Small and medium enterprises (SMEs) are pivotal drivers of employment, Gross Domestic Product (GDP) growth, and innovation globally, yet they continue to face persistent barriers to adopting data-driven technologies, and literature lacks actionable, resource-sensitive guidance for practitioners. The systematic review maps algorithm-specific applications across industries including XGBoost for credit risk, LightGBM for real-time BI automation, and Support Vector Machines for high-dimensional small datasets while identifying persistent gaps in methodological transparency, geographic diversity, and prescriptive guidance. Addressing these gaps, the paper makes three original contributions: (1) a critical synthesis distinguishing access gaps from outcome gaps in SME BI adoption; (2) an analysis of why cost-efficiency remains under-weighted relative to strategic growth as an adoption driver, despite resource constraints; and (3) a phased three-stage implementation roadmap guiding SMEs from frugal descriptive dashboards through predictive modeling to prescriptive decision automation.
Preprocessing strategy selection in business analytics typically relies on convention rather than systematic evidence, despite consuming 60-80% of project effort. This study introduces REPROPREP (v1.0), a methodological framework for validating preprocessing effectiveness assumptions through statistical analysis and cost-benefit assessment. The framework applies Benjamini-Hochberg false discovery rate correction, quality degradation protocols, and cost-effectiveness evaluation. A demonstration across 10 UCI datasets, three preprocessing strategies, and gradient boosting classifiers with 5-fold stratified cross-validation yielded no statistically significant performance differences after multiple comparisons correction (mean effect size: 0.001 AUC), with implementation cost differences ranging from $150-$800. Focused on numeric preprocessing, REPROPREP provides organizations with a rigorous, context-specific methodology for evaluating preprocessing assumptions. Generalizability requires validation beyond tested conditions. Reproducible code is publicly available.
Preprocessing strategy selection in business analytics typically relies on convention rather than systematic evidence, despite consuming 60–80% of project effort. This study introduces REPROPREP (v1.0), a methodological framework for validating preprocessing effectiveness assumptions through statistical analysis and cost-benefit assessment. The framework applies Benjamini-Hochberg false discovery rate correction, quality degradation protocols, and cost-effectiveness evaluation. A demonstration across 10 UCI datasets, three preprocessing strategies, and gradient boosting classifiers with 5-fold stratified cross-validation yielded no statistically significant performance differences after multiple comparisons correction (mean effect size: 0.001 AUC), with implementation cost differences ranging from $150–$800. Focused on numeric preprocessing, REPROPREP provides organizations with a rigorous, context-specific methodology for evaluating preprocessing assumptions. Generalizability requires validation beyond tested conditions. Reproducible code is publicly available.
Organizations need to evolve to stay competitive, given the constant influx of data. Traditional analysis struggles with this volume and complexity. Data democratization, enabling broader employee access to data, is crucial. This trend, alongside self-service business intelligence and data literacy, empowers data-driven decisions. While data democratization removes access barriers and self-service business intelligence provides tools, data literacy ensures meaningful comprehension. Balancing this freedom with strong governance is key. Data democratization and self-service business intelligence offer faster decisions and innovation but risk data accuracy without robust governance (discoverability, tagging, security). Artificial intelligence further enhances self-service business intelligence, transforming it into an intelligent analytics partner. Successfully integrating data democratization and self-service business intelligence with strong literacy and governance is vital for organizations to leverage their data's full potential. Through an integrative literature review, this study synthesizes insights from technical, organizational, and behavioural domains to provide a comprehensive understanding of these interrelated concepts.
This study explores the link between user sentiment and credit risk on FinTech lending platforms using sentiment analysis techniques like Latent Dirichlet Allocation (LDA) and the Liu Hu method. Analyzing data from 2020 to 2023, findings reveal Kiva leads with 91.16% positive feedback and a 4.7-star rating but fewer reviews (617). LendingClub, with 1.58K reviews, has mixed sentiment (56.08% positive, 39.99% negative) and a lower rating (3.3 stars). Plenti achieves 58.33% positive sentiment but lower coherence, while Mintos balances sentiment (66.69% positive) with the largest review base (100K+). Results show platforms with higher positive sentiment and topic coherence mitigate credit risk more effectively, underscoring the value of user feedback in optimizing marketplace lending. The study offers actionable insights for FinTech stakeholders to improve app performance and user-centric financial solutions through effective sentiment analysis.
This study explores the link between user sentiment and credit risk on FinTech lending platforms using sentiment analysis techniques like Latent Dirichlet Allocation (LDA) and the Liu Hu method. Analyzing data from 2020 to 2023, findings reveal Kiva leads with 91.16% positive feedback and a 4.7-star rating but fewer reviews (617). LendingClub, with 1.58K reviews, has mixed sentiment (56.08% positive, 39.99% negative) and a lower rating (3.3 stars). Plenti achieves 58.33% positive sentiment but lower coherence, while Mintos balances sentiment (66.69% positive) with the largest review base (100K+). Results show platforms with higher positive sentiment and topic coherence mitigate credit risk more effectively, underscoring the value of user feedback in optimizing marketplace lending. The study offers actionable insights for FinTech stakeholders to improve app performance and user-centric financial solutions through effective sentiment analysis.
In Serbian medium-sized enterprises (MSEs) (2009-2018), the study found a 14.8% domestic capital decline offset by foreign capital inflows (p<0.05). With regional disparities, Šumadija achieved 17.34%*** foreign capital growth, South/East Serbia suffered a -7.13%*** domestic capital decline. Proximity to Belgrade, as the capital city, significantly correlated with financing success (β=0.42, p<0.1). Manufacturing dominated with 10.25%*** foreign capital growth versus agricultural slump (1.93% ns). WCM optimization yielded 24-day cash conversion cycle reductions and +1.7*** inventory turnover improvements in manufacturing. Simulations project 8-10% manufacturing growth with optimal WCM. Foreign capital's high variability (SD=6.42) exacerbated regional inequalities, particularly in Vojvodina (-1.28% foreign capital) versus Šumadija's exceptional performance. Findings show foreign capital mitigates domestic capital decline in certain regions while creating new spatial economic imbalances, necessitating regional policies accounting for sectoral composition and WCM infrastructure.
This study conducts a systematic evaluation of the European Union Artificial Intelligence Act, assessing its regulatory alignment with the rapidly evolving risk landscape posed by Generative AI systems. Employing large language models within a governance-oriented analytical framework, the analysis critically examines the extent to which the Act addresses foundational concerns such as algorithmic fairness, model explainability, environmental sustainability, and financial stability. While the Act advances transparency and accountability, analysis reveals notable limitations in operational guidance and alignment with high-level ethical principles. Overall, the Act represents a positive foundation, yet targeted enhancements are essential for enabling responsible innovation and ensuring that Generative AI advances align with societal and economic values. The proposed framework also enables practical self-audits and supports future regulatory design, making it a valuable tool for both public and private stakeholders.
This study employs the gravity model to explore the key drivers of Vietnam's agricultural exports to the European Union (EU) from 2015 to 2021. The findings reveal that the EU's demand for agricultural products and favorable exchange rates are pivotal in boosting Vietnam's export performance, while geographic distance and inflation present significant challenges. Additionally, Vietnam's agricultural capacity and land resources emerge as vital contributors to enhancing export growth. The study highlights the complexities of navigating the EU's stringent quality standards and technical requirements, providing a nuanced understanding of the factors shaping Vietnam's agricultural export dynamics. These insights bridge theory and real-world practice, offering a clear perspective on the opportunities and obstacles within this competitive market.
This book review examines Exploring Research Methodology and Research Design: Doing Research Across the Business Disciplines, edited by Peter John Sandiford and Sabine Sch & uuml;hrer. The book is a reflective and practice-based resource for researchers navigating the complexities of methodology and design in business disciplines. It is structured in three parts: foundational perspectives on research, considerations in design and planning, and the realities of conducting research. Through diverse voices and case-based insights, the text challenges linear, formulaic approaches to research and instead promotes critical engagement, methodological flexibility, and reflexivity. The book equips doctoral students and early-career researchers with tools for thoughtful and context-sensitive research practice by addressing philosophical, ethical, and social dimensions.
In Serbian medium-sized enterprises (MSEs) (2009-2018), the study found a 14.8% domestic capital decline offset by foreign capital inflows (p<0.05). With regional disparities, & Scaron;umadija achieved 17.34%*** foreign capital growth, South/East Serbia suffered a-7.13%*** domestic capital decline. Proximity to Belgrade, as the capital city, significantly correlated with financing success (beta=0.42, p<0.1). Manufacturing dominated with 10.25%*** foreign capital growth versus agricultural slump (1.93% ns). WCM optimization yielded 24-day cash conversion cycle reductions and +1.7*** inventory turnover improvements in manufacturing. Simulations project 8-10% manufacturing growth with optimal WCM. Foreign capital's high variability (SD=6.42) exacerbated regional inequalities, particularly in Vojvodina (-1.28% foreign capital) versus & Scaron;umadija's exceptional performance. Findings show foreign capital mitigates domestic capital decline in certain regions while creating new spatial economic imbalances, necessitating regional policies accounting for sectoral composition and WCM infrastructure.
This book review examines Exploring Research Methodology and Research Design: Doing Research Across the Business Disciplines, edited by Peter John Sandiford and Sabine Schührer. The book is a reflective and practice-based resource for researchers navigating the complexities of methodology and design in business disciplines. It is structured in three parts: foundational perspectives on research, considerations in design and planning, and the realities of conducting research. Through diverse voices and case-based insights, the text challenges linear, formulaic approaches to research and instead promotes critical engagement, methodological flexibility, and reflexivity. The book equips doctoral students and early-career researchers with tools for thoughtful and context-sensitive research practice by addressing philosophical, ethical, and social dimensions.
This study employs the gravity model to explore the key drivers of Vietnam's agricultural exports to the European Union (EU) from 2015 to 2021. The findings reveal that the EU's demand for agricultural products and favorable exchange rates are pivotal in boosting Vietnam's export performance, while geographic distance and inflation present significant challenges. Additionally, Vietnam's agricultural capacity and land resources emerge as vital contributors to enhancing export growth. The study highlights the complexities of navigating the EU's stringent quality standards and technical requirements, providing a nuanced understanding of the factors shaping Vietnam's agricultural export dynamics. These insights bridge theory and real-world practice, offering a clear perspective on the opportunities and obstacles within this competitive market.
Gemini, an advanced artificial intelligence tool, has generated significant interest because of its complex capabilities and versatility. Notwithstanding its worldwide acclaim, several nations, particularly Turkey, have exhibited reluctance to implement Gemini domestically. This article aims to investigate the public discourse regarding Gemini in Turkey, assess the factors contributing to its limited acceptance, and provide insights into the future of artificial intelligence globally. The authors utilize the latent dirichlet allocation topic modeling algorithm to examine 35,974 social media posts and comments from Turkish users regarding Gemini. The results indicate that negative attitudes prevail over positive and neutral sentiments. Furthermore, the authors observe two notable peaks in Gemini's focus from March to May 2024. The principal terms signifying public interest and concern are “innovation,” “enterprise,” and “advancement.” The analysis reveals three principal topics in the discourse: societal influence, technical advancement, and educational applications.
Global trade volumes which set new records yearly indicate growth of commercial operations among an increasing number of companies. This expansion inevitably brings with it the challenge of managing exchange rate risks. As a standard practice, international payments are made within a specified period agreed to in a commercial contract leading to the elevated risks of the exchange rate exposure. To avoid that, many companies decide to use hedging instruments. This paper aims to reduce the extra expenses of hedging by designing an expert decision system. The expert decision system facilitates daily recommendations to a company on the decision of exchange rate hedging. The contribution of this research is twofold. First, it applies the latest machine learning techniques and obtains 79% accuracy in predicting the following day's exchange rate trend. Second, it designs an expert decision system that helps a company reduce its foreign exchange rate exposure managing. The results of backtesting on real data prove the efficiency of the expert decision system.
This paper proposes a novel framework for a real-time music visualization system designed for the hearing-impaired, utilizing AI and serverless computing. The system converts audio signals into visual representations that capture both the physical and emotional aspects of music. A neural network-based Music Emotion Recognition (MER) model extracts emotional cues, which are integrated into the visualizations. The serverless computing ensures accessibility, while an account management system and comment collection system enable customization and regular retraining of the model for better accuracy. Results demonstrate the framework's effectiveness, highlighting the scalability and cost-efficiency of serverless computing. This work significantly advances music accessibility for the hearing-impaired, enhancing sensory experiences and promoting mental well- being. The MER model shows superior performance, with a 46.8% lower Root Mean Squared Error (RMSE) compared to other works targeting the same 10-second audio fragment length and a 13.5% higher Pearson's correlation coefficient (PCC) for 30-second fragments.
The ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.
