Author(s)

Wentsao Pan

Affiliation(s)

School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.

Corresponding Author

Wentsao Pan

ABSTRACT

The proliferation of digital data requires the establishment of a new research paradigm based on complex data and methods. Building a computable research paradigm is a new path for knowledge management research in the era of digital intelligence. Through systematic digital representation and execution of computable Knowledge objects are of great significance for the exploration of application modes that promote knowledge to practice. Drawing on the development rules and research results of computational social science, the concept and core content of constructing computational knowledge management science are proposed. Comparing the three research methods of the computational social science research paradigm, three research methods for the development of computational knowledge management science are proposed. Computational knowledge management science is a new path for the development of knowledge management in the era of digital intelligence, and its core is to integrate computer science and technology to bridge the gap between "data-knowledge-practice-data". The scientific development of computational knowledge management is driven by the trinity of data, algorithms, and computing power. Building a computable knowledge management system and interpretability is the only way to adapt to the complex knowledge environment in the future. The research methods of computational knowledge management science are integrated, self-adaptive and time-sensitive, dynamic evolution and multi-dimensional verification are the core requirements of computational knowledge management system.

KEYWORDS

Knowledge management; Computational social science; Computational knowledge management science; Knowledge twin; Metaverse; Digital intelligence.

CITE THIS PAPER

Wentsao Pan. Computational knowledge management science: the research path of knowledge management in the era of digital intelligence. World Journal of Management Science. 2023, 1(1): 15-25.

REFERENCES

[1] Peng Taiquan, Hai Liang, Zhu J H. Introducing computa-tional social science for Asia-Pacific communication research. Asian Journal of Communication, 2019, 29 (3): 205-216.

[2] Mann A. Computational social science. Proceedings of the National Academy of Sciences, 2016, 113 (3): 468-470.

[3] Welles BF, Meirelles I. Visualizing computational social science: the multiple lives of a complex image. Science Communication, 2015, 37 (1): 34-58.

[4] Meng Xiaofeng, Zhang Yi. Computational social science promotes the transformation of social science research. Social Science, 2019 (7): 3-10.

[5] Zhang Shuang. Knowledge Management—Past, Present and Future. Library and Information Work, 2013, 57 (S1): 20-22.

[6] Wiig K M. Knowledge management : an introduction and perspective. Journal of Knowledge Management, 1997, 1 (1): 6-14.

[7] Ruggles R. The state of the notion: knowledge management in practice ice. California Management Review , 1998 , 40 (3): 80-89.

[8] Demarest M. Understanding knowledge management. Long Range Planning, 1997 , 30 (3): 374-384.

[9] Yang Jianxiu, Liu Xu. Several basic issues of knowledge management. Library and Information Work, 2007 (7): 62-65.

[10] Li Dan. On the theoretical basis of knowledge management in scientific research activities. Library and Information Services, 2006 (2): 67-71.

[11] Lazer D, Pentland A, Adamic L. social science. Science, 2009, 323 (5915): 721-723.

[12] Lazer DMJ, Pentland A, Watts DJ, et al. Computational social science: obstacles an do opportunities. Science, 2020, 369 (6507): 1060-1062.

[13] Conte R, Gilbert N, Bonelli G. Manifesto of computational social science. The European Physical Journal Special Topics, 2012, 214 (1): 325-346.

[14] Theocharis Y, Jungherr A. Computational social science and the study of political communication. Political Communication, 2021, 38 (1-2): 1-22.

[15] Hofman JM, Watts DJ, Athey S. Integrating explanation and prediction in computational social science. Nature, 2021, 595 (7866): 181-188.

[16] Wang Lin. The Three-Dimensional Structure of Knowledge Exchange Theory in the Digital Age. Library Science Research, 2018 (1): 18-23.

[17] Liu Libin. Discuss the concept of knowledge management from the origin of knowledge management thought. Library Journal, 2008 (6): 2-7, 38.

[18] Chung W, Mustaine E, Zeng D. A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSS. Enterprise Information Systems, 2021, 15 (10): 1460-1482.

[19] Kitts JA, Quintane E. Rethinking social networks in the era of computational social science [M] //The Oxford hand-book of social networks. Oxford, UK : Oxford University Press, 2020 : 71-97.

[20] Kitts J A. Beyond networks in structural theories of exchange:

promises from computational social science [M] //Advances in group processes. Bingley: Emerald Group Publishing Limited- ed, 2014 : 263-298.

[21] O'DONNELL MB , Falk E B. Big data under the microscope

and brains in social context: integrating methods from computational social science and neuroscience. The Annals of the American Academy of Political and Social Science, 2015 , 659 (1): 274-289.

[22] Du Jian, Kong Guilan, Li Pengfei, Bai Yongmei, Zhang Luxia. The basic concept and realization path of computable medical knowledge. Journal of Information Science, 2021, 40 (11): 1221-1233.

[23] Wang Wentao, Wen Jiayi, Zhang Zhen, Yang Min, Liu Yongmei, Xie Yangqun. Sticky Knowledge Transfer in Online Health Community: From the Perspective of Privacy Computing. Information Theory and Practice, 2020, 43 (2): 121-128.

[24] Ruppert E. Rethinking empirical social sciences. Dialogues in Human Geography, 2013, 3 (3): 268-273.

[25] Bravo G, Farjam M. Prospects and challenges for the computational social sciences. Journal of Universal Computer Science (Online), 2017, 23 (11): 1057-1069.

[26] Cappella J N. Vectors into the future of mass and interpersonal communication research : Big data, social media, and computational social science. Human Communication Research, 2017 , 43 (4): 545-558.

[27] Longo F, Nicoletti l, Padovano A. Ubiquitous knowledge empowers the Smart Factory: the impacts of a Service-oriented Digital Twin on enterprises' performance. Annual Reviews in Control, 2019 , 47 (9): 221-236.

[28] Sun Dongliang, Yao Wei, Zhou Peng, Pan Liyun. Research on Value Co-creation of Socialized Multimedia Knowledge Demanders. Information Theory and Practice, 2022, 45 (10): 75-81 53.

[29] Yao Wei, Zhou Peng, Yu Huiling, Wang Zheng. From digital twins to knowledge twins: Empowering virtual community members to perceive benefits and promote knowledge transformation. Information Theory and Practice, 2022, 45 (9): 67-74, 82.

[30] Feng Shipeng. Analysis of Peter Drucker's Knowledge Management Thought. Information Exploration, 2012 (7): 32-34.

[31] Yuan Qiaoyun, Gloor P A. Research on the SE-IE-CI Model of Network Knowledge Innovation Spiral Transformation under Web2.0 Environment. Chinese Library Journal, 2013, 39 (2): 63-70.

[32] Hao Long, Li Fengxiang. Big Data Computing in Social Sciences - The Core Issue of Computing Social Science in the Big Data Era. Library Science Research, 2017 (22):20-29 35.

[33] Luo Jun. Computation·Simulation·Experiment: Three Research Methods of Computational Social Sciences. Academic Forum, 2020, 43 (1): 35-49.