One of the key elements for the development of an agro-tourism destination can be specific gastronomy. In this context, research and understanding of the motivation of tourists who visit those destinations can influence the development of the concept of their sustainability, which was the goal of this research. The research was conducted during 2022 on a total sample of 607 respondents who stayed in agro-tourism destinations in the Republic of Serbia. The paper presents motivational factors that potentially influence the frequency of gastronomic tourists to try local gastronomic specialties and food in agro-tourism destinations. The results indicate that the cultural experience dimension, excitement dimension, interpersonal, relation dimension, and sensory appeal dimension show a significant influence on the visitation of agro-tourism destinations by gastronomic tourists, while the health concern dimension factor does not show statistical significance. The results undoubtedly indicate that certain socio-demographic characteristics of gastro-tourists have an impact on the development of an agro-tourism destination, in the direction that the more educated as well as women have more positive attitudes towards the influence of motivational factors on the development of an agro-tourism destination.

This study investigates the perceptions of employees in the hotel industry of the Republic of Serbia regarding the acceptance and importance of artificial intelligence (AI). Through a modified UTAUT model and the application of structural equation analysis (SEM), we investigated the key factors shaping AI acceptance. Research results show that behavioral intention and habit show a significant positive impact on AI usage behavior, while facilitating conditions have a limited but measurable impact on behavioral intention. Other factors, including social influence, hedonic motivation, performance expectancy, and effort expectancy, have minimal influence on the examined variables. The analysis reveals the crucial mediating role of behavioral intention, effectively bridging the gap between various predictors and AI usage behavior, thereby highlighting its significance in the broader context of technology adoption in the hotel industry. The primary goal of the study, which closes significant research gaps, as well as the manner in which it uses a specific model and statistical analysis to accomplish this goal, shows how innovative the work is. This method not only broadens the field’s understanding but also offers valuable insights for shaping sustainable development practices in the hospitality sector in the Republic of Serbia.

Most COVID-19 patients were treated in primary health care (PHC) in Europe.To demonstrate the scope of PHC workflow during the COVID-19 pandemic emphasising similarities and differences of patient’...

Purpose: The study aims to examine how AI contributes to food waste reduction and improves operational efficiency in the hospitality sector. In the context of sustainability, the research investigates AI’s role in inventory management, process automation and waste tracking. The findings provide insights into the potential of AI technologies to optimize kitchen operations, reduce environmental footprints and enhance resource utilization. The results can assist industry stakeholders in developing AI-driven strategies to improve efficiency and business sustainability. Design/methodology/approach: This study employs a quantitative approach and structural equation modeling (SEM) to analyze the impact of artificial intelligence (AI) on food waste reduction in the hospitality industry. A total of 234 managers and head chefs from 117 hospitality establishments in Serbia and Montenegro participated in the survey. The data were analyzed using SmartPLS, focusing on AI applications in inventory management, menu planning, process automation, waste tracking and recycling. The study also incorporates exploratory factor analysis and regression models to assess the significance of AI in optimizing food management and enhancing operational efficiency. Findings: The results confirm that AI significantly reduces food waste through improved inventory control, personalized menu planning and automated waste tracking. The modeling demonstrates a positive impact of AI on waste reduction, while real-time monitoring enables swift corrective actions. The findings highlight the economic and environmental benefits of AI, emphasizing its crucial role in optimizing hospitality business operations. Empirical evidence supports AI as a strategic tool for more efficient food management and a more sustainable hospitality sector. Research limitations/implications: The study is limited to the hospitality industry in Serbia and Montenegro, which may reduce its applicability to other regions with different economic conditions. The research focuses on managerial perspectives, with less emphasis on consumer habits and behaviors. Although AI yields positive outcomes, challenges such as high implementation costs, staff training and technical integration require further analysis. Future research should include longitudinal studies to assess AI’s long-term impact and explore cultural differences in its adoption. Practical implications: The findings suggest that hospitality establishments should invest in AI for inventory tracking, kitchen process automation and real-time waste monitoring to reduce costs and improve efficiency. AI can enhance portion control, demand forecasting and sustainable food sourcing. Additionally, AI analytics can help identify inefficiencies and develop waste reduction strategies, enabling managers to make data-driven decisions, increase profitability and promote environmentally responsible practices. Originality/value: This study is among the first to empirically assess the role of AI in food waste reduction in the hospitality sector. It provides novel insights into kitchen operation optimization and sustainability through AI implementation. The study contributes to the literature by integrating various AI functionalities – such as inventory management, process automation and waste tracking – into a comprehensive framework. The findings are valuable for professionals, policymakers and researchers interested in the application of AI for efficient food management and sustainable hospitality services.

Background and aim: Primary health care (PHC) supported long-term care facilities (LTCFs) in attending COVID-19 patients. The aim of this study is to describe the role of PHC in LTCFs in Europe during the early phase of the pandemic. Methods: Retrospective descriptive study from 30 European countries using data from September 2020 collected with an ad hoc semi-structured questionnaire. Related variables are SARS-CoV-2 testing, contact tracing, follow-up, additional testing, and patient care. Results: Twenty-six out of the 30 European countries had PHC involvement in LTCFs during the COVID-19 pandemic. PHC participated in initial medical care in 22 countries, while, in 15, PHC was responsible for SARS-CoV-2 test along with other institutions. Supervision of individuals in isolation was carried out mostly by LTCF staff, but physical examination or symptom’s follow-up was performed mainly by PHC. Conclusion: PHC has participated in COVID-19 pandemic assistance in LTCFs in coordination with LTCF staff, public health officers, and hospitals.

Purpose - The study aimed to analyze the quality of gastronomic services in the agrarian regions of Eastern Serbia, focusing on the experiences of tourists visiting these destinations. The research was motivated by the increasing importance of gastronomic services in contemporary studies, particularly in rural areas like Eastern Serbia, known for its unique offerings in agritourism. Methodology - The research was conducted in 2024 on a sample of 189 respondents across 14 agritourism farms in Eastern Serbia. Data were analyzed using SPSS software, version 26.00, and multiple regression analysis was employed to identify the key predictors influencing overall service quality. Findings - The results revealed a statistically significant impact of the quality of gastronomic services on tourist satisfaction. The analysis identified key predictors of the quality of gastronomic services that significantly contribute to the overall tourist experience, highlighting the importance of high-quality service in enhancing visitor satisfaction. Implications - The study's findings contribute to the theoretical understanding of service quality in rural tourism and provide practical insights for agritourism operators in Eastern Serbia. By improving the quality of their gastronomic offerings, these operators can enhance tourist satisfaction and potentially increase visitor numbers.