The objective of this research paper is to investigate potential avenues for value creation in the refined sugar market for domestic use, a market currently facing a critical juncture. The growing concerns about the health impacts of sugar have resulted in a notable decline in demand. Furthermore, changes in European Union regulations have introduced additional operators into the Spanish market, increasing competition and amplifying the need for innovation. This study examines how brands can respond to these challenges by enhancing their value proposition through market segmentation, targeted marketing strategies, and adaptive packaging solutions. To achieve this objective, we have conducted market research, which involved an in-depth interview, and a questionnaire distributed to 402 individuals responsible for household purchases. The findings suggest potential approaches for addressing the needs of consumers with a focus on health and well-being, while simultaneously enhancing the durability of products, thus facilitating greater brand differentiation. Furthermore, the study underscores the pivotal role of public policies and regulatory frameworks in influencing consumer behavior and market dynamics. Policies promoting sugar alternatives, labelling requirements, and packaging innovations have been demonstrated to impact brand strategies and consumer preferences. By aligning with these policy-driven shifts, companies can enhance their positioning in a mature and competitive market. This research contributes to the existing literature on brand value in commodity markets by integrating insights into the impact of regulation and consumer segmentation. Our recommendations emphasize the importance of marketing strategies that are informed by an understanding of the policy context, which not only enhances brand equity but also promotes sustainable growth in the retail sugar industry.

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.

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.

The transition to sustainable agricultural practices is critical in the face of escalating climate challenges. Despite significant advances, the integration of green technologies within agribusiness remains underexplored. This study undertakes a comprehensive bibliometric analysis, utilizing data from the Web of Science Core Collection (1990–2023), to elucidate the integration of green technologies within agribusiness strategies. The research highlights key trends, influential authors, prominent journals, and significant thematic clusters, including biogas, biochar, biotech remediation, sustainable agriculture transition, low-carbon agriculture, and green strategies. By employing R, Bibliometrix, and VOSviewer, the study provides a nuanced understanding of the research landscape, emphasizing the critical role of strategic planning, policy frameworks, technological innovation, and interdisciplinary approaches in promoting sustainable agricultural development. The findings underscore the growing scholarly interest in sustainable practices, driven by global initiatives such as the UN’s 2030 Agenda and the Paris Agreement. This study contributes to the literature by offering qualitative insights and policy implications, highlighting the necessity for a holistic integration of green technologies to enhance the environmental and economic viability of agribusinesses.