Abstract
To accelerate the transformation and application of basic research results, the Chinese government has repeatedly mentioned in a government work report that it is necessary to support research and innovation collaborations between knowledge research institutions and enterprises. However, few studies have focused on the evolution of collaborations between these organizations and the impact of collaborations on innovation performance (IP) in the field of renewable energy under the background of government-funded support (GFS). Based on scientific publications, we construct a GFS collaboration network in the wind power field to investigate the evolution of network structure characteristics, attribute proximity variables, and applied research collaboration (ARC), and we study the impact of network evolution on the IP of actors. The results show that the focal actor of the collaboration network prefers to engage in ARC with partners who are familiar and have the same knowledge base in different provinces. This collaboration tendency will reduce geographical proximity and increase the direct ties, indirect ties, technological proximity, and ARC of the ego network. Among them, direct ties have an inverted U-shaped effect on IP, geographical proximity has a significantly negative impact on IP, and the remaining variables have positive impacts on IP. Taken together, when the direct ties is within a certain range, these collaboration tendencies in a GFS collaboration network positively affect the IP of research institutions and enterprises.
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Source: http://output.nsfc.gov.cn/
References
Ahuja G (2000) Collaboration networks, structural holes, and innovation: a longitudinal study. Adm Sci Q 45:425. https://doi.org/10.2307/2667105
Ahuja G, Katila R (2001) Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study. Strateg Manag J 22:197–220. https://doi.org/10.1002/smj.157
Audretsch DB, Feldman MP (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640
Bäck I, Kohtamäki M (2015) Boundaries of R&D collaboration. Technovation 45–46:15–28. https://doi.org/10.1016/j.technovation.2015.07.002
Bae SJ, Lee H (2019) The role of government in fostering collaborative R&D projects: empirical evidence from South Korea. Technol Forecast Soc Change 119826:119826. https://doi.org/10.1016/j.techfore.2019.119826
Bai C (2016) The Pioneer initiative: a new era in Chinese research. Small 12:2115–2117. https://doi.org/10.1002/smll.201503720
Balland P-A (2012) Proximity and the evolution of collaboration networks: evidence from research and development projects within the global navigation satellite system (GNSS) industry. Reg Stud 46:741–756. https://doi.org/10.1080/00343404.2010.529121
Balland PA, Boschma R, Frenken K (2015) Proximity and innovation: from statics to dynamics. Reg Stud 49:907–920. https://doi.org/10.1080/00343404.2014.883598
Bell GG, Zaheer A (2007) Geography, networks, and knowledge flow. Organ Sci 18:955–972. https://doi.org/10.1287/orsc.1070.0308
Belussi F, Sammarra A, Sedita SR (2010) Learning at the boundaries in an “open regional innovation system”: a focus on firms’ innovation strategies in the Emilia Romagna life science industry. Res Policy 39:710–721. https://doi.org/10.1016/J.RESPOL.2010.01.014
Bielecki A, Ernst S, Skrodzka W, Wojnicki I (2020) The externalities of energy production in the context of development of clean energy generation. Environ Sci Pollut Res 27:11506–11530. https://doi.org/10.1007/s11356-020-07625-7
Boschma R (2005) Proximity and innovation: a critical assessment. Reg Stud 39:61–7410.1080/0034340052000320887
Brem A, Radziwon A (2017) Efficient triple helix collaboration fostering local niche innovation projects – a case from Denmark. Technol Forecast Soc Change 123:130–141. https://doi.org/10.1016/j.techfore.2017.01.002
Cardamone P (2018) Firm innovation and spillovers in Italy: does geographical proximity matter? Lett Spat Resour Sci 11:1–16. https://doi.org/10.1007/s12076-017-0193-y
Chen H, Xie F (2018) How technological proximity affect collaborative innovation? An empirical study of China’s Beijing–Tianjin–Hebei region. J Manag Anal 5:287–308. https://doi.org/10.1080/23270012.2018.1478329
Chen K, Zhang Y, Zhu G, Mu R (2017) Do research institutes benefit from their network positions in research collaboration networks with industries or/and universities? Technovation. 94-95:102002. https://doi.org/10.1016/J.TECHNOVATION.2017.10.005
Cohen WM, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35:128. https://doi.org/10.2307/2393553
Demirkan I, Deeds DL (2013) Evolution of research collaboration networks and their impact on firm innovation output. 67–95
Du J, Leten B, Vanhaverbeke W (2014) Managing open innovation projects with science-based and market-based partners. Res Policy 43:828–840. https://doi.org/10.1016/j.respol.2013.12.008
Estrada I, Faems D, Martin Cruz N, Perez Santana P (2016) The role of interpartner dissimilarities in Industry-University alliances: insights from a comparative case study. Res Policy 45:2008–2022. https://doi.org/10.1016/j.respol.2016.07.005
Fan J-L, Hu J-W, Zhang X, Kong LS, Li F, Mi Z (2018) Impacts of climate change on hydropower generation in China. Math Comput Simul 167:4–18. https://doi.org/10.1016/j.matcom.2018.01.002
Feldman MP, Florida R (1994) The geographic sources of innovation: technological infrastructure and product innovation in the United States. Ann Assoc Am Geogr 84:210–229. https://doi.org/10.1111/j.1467-8306.1994.tb01735.x
Fung MK (2003) Technological proximity and co-movements of stock returns. Econ Lett 79:131–136. https://doi.org/10.1016/S0165-1765(02)00297-5
Gilsing V, Nooteboom B, Vanhaverbeke W, Duysters G, van den Oord A (2008) Network embeddedness and the exploration of novel technologies: technological distance, betweenness centrality and density. Res Policy 37:1717–1731. https://doi.org/10.1016/J.RESPOL.2008.08.010
Giuliani E, Arza V (2009) What drives the formation of ‘valuable’ university–industry linkages?: insights from the wine industry. Res Policy 38:906–921. https://doi.org/10.1016/j.respol.2009.02.006
Granovetter MS (1977) The Strength of Weak Ties11This paper originated in discussions with Harrison White, to whom I am indebted for many suggestions and ideas. Earlier drafts were read by Ivan Chase, James Davis, William Michelson, Nancy Lee, Peter Rossi, Charles Tilly, and a. In: Leinhardt SBT-SN (ed). Academic Press, pp 347–367
Guan J, Liu N (2016) Exploitative and exploratory innovations in knowledge network and collaboration network: a patent analysis in the technological field of nano-energy. Res Policy 45:97–112. https://doi.org/10.1016/J.RESPOL.2015.08.002
Guan JC, Yan Y (2016) Technological proximity and recombinative innovation in the alternative energy field. Res Policy 45:1460–1473. https://doi.org/10.1016/j.respol.2016.05.002
Guan J, Zhao Q (2013) The impact of university–industry collaboration networks on innovation in nanobiopharmaceuticals. Technol Forecast Soc Change 80:1271–1286. https://doi.org/10.1016/j.techfore.2012.11.013
Gulati R, Gargiulo M (1999) Where do interorganizational networks come from? Am J Sociol 104:1439–1493. https://doi.org/10.1086/210179
He L, Zhang L, Zhong Z, Wang D, Wang F (2019) Green credit, renewable energy investment and green economy development: empirical analysis based on 150 listed companies of China. J Clean Prod 208:363–372. https://doi.org/10.1016/j.jclepro.2018.10.119
Hou B, Hong J, Wang H, Zhou C (2019) Academia-industry collaboration, government funding and innovation efficiency in Chinese industrial enterprises. Technol Anal Strateg Manag 31:692–706. https://doi.org/10.1080/09537325.2018.1543868
Jaffe AB, Newell RG, Stavins RN (2005) A tale of two market failures: technology and environmental policy. Ecol Econ 54:164–174. https://doi.org/10.1016/j.ecolecon.2004.12.027
Kapoor R, Karvonen M, Ranaei S, Kässi T (2015) Patent portfolios of European wind industry: new insights using citation categories. World Pat Inf 41:4–10. https://doi.org/10.1016/j.wpi.2015.02.002
Katz JS, Martin BR (1997) What is research collaboration? Res Policy 26:1–18. https://doi.org/10.1016/S0048-7333(96)00917-1
Lahiri N (2010) Geographic distribution of R&D activity: how does it affect innovation quality? Acad Manag J 53:1194–1209
Lavrakas PJ (2008) Encyclopedia of survey research methods. Sage Publications
Li H, Jiang H-D, Dong K-Y, Wei YM, Liao H (2019) A comparative analysis of the life cycle environmental emissions from wind and coal power: evidence from China. J Clean Prod 119192:119192. https://doi.org/10.1016/j.jclepro.2019.119192
Li D, Zhao R, Peng X, Ma Z, Zhao Y, Gong T, Sun M, Jiao Y, Yang T, Xi B (2020) Biochar-related studies from 1999 to 2018: a bibliometrics-based review. Environ Sci Pollut Res 27:2898–2908. https://doi.org/10.1007/s11356-019-06870-9
Liang X, Liu AMM (2018) The evolution of government sponsored collaboration network and its impact on innovation: a bibliometric analysis in the Chinese solar PV sector. Res Policy 47:1295–1308. https://doi.org/10.1016/J.RESPOL.2018.04.012
Lind JT, Mehlum H (2010) With or without U? The Appropriate Test for a U-Shaped Relationship*. Oxf Bull Econ Stat 72:109–118. https://doi.org/10.1111/j.1468.0084.2009.00569.x
Liu N, Guan J (2015) Dynamic evolution of collaborative networks: evidence from nano-energy research in China. Scientometrics 102:1895–1919. https://doi.org/10.1007/s11192-014-1508-z
McEvily B, Zaheer A (1999) Bridging ties: a source of firm heterogeneity in competitive capabilities. Strateg Manag J 20:1133–1156. https://doi.org/10.1002/(SICI)1097-0266(199912)20:12<1133::AID-SMJ74>3.0.CO;2-7
Oerlemans LAG, Knoben J, Pretorius MW (2013) Alliance portfolio diversity, radical and incremental innovation: the moderating role of technology management. Technovation 33:234–246. https://doi.org/10.1016/J.TECHNOVATION.2013.02.004
Papazoglou ME, Spanos YE (2018) Bridging distant technological domains: a longitudinal study of the determinants of breadth of innovation diffusion. Res Policy 47:1713–1728. https://doi.org/10.1016/J.RESPOL.2018.06.006
Park HW, Leydesdorff L (2010) Longitudinal trends in networks of university–industry–government relations in South Korea: the role of programmatic incentives. Res Policy 39:640–649. https://doi.org/10.1016/j.respol.2010.02.009
Perkmann M, Tartari V, McKelvey M, Autio E, Broström A, D’Este P, Fini R, Geuna A, Grimaldi R, Hughes A, Krabel S, Kitson M, Llerena P, Lissoni F, Salter A, Sobrero M (2013) Academic engagement and commercialisation: a review of the literature on university–industry relations. Res Policy 42:423–442. https://doi.org/10.1016/j.respol.2012.09.007
Qiu Y, Anadon LD (2012) The price of wind power in China during its expansion: technology adoption, learning-by-doing, economies of scale, and manufacturing localization. Energy Econ 34:772–785. https://doi.org/10.1016/j.eneco.2011.06.008
Qu J, Cao J, Wang X, Tang J, Bukenya JO (2017) Political connections, government subsidies and technical innovation of wind energy companies in China. Sustain. 9
Ritala P, Hallikas J (2011) Network position of a firm and the tendency to collaborate with competitors – a structural embeddedness perspective. Int J Strateg Bus Alliances 2:307–328. https://doi.org/10.1504/IJSBA.2011.044859
Salman N, Saives A-L (2005) Indirect networks: an intangible resource for biotechnology innovation. R&D Manag 35:203–215. https://doi.org/10.1111/j.1467-9310.2005.00383.x
Santoro MD (2000) Success breeds success: the linkage between relationship intensity and tangible outcomes in industry–university collaborative ventures. J High Technol Manag Res 11:255–273. https://doi.org/10.1016/S1047-8310(00)00032-8
Scandura A (2016) University–industry collaboration and firms’ R&D effort. Res Policy 45:1907–1922. https://doi.org/10.1016/j.respol.2016.06.009
Schilling MA, Phelps CC (2007) Interfirm collaboration networks: the impact of large-scale network structure on firm innovation. Manag Sci 53:1113–1126. https://doi.org/10.1287/mnsc.1060.0624
Steinmo M, Rasmussen E (2016) How firms collaborate with public research organizations: the evolution of proximity dimensions in successful innovation projects. J Bus Res 69:1250–1259. https://doi.org/10.1016/J.JBUSRES.2015.09.006
Subtil Lacerda J, van den Bergh JCJM (2020) Effectiveness of an ‘open innovation’ approach in renewable energy: empirical evidence from a survey on solar and wind power. Renew Sust Energ Rev 118:109505. https://doi.org/10.1016/j.rser.2019.109505
Tang T, Popp D (2016) The learning process and technological change in wind power: evidence from China’s CDM wind projects. J Policy Anal Manag 35:195–222. https://doi.org/10.1002/pam.21879
Ubfal D, Maffioli A (2011) The impact of funding on research collaboration: evidence from a developing country. Res Policy 40:1269–1279. https://doi.org/10.1016/j.respol.2011.05.023
Uzzi B (1997) Social structure and competition in Interfirm networks: the paradox of embeddedness. Adm Sci Q 42:35–67. https://doi.org/10.2307/2393808
van Beers C, Zand F (2014) R&D cooperation, partner diversity, and innovation performance: an empirical analysis. J Prod Innov Manag 31:292–312. https://doi.org/10.1111/jpim.12096
van Beers C, Berghäll E, Poot T (2008) R&D internationalization, R&D collaboration and public knowledge institutions in small economies: evidence from Finland and the Netherlands. Res Policy 37:294–308. https://doi.org/10.1016/J.RESPOL.2007.10.007
Van Noorden R (2014) China tops Europe in R&D intensity. Nature 505:144–145. https://doi.org/10.1038/505144a
Villani E, Rasmussen E, Grimaldi R (2017) How intermediary organizations facilitate university–industry technology transfer: a proximity approach. Technol Forecast Soc Change 114:86–102. https://doi.org/10.1016/J.TECHFORE.2016.06.004
Wang X, Zou H (2018) Study on the effect of wind power industry policy types on the innovation performance of different ownership enterprises: evidence from China. Energy Policy 122:241–252. https://doi.org/10.1016/J.ENPOL.2018.07.050
Wang C, Rodan S, Fruin M, Xu X (2014) Knowledge networks, collaboration networks, and exploratory innovation. Acad Manag J 57:484–514. https://doi.org/10.5465/amj.2011.0917
Wieczorek AJ, Negro SO, Harmsen R, Heimeriks GJ, Luo L, Hekkert MP (2013) A review of the European offshore wind innovation system. Renew Sust Energ Rev 26:294–306. https://doi.org/10.1016/j.rser.2013.05.045
Wong PK, Singh A (2013) Do co-publications with industry lead to higher levels of university technology commercialization activity? Scientometrics 97:245–265. https://doi.org/10.1007/s11192-013-1029-1
Yuan L, Xi J (2019) Review on China’s wind power policy (1986–2017). Environ Sci Pollut Res 26:25387–25398. https://doi.org/10.1007/s11356-019-05540-0
Zhang G, Tang C (2018) How R&D partner diversity influences innovation performance: an empirical study in the nano-biopharmaceutical field. Scientometrics 116:1487–1512. https://doi.org/10.1007/s11192-018-2831-6
Zhang S, Wang W, Wang L, Zhao X (2015) Review of China’s wind power firms internationalization: status quo, determinants, prospects and policy implications. Renew Sust Energ Rev 43:1333–1342. https://doi.org/10.1016/J.RSER.2014.11.100
Zhang Y-J, Peng Y-L, Ma C-Q, Shen B (2017) Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100:18–28. https://doi.org/10.1016/j.enpol.2016.10.005
Zhang H, Zheng Y, Zhou D, Long X (2018) Selection of key technology policies for Chinese offshore wind power: a perspective on patent maps. Mar Policy 93:47–53. https://doi.org/10.1016/j.marpol.2018.03.030
Zhang N, Choi Y, Wang W (2019a) Does energy research funding work? Evidence from the Natural Science Foundation of China using TEI@I method. Technol Forecast Soc Change 144:369–380. https://doi.org/10.1016/j.techfore.2018.02.001
Zhang S, Yuan C, Wang Y (2019b) The impact of industry–university–research alliance portfolio diversity on firm innovation: evidence from Chinese manufacturing firms. Sustainability 11:2321. https://doi.org/10.3390/su11082321
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This research was funded by the National Natural Science Foundation of China (71,972,064, 71,573,069) and the Project of Key Research Institute of Humanities and Social Science in University of Anhui Province.
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Conceptualization, Jianling Jiao and Yuwen Xu; methodology, Jianling Jiao and Yuwen Xu; software, Yuwen Xu; validation, Jianling Jiao, Jingjing Li, and Ranran Yang; formal analysis, Yuwen Xu; investigation, Yuwen Xu; resources, X.X.; data curation, Yuwen Xu; writing—original draft preparation, Yuwen Xu; writing—review and editing, Jianling Jiao, Jingjing Li, and Ranran Yang; visualization, Yuwen Xu; supervision, Jianling Jiao; project administration, Jianling Jiao and Jingjing Li; funding acquisition, Jianling Jiao. All authors have read and agreed to the published version of the manuscript.
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Appendices
Appendix A
The search formula for wind power scientific publications is: TS=(“wind power” OR “power of wind” OR” power from wind” OR “wind turbine*” OR” wind energy” OR” energy of wind” OR” energy from wind” OR “wind-energy” OR” wind rotor*” OR” wind generat*” OR” wind farm*” OR windmill OR” wind axis” OR” wind blade*” OR “wind force” OR “wind drive*” OR windpark or aerogenerator* OR “Power-Wind” OR wind-power OR “wind electricity” OR “wind-electricity” OR “wind-driven” OR “wind-turbine*” OR “wind-stress” OR “wind park” OR “wind sensor” OR “electric wind” OR “fed induction generator” OR “DFIG” OR “FTPR”) AND CU=(CHINA)
Appendix B
The search formula for wind power patents is: Application Date=(20020101:20161231)AND (IPC Classification Number =(F03D H02J1/12 H02J3/38 H02J3/18 H02P9/04 H02K7/18 H02K1/27 F03B13/00 H02P9/00 H02P9/00 F01D5/14 F01D15/10 F03B17/06 E02B9/00 E04H5/02 E04H12/00 B63H9/00 B63H13/00) OR Invention Title=(wind power, wind force, wind-driven generator, wind wheel, wind field, wind blade, wind speed, wind resources) OR Abstract=(wind power, wind force, wind-driven generator, wind wheel, wind field, wind blade, wind speed, wind resources)) AND Invention type=(“I” OR “U” OR “D”)) AND Country Code=(HK OR MO OR TW OR CN)) AND Applicant/Assignee = ()
Appendix C
Appendix D
We conduct the U test as proposed by Lind and Mehlum (2010) to test the significance of the existence of this inverted U-shape relationship between direct ties and innovation performance. The results are exhibited in Table 4. The results of model 2 revealed that direct ties has an inverted U-shape effect on innovation performance as the results sufficiently met the three conditions of an inverted U-shape effect: (1) the coefficient of direct ties squared is negative and significant (p = 0.005), (2) the slope is steep and significant at both the lower end (slope = 0.032, 0.008) and the higher end (slope = −0.053, p = 0.011) of the data range, and (3) the turning point (direct ties = 17.97) is well located within the data range. The overall test of the presence of an inverted U-shape effect is also significant (p = 0.011). Hence, we find support for Hypothesis 1. This relationship is depicted in Fig. 4.
Relationship between direct ties and innovation performance
Appendix E
This paper tests the robustness of the results by replacing the annual number of issued patents with the number of patent applications of wind power technology.
The regression results in Table 5 are similar to the results in Table 2. The regression results show that direct ties have an inverted U-shaped effect on the innovation performance of the focal actor, indirect ties contributes to organizational innovation performance. GP can negatively regulate the relationship between direct ties and innovation performance, TP can negatively regulate the relationship between indirect ties and innovation performance, and ARC can positively regulate the relationship between direct ties and innovation performance and the relationship between nonredundant ties and innovation performance. This finding shows that our results are robust to some extent.
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Jiao, J., Xu, Y., Li, J. et al. The evolution of a collaboration network and its impact on innovation performance under the background of government-funded support: an empirical study in the Chinese wind power sector. Environ Sci Pollut Res 28, 915–935 (2021). https://doi.org/10.1007/s11356-020-10528-2
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DOI: https://doi.org/10.1007/s11356-020-10528-2