Evaluation of Innovative Performance of Knowledge based Company by Network Data Envelopment Analysis-Game Theory Approach

Document Type : Research Paper

Authors

1 Associate Prof., Faculty of Management University of Tehran, Iran

2 Ph.D., Industrial Management, University of Tehran, Iran

3 Associate Prof., Faculty of Entrepreneurship University of Tehran, Iran

Abstract

The performance evaluation and efficiency measurement of software companies and compare them together as a set of knowledge-based activities due to the unknown of internal processes and stages common to these companies and ignoring these processes in performance evaluation by data envelopment analysis models and how to consider these processes and procedures for calculating efficiency of each stage and overall efficiency of each company are the issues that this article effort to study them. The main objects of this study are determination the common stages and processes of innovation activities in software companies and measurement of the innovation efficiency of each stages and the overall efficiency of each company over a period of four years. The literature review and experts interviews revealed that each company has two series stages of knowledge production and exploitation processes. To determine the efficiency of each stage and the overall efficiency we used network data envelopment analysis- game theory approach. The modeling is done by the leader-follower method or Stackelberg game for two stages and their solved using GAMS software. The results show that the overall efficiency of all companies is less than one and in the first stage, only two of the 38 companies and in the second stage, only three of them are full efficient. It is suggested that inefficient firms at every stage follow its determined benchmarks to improve their operations.

Keywords

References

  1. Anand, N., Gardner, H.,Morris, T.(2007).Knowledge-based innovation: emergence and embedding of new practice areas in management consulting firms. Academy of Management Journal,50(2): 406-428.‌
  2. Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multi-disciplinary definition of innovation. Mang.Decision, 47(8):23-39.‌
  3. Carlsson, B., Jacobsson, S., Holmén, M., & Rickne, A. (2002). Innovation systems: analytical and methodological issues. Research policy, 31(2): 233-245.
  4. Chen, C., & Yan, H. (2011). Network DEA model for supply chain performance evaluation.Euro. J. of Operational Res,213(1): 147-155.‌
  5. Chen, Y., Cook, W. D., Kao, C., & Zhu, J. (2013). Network DEA pitfalls: Divisional efficiency and frontier projection under general network structures.Euro. J. of Operational Research, 226(3): 507-515.
  6. Cook, W. D., Zhu, J., Bi, G., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2): 1122-1129.
  7. Cook, W., Zhu, G., F. Yang (2010). Network DEA: Additive efficiency decomposition. Euro.J.of Operational Res., 207(2): 1122–1129.
  8. Cooper, W., M. Seiford, and K. Tone (2007). Introduction to data envelopment analysis and its uses: with DEA-solver software. Springer.
  9. Costa, R. (2012). Assessing intellectual capital efficiency and productivity: An application to the Italian yacht manufacturing sector. Expert Systems with Applications, 39(8): 7255–7261.
  10. Du, J., Liang, L., Chen, Y., Cook, W., & Zhu, J. (2011). A bargaining game model for measuring performance of two-stage network structures. Euro. J. of Operational Research, 210(2): 390-397.‌
  11. Dutta, S., O. Narasimhan, and S. Rajiv (2005). Conceptualizing and measuring capabilities: Methodology and empirical application. Strategic Management Journal, 26(3): 277–285.
  12. Färe, R. & S. Grosskopf (2000). Network DEA. Socio-Economic Planning Sciences, 34(1):35–49.
  13. Färe, R., S. Grosskopf, and G. Whittaker (2007). Network DEAin Modeling data irregularities and structural complexities in data envelopment analysis, 209–240. Springer.
  14. Fukuyama, H. and S. Mirdehghan (2012). Identifying the efficiency status in network DEA. Euro. J. of Operational Res., 220(1): 85–92.
  15. Guan, J., & Chen, K. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1): 102-115.
  16. Holt, D.(1992). Entrepreneurship: New venture creation. Prentice Hall.‌
  17. Kao, C. (2009b). Efficiency measurement for parallel production systems. Euro. J. of Operational Research, 196(3): 1107–1112.
  18. Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. Euro.J. of Operational Res., 185(1): 418-429.‌
  19. Kao,C.(2009a). Efficiency decomposition in network DEA: A relational model. Euro. J. of Operational Res., 192(3): 949–962.
  20. Kravtsova, V., & Radosevic, S. (2012). Are systems of innovation in Eastern Europe efficient?. Economic Systems, 36(1): 109-126.
  21. Li, Y., Y. Chen, L. Liang, and J. Xie (2012). DEA models for extended two-stage network structures. Omega, 40(5): 611–618.
    1. Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two‐stage processes: Game approach and efficiency decomposition. Naval Research Logistics (NRL),55(7): 643-653.‌
    2. Liu, X., & Buck, T. (2007). Innovation performance and channels for international technology spillovers: Evidence from Chinese high-tech industries.Research Policy, 36(3): 355-366.‌
    3. Löthgren, M., & Tambour, M. (1999). Productivity and customer satisfaction in Swedish pharmacies: A DEA network model. European Journal of Operational Research, 115(3): 449-458.‌
    4. Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management science, 45(9): 1270-1288.
    5. Sexton, T. & H. Lewis (2003). "Two-Stage DEA: An Application to Major League Baseball." Productivity Analysis,19(2-3): 227-249.
    6. Shahriari, S.(2013). A Network Data Envelopment Analysis (NDEA) Model to evaluate firm’s Strategic Entrepreneurship.(Unpublished doctoral dissertation). Tehran University, Tehran, Iran. (in Persian)
      1. Shahriari, S., Razavi, M. & Asgharizadeh, A.(2013), Fuzzy DEA and new approach FIEP / AHP units for the full ranking decision makers: A Case Study of Humanities Faculty of Tehran University, Industrial Management Journal, 5(1): 21-42. (in Persian)
      2. Sharif, N. (2006). Emergence and development of the National Innovation Systems concept. Research policy, 35(5): 745-766.
      3. Teece, D. (2000). Strategies for managing knowledge: the role of firm structure and industrial context. Long range planning, 33(1): 35-54.‌
      4. Tone, K. and M. Tsutsui (2009). Network DEA: a slacks-based measure approach. . Euro.J. of Operational Res.,197(1): 243–252.
      5. Von Hippel, E. (2007). The sources of innovation. Gabler.‌
      6. Wacker, J. G. (1998). "A definition of theory: research guidelines for different theory-building research methods in operations management." Journal of Operations Management, 16(4): 361-385.
      7. Yu, M., & Lin, T. (2008). Efficiency and effectiveness in railway performance using multi-activity network DEA. Omega, 36(6): 5-17.‌
Industrial Management Journal
Volume 7, Issue 4
January 2016
Pages 721-742

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