388 Organizacija, V olume 58 Issue 4, November 2025 Research Papers 1 Received: 20th August 2025; Accepted: 20th October 2025 Use of Chatbots in Human Resource Management for More Efficient Knowledge Sharing – Systematic Literature Review Nejc BERNIK, Polona ŠPRAJC University of Maribor, Faculty of Organizational Sciences, Kranj, Slovenia, nejc.bernik1@um.si, polona.sprajc@um.si 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 shar- ing (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 dig- ital learning, thereby supporting competitive advantage in knowledge-intensive settings. Keywords: Chatbots, Generative artificial intelligence (GenAI), Human resource management (HRM), Knowledge management (KM), Knowledge sharing (KS) DOI: 10.2478/orga-2025-0024 1 Introduction In recent years, chatbots have become integral to both professional and personal life, with tools such as ChatGPT, Siri, and Google Gemini. Powered by generative artificial intelligence (GenAI), they enable natural language interac- tion, simulate human communication, and provide relevant and desired information to users (Venusamy et al., 2020). Their organisational adoption in recent years demonstrates their adaptability and value by offering intuitive commu- nication and automating routine tasks to help employees resolve queries and problems efficiently. Chatbots also have potential in knowledge manage- ment (KM), particularly in knowledge sharing (KS), which remains a persistent challenge in many organisations. This occurs when older employees retire without distributing expertise to younger employees, mainly due to the absence of systematic KM practices such as mentoring protocols, succession planning, and structured KS. These gaps risk the loss of critical organisational know-how, experience, 389 Organizacija, V olume 58 Issue 4, November 2025 Research Papers and best practices. With the integration of chatbots into human resource management (HRM) processes, they can enhance KS systems and help preserve and share organisa- tional knowledge. HRM plays a central role in this process, and combining chatbot capabilities with HRM aligns with the strategic needs of knowledge-intensive organisations (Mogea, 2023), where intellectual capital—employee ex- pertise, organisational know-how, and innovation capaci- ty—is a vital competitive asset. A keyword-based literature search showed that rela- tively little research has examined the intersection of chat- bots, KS, and HRM. Combining KM and HRM theories with socio-technical perspectives provides a solid concep- tual base for studying the role of chatbots in organisational settings. It is therefore important to investigate how chat- bots can be effectively integrated into HRM practices to support KS, while also addressing ethical, organisational, and security-related challenges. This perspective guid- ed the design of our systematic literature review (SLR), which is presented in the following section. 2 Theoretical background The rapid emergence of GenAI technologies has re- shaped both the theoretical and practical approaches to KM and HRM. Chatbots, as conversational GenAI sys- tems, bring together these two perspectives by functioning as intelligent intermediaries between knowledge repos- itories and employees, thereby enhancing accessibility, reducing communication barriers, and supporting a cul- ture of continuous learning (Chowdhury et al., 2023). At the same time, their introduction raises new theoretical questions concerning trust, employee acceptance, and the socio-technical integration of AI into organisational pro- cesses (Meyer et al., 2023). To address these dynamics, it is useful to frame the discussion of chatbots within ex- isting KM and HRM theories, while also recognising the transformative impact of GenAI on organisational practic- es and the broader transition toward Society 5.0 (Roblek et al., 2021). Following Trist & Bamforth (1951) these developments align with socio-technical systems theory, which emphasises the joint optimisation of social and tech- nical structures when new technologies are introduced into work environments. 2.1 Knowledge sharing KM is a strategic and systematic process for creating, sharing, storing, and applying knowledge within organi- sations (Teece, 1998; Vrontis et al., 2019). According to Summerscales (2024) the goal of KM is to strengthen or- ganisational effectiveness, foster innovation, and improve competitiveness by making sure that relevant knowledge is accessible to the appropriate individuals when needed. Organisational knowledge is commonly divided into ex- plicit and implicit forms—explicit being formalised and documented, and implicit (tacit) being personal and ex- perience-based (Brauner & Becker, 2006; Krišelj et al., 2025). A central component of KM is KS, defined as the exchange of explicit and implicit knowledge, skills and expertise among employees or teams. While often used interchangeably with “knowledge transfer,” KS has been described as more specific to the KM context and particu- larly linked to the use of information systems (Chou & Tang, 2014; Paulin & Suneson, 2012). Following (Argote, 2024), knowledge transfer is broader in scope and may oc- cur across different domains and settings. For this reason, the present study adopts the KS perspective, recognising its importance in fostering collaboration, problem-solving, and continuous learning in organisations. The encouragement of KS depends largely on HR managers, who implement policies, procedures, and sys- tems to facilitate knowledge exchange across teams and departments.(Matošková & Směšná, 2018; Sammarra et al., 2017). Strategic HRM practices—such as mentoring, digital repositories, and peer collaboration—are typical of knowledge-intensive organisations, enabling informed decision-making, higher efficiency, and more substantial commitment (Battistelli et al., 2019; Swart & Kinnie, 2003). With the rise of GenAI, (Chowdhury et al., 2023) wrote that KS has become even more important, as GenAI tools can organise, contextualise, and update knowledge assets, making them more accessible. 2.2 Chatbots in HRM As part of GenAI tools, chatbots are increasingly used in organisations to support effective KM (Al-Sharafi et al., 2023). Powered by large language models (LLMs), they can understand and generate human-like text (Meyer et al., 2023). This enables them to answer employee questions, contextualise information, summarise documents, recom- mend solutions, and adapt to specific communication styles and knowledge needs (Ashfaq et al., 2020; Majumder & Mondal, 2021). Acting as intelligent interfaces, chatbots capture, distribute, and reuse organisational knowledge across departments (Abdelwhab Ali et al., 2019) and con- tribute to organisational memory by preserving procedur- al and experiential knowledge in structured digital form (Madanchian, 2024). They improve HRM processes, such as selection, on- boarding, training, employee education, etc (Frischen & Fiebig, 2025; Sharma, 2021). Chatbots can also provide employees with real-time, consistent responses to their queries, functioning as digital coaches (Alhusban et al., 2025) and contributing to talent development. By analys- ing employee interactions, chatbots support skills mapping 390 Organizacija, V olume 58 Issue 4, November 2025 Research Papers and competency-based workforce planning, acting as stra- tegic enablers within knowledge-intensive environments where intellectual capital drives innovation and adaptabil- ity (Mogea, 2023) With the strategic integration of chatbots in HRM, as Sharma (2021) wrote, chatbots can deliver a rapid return on investment and contribute to preserving organisation- al knowledge that might otherwise be lost (Deepa et al., 2024). By safeguarding knowledge and enabling continu- ous development, chatbots can also support the transition toward a digitalised Society 5.0 (Roblek et al., 2021). 3 Methodology Our motivation for conducting this study stems from recognising the significant potential of integrating chat- bots into HRM processes. Our experience working in large mid-European organisations showed that chatbots were rarely used in HRM, particularly in KM and KS. A pre- liminary review of the relevant literature confirmed this observation, as relatively few studies have examined the intersection of chatbots, KS, and HRM. This gap motivat- ed us to conduct an extensive SLR. The research problem we address is the limited adop- tion of chatbots in HRM to support KS. To explore this issue, we formulated three research questions (RQ): RQ1: How do chatbots improve KS in organisations? RQ2: How can chatbots enable HRM to support more effective KS? RQ3: What should organisations be aware of when im- plementing chatbots for KS? We used two of the largest scientific databases for SLR: Web of Science (WoS) and Scopus (Zhu & Liu, 2020). To identify the most relevant studies on using chatbots in HRM for KS, we focused on four core terms: chatbots, HRM, KS, and AI. These were further expanded with synonyms to capture a broader range of publications. Figure 1: PRISMA flow diagram 391 Organizacija, V olume 58 Issue 4, November 2025 Research Papers The terms were combined using Boolean operators (AND, OR). The whole search string was as follows: (“chatbot” OR “LLM” OR “virtual assistant”) AND (“AI” OR “artificial intelligence”) AND (“knowledge transfer” OR “knowledge sharing” OR “knowledge distri- bution”) AND (“” OR “human resource”). We applied the following inclusion criteria: only peer-reviewed journal articles published between 2020 and 2025 and written in English. Using this procedure, we identified a total of 16 relevant articles. Figure 1 presents the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRIS- MA) flow diagram with the complete search strategy and screening process. The research was conducted on 12 August 2025, and based on the initial search across both databases, we identi- fied 1,064 articles. After removing duplicates, 461 articles were screened by reviewing their titles and abstracts. From this step, 363 articles were excluded due to non-compliant or irrelevant titles and abstracts. This left 98 articles, which were thoroughly reviewed, including their results, findings, and discussions. At this stage, we excluded papers that did not address at least two of our research questions, presented findings that would be difficult to implement in HR practices (as not all studies were HR-oriented), and were not published in Q1 or Q2 ranking journals. In the final step, 16 articles were selected, fully ob- tained, and analysed in detail. These studies formed the primary evidence base for our research and provide the foundation for addressing the three research questions. 4 Results With the program VOSviewer, we conducted litera- ture visualisation of selected 16 articles (Figure 2). We got four different colours representing keyword clusters that frequently co-occur in the analysed literature. The blue cluster highlights KS linked to HRM, which relates to in- formation flow and impact. The green cluster centres on AI connected with ChatGPT, KM and opportunities. The red cluster relates to management and performance, empha- sising the role of AI-based virtual assistants in supporting knowledge exchange in organisations. The yellow cluster reflects technology and challenges, pointing to barriers in adopting AI tools. The map illustrates that research con- nects chatbots, HRM and KS through opportunities, per- formance, and technological challenges. Figure 2: VOSviewer keywords visualisation 392 Organizacija, V olume 58 Issue 4, November 2025 Research Papers These clusters illustrate how the field is still in an emerging stage, where research streams are fragmented but interconnected. The concentration of studies around KS and HRM (blue) suggests that scholars recognise the importance of knowledge flows but have not yet fully ex- amined chatbot-specific contributions. The presence of AI and ChatGPT in the green cluster reflects the dominance of technological discussions, whereas the red and yellow clusters highlight the managerial and ethical concerns that are increasingly shaping debates on AI adoption. Togeth- er, these clusters indicate that chatbot research in HRM is developing along both technological and human-centred dimensions, underscoring the relevance of a socio-techni- cal perspective. All 16 selected articles were published from 2021 to 2025. Most of them were published in 2025. The most cit- ed article was from Duong et al. (2023), which received 84 citations by the time of review, followed by (Soleimani et al., 2021) with 48 citations and Alavi et al. (2024) with 43 citations. Selected articles were chosen because they were relat- ed to at least two of our research questions. Specifically, they explored combinations such as HRM and KS, chat- bots and KS, HRM and chatbots, or all three domains— chatbots, KS, and HRM. The criteria for selecting these articles are presented in the middle column of Table 1, ex- plaining how each article addresses our research questions. The last column indicates the journal ranking (Q1 or Q2) in which each article was published. Table 1: Selected articles and their connection to our RQs Authors Related to our RQ Q (Alavi et al., 2024) RQ1: GenAI can significantly transform KM by automating and enhancing KS, as it can convert implicit knowledge into explicit knowledge. RQ3: The transformation of KM and KS raises ethical concerns and requires building trust in GenAI-generated knowledge among employees. Q1 (Arbulú Ball- esteros et al., 2024) RQ1: Effort expectancy and performance expectancy significantly influence students’ behavioural intention to use ChatGPT, which strongly predicts its actual use. KS correlates with actual use and the behavioural intention to use ChatGPT. RQ3: The use of ChatGPT poses a threat to students’ critical and creative skills and thinking. Q1 (Murugan et al., 2024) RQ2: GenAI is highly effective for facilitating KS when onboarding new employees. It can tailor content to be relevant and engaging through interactive tutorials and simulations, easing integration into the organisation, particularly for roles requiring hands-on learning and experience. Employees may also feel less inhibited when asking GenAI questions than when approaching supervisors or co- workers. RQ3: The organisation may lose control over what knowledge is transferred to employees. Moreover, excessive reliance on GenAI and chatbots can reduce employee creativity. Q1 (Nguyen & Fry, 2022) RQ1: Online AI knowledge sharing capabilities can foster trust, build employee self-confidence using digital platforms, and cultivate a KS culture among younger employees. This can enhance their knowledge self-efficacy and lead to improved job performance. RQ3: A lack of technical support for employees who encounter problems or have questions can hinder effective implementation. Q1 (Rezaei et al., 2024) RQ2: AI within KS can help decision-makers identify best work practices and improve strategies. RQ3: Organisations should adopt strong privacy measures, address AI-related ethical issues, and use fairness-aware algorithms supported by legal safeguards to protect rights, ensure transparency, and safeguard data ownership (including intellectual property). Failure to do so may provoke employees’ mistrust, uncertainty, and anticipatory anxiety. Q1 (Malik et al., 2024) RQ1: AI can automatically collect, organise, and update HRM knowledge from diverse sources (e.g., performance data, recruitment records, training outcomes). RQ2: AI in HRM improves decision-mak- ing, personalises KS for each employee, and motivates them to participate in AI-mediated KS (AI- MKS). Q1 (He et al., 2025) RQ1: Intelligent agents enable more informed, data-driven decisions, fostering stronger managerial innovation and KS. RQ2: AI enhances managerial creativity by leveraging organisational knowledge, improving information distribution, and applying advanced analytics. RQ3: High costs of AI technol- ogy, expertise, and infrastructure, integration difficulties, and competition for skilled professionals often hinder implementation and cause delays. Q1 393 Organizacija, V olume 58 Issue 4, November 2025 Research Papers (Terblanche & Tau, 2025) RQ1: Chatbots as coaches offer guidance, motivation, and personalised feedback to boost students’ clarity, commitment, and progress, enhancing self-regulation, accountability, and time management for improved goal achievement. RQ3: Due to high costs and the inability to predict final expenses, organisations are often unwilling to adopt chatbots. Q2 (Dutta & Mishra, 2024) RQ1: Virtual assistants (VAs) can enhance employee engagement. RQ2: VAs are emerging as a value-adding HRM practice that raises expectations while fostering a greater sense of meaningful- ness and psychological safety. They promote interactive communication with employees, positively influence engagement, and encourage employees to share their feelings and concerns openly. Q1 (Alhusban et al., 2025) RQ1: Chatbots offer 24/7 support. RQ2: ChatGPT can mentor new employees by providing them with training, guidance, and resources to help them integrate effectively into the organisation. RQ3: Adopting ChatGPT may lead to job losses, particularly in positions involving repetitive or easily au- tomatable tasks. Q1 (Duong et al., 2023) RQ1: ChatGPT is easy to use and helps students complete learning tasks quickly and accurately—such as providing feedback or correcting grammar—thereby enhancing learning outcomes while reducing teachers’ workload. RQ3: Concerns regarding data privacy. Q1 (Hui et al., 2024) RQ1: AI virtual assistants serve as catalysts for team innovation by bridging explicit and tacit knowledge through decision-support capabilities and facilitating social network connections. RQ2: AI virtual assistants enhance HRM by automating repetitive tasks, providing data-driven insights, improving recruitment and performance decisions, and delivering personalised training through adaptive learning. Q2 (Lambiase et al., 2025) RQ1: Chatbots with speech recognition can become more desirable among employees. RQ3: Privacy violations and a lack of trust in chatbots. Q1 (Soleimani et al., 2021) RQ2: AI in recruitment holds significant potential to improve recruitment and selection processes while reducing unconscious bias in hiring decisions. RQ3: Organisations should be careful not to rely entirely on AI, as excluding human judgment can result in biased, unethical, or contextually inappro- priate decisions. Q2 (Sumbal et al., 2024) RQ1: The utilisation of ChatGPT by employees enhances work efficiency and saves time, which can be redirected toward creative and value-adding activities. RQ2: ChatGPT can be adapted to retrieve relevant information required by employees for informed decision-making while supporting tacit KM through collaborative KS. RQ3: It may reduce employees’ critical thinking skills and weaken collabora- tion among team members. Q1 (Olan et al., 2024) RQ1: Proper training and involvement in AI adoption can boost acceptance, enhance skills, and maintain motivation. AI-supported KS environments improve innovation and task execution. RQ2: Candidate screening and administrative tasks free HRM professionals for strategic work and employ- ee engagement, while AI-driven analytics provide data-based insights to improve decisions in talent acquisition, retention, and performance management. Q1 Table 1: Selected articles and their connection to our RQs (continues) Most observed articles showed strong alignment be- tween research aims, data collection, and analysis, indi- cating satisfactory methodological rigour. However, few reported bias diagnostics or ethical approval, and general- isability was often limited to sector-specific samples. By establishing a clear methodological framework, our study ensures transparency and replicability, which are crucial for strengthening the reliability of systematic liter- ature reviews (Zhu & Liu, 2020). The rigorous screening process and reliance on peer-reviewed Q1 and Q2 jour- nals provide a solid empirical foundation for addressing the proposed research questions. At the same time, the ap- plied procedure reflects broader methodological standards in management and information systems research, where structured reviews are increasingly used to map emerging fields such as AI-driven HRM (Deepa et al., 2024). Build- ing on these methodological choices, the following section presents the results of the review, highlighting the main thematic clusters and research trends at the intersection of chatbots, HRM, and KS. 394 Organizacija, V olume 58 Issue 4, November 2025 Research Papers 5 Discussion To present the research findings as effectively as pos- sible to HR managers and organisations, and to encourage them to consider the introduction of chatbots into HRM processes as a potential source of competitive advantage, we summarised the insights from all 16 selected articles in the form of answers to the three research questions. Our findings complement existing research on the role of chatbots in HRM and place them within a broader theo- retical, organisational, and societal framework. 5.1 RQ1: How do chatbots improve KS in organisations? Chatbots, powered by GenAI, can significantly en- hance KS by fostering employee innovation and creativity (He et al., 2025). Their ease of use enables employees to complete learning tasks efficiently—such as receiving in- stant feedback—thus improving job performance (Duong et al., 2023). Hui et al. (2024) conducted a survey where they discovered that chatbots used in connection with or- ganisational leadership, KS, absorptive capacity and team innovation play a significant role in strengthening employ- ees’ decision-making. With that in mind, they can serve as coaches for effective KS (Terblanche & Tau, 2025). Within KS, chatbots also facilitate the organisation, retrieval, and utilisation of diverse knowledge resources, helping organisations leverage knowledge for competi- tive advantage (Nguyen & Fry, 2022). This advantage is achieved by influencing employees’ behavioural inten- tions, motivating them to perform better, and encouraging greater effort in task execution (Arbulú Ballesteros et al., 2024). Alavi et al. (2024) described GenAI as the core technology behind chatbots, further supporting KS by pro- viding employees with immediate answers, reducing hesi- tation in asking questions, and increasing the overall flow of shared knowledge. Trust in chatbots is also critical because when em- ployees perceive them as reliable, AI-mediated KS acts as a social exchange mechanism that enhances job perfor- mance, increases satisfaction, and reduces turnover inten- tions (Malik et al., 2024; Olan et al., 2024). Personalised chatbot-driven training programmes can strengthen KS by identifying employee needs and delivering tailored, up-to- date content (Alhusban et al., 2025). Sumbal et al. (2024) conducted a case study where they found out that chatbot programmes boost efficiency, save time for creative and value-adding activities and ultimately drive innovation within organisations. Authors’ findings confirm that chatbots can significant - ly improve and affect KS practices in HRM, which opens the way for connecting them with established models of technology adoption. The Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) emphasise that perceived useful- ness, ease of use, social influence, and facilitating condi- tions are key factors influencing employees’ readiness to adopt digital tools (Hashim et al., 2022; Leesakul et al., 2022). In the context of chatbot adoption, trust emerges as a particularly critical dimension, as KS relies heavily on employees’ confidence in the quality, reliability, and fairness of GenAI-generated content (Malik et al., 2024; Nguyen & Fry, 2022). Without such trust, employees may resist adoption or limit their engagement with chatbot-sup- ported KS processes. In conclusion, the findings clearly address RQ1 by demonstrating that chatbots, as GenAI-driven tools, im- prove KS through enhanced accessibility, automation, trust, and behavioural motivation, leading to higher inno- vation, engagement, and organisational performance. 5.2 RQ2: How can chatbots enable HRM to support more effective KS? Chatbots can enable HRM to support more effective KS by helping HR professionals identify best practices and refine strategies (Rezaei et al., 2024). They improve effi - ciency, accuracy, and accessibility by taking over routine and administrative tasks, allowing employees to concen- trate on more complex, value-adding activities (Malik et al., 2024; Nguyen & Fry, 2022). In this regard, He et al. (2025) argued that chatbots can relieve HR professionals from administrative routine tasks, enabling them to ad- dress strategic challenges, foster creativity, and enhance information distribution. Beyond administrative support, chatbots strengthen HRM through automation, data-driven insights, improved recruitment and performance decisions, and personalised training via adaptive digital learning (Hui et al., 2024). They can act as digital coaches across HRM processes. For instance, in recruitment, the GenAI recruitment sys- tems demonstrate how chatbots can support data labelling, job function analysis, and machine learning improve- ments, leading to faster recruitment, reduced bias, fairer decision-making, and timely candidate communication (Soleimani et al., 2021). Similarly, as digital coaches, chat- bots can provide employees with relevant information for informed decision-making (Sumbal et al., 2024). Their constant availability makes them also valuable for onboarding and coaching, offering rapid answers, per- sonalised guidance, and interactive communication that enhance employee engagement (Duong et al., 2023). This coaching role encourages employees to express ideas and concerns openly, reducing HRM workload and increasing job satisfaction (Alhusban et al., 2025; Dutta & Mishra, 2024; Olan et al., 2024). Organisations implementing ad- vanced chatbots for automating recruitment, screening, 395 Organizacija, V olume 58 Issue 4, November 2025 Research Papers and training tasks will lower operational costs and improve long-term HRM efficiency (Pejić Bach et al., 2025; Tur- sunbayeva & Renkema, 2023). Thus, the evidence directly answers RQ2 by confirm- ing that chatbots enable HRM to support more effective KS through automation, data-driven personalisation, and digital coaching, allowing HR professionals to focus on strategic and developmental tasks that strengthen their sat- isfaction and commitment. 5.3 RQ3: What should organisations be aware of when implementing chatbots for KS? Chatbots collect knowledge from various sources, which requires organisations to carefully assess the legiti- macy of the information to avoid losing control over what is shared with employees. They must carefully manage the type and scope of knowledge that GenAI distributes to protect privileged data and prevent undermining employ- ee curiosity and creativity (Alavi et al., 2024; Duong et al., 2023). Furthermore, the use of chatbots may reduce participation among employees and limit social interaction within the workplace. As noted by (Alhusban et al., 2025) the adoption of chatbots carries potential risks of workforce reduction, particularly in roles dominated by repetitive and autom- atable activities. Since not all employees are familiar with the latest digital and GenAI technologies, organisations should implement targeted technical support and tailored educational programmes (Nguyen & Fry, 2022). Accord- ing to Rezaei et al. (2024), such programmes should help develop basic skills for each employee privacy protections, address GenAI-related ethical issues, and promote the use of fairness-aware algorithms supported by legal safe- guards that protect individual rights, ensure transparency, and secure data ownership, including intellectual property. In addition, organisations should incorporate cybersecu- rity measures to safeguard sensitive organisational data, protect against unauthorised access, and ensure the secure integration of chatbots and connected GenAI technology within existing infrastructure (Bernik et al., 2022; Podbre- gar & Šprajc, 2018). Employees should also be prepared for the psychological and organisational impacts of GenAI adoption using continuous awareness-building, structured training, and open communication, which can help reduce mistrust, uncertainty, and anticipatory anxiety (Lambiase et al., 2025). At the same time, overreliance on chatbots can nega- tively affect employees’ critical thinking, weaken collabo- ration, and create risks related to intellectual property rights and information accuracy (Sumbal et al., 2024). Many or- ganisations also lack awareness of the potential benefits of chatbots or how to effectively integrate them into opera- tions, which may lead to hesitation in adoption (He et al., 2025). In some cases, costs may escalate unexpectedly, as organisations underestimate the financial implications of chatbot implementation (Terblanche & Tau, 2025). Final- ly, organisations must avoid relying exclusively on GenAI technologies in decision-making, since excluding human judgment can result in biased, unethical, or contextually inappropriate outcomes (Soleimani et al., 2021). Overall, these insights address RQ3 by highlighting that successful chatbot implementation for KS depends on ethical governance, employee trust, data security, and re- sponsible integration that balances technological efficien- cy with human judgment and creativity. 5.4 Practical guidelines for organisations Based on our analysis, several guidelines can be pro- posed for the effective integration of chatbots in HRM: • Gradual implementation – Start with routine tasks (e.g., answering FAQs, administrative support) and progressively expand toward more complex processes such as onboarding, competency devel- opment, and decision support. • Human–AI complementarity – Ensure chatbots complement rather than replace human interac- tion, particularly in processes that require empa- thy, complex judgment, and ethical decision-mak- ing. • Training and digital literacy – Provide targeted training programmes that address both technical use and broader awareness of ethics, data protec- tion, and cybersecurity. • Security and ethics – Establish robust policies for data protection, intellectual property, and trans- parent decision-making to prevent bias, misuse, and loss of trust. • Monitoring outcomes – Continuously assess the effects of chatbot adoption on employee satisfac- tion, engagement, and the quality of KS to enable timely adjustments. By following these guidelines, organisations can ap- proach chatbot implementation in a structured and respon- sible way that balances efficiency gains with ethical and human-centred considerations. The emphasis on gradual integration, complementarity with human interaction, and continuous monitoring highlights that the successful use of chatbots in HRM is not only a technological challenge but also a cultural and strategic one. This perspective un- derscores the need for leadership commitment, transparent communication, and employee involvement throughout the adoption process, ensuring that chatbots become genu- ine enablers of KS and organisational learning rather than isolated digital tools. By demonstrating that chatbots are not merely op- 396 Organizacija, V olume 58 Issue 4, November 2025 Research Papers tional digital tools, this study advances understanding of HRM and KM by integrating socio-technical and knowl- edge-based perspectives to explain how chatbots can re- shape organisational knowledge dynamics. It conceptual- ises chatbots as mediators that enhance human capabilities and transform KS into a continuous, technology-aug- mented process. Building on this integration, the study contributes to the scholarly discourse on the integration of chatbots within HRM by examining their transforma- tive potential for KS. The conclusions are derived from a PRISMA-based systematic literature review (SLR) of 16 Q1–Q2 articles indexed in Web of Science and Scopus. As a synthesis of secondary data, these findings should be interpreted with caution and substantiated through future multi-site empirical investigations. In practical terms, the study provides actionable guid- ance for HR professionals. Chatbots should be implement- ed gradually, designed to complement rather than replace human interaction, and governed by robust data and ethi- cal frameworks. Continuous monitoring through employee experience and KS quality indicators is essential to ensure sustainable performance outcomes. 6 Conclusion Study contributes to the growing body of research on the integration of chatbots into HRM processes by empha- sising their potential to transform KS and organisational learning. The findings demonstrate that chatbots, as part of GenAI technologies, can automate routine HRM tasks, preserve organisational memory, and enhance employee engagement, thereby supporting long-term competitive- ness in knowledge-intensive environments. At the same time, their successful use requires careful attention to trust, ethical concerns, and the socio-technical interplay between employees and technology. This study also demonstrates that despite the increasing visibility of GenAI, empirical evidence on chatbot adoption in HRM remains limited, leaving considerable room for future cross-national and cross-sectoral research. Beyond organisational outcomes, the integration of chatbots also resonates with the broader vision of Socie- ty 5.0, which highlights the importance of human-centred digital transformation (Roblek et al., 2021). By reducing digital divides, supporting intergenerational KS and ena- bling adaptive learning, chatbots can help organisations respond to the demands of a rapidly changing socio-eco- nomic landscape. However, this transformation will only succeed if leadership provides clear guidance, transparent communication, and continuous training to ensure that AI complements rather than replaces human judgement. 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Routledge. Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers (No. arXiv:2002.02608). arXiv. https://doi.org/10.48550/ arXiv.2002.02608 399 Organizacija, V olume 58 Issue 4, November 2025 Research Papers Nejc Bernik is a doctoral student and young researcher at the Faculty of Organizational Sciences, University of Maribor. His primary research interests focus on knowledge management, digital literacy, teamwork and human resource training. Polona Šprajc is a Full Professor at the Faculty of Organizational Sciences, University of Maribor. Her research interests encompass organisation and management, crisis management, critical infrastructure, and human resource management. 400 Organizacija, V olume 58 Issue 4, November 2025 Research Papers Appendix On the day 12.08.2025 WoS Scopus Search criteria English Article 2020-2025 English Article 2020-2025 TOPIC: ( “chatbot” OR “llm” OR “virtual assistant” ) AND ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge sharing” OR “knowledge distri- bution” ) AND (“hr” OR “human resource”) = 0 ( “chatbot” OR “llm” OR “virtual assistant” ) AND ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge shar- ing” OR “knowledge distribution” ) AND (“hr” OR “human resource”) = 0 TOPIC: ( “chatbot” OR “llm” OR “virtual assistant” ) AND ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge sharing” OR “knowledge distribution” ) = 12 ( “chatbot” OR “llm” OR “virtual assistant” ) AND ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge shar- ing” OR “knowledge distribution” ) = 13 TOPIC: ( “ai” OR “artificial intelligence” ) AND ( “knowl- edge transfer” OR “knowledge sharing” OR “knowledge distribution” ) AND ( “hr” OR “human resource” ) = 11 ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge shar- ing” OR “knowledge distribution” ) AND ( “hr” OR “human resource” ) = 13 TOPIC: ( “ai” OR “artificial intelligence” ) AND ( “knowl- edge transfer” OR “knowledge sharing” OR “knowledge distribution” )= 469 ( “ai” OR “artificial intelligence” ) AND ( “knowledge transfer” OR “knowledge shar- ing” OR “knowledge distribution” )= 537 TOPIC: (“chatbot” OR “llm” OR “virtual assistant”) AND (“knowledge transfer” OR “knowledge sharing” OR “knowledge distribution”) AND (“hr” OR “human resource”) = 0 (“chatbot” OR “llm” OR “virtual assistant”) AND (“knowledge transfer” OR “knowledge sharing” OR “knowledge distribution”) AND (“hr” OR “human resource”) = 0 TOPIC: (“chatbot” OR “llm” OR “virtual assistant”) AND (“knowledge transfer” OR “knowledge sharing” OR “knowledge distribution”) = 39 (“chatbot” OR “llm” OR “virtual assistant”) AND (“knowledge transfer” OR “knowledge sharing” OR “knowledge distribution”) = 52 All together 491 573