Global Academy of Business Studies (GABS) ISSN: 3110-3197.
Vol 2.
No 2, 2025, 117-133 https://doi.
org/10.
35912/gabs.
Analysis of factors that are the challenges of digital transformation in public service complaint management through the SP4NLAPOR! Application in the Ombudsman of the Republic of Indonesia Dana Utama1.
Dian Indiyati2 Telkom University.
Bandung.
Jawa Barat.
Indonesia1,2 bayanaka@gmail.
Abstract Purpose: This study aims to analyze the challenging factors in the digital transformation of public service complaint management through the SP4N-LAPOR! Application at the Ombudsman of the Republic of Indonesia.
Methodology/approach: The research adopts a quantitative approach, collecting data through interviews and questionnaires from employees/assistant managers of SP4N-LAPOR! at the Ombudsman of the Republic of Indonesia.
Exploratory Factor Analysis (EFA) serves as the analytical method to identify the primary factors Article History influencing difficulties and challenges in the digital transformation Received on 27 August 2025 Revised on 11 September 2025 Results/findings: The analysis reveals several challenging factors in Revised on 21 October 2025 the digital transformation of managing public service complaints Accepted on 30 October 2025 through the SP4N-LAPOR! Application at the Indonesian Ombudsman.
The identified challenge factors, ranging from the most dominant to less prominent, include Digital Capability.
Information Network Systems.
Complaint Management.
Digital Solutions.
Leadership 4.
Business Ecosystem.
Integrated Regulation.
Digital Talent.
Digital Conversion and Digital Implementation.
Conclusion: Digital transformation of complaint management at the Ombudsman of Indonesia faces major challenges, notably limited .
93%),
SP4N-LAPOR!
implementation effectiveness and overall public service efficiency.
Limitations: This research aimed to identify factors challenging the digital transformation of public service complaint management, providing a foundation for future studies to develop effective strategies for improvement.
However, the studyAos respondents were limited to employees or assistants of the Ombudsman of the Republic of Indonesia, restricting broader generalization.
Contribution: This article contributes to a comprehensive understanding of critical aspects essential for improving the implementation of digital transformation in managing public service Keywords: Complaint Management.
Digital Transformation.
Exploratory Factor Analysis.
Public Services.
SP4N-LAPOR! How to Cite: Utama.
, & Indiyati.
Analysis of factors that are the challenges of digital transformation in public service complaint management through the SP4N-LAPOR! Application in the Ombudsman of the Republic of Indonesia.
Global Academy of Business Studies, 2.
, 117-133.
Introduction Public services are an element of the bureaucratic system that every country has.
A series of activities in public service are aimed at fulfilling the rights of every citizen (Nasura, 2.
In Indonesia, the implementation of public services is an important and interesting issue to study because it involves fulfilling the rights of citizens.
This is of course in line with the mandate of Law Number 25 of 2009 concerning Public Services, especially in Article 1 where public services are a series of activities in order to fulfill service needs in accordance with statutory regulations for every citizen and resident for goods, services and/or administrative services provided by public service providers.
Every citizen has the right to receive quality public services.
Quality service is when people get easy service with procedures that are short, fast, precise and satisfying.
In Indonesia itself, there are still many government agencies that are not optimal in providing services to the community.
The quality of public services in Indonesia still needs to be improved.
Indonesia's Ease of Doing Business ranking is number 73 in the world, which is behind several countries in ASEAN.
Then, the results of the 2022 compliance assessment by the Indonesian Ombudsman, most of Ministries.
Institutions, central and regional governments are still in the yellow zone, namely in the moderate maladministration category.
The management of public service complaints encounters several challenges, including normative follow-up procedures, the archival of numerous reports deemed nonactionable, public distrust in the safeguarding of complainants' personal data by complaint service managers, lack of integration and inactivity of certain government agencies in complaint management, constrained human resources, and inadequate facilities and infrastructure (Astuti, 2.
Furthermore, there is minimal commitment from institutional leaders and regional governments, limited outreach efforts, and the continued use of disparate complaint management applications by individual agencies and regional governments.
As highlighted by Indiyati.
Kurniawan, and Choirunnisa .
, employees also express concerns about the absence of rules specifying job responsibilities, overtime regulations, and the work reporting system.
Similarly by Sanjaya.
Wibisono, and Sajiyo .
, work motivation is identified as a crucial factor in achieving optimal results for employees.
Addressing these challenges is vital for enhancing the efficiency and effectiveness of the public service complaint management system.
In order to address the realization of quality and fair public services, the digital transformation of public services has been carried out by implementing the no wrong door policy, connecting and integrating all public service complaint managers in government agencies at both central and regional levels in Indonesia, presenting the National Public Service Complaint Management System.
People's Online Aspiration Service (SP4N-LAPOR!) where the Ombudsman of the Republic of Indonesia acts as a supervisory agency or President Joko Widodo in his speech at the launch of the 2021 Ombudsman of the Republic of Indonesia annual report book stated that public service is the concrete face of the state's presence in people's daily The state is said to be present if it is able to provide excellent public services, which are fast, professional and fair.
Realizing excellent public services requires sustainable efforts, requires system transformation, requires governance, requires a change in the mindset and work culture of our bureaucracy from a culture of being happy to be served to a culture of service.
Once again, this is a big work for us together.
It requires participation from all elements of society and also requires supervision from the Ombudsman of the Republic of Indonesia, both in the form of input, in the form of criticism and support so that public services in our country become increasingly high quality.
Starting from the description above, this research wants to describe the implementation of the digital transformation of SP4N-LAPOR! at the Ombudsman of the Republic of Indonesia and explains what factors challenges are in managing the digital transformation process for managing public services through the SP4N-LAPOR! application in the Ombudsman of the Republic of Indonesia is one of the dominant factors.
The aim of this research is to analyze the implementation process and factors that are challenges in the digital transformation of public service management through the SP4N-LAPOR! in the Indonesian Ombudsman is one of the dominant factors.
This research has the benefit of being an evaluation material for the Indonesian Ombudsman and/or the Ministry for Administrative Reform and 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Bureaucratic Reform as well as Ministries/Institutions/Regional Governments in terms of managing SP4N-LAPOR!.
Apart from that, it can be used as a recommendation to support the success and development of SP4N-LAPOR!.
Literature review Digital Transformation Although the specific definition of digital transformation has not been agreed upon by researchers.
Almost all activity processes carried out by humans have been transferred to digital media.
Digital transformation, in general, can be interpreted as a radical process that occurs in an organization in utilizing technology, human resources and business processes which causes the business performance of the organization to change drastically (Nurhikmah & Fasa, 2.
This is in line with what was conveyed by Wulandari et al.
who defined digital transformation as the radical use of digital technology to improve performance or achieve company goals.
Apart from that, digital transformation can be interpreted as the process of utilizing existing digital technology such as virtualization technology, mobile computing, cloud computing, integration of all existing systems in the organization and so on (Loonam.
Eaves.
Kumar, & Parry, 2.
There are also those who interpret it as the impact obtained from the use of a combination of digital innovations resulting in changes to the structure, values, processes, positions or ecosystems within the organization and the environment outside the organization (Hinings.
Gegenhuber, & Greenwood, 2.
The need for digital transformation in the era of technological disruption is motivated by conditions where companies are increasingly dependent on technology to make business processes and operations more efficient (Osmundsen.
Iden, & Bygstad, 2.
These factors are changes in regulations, changes in the competitive landscape, shifts/changes from manual to digital forms, and changes in consumer behavior and expectations.
Digital transformation is the process by which organizations adopt digital technologies to change the way they operate, interact with customers and provide added value (Norliani et al.
, 2.
So digital transformation can generally be interpreted as a process implemented by an organization to radically integrate digital technology in all areas of business.
Digital transformation also brings many new challenges to organizations and requires them to be more careful than in previous eras.
This process can fundamentally change an organization's pattern of delivering results to customers.
Companies are adopting innovative digital technologies to make cultural and operational changes that better adapt to changing customer demands (Panjaitan & Lupiana.
Examples of digital transformation, namely companies starting to build digital solutions .
obile applications or electronic trading platform.
, companies migrating from on-premises computer infrastructure to cloud computing, companies adopting smart sensors to reduce operating costs.
The use of the term digital transformation explains the implementation of new technology, talent and processes in order to remain competitive in a technological landscape that is always changing very quickly.
There is a need for digital transformation because implementing digital transformation is needed by companies to be able to increase costs and improve efficiency (Bobro.
Lisova.
Parfentieva.
Dmytrovska, & Kyrylenko, 2.
Digital transformation also has various benefits that can be felt by companies or organizations such as increasing productivity, improving customer experience, reducing operational costs (Pohan & Yosepha.
Benefits related to increasing productivity include the emergence of cloud service technology which can save time and increase efficiency in all types of business processes.
Apart from that, there is also data analytics with machine learning which can provide insights to achieve business goals more Meanwhile, the benefits related to improving customer experience are evident in the postpandemic era, where customers want constant service availability across many channels.
Apart from that, there is also a demand for websites and communications that are easy and comfortable to use on mobile devices.
Finally the benefit of digital transformation is that it reduces ongoing operational costs significantly.
This can optimize existing business processes and reduce costs such as: equipment maintenance, logistics and shipping, energy costs, human resources costs and customer support costs (Yulianto & 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Wulandari, 2.
In particular, cost savings due to digital transformation can help to do things like:
eliminate or replace certain resource-intensive workflows, reduce costs on expensive infrastructure and equipment through managed services and cloud computing.
The benefits of digital transformation above are in line with what Hendarsyah .
said that the benefits of transformation are to increase competitiveness, increase flexibility and market reach, as well as increase the effectiveness and efficiency of operational processes.
Digitization essentially refers to the process of taking analog information and encoding it into zeros and ones so that computers can store, process, and transmit this information as conveyed (Bloomberg, 2.
Digitalization is defined as the utilization of digital technology to transform the socio-technical structure referring to social .
uman interactions, relationships, norms, etc.
) and technical aspects .
echnology, tasks, routines, etc.
) (Govers & Amelsvoort, 2.
Digital technology as Hanelt.
Bohnsack.
Marz, and Antunes Marante .
identifies social media, mobile, analytics, cloud, and Internet of Things (SMACIT) as fundamental driving forces for digital business transformation.
Firican .
explain that the existence of digital culture can be recognized if there are emerging changes capable of transitioning from analog culture to digital culture, with benchmarks in the following three categories: customer and demand, organization and human resources, and attitude and work approach.
According to Lawrence Lessig, a law professor and activist who has contributed in the context of law and technology, the "Code" theory by Lawrence Lessig states that in the era of information and communication technology, law and technology play a crucial role in shaping and directing human life.
Lessig .
states that: "Cyberspace teaches a new threat to libertyAThus, four constraints regulate this pathetic dot--the law, social norms, the market, and architecture-and the 'regulation' of this dot is the sum of these four constraints.
Government has a range of tools that it uses to regulate.
Cyberspace expands that range.
The code of cyberspace is becoming just another tool of state regulation.
" There are primary driving factors that he refers to as "digital transformation regulatory forces.
" He argues that there are four major regulatory forces that influence the behavior of individuals and companies in the context of digital transformation.
The driving factors of digital transformation regulation according to Lawrence Lessig are Code, law, market, and culture.
According to Lessig, these four driving factors interact with each other and mutually influence behavior and regulate digital transformation.
Their influence can take place in more centralized forms such as government laws and regulations, or in more decentralized forms such as regulation through code and market forces.
Lessig emphasizes the importance of understanding these regulatory forces in the context of digital transformation, so that appropriate regulations can be applied to maintain a balance between relevant interests and values, including privacy protection, security, and fairness in technology use.
E-government E-governmentis the use of information and communication technology formed from a management system and work activities in the government environment.
E-government by Irawan and Saputro .
states that e-government is an effort to create an atmosphere of government services that is in accordance with the common objectives .
hared goal.
of a number of interested communities, therefore the vision launched must also reflect the shared vision of related stakeholders, such as improving the productivity and operational performance of the government in serving the community, promoting a clean and transparent government, improving the quality of people's lives through the performance of public services, ensuring the creation of democratic state administration.
In the journal Habibullah .
e-government can also be understood as the use of technology based on WEB .
, internet communication and in certain cases interconnection applications to facilitate communication and expand access to and/or from government service providers and information to residents, businesses, job seekers and other governments, both institutional as well as between countries.
Irawan .
quotes that e-government is "E-government refers to the use by government agencies of information technologies .
uch as wide area networks, the internet, and mobile computin.
that have the ability to transform relations with citizens, businesses, and other arms of 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 governmentAy.
Meanwhile e-government .
according to Ayuningtiyas .
it is a new interaction mechanism between government, society and other interested groups which involves the use of information technology with the aim of improving the quality of services that are currently running.
The implementation of e-government innovation has been widely conducted in Indonesia, albeit with varying degrees of success.
E-government implementation not only involves the use of technology but also relies on good regulations and policies.
Several issues contributing to the failure of e-government development and implementation include the lack of readiness of human resources, available information technology, and insufficient attention from directly involved parties.
Presidential Instruction Number 3 of 2003 regarding the Policy and National Strategy for e-Government Development explains that there are 5 .
success factors or readiness factors in implementing egovernment in governance, namely: e-leadership, information network infrastructure, information management, business environment, and human resources.
E-leadership is defined as virtual leadership, which is leadership that directs people from a distance to accomplish tasks to achieve organizational goals.
The strategic alignment theory by identifies four critical domains for aligning business and Information Technology (IT) strategies.
The four domains driving e-leadership capabilities are Strategy execution alignment.
Technology transformation alignment.
Competitive potential alignment, and Service-level alignment.
Information technology infrastructure is a combination of a set of hardware, software, computer networks, facilities, and others .
ncluding all information technolog.
, to develop, test, provide, monitor, and control information technology services.
There are several indicators that can be used to measure the quality of digital-based public services, namely Efficiency as the quality of information/service.
Reliability as the accessibility of services.
Trust as the extent to which services can be trusted to be safe from interference and protect personal information, and Community Support.
The influential factors on the success of digital businesses in achieving their business goals are Market complexity.
Customer empowerment.
Dynamics of digitalization and innovation, and Convergence and technology.
The four digital skills that need to be developed to support digital transformation in companies are digital literacy, data literacy, technical skills, and digital threat awareness.
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 The research model for this study is shown below in figure 1.
Figure 1.
Research Model, 2023 Research methods The research method employed in this study is a quantitative approach.
According to Sugiyono .
, quantitative research method can be defined as a research method based on positivism philosophy, used to investigate a specific population or sample, with sampling techniques generally conducted randomly, collection and use of research instruments, quantitative/statistical data analysis aimed at testing established hypotheses.
Quantitative research is defined as research based on an assumption where from that assumption variables that will affect will be determined, which will then be analyzed using valid research methods.
The research instrument uses a scale, specifically an ordinal scale, aimed at providing information in the form of values for responses.
Certain variables can be measured by measurement instruments in the form of ordinal scale questionnaires containing Likert scale statements.
The research instrument is measured using the Likert scale standard.
The author's research uses a 5-point Likert scale with the following Likert scale standards:
Table 1.
Linkert Scale Value Scale Strongly Disagree Disagree Netral Agree Strongly Agree To reveal the factors that challenge the digital transformation of public service complaint management through the SP4N-LAPOR! application at the Indonesian Ombudsman, the author uses a quantitative 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 approach using the Exploratory Factor Analysis (EFA) factor analysis technique.
Factor analysis is a technique that looks for factors that are able to explain the relationship between various independent indicators that are observed.
Through factor analysis, we can find out what factors have the most influence on the digital transformation challenges of SP4N-LAPOR! at the Indonesian Ombudsman.
Determination of the number of factors is done by extracting or reducing a number of variables into a smaller set of new variables or factors.
Hair et al.
mention that several approaches that can be used in determining the number of formed factors include the characteristic root approach, the percentage of variance approach .
, and the scree test approach.
The number of factors formed from this study will be determined by the approach based on the percentage value of variance or eigenvalue .
he amount of variance explained by each facto.
and also the scree test approach.
Based on the Latent Root Criterion, only factors with a minimum latent root .
of 1 will be retained.
This means that a factor can be considered significant if it can explain at least one variable's variation or each variable contributes a value of 1 to the total eigenvalues.
Thus, only factors with eigenvalues > 1 are considered significant.
Testing is conducted for validity and reliability.
In validity testing, valid means the instrument can measure what should be measured.
Data obtained from the study are empirical data with specific validity criteria.
Validity indicates the degree of accuracy between the actual data occurring on the object and the data that can be collected by the researcher (Sugiyono, 2.
For testing.
Pearson formula is used with the following assessment standards:
Table 2.
Validity Assessment Standard Category Good Acceptable Margin Poor Value 0,50 0,30 0,20 0,10 As for the reliability test, a reliable instrument is one that, when used several times to measure the same object, will produce the same data.
The technique used is the Cronbach's Alpha technique with the following assessment standards.
Table 3.
Relability Assessment Standard Category Good Acceptable Margin Poor Value 0,80 0,70 0,60 0,50 The calculation is computed using computer assistance with SPSS (Statistical Product and Service Solutio.
As defined by Sugiyono .
, population is the generalization area consisting of objects/subjects that have certain qualities and characteristics determined by the researcher to be studied and then conclusions drawn.
The population in this study consists of employees .
of the Indonesian Ombudsman both at the central office and representatives spread across 34 provinces in Indonesia.
The total number of employees .
The sample is a part of the total and characteristics owned by the population (Sugiyono, 2.
Calculation of the appropriate sample size, one of which uses the Slovin formula, is 202 respondents.
The sampling method used is Proportional Stratified Random Sampling technique.
This technique is used when the population has heterogeneous and proportionally stratified members/elements (Sugiyono, 2.
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Result and discussion Characteristics Respondents Descriptive Statistics The demographic information for the respondents is displayed in Tabel 1.
The number of employees or assistants of the Ombudsman based on respondents' age in this study is 202.
Out of these respondents, 62% are assistants aged 30-39, totaling 125 respondents, 22% are assistants aged 20-29, totaling 44 respondents, and the remaining 16% are assistants aged 40-59, totaling 16 respondents.
According to gender, 57% or 116 respondents are male assistants and 86 respondents, or 43%, are female respondents.
Based on the educational background, the employees or assistants who were respondents with a bachelorAos degree amounted to 59%, totaling 119 respondents, while those with a masterAos degree amounted to 41%, totaling 83 respondents.
Table 4.
Analysis of Demographic Profile of Respondents Variabel Item Age Total Male Gender Female Total Bachelor's Degree Education Background Master's Degree Total Associate Assistant Young Assistant Position Hierarchy Middle Assistant Senior Assistant Total <10 Tahun 10-20 Tahun Length of Service >20 Tahun Total Head Office Work Location Representative Office Total Source: Research Data of Respondents, 2023 Frequency Percent From the questionnaire responses, 56% of the respondents hold the position of Assistant Associate, with a total of 114 respondents.
38% or 76 respondents hold the position of Young Assistant, followed by 5% or 10 respondents hold the position of Middle Assistant, and the remaining 1% are Senior Assistant, totaling 2 respondents.
The distribution of respondents based on length of service indicates that 74% or 150 respondents have worked for less than 10 years, while 23%, totaling 47 respondents, have worked for 10-20 years, and the remaining 3%, totaling 5 respondents, have worked for more than 20 years.
Regarding the distribution of respondents based on work location, it is found that 16%, or 32 respondents, are located at the head office, while 84%, or 170 respondents, are located at representative Data and Validity Test A total of 202 questionnaires were distributed to 202 employees/assistants within the scope of the Indonesian Ombudsman.
The distribution of questionnaires was carried out randomly and manually with the assistance of the Ombudsman and all questionnaires were responded to and returned.
The number of samples of 202 samples has met the minimum required for carrying out factor analysis.
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 item is valid if the calculated r value > r table, where the calculated r is the value in the Corrected ItemTotal Correlation and the r table for the two-way test is a statistical provision with n 202 and alpha 0.
obtaining r table of 0.
The results of validity testing by comparing the calculated r value for each question item with the r table can be seen in the following Table 5.
Table 5.
Validity and Communalities Test Validity (Corrected Item-Total Correlatio.
Variabel Indicators R value table >0,138 Validity Item 1
Valid
Item 2
Valid
Item 3
Valid
Item 4
Valid
Item 5
Valid
Item 6
Valid
Item 7
Valid
Item 8
Valid
Item 9
Valid
Item 10
Valid
Item 11
Valid
Item 12
Valid
Item 13
Valid
Item 14
Valid
Item 15
Valid
Item 16
Valid
Item 17
Valid
Item 18
Valid
Item 19
Valid
Item 20
Valid
Item 21
Valid
Item 22
Valid
Item 23
Valid
Item 24
Valid
Item 25
Valid
Item 26
Valid
Item 27
Valid
Item 28
Valid
Item 29
Valid
Item 30
Valid
Item 31
Valid
Item 32
Valid
Item 33
Valid
Item 34
Valid
Item 35
Valid
Item 36
Valid
Item 37
Valid
Item 38
Valid
Item 39
Valid
Item 40
Valid
Item 41
Valid
Item 42
Valid
Item 43
Valid
Item 44
Valid
Communalities Tabel
Initial Extraction >0,5 0,736 0,695 0,660 0,639 0,605 0,926 0,888 0,673 0,638 0,925 0,888 0,818 0,921 0,878 0,898 0,880 0,890 0,633 0,696 0,687 0,716 0,699 0,539 0,667 0,605 0,648 0,729 0,625 0,564 0,588
0,952
0,920
0,964
0,563
0,988
0,955
0,970
0,704
0,951
0,903
0,913
0,885
0,970
0,659
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Item 45 Item 46 Item 47 Item 48 Item 49 Item 50 Item 51 Source: SPSS.
25 Output on Research Data, 2023
Valid
Valid
Valid
Valid
Valid
Valid
Valid
0,691
0,548
0,618
0,585
0,706
0,582
0,757
Reliability Test The results of the data reliability test on 55 valid questions carried out using SPSS.
25 can be seen in Table 6.
Table 6.
Reliability Test Results Cronbanch's Alpha value Acceptable Terms Source: SPSS.
25 Output on Research Data, 2023 Information Reliable The results of the reliability test show that the Cronbach Alpha value is 0.
and it can be concluded that the instrument used is reliable with a very high reliability category > 0.
The results of the reliability test for each factor can be seen in Table 5.
Correlation Test Between Variables From the test results on 55 question items, the resulting KMO Measure of Sampling Adequacy was 878 (>0.
and the Bartlett test of sphericity was 0.
000 (<0.
so it was feasible to carry out further factor analysis.
The test results are presented in table 7 below.
Table 7.
Correlation Test Results Between Variables Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity Approx.
Chi-Square Sig.
Source: SPSS.
25 Output on Research Data, 2023
13728,108
0,000
Another test was carried out with anti-image matrices correlation by looking at the Measure Sampling Adequacy (MSA) value.
From the test results on 55 items, all of them passed the test because they had an MSA value > 0.
5 as stated in Table 8 below.
Table 8.
Anti-Image Matrices Correlation Results Item 1 Item 2 Item 3 Item 4 Item 5 Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item Item 6 Item Item Item Item Item 7 Item Item Item Item Item 8 Item Item Item Item 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Item 9 Item Item Item Item Item 10 Item 20 Item 30 Item 40 Item 50 Item Item Item Item Item Source: SPSS.
25 Output on Research Data, 2023 Factoring and Rotation To perform factoring and rotation, the steps involve using analysis based on the results of the communalities table, which explains how much a variable can or cannot explain a factor.
Each variable is considered capable of explaining a factor if the extraction obtained is greater than 0.
5 (> 0.
In the communalities table output in Tabel 5 above, the initial value is 1, which means that each variable is fully captured by the dimensional structure.
For the extraction value, all values are greater than 0.
5 (>
, which means that all variables are considered capable of explaining the factor.
Based on the output values in the communalities table, it can be concluded that the existing variables can be used to explain the formed factors, where the larger the communalities value, the closer its relationship with the formed Determination of the Number of Factors The determination of the number of factors in this study was done in two ways.
The first method involved interpreting the Scree Plot generated and using the diversity percentage approach .
The Scree Plot image generated from the SPSS.
25 output is as shown in the Figure 2 below.
Figure 2.
Scree Plot Source: SPSS.
25 Output on Research Data, 2023 The Scree Plot above illustrates the relationship between factors or variables and their eigenvalues.
The number of reduced factors can be determined by drawing a horizontal line at 1 eigenvalue against the component number.
The number of factors is determined from the plotted values above 1 eigenvalue >1.
From the plot above, it can be observed that the curve starts to flatten with eigenvalue >1 when 10 components are formed.
This indicates that 10 components or factors are the ideal number of factors causing the issue to be analyzed.
The second method involves using the total variance explained approach, i.
, the percentage of diversity or eigenvalue.
The number of factors is determined by the number of variables that obtain 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 eigenvalues >1 after factor extraction.
The total variance explained in the Tabel 9 bellow can be analyzed in two ways to explain a variance.
First, through initial eigenvalues analysis.
Based on the initial eigenvalues analysis, it is evident that 10 factors are formed from the 55 analyzed variables, as seen from the total eigenvalues greater than 1 (>.
Second, through extraction sums of squared loadings analysis, which indicates the amount of variation or the number of factors that can be formed.
From the output table of total variance explained above, 10 factors are obtained.
Table 9.
Total Variance Explained Initial Eigenvalues Component % of Cumulative % Variance 19,212 34,932 34,932 5,110 9,291 44,222 4,625 8,410 52,632 3,071 5,584 58,216 2,325 4,227 62,443 1,927 3,503 65,946 1,798 3,269 69,215 1,368 2,487 71,701 1,240 2,255 73,957 1,120 2,037 75,994
0,992
1,803
77,797
0,949
1,725
79,522
0,837
1,521
81,043
0,783
1,424
82,467
0,006
0,010
99,996
0,002
0,004
100,000
Source: SPSS.
25 Output on Research Data, 2023
Total
Extraction Sums of Squared Loadings % of Total
Cumulative % Variance 19,212 34,932 34,932 5,110 9,291 44,222 4,625 8,410 52,632 3,071 5,584 58,216 2,325 4,227 62,443 1,927 3,503 65,946 1,798 3,269 69,215 1,368 2,487 71,701 1,240 2,255 73,957 1,120 2,037 75,994
Distribution of Variables in Factors and Factor Rotation After forming 10 factors, the next step is to distribute the 55 question items into the 10 factors based on their factor loadings using a rotated component matrix with the varimax rotation method so that all variables can be filled into the 10 factors that are formed optimally, and the factor matrix looks simpler.
The results of factor rotation can be seen in Table 10.
Table 10.
Rotate Component Matrix
Item1
Item2
Item3
Item4
Item5
Item6
Item7
Item8
Item9
Item10
Item11
0,194
0,231
0,107
0,208
0,213
0,237
0,205
0,088
0,049
0,223
0,888
0,149
0,207
0,133
0,028
0,244
0,138
0,163
0,035
0,239
0,129
0,174
0,216
0,092
0,120
0,139
0,071
0,286
0,293
0,158
0,128
0,277
0,183
0,014
0,173
0,619
0,646
0,521
0,847
0,803
0,662
0,555
0,853
0,152
Component 0,280 0,156 0,365 0,138 0,341 -0,003 -0,070 0,116 0,061 0,129 0,066 0,155 0,074 0,190 0,212 0,035 0,061 0,135 0,075 0,150 0,030 0,043 0,127 -0,014 0,215 0,223 0,389 -0,043 -0,030 0,007 0,031 -0,052 0,062 0,132 0,088 0,024 0,102 0,057 0,082 0,115 0,055
0,127
0,083
-0,015
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133
0,702
0,098
-0,167
-0,028
0,005
-0,006
-0,002
0,332
0,398
-0,008
0,056
-0,027
0,625
0,205
0,281
0,220
-0,121
-0,182
0,203
0,239
-0,128
-0,034
Component Item12 0,861 0,168 0,174 0,033 0,103 -0,003 Item13 0,894 0,177 0,148 0,129 0,053 0,174 Item14 0,856 0,131 0,122 0,290 0,027 0,084 Item15 0,898 0,166 0,129 0,135 0,073 0,132 Item16 0,864 0,163 0,167 0,115 0,096 0,223 Item17 0,893 0,169 0,062 0,156 0,092 0,070 Item18 0,156 0,130 0,106 0,114 0,185 0,353 Item19 0,115 0,180 0,270 0,019 0,281 0,297 Item20 0,113 0,258 0,264 0,032 0,128 0,335 Item21 0,126 0,119 0,281 0,103 0,223 0,182 Item22 0,106 0,147 0,159 0,041 0,112 0,066 Item23 0,067 0,113 0,266 0,160 0,630 0,063 Item24 -0,061 0,185 0,027 0,177 0,656 0,309 Item25 0,119 0,221 0,089 0,065 0,587 0,141 Item26 0,110 -0,084 0,204 0,010 0,633 0,262 Item27 0,097 0,111 0,109 0,319 0,536 0,133 Item28 0,130 0,104 0,211 0,071 0,709 0,028 Item29 0,088 0,170 0,286 0,005 0,583 0,171 Item30 -0,020 0,137 0,166 -0,030 0,536 0,362 Item31 0,181 0,925 0,026 0,173 0,087 0,100 Item32 0,199 0,904 0,061 0,097 0,156 0,094 Item33 0,174 0,929 0,058 0,020 0,114 0,172 Item34 0,151 0,422 0,148 0,092 0,199 0,420 Item35 0,184 0,947 0,043 0,111 0,101 0,127 Item36 0,182 0,929 0,030 0,155 0,093 0,112 Item37 0,172 0,933 0,043 0,115 0,108 0,144 Item38 0,109 0,261 0,635 0,135 0,298 0,253 Item39 0,179 0,040 0,878 0,146 0,152 0,228 Item40 0,173 0,026 0,846 0,344 0,129 0,062 Item41 0,160 0,015 0,867 0,264 0,174 0,115 Item42 0,204 -0,020 0,861 0,211 0,157 0,104 Item43 0,160 0,025 0,897 0,127 0,195 0,202 Item44 0,205 0,106 0,556 0,130 0,301 0,149 Item45 0,015 0,215 0,150 0,162 0,144 0,517 Item46 0,106 0,122 0,184 0,124 0,120 0,625 Item47 0,153 0,131 0,293 0,163 0,072 0,550 Item48 0,181 0,182 0,286 0,129 0,182 0,564 Item49 0,197 0,222 0,117 0,141 0,340 0,665 Item50 0,047 0,227 0,042 0,151 0,122 0,594 Item51 0,244 0,030 0,237 0,108 0,244 0,663 Item52 -0,064 0,155 0,187 0,037 0,130 0,019 Item53 0,094 0,058 0,064 0,050 0,118 0,364 Item54 -0,044 0,060 0,102 0,206 0,223 0,180 Item55 -0,089 0,065 0,043 0,122 0,226 0,145 Source: SPSS.
25 Output on Research Data, 2023
0,055
0,123
0,066
0,053
0,063
0,091
0,503
0,604
0,480
0,674
0,764
0,137
0,091
0,127
0,309
-0,078
0,080
0,169
-0,033
0,087
0,055
0,124
0,134
0,103
0,088
0,102
0,079
0,155
0,106
0,131
0,105
0,176
0,188
0,390
0,231
0,223
0,141
0,081
-0,139
0,261
0,203
0,255
-0,068
-0,058
-0,054
0,014
-0,012
0,008
0,017
-0,042
0,054
0,086
0,050
0,226
-0,061
0,020
0,184
0,302
0,087
0,008
0,122
0,204
0,163
0,042
0,075
0,075
0,141
0,059
0,045
0,068
0,046
0,146
0,029
0,039
0,022
0,099
0,246
-0,027
0,077
0,125
0,113
0,011
0,265
0,191
0,756
0,530
0,815
0,872
0,009
0,050
0,036
0,011
0,049
0,076
0,388
0,066
0,231
0,085
-0,036
0,037
0,065
0,143
-0,024
-0,105
0,145
0,069
0,092
0,035
0,086
0,054
0,024
0,041
0,027
0,057
0,193
0,062
0,002
0,009
0,044
0,034
0,264
0,390
0,094
0,258
0,193
0,108
-0,113
-0,148
-0,066
-0,237
0,198
0,147
0,009
0,029
0,128
0,054
0,029
0,082
0,053
0,181
0,350
0,005
-0,184
0,071
-0,159
0,194
0,116
0,520
0,032
0,019
0,293
0,063
0,022
0,028
0,278
0,042
0,040
0,059
-0,066
0,011
0,076
0,059
0,084
0,051
0,035
0,049
-0,006
0,159
0,030
-0,092
0,184
0,061
0,197
-0,136
-0,017
0,003
Naming the Factors Formed To give names to the factors formed as a result of matrix rotation, the ten factors are given names according to the characteristics of the question items or variables that form them.
There is no standard or standard reference in naming factors, for this reason, in naming the factors that are formed, appropriate justification is needed based on the characteristics of the existing variables.
After looking at the characteristics of the variables that form it, the author names the ten factors with names that can be seen in Table 11.
2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Table 11.
Naming of the Factors Formed Factor Factor Name Formed Eigen Value / Total Variance Factor 1 Digital Capability 19,212 / 34,932% Factor 2 Information Network Systems 5,110 / 9.
Factor 3 Complaint Management 4,625 / 8.
Factor 4 Digital Solutions 3,071/ 5.
Factor 5 Leadership 4.
2,325 / 4.
Factor 6 Business Ecosystem 1,927 / 3.
Factor7 Integrated Regulation 1,798 / 3.
Indicators Item 11 Item 12 Item 13 Item 14 Item 15 Item 16 Item 17 Item 31 Item 32 Item 33 Item 34 Item 35 Item 36 Item 37 Item 38 Item 39 Item 40 Item 41 Item 42 Item 43 Item 44 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item 10 Item 23 Item 24 Item 25 Item 26 Item 27 Item 28 Item 29 Item 45 Item 46 Item 47 Item 48 Item 49 Item 50 Item 51 Item 45 Item 18 Item 19 Item 20 Item 21 Item 22 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 Factor Loading Factor 8 Digital Talent 1,368 / 2.
Factor 9 Digital Conversion 1,240 / 2.
Factor 10 Digital Implementation 1,120 / 2.
Source: SPSS.
25 Output on Research Data, 2023 Item 52 Item 53 Item 54 Item 55 Item 1 Item 2 Discussion Based on the analysis of 55 variables using SPSS 25, the study identified 10 factors that challenge the digital transformation of managing public service complaints through the SP4N-LAPOR application.
These factors include Digital Capability.
Information Network Systems.
Complaint Management.
Digital Solutions.
Leadership 4.
Business Ecosystem.
Integrated Regulation.
Digital Talent.
Digital Conversion, and Digital Implementation.
Among these factors.
Digital Capability emerged as the most dominant, with an Eigenvalue of 19.
212 and accounting for 34.
932% of the total variance.
This finding underscores the importance of enhancing Digital Capability in public service management through the SP4N-LAPOR application to address the challenges of digital transformation effectively.
Conclusions Conclusion Factors that pose challenges in the digital transformation process of public service management through the SP4N-LAPOR! application at the Ombudsman of the Republic of Indonesia.
The challenging factors generated successively from the most dominant are Digital Capability.
Information Network Systems.
Complaint Management.
Digital Solutions.
Leadership 4.
Business Ecosystem.
Integrated Regulation.
Digital Talent.
Digital Conversion, and Digital Implementation, which means in this study indicates that the digital transformation of complaint management at the Ombudsman of Indonesia still faces various challenges.
This can hinder the efforts of the Ombudsman of Indonesia in improving the effectiveness and efficiency of public service complaint management.
Based on the 10 .
factors outlined above, the most dominant factor posing a challenge in conducting transformation at the Ombudsman of the Republic of Indonesia is Digital Capability because it can explain 34.
932% of the challenging factors that arise at the Ombudsman of the Republic of Indonesia when undergoing transformation towards the SP4N-LAPOR! application, which means digital capability at the Ombudsman of Indonesia needs to be improved.
Digital capability is the ability of individuals, organizations, or society to effectively use and utilize digital technology to achieve specific goals.
This includes skills, knowledge, understanding, and attitudes needed to interact with digital technology in a productive, innovative, and safe manner.
Limitations Although this study adds value to existing knowledge to serve as an evaluation and recommendation material in the digital transformation process of public service complaint management through the SP4N-LAPOR! application, especially at the Ombudsman of the Republic of Indonesia and other stakeholders, several limitations need to be mentioned.
First, the research aimed to identify the factors that pose challenges in the digital transformation of public service complaint management, so that future research can conduct more specific studies related to the appropriate and effective strategies to overcome these challenges and formulate strategies that can be implemented.
Second, the respondents of the current study are limited to employees/assistants of the Ombudsman of the Republic of Indonesia.
Suggestions For future research, comparative studies can be conducted to compare the perceptions of the Ombudsman of the Republic of Indonesia's employees with those of the public service users to obtain a broader and deeper perspective on the experiences and perceptions of the digital transformation of complaint management using the SP4N-LAPOR! application.
Third, this study is quantitative and uses survey-based questionnaires to collect data.
future research can employ more diverse research methods, such as mixed qualitative and quantitative methods.
This can provide a more comprehensive overview 2025 | Global Academy of Business Studies/ Vol 2 No 2, 117-133 of the factors challenging the digital transformation of public service complaint management using the SP4N-LAPOR! application at the Ombudsman of the Republic of Indonesia.
References