| Title: | Use of chatbots in human resource management for more efficient knowledge sharing – systematic literature review |
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| Authors: | ID Bernik, Nejc (Author)
ID Šprajc, Polona (Author) |
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| Files: |  https://organizacija.fov.um.si/issues/vol-58-no-4-2025/
 RAZ_Bernik_Nejc_2025.pdf (2,62 MB)
MD5: 2E813E7CB6C549B30BCB5C8AC2D335A0
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.02 - Review Article |
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| Organization: | FOV - Faculty of Organizational Sciences in Kranj
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| 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. |
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| Keywords: | Chatbots, generative artificial intelligence, human resource management, knowledge management, knowledge sharing |
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| Publication date: | 01.01.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 388-400 |
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| Numbering: | Vol. 58, issue 4 |
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| PID: | 20.500.12556/DKUM-95953  |
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| UDC: | 005.94:004.8 |
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| ISSN on article: | 1318-5454 |
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| COBISS.SI-ID: | 257099523 |
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| DOI: | 10.2478/orga-2025-0024 |
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| Publication date in DKUM: | 14.11.2025 |
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| Views: | 205 |
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| Downloads: | 13 |
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| Metadata: |    |
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| Categories: | Misc.
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