Search Results (2,915)

In light of the rapid adoption of text-to-image (T2I) tools in higher education, this study develops a stimulus–organism–response (S-O-R) model to explain the sustainable and responsible use intentions of text-to-image generative AI tools in higher education. Focusing on both university students and faculty, the model conceptualizes perceptions of ease of use, information quality, and ethical awareness as external stimuli; technology- and ethics-related anxiety as internal emotional states; and algorithmic trust, perceived risk, and sustainable use intention as behavioral evaluations and responses. Grounded in the Stimulus–Organism–Response (S–O–R) framework, we integrate the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory (TTAT), and the DeLone–McLean (D&M) model to propose a layered mechanism, with personal innovativeness serving as a moderator. Utilizing 807 valid survey responses, we employed structural equation modeling and fuzzy-set qualitative comparative analysis. The results reveal that (1) the overall chain is supported: perceived ease of use, information quality, and ethical awareness primarily influence sustainable use intention indirectly through anxiety, trust, and risk; (2) although higher usability and quality do not alleviate anxiety, they coexist within a complex pattern of trust amid anxiety; and (3) high levels of personal innovativeness diminish the linear effects of trust and risk on intention. Configurational evidence further indicates multiple pathways leading to high sustainable intention, whereas low intention is typically characterized by uniformly low perceptions, emotions, evaluations, and innovativeness. By framing sustainable adoption through a coupled trust–risk–anxiety lens, this study extends the understanding of generative AI use in education and offers actionable implications for promoting responsible and sustainable practices in universities. Full article

Understanding residents’ support is essential for the social sustainability of tourism development, particularly in rapidly transforming destinations. Drawing on Social Exchange Theory, Social Identity Theory, and Destination Image Theory, this study proposes a process-oriented model in which perceived economic benefits and environmental concerns influence residents’ Behavioral Intention Support for Tourism through destination image and national identity. Using survey data from 418 young, educated Saudi residents (predominantly undergraduate university students) and structural equation modeling, the findings show that support is driven primarily by indirect pathways rather than direct cost–benefit evaluations. Economic benefits enhance destination image and strengthen national identity, which in turn foster supportive behavioral intentions. Environmental concerns do not directly reduce support; instead, they operate through sustainability-oriented perceptions of destination image and identity. The results extend existing models by showing how young residents’ behavioral intention support for tourism in Saudi Arabia is shaped through a cognitive–identity process in which destination image and national identity translate economic and environmental evaluations into behavioral intention. This framework offers actionable insights for destinations pursuing state-led tourism development. Full article

Generative artificial intelligence (AI) is rapidly reshaping higher education, yet the social pathways that trigger intensive use and may evolve into dependency remain insufficiently understood. This study examines how social drivers shape sustainable AI usage and the potential progression toward dependency. We surveyed 965 university students and analyzed the data using partial least squares structural equation modeling (PLS-SEM). The findings show that social factors, including fear of missing out, word-of-mouth, and subjective norms, primarily influence AI usage through trust in AI. AI usage has a limited direct effect on dependency, whereas dependency is more strongly associated with psychological evaluations of AI benefits, including perceived competence enhancement and perceived intelligence of AI systems. These results support a staged sociotechnical account of AI engagement and point to sustainability-relevant implications for responsible AI integration in higher education (Sustainable Development Goal 4), including trust calibration, competence building, and safeguards against over-reliance that may undermine long-term learning outcomes. Full article

Ensuring household food security through climate resilient and sustainable crop production continues to be a central challenge for rural farming households in Kenya. Therefore, the adoption of adaptation strategies to a changing climate is crucial in maize-growing regions. A multivariate probit model was deployed to understand determinants of the adoption of climate adaptation strategies and drought-tolerant maize varieties among 819 smallholder farmers in arid and semi-arid areas. The survey was conducted in four major maize-growing counties in Kenya. Results show that most climate change adaptation strategies implemented by maize-dependent smallholders are complementary. Multivariate logistic coefficients showed a significant inverse relationship between marital status and the adoption of soil and water conservation strategy in Machakos (−2.321; p = 0.01). Secondary education was significantly associated with the adoption of water harvesting in Machakos (2.538; p = 0.001), while it was associated with soil and water conservation in Homa Bay (2.208; p = 0.0001) and Migori (1.538; p = 0.01), respectively. Unemployment was positively (21.726; p = 0.01) linked with the adoption of water harvesting in Machakos, with the probability of a farmer adopting water harvesting strategies in Machakos (1.460; p = 0.01). Remarkably, soil and water conservation strategies in Machakos (1.807; p = 0.001) and Migori (2.458; p = 0.0001) positively correlated with food insecurity. Incidentally, only farmers in Migori County had a significant (1.024; p = 0.01) likelihood of adopting drought-tolerant maize varieties with increasing land size. In the same county, the source of maize variety was positively associated with the adoption of drought-tolerant varieties. There is a need to promote policies like informal and formal education and awareness creation to enhance smallholder farmers’ capacity to adopt multiple sustainable climate-smart adaptation strategies that can promote the continued adoption of drought-tolerant maize varieties. Full article

The disparities in accessibility of public charging stations (PCSs) have emerged as a critical bottleneck constraining urban social equity and sustainable development. This study focuses on PCS accessibility within the central urban area of Tianjin. Employing an enhanced Gaussian-based two-step floating catchment area method, spatial autocorrelation analysis, and multi-scale geographically weighted regression, the research investigates spatial configuration, spatial dependence, and influencing factors from three analytical dimensions. The study elaborates on the disparities in PCS accessibility among different income groups and examines the determinants contributing to these variations. Research findings indicate that: (1) PCS accessibility1 in the central urban area exhibits a core-periphery spatial structure, with notable heterogeneity in the spatial distribution patterns of PCS accessibility across different income groups. (2) The MGWR model demonstrates superior performance compared to conventional models, with an increase in R² explanatory power of at least 23.7% and a reduction in AICc values ranging from 3.94% to 47.45%. (3) Housing prices are positively correlated with PCS accessibility across different income groups, while per capita income and education and cultural services differentiated associations with PCS accessibility. From the perspective of different income groups, this study systematically analyzes the disparities in PCS accessibility and their associated factors, providing a theoretical foundation for sustainable urban planning and development. Full article

The COVID-19 pandemic (2020–2023) profoundly disrupted healthcare systems and imposed sustained psychological burdens on healthcare professionals and trainees. Reliable instruments are essential for assessing these impacts. This study evaluated the construct validity and reliability of the Fear of COVID-19 Scale (FCV-19S) in a convenience sample of 1761 healthcare professionals, trainees, and academic staff at a single U.S. academic health sciences center (the University of Oklahoma Health Campus). Participants completed the FCV-19S; confirmatory factor analysis (CFA) examined its dimensional structure; and internal consistency was assessed using Cronbach’s α and McDonald’s ω. The one-factor model demonstrated good internal consistency (α = 0.89; ω = 0.89) but exhibited sub-optimal model fit (CFI = 0.89; TLI = 0.83; SRMR = 0.06; RMSEA = 0.18). The two-factor model, distinguishing emotional and somatic fear, showed substantially improved fit (CFI = 0.97; TLI = 0.96; SRMR = 0.03; RMSEA = 0.09) and acceptable internal consistency for both factors (α = 0.85 and 0.86; ω = 0.85 and 0.87), although RMSEA remained above conventional thresholds for close fit. Overall, findings support a two-factor structure as a comparatively superior and more nuanced representation of fear responses among an academic health workforce. The validated FCV-19S offers a reliable tool for assessing COVID-19-related distress in clinical and educational health settings, informing targeted interventions to strengthen workforce resilience. Full article

Sustaining Ghana’s construction workforce requires more than expanding training as it requires integrating apprenticeships into a coherent skills system that links education, industry, and employability. This study tests how institutional integration, practical training, and industry collaboration jointly shape the effectiveness of apprenticeship training as a pathway to a sustainable construction workforce. Using survey data from 212 students, 36 instructors, and 129 industry and policy stakeholders, the study applies Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to validate a latent construct of Effective Apprenticeship Strategies and quantify how its components explain training and workforce outcomes. A first-order measurement SEM was estimated to examine the causal relationships between the latent strategy construct and its observed indicators across the three respondent groups within a single analytical framework. The model shows strong construct validity (CFI = 0.904; RMSEA = 0.032) and reveals that structured workplace learning, institutional support, SME engagement, and technology-oriented training are the most influential components of effective apprenticeship integration, together explaining a substantial proportion of variance in apprenticeship quality and workforce readiness. The results further reveal a highly gender-polarised training pipeline (87.7% of students and 94.4% of instructors are male), indicating that current apprenticeship structures risk constraining Ghana’s future skilled labour supply and undermining long-term workforce sustainability. The study demonstrates that apprenticeship integration is not merely a training reform but a workforce sustainability mechanism. By empirically identifying which integration strategies matter most and showing how gender exclusion limits future labour capacity, the study provides a quantitative basis for redesigning Ghana’s apprenticeship system toward a more inclusive, industry-aligned, and sustainable construction workforce. Full article

Sustainability has become a central pillar of public policy and higher education, with university students playing a key role both as recipients of knowledge and as agents of change toward more responsible practices. Existing literature shows that students’ attitudes, beliefs, and behaviors related to sustainability are shaped by multiple explanatory factors; however, prior research has often addressed these factors in isolation, resulting in a fragmented understanding of how sustainability is constructed within the university context. Students’ engagement with sustainability emerges from the interaction of several interconnected dimensions, including conceptual clarity, everyday lifestyle practices, academic experiences, institutional environments, and sustainability-related training. This study provides a descriptive and exploratory empirical overview of the dimensions that shape university students’ understanding of sustainability, enabling the identification of patterns, trends, and key influences on attitudes, intentions, and sustainable behaviors. Data were collected from a sample of university students in Spain using a structured questionnaire designed to capture perceptions, behaviors, and experiences related to sustainability. The data were analyzed using quantitative descriptive techniques. The findings reveal distinct sustainability dimensions and highlight the interplay between conceptual understanding, educational experiences, institutional initiatives, and lifestyle practices in shaping students’ engagement with sustainability. By offering a comprehensive, non-manipulative empirical perspective, the study lays the groundwork for the development of more effective educational and university management strategies aimed at strengthening student commitment to the Sustainable Development Goals. Beyond its descriptive contribution, the study proposes an integrated conceptual model of sustainable education that brings together conceptual, attitudinal, educational, and institutional dimensions from the students’ perspective. This holistic framework provides actionable guidance for universities seeking to adapt curricula, pedagogical approaches, and institutional initiatives to foster more coherent, inclusive, and effective sustainability education. Full article

This study examines the determinants of renewable energy consumption among BRICS countries (Brazil, Russia, India, China, South Africa, Saudi Arabia, Egypt, the United Arab Emirates, Ethiopia, Iran, and Indonesia) between 2000 and 2022. Using static (Fixed and Random Effects) and dynamic (First-Difference GMM) panel data models, the research investigates how economic, institutional, and social factors influence renewable energy transition. The results reveal structural heterogeneity within the bloc. Among the founding members, renewable energy consumption is positively associated with governance quality and trade openness, while GDP per capita exhibits a negative relationship, consistent with the Environmental Kuznets Curve hypothesis. In contrast, the new members show strong energy dependence and limited institutional capacity, with dynamic models confirming high persistence in energy consumption and weak responsiveness to economic and policy changes. Variables such as education and life expectancy were omitted in the dynamic specification due to limited temporal variation, without compromising model consistency. Diagnostic tests (Hansen, Sargan, and AR(2)) confirm the robustness of the estimates. Overall, the findings highlight the importance of strengthening institutional governance, technological innovation, and intra-bloc cooperation to advance energy transition and achieve sustainable development across the BRICS economies. Full article

In today’s society, children are increasingly exposed to negative environmental information. How such exposure shapes pro-environmental behavioral intentions matters for the Sustainable Development Goals (SDGs). However, empirical evidence specific to Chinese children remains limited. An explanatory sequential mixed-methods study was conducted with Grade 4 to 6 students in N City, China (survey n = 253; focus groups n = 16). The survey assessed negative environmental information exposure, environmental worry, environmental self-efficacy, and behavioral intentions, and tested mediation and moderation models. Focus groups were analyzed thematically to refine the mechanisms. Quantitative results revealed a clear “double-edged” pattern: exposure to negative environmental information was positively associated with pro-environmental behavioral intentions via heightened environmental worry, yet negatively associated with intentions via reduced environmental self-efficacy. Moreover, environmental self-efficacy moderated the link between worry and intention. Qualitative findings further corroborated and specified these pathways, indicating that children interpret negative messages through crisis narratives, blame attribution, and scale comparison, whereas actionable scripts and positive feedback help sustain perceived control and support translating worry into intention. Sustainability communication and education should therefore pair risk information with efficacy cues, feasible actions, and meaningful feedback rather than relying solely on threat narratives. Full article

Higher education is in a period of change driven by increasing demands for student-centred learning, flexible delivery, and stronger industry relevance. While innovation in course design is widely recognised as essential, academics often face barriers such as limited time, institutional constraints, budget and financial constraints and risk aversion. Building on previous pedagogical and innovation models, this paper presents the enabling innovation framework, developed through an iterative, design-thinking process and grounded in Rogers’ Diffusion of Innovation theory. The framework conceptualises three interconnected modes of innovation: everyday, strategic, and radical. The development of each mode highlights the importance of time and scholarly activity as underpinning concepts of the framework. Everyday innovation involves small, often spontaneous adjustments to teaching practice; strategic innovation is collaborative and aligns with institutional or program-level goals; and radical innovation is transformative, disrupting existing practices to create new cultures of learning. Together, these modes offer multiple entry points into innovation, encouraging academics to engage meaningfully with course design regardless of their level of risk appetite or institutional positioning. By framing innovation as a continuum supported by scholarship, the framework provides educators with a practical scaffold to initiate and sustain pedagogical change. This work argues that enabling innovation at different levels fosters a stronger culture of creativity, adaptability, and quality in higher education teaching and learning. Full article

Participatory decision-making (PDM) is increasingly recognized as a critical governance mechanism for enhancing institutional sustainability in higher education. However, the internal pathways through which participation contributes to organizational learning and adaptive governance remain insufficiently understood and empirically tested. Drawing on collaborative governance and organizational learning theories, this study develops a feedback-based governance framework that links PDM, learning feedback (LF), and adaptive governance capacity within universities. This study adopts a quantitative research design and employs covariance-based structural equation modeling (CB-SEM) to examine the proposed framework. The analytical strategy integrates confirmatory factor analysis, mediation analysis, moderation analysis, and multi-group comparisons to capture both internal transmission mechanisms and contextual heterogeneity. As an empirical illustration, questionnaire data were collected from 512 respondents across 32 universities in China, encompassing both research-oriented and practice-oriented institutions. The survey instruments measured respondents’ perceptions of PDM, LF, adaptive governance capacity (AGC), managerial support, and digital collaboration tools using established multi-item scales. The results show that PDM enhances adaptive governance primarily through LF, indicating that participation contributes to sustainability by operating as a perceived learning mechanism rather than a one-off procedural input. Overall, the findings suggest interpreting participatory governance as a perception-based, feedback-oriented decision–learning–adaptation process, highlighting the role of learning in adaptive university governance. Full article

The Thailand 4.0 agenda elevates entrepreneurship education (EE) as a lever to escape the middle-income, inequality, and imbalance traps, yet EE remains weakly embedded in basic education—especially in Thai language. We designed and piloted a community-economy context-based learning model integrating EE (CEC-EE) for Grade 12 Thai via a two-cycle R&D process: needs analysis (surveys and focus groups with teachers and students) and prototype development. The model operationalizes six instructional steps (6Cs: connect, comprehend, clarify, construct, carry over, and conclude) anchored in Mae Chan’s community economy and targets entrepreneurial language skills (ELSs) consisting of analytical reading and creative writing. In a one-group pretest–posttest with Grade 12 students (n = 32), academic achievement and ELSs—analytical reading and creative writing—improved markedly. Posttest means exceeded pretests with very large effect. Experts rated the model appropriate, feasible, and useful; teachers and students reported high perceived value alongside concerns about implementation cost, support capacity, and student readiness. The CEC-EE model offers a context-responsive pathway for embedding EE in Thai-language instruction; future work should employ comparative designs, multi-site samples, and cost-effectiveness analyses to assess scalability and sustained impact. Full article

This study examines the determinants of cybersecurity behaviour among employees in higher education institutions (HEIs) in the United Arab Emirates (UAE) through an integrated framework combining the Protection Motivation Theory (PMT), the Theory of Planned Behaviour (TPB), and the COM-B model. Using a convergent mixed-methods design, survey data from 250 participants and interviews with 18 staff members were analysed to identify behavioural drivers and construct a predictive model. Results showed that policy familiarity, security culture, and intrinsic motivation were the strongest predictors of secure behaviour, collectively explaining nearly 70% of variance. The predictive neural network model achieved high accuracy (F1 = 91.99%, R² = 0.94), with interpretability analysis ranking policy familiarity as the top determinant. Qualitative insights reinforced these findings, revealing that policy clarity, leadership engagement, and supportive culture enable consistent compliance. The study contributes a validated, theory-driven predictive framework for HEIs, bridging behavioural science and cybersecurity governance. The findings are based on self-reported cybersecurity behaviour and should be interpreted accordingly. It concludes that effective institutional cybersecurity depends on clear policies, inclusive culture, and sustained motivation rather than fear-based enforcement. Full article

This study investigates the nexus between sustainable development indicators and economic growth across seven strategic emerging economies: China, Turkey, Brazil, Malaysia, Iran, Egypt, and Argentina, from the period 2002 to 2023. Utilizing panel data regression analysis, the Random Effects model was identified as the most appropriate estimation method based on rigorous statistical criteria. The empirical results reveal that R&D expenditures, health expenditures, the renewable energy share, and CO₂ emissions exert a positive and significant influence on GDP. In contrast, education expenditures were found to have a negative and statistically insignificant effect on growth. This study emphasizes the necessity of supporting vital sectors, such as agriculture and industry, while simultaneously adopting effective environmental policies to reduce emissions and ensure long-term sustainable development goals in the analyzed countries. Full article