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A study on framework for effective R&D performance analysis of Korea using the Bayesian network and pairwise comparison of AHP

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Abstract

To effectively evaluate and analyze R&D performance, it is necessary to measure the relative importance of performance analysis factors and quantitative analysis methods that consider the objectivity and relevance of detail factors that constitute performance evaluation.

This study suggests a framework for R&D performance evaluations by computing weights through an AHP (Analytical Hierarchy Process) expert survey and by applying a Bayesian Network approach whereby, through which, giving objectivity and allowing inference analyses. This framework can be used as a performance analysis indicator, which uses input and output performance factors in order to perform quantitative analysis for projects. We can quantitatively define the satisfactory level of each project and each performance analysis factor by assigning probability values. It is possible to analyze the relationship between project evaluation results (qualitative evaluation) and performance analysis indicator (quantitative performance). This performance analysis framework can infer posteriori probability using the prior probability and the likelihood function of each performance factor. In addition, by inferring the relationships among performance factors, it allows performing probability analyses on the successful and unsuccessful factors, which can provide further feedback.

In conclusion, the framework would improve the national R&D program in terms of financial investment efficiency by aligning budget allocation and performance evaluation.

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References

  1. Pearl J (2000) Causality: models, reasoning, and inference. Cambridge University Press, New York

    MATH  Google Scholar 

  2. Yang HM (2011) Strategy for effective management of basic research through performance analysis. Ministry of Education and Technology in Korea, Seoul, pp 109–230

    Google Scholar 

  3. Lee SY (2008) Improving the BK21 program evaluation—performance analysis of the phase I. Korea Research Foundation, Seoul, pp 28–37

    Google Scholar 

  4. Vonortas N, Lackey M (2002) Real options approach for evaluating public sector R&D investments. In: Learning from science and technology policy evaluation

    Google Scholar 

  5. STAR Metrics (2010) Science & technology for America’s reinvestment: measuring the effects of research on innovation, competitiveness and science

  6. Mitsubishi Research Institute (2009) Review on the progress and performance of R&D evaluation: focused on R&D evaluation of ministry of economy, trade and industry in Japan

  7. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    MATH  Google Scholar 

  8. Choi YM, Choo MW, Chin SA (2005) Prototyping a student model for educational games. J Inf Process Syst 1(1):107–111

    Google Scholar 

  9. Huang Y-P, Lai S-L (2012) Novel query-by-Humming/Singing method with fuzzy inference system. J Converg 3(4):1–8

    Google Scholar 

  10. Cho J-H (2012) A context awareness and prediction support system for efficient management of u-city. J Internet Technol 13(3):509–520

    Google Scholar 

  11. Viswanathan V, Krishnamurthi (2012) Finding relevant semantic association paths through user-specific intermediate entities. Hum-Cent Comput Inf Sci. doi:10.1186/2192-1962-2-9

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012).

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Authors and Affiliations

  1. Center of R&D Feasibility Analysis, Korea Institute of S&T Evaluation and Planning, Seoul, Korea

    Jae-Hyuk Cho

  2. Division of R&D Results Diffusion, Korea Institute of S&T Evaluation and Planning, Seoul, Korea

    Kil-Woo Lee

  3. Division of Knowledge & Information, Korea Institute of S&T Evaluation and Planning, Seoul, Korea

    Hong-Min Son

  4. Team of Panning, Korea Institute of S&T Evaluation and Planning, Seoul, Korea

    Hyun-Sik Kim

Authors
  1. Jae-Hyuk Cho
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  2. Kil-Woo Lee
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  3. Hong-Min Son
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  4. Hyun-Sik Kim
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Correspondence to Jae-Hyuk Cho.

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Cho, JH., Lee, KW., Son, HM. et al. A study on framework for effective R&D performance analysis of Korea using the Bayesian network and pairwise comparison of AHP. J Supercomput 65, 593–611 (2013). https://doi.org/10.1007/s11227-013-0876-0

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  • DOI: https://doi.org/10.1007/s11227-013-0876-0

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