IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i4d10.1007_s11192-020-03829-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Yongqi Feng

    (Jilin University)

  • Haolin Zhang

    (Jilin University)

  • Yung-ho Chiu

    (Soochow University)

  • Tzu-Han Chang

    (Soochow University)

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.

Suggested Citation

  • Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 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, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-020-03829-3
    DOI: 10.1007/s11192-020-03829-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03829-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03829-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hussinger, Katrin & Pacher, Sebastian, 2019. "Information ambiguity, patents and the market value of innovative assets," Research Policy, Elsevier, vol. 48(3), pages 665-675.
    2. Kao, Chiang, 2009. "Efficiency decomposition in network data envelopment analysis: A relational model," European Journal of Operational Research, Elsevier, vol. 192(3), pages 949-962, February.
    3. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    4. Colombelli, Alessandra & Grilli, Luca & Minola, Tommaso & Mrkajic, Boris, 2020. "To what extent do young innovative companies take advantage of policy support to enact innovation appropriation mechanisms?," Research Policy, Elsevier, vol. 49(10).
    5. Kontolaimou, Alexandra & Giotopoulos, Ioannis & Tsakanikas, Aggelos, 2016. "A typology of European countries based on innovation efficiency and technology gaps: The role of early-stage entrepreneurship," Economic Modelling, Elsevier, vol. 52(PB), pages 477-484.
    6. Edinaldo Tebaldi & Bruce Elmslie, 2013. "Does institutional quality impact innovation? Evidence from cross-country patent grant data," Applied Economics, Taylor & Francis Journals, vol. 45(7), pages 887-900, March.
    7. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    8. Song, Malin & Ai, Hongshan & Li, Xie, 2015. "Political connections, financing constraints, and the optimization of innovation efficiency among China's private enterprises," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 290-299.
    9. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    10. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    11. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    12. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    13. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    14. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    15. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    16. Li, Lan-bing & Liu, Bing-lian & Liu, Wei-lin & Chiu, Yung-Ho, 2017. "Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 24-33.
    17. Haibo Zhou & Ronald Dekker & Alfred Kleinknecht, 2011. "Flexible labor and innovation performance: evidence from longitudinal firm-level data," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 20(3), pages 941-968, June.
    18. Ana Lozano-Vivas & Jesús Pastor & José Pastor, 2002. "An Efficiency Comparison of European Banking Systems Operating under Different Environmental Conditions," Journal of Productivity Analysis, Springer, vol. 18(1), pages 59-77, July.
    19. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    20. Astrid Cullmann & Jens Schmidt-Ehmcke & Petra Zloczysti, 2009. "Innovation, R&D Efficiency and the Impact of the Regulatory Environment: A Two-Stage Semi-Parametric DEA Approach," Discussion Papers of DIW Berlin 883, DIW Berlin, German Institute for Economic Research.
    21. I. L. Beilin* & V. V. Khomenko & N. M. Yakupova & E. I. Kadochnikova & D. D. Aleeva, 2018. "Modeling of Economic Effects of commercialization of High-Tech Developments at Small Innovative Enterprises of Polymer Profile," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 188-193:5.
    22. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    23. Chen, Ping-Chuan & Hung, Shiu-Wan, 2016. "An actor-network perspective on evaluating the R&D linking efficiency of innovation ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 303-312.
    24. Salas-Velasco, Manuel, 2018. "Production efficiency measurement and its determinants across OECD countries: The role of business sophistication and innovation," Economic Analysis and Policy, Elsevier, vol. 57(C), pages 60-73.
    25. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    26. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 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, Elsevier, vol. 12(3), pages 784-805.
    27. Siebert, Ralph Bernd, 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, Elsevier, vol. 54(C), pages 89-124.
    28. Wang, Eric C., 2007. "R&D efficiency and economic performance: A cross-country analysis using the stochastic frontier approach," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 345-360.
    29. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    30. Nemoto, Jiro & Goto, Mika, 1999. "Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies," Economics Letters, Elsevier, vol. 64(1), pages 51-56, July.
    31. Beneito, Pilar & Rochina-Barrachina, María Engracia & Sanchis, Amparo, 2015. "The path of R&D efficiency over time," International Journal of Industrial Organization, Elsevier, vol. 42(C), pages 57-69.
    32. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    33. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    34. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    35. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    36. Gao, Wenlian & Chou, Julia, 2015. "Innovation efficiency, global diversification, and firm value," Journal of Corporate Finance, Elsevier, vol. 30(C), pages 278-298.
    37. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Panagiotis Mitropoulos & Alexandros Mitropoulos, 2023. "Evaluating efficiency and technology gaps of the national systems of entrepreneurship using stochastic DEA and club convergence," Operational Research, Springer, vol. 23(1), pages 1-28, March.
    2. Yi Ji & Hechang Cai & Zilong Wang, 2023. "Impact of Industrial Synergy on the Efficiency of Innovation Resource Allocation: Evidence from Chinese Metropolitan Areas," Land, MDPI, vol. 12(1), pages 1-16, January.
    3. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    4. Zhang, Zhongqingyang & Zhu, Huiming & Zhou, Zhongbao & Zou, Kai, 2022. "How does innovation matter for sustainable performance? Evidence from small and medium-sized enterprises," Journal of Business Research, Elsevier, vol. 153(C), pages 251-265.
    5. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    6. German Blanco & Rajeev K. Goel, 2023. "Do weak institutions undermine global innovation production efficiency?," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1813-1838, October.
    7. Xueling Guan & Lijiang Chen & Qing Xia & Zhaohui Qin, 2022. "Innovation Efficiency of Chinese Pharmaceutical Manufacturing Industry from the Perspective of Innovation Ecosystem," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    8. Yukun Shi & Duchun Wang & Zimeng Zhang, 2022. "Categorical Evaluation of Scientific Research Efficiency in Chinese Universities: Basic and Applied Research," Sustainability, MDPI, vol. 14(8), pages 1-16, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    2. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.
    3. Zhen Shi & Yingju Wu & Yung-ho Chiu & Fengping Wu & Changfeng Shi, 2020. "Dynamic Linkages among Mining Production and Land Rehabilitation Efficiency in China," Land, MDPI, vol. 9(3), pages 1-25, March.
    4. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    5. Chiu, Yung-ho & Huang, Kuei-Ying & Chang, Tzu-Han & Lin, Tai-Yu, 2021. "Efficiency assessment of coal mine use and land restoration: Considering climate change and income differences," Resources Policy, Elsevier, vol. 73(C).
    6. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    7. Chen, Kaihua, 2014. "Measuring and decomposing the overall efficiency of multi-period and -division systems associated with DEA," MPRA Paper 55073, University Library of Munich, Germany.
    8. Yu, Ming-Miin & Lin, Chung-I & Chen, Kuan-Chen & Chen, Li-Hsueh, 2021. "Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach," Omega, Elsevier, vol. 98(C).
    9. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    10. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    11. Hsiao-Yen Mao & Wen-Min Lu & Hsin-Yen Shieh, 2023. "Exploring the Influence of Environmental Investment on Multinational Enterprises’ Performance from the Sustainability and Marketability Efficiency Perspectives," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    12. Junhee Bae & Yanghon Chung & Hyesoo Ko, 2021. "Analysis of efficiency in public research activities in terms of knowledge spillover: focusing on earthquake R&D accomplishments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2249-2264, September.
    13. Suvvari Anandarao & S. Raja Sethu Durai & Phanindra Goyari, 2019. "Efficiency Decomposition in two-stage Data Envelopment Analysis: An application to Life Insurance companies in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 271-285, June.
    14. Mohammad Nourani & Qian Long Kweh & Irene Wei Kiong Ting & Wen-Min Lu & Anna Strutt, 2022. "Evaluating traditional, dynamic and network business models: an efficiency-based study of Chinese insurance companies," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(4), pages 905-943, October.
    15. Kao, Chiang, 2014. "Efficiency decomposition in network data envelopment analysis with slacks-based measures," Omega, Elsevier, vol. 45(C), pages 1-6.
    16. Akther, Syed & Fukuyama, Hirofumi & Weber, William L., 2013. "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking," Omega, Elsevier, vol. 41(1), pages 88-96.
    17. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2022. "Innovation efficiency and technology heterogeneity within China's new energy vehicle industry: A two-stage NSBM approach embedded in a three-hierarchy meta-frontier framework," Energy Policy, Elsevier, vol. 161(C).
    18. Xiao-Ning Li & Ying Feng & Pei-Ying Wu & Yung-Ho Chiu, 2021. "An Analysis of Environmental Efficiency and Environmental Pollution Treatment Efficiency in China’s Industrial Sector," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    19. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    20. Aparicio, Juan & Kapelko, Magdalena, 2019. "Accounting for slacks to measure dynamic inefficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 463-471.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-020-03829-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.