PENA
JURNAL ILMU PENGETAHUAN DAN TEKNOLOGI
p-ISSN : 0854-7521 e-ISSN : 2301-6450 Vol.
39 No.
1 Maret 2025 Hal.
15- 26
https://jurnal.
id/index.
php/pena The Influence of Digital Competence and Knowledge Sharing on Educational Staff Performance with Motivation as an Intervening Variable Feny Yulita1.
Chalimah2 Wahyuningsih3 1,2Magister Manajemen Fakultas Ekonomi dan Bisnis.
Universitas Pekalongan 3BKPSDM Kota Pekalongan unikal@gmail.
Submitted : 20/03/25 Accepted :28/05/25 Published:23/06/25 ABSTRACT The difference in research results on the influence of digital competence, knowledge sharing, motivation on performance underlay this research.
The purpose of this study was to test and analyze the influence of digital competence and knowledge sharing on the performance of educational staff at Pekalongan University with motivation as an intervening variable.
Data analysis in this study used the Partial Lest Square (PLS) approach.
The results of the study explained that: First, digital competence had a positive and significant effect on motivation.
Second, digital competence had a positive and insignificant effect on Third, knowledge sharing had a positive and significant effect on motivation.
Fourth, knowledge sharing had a positive and significant effect on performance.
Fifth, motivation had a negative and insignificant effect on performance.
Sixth, digital competence had a direct effect on performance, meaning that motivation did not play a role in mediating the relationship between digital competence and performance.
Seventh, knowledge sharing had a direct effect on performance, meaning that motivation did not play a role in mediating the relationship between knowledge sharing and Keywords : digital competence, knowledge sharing, performance, motivation ABSTRAK Perbedaan hasil penelitian tentang pengaruh kompetensi digital, knowledge sharing, motivasi terhadap kinerja mendasari dilakukan penelitian ini.
Tujuan penelitian ini untuk menguji dan menganalisis pengaruh kompetensi digital dan knowledge sharing terhadap kinerja tenaga kependidikan Universitas Pekalongan dengan motivasi sebagai variabel intervening.
Analisis data dalam penelitian ini menggunakan pendekatan Partial Lest Square (PLS).
Hasil penelitian menjelaskan bahwa: Pertama, kompetensi digital berpengaruh positif dan signifikan terhadap motivasi.
Kedua kompetensi digital berpengaruh positif dan tidak signifikan terhadap kinerja.
Ketiga knowledge sharing berpengaruh positif dan signifikan terhadap Keempat knowledge sharing berpengaruh positif dan signifikan terhadap kinerja.
Kelima motivasi berpengaruh negatif dan tidak signifikan terhadap kinerja.
Keenam kompetensi digital berpengaruh langsung terhadap kinerja, artinya motivasi tidak berperan dalam memediasi hubungan kompetensi digital terhadap Kinerja.
Ketujuh knowledge sharing berpengaruh langsung terhadap kinerja, artinya motivasi tidak berperan dalam memediasi hubungan knowledge sharing terhadap kinerja.
Kata Kunci : kompetensi digital, knowledge sharing, kinerja, motivasi Pena Jurnal Ilmu Pengetahuan dan Teknologi is licensed under CC-BY-SA 4.
INTRODUCTION
According Government Regulation of the Republic of Indonesia Number 4 of 2014, a higher education institution is an educational unit that provides higher education.
A private university is a higher education institution established and/or managed by the Universitas Pekalongan is a private university established by the Samarthya Mahotsaha Paramadharma Foundation.
The operation of a higher education institution requires human resources (HR).
Human resources are the main component in supporting the success of a university, specifically in achieving its vision, mission, and goals.
According to Chalimah .
, human resources are a Organizational goals can be more easily achieved if the organization has qualified and productive human resources.
Universitas Pekalongan has a vision, mission, and goals.
To achieve the vision.
Universitas Pekalongan, qualified human resources are No matter how grand the university building is or how advanced and complete its facilities are, without qualified human resources, the university will not be able to develop optimally.
Human resources play a very important and strategic role in achieving the goals of higher education To acquire and develop qualified human resources, professional human resource management is essential.
Professional human resource management is one of the key factors supporting the success of higher education institutions.
In the context of educational work.
Nurul Ulfatin .
classified people involved in the operational aspects of education into two groups: learners .
and those referred to as educators and educational staff.
All individuals participating in the education process are referred to as students .
Meanwhile, those who provide education are referred to as educators and educational Educators and educational personnel are what is referred to as Pena Vol.
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educational human resources.
In higher personnel include librarians, laboratory staff, technicians, administrative staff, and support staff who are responsible for achieving the quality targets of university The rapid development of technology demands that educational personnel always be ready and responsive in facing various changes and challenges by adapting to dynamic regulations and policies in order to continue contributing and providing good performance in advancing and developing their universities.
Educational staff must continuously develop and enhance their There are many competencies that educational staff must possess, one of which is digital competence.
Digital competence, according to Elisnawati et al.
, is the ability to enhance the positive outcomes of ICT use and to minimize the negative results Meanwhile, according to Ilomyki et al.
, digital competence is based on basic ICT skills, namely the use of computers to retrieve, evaluate, store, produce, present, and exchange information, as well as to collaborative networks via the internet.
The definition of digital competence as stated by Ilomyki et al.
, .
can be used to measure the variable of digital competence.
To improve the performance of educational personnel, in addition to possessing various competencies, a culture of knowledge sharing among educational staff is also necessary.
According to Tupamahu.
Pelamonia .
, knowledge sharing is an individual's behavior in sharing what they have learned and transferring what they know to others who have common interests and have found the knowledge to be Partogi & Tjahjawati .
state that a culture of knowledge sharing, if well implemented and applied by employees, will bring great benefits to the organization, including enabling new employees to carry out their tasks, thus forming a high-quality workforce that helps the organization Feny Y.
Chalimah Wahyuningsi.
The Influence of Digital Competence achieve its goals more easily.
Partogi & Tjahjawati .
conclude that knowledge sharing is the process of sharing and distributing knowledge among individuals, which is beneficial in stimulating individuals to think more creatively and effectively, organizational performance.
The indicators used to measure knowledge sharing, according to Tupamahu.
Pelamonia, .
, are: .
knowledge collection, where employees learn new knowledge both inside and outside the .
knowledge contribution, where employees contribute ideas to their colleagues, supervisors, or the organization.
sharing experiences and information, where employees share work-related colleagues, supervisors, or the organization.
In addition to competencies and a culture of knowledge sharing among educational staff, another factor influencing their performance is motivation.
Motivation, according to HB Uno dan N Lamatenggo .
, is an internal drive that causes an individual to act or perform a task.
Motivation is difficult to observe directly, but it can be clarified through behavior, such as impulses or stimuli that trigger certain Another view on work motivation, according to Anandita et.
, .
, is the internal drive that a person possesses to inspire or ignite enthusiasm in relation to the work environment, ultimately leading to goal It can be said that to achieve the goals of a higher education institution, strong work motivation is needed from educational personnel in order to deliver good performance.
According to Hadeli .
, there are six indicators to measure work motivation: .
a strong feeling in achieving goals, .
taking responsibility for oneself, .
being evaluative, .
taking moderate risks, .
being creative and innovative, and .
enjoying challenges or achievement-oriented.
The competencies possessed by educational personnel, supported by a culture of knowledge sharing and strong work motivation, is expected to improve performance, which in turn will contribute to the achievement of the vision, mission, and Universitas Pekalongan.
Performance, according to Mangkunegara .
, is the result of an employeeAos work in terms of quality and quantity obtained while carrying out the duties and responsibilities assigned to them.
Employee performance can be linked to the individualAos ability or competence to develop themselves in order to work toward the goals desired by the organization (Anandita et.
, 2.
According to Robbins .
as cited in Bintoro & Daryanto, .
, there are five indicators used to measure individual employee performance: .
Quantity, .
Quality, .
Timeliness, .
Effectiveness, and .
Independence.
Based on previous studies, there are many factors that influence employee A study by Nugraha & Sukiman .
stated that knowledge sharing has a significant positive effect on work motivation, and that knowledge sharing has a positive but not significant effect on employee performance.
Work motivation has a significant positive effect on employee performance.
Knowledge sharing has a significant positive effect on employee performance through work motivation, with work motivation playing a mediating role in the relationship between knowledge sharing and performance.
Fadila et al.
, .
, in their research, found that knowledge sharing and transformational leadership style have a significant positive effect on employee A study conducted by Rahmah & Tania .
showed different results for the knowledge sharing variable.
Their findings indicate that knowledge sharing has a negative but not significant effect on motivation has a significant positive effect on employee performance.
Knowledge sharing and work motivation do not directly affect performance through competence as an intervening variable.
The study conducted by Baharrudin et , .
showed that digital competence had a positive but not significant effect, while human resource engagement had a performance during the work-from-home Meanwhile, the findings of Elisnawati et .
showed that digital competence had a significant positive effect on employee Work motivation had a significant positive effect on employee performance, and work discipline also had a significant positive effect on employee The influence of digital competence and knowledge sharing on performance cannot be separated from psychological aspects, one of which is work motivation.
Motivation plays an important role as an internal driving force that encourages individuals to apply their knowledge and skills optimally.
Therefore, it is essential to examine the role of motivation as an intervening variable that can strengthen or even mediate the relationship between digital competence and knowledge sharing on the performance of educational Considering the differing results of previous studies and in line with the actual needs at Universitas Pekalongan in facing the era of digitalization, as well as the importance of providing empirical evidence to support efforts to improve the quality of educational staff performance through a holistic approach that includes aspects of competence, collaboration, and motivation, the authors have chosen the title The Influence of Digital Competence and Knowledge Sharing on the Performance of Educational Staff with Motivation as an Intervening Variable.
This research is expected to provide both theoretical and practical contributions to human resource development in the educational sector.
The conceptual model in this study is divided into three groups of variables:
independent variables, dependent variables, and intervening or mediating variables.
The independent variables in this study are digital competence and knowledge sharing.
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Performance is the dependent variable, while motivation is the intervening or mediating Based on the explanation above, the research hypotheses can be formulated as shown in Table 1.
Table 1 : Hypotheses of the study Hypotheses Digital competence has a significant positive effect on motivation Digital competence has a significant positive effect on performance Knowledge sharing has a significant positive effect on motivation Knowledge sharing has a significant positive effect on performance Motivation has a significant positive effect on performance Digital competence has an indirect effect on performance through motivation as an intervening variable Knowledge sharing has an indirect effect on performance through motivation as an intervening variable METHODS This study is a correlational study.
Correlational research is a type of descriptive research that explains the relationship between two or more variables (Hasanah.
Operationalization of Variables This study examines four variables:
digital competence (X.
and knowledge sharing (X.
as independent variables, motivation (Z) as the intervening variable, and the performance of educational staff (Y) as the dependent variable.
In order for these four variables to be measurable and clearly definitions are provided in Table 2.
Feny Y.
Chalimah Wahyuningsi.
The Influence of Digital Competence Tabel 2.
Definisi Operasional Variabel Variable Indicators Source Retrieving Evaluating Storing Producing Presenting and Digital Ilomyki et Competence , .
Communicating participating in networks via the Internet Collecting Tupamahu.
Pelamonia.
Contributing .
Knowledge Sharing Sharing experiences and Robbins Quality .
as Quantity cited in Performance Timeliness Bintoro & Effectiveness Daryanto.
Independence .
Motivation Strong sense of Accountability to oneself Evaluative Moderate risktaking Creativity and Enjoyment of competitive or challenges and Hadeli, .
Population and Sample The population in this study consists of educational staff at Universitas Pekalongan.
A total of 30 individuals were selected as the sample for this study.
The sampling technique used was accidental sampling.
Data Collection Method This study utilized primary data collected through questionnaires filled out by respondents using a Likert scale.
Data Analysis Technique The data analysis technique employed in this study is the Partial Least Squares (PLS-SEM) method using the SmartPLS 3.
The use of PLS-SEM is based on including its suitability for handling nonnormal data and situations involving small sample sizes, making it ideal for exploratory research where collecting large amounts of data is not feasible (Ghozali, 2.
RESULTS AND DISCUSSION
Measurement Model Test (Outer Mode.
The Outer Model test is conducted to evaluate the validity and reliability of the indicators used to measure the latent variables .
This test ensures that the indicators genuinely measure the intended concept and measure consistently.
The purposes of the Outer Model test are as To evaluate Convergent Validity, assesses whether the indicators used have a strong correlation with the latent variable being measured.
It means that the indicators measure the same To evaluate Discriminant Validity, assesses whether the indicators are able to distinguish between different latent It indicates that the indicators not only measure one latent variable but also have the ability to differentiate it from other latent variables.
To evaluate Construct Reliability, assesses the internal consistency of the indicators used.
It means that the indicators consistently measure the same concept.
The measurement model for validity and reliability testing, the model's coefficient of determination, and the path coefficients for the structural equation model can be seen in Figure 1 Pena Vol.
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The AVE (Average Variance Extracte.
value is a measure used in statistics, particularly in factor analysis and structural models, to assess the proportion of variance explained by a construct .
atent variabl.
in the measurement.
An indicator of a variable is considered valid if the AVE value is greater than 0.
Table 3 presents the results of the discriminant validity measurement.
Tabel 3.
Construct Reliability and Validity Cronbach's Composite Alpha Reliability Variable Figure 1: Outer Model In Figure 1 above.
X1.
1 to X1.
10 are the items used to measure the Digital Competence X2.
1 to X2.
6 are the items used to measure the Knowledge Sharing variable.
to Z8 are the items used to measure the Motivation variable, and Y1 to Y16 are the items used to measure the Performance Validity Test A Convergent Validity This is used to assess the extent to which the questionnaire instrument accurately explains the variables included in the study.
An indicator is considered valid if its outer loading value is greater than 0.
Based on the data analysis shown in Figure 1 above, it was found that all outer loading values for all indicator items were greater than 0.
indicating that all indicators are valid and the convergent validity requirement is fulfilled.
Discriminant Validity Discriminant conducted to ensure that each construct or variable in the latent model is distinct and unique from other variables in the model.
Discriminant validity can be assessed using the Average Variance Extracted (AVE) value and the square root of the AVE.
AVE
Performance Knowledge Sharing Digital Competence Motivation Source: Processed Primary Data, 2024 Based on Table 3, the results of the discriminant validity measurement show that the AVE values for all variables are greater than 0.
5, indicating that all variable indicators are valid and the Discriminant Validity test is fulfilled.
The values of the square roots of AVE were shown in Table 4.
In Table 4, it can be seen that the square root of the AVE for each variable is greater than its correlation with other variables, thus it can be stated that the Discriminant Validity is fulfilled.
Table 4.
Fornell-Larcker Criterion Variable Performance Knowledge Digital Motivation Sharing Competence Performance Knowledge Sharing Digital Competence Motivation Source: Processed Primary Data, 2024 Reliability Test A research instrument is considered reliable if the values of CronbachAos Alpha > 7, rho_A > 0.
7, and Composite Reliability Feny Y.
Chalimah Wahyuningsi.
The Influence of Digital Competence > 0.
As shown in Table 3, the values of CronbachAos Alpha, rho_A, and Composite Reliability are all greater than 0.
7, thus it can be concluded that the reliability test is Structural Model Test (Inner Mode.
The purpose of the inner model test is to evaluate the relationships between latent variables .
in the research This test is conducted by examining the R-Square value.
F-Square, and Estimates for Path Coefficients.
R-Square R-Square is a measure of the proportion of variation in the dependent variable that can be explained by the influencing The criteria are as follows:
RA = 0.
75 indicates substantial .
RA = 0.
50 indicates moderate RA = 0.
25 indicates weak .
Table 5 shows the results of the R2.
Table 5.
R2 results Variable R2 Adjusted Performance Motivation Source: Processed Primary Data, 2024 Based on Table 5, it is shown that:
R-Square for Path Model I = 0.
which means that variables X1 and X2 are able to explain variable Z by 39% .
oderate criteri.
R Square for Path Model II = 0.
which means that variables X1 and X2 through Z are able to explain variable Y 9% .
oderate criteri.
F Square FA effect size is a measure used to assess the relative impact of an influencing .
variable on an affected .
The criteria are as follows:
FA = 0.
mall/wea.
FA = 0.
FA = 0.
arge/stron.
Table 6.
F Square Variable Performance Motivation Knowledge Sharing Digital Competence Motivation Source: Processed Primary Data, 2024 Based on the data analysis results as shown in Table 6, it was found that:
Knowledge Sharing Ie Motivation = 543 .
arge/stron.
Knowledge Sharing Ie Performance = 350 .
arge/stron.
Digital Competence Ie Motivation = 200 .
Digital Competence Ie Performance = 000 .
mall/wea.
Motivation Ie Performance = 0.
mall/wea.
Hypothesis Testing Direct Effect (Path Coefficien.
The analysis of direct effects is used to test hypotheses regarding the direct influence of an exogenous variable on an endogenous variable.
Criteria:
ue Path Coefficient A If the Path Coefficient value is positive, the influence of the endogenous variable is in the same This means that if the value of the exogenous variable increases, the value of the endogenous variable also increases.
A If the Path Coefficient value is negative, the influence is in the opposite direction.
This means that if the value of the exogenous variable increases, the value of the endogenous variable decreases.
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ue Probability / Significance Value (P-Valu.
A If the P-Value < 0.
05, the effect is considered significant.
A If the P-Value > 0.
05, the effect is considered not significant.
The results of the Path Coefficient analysis are presented in Table 7.
Table 7.
Path Coefficient Path Knowledge Sharing Ie Performance Knowledge Sharing Ie Motivation Digital Competence Ie Performance Digital Competence Ie Motivation Motivation Ie Performance Original Sample(O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Source: Processed Primary Data, 2024 Based on the results of the path coefficient analysis presented in Table 7, the following findings were obtained:
H1 Digital Competence Ie Motivation = 345 .
P Value 0.
003 < 0.
, indicating that digital competence has a positive and significant effect on motivation.
This finding shows that the higher the level of digital competence possessed by educational staff, the higher their work motivation.
This can be explained by the fact that mastering technology provides a sense of confidence, ease in completing tasks, and a feeling of relevance to current demands, which ultimately increases enthusiasm and motivation to work.
This finding demonstrate a strong relationship between digital skills and increased work motivation in educational environments, thus H1 is accepted.
H2 Digital Competence Ie Performance = 010 .
P Value 0.
979 > 0.
ot significan.
, indicating that digital competence has a positive but not significant effect on performance.
Although the relationship is positive, this shows that even though educational staff may have good digital skills, it is not sufficient to directly improve their Therefore.
H2 is rejected.
H3 Knowledge Sharing Ie Motivation = 567 .
P Value 0.
000 < 0.
, indicating that knowledge sharing has a positive and significant effect on motivation.
The higher the level of knowledge-sharing practices in the work environment, the higher the motivation of the educational staff.
This aligns with the theory that knowledge sharing can create a collaborative and supportive work atmosphere, enhance a sense of belonging to the organization, all of which contribute to increasing individual motivation.
Therefore.
H3 is H4 Knowledge Sharing Ie Performance = 849 .
P Value 0.
010 < 0.
, indicating that knowledge sharing has a positive and significant effect on performance.
This finding shows that educational staff who actively share knowledge not only help their colleagues but also expand their own insights and their skills.
environment that supports knowledge exchange will accelerate task completion and encourage innovation, thereby directly contributing to improved Therefore.
H4 is accepted Feny Y.
Chalimah Wahyuningsi.
The Influence of Digital Competence H5 Motivation Ie Performance = -0.
P Value 0.
320 > 0.
ot significan.
, indicating that motivation has a negative and not significant effect on Therefore.
H5 is rejected.
If the P-Value < 0.
05, the effect is significant or indirect, meaning that the intervening variable plays a mediating role in the relationship between the exogenous and endogenous variables.
Indirect Effect If the P-Value > 0.
05, the effect is not significant or is considered a direct effect, meaning that the intervening variable does not play a role in mediating the relationship between the exogenous and endogenous variables.
The analysis of indirect effects is useful for testing hypotheses regarding the indirect influence of an exogenous variable on an endogenous variable mediated by an intervening variable.
The results of the Indirect Effect analysis can be seen in Table 8.
Criteria:
Path Tabel 8 Specific Indirect Effect Original Sample Standard Sample (O) Mean (M) Deviation (STDEV) Knowledge Sharing Ie Motivation Ie Performance Digital Competence -0.
Ie Motivation Ie Performance Source: Processed Primary Data, 2024 Based on Table 6 above, the following conclusions can be drawn:
H6: Digital Competence Ie Motivation Ie Performance.
P-Value = 0.
irect effec.
, meaning that Digital Competence has a direct effect on Performance.
Motivation does not play a mediating role in the Digital Competence Performance.
H6 is rejected.
H7: Knowledge Sharing Ie Motivation Ie Performance.
P-Value = 0.
irect effec.
, meaning that Knowledge Sharing has a direct effect on Performance.
Motivation does not play a mediating role in the Knowledge Sharing and performance.
H7 is rejected.
T Statistics (|O/STDEV|) P Values CONCLUSION The results of the study involving 30 educational staff at Universitas Pekalongan regarding the role of digital competence and knowledge sharing on staff performance, with motivation as an intervening variable, revealed several findings.
First, digital competence has a significant positive effect on motivation.
Second, digital competence has a positive but not significant effect on performance, which is consistent with the findings of Baharrudin et al.
, .
Third, knowledge sharing has a significant positive effect on motivation, in line with the study by Nugraha & Sukiman .
Fourth, knowledge sharing has a significant positive effect on performance, consistent with the findings of Nugraha & Sukiman .
and Fadila et al.
, .
Fifth, motivation has a negative but not significant effect on Sixth, digital competence has a direct effect on performance, indicating that motivation does not mediate the relationship Seventh, knowledge sharing also has a direct effect on performance, meaning that motivation does not mediate the relationship between knowledge sharing and performance.
From these findings, it can be concluded that knowledge sharing is the most consistently influential factor on both motivation and performance.
Therefore, it is necessary to strengthen the culture of knowledge sharing across units and departments, as it significantly impacts both motivation and performance.
Universitas Pekalongan needs to encourage the development of a collaborative work culture.
Meanwhile, digital competence plays more of a role as a motivator, but it does not directly enhance performance without the support of other contributing factors.
These findings provide practical implications: improving the performance of educational staff not only depends on enhancing digital skills but also requires support through a work culture that promotes collaboration, as well as a management system capable of transforming motivation into tangible performance REFERENCES