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This study aims to analyze the relationship between salaries, allowances, and employee productivity on company profitability. Human resources are an important factor in determining organizational success because employee quality and performance directly influence the achievement of company goals. Providing appropriate compensation, including salaries and allowances, can increase employee motivation, job satisfaction, and loyalty, thereby encouraging higher work productivity. High productivity reflects the company’s ability to utilize resources effectively and efficiently in order to produce optimal output. In addition, employee turnover is an important aspect that must be considered because it may affect operational stability and the company’s effectiveness in achieving business targets. Profitability is used as the main indicator to assess the company’s ability to generate profits from its operational activities. This study applies a quantitative method with an approach that examines the relationships among variables to obtain an overview of the influence of compensation and productivity on company profitability. The results of this study are expected to provide insights and recommendations for companies in managing human resources more effectively in order to improve financial performance sustainably.

This study was conducted to examine the effect of compensation on employee performance through a literature review approach. The background of this research is based on the importance of compensation as a factor that contributes to improving employee work outcomes. Compensation is not only considered as remuneration for work performed, but also as an element that can encourage motivation and productivity. The study applied a descriptive method using a literature review approach. The data used were secondary data obtained from 20 national scientific journals published since 2020, discussing compensation and employee performance. Data collection was carried out through library research by selecting, classifying, and reviewing relevant literature sources. Furthermore, the data were analyzed descriptively by interpreting findings from previous studies. The results indicate that 19 out of 20 journals reported a positive and significant relationship between compensation and employee performance. Therefore, it can be concluded that compensation plays an essential role in enhancing employee performance, making an effective compensation system a crucial aspect of human resource management.

The effectiveness of public sector organizations in delivering quality services and achieving their mandates depends critically on how they manage their human resources. This study examines the relationship between strategic human resource management (SHRM) practices and organizational performance in Indonesian public sector organizations. Employing a mixed-methods research design, data were collected from 312 public sector managers and HR professionals across 45 government agencies through surveys and 32 in-depth interviews. The research investigates five key SHRM dimensions: strategic recruitment and selection, performance management systems, employee development and training, compensation and rewards, and employee engagement. Findings reveal significant positive relationships between SHRM practices and multiple performance indicators including service quality, operational efficiency, employee productivity, and organizational innovation. Regression analysis demonstrates that SHRM practices collectively explain 47.3% of variance in organizational performance scores. Qualitative data illuminate implementation challenges including limited HR professional capacity, rigid civil service regulations, political interference, and resistance to performance-based management. The study identifies critical success factors such as top management commitment, alignment between HR strategy and organizational strategy, investment in HR analytics capabilities, and cultural transformation toward merit-based practices. Results indicate that high-performing public organizations distinguish themselves through systematic talent management, data-driven HR decision-making, continuous learning cultures, and stronger linkages between individual performance and organizational outcomes. This research contributes empirical evidence on SHRM effectiveness in public sector contexts and provides actionable recommendations for HR practitioners and policymakers seeking to leverage human capital for improved public service delivery.

This study was carried out at RS Hj. Fatimah Sulhan in Kabupaten Demak, Jawa Tengah, which functioned as both the research site and the moderating context of the investigation. The research aims to examine how personal compensation, Organizational Citizenship Behavior (OCB), and organizational commitment influence employee work performance within the hospital environment. A quantitative research design was implemented, involving 150 employees selected as respondents through appropriate sampling procedures. Data were collected using structured questionnaires and subsequently analyzed using Structural Equation Modeling (SEM) with the AMOS software to test the hypothesized relationships among variables. The findings reveal that personal compensation, OCB, and organizational commitment each exert a positive and statistically significant impact on employee performance. These results indicate that fair and competitive compensation, strong voluntary work behaviors, and a high level of commitment to organizational goals collectively enhance productivity, service effectiveness, teamwork quality, and overall institutional performance in the healthcare setting.

Rapid developments in the Artificial Intelligence (AI) industry have triggered an increased need for workers with specialized competencies, which has implications for significant variations in salary levels. This research aims to analyze the factors that influence salaries in the AI sector using the multiple linear regression method. The dataset used includes 15,000 AI job vacancies with variables including job and company characteristics. The data was engineered via the one-hot encoding method and divided into two parts: training data (80%) and test data (20%). The analysis results show that the regression model is able to explain 85% of the variation in salary, with an R² value of 0.85 and a Root Mean Square Error (RMSE) of USD 23,221. The three main factors identified as having a significant influence on salaries in the AI field are work experience, company location, and the industry in which the company operates. The experience factor reflects the skills and knowledge developed over many years, which can increase productivity (Rony et al., 2023). Company location also plays an important role, as the cost of living and demand for skilled labor varies by region (Badran, 2019). Additionally, the specific industry in which an employee works influences salary, given that more developed industries can often offer higher compensation (Huang, 2025). This research makes a significant empirical contribution to the understanding of compensation structures in the AI labor market.