Purpose of the article:The present study investigates the role of University Centers for Entrepreneurial Support (UCES) in regional innovation systems, with a particular emphasis on the integration of university sports. Utilising the Triple Helix model as a foundation, the paper delves into the potential of university sports to augment STEM outreach, promote entrepreneurship, and foster collaboration between academia, industry, and government. The findings, which are based on case studies conducted in the Czech Republic, the United States, and Qatar, and incorporate insights from Generation Z, suggest that initiatives focused on university sports, when embedded within entrepreneurial ecosystems, have the potential to enhance institutional visibility, attract students, and stimulate sustainable regional development. The paper concludes with policy recommendations and a framework for assessing the socio-economic impacts of these initiatives. Methodology/methods: The methodology employed in this paper is based on a critical review of academic literature, supported by original sources and empirical data from key stakeholders involved in the development of entrepreneurial universities, innovation ecosystems, and university sports. The present corpus comprises both theoretical contributions and practical insights, which have been gathered through qualitative case studies and stakeholder engagement. Scientific aim: The objective of this paper is to explore the potential of university sports to effectively support entrepreneurial thinking and to enhance collaboration between academia, business, and public institutions. The paper also considers how this integration can help attract students to STEM fields and contribute to broader regional development goals. Findings: The findings indicate that participation in university sports has the potential to facilitate the establishment of stronger regional ties, enhance student engagement, and promote entrepreneurial initiatives. The extant data suggest that these programs can be effective in creating a more dynamic and inclusive academic environment, especially when combined with support from innovation centres such as UCES. Conclusions: The combination of sports and entrepreneurship support structures presents new opportunities for universities looking to improve their relevance and impact. While the initial data are encouraging, further research is required to enhance the understanding of how these efforts can be expanded and the long-term effects of these measures can be measured.
Purpose of the Article: This paper investigated the effect of management innovativeness on the organisational sustainability of the small enterprises in Ogun West Senatorial District, Ogun State, Nigeria. Research Aim: The aim of this research paper is to investigate the effect of management innovation on sustainability of Small-Scale Businesses in Ogun West Senatorial District, Ogun State, Nigeria. Methodology/Methods: A survey design was used to collect data on 400 SMEs and analysed through Structural Equation Modelling (SEM). Findings: Findings indicate that the impact of innovation strategy and human resource innovation on business sustainability was positive and insignificant, whereas financial management innovation had negative and insignificant effects. A great positive predictor that was observed was organisational culture (0.637, p = 0.001), and the model had 46.9% variance in sustainability. Practical Implication: These observations imply that local organisational culture is more crucial in keeping SMEs striving as compared to strategy and financial innovation. The research has an empirical interest in the contribution of management innovation to the sustainability of SMEs in a developing setting, which is encouraging businesses to include more dimensions of innovation in their business sustainability plans.
Purpose of the article: The aim of this article is to investigate the asymmetric cost behavior of small and medium-sized enterprises (SMEs) in the Italian manufacturing industry during the period 2020–2023, with a focus on cost behavior during the crisis and the post-crisis period. The study examines how the economic conditions of the COVID-19 pandemic affected traditional cost behavior theories in the manufacturing SME sector. Methodology/methods: The research utilizes the model by Anderson et al. (2003) applied to a large dataset of 56,017 Italian manufacturing SMEs obtained from the Orbis Europe database. Statistical analysis covers three periods: 2020/2021, 2021/2022, and 2022/2023, which allows tracking the evolution of cost behavior asymmetry over time. The regression models test the relationship between logarithmic changes in total operating costs and sales, including an interaction dummy variable to capture asymmetric cost behavior. Statistical significance is tested using F-statistics at the α = 0.05 level. Scientific aim: The scientific objective is to empirically document the dynamic evolution of asymmetric cost behavior (cost stickiness) in manufacturing SMEs over the economic periods in question, thereby contributing to the theory of asymmetric cost behavior. Findings: The results confirm an evolution in cost behavior across the three periods in the manufacturing sector. The period 2020/2021 exhibits an anti-sticky cost behavior with a positive interaction coefficient (+0.107), reflecting a cost-cutting response to supply chain shocks. The subsequent periods 2021/2022 and 2022/2023 demonstrate pronounced cost stickiness (interaction coefficients –0.255 and –0.222, respectively). All models show high explanatory power, with R² in the range of 0.708–0.772, and are statistically significant at p < 0.001. Conclusions: The article provides evidence that crisis conditions in the manufacturing industry can temporarily alter the established asymmetric cost behavior, but that traditional sticky cost behavior gradually re-stabilizes after the crisis subsides. These findings have important implications for corporate crisis management and cost management strategies of manufacturing firms. Keywords: cost stickiness; manufacturing SME; COVID-19; industrial cost behavior; Italy; industry JEL Classification: M15, M21
Purpose of the article: The article examines the suitability of using the Modern Portfolio Theory (MPT) principle in cryptocurrency portfolio optimization. Methodology/methods: As an input data was used the daily data of 10 most traded cryptocurrencies extracted from the web CoinMarketCap. The data for the period from 2 January 2021 to 1 September 2021 was selected as a sample. The Python programming language was used as a tool for implementation of the Modern Portfolio Theory. The Pandas open source tool was used for data analysis and manipulation. Mathematical operations were performed using the NumPy package. Library Matplotlib was used for creating static, animated, and interactive visualizations in Python. Scientific aim: The research objective is to determine the possibilities of diversifying the cryptocurrency investment portfolio risk based on MPT. Findings: The proposed optimal investment portfolio demonstrates that its performance is profitable not only in the optimization period but also in the validation period. Verification of the optimal investment portfolio confirms that the risk and profitability characteristics are fully respected by the proposed model. The article benefit is also the presentation of a comprehensive methodology for the creation and verification of an optimal portfolio based on MPT principles in the context of software support. Conclusions: The case study of investment portfolio optimization (with the top 10 most traded cryptocurrencies) using MPT was developed, including verification of the proposed investment portfolio. Compared to traditional approaches, which include investor’s or portfolio manager’s decision-making, this approach provides investors with automated portfolio optimization processing based on mathematical and statistical calculations.
Purpose of the article: The purpose of this article is to provide a comprehensive analysis of the role of artificial intelligence (AI) in the banking sector, focusing on its applications, challenges, and implications. By synthesizing existing research and empirical studies, the article aims to inform researchers about the transformative potential and inherent challenges of AI-driven innovation in banking. Methodology/methods: Using a systematic review approach, the relevant literature on AI integration in banking was identified from electronic databases and leading corporate research departments, ensuring a synthesis of scholarly and industry perspectives. Scientific aim: With limited academic research on AI in banking, this study aims to shed light on its applications, challenges, and implications. Findings: The integration of AI in the banking sector has significantly transformed various operational areas, including customer interactions, risk management, compliance, and operational efficiency. AI applications, such as chatbots and smart virtual assistants, have enhanced customer service by offering personalized, 24/7 support, and have demonstrated significant cost and revenue benefits. AI-driven credit scoring and fraud detection have improved risk assessment and mitigation, enabling more precise and informed decision-making. However, AI adoption faces challenges such as high computational costs, data quality issues, the "curse of recursion" where models trained on AI-generated data degrade, and the need to balance trust in AI outputs with their reliability. Furthermore, regulatory considerations play a crucial role in AI integration. While the European Union's AI Act aims to ensure the ethical use of AI in finance, it also presents challenges related to compliance and potential over-regulation. Conclusions: In conclusion, the integration of AI in the banking sector has revolutionized customer service, risk management, compliance, and operational efficiency. However, the adoption of AI also raises concerns about data privacy, security, and the need for regulatory frameworks to ensure ethical use. As AI continues to evolve, it will be crucial for banks to balance technological innovation with responsible practices to maximize benefits and mitigate risks.
