In the agricultural sector of Huila, particularly among SMEs in coffee, cocoa, fish, and rice subsectors, the transition to the International Financial Reporting Standards (IFRS) is paramount yet challenging. This research aims to offer management guidelines to support Huila’s agricultural SMEs in their IFRS transition, underpinning the region’s aspirations for financial standardization and economic advancement. Utilizing a mixed-methods managerial approach, data was gathered from 13 representative companies using validated questionnaires, interviews, and analyzed with SPSS and ATLAS.ti. Results indicate that while there is evident progress in IFRS adoption, 12 out of 13 firms adopted IFRS, with rice leading in terms of adoption duration. While 77% found IFRS useful for financial statements, half reported insufficient staff training. The transition highlighted challenges, including asset recognition and valuation, and emphasized enhancing institutional support and IFRS training. Interviews revealed managerial commitment and expertise as significant factors. Recommendations for successful implementation include leadership involvement, continuous professional development, anticipating costs, clear accounting policies, and meticulous record-keeping. The study concludes that adopting IFRS enhances financial reporting quality, urging entities to converge their reporting practices without hesitation for improved comparability, relevance, and reliability in their financial disclosures.

The present study focuses on improving Cognitive Radio Networks (CRNs) based on applying machine learning to spectrum sensing in remote learning scenarios. Remote education requires connection dependability and continuity that can be affected by the scarcity of the amount of usable spectrum and suboptimal spectrum usage. The solution for the proposed problem utilizes deep learning approaches, namely CNN and LSTM networks, to enhance the spectrum detection probability (92% detection accuracy) and consequently reduce the number of false alarms (5% false alarm rate) to maximize spectrum utilization efficiency. By developing the cooperative spectrum sensing where many users share their data, the system makes detection more reliable and energy-saving (achieving 92% energy efficiency) which is crucial for sustaining stable connections in educational scenarios. This approach addresses critical challenges in remote education by ensuring scalability across diverse network conditions and maintaining performance on resource-constrained devices like tablets and IoT sensors. Combining CRNs with new technologies like IoT and 5G improves their capabilities and allows these networks to meet the constantly changing loads of distant educational systems. This approach presents another prospect to spectrum management dilemmas in that education delivery needs are met optimally from any STI irrespective of the availability of resources in the locale. The results show that together with machine learning, CRNs can be considered a viable path to improving the networks’ performance in the context of remote learning and advancing the future of education in the digital environment. This work also focuses on how machine learning has enabled the enhancement of CRNs for education and provides robust solutions that can meet the increasing needs of online learning.

The coronavirus pandemic has reinforced the need for sustainable, smart tourism and local travel, with rural destinations gaining in their popularity and leading to increased potential of smart rural tourism. However, these processes need adjustments to the current trends, incorporating new transformative business concepts and marketing approaches. In this paper we provide real life examples of new marketing approaches, together with new business models within the context of the use of new digital technologies. Via hermeneutic research approach, consisting of the secondary analysis of the addressed subject of smart rural tourism in adversity of the COVID-19 and 6 semi-structured interviews, the importance of technology is underscored in transforming rural tourism to smart rural tourist destinations. The respondents in the interview section were chosen based on their direct involvement in the presented examples and geographical location, i.e. France, Slovenia and Spain, where presented research examples were developed, concretely within European programmes, i.e. Interreg, Horizon and Rural Development Programme (RDP). Interviews were taking place between 2022 and 2023 in person, email or via Zoom. This two-phased study demonstrates that technology is important in transforming rural tourism to smart tourist destinations and that it ushers new approaches that seem particularly useful in applying to rural areas, creating a rural digital innovation ecosystem, which acts as s heuristic rural tourist model that fosters new types of tourism, i.e. smart rural tourism.

This study aims to structure guidelines for an intervention model from the perspective of Integral Project Management to improve the competitiveness level of cacao associations in south region of Colombia. The research followed a mixed-method approach with a non-experimental cross-sectional design and a descriptive scope. The study employed a stage-based analytical framework which included: identifying the factors influencing the competitiveness of the cacao sector; grouping these factors under the six primary determinants of competitiveness with reference to Porter’s Diamond Model; and proposing guidelines for an intervention model to enhance the competitiveness of the studied associations through project management. The first stage was conducted via literature review. The second stage involved primary data collected through surveys and interviews with the associations, members, and cacao sector experts in Huila. The third stage entailed grouping the factors within the main determinants that promote and limit the competitiveness of the cacao sector in the context of Porter’s Diamond Model. Based on the analysis of the corresponding restrictive and promoting factors, strategic recommendations were formulated for the various sector stakeholders on the measures that can be adopted to address restrictive factors and maintain promoting factors to enhance and sustain the sector's competitiveness.

This paper investigates the factors influencing credit growth in Kosovo, focusing on the relationship between credit activity and key economic variables, including GDP, FDI, CPI, and interest rates. Its analysis targets loans issued to businesses and households in Kosovo, employing a VAR model integrated into a VEC model to investigate the determinants of credit growth. The findings were validated using OLS regression. Additionally, the study includes a normality test, a model stability test (Inverse Roots AR Characteristic Polynomial), a Granger causality test for short-term relationships, and variance decomposition to analyze variable shocks over time. This research demonstrates that loan growth is primarily driven by its historical values. The VEC model shows that, in the long run, economic growth in Kosovo leads to less credit growth, showing a negative link between it and GDP. Higher interest rates also reduce credit growth, showing another negative link. On the other hand, more foreign direct investment (FDI) increases credit demand, showing a positive link between credit growth and FDI. The results show that loans and inflation (CPI) are positively linked, meaning higher inflation leads to more credit growth. Similarly, more foreign direct investment (FDI) increases credit demand, showing a positive link between FDI and credit growth. In the long term, higher inflation is connected to greater credit growth. In the short term, the VAR model suggests that GDP has a small to moderate effect on loans, while FDI has a slightly negative effect. In the VAR model, interest rates have a mixed effect: one coefficient is positive and the other negative, showing a delayed negative impact on loan growth. CPI has a small and negative effect, indicating little short-term influence on credit growth. The OLS regression supports the VAR results, finding no effect of GDP on loans, a small negative effect from FDI, a strong negative effect from interest rates, and no effect from CPI. This study provides a detailed analysis and adds to the research by showing how macroeconomic factors affect credit growth in Kosovo. The findings offer useful insights for policymakers and researchers about the relationship between these factors and credit activity.