Skip to main content
SpringerLink
Log in

Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Improving the development of science and technology through innovation is the core of a country's economic development. This study employed the two-stage meta-frontier dynamic network DEA model to explore the innovation efficiency from the R&D resources to charges received for the use of intellectual property and high-technology exports in 34 high-income and 23 middle-income countries from 2013 to 2017. We calculated the overall efficiency scores and the technology gap ratios of each country and the scores of input and output variables in the research and development (R&D) stage and marketing stage. The results showed that the average overall efficiency scores of middle-income countries were higher than those of high-income countries from 2013 to 2015, but the five-year total score of high-income countries was higher. The R&D efficiency scores were higher in middle-income countries, while the marketing efficiency scores were higher in high-income countries. In the R&D stage, the scores of all input and output variables were higher in middle-income countries but in the marketing stage, the scores of the output variables in high-income countries were obviously higher. High-quality institution can help improve the innovation efficiency in both high-income and middle-income countries. The efficiency improvements are higher in high-income countries during the R&D stage and higher in middle-income countries during the marketing stage. Therefore, both high-income and middle-income countries should strengthen institutional construction in order to improve the efficiency of innovation. And the researches in middle-income countries should pay more attention to local practical issues and their solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Amirteimoori, A. (2006). Data Envelopment Analysis in Dynamic Framework. Applied Mathematics and Computation, 181(1), 21–28.

    MathSciNet  MATH  Google Scholar 

  • Azad, S. M., Khodabakhsh, P., Roshannafas, F., et al. (2019). Modelling techno-sectoral innovation system: A new hybrid approach for innovation motors policymaking. Kybernetes, 49(2), 332–361.

    Google Scholar 

  • Banker, R., Charnes, A., & Cooper, W. (1984). Some Models for Estimating Technical and Scale Efficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078–1092.

    Article  Google Scholar 

  • Battese, G. E., & Rao, D. S. P. (2002). Technology Gap, Efficiency and A StochasticMetafrontier Function. International Journal of Business and Economics, 1(2), 87–93.

    Google Scholar 

  • Battese, G. E., Rao, D. S. P., & O’Donnell, C. J. A. (2004). Meta frontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies. Journal of Productivity Analysis, 21(1), 91–103.

    Google Scholar 

  • Beilin, I. L., Khomenko, V. V., Yakupova, N. M., et al. (2018). Modeling of Economic Effects of commercialization of High-Tech Developments at Small Innovative Enterprises of Polymer Profile. The Journal of Social ences Research, 20(2), 188–193.

    Google Scholar 

  • Beneito, P., Rochina-Barrachina, M. E., & Sanchis, A. (2015). The path of R&D efficiency over time. International Journal of Industrial Organization, 42(2), 57–69.

    Google Scholar 

  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 8(2), 429–444.

    MathSciNet  MATH  Google Scholar 

  • Castelli, C. L., Pesenti, R., & Ukovich, W. (2010). A classification of DEA models when the internal structure of the Decision Making Units is considered. Annals of Operations Research, 173(1), 207–235.

    MathSciNet  MATH  Google Scholar 

  • Chen, H., He, P., Zhang, C. X., & Liu, Q. (2017). Efficiency of technological innovation in China’s high tech industry based on DEA method. Journal of Interdisciplinary Mathematics, 20(6), 1493–1496.

    Google Scholar 

  • Chen, K., Mingting, K., & Xiaolan, F. (2018). Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China’s regional R&D systems. Omega, 74, 103–114.

    Google Scholar 

  • Chen, P. C., & Hung, S. W. (2016). An actor-network perspective on evaluating the R&D linking efficiency of innovation ecosystems. Technological Forecasting and Social Change, 112, 303–312.

    Google Scholar 

  • Chen, Y., & Zhu, J. (2004). Measuring Information Technology’s Indirect Impact on Firm Performance. Information Technology and Management., 5(1), 9–22.

    Google Scholar 

  • Colombelli, A., Grilli, L., Minola, T., et al. (2020). To what extent do young innovative companies take advantage of policy support to enact innovation appropriation mechanisms? Research Policy, 49(10), 61–77.

    Google Scholar 

  • Costa-Campi, M. T., & Duch-Brown, N. (2014). Garc al. To what extent do young innovative companies take advantage of policy sup Energy Economics, 46, 20–30.

    Google Scholar 

  • Cullmann, A., Schmidt-Ehmcke, J., & Zloczysti, P. (2009). Innovation, R&D Efficiency and the Impact of the Regulatory Environment: A Two-Stage Semi-Parametric DEA Approach. SSRN Electronic Journal, 8(883), 36–42.

    Google Scholar 

  • Färe, R., & Grosskopf, S. (2009). A Comment on dynamic DEA. Applied Mathematics and Computation., 213(1), 275–276.

    MathSciNet  MATH  Google Scholar 

  • Färe, R., Grosskopf, S., Norris, S., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review., 84(1), 66–83.

    Google Scholar 

  • Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: a frontier approach. Economics Letters., 50, 65–70.

    MATH  Google Scholar 

  • Färe, R.; Grosskopf, S.; Whittaker, G. Network DEA. Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis. 2007, 209–240.

  • Feng, Y. Technological progress, intellectual property protection and economic growth. PhD dissertation, Nankai University, Economics College, China, 2012.

  • Gao, W., & Chou, J. (2015). Innovation efficiency, global diversification, and firm value. Journal of Corporate Finance, 30, 278–298.

    Google Scholar 

  • González, X., & Pazó, C. (2004). Firms’ R&D dilemma: to undertake or not to undertake R&D. Applied Economics Letters, 11(1), 55–59.

    Google Scholar 

  • Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102–115.

    MathSciNet  Google Scholar 

  • Han, C., Thomas, S. R., Yang, M., Ieromonachou, P., & Zhang, H. (2017). Evaluating R&D investment efficiency in China’s high-tech industry. The Journal of High Technology Management Research, 28(1), 93–109.

    Google Scholar 

  • Hong, J., Hong, S., Wang, L., Xu, Y., & Zhao, D. (2015). Government Grants, Private R&D Funding and Innovation Efficiency in Transition Economy. Technology Analysis and Strategic Management, 27(9), 1068–1096.

    Google Scholar 

  • Hong, J., Feng, B., Wu, Y., & Wang, L. (2016). Do government grants promote innovation efficiency in China’s high-tech industries? Technovation, 57–58, 4–13.

    Google Scholar 

  • Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34, 3206–3217.

    Google Scholar 

  • Hussinger, K., & Pacher, S. (2019). Information ambiguity, patents and the market value of innovative assets. Research policy, 48(3), 665–675.

    Google Scholar 

  • Jiang, Z., Wang, Z., & Li, Z. (2018). The effect of mandatory environmental regulation on innovation performance: Evidence from China. Journal of Cleaner Production, 203, 482–491.

    Google Scholar 

  • Jiyoung, L., Chulyeon, K., & Gyunghyun, C. (2019). Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea. European Journal of Operational Research, 278(2), 533–545.

    MathSciNet  MATH  Google Scholar 

  • Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949–962.

    MATH  Google Scholar 

  • Kao, C., & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1), 418–429.

    MATH  Google Scholar 

  • Kloop, G. A. (1985). The analysis of the efficiency of production system with multiple inputs and outputs PhD dissertation. Industrial and System Engineering College, Chicago: University of Illinois.

    Google Scholar 

  • Kontolaimou, A., Giotopoulos, I., & Tsakanikas, A. (2016). A typology of European countries based on innovation efficiency and technology gaps: The role of early-stage entrepreneurship. Economic Modelling, 52, 477–484.

    Google Scholar 

  • Li, L. B.; Liu, B. l.; Liu, W. L.; Chiu, Y. H. Efficiency Evaluation of the Regional High-Tech Industry in China: A New Framework Based on Meta-frontier Dynamic DEA Analysis. Socio-Economic Planning Sciences, 2017, 60, 24–33.

  • Li, Y. (2018). Preferential Tax Policy and Innovation Efficiency of High-Tech Industry. Journal of Quantitative and Technical Economics, 35(1), 60–76.

    Google Scholar 

  • Liu, Z., Chen, X., Chu, J., & Zhu, Q. (2018). Industrial Development Environment and Innovation Efficiency of High-Tech Industry: Analysis Based on the Framework of Innovation Systems. Technology Analysis and Strategic Management, 30(4), 434–446.

    Google Scholar 

  • Liu, S., Jiang, X., & Yu, Q. (2015). Evolving Law of Technology Innovation in High-Tech Industry of China. Journal of Quantitative and Technical Economics, 32(7), 104–116.

    Google Scholar 

  • Liu, K. (2016). Researches on the Inter-provincial R&D Innovation Efficiency for Chinese High-Tech Industry. Modern Economy, 7(9), 921–932.

    Google Scholar 

  • Lozano-Vivas, A., Pastor, J. T., & Pastor, J. M. (2002). An Efficiency Comparison of European Banking Systems Operating under Different Environmental Conditions. Journal of Productivity Analysis, 18(1), 59–77.

    Google Scholar 

  • Lundvall, B. A. (1992). National Systems of Innovation: Toward a Theory of Innovation and Interactive Learning. Research Policy, 24(4), 318–330.

    Google Scholar 

  • Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadistica, 4(2), 209–242.

    MathSciNet  MATH  Google Scholar 

  • Nemoto, J., & Goto, M. (1999). Dynamic Data Envelopment Analysis: Modeling Intertemporal Behavior of a Frim in the Presence of Productive Inefficiencies. Economics Letters., 64(1), 51–56.

    MATH  Google Scholar 

  • O’Donnell, C. J., Prasada Rao, D. S., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231–255.

    Google Scholar 

  • Olefirenko, O., & Shevliuga, O. (2017). Commercialization of innovations: peculiarities of sales policy at innovation active enterprise. Innovative Marketing, 13(2), 6–12.

    Google Scholar 

  • Rayyes, A. E., & Vallspasola, J. (2013). The Effect of Research & Development Activities and Open Innovation Activities: A Key to Low/ Medium Technology Industries and Firms in Catalonia. International Journal of Innovation Science, 5(4), 225–236.

    Google Scholar 

  • 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.

    Google Scholar 

  • Science & Engineering Indicators 2018. Available online: https://www.nsf.gov/statistics/2018/nsb 20181/report/sections/overview/knowledge--and-technology-intensive-economic-activity (accessed on 8 January 2020)

  • Science & Engineering Indicators 2018. Available online: https://www.nsf.gov/statistics/2018 /nsb20181/report/sections/overview/knowledge--and-technology-intensive-economic-activity(accessed on 8 January 2020)

  • Science & Engineering Indicators 2018. Available online :https ://www.nsf.gov/statistics/2018/nsb 20181/report/sections/overview/knowledge--and-technology-intensive-economic-activity(accessed on 8 January 2020)

  • Siebert, R. B. (2017). A structural model on the impact of prediscovery licensing and research joint ventures on innovation and product market efficiency. International Journal of Industrial Organization, 54, 89–124.

    Google Scholar 

  • Song, M., Ai, H., & Li, X. (2015). Political connections, financing constraints, and the optimization of innovation efficiency among China’s private enterprises. Technological Forecasting and Social Change, 92, 290–299.

    Google Scholar 

  • Swati, M. (2018). National Innovation System of India: An Empirical Analysis. Millennial Asia, 9(2), 203–224.

    Google Scholar 

  • Tebaldi, E., & Elmslie, B. (2013). Does institutional quality impact innovation? Evidence from cross-country patent grant data. Applied Economics, 45(7), 887–900.

    Google Scholar 

  • The World Bank. Available online: https://data.worldbank.org.cn/indicator/GB.XPD.RSDV.GD.ZS (accessed on 20 November 2019)

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research., 130, 498–509.

    MathSciNet  MATH  Google Scholar 

  • Tone, K., & Tsutsui, M. (2014). Dynamic DEA with Network Structure: A Slacks-Based Measure Approach. Omega., 42(1), 124–131.

    MATH  Google Scholar 

  • Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A Slacks-based Measure Approach. Omega., 38, 145–156.

    MATH  Google Scholar 

  • Tone, K., & Tsutsui, M. (2009). Network DEA: A Slacks Based Measurement Approach. European Journal of Operational Research., 197, 243–252.

    MATH  Google Scholar 

  • Wang, E. C. (2007). R&D efficiency and economic performance: A cross-country analysis using the stochastic frontier approach. Journal of Policy Modeling, 29(2), 345–360.

    Google Scholar 

  • Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260–273.

    Google Scholar 

  • Wang, Q., Hang, Y., & Sun, L. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261.

    Google Scholar 

  • Xiong, X., Yang, G. L., & Guan, Z. C. (2018). Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences. Journal of Informetrics, 12(3), 784–805.

    Google Scholar 

  • 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.

    Google Scholar 

  • Zhou, H., Dekker, R., & Kleinknecht, A. (2011). Flexible labor and innovation performance: evidence from longitudinal firm-level data. Industrial & Corporate Change, 20(3), 941–968.

    Google Scholar 

Download references

Funding

This study was supported by the international innovation team program in philosophy and social sciences of Jilin university “China's foreign trade and financial cooperation under the BRI” (2019GJTD03), Major Program of the Key Research Base of Philosophy and Social Sciences of Jilin University “The Economic Dependence of China, Japan and Korea and the Construction of New Economic Cooperation under the BRI” (2019XXJD04).

Author information

Authors and Affiliations

  1. School of Economics, Jilin University, No. 2699, Qianjin Street, Changchun, 130012, China

    Yongqi Feng & Haolin Zhang

  2. Department of Economics, Soochow University, No. 56, Kueiyang St., Sec. 1, Taipei, 100, Taiwan

    Yung-ho Chiu & Tzu-Han Chang

Authors
  1. Yongqi Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haolin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yung-ho Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tzu-Han Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, Y.F. and H.Z.; methodology, Y.-h. C.; software, T.-H. C.; validation, Y.-h. C., T.-H. C. and Y.F.; formal analysis, Y.F. and H.Z.; investigation, H.Z.; resources, Y.-h. C. and T.-H. C.; data curation, H.Z.; writing—original draft preparation, H.Z.; writing—review and editing, Y.F., Y.-h. C. and T.-H. C.; visualization, T.-H. C.; supervision, Y.F.; project administration, Y.-h. C.; funding acquisition, Y.F..

Corresponding author

Correspondence to Yung-ho Chiu.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Zhang, H., Chiu, Yh. et al. 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, 3091–3129 (2021). https://doi.org/10.1007/s11192-020-03829-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-020-03829-3

Keywords

Search

Navigation