1. Beyond differences : a generational convergence in technology use among business studentsVesna Čančer, Maja Rožman, Polona Tominc, 2025, original scientific article Abstract: The rapid digitalization of higher education has transformed how students learn, collaborate, and engage with emerging technologies such as artificial intelligence (AI). While earlier research emphasized generational or academic-level differences in digital behavior, recent evidence suggests convergence in technology use. This study explores whether undergraduate and postgraduate students at the Faculty of Economics and Business, University of Maribor, display distinct technology engagement patterns across five constructs: excessive technology use, online collaboration, the use of digital learning tools (E-boards), the use of AI in education, and perceived academic success. A survey among 285 students was analyzed using the non-parametric Mann–Whitney U test due to a non-normal data distribution. The findings showed that postgraduate students did not report higher levels of E-Board use, online collaboration, or perceived academic success. Undergraduate students scored higher on one item related to excessive technology use, but not across the full construct. However, significant differences emerged in AI use, where postgraduate students showed greater confidence and willingness to integrate AI tools. The findings suggest that digital competence and the quality of technology integration, rather than study level, shape students’ learning experiences. Higher education institutions should promote balanced and ethical technology use, strengthen AI literacy, and foster self-regulated learning skills.
Keywords: higher education, technology use, online collaboration, artificial intelligence, perceived academic success, undergraduate and postgraduate students
Published in DKUM: 23.12.2025; Views: 0; Downloads: 0
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2. Collaborative leadership for quality assurance : a case study on developing a strategic quality manual in higher educationKarin Širec, Maja Rožman, 2025, original scientific article Abstract: Higher education institutions face growing pressure to demonstrate excellence and strategic alignment in quality assurance, particularly to meet international accreditation standards. This paper presents a case study of a business faculty that undertook a leadership-driven initiative to develop a comprehensive Quality Manual aimed at continuous improvement and accreditation readiness. The project was structured as a multi-phase, collaborative effort involving broad stakeholder engagement, benchmarking of best practices, and alignment with international accreditation frameworks and the European Standards and Guidelines (ESG), as well as relevant national quality assurance requirements. The resulting Quality Manual provides a unified framework for quality assurance (QA) across all key areas—governance, academic programmes, research, student support, partnerships—linking institutional strategy to daily processes. Key outcomes of the case include clarified roles and procedures, integrated feedback loops, and enhanced readiness for accreditation. The development process highlighted the critical role of educational leadership in fostering a quality culture; by engaging faculty, staff, students, and external partners, the leadership built shared ownership of QA goals. The study is guided by two research questions: (1) How can collaborative and distributed leadership support the development of a strategically aligned Quality Manual at the faculty level? (2) How does the process of designing and implementing a Quality Manual contribute to strengthening internal QA and fostering a quality culture? The study discusses challenges and lessons learned in leading such change, emphasising the importance of stakeholder collaboration, strategic alignment, and change management. The findings contribute theoretically by illustrating how faculty-level leadership operationalizes QA frameworks into concrete institutional tools, and practically by offering a structured model for developing a strategic Quality Manual in higher education.
Keywords: educational leadership, quality assurance, strategic alignment, accreditation, higher education, quality culture, stakeholder engagement
Published in DKUM: 23.12.2025; Views: 0; Downloads: 0
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3. Podjetništvo mladih in umetna inteligenca: primerjalna analiza zaznavanja, sprejemanja in uporabe umetne inteligencePina Slaček, 2025, master's thesis Abstract: Magistrsko delo obravnava generacijske razlike v zaznavanju, sprejemanju in uporabi umetne inteligence med podjetniki v Sloveniji ter primerjalno analizira zaznane koristi, tveganja, priložnosti in ovire njenega uvajanja. V ospredju je podjetništvo mladih, pri čemer so analizirane značilnosti, motivacija, priložnosti in ovire za mlade, ki jih spremljajo pri uvajanju novih tehnologij v poslovne modele. V teoretičnem delu so predstavljeni ključni koncepti podjetništva mladih, procesi digitalizacije ter vloga umetne inteligence kot orodja za optimizacijo procesov, personalizacijo storitev, podporo pri odločanju in razvoj inovativnih poslovnih modelov. Empirični del temelji na primerjalni analizi zaznav pozitivnih učinkov umetne inteligence (povečanje produktivnosti, inovativnosti, poslovne rasti in izboljšanje uporabniške izkušnje) ter negativnih učinkov (stroški implementacije, varnostni in etični pomisleki, odpor zaposlenih in strank). Primerjalno analizo smo izvedli med mlajšimi (18–34 let) in starejšimi (34+ let) podjetniki, pri čemer smo s statističnimi preizkusi ocenili razlike v percepciji pozitivnih in negativnih učinkov ter v pripravljenosti za uvajanje umetne inteligence v poslovne procese. Rezultati analize so pokazali, da mlajši podjetniki statistično značilno pogosteje zaznavajo pozitivne učinke in so bolj optimistični glede prihodnje vloge umetne inteligence, medtem ko med generacijami ni bilo razlik pri zaznavanju negativnih učinkov, kar kaže na univerzalnost teh pomislekov. Analiza velikosti učinka je potrdila, da imajo ugotovljene razlike praktični pomen, zlasti pri prihodnjih pričakovanjih glede dolgoročnega potenciala umetne inteligence. Sklepi naloge opozarjajo na potrebo po oblikovanju prilagojenih podpornih politik in programov, ki bodo mladim podjetnikom olajšali sprejemanje in uporabo umetne inteligence, hkrati pa zmanjšali tveganja in digitalne neenakosti med podjetniki različnih generacij.
Keywords: podjetništvo mladih, umetna inteligenca, generacijske razlike, digitalna transformacija, digitalizacija, percepcija in sprejemanje umetne inteligence, podporne politike in programi, Slovenija.
Published in DKUM: 07.11.2025; Views: 0; Downloads: 46
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4. AI Literacy Among University Students : a comparative study of three countries—Slovenia, Croatia, and IndiaMaja Rožman, Dijana Oreški, Arun A. Elias, Minnu F. Pynadath, Polona Tominc, 2025, original scientific article Abstract: This study investigates artificial intelligence (AI) literacy among university students from Slovenia, Croatia, and India, focusing on variations in their understanding of AI concepts and applications across different educational systems. The sample comprised 471 students from the business studies programmes in Slovenia, Croatia and India. Data were collected through a structured online questionnaire, and statistical analyses utilized descriptive statistics and independent samples proportions to identify significant differences in AI literacy levels across the three countries. The findings reveal substantial disparities in AI literacy. Croatian students demonstrated foundational and practical knowledge of AI, reflecting well-developed educational frameworks. Indian students excelled in interdisciplinary and applied aspects of AI, though their understanding of theoretical concepts revealed notable gaps. Slovenian students showed the greatest need for improvement, particularly in foundational knowledge, practical applications, and ethical considerations. The results also highlight universal challenges, such as gaps in understanding AI’s ethical and legal implications, underscoring the need for all educational systems to incorporate discussions on accountability and societal impacts into their curricula. These findings offer actionable recommendations for educational policy and curriculum design to enhance AI literacy globally and prepare students for the challenges and opportunities of an AI-driven world. This study contributes to the literature by enhancing the understanding of how AI literacy manifests across diverse educational systems, providing comparative insights to support globally relevant curriculum development and digital skills advancement.
Keywords: AI literacy, students, higher education, artificial intelligence
Published in DKUM: 05.11.2025; Views: 0; Downloads: 0
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6. Systematic risk management practices and improved risk culture in Slovenian non-financial corporations : a SEM ApproachTimotej Jagrič, Maja Rožman, Vita Jagrič, 2025, original scientific article Abstract: There is limited empirical evidence on how the risk management process is implemented in non-financial corporations, resulting in an incomplete understanding—particularly in post-transitional economies such as Slovenia. Using Structural Equation Modeling on data collected from Slovenian non-financial corporations in 2023, we find that a well-structured risk management process positively influences the adoption of advanced risk management methods and practices. This, in turn, significantly reduces challenges associated with risk management, enhances its overall effectiveness, and directly contributes to corporate value creation. These findings highlight the strategic importance of risk management as an essential component of organizational governance.
Keywords: risk management, risk culture, risk metrics, corporate value, structural equation modeling (SEM) approach, Slovenia
Published in DKUM: 03.09.2025; Views: 0; Downloads: 10
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7. Measuring students’ use of digital technology to support their studiesVesna Čančer, Polona Tominc, Maja Rožman, 2025, original scientific article Abstract: To provide a more holistic understanding of how digital tools shape the educational environment, this paper includes a comprehensive analysis that explores several dimensions of technology use in higher education: use of artificial intelligence in education, online collaboration, use of an E-Board for learning, and excessive use of technology. With the aim of measuring students’ use of digital technology to support their studies, this research meets the goals of developing the measurement process, building a multi-criteria model, and applying it to a real-life example of determining the degree of students’ use of digital technology in relation to the demonstrated quality of academic performance. The analysis is based on a survey conducted among students at the University of Maribor’s Faculty of Economics and Business. Using factor analysis and multi-criteria evaluation, the findings reveal that students who demonstrate very-high-quality achievements also report the highest level of technology use to support their studies. They are followed by students with outstanding achievements, who excel in using an E-Board for learning and in demonstrating responsibility regarding excessive technology use. Students who achieve acceptable-quality results with certain defects stand out in online collaboration and the use of AI in the study process. The lowest level of technology use was reported by students demonstrating moderate-quality achievements. Theoretically, this research contributes to a better understanding of the multidimensional use of digital technology in higher education, while, practically, it provides useful guidelines for optimizing digital learning tools and enhancing the overall quality of the academic process
Keywords: digital technology in education, artificial intelligence in education, students, academic performance, multi-criteria analysis
Published in DKUM: 20.08.2025; Views: 0; Downloads: 10
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8. Razvojni potenciali podjetja : študija primera malega podjetja krožnega gospodarstvaTjaša Štrukelj, Maja Rožman, 2025, independent scientific component part or a chapter in a monograph Keywords: razvojni potenciali podjetja, politika podjetja, strateški management, izvedbeni management, uspešnost podjetja, malo podjetje, krožno gospodarstvo
Published in DKUM: 24.07.2025; Views: 0; Downloads: 12
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10. Comparative analysis of nonlinear models developed using machine learning algorithmsMaja Rožman, Alen Kišić, Dijana Oreški, 2024, original scientific article Abstract: Machine learning algorithms are increasingly used in a vast spectrum of domains where statistical approaches were previously used. Algorithms such as artificial neural networks, classification, regression trees, or support vector machines provide various advantages over traditional linear regression or discriminant analysis. Advantages such as flexibility, scalability, and improved accuracy in dealing with diverse data types, nonlinear problems, and dimensionality reduction, compared to traditional statistical methods are empirically demonstrated in many previous research papers. In this paper, two machine learning algorithms are compared with one statistical method on highly nonlinear data. Results indicate a high level of effectiveness for machine learning algorithms when dealing with nonlinearity.
Keywords: machine learning, decision tree algorithm, artificial neural network, predictive models, data characteristics, nonlinear data, artificial intelligence
Published in DKUM: 02.07.2025; Views: 0; Downloads: 8
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