ISSN .
: 2829-7350 | ISSN.
: 2963-9441 The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 Univesitas Negeri Jakarta.
Indonesia E-mail: angga.
dhaniswara@gmail.
com1, susita2323@gmail.
com2, wahono@unj.
Abstract Taxes play an essential role in realizing the vision of "Indonesia Emas 2045".
Low tax ratios and voluntary taxpayer compliance influence the achievement of tax revenues.
The causal factor is that the quality of education provided is not optimal because the number of tax educators is still limited.
The DGT report stated that digital competence, knowledge sharing, and tax instructors' work motivation needed to be improved.
This research aims to determine the effect of digital competence and knowledge sharing on employee performance with work motivation as an intervening variable for tax educators at the Directorate General of Taxes.
The research methodology uses quantitative survey methods.
The population is 2,340 employees, with a total of 342 respondents.
The hypothesis was tested using PLS-SEM analysis using Smart-PLS version 4.
SPSS Version 27, and the Embedded Two-Stage Approach method.
The research results show that digital competence and work motivation have a significant positive influence on employee performance.
Knowledge sharing does not have a significant effect on employee performance.
Digital Competence and Knowledge Sharing positively and significantly affect work motivation.
Work motivation significantly mediates the indirect effect of digital competence and knowledge sharing on employee Keywords Digital Competence.
Employee Performance.
Knowledge Sharing.
Tax Educators.
Work motivation.
INTRODUCTION
To realize the vision of "Indonesia Emas 2045," the country needs quality human resources (HR) as the central strong driving development (Saleh et al.
, 2.
Investment in education, technology, and other sectors that support human resource development is urgently needed (Arifin, 2.
Taxes are crucial because their contribution reaches 79.
of total state revenue (BPS, 2.
However, tax revenues are not optimal due to the low tax ratio (OECD, 2.
This low tax ratio is due to minimal voluntary compliance (Kurniawan et al.
, 2.
and yet-to-be-optimal tax education ((Hidayati et al.
(Sari et al.
(Yap & Mulyani, 2.
Referring to Permenpan-RB Number 11 of 2023, the task of tax education is carried out by the Functional State Financial Supervisor with the scope of Technical Services and Tax Counseling .
urrently still using the Functional Tax Educator nomenclatur.
However, the current staff composition is not ideal because one tax educator must educate more than 10,286 taxpayers.
The 2021 (DJP, 2.
and 2022 (DJP, 2.
Service Satisfaction and Effectiveness of Counseling and Public Relations Survey Results Report of the Directorate General of Taxes notes that there are employee performance problems related to tax counseling and services, especially issues regarding the ability to use technology and the ability to share.
According to empirical research, employee performance is influenced by SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
digital competence (He et al.
, 2.
, knowledge sharing (Meher & Mishra, 2.
, and work motivation (Hanandeh et al.
, 2.
There is a limited number of employees, and so that the implementation of education can run effectively and efficiently and have a broad reach, it is necessary to optimize the use of digital media (Pramujo, 2.
Tax educators must be more creative and skilled in optimizing information technology such as Zoom.
Google Meet, and so on (Tynnessen et , 2.
Successful use of this media depends on a person's ability to adapt to information technology (Setiaji & Dinata, 2.
This ability is obtained through digital competence (Hibana & Surahman, 2.
However, based on the DGT survey report, it was noted that there was a gap in the digital competency of Tax Educators.
According to Lee (Tynnessen et al.
, 2.
, knowledge sharing is sharing and distributing knowledge to individuals or organizations through various media.
This implementation will encourage organizations to achieve sustainable competitive advantage (Bhatti et al.
, 2.
and future success (Manfredi Latilla et al.
, 2.
Knowledge sharing is also a key component in knowledge management, which can encourage employees to work more efficiently (Fraihat et al.
, 2.
However, the DGT survey report noted that there were problems related to the ability of tax officers to share, especially regarding methods and styles of education or service.
Meanwhile, on the other hand, the emergence of cases involving unscrupulous tax employees has also influenced tax employees' motivation to work (Yuniar, 2.
This motivation will influence how a person will devote their energy to planning, achieving success, and achieving a goal (Andriana & Ardi, 2.
From the description above, it is known that problems exist related to digital competence, knowledge sharing, work motivation, and the performance of Tax Educator This research aims to determine the influence of these variables and develop a theory that can be useful for readers and organizations in improving performance in the LITERATURE REVIEW Digital Competence Digital competence is the ability to use information and communication technology creatively, critically, and confidently to achieve goals related to employment, employability, learning, leisure, inclusion, and participation in society (INTEF, 2.
Dimensions related to digital competence include Information and data literacy.
Communication and collaboration.
Digital Content Creation.
Safety, and Problem Solving (INTEF, 2.
Knowledge Sharing Knowledge sharing is exchanging ideas related to information, input, expertise, and collaboration with others to carry out several daily tasks to overcome problems and grow new ideas (Ahmad, 2.
Meanwhile.
Budiyarti et al.
define knowledge sharing as a person's behavior in sharing knowledge and skills with colleagues as a form of cooperation to improve work patterns and performance.
Wang and Noe, quoted by (Zheng, 2.
stated that knowledge sharing is different from knowledge transfer and knowledge exchange.
SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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According to Van Den Hoof and De Ridder .
, knowledge sharing has two dimensions: knowledge donation and knowledge collecting.
Work Motivation Work motivation is defined as the desires and desires within humans related to human psychological factors (Susita et al.
, 2.
Colquitt et al.
explain that motivation is a set of energetic forces that connect a person's internal and external sides and encourage direction, intensity, and persistence in completing a job.
Mathis et al.
convey the same thing, stating that motivation is a desire that drives a person to act.
According to McClelland's three needs theory, motivation consists of three dimensions: The need for achievement, the need for power, and the need for affiliation.
Employee Performance Herzberg .
explains that performance results from the interaction between job satisfaction and motivation, influencing employee work effectiveness and efficiency.
Drucker .
explains that performance is the achievement of organizational goals, measured based on effectiveness and efficiency in utilizing resources.
Robbins and Judge .
also define something similar, that employee performance is a combination of effectiveness and efficiency in carrying out core work.
Dessler .
defines performance as work performance, namely the comparison between the work results achieved and the standards set.
In line with this.
Wibisono et al.
define performance as tasks or work behaviors arranged to meet predetermined organizational requirements and goals.
According to Koopmans et al.
, performance dimensions are divided into Task Performance and Contextual Performance.
Figure 1.
Model of Relationships Between Constructs source: processed primary data, 2024.
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
From Figure 1, the following hypothesis is proposed:
H1: Digital competence influences employee performance.
H2: Knowledge sharing influences employee performance H3: Work motivation influences employee performance H4: Digital competence influences work motivation.
H5: Knowledge sharing influences work motivation H6: Digital competence influences employee performance through work motivation.
H7: Knowledge sharing influences employee performance through work motivation.
METHOD
This research uses questionnaire data in the form of answers from the perceptions of Tax educator employees regarding research variables.
Data was obtained by distributing questionnaires online via Google Forms using a Likert scale of 1 (Strongly Disagre.
to 5 (Strongly Agre.
The population in the study was 2,340 Tax educator employees spread throughout Indonesia, and samples were obtained using the Slovin formula .
% error toleranc.
with the following proportionate stratified random sampling method.
Table 1.
Distribution of Research Respondents Position Level Number of Employees Proporti Responden
Ahli Madya
1,97%
Ahli Muda
14,87%
Ahli Pertama 21,24% Penyelia 4,10% Mahir
16,88%
Terampil
40,94%
Total Table 2.
Variables.
Dimensions and Measuring Items Variable Digital Competence Dimension Information & Data Literacy Code
IDL1
IDL2
Communication CCO1
Collaboration CCO2
Digital Content
Creation
DCC1
Measuring Items The ability to select relevant sources of information is essential.
Information found on the internet must be evaluated critically.
It is important to be able to use digital presentation applications .
uch as Microsoft PowerPoint.
Freeze.
Canva, etc.
The ability to operate video conferencing applications .
uch as Zoom.
Google Meet.
Skype.
Microsoft Teams, etc.
) must support shared knowledge.
Creating digital audio content with a mixture of sound and music is essential.
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DCC2
Safety SFT1
SFT2
Problem
Solving PSV1
PSV2
Knowledge Donation KDO1
KDO2
KDO3
KDO4
Knowledge Sharing Knowledge Collecting KCO1
KCO2
KCO3
KCO4
Work
Motivation Need for Achievement
NAC1
NAC2
NAC3
Need for Power NOP1
NOP2
The ability to design digital presentations by combining images, graphics, and text is Sensitive data needs to be protected.
Maintaining basic device security .
ntivirus, system updates, etc.
) is essential.
It is important to be able to identify technical problems that occur with extension support devices .
aptops, projectors, etc.
Trying new digital technologies is essential to reduce the technology gap.
Information related to tax regulations is essential to share with colleagues.
Skills in using tax and extension support applications are necessary to share with Information related to tax regulations is essential to share with taxpayers and other Skills in using tax applications are essential for sharing with taxpayers and other Colleagues in the office share information related to tax regulations.
Colleagues in the office informed them regarding the use of tax applications and extension support applications.
Taxpayers or other stakeholders notify problems regarding tax regulations.
Taxpayers or other stakeholders notify tax application problems.
Rewards at work can motivate employees to work better.
Employees have the opportunity to take part in education and training that supports Every employee has the same opportunity to develop a career.
Employees have authority and responsibility for the success of the office.
Employees dare to express different opinions SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
Need for NAF1
NAF2
NAF3
Task
Performance TAP1
TAP2
TAP3
Employee Performance Contextual Performance TAP4
TAP5
COP1
COP2
COP3
COP4
COP5
Employees enjoy working with others rather than working alone.
Building relationships with superiors and fellow employees is one of the priorities in the Employees respect each other and can work together well.
Employees work on time according to established plans.
Employee work achievements are by the targets and objectives set Employees work according to predetermined Employees carry out work quite efficiently.
Employees try to get optimal work results.
Employees take on extra responsibilities from their jobs.
Employees have the initiative to start new tasks after old work is completed.
Employees update knowledge related to work.
Employees find creative solutions to every Employees act actively in work meetings.
The analysis technique used is Structural Equation Modeling (SEM) based on Partial Least Square (PLS) with the help of Smart-PLS version 4 software.
The PLS-SEM analysis stage in this research uses the Embedded Two-Stage Approach estimation, wherein, in the initial stage, the measurement model uses repeated indicators to evaluate first-order measurements .
he relationship between dimensions and variable.
and creates latent Next, the latent variable scores obtained in the initial stage represent the dimension measurements in the second stage.
Meanwhile, testing the research model consists of evaluating the measurement model .
uter mode.
, the structural model .
nner mode.
, and the goodness of fit model.
The measurement model in this research uses a reflectivereflective measurement model.
In Hair et al.
, the evaluation of the reflective measurement model consists of Loading factor (LF) Ou 0.
Composite Reliability (CR) Ou 70.
Cronbach's Alpha Ou 0.
Average Variance Extracted (AVE) Ou 0.
50, and Discriminant Validity, namely fornell and lacker criteria and HTMT (Heterotrait Monotrait Rati.
below 90 and Cross Loading.
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RESULTS AND DISCUSSION
Outer Model Analysis Stage I Figure 2.
Output Graphic First-Order Level source: processed primary data, 2024.
Table 3.
Outer Loading.
Composite Reliability (CR) and Average Variance Extracted (AVE) - First Order Level Dimensions Information & Data
Literacy Communication & Collaboration Digital Content
Creation
Safety Problem Solving Knowledge Donation Knowledge Collecting Measurement Items Outer
Loading IDL1
IDL2
CCO1
CCO2
DCC1
DCC2
SFT1
SFT2
PSV1
PSV2
KDO1
KDO2
KDO3
KDO4
KCO1
Cronbach's Composite Alpha Reliability AVE SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
Need for Achievement
Need for Power
Need for Affiliation Task Performance Contextual Performance KCO2
KCO3
KCO4
NAC1
NAC2
NAC3
NOP1
NOP2
NAF1
NAF2
NAF3
TAP1
TAP2
TAP3
TAP4
TAP5
COP1
COP2
COP3
COP4
COP5
source: processed primary data, 2024.
Figure 4.
1 and table 4.
1 above show that all dimensions have a Composite Reliability value above 0.
70, so they can be said to be reliable.
Meanwhile, for AVE, all values are This AVE's value indicates that the average variance of the measurement items contained by the variable is above 50%.
From the results of this measurement, it can be concluded that the evaluation of the measurement model from the Convergent Validity aspect has been fulfilled.
The next test is Discriminant Validity.
Test results for Fornell Larcker Criterion.
Heterotrait Monotrait Ratio (HTMT), and Cross Loading indicate that the distribution's validity has been met.
SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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Output Grafik Second Order Level source: processed primary data, 2024.
Table 4.
Composite Reliability (CR).
Average Variance Extracted (AVE) - Second Order Level Average Composite Composite Cronbach's Variance Reliability Reliability (Rhoalpha Extracted (Rho-.
(AVE) Employee Performance Knowledge Sharing Digital Competence Work Motivation source: processed primary data, 2024.
Figure 4.
2 and table 4.
21 show that all variables have Composite Reliability values 70, so they can be said to be reliable.
Meanwhile, for AVE, all values are above This indicates that the average variance of the measurement items contained by the variable is above 50%.
From the results of this measurement, it can be concluded that the evaluation of the measurement model from the Convergent Validity aspect has been fulfilled.
The next test is Discriminant Validity.
Test results for Fornell Larcker Criterion.
Heterotrait Monotrait Ratio (HTMT), and Cross Loading indicate that the distribution's validity has been SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
Inner Model Analysis source: processed primary data, 2024.
The structural model evaluation examination was carried out in three stages: .
Multicollinearity test, namely by checking whether there is multicollinearity between variables with the Inner VIF (Variance Inflated Facto.
Inner VIF value < 5 indicates no multicollinearity between variables (Hair et al.
, 2.
Hypothesis testing, namely testing hypotheses between variables by looking at the statistical t-value or p-value.
If the calculated t statistic is more significant than 1.
-tabl.
or the p-value of the test results is <0.
05, then there is a significant influence between the variables.
In addition, it is necessary to convey the results and the 95% confidence interval for the estimated path coefficient parameters.
f square value, namely the influence of direct variables at the structural level with f square criteria of 0.
, 0.
, 0.
(Hair et al.
, and f square mediation effects .
alled upsilon .
statistic obtained by squaring the mediation coefficient, based on Ogbeibu et al.
is that the mediation effect is declared low if the value is 0.
01, medium 0.
075 and high 0.
Table 5.
Hypothesis Testing - Direct Effect 95% Confidence Path Interval PHypothesis Coefficien Value Lower Upper Limit Limit H1: Digital Competence -> Employee Performance H2: Knowledge Sharing -> Employee Performance FSqure Result Accepted .
Rejected SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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Accepted (Moderat.
Accepted (Moderat.
source: processed primary data, 2024.
Hypothesis H6: Digital Competence -> Work Motivation -> Employee Performance H7: Knowledge Sharing -> Work Motivation -> Employee Performance Tabel 6.
Hypothesis Testing - Mediation Test
Confidence Path
PUpsilo
Interval
Coefficie VAF
Value Lower Upper
Limit
Limit
0,077
2 x 0,138/0,465 .
2 = 0,0191 29,6% Result Accept .
2 x 0,198/0,273 .
2 72,5% 0,0392 Accept .
source: processed primary data, 2024.
Goodness and Fit Evaluation of Model PLS is a variance-based SEM analysis to test theoretical models emphasizing prediction studies.
Therefore, several measures were developed to express the accepted model, such as R square.
Q Square.
SRMR.
PLS Predict.
Goodness of Fit Index, and Robustness Check (Hair et al.
, 2.
According to Chin .
, the qualitative interpretation value of R Square is 0.
ow influenc.
, 0.
oderate influenc.
, and 0.
igh influenc.
The data processing results show that the joint influence of digital competence and knowledge sharing on work motivation is 0.
early high influenc.
The magnitude of the joint influence between digital competence and knowledge sharing on work motivation and employee performance is 0.
oderate influenc.
Q Square describes a measure of prediction accuracy, namely how well each change in exogenous/endogenous variables can predict endogenous variables.
A q-square value above 0 indicates that the model has predictive relevance.
However, according to Hair et al.
the Q Square interpretation value is qualitatively 0 .
ow influenc.
, 0.
oderate influenc.
, and 0.
igh influenc.
Based on the processing results, the Q Square value SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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The Influence of Digital Competence and Knowledge Sharing on Employee Performance with Work Motivation as an Intervening Variable Angga Sukma Dhaniswara1.
Dewi Susita2.
Puji Wahono3 DOI: https://doi.
org/10.
54443/sj.
for the motivation variable is 0.
ear high influenc.
, while the Q Square value for the employee performance variable is 0.
ear high influenc.
SRMR measures model fit, namely the difference between the data correlation matrix and the correlation matrix of the estimated model results.
In Hair et al.
SRMR values 08 indicate a fit model.
From the measurement results, it is known that the SRMR result in this study was 0.
064 (<0.
this means that the model built matches the empirical Meanwhile, the model is said to have high prediction accuracy if the RMSE or MAE value of the PLS model is lower than the linear regression model (LM).
The evaluation results show that the predictive power is low.
The Goodness of Fit Index (GoF Inde.
is an evaluation of the entire model, which evaluates measurements and structural models.
This GoF index can only be calculated from a reflective measurement model, namely the root of multiplying the geometric average communality by the average R square.
According to Wetzels et al.
, the interpretation of the GoF index values is 0.
ow GoF), 0.
edium GoF), and 0.
igh GoF).
The calculation results show that the GoF model value 697, which is in the high category.
The results of the robustness check test, the p-value of the squared effect of digital competence, knowledge sharing, and work motivation on satisfaction is not significant .
>0.
, so it can be concluded that the influence of digital competence, knowledge sharing, and work motivation is linear or the effect of linearity of the model fulfilled .
The results of testing the p-value of the squared effect of Digital Competence and Knowledge Sharing on Work Motivation are not significant .
>0.
, so it can be concluded that the influence of digital competence and knowledge sharing is linear, or the linearity effect of the model is met .
CONCLUSION
Based on the interpretation, several conclusions were drawn, namely that Digital Competence has a positive and significant influence on Employee Performance with a path coefficient of 0.
326 and a p-value of 0.
000 (<0.
However, its presence has a low influence at the structural level .
-square=0.
In the 95% confidence interval, the magnitude of the influence of digital competence in improving employee performance is 179 and 0.
Knowledge sharing does not significantly influence employee performance with a path coefficient of 0.
075 and a p-value of 0.
249 (>0.
within the 95% confidence interval, the influence of knowledge sharing on employee performance is 006 and 0.
Work motivation has a positive and significant effect on employee performance, with a path coefficient of 0.
404 and a p-value of 0.
000 (<0.
However, its presence has a moderate influence at the structural level .
-square = 0.
the 95% confidence interval, the influence of work motivation is significant in improving employee performance, which lies between 0.
269 and 0.
Digital Competence positively and significantly affects work motivation with a path coefficient of 0.
342 and a p-value of 0.
000 (<0.
However, the effect tends to be moderate at the structural level .
-square = 0.
In the 95% confidence interval, the magnitude of the influence of digital competence in increasing work motivation lies between 0.
203 and Knowledge sharing has a positive and significant effect on work motivation .
ath SINOMICS JOURNAL | VOLUME 3 ISSUE 1 .
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000 (<0.
), but the effect is moderate at the structural level.
In the 95% confidence interval, the magnitude of the influence of knowledge sharing in increasing work motivation is between 0.
348 and 0.
Work motivation plays a significant role in mediating the indirect influence of digital competence on employee performance with a mediation path coefficient .
and a pvalue of 0.
000 (<0.
However, at the structural level, it still has a low influence .
psilon v=0.
In the 95% confidence interval, by increasing improvements in work motivation, the mediating role will increase to 0.
Work motivation plays a significant role in mediating the indirect influence of knowledge sharing on employee performance with a mediation path coefficient .
and a p-value of 0.
000 (<0.
However, at the structural level, it still has a low influence .
psilon v=0.
In the 95% confidence interval, by increasing improvements in work motivation, the mediating role will increase to 0.
Suggestions for future research The researcher recommends that other researchers conduct qualitative studies to understand better how and why knowledge sharing impacts .
r does no.
Interviews or focus group discussions with employees can provide insight into barriers or supporting factors for knowledge sharing.
Other researchers can also focus on types of knowledge sharing, such as tacit knowledge sharing .
mplicit knowledge shared through direct experienc.
and explicit knowledge sharing .
xplicit knowledge shared through documents, guides, etc.
), and evaluate their impact on performance separately.
Other researchers can also try to conduct exploratory studies on the influence of gender, age, education level, and length of work on each variable to get a more in-depth picture of the variables studied.
REFERENCES