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X-Efficiency of Innovation Processes: Concept and Evaluation based on Data Envelopment Analysis

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  • Herimalala, Rahobisoa
  • Gaussens, Olivier
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    Abstract

    This paper investigates X-(in)efficiency of innovation processes in Small and Medium-sized Enterprises (SMEs). We have adopted the following approach: (a) we provide both a concept of X-(in)efficiency and a model of innovation processes for each SME; (b) from this model we evaluate both the dimensions of the innovation processes and the X-(in)efficiency of these processes using a variant of the Data Envelopment Analysis (DEA) model; (c) finally, we characterize X-inefficiency by using techniques of exploratory analysis derived from an empirical analysis. Our approach has been applied to regional SMEs in Normandy (France) with a representative random sample of 80 innovative businesses. The results show the existence of X- inefficiency in the innovation processes of SMEs in 71% of cases. This X-inefficiency arises primarily from the difficulties that entrepreneurs face in implementing the interacting rules and standards of exploitation and exploration activities.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 41887.

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    Date of creation: Oct 2012
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    Handle: RePEc:pra:mprapa:41887

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    Related research

    Keywords: Data Envelopment Analysis; Multiple Projections; X-Efficiency; Innovation Process;

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    References

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    1. Mark Dodgson & Sybille Hinze, 2000. "Indicators used to measure the innovation process: defects and possible remedies," Research Evaluation, Oxford University Press, vol. 9(2), pages 101-114, August.
    2. Patrick Brockett & William Cooper & Honghui Deng & Linda Golden & T. Ruefli, 2004. "Using DEA to Identify and Manage Congestion," Journal of Productivity Analysis, Springer, vol. 22(3), pages 207-226, November.
    3. Forsman, Helena, 2011. "Innovation capacity and innovation development in small enterprises. A comparison between the manufacturing and service sectors," Research Policy, Elsevier, vol. 40(5), pages 739-750, June.
    4. Cooper, W. W. & Gu, Bisheng & Li, Shanling, 2001. "Comparisons and evaluations of alternative approaches to the treatment of congestion in DEA," European Journal of Operational Research, Elsevier, vol. 132(1), pages 62-74, July.
    5. F. Hosseinzadeh Lotfi & A. Noora & G. Jahanshahloo & J. Jablonsky & M. Mozaffari & J. Gerami, 2009. "An MOLP based procedure for finding efficient units in DEA models," Central European Journal of Operations Research, Springer, vol. 17(1), pages 1-11, March.
    6. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    7. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    8. Guan, Jian Cheng & Yam, Richard C.M. & Mok, Chiu Kam & Ma, Ning, 2006. "A study of the relationship between competitiveness and technological innovation capability based on DEA models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 971-986, May.
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