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Has enterprise digital transformation improved the efficiency of enterprise technological innovation? A case study on Chinese listed companies

  • Received: 07 July 2022 Revised: 02 August 2022 Accepted: 08 August 2022 Published: 30 August 2022
  • Digital transformation is a new driving force of enterprise efficiency reform. Enterprises' digital transformation can effectively improve their technological innovation efficiency, thereby promoting their high-quality development. Using the panel data of 930 Chinese A-share listed companies from 2015 to 2020, we have studied the impact and heterogeneity of digital transformation on enterprise technological innovation efficiency with a panel data model. Further, a mediating effect model and a moderating effect model were constructed to study the mechanism of digital transformation affecting the efficiency of enterprise technological innovation. The conclusions are as follows. First, enterprise digital transformation significantly improves the efficiency of enterprise technological innovation. Second, the impact of digital transformation on the efficiency of enterprise technological innovation is heterogeneous, which is reflected in two aspects: the factor intensity and the nature of ownership. Third, financing constraints and equity concentration play a mediating and a moderating role, respectively, in the impact of digital transformation on the efficiency of enterprise technological innovation.

    Citation: Tinghui Li, Jieying Wen, Danwei Zeng, Ke Liu. Has enterprise digital transformation improved the efficiency of enterprise technological innovation? A case study on Chinese listed companies[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12632-12654. doi: 10.3934/mbe.2022590

    Related Papers:

  • Digital transformation is a new driving force of enterprise efficiency reform. Enterprises' digital transformation can effectively improve their technological innovation efficiency, thereby promoting their high-quality development. Using the panel data of 930 Chinese A-share listed companies from 2015 to 2020, we have studied the impact and heterogeneity of digital transformation on enterprise technological innovation efficiency with a panel data model. Further, a mediating effect model and a moderating effect model were constructed to study the mechanism of digital transformation affecting the efficiency of enterprise technological innovation. The conclusions are as follows. First, enterprise digital transformation significantly improves the efficiency of enterprise technological innovation. Second, the impact of digital transformation on the efficiency of enterprise technological innovation is heterogeneous, which is reflected in two aspects: the factor intensity and the nature of ownership. Third, financing constraints and equity concentration play a mediating and a moderating role, respectively, in the impact of digital transformation on the efficiency of enterprise technological innovation.



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    [1] H. Liu, L. Lobschat, P. C. Verhoef, Digital transformation: A multidisciplinary reflection and research agenda, J. Bus. Res., 12 (2019), 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 doi: 10.1016/j.jbusres.2019.09.022
    [2] V. Scuotto, G. Santoro, S. Bresciani, M. Del Giudice, Shifting intr- and inter-organizational innovation processes towards digital business: An empirical analysis of SMEs, Creativity Innovation Manage., 26 (2017), 247–255. https://doi.org/10.1111/caim.12221 doi: 10.1111/caim.12221
    [3] L. Wessel, A. Baiyere, R. Ologeanu-Taddei, J. Cha, T. B. Jensen, Unpacking the difference between digital transformation and IT-enabled organizational transformation, J. Assoc. Inf. Syst., 22 (2021). https://doi.org/10.17705/1jais.00655 doi: 10.17705/1jais.00655
    [4] T. D. Oesterreich, F. Teuteberg, Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry, Comput. Ind., 83 (2016), 121–139. https://doi.org/10.1016/j.compind.2016.09.006 doi: 10.1016/j.compind.2016.09.006
    [5] J. Müller, O. Buliga, K. I. Voigt, Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0, Technol. Forecasting Social Change, 132 (2018), 2–17. https://doi.org/10.1016/j.techfore.2017.12.019 doi: 10.1016/j.techfore.2017.12.019
    [6] M. Ghobakhloo, M. Fathi, Corporate survival in Industry 4.0 era: the enabling role of lean-digitized manufacturing, J. Manuf. Technol. Manage., 31 (2019), 1–30. https://doi.org/10.1108/JMTM-11-2018-0417 doi: 10.1108/JMTM-11-2018-0417
    [7] A. C. Taymans, Tarde and Schumpeter: A similar vision, Q. J. Econ., 64 (1950), 611–622. https://doi.org/10.2307/1884391 doi: 10.2307/1884391
    [8] P. Aghion, N. Bloom, R. Griffith, P. Howitt, Competition and innovation: An inverted U relationship, Q. J. Econ., 120 (2005), 701–728. https://doi.org/10.1093/qje/120.2.701 doi: 10.1093/qje/120.2.701
    [9] B. J. Lee, Firm age and innovation, Ind. Corporate Change, 17 (2008), 1019–1047. https://doi.org/10.1093/icc/dtn028 doi: 10.1093/icc/dtn028
    [10] V. Souitaris, Technological trajectories as moderators of firm-level determinants of innovation, Res. Policy, 31 (2002), 877–898. https://doi.org/10.1016/S0048-7333(01)00154-8 doi: 10.1016/S0048-7333(01)00154-8
    [11] R. E. Hoskisson, M. A. Hitt, R. A. Johnson, W. Grossman, Conflicting voices: The effects of institutional ownership heterogeneity and internal governance on corporate innovation strategies, Acad. Manage. J., 45 (2002), 697–716. https://doi.org/10.5465/3069305 doi: 10.5465/3069305
    [12] H. Curtis, Artificial intelligence, gender and the future of work, in AI@Work 2020 Conference, Amsterdam, 2020.
    [13] C.W. Su, M. Qin, S. K. A. Rizvi, M. Umar, Bank competition in China: a blessing or a curse for financial system, Econ. Research-Ekonomska Istrazivanja, 34 (2020), 1244–1264. https://doi.org/10.1080/1331677X.2020.1820361 doi: 10.1080/1331677X.2020.1820361
    [14] R. Falvey, N. Foster, The Role of Intellectual Property Rights in Technology Transfer and Economic Frowth: Theory and Evidence, 2006. Available from: http://www.ipr-policy.eu/media/pts/1/UNIDO_Report_about_IPR.pdf.
    [15] M. Danquah, J. Amankwah-Amoah, Assessing the relationships between human capital, innovation and technology adoption: Evidence from sub-Saharan Africa, Technol. Forecasting Social Change, 122 (2017), 24–33. https://doi.org/10.1016/j.techfore.2017.04.021 doi: 10.1016/j.techfore.2017.04.021
    [16] O. Dincer, Does corruption slow down innovation? Evidence from a cointegrated panel of US states, Eur. J. Political Econ., 56 (2019), 1–10. https://doi.org/10.1016/j.ejpoleco.2018.06.001 doi: 10.1016/j.ejpoleco.2018.06.001
    [17] N. A. S. Burhan, R. C. Razak, F. Salleh, M. E. Labastida Tovar, The higher intelligence of the 'creative minority' provides the infrastructure for entrepreneurial innovation, Intelligence, 65 (2017), 93–106. https://doi.org/10.1016/j.intell.2017.09.007 doi: 10.1016/j.intell.2017.09.007
    [18] S. Berg, M. Wustmans, S. Broring, Identifying first signals of emerging dominance in a technological innovation system: A novel approach based on patents, Technol. Forecasting Social Change, 146 (2019), 706–722. https://doi.org/10.1016/j.techfore.2018.07.046 doi: 10.1016/j.techfore.2018.07.046
    [19] K. Xin, X. Chen, R. Zhang, Y. Sun, R&D intensity, free cash flow, and technological innovation: evidence from high-tech manufacturing firms in China, Asian J. Technol. Innovation, 27 (2019), 214–238. https://doi.org/10.1080/19761597.2019.1635894 doi: 10.1080/19761597.2019.1635894
    [20] M. Matarazzo, L. Penco, G. Profumo, R. Quagliac, Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective, J. Bus. Res., 123 (2021), 642–656. https://doi.org/10.1016/j.jbusres.2020.10.033 doi: 10.1016/j.jbusres.2020.10.033
    [21] R. B. Bouncken, S. Kraus, N. Roig-Tierno, Knowledge- and innovation-based business models for future growth: digitalized business models and portfolio considerations, Rev. Managerial Sci., 15 (2021), 1–14. https://doi.org/10.1007/s11846-019-00366-z doi: 10.1007/s11846-019-00366-z
    [22] P. Mikalef, A. Pateli, Information technology-enabled dynamic capabilities and their indirect effect on competitive performance: Findings from PLS-SEM and fsQCA, J. Bus. Res., 70 (2017), 1–16. https://doi.org/10.1016/j.jbusres.2016.09.004 doi: 10.1016/j.jbusres.2016.09.004
    [23] C. Llopis-Albert, F. Rubio, F. Valero, Impact of digital transformation on the automotive industry, Technol. Forecasting Social Change, 162 (2021), 9. https://doi.org/10.1016/j.techfore.2020.120343 doi: 10.1016/j.techfore.2020.120343
    [24] H. L. Li, Y. Wu, D. M. Cao, Y. C. Wang, Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility, J. Bus. Res., 122 (2021), 700–712. https://doi.org/10.1016/j.jbusres.2019.10.036 doi: 10.1016/j.jbusres.2019.10.036
    [25] V. Jafari-Sadeghi, A. Garcia-Perez, E. Candelo, J. Couturier, Exploring the impact of digital transformation on technology entrepreneurship and technological market expansion: The role of technology readiness, exploration and exploitation, J. Bus. Res., 124 (2021), 100–111. https://doi.org/10.1016/j.jbusres.2020.11.020 doi: 10.1016/j.jbusres.2020.11.020
    [26] S. K. Sia, P. Weill, N. L. Zhang, Designing a future-ready enterprise: The digital transformation of DBS bank, Calif. Manage. Rev., 63 (2021), 35–57. https://doi.org/10.1177/0008125621992583 doi: 10.1177/0008125621992583
    [27] N. Moretti, C. Ellul, F. R. Cecconi, N. Papapesios, M. Claudio Dejaco, GeoBIM for built environment condition assessment supporting asset management decision making, Autom. Constr., 130 (2021), 14. https://doi.org/10.1016/j.autcon.2021.103859 doi: 10.1016/j.autcon.2021.103859
    [28] M. F. Manesh, M. M. Pellegrini, G. Marzi, M. Dabic, Knowledge management in the fourth industrial revolution: Mapping the literature and scoping future avenues, IEEE Trans. Eng. Manage., 68 (2021), 289–300. https://doi.org/10.1109/TEM.2019.2963489 doi: 10.1109/TEM.2019.2963489
    [29] S. Pizzi, A. Venturelli, M. Variale, G. P. Macario, Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis, Technol. Soc., 67 (2021), 11. https://doi.org/10.1016/j.techsoc.2021.101738 doi: 10.1016/j.techsoc.2021.101738
    [30] Y. J. Yoo, O. Henfridsson, K. Lyytinen, The new organizing logic of digital innovation: An agenda for information systems research, Inf. Syst. Res., 21 (2010), 724–735. https://doi.org/10.1287/isre.1100.0322 doi: 10.1287/isre.1100.0322
    [31] M. A. Afonasova, E. E. Panfilova, M. A. Galichkina, B. Ślusarczyk, Digitalization in economy and innovation: The effect on social and economic processes, Pol. J. Manage. Stud., 19 (2019), 22–32. https://doi.org/10.17512/PJMS.2019.19.2.02 doi: 10.17512/PJMS.2019.19.2.02
    [32] S. J. Yuan, H. O. Musibau, S. Y. Genc, R. Shaheen, A. Ameen, Z. Tan, Digitalization of economy is the key factor behind fourth industrial revolution: How G7 countries are overcoming with the financing issues, Technol. Forecasting Social Change, 165 (2021), 120533. https://doi.org/10.1016/j.techfore.2020.120533 doi: 10.1016/j.techfore.2020.120533
    [33] H. H. Liu, P. Wang, Z. J. Li, Is there any difference in the impact of digital transformation on the quantity and efficiency of enterprise technological innovation? Taking China's agricultural listed companies as an example, Sustainability, 13 (2021), 12972. https://doi.org/10.3390/su132312972 doi: 10.3390/su132312972
    [34] J. K. Nwankpa, Y. Roumani, IT capability and digital transformation: A firm performance perspective, in CIS 2016 Proceedings, 2016.
    [35] J. Ferreira, C. I. Fernandes, F. Ferreira, To be or not to be digital, that is the question: Firm innovation and performance, J. Bus. Res., 101 (2019), 583–590. https://doi.org/10.1016/j.jbusres.2018.11.013 doi: 10.1016/j.jbusres.2018.11.013
    [36] B. Stutzmann, P. Sailer, L. Kobold, Successful Digital Transformation—How Change Management Helps You to Hold Course, Siemens IoT Services, 2019. Available from: https://www.siemens-advanta.com/whitepapers/successful-digital-transformation.
    [37] A. S. AL-Adwan, Information systems quality level and its impact on the strategic flexibility: A field study on tourism and travel companies in the jordanian capital amman, Int. J. Hum. Resour. Stud., 7 (2017), 164–187. http://dx.doi.org/10.5296/ijhrs.v7i3.11436 doi: 10.5296/ijhrs.v7i3.11436
    [38] M. Liu, S. Fang, H. Dong, C. Xu, Review of digital twin about concepts, technologies, and industrial applications, J. Manuf. Syst., 58 (2020), 346–361. https://doi.org/10.1016/j.jmsy.2020.06.017 doi: 10.1016/j.jmsy.2020.06.017
    [39] M. I. Sanchez-Segura, G. L. Dugarte-Pena, A. Amescua, F. Medina-Domínguez, E. López-Almansa, E. Barrio Reyes, Smart occupational health and safety for a digital era and its place in smart and sustainable cities, Math. Biosci. Eng., 18 (2021), 8831–8856. https://doi.org/10.3934/mbe.2021436 doi: 10.3934/mbe.2021436
    [40] P. F. Borowski, Digitization, digital twins, blockchain, and Industry 4.0 as elements of management process in enterprises in the energy sector, Energies, 14 (2021), 1885. https://doi.org/10.3390/en14071885 doi: 10.3390/en14071885
    [41] A. Ferraris, A. Mazzoleni, A. Devalle, J. Couturier, Big data analytics capabilities and knowledge management: impact on firm performance, Manage. Decis., 57 (2019), 1923–1936. https://doi.org/10.1108/MD-07-2018-0825 doi: 10.1108/MD-07-2018-0825
    [42] D. G. Schniederjans, C. Curado, M. K. Hedayati, Supply chain digitisation trends: An integration of knowledge management, Int. J. Prod. Econ., 220 (2019), 107439. https://doi.org/10.1016/j.ijpe.2019.07.012 doi: 10.1016/j.ijpe.2019.07.012
    [43] Q. Qi, F. Tao, Digital twin and big data towards smart manufacturing and Industry 4.0: 360 ddegree comparison, IEEE Access, 6 (2018), 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265 doi: 10.1109/ACCESS.2018.2793265
    [44] M. Ghobakhloo, Industry 4.0, digitization, and opportunities for sustainability, J. Cleaner Prod., 252 (2019), 119869. https://doi.org/10.1016/j.jclepro.2019.119869 doi: 10.1016/j.jclepro.2019.119869
    [45] S. Nambisan, M. Wright, M. Feldman, The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes, Res. Policy, 48 (2019), 103773. https://doi.org/10.1016/j.respol.2019.03.018 doi: 10.1016/j.respol.2019.03.018
    [46] K. Lyytinen, Y. Yoo, R. J. Boland Jr., Digital product innovation within four classes of innovation networks, Inf. Syst. J., 26 (2016), 47–75. https://doi.org/10.1111/isj.12093 doi: 10.1111/isj.12093
    [47] R. Rajapathirana, Y. Hui, Relationship between innovation capability, innovation type, and firm performance, J. Innovation Knowl., 3 (2017), 44–55. https://doi.org/10.1016/j.jik.2017.06.002 doi: 10.1016/j.jik.2017.06.002
    [48] C. Yang, T. Li, K. Albitar, Does energy efficiency affect ambient PM2.5? The moderating role of energy investment, Front. Environ. Sci., 9 (2021), 1–16. https://doi.org/10.3389/fenvs.2021.707751 doi: 10.3389/fenvs.2021.707751
    [49] M. Abrigo, I. Love, Estimation of panel vector autoregression in stata, Stata J. Promot. Commun. Stat. Stata, 16 (2016), 778–804. https://doi.org/10.1177/1536867X1601600314 doi: 10.1177/1536867X1601600314
    [50] F. Li, C. Yang, Z. Li, P. Failler, Does geopolitics have an impact on energy trade? empirical research on emerging countries, Sustainability, 13 (2021), 5199. https://doi.org/10.3390/su13095199 doi: 10.3390/su13095199
    [51] J. Wolszczak-Derlacz, An evaluation and explanation of (in) efficiency in higher education institutions in Europe and the US with the application of two-stage semi-parametric DEA, Res. Policy, 46 (2017), 1595–1605. https://doi.org/10.1016/j.respol.2017.07.010 doi: 10.1016/j.respol.2017.07.010
    [52] F. Quiroga-Martinez, E. Fernandez-Vazquez, C. L. Alberto, Efficiency in public higher education on Argentina 2004–2013: institutional decisions and university-specific effects, Lat. Am. Econ. Rev., 27 (2018), 18. https://doi.org/10.1186/s40503-018-0062-0 doi: 10.1186/s40503-018-0062-0
    [53] L. Fang, Stage efficiency evaluation in a two-stage network data envelopment analysis model with weight priority, Omega, 97 (2020), 12. https://doi.org/10.1016/j.omega.2019.06.007 doi: 10.1016/j.omega.2019.06.007
    [54] R. D. Banker, A. Charnes, W. Cooper, Some models for estimating technical and scale inefficiency in Data envelopment analysis, Manage. Sci., 30 (1984), 1078–1092. http://dx.doi.org/10.1287/mnsc.30.9.1078 doi: 10.1287/mnsc.30.9.1078
    [55] W. D. Cook, K. Tone, J. Zhu, Data envelopment analysis: Prior to choosing a model, Omega, 44 (2014), 1–4. https://doi.org/10.1016/j.omega.2013.09.004 doi: 10.1016/j.omega.2013.09.004
    [56] N. Neykov, S. Kristakova, I. Hajdúchová, M. Sedliačiková, P. Antov, B. Giertliová, Economic efficiency of forest enterprises-empirical study based on data envelopment analysis, Forests, 12 (2021), 462. https://doi.org/10.3390/f12040462 doi: 10.3390/f12040462
    [57] Z. Griliches, Patent statistics as economic indicators: A survey, J. Econ. Lit., 28 (1990), 1661–1707. http://www.jstor.org/stable/2727442
    [58] M. Croby, Patents, innovation and growth, Econ. Rec., 7 (2000), 255–262. https://doi.org/10.1111/j.1475-4932.2000.tb00021.x doi: 10.1111/j.1475-4932.2000.tb00021.x
    [59] G. H. Jefferson, H. Bai, X. Guan, R&D performance in Chinese industry, Econ. Innovation New Technol., 15 (2006), 345–366. https://doi.org/10.1080/10438590500512851 doi: 10.1080/10438590500512851
    [60] D. Skuras, K. Tsegenidi, K. Tsekouras, Product innovation and the decision to invest in fixed capital assets: Evidence from an SME survey in six European Union member states, Res. Policy, 37 (2008), 1778–1789. https://doi.org/10.1016/j.respol.2008.08.013 doi: 10.1016/j.respol.2008.08.013
    [61] D. Guo, Y. Guo, K. Jiang, Government-subsidized R&D and firm innovation: Evidence from China, Social Sci. Electron. Publ., 45 (2016), 1129–1144. https://doi.org/10.1016/j.respol.2016.03.002 doi: 10.1016/j.respol.2016.03.002
    [62] Z. H. Li, Z. M. Ao, B. Mo, Revisiting the valuable roles of global financial assets for international stock markets: Quantile coherence and causality-in-quantiles approaches, Mathematics, 9 (2021), 1750. https://doi.org/10.3390/math9151750 doi: 10.3390/math9151750
    [63] C. J. Hadlock, J. R. Pierce, New evidence on measuring financial constraints: Moving beyond the KZ index, Rev. Financ. Stud., 23 (2010), 1909–1940. https://doi.org/10.1093/rfs/hhq009 doi: 10.1093/rfs/hhq009
    [64] R. Haas, A. Ajanovic, J. Ramsebner, T. Perger, J. Knápek, J. W. Bleyl, Financing the future infrastructure of sustainable energy systems, Green Finance, 3 (2021), 90–118. https://doi.org/10.3934/GF.2021006 doi: 10.3934/GF.2021006
    [65] J. H. Zhu, Z. H. Huang, Z. H. Li, K. Albitar, The impact of urbanization on energy intensity-An empirical study on OECD countries, Green Finance, 3 (2021), 508–526. https://doi.org/10.3934/GF.2021024 doi: 10.3934/GF.2021024
    [66] Z. Li, F. Q. Zou, Y. Tan, J. Zhu, Does financial excess support land urbanization-An empirical study of cities in China, Land, 10 (2021), 635. https://doi.org/10.3390/land10060635 doi: 10.3390/land10060635
    [67] Z. H. Li, L. Chen, H. Dong, What are bitcoin market reactions to its-related events, Int. Rev. Econ. Finance, 73 (2021), 1–10. https://doi.org/10.1016/j.iref.2020.12.020 doi: 10.1016/j.iref.2020.12.020
    [68] T. Li, X. Li, G. Liao, Business cycles and energy intensity, evidence from emerging economies, Borsa Istanbul Rev., 22 (2021), 560–570. https://doi.org/10.1016/j.bir.2021.07.005 doi: 10.1016/j.bir.2021.07.005
    [69] Z. H. Li, F. Q. Zou, B. Mo, Does mandatory CSR disclosure affect enterprise total factor productivity, Econ. Research-Ekonomska Istrazivanja, 20 (2021). https://doi.org/10.1080/1331677X.2021.2019596 doi: 10.1080/1331677X.2021.2019596
    [70] T. Li, X. Li, K. Albitar, Threshold effects of financialization on enterprise R&D innovation: a comparison research on heterogeneity, Quantitative Finance Econ., 5 (2021), 496–515. https://doi.org/10.3934/QFE.2021022 doi: 10.3934/QFE.2021022
    [71] R. B. James, G. Martinsson, B. C. Petersen, Do financing constraints matter for R&D, Social Sci. Electron. Publ., 56 (2012), 1512–1529. https://doi.org/10.1016/j.euroecorev.2012.07.007 doi: 10.1016/j.euroecorev.2012.07.007
    [72] S. Gerlach, F. Browne, P. Honohan, The two pillars of the European Central Bank, Econ. Policy, 19 (2004), 390–439. https://doi.org/10.1111/j.1468-0327.2004.00128.x doi: 10.1111/j.1468-0327.2004.00128.x
    [73] M. L. Song, H. S. Ai, X. Li, Political connections, financing constraints, and the optimization of innovation efficiency among China's private enterprises, Technol. Forecasting Social Change, 92 (2015), 290–299. https://doi.org/10.1016/j.techfore.2014.10.003 doi: 10.1016/j.techfore.2014.10.003
    [74] Z. L. Wen, B. J. Ye, Analyses of mediating effects: The development of methods and models, Adv. Psychol. Sci., 22 (2014), 731–745. https://doi.org/10.3724/SP.J.1042.2014.00731 doi: 10.3724/SP.J.1042.2014.00731
    [75] H. Li, Y. Wu, D. Cao, Y. Wang, Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility, J. Bus. Res., 122 (2021), 700–712. https://doi.org/10.1016/j.jbusres.2019.10.036 doi: 10.1016/j.jbusres.2019.10.036
    [76] D. Horvath, R. Z. Szabo, Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities, Technol. Forecasting Social Change, 146 (2019), 119–132. https://doi.org/10.1016/j.techfore.2019.05.021 doi: 10.1016/j.techfore.2019.05.021
    [77] J. Wang, H. Wang, D. Wang, Equity concentration and investment efficiency of energy companies in China: Evidence based on the shock of deregulation of QFIIs, Energy Econ., 93 (2020), 105032. https://doi.org/10.1016/j.eneco.2020.105032 doi: 10.1016/j.eneco.2020.105032
    [78] Z. H. Li, H. Dong, C. Floros, A. Charemis, P. Failler, Re-examining Bitcoin volatility: A CAViaR-based approach, Emerging Markets Finance Trade, 58 (2022), 1320–1338. https://doi.org/10.1080/1540496X.2021.1873127 doi: 10.1080/1540496X.2021.1873127
    [79] Y. Liu, X. Zhao, F. Mao, The synergy degree measurement and transformation path of China's traditional manufacturing industry enabled by digital economy, Math. Biosci. Eng., 19 (2022), 5738–5753. https://doi.org/10.3934/mbe.2022268 doi: 10.3934/mbe.2022268
    [80] I. S. Farouq, N. U. Sambo, A. U. Ahmad, A. H. Jakada, Does financial globalization uncertainty affect CO2 emissions? Empirical evidence from some selected SSA countries, Quantitative Finance Econ., 5 (2021), 247–263. https://doi.org/10.3934/QFE.2021011 doi: 10.3934/QFE.2021011
    [81] Z. H. Li, Z. M. Huang, P. Failler, Dynamic correlation between crude oil price and investor sentiment in China: Heterogeneous and asymmetric effect, Energies, 15 (2022), 22. https://doi.org/10.3390/en15030687 doi: 10.3390/en15030687
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