Abstract This study explores the connection between transformational leadership (TL) and employee performance (EP), with job satisfaction (JS) acting as a mediating factor, at Jeddah Islamic Port (JIP). A quantitative approach employing structural equation modeling (SEM) was utilized, with data gathered from 198 employees and subsequently analyzed through AMOS software. The results indicate that each of the four dimensions of transformational leadership—idealized influence (II), inspirational motivation (IM), intellectual stimulation (IS), and individualized consideration (IC) —has a significant and positive impact on JS and EP. The relationship between transformational leadership and EP is partially mediated by JS. The variables associated with transformational leadership account for 26.0% of the variance in JS and 53.9% of the variance in EP. The investigation offers empirical data that underscores the essential function of transformational leadership in improving organizational performance within the maritime logistics sector.

This research aims to determine and analyze the relationship between job satisfaction, work-life balance, and work motivation variables on employee performance. Employee performance can be influenced by various factors, including job satisfaction, work-life balance, and work motivation. Effective strategies are needed to improve employee performance. Using a quantitative approach, data were collected from Millennial Generation employees in various hospitals and analyzed using multiple regression techniques. The data collection technique was by distributing questionnaires to research objects to Millennial Generation employees and through a documentation study of 100 Millennial Generation employees. The findings of this study empirically prove that all three factors can have a significant impact on employee performance, with job satisfaction being the strongest predictor. From the results of this study, partially, the job satisfaction variable has a positive and significant effect on the performance of Millennial Generation employees in various hospitals, the work-life balance variable has a positive and significant effect on the performance of Millennial Generation employees in various hospitals, the work-motivation variable has a positive and significant effect on the performance of Millennial Generation employees in various hospitals. The simultaneous findings show that job satisfaction, work-life balance, and work motivation have a positive and significant effect on the performance of Millennial Generation employees in various hospitals. This research result value of Adjusted R Square was proven to be 45.7%.

Employee performance is the main result that can determine whether an employee has contributed to their productivity, therefore an organization is required to be able to analyze and know the factors of employee performance along with efforts in developing these employees. This study aims to analyze the effect of Occupational Safety and Health (K3) and Work Environment on Employee Performance. This study is a quantitative study with multiple linear regression methods. The total sample in this study was 90 people who were employees of PT. Adovelin Raharja Shipping. The results of the study showed that partially and simultaneously Occupational Safety and Health (K3) and Work Environment had a positive and significant effect on Employee Performance.

Blue Monday, work stress and employee performance: an empirical investigation

How does artificial intelligence adoption shape employee performance? A novel exploration of mimetic artificial intelligence performance through a hybrid approach based on PLS-SEM and ANN

This study aimed to validate the digital transformation model in human resource management of the Civil Registration Organization of Iran. The research method was mixed (qualitative–quantitative) and employed a descriptive–analytical approach. In the qualitative phase, by applying grounded theory and conducting interviews with experts, the main and subcategories of digital transformation were extracted, followed by open, axial, and selective coding. Then, based on the qualitative findings, a questionnaire was designed and implemented in the quantitative phase with a sample of 380 managers and specialists. The data were analyzed using structural equation modeling (PLS-SEM) and validity and reliability tests. The reliability of the questionnaire was confirmed with Cronbach’s alpha above 0.7 and composite reliability above 0.8. Convergent validity was verified with AVE greater than 0.5, and discriminant validity was confirmed with HTMT less than 0.9. The findings indicated that digital transformation in human resource management is a multidimensional process influenced by causal conditions (β = 0.34), intervening conditions (β = 0.49), and contextual conditions (β = 0.56). Strategies such as organizational culture development, human resource empowerment, smart network development, and continuous monitoring were identified as key drivers. The outcomes of this transformation emerged at three levels — individual, organizational, and social — including enhanced employee performance, increased organizational agility, and improved social capital. The modeling results showed that all hypotheses were statistically significant (t > 1.96). Additionally, the coefficient of determination (R² = 0.20) and the global goodness-of-fit index (GOF = 0.411) indicated an acceptable model fit. Ultimately, the proposed model can serve as a comprehensive framework for digital human resource management in the Civil Registration Organization and similar organizations.

The purpose of this study is to analyze the relationship between industrial digital strategy in the area of transformational technopreneurship development and analyze the relationship between human creativity in the area of transformational technopreneurship development. This study uses a quantitative approach with an explanatory research design, which aims to examine the influence of integrity, organizational commitment, and motivation on sustainable employee performance with job satisfaction as a mediating variable. The population consists of all employees of SME organizations, totaling 765 employees. The sampling technique applied is simple random sampling. The research instrument is a questionnaire using a 5-point Likert scale. The research variables are: Digital Work Environment (X1), Job Satisfaction (X2), Organizational Culture (X3) and Employee Work Productivity (Y). Data were analyzed using Partial Least Square – Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The analysis consists of two stages: Outer Model (Measurement Model): Convergent validity, discriminant validity, and reliability testing. Inner Model (Structural Model): Testing path coefficients, R² values, and direct and indirect influences between variables. The results of the study show that the Industrial digital strategy variable has a positive relationship with the transformational development area of technopreneurship, human creativity has a positive relationship with the transformational development area of technopreneurship.

In the age of digital revolution, organizations increasingly use artificial intelligence (AI) and machine learning (ML) to improve their data-driven decision-making, especially in human resource management. This paper makes a comparative evaluation of AI-driven data warehousing systems and ML methods for forecasting employee turnover and maximizing employee performance. The study compares top data warehousing platforms like Redshift, BigQuery, Snowflake, and Databricks and their coupling with ML models with regard to prominent workforce features. Qualitative findings from HR managers were also examined, in order to evaluate the real-world effect of these technologies on the productivity of the workforce and employment strategies. Research shows that AI-based data warehousing integrated with competent machine learning models drastically enhances attrition prediction accuracy, performance tracking, and strategic workforce planning. This research identifies the strategic advantages of combining AI-driven data warehousing with HR analytics, offering organizations actionable findings to choose the best AI-enabled solutions. The findings contribute to extending knowledge on efficient data strategies in lessening attrition as well as improving employee performance, aiding organizations in their pursuit of strategic human capital objectives.

This study aims to examine the influence of competence and discipline on employee performance at the Kecamatan Sitinjau Laut Kabupaten Kerinci, both simultaneously and partially, and to identify the most dominant variable affecting performance. The research questions focus on whether competence and discipline significantly affect employee performance, the extent of their influence, and which variable contributes most prominently. The analysis is based on indicators representing competence and discipline and their relationship to performance outcomes. The findings indicate that competence positively influences employee performance, as supported by relevant statistical tests. Discipline also contributes to performance improvement, though with a different level of impact compared to competence. When analyzed simultaneously, both variables show a significant effect on employee performance, as demonstrated through regression analysis. Among the two, competence emerges as the more dominant factor. These results suggest that enhancing employee competence and reinforcing workplace discipline are effective strategies for improving performance in local government institutions.

This study aims to analyze the influence of motivation (X1) and work discipline (X2) on employee performance (Y) at the Bogor City Regional Secretariat. The research method used is quantitative with a descriptive and verificative approach, collecting data through observation and distributing questionnaires to employees. The sample in this study includes all employees at the Bogor City Regional Secretariat, totaling 129 people, to fill out the motivation and work discipline variables, with one leader filling out the employee performance variable. The data were analyzed using validity and reliability tests, as well as multiple linear regression analysis with the help of SPSS software. The results of the study indicate that motivation and work discipline, both partially and simultaneously, have a significant effect on employee performance, as evidenced by the F-test result of 5.303 with a significance level of 0.03. This means that the higher the motivation and work discipline, the better the employee performance will be.

The purpose of this study was to examine and analyze the effects of leaders’ empowering leadership on job satisfaction and job performance of employees in beauty companies, and to propose practical strategies for enhancing organizational and employee performance. A self-administered survey was conducted among hair salon employees in their 20s to 40s working for the ‘J’ beauty franchise, and a total of 305 questionnaires were used for the final analysis. The results indicated that providing training to enhance employees’ professional competencies and creating an environment conducive to task focus led to improved job performance. In addition, when opportunities to participate in key decision-making processes were guaranteed and autonomy was expanded, a horizontal organizational culture was established, resulting in increased job ownership and job satisfaction. Leaders’ empowering leadership had positive effects on both job satisfaction and job performance, and job satisfaction was found to mediate the relationship between empowering leadership and job performance. These findings suggest that enhancing empowering leadership and job satisfaction can positively influence job performance and customer satisfaction, and may serve as a basis for human resource management and organizational strategy development in beauty service organizations.

The tea plantation sector has a complex supply chain, and the production process at the black tea processing company is often affected by various operational issues. One of the main problems is delays in raw material processing caused by the late arrival of wet tea leaves, which are further aggravated by machine breakdowns and a lack of worker discipline. These conditions can disrupt or even halt production, emphasizing the need for effective risk mitigation measures. This study aims to identify risk factors in the black tea production supply chain using the Supply Chain Operations Reference (SCOR) model and to formulate priority mitigation strategies through the House of Risk (HOR) method. The results of the HOR Phase I analysis identified 21 risk events and 4 priority risk agents with a cumulative Aggregate Risk Potential (ARP) value of 76.82%. The four main risk agents are: (A2) irregularities or delays in the transportation of wet tea leaves, (A6) lack of worker discipline, (A7) machine damage or malfunction, and (A8) low employee awareness during work. The HOR Phase II analysis produced seven mitigation actions, which were ranked based on the Effectiveness to Difficulty Ratio (ETD). The top three priority actions are (PA₁) conducting analysis and evaluation of employee performance (ETD = 6.00), (PA₃) increasing supervision of foremen at each station (ETD = 5.00), and (PA₂) creating a schedule for picking wet tea leaves (ETD = 5.00). Supporting actions include quality control of raw materials (PA₄), routine machine maintenance (PA₅), increasing awareness to work carefully (PA₇), and limiting machine loads to maximum capacity (PA₆). The results indicate that human and managerial factors—including employee performance, supervision, and work scheduling—are the dominant contributors to supply chain risk. Therefore, mitigation strategies should prioritize management and behavioral improvements, supported by technical maintenance and process control, to enhance the efficiency, reliability, and resilience of the black tea supply chain.

This study aims to analyze the influence of supervision on employee work discipline at the Department of Housing, Settlement Areas, and Land Affairs of Cirebon Regency. Supervision is defined as an organizational control mechanism consisting of direct and indirect supervision, while work discipline includes preventive and corrective discipline. This research employed a quantitative approach using a saturated sampling technique, involving all 79 employees as respondents. Data were collected through questionnaires and analyzed using descriptive statistics and simple linear regression. The results indicate that the level of supervision is categorized as “good,” with an average score of 3.70, while employee work discipline is categorized as “high,” with an average score of 3.85. The regression analysis confirms that supervision has a positive and significant influence on employee discipline. These findings highlight that strengthening supervision—both direct and indirect—plays a crucial role in improving employee discipline. The study contributes to the development of internal supervision policies for local government agencies.

Promotion policies are critical to the functioning of public service organizations, shaping leadership progression, employee morale, and institutional performance. In local governments, seniority-based promotion systems have historically been adopted to ensure fairness, reduce political interference, and provide predictable career pathways. Drawing on equity theory, institutional theory, and human capital theory, this paper examines the effects of seniority-based promotions on employees and local government institutions. While these systems enhance job security, loyalty, and retention, they may also reduce intrinsic motivation, discourage innovation, and alienate high-performing or younger employees. Using Ghana’s Local Government Service as a case study, the paper highlights how seniority-based promotions preserve institutional knowledge, facilitate workforce planning, and maintain stability, but also pose challenges for performance, modernization, and talent attraction. The findings suggest that integrating merit-based criteria alongside seniority can balance stability with innovation, supporting both effective governance and employee development. This study contributes to understanding the evolving role of promotion policies in modern public administration and offers guidance for designing hybrid promotion frameworks that promote fairness, competence, and organizational effectiveness.

Lime-transformational headship has gained more significance due to the growing use of sustainability-based processes in officialdoms and educational companies. Powerful individuals that advance ecological responsibility and support ecological ethics are actively involved in turning staff flexibility and ecological efficiency. Two hypotheses were formulated: to investigate how Lime-transformational headship influence the staff flexibility and performance within higher education companies. The study was conducted using a validated questionnaire as quantitative cross-sectional research. Multistage sampling was applied to obtain data among faculty and administrative personnel employed in Islamabad and Karachi, Pakistan, in HEC-recognized private and public universities. A total of 416 respondents were used to collect the data. Data analysis was conducted with the SPSS version 26 and SmartPLS 4. The results revealed that Lime- transformational headship has a wide scope of improving staff adaptive performance and ecological performance. When staff under headship develop good ecological commitment, vision and support, they will be better positioned to adjust to structural requirements and better placed to practice sustainable work. Lime-transformational direction is a real approach to ensuring sustainability and streamlining employee activity in the higher education companies. The toleration of this headship style may help the development of institutions, Lime work environment, and greater staff flexibility. Institutional leaders and officials are motivated to incorporate sustainable headship in order to be able to optimize productivity and environmental responsibility.

Artificial Intelligence (AI) continuously transforms how organizations operate by enhancing decision-making, automating routine tasks, and improving overall accuracy. This study examined the relationship between Artificial Intelligence (AI) utilization and job performance of employees at SMTI Cargo Transport, a logistic company that has begun integrating AI tools into its daily operations. A quantitative descriptive–correlational design was employed, and data were collected from 34 employees using a structured questionnaire adapted from validated instruments on technology acceptance and work performance. This study used frequency and percentage, weighted mean and Spearman’s rho correlation to determine the relationship between the two variables. Results showed that employees generally hold positive perceptions of artificial intelligence, rating it highly in terms of usefulness, ease of use, and overall acceptance. Their job performance—including task performance, contextual performance, and counterproductive work behavior—was also reported as satisfactory. However, the Spearman’s rho correlation analysis revealed that there is no significant relationship between AI utilization and job performance (ρ = –0.005, p = 0.976). This finding indicates that the use of AI tools alone does not significantly influence employees’ job performance. Although AI systems are already embedded in work processes, their effectiveness in enhancing performance appears to depend on other factors such as employee training, task–technology fit, skills development, and organizational support. Overall, this study concludes that AI should be viewed as a supportive tool rather than a standalone determinant of job performance. The findings provide useful insights for SMTI Cargo Transport management in improving AI implementation strategies and highlight the need for future research to explore additional variables that may affect employee performance. By addressing these factors, SMTI can better support its workforce and maximize the potential benefits of AI as it continues to modernize its operations.

This research aims to assess the effect of leadership styles on the performance of the workforce in the nonprofit organization in Nepal. The accomplishment of employees’ responsibilities depends on the leadership approaches adopted by leaders that help to establish a foundation for stimulation. It reflects that leadership doctrine congruent with organizational conditions, employees, and stakeholders enhances the overall performance of the workforce. However, earlier evidence manifests contradictory consequences and exhibits that there is no conclusive statement on the role of leadership in boosting workforces’ performance. Therefore, this research aims to address the existing research gap. This research utilized 371 useful primary data and analysed using Smart PLS version 4.1.1.4. The research outcomes show a favourable and significant effect of autocratic, democratic, and laissez-faire leadership on the performance of the workforce. Therefore, this advocates that organizations adopting diverse and appropriate leadership approaches significantly contribute to cultivate employee performance.

AI and predictive analytics have revolutionized corporate management by replacing guesswork with facts. A comprehensive literature review reveals that AI-enhanced decision support systems are increasingly incorporating machine learning predictive analytics models. This paper summarizes research from academic studies and case studies performed by businesses to demonstrate how predictive analytics becomes an integrated part of planning corporate strategy, allocating resources among departments, and ensuring administrative efficiency. This study focuses on classifiers, which are fundamental machine learning techniques for predicting and simplifying complex decision-making processes. Such techniques include neural networks, regression analysis, decision trees, and others. The authors explain the various aspects to consider when implementing AI-driven solutions successfully, including data quality, model interpretability, and ethics. The findings show that organizations adopting predictive analytics report measurable improvements, including up to 15% reduction in employee turnover, 20 -30% improvement in risk mitigation, 25% sales growth, and 40% reduction in operational inefficiencies when integrated with ERP systems. The study also examines how predictive analytics is affecting various disciplines, such as risk management, market trend forecasting, and employee performance appraisal, by analyzing specific real-life examples. Findings suggest that the implementation of real-time analytics in ERP systems has the potential to enhance strategic decision-making significantly. The review also reveals gaps in the literature and contributes to future research by highlighting the need to scale solutions to problems and applications across industries.

E-HRM as Driver of Financial Firm's Service Quality: Exploring the Mediating Role of Employee Performance from a Resource-Based View

Prospects for Using Blockchain-Based Mobile Apps in Employee Performance Evaluation