1. Use of chatbots in human resource management for more efficient knowledge sharing – systematic literature reviewNejc Bernik, Polona Šprajc, 2025, review article Abstract: Purpose: This study examines how chatbots, as part of generative artificial intelligence (GenAI), can assist human resource (HR) professionals in supporting more effective knowledge management (KM), especially knowledge sharing (KS). The research aims to understand the strategic roles of chatbots in Human Resource Management (HRM). It offers propositions for their effective deployment to support KS and enhance their utilisation within organisations. Methodology: A systematic literature review (SLR) was carried out using the databases Web of Science (WoS) and Scopus. After applying inclusion and exclusion criteria, 16 relevant articles were selected for detailed analysis. Results: The findings show that chatbots can significantly enhance KS by automating HRM processes. They enable personalised training, offer continuous support, and promote employee performance, engagement, and innovation. Furthermore, chatbots assist HR professionals in focusing on strategic tasks by lowering administrative workload. Several challenges are also identified, including ethical concerns, privacy issues, data quality problems, reduced social interaction, and risks to creativity and critical thinking. Conclusion: Chatbots offer a transformative opportunity for HRM to enhance KS, organisational memory, and digital learning, thereby supporting competitive advantage in knowledge-intensive settings.
Keywords: Chatbots, generative artificial intelligence, human resource management, knowledge management, knowledge sharing
Published in DKUM: 14.11.2025; Views: 0; Downloads: 0
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2. The efficiency of manual editing of high-density surface electromyogram decomposition depends on the recorded muscle and contraction level but less on the operator’s experienceNina Murks, Jakob Škarabot, Matej Kramberger, Matjaž Divjak, Gašper Sedej, Tamara Valenčič, Christopher D. Connelly, Haydn Thomason, Aleš Holobar, 2025, original scientific article Abstract: We investigated the agreement and accuracy of manual editing of the high-density electromyogram (hdEMG) decomposition results by seven human operators with various experience levels. All operators edited the same automatically decomposed experimental hdEMG from the first dorsal interosseous (FDI), tibialis anterior (TA), vastus lateralis (VL), and biceps brachii (BB) muscles, and synthetic hdEMG from soleus (SO) and BB muscles at 10%, 30%, 50% and 70% of maximum voluntary contraction. On average, operators kept 13.7 ± 7.4 motor units (MUs) after editing and demonstrated relatively large disagreement in the calculated MU pulse trains (normalized root mean square difference) but relatively high agreement in the identified MU discharges. Inter-operator agreement positively correlated with the initial MU Pulse-to-Noise Ratio used as a quality measure of automatic MU identification, and negatively correlated with the muscle contraction level. Operators agreed more on the results of the simulated than experimental hdEMG. Among the experimental muscles tested, the greatest agreement was demonstrated for VL and the lowest for BB. We obtained similar results when comparing editing to the results of the most experienced operator and to ground truth in simulated cases: the greatest precision and sensitivity were demonstrated for VL, and the lowest for BB. The level of the operator’s experience had a significant impact on the editing of synthetic hdEMG and the detection of the first MU discharge, but not on the rate of agreement or editing time of experimental hdEMG.
Keywords: hdEMG, manual editing, decomposition results, human operators, motor unit, človeški operaterji
Published in DKUM: 14.10.2025; Views: 0; Downloads: 4
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3. Scoping out the common-sense perspective on meaningful work : theory, data and implications for human resource management and developmentFrancesco Tommasi, Riccardo Sartori, Andrea Ceschi, 2023, original scientific article Abstract: Background/Purpose: Meaningful work is a topic of relevant interest to management and organizational scholars. The study of meaningful work has been heavily characterized by theories from different disciplines, yet the common-sense perspective is often overlooked, i.e., a non-academic perspective of meaningful work. The paper presents a qualitative study on how non-academics define meaningful work. Methods: Adopting the lens of Aristotelian logic, the paper presents a methodological-theoretical approach to explore how non-academics define human resource management concepts. We asked 194 workers to propose ultimate definitions of the concept of meaningful work. The questions were submitted via a short survey collecting demographics. Results: The analysis of the collected definitions led to the proposition of an intensive definition of meaningful work according to which meaningful work is a positive experience associated with a sense of competence, the presence of positive relation with others, significance and purpose of work. Yet, there must be good reasons to experience work as meaningful as the environment may contain barriers to the presence of meaningfulness. Conclusion: Methodologically, the paper advances a novel approach to the study of human resource management and development concepts. Theoretically, the study proposes a novel perspective of meaningful work prioritizing concerns on the common-sense.
Keywords: meaningful work, common-sense, employee wellbeing, human resource management and development
Published in DKUM: 13.10.2025; Views: 0; Downloads: 2
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4. The application of the sensemaking perspective for the examination of employees’ behavioural responses to the HR chatbotVanda Harmat, 2023, original scientific article Abstract: Background and Purpose: The emergence of new technologies affects different business areas, including HR activities. Employee communication is an HR activity that can be automated with the application of chatbots. Besides the numerous advantages these artificial entities offer, the challenges generated by them also need to be considered. One such challenge is user acceptance, which plays a substantial role in the implementation. This study aims to explore employees’ behavioural responses to HR chatbots. Thus, we applied the sensemaking perspective, according to which new, unknown phenomena induce the need for interpretation in individuals that simultaneously shape individuals’ behaviour toward the phenomenon.
Methodology: For data collection, we conducted semi-structured interviews that were analysed with interpretive phenomenological analysis (IPA). The sample consisted of 6 HR professionals and ten general users. The study took place at the Hungarian organisations of a multinational company.
Results: The analysis revealed that subjects who attributed positive meanings to the HR chatbot typically engaged in actions that aligned with organisational expectations, i.e., using the digital assistant became part of their routine. Interestingly, the closer HR professionals are to technology in their work activities, the more positively they perceive it. However, having ambivalent feelings towards technology resulted in occasional use or avoidance, while experiencing negative feelings led to the rejection of use.
Conclusion: We explored the different meanings employees attributed to the HR bot and their actions directed towards it both in the initial and transitional technology adoption phase. The results contribute to understanding how an HR chatbot can be successfully implemented in an organisation.
Keywords: HR chatbot, HR bot, human-chatbot interaction, chatbot, sensemaking
Published in DKUM: 07.10.2025; Views: 0; Downloads: 2
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5. Agricultural and food product assessment : methodological choices in sustainability reporting using the LCA methodTinkara Ošlovnik, Matjaž Denac, 2025, original scientific article Abstract: Consumers are increasingly exposed to environmental claims on food products. These claims often lack scientific validation and there are different methodologies that can be used for grounding these claims, which can lead to misleading results. The European Union’s (EU) Environmental Footprint methodology excludes the aggregation of environmental impacts, including damage to human health. This fact reduces transparency and limits the consumers’ ability to make information-based sustainable choices. This study aims to address this issue by calculating aggregated impacts on human health via life cycle assessment (LCA) in the agriculture and food-production sectors. In the study the IMPACT World+ method was used, including trustworthy databases and proper functional unit definition. The assessment encompassed three types of vegetables, four types of fruit, and four types of ready meals. The study also attempts to assess the impact of different farming systems (organic and conventional) on human health. Two standardised functional units, i.e., the unit based on product weight and product energy value were considered for each group of products. Our findings showed significant differences in results when different functional units were used. Additionally, no conclusion could be drawn regarding which farming system is more sustainable. Therefore, it is essential that the regulator clearly defines the criteria for selecting the appropriate functional unit in LCA within the agriculture and food-production sectors. In the absence of these criteria, results should be presented for all alternatives. Although not required by EU regulation, the authors suggest that companies should nevertheless disclose information regarding the environmental impact of agriculture and food production on human health, as this is important for consumers.
Keywords: sustainable food production, environmental concerns, life cycle assessment, human health, responsible consumption
Published in DKUM: 24.09.2025; Views: 0; Downloads: 9
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6. Method review for assessing damage to human health in LCA : implementation in the production of selected food productsMatjaž Denac, Tinkara Ošlovnik, 2025, original scientific article Abstract: Agriculture and food production can cause a variety of adverse environmental impacts. Life Cycle Assessment (LCA) is the only standardised method so far that provides a comprehensive assessment of the environmental burden of products. LCA results can be expressed as environmental impacts (midpoint level) or environmental damage (endpoint level). Communication on environmental impact is mostly carried out as a part of the Carbon Footprint (CF) or Environmental Product Declaration (EPD), while reporting on environmental damage is rarely used. Due to the growing interest of consumers in ‘healthy’ food, it is important that food producers address the environmental damage. Therefore, an overview of existing Life Cycle Impact Assessment (LCIA) methods that provide insight into endpoint indicators describing damage to human health has been performed and presented in this paper. The application of the selected LCIA methods is demonstrated for the case of food products, highlighting the fact that the results obtained by different LCIA methods are not directly comparable due to a variety of methodological limitations, such as the existence of different environmental indicators and their structure. Finally, the usage of the LCA method for designing food products is presented, with the aim of enabling consumers to choose diets with lower environment and human health impacts.
Keywords: sustainable food consumption, eco-conscious choices, life cycle assessment, environmental impact, human health
Published in DKUM: 23.09.2025; Views: 0; Downloads: 4
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7. Usage of Artificial Intelligence Tools in Human Resources ManagementVanna Jensen, 2025, undergraduate thesis Abstract: Artificial intelligence is one of the largest technological trends in the 2020s, and many processes have been transformed by it, just like all technological innovations have. It is no longer based exclusively on corporate level, but since its free public launch, AI has become much more accessible that people are even using it for personal needs. Human Resources Management is not immune to the changes and HR leaders are utilising it as well to improve efficiency, data-driven leadership and decision-making. However, there is one concern from general public: how smart and ethical is it to have AI deployed in a department that is about managing humans and its relationships, where we need to have a human to understand the context and emotions?
The theoretical part of this thesis explores technological evolution of Human Resources Management, brief fundaments for the AI technology, HRM areas where it can be applied, along with its benefits and detriments, which serves as the basis to gain knowledge and insights through literature review. Furthermore, the empirical part explores the actual usage by interviewing HR leaders that have deployed AI into their technical stack. With rich qualitative data from different perspectives, we are able to see how AI is being used, and HR professionals’ respective opinions, who all agree that AI is a powerful tool that transforms the HRM processes by dealing with repetitive and mostly administrative tasks, leaving plenty of space to focus on more creative and strategic tasks. One of the largest concerns regarding AI is in people; how they train the model and to what extent is AI being relied on.
Keywords: human resources management, artificial intelligence, machine learning
Published in DKUM: 10.09.2025; Views: 0; Downloads: 12
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8. Legal aspect of organ donation after death across Europe in human rights contextIlona Kiel-Puślecka, Mateusz Puślecki, Dąbrowski Marek, Bartłomiej Janyga, Bartłomiej Perek, Agnieszka Zawiejska, 2022 Keywords: medicine law, organ donation, human rights, European Union
Published in DKUM: 04.09.2025; Views: 0; Downloads: 5
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