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Performance Evaluation of Research and Business Development: A Case Study of Korean Public Organizations

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  • Jaehun Park

    (Division of Cosmetic Science and Technology, Daegu Haany University, Gyeongsan, Gyeongsangbuk-do 38610, Korea)

  • Joonyoung Kim

    (Defense Agency for Technology and Quality, Jinju, Gyeongsangnam-do 52851, Korea)

  • Si-Il Sung

    (Department of Industrial and Management Engineering, Inje University, Gimhae, Gyeongsangnam-do 50834, Korea)

Abstract

This study presents data envelopment analysis (DEA)-based systematic and simultaneous performance measures for research and business development (R&BD) and internal processes. In particular, we focus on the relationship between the research and development (R&D) process and business development (BD) process in evaluating R&BD overall performance, and provide effective guidelines for relatively underperforming decision-making units to help them establish an optimal strategy to increase their performance. To that end, we develop a two-stage network DEA model based on performance measures by defining variables and transforming R&D and BD performances into a serial network structure. In addition, based on the proposed method, we present a practical application of R&BD performance measurement for Korean public organizations, specifically public research institutes and universities.

Suggested Citation

  • Jaehun Park & Joonyoung Kim & Si-Il Sung, 2017. "Performance Evaluation of Research and Business Development: A Case Study of Korean Public Organizations," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2297-:d:122520
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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    3. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, January.
    4. 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.
    5. 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.
    6. Beasley, J. E., 1990. "Comparing university departments," Omega, Elsevier, vol. 18(2), pages 171-183.
    7. Liu, John S. & Lu, Wen-Min, 2010. "DEA and ranking with the network-based approach: a case of R&D performance," Omega, Elsevier, vol. 38(6), pages 453-464, December.
    8. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    9. Hsu, Fang-Ming & Hsueh, Chao-Chih, 2009. "Measuring relative efficiency of government-sponsored R&D projects: A three-stage approach," Evaluation and Program Planning, Elsevier, vol. 32(2), pages 178-186, May.
    10. Andrea Bonaccorsi & Cinzia Daraio, 2003. "A robust nonparametric approach to the analysis of scientific productivity," Research Evaluation, Oxford University Press, vol. 12(1), pages 47-69, April.
    11. Lee, Hakyeon & Park, Yongtae & Choi, Hoogon, 2009. "Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach," European Journal of Operational Research, Elsevier, vol. 196(3), pages 847-855, August.
    12. Meng, Wei & Zhang, Daqun & Qi, Li & Liu, Wenbin, 2008. "Two-level DEA approaches in research evaluation," Omega, Elsevier, vol. 36(6), pages 950-957, December.
    13. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    14. 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.
    15. Younhee Kim, 2013. "The ivory tower approach to entrepreneurial linkage: productivity changes in university technology transfer," The Journal of Technology Transfer, Springer, vol. 38(2), pages 180-197, April.
    16. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    17. Bozeman, Barry & Rimes, Heather & Youtie, Jan, 2015. "The evolving state-of-the-art in technology transfer research: Revisiting the contingent effectiveness model," Research Policy, Elsevier, vol. 44(1), pages 34-49.
    18. Seema Sharma & V. J. Thomas, 2008. "Inter-country R&D efficiency analysis: An application of data envelopment analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(3), pages 483-501, September.
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    Cited by:

    1. Kun Chen & Xian-tong Ren & Guo-liang Yang & Hai-bo Qin, 2022. "The other side of the coin: The declining of Chinese social science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 127-143, January.
    2. Yoo Hwan Lee & Young Wook Seo, 2018. "Strategies for Sustainable Business Development: Utilizing Consulting and Innovation Activities," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    3. Hongbo Li & Bowen Yao & Xin Yan, 2021. "Data-Driven Public R&D Project Performance Evaluation: Results from China," Sustainability, MDPI, vol. 13(13), pages 1-14, June.

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