The development of the maize agribusiness system is highly dependent on the role of social capital in facilitating interaction among actors in the chain of activities ranging from the provision of farm supplies to marketing. Therefore, this research aimed to characterize the key elements of social capital specifically bonding, bridging, and linking, as well as to demonstrate their respective roles. Data were collected from farmers and non-farmers actors engaged in various activities in the maize agribusiness system. The data obtained were processed using ATLAS Ti, applying open, axial, and selective coding techniques. The results showed the roles played by bonding, bridging, and linking social capital in the interaction between farmers and multiple actors in activities such as providing farm supplies, farming production, harvesting, post-harvest, and marketing. The combination of these social capital forms acted as the glue and wires that facilitated access to resources, collective decision-making, and reduced transaction costs. These results have theoretical implications, suggesting that bonding, bridging, and linking should be combined with the appropriate role composition for each activity in the agribusiness system.

Social media interactivity creates consumer’s space of information seeking-sharing where its intensity could produce knowledge, creates new values and changes behavior. The aim of this study is to exploratory investigate the dual role of Generation Z’s information seeking-sharing behavior within green context through the interactive space of social media as a resource for the development of social media marketing strategy. The research employs mixed-method approach of qualitative-explorative data mining, quantitative cross-tabulation Chi-Square test, and integration. Two findings of this research are elaborated. First, consumer’s space of information-seeking leads to the process of green awareness rationalization, i.e., how environment-oriented actions can be rationalized. Second, consumer’s space of information-sharing leads to green social values, i.e., How environment-oriented actions can be socially recognized. The marketing implications of these two findings are business’ efforts to develop green-oriented strategic mindset through space of social media marketing “customer engagement” where the dual role of information seeking-sharing within green context is facilitated.

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.

Our previous research on social innovation examined the process, levels, and stakeholders of social innovation, as well as its relationship with technical and technological innovation. The present study analyzes the spatial image created by the social innovation potential and investigates its relationship with the economic power of the neighborhoods. The most important conclusion of the study is that the basic territorial inequality dimensions are the same in the case of both the social innovation potential and the district’s economic strength. The difference is primarily to be found in concentration, as economic power is much more concentrated in the capital and the most important economic and tourism centers than the social innovation potential. We can therefore state that developments based on social innovation can solve a lot of the highly concentrated spatial structure in Hungary.