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Capital Stock and Performance of R&D Organizations: A Dynamic DEA-ANP Hybrid Approach

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Handbook of Operations Analytics Using Data Envelopment Analysis

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 239))

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Abstract

Assessing resource allocation in R&D organizations is an important issue that requires a comprehensive measure to characterize it. To provide a greater picture, we first construct a dynamic three-stage network DEA model, which evaluates the R&D efficiency, technology-diffusion efficiency, and value-creation efficiency of Taiwanese R&D organizations over the period 2005–2009. Before integrating window analysis and network data envelopment analysis (DEA) to estimate dynamic efficiencies, we apply Analytic Network Process (ANP) to determine the relative importance of each stage. Subsequently, we employ panel data regression to examine whether the capital stock of patents, quality of human resources, and capability of service support affect the dynamic efficiencies of the R&D organizations. Our findings show that the mean R&D efficiency score is greater than that of the technology-diffusion efficiency, with the value-creation efficiency score being the lowest, suggesting that R&D organizations have to firstly work on improving the technology-diffusion inefficiency, and finally improving the value-creation inefficiency. Our panel data regression analysis indicates that the capital stock of patents do affect the efficiencies of the R&D organizations, even including the quality of human resources and capability of service support. That is, managers should focus on technological development and innovation to improve their corporate performance.

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References

  • Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56(1), 48–58.

    Article  Google Scholar 

  • Chakravarty, S., Feinberg, R., & Widdows, R. (1995). What do consumers want from banks? Journal of Retail Banking, 12, 15–19.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Chen, Y., & Zhu, J. (2004). Measuring information technology’s indirect impact on firm performance. Information Technology and Management, 5(1), 9–22.

    Article  Google Scholar 

  • Cherchye, L., & Abeele, P. V. (2005). On research efficiency: A micro-analysis of Dutch university research in Economics and Business Management. Research Policy, 34(4), 495–516.

    Article  Google Scholar 

  • Co, H., & Chew, K. (1997). Performance and R&D expenditures in American and Japanese manufacturing firms. International Journal of Production Research, 35(12), 3333–3348.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: With DEA-solver software and references. New York: Springer Science.

    Google Scholar 

  • Deeds, D. L., & Hill, C. W. (1996). Strategic alliances and the rate of new product development: An empirical study of entrepreneurial biotechnology firms. Journal of Business Venturing, 11(1), 41–55.

    Article  Google Scholar 

  • Edquist, C. (1997). Systems of innovation: Technologies, institutions, and organizations. London: Pinter Publishers/Cassell Academic.

    Google Scholar 

  • Efrat, K. (2014). The direct and indirect impact of culture on innovation. Technovation, 34(1), 12–20.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.

    Google Scholar 

  • Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.

    Article  Google Scholar 

  • González, E., & Gascón, F. (2004). Sources of productivity growth in the Spanish pharmaceutical industry (1994–2000). Research Policy, 33(5), 735–745.

    Article  Google Scholar 

  • Griliches, Z. (1988). Productivity puzzles and R&D: Another nonexplanation. The Journal of Economic Perspectives, 2(4), 9–21.

    Article  Google Scholar 

  • Griliches, Z. (2007). R&D, Patents and Productivity. Chicago: University of Chicago Press.

    Google Scholar 

  • Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102–115.

    Article  Google Scholar 

  • Guellec, D., & Bruno, V. P. P. (2004). From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter? Oxford Bulletin of Economics and Statistics, 66(3), 353–378.

    Article  Google Scholar 

  • Hall, L. A., & Bagchi-Sen, S. (2007). An analysis of firm-level innovation strategies in the US biotechnology industry. Technovation, 27(1), 4–14.

    Article  Google Scholar 

  • Hartmann, G. C. (2003). Linking R&D spending to revenue growth. Research-Technology Management, 46(1), 39–46.

    Google Scholar 

  • Hsu, K. H. (2005). Using balanced scorecard and fuzzy data envelopment analysis for multinational R&D project performance assessment. Journal of American Academy of Business, 7(1), 189–196.

    Google Scholar 

  • Hung, C.-L., Kuo, S.-J., & Dong, T.-P. (2013). The relationship between team communication, structure, and academic R&D performance: Empirical evidence of the national telecommunication program in Taiwan. R&D Management, 43(2), 121–135.

    Article  Google Scholar 

  • Klopp, G. (1985). The analysis of the efficiency of productive systems with multiple inputs and outputs. Ph.D. thesis, University of Illinois at Chicago, IL, USA. Retrieved from http://search.proquest.com/docview/303474777?accountid=15184, ProQuest Dissertations & Theses A&I database.

  • Lee, H. Y., & Park, Y. T. (2005). An international comparison of R&D efficiency: DEA approach. Asian Journal of Technology Innovation, 13(2), 207–222.

    Article  Google Scholar 

  • Lee, H., Park, Y., & Choi, H. (2009). Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach. European Journal of Operational Research, 196(3), 847–855.

    Article  Google Scholar 

  • Liu, J. S., & Lu, W.-M. (2010). DEA and ranking with the network-based approach: A case of R&D performance. Omega, 38(6), 453–464.

    Article  Google Scholar 

  • Lu, W.-M., & Hung, S.-W. (2011). Exploring the operating efficiency of technology development programs by an intellectual capital perspective—A case study of Taiwan. Technovation, 31(8), 374–383.

    Article  Google Scholar 

  • Lu, W.-M., Kweh, Q. L., & Huang, C.-L. (2014). Intellectual capital and national innovation systems performance. Knowledge-Based Systems, 71, 201–210.

    Article  Google Scholar 

  • Lu, W.-M., Wang, W.-K., Tung, W.-T., & Lin, F. (2010). Capability and efficiency of intellectual capital: The case of fabless companies in Taiwan. Expert Systems with Applications, 37(1), 546–555.

    Article  Google Scholar 

  • Mansfield, E. (1980). Basic research and productivity increase in manufacturing. The American Economic Review, 70(5), 863–873.

    Google Scholar 

  • Mansfield, E. (1988). Industrial R&D in Japan and the United States: A comparative study. The American Economic Review, 78(2), 223–228.

    Google Scholar 

  • Mowery, D. C., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17, 77–91.

    Article  Google Scholar 

  • Nasierowski, W., & Arcelus, F. (2003). On the efficiency of national innovation systems. Socio-Economic Planning Sciences, 37(3), 215–234.

    Article  Google Scholar 

  • Rousseau, S., & Rousseau, R. (1997). Data Envelopment Analysis as a tool for constructing scientometric indicators. Scientometrics, 40(1), 45–56.

    Article  Google Scholar 

  • Rousseau, S., & Rousseau, R. (1998). The scientific wealth of European nations: Taking effectiveness into account. Scientometrics, 42(1), 75–87.

    Article  Google Scholar 

  • Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). Pittsburgh: RWS Publications.

    Google Scholar 

  • Saiki, T., Akano, Y., Watanabe, C., & Tou, Y. (2006). A new dimension of potential resources in innovation: A wider scope of patent claims can lead to new functionality development. Technovation, 26(7), 796–806.

    Article  Google Scholar 

  • Sharma, S., & Thomas, V. (2008). Inter-country R&D efficiency analysis: An application of data envelopment analysis. Scientometrics, 76(3), 483–501.

    Article  Google Scholar 

  • Sipahi, S., & Timor, M. (2010). The analytic hierarchy process and analytic network process: An overview of applications. Management Decision, 48(5), 775–808.

    Article  Google Scholar 

  • Souitaris, V. (2002). Firm–specific competencies determining technological innovation: A survey in Greece. R&D Management, 32(1), 61–77.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. OMEGA—The International Journal of Management Science, 38(3), 145–156.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. OMEGA—The International Journal of Management Science, 42(1), 124–131.

    Article  Google Scholar 

  • Trajtenberg, M. (1990). A penny for your quotes: Patent citations and the value of innovations. The Rand Journal of Economics, 21, 172–187.

    Article  Google Scholar 

  • Upton, D. M. (1995). What really makes factories flexible? Harvard Business Review, 73(4), 74–84.

    Google Scholar 

  • Walwyn, D. (2007). Finland and the mobile phone industry: A case study of the return on investment from government-funded research and development. Technovation, 27(6), 335–341.

    Article  Google Scholar 

  • Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260–273.

    Article  Google Scholar 

  • Wu, W.-Y., Tsai, H.-J., Cheng, K.-Y., & Lai, M. (2006). Assessment of intellectual capital management in Taiwanese IC design companies: Using DEA and the Malmquist productivity index. R&D Management, 36(5), 531–545.

    Article  Google Scholar 

  • Zhang, A., Zhang, Y., & Zhao, R. (2003). A study of the R&D efficiency and productivity of Chinese firms. Journal of Comparative Economics, 31(3), 444–464.

    Article  Google Scholar 

  • Zhu, J. (2009). Quantitative models for performance evaluation and benchmarking: DEA with spreadsheets. Boston: Springer.

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Cultural Vocation Development, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei, 10608, Taiwan

    Yueh-Cheng Wu

  2. Department of Accounting, College of Business Management and Accounting, Universiti Tenaga Nasional, Sultan Haji Ahmad Shah Campus, Muadzam Shah, Pahang, 26700, Malaysia

    Qian Long Kweh

  3. Department of Financial Management, National Defense University, No. 70, Sec. 2, Zhongyang North Rd., Beitou, Taipei, 112, Taiwan

    Wen-Min Lu & Chia-Fa Chang

  4. Department of Business Administration, National Central University, No. 300, Jung-Da Road, Jung-Li City, 320, Tao-Yuan, Taiwan

    Shiu-Wan Hung

Authors
  1. Yueh-Cheng Wu
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  2. Qian Long Kweh
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  3. Wen-Min Lu
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  4. Shiu-Wan Hung
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  5. Chia-Fa Chang
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Corresponding author

Correspondence to Wen-Min Lu .

Editor information

Editors and Affiliations

  1. Dept of Business Administration, Ming Chuan University, Taipei, Taiwan

    Shiuh-Nan Hwang

  2. College of Maritime Science & Mgmt, National Taiwan Ocean University, Keelung, Taiwan

    Hsuan-Shih Lee

  3. School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Joe Zhu

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Wu, YC., Kweh, Q.L., Lu, WM., Hung, SW., Chang, CF. (2016). Capital Stock and Performance of R&D Organizations: A Dynamic DEA-ANP Hybrid Approach. In: Hwang, SN., Lee, HS., Zhu, J. (eds) Handbook of Operations Analytics Using Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 239. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7705-2_7

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