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State of an innovation system: theoretical and empirical advance towards an innovation efficiency index

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  • Montalvo, Carlos
  • Moghayer, Saeed
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    Abstract

    Innovation is currently seen as a cornerstone not only for economic development but also as an intrinsic human activity that could help to face the great challenges of human kind. Given the importance of innovation in the new European 2020 Strategy, measuring progress but also monitoring what drives innovation becomes crucial for policy development. Following upon this strategy the new European flag initiative “Innovation Union” called for a new “single” indicator on innovation. Currently the information infrastructure on innovation in Europe contains a number of indicators. Most of the current indicators at the national or sector levels use a performance theoretical framework based on an efficiency model of inputs and outputs. The last five editions of CIS have been a bastion of innovation policy research during the last decade. Despite this, CIS has been criticised for not having an umbrella framework that unifies its different underpinnings to explain what drives innovation to actual innovation and economic outcomes. In this paper we propose a framework that enables the theoretical and empirical linkages between the drivers of innovation to innovation performance via the integration of core features determining innovative behaviour in to a single composite. This index enables to assess the total propensity of firms to innovate and assess the relative innovation performance at the sector and country level. The approach adopted here to create the index overcomes long standing theoretical and methodological issues related to the reduction of complexity in a meaningful form, scope, aggregation, normalisation and validation of innovation composites. The empirical demonstration of the index was done using CIS4 data and the results validate the theoretical structure and robustness of the proposed model. This enables its replication for innovation policy analysis in different settings. The model underlying the proposed index provides not only a depiction of the efficiency of the innovation system but also a link to economic performance and to the factors that determine relative performance.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 38002.

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    Date of creation: 28 Oct 2011
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    Handle: RePEc:pra:mprapa:38002

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    Related research

    Keywords: Innovation indicators; Innovation performance; innovation efficiency; innovation intensity; theory of planned behaviour; CIS; single indicator; composite indicators; sectoral innovation indicators; behavioral economics; psychological economics;

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    References

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    1. Laurens Cherchye & Wim Moesen & Nicky Rogge & Tom Van Puyenbroeck & Michaela Saisana & A. Saltelli & R. Liska & S. Tarantola, 2006. "Creating Composite Indicators with DEA and Robustness Analysis: the case of the Technology Achievement Index," Public Economics Working Paper Series, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics ces0613, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics.
    2. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, Elsevier, vol. 32(8), pages 1365-1379, September.
    3. Laurens Cherchye & Wim Moesen & Tom Van Puyenbroeck, 2003. "Legitimately Diverse, yet Comparable: On Synthesising Social Inclusion Performance in the EU," Public Economics Working Paper Series, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics ces0301, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics.
    4. Giulio Cainelli & Rinaldo Evangelista & Maria Savona, 2006. "Innovation and economic performance in services: a firm-level analysis," Cambridge Journal of Economics, Oxford University Press, Oxford University Press, vol. 30(3), pages 435-458, May.
    5. Chris Freeman & Luc Soete, 1997. "The Economics of Industrial Innovation, 3rd Edition," MIT Press Books, The MIT Press, The MIT Press, edition 3, volume 1, number 0262061953, December.
    6. Carlos Montalvo, 2007. "Explaining and predicting the impact of regulation on innovation: towards a dynamic model," International Journal of Public Policy, Inderscience Enterprises Ltd, Inderscience Enterprises Ltd, vol. 2(1/2), pages 5-31.
    7. Carlsson, Bo & Jacobsson, Staffan & Holmen, Magnus & Rickne, Annika, 2002. "Innovation systems: analytical and methodological issues," Research Policy, Elsevier, Elsevier, vol. 31(2), pages 233-245, February.
    8. Nasierowski, W. & Arcelus, F. J., 2003. "On the efficiency of national innovation systems," Socio-Economic Planning Sciences, Elsevier, Elsevier, vol. 37(3), pages 215-234, September.
    9. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, Elsevier, vol. 50(2), pages 179-211, December.
    10. Alfred Kleinknecht & Kees Van Montfort & Erik Brouwer, 2002. "The Non-Trivial Choice between Innovation Indicators," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 11(2), pages 109-121.
    11. Malerba, Franco, 2002. "Sectoral systems of innovation and production," Research Policy, Elsevier, Elsevier, vol. 31(2), pages 247-264, February.
    12. Katz, J. Sylvan, 2006. "Indicators for complex innovation systems," Research Policy, Elsevier, Elsevier, vol. 35(7), pages 893-909, September.
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