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Abstract: In endurance sports, athletes and coaches shift increasingly from intuition-based decisionmaking to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes.
Keywords: smart sports training, endurance sports, intelligent data analysis, machine learning, artificial intelligence, computational intelligence, systematic literature review
Published in DKUM: 02.10.2025; Views: 0; Downloads: 6
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Abstract: This study seeks to examine pull factors of capital inflows, offering an empirical analysis based on a panel study of eleven Southeast European countries (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Greece, Montenegro, North Macedonia, Kosovo, Romania, Serbia, and Türkiye) over the period of 2004 – 2021. Methodologically, the study utilizes a fixed effects (FE) regression model with robust Driscoll-Kraay standard errors to address issues of heteroskedasticity, autocorrelation, and potential cross-country correlation. The study finds that several pull factors can be relevant in driving capital inflows as follows: market size, inflation, financial and trade openness. The empirical analysis confirms that the forces of trade liberalization, financial liberalization, market size, real interest rates and inflation stability are the elements that encourage capital inflows. On the other hand, the estimated effects of current account balance and real economic growth are not very convincing. Finally, we stress that more study is required to fully understand the pull variables' ultimate macroeconomic implications at the national level. The overall influence of these positive (or negative) inflows may be moderated by several characteristics, even if certain countries may be extremely susceptible to these factors.
Keywords: capital inflows, pull factors, southeast Europe, panel data analysis, Driscoll-Kraay standard errors
Published in DKUM: 28.05.2025; Views: 0; Downloads: 2
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Abstract: Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, December 2023 version), and Gemini (formerly Google Bard, December 2023 version). Participants (N = 85), Master’s students from the Faculty of Electrical Engineering and Computer Science with prior experience in UX evaluations and familiarity with AI-based chatbots, performed sentiment analysis and data annotation tasks using these tools, enabling a comparative evaluation. The results show that AI tools were associated with lower cognitive effort and more positive emotional responses compared to Taguette, which caused higher frustration and workload, especially during cognitively demanding tasks. Among the tools, ChatGPT achieved the highest usability score (SUS = 79.03) and was rated positively for emotional engagement. Trust levels varied, with Taguette preferred for task accuracy and ChatGPT rated highest in user confidence. Despite these differences, all tools performed consistently in identifying qualitative patterns. These findings suggest that AI-driven tools can enhance researchers’ experiences in QDA while emphasizing the need to align tool selection with specific tasks and user preferences.
Keywords: user experience, UX, usability, qualitative data analysis, QDA, chatbots
Published in DKUM: 07.02.2025; Views: 0; Downloads: 20
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Abstract: The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.
Keywords: big data, cluster analysis, digital divide, k-means, enterprise, industry, Europe, quality
Published in DKUM: 14.01.2025; Views: 0; Downloads: 7
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Abstract: The publication "Analysis of quantitative research data in nursing research: A guide to SPSS" provides nursing students and nurses with the knowledge and skills to interpret the different statistical methods in their field, which can improve users' skills in collecting, analysing and interpreting results from clinical practice, thus contributing to improving the quality of health care. It provides detailed instructions on how to use IBM SPSS and perform statistical analyses that nurses need to be familiar with as they use and generate data in their daily work with patients. The main aim of patient care is to provide high quality, evidence-based care, so nurses have a duty to keep up to date with the latest research and evidence and apply it to their work. The knowledge gained in this book can also help nurses to better understand and interpret previously published results, and thus critically assess the validity and reliability of the results they will use in clinical practice.
Keywords: quantitative analysis, statistics, IBM SPSS, reliability, validity, data analysis
Published in DKUM: 18.07.2024; Views: 113; Downloads: 27
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