As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies. These findings provide valuable insights for researchers exploring collaboration opportunities, practitioners implementing AI solutions, and policymakers developing strategic frameworks for AI adoption in HRM. This research contributes to understanding the evolving landscape of AI-HRM research and offers practical guidelines for leveraging AI in HR practices.

This study maps how Natural Language Processing (NLP) contributes to sustainability. Using a PRISMA‐guided review of 131 English‐language articles from Web of Science (2018–2025), we combine bibliometric co‐word mapping with BERTopic to derive complementary structural and semantic views. Four themes emerge: Topic 0—Climate Change Discourse and Energy Innovation (SDG 13, SDG 7); Topic 1—Sustainability Policy and Public Engagement (SDG 11, SDG 16); Topic 2—Tech‐Driven Environmental Solutions (SDG 6, SDG 9, SDG 15); and Topic 3—Corporate Sustainability and Sustainable Infrastructure (SDG 8, SDG 9, SDG 12). Social equity remains comparatively under‐covered, with limited attention to SDG 5 and SDG 10. Theoretically, we position NLP as a bridge that supports sensemaking, coordination, and accountability across the environmental, social, and economic pillars. Practically, our synthesis highlights deployable uses of NLP for real‐time narrative monitoring, environmental, social, governance (ESG) claim assessment, and horizon scanning for emerging risks and innovation niches. For policy, coupling narrative analytics with policy‐text tracking can create feedback loops that enhance communication, legitimacy, and policy‐mix coherence for SDG implementation. By jointly leveraging network‐based and embedding‐based evidence, the review reveals thematic frontiers, makes SDG linkages explicit, and offers a topic‐based agenda with testable propositions.

The allocation of human resources is considered as one of daily challenges of many organizations to achieve optimal efficiency. After investigating this challenge, in this research, a multi-agent system is used to make a cooperative game between organizational units for allocation of tasks and human resources in an organization. In the game, every unit provides a proposal for promoting his and other unit’s performance. Our model uses fuzzification to facilitate game process and increase probability of reaching an agreement in the game. Meanwhile it helps for simulation of real conditions in organizations. Results of the fuzzy game have shown that for special allocations of human resources and tasks, organizational performance is more desirable, and its productivity will be higher. We used an example to apply the model and show the results. The model can improve the human resources and task allocation in organizations by using a fuzzy game theory.

The rapid proliferation of Internet of Things (IoT) technologies necessitates robust forecasting mechanisms to guide strategic decision-making amid increasingly complex innovation landscapes. Despite extensive research employing patent analysis for technology forecasting, existing studies lack systematic integration of social network analysis, advanced text mining, and life cycle modeling to comprehensively map IoT technological evolution and collaborative dynamics. This study addresses these gaps by analyzing 154,227 IoT-related patents through a unified methodological framework combining BERT-based text embeddings, k-means clustering with Davies-Bouldin optimization, S-curve life cycle modeling, and Louvain community detection. The analysis identified nine distinct technology clusters spanning foundational infrastructure (Smart Monitoring and Sensor Systems, Network Communication and Data Transmission) to domain-specific applications (Agricultural IoT, Connected Vehicle Technologies). Life cycle assessment revealed temporal convergence, with eight clusters reaching saturation between 2023 and 2027, reflecting ecosystem-wide synchronization driven by standardization imperatives, platform consolidation, and pandemic-accelerated digital transformation. Social network analysis uncovered five major collaborative communities exhibiting divergent strategic orientations: from extreme specialization (Global Telecommunications Technology Leaders: 87.7% network communication focus) to diversified portfolios (China State Grid IoT Consortium: balanced infrastructure investment). Cross-analysis revealed complementary innovation strategies where infrastructure operators pursue breadth, telecommunications specialists maintain focused expertise, and academic researchers emphasize development-aligned agendas. These findings provide actionable intelligence for corporations navigating saturation domains, SMEs adopting focused positioning strategies, and policymakers optimizing intervention timing. The study advances methodological rigor in patent-based forecasting while delivering practical insights for strategic planning, resource allocation, and partnership formation within the evolving IoT ecosystem.

In the context of global sustainability, buildings are significant consumers of energy, emphasizing the necessity for innovative strategies to enhance efficiency and reduce environmental impact. This research leverages extensive raw data from building infrastructures to uncover energy consumption patterns and devise strategies for optimizing resource use. We investigate the factors influencing energy efficiency and cost reduction in buildings, utilizing Lasso Regression, Decision Tree, and Random Forest models for accurate energy use forecasting. Our study delves into the factors affecting energy utilization, focusing on primary fuel and electrical energy, and discusses the potential for substantial cost savings and environmental benefits. Significantly, we apply metaheuristic techniques to enhance the Decision Tree algorithm, resulting in improved predictive precision. This enables a more nuanced understanding of the characteristics of buildings with high and low energy efficiency potential. Our findings offer practical insights for reducing energy consumption and operational costs, contributing to the broader goals of sustainable development and cleaner production. By identifying key drivers of energy use in buildings, this study provides a valuable framework for policymakers and industry stakeholders to implement cleaner and more sustainable energy practices.