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X-Efficiency of Innovation Processes: Evaluation Based on Data Envelopment Analysis

Author

Listed:
  • Rahobisoa Herimalala

    (UFR de sciences économiques et de gestion, Université de Caen Basse-Normandie, CREM-CNRS, UMR 6211)

  • Olivier Gaussens

    (UFR de sciences économiques et de gestion, Université de Caen Basse-Normandie, CREM-CNRS, UMR 6211)

Abstract

Innovation in small and medium-sized enterprises (SMEs) is a source of regional development and enables enterprises to improve their competitiveness. However, the intensification of innovation effort depends upon a better understanding of the innovation process, in particular the assessment of its innovation capacity to process its resources and various activities in efficient manner into better results. This paper deals with innovation process modeling and innovation measurement, in order to provide answers to these recurrent questions of the entrepreneurs in these SMEs. Thus, first we propose a model of innovation process as a collective design process that involves the interplay of two categories of activities, such as exploratory activities and value oriented activities, centered on the entrepreneur. Then from this model, we evaluate: (a) the innovation capacity from the process activities and too the outputs of innovation process; (b) the X-(in)efficiency using multiobjective (MOLP) data envelopment analysis (DEA) model of innovation processes. Through MOLP-DEA method, we decompose the X-inefficiency in technical inefficiency and congestion to highlighting the miss-use or the under-utilization of innovation capacity, as resources of process. Finally we measure X-inefficiency by an overall index taking into account of all aspects of inefficiency as the enhanced DEA Russell graph efficiency measure. For the empirical analysis, we use the data from a representative random sample formed by 80 innovative enterprises of regional SMEs of Normandy in France. The results show that most of innovation processes are X-inefficient in SMEs of Normandy. This X-inefficiency is more characterized by the congestion problem than the technical inefficiency. That shows the difficulties of some entrepreneurs to implement the rules and standards of interplay between some activities.

Suggested Citation

  • Rahobisoa Herimalala & Olivier Gaussens, 2012. "X-Efficiency of Innovation Processes: Evaluation Based on Data Envelopment Analysis," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 201215, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    • Handle: RePEc:tut:cremwp:201215
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    References listed on IDEAS

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    1. M P Estellita Lins & L Angulo-Meza & A C Moreira Da Silva, 2004. "A multi-objective approach to determine alternative targets in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1090-1101, October.
    2. Pascal Le Masson & Armand Hatchuel & Benoit Weil, 2011. "The Interplay Between Creativity issues and Design Theories: a new perspective for Design Management Studies?," Post-Print hal-00696122, HAL.
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    Cited by:

    1. Movahedi, Mohammad & Shahbazi, Kiumars & Gaussens, Olivier, 2017. "Innovation and willingness to export: Is there an effect of conscious self-selection?," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-22.
    2. Movahedi, Mohammad & Gaussens, Olivier, 2011. "Innovation, productivity, and export: Evidence from SMEs in Lower Normandy, France," MPRA Paper 40443, University Library of Munich, Germany, revised 07 Jun 2012.

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    More about this item

    Keywords

    Innovation Process; X-Efficiency; Multiobjective Linear Programming; Data Envelopment Analysis; Russell measure;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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