Search Results (739)

Indonesia’s worsening air pollution and traffic emissions have thrust electric vehicles (EVs) into the spotlight, but what really drives Indonesians to make the switch? This study integrates Protection Motivation Theory with green branding and policy frameworks to explain electric vehicle (EV) adoption in Indonesia. Using a nationwide survey (n = 986) and partial-least-squares structural-equation modeling, we test how environmental awareness, consumer expectancy, threat appraisal, and coping appraisal shape adoption both directly and through green brand image (GBI), while perceived policy incentives moderate the GBI–adoption link. The model accounts for 54% of the variance in adoption intention. These findings highlight that combining public awareness campaigns, compelling green brand messaging, and carefully calibrated policy incentives is essential for accelerating Indonesia’s transition to cleaner transport. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

Extreme heat poses growing health risks in tropical regions, yet public perception of this threat remains understudied in the Caribbean. This study examines how residents in Puerto Rico perceived heat-related health risks and how these perceptions relate to vulnerability and protective behaviors during the extreme heat events of the summer of 2020. We conducted a cross-sectional telephone survey of 500 adults across metropolitan and non-metropolitan areas of Puerto Rico, using stratified probability sampling. The questionnaire assessed heat risk perception, sociodemographic characteristics, health status, prior heat exposure, and heat-related behaviors. While most participants expressed concern about climate change and high temperatures, fewer than half perceived heat as a high level of personal health risk. Higher levels of risk perception were significantly associated with being male, aged 50–64, unemployed, and in fair health, having multiple chronic conditions, and prior experience with heat-related symptoms. Those with symptoms were nearly five times more likely to report high levels of risk perception (OR = 4.94, 95% CI: 2.93–8.34). In contrast, older adults (65+), despite their higher level of vulnerability, reported lower levels of risk perception and fewer symptoms. Nighttime heat exposure was widespread and strongly associated with heat-related symptoms. Common coping strategies included the use of fans and air conditioning, though economic constraints and infrastructure instability limited access. The findings highlight the disparity between actual and perceived vulnerability, particularly among older adults. Public health strategies should focus on risk communication tailored to vulnerable groups and address barriers to heat adaptation. Strengthening heat resilience in Puerto Rico requires improved infrastructure, equitable access to cooling, and targeted outreach. Full article

Background/Objectives: Chickenpox is an ongoing health threat for young children. This study aimed to investigate varicella vaccination uptake among children and its determinants at both the individual and interpersonal levels. Methods: A cross-sectional survey of parents of children aged 0–15 years and with administrative health records was conducted between September and October 2024 in Shenzhen, China. Participants were recruited through multistage random sampling. This analysis was based on a subsample of 996 parents whose children were 1–10 years old and without a prior history of chickenpox. Multivariate logistic regression models were fitted. Results: Among the participants, 47.0% reported that their children had received a varicella vaccination. Parents who believed that chickenpox was highly contagious (adjusted odds ratios [AOR]: 1.62, 95% confidence interval [CI]: 1.23, 2.13), perceived more benefits (AOR: 1.22, 95% CI: 1.05, 1.41) and cues to action (AOR: 1.33, 95% CI: 1.04, 1.69), and exhibited greater self-efficacy (AOR: 1.40, 95% CI: 1.09, 1.80) related to children’s varicella vaccination reported higher varicella vaccination uptake for their children. Greater perceived barriers related to vaccination (AOR: 0.89, 95% CI: 0.83, 0.95) and dysfunctional interactions with children (AOR: 0.97, 95% CI: 0.94, 0.99) were associated with lower varicella vaccination uptake for children. In addition, higher exposure to information encouraging parents to vaccinate their children against chickenpox (AOR: 1.24, 95%CI: 1.08, 1.41) and thoughtful consideration of the veracity of the information were associated with higher varicella vaccination uptake among children (AOR: 1.19, 95% CI: 1.05, 1.36). Conclusions: There is a strong need to promote varicella vaccination for children in China. Full article

The uptake of the COVID-19 and seasonal influenza (SI) vaccines have decreased in Europe and especially in Malta. The present study aimed to investigate the attitudes toward COVID-19 and SI vaccines and determine if individuals perceive that these vaccines are relevant to protect their health and identify reasons for their responses. A cross-sectional study using an anonymous questionnaire, informed by the Theory of Planned Behavior, addressing behavior beliefs and attitudes, and targeted at adult residents in Malta, was designed on Google Forms and disseminated using social media between January and March 2024. A total of 555 responses were received. The majority of respondents did not take/intend to take the COVID-19 (75%, n = 417) or SI (64.3%, n = 362) vaccines, with females being less likely to do so (p = 0.033). Perceived lack of safety (31.3%, n = 174) was the primary reason for rejecting the COVID-19 vaccine, and perceived lack of a threat from SI (26%, n = 144) was the reason for rejecting the SI vaccine. Those having chronic conditions were positively associated with uptake of both vaccines. In the post-pandemic era, these vaccines are not envisaged as having a major role in protecting one’s health. A high degree of skepticism especially toward the combined COVID-19 and SI vaccine in terms of safety, mostly in women, is still present. Full article

Cybersecurity threats increasingly originate from human actions within organizations, emphasizing the need to understand behavioral factors behind non-compliance with information security policies (ISPs). Despite the presence of formal security policies, insider threats—whether accidental or intentional—remain a major vulnerability. This study addresses the gap in behavioral cybersecurity research by developing an integrated conceptual model that draws upon Operant Conditioning Theory (OCT), Protection Motivation Theory (PMT), and the Theory of Planned Behavior (TPB) to explore ISP compliance. The research aims to identify key cognitive, motivational, and behavioral factors that shape employees’ intentions and actual compliance with ISPs. The model examines seven independent variables of perceived severity: perceived vulnerability, rewards, punishment, attitude toward the behavior, subjective norms, and perceived behavioral control, with intention serving as a mediating variable and actual ISP compliance as the outcome. A quantitative approach was used, collecting data via an online survey from 302 employees across the public and private sectors. Structural Equation Modeling (SEM) with SmartPLS software (v.4.1.1.2) analyzed the complex relationships among variables, testing the proposed model. The findings reveal that perceived severity, punishment, attitude toward behavior, and perceived behavioral control, significantly and positively, influence employees’ intentions to comply with information security policies. Conversely, perceived vulnerability, rewards, and subjective norms do not show a significant effect on compliance intentions. Moreover, the intention to comply strongly predicts actual compliance behavior, thus confirming its key role as a mediator linking cognitive, motivational, and behavioral factors to real security practices. This study offers an original contribution by uniting three well-established theories into a single explanatory model and provides actionable insights for designing effective, psychologically informed interventions to enhance ISP adherence and reduce insider risks. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article

Pteridium aquilinum (bracken fern) poses a global threat to biodiversity and to the health of both animals and humans due to its toxic metabolites and aggressive ecological expansion. In northern Spain, particularly in regions of intensive livestock farming, these risks may be exacerbated, calling for urgent assessment and monitoring strategies. In this study, we implemented a multidisciplinary approach to evaluate the toxicological and ecological relevance of P. aquilinum through four key actions: (a) quantification of pterosins A and B in young fronds (croziers) using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS); (b) analysis of in vivo genotoxicity of aqueous extracts using Drosophila melanogaster as a model organism; (c) a large-scale survey of local livestock farmers to assess awareness and perceived impact of bracken; and (d) the development and field application of a drone-based mapping tool to assess the spatial distribution of the species at the regional level. Our results confirm the consistent presence of pterosins A and B in croziers, with concentrations ranging from 0.17 to 2.20 mg/g dry weight for PtrB and 13.39 to 257 µg/g for PtrA. Both metabolite concentrations and genotoxicity levels were found to correlate with latitude and, importantly, with each other. All tested samples exhibited genotoxic activity, with notable differences among them. The farmer survey (n = 212) revealed that only 50% of respondents were aware of the toxic risks posed by bracken, indicating a need for targeted outreach. The drone-assisted mapping approach proved to be a promising tool for identifying bracken-dominated areas and provides a scalable foundation for future ecological monitoring and land management strategies. Altogether, our findings emphasize that P. aquilinum is not merely a local concern but a globally relevant toxic species whose monitoring and control demand coordinated scientific and policy-based efforts. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

During the 1970s’ military dictatorships in Chile and Brazil, the Catholic Church took the position of moral opposition to the authoritarian regimes, while in Argentina, the Church sided with the dictatorship. In his pathbreaking book utilizing the religious economy model, Anthony Gill theorized that the Church in Chile adopted a critical stance against the regime due to competition from Protestant Churches. Subsequently, Gill’s specific arguments and the religious economy model in general have been widely applied but also critiqued by sociologists of religion. Through a deeper analysis of the three case studies of Argentina, Chile, and Brazil, we support and extend theoretical critiques of the religious economy model. We argue that that the religious economy model as applied by Gill is an insufficient predictor of church and state behavior because it lacks specificity on the amount of competition necessary to warrant taking progressive action, lacks specificity about the timing of Protestant competition, ignores the pre-existing position of the Catholic Church, treats the demand side of the religious market as inelastic, assumes the Catholic Church intentionally implemented progressive policies in response to competition, presumes the Church is a unitary actor, and disregards the impact of a perceived Marxist threat on both church and state actors. Our critique of the religious economy model points to refinements of the model and alternative approaches to understanding church and state behavior in Latin America and elsewhere. Full article

In recent years, significant changes in food consumption habits have emerged due to various factors, including climate change, population growth, urbanization, and the depletion of natural resources. These changes pose a threat to the stability of global food systems and raise serious concerns about food security. Although this process encourages innovative and sustainable food consumption, it also makes individuals more skeptical and concerned about new foods. In this context, understanding consumer intentions regarding behaviors such as purchasing genetically modified (GM) foods is critical for predicting consumer responses and promoting responsible consumption patterns within the scope of sustainability. This study examined the effects of food technology neophobia and perceived information on attitudes and purchase intentions toward genetically modified (GM) foods. Survey data were collected from 324 participants across Turkey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that food technology neophobia reduces perceived benefits and increases perceived risks, whereas perceived information enhances perceived benefits and lowers perceived risks. Additionally, attitudes were found to influence the intention to purchase GM foods significantly. Global issues, such as climate change and the depletion of natural resources, highlight the importance of innovations in food technology for sustainable food production. Understanding consumer concerns and perceived knowledge levels regarding genetically modified (GM) foods is critical to ensuring that these products are accepted at the societal level in an informed and conscious way. This study contributes to the promotion of sustainable food technologies and responsible consumer behavior, in line with the objectives of Sustainable Development Goal 12 (Responsible Consumption and Production). Full article

Coastal forests in Bangladesh provide various essential ecosystem services. However, these face severe threats from deforestation, land-use changes, and climate change. Understanding community perceptions of these services and their willingness to support restoration is crucial for effective conservation strategies. To analyze these, this study employs machine learning on survey data collected from Chakaria and Moheshkhali Upazilas of Cox’s Bazar District. Gradient Boosting Machine (GBM) model was used to identify key predictors of restoration willingness and compared with Random Forest (RF) and Generalized Linear Model (GLM). Proximity to forests shapes community priorities with stronger support for restoration among those living near the forest. Higher perception levels were linked to various ecosystem services provided by the forest, while forest dependence, age, and education also influenced perception with education and age showing strong negative correlations. The GBM model outperformed RF and the GLM in predicting restoration willingness due to its ability to capture complex relationships. The perceived importance of provisioning and supporting services and restoration awareness influenced willingness, accounting for 55.56% of the variation. Willingness was also shaped by income, forest dependency and awareness. Overall, this study leverages machine learning to align conservation efforts with socio-economic priorities, ensuring the resilience of Bangladesh’s coastal ecosystems. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

Global data reveal ongoing inequalities faced by minorities, often reinforced by media portrayals that depict them as threats, victims, or passive individuals without agency. While media framing has been extensively studied, especially in terms of media content and representation, few studies have examined how journalists perceive and navigate the coverage of minorities. This study addresses that gap by examining how Greek journalists perceive mainstream media coverage of refugees and migrants, LGBTQ+ individuals, and people with mental health challenges, with particular attention to their sourcing practices and sense of ethical responsibility. Fourteen journalists participated in semi-structured interviews, and thematic analysis was applied to identify key patterns. Journalists described dominant media narratives as fragmented, stereotypical, and dehumanizing, noting the frequent use of linguistic inaccuracies, misinformation, and the absence of personal stories. At the same time, they reported opportunities within their own sourcing practices to promote more inclusive and accurate coverage. Ethical concerns were expressed on three levels—union; corporate; and personal—with calls for clearer editorial guidelines and dedicated training. Many participants emphasized the role of personal ethics as a guiding compass in navigating complex newsroom pressures. Full article

This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies (e.g., ERP systems, digital audit tools). The data collected through a convenience sampling method was analyzed using SPSS 27 and SmartPLS 4 software. The findings reveal that the direct effect of technostress on job performance is not significant; however, this stress indirectly contributes to performance through techno-eustress. In this study, techno-eustress refers to the cognitive appraisal of technology-related demands as development-enhancing challenges rather than threats. This concept is theoretically grounded in the broader eustress framework, which views stressors as potentially motivating and growth-promoting when positively interpreted. The model is based on Cognitive Evaluation Theory, the Job Demands–Resources Model, and Self-Determination Theory. This study demonstrates that digital transformation can promote not only operational improvements but also organizational resilience by enhancing employees’ psychological resources and adaptive capacities. By highlighting the mediating role of techno-eustress, this research offers a nuanced perspective on how human-centered cognitive mechanisms can strategically support performance and sustainability in the face of technological disruption—an increasingly relevant area for organizations striving to thrive amid uncertainty. Full article