Search Results (864)

Operational labor shortages have become a pressing challenge for hospitality organizations, especially in highly seasonal tourism destinations such as Greece, where service experiences are deeply tied to cultural identity and authentic hospitality. While much of the existing research has examined understaffing from operational or human resource management perspectives, limited attention has been paid to its impact on the organizational capacity to sustain authentic hospitality experiences. Using Service-Dominant Logic (SDL) as an interpretive framework, this study views authentic hospitality as an organizational process shaped by employee interaction, cultural transmission, and service delivery practices. Drawing on survey data from 201 hotel employees in Greece, it investigates the relationship between operational labor shortages, organizational pressures, and perceived threats to authentic hospitality within hotel operations. The findings reveal significant positive relationships between work stress and service quality decline, as well as between cultural knowledge and perceived challenges in maintaining authentic hospitality. Multiple regression analysis further shows that reactive hiring, serious understaffing, and payroll cost pressure are significantly linked to perceived challenges in sustaining authentic hospitality, while service quality decline exhibits a positive but statistically non-significant effect in the final model. The study contributes to hospitality authenticity literature by emphasizing employee perceptions of authenticity as an organizationally supported process rather than merely a guest-centered outcome. The results also highlight the importance of workforce planning, recruitment quality, and cultural onboarding in supporting authentic hospitality within Greek hotel operations. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

Background/Objectives: Discharging patients after hospitalization for an acute exacerbation of chronic obstructive pulmonary disease (COPD) is a critical transition in care associated with a high risk of early readmission. This survey aimed to describe physician-reported discharge practices following COPD exacerbations, identify perceived gaps and organizational barriers, explore attitudes toward structured COPD discharge summaries, and use a SWOT analysis as an interpretative framework. Methods: In this cross-sectional observational survey, 100 physicians involved in COPD care were recruited from the official mailing list of the Respiratory Society of Serbia, which represents approximately 71% of the Society’s members. The survey assessed discharge procedures, multidisciplinary practices, patient education, comorbidity management, perceived causes of readmission, and barriers to structured discharge summaries. Data were analyzed descriptively and complemented with a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Results: Most respondents worked in tertiary care settings and were involved in managing patients hospitalized for COPD exacerbations. Although 24% of physicians routinely used structured discharge summaries, 45% reported never using them. The most frequently perceived contributors to 30-day readmissions were active smoking (90%), poor treatment adherence (81%), comorbidities (77%), and incorrect inhaler technique (72%). Major barriers to implementing structured discharge summaries included the lack of standardized templates, time constraints, poor coordination across healthcare levels, and technical limitations. Willingness to implement structured discharge tools was high (mean score 8.86/10). SWOT analysis identified strong professional support for discharge standardization alongside organizational and system-level barriers to implementation. Conclusions: This exploratory survey identified important gaps between recommended and routine COPD discharge practices and highlighted organizational barriers to implementation. The findings may inform future evaluation and development of structured discharge tools in this healthcare setting. Full article

Background: Panvascular disease (PVD) is a systemic atherosclerotic condition that poses a substantial threat to global health. Despite the recognized importance of early proactive screening, proactive screening beliefs among high-risk populations are poorly understood. Objective: To explore the proactive screening beliefs among Chinese high-risk patients for PVD based on the Health Belief Model (HBM), so as to provide evidence for developing targeted nursing intervention strategies and health policies. Methods: A descriptive qualitative study was conducted. Employing a purposive sampling strategy with maximum variation, participants at elevated risk for PVD were recruited from a tertiary hospital in Shanghai between October and December 2025 to conduct semi-structured interviews. Data saturation guided sample size (n = 22; 14 male, 8 female; mean age 62.68 years). Data were analyzed using qualitative content analysis. Results: Five main themes were extracted: multifaceted perceptions of susceptibility, multidimensional fear of severity, positive attitudes toward the benefits of proactive screening, multiple perceived barriers to proactive screening, and significant differences in self-efficacy for proactive screening. Conclusions: The proactive screening beliefs in Chinese high-risk patients of PVD were deeply embedded in local cultural values and healthcare realities. Tailored health education, age-friendly service optimization, and stratified intervention strategies are urgently needed to reduce screening barriers and improve population-wide proactive screening beliefs. Full article

AI technologies are changing the world of work in ways that are hard to predict, and this uncertainty is felt particularly strongly by young people who are just beginning to think about their futures. This study explores how high school students in Turkey perceive AI’s potential impact on their career choices, using Social Cognitive Career Theory (SCCT) and Uncertainty Management Theory (UMT) as interpretive lenses rather than formally tested models. SCCT helps frame AI as an environmental force that shapes how students think about their career options, while UMT helps explain how students emotionally and cognitively respond to uncertainty that cannot easily be resolved. Using a cross-sectional survey of 354 12th-grade students, we developed and validated the AI-Related Career Perception Questionnaire (AICP-Q), which yielded four factors: AI Anxiety and Career Precarity, AI Literacy and Technological Awareness, Proactive Career Adaptation, and Socio-Technical Uncertainty. Students showed moderate AI awareness but relatively high levels of socio-technical uncertainty. Academic track emerged as an exploratory statistical correlate of AI Anxiety, a descriptive association suggesting that students’ sense of threat from AI may relate more to the specific skill demands of their chosen field than to the prestige of their school, though no causal inference can be drawn from these cross-sectional data. A key finding is “the planning gap”: students recognized the potential career disruptions associated with AI but did not consistently respond with adaptive behaviors. Drawing on UMT, we advance the tentative hypothesis, to be tested in future research, that this pattern may relate to a lack of the appraisal resources needed to translate awareness into action; because these constructs were not directly measured, this remains an interpretive suggestion rather than an empirical finding. Full article

Artificial intelligence (AI) is increasingly embedded in marketing work, yet employees who use AI tools may not always disclose AI’s role in producing their outputs. This study examines AI disclosure silence, defined as employees’ intentional withholding of information about the use, role, or contribution of AI tools in work-related outputs after AI has already been used. Unlike AI avoidance or resistance, this construct concerns post-adoption concealment; unlike general employee silence, it focuses on the hidden technological contribution behind visible work. Drawing on Conservation of Resources Theory and Psychological Safety Theory, the study investigates how threat-based conditions, safety and governance conditions, and AI-related capability are associated with AI disclosure silence. Data were collected through a two-wave survey of 635 marketing employees who actively used AI tools at work. The analysis combined measurement validation, Necessary Condition Analysis (NCA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and explainable machine learning. The findings show that no single condition operated as a strong necessary bottleneck. Instead, AI disclosure silence appeared through multiple pathways involving AI anxiety, fear of negative evaluation, perceived creativity threat, perceived job insecurity, low trust in management, weak psychological safety, and unclear AI policy. SHapley Additive exPlanations (SHAP)-based interpretation further indicated that fear of negative evaluation, AI anxiety, perceived creativity threat, and trust in management had the strongest model-based predictive relevance. The study contributes to workplace AI and employee silence research by positioning AI disclosure silence as an emerging post-adoption disclosure construct. It also highlights the need for clear AI disclosure norms, non-punitive managerial responses, AI-assisted authorship guidelines, and psychologically safe AI-governance practices. The findings should be interpreted as configurational and predictive evidence rather than causal effects, and further scale validation across sectors and cultures is encouraged. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Aircraft cargo compartment fires constitute a significant type of aviation fire, posing a grave threat to aviation safety. To guard against and respond to such fires, existing aircraft cargo compartments are equipped with smoke detection fire detectors, which rely on perceiving changes in smoke transmittance to determine the onset of a fire. However, these detectors offer relatively low recognition accuracy and cannot provide a direct visual representation of the fire. In this work, we introduce a fire recognition method built on image sensors and a deep learning model. In light of the irregular shapes of flames and smoke, an improved interactive triplet attention mechanism (ITAM) is integrated into the You Only Look Once version 5 (YOLOv5) model, enhancing the model’s recognition accuracy. Furthermore, the original Neck structure is replaced with an Asymptotic Feature Pyramid Network (AFPN), improving the model’s ability to recognize small targets, which is particularly useful for detecting flames and smoke early in a fire. This paper further improves the model’s recognition accuracy by introducing the Focaler-IoU loss function, which balances the feature learning of hard and easy samples. Therefore, the network model in this paper is named IATF-YOLO. Ablation experiments demonstrate that our algorithm improves accuracy by 2%, while comparative experiments with several mainstream baseline models show that our algorithm achieves a 0.7% accuracy improvement, with a final peak accuracy of 93.6%. Full article

Background: Open defecation (OD) has remained a threat to the attainment of SDG 6 (sanitation and hygiene). This study measured the level of exposure to the “Clean Nigeria, Use the Toilet” campaign against open defecation, determined the level of public knowledge about open defecation-related harms and diseases, ascertained the public attitude towards open defecation, and established the prevailing defecation practices and the perceived barriers to toilet usage in Ebonyi state, the most prevalent OD state in Nigeria. Methods: The study employed a survey design, using a structured questionnaire for data collection. The multi-stage sampling technique was employed in selecting the respondents from two randomly selected Local Government Areas (LGAs) in the state. Analysis was conducted using 384 valid responses. Results: The results were presented in simple percentage frequency tables and interpreted through the descriptive method, while the Chi-Square test was used to analyse the formulated hypotheses, using the decision rule of p < 0.05. The findings show a high level of awareness of the campaign against open defecation, through the radio and community engagements by environmental activists/NGOs, even though regular access to such information was limited. The results also showed inadequate knowledge of the public health implications of open defecation, whereas good knowledge of environmental consequences was reported. The study found favourable attitudes toward OD practice and persistent open defecation, and major barriers to toilet usage include the high cost of toilet construction, lack of access to toilet facilities, poor sanitation and management of available toilets, and perceived risks of contracting infection from public toilets. However, the Chi-Square values showed that the SBCC campaign was significantly associated with knowledge, attitude, and practice (p < 0.05). Conclusions: The study concluded that localised, culturally relevant and socio-demographically targeted communication interventions, grassroot advocacy, community watch, and neighbourhood taskforce on open defecation, in addition to the provision of aids for the construction of modern toilets with water facilities, are required to combat open defecation in Ebonyi and related contexts in Nigeria. Full article

Millions of chicks are hatched daily in commercial hatcheries and due to ease of access and the large availability of chicks produced daily, such chicks have been accepted as a reference and control. Unfortunately, this is not a correct assumption. Chickens evolved to be hatched in nests and to remain in close contact with adult hens, which is important for the transfer of chicken-adapted microbiota from hens to offspring. In the absence of adult hens, chicks from hatcheries are colonised by microbiota of environmental origin. Forgetting this fact has led to many confounding conclusions, including a dogma on the age-dependent development of gut microbiota. In this sense, chicks from hatcheries represent a threat. However, if correctly perceived, the same chicks represent a unique opportunity for host–microbiota studies since there is no alternative animal model in which offspring free of any paternal influence are that readily available. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Apart from vigilance and flight, anti-predator defense behavior in horses has not been well documented despite its importance during natural selection. In this study, observations of a feral herd (around 140) of Venezuelan horses sympatric with puma and jaguar divided such defense into precaution and reaction. Group living and the avoidance of danger areas are precautionary measures enhanced by the stallion’s vigilance and his actions to keep small foals with the band. Reactions to perceived threats comprise communication of alarm; bunching, or cohesion, as a primary response; massed flight following self-organizing principles; and reassembly of bands. Stallions usually initiated this behavioral process. Stallions’ initial reactions to perceived threats were “investigation”, “move away”, “run away”, and “stampede”, and resulting herd behavior was categorized into 27 responses. Data analysis through Observation Oriented Modeling indicated that each category of initial stallion response to perceived threats was associated with a recurring pattern of subsequent herd behavior. Prominent behaviors enhanced cohesion and synchrony, as well as velocity and direction matching. A fourth observed category was the cohesive “run to band” of a startled outlying member, in which the individual’s alarm might transmit to the band or the band’s calm transmit to the individual. The results emphasize the importance of communication, social cohesion, and synchronous action in times of perceived threats, their continuous practice during maintenance activities, and the social needs and understanding management of domestic horses. Full article

Western theories and empirical comparative research on attitudes toward immigrants and their rights have largely overlooked Latin America. To address this gap, we conducted multilevel ordered logistic regression analyses on Latinobarómetro surveys from 17 countries (N = 19,004). The findings show that support for immigrants’ social rights is more contingent on immigration-related benefits—especially cultural enrichment—than on perceived threats. When threats do mobilize opposition, the perceived fiscal burden emerges as the sole significant driver, overriding both concerns about labor market competition and fears of rising crime. Furthermore, right-wing individuals were no less supportive of immigrants’ social rights than left-wing individuals. Instead, the most welfare-chauvinist attitudes were found among the politically disengaged. At the macrosocial level, the results provide evidence that contextual factors not only exert a direct statistical effect on public support for immigrants’ social rights but also moderate the influence of perceived micro-level threats. In particular, the national unemployment rate and the immigrant stock exacerbate the exclusionary effect of the perceived fiscal burden on levels of support among citizens. Ultimately, these findings challenge some theoretical assumptions derived from intergroup threat theory, provide novel evidence for the Threat-Benefit Model, and further suggest a distinct political dynamic in the region. Full article

Extreme high-temperature events driven by global climate change are occurring with increasing frequency, posing a serious threat to the stability of aquatic ecosystems. The Tibetan schizothoracin (Gymnocypris eckloni), a cold-water fish species endemic to the Qinghai–Tibet Plateau, is highly sensitive to temperature fluctuations and serves as an ideal model for studying the effects of climate change on fish. As a key organ for fish to perceive environmental changes, the gills’ comprehensive response mechanism has not yet been fully elucidated. This study investigated the effects of acute heat stress on the gill tissue of G. eckloni. The results showed that acute heat stress caused severe histopathological damage in the gills, including lamellar curling, epithelial cell detachment, and edema, with a significant increase in apoptosis. Biochemical analysis revealed elevated levels of cortisol, glucose, and ATPase activity in serum, as well as increased MDA content and CAT activity in the gills. Transcriptomic analysis identified 2304 DEGs. Upregulated DEGs were significantly enriched in pathways related to inflammatory response, TNF signaling, ferroptosis, and apoptosis, while downregulated DEGs were primarily involved in peroxisome metabolism, cell cycle, and steroid biosynthesis. This study confirms that acute heat stress induces structural damage and functional impairment in the gills by activating inflammatory and apoptotic pathways and disrupting redox homeostasis. It elucidates the immediate molecular and physiological responses of G. eckloni gills to acute heat stress. Follow-up experiments will be conducted at multiple time points, across different temperature gradients, and under chronic stress conditions to gain a more comprehensive understanding of the adaptive potential of high-altitude fish to climate warming, thereby providing a scientific basis for the development of conservation strategies. Full article

Climate change is significantly impacting sustainable agriculture and poses a threat that is likely to motivate farmers to adapt by applying AI technology to reduce risks, costs, expenses, and the impact on greenhouse gas emissions. In other contexts related to climate change, it is important to assess whether perceived climate threats and perceived vulnerability to climate change influence farmers’ intention to use artificial intelligence and whether farmers believe AI is an effective method for addressing climate change, as well as their confidence in its effectiveness. This research examines whether the ability to learn about AI independently affects the intention to use AI, aligning with Protection Motivation Theory. It further evaluates whether perceived ease of use of AI influences perceived usefulness, considering the core factors of perceived ease of use and perceived usefulness based on the Technology Acceptance Model as influencing the intention to use AI. Furthermore, it investigates whether PEOU (Perceived ease of use) and PU (Perceived usefulness) affect attitude (a key factor in the Theory of Planned Behavior) and subjective norm (another core factor in TPB (Theory of Planned Behavior)) influencing farmers’ behavioral adaptation to AI use. Therefore, exploring farmers’ behavioral intention to use AI integrates three theories: PMT (Protection Mo-tivation Theory), TPB, and TAM (Technology Acceptance Model), presenting them as a conceptual model to examine the motivating factors influencing behavioral change. This research surveyed 471 farmers in Thailand using data analyzed from PLS-SEM (Partial Least Squares Structural Equation Mod-eling). The findings revealed that only eight hypotheses (AI self-efficacy, PEOU, PU, ATT (Attitude), and SN (Social Norm)) significantly influenced the intention to use AI, while three hypotheses (PS (Perceived severity), PV (Perceived vulnerability), and RE (Response efficacy)) did not. This will be useful for planning or strategizing AI adoption among farmers, focusing on reducing problems and obstacles from insignificant factors to achieve sustainable agriculture and minimize the impact that may lead to inequality from AI use, or the AI divide, in the future. Full article