Journal of Management Economics and Financial Accounting e-ISSN: 3110-4924 p-ISSN: 3110-5475 The Effect of Digital Competence.
Work Discipline, and Work Motivation on Employee Performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency Aulia Rahma Dini 1*.
Kurniawaty Fitri 2, and Prima Andreas 3 Fakultas Ekonomi dan Bisnis.
Universitas Riau.
Pekanbaru.
Indonesia.
e-mail : aulia.
rahma3962@student.
2 Fakultas Ekonomi dan Bisnis.
Universitas Riau.
Pekanbaru.
Indonesia.
e-mail : kurniawaty.
fitri@lecturer.
3 Fakultas Ekonomi dan Bisnis.
Universitas Riau.
Pekanbaru.
Indonesia.
e-mail : prima.
andreas@lecturer.
* Corresponding Author : Aulia Rahma Dini Abstract: This study aims to analyze the effects of digital competence, work discipline, and work motivation on employee performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency.
The research problem focuses on the suboptimal level of employee performance amid increasing demands for public service quality and the growing use of digital technology, making an empirical investigation necessary to identify the determining factors and the most dominant variable influencing performance.
This study employs a quantitative approach using a survey method.
The research population includes all employees of BKPSDM Kampar Regency, with a total sample of 52 respondents selected using a census method.
Data were collected through questionnaires that had been tested for validity and reliability, and were analyzed using multiple linear regression with the assistance of SPSS version 25.
The results indicate that digital competence and work motivation have a positive and significant effect on employee performance, while work discipline has a positive but not statistically significant effect in partial testing.
Simultaneously, the three independent variables significantly influence employee performance, with work motivation identified as the most dominant factor.
These findings confirm that improving employee performance is not only dependent on digital technical competence but is also strongly influenced by work motivation, while work discipline functions as a supporting factor in task implementation.
Therefore, efforts to enhance employee performance at BKPSDM Kampar Regency should focus on strengthening work motivation and continuously developing digital competence.
Received: November 15, 2025 Revised: November 21, 2025 Accepted: December 23, 2025 Published: December 30, 2025 Curr.
Ver.
: December 30, 2025 Copyright: A 2025 by the authors.
Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY SA) license .
ttps://creativecommons.
org/licenses/by-sa/4.
Keywords: Employee Performance.
Digital Competence.
Work Discipline.
Work Motivation.
Introduction Human resources are the main determinant of organizational success, because organizational performance is largely determined by the ability of individuals to carry out their duties and responsibilities effectively .
In the public sector, employee performance has broader implications because it is directly related to the quality of public services and the accountability of government administration.
Along with the acceleration of globalization and bureaucratic reform, government organizations are required not only to operate administratively but also to be adaptive to technological changes and performance oriented .
The object of this study is the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency, a local government institution that plays a strategic role in managing civil servant human resources, ranging from workforce planning and personnel administration to the development of employee competencies.
As an institution responsible for the quality of public sector human resources.
BKPSDM is required to produce DOI : https://doi.
org/10.
69714/48ne1009 https://journal.
id/index.
php/jomefa Journal of Management Economics and Financial Accounting 2025 (Decembe.
vol 1 no 2 Dini, et al.
16 of 28 high performing employees in order to support effective governance and high quality public Previous studies indicate that employee performance is influenced by various internal organizational factors, such as competence, work discipline, and work motivation .
Most of these studies use a quantitative approach with survey methods and regression analysis to examine the relationships among variables.
This method is considered capable of providing empirical evidence of the statistical influence of independent variables on employee performance.
However, the majority of prior research including recent international studies has predominantly focused on organizational settings where digitalization is nascent or examined from a citizen-centric perspective.
For instance, previous research .
highlighted the role of digital competence in improving service quality in broader public sector contexts, while other studies .
emphasized the critical gap in digital skills for digital transformation in government agencies.
Despite these findings, there is a scarcity of empirical literature examining how these digital competencies interact with traditional performance determinants like work discipline within a local government agency that has undergone a complete digital transformation (SPBE).
This study addresses this identified gap by focusing on the implementation of the ElectronicBased Government System (SPBE), as mandated by Presidential Regulation Number 95 of The transition to a fully digital work environment requires a fundamental reassessment of human resource readiness.
The specific novelty of this research lies in its investigation of the BKPSDM of Kampar Regency, an institution that has fully adopted national digital personnel applications (SIMPEGNAS).
Unlike traditional bureaucratic settings, this study posits that in a digitized ecosystem, the weight of digital competence may shift the impact of conventional factors like work discipline.
Therefore, this study aims to empirically analyze the determinants of employee performance in a high-intensity digital public sector environment.
The research problem arises from the phenomenon of declining employee performance quality at BKPSDM Kampar Regency.
Performance evaluation data for the period 2020 to 2024 show that although the number of employees has increased, the proportion of employees categorized as AuVery GoodAy has significantly decreased in 2024.
Most employees fall into the AuGoodAy category, indicating that they only meet performance standards without exceeding organizational expectations.
This condition suggests the existence of performance management problems that need to be examined more deeply.
On the other hand.
BKPSDM Kampar Regency has implemented a fully digital work system through the use of national personnel applications such as SIMPEGNAS.
However, interviews and pre survey results indicate that employeesAo digital competence is still not optimal.
This is consistent with previous research findings stating that digital competence has a significant effect on employee performance in the era of bureaucratic digitalization.
Digital competence is part of work competence that influences the effectiveness and efficiency of performance, especially in a technology based work environment.
Low digital competence has the potential to cause work delays, administrative errors, and low productivity.
In addition to digital competence, work discipline is also an important factor that influences employee performance.
Work discipline reflects the level of employee compliance with rules, procedures, and working time.
Although it is regulated in Law Number 5 of 2014 on Civil Servants and its implementing regulations, disciplinary violations still frequently occur in many government institutions.
Data from the National Civil Service Agency (BKN) recorded thousands of cases of civil servant disciplinary violations throughout 2023.
Similar conditions are also found at BKPSDM Kampar Regency, such as lateness and ineffective use of working time, which can potentially reduce employee performance.
Another factor that also influences employee performance is work motivation.
Motivation is the main driving force that directs employeesAo energy and potential to work productively in achieving organizational goals.
The results of the pre survey indicate that the level of work motivation among employees of BKPSDM Kampar Regency is still relatively low, especially in terms of rewards, job challenges, and trust in decision making.
Low motivation has Journal of Management Economics and Financial Accounting 2025 (Decembe.
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17 of 28 the potential to reduce employeesAo initiative, responsibility, and commitment to their work.
Based on these phenomena, this study proposes a quantitative approach to analyze the effects of digital competence, work discipline, and work motivation on employee performance at BKPSDM Kampar Regency.
This study is expected to provide empirical evidence regarding the dominant factors that influence employee performance in a fully digitalized government work environment.
The contributions of this study include: .
a theoretical contribution by enriching the literature on public sector human resource management related to the integration of digital competence, work discipline, and work motivation in influencing employee performance.
an empirical contribution in the form of evidence from a local government institution that has implemented a digital based work system.
a practical contribution as a reference for policymakers in formulating strategies to improve civil servant performance.
Preliminaries or Related Work or Literature Review Employee Performance Employee performance is the work outcome achieved by an employee in carrying out the duties and responsibilities assigned to them.
Performance refers to employee work results viewed in terms of quality, quantity, working time, and cooperation in achieving organizational goals.
Performance is the quality and quantity of work achieved by employees in accordance with the responsibilities assigned to them.
Based on these views, employee performance can be defined as the results of task execution measured through the quality and quantity of work, the timeliness of task completion, and the ability to work collaboratively in achieving organizational goals.
In government organizations, employee performance plays a crucial role because it is directly related to the quality of public services.
Good performance reflects the ability of employees to carry out their duties in a professional, accountable, and responsible manner.
Therefore, improving employee performance is a primary objective in human resource management, especially in government institutions such as the BKPSDM of Kampar Regency.
2 Digital Competence Digital competence refers to employeesAo ability to use and utilize digital technologies to support the execution of their work.
Digital competence includes the ability to operate hardware and software, to understand and critically evaluate information, to collaborate online, and to maintain digital security and privacy .
Digital competence is also related to the mastery of digital technologies, products, and services.
Competence is acquired through education, training, and work experience, which enable employees to produce performance.
Based on these views, it can be concluded that digital competence is the ability possessed by employees to use digital technology effectively in carrying out their work tasks and responsibilities.
Digital competence enables employees to operate application-based work systems more easily, speeds up task completion, and reduces errors in data processing.
Employees with good digital competence are able to work more effectively and efficiently, thereby producing more optimal performance.
In contrast, limited digital competence can hinder task completion and reduce the quality of work outcomes.
Digital competence influences employee performance .
Other research results also show a positive and significant influence between digital competence and performance.
Thus, the higher the employeesAo digital competence, the better the performance they produce.
Journal of Management Economics and Financial Accounting 2025 (Decembe.
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18 of 28 3 Work Discipline Work discipline refers to the attitudes and behaviors of employees in complying with the rules and regulations that apply within the organization .
Work discipline is the attitude, behavior, and actions that are in accordance with both written and unwritten rules, the violation of which is subject to sanctions .
Work discipline is used to build employeesAo awareness in complying with organizational rules.
It reflects the level of individual compliance with work norms and regulations .
Based on these definitions, work discipline can be concluded as the attitude of obedience and compliance of employees toward the rules, procedures, and work norms that apply within the organization.
Work discipline creates order in the implementation of employeesAo duties and responsibilities.
Disciplined employees work according to the established schedule, follow work procedures, and take responsibility for their tasks.
This encourages improvements in both the quality and quantity of work results, which in turn has a direct impact on employee performance.
Work discipline has a positive and significant effect on employee performance.
Thus, the better the employeesAo work discipline, the higher the performance that will be achieved.
4 Work Motivation Work motivation is the drive that encourages a person to strive and persist in completing It generates the intensity, direction, and persistence of individuals in achieving goals.
Work motivation influences individual behavior at work.
Work motivation is a fundamental drive that moves a person to devote energy and effort in order to achieve goals.
Based on these views, work motivation can be concluded as an inner drive that encourages employees to work seriously in order to achieve work goals.
Work motivation stimulates employees to show enthusiasm, responsibility, and commitment toward their assigned tasks.
Employees with high work motivation will strive to achieve optimal work results, whereas low work motivation can reduce employee performance.
Work motivation has a positive and significant effect on employee performance.
Therefore, the higher the employeesAo work motivation, the better the performance that will be Based on the above explanation, the hypotheses of this study are formulated as follows:
HCA: Digital competence has a positive and significant effect on employee performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency.
HCC: Work discipline has a positive and significant effect on employee performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency.
HCE: Work motivation has a positive and significant effect on employee performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency.
HCE: Digital competence, work discipline, and work motivation simultaneously have a positive and significant effect on employee performance at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Kampar Regency.
Digital Competency Employee Performance Work Discipline Work Motivation Figure 1.
Research model Journal of Management Economics and Financial Accounting 2025 (Decembe.
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19 of 28 Proposed Method This study employs a quantitative research method with an associative approach, which aims to analyze the relationships and effects between independent variables and the dependent The quantitative approach is chosen because it allows the measurement of research variables in numerical form and enables objective hypothesis testing through statistical analysis.
The object of this study is employees of the Regional Personnel and Human Resource Development Agency (BKPSDM) of Kampar Regency.
The study focuses on the effects of digital competence, work discipline, and work motivation on employee performance.
The variables in this study were measured using a 5-point Likert Scale, ranging from score 1 (Strongly Disagre.
to score 5 (Strongly Agre.
To facilitate interpretation, the mean scores are classified into specific intervals: 1.
00Ae1.
80 (Very Lo.
, 1.
81Ae2.
60 (Lo.
, 2.
61Ae3.
40 (Moderat.
, 3.
41Ae4.
20 (Hig.
, and 4.
21Ae5.
00 (Very Hig.
This classification ensures a clear understanding of the respondents' perception levels regarding digital competence, discipline, and 1 Operations Definitions of Variables Table 1.
Operations Definitions of Variables Variable Employee Performance (Y) Digital Competence (XCA) Work Discipline (XCC) Work Motivation (XCE) Definition Indicators It is the work result in terms of quality and quan- 1.
Work results tity achieved by an employee in carrying out their 2.
Work behavior duties in accordance with the responsibilities (Performance achievement indicators of given to them.
(Mulyani et al.
, 2.
BKPSDM Kampar Regenc.
A person's ability to use, understand, and partici- 1.
Knowledge pate effectively in a digital environment, such as 2.
Skills operating hardware and software, understanding 3.
Usage and assessing information critically, collaborating 4.
Speed online, and maintaining security and privacy in the 5.
Quality digital space.
(Liana et al.
, 2.
(Marguna and Sangiaserri, 2.
Attendance and punctuality An attitude, behavior, and action that is in accord- 2.
Compliance with Work Rules and Proance with both written and unwritten regulations, where violations will result in sanctions or punish- 3.
Work productivity (Arisanti et al.
, 2.
Work quality (Nawir et al.
, 2.
Achievement Recognition Work motivation is the basic drive that moves a Responsibility person or the desire to devote all energy due to a The work itself specific goal.
(Fauziah et al.
, 2.
Advancement (Wuldanari and Bagia, 2.
Scale Likert Likert Likert Likert 2 Population and Sample The population in this study consists of all employees of the BKPSDM of Kampar Regency, totaling 52 individuals.
Given the relatively small population size, a census sampling technique was applied, in which all members of the population were included as research respondents.
Therefore, the sample size of this study is 52 respondents.
The use of the census method is expected to provide a more comprehensive and accurate representation of the actual conditions of the research variables and to minimize potential bias that could arise from selecting only a portion of the population.
3 Data Collection Technique The data in this study were collected using a questionnaire developed based on the indicators of each research variable.
The questionnaire was distributed directly to all respondents and completed according to each employeeAos perceptions and work experience.
Each statement in the questionnaire was measured using a Likert scale, which is intended to assess the level Journal of Management Economics and Financial Accounting 2025 (Decembe.
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20 of 28 of respondentsAo agreement with the given statements.
This scale allows the collected data to be processed quantitatively for statistical analysis.
4 Data Analysis Technique The initial stage involved testing data quality, including validity and reliability tests, to ensure that the research instruments were appropriate for use.
Next, classical assumption tests were conducted as prerequisites for multiple linear regression analysis, consisting of normality, multicollinearity, and heteroscedasticity tests.
After all assumptions were met, multiple linear regression analysis was carried out to determine the effects of digital competence, work discipline, and work motivation on employee performance.
Hypothesis testing was performed using partial tests .
-test.
to examine the effect of each independent variable individually, and a simultaneous test (F-tes.
to examine the joint effect of the independent variables on the dependent variable.
In addition, the coefficient of determination (RA) was used to determine the extent to which the independent variables explain the variation in employee performance.
Results Data analysis in this study was conducted using the Statistical Package for the Social Sciences (SPSS) version 25 as statistical software to process and analyze the research data.
The data were obtained from questionnaire responses completed by 52 respondents, representing all employees of the BKPSDM of Kampar Regency.
The collected data were then processed through several stages of statistical analysis, including validity testing, reliability testing, and multiple linear regression analysis to examine the effects of digital competence, work discipline, and work motivation on employee performance as well as to test the research hypotheses.
Data Quality Testing Before hypothesis testing was conducted, the research data were first examined to ensure the adequacy of the instruments used.
Data quality testing in this study included validity and reliability tests for all statement items of each research variable.
1 Validity Test The validity test aims to determine the extent to which each statement item is able to accurately and consistently measure the intended variable.
If the calculated r value > r table value, the statement item is considered valid and suitable for further analysis.
Table 2.
Validity Test Results Variable Item r Calculated Comparison r Table (N = 52, = 0.
Decision Employee Performance Y1.
0,660
0,273
Valid Y1.
0,893
0,273
Valid Y1.
0,813
0,273
Valid Y1.
0,839
0,273
Valid Y1.
0,862
0,273
Valid Y1.
0,876
0,273
Valid Y1.
0,891
0,273
Valid Y1.
0,579
0,273
Valid XCA.
0,959
0,273
Valid XCA.
0,932
0,273
Valid XCA.
0,857
0,273
Valid XCA.
0,912
0,273
Valid XCA.
0,923
0,273
Valid Digital Competence Journal of Management Economics and Financial Accounting 2025 (Decembe.
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Work Discipline Work Motivation 21 of 28 XCC.
0,723
0,273
Valid XCC.
0,825
0,273
Valid XCC.
0,776
0,273
Valid XCC.
0,813
0,273
Valid XCE.
0,919
0,273
Valid XCE.
0,907
0,273
Valid XCE.
0,831
0,273
Valid XCE.
0,947
0,273
Valid XCE.
0,878
0,273
Valid The results of the validity test indicate that all indicators of digital competence, work discipline, work motivation, and employee performance have correlation values that meet the established criteria.
therefore, all statement items are declared valid.
2 Uji Reliabilitas In this study, reliability was measured using CronbachAos Alpha.
Reliability testing was conducted by calculating the CronbachAos Alpha value for each instrument within a variable.
An instrument is considered reliable if the CronbachAos Alpha value is > 0.
Table 3.
Reliability Test Results Variable CronbachAo Alpha Critical Value Conclusion Employee Performance 0,916 0,70 Reliable Digital Competence 0,935 0,70 Reliable Work Discipline 0,779 0,70 Reliable Work Motivation 0,938 0,70 Reliable The results of the reliability test show that each variable has a good level of internal consistency, with reliability coefficient values exceeding the minimum required threshold.
therefore, the research instruments are considered reliable.
Classical Assumption Tests Classical assumption tests were conducted to ensure that the data met the required statistical These tests aim to determine whether the regression model used in this study produces accurate, unbiased, and efficient estimates.
Three types of classical assumption tests were performed, namely the normality test using a histogram and normal PAeP plot, the multicollinearity test, and the heteroscedasticity test.
1 Normality Test 1 Histogram Graph Figure 2.
Normality Test Results (Histogram Grap.
Journal of Management Economics and Financial Accounting 2025 (Decembe.
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22 of 28 The results show that the frequency distribution of the Regression Standardized Residual forms a symmetric bell-shaped pattern, with the highest concentration of frequencies .
he pea.
located around the zero mean value.
This bell-shaped curve indicates that the residual data are normally distributed.
Therefore, based on visual analysis, the assumption of residual normality in the regression model is satisfied.
2 Normal PAeP Plot Figure 3.
Normality Test Results (PAeP Plo.
The normality test of the residuals using the Normal PAeP Plot aims to compare the cumulative distribution of the model residuals with the theoretical normal distribution.
Based on the graph, the residual data points .
ray circle.
are distributed along and close to the diagonal line .
lack straight lin.
Although there are slight deviations at some points, particularly in the middle and upper sections, the overall pattern does not show a systematic departure from the diagonal line.
Therefore, based on the visual interpretation of the PAeP Plot, it can be concluded that the assumption of residual normality has been satisfied.
2 Multikolinearity Test The purpose of the multicollinearity test is to identify and address multicollinearity problems that may cause regression coefficients to be unstable.
The testing methods used are the Variance Inflation Factor (VIF) and tolerance.
This test is conducted by examining the Tolerance and Variance Inflation Factor (VIF) values.
The criteria used are that the Tolerance value must be > 0.
10 and the VIF value must be < 10.
Table 4.
Multicollinearity Test Results Coefficients Model
Tolerance Digital Competence 0,112 Work Discipline 0,476 Work Motivation 0,127 Dependemt Variable: Employee Performance VIF
8,921
2,100
7,889
Based on these results, all independent variables have VIF values < 10 and tolerance values > 0.
Therefore, it can be concluded that this regression model is free from multicollinearity problems, and the classical assumption of multicollinearity has been satisfied.
3 Heteroscedasticity Test The heteroscedasticity test aims to examine whether there is inequality of variance in the residuals from one observation to another.
This test is conducted visually using a scatterplot that plots the Regression Standardized Predicted Value (X-axi.
against the Regression Studentized Residual (Y-axi.
Journal of Management Economics and Financial Accounting 2025 (Decembe.
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23 of 28 Figure 4.
Heteroscedasticity Test Results (PAeP Plo.
The distribution of residual data points is random and does not form any clear pattern, such as waves, widening, or narrowing, and the points are scattered both above and below 0 on the vertical axis.
Therefore, since the residuals do not exhibit a systematic pattern, it is concluded that there is no heteroscedasticity problem in this regression model.
Thus, the classical assumption of heteroscedasticity has been satisfied.
3 Multiple Linear Regression Analysis After the classical assumption tests are satisfied, the analysis is continued by interpreting the Coefficients table to construct the multiple regression equation.
The tool used by the researcher is SPSS 25, applying the enter method in which all variables are included to examine the effect of the independent variables on the dependent variable through the regression of employee performance as the dependent variable and digital competence, work discipline, and work motivation as the independent variables.
The test results are presented in the table Table 5.
Results of Multiple Linear Regression Analysis Model Unstandardized Coefficients Coefficients Standardized Std.
Error
Coefficients Beta
0,666
0,077
0,357
(Constan.
13,774 Digital Com0,371 Work Disci0,017 0,062 Work Moti0,686 0,075 Dependemt Variable: Employee Performance Sig.
4,808
0,000
0,000
0,010
0,278
0,782
0,636
9,122
0,000
Based on the Unstandardized Coefficients (B), the multiple linear regression equation obtained is as follows:
Y = 13.
371XCA 0.
017XCC 0.
686XCE e Where:
Y = Employee Performance XCA = Digital Competence XCC = Disiplin Kerja XCE = Work Discipline = standar error Journal of Management Economics and Financial Accounting 2025 (Decembe.
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24 of 28 From the results of the multiple linear regression analysis, it can be concluded that:
The constant .
indicates that if Digital Competence (XCA).
Work Discipline (XCC), and Work Motivation (XCE) are equal to zero.
Employee Performance (Y) has a value of 13.
The coefficient of XCA .
means that for every one-unit increase in XCA (Digital Competenc.
, assuming XCC and XCE remain constant.
Employee Performance (Y) will increase by 0.
371 units.
This indicates that Digital Competence has a positive effect on performance.
The coefficient of XCC .
means that for every one-unit increase in XCC (Work Disciplin.
, assuming XCA and XCE remain constant.
Employee Performance (Y) will increase by 0.
017 units.
This implies that Work Discipline has a positive effect on employee performance.
The coefficient of XCE .
means that for every one-unit increase in XCE (Work Motivatio.
, assuming XCA and XCC remain constant.
Employee Performance (Y) will increase by 0.
686 units.
Thus.
Work Motivation has the strongest positive effect on employee performance compared to the other variables.
The standard error is a measure used to indicate the degree of deviation or sampling error of the estimated regression coefficients.
It shows how far the estimated results obtained from the sample may differ from the true values in the population.
In other words, the standard error is used to assess the accuracy of the regression coefficients produced in the research model 4 Hypothesis Testing The researcher conducted two types of hypothesis tests, namely the partial significance test .
and the simultaneous test (F-tes.
1 Partial Significance Test .
-tes.
The t-test is used to determine the partial effect of each independent variable on the dependent In other words, this test aims to examine whether each independent variable, namely Digital Competence (XCA).
Work Discipline (XCC), and Work Motivation (XCE), has a significant effect on Employee Performance (Y) at BKPSDM Kampar Regency.
If t calculated > t table and significance < 0.
05, then HCA is rejected and HCa is accepted, meaning that the independent variable has a significant effect on the dependent variable.
Conversely, if t calculated O t table or significance Ou 0.
05, then HCA is accepted and HCa is rejected, indicating that the independent variable does not have a significant effect The t table value in this study is based on df = n Oe k .
f = 52 Oe 4 = .
, resulting in a t table value of 2.
Table 6.
t Test Result Digital Competence Work Discipline Work Motivation t Calculated 4,808 Significance 0,000 t Table .
f=n-.
2,010 Decision Significant 0,278 9,122 0,782 0,000 2,010 2,010 Not Significant Significant Based on the t-test results presented in the table above, the findings can be explained as follows:
Digital Competence (XCA) has a t calculated value of 4.
808 > t table 2.
010 with a significance value of 0.
000 < 0.
05, indicating that Digital Competence has a significant effect on Employee Performance.
This means that the higher the employeesAo ability to understand and utilize digital technology, the better their performance.
Work Discipline (XCC) has a t calculated value of 0.
278 < t table 2.
010 with a significance value of 0.
278 > 0.
05, indicating that Work Discipline does not have a significant effect Journal of Management Economics and Financial Accounting 2025 (Decembe.
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25 of 28 on Employee Performance.
This finding suggests that although work discipline is descriptively assessed as good, statistically it has not been able to make a meaningful contribution to improving employee performance.
Work Motivation (XCE) has a t calculated value of 9.
122 > t table 2.
010 with a significance value of 0.
000 < 0.
05, indicating that Work Motivation has the strongest and most significant effect on Employee Performance.
This shows that both internal and external motivation among employees is a key factor in increasing work enthusiasm and productivity.
2 Simultaneous Test (F-tes.
The simultaneous test or F-test is used to determine whether all independent variables in this study, namely Digital Competence (XCA).
Work Discipline (XCC), and Work Motivation (XCE), jointly have a significant effect on the dependent variable, namely Employee Performance (Y).
If F calculated > F table and significance < 0.
05, then HCA is rejected and HCa is accepted, meaning that the independent variables simultaneously have a significant effect on the dependent variable.
Conversely, if F calculated O F table or significance Ou 0.
05, then HCA is accepted and HCa is rejected, indicating that there is no significant simultaneous effect (Ghozali, 2.
The results are presented as follows:
Table 7.
F-test Results Model Regression Residual Total Sum of Squares 862,5999 26,324 888,923 ANNOVA Mean Square 287,5333 0,548 524,292 Sig.
0,000
Based on the ANOVA table above, the F calculated value is 524.
292 with a significance value 000 < 0.
05, and the F table value is 2.
ith df1 = 3 and df2 = .
Since F calculated > F table and the significance value < 0.
HCA is rejected and HCa is accepted.
Thus, it can be concluded that Digital Competence.
Work Discipline, and Work Motivation simultaneously have a positive and significant effect on Employee Performance at BKPSDM Kampar Regency.
These results indicate that the three independent variables jointly contribute to improving employee performance.
This means that the better the digital competence, work discipline, and work motivation possessed by employees, the higher the overall performance achieved.
4 Coefficient of Determination Test The coefficient of determination test is conducted to determine how well the independent variables, namely Digital Competence (XCA).
Work Discipline (XCC), and Work Motivation (XCE), explain the variation in the dependent variable.
Employee Performance (Y).
This test aims to assess how strong the relationship and the joint contribution of these three independent variables are in improving employee performance at BKPSDM Kampar Regency.
Table 8.
Coefficient of Determination Test Results Model
0,985
Model Summary R Square Adjusted R Square 0,970 0,969 Std.
Error of the Estimate 0,74055 Based on the table, the R Square (RA) value is 0.
970, which means that 97% of the variation in employee performance can be explained simultaneously by Digital Competence.
Work Discipline, and Work Motivation, while the remaining 3% is explained by other factors.
Note on Model Fit: It is important to acknowledge that this R-squared value is exceptionally high.
While this suggests strong explanatory power, it may also reflect specific characteristics of the Journal of Management Economics and Financial Accounting 2025 (Decembe.
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26 of 28 dataset, such as the use of a census method (N=.
within a single homogeneous agency.
Readers should interpret the generalizability of these results with caution.
Discussion 1 The Effect of Digital Competence on Employee Performance The regression analysis results show that digital competence (Total_x.
has a regression coefficient of 0.
371 with a t value of 4.
808 and a significance level of 0.
000 < 0.
05, indicating that digital competence has a positive and significant effect on employee performance at BKPSDM Kampar Regency.
The positive coefficient indicates that the higher the employeesAo digital competence, the higher the performance achieved.
This finding shows that employeesAo ability to use applications, information systems, and work-related technology strongly determines their success in completing tasks effectively and accurately.
With BKPSDMAos work system being fully digital-based, employees who master technology tend to work faster, more systematically, and with fewer errors.
Therefore.
HCA is accepted.
2 The Effect of Work Discipline on Employee Performance Based on the regression results, work discipline (Total_x.
has a coefficient of 0.
017 with a t value of 0.
278 and a significance level of 0.
782 > 0.
05, indicating that work discipline does not have a significant effect on employee performance.
This finding can be explained through the Result-Based Management (RBM) theory, which posits that in modernized organizations, performance is measured by outcomes rather than process compliance.
In a digital ecosystem like SPBE, physical presence .
raditional disciplin.
becomes less relevant than the ability to utilize digital tools effectively to produce outputs.
An employee might be punctual .
igh discipline scor.
but lack the digital skills or motivation to utilize the SIMPEGNAS system effectively, resulting in average performance.
This suggests a paradigm shift in the public sector where 'presence' is no longer a sole proxy for 'productivity'.
Therefore.
HCC is rejected.
3 The Effect of Work Motivation on Employee Performance Work motivation (Total_x.
has a regression coefficient of 0.
686 with a t value of 9.
122 and a significance level of 0.
000 < 0.
05, indicating that work motivation has a positive and highly significant effect on employee performance.
The largest coefficient among the variables shows that motivation is the most dominant factor in improving performance.
Employees who have the drive to achieve, receive recognition, and develop themselves tend to work more diligently, resulting in better output.
Therefore.
HCE is accepted.
4 The Effect of Digital Competence.
Work Discipline, and Work Motivation on Employee Performance Simultaneously, digital competence and work motivation play important roles in improving employee performance, while work discipline does not show a significant partial effect.
However, in the overall regression model, the three variables together form a system that explains variations in employee performance at BKPSDM Kampar Regency.
This indicates that employee performance is determined not by a single factor but by a combination of technological competence, work motivation, and behavioral governance.
Therefore, the research model that combines these three variables is relevant for explaining employee performance, and HCE is Conclusions Based on the analysis and discussion, it is concluded that simultaneously.
Digital Competence.
Work Discipline, and Work Motivation have a positive and significant effect on Employee Performance at BKPSDM Kampar Regency.
However, partially, only Digital Competence and Work Motivation demonstrate a significant effect, whereas Work Discipline does not significantly influence performance.
The high determination coefficient suggests that these variables collectively are primary predictors of performance in this agency, driven primarily by motivation and technical competence.
Managerial Implications: Based on these findings, several practical strategies are recommended for the management of BKPSDM Kampar Regency:
Journal of Management Economics and Financial Accounting 2025 (Decembe.
vol 1 no 2 Dini, et al.
27 of 28 Digital Competence Development: Given the significant impact of digital skills, the agency should move beyond basic computer training to specialized workshops on Advanced Public Sector Digital Tools and Data Security.
Regular "digital clinics" can be established to assist employees struggling with specific software updates.
Motivation Enhancement Strategy: Management should implement a recognition-based reward system.
This could include awards specifically linked to innovation in digital service delivery or non-monetary incentives for high performers.
Redefining Discipline: The focus should shift from monitoring physical attendance to output-based monitoring.
The agency should evaluate performance based on deliverables completed within the digital system (SIMPEGNAS) rather than mere presence.
References