Institute of Commerce and Law

This chapter explores the shift from platform capitalism to post-platform health infrastructures, emphasizing the role of digital innovation in reimagining public health systems. It introduces a transdisciplinary framework that integrates health sociology, critical data studies, and science and technology studies (STS), exploring the structural disruptions caused by data monopolies and the emergent capabilities enabled by digital health technologies. Focusing on decentralization, data sovereignty, and open protocols, the chapter examines case studies like India's Ayushman Bharat and Estonia's e-Health system. Ethical, political, and epistemological tensions, such as algorithmic bias and digital divides, are critically analysed. The chapter concludes by offering a transdisciplinary model for equitable, participatory, and resilient digital public health systems in a post-platform era.

This study investigates how various audience segments view social media advertising. The study investigates the ways in which several demographic groups react to different advertising tactics on social media sites like LinkedIn, Facebook, Instagram, Twitter, and TikTok. The study examines elements that impact customer perception, such as ad type, platform context, personalization, and content relevancy, using both primary and secondary data. The results show notable differences in perception according on platform choices, age, and digital proficiency, which has significant ramifications for marketers looking to maximize the efficacy of their ads. The findings show that while intrusive formats and over-personalization frequently elicit unfavorable reactions from audiences, transparency, genuine messaging, and value-oriented content greatly enhance audience receptivity to social media advertising. The research contributes to understanding the evolving relationship between consumers and digital advertisers in the increasingly saturated social media landscape.

Objective: to formulate proposals to form a system of subsidiary liability for harm resulting from the use of artificial intelligence systems. Methods: the research is based on a comprehensive methodological basis, including the abstract logical method for theoretical understanding of the legal nature of artificial intelligence as an object of legal regulation; the method of comparison to analyze the Russian and European legislations on tort liability; generalization to systematize the existing concepts of responsibility distribution between subjects of law; and correlation analysis to identify the relationships between the typology of artificial intelligence systems and the mechanisms of legal responsibility for their functioning. Results: the study summarizes and systematizes modern theoretical and legal concepts and regulations of the European Union and the Russian Federation on the distribution of subsidiary responsibility for the adverse effects of artificial intelligence. Potential subjects of responsibility were identified, as well as the key factors influencing the distribution of responsibility between them. A multidimensional matrix was developed for responsibility distribution between the subjects, taking into account their impact on the specific artificial intelligence system functioning and the systems typologization under the risk-based approach. Scientific novelty: for the first time, an original concept is proposed, which combines the differentiation of the subjects’ roles in terms of their real impact on the artificial intelligence results; the differentiation of artificial intelligence systems under the risk-based approach; and the system of legal presumptions of responsibility distribution corresponding to the above two classifications. The novelty lies in the creation of a multidimensional matrix of subsidiary liability, which allows taking into account many factors when determining the subject of responsibility in each specific case of harm caused by artificial intelligence systems, which differs significantly from existing unilateral approaches to this issue. Practical significance: the research conclusions and suggestions can be used to develop the doctrine of subsidiary responsibility in the field of artificial intelligence use, to develop and modify the legal norms regulating artificial intelligence. The proposed multidimensional matrix of responsibility distribution can serve as a theoretical basis for improving judicial practice in cases of compensation for damage caused by artificial intelligence systems, as well as for creating an effective balance between stimulating the development of AI technologies and ensuring the protection of the rights and legitimate interests of individuals and legal entities.

This research employs an advanced Graph Attention Network (GAT) model to assess user influence in e-commerce social networks. As social networks become increasingly embedded in online shopping environments, it is essential for businesses to comprehend how user relationships, behaviors, and interactions shape purchasing behavior in order to refine marketing tactics. Conventional influence analysis techniques are inadequate for handling the intricate and varied nature of these networks, which include diverse user engagements and transaction records. This research proposes a modified GAT model tailored to e-commerce environments, which incorporates advanced attention mechanisms to better capture the significance of various user interactions, product categories, and demographic attributes. The study first constructs a graph-based representation of the e-commerce social network by integrating transactional, demographic, and social connection data. It then applies the enhanced GAT model to quantify user influence, emphasizing the importance of contextual interactions and varying user behaviors. Experimental results, validated using real-world e-commerce data, show that the modified GAT model outperforms traditional influence metrics in terms of accuracy, offering businesses actionable insights into identifying key influencers within their networks. Key contributions of the research include the development of a comprehensive graph-based framework for influence analysis, the introduction of domain-specific modifications to GAT, and the empirical validation of the proposed model’s effectiveness. These developments allow for more precise detection of key influencers, supporting e-commerce platforms in enhancing marketing efficiency, strengthening customer interaction, and boosting sales outcomes. By introducing a new method of influence analysis specifically designed for e-commerce social networks, this study addresses important gaps in existing research and provides a foundation for future investigations in the field.

Congestion in Malta is a persistent national problem. In a country which has a land mass of just 316km2, with a total population verging near a million, the major means of transportation is road transport, making it the fifth most densely populated nation and the fifth most dense transport network in the world. In order to alleviate or solve this problem, MRT (Mass Rapid Transport or Transit) is being proposed to make a shift from single private vehicles to mass public transportation. This study adopted a postpositivist quantitative approach, employing a convenience, non-representative stratified sample, and using second generation statistical methods, specifically Partial Least Squares Structural Equation Modelling (PLS-SEM), to test the validity, reliability, and predictability of a proposed Extended Theory of Planned Behavior (ETPB) model, that combined the Theory of Planned Behavior (TPB) and elements of Behavioural Economics (BE), specifically Prospect Theory (PT). The study examines the social, economic, and political barriers faced by licenced drivers, who would obviously be the key element needed in this modal transportation switch. The conclusion that is derived from this study is that is that TPB is significantly intrinsic to the PT, and whilst the inclination of the attitude of Maltese licenced drivers to switch and adopt the use of MRT is weak, there is still a positive inclination.

The Phoenix period (1952–1955) was crucial in the development of Scientology. In Phoenix L. Ron Hubbard shifted from a focus on Dianetics to Scientology, which he defined as a religion. He continued to train auditors and create new content. He also wrote two foundational texts, Scientology: A History of Man and The Creation of Ability. Important innovations introduced during the period include the E-meter and the concept of the thetan, the immortal soul. Hubbard also began to expand internationally, especially to London and Australia. He also turned his creative energies to organizational issues, moving away from the use of foundations to centrally-controlled forms of organization. Using original sources, this paper analyzes how Phoenix set the stage for Scientology’s future growth.

The research aimed to study the impact of green marketing on the purchasing behavior (consumer purchasing awareness - consumer purchasing culture - consumer purchasing trend) of consumers of consumer goods, by application to one of the major marketing institutions. To achieve the objectives of the research, the “researchers” designed a questionnaire that was distributed to a sample of the research community. Of consumers of consumer goods, the “researchers” used the descriptive analytical approach and the quantitative method to complete the research, as well as using appropriate statistical tests to test the relationship between the independent and dependent variables, as well as testing the validity of the hypotheses and answering the research questions. The research results concluded that there is a statistically significant influence relationship. Between green marketing and developing awareness of purchasing behavior, as well as the existence of a statistically significant influence relationship between green marketing and developing a culture of purchasing behavior. It was also shown that there is a statistically significant influence relationship between green marketing and developing the purchasing behavior trend of consumers of consumer goods.

The article is devoted to issues affecting the implementation of ESG standards by the Russian business community and the role of the Russian Chambers of Commerce and industry in this process. This scientific article presents the results of a large-scale sociological study (by means of a questionnaire survey) conducted in order to identify what semantic meaning representatives of Russian business put into understanding the essence of ESG principles, how ready they are to follow them in their current professional activities, etc.
 The respondents were both direct representatives of Russian business and representatives of the system of Chambers of commerce and industry of the Russian Federation. The survey results served as the basis for the development of a set of measures to support the implementation of ESG standards by the Chambers of Commerce and Industry of Russia.

Abstract This research seeks to propose an innovative mathematical approach for measuring student performance in engineering education. This paper also includes a detailed analysis and the numerical solutions to this mathematical model. The designed mathematical model is constructed upon four categories, average candidates, weak candidates, below average candidates and good candidates. For the numerical results of the designed nonlinear mathematical model, the analysis through the Adams numerical scheme is applied for solving the differential system based on the migration rate and average student rate moves to weak and above average. Moreover, artificial neural network is also applied to get the stochastic results ANNs-LMB, also known as Levenberg-Marquardt training algorithm. The ANNs-LMB procedures have been implemented with three samples of data scales using the authentication, testing and training, which are chosen as 75%, 15% and 10%, respectively. According to the findings, when the rate of students leaving engineering studies increased, good students performed better, and when the rate of students below average moved, it was due to an increase in the rate of migration above average, the performance of the good students was only impacted in this way. This research material can be used in different designs and models to improve the students’ performance.