Search Results (1,672)

Despite strong evidence that gender-diverse leadership improves organizational innovation and performance, women remain underrepresented in leadership pipelines worldwide, particularly in Asia. While prior research largely examines the outcomes of gender diversity at the firm level, far less is known about the psychological and social factors that shape women’s leadership aspirations in the first place. Addressing this gap, this study applies Social Cognitive Career Theory (SCCT) to explain how contextual support and developmental experiences influence women’s leadership aspirations in a collectivist business environment. Using survey data from 400 adult women in Indonesia and structural equation modelling, the study examines how parental involvement shapes personal mastery, how personal mastery strengthens leadership self-efficacy, and how self-efficacy, role models, and perceived leadership traits jointly predict leadership aspiration. The findings show that parental involvement indirectly contributes to leadership aspiration through personal mastery and self-efficacy, while role models and leadership traits also play significant roles. Among all predictors, self-efficacy emerges as the strongest driver of women’s leadership aspiration. This study makes three contributions. First, it extends SCCT beyond traditional STEM career research into the domain of leadership aspiration. Second, it provides rare empirical evidence from a collectivist Asian context, highlighting the role of family and social environment in shaping women’s leadership pathways. Third, it shifts the focus of gender diversity research from representation outcomes to the formation of the female leadership pipeline, offering actionable insight for educators, families, and organizations seeking to foster future women leaders. Full article

This study examines the roles of startup hubs within the cultural and creative industries (CCIs) and their implications for cultural innovation and tourism in European cities. Despite the growing importance of CCIs in urban development and destination branding, few studies have explored the organisational, social and communicative dynamics of cultural startup hubs. To address this gap, a comparative mixed-methods approach is applied to analyse 91 incubated startups in three European hubs: 104factory (Paris, France), Makerversity (London, UK) and A Lab (Amsterdam, The Netherlands). This study integrates structural variables (sustainability and institutionalisation), social variables (gender representation in leadership) and communication variables (activity and engagement on Instagram). The results reveal distinct organisational models, from highly institutionalised structures to more flexible, community-oriented approaches, with notable differences in terms of sustainability and gender distribution. In terms of communication, greater engagement is associated with content focused on community, identity and collective creativity, rather than promotional strategies. These findings highlight the role of startup hubs as hybrid intermediaries that not only support cultural entrepreneurship, but also contribute to the symbolic positioning and tourist appeal of the cities in which they are located. This study offers theoretical and practical insights for the development of more inclusive, sustainable and effectively communicative cultural ecosystems. Full article

Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading to loss of critical substructures and limited interpretability—key limitations in molecular analysis and social network mining. To address these issues, we propose SparsePool, a graph pooling method that integrates node features and structural patterns through atomic decomposition. By dynamically decomposing graphs into interpretable atomic units via Boolean matrix factorization, SparsePool preserves semantically meaningful substructures while providing transparent evidence of retained patterns. We further introduce an Atomic Pooling Neural Network (APNN) for graph representation learning. Extensive experiments on relevant benchmarks including biochemical and social network datasets demonstrate that SparsePool outperforms state-of-the-art pooling methods, achieving an average classification accuracy improvement of 1.03% over baseline models while reducing structural information loss. We also discuss its compatibility with emerging quantum computing paradigms, such as quantum-accelerated sparse decomposition, as a promising direction for scaling graph processing in industrial contexts. Full article

As image–text posts have become a dominant form of social media communication, understanding how the two modalities jointly convey meaning remains a key challenge in multimodal analysis. This study aims to examine whether image–text consistency is inherently multidimensional rather than reducible to a single similarity metric. Existing studies often reduce consistency to a single relevance score, which cannot capture semantic, emotional, and functional interactions. We construct a dataset of 28,650 multimodal posts and model image–text relationships along three dimensions: semantic consistency (CSC), emotional consistency (CEC), and informational matching consistency (IMC). Semantic and emotional alignment are measured using cross-modal representation and similarity computation, while IMC is defined through rule-based classification of informational roles. Results show that emotional consistency (CEC = 0.621) is higher than semantic consistency (CSC = 0.549, $p<0.001$), while 61.0% of posts maintain consistent informational orientation. These findings demonstrate that image–text consistency exhibits distinct cross-dimensional patterns that cannot be captured by single-metric approaches. Full article

This study examines how the Science of Reading is represented in Twitter discourse and compares these representations to contemporary models of reading development. Although the Science of Reading is frequently positioned as an equity-oriented reform, little is known about how related ideas circulate in public discourse, particularly across social media platforms that increasingly shape teacher learning, policymaking, and public opinion. This content analysis study analyzed a sample of 14,165 tweets containing the hashtag #scienceofreading from 2020–2021 and 2022–2023. It explores two primary questions investigating (1) the extent to which essential literacy skills (e.g., phonological awareness, phonics, comprehension, vocabulary) are referenced in tweets or linked content and (2) the extent to which specific subgroup classifications identified by the National Assessment of Educational Progress (e.g., Black, Hispanic, students with disabilities, low-income, and other populations) are mentioned in the same sample of discourse on Twitter. Findings demonstrate that online discourse on Twitter (now X) includes more references to decoding-related skills such as phonological awareness and phonics, with far fewer mentions of language-related skills such as comprehension or vocabulary. Mentions of subgroups were minimal, while references to students with disabilities with explicit mention of dyslexia occurred at four times the frequency of race- or income-related subgroups. These distributions contrast with persistent national achievement disparities and suggest that contemporary Science of Reading discourse is more strongly oriented toward decoding-related skills than toward equity-focused concerns. Implications for teacher preparation, policy enactment, and critical media literacy are discussed. Full article

Event detection is crucial for disaster response, public safety, and trend analysis, enabling real-time identification of critical events. Social media platforms provide a vast content source, offering timely and diverse event coverage compared to traditional news reports. However, challenges arise due to the informal and noisy nature of the text, along with the limited availability of ground truth data for training models. This study introduces SOCIAL (Social Media Event Classification using Integrated Artificial Learning and Natural Language Processing), a mathematically grounded framework for real-time social media event detection. SOCIAL integrates a formal representation of social media text with a customized CNN–LSTM architecture, combining convolutional operations for local feature extraction with sequential modeling to capture temporal dependencies, thereby enhancing classification accuracy. Generative AI is employed to create synthetic event-related samples, addressing data scarcity and ensuring a balanced dataset, while the design incorporates quantitative principles to guide embedding selection and model optimization. This study systematically evaluates six experimental configurations with TF-IDF and Word2Vec embeddings. The TF-IDF-based CNN–LSTM model achieved top performance with 98.59% accuracy, 98.13% precision, 99.06% recall, and 0.9719 MCC. Additionally, the $F_{0.5}$, $F_1$, and $F_2$ scores were 98.31%, 98.59%, and 98.87%, respectively, confirming the model’s strong predictive capabilities. TF-IDF integration enhanced event-specific term recognition, reducing misclassifications and improving reliability. These results demonstrate that SOCIAL is not only a fast, accurate, and scalable tool for crisis event detection, but also a formally principled framework for modeling and analyzing social media signals. Full article

Understanding the sustainable management of the complex adaptive tourism systems requires an integrated research approach that combines environmental processes with stakeholder behaviors. Agent-based modelling (ABM) has emerged as a pivotal tool for decoding the resilience, adaptability, and sustainability of tourism systems. However, the current application landscape, methodological limitations, and future research directions of ABM remain insufficiently synthesized, thereby constraining its full potential in advancing sustainable tourism management. This study examines 137 publications on the application of ABM in tourism research between 1989 and 2025, aiming to clarify the application characteristics and evolutionary trajectories. The results show the following: (1) ABM applications in tourism have become increasingly comprehensive and refined, evolving from simplistic simulations based on simplex agents and static spatial representations toward integrated models incorporating heterogeneous agents, fine-grained spatial environments, and multiple contextual factors. (2) Behavioral modeling has progressed from basic human–space interactions to complex, co-evolutionary dynamics among human, social, and ecological systems. (3) ABM applications exhibit context specificity: climate-sensitive scenarios emphasize resource dynamics and adaptation strategies; disaster-prone contexts focus on multi-agent responses and emergency management; conservation-oriented systems support sustainable policy development; and management-centric scenarios prioritize technological innovation and macro-level regulation. Future research should prioritize refining agent interactions through dynamic social network integration, incorporating cross-scale and long-distance system linkages, and strengthening the connection between theoretical modeling and real-world applications. This study would provide a comprehensive knowledge base for advancing the innovative application of ABM in sustainable tourism research and contribute to strengthening resilience, adaptive governance, and long-term sustainability within complex tourism systems. Full article

Trajectory planning in dynamic multi-vehicle interaction environments faces three critical challenges, including the difficulty of quantifying spatial risk distributions, the complexity of characterizing behavioral uncertainty arising from the multimodal maneuvers of surrounding vehicles, and the challenge of simultaneously optimizing multiple competing objectives such as safety, efficiency, comfort, and energy consumption. To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method. First, a joint spatiotemporal social attention mechanism achieves unified modeling of spatial interactions, temporal dependencies, and spatiotemporal coupling, combined with a lateral–longitudinal intent strategy for multimodal trajectory prediction. Second, an improved dynamic risk field model is constructed comprising three components: a vehicle risk field that incorporates spatial orientation and motion direction factors for anisotropic risk representation, along with a collision tendency factor that converts objective risk into effective risk; a predicted trajectory risk field that achieves anticipatory quantification of future risk from surrounding vehicles through confidence-weighted fusion; and a driving environment risk field that encapsulates road geometry, static obstacles, and environmental conditions. Finally, a multi-objective cost function embedding risk field gradients is formulated, and multi-objective coordinated optimization is realized through a three-dimensional spatiotemporal situation graph with adaptive safety sampling. Simulation results demonstrate that the proposed method enhances safety while simultaneously improving comfort and efficiency and reducing energy consumption, exhibiting excellent planning performance in complex dynamic environments. Full article

As social robots are increasingly deployed in domains such as healthcare, education, and entertainment, there is growing demand for affective agents that can interpret users’ affective states and respond in contextually appropriate ways. Existing work has established strong foundations for emotion generation and appraisal, but the step that connects generated emotion to behavioral execution still relies heavily on model-specific rules or implicit links. We frame this issue as a Mechanism Gap and propose an Emotional BDI framework that introduces Frijda’s action tendency as an intermediate representation layer between the Affective Core and the Belief–Desire–Intention (BDI) Executor. Rather than mapping emotion directly to concrete behavior, the framework first transforms affective state into a directional action tendency and then lets BDI reasoning realize that tendency according to role and context. This creates an explicit emotion-to-behavior mediation structure through which the same emotion can be expressed differently across situations and roles. In an exploratory user evaluation with 26 participants, the proposed model received more favorable ratings than an Emotion-Driven Agent in satisfaction ($p=0.010$) and appropriateness ($p=0.002$). Compared with a Cooperative Agent, the proposed model showed a significant advantage only in satisfaction ($p=0.030$). These findings suggest that the proposed framework offers a useful architectural direction for affective decision making beyond direct mapping or unconditional compliance. Full article

Background/Objectives: Cancer-related terminology is not merely descriptive and plays a critical role in shaping emotional responses, personal identity, and communication across clinical, social, and public spheres. Despite growing interest in the psychosocial dimensions of illness language, few studies have centered the lived experiences of individuals navigating cancer through the lens of terminology. This study explores how people living with and beyond cancer perceive, interpret, and emotionally respond to cancer-related language, focusing on the way terminology influences identity, stigma, and communicative interaction. Methods: A sequential mixed-methods design was employed. The quantitative phase involved 146 participants with a cancer diagnosis completing a structured questionnaire on preferred terminology and emotional impact. The qualitative phase followed, using open-ended questionnaires with 11 participants to deepen understanding of linguistic experiences. Thematic content analysis was used to identify patterns across narratives. Results: These findings reveal that labels such as “cancer patient” evoke strong negative emotional reactions, associated with stigma, fear, and identity reduction. Person-first and context-sensitive language was perceived as more respectful and empowering. Emotional responses to language varied widely, from fear to neutrality, shaped by speaker role, context, and time since diagnosis. Media representations were often seen as dramatizing or moralizing, reinforcing the need for communicative clarity, empathy, and education in both clinical and public discourse. Conclusions: Cancer-related language is a powerful psychosocial force. It shapes how individuals are seen and see themselves and can either reinforce stigma or foster dignity and resilience. This study highlights the urgent need for person-centered, context-aware communication practices across healthcare, media, and society. Full article

This study analyses NoViolet Bulawayo’s We Need New Names through the framework of intersectional feminism, a concept introduced by Kimberlé Crenshaw that examines how multiple identities, such as race, gender, and class, intersect to shape distinct experiences of marginalization. Bulawayo’s narrative, centred on the protagonist Darling, reveals the complex social forces she encounters as she navigates cultural and geographic transitions. Through a blend of English and Shona, the text reflects cultural duality and the tensions of migration, including acculturation and displacement. The episodic structure mirrors the fragmentation inherent in Darling’s African upbringing and her transcontinental journey. The analysis situates the novel alongside contemporary works such as Chimamanda Ngozi Adichie’s Americanah and Yaa Gyasi’s Homegoing, highlighting shared thematic concerns with identity, oppression, and the migrant experience. Ultimately, the study argues that Bulawayo’s representation of intersecting identities enriches the novel’s engagement with gender, race, class, and the transformative potential of language in articulating minority experiences. Full article

Computational thinking (CT) has become a cross-curriculum priority in many educational jurisdictions, yet a growing body of research reports uneven integration in initial teacher education (ITE), limited preservice teacher confidence, and persistent misconceptions that equate CT with coding. Concurrently, generative artificial intelligence (GenAI) has rapidly entered university programmes, offering new possibilities for modelling problem-solving, generating multiple representations, and supporting iterative design. However, while constructs such as self-efficacy, cognitive load, and affect are well established in educational psychology, their specific application to the intersection of CT and GenAI in teacher education remains under-theorised: existing research has not systematically examined how these psychological dimensions interact when preservice teachers learn CT through GenAI-mediated tasks. This thematic literature review synthesises 54 sources across three intersecting domains: CT frameworks and their pedagogical implications, CT integration in preservice teacher preparation, and GenAI in teacher education and learning design. Drawing on Bandura’s social cognitive theory, cognitive load theory, and research on technology-related affect, the review foregrounds the affective, cognitive, and cultural dimensions of preservice teachers’ engagement with CT and GenAI. The review proposes the GenAI-Enabled Computational Thinking for Preservice Teachers (GECT-P) model, which integrates CT dimensions with GenAI-supported learning cycles, psychological mediators, and teacher education outcomes. The model positions prompting as an epistemic and pedagogical practice that can make CT visible, supports cycles of decomposition, abstraction, pattern recognition, and algorithmic design, and embeds critical AI literacy, ethics, affective scaffolding, and classroom enactment. Design principles and practical pathways are offered for teacher educators seeking to prepare graduates who can develop CT with and beyond GenAI across diverse curriculum areas. Full article

Understanding and predicting how tourists move through a city is a challenging task, as it involves a complex interplay of spatial, temporal, and social factors. Traditional recommender systems often rely on structured data, trying to capture the nature of the problem. However, recent advances in Large Language Models (LLMs) open new possibilities for reasoning over richer, text-based representations of user context, even without a dedicated pre-training phase. In this study, we investigate the potential of LLMs to interpret and predict tourist movements in a real-world application scenario involving tourist visits to Verona, a municipality in Northern Italy, between 2014 and 2023. We propose an incremental prompt engineering approach that gradually enriches the model input, from spatial features alone to richer behavioral information, including visit histories, time information, and user cluster patterns. The approach is evaluated using six open-source models, enabling us to compare their accuracy and efficiency across various levels of contextual enrichment. The results provide a first insight about the abilities of LLMs to incorporate spatio-temporal contextual factors, thus improving predictions, while maintaining computational efficiency. The analysis of the model-generated explanations completes the picture by adding an interpretability dimension that most existing next-PoI prediction solutions lack. Overall, the study demonstrates the potential of LLMs to integrate multiple contextual dimensions in tourism mobility, highlighting the possibility of a more text-oriented, adaptive, and explainable T-RS. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

Archaeological reports show that about 70% of Han dynasty pictorial stone sites feature historical figures, revealing a significant yet understudied aspect of tomb ritual practice (muji yishi). This study examines how these depictions may reflect ritual characteristics and their relationship to temple ritual practice (miaoji yishi). From the Qin to Han period (221 BCE–220 CE), tomb and temple rituals increasingly converged; temple rituals were sometimes performed by tombs, and the imagery incorporated cosmological models alongside representations of daily life, including clothing, diet, dwellings, and mobility. The historical figures depicted can be grouped into three categories: emperors and sages, loyal ministers and righteous heroes, and filial sons and chaste women. These figures were closely associated with ideals of transcendence and immortality, suggesting a ritual framework that connected temple and tomb practices, with emperors and sages appearing most frequently, accounting for about 80% of the depictions. Notably, these images occur predominantly in commoners’ tombs (approximately 95%), where fewer social restrictions may have allowed greater creative freedom. While research on tomb ritual practices has traditionally relied on textual sources, the present study emphasizes archaeological evidence, offering an analytical perspective on the relationship between temple and tomb rituals in Han funeral art and highlighting their potential role in shaping Han ritual logic and religious expression. Full article