Green Human Resource Management (HRM) is considered an emerging field of management that evaluates and ensures green performance and outcomes in organizations. In today’s dynamic business environment, work-life balance has become one of the key issues faced by many employees all over the world. Maintaining work-life balance is an issue increasingly recognized as of strategic importance to the organization and significance to employees. In doing so, the present study introduced independent and dependent variables to explain the underlying mechanisms of green HRM and work-life balance and its impact on employee performance. A total of 90 employees of the calibration services company have completed a set of questionnaires through Google Forms to provide data for the analysis. This study is using census method as one of the best probability sampling techniques to be used it’s a systematic method that collects and records the data about the members of the population and is suitable when the case-intensive study is required or the area is limited. This study has adopted the quantitative method in this research as the method allows the researcher to focus on the research. The data were analyzed through SPSS which facilitates descriptive statistics, correlation, and multiple regressions. Multiple regression analysis was used to test the hypotheses in this research. The findings showed that green HRM and work-life balance were the significant variables influencing employee performance in the study. In addition, the significance of the study included providing new knowledge from the theoretical perspective, obtaining a better understanding of the importance of green HRM and work-life balance from the perspective of employee performance, and contributing to the efforts made by the government to improve the probability of green culture in organizational and balancing professional life and family life employment of employees through policies from the perspective of the government. Lastly, recommendations for employers, employees, government, and future research are made to improve employee performance.

This study investigates the impact of corporate carbon performance on financing costs, focusing on S&P 500 companies from 2015 to 2022. Utilizing a fixed-effects regression model, the research reveals a complex U-shaped nonlinear relationship between carbon intensity (CI) and cost of debt (COD). The sample comprises 2896 firm-year observations, with CI measured by the ratio of Scope 1 and 2 greenhouse gas (GHG) emissions to annual sales. The findings indicate that companies with higher CI initially face increased COD due to heightened regulatory and operational risks. However, as CI falls below a certain threshold, further reductions in emissions can paradoxically lead to increased COD, likely due to the substantial investments required for advanced technologies. Additionally, a positive relationship between CI and cost of equity (COE) is observed, suggesting that shareholders demand higher returns from companies with greater environmental risks. These results underscore the importance of balancing short-term and long-term environmental strategies. The study highlights the need for corporate managers to communicate the long-term benefits of environmental efforts effectively to creditors and investors. Policymakers should consider these dynamics when designing regulations that incentivize lower carbon emissions.

The performance of Public Enterprises (PEs) in Namibia has been a long and contentious issue, clamored by continuous bailouts in the face of constant poor performance. The trend of financial bailouts to PEs in Namibia over the years has attracted increased attention into the dynamics of poor PE performance and their fiscal burden on the state. The Namibian government has taken active steps in cutting on PE bailouts and demanding improved performance or face closure. By looking at recent developments in the governance of PEs in Namibia, the purpose and objective of the current study is to analyze whether the current stance and trajectory of government decisions spells a post-honeymoon period in which poor performing PEs will ‘wither and survive or die’ if they do not improve their sustainability index by not relying on financial bailouts. This analysis is aided by the insights provided by the stakeholder, institutional and principal-agent theories. Through the qualitative research method, this study finds that the Namibian government has taken a new attitude and approach in which it will no longer blindly accept and tolerate the poor performance of PEs through continuous bailouts as seen in the past. PEs that are withering will now either survive (through reforms) or die (through liquidation or dissolution).

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The research aims to explore the role of Electronic Human Resources Management on employee performance through employee engagement. The present research’s population included all Jordanian Service and Public Administration Commission employees. The data was collection through a questionnaire that was administered for the study Population. 262 questionnaires collected from employees working in Service and Public Administration Commission in Jordan valid for statistics. The analysis of the data was undertaken through the use of SEM (structural equation modelling). The results showed that E-HRM has a direct impact on employee performance and employee engagement. Consequently, the indication from the results was that a significant role in mediation within the effect that E-HRM had upon employee performance been played by employee engagement. The conclusion reached was that transformation of the public sector through implementation of technological HRM methods fosters employee engagement, with that being a key driver for the alignment of employee behaviors for the achievement of high levels of employee performance.