Purpose: This study investigates the mediating effect of Environmental Attachment (EA) among consumers in an emerging market, concentrating on the impact of two key factors: Green Environmental Awareness (GEA) and Sense of Responsibility (SOR) on Sustainable Product Consumption (SPC). Design/methodology/approach: A thorough online survey was carried out with Google Docs and distributed to 304 Pakistani consumers who now use or are considering purchasing sustainable or green products. Structural Equation Modeling (SEM) was used to rigorously test the suggested model utilizing a non-probability sampling technique, specifically the stratified purposive sampling approach. Findings: Green environmental awareness (GEA) and a sense of responsibility (SOR) have been shown to have a substantial impact on creating environmental attachment (EA) in both existing and potential customers of sustainable products. The findings of this study also revealed that environmental attachment (EA) plays an important role as a mediator in the links between green environmental awareness (GEA) and the consumption of sustainable goods (SPC), as well as between a sense of responsibility (SOR) and SPC. Despite this, it is crucial to note that the projected direct effect of GEA on SPC was shown to be statistically insignificant. This conclusion implies that additional factors outside the scope of this study may influence the relationship between GEA and SPC. Research limitations/implications: It is vital to highlight that the focus of this study is on an online sample of consumers near Punjab, Pakistan. Future studies should look at other parts of Pakistan to acquire a more complete picture of sustainable consumption trends. Furthermore, our findings suggest that characteristics impacting sustainable consumption, such as Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), may differ among countries. As a result, performing a comparison analysis involving two or more countries could provide valuable insights into projecting sustainable product consumption among current and potential sustainable product customers. Originality/Value: This study contributes to the literature by investigating the factors of sustainable consumption using the lens of the Norm Activation Model theory (NAM), notably Green Environmental Awareness (GEA) and Sense of Responsibility (SOR), to predict sustainable product consumption. The findings are important for promoting long-term goals in Pakistan and provide a framework that can be applied in other emerging markets.

In the process of constructing and building the industry English curriculum system in the new era, higher education institutions should clarify the corresponding curriculum teaching focus and direction, analyze, optimize, and improve the defects and deficiencies in the English curriculum teaching system. They should also combine refined and beneficial teaching ideas and strategies, innovate existing teaching methods, and integrate more ideological and political elements into curriculum teaching, to achieve more efficient teaching guidance for students. This article briefly analyzes and explores the strategies for constructing the English course system for waterway transportation and maritime management majors at present.

China’s rapid development in modern times has become an important country in the world. Therefore, the cultures of various countries continue to cross and integrate in China. Language is the carrier of culture. Under the function of language, international culture is constantly spread and exchanged. As an important part of basic education, language education, especially college English education, significantly contains multiple international cultures. From the perspective of multiculturalism, the development status of college English teaching reflects the importance of multicultural infiltration. The following explores college English teaching strategies from the perspective of multiculturalism from three aspects: establishing “double qualified” teachers, paying attention to mother tongue and cultural differences, establishing diversified teaching concepts and establishing teaching culture evaluation system.

Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply chain transparency, technological integration, and food safety practices and procedures as independent variables and predictive analytics as a mediator. The results show that supply chain managers’ capacity to anticipate and control risks related to food safety can be improved by predictive analytics, leading to safer food production and distribution methods. The research recommends that businesses create scalable cloud-based predictive model solutions, combine data sources, and employ cutting-edge AI and machine learning tools. Companies should also note that strong, data-driven approaches to food safety require cooperative data sharing, regulatory compliance, training initiatives and ongoing improvement.

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

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.