The traditional global food supply chains are not just complex, but they do not support the sustainability of agriculture. The business models with the greatest growth potential are those that would allow consumers to buy more directly from producers. Before COVID-19, these alternatives were not just popular but had a relatively high market share compared to post COVID-19 era. However, due to the pandemic and the changes in consumers’ purchasing behavior, the players of short food supply chains had to adapt to the new circumstances. This is why business model innovation is nowadays a priority, which means a substantial renewal of the value delivered to customers, and a significant transformation of the processes and activities of the company/network. The study aimed to examine the dynamic innovation through applied digital marketing solutions that could open up new sales channels and increase the competitiveness of the companies involved. Since Austria is one of the “greenest” countries, its consumers are interested in purchasing sustainably, so they strive to buy directly from the producers. This motivates the authors to use its representative sample to investigate how well adapted the Austrian direct sellers are to the unsteady circumstances. Based on a previous research framework, a principal component analysis was applied and the elements defined therein were the variables of non-hierarchical clustering. The used methods highlight the lack of online distribution and marketing of the farmer shops, which could multiply the harmful effects of the pandemic. The classification of the shops demonstrated the generally low share of innovative direct sellers. We conclude that in the new era, businesses that effectively apply open business innovation models will be able to compete in the market.

This review synthesises research on fintech implications of integrating machine learning algorithms into cryptocurrency trading strategies to address the fragmented understanding of their impact on trading efficacy, risk management, and financial innovation. The review aimed to evaluate current knowledge on machine learning applications, benchmark algorithmic trading performance, identify risk mitigation techniques, compare algorithm effectiveness, and examine regulatory and ethical considerations. A systematic analysis of diverse methodologies, including supervised, reinforcement, and hybrid learning models across global computational finance and AI literature, was conducted. Findings indicate that deep learning and ensemble methods significantly enhance predictive accuracy and trading profitability under volatile market conditions, while reinforcement learning frameworks improve dynamic portfolio optimisation and risk-adjusted returns. Risk management benefits arise from integrating technical indicators and reward-based safety mechanisms, though universal frameworks remain lacking. Fintech integration advances through blockchain-enabled transparency and automation, yet practical deployment faces scalability and interoperability challenges. Ethical and regulatory discourse is nascent, underscoring the need for responsible AI frameworks to ensure market integrity and investor protection. These findings collectively demonstrate that machine learning substantially transforms cryptocurrency trading strategies, offering enhanced performance and risk control within evolving fintech infrastructures, while highlighting critical gaps in regulatory compliance and ethical governance that warrant focused future research.

Coffee is one of the most consumed beverages in the world. It is the second largest traded commodity after petroleum. It is crucial for the economy and politics of many developing countries, as its cultivation, processing, trade, transportation and distribution create jobs for millions of people. The analysis of network connections and structures became popular in the field of sociology, whereby the term Social Network Analysis (SNA) is derived. The method is well suited for the complex examination and modelling of composite social structures. The social network is defined as a finite set of social actors and the relationships between them. Modelling of complex networks now apply in countless locations, from social media through the pharmaceutical industry to economic and commercial processes. Our study examines changes in international coffee trade through a network analysis. Introducing the complex networks investigate by our, highlighting major changes in international coffee trade. A kávé az egyik legtöbbet fogyasztott ital a világon. Az árucikkek kereskedelmében második helyet foglalja el a kőolaj után. Számos fejlődő ország gazdasága és politikája szempontjából döntő fontosságú, mivel termesztése, feldolgozása, kereskedelme, szállítása és forgalmazása munkahelyeket teremt emberek millióinak. A hálózati kapcsolatok és struktúrák elemzése elsősorban a szociológia területén vált népszerűvé, innen ered a társadalmi kapcsolatháló-elemzés (Social Network Analysis, SNA) kifejezés is. A módszer kiválóan alkalmas bonyolult társadalmi struktúrák komplex vizsgálatára és azok modellezésére. A kapcsolatháló definíció szerint társadalmi szereplők véges számú készletéből és a közöttük lévő kapcsolatokból áll. A komplex hálózatok modellezése ma már számtalan helyen előfordul a közösségi médiától a gyógyszeriparon át a gazdasági és kereskedelmi folyamatokig. A tanulmányunk a nemzetközi kávékereskedelem változásait vizsgálja kapcsoltháló-elemzéssel. Bemutatjuk az általunk vizsgált komplex hálózatokat, melyek rávilágítanak a nemzetközi kávékereskedelem fontosabb változásaira.