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Research on the Correlation and Influencing Factors of Digital Technology Innovation in the Guangdong–Hong Kong–Macao Greater Bay Area

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  • Diexin Chen

    (Department of Economics and Trade, Guangdong University of Technology, 161 St. Yin Long’s Street, Guangzhou 510630, China)

  • Yuxiang Xiao

    (Department of Management, Guangdong University of Technology, 161 St. Yin Long’s Street, Guangzhou 510630, China)

  • Kaicheng Huang

    (Department of Economics and Trade, Guangdong University of Technology, 161 St. Yin Long’s Street, Guangzhou 510630, China)

  • Xiumin Li

    (Department of Economics and Trade, Guangdong University of Technology, 161 St. Yin Long’s Street, Guangzhou 510630, China)

Abstract

We investigated the digital technology innovation association’s spatial distribution characteristics and influencing factors using social network analysis and a negative binomial gravity regression model. The model was based on the transfer of digital technology patent rights among cities in the Guangdong–Hong Kong–Macao Greater Bay Area from 2010 to 2020. The following are the paper’s main findings: First, the digital technology innovation association among cities in the Guangdong–Hong Kong–Macao Greater Bay Area is strengthening, and the accessibility and agglomeration of each city node are improving, as are small-world characteristics. Second, for a long time, the four cities of Guangzhou, Shenzhen, Dongguan, and Foshan have been at the epicenter of digital technology innovation. Third, in a more peripheral position, Zhongshan, Huizhou, and Zhaoqing have gradually increased the number of digital technology innovation linkages with other cities. Fourth, technological and institutional proximity positively impact digital technology innovation associations in the Greater Bay Area, whereas geographical distance has a negative impact. The study’s findings can be used to help promote digital technology innovation linkages and develop policies for innovation development in the Greater Bay Area.

Suggested Citation

  • Diexin Chen & Yuxiang Xiao & Kaicheng Huang & Xiumin Li, 2022. "Research on the Correlation and Influencing Factors of Digital Technology Innovation in the Guangdong–Hong Kong–Macao Greater Bay Area," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14864-:d:969110
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    References listed on IDEAS

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    1. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, vol. 32(8), pages 1365-1379, September.
    2. Zhijun Feng & Hechang Cai & Wen Zhou, 2020. "Structural Characteristics and Spatial Patterns of the Technology Transfer Network in the Guangdong–Hong Kong–Macao Greater Bay Area," Sustainability, MDPI, vol. 12(6), pages 1-28, March.
    3. Mowery, David C. & Oxley, Joanne E. & Silverman, Brian S., 1998. "Technological overlap and interfirm cooperation: implications for the resource-based view of the firm," Research Policy, Elsevier, vol. 27(5), pages 507-523, September.
    4. José María Liberti & Mitchell A Petersen, 2019. "Information: Hard and Soft," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 8(1), pages 1-41.
    5. Jaffe, Adam B, 1986. "Technological Opportunity and Spillovers of R&D: Evidence from Firms' Patents, Profits, and Market Value," American Economic Review, American Economic Association, vol. 76(5), pages 984-1001, December.
    6. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    7. Wilson, Paul, 2015. "The misuse of the Vuong test for non-nested models to test for zero-inflation," Economics Letters, Elsevier, vol. 127(C), pages 51-53.
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    Cited by:

    1. Yang, Senmiao & Wang, Jianda & Dong, Kangyin & Jiang, Qingzhe, 2023. "A path towards China's energy justice: How does digital technology innovation bring about a just revolution?," Energy Economics, Elsevier, vol. 127(PA).

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