Quality, Information Quality, Service Quality, Use, User Satisfaction,

and Net Benefits. These variables are aligned with the research hypotheses and follow the

DeLone and McLean model.

Hypothesis

This study applies the information system success model, but previous research has

identified a relationship between the dimensions in the model. Therefore, we formulated

a hypothesis to describe this correlation. According to (Urbach, Smolnik, & Riempp,

2008), the most significant associative relationship in establishing a success model using

the DeLone and McLean method is between system quality and user satisfaction. Other

relationships described involve information quality with user satisfaction, system use

with user satisfaction, and system quality with system use. However, the interest in using

variables (Intention to Use) is irrelevant because users with related roles must use the

PEN Guarantee Application's digital signature and stamp features. The image of the

model accompanies the relevant references. The hypothesis of this study reflects previous

Nur Syamsuhadi, Sfenrianto

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1700

research and includes variables such as System Quality, Information Quality, Service

Quality, Use, User Satisfaction, and Net Benefits. The variables used are:

H1: System quality has a positive influence on Use.

H2: System quality has a positive influence on User Satisfaction.

H3: The quality of information (Information Quality) positively influences the use (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 effect on Net Benefits.

H9: User Satisfaction has a positive influence on Net Benefits.

Population and Sample

Population is the totality of analysis units with similar characteristics, including the

research object and its attributes. The population is the area of generalization tested to

reach study conclusions. This study focuses on the population of Branch Managers and

Business Managers at credit guarantee companies in Indonesia who use the PEN

Guarantee application. The sample, as a representation of the population with similar

attributes, is selected using the Saturated Sampling method, in which the entire population

is sampled without selection. The study took a sample of 108 employees who use the PEN

Guarantee application system, representing the entire population.

Data Collection Methods

Researchers use the data collection method to gather information in a study. The

study design and report provide a detailed description of the data collection techniques.

Questionnaires are a standard collection method for presenting written questions or

statements to respondents. The questionnaire should be pertinent to the research problem

and operational definition. In order to reduce the amount of paper used, we chose to use

an online questionnaire for this study. The Microsoft Forms platform allows respondents

from over Indonesia to complete the questionnaire through the provided link. This

approach has speed, low cost, and better response quality advantages. Making a

questionnaire refers to the model proposed by (Iivari, 2005) by separating variables into

categories of independent and dependent variables. The independent variables include

System Quality, Information Quality, and service quality, while the dependent variable

includes Use and Net Benefits.

Data Analysis Methods

The study uses inferential statistical analysis with the Partial Least Squares (PLS)

technique to examine relationships and influences of variables in complex models.

Evaluation includes R-square, F-square, and Q-square to measure model quality. PLS

evaluation involves Measurement Model Evaluation and Structural Model Evaluation,

which includes analyzing the outer model to measure the validity and reliability of

indicators in the model and calculating factors like convergent and discriminant validity,

composite reliability, average variance extracted (AVE), and Cronbach's alpha.

Evaluation of the Structural Model involves an analysis of the inner model to assess the

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1701

relationship between latent variables according to the proposed hypothesis. The

parameters evaluated include the path coefficient (path coefficient), coefficient of

determination (R-Square), T-test, effect size (F-square), and prediction relevance (Q-

square) [21], [22]. This evaluation helps researchers ensure the quality and validity of the

resulting model and identify the contribution of variables to the research model [21], [22].

Results and Discussion

After collecting data, the next step is to process it using the Partial Least Squares

(PLS) method via SmartPLS 3 software. The PLS method in structural equation analysis

involves building a measurement model and evaluating a structural model to test

previously proposed models and the relationship between variables.

Testing the Measurement Model (Outer Model Analysis)

1. Convergent Validity Testing

The data testing results show a loading factor below 0.7. The results indicate that

indicators meet convergent validity and are valid for construct dimensions. See Table 1

for loading factor results.

Table 1

Results of Factor Loading

Variable

Components

Indicato

Outer

Loadin

Validit

System Quality

(SQ)

SQ1

0.72

Vali

SQ2

0.76

Vali

SQ3

0.85

Vali

SQ4

0.76

Vali

SQ5

0.90

Vali

SQ6

0.89

Vali

Information

Quality (IQ)

IQ1

0.81

Vali

IQ2

0.94

Vali

IQ3

0.92

Vali

IQ4

0.83

Vali

IQ5

0.93

Vali

Service

Quality (SQ)

SEQ1

0.95

Vali

SEQ2

0.91

Vali

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Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1702

SEQ3

0.94

Vali

Use (U)

0.87

Vali

0.90

Vali

User

Satisfaction

(US)

US1

0.96

Vali

US2

0.96

Vali

Net Benefits

(NB)

NB1

0.93

Vali

NB2

0.94

Vali

NB3

0.96

Vali

Average Variance Extracted (AVE) Test

After conducting the tests, we can conclude that all variables exhibit a construct

validity level exceeding 0.50.

Discriminant Validity Testing

The results of the Cross Loading test show that the indicators have a higher

correlation with the construct itself than with other constructs. The results indicate that

each indicator is part of the appropriate construct. Furthermore, when looking at Fornell-

Lacker's Cross Loading value, the test results show that the AVE root of each construct

is more significant than its correlation with other constructs. The results show that this

model has good Discriminant Validity based on the AVE root test.

Composite Reliability Testing

The test results show that each variable has a value above 0.60. The results mean

that the variables in this study are very reliable. These results confirm that the data

collected is consistent and reliable. Therefore, the analysis carried out has a high level of

confidence. The reliability of the variables also shows that the measurements and

indicators used are suitable. In other words, these results support that the measuring

instrument used in this study effectively measures the concept in question.

Structural Model Testing (Inner Model Analysis)

When testing the Inner Model, there are steps to evaluate the relationship between

latent variables. There are five main stages with their respective roles to ensure the

integrity and validity of the hypothesized relationship. These stages form the basis of

analyzing variable relationships in research, helping to understand interactions and their

impacts.

1. Path Coefficient Testing (𝛽)

Based on the following table, out of 9 paths in the research model, four paths are

without statistical significance because the values are below the threshold of 0.1. These

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1703

results show that the relationship between these paths is not significant. Special attention

is needed to understand why and what factors influence its significance.

Table 2

Path Coefficient Test Results

Correlation

(Relationship)

Path

Coefficient

(𝛽)

Information Quality) →

Use

-0,055

Information Quality) →

User Satisfaction

0,014

Service Quality) → Use

0,471

Service Quality → User

Satisfaction

0,083

System Quality → Use

0,164

System Quality → User

Satisfaction

0,527

Use → Net Benefits

0,042

Use → User Satisfaction

0,341

User Satisfaction → Net

Benefits

0,641

2. Testing the Coefficient of Determination (R-Square)

Based on the data in Table 3 below, the results show that the research model used

has a moderate level.

Table 3

Coefficient of Determination (R Square) Test Results

Component

Variable

R Square

Results

Net Benefits

0,447

Moderate

Use

0,311

Moderate

User

Satisfaction

0,690

Good

3. Testing the T-test (T-Statistics)

Based on the results in Table 4, this study only obtains the accepted hypothesis path

Y from 9 existing hypotheses. The path has been rejected as the T-test value falls below

the threshold of 1.96.

Table 4

T-test results (T-Statistics)

Hypothesis

T Statistics

(|O/STDEV|)

Analysis

System

Quality →

Use

1,339

Rejected

System

Quality →

4,321

Accepted

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Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1704

Hypothesis

T Statistics

(|O/STDEV|)

Analysis

User

Satisfaction

Information

Quality →

Use

0,413

Rejected

Information

Quality →

User

Satisfaction

0,122

Rejected

Service

Quality →

Use

3,051

Accepted

Service

Quality →

User

Satisfaction

0,645

Rejected

Use → User

Satisfaction

3,294

Accepted

Use → Net

Benefits

0,267

Rejected

User

Satisfaction

→ Net

Benefits

4,262

Accepted

4. Effect Size Test (f2)

Based on the test results, it was found that User Satisfaction has the most

considerable effect size value on the hypothetical path of Net Benefits, with a value of

0.431. System Quality and Use both have a medium effect on User Satisfaction. The

remaining hypotheses have little effect on the model structure, with an effect size value

(f2) below 0.15.

5. Predictive Relevance Testing (Q2)

This testing process uses the blindfolding method. Where the results obtained show

that the Q2 value of the dependent variable has a value above zero, these results can be

interpreted that these variables have a predictive relationship.

Hypothesis Testing Results

The following results are based on data measurements for associated hypotheses.

H1: System Quality has a positive influence on Use.

The results of the T-test on the structural model analysis in Table 4 reveal a T-test

value of 1.339, below the critical limit of the T-test of 1.96. Therefore, we can conclude

that the relationship is invalidated. The results indicate no significant positive impact of

system quality on usage. The results imply that the system quality hypothesis might play

a minor role in explaining the extent of application or system use under investigation.

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Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1705

H2: System quality (System Quality) has a positive influence on user satisfaction

(User Satisfaction)

Derived from the outcomes of the T-test in Table 4 during the structural model

analysis, we identified a T-test value of 4.321, signifying that the value surpassed the

threshold of 1.96. The results indicate that the relationship between the two variables is

accepted. This finding also shows that the system's quality (System Quality) positively

impacts user satisfaction. This finding is reinforced by the path coefficient in Table 2 of

0.527, indicating that the hypothesis regarding System Quality → User Satisfaction has a

positive and significant effect.

H3: The quality of information (Information Quality) has a positive influence on the

use (Use)

Derived from the findings of the T-test in Table 4 during the structural model

analysis, we determined a T-test value of 0.413. This value suggests that it falls below the

threshold of 1.96. Consequently, the connection between the two variables is invalidated.

Furthermore, these outcomes imply that the quality of information (Information Quality)

does not positively impact usage (Use). This result is reinforced by the path coefficient in

Table 2, which is -0.055, indicating that this hypothesis has no positive influence.

H4: The quality of information (Information Quality) positively influences user

satisfaction (User Satisfaction).

The T-test results in the structural model analysis, as presented in Table 4, reveal a

T-test value of 0.122. This value signifies that the figure falls below the threshold of 1.96.

These outcomes suggest the rejection of the relationship between the two variables. These

findings also reveal that the quality of information (Information Quality) does not

positively impact user satisfaction (User Satisfaction). The path coefficient in Table 2

further strengthens this discovery, as it holds a value of 0.014. This value indicates that

this hypothesis lacks a positive influence.

H5: Service Quality has a positive influence on Use.

Derived from the results of the T-test in the structural model analysis presented in

Table 4, we identified a T-test value of 3.051, indicating that this figure surpassed the

threshold of 1.96. These results indicate that the relationship between the two variables is

accepted. These findings also indicate that service quality (Service Quality) positively

affects usage (Use). The path coefficient in Table 2 further confirms this discovery, with

a value of 0.471. This value suggests that the Service Quality → Use hypothesis holds a

meaningful and positive influence.

H6: Service quality has a positive influence on user satisfaction.

The results of the T-test in the structural model analysis recorded in Table 4 reveal

a T-test value of 0.645, which illustrates that this value is below the threshold value of

1.96. This finding suggests the rejection of the relationship between the two variables.

These findings also reflect that service quality (Service Quality) does not positively affect

user satisfaction (User Satisfaction). The path coefficient in Table 2 further strengthens

this finding, with a value of 0.083. This value indicates the lack of a positive impact on

this hypothesis.

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Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1706

H7: Use has a positive influence on User Satisfaction.

The results of the T-test in the structural model analysis presented in Table 4 reveal

a T-test value of 3.294. This value indicates that the figure surpassed the threshold of

1.96. This result indicates that the relationship between the two variables is accepted.

These results also show that the use (Use) positively impacts user satisfaction (User

Satisfaction). The path coefficient in Table 2 further reinforces this finding, standing at

0.341. This value indicates a significant positive impact on the Use → User Satisfaction

hypothesis.

H8: Use has a positive effect on Net Benefits.

Based on the results of the T-test in the structural model analysis shown in Table 4,

a T-test value of 0.267 was found, which indicates that this figure is below the threshold

of 1.96. This result signifies the rejection of the relationship between the two variables.

This finding also shows that the quality of use (Use) does not positively impact net

benefits (Net Benefits). The path coefficient in Table 2 further strengthens this finding,

registering at 0.042. This value implies that this hypothesis lacks a positive effect.

H9: User Satisfaction has a positive influence on Net Benefits.

The results of the T-test on the structural model analysis in Table 4 indicate a T-test

value of 4.262, which exceeds the limit of 1.96. This result shows that the relationship

between the two variables is accepted. This finding also reveals that user satisfaction

(User Satisfaction) positively impacts net benefits (Net Benefits). The path coefficient in

Table 2 of 0.641 further confirms this finding. This value indicates a significant and

positive influence on the hypothesis User Satisfaction → Net Benefits.

Conclusion

The results of the conducted data analysis and testing concerning the

implementation of digital signature and stamp features in the PEN Guarantee Application

System led to the conclusion that the model derived from the DeLone and McLean

information system success model needs comprehensive empirical validation in this

study. Among the nine proposed hypotheses, only four were approved. These include the

correlations between system quality, service quality, usage, and user satisfaction with net

benefits. These findings highlight that the outcomes of the information system success

model framework could differ based on the context and characteristics of the application

examined in subsequent research.

Assessment of the Success of Digital Signature and Stamp Implementation in the National

Economic Rescue (PEN) Application System at Credit Insurance Companies in Indonesia

Indonesian Journal of Social Technology, Vol. 5, No. 4, April, 2024 1707

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