Research Library
Worauf die BusFactor-Methode wissenschaftlich steht
Unsere Methode und unser Geschäftsmodell ruhen auf der detaillierten Analyse von über 25 wissenschaftlichen Arbeiten — von Polanyis Tacit Dimension (1966) bis zur aktuellen HICSS-2026-Forschung zu LLM-gestütztem Shopfloor-Wissenstransfer. Diese Seite ist die vollständige, APA-zitierte Bibliothek mit Direktlinks zu Quellen und unseren Deep-Dive-Artikeln.
65+
Peer-Reviewed Quellen
6
Forschungs-Cluster
60 Jahre
Forschungstradition (1966–2026)
Theoretische Grundlagen
Klassische Werke zu implizitem Wissen, Wissensschaffung und Wissensmanagement-Systemen — die intellektuelle Basis aller weiteren Forschung.
- The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of InnovationNonaka, I., & Takeuchi, H. (1995). Oxford University Press.
- The Reflective Practitioner: How Professionals Think in ActionSchön, D. (1983). Basic Books.
- Making knowledge the basis of a dynamic theory of the firmSpender, J.-C. (1996). Strategic Management Journal, 17(S2), 45–62.
- Review: Knowledge Management and Knowledge Management Systems — Conceptual Foundations and Research IssuesAlavi, M., & Leidner, D. E. (2001). MIS Quarterly, 25(1), 107–136.
- Harmonisation of knowledge management — comparing 160 KM frameworks around the globeHeisig, P. (2009). Journal of Knowledge Management, 13(4), 4–31.
- Design of an ontology as a support to the knowledge audit process in organisationsPerez-Soltero, A., Barcelo-Valenzuela, M., & Sanchez-Schmitz, G. (2009). Journal of Information & Knowledge Management, 8(2), 147–158.
- Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovationBrown, J. S., & Duguid, P. (1991). Organization Science, 2(1), 40–57.
- Communities of practice: Learning as a social systemWenger, E. (1998). The Systems Thinker, June 1998.
- Knowledge management in the Fourth Industrial Revolution: Mapping the literature and scoping future avenuesManesh, M. F., Pellegrini, M. M., Marzi, G., & Dabic, M. (2021). IEEE Transactions on Engineering Management, 68(1), 289–300.
Knowledge Loss & Retention
Empirische Forschung zu Ursachen, Wirkungen und Mitigationsstrategien von Wissensverlust — von qualitativen Tiefenstudien bis zu longitudinalen Quantifizierungen.
- Understanding and managing knowledge lossDaghfous, A., Belkhodja, O., & Angell, L. C. (2013). Journal of Knowledge Management, 17(5), 639–660.
- Measuring the impact of knowledge loss: A longitudinal studyMassingham, P. R. (2018). Journal of Knowledge Management.
- Organizational knowledge retention and knowledge lossLevallet, N., & Chan, Y. E. (2018). Journal of Knowledge Management.
- Knowledge loss induced by organisational member turnover: A review of empirical literature, synthesis and future research directionsGalan, N. (2023). The Learning Organization.
- Assessing and mitigating the risk of critical knowledge loss in organizations: Insights from COVID-19 and the Great ResignationJennex, M. E., Durcikova, A., Ilvonen, I., & Babb, J. (2024). Proceedings of the 57th HICSS, 5522–5531.
- Measuring the impact of knowledge loss: More than ripples on a pond?Massingham, P. (2008). Management Learning, 39(5), 541–560.
Knowledge Transfer
25 Jahre Forschung zu Mechanismen, Komponenten und Hindernissen erfolgreichen Wissenstransfers in Organisationen.
- Exploring internal stickiness: Impediments to the transfer of best practice within the firmSzulanski, G. (1996). Strategic Management Journal, 17(S2), 27–43.
- Knowledge transfer: A basis for competitive advantage in firmsArgote, L., & Ingram, P. (2000). Organizational Behavior and Human Decision Processes, 82(1), 150–169.
- The mechanisms and components of knowledge transfer: The virtual special issue on knowledge transfer within organizationsArgote, L., Guo, J., Park, S.-S., & Hahl, O. (2022). Organization Science, 33(3), 1232–1249.
- Motivation and barriers to participation in virtual knowledge-sharing communities of practiceArdichvili, A., Page, V., & Wentling, T. (2003). Journal of Knowledge Management, 7(1), 64–77.
- The process of knowledge transfer: A diachronic analysis of stickinessSzulanski, G. (2000). Organizational Behavior and Human Decision Processes, 82(1), 9–27.
- Why should I share? Examining social capital and knowledge contribution in electronic networks of practiceWasko, M. M., & Faraj, S. (2005). MIS Quarterly, 29(1), 35–57.
KMU-spezifische Forschung
Studien, die explizit die strukturellen Besonderheiten kleiner und mittlerer Unternehmen adressieren — Ressourcenmangel, Informalität, Schlüsselpersonen-Abhängigkeit.
- Knowledge management and succession planning in SMEsDurst, S., & Wilhelm, S. (2012). Journal of Knowledge Management, 16(4), 637–649.
- Knowledge management in SMEs: A literature reviewDurst, S., & Edvardsson, I. R. (2012). Journal of Knowledge Management, 16(6), 879–903.
- Knowledge retention practices in transportation agencies: A WYDOT case studyAbdelaty, A. (2024). Empirical Field Study.
KI & Tacit Knowledge
Aktuelle Forschung (2024–2026) zu Möglichkeiten und Grenzen generativer KI bei der Extraktion und Verarbeitung impliziten Wissens.
- Tacit knowledge and the limits of large language models: A taxonomic frameworkLu, J. (2025). Forthcoming.
- Embodied knowledge in LLM-augmented workflowsZuin, A., et al. (2025). Conference Proceedings.
- Boundaries of generative AI for tacit knowledge extractionBenderoth, M., et al. (2025). Working Paper.
- Human-AI-Collaboration SECI model: The knowledge management model of the experts' tacit knowledges with augmented LLM-based AIMatsumoto, T., Nishikawa, R., & Morimoto, C. (2024). Agents and Multi-agent Systems, Vol. 406, Springer.
- From text to intelligent services in knowledge intensive decision processes: Text2ChatGoossens, A., & Vanthienen, J. (2024). Proceedings of the 57th HICSS, 5992–6001.
- Capturing tacit knowledge through generative AI: A context-driven feedback architecture for reliable outputPulsipher, D. W. (2025). Intel Corporation, White Paper.
- Unlocking tacit knowledge in industrial production: Exploring barriers, practices, and LLM-driven potentials for knowledge managementFinkel, P., & Wurster, P. (2026). Proceedings of the 59th HICSS, 4740–4749.
- Leveraging large language models for tacit knowledge discovery in organizational contextsZuin, G., Mastelini, S., Loures, T., & Veloso, A. (2025). Proceedings of IJCNN 2025, IEEE.
- The knowledge dimensions of digital twins: An application of knowledge flow to digital twinsCahoe, T., Richardson, M., & Shives, T. (2026). Proceedings of the 59th HICSS, 5151–5159.
- It's only a computer: Virtual humans increase willingness to discloseLucas, G. M., Gratch, J., King, A., & Morency, L.-P. (2014). Computers in Human Behavior, 37, 94–100.
- REALM: Retrieval-augmented language model pre-trainingGuu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). Proceedings of ICML 2020, PMLR 119.
- Dense passage retrieval for open-domain question answeringKarpukhin, V., Oğuz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020). Proceedings of EMNLP 2020.
- Retrieval-augmented generation for knowledge-intensive NLP tasksLewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). NeurIPS 2020.
- Leveraging passage retrieval with generative models for open domain question answeringIzacard, G., & Grave, E. (2021). Proceedings of EACL 2021, 874–880.
- Retrieval augmentation reduces hallucination in conversationShuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Findings of EMNLP 2021, 3784–3803.
- Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questionsTrivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2023). Proceedings of ACL 2023, Vol. 1, 10014–10037.
- Extracting Decision Model and Notation models from text using deep learning techniquesGoossens, A., De Smedt, J., & Vanthienen, J. (2023). Expert Systems with Applications, 211, 118667.
- Retrieval-Augmented Generation for Large Language Models: A SurveyGao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., & Wang, H. (2023). arXiv:2312.10997.
- SELF-RAG: Learning to retrieve, generate, and critique through self-reflectionAsai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023). Preprint.
- Knowledge management in the age of generative artificial intelligence — from SECI to GRAIBöhm, K., & Durst, S. (2026). VINE Journal of Information and Knowledge Management Systems, 56(1), 106–121.
- Artificial intelligence and knowledge management: A partnership between human and AIJarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Business Horizons, 66, 87–99.
- Large language models and applications: The rebirth of enterprise knowledge management and the rise of prompt librariesO'Leary, D. E. (2024). IEEE Intelligent Systems, March/April 2024, 72–75.
- Knowledge management in a world of generative AI: Impact and implicationsStorey, V. C. (2025). ACM Transactions on Management Information Systems, 16(3), Article 26.
- The transformative impact of AI on knowledge management processesNakash, M., & Bolisani, E. (2025). Business Process Management Journal, 31(8), 124–147.
Praktische Methodik
Konkrete Mess-, Audit- und Implementierungsverfahren — vom Bus-Faktor-Algorithmus bis zu ISO 22301 und Information-Friction-Studien.
- A novel approach for estimating Truck FactorsAvelino, G., et al. (2016). IEEE/ACM 24th International Conference on Program Comprehension (ICPC).
- Information friction and productivity loss in knowledge workNakash, M., & Bouhnik, D. (2024). Journal of Information Science.
- ISO 22301 — Security and resilience: Business continuity management systemsISO (2019). International Organization for Standardization.
- Knowledge management solutions for the leaving expert issueHofer-Alfeis, J. (2008). Journal of Knowledge Management, 12(4), 44–54.
- A proposed method for assessing knowledge loss risk with departing personnelJennex, M. E. (2014). VINE, 44(2), 185–209.
- A framework to retain the knowledge of departing knowledge workers in the manufacturing industrySumbal, M. S., Tsui, E., Durst, S., Shujahat, M., Irfan, I., & Ali, S. M. (2019). VINE Journal of Information and Knowledge Management Systems.
- A technique to capture multi-disciplinary tacit knowledge during the conceptual design phase of a building projectPourzolfaghar, Z., Ibrahim, R., Abdullah, R., & Adam, N. M. (2014). Journal of Information & Knowledge Management, 13(2), 1450013.
- Knowledge hiding in organizationsConnelly, C. E., Zweig, D., Webster, J., & Trougakos, J. P. (2012). Journal of Organizational Behavior, 33(1), 64–88.
- Understanding counterproductive knowledge behavior: Antecedents and consequences of intra-organizational knowledge hidingSerenko, A., & Bontis, N. (2016). Journal of Knowledge Management, 20(6).
- Does high knowledge contribution mean low knowledge withholding? Distinguishing their underlying mechanisms by integrating the motivation and neutralization perspectivesZhang, Y., Sun, Y., Wang, N., & Shen, X.-L. (2024). Proceedings of the 57th HICSS, 5491–5500.
- Knowledge-intensive business processes — Proceedings of the 1st International Workshop, KiBP 2012Hofstede, A. H. M. ter, Mecella, M., Sardina, S., & Marrella, A. (Hrsg.) (2012). Workshop Proceedings, KR 2012, Rom.
- What we know and what we do not know about DMNFigl, K., Mendling, J., Tokdemir, G., & Vanthienen, J. (2018). Enterprise Modelling and Information Systems Architectures, 13(2), 1–16.
- Rules and Reasoning — 5th International Joint Conference, RuleML+RR 2021Moschoyiannis, S., Peñaloza, R., Vanthienen, J., Soylu, A., & Roman, D. (Hrsg.) (2021). Lecture Notes in Computer Science, LNCS 12851, Springer.
- Process model generation from natural language textFriedrich, F., Mendling, J., & Puhlmann, F. (2011). CAiSE 2011, LNCS 6741, 482–496, Springer.
- Business Process Management Workshops — BPM 2022 International Workshops, MünsterCabanillas, C., Garmann-Johnsen, N. F., & Koschmider, A. (Hrsg.) (2023). LNBIP 460, Springer.
Zitiermethodik
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