The purpose of this research is to investigate the influence of green recruitment and selection, green training, and green intellectual capital on employee performance in Yogyakarta, Indonesia. The data collection method used in this research is to look for observation, interviews, and the distribution of questionnaires. Structural Equation Modeling is used to be an analysis method with SmartPLS assistance. Within the SEM model, this research predominantly employs the Partial Least Squares (PLS) technique, a component-based or variance-based structural equation modeling approach. PLS enables the testing of theories or theory development for predictive purposes based on experimental results. Based on the results of this study, green recruitment and selection have a significant and positive impact on employee performance. However, green training does not have any statistically significant positive effect on employee performance in the same region. On the other hand, green intellectual capital shows a significant and positive influence on employee performance. These results contribute to understanding the relationship between green human resource practices and employee performance.

Small Medium Enterprises (SMEs) play a vital role for the development of any economy. Mul-tiple studies with respect to various aspects and dimensions of SMEs have been undertaken. Some of the problems faced by SMEs from the side of the employees include performance management issues, employee issues like strikes, go slow tactics, and so on. However, studies that explore the issues as perceived by employers are scarce. The present research is undertak-en to find out the various human resource issues as perceived by employers. Data for the present study was collected from 60 SMEs. The organizations belonged to various sectors. Factor Analysis was performed to identify the factors. The analysis identified two factors as perceived by employers regarding human resource issues in SMEs. The reliability and validity of the factors were also assessed, which was found to be sufficiently high. A few suggestions for further research are also presented.

This study has examined factors, such as technology and employee influence on artificial intelligence (AI) adoption of e-innovative projects in the United Arab Emirates. The present study revealed the success or failure of e-innovation adoption in the public sector of the UAE and hinted at potential e-innovative projects to consider essential factors before adopting it. The study's sample covered the government sector, and the data collection method was a survey questionnaire with a sample size of 1037 responses made up of government employees. This paper was mainly built upon the diffusion of innovation and technology acceptance theories. The analysis findings showed that technology (an external factor) significantly and positively contributed to adopting AI e-innovation technology. Further analysis revealed that employee (internal factor) proxies directly influenced the adoption of AI e-innovation technology. Overall, internal and external factors contributed to adopting e-innovation technology in the United Arab Emirates. For future directions, additional factors related to the market should be considered to explore their contribution.