How AI enabled human resource management drives sustainable hybrid performance through AI centric green human resource management in Saudi Arabian private sector firms
This study examines the relationship between AI-enabled human resource management (HRM) and AI-centric Green HRM (GHRM) and how these capabilities influence sustainable hybrid performance, defined as the integration of productivity, adaptability, and environmentally responsible work practices. The study further investigates the mediating roles of governance maturity, HR–IT collaboration, and workforce AI literacy within the Saudi Arabian private-sector firms. Using a quantitative research design, data were collected through an online survey employing purposive sampling among employees from private-sector firms in Saudi Arabia (N = 161). Six multidimensional constructs were measured using established scales from prior literature and analyzed using covariance-based structural equation modeling with bootstrapping. The measurement model demonstrated satisfactory fit (RMSEA = .042, CFI = .92, TLI = .91). The results reveal that AI-enabled HRM significantly influences AI-centric GHRM (β = .78, p < .05), while AI-centric GHRM positively affects sustainable hybrid performance (β = .71, p < .05). The model explains 64% of the variance in AI-centric GHRM and 55% of the variance in sustainable hybrid performance. Governance emerged as the strongest mediator, with indirect effects of β = .20 between AI-enabled HRM and AI-centric GHRM and β = .14 between AI-centric GHRM and sustainable hybrid performance. HR–IT collaboration and workforce AI literacy also exhibited significant complementary mediation effects. Theoretically, the study contributes to sustainability research by explaining how socio-technical mechanisms transform AI-enabled HR capabilities into environmental and organizational sustainability outcomes. Practically, the findings highlight the importance of sustainability-oriented AI governance, HR–IT collaboration, and workforce capability development in supporting sustainable hybrid work systems. Because the study employed a cross-sectional design, the findings should be interpreted as relational rather than causal.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.