Analysis of the Influence of Workload on Digital Competence and Its Impact on Employee Performance in the State-Owned Enterprise Service Division of PT XYZ

International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6284 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Analysis of the Influence of Workload on Digital Competence and Its Impact on Employee Performance in the State-Owned Enterprise Service Division of PT XYZ Ganjar Adi Saputra1, Dian Indiyati2 1,2 Master of Management Study Program, School of Economics and Business, Telkom University, Bandung, Indonesia ABSTRACT: Employee performance is a crucial factor in achieving organizational goals, especially within the telecommunications industry that is currently undergoing digital transformation. This study aims to analyze the effect of workload on digital competence and employee performance in the State-Owned Enterprise Service Division of PT XYZ. A quantitative approach was used, employing a questionnaire distributed to 126 respondents determined using the Slovin formula. Data analysis was conducted through the Structural Equation Modeling (SEM) technique with the Partial Least Squares (PLS) approach. The results show that workload has a significant effect on digital competence, workload significantly affects employee performance, and digital competence has a significant effect on employee performance. These findings emphasize the importance of workload management and the enhancement of digital competence to improve performance amid technology-driven work demands. KEYWORDS: Digital Competence, Employee Performance, Workload. INTRODUCTION The rapid advancement of digital technologies has driven fundamental changes in how organizations operate, communicate, and deliver services. Digital transformation requires every work unit to adapt to business processes that are more efficient, integrated, and data-driven. In the telecommunications sector, these changes are accelerating as the demand for digital services, the use of big data, and process automation continues to grow. PT XYZ, as one of Indonesia’s state-owned enterprises in the telecommunications industry, holds a strategic role in supporting the national digitalization agenda—particularly through the State-Owned Enterprise Service Division (SOE Service), which is responsible for managing services for state-owned enterprises, their subsidiaries, and affiliated entities across various strategic sectors. The work environment within the SOE Service Division is dynamic and highly complex, characterized by heavy workloads, tight deadlines, and the constant expectation to maintain service quality. These conditions create physical, mental, and psychological pressures. When workload is not managed in a balanced manner—whether excessively high or disproportionately low—it can diminish productivity, work quality, and employee motivation. As task complexity continues to increase, employees face significantly higher demands in terms of volume, time pressure, and responsibility. If not properly managed, these conditions can undermine work effectiveness. It has been observed that employees often continue to accept additional tasks despite experiencing fatigue in order to maintain performance, indicating that workload imbalance has become a major source of strain that negatively affects productivity [1]. On the other hand, digital competence plays a strategic role in enhancing employee performance through the effective use of technology, encompassing technical skills, digital ethics, data security, and adaptive capabilities. These competencies contribute to greater efficiency, accuracy, timely decision-making, and innovation. Employees with strong digital competence are better able to automate routine tasks and reduce administrative burdens, whereas limited digital skills can hinder workflow and elevate stress due to technical challenges. As the use of digital technologies in organizational processes continues to expand, systematic knowledge management becomes increasingly essential to ensure that information and work experiences are captured and utilized optimally. Within this context, organizational knowledge management emerges as an integral component of digital transformation. Among these factors, the human element is considered the most critical, as the success of knowledge management largely depends on individual participation within the program [2]. Organizational success is strongly influenced by employee creativity and skills, International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6285 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 including their ability to adapt to digital work systems [3], while technology has become a key element in business strategies aimed at achieving and sustaining competitive advantage in the digital transformation era [4]. In the context of the SOE Service Division at PT XYZ, the relationship between workload and digital competence directly influences employee performance. A high workload that is not supported by adequate digital competence often leads to fatigue, stress, reduced motivation, and diminished work effectiveness. Conversely, enhanced digital competence enables technology to serve as a tool that eases workloads and boosts productivity. Thus, maintaining a balance between workload and digital competence is essential for achieving optimal performance. This study aims to examine the effect of workload on digital competence and its subsequent impact on employee performance within the SOE Service Division of PT XYZ. The findings are expected to contribute theoretically to the development of human resource management studies in the digital era, while also providing practical insights for organizations in formulating policies on workload management, digital competence development, and the creation of a productive and sustainable work environment. In addition, the results can serve as a form of intellectual capital that helps organizations identify and assess intangible assets through measurable indicators. Once quantified, these assets become easier to communicate and interpret. Several scholars argue that intellectual capital can serve as a representation of organizational performance and may function as an alternative approach to the balanced scorecard [5]. LITERATURE REVIEW A. Workload Workload refers to the amount of tasks or responsibilities that an individual is required to complete within a specified timeframe in accordance with organizational standards. An imbalance between job demands and an individual’s capacity can lead to work stress, fatigue, and decreased motivation [6]. Such conditions make employees vulnerable to stress, which occurs when environmental demands exceed an individual’s ability to respond effectively [7]. Workload may be quantitative in nature (excessive amounts of work) or qualitative (high levels of task difficulty). Three primary dimensions of workload are commonly identified, as follows: Table 1. Workload Dimension [8] No. Dimension Description 1 Work Conditions Encompasses the alignment of assigned tasks with available facilities, organizational support, and the work environment. 2 Performance Targets Refers to organizational standards and expected results. Unrealistic targets may intensify the workload. 3 Use of Working Time Concerns the effectiveness with which employees manage and utilize their time to achieve optimal outcomes. In the context of the State-Owned Enterprise Service Division at PT XYZ, workload has increased alongside digital transformation, which demands task completion through new systems, cross-platform coordination, and a growing volume of data-driven administrative work. An imbalance between workload and the resources available can reduce efficiency and place downward pressure on individual performance. B. Digital Competence Digital competence is defined as an individual's ability to use information technology effectively, creatively, and safely within a work context [9]. According to the European Commission, digital competence extends beyond technical skills and encompasses data literacy, online communication, digital security, and problem-solving abilities [10]. UNESCO categorizes digital competence into three levels [11]: 1. Functional skills, referring to basic understanding and operation of technology. 2. General skills, which represent the generic ability to use digital tools in daily activities. 3. Advanced skills, which involve professional-level capabilities in innovation and technology-based problem-solving. There are five key dimensions of digital competence, as outlined below: International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6286 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Table 2. Digital competence dimensions [9] No. Dimension Description 1 Information and Data Literacy The ability to identify, evaluate, and manage digital information effectively. 2 Digital Communication and Collaboration Skills for interacting online with proper ethics and data security. 3 Digital Content Creation The ability to produce and adapt digital content creatively. 4 Digital Safety Understanding personal data protection and awareness of cybersecurity risks. 5 Digital Problem-Solving The capability to address technological challenges effectively and innovatively. High levels of digital competence enable employees to adapt to complex workloads and enhance overall organizational performance. In the context of this study, digital competence is viewed as a variable that may mediate or strengthen the influence of workload on work outcomes. Employee performance refers to the quality and quantity of work achieved by an individual in carrying out their responsibilities [12]. Performance is influenced by various factors, including capability, motivation, work environment, and organizational support. A balanced workload coupled with adequate digital competence contributes to higher productivity, effectiveness, and job satisfaction. To comprehensively assess employee performance, a dimensional framework is required to capture the diverse aspects of work behavior and outcomes. Six key dimensions are commonly used to measure individual performance within organizations, namely: Table 3. Dimensions of Employee Performance [13] No. Dimension Description 1 Work Quality The degree of excellence reflected in an employee’s work outcomes, including consistency in maintaining quality standards and accuracy in completing tasks in accordance with procedures and expectations. 2 Work Quantity The volume of work completed within a specified period, assessing productivity capacity quantitatively without compromising quality. 3 Timeliness The employee’s ability to complete tasks within established deadlines. Timeliness serves as a critical indicator in performance environments driven by targets and process discipline. 4 Work Effectiveness The employee’s ability to utilize physical, financial, and technological resources efficiently to produce optimal outputs. 5 Independence The ability to work autonomously without excessive reliance on supervisory direction, reflecting professional maturity and proactive initiative. 6 Willingness to Develop Intrinsic motivation to enhance knowledge, skills, and contribution, demonstrated through participation in training, openness to innovation, and rapid adaptation to digital technological advancements. C. Research Methodology This study employs a descriptive and verificative quantitative approach to examine the causal relationships among workload, digital competence, and employee performance. A survey method was used because the data were collected directly through questionnaires that capture the perceptions and experiences of employees in the State-Owned Enterprise Service Division of PT XYZ. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6287 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 D. Data Analysis Technique Data were analyzed using SEM-PLS through SmartPLS 4.0, which is appropriate for multivariate data and sample sizes of fewer than 200 respondents. The analysis was conducted through the following stages: 1. Evaluation of the Outer Model: assessing convergent validity (factor loadings > 0.70; AVE > 0.50), discriminant validity (Fornell–Larcker criterion and cross-loadings), and reliability (Composite Reliability and Cronbach’s Alpha > 0.70). 2. Evaluation of the Inner Model: examining relationships among variables using R², t-statistics, p-values, predictive relevance (Q²), and effect size (f²). The results of the SEM-PLS analysis were used to explain the influence of workload on digital competence and employee performance, as well as the effect of digital competence on performance within the context of PT XYZ’s digital transformation. RESULTS AND DISCUSSION A. Outer Model Assessment The outer model assessment was conducted to evaluate the validity and reliability of the research instruments used to measure the variables of workload, digital competence, and employee performance. The evaluation was carried out through four main components: (1) convergent validity testing, (2) reliability testing using Cronbach’s Alpha, (3) reliability testing using Composite Reliability, and (4) discriminant validity testing. To analyze the relationships among the variables in greater depth, Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach was applied. Data processing was performed using SmartPLS 4.0. Based on the results of the analysis, the following findings were obtained. Figure 1. Struktur variabel penelitian International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6288 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 B. Convergent Validity Test The results indicate that all indicators meet the criteria for convergent validity, as shown below: Table 4. Convergent Validity Test Results Variabel Item Code Outer Loading Workload X1.1 0,886 X1.2 0,858 X1.3 0,825 X1.4 0,763 X1.5 0,820 X1.6 0,865 X1.7 0,723 X1.8 0,787 Digital Competency X2.1 0,851 X2.10 0,770 X2.11 0,734 X2.12 0,735 X2.13 0,788 X2.14 0,783 X2.2 0,734 X2.3 0,799 X2.4 0,831 X2.5 0,742 X2.6 0,794 X2.7 0,771 X2.8 0,742 X2.9 0,804 Employee Performance Y1.1 0,777 Y1.10 0,869 Y1.11 0,798 Y1.12 0,837 Y1.13 0,710 Y1.14 0,832 Y1.15 0,762 Y1.16 0,710 Y1.17 0,796 Y1.18 0,802 Y1.2 0,806 Y1.3 0,806 International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6289 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Variabel Item Code Outer Loading Y1.4 0,777 Y1.5 0,803 Y1.6 0,786 Y1.7 0,862 Y1.8 0,810 Y1.9 0,822 All outer loading values exceed the minimum threshold of 0.50, indicating that the indicators meet the required validity criteria. Thus, all indicators used in this study are considered valid [15]. C. Discriminant Validity Test Based on the results presented in the table above, all constructs in this study show that the square root of the AVE values is greater than the correlations between constructs. Therefore, the criteria for discriminant validity are fulfilled, and the discriminant validity of the model is deemed adequate. Table 5. Discriminant Validity Test Results (Heterotrait–Monotrait Ratio / HTMT) Heterotrait-monotrait ratio (HTMT) Employee Performance ↔ Workload 0,540 Digital Competence ↔ Workload 0,514 Digital Competence ↔ Employee Performance 0,710 D. Reliability Test The reliability test is the final stage in the outer model evaluation and is conducted to ensure that the instrument does not exhibit measurement issues. Reliability is assessed using Composite Reliability and Cronbach’s Alpha. If all constructs have values ≥ 0.70, the instrument is considered reliable, and the questionnaire is deemed consistent. Table 6. Reliability Test Results Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Workload 0,930 0,950 0,941 Employee Performance 0,967 0,969 0,969 Digital Competence 0,950 0,956 0,955 The assessment of validity and reliability provides a strong foundation for the subsequent analysis. The high Cronbach’s Alpha values indicate good internal consistency, confirming that the constructs are reliable. This accuracy ensures that the data used are consistent and trustworthy for interpreting the path coefficient analysis in the following stages. E. Inner Model After the model fulfilled the outer model criteria, the inner model assessment was conducted to evaluate the relationships among the variables within the conceptual framework. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6290 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Model Fit Test Table 7. Model Fit Test Saturated model Estimated model SRMR 0,090 0,090 d_ULS 6,573 6,573 d_G 3,088 3,088 Chi-square 1738,802 1738,802 NFI 0,680 0,680 The results of the model fit test show an SRMR value of 0.090 (< 0.90), indicating that the model demonstrates an acceptable level of fit. R-Square (R²) Values R² is used to assess the model’s ability to explain the variance of the endogenous variables. Table 8. R-Square (R²) Values R-square R-square adjusted Employee Performance 0,549 0,541 Digital Competency 0,264 0,258 For Employee Performance, the Adjusted R² value is 0.541, indicating that 54.1% of the variance in employee performance is explained by workload and digital competence. This value falls within the moderate category. For Digital Competence, the Adjusted R² value is 0.258, meaning that 25.8% of the variance in digital competence is explained by workload, which is considered a low category. Effect Size (f²) Table 8. Effect Size (f²) f-square Workload > Employee Performance 0,103 Workload > Digital Competency 0,358 Digital Competency > Employee Performance 0,547 Effect size values indicate the strength of influence exerted by each predictor variable: a. Workload → Employee Performance: f² = 0.103 (medium effect). b. Workload → Digital Competence: f² = 0.358 (strong effect). c. Digital Competence → Employee Performance: f² = 0.547 (very strong effect). Q-Square (Predictive Relevance) Table 9. Q-Square (Predictive Relevance) Q²predict Employee Performance 0,269 Digital Competency 0,222 A Q² value greater than 0 indicates that the model has predictive relevance. a. Employee Performance → Q² = 0.269 (moderate predictive relevance). b. Digital Competence → Q² = 0.222 (moderate predictive relevance). International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6291 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Hypothesis Testing (Bootstrapping) Figure 2. Hypothesis Testing (Bootstrapping) Hypothesis testing was conducted using the bootstrapping procedure to assess the significance of the path coefficients. A relationship is considered statistically significant when the T-statistic value exceeds 1.96 (α = 0.05) and the P-value is less than 0.05. Table 10. Hypothesis Testing Results Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Workload → Employee Performance 0,252 0,256 0,071 3,568 0,000 Workload → Digital Competence 0,514 0,523 0,082 6,283 0,000 Digital Competence → Employee Performance 0,579 0,575 0,061 9,443 0,000 a. Effect of Workload on Employee Performance The bootstrapping results indicate a significant effect, with a T-statistic of 3.568 (> 1.96) and a P-value of 0.000 (< 0.05). The positive path coefficient (0.252) suggests that workload has a positive influence on employee performance. Thus, the hypothesis is accepted. b. Effect of Workload on Digital Competence The effect of workload on digital competence is also significant. The T-statistic value of 6.283 and the P-value of 0.000 indicate that the positive path coefficient (0.514) is statistically significant. Therefore, the hypothesis is accepted. c. Effect of Digital Competence on Employee Performance The bootstrapping results show a significant effect, with a T-statistic of 9.443 (> 1.96) and a P-value of 0.000. The positive path coefficient (0.579) confirms that digital competence has a positive influence on employee performance. Hence, the hypothesis is accepted. International Journal of Current Science Research and Review ISSN: 2581-8341 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijcsrr/V8-i12-40, Impact Factor: 8.048 IJCSRR @ 2025 www.ijcsrr.org 6292 *Corresponding Author: Ganjar Adi Saputra Volume 08 Issue 12 December 2025 Available at: www.ijcsrr.org Page No. 6284-6294 Table 10. Indirect Effect Hypothesis Testing Results: Influence of X on Y through Z Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Workload → Digital Competence → Employee Performance 0,297 0,302 0,064 4,668 0,000 The indirect effect of workload on employee performance through digital competence is proven to be significant. The T-statistic value of 4.668 (> 1.96) and the P-value of 0.000 (< 0.05) indicate that the indirect path coefficient is positive (0.297). This suggests that workload enhances employee performance through an increase in digital competence. RESULTS AND DISCUSSION All workload indicators were found to be valid and reliable, with outer loading values above 0.70 and an AVE of 0.669. The strongest contributing indicators originate from the dimensions of work volume and time pressure (highest loading = 0.886), indicating that employees in the SOE Service Division face substantial demands related to project completion and service delivery for state-owned enterprises. Nevertheless, these workloads remain manageable through structured work systems and clear task allocation. These findings align with Priyandi [16], who notes that well-managed workload can enhance productivity. The digital competence variable achieved an AVE of 0.605, with all indicators demonstrating outer loading values greater than 0.70. The most dominant dimensions were the use of work applications and internal systems (0.851), as well as data management and technological adaptation (0.831). These results indicate that employees have adapted well to digitalization processes. Methodologically, the findings confirm that the digital competence construct meets the convergent validity criteria recommended by Chin [17] and Hair et al. [15]. Employee performance indicators were also validated and deemed reliable, with an AVE of 0.639. The strongest dimensions were work effectiveness and work quality, reflecting employees’ ability to meet targets efficiently, while the desire-to-develop dimension showed relatively lower values. This suggests a need to strengthen a culture of continuous learning. These findings are consistent with Rolos [18], who highlights the importance of workload management in sustaining employee performance. Workload has a positive and significant effect on employee performance (β = 0.252; T = 3.568; P < 0.001). This result indicates that a proportional increase in workload can enhance focus and responsibility, thereby improving performance. These findings are consistent with Priyandi et al. [16] and Silaban et al. [19]. The results also show that workload has a positive and significant effect on digital competence (β = 0.514; T = 6.283; P < 0.001; f² = 0.358). This finding demonstrates that increasing job demands encourage employees to enhance their digital skills as an adaptive strategy. This is supported by Wahjono [20], who discusses the role of work pressure in driving innovative behavior. Digital competence has a strong and significant effect on employee performance (β = 0.579; T = 9.443; P < 0.001; f² = 0.547). This result indicates that digital competence is a key determinant of productivity improvement within the SOE Service Division, particularly in data management, the use of information systems, and cross-unit collaboration. The finding is consistent with Dhaniswara [21], who reports that digital competence has a positive and significant impact on employee performance. CONCLUSION The findings of this study indicate that workload has a significant and positive influence on both digital competence and employee performance, with a strong effect on digital competence (f² = 0.358) and a moderate effect on employee performance (f² = 0.103). Digital competence, in turn, has a significant and very strong influence on employee performance (f² = 0.547). Collectively, workload and digital competence explain 68% of the variance in employee performance (R² = 0.680), demonstrating substantial predictive power. Workload also accounts for a significant proportion of the variance in digital competence. In addition, the structural model shows good predictive relevance, as reflected by Q² values of 0.269 for employee performance and 0.222 for digital competence.