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This paper examines the existing research on generative artificial intelligence (GenAI) in language education and highlights its potential to reshape teaching practices, learner engagement, and pedagogical design. By leveraging co-word analysis and BERTopic modeling on 908 publications from 2023 to the end of 2025, the article traces thematic patterns and conceptual developments in the GenAI field. The co-word analysis identifies key clusters at the intersection of GenAI and English language instruction, including its use in academic writing, translation, assessment, and learner-centered pedagogy. These themes demonstrate the critical role of GenAI in enabling personalized feedback, adaptive learning environments, and enhanced teacher–student interaction. The application of BERTopic modeling adds a semantic layer to this analysis and reveals diverse topics such as AI-assisted writing, teacher engagement with AI tools, emotional and motivational dynamics, and technology acceptance among learners. Results also indicate a progression from broad pedagogical experimentation toward more focused inquiries into learner psychology, ethical use, and professional development. This trend reflects the maturation of GenAI applications in both formal and informal educational contexts. The findings offer implications for language educators, curriculum designers, and policymakers by highlighting the need to integrate GenAI through pedagogically grounded, ethically responsible, and learner-centered approaches. They also suggest that future language education practices should combine AI literacy, transparent assessment policies, and teacher professional development to support effective and equitable GenAI adoption. Ultimately, the article positions GenAI as a transformative force in language education that offers technological innovation and new frameworks for inclusive, responsive, and emotionally attuned instruction.

This article offers a comprehensive bibliometric analysis of research on physical work environments and maps its intellectual structure, thematic evolution, and collaborative dynamics over the past four decades from a business and management perspective. It reveals how previously fragmented streams of research on workplace design, employee well-being, and organizational performance are embedded in the broader management literature. The analysis provides insights into how the physical work environment has evolved from a peripheral topic to a topic of strategic concern in organizational research. Based on a dataset of 834 publications indexed in Scopus and Web of Science between 1978 and 2024, it captures the contributions of 1,906 authors publishing across 214 sources. Using advanced bibliometric techniques such as co-citation analysis, keyword co-occurrence, and strategic mapping, the study reveals a transition from early concerns with office layout and facility management to more recent emphases on employee well-being, workplace design, and the impacts of COVID-19. The thematic structure shows well-developed motor themes, such as office buildings and sustainability, alongside basic themes, such as workplace, productivity, and the physical work environment. Emerging topics include coworking spaces and workplace learning, while niche areas such as indoor air quality and workplace health highlight growing specialization. The results uncover the underlying knowledge structure of physical work environment research and highlight emerging research areas related to workplace transformation, hybrid work arrangements, and employee-centered workspace design. This study provides the conceptual foundation that enables researchers to better position future workplace research.

Blockchain technology has gained growing scholarly and industrial attention for its ability to improve transparency, optimize operational efficiency, and support food safety protocols in food supply chain (FSC) traceability systems. Yet, despite this fast rise in prominence, current scholarly discourse demonstrates a noticeable gap in knowledge regarding the systemic implementation and wider implications of blockchain in FSC traceability. To fill this research gap, the present study delivers what is, to the best of our knowledge, the first large-scale bibliometric examination exclusively focused on blockchain-enabled FSC traceability. Using 620 publications indexed in the Scopus database, this study maps the intellectual structure of the field and traces the evolution of research themes concerning blockchain-enabled traceability. The review uncovers several dominant thematic trajectories. More specifically, the adoption of blockchain with complementary advanced technologies such as the Internet of Things (IoT) and artificial intelligence (AI) has become a focal trend. Concurrently, blockchain acts as a mechanism to mitigate concerns related to food safety, authenticity, security, and sustainable supply governance. The findings illustrate the multifaceted utility of blockchain in FSC traceability and signal its potential to impact future trends in supply chain management. Overall, this article addresses a critical gap in the current literature and lays a foundation for further interdisciplinary exploration. By aligning theoretical discourse with practical relevance, the review aims to contribute to the development of transparent, efficient, and sustainable food supply systems, thereby supporting global food safety and security objectives.

Digital technology has revolutionized the communication between suppliers and buyers in logistics and transportation. Previous studies focused on individual technolo-gies and their benefits but have not sufficiently explored how these technologies interact together as a communication interface, and what specific data they exchange. The review conducts a systematic literature analysis of 51 peer reviewed papers (2016-2026), screened from 711 records from Scopus and Web of Sciences. The paper provides three contributions, first it identifies digital communication mechanism that enables supplier buyer coordination with documented outcomes from in-cluding reducing freight negotiations from weeks to hours and warehouse waiting time by 80%. Second the review consolidates exchange data parameters into the Digital Logistics Communication Architecture (DLCA), a four-layer taxonomy offering empirically grounded data specification. Moreover, third it finds that digital communication contrib-utes to sustainability primarily through environmental and economic outcomes, with so-cial outcomes remaining underrepresented. The most significant finding is structural; across most 51 peer reviewed papers it is observed that each digital communication system depends on human initiation. Building on this structural gap, the study proposes agentic AI as a Tier 5 extension of the DLCA framework, positioned as the technology most capable of overcoming human-initiation dependence. A future research agenda is outlined.

Human-Centered Artificial Intelligence (HCAI) has emerged as a promising paradigm to increase transparency, usability, and trust in AI-driven Decision Support Systems (DSS). However, existing research lacks technically detailed accounts of how HCAI principles can be operationalized, implemented, and empirically validated in real decision environments. This study proposes a technically grounded HCAI-oriented DSS framework and presents a concrete prototype implemented in two high-stakes domains: clinical decision support and financial risk assessment. The architecture integrates interpretable machine learning models, SHAP-based explanations, structured user-feedback loops, and governance mechanisms aligned with the EU Trustworthy AI Guidelines. We trained and evaluated domain-specific models using publicly available medical and financial datasets, describing all data preprocessing, model selection, and hyperparameter settings to ensure reproducibility. An empirical study involving 30 domain experts (15 clinicians, 15 financial analysts) compared the HCAI-DSS with a functionally identical black-box DSS. Statistical analyses (paired t-tests with 95% confidence intervals and Cohen’s d) revealed that the HCAI-DSS significantly improved trust (d = 1.23), transparency and understanding (+1.76 mean difference), usability (SUS difference = +15.4), and decision accuracy (+10.2%), without a significant increase in decision time (p = 0.08). Qualitative feedback further demonstrated that explanations, control, and human-in-the-loop features increased confidence and reduced uncertainty. The results provide empirical evidence that HCAI principles tangibly enhance DSS effectiveness and user acceptance. The study contributes (1) a reproducible technical implementation, (2) a validated HCAI-DSS architecture, and (3) multi-domain evidence of improved decision quality. These findings support sustainable and trustworthy AI adoption across sectors and align with emerging regulatory frameworks such as the EU AI Act.

Social robots are becoming increasingly prominent across various domains, yet comprehensive bibliometric studies detailing the field's development are scarce. To bridge this gap, this study conducts an extensive bibliometric and network analysis of social robot research and illustrates its dynamic evolution from 1989 to 2024. Based on 5338 publications from the Scopus database, authored by 14,693 researchers, this study highlights the most prominent scholars and influential academic articles. The analysis reveals various bibliometric networks, including citation, co-citation, and collaboration networks, as well as keyword co-occurrence networks, and presents two intellectual structure maps: a conceptual structure map and a thematic map. The findings identify five distinct growth phases in social robot research, with publication output peaking in 2021. The United States, China, Italy, Japan, and the United Kingdom are the most productive and collaborative countries, and the International Journal of Social Robotics is recognized as the leading publication venue. Citation and co-citation analyses indicate that Bartneck, Belpaeme, and Scassellati are central influential authors in the field. Keyword and thematic analyses reveal four primary thematic clusters: human–robot interaction and design, assistive and healthcare applications, educational and developmental uses, and AI-driven technological advancements. These insights emphasize evolving research patterns and highlight areas with strong future growth potential, particularly in healthcare and socially assistive contexts. The key trends and influential contributions presented in the paper provide critical insights for future research and practical applications in the field of social robots.

This chapter delves into the urban planning framework, with a particular focus on Sustainable Urban Mobility Plans (SUMPs) implemented across Europe. These strategic documents, encouraged by the European Commission (EC) aim to enhance transport sustainability, reduce environmental impacts, and improve citizens’ quality of life by tackling challenges like congestion, air and noise pollution, climate change, road safety, and parking issues. However, the customization of SUMPs to fit each city’s unique context presents challenges in standardizing and comparing the effectiveness of implemented measures. This study examines Sustainable Urban Mobility Plans (SUMPs) from various European cities over the past decade, with a particular focus on Eastern and Central European countries to highlight best practices and strategies, using Budapest as a case study to validate the effect of infrastructure coverage on public transport usage. The findings reveal that while governments prioritize strategies, there is a poor tracking of regional key performance indicators, with much data being reported at a national level. The quantitative analysis in Budapest faces challenges due to limited historical data; however, the linear regression model shows a correlation between infrastructure and passenger kilometers. By comparing urban measures and their outcomes, this research aims to provide policymakers and urban planners with actionable insights on the most effective interventions for promoting sustainable mobility, offering guidelines for future variables analysis.

The electric vehicle (EV) battery supply chain plays a critical role in promoting sustainable transportation and tackling scarce resources, environmental costs, and supply chain vulnerabilities. The current study aims to conduct an extensive literature review of the EV battery supply chain given its importance for developing sustainable and efficient EVs. Using keyword co-occurrence and article co-citation analyses, this study analyses more than 681 publications from 2005 to 2024 and sourced from the Scopus database. Findings show that the number of articles increased considerably after 2020, which can be attributed to the global focus on decarbonization, electromobility, and circular economy practices. The review identifies important themes such as sustainability challenges, critical materials management, reverse logistics, and policy-driven frameworks for closed-loop supply chains. The findings from this study highlight a multidimensional approach where the integration of technologies, innovative policies, and collaborative actions can contribute to the resilience and sustainability of EV battery supply chains. It offers practical insights for stakeholders, strategic directions to maximize EV battery lifecycle management, and outlines the pathways to reach carbon neutrality in the transportation sector. By identifying the intellectual structure of this emerging field, the study contributes to academic discourse and informs the formulation of practical strategies to advance sustainable mobility.

Background: Globally, in the Fast-Moving Consumer Goods (FMCG) industry, excess inventory results from the bullwhip effect. Earlier, barcode-based two-bin systems were limited by manual scanning; hence, a more responsive system is needed to align the inventory with real-time demand. Prior studies have predominantly concentrated on mitigating demand fluctuations and employed comparatively low-efficiency systems, hindering excess inventory (EI) reduction. Methods: This study proposes identifying research gaps, considering the distributor-manufacturer relationship, and developing an RFID-based modified two-bin system and mathematical model to reduce EI and control over manufacturers’ excessive cost. Results: This study tested through Python-based simulation using historical data from an FMCG manufacturer, and the proposed model achieved a reduction in 67% EI and 73% month-wise holding costs. Moreover, the integration of the Artificial Bee Colony algorithm optimizes rework rates within budget, including reworking shop-floor and holding costs, contributing to a monthly excessive cost reduction of 34–48%, alongside a corresponding 41–44% cumulative excessive cost reduction. Conclusions: Bringing significant implications on digitalized SCM, this study offers a practical and scalable solution for perishable FMCG items facing demand variability and budget constraints. Collectively, this novel perspective bridges research gaps and motivates future research for embedding trend-aligned parameters, enhancing the model’s performance through diverse SCM contexts like safety stock and backorder cost optimization.

Background: This study analyzes modal distribution patterns across Europe and 28 European countries, employing clustering analysis to identify trends in transport mode utilization. The research quantifies logistics diversification, examining extreme cases and addressing gaps in understanding modal transitions affecting environmental and economic performance. Methods: Statistical testing using compositional data transformations revealed significant differences between modal distributions (p < 0.001), justifying country-specific and European-level assessments. K-means clustering was applied to identify groups of countries with similar modal distribution patterns. Results: Maritime and road transport constitute the predominant modes across all analyzed countries in the study period. Among terrestrial modes, road transport dominates universally, exhibiting systematic growth, while rail transport experienced a corresponding decline. This trend directly contradicts European sustainability objectives promoting modal shift toward environmentally superior alternatives. Romania demonstrates the highest logistics diversification with the most balanced modal distribution, while Portugal exhibits the lowest diversification due to maritime transport dominance. K-means clustering positioned Portugal within a maritime-dominated group alongside Greece, Cyprus, and Ireland, reflecting similar geographical constraints and distribution patterns. Conclusions: The findings reveal critical aspects requiring further investigation concerning European modal distribution trends that challenge current policy effectiveness, highlighting the divergence between observed transport patterns and stated sustainability goals. These results provide essential insights for addressing persistent modal shift challenges in European transport systems.