This study replicates and extends Corbett and Kirsch (2001) and Vastag (2004) using a new data set to investigate the drivers of ISO 14000 certification diffusions using decision tree analysis. The findings indicate that at the national level, ISO 14000 certification diffusions are influenced by factors other than ISO 9000 certification diffusions, such as the number of environmental treaties signed and ratified, industrial activities as a percentage of GDP, and GDP per capita, thus provides a range of managerial insights and enhances scholarly understanding of sustainability beyond the influence of ISO 9000. Future studies might extend the countries included in this study to see if the results are the same. Future research may include other factors like a country’s Environmental, Social, and Governance (ESG) indicators to better understand its commitment to sustainability, including environmental sustainability. The country’s culture may influence customers, investors, and other stakeholders’ knowledge and desire for sustainable practices and inspire firms to obtain ISO 14000 certifications. Since larger firms may seek ISO 14000 certification, future studies may evaluate the influence of the number of large firms in various countries as drivers of ISO certification diffusions.

In order to overcome negative demographic trends in the Russian Federation, measures to stimulate the birth rate have been developed and financed at the federal and sub-federal levels. At the moment, on the one hand, there is a tendency to centralize expenditures for these purposes at the federal level, on the other hand, the coverage of the subjects of the Russian Federation, which introduce sub-federal (subnational) maternity capital (SMC), is expanding. The study was recognized to answer the question: whether the widespread introduction of SMC is justified, whether the effect of its use depends on the level of subsidization of the region and the degree of decentralization of expenditures.

This study explores the interconnected roles of organizational atmosphere, psychological capital, work engagement, and psychological contract on the work performance. Structural equation modeling and moderated mediation analyses were conducted to test the hypothesized relationships. Methodologically, the study employed a stratified random sampling of 369 faculty members across various disciplines. Key findings reveal that both organizational atmosphere and psychological capital have a significant positive impact on work engagement, which in turn, enhances work performance. Work engagement acted as a mediator in these relationships. Moreover, the psychological contract was found to moderate the relationship between work engagement and work performance, indicating that the engagement-performance link is stronger when employees perceive their psychological contract has been fulfilled. The implications of this research are multifaceted. Theoretically, it contributes to organizational behavior literature by integrating psychological contracts into the engagement-performance narrative. Practically, it provides actionable insights for university administrators, suggesting that investments in a supportive organizational atmosphere and the development of faculty psychological capital are likely to yield improvements in engagement and performance. The study also underscores the importance of effectively managing psychological contracts to maximize employee output.

This article investigates the income and expenditure patterns of individuals, with a specific focus on investments in luxury items, real estate, and expensive modes of transportation. Using global databases such as “Luxury Goods—Worldwide/Statista Market Forecast” and “Data—WID—World Inequality Database”, the authors explore the correlation between high demand for luxury items and economic inequality. The study emphasizes the role of luxury tax as essential for implementing a progressive personal income tax system in Russia. By examining country-specific factors, particularly in China and Russia, and conducting a comparative analysis of progressive tax systems globally, the research highlights the potential of luxury tax to enhance the efficacy of income tax in reducing inequality.

The research aims to examine the determinants influencing the business commitment toward sustainable goals in Vietnam. To employ a quantitative research approach, we surveyed 208 business leaders in Vietnam to assess their perceptions and actions regarding sustainable goals. We explored the impact of internal enterprise characteristics and external facilitating factors on different dimensions of sustainable goals by using logistic regression models. This paper’s findings reveal that enterprise attributes, corporate leadership traits, and external factors significantly influence sustainable goal engagement. Notably, corporate leaders emerge as pivotal factors, particularly in their willingness to embrace risks and uncertainties. Moreover, this paper’s analysis identifies external factors with limited efficacy in fostering sustainable business operations. These insights hold significant implications for governmental institutions in Vietnam, offering valuable guidance for updating and refining policies.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.