Economic and environmental efficiency of joint R&D between universities and firms
Abstract
China’s total R&D funding has increased from CNY 89.6 billion in 2000 to CNY 2,442.6 billion in 2020 or by 27 times in 20 years. Although a large amount of literature has analyzed China’s R&D efficiency, scant studies have targeted second-stage economic and environmental efficiencies and rarely considered both university and industrial R&D. This research thus uses the Parallel Two-stage Undesirable Dynamic Model to evaluate the R&D efficiencies of universities and industry and examines their impact on the economy and the environment. The empirical results are as follows. 1) There are differences in the R&D and environmental efficiency of various regions in China with the eastern part being the highest, the western part second, and the central part the lowest. 2) The input index efficiency of universities is generally higher than that of industry. 3) The linkage effect between universities and the local economy and the environment is higher than that of industry.
First published online 13 February 2023
Keyword : R&D efficiency, environment, Parallel Two-stage
How to Cite
Fang, Z., Lv, S., Chiu, Y.- ho, Lin, T.-Y., & Lin, Y.-N. (2023). Economic and environmental efficiency of joint R&D between universities and firms. Technological and Economic Development of Economy, 29(2), 591–617. https://doi.org/10.3846/tede.2023.18336
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abramovsky, L., Harrison, R., & Simpson, H. (2007). University research and the location of business R&D. The Economic Journal, 117(519), C114–C141. https://doi.org/10.1111/j.1468-0297.2007.02038.x
Amirteimoori, A. (2013). A DEA two-stage decision processes with shared resources. Central European Journal of Operations Research, 21, 141–151. https://doi.org/10.1007/s10100-011-0218-3
Beasley, J. E. (1995). Determining teaching and research efficiencies. Journal of the Operational Research Society, 46(4), 441–452. https://doi.org/10.2307/2584592
Belderbos, R., Carree, M., & Lokshin, B. (2004). Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492. https://doi.org/10.1016/j.respol.2004.07.003
Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154(2), 465–476. https://doi.org/10.1016/S0377-2217(03)00182-6
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156, 107234. https://doi.org/10.1016/j.cie.2021.107234
Chen, Y., Du, J., David Sherman, H., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207(1), 339–349. https://doi.org/10.1016/j.ejor.2010.03.031
Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48(1), 1–23. https://doi.org/10.1287/mnsc.48.1.1.14273
Cook, W. D., & Hababou, M. (2001). Sales performance measurement in bank branches. Omega, 29(4), 299–307. https://doi.org/10.1016/S0305-0483(01)00025-1
Costa-Campi, M. T., Duch-Brown, N., & García-Quevedo, J. (2014). R&D drivers and obstacles to innovation in the energy industry. Energy Economics, 46, 20–30. https://doi.org/10.1016/j.eneco.2014.09.003
D’Aspremong, C., & Jacquemin, A. (1988). Cooperative and noncooperative R&D in duopoly with spillovers. American Economic Review, 78(5), 1133–1137. https://www.jstor.org/stable/1807173
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65–70. https://doi.org/10.1016/0165-1765(95)00729-6
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/S0038-0121(99)00012-9
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In J. Zhu & W. D. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209–240). Springer. https://doi.org/10.1007/978-0-387-71607-7_12
Feng, Y., Zhang, H., Chiu, Y. H., & Chang, T. H. (2021). Innovation efficiency and the impact of the institutional quality: A cross-country analysis using the two-stage meta-frontier dynamic network DEA model. Scientometrics, 126(4), 3091–3129. https://doi.org/10.1007/s11192-020-03829-3
Hervás-Oliver, J. L., Parrilli, M. D., Rodríguez-Pose, A., & Sempere-Ripoll, F. (2021). The drivers of SME innovation in the regions of the EU. Research Policy, 50(9), 104316. https://doi.org/10.1016/j.respol.2021.104316
Kao, C. (2009). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196(3), 1107–1112. https://doi.org/10.1016/j.ejor.2008.04.020
Kao, C. (2012). Efficiency decomposition for parallel production systems. European Journal of Operational Research, 63(1), 64–71. https://doi.org/10.1057/jors.2011.16
Kao, C. (2017). Network data envelopment analysis: Foundations and extensions. Springer. https://doi.org/10.1007/978-3-319-31718-2
Kao, C., & Hwang, S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3), 437–446. https://doi.org/10.1016/j.dss.2009.06.002
Kim, C. H., & Shin, W. S. (2019). Does information from the higher education and R&D institutes improve the innovation efficiency of logistic firms? The Asian Journal of Shipping and Logistics, 35(1), 70–76. https://doi.org/10.1016/j.ajsl.2019.03.010
Kreiling, L., Serval, S., Peres, R., & Bounfour, A. (2020). University technology transfer organizations: Roles adopted in response to their regional innovation system stakeholders. Journal of Business Research, 119, 218–229. https://doi.org/10.1016/j.jbusres.2019.08.031
Li, H., He, H., Shan, J., & Cai, J. (2019). Innovation efficiency of semiconductor industry in china: A new framework based on generalized three-stage DEA analysis. Socio-Economic Planning Sciences, 66, 136–148. https://doi.org/10.1016/j.seps.2018.07.007 https://doi.org/10.1016/j.frl.2019.101364
Li, X., & Tan, Y. (2020). University R&D activities and firm innovations. Finance Research Letters, 37, 101364. https://doi.org/10.1016/j.frl.2019.101364
Liu, H. H., Yang, G. L., Liu, X. X., & Song, Y. Y. (2020). R&D performance assessment of industrial enterprises in China: A two-stage DEA approach. Socio-Economic Planning Sciences, 71, 100753. https://doi.org/10.1016/j.seps.2019.100753
Maietta, O. W. (2015). Determinants of university–firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, 44(7), 1341–1359. https://doi.org/10.1016/j.respol.2015.03.006
Ministry of Science and Technology of the People’s Republic of China. (2021). The Ministry of Science and Technology released the analysis of the characteristics of R&D investment in 2019. https://www.edu.cn/rd/gao_xiao_cheng_guo/gao_xiao_zi_xun/202106/t20210609_2120331.shtml
National Bureau of Satistics. (2021). China Statistical Yearbook. https://www.bloomberg.com/news/articles/2021-03-01/china-s-r-d-spending-rises-10-to-record-378-billion-in-2020
Nie, P. Y., Wang, C., & Wen, H. X. (2022a). Technology spillover and innovation. Technology Analysis & Strategic Management, 34(2), 210–222. https://doi.org/10.1080/09537325.2021.1893294
Nie, P. Y., Wen, H. X., & Wang, C. (2022b). Cooperative green innovation. Environmental Science and Pollution Research, 29(20), 30150–30158. https://doi.org/10.1007/s11356-021-18389-z
OECD. (2021). Gross domestic spending on R&D from 2000–2018. https://data.oecd.org/rd/gross-domestic-spending-on-r-d.htm
Salas-Velasco, M. (2018). Production efficiency measurement and its determinants across OECD countries: The role of business sophistication and innovation. Economic Analysis and Policy, 57, 60–73. https://doi.org/10.1016/j.eap.2017.11.003
Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270–1288. https://doi.org/10.1287/mnsc.45.9.1270 https://www.jstor.org/stable/2634837
Sun, H. P., Edziah, B. K., Sun, C. W., & Kporsu, A. K. (2019). Institutional quality, green innovation and energy efficiency. Energy Policy, 135, 111002. https://doi.org/10.1016/j.enpol.2019.111002
Temel, S., Dabić, M., Ar, I. M., Howells, J., Mert, A., & Yesilay, R. B. (2021). Exploring the relationship between university innovation intermediaries and patenting performance. Technology in Society, 66, 101665. https://doi.org/10.1016/j.techsoc.2021.101665
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/J.EJOR.2008.05.027
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131. https://doi.org/10.1016/j.omega.2013.04.002
Wang, C., & Nie, P. Y. (2021). Analysis of cooperative technological innovation strategy. Economic Research-Ekonomska Istraživanja, 34(1), 1520–1545. https://doi.org/10.1080/1331677X.2020.1844580
Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261. https://doi.org/10.1016/j.techfore.2016.04.019
Wang, Y., Pan, J. F., Pei, R. M., Yi, B. W., & Yang, G. L. (2020). Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Economic Planning Sciences, 71, 100810. https://doi.org/10.1016/j.seps.2020.100810
Wu, J., Zhu, Q., Chu, J., Liu, H., & Liang, L. (2016a). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460–472. https://doi.org/10.1016/j.trd.2015.08.001
Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang L. (2016b). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182–197. https://doi.org/10.1016/j.ejor.2015.10.049
Yu, Y., & Shi, Q. (2014). Two-stage DEA model with additional input in the second stage and part of intermediate products as final output. Expert Systems with Applications, 41(15), 6570–6574. https://doi.org/10.1016/j.eswa.2014.05.021
Yanhui, W., Huiying, Z., & Jing, W. (2015). Patent elasticity, R&D intensity and regional innovation capacity in China. World Patent Information, 43, 50–59. https://doi.org/10.1016/j.wpi.2015.10.003
Zeng, D. Z. (2017). Measuring the effectiveness of the Chinese innovation system. A global value chain approach. International Journal of Innovation Studies, 1(1), 57–71. https://doi.org/10.3724/SP.J.1440.101005
Zha, Y., & Liang, L. (2010). Two-stage cooperation model with input freely distributed among the stages. European Journal of Operational Research, 205(2), 332–338. https://doi.org/10.1016/j.ejor.2010.01.010
Zhong, W., Yuan, W., Li, S. X., & Huang, Z. (2011). The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data. Omega, 39(4), 447–455. https://doi.org/10.1016/j.omega.2010.09.004
Amirteimoori, A. (2013). A DEA two-stage decision processes with shared resources. Central European Journal of Operations Research, 21, 141–151. https://doi.org/10.1007/s10100-011-0218-3
Beasley, J. E. (1995). Determining teaching and research efficiencies. Journal of the Operational Research Society, 46(4), 441–452. https://doi.org/10.2307/2584592
Belderbos, R., Carree, M., & Lokshin, B. (2004). Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492. https://doi.org/10.1016/j.respol.2004.07.003
Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operational Research, 154(2), 465–476. https://doi.org/10.1016/S0377-2217(03)00182-6
Chen, X., Liu, X., Gong, Z., & Xie, J. (2021). Three-stage super-efficiency DEA models based on the cooperative game and its application on the R&D green innovation of the Chinese high-tech industry. Computers & Industrial Engineering, 156, 107234. https://doi.org/10.1016/j.cie.2021.107234
Chen, Y., Du, J., David Sherman, H., & Zhu, J. (2010). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207(1), 339–349. https://doi.org/10.1016/j.ejor.2010.03.031
Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: The influence of public research on industrial R&D. Management Science, 48(1), 1–23. https://doi.org/10.1287/mnsc.48.1.1.14273
Cook, W. D., & Hababou, M. (2001). Sales performance measurement in bank branches. Omega, 29(4), 299–307. https://doi.org/10.1016/S0305-0483(01)00025-1
Costa-Campi, M. T., Duch-Brown, N., & García-Quevedo, J. (2014). R&D drivers and obstacles to innovation in the energy industry. Energy Economics, 46, 20–30. https://doi.org/10.1016/j.eneco.2014.09.003
D’Aspremong, C., & Jacquemin, A. (1988). Cooperative and noncooperative R&D in duopoly with spillovers. American Economic Review, 78(5), 1133–1137. https://www.jstor.org/stable/1807173
Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65–70. https://doi.org/10.1016/0165-1765(95)00729-6
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49. https://doi.org/10.1016/S0038-0121(99)00012-9
Färe, R., Grosskopf, S., & Whittaker, G. (2007). Network DEA. In J. Zhu & W. D. Cook (Eds.), Modeling data irregularities and structural complexities in data envelopment analysis (pp. 209–240). Springer. https://doi.org/10.1007/978-0-387-71607-7_12
Feng, Y., Zhang, H., Chiu, Y. H., & Chang, T. H. (2021). Innovation efficiency and the impact of the institutional quality: A cross-country analysis using the two-stage meta-frontier dynamic network DEA model. Scientometrics, 126(4), 3091–3129. https://doi.org/10.1007/s11192-020-03829-3
Hervás-Oliver, J. L., Parrilli, M. D., Rodríguez-Pose, A., & Sempere-Ripoll, F. (2021). The drivers of SME innovation in the regions of the EU. Research Policy, 50(9), 104316. https://doi.org/10.1016/j.respol.2021.104316
Kao, C. (2009). Efficiency measurement for parallel production systems. European Journal of Operational Research, 196(3), 1107–1112. https://doi.org/10.1016/j.ejor.2008.04.020
Kao, C. (2012). Efficiency decomposition for parallel production systems. European Journal of Operational Research, 63(1), 64–71. https://doi.org/10.1057/jors.2011.16
Kao, C. (2017). Network data envelopment analysis: Foundations and extensions. Springer. https://doi.org/10.1007/978-3-319-31718-2
Kao, C., & Hwang, S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3), 437–446. https://doi.org/10.1016/j.dss.2009.06.002
Kim, C. H., & Shin, W. S. (2019). Does information from the higher education and R&D institutes improve the innovation efficiency of logistic firms? The Asian Journal of Shipping and Logistics, 35(1), 70–76. https://doi.org/10.1016/j.ajsl.2019.03.010
Kreiling, L., Serval, S., Peres, R., & Bounfour, A. (2020). University technology transfer organizations: Roles adopted in response to their regional innovation system stakeholders. Journal of Business Research, 119, 218–229. https://doi.org/10.1016/j.jbusres.2019.08.031
Li, H., He, H., Shan, J., & Cai, J. (2019). Innovation efficiency of semiconductor industry in china: A new framework based on generalized three-stage DEA analysis. Socio-Economic Planning Sciences, 66, 136–148. https://doi.org/10.1016/j.seps.2018.07.007 https://doi.org/10.1016/j.frl.2019.101364
Li, X., & Tan, Y. (2020). University R&D activities and firm innovations. Finance Research Letters, 37, 101364. https://doi.org/10.1016/j.frl.2019.101364
Liu, H. H., Yang, G. L., Liu, X. X., & Song, Y. Y. (2020). R&D performance assessment of industrial enterprises in China: A two-stage DEA approach. Socio-Economic Planning Sciences, 71, 100753. https://doi.org/10.1016/j.seps.2019.100753
Maietta, O. W. (2015). Determinants of university–firm R&D collaboration and its impact on innovation: A perspective from a low-tech industry. Research Policy, 44(7), 1341–1359. https://doi.org/10.1016/j.respol.2015.03.006
Ministry of Science and Technology of the People’s Republic of China. (2021). The Ministry of Science and Technology released the analysis of the characteristics of R&D investment in 2019. https://www.edu.cn/rd/gao_xiao_cheng_guo/gao_xiao_zi_xun/202106/t20210609_2120331.shtml
National Bureau of Satistics. (2021). China Statistical Yearbook. https://www.bloomberg.com/news/articles/2021-03-01/china-s-r-d-spending-rises-10-to-record-378-billion-in-2020
Nie, P. Y., Wang, C., & Wen, H. X. (2022a). Technology spillover and innovation. Technology Analysis & Strategic Management, 34(2), 210–222. https://doi.org/10.1080/09537325.2021.1893294
Nie, P. Y., Wen, H. X., & Wang, C. (2022b). Cooperative green innovation. Environmental Science and Pollution Research, 29(20), 30150–30158. https://doi.org/10.1007/s11356-021-18389-z
OECD. (2021). Gross domestic spending on R&D from 2000–2018. https://data.oecd.org/rd/gross-domestic-spending-on-r-d.htm
Salas-Velasco, M. (2018). Production efficiency measurement and its determinants across OECD countries: The role of business sophistication and innovation. Economic Analysis and Policy, 57, 60–73. https://doi.org/10.1016/j.eap.2017.11.003
Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270–1288. https://doi.org/10.1287/mnsc.45.9.1270 https://www.jstor.org/stable/2634837
Sun, H. P., Edziah, B. K., Sun, C. W., & Kporsu, A. K. (2019). Institutional quality, green innovation and energy efficiency. Energy Policy, 135, 111002. https://doi.org/10.1016/j.enpol.2019.111002
Temel, S., Dabić, M., Ar, I. M., Howells, J., Mert, A., & Yesilay, R. B. (2021). Exploring the relationship between university innovation intermediaries and patenting performance. Technology in Society, 66, 101665. https://doi.org/10.1016/j.techsoc.2021.101665
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252. https://doi.org/10.1016/J.EJOR.2008.05.027
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131. https://doi.org/10.1016/j.omega.2013.04.002
Wang, C., & Nie, P. Y. (2021). Analysis of cooperative technological innovation strategy. Economic Research-Ekonomska Istraživanja, 34(1), 1520–1545. https://doi.org/10.1080/1331677X.2020.1844580
Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261. https://doi.org/10.1016/j.techfore.2016.04.019
Wang, Y., Pan, J. F., Pei, R. M., Yi, B. W., & Yang, G. L. (2020). Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Socio-Economic Planning Sciences, 71, 100810. https://doi.org/10.1016/j.seps.2020.100810
Wu, J., Zhu, Q., Chu, J., Liu, H., & Liang, L. (2016a). Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transportation Research Part D: Transport and Environment, 48, 460–472. https://doi.org/10.1016/j.trd.2015.08.001
Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang L. (2016b). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182–197. https://doi.org/10.1016/j.ejor.2015.10.049
Yu, Y., & Shi, Q. (2014). Two-stage DEA model with additional input in the second stage and part of intermediate products as final output. Expert Systems with Applications, 41(15), 6570–6574. https://doi.org/10.1016/j.eswa.2014.05.021
Yanhui, W., Huiying, Z., & Jing, W. (2015). Patent elasticity, R&D intensity and regional innovation capacity in China. World Patent Information, 43, 50–59. https://doi.org/10.1016/j.wpi.2015.10.003
Zeng, D. Z. (2017). Measuring the effectiveness of the Chinese innovation system. A global value chain approach. International Journal of Innovation Studies, 1(1), 57–71. https://doi.org/10.3724/SP.J.1440.101005
Zha, Y., & Liang, L. (2010). Two-stage cooperation model with input freely distributed among the stages. European Journal of Operational Research, 205(2), 332–338. https://doi.org/10.1016/j.ejor.2010.01.010
Zhong, W., Yuan, W., Li, S. X., & Huang, Z. (2011). The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data. Omega, 39(4), 447–455. https://doi.org/10.1016/j.omega.2010.09.004