Search the digital library catalog

Abstract: Introduction: Risk management is essential for quality assurance in modern healthcare organizations. Risk matrices are widely used to evaluate risks in healthcare settings; however, this approach has noteworthy weaknesses and limitations. This paper introduces a novel risk evaluation model that utilizes multicriteria decision-making and fuzzy logic, to enhance the transparency and quality of the risk evaluation process in healthcare. Methods: The Multicriteria Evaluation Model was developed using the Decision Expert method and expert knowledge integration. Fuzzy logic was integrated within the model, using partial degrees of membership and probabilistic analysis, to address uncertainties inherent to healthcare risk evaluation. The evaluation model was tested with healthcare professionals active in the field of risk management in clinical practice and compared with the risk matrix. Results: The designed evaluation model utilizes multicriteria decision-making while encompassing the risk matrix framework to boost user understanding and enable meaningful comparison of results. Compared with the risk matrix, the model provided similar or marginally higher risk-level evaluations. The use of degrees of membership enables evaluators to articulate a wide range of plausible risk consequences, which are often overlooked or ambiguously addressed in the traditional risk matrix approach. Discussion and Conclusions: The evaluation model demonstrates increased transparency of the decision-making process and facilitates in-depth analysis of the evaluation results. The utilization of degrees of membership revealed distinct strategies for handling uncertainty among participants, highlighting the weaknesses of using single value evaluation approach for the presented and similar decision problems. The presented approach is not limited to healthcare-related risk evaluation, but has the capacity to improve risk evaluation practices in diverse settings
Keywords: multi-criteria decision-making, risk management, fuzzy logic, decision support, patient care, DEX
Published in DKUM: 01.12.2025; Views: 0; Downloads: 0
Full text (4,13 MB)
This document has many files! More...

Abstract: Background and purpose: This study aimed to fill a gap in the literature by identifying how employee burnout shapes decision-making styles in the post-COVID-19 business environment. The main goal was to examine the impact of three dimensions of burnout—exhaustion, cynicism, and professional efficacy—on four conflict-related decision-making styles: vigilance, buck-passing, procrastination, and hypervigilance. Design/Methodology/Approach: A total of 567 employees from various companies in Croatia participated in the online survey conducted in March 2023. Multiple regression analysis examined the impact of exhaustion, cynicism, and professional efficacy on decision-making styles under conflict. Results: The results of the multiple regression analysis revealed that professional efficacy leads to a vigilant decision-making style, while simultaneously diminishing procrastination, buck-passing, and hypervigilance. Cynicism, in contrast, was a positive predictor of procrastination, buck-passing, and hypervigilant decision-making. Finally, exhaustion was found to have a positive impact on hypervigilance. Conclusion: The study is significant because it contributes to the body of knowledge on the impact of burnout dimensions on professional decision-making styles in organisational settings, and it also offers practical implications of considerable importance.
Keywords: employee burnout, decision-making style, work-related stress, Croatian companies, post-COVID-19 period
Published in DKUM: 14.11.2025; Views: 0; Downloads: 4
Full text (797,73 KB)
This document has many files! More...

Abstract: Background and purpose: This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies. Methodology: Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design. Results: Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented. Conclusion: This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results. Acknowledgement: The author Hyrmet Mydyti’s PhD thesis has been extended in this paper.
Keywords: digital transformation, data mining, decision tree algorithm, decision-making, home appliances after-sales services
Published in DKUM: 08.10.2025; Views: 0; Downloads: 2
Full text (1,95 MB)
This document has many files! More...

Abstract: Seasonal influenza is a highly contagious virus posing significant health risks, especially tochildren, pregnant women, and older adults. Despite its crucial role in prevention andreducing severity, vaccination rates among medical and health students, who frequentlyinteract with vulnerable groups, remain low. This study investigates attributes of influenzavaccines that predict vaccination decisions within this population, while also examininghow these preferences vary based on the health literacy levels of the respondents.Between May and June 2024, 239 Slovenian medical and health students completed anonline discrete choice experiment presenting vaccines varying in efficacy, side effects, andadministration site. Preferences were analyzed to identify key attributes. Results showedthat higher efficacy and fewer side effects drove vaccine choice across all disciplines.Medical students favored faculty-based vaccination, whereas nursing and other healthdiscipline students preferred GP offices and NIPH units. Health literacy did not alter theimportance of efficacy and safety, but those with sufficient literacy showed a strongerpreference for GP settings over faculty sites. Campaigns should emphasize vaccine efficacyand the low risk of severe side effects, reassuring that mild reactions are transient.Facilitating GP-based vaccination for nursing and other health discipline students mayfurther improve uptake.
Keywords: seasonal influenza, vaccination, decision-making, health behavior, students, discrete choice experiment
Published in DKUM: 25.08.2025; Views: 0; Downloads: 7
Full text (2,18 MB)
This document has many files! More...

Abstract: The integration of artificial intelligence (AI) within the decision-making processes of small- and medium-sized enterprises (SMEs) presents both significant opportunities and substan- tial ethical challenges. The aim of this paper is to provide a theoretical model depicting the interdependence of organisational decision-making levels and decision-making styles, with an emphasis on exploring the role of AI in organisations’ decision making, based on selected process dimension of the MER model of integral governance and management, particularly in relation to routine, analytical, and intuitive decision-making capabilities. The research methodology employs a comprehensive qualitative analysis of the scientific literature published between 2010 and 2024, focusing on AI implementation in SMEs, ethi- cal decision making in integral management, and regulatory frameworks governing AI use in business contexts. The findings reveal that AI technologies influence decision making across business policy, strategic, tactical, and operative management levels, with distinct implications for intuitive, analytical, and routine decision-making approaches. The analysis demonstrates that while AI can enhance data processing capabilities and reduce human biases, it presents significant challenges for normative–ethical decision making, requiring human judgment and stakeholder consideration. We conclude that effective AI integration in SMEs requires a balanced approach where AI primarily serves as a tool for data collection and analysis rather than as an autonomous decision maker. These insights contribute to the discourse on responsible AI implementation in SMEs and provide practical guidance for leaders navigating the complex interplay between (non)technological capabilities, ethical considerations, and regulatory requirements in the evolving business landscape.
Keywords: MER model of integral governance and management, strategic management, management process, intuitive decision making, responsible leadership, small- and medium-sized enterprises (SMEs), artificial intelligence (AI), AI regulatory frameworks, analytical decision making, routine decision making
Published in DKUM: 15.07.2025; Views: 0; Downloads: 11
Full text (774,29 KB)
This document has many files! More...