|
|
|
| Dimension Identification, Feature Extraction and Layered Model of Digital Economy Security: A Mixed Research of LDA Thematic Analysis and Grounded Theory Coding |
| Fan Bonai1,2,3, Sheng Zhonghua1,3 |
1.School of Public Affairs, Zhejiang University, Hangzhou 310058, China
2.Institute of Public Policy, Zhejiang University, Hangzhou 310058, China
3.Chinese Organization Development and Performance Evaluation Research Center, Zhejiang University, Hangzhou 310058, China |
|
|
|
|
Abstract Digital economy security is an important support for modernizing national security systems and capabilities. The existing studies have carried out theoretical exploration from the perspective of multiple disciplines but lack dimensional identification as well as structural feature analysis of digital economy security. This paper adopts a mixed research method to address this problem. Study 1 is a text analysis of digital economy security. Based on 1,838 articles from 62 top international journals, a Latent Dirichlet Allocation (LDA) topic model is constructed in Study 1. The results show that the Western academic community divides the digital economy security into five categories: digital infrastructure risk, key core technology risk, digital industry security, industrial digital security, and digital social stability, showing different topic features. Social network analysis further shows that these five types of security present a core-peripheral layered structure, among which the digital infrastructure risk is located in the core layer, digital social stability is in the peripheral layer, and the other risks are in the middle layer.Study 2 is an empirical research of digital economy security in China. Through semi-structured in-depth interviews with 42 practitioners, we construct 5 types of main risks and 18 types of sub-risks based on grounded theory coding. The Delphi method of risk ranking further shows that, China mainly faces five security categories according to the importance of security: key core digital technology security, digital security, digital industry security, industrial digital security, and digital economy ecological environment. Among them, key core digital technology security is represented by digital technology design security, digital technology manufacturing security, digital technology supply security and other features. Digital security includes data security, cyber security, information security, artificial intelligence security and other features. Digital industry security is characterized by digital industrial chain and supply chain security, platform enterprise monopoly, digital product price discrimination, digital industry competitiveness and so on. Industrial digital security is manifested in the features of digital transformation of manufacturing industry, digital agriculture construction, and digital financial security. The features of digital economy ecological environment include digital infrastructure environment, digital market environment, digital policy environment, digital social environment. Study 2 further verifies and modifies the conclusions of Study 1.According to the comparative results of Study 1 and 2, the manifestation of digital economy security in China is not quite different from that in the West. However, in terms of risk ranking, China pays attention to the key core digital technologies security, while the West focuses more on digital infrastructure security. The West regards digital social stability as the peripheral layer of digital economy security, while China integrates the ecological environment of digital economy into the security system. It should be noted that security types with Chinese characteristics, such as artificial intelligence security, digital industrial chain and supply chain security, digital industry competitiveness, digital agriculture construction and digital infrastructure environment, have also been identified.This paper has three major theoretical contributions. First, it deeply reveals the dimension types and topic features of digital economy security and builds a layered conceptual model. Although the topic of digital economy security has attracted more attention in the recent years, its internal dimension form and relationship structure are still a black box, with a lack of detailed and in-depth theoretical elaboration. This research provides a more fine-grained explanation by dividing the five dimensions of digital economy security, revealing the preference ranking of risk, indicating that it is no longer regarded as a homogeneous and unitary whole, and expanding the research on the typology structure of digital economy security. Second, comparing the differences between China and the West in digital economy security it clarifies the direction for future researches in China. At present, the international academic community has carried out a large number of exploratory research but the domestic research is still in the initial stage, so it is necessary to keep up with the theoretical frontier and grasp the academic innovation. Our results reveal the dimensional division of 62 international top journals, which provides implications for future researches. At the same time, combined with the Chinese situation and local practice, the study refines the security elements of digital economy with Chinese characteristics, and promotes the dialogues between international theoretical literature and Chinese practice. Third, the mixed research methods are adopted to make up for the limitations of the existing literature focused on speculative discussion, and for the first time to provide empirical evidence as well as support for the conceptual model of digital economy security. Study 1 uses the LDA topic model of unsupervised machine learning to overcome the subjective bias brought about by traditional manual coding by automatically extracting potential topics from massive data and provide evidences for dimension identification. Based on the Chinese reality, Study 2 abstracts new security dimensions through grounded theory. These two studies confirm each other, improving the external validity of the conclusion, promoting the dialogues between Chinese and Western theories. At the same time, they complement each other, bridging the gap in theory and practice, and contributing the knowledge increment to the construction of digital economy security theory with Chinese characteristics.From the practical level, the layered conceptual model constructed in this research is conducive to helping government officials and enterprise managers to better conduct hierarchical governance of digital economy security. For the core digital technology security in the core layer, we should give full play to the advantages of the new national system, concentrate on building a large-depth, interdisciplinary collaborative research mechanism to solve the bottleneck problem. For the security in the peripheral layer, we should keenly capture weak signals in digital ecological environment and provide theoretical guidance for government agencies to regularly issue dynamic warning of digital economy risks and establish a scientific and effective security monitoring system.
|
|
Received: 27 February 2023
|
|
|
|
1 中国信息通信研究院： 《中国数字经济发展报告（2023年）》，http：//www.caict.ac.cn/kxyj/qwfb/bps/202304/P020230427572038320317.pdf，2023年2月27日。
2 习近平： 《加快推进国家安全体系和能力现代化 以新安全格局保障新发展格局》，《人民日报》2023年5月31日，第1版。
3 Williamson S.， “Central bank digital currency： welfare and policy implications，” Journal of Political Economy， Vol. 130， No. 11 （2022）， pp. 2829-2861.
4 Mikalef P.， Conboy K. & Lundstrom J. E. et al.， “Thinking responsibly about responsible AI and ‘the dark side’ of AI，” European Journal of Information Systems， Vol. 31， No. 3 （2022）， pp. 257-268.
5 Rahman H. A.， “The invisible cage： workers’ reactivity to opaque algorithmic evaluations，” Administrative Science Quarterly， Vol. 66， No. 4 （2021）， pp. 945-988.
6 郭雪慧： 《人工智能时代的个人信息安全挑战与应对》，《浙江大学学报（人文社会科学版）》2021年第5期，第157-169页。
7 史金易、王志凯： 《加强数字经济认知，推动经济社会迭代创新》，《浙江大学学报（人文社会科学版）》2021年第5期，第149-156页。
8 Solow R. M.， “We’d better watch out，” New York Times Book Review， 1987-07-12， http：//digamo.free.fr/solow87.pdf， 2023-02-27.
9 李苍舒、沈艳： 《数字经济时代下新金融业态风险的识别、测度及防控》，《管理世界》2019年第12期，第53-69页。
10 Sikorski R. & Peters R.， “Digital security， Part I，” Science， Vol. 283 （1999）， pp. 348-349.
11 OECD， Digital Security Risk Management for Economic and Social Prosperity： OECD Recommendation and Companion Document， 2015-10-01， https：//doi.org/10.1787/9789264245471-en， 2023-02-27.
12 OECD， Measuring Digital Security Risk Management Practices in Businesses， 2019-06-21， https：//doi.org/10.1787/7b93c1f1-en， 2023-02-27.
13 Mehrizi M. H. R.， Nicolini D. & Modol J. R.， “How do organizations learn from information system incidents？ a synthesis of the past， present， and future，” MIS Quarterly， Vol. 46， No. 2 （2022）， pp. 531-590.
14 Esposti S. D.， Ball K. & Dibb S.， “What’s in it for us？ benevolence， national security， and digital surveillance，” Public Administration Review， Vol. 81， No. 5 （2021）， pp. 862-873.
15 Atkins S. & Lawson C.， “Integration of effort： securing critical infrastructure from cyberattack，” Public Administration Review， Vol. 82， No. 4 （2022）， pp. 771-775.
16 Schiff D. S.， Schiff K. J. & Pierson P.， “Assessing public value failure in government adoption of artificial intelligence，” Public Administration， Vol. 100， No. 3 （2022）， pp. 653-673.
17 Harknett R. J. & Stever J. A.， “The new policy world of cybersecurity，” Public Administration Review， Vol. 71， No. 3 （2011）， pp. 455-460.
18 Daniel E. M.， Ward J. M. & Franken A.， “A dynamic capabilities perspective of IS project portfolio management，” Journal of Strategic Information Systems， Vol. 23， No. 2 （2014）， pp. 95-111.
19 Nylén D. & Holmstr?m J.， “Digital innovation in context： exploring serendipitous and unbounded digital innovation at the church of Sweden，” Information Technology and People， Vol. 32， No. 3 （2019）， pp. 696-714.
20 Burt R. S.， Structural Holes： The Social Structure of Competition， Cambridge： Harvard University Press， 1992.
21 Hannigan T. R.， Haans R. F. J. & Vakili K. et al.， “Topic modeling in management research： rendering new theory from textual data，” Academy of Management Annals， Vol. 13， No. 2 （2019）， pp. 586-632.
22 Blei D. M.， Ng A. Y. & Jordan M. I.， “Latent dirichlet allocation，” Journal of Machine Learning Research， Vol. 3， No. 4-5 （2003）， pp. 993-1022.
23 Walker R. M.， Chandra Y. & Zhang J. S. et al.， “Topic modeling the research-practice gap in public administration，” Public Administration Review， Vol. 79， No. 6 （2019）， pp. 931-937.
24 Gentzkow M.， Kelly B. & Taddy M.， “Text as data，” Journal of Economic Literature， Vol. 57， No. 3 （2019）. pp. 535-574.
25 George G.， Osinga E. C. & Lavie D. et al.， “Big data and data science methods for management research，” Academy of Management Journal， Vol. 59， No. 5 （2016）， pp. 1493-1507.
26 Roberts M. E.， Stewart B. M. & Tingley D. et al.， “Structural topic models for open-ended survey responses，” American Journal of Political Science， Vol. 58， No. 4 （2014）， pp. 1064-1082.
27 Rosemann M. & Vessey I.， “Toward improving the relevance of information systems research to practice： the role of applicability checks，” MIS Quarterly， Vol. 32， No. 1 （2008）， pp. 1-22.
28 Glaser B. G.， Theoretical Sensitivity： Advances in the Methodology of Grounded Theory， Mill Valley： Sociology Press， 1978.
29 Charmaz K.， “Stories of suffering： subjective tales and research narratives，” Qualitative Health Research， Vol. 9， No. 3 （1999）， pp. 362-382.
30 Okoli C. & Pawlowski S. D.， “The Delphi method as a research tool： an example， design considerations and applications，” Information & Management， Vol. 42， No. 1 （2004）， pp. 15-29.
31 Schmidt R. C.， “Managing Delphi surveys using nonparametric statistical techniques，” Decision Sciences， Vol. 28， No. 3 （1997）， pp. 763-774.
32 Tilson D.， Lyytinen K. & Sorensen C.， “Digital infrastructures： the missing is research agenda，” Information Systems Research， Vol. 21， No. 4 （2010）， pp. 748-759.
33 刘志鹏、代涛、李怡洁等： 《技术经济安全评估的“三力”模型构建研究》，《科研管理》2018年第5期，第77-85页。
34 Boyens J.， Smith A. & Bartol N. et al.， Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations， https：//nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-161r1.pdf， 2023-02-27.
35 全国信息安全标准化技术委员会大数据安全标准特别工作组： 《人工智能安全标准化白皮书（2019版）》，2019年10月，http：//www.cesi.cn/images/editor/20191101/20191101115151443.pdf，2023年2月27日。
36 孙毅： 《数字经济学》，北京：机械工业出版社，2022年。
37 Stonig J.， Schmid T. & Muller-Stewens G.， “From product system to ecosystem： how firms adapt to provide an integrated value proposition，” Strategic Management Journal， Vol. 43， No. 9 （2022）， pp. 1927-1957.
38 Goldfarb A. & Tucker C.， “Digital economics，” Journal of Economic Literature， Vol. 57， No. 1 （2019）， pp. 3-43.
39 Dolata M.， Feuerriegel S. & Schwabe G.， “A sociotechnical view of algorithmic fairness，” Information Systems Journal， Vol. 32， No. 4 （2022）， pp. 754-818.
40 习近平： 《不断做强做优做大我国数字经济》，《求是》2022年第2期，第4-8页。
41 范柏乃、段忠贤： 《数字经济安全风险防控机制建设路径探讨》，《国家治理》2022年第5期，第43-46页。
42 Waldfogel J. & Reimers I.， “Storming the gatekeepers： digital disintermediation in the market for books，” Information Economics & Policy， Vol. 31， No. 2 （2015）， pp. 47-58.
43 Padilla J.， Perkins J. & Piccolo S.， “Self-preferencing in markets with vertically integrated gatekeeper platforms，” Journal of Industrial Economics， Vol. 70， No. 2 （2022）， pp. 371-395. |
|
|
|
2024, Vol. 54 
