The essential point of the ponder is to investigate and discover the affiliation between organizational natural maintainability and utilizing green HRM (green compensation, green hiring, green involvement, and green training), as well as to ascertain the role that an employee's green behavior may play in making the entire work settings green. Environmental responsibility is a crucial area of study. Manufacturing companies worldwide are calling for the advancement of their green center projects due to the growing concern for the environment and its effects on global warming and climate change. In a similar vein, it was emphasized that to gain a competitive edge, there is a constant need for the production and exchange of innovative green ideas while taking into account diverse environmental groups throughout the world. The researchers adopted a qualitative methodology. The study examines the relationship between variables, finding that environmental sustainability and staff green behavior correlate with green HRM.

This literature review explores Green Human Resource Management (Green HRM) initiatives aimed at nurturing employees' commitment towards eco-friendly work output and environmental sustainability in organisations. Green HRM is a strategy that integrates an organisation's environmental values into its human resource strategy and practices. This review reflects on the interplay between Green HRM practices and employees' green commitment, that is, the degree to which they feel psychologically bonded to environmental goals and pursue them. Moreover, it focuses on how green commitment contributes to achieving specific organisational environmental sustainability outcomes, including waste reduction, energy saving, and improved corporate social responsibility. Drawing on empirical research, the review outlines specific mechanisms by which Green HRM practices (e.g., green training programs and green performance appraisal) influence employees' sustainability-related attitudes and behaviours. It also addresses barriers to adopting Green HRM and developing green dedication, such as inadequate resources, a lack of management interest, and resistance within the company. The review findings are concluded with suggestions for future research direction, requesting the need for cross-sector studies and the examination of the long-term implications of Green HRM implementations on environmental sustainability. Through qualitative methodology, the study establishes a link between the main variables involving environmental sustainability, employees' green behavior, and GHRM (Green HRM). Future research can examine more closely how industry-specific elements affect workers' green commitment and how digital technologies like artificial intelligence and big data might assist businesses in meeting their sustainability goals. Further investigation into the cultural factors that can account for variations in green commitment among various geographic areas will also yield a more comprehensive comprehension of this idea. The impact of employee green commitment on organizational sustainability performance requires longitudinal studies to understand organizational practice better. Finally, future research topics must look into the barriers to Green HRM practices to promote their successful impact.

We examined the role that Green Work Commitment plays in the context of green HR practices, such as green hiring, green compensation, green training, AI components, and organisational sustainability. It is a quantitative research method that uses questionnaires as a data-collecting tool. The study population was the study population of employees associated with multinational corporations and essential sectors in Pakistan, such as Pakistan International Airlines, Procter & Gamble Pakistan, banks, educational institutions, and IT organisations. Likewise, key organisations such as Navoi Mining and Metallurgy Combinat JSC, POSCO International Textile, LUKOIL Uzbekistan Operating Company, Nestlé Uzbekistan, Huawei Uzbekistan, various banks, educational institutions, as well as the IT sector, collected data from Uzbekistan. In both cases, quantitative methods were used, and random and convenience sampling were used to elicit 373 responses. Descriptive statistics were performed in SPSS version 23 (mean, rates, and standard deviation), and inferential statistics in Smart PLS version 3.0. Based on these goals, 12 hypotheses were developed to investigate the relationships between green HR practices and environmental sustainability. Considering these hypotheses, ten had strong backing, while two had less strength.This study adds to the expanding body of information on Green HR practices by emphasising the mediating function of green work commitment in promoting environmental sustainability inside multinational firms in Pakistan. In this study, we outline the path for future research into the broader implications of Green HR practices for sustaining organisations across regions and industries. It would be interesting to study how green HR approaches evolve alongside AI aspects in future research, emphasising how employee engagement and organisational success are impacted by green work commitments. Furthermore, an analysis of how leadership and company culture impact green HR policies may provide more insight. To better understand how these techniques are applicable globally, a comparison can be made between emerging and developing economies.

This study examines how green recruitment, green training, green compensation, and green leadership influence environmental sustainability through employees’ green work commitment. Using a quantitative cross-sectional survey of 407 employees working in international organizations in the IT, manufacturing, and education sectors in Pakistan and Uzbekistan, the study estimates a mediation model in SmartPLS 4. The model tests 13 hypotheses, comprising nine direct effects and four indirect effects through green work commitment. Findings show that green recruitment, green training, green compensation, and green leadership positively influence green work commitment, and green work commitment positively influences environmental sustainability. The direct effects of the antecedent variables on environmental sustainability are not statistically significant, whereas the indirect effects through green work commitment are significant, indicating full mediation. This study is meaningful because it shows that the mechanism that links green HRM practices to green environmental sustainability outcomes is green work commitment of employees. It demonstrates that employee buy-in and commitment to organizational sustainability efforts are more likely to be effective when they are ingrained in their daily practices. This study contributes to the sustainability literature by integrating leadership and GHRM practices within a single employee-commitment framework in two emerging-market contexts. Limitations include the cross-sectional design, self-reported data, and non-probability sampling.

This study explores the usage of Artificial Intelligence and machine learning technology in modern cybersecurity. When cyberattacks becoming more frequent, organizations are turning to AI-powered solutions to improve their defense systems. Adoption of AI technologies like machine learning and deep learning is shown to improve the accuracy of threat detection, but data reliability, model interpretability, and bias in decision-making systems remain significant issues. Research has proven AI to be most successful in preventing large-scale attacks, i.e., DDoS, through the recognition of patterns in network traffic based on models like Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The application of AI technologies, such as machine learning and deep learning, has been shown to improve the speed and precision of threat detection so that it becomes easier to identify and counteract previously unknown vulnerabilities. Despite these advantages, very low rates of organizations have fully implemented AI-driven security solutions, and confidence in AI decision-making still affects deployment. This article also highlights increasing incidents of AI-driven cyber attacks, such as AI-created malware, automated phishing, and deepfake identity theft, that are outrunning traditional security controls. This analysis also emphasizes the growing importance of application of AI in cybersecurity with underscoring its potential to strengthen defens systems against zero-day attacks and similar evolving threats. However, it also reviews some of the challenges that need to be addressed for more effective integration of AI models in cybersecurity, such as the reliability of the data that is being used for its' training. With the development in AI technologies, machine learning based models are expected to play an increasingly crucial role in protection of both businesses and individuals from sophisticated cyberattacks. And despite all the advantages, the full implementation of AI-driven security solutions remains low. What is more, concerns about the trustworthiness of AI decisions and the lack of transparency in AI models still remains.