8,098
Views
12
CrossRef citations to date
0
Altmetric
Review

Integrating artificial intelligence for knowledge management systems – synergy among people and technology: a systematic review of the evidence

, , , , , , & show all
Pages 7043-7065 | Received 20 Aug 2021, Accepted 23 Mar 2022, Published online: 11 Apr 2022

References

  • Aamodt, A., & Nygård, M. (1995). Different roles and mutual dependencies of data, information, and knowledge—An AI perspective on their integration. Data & Knowledge Engineering, 16(3), 191–222. https://doi.org/10.1016/0169-023X(95)00017-M
  • Al Hakim, S., Sensuse, D. I., Budi, I., Winarni, M. M., & Khusni, U. (2020). An empirical study of knowledge mapping implementation in Indonesian organizational context. VINE Journal of Information and Knowledge Management Systems, 21(5), 773–791.
  • Ankitha, S., & Basri, S. (2019). The effect of relational selling on life insurance decision making in India. International Journal of Bank Marketing, 37(7), 1505–1524. https://doi.org/10.1108/IJBM-09-2018-0236
  • Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886
  • Bate, S. P., & Robert, G. (2002). Knowledge management and communities of practice in the private sector: Lessons for modernizing the National Health Service in England and Wales. Public Administration, 80(4), 643–663. https://doi.org/10.1111/1467-9299.00322
  • Bencsik, A. (2021). The sixth generation of knowledge management – The headway of artificial intelligence. Journal of International Studies, 14(2), 84–101. https://doi.org/10.14254/2071-8330.2021/14-2/6
  • Bennett, J., & Lanning, S. (2007). Proceedings of KDD Cup and Workshop, 2007.
  • Berk, R. (2012). Criminal justice forecasts of risk: A machine learning approach. Springer.
  • Bhardwaj, M., & Monin, J. (2006). Tacit to explicit: an interplay shaping organization knowledge. Journal of Knowledge Management, 10(3), 72–85. https://doi.org/10.1108/13673270610670867
  • Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science (New York, N.Y.), 352(6293), 1573–1576. https://doi.org/10.1126/science.aaf265427339987
  • Boy, G. (1991). Intelligent assistant systems. Academic Press.
  • Burnett, S. (2012). Explicit to tacit: The role of explicit knowledge in technological innovation. Libri, 62(2). https://doi.org/10.1515/libri-2012-0011
  • Busch, P. (2008). Tacit knowledge in organizational learning. IGI Pub.
  • Chai, H., Leng, S., Chen, Y., & Zhang, K. (2020). A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 14(8), 1–11.
  • Champy, J., & Hammer, M. (2001). Reengineering the corporation (p. 30). Bookbytes.
  • Chen, Z., & Liu, B. (2016). Lifelong machine learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 10(3), 1–145. https://doi.org/10.2200/S00737ED1V01Y201610AIM033
  • Chua, L. O., & Yang, L. (1988). Cellular neural networks: theory. IEEE Transactions on Circuits and Systems, 35(10), 1257–1272. https://doi.org/10.1109/31.7600
  • Davenport, T. H. (1997). Ten principles of knowledge management and four case studies. Knowledge and Process Management, 4(3), 187–208. https://doi.org/10.1002/(SICI)1099-1441(199709)4:3<187::AID-KPM99>3.0.CO;2-A
  • Davenport, T. H., & Beers, M. C. (1995). Managing information about processes. Journal of Management Information Systems, 12(1), 57–80. https://doi.org/10.1080/07421222.1995.11518070
  • Djenouri, Y., Srivastava, G., Belhadi, A., & Lin, J. C. (2021). Intelligent blockchain management for distributed knowledge graphs in IoT 5G environments. Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.4332
  • Fakhar Manesh, M., Pellegrini, M. M., Marzi, G., & Dabic, M. (2020). Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues. IEEE Transactions on Engineering Management, 1–12. https://doi.org/10.1109/tem.2019.2963489
  • Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104. https://doi.org/10.1145/2818717
  • Fowler, A. (2000). The role of AI-based technology in support of the knowledge management value activity cycle. The Journal of Strategic Information Systems, 9(2-3), 107–128. https://doi.org/10.1016/S0963-8687(00)00041-X
  • Fu, L. (1998). A neural-network model for learning domain rules based on its activation function characteristics. IEEE Transactions on Neural Networks, 9(5), 787–795. https://doi.org/10.1109/72.712152
  • Gao, B. (2021). Exploration of talent mining based on machine learning and the influence of knowledge acquisition. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2021.1955631
  • Goncharova, A., & Murach, D. (2020). Artificial intelligence as a subject of civil law. Knowledge, Education, Law, Management, 1(3), 153–159. https://doi.org/10.51647/kelm.2020.3.1.26
  • Grum, M., Kotarski, D., Ambros, M., Biru, T., Krallmann, H., & Gronau, N. (2021, July). Managing knowledge of intelligent systems. In International symposium on business modeling and software design (pp. 78–96). Springer.
  • Gupta, R., Kewalramani, M. A., & Goel, A. (2006). Prediction of concrete strength using neural-expert system. Journal of Materials in Civil Engineering, 18(3), 462–466. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:3(462)
  • Harrigan, K. R., & Dalmia, G. (1991). Knowledge workers: The last bastion of competitive advantage. https://doi.org/10.1108/EB054337
  • Hobbs, J. (1993). The generic information extraction system [Paper presentation]. In B. Sundheim (Ed.), Fifth Message Understanding Conference (MUC5).
  • John, G. (1998). Share strength. People Management, 4(16), 44–47.
  • Kasabov, N. K. (1996). Foundations of neural networks, fuzzy systems, and knowledge engineering. Cambridge.
  • Kock, N. F., McQueen, R. J., & Baker, M. (1996). Learning and process improvement in knowledge organizations: a critical analysis of four contemporary myths. The Learning Organization, 3(1), 31–41. https://doi.org/10.1108/09696479610106790
  • Kot, S., Hussain, H. I., Bilan, S., Haseeb, M., & Mihardjo, L. W. W. (2021). The role of artificial intelligence recruitment and quality to explain the phenomenon of employer reputation. Journal of Business Economics and Management, 22(4), 867–883. https://doi.org/10.3846/jbem.2021.14606
  • Krulwich, B., Burkey, C., & Consulting, A. (1996). The ContactFinder agent: Answering bulletin board questions with referrals. AAAI/IAAI, 1, 10–15.
  • Kunnathur, A. S., Ahmed, M. U., & Charles, R. J. S. (1996). Expert systems adoption. An analytical study of managerial issues and concerns. Information & Management, 30(1), 15–25. https://doi.org/10.1016/0378-7206(95)00039-9
  • Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11(1), 65–100. https://doi.org/10.1016/S0364-0213(87)80026-5
  • Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892
  • Liebowitz, J. and Wilcox, L. (Eds.), (1997). Knowledge management and its integrative elements. CRC Press.
  • Liebowitz, J. (2000). Knowledge management receptivity at a major pharmaceutical company. Journal of Knowledge Management, 4(3), 252–258. https://doi.org/10.1108/13673270010350057
  • Liebowitz, J. (2001). Knowledge management and its link to artificial intelligence. Expert Systems with Applications, 20(1), 1–6. https://doi.org/10.1016/S0957-4174(00)00044-0
  • Litvaj, I., & Stancekova, D. (2015). Knowledge management embedment in company, knowledge repositories, knowledge management significance and usage in company. Procedia Economics and Finance, 23, 833–838. https://doi.org/10.1016/S2212-5671(15)00549-3
  • Mohapatra, S. (2021). Human and computer interaction in information system design for managing business. Information Systems and e-Business Management, 19(1), 1–11. https://doi.org/10.1007/s10257-020-00475-3
  • Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/10.1186/2046-4053-4-1
  • Morais, C., Yung, K. L., Johnson, K., Moura, R., Beer, M., & Patelli, E. (2022). Identification of human errors and influencing factors: A machine learning approach. Safety Science, 146, 105528. https://doi.org/10.1016/j.ssci.2021.105528
  • Nethravathi, P. S. R., Bai, G. V., Spulbar, C., Suhan, M., Birau, R., Calugaru, T., Hawaldar, I. T., & Ejaz, A. (2020). Business intelligence appraisal based on customer behaviour profile by using hobby-based opinion mining in India: a case study. Economic Research-Ekonomska Istraživanja, 33(1), 1889–1908. https://doi.org/10.1080/1331677X.2020.1763822
  • Nonaka, I., & Takeuchi, H. (1996). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Long Range Planning, 29(4), 592. https://doi.org/10.1016/0024-6301(96)81509-3
  • Obrenovic, B., Obrenovic, S., & Hudaykulov, A. (2015). The value of knowledge sharing: impact of tacit and explicit knowledge sharing on team performance of scientists. The International Journal of Management Science and Business Administration, 1(2), 33–52. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.12.1003
  • Pereira, T., & Santos, H. (2013). The matrix of quality dimensions of knowledge management: Knowledge management assessment models review. Knowledge Management: An International Journal, 12(1), 33–41. https://doi.org/10.18848/2327-7998/cgp/v12i01/50839
  • Pradhan, B., Lee, S., & Buchroithner, M. F. (2010). A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 34(3), 216–235. https://doi.org/10.1016/j.compenvurbsys.2009.12.004
  • Qi, G., & Zhu, Z. (2021). Blockchain and Artificial Intelligence Applications. Special Issue: Blockchain and Artificial Intelligence Applications, 1(2). https://doi.org/10.37965/2021.0019
  • Raquel Merlo, T. (2017). Knowledge management technology: human-computer interaction & cultural perspective on pattern of retrieval, organization, use, and sharing of information and knowledge. Knowledge and Performance Management, 1(1), 46–54. https://doi.org/10.21511/kpm.01(1).2017.05
  • Safarnezhad, S. M., Shahcheraghi, A., & Zabihi, H. (2021). Explaining the theoretical model of knowledge management process in building automated facade design intelligence. Iranian Journal of Information Processing and Management, 36(4), 923–943.
  • Sanzogni, L., Guzman, G., & Busch, P. (2017). Artificial intelligence and knowledge management: Questioning the tacit dimension. Prometheus, 35(1), 37–56. https://doi.org/10.1080/08109028.2017.1364547
  • Sharda, R. (1994). Neural networks for the MS/OR analyst: An application bibliography. Interfaces, 24(2), 116–130. https://doi.org/10.1287/inte.24.2.116
  • Shetty, A., & Basri, S. (2018). Relationship orientation in banking and insurance services – A review of the evidence. Journal of Indian Business Research, 10(3), 237–255. https://doi.org/10.1108/JIBR-10-2017-0176
  • Soleimani, M., Intezari, A., & Pauleen, D. J. (2022). Mitigating cognitive biases in developing AI-assisted recruitment systems: A knowledge-sharing approach. International Journal of Knowledge Management (IJKM), 18(1), 1–18.
  • Stanciu, A., Titu, A. M., & Deac-Şuteu, D. V. (2021, July). Driving digital transformation of knowledge-based organizations through artificial intelligence enabled data centric, consumption based, As-a-service models. In 2021 13th international conference on electronics, computers and artificial intelligence (ECAI) (pp. 1–8). IEEE.
  • Sturm, T., Gerlacha, J., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., Nan, N., & Buxmann, P. (2021). Coordinating human and machine learning for effective organizational learning. MIS Quarterly, 45(3), 1581–1602. https://doi.org/10.25300/MISQ/2021/16543
  • Swan, J., & Scarbrough, H. (2019). Knowledge, purpose and process: linking knowledge management and innovation. Proceedings of the 34th Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/hicss.2001.926486
  • Takagi, H. (1997). Introduction to fuzzy systems, neural networks, and genetic algorithms. In Intelligent hybrid systems (pp. 3–33). Springer. https://doi.org/10.1007/978-1-4615-6191-0_1
  • Wasserman, P. D. (1989). Neural computing: Theory and practice. Van Nostrand Reinhold Co.
  • Wu, I.-L., & Hu, Y.-P. (2018). Open innovation-based knowledge management implementation: A mediating role of knowledge management design. Journal of Knowledge Management, 22(8), 1736–1756. https://doi.org/10.1108/JKM-06-2016-0238
  • Yeşil, S., & Hırlak, B. (2019). Exploring knowledge-sharing barriers and their implications. In Effective knowledge management systems in modern society (pp. 99–122). IGI Global. https://doi.org/10.4018/978-1-5225-5427-1.ch006