IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i12p2297-d122520.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/12/2297/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/12/2297/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    7. 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.
    8. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, October.
    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. 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.
    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. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Beasley, J. E., 1990. "Comparing university departments," Omega, Elsevier, vol. 18(2), pages 171-183.
    18. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    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. 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.

    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. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    2. Ibrahim Alnafrah, 2021. "Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-28, December.
    3. 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.
    4. 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.
    5. Lee, Hakyeon & Shin, Juneseuk, 2014. "Measuring journal performance for multidisciplinary research: An efficiency perspective," Journal of Informetrics, Elsevier, vol. 8(1), pages 77-88.
    6. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    7. Mehdi Rhaiem, 2017. "Measurement and determinants of academic research efficiency: a systematic review of the evidence," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 581-615, February.
    8. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    9. Eucabeth Majiwa & Boon L. Lee & Clevo Wilson & Hidemichi Fujii & Shunsuke Managi, 2018. "A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya’s rice processing industry," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 10(3), pages 631-648, June.
    10. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    11. Chiang Kao & Shiang-Tai Liu, 2022. "Stochastic efficiencies of network production systems with correlated stochastic data: the case of Taiwanese commercial banks," Annals of Operations Research, Springer, vol. 315(2), pages 1151-1174, August.
    12. He, Haoran & Weng, Qian, 2012. "Ownership, autonomy, incentives and efficiency: Evidence from the forest product processing industry in China," Journal of Forest Economics, Elsevier, vol. 18(3), pages 177-193.
    13. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    14. Vladimír Holý & Karel Šafr, 2018. "Are economically advanced countries more efficient in basic and applied research?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(4), pages 933-950, December.
    15. Kao, Chiang & Liu, Shiang-Tai, 2014. "Measuring performance improvement of Taiwanese commercial banks under uncertainty," European Journal of Operational Research, Elsevier, vol. 235(3), pages 755-764.
    16. Sokol, Ondřej & Frýd, Lukáš, 2023. "DEA efficiency in agriculture: Measurement unit issues," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    17. Yang, Guoliang & Ahlgren, Per & Yang, Liying & Rousseau, Ronald & Ding, Jielan, 2016. "Using multi-level frontiers in DEA models to grade countries/territories," Journal of Informetrics, Elsevier, vol. 10(1), pages 238-253.
    18. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    19. Aggelopoulos, Eleftherios & Georgopoulos, Antonios, 2017. "Bank branch efficiency under environmental change: A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1170-1188.
    20. Ohene-Asare, Kwaku & Turkson, Charles & Afful-Dadzie, Anthony, 2017. "Multinational operation, ownership and efficiency differences in the international oil industry," Energy Economics, Elsevier, vol. 68(C), pages 303-312.

    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:gam:jsusta:v:9:y:2017:i:12:p:2297-:d:122520. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.