Search Results (2,612)

This study investigates the psychological and contextual mechanisms through which consumers’ awareness of agriculture’s contribution to climate change translates into a willingness to pay (WTP) for low-carbon agricultural products. Drawing on data from Eurobarometer 93.2 (ZA7739; N = 24,193), the research applies a moderated mediation model (Hayes’ PROCESS Model 14) to examine the mediating role of support for mandatory environmental labelling and the moderating effect of residential context. The results indicate that climate change awareness is significantly and positively associated with WTP. Moreover, support for mandatory labelling partially mediates this relationship, suggesting that institutionalized transparency may serve as a key mechanism through which environmental concern becomes economically actionable. The findings further reveal that this indirect effect is moderated by the level of urbanization, being stronger in urban areas than in rural settings. This highlights the importance of socio-spatial context in shaping consumer responses to sustainability information. Overall, the study contributes to the literature on sustainable consumption by demonstrating that willingness to financially support low-carbon agriculture depends not only on environmental awareness but also on trust-enhancing policy instruments and contextual factors. The findings offer important implications for policymakers aiming to promote sustainable food systems through information-based regulation. Full article

Background: Transformational leadership positively influences safety climate perceptions. Transformational leadership has four dimensions, and safety climate can be operationalized at different levels. Few research efforts, however, have been made to investigate the association between specific transformational leadership dimensions and safety climate at the individual level (i.e., psychological safety climate, PSC). Methods: Drawing on the interactive approach to forming safety climate, this study developed a multiple mediation model linking project team leaders’ intellectual stimulation (IS) to construction personnel’s PSC via safety-specific leader–member exchange (LMX) and team member exchange (TMX). A random sample (N = 292) of construction personnel based in Hong Kong is employed to validate the model. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data. Results: Both safety-specific LMX (specific indirect effect = 0.189) and safety-specific TMX (specific indirect effect = 0.032) significantly mediate the relationship between IS and PSC, although the direct association between them is insignificant. Age significantly moderates the IS–PSC relationship, with a stronger association for younger personnel (β = 0.481, p < 0.001) than for older personnel (β = 0.195, p = 0.029). Conclusions: The findings reveal that the relationship between IS and PSC is fully mediated by safety-specific LMX and TMX, with vertical exchange (LMX) playing a substantially more prominent mediating role than lateral exchange (TMX). These results suggest that improving construction personnel’s PSC requires developing project team leaders’ intellectually stimulating skills and fostering high-quality leader–follower safety exchange within project teams. Full article

In the new development stage of China’s green and low-carbon transition, agricultural industry agglomeration serves as a key catalyst for sustainable agricultural practices. Its effects on agricultural carbon reduction and energy conservation urgently need investigation. This research uses panel data from 31 Chinese provinces spanning 2005 to 2021 to investigate the nonlinear effects of agricultural industry agglomeration on agricultural carbon emissions and energy consumption, employing econometric models such as the two-way fixed effects model, mediation model, and moderation model. The findings indicate that (1) there’s a clear inverted U-shaped pattern linking agricultural industry agglomeration to both carbon emissions and energy consumption in agriculture; (2) agricultural scale effects and socialized services are key mechanisms; (3) marketization and environmental regulation positively moderate this relationship; and (4) the carbon reduction and energy-saving effects are more pronounced in regions with higher agricultural modernization levels, higher urbanization rates, and plain areas. This finding contributes to optimizing the path of agricultural industry agglomeration and facilitates the synergy of carbon reduction and energy conservation in such agglomeration. Full article

Freshwater aquaculture in China has expanded rapidly in recent decades, raising growing concerns about its environmental sustainability. However, the relationship between industrial agglomeration and environmental efficiency in freshwater aquaculture remains insufficiently understood. Using panel data from 18 major freshwater aquaculture provinces in China from 2009 to 2023, this study investigates the nonlinear effects of industrial agglomeration on environmental efficiency. Environmental efficiency is evaluated using a Global Super-SBM model incorporating undesirable outputs, while industrial agglomeration is measured by the location quotient index. A two-way fixed-effects model is employed for empirical estimation. The results reveal a significant inverted U-shaped relationship between industrial agglomeration and environmental efficiency, with a turning point at an agglomeration level of 2.519. Moderate agglomeration improves environmental efficiency through economies of scale and technology diffusion, whereas excessive agglomeration generates crowding effects that reduce efficiency. Further mechanism analysis shows that technology diffusion, proxied by the number of trained fishermen, plays a significant mediating role in this relationship. This study provides new empirical evidence on the nonlinear environmental effects of industrial agglomeration in freshwater aquaculture and offers policy implications for optimizing industrial spatial layout and developing differentiated environmental regulations to support the green and sustainable development of the sector. Full article

This study examines how the relationship between perceived financial accounting disclosures (FAD) and investor reactions to negative financial news (IRNFN) is conditioned by two individual-level moderators among 310 retail investors holding shares in project-based organisations (PBOs) listed on the Dubai Financial Market and Abu Dhabi Securities Exchange. The two moderators are framing bias susceptibility, a cognitive predisposition to be influenced by presentational form, and AI channel reliance (AICR), the extent to which investors rely on AI-mediated information channels—including algorithmic news aggregators, robo-advisory tools, AI-curated social media feeds, and automated sentiment-scored financial alerts—for receiving and interpreting corporate disclosures. Drawing on Behavioural Finance Theory and the Theory of Planned Behaviour, the study investigates whether the strength of the FAD–IRNFN association depends on these cognitive and informational processing conditions. The measurement model was estimated using confirmatory factor analysis in AMOS 25, and the moderation hypotheses were tested through path analysis with mean-centred composite scores and bias-corrected bootstrap inference, with a latent interaction robustness check reported in parallel. AI channel reliance emerged as a substantial moderator of the FAD–IRNFN relationship, while framing bias provided a smaller, marginally significant moderating effect. The findings are consistent with the theoretical expectation that, in AI-mediated information environments, the perceived quality and presentation of complex disclosures are associated with stronger, rather than weaker, investor reactions to negative news. Because the design is cross-sectional and based on self-reported data, the results are interpreted as associations rather than causal effects, with implications for disclosure regulation, corporate communication, and AI platform design in the UAE and comparable emerging markets. Full article

Agricultural carbon reduction is increasingly important for advancing low-carbon and sustainable agricultural development. Using provincial panel data for the Yangtze River Economic Belt (YEB) from 2007 to 2022, this study examines the spatiotemporal evolution of the digital economy (DE) and agricultural carbon emissions (ACE), and applies a two-way fixed-effects model with mediation and moderation analyses. The results show that digital economy (DE) increased steadily across the YEB, while agricultural carbon emissions (ACE) showed clear spatiotemporal variation. Digital economy (DE) is significantly negatively associated with agricultural carbon emissions (ACE), indicating that digital development can support agricultural carbon reduction. The Bootstrap results show that technological innovation and industrial agglomeration are statistically supported mediating pathways. Technological innovation is the primary mechanism, accounting for 44.02% of the total effect, while industrial agglomeration has a smaller but significant mediation share of 0.25%. Industrial structure optimization and fiscal investment are not confirmed as robust indirect pathways. The moderation results show that environmental regulation weakens the negative DE–ACE relationship, whereas agricultural fiscal expenditure strengthens it. These findings highlight the importance of green innovation, agglomeration effects, and supportive fiscal conditions in digital agricultural carbon reduction. Full article

Despite rapid growth in the AI endorser market, the psychological mechanisms governing their effectiveness remain theoretically fragmented. This study proposes the AHICC (AI–Human Interface in Consumer Cognition) model—integrating the Stereotype Content Model, Uncanny Valley hypothesis, anthropomorphism theory, Source Credibility Model, and Parasocial Interaction theory—to explain consumer responses to AI endorsers. A fully crossed 3 (endorser type: AI vs. hybrid vs. human) × 3 (anthropomorphism level: low vs. moderate vs. high) × 2 (technological transparency: low vs. high) between-subjects factorial experiment (n = 252) was conducted. Twenty-one sub-hypotheses were tested using MANOVA, polynomial regression, SEM, and bootstrap mediation analysis. All 21 sub-hypotheses were supported. AI endorsers outperformed human counterparts on brand attitude and purchase intention. Polynomial regression confirmed an inverted U-shaped Uncanny Valley effect with an optimal anthropomorphism level of 4.7 (7-point scale). High technological transparency attenuated the Uncanny Valley effect by approximately 60%. Dual-pathway mediation through cognitive and affective routes was confirmed, and TRI and product complexity emerged as significant boundary conditions. The AHICC model offers the first comprehensive framework for the AI endorser context, providing theoretically grounded guidance on anthropomorphism calibration, transparency strategy, and product-category-specific endorser selection. Full article

The rapid development of generative artificial intelligence (GAI) has exerted extensive and far-reaching impacts on college students’ learning, making it a topic worthy of in-depth investigation. This study aims to explore the impact of GAI usage on college students’ innovative learning behaviors, drawing on the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, the research explores the mediating mechanisms of effort expectancy and performance expectancy, as well as the moderating role of growth mindset in this process. Based on a sample of 430 Chinese college students recruited from diverse academic majors, the proposed moderated mediation model is empirically examined through latent structural equation modeling analysis. The results indicate that using GAI in learning significantly enhances students’ perceptions of effort expectancy and performance expectancy, thereby fostering their subsequent innovative behavior. Notably, the findings reveal that while performance expectancy mediates the relationship between GAI usage and innovative behavior, a growth mindset weakens this indirect pathway. The practical implications of this study are also discussed for both students and universities. Full article

The influenza A virus (IAV) has been responsible for multiple seasonal epidemics and poses a pandemic threat, and the growing number of variant strains constitutes a persistent threat to humanity. This study aimed to identify anti-influenza compounds from a traditional Chinese medicine (TCM) monomer library using a machine learning approach, with calmodulin as a hypothesis-driven target. The antiviral efficacy of the compounds with the highest predicted binding scores from virtual screening was evaluated using integrated computational and experimental approaches. A pre-trained protein language model (ConPLex) was employed for virtual screening. Molecular docking was used to predict binding characteristics, and network pharmacology was applied to generate hypotheses on multi-target mechanisms. The cytotoxicity and anti-H1N1 activity of the selected compounds were assessed in vitro, followed by in vivo evaluation of survival, lung pathology, viral load, and inflammatory mediators in a lethal mouse infection model. Sodium deoxycholate (NaDC) and deoxycholic acid (DCA) were identified as promising lead compounds. Both exhibited dose-dependent inhibition of viral replication in vitro with low cytotoxicity. Treatment with NaDC and DCA significantly improved survival rates and reduced lung pathology in H1N1-infected mice. Treatment was associated with suppression of nuclear factor kappa-B (NF-κB) activation, reduced pro-inflammatory cytokines, and elevated interleukin-10 (IL-10) levels. Molecular docking predictions indicated that NaDC and DCA exhibit moderate binding affinity for calmodulin, with binding energies of −8.38 kcal/mol and −7.61 kcal/mol, respectively. Furthermore, network pharmacology analysis suggested that these compounds may modulate pathways related to viral infection, inflammation, and immune regulation. NaDC and DCA demonstrate anti-influenza activity both in vitro and in vivo, reducing viral replication and alleviating inflammatory lung injury. These findings position NaDC and DCA as promising lead compounds for anti-influenza drug development and provide a foundation for further mechanistic validation. Full article

Background: Previous research has confirmed that dietary diversity is positively linked to mental health outcomes in older populations. Nevertheless, relevant evidence focusing specifically on Chinese older adults remains limited, and the internal mechanisms underlying this association I confirm. are not fully understood. Against this background, this study intended to investigate the association between dietary diversity and mental health among Chinese older individuals, explore the chain mediating roles of sleep quality and self-perceived quality of life, and further test whether gender moderates the above direct and mediating pathways. Methods: Using 2018 CLHLS data, 10,089 older adults aged 60 and above were selected as valid samples. Pearson correlation analysis was employed to determine the relationships between key variables. Hayes’ PROCESS macro Model 6 was used for baseline serial mediation analysis, and Model 85 was used for moderated serial mediation with gender as the moderator, adopting 5000 bootstrap samples. Results: The results revealed significant positive correlations (p < 0.01) between key variables, including dietary diversity, sleep quality, self-rated quality of life, and mental health. Model 6 showed that dietary diversity serves as a positive and significant predictor of mental health (B = 0.130, p < 0.001). Three significant mediating pathways were identified through which dietary diversity affects mental health: (1) sleep quality (B = 0.076, 95% CI: 0.062, 0.092), (2) self-rated quality of life (B = 0.100, 95% CI: 0.083, 0.118), and (3) sleep quality and self-rated quality of life (B = 0.020, 95% CI: 0.016, 0.025). The total mediating effect of the three pathways reached 59.94%. Model 85 found that the interaction term of dietary diversity x gender was non-significant (p > 0.05), demonstrating no statistically significant gender moderation of any pathway. Gender-stratified conditional effects revealed numerical differences across subgroups. Conclusions: Higher dietary diversity is significantly correlated with better mental health among Chinese older adults. Sleep quality and self-rated quality of life play significant roles as serial mediators in this association. Although gender does not statistically moderate the whole association mechanism, subtle gender heterogeneity exists in the pathway effect magnitude. The above findings offer novel insights into the underlying mechanisms. Strategies aimed at improving dietary diversity, combined with targeted interventions to enhance sleep quality and self-rated quality of life, with slight gender-differentiated auxiliary suggestions, may effectively promote mental health and contribute to active aging in later life. Full article

This study examines how international tourists’ perceptions of environmental, social, and governance (ESG) practices influence their willingness to pay a premium for community-based homestays. Grounded in signaling theory, ESG perception is conceptualized as a credibility signal that reduces perceived uncertainty in community-based accommodation settings. Data were collected from 300 international tourists visiting Mae Kampong Village, Chiang Mai, Thailand, and analyzed using partial least squares structural equation modeling (PLS-SEM). To strengthen predictive assessment, the model was additionally evaluated using PLSpredict, Q²_predict, and the Cross-Validated Predictive Ability Test (CVPAT). The results indicate that ESG perception significantly enhances community sustainability image, trust, and booking intention. Trust partially mediates the relationships between ESG perception and both booking intention and willingness to pay a premium, while booking intention demonstrates the strongest effect on willingness to pay a premium. Community sustainability image does not directly influence booking intention but instead operates indirectly through trust. Environmental concern significantly influences willingness to pay a premium, although its moderating effect is not supported. The findings suggest that tourists in community-based homestay environments rely heavily on trust-based psychological assurance when making accommodation decisions. This study extends ESG tourism research into community-based accommodation contexts and highlights the importance of trust in high-uncertainty tourism environments. The findings also emphasize the importance of transparent ESG communication and trust-building strategies for strengthening sustainable tourism competitiveness. Full article

Despite the growing popularity of digital payment methods and online shopping environments, little is known about the psychological mechanisms through which they affect consumer buying patterns. Drawing on the Stimulus–Organism–Response (SOR) framework, this study introduces the concept of spendception and examines its dual dimensions: perceived spendception ease (PSE) and perceived spendception control (PSC). These dimensions serve as mechanisms linking digital twin environments (DTEs) to impulse buying. Data were collected from 571 Generation Z consumers engaged in social commerce in Shanghai, China. Structural equation modeling (SEM) and machine learning techniques were employed to test the proposed relationships and evaluate predictive validity. The results reveal that DTE significantly increases impulse buying behavior both directly and indirectly through PSE. Specifically, PSE serves as a significant mediator by reducing the psychological friction associated with spending, thereby encouraging impulse buying decisions. In contrast, PSC acts as a significant moderator that weakens the positive relationship between DTE and impulse buying by enhancing consumers’ perceived ability to regulate their spending behavior. These findings demonstrate that spendception operates through two opposing psychological mechanisms: spending facilitation and spending control. This study contributes to the literature by conceptualizing spendception as a distinct transaction-centered construct and by extending the SOR framework through a dual-mechanism explanation of how digital commerce environments simultaneously encourage and restrain impulsive consumption. Full article

Against the backdrop of carbon peaking, carbon neutrality, and digital economy development, exploring the pathways through which artificial intelligence (AI) applications in manufacturing enterprises empower green transformation is of great significance. Using panel data on Chinese A-share listed manufacturing companies from 2005 to 2024 and a difference-in-differences (DID) model, this study examined the impact of the National Artificial Intelligence Innovation and Application Pilot Zones (AI Pilot Zones) policy on corporate green innovation. The results showed that the establishment of AI Pilot Zones significantly promoted green innovation among manufacturing enterprises, and this conclusion remained robust after parallel trend tests, PSM-DID estimation, and alternative variable measurements. Mechanism analysis revealed that financing constraints served as a key mediating channel, and that AI policies promoted green innovation through a serial mediation mechanism involving fintech development and the alleviation of financing constraints. Moderation analysis indicated that both human capital and digital transformation enhanced the policy effect. Heterogeneity analysis suggested that the policy’s impact was more pronounced among non-state-owned enterprises, large enterprises, and firms located in eastern regions. This study provides empirical evidence on the effectiveness of AI Pilot Zones in promoting green innovation among manufacturing firms and clarifies the underlying mechanisms. Full article

This study investigates the relationships among clean energy tourism potential (CETP), sustainable logistics capability (SLC), stakeholder collaboration (SCN), environmental concern (ECN), and sustainable community wellbeing and tourism outcomes (SCWTO) in island tourism destinations. The aim of this study is to examine the direct and indirect relationships among clean energy tourism potential, sustainable logistics capability, stakeholder collaboration, environmental concern, and sustainable community wellbeing and tourism outcomes in island tourism destinations. The study focuses on Koh Larn Island, Thailand, and Jeju Island, South Korea, as representative destinations with strong renewable energy tourism potential and sustainability-oriented tourism development. A quantitative research design was employed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 400 international tourists through an online questionnaire survey. The results reveal that clean energy tourism potential is positively associated with sustainable logistics capability and stakeholder collaboration. Sustainable logistics capability is positively associated with stakeholder collaboration and sustainable community wellbeing and tourism outcomes. Stakeholder collaboration also demonstrates a significant positive relationship with sustainable community wellbeing and tourism outcomes. Furthermore, the mediation analysis indicates that stakeholder collaboration serves as an important mechanism linking clean energy tourism potential and sustainable logistics capability with broader sustainability outcomes. Although the interaction term between environmental concern and sustainable logistics capability yielded a negative coefficient, no statistically significant moderating effect was observed. The findings highlight the strategic importance of renewable energy tourism development, sustainable logistics systems, and collaborative governance in promoting sustainable tourism and long-term community wellbeing in island destinations. This study contributes to the sustainable tourism literature by integrating clean energy tourism potential, sustainable logistics capability, stakeholder collaboration, and environmental concern within a comprehensive sustainability-oriented framework. Full article

Background: Circular logistics translates circular economy principles into practical supply chain processes, but its adoption varies across firms because organizations differ in sustainability commitment, circular supply chain motivation, shared circular identity, and digital traceability capability. This study examines how supplier sustainability commitment and circular supply chain motivation influence circular logistics adoption through circular supply chain identity, while also testing the moderating role of digital traceability capability. Methods: Data were collected from 350 supply chain professionals in Saudi Arabia and analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Supplier sustainability commitment and circular supply chain motivation positively influenced both circular logistics adoption and circular supply chain identity. Circular supply chain identity also positively affected circular logistics adoption and partially mediated the effects of both antecedents. Digital traceability capability acted as a selective moderator: it weakened the circular supply chain motivation–identity relationship, did not significantly moderate the supplier sustainability commitment–adoption relationship, but strengthened the circular supply chain identity–adoption relationship. It also moderated the indirect effect of circular supply chain motivation on circular logistics adoption through circular supply chain identity. Conclusions: Circular logistics adoption is driven not only by commitment and motivation, but also by shared circular identity and digitally enabled traceability. Full article