This research aims to investigate how technological innovation influences social sustainability via the mediating role of organizational innovation and digital entrepreneurship. This investigation employed a quantitative research approach and used data from survey questionnaires based on a set of suppositions evaluated using structural equation modeling. A total of 320 respondent companies from digital provider companies in Thailand. The findings of the research expose that technological innovation has a positive effect on organizational innovation and digital entrepreneurship. Both serve as mediators in the correlation between technology innovation and social sustainability. Moreover, this research will be beneficial for businesses that are implementing new technologies and innovation, considering their role in attaining both environmental and social sustainability.

Chinese multinational enterprises (MNEs) have increasingly engaged in outward foreign direct investment in recent years, and particularly into the infrastructure sector of developing economies. This has been prompted by the infrastructure-led economic integration plan of China’s Belt and Road Initiative. However, such collaboration faces many challenges. Infrastructure projects are often undertaken in industries, countries, and regions posing particular and difficult challenges, and with divergent, often conflicting interests, with the ensuing conclusion that the MNE is simply exploiting the project and not delivering value to the host country. Overall, not only does the infrastructure project have to be well-functioning with expected returns (or savings) realized, but these projects face close scrutiny from local communities, labor, opposition parties, neighboring countries, and various international bodies and nonprofits, requiring delicate handling of the principals involved. The unfolding of these issues and their management by the multinational are examined through an in-depth longitudinal case study. The data are drawn from major participants and stakeholders around a leading Chinese MNE and the mega project of the construction of a major hydropower plant in Pakistan.

The intersex person’s social isolation condition is the leading concern in inclusive educational practices. It provides for the relevance of intersex communities with the influence of social isolation on their education and well-being. Given the underlying problem, this paper stresses the isolation-free condition of the intersex community by facilitating inclusive education. The Atkinson and Shiffrin Model and Behaviorism-Based Intersex Theory supports inclusive education by extending the desire to significantly manage stereotypes, quality teaching, parental beliefs, expressions, physique, and intersex attribution. The qualitative research method analyses the reducing role of social isolation for inclusive education. The semi-structured interview research instrument is used for the data collection from the Ministry of Human Rights, Educational Institutions, and inter-sex Representatives. The results show that managing directors and heads of educational institutions frame policy management for the free social isolation of intersex persons, which is relevant through inclusive education. This paper aims to provide a better social condition for intersex persons and promote inclusive education through effective policy management.

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.