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Link to original content: https://doi.org/10.1007/S10479-017-2728-4
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Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions

  • S.I.: BALCOR-2017
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Abstract

The main aim of this paper is to investigate the impact of patent applications, development level, employment level and degree of technological diversity on innovation efficiency. Innovation efficiency is derived by relating innovation inputs and innovation outputs. Expenditures in Research and Development and Human Capital stand for innovation inputs. Technological knowledge diffusion that comes from spatial and technological neighborhood stands for innovation output. We derive innovation efficiency using Data Envelopment Analysis for 192 European regions for a 12-year period (1995–2006). We also examine the impact of patents production, development and employment level and the level of technological diversity on innovation efficiency using Structural Equation Modeling. This paper contributes a method of innovation efficiency estimation in terms of regional knowledge spillovers and causal relationship of efficiency measurement criteria. The study reveals that the regions presenting high innovation activities through patents production have higher innovation efficiency. Additionally, our findings show that the regions characterized by high levels of employment achieve innovation sources exploitation efficiently. Moreover, we find that the level of regional development has both a direct and indirect effect on innovation efficiency. More accurately, transition and less developed regions in terms of per capita GDP present high levels of efficiency if they innovate in specific and limited technological fields. On the other hand, the more developed regions can achieve high innovation efficiency if they follow a more decentralized innovation policy.

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Notes

  1. Data were retrieved from http://ec.europa.eu/eurostat.

  2. Following Kalapouti and Varsakeli’s (2015) methodology we construct inter- regional knowledge spillovers.

  3. Based on the report of European Commission (2014).

  4. Variety, according to Audretsch et al. (2010), refers to “richness”, that is the sectors that are present in the specific region and the distribution of those different sectors within the regional economic activity. Raw data reveal that not all sectors are present in the innovative activity of every region

  5. Kalapouti and Varsakeli’s (2015) methodology is used.

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Author information

Authors and Affiliations

  1. Department of Economics, Aristotle University of Thessaloniki, Thessaloníki, Greece

    Kleoniki Kalapouti

  2. Information Systems and e-Business Lab, Room 332, Department of Applied Informatics, University of Macedonia, 156 Egnatia Str., 54006, Thessaloníki, Greece

    Konstantinos Petridis

  3. Department of Agricultural Development, Democritus University of Thrace, Pantazidou 193, Orestiada, Greece

    Chrisovalantis Malesios

  4. Aston Business School, Aston University, Aston Triangle, Birmingham, B4 7ET, UK

    Prasanta Kumar Dey

Authors
  1. Kleoniki Kalapouti
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  2. Konstantinos Petridis
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  3. Chrisovalantis Malesios
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  4. Prasanta Kumar Dey
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Corresponding author

Correspondence to Konstantinos Petridis.

Appendix

Appendix

See Tables 3 and 4.

Table 3 Fully efficient DMUs under CRS technology
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Table 4 Fully efficient DMUs under VRS technology
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Kalapouti, K., Petridis, K., Malesios, C. et al. Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions. Ann Oper Res 294, 297–320 (2020). https://doi.org/10.1007/s10479-017-2728-4

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