The scientific discourse on university towns (UT) has progressed for a long time, with a surge of interest in recent years. However, a global overview of the research conducted on this topic have yet to exist. This paper aims to re-examine the relationship between UT and urbanization in literature. Built environment and people are often the most talked aspects in UT literatures. The variety of definitions remains largely uncharted. Policies behind UT development are also rarely studied. This article used an R studio-based bibliometric literature review to synthesize findings from various scientific literature. Keywords related to university towns and urban were used in digital search engines to examine and analyse the literature. Results revealed a significant gap in scientific research on critical theoretical concepts that planners can use as a guide in creating, formulating, and evaluating UT, especially in developing countries. This study promotes simplification of existing literature by examining the impact of UT on the stakeholders involved.

Ticket revenues are crucial for the financial success of sports teams. To maximize these revenues, teams continuously explore effective ticket promotional strategies. One such strategy includes partial season plans, which mirror bundle offers common across various industries. Another widespread promotional strategy across industries is offering discounted credit (i.e., store credit purchased at a lower price than its face value). However, its application in sports (e.g., providing a $500 credit for tickets at $450) remains limited. Therefore, this study explores critical questions such as: “How effective is offering discounted credit compared to partial season plans?” and “What factors influence ticket promotion preferences?” Consequently, the study employed a 2 × 2 × 2 experimental designs, considering three independent variables: promotion type (discounted credit vs. partial season plans), promotion flexibility (predefined vs. customizable), and the consumer’s distance to the venue (near vs. distant). Results indicated that partial season plans generated significantly higher perceived value and purchase intentions  while presenting lower perceived risks than discounted credit . Promotion flexibility did not significantly influence the three dependent variables , but the distance to the venue did . Both practical and theoretical implications of these findings are discussed.

Praxeology is the study of practice, i.e., human activity, primarily in the context of its rationality. The study of manager’s praxeological activity from the point of view of management theory is an important direction of modern science, since it contributes not only to improving the management effectiveness in an organization, but also to the development of new managerial concepts and techniques. In the article, the authors’ concept of praxeological managerial activity is proposed based on the analysis of existing scientific approaches to praxeology. An extended list of criteria for the manager’s praxeological activity efficiency was developed. These criteria include performance, productivity, accuracy of the decisions taken, purposefulness, reliability, innovativeness, quality, and ethics. The authors’ model of the manager’s praxeological activity includes the following elements: a subject (a manager), an object (a company, its staff and activities, etc.), motives (success, growth, profit, etc.), the goal (to ensure the effectiveness of the company’s activities), methods and tools (analysis, planning, organization, motivation, and control), process (praxeological activity), result (efficiency improvement), and reflexivity, correction and iteration. Within the framework of the model of praxeological managerial activity, the manager’s ability to influence the managed object (an organization, employees or the manager’s activities) is particularized. This influence should result in an increase in the employees’ performance, an increase in the managers’ performance, and an increase in the performance of the organization as a whole. The article will be of interest to specialists in the field of management, and corporate governance, as well as for anyone interested in the problems of effective management.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

The principal objective of this article is to gain insight into the biases that shape decision-making in contexts of risk and uncertainty, with a particular focus on the prospect theory and its relationship with individual confidence. A sample of 376 responses to a questionnaire that is a replication of the one originally devised by Kahneman and Tversky was subjected to analysis. Firstly, the aim is to compare the results obtained with the original study. Furthermore, the Cognitive Reflection Test (CRT) will be employed to ascertain whether behavioural biases are associated with cognitive abilities. Finally, in light of the significance and contemporary relevance of the concept of overconfidence, we propose a series of questions designed to assess it, with a view to comparing the various segments of respondents and gaining insight into the profile that reflects it. The sample of respondents is divided according to gender, age group, student status, professional status as a trader, status as an occasional investor, and status as a behavioural finance expert. It can be concluded that the majority of individuals display a profile of underconfidence, and that the hypotheses formulated by Kahneman and Tversky are generally corroborated. The low frequency of overconfident individuals suggests that the results are consistent with prospect theory in all segments, despite the opposite characteristics, given the choice of the less risk-averse alternative. These findings are useful for regulators to understand how biases affect financial decision making, and for the development of financial literacy policies in the education sector.

This study employed a deductive approach to examine external HRM factors influencing job satisfaction in the post-pandemic hybrid work environment. Explores the intermediary functions of age, gender, and work experience in this particular environment. The data-gathering procedure consisted of conducting semi-structured interviews with carefully chosen 50 managers representing various sectors, industries, organizations, and professions. The applied approach was adopted to allow a systematic and unbiased investigation of the mediating variables. The study used SPSS 25 and Smart PLS 4 to analyze the model, enhancing understanding of HRM challenges in a constantly evolving workplace. The findings offer valuable insights for HR experts and businesses, highlighting the value of comprehending what methods HRM components influence job satisfaction to optimize employee well-being and productivity. The study provides applied recommendations designed for enhancing employee contentment in the AI-evolving professional atmosphere, shedding light on the importance of supportive leadership strategies, particularly during AI-triggered downsizing. Additionally, we welcome a new era to push forward in integrating and managing AI tools and technologies to automate decision-making and data processing. Results propose that Exogenous influences of human resource management (HRM) influence manager job satisfaction considerably. Specifically, downsizing caused by AI was found to have negative consequences, whereas diversity and restructuring have favorable effects. Gender was recognized as a crucial factor that influences outcomes, then age and years of experience have the most visible effect.