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Efficiency Analysis of EU and Non-EU R&D Investor Firms on Matched Samples

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  • Onder Belgin

    (Republic of Türkiye Ministry of Industry and Technology
    Atılım University)

Abstract

This study is on the efficiency analysis of EU and non-EU R&D investor firms. The study mainly aims to understand if there is a difference between the efficiency level of EU and non-EU R&D investor firms and what the effecting factors of firm efficiency are. To construct an unbiased group of EU and non-EU firms, propensity score matching (PSM) is employed and thereby the analysis is made with the firms that have similar features. In the efficiency analysis stage, a slacks-based measure data envelopment analysis (SBM DEA) model is used for 2017–2019 period. After that, a panel Tobit regression model is used to examine the factors effecting the efficiency of the EU and non-EU firms. The results showed that EU firms have higher efficiency than non-EU firms only in 2018 and EU firms have very high improvement potential in market capitalization. By panel Tobit regression model, it was understood that capital expenditure intensity has negative effect on both the efficiency of EU and non-EU firms. Size of the firms has negative effect on only non-EU firms.

Suggested Citation

  • Onder Belgin, 2024. "Efficiency Analysis of EU and Non-EU R&D Investor Firms on Matched Samples," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 13601-13621, September.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:3:d:10.1007_s13132-023-01605-1
    DOI: 10.1007/s13132-023-01605-1
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    References listed on IDEAS

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    1. Ha Sung Park & Tae Youn Kim & Daecheol Kim, 2019. "Efficiency Analysis of Zinc Refining Companies," Sustainability, MDPI, vol. 11(22), pages 1-13, November.
    2. Zhang, Bing & Bi, Jun & Fan, Ziying & Yuan, Zengwei & Ge, Junjie, 2008. "Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach," Ecological Economics, Elsevier, vol. 68(1-2), pages 306-316, December.
    3. Dongphil Chun & Yanghon Chung & Chungwon Woo & Hangyeol Seo & Hyesoo Ko, 2015. "Labor Union Effects on Innovation and Commercialization Productivity: An Integrated Propensity Score Matching and Two-Stage Data Envelopment Analysis," Sustainability, MDPI, vol. 7(5), pages 1-19, April.
    4. G D Ferrier & V G Valdmanis, 2004. "Do mergers improve hospital productivity?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1071-1080, October.
    5. Aristovnik, Aleksander, 2012. "The impact of ICT on educational performance and its efficiency in selected EU and OECD countries: a non-parametric analysis," MPRA Paper 39805, University Library of Munich, Germany.
    6. Jin-Li Hu & Chih-Hai Yang & Chiang-Ping Chen, 2014. "R&D Efficiency And The National Innovation System: An International Comparison Using The Distance Function Approach," Bulletin of Economic Research, Wiley Blackwell, vol. 66(1), pages 55-71, January.
    7. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    8. Bogetoft, Peter & Kromann, Lene, 2018. "Evaluating treatment effects using data envelopment analysis on matched samples: An analysis of electronic information sharing and firm performance," European Journal of Operational Research, Elsevier, vol. 270(1), pages 302-313.
    9. 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.
    10. Yongrok Choi & Hua Wen & Hyoungsuk Lee & Hang Yang, 2020. "Measuring Operational Performance of Major Chinese Airports Based on SBM-DEA," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    11. 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.
    12. Thomas, V.J. & Sharma, Seema & Jain, Sudhir K., 2011. "Using patents and publications to assess R&D efficiency in the states of the USA," World Patent Information, Elsevier, vol. 33(1), pages 4-10, March.
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