Search Results (99)

Background/Objective: Maternal immunization is highly recommended, particularly in developed countries. However, its awareness among pregnant women in Japan remains low. This study aimed to assess the awareness and attitudes toward maternal immunization among pregnant women in Japan and to identify the factors that may promote its acceptance. Methods: We conducted a cross-sectional questionnaire survey among pregnant women attending antenatal checkups at nine facilities in Kanagawa Prefecture, Japan, from August 2024 to January 2025. The survey assessed knowledge and intention regarding maternal immunization for influenza, pertussis, respiratory syncytial virus (RSV), and group B streptococcus (GBS) as well as attitudes toward vaccination costs and information sources. Results: Overall, 523 respondents were included in this study. The overall awareness of maternal immunization was 16%. Willingness to receive vaccinations during pregnancy was reported for influenza (68%), pertussis (58%), RSV (59%), and GBS (71%). A common reason for vaccine hesitancy included uncertainty about its effects on the fetus. The key factors associated with vaccine acceptance were higher educational attainment and prior knowledge of maternal immunization. Regarding costs, most respondents were willing to pay up to JPY 5000 (approximately USD 35). The most frequently prioritized sources were explanations from physicians, followed by explanations from midwives. Conclusions: Despite low awareness, vaccination intention was comparable to that reported in other countries. Points that may contribute to improved vaccine uptake were also identified. These findings may lead to the prevention of infectious diseases in newborns and infants in Japan and possibly improve public health. Full article

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces. Full article

As artificial intelligence (AI) becomes increasingly integrated into higher education, understanding perceptions across different demographic groups is essential for its effective implementation. This study examines attitudes toward AI among students, lecturers, and academic staff, considering factors such as gender, age, occupation, academic discipline, ethical concerns, and experience level. The findings indicate that while overall perceptions of AI in education are positive, concerns about ethics and uncertainty regarding its role persist. Gender and age differences in AI perceptions are minimal, though female students, educators, and individual in humanities disciplines express slightly higher ethical concerns. Teachers exhibit greater skepticism, emphasizing the need for transparency, ethical guidelines, and training to build trust. The study also highlights the influence of AI experience and perceptions. Frequent users tend to have a more positive outlook, whereas those with advance expertise engage with AI more selectively, suggesting a shift toward intentional and strategic use. Full article

Background/Objectives: Vaccine hesitancy has increased during the coronavirus pandemic, a period marked by the spread of disinformation and conspiracy theories about COVID-19 vaccines. This qualitative study aimed to explore the beliefs and attitudes of Spaniards towards COVID-19 vaccines and analyze the role of information sources in this process. Methods: Semi-structured interviews were conducted with 35 residents of Spain who exhibited varying levels of vaccine hesitancy. Through thematic content analysis, the narratives supporting vaccination-related decisions, as well as the influence and trust in information sources, were examined. Results: Reasons for getting vaccinated included perceptions of it being “almost an obligation” and “fear of illness and death”. Conversely, reasons for not getting vaccinated included “uncertainty about vaccines”, the belief that “the risk is not real”, and the perception that “vaccination is a personal decision”. Regarding vaccine-related information sources, interviewees expressed distrust of the media, particularly television, as they considered news about vaccine effectiveness and characteristics to be contradictory and constantly changing, which created uncertainty about its reliability. Most interviewees were unsure if social media influenced their decision not to get vaccinated. However, those who acknowledged its impact mentioned trusting sources such as people with a university education. Additionally, close contacts—particularly healthcare professionals—had a significant influence on the decision not to get vaccinated. Conclusions: This study shows that the decision not to vaccinate is shaped by personal beliefs and sources of information—particularly social media and close acquaintances, including healthcare professionals. Full article

Background: The Dementia Attitude Scale (DAS) is a validated instrument used to capture the affective, behavioural, and cognitive components of attitudes toward people living with dementia (PLwD). This study conducts confirmatory and exploratory factor analyses (CFA and EFA) of the DAS assessed by caregivers of PLwD and BPSD enrolled in the RECage multicentre clinical trial. Methods: The baseline questionnaire was completed by 485 caregivers (29.7% male, 70.3% female), from six European countries, reflecting diverse cultural contexts. CFA tested the two-factor structure of the original model, while EFA thoroughly explored the factor structure. Results: The CFA results showed a poor model fit, with significant deviations from ideal values for RMSEA (0.0861), SRMSR (0.0781), and CFI (0.7117), showcasing an inadequate representation of the data. EFA revealed a three-factor structure, explaining the 45.2% variance for social comfort, 28.8% for social discomfort, and 25.9% for dementia knowledge. The social comfort items reflected positive caregiver attitudes toward PLwD, while social discomfort captured feelings of discomfort and uncertainty about caregiving. Dementia knowledge included items related to understanding dementia’s symptoms and needs. Conclusions: The three-factor model highlights the importance of emotional comfort, knowledge of dementia, and social discomfort as key dimensions in caregiver attitudes. Full article

This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver’s attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver’s requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation. Full article

This study aims to assess the influence of various factors on young customers’ intention to purchase renewable energy products by extending the Theory of Planned Behavior (TPB) model. Based on data collected from 690 respondents, the results of the SEM model analysis revealed that the intention to purchase renewable energy products is influenced by all three primary TPB constructs: attitude toward renewable energy products, perceived behavioral control, and subjective norm. Furthermore, knowledge of sustainable development goals significantly affects perceived behavioral control, attitude toward renewable energy products, and intention to purchase renewable energy products. Additionally, perceived government policies uncertainty emerged as a significant barrier to the intention to purchase renewable energy products. Conversely, trust in retailers has no impact on the purchasing intention. Notably, this study identifies the indirect effect of sustainable development goals (SDGs) knowledge on the intention to purchase renewable energy products through the mediating roles of attitude toward renewable energy products and perceived behavioral control. Based on these findings, this study makes both theoretical and practical contributions, proposing several implications for businesses and policymakers. Full article

This study investigates how consumers’ information-seeking motivation and perceptions of cross-media campaigns influence their attitudes toward innovative products. Drawing from the perspective of interactive marketing, it highlights the role of consumer–brand interaction in shaping product evaluation and acceptance. The findings indicate that when consumers perceive a product as highly innovative, they tend to experience both curiosity and uncertainty. This activates their need for information-seeking, which subsequently increases their engagement with cross-media campaigns designed with interactive marketing elements. Through this process, consumers develop more favorable attitudes toward the product. The results also reveal a significant dual mediation effect between perceived innovativeness and product attitude, mediated by the need for information-seeking and perception of cross-media campaigns. Although each path may not independently reach significance, the combined sequential mechanism—where consumers actively explore and interact with brand content—plays a critical role in shaping product attitudes. Full article

Background: The integration of artificial intelligence (AI) in medicine has progressed from rule-based systems to advanced models and is showing potential in clinical decision-making. In this study, the psychological impact of AI collaboration in clinical practice is assessed, highlighting its role as a support tool for medical residents. This study aimed to compare clinical decision-making approaches of junior rheumatology residents with both trained and untrained AI models in clinical reasoning, pre-diagnosis, first-line, and second-line management stages. Methods: Ten junior rheumatology residents and two GPT-4 models (trained and untrained) responded to 10 clinical cases, encompassing diagnostic and treatment challenges in inflammatory arthritis. The cases were evaluated using the Revised-IDEA (R-IDEA) scoring system and additional case management metrics. In addition to scoring clinical case performance, residents’ attitudes toward AI integration in clinical practice were assessed through a structured questionnaire, focusing on perceptions of AI’s potential after reviewing the trained GPT-4’s answers. Results: Trained GPT-4 outperformed residents across all stages, achieving significantly higher median R-IDEA scores and superior performance in pre-diagnosis, first-line, and second-line management phases. Residents expressed a positive attitude toward AI integration, with 60% favoring AI as a supportive tool in clinical practice, anticipating benefits in competence, fatigue, and burnout. Conclusions: Trained GPT-4 models outperform junior residents in clinical reasoning and management of rheumatology cases. Residents’ positive attitudes toward AI suggest its potential as a supportive tool to enhance confidence and reduce uncertainty in clinical practice. Trained GPT-4 may be used as a supplementary tool during the early years of residency. Full article

Objectives: In China, primary healthcare (PHC) facilities have high antibiotic prescribing rates for upper respiratory tract infections (URTIs), which are primarily viral and self-limited. This study aimed to identify the main factors influencing PHC physicians’ antibiotic decisions for URITs based on the theory of planned behavior. Methods: A convergent mixed-methods study was conducted at 30 PHC facilities across Shaanxi Province, China. A total of 108 PHC physicians completed a five-point Likert Scale questionnaire focused on behavioral components of antibiotic prescribing, including attitudes, subjective norms, perceived behavioral control, belief in past experiences, and prescribing intentions. Twenty-two physicians participated in semi-structured interviews. Results: Respondents had a good awareness of AMR (Mean = 4.49) and a weak belief regarding the benefit of antibiotics (Mean = 2.34). The mean score for subjective norms was 3.36, and respondents had good control over their prescribing behavior (Mean = 4.00). A reliance on past prescribing experiences was observed (Mean = 3.34), and physicians’ antibiotic prescribing intention was 3.40 on average. Multiple linear regression revealed that physicians showing a more favorable attitude towards antibiotics (p = 0.042) and relying more on their past experiences (p = 0.039) had a higher antibiotic prescribing intention. Qualitative interviews indicated that most physicians would consider prescribing antibiotics when facing diagnostic uncertainty. Low utilization of diagnostic tests, limited effectiveness of training programs, inadequate knowledge of guidelines, and lack of feedback on antibiotic prescriptions all contributed to antibiotic overprescribing. Conclusions: PHC physicians in China demonstrated strong intentions to prescribe antibiotics for URTIs when facing diagnostic uncertainty. Beliefs about antibiotics and previous prescribing behavior were significantly linked to prescribing intentions. Multifaceted interventions that focus on facilitating diagnostic tests, improving the quality of training, effectively implementing clinical guidelines, and providing practical feedback on antibiotic prescriptions may help reduce antibiotic overprescribing in China’s PHC facilities. Full article

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker’s attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies. Full article

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns. Full article

Literature is sparse regarding men’s attitudes towards male sexual assault and the role that the sexuality of those involved may have. Despite the high prevalence of chemsex and GHB (gamma-hydroxybutyrate) participation among men who have sex with men, no study has yet investigated attitudes towards such. Utilising a community sample of 141 UK men, participants were randomly assigned into one of six conditions based on victim sexuality (heterosexual or homosexual) and the drug used present during the sexual assault (chemsex, chemical submission, or no drugs). All participants completed the Male Rape Victim and Perpetrator Blaming Scale and Victim-Blaming Empathy Scale to measure victim-blame and empathy attributions. Results of a two-way MANOVA revealed a significant difference between participant gender and empathic ratings, with heterosexual participants significantly less likely to empathise than their homosexual counterparts. A non-significant difference was observed between the conditions alongside a non-significant interaction. Nevertheless, results indicate that victims in the chemsex condition, along with heterosexual victims, encountered the greatest victim-blaming attributions and the lowest rates of participant empathy overall. Findings overall appear to indicate a general decline in victim-blame attitudes towards men who have sex with men, though a level of uncertainty was apparent among the sample. Implications and limitations of the work are discussed alongside the importance of future research and psychoeducation interventions. Full article

Understanding and estimating the economic value that society places on agricultural soil quality protection and improvement can guide the development of policies aimed at mitigating pollution, promoting conservation, or incentivizing sustainable land management practices. We estimate the general public’s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n = 1000) and the UK (n = 984) using data from a cross-sectional survey via Qualtrics panels in March–April 2021. We use a double-bound dichotomous choice contingent valuation approach to elicit the individuals’ WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals’ WTP for agricultural soil quality protection and improvement, we model individuals’ WTP through individuals’ awareness and attitudes toward agricultural soil quality protection and the environment; trust in institutions; risk and time preferences; pro-social behavior; and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However, the individual’s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of the general public’s perceived uncertainty and geographic location. Full article

Political risk, one of the most significant uncertainty shocks, affects firms’ future attitudes toward risks and plays a crucial role in their decision making. A stock price crash risk is a classical topic in financial markets; therefore, this paper probes the relationship between firm-level political risk and stock price crash risk based on a sample of Chinese listed firms from 2011 to 2020. This paper collects the MD&A textual material of Chinese listed firms and calculates the firm-level political risk of Chinese listed firms. Our results show that a firm’s stock price crash risk is positively associated with its firm-level political risk exposure. Our findings hold after conducting various robustness tests, including instrument variable regression and altering the measurement of stock price crash risk. Further discussion reveals that political involvement mitigates the positive effect of firm-level political risk on the risk of a stock price jump. Full article