Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

Previous studies support the direct relationship between outdoor physical activity and natural spaces in cities. The Active City and Nature concept explores the relationship between urban, green and active environments; it aims to demonstrate the scientific evidence for the need for action to be taken to increase participation in active living and sport, leading to healthier cities and communities. Our research seeks to analyse the city’s natural spaces as scenarios to encourage physical activity and sport, through a combined study of qualitative research techniques: the use of a digital webGIS platform, collaborative maps made by citizens, and surveys conducted with citizens and the local government. This methodology has been tested in the city of Malaga, the European City of Sport 2020. The study of the city’s main sport areas, the waterfront and natural green spaces provided data on the types of physical activity taking place in each of these areas and the physical activity needs of citizens. This research argues that it is important to know the criteria of local communities for physical activity and/or sport in natural environments, as well as the main demands expressed. This will provide valuable information to design and manage natural public spaces as a means of promoting physical activity and healthy habits.

This paper aims to research the impact of psychological contract fulfilment on employee innovative work behaviour, and the mediating role of work engagement and the moderating role of social support. A quantitative analysis was adopted to address in research. Two-wave data were collected from 332 respondents working in China. Hierarchical regression analyses were conducted to assess the proposed hypotheses. Results revealed that psychological contract fulfilment positively impacted innovative work behaviour. In addition, engagement partially mediated the relationship between psychological contract fulfilment and innovative work behaviour. Furthermore, the findings suggest that social support moderates the relationship between work engagement and innovative work behaviour, and, in turn, moderates the indirect effect of psychological contract fulfilment on innovative work behaviour through work engagement. This research extends the generalizability of findings in the psychological contract literature. The results bear significant implications for the management of employees’ innovative work behaviour.