The continuous advancement of artificial intelligence technology is impacting the operational mechanisms and market competition structures of closed-loop supply chains. How to integrate AI empowerment with channel strategies for coordinated optimization has become a core decision-making issue for manufacturers. By establishing three progressive game models: the benchmark model (M0), the encroachment model (M1), and the AI empowerment model (M2), this study investigates the strategic interplay arising from manufacturers' channel encroachment decisions and AI technology deployment in the context of remanufacturing outsourcing, along with the resulting economic implications. The study finds that when channel differentiation is significant, the manufacturer's encroachment can enhance its profits through market expansion effects. On this basis, by introducing AI technology, its enabling effect exhibits a non-linear characteristic. Only when the channel substitution degree is low and the price sensitivity is moderate, can AI achieve the synergistic benefits of cost savings and market expansion. In terms of revenue distribution, the AI dividend has extensive penetration among supply chain members. Retailers only suffer losses in extreme scenarios where the channels are highly homogeneous and consumers are not price-sensitive. In most market conditions, they can share the efficiency improvement brought about by technology empowerment. This study theoretically reveals the interaction mechanism between channel encroachment and AI empowerment, expands the analytical boundaries of closed-loop supply chain management, and provides strategic guidance for manufacturing enterprises on how to formulate collaborative strategies in the wave of digital transformation.

In the last few years, artificial intelligence has grown rapidly and has become increasingly popular in daily life. As a powerful branch of artificial intelligence, generative AI (Gen AI) offers immense potential for application within the new product development (NPD) process. In this study, we investigate the impact of Gen AI on NPD by utilizing a closed-form analytical model within a single-product monopoly. The results show that the profit and productivity levels of a company adopting Gen AI technology are lower than those of a non-Gen AI company. However, the company that adopts Gen AI technology achieves better product quality than a company without Gen AI implementation. Furthermore, by increasing design costs by a small amount, the Gen AI adopting company improves its product quality level, while non-Gen AI companies experience only a slim increase in product quality. Additionally, under lower fixed costs, outcomes improve for both Gen AI and non-Gen AI companies. Our findings suggest that a company which has already adopted Gen AI technology should not reduce the cost of quality. Instead, it should focus more on increasing the cost of quality to realize greater benefits from Gen AI implementation.

The COVID-19 pandemic highlighted humanity's vulnerability to emerging infectious diseases and the critical need for proactive strategies. While COVID-19's immediate crisis recedes, it's crucial not to underestimate other threats like Hantavirus, which, despite lower transmissibility, carries high fatality rates and lacks specific treatments or vaccines. This paper argues that the lessons and technological advancements from the COVID-19 response offer a vital blueprint for Hantavirus mitigation. We advocate for a rapid, multi-faceted approach, mirroring COVID-19's urgency. This includes implementing personal protective measures (e.g., masks, gloves) proven effective in reducing viral transmission. Crucially, we propose leveraging the significant advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) that matured during the pandemic. These technologies can revolutionize Hantavirus drug discovery by accelerating drug repurposing from existing libraries, executing rapid virtual screening and molecular docking of vast chemical databases to identify inhibitors, and informing de novo drug design. This accelerated process demands a 'Human-in-the-Loop' (HITL) paradigm. While AI/ML/DL offer unparalleled speed in hypothesis generation, human expertise from virologists, clinicians, and public health officials remains indispensable for validating models, interpreting complex biological data, guiding experimental design, and translating insights into effective interventions. By synergizing advanced computational power with invaluable human intuition, we can proactively develop robust therapeutic and preventive strategies for Hantavirus, shifting from reactive crisis management to a data-driven defense against future viral threats.

This study focuses on human resource management issues in the digital age from a legal perspective, based on dual-core theory, and empirically examines the impact of AI-enabled HR practices on organizational performance. The core dimensions of AI-enabled HR in this study include AI-powered recruitment, training, and performance appraisal, with employee adaptability introduced as a mediating variable. The study employed a quantitative research design, collecting 247 validated questionnaires from employees in digital organizations using partial least squares structural equation modeling (PLS-SEM) for empirical testing. The reliability and validity of the measurement model exceeded 0.7, the average extracted variance value was greater than 0.5, and the model passed the discriminant validity test according to the Fornell-Larcker criterion. The path test results demonstrate that all types of AI-enabled HR practices that comply with laws and regulations, along with employee adaptability, have a significant positive impact on organizational performance. Furthermore, employee adaptability is a positive mediating factor in the relationship between AI-enabled recruitment and organizational performance. This study complements the empirical evidence in this field regarding the interactive impact of AI-assisted human resources systems and the adaptability of employees.

The rapid development of artificial intelligence is driving human resource management to transform from its traditional administrative support function into an intelligent strategic system capable of strengthening an organization's core capabilities. A new generation of agentic artificial intelligence (Agentic AI) human resource systems with independent adaptive capabilities has gradually been implemented in enterprise application scenarios. Currently, various types of AI-enabled human resource systems have become widespread in the market, but both academic and industry communities still lack empirical research on the internal mechanisms through which these agentic AI human resource systems impact organizational performance. This study is conducted specifically to fill this gap. This study adopts a quantitative research design. Using SmartPLS 4 software, it analyzes cross-sectional survey data through partial least squares structural equation modeling (PLS-SEM). Its core variables include the independent variable agentic AI human resource system, the mediating variables data-driven decision-making and workforce adaptability, and the dependent variable organizational performance. This study relies on dynamic capabilities theory, information processing theory, and the knowledge-based view as its core theoretical supports. Empirical findings show that the core independent variable has a significant direct positive effect on the dependent variable, and can also produce a significant indirect positive effect transmitted through two mediating variables. Among these mediators, employee adaptability exerts a stronger impact on organizational performance than data-driven decision-making. In subsequent work, this paper will sort out the study's theoretical contributions, put forward practical suggestions for corporate practice, and clarify potential directions for future research expansion.

This study aims to develop and test an integrative model of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to explain farmers' intentions to adopt artificial intelligence (AI) in the agricultural sector. The model positions Perceived Ease of Use (PEOU) as an initial determinant influencing Perceived Usefulness (PU), Attitude Toward AI (ATT), and Behavioral Intention to Use AI (BI), both directly and through mediating mechanisms. The study employed a quantitative explanatory design with a cross-sectional survey approach. Data were collected from 203 respondents, consisting of farmers and MSMEs and small-to-medium-scale agricultural businesses in the Greater Bandung area, from January to March 2024. The research instrument was an online questionnaire with a five-point Likert scale, and data analysis was conducted using covariance-based Structural Equation Modeling (SEM). The results showed that PEOU had a positive and significant effect on PU and ATT. Furthermore, PU, ATT, Perceived Behavioral Control, and Perceived Social Norms were shown to increase the intention to use AI in agricultural activities. Mediation tests confirmed that PU and ATT act as partial mediators in the relationship between ease of use and AI adoption intention. These findings emphasize that AI adoption in the agricultural sector is a gradual process that simultaneously involves cognitive, affective, and social factors. Theoretically, this study strengthens the relevance of TAM–TPB integration in the context of modern agriculture, while practically providing a basis for formulating strategies to accelerate sustainable digital transformation in agriculture.

Artificial Intelligence (AI) technologies have revolutionized maritime logistics and supply chain operations as much as they have revolutionized the manner in which other basic services are provided and exported in the maritime world, especially for the export-oriented Micro, Small and Medium Enterprises (MSMEs). This study delves into how the adoption of AI affects maritime supply chain resilience for MSME exporters in the India–Thailand trade corridor. The research combines the Technology Acceptance Model (TAM) and Dynamic Capability Theory, aiming to analyse the influence of perceived usefulness, technological readiness, digital integration and organizational adaptability on AI adoption and resilience of the maritime supply chain. The research method utilized was a cross-sectional survey which uses quantitative empirical research design. The primary data obtained were 268 responses from MSME exporters of maritime trade between India and Thailand. The collected data were analysed using a software tool called a Partial Least Squares (PLS) and a Software Statistical Package (SPSS) version 29 (SmartPLS 4). The results indicate that the use of AI has a positive and significant impact on maritime supply chain resilience. Perceived usefulness and technological readiness emerged as significant factors affecting AI adoption, and digital integration and organization's adaptability as factors affecting maritime supply chain resilience. AI-powered solutions like predictive analytics, intelligent cargo tracking, route optimization, and automated logistics systems are pivotal in bolstering the operational continuity and resilience of maritime supply chains, as emphasized in the study. The research has theoretical contributions by building on TAM and Dynamic Capability Theory and applying them to the maritime logistics context and implications for MSMEs, policy makers and maritime authorities on digital transformation and the resilient maritime trade systems. The study provides a comprehensive overview of the role of AI in maritime supply chains for achieving greater competitiveness and sustainability in new global trade contexts.

Natural fiber reinforced composites (NFRCs) have gained recognition as sustainable and environmentally friendly alternatives to synthetic composites. However, estimating their fatigue durability remains a major challenge due to their complex and heterogeneous behaviour. This study develops a machine learning framework to predict the fatigue life of hemp fiber-reinforced HDPE composites from experimental data, addressing the specific limitations of conventional empirical models. The dataset is divided into training and testing subsets following a preprocessing phase that includes logarithmic transformation, normalization, and outlier removal. Four regression models are compared: Multiple Linear Regression, Random Forest, Gradient Boosting Regressor, and Support Vector Regression. Grid Search with 5-fold cross-validation is used to optimize hyperparameters and improve predictive accuracy. Model performance is evaluated using the coefficient of determination (R²), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Results show that ensemble methods, particularly Random Forest (R² = 99.91%, MAPE = 1.11%) and Gradient Boosting (R² = 99.87%, MAPE = 1.52%), substantially outperform linear models and traditional S-N curve fitting. Feature importance analysis reveals that maximum stress accounts for the majority of prediction variance (approximately 68%–81% depending on the dataset), offering actionable insights for material design. The proposed framework demonstrates strong generalization potential and provides a reproducible template for data-driven fatigue modeling in sustainable composite materials.

The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.

This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.