The rise of financial inclusion has notably increased household engagement in risky financial asset allocation, posing challenges to macro-financial stability. This study explored the crucial role of financial literacy in enabling households to effectively engage with complex financial markets and products. Specifically, it examined how different aspects of financial literacy—knowledge, attitudes, and skills—influence both the participation and depth of household investment in risky financial assets in China. Utilizing a comprehensive dataset from the 2019 China Household Finance Survey, which included 32,458 households, this study employed a robust indicator system and regression analysis via STATA 17.0 to assess these impacts. The results demonstrated that enhancements in financial literacy significantly foster increased engagement and deeper involvement in risky asset allocation, particularly through improved financial attitudes. Additionally, the analysis revealed that households led by women show a higher propensity towards risky asset investments than those led by men. These insights suggested the potential for targeted financial education to improve the financial health and economic resilience of Chinese households.

The study aims to explore the impact of examination-oriented education on Chinese English learners and the importance of cultural intelligence in second language acquisition. Through a questionnaire administered to postgraduate students majoring in English in China, the research discovered that the emphasis on test scores and strategies in China’s higher English education system has led to a neglect of cultural backgrounds and cross-cultural communication. The findings underscore the necessity for reforms in English teaching within Chinese higher education to cultivate students’ intercultural intelligence and enhance their readiness for international careers in the era of globalization.

Although various actors have examined the user acceptance of e-government developments, less attention has so far devoted to the relationship between attitudes of certain commuter groups against digital technologies and their intention to engage in productive time-use by mobile devices. This paper aims to fill this gap by establishing an overall framework which focuses on Hungarian commuters’ attitudes toward e-government applications as well as their possible demands of developing them. Relying on a representative questionnaire survey conducted in Hungary in March and April 2020, the data were examined by a machine learning and correlations to identify the factors, attitudes and demands that influence the use of mobile devices during frequent commuting. The paper argues that the regularity of commuting in rural areas, as well as the higher levels of qualification and employment status in cities show a more positive, technophile attitude to new ICT and mobile technologies that strengthen the demands for digital development, with special regard to optimising e-government applications for certain types of commuting groups. One of the main limitations of this study is that results suggest a picture of the commuters in a narrow timeframe. The findings suggest that developing e-government applications is necessary and desirable from both of the supply and demand sides. Based on prior scholarly knowledge, no research has ever analysed these correlations in Hungary where commuters are among the European citizens who spend extensive time with commuting.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

Currently, there is little study on managing organizational silence in Malaysia post COVID-19 pandemic. This study aims to examine the determinants of organizational silence and the impacts of silence on private sectors and employees. The target respondents are two hundred individuals above 21 years old working in private sectors across Malaysia. Purposive sampling is selected for this study because the target respondents must be individuals working in private sectors across Malaysia. The strongest predictor of organizational silence is the attitudes of immediate superior, followed by attitudes of top management and communication opportunities. This study provides valuable information to the employees and management in the private sector to recognize the behaviors that will create silence within the organization.