This paper systematically restructures and methodologically improves existing research on the integration of microtransactions, battle pass systems, and loot box mechanics into the software architecture of modern video games. By combining a theoretical literature review, quantitative analysis (ARPU, correlation between spending and indicators of problematic behavior), and comparative regulatory analysis, the paper addresses three research questions: differences in ARPU between monetization models, the relationship between spending on loot boxes and problematic behavior, and the effects of regulatory interventions on publisher revenues. Based on the findings, technical, ethical, and regulatory recommendations are proposed for sustainable monetization practices.
The fight against money laundering and terrorist financing gains a new dimension with the advent of digital currencies. Digital currencies also include cryptocurrencies, which very quickly attract a large number of users, mostly legitimate users. Due to their nature, cryptocurrencies are becoming a preferred vehicle for money laundering and terrorist financing (AML/CFT). Cryptocurrencies themselves rely heavily on pseudo-anonymity as well as high transaction speed. Namely, the volume and speed of transactions greatly complicates the work of agencies tasked with fighting money laundering and terrorist financing. The emergence of artificial intelligence as well as specialized tools based on artificial intelligence greatly help agencies to more quickly and accurately locate transactions that can be linked to money laundering and terrorist financing.
During the period from 1904 to 1908, mining production in the Kingdom of Serbia exhibited significant dynamism and diversity, both in terms of the types of raw materials extracted and production volumes. A total of 13 mineral types were exploited, with coal and copper dominating both in quantity and economic value. Descriptive statistical analysis reveals notable fluctuations among metallic ores, particularly silver, antimony, and lignite, whereas gold, cement, and hard coal demonstrated relatively stable production trends. Financial analysis confirms copper as the most profitable mineral, while lignite and hard coal made a stable, albeit variable, contribution to the overall economy. These findings indicate a mining sector undergoing growth and diversification, characterized by intensive exploitation cycles of specific mines and the initial development of new resources, such as pyrite.
Rural development in the Republic of Serbia takes place under conditions of pronounced structural disparities, demographic depopulation, and limited economic diversification, particularly in predominantly rural local government units. The aim of this paper is to identify the key factors shaping the development potentials and constraints of rural development in the Municipality of Sečanj through the application of an integrated SWOT-TOWS approach. The research is based on the analysis of official statistical data, relevant strategic and planning documents, and secondary sources, using descriptive, analytical, and comparative methods. The results of the SWOT analysis indicate the presence of significant natural and agricultural resources, a favorable geographical and cross-border position, and a preserved base of family farms, while adverse demographic trends, limited local labor market absorption capacity, and a low level of agricultural product processing are identified as the main weaknesses. The TOWS matrix was used to formulate priority strategic options aimed at strengthening agri-food value chains, diversifying the local economy, and improving institutional conditions for development.
The contemporary business environment is characterized by rapid technological change and an increasing reliance on data-driven decision-making, making artificial intelligence (AI) a key driver of digital transformation and organizational efficiency. However, AI implementation is particularly challenging for small and medium-sized enterprises (SMEs) due to limited resources, a lack of skilled personnel, and various technical, financial, organizational, legal, and ethical risks associated with the process of digital transformation. Building on these challenges, the main aim of this paper is to identify the key risks associated with AI implementation in SMEs and to formulate appropriate strategies for their management and mitigation. The paper seeks to offer a systematic approach to risk management in AI projects, drawing on the principles of contemporary risk management standards and the specific characteristics of SME operations. It is based on the premise that digital transformation, including the adoption of artificial intelligence, is inevitable in the modern business environment, yet its implementation entails significant and multifaceted risks. At the same time, SMEs, due to their limited resources and specific organizational structures, occupy a particularly vulnerable position, which requires a systematic, thoughtful, and strategic approach to managing the risks associated with AI projects.
