Patent-Based Analysis of China’s Emergency Logistics Industry Convergence
Abstract
:1. Introduction
2. Literature Review
2.1. Industrial Convergence Background
2.2. Measurement of Industry Convergence
3. Research Method
3.1. Constructing the Relation Matrix by IPC Co-Classification
3.2. Social Network Analysis
3.3. Determine the Index to Measure the Convergence of the Emergency Logistics Industry
4. Data Sources and Processing
5. Discussion
5.1. General Trends in the Tonvergence of the Emergency Logistics Industry
5.1.1. Regional Distribution Trends
5.1.2. Time Development Trend
5.2. Analysis of Node Strength and Convergence in the Emergency Logistics Industry
5.2.1. Industry Node Strength
5.2.2. Convergence Strength between Industries
5.3. Identification of the Key Roles in the Convergence of the Emergency Logistics Industry
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Gao, Y.; Feng, Z.; Zhang, S. Managing supply chain resilience in the era of VUCA. Front. Eng. Manag. 2021, 8, 465–470. [Google Scholar] [CrossRef]
- Ge, X.; Yang, J.; Wang, H.; Shao, W. A fuzzy-TOPSIS approach to enhance emergency logistics supply chain resilience. J. Intell. Fuzzy Syst. 2020, 38, 6991–6999. [Google Scholar] [CrossRef]
- Spieske, A.; Birkel, H. Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic. Comput. Ind. Eng. 2021, 158, 107452. [Google Scholar] [CrossRef]
- Lan, Q.; Wang, C.; Zheng, X. Study on the reality and countermeasures of the stability and competitiveness of China’s industrial chain supply chain under the dual-cycle pattern. J. Yunnan Norm. Univ. (Philos. Soc. Sci. Ed.) 2021, 288, 132–145. [Google Scholar]
- Lv, J.; Zhang, Y.; Zhuang, Y. Research on the optimization of emergency logistics capacity based on smart logistics under public health crisis. China Soft Sci. 2020, 16–22. [Google Scholar]
- Costa, I.P.D.A.; Maêda, S.M.D.N.; Teixeira, L.F.H.D.S.D.B.; Gomes, C.F.S.; Santos, M.D. Choosing a hospital assistance ship to fight the covid-19 pandemic. Rev. De Saúde Pública 2020, 54, 79. [Google Scholar] [CrossRef]
- Hacklin, F.; Marxt, C.; Fahrni, F. Coevolutionary cycles of convergence: An extrapolation from the ICT industry. Technol. Forecast. Soc. Chang. 2009, 76, 723–736. [Google Scholar] [CrossRef]
- Geum, Y.; Kim, M.-S.; Lee, S. How industrial convergence happens: A taxonomical approach based on empirical evidences. Technol. Forecast. Soc. Chang. 2016, 107, 112–120. [Google Scholar] [CrossRef]
- Li, Y.; Li, Y.; Zhao, Y.; Wang, F. Which Factor Dominates the Industry Evolution? A Synergy Analysis Based on China’s ICT Industry. Inz. Ekon.-Eng. Econ. 2014, 25, 273–282. [Google Scholar] [CrossRef] [Green Version]
- Kim, N.; Lee, H.; Kim, W.; Lee, H.; Suh, J.H. Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data. Res. Policy 2015, 44, 1734–1748. [Google Scholar] [CrossRef]
- Huang, X.; Wang, S. Research on emergency material stockpiling strategy based on stability analysis. Oper. Res. Manag. 2014, 112, 15–19. [Google Scholar]
- Zhang, H.-B.; Tao, Z.-G. Changes in the cooperation network among government departments in emergency management of public health events. J. Wuhan Univ. (Philos. Soc. Sci. Ed.) 2021, 74, 114–126. [Google Scholar]
- Su, T.-Y.; Zhang, L.-L.; Zhao, X. The impact of manufacturing and logistics coupling on the productivity of manufacturing firms—Based on industrial symbiosis perspective. Ind. Eng. Manag. 2020, 142, 42–49. [Google Scholar]
- Moreira, M.L.; Gomes, C.F.S.; dos Santos, M.; Júnior, A.C.D.S.; Costa, I.P.D.A. Sensitivity Analysis by the PROMETHEEGAIA method: Algorithms evaluation for COVID-19 prediction. Procedia Comput. Sci. 2022, 199, 431–438. [Google Scholar] [CrossRef]
- Heoa, P.S.; Lee, D.H. Evolution patterns and network structural characteristics of industry convergence. Struct. Change Econ. Dyn. 2019, 52, 405–426. [Google Scholar] [CrossRef]
- Wang, C.; Liu, X. Using patent information analysis to examine the development of blockchain. Technol. Anal. Strateg. Manag. 2022. [Google Scholar] [CrossRef]
- Qiu, Z.; Wang, Z. Technology Forecasting Based on Semantic and Citation Analysis of Patents: A Case of Robotics Domain. IEEE Trans. Eng. Manag. 2022, 69, 1216–1236. [Google Scholar] [CrossRef]
- Ashouri, S.; Mention, A.; Smyrnios, K. Anticipation and analysis of industry convergence using patent-level indicators. Scientometrics 2021, 126, 5727–5758. [Google Scholar] [CrossRef]
- Curran, C.-S.; Bröring, S.; Leker, J. Anticipating converging industries using publicly available data. Technol. Forecast. Soc. Change 2010, 77, 385–395. [Google Scholar] [CrossRef]
- Verhoeven, D.; Bakker, J.; Veugelers, R. Measuring technological novelty with patent-based indicators. Res. Policy 2016, 45, 707–723. [Google Scholar] [CrossRef] [Green Version]
- MortezaGhobakhloo, Industry 4.0, digitization, and opportunities for sustainability. J. Clean. Prod. 2020, 252, 119869. [CrossRef]
- Bongomin, O.; Yemane, A.; Kembabazi, B.; Malanda, C.; Mwape, M.C.; Mpofu, N.S.; Tigalana, D. Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. J. Eng. 2020, 45, 8090521. [Google Scholar] [CrossRef]
- Kwon, H.-I.; Hee, K.S. The Study on Selection of Proper Convergence Industry for 3D Printing: Focused on Manufacturing Industry. Korean Bus. Educ. Rev. 2017, 32, 303–322. [Google Scholar] [CrossRef]
- Kim, S.M. A Study on Directions of Convergence Complex for Design Industry’s Response to the Challenges of the Domestic Industry 3D Printing Technology Roadmap Strategy. J. Basic Des. Art 2015, 16, 89–99. [Google Scholar]
- Song, C.; Zhang, Z.; Xi, H. Decentralized Optimal Control in Shared Resource Pools of Video Service Nodes in Network Convergence. In Proceedings of the 2014 33rd Chinese Control Conference (CCC), Nanjing, China, 28–30 July 2014; pp. 1551–1556. [Google Scholar]
- Davy, P.; Wouter, J.; Elisabeth, I.-Z. Trustworthy data-driven networked production for customer-centric plants. Ind. Manag. Data Syst. 2017, 117, 2305–2324. [Google Scholar]
- Fai, F.; Von, N. Industry-specific competencies and converging technological systems: Evidence from patents. Struct. Chang. Econ. Dyn. 2001, 12, 141–170. [Google Scholar] [CrossRef]
- Karvonen, M.; Kässi, T. Patent citation analysis as a tool for analysing industry convergence. In Proceedings of the 2011 Proceedings of PICMET’11: Technology Management in the Energy Smart World (PICMET), Portland, OR, USA, 31 July–4 August 2011; pp. 1–13. [Google Scholar]
- Breschi, S.; Lissoni, F.; Malerba, F. Knowledge-relatedness in firm technological diversification. Res. Policy 2003, 32, 69–87. [Google Scholar] [CrossRef]
- Lu, C.; Qiao, J.; Chang, J. Herfindahl–Hirschman Index based performance analysis on the convergence development. Cluster Comput. 2017, 20, 121–129. [Google Scholar] [CrossRef]
- Xing, W.; Ye, X.; Kui, L. Measuring convergence of China’s ICT industry: An input–output analysis. Telecommun. Policy 2011, 35, 301–313. [Google Scholar] [CrossRef]
- Frank, A.G.; Mendes, G.H.S.; Ayala, N.F.; Ghezzi, A. Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective. Technol. Forecast. Soc. Chang. 2019, 141, 341–351. [Google Scholar] [CrossRef]
- Lee, H.; Kim, N.; Kwak, K.; Kim, W.; Soh, H.; Park, K. Diffusion Patternsin Convergence among High-Technology Industries: A Co-Occurrence-Based Analysis of Newspaper Article Data. Sustainability 2016, 8, 1029. [Google Scholar] [CrossRef] [Green Version]
- You, Y.; Yang, H.; Wang, J. The structure evolution of China’s urban networks from the perspective of HSR flows. World Reg. Stud. 2020, 29, 773–780. [Google Scholar]
- Choi, Y.; Park, S.; Lee, S. Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data. Metrics 2021, 126, 5431–5476. [Google Scholar] [CrossRef]
- Jiang, Y.; Yuan, Y. Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges. Int. J. Environ. Res. Public Health 2019, 16, 779. [Google Scholar]
- Xiao, X.; Li, S. Industrial chain resilience under great changes: Generation logic, practical concerns and policy orientation. Reform 2022, 345, 1–14. [Google Scholar]
Industry | 1 | 2 | 3 | …… | 33 | 34 | 35 |
---|---|---|---|---|---|---|---|
1 | 0 | 157 | 65 | …… | 2 | 41 | 95 |
2 | 157 | 0 | 128 | …… | 17 | 19 | 186 |
3 | 65 | 128 | 0 | …… | 0 | 8 | 47 |
…… | …… | …… | …… | …… | …… | …… | …… |
33 | 2 | 17 | 0 | …… | 0 | 2 | 20 |
34 | 41 | 19 | 8 | …… | 2 | 0 | 35 |
35 | 95 | 186 | 47 | …… | 20 | 35 | 0 |
NO. | Industry | Node Strength |
---|---|---|
1 | 12 (control) | 5.99 |
2 | 6 (computer technology) | 4.45 |
3 | 4 (digital communication) | 4.16 |
4 | 2 (Audiovisual technology) | 3.70 |
5 | 7 (IT management methods) | 2.28 |
6 | 3 (telecommunications) | 2.15 |
7 | 32 (transportation systems) | 1.89 |
8 | 35 (civil engineering) | 1.85 |
9 | 1 (electrical machinery, equipment, energy) | 1.58 |
10 | 10 (measurement) | 1.31 |
11 | 25 (processing) | 1.25 |
12 | 31 (mechanical components) | 1.24 |
13 | 24 (environmental technology) | 0.86 |
…… | …… | …… |
35 | 18 (food chemistry) | 0 |
NO. | Industry Pair | Convergence Strength between Industries |
---|---|---|
1 | 6–7 (computer technology–information technology management methods) | 29.37 |
2 | 6–12 (computer technology–control) | 29.09 |
3 | 4–12 (digital communications–control) | 28.78 |
4 | 3–4 (telecommunications–digital communications) | 28.61 |
5 | 2–12 (audiovisual technology–control) | 24.29 |
6 | 4–6 (digital communications–computer technology) | 14.10 |
7 | 2–6 (audiovisual technology–computer technology) | 13.51 |
8 | 7–12 (information technology management methods–control) | 12.37 |
9 | 25–31 (processing–mechanical components) | 10.78 |
… | …… | …… |
595 | 31–34 (mechanical components–other consumer goods) | 0 |
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He, J.; Wang, Y. Patent-Based Analysis of China’s Emergency Logistics Industry Convergence. Sustainability 2023, 15, 4419. https://doi.org/10.3390/su15054419
He J, Wang Y. Patent-Based Analysis of China’s Emergency Logistics Industry Convergence. Sustainability. 2023; 15(5):4419. https://doi.org/10.3390/su15054419
Chicago/Turabian StyleHe, Jianjia, and Yue Wang. 2023. "Patent-Based Analysis of China’s Emergency Logistics Industry Convergence" Sustainability 15, no. 5: 4419. https://doi.org/10.3390/su15054419