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RETRACTED ARTICLE: Appraisal of Science and Economic Factors on Total Number of Granted Patents

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This article was retracted on 12 November 2019

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Abstract

The number of granted patents, as an innovation output at macro level, may be influenced by different factors. In this study the total number of granted European patents was analyzed based on key science and economic factors, i.e. innovation potential indicators. Nine input factors were considered: total number of researchers in the higher education sector, total number of researchers in the government sector, total number of researchers in the business enterprise sector, research and development (R&D) expenditure in the higher education sector, R&D expenditure in the government sector, human resources in science and technology, employment rate, unemployment rate and gross domestic expenditure on R&D. The main goal was to determine which factor has the highest impact on the number of the granted patents. The total number of the granted patents belongs to the electrical engineering, instruments, chemistry, mechanical engineering and other fields. Adaptive neuro-fuzzy inference system (ANFIS) was used as the searching methodology. In general the total number of researchers in the business enterprise sector is the most influential factor for the total number of granted patents.

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Change history

  • 12 November 2019

    The Editor-in-Chief has retracted this article (Markovi�� 2018) because validity of the content of this article cannot be verified.

References

  • Andergassen R, Nardini F, Ricottilli M (2015) Emergence and resilience in a model of innovation and network formation. Netw Spat Econ 15(2):293–311

    Article  Google Scholar 

  • Audretsch DB (2004) Sustaining Innovation and Growth: Public Policy Support for Entrepreneurship. Ind Innov 11(3):167–191

    Article  Google Scholar 

  • Audretsch D, Feldman M (1996) R&D spillovers and the geography of innovative production. Am Econ Rev 86:630–640

    Google Scholar 

  • Bass SD, Kurgan LA (2010) Discovery of factors influencing patent value based on machine learning in the field of nanotechnology. Scientometrics 82(2)

  • Batabyal AA, Beladi H (2017) Patent Protection in a Model of Economic Growth in Multiple Regions. Netw Spat Econ 17:255–268

    Article  Google Scholar 

  • Batabyal AA, Nijkamp P (2014) Innovation, decentralization, and planning in a multi-region model of Schumpeterian Economic Growth. Netw Spat Econ 14(3-4):605–628. https://doi.org/10.1007/s11067-014-9258-2

    Article  Google Scholar 

  • Bottazzi L, Giovanni P (2002) Innovation and Spillovers in Regions: Evidence from European Patent Data. Eur Econ Rev 47:687–710

    Article  Google Scholar 

  • Budescu DV (1993) Dominance Analysis: A New Approach to the Problem of Relative Importance of Predictors in Multiple Regression. Psychol Bull 114:542–551

    Article  Google Scholar 

  • Capello R, Lenzi C (2013) Territorial patterns on innovation and economic growth in European Regions. Growth Chang 44(2):195–227

    Article  Google Scholar 

  • Coto-Millán P, Fernández XL, Pesquera MÁ, Agüeros M (2016) Impact of logistics on technical efficiency of world production (2007–2012). Netw Spat Econ 16(4):981–995

    Article  Google Scholar 

  • Freeman C (1969) Measurement of output of research and experimental development, a review paper. UNESCO, Paris. http://unesdoc.unesco.org/images/0013/001317/131744eo.pdf

  • Gössling T, Rutten R (2007) Innovation in regions. Eur Plan Stud 15(2):253–270

    Article  Google Scholar 

  • Grömping U (2006) Relative importance for linear regression in R: The Package relaimpo. J Stat Softw 17:1. Available at http://www.jstatsoft.org/v17/i01/. (2007)

  • Grömping U (2007) Estimators of Relative Importance in Linear Regression Based on Variance Decomposition. Am Stat 61:139–147

    Article  Google Scholar 

  • Grömping U (2009) Variable Importance Assessment in Regression: Linear Regression versus Random Forest. Am Stat 63(4):308–319. https://doi.org/10.1198/tast.2009.08199

    Article  Google Scholar 

  • Hu M-C, Mathews JA (2008) China’s national innovative capacity. Res Policy 37:1465–1479

    Article  Google Scholar 

  • Jang J-SR (1993) ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Markovic D (2017) Comparative analysis of the most influential parameters that impact innovations in two EU states: Romania as “Modest Innovator” and Italy as “Moderate Innovator”, DSCIM 2017, pp. 58–62

  • Meek VL, Teichler U, Kearney M-L (2009) Higher education, research and innovation: changing dynamics, report on the UNESCO forum on higher education, research and knowledge, 2001–2009. https://www.unikassel.de/einrichtungen/fileadmin/datas/einrichtungen/incher/T_18__UNESCO_Forum_Report__2009.pdf

  • OECD (1963) Frascati manual, the measurement of scientific and technical activities, proposed standard practice for surveys on research and development. OECD. https://www.oecd.org/sti/inno/Frascati-1963.pdf

  • OECD (2013) Main science and technology indicators, Volume 2013/1. OECD. http://www.oecd.org/sti/2013_1_documentation_e.pdf

  • Prodan I (2005) Influence of research and development expenditures on number of patent aplications: selected case studies in OECD countries and Central Europe, 1981–2001. Appl Econ Int Dev 5-4. AEID

  • Rahimi AM (2017) Determination of the main influencing factors on road fatalities using an integrated neuro-fuzzy algorithm. Lat Am J Solids Struct, Print version ISSN 1679-7817, On-line version ISSN 1679–7825

  • Soete B, Stephan A (2004) Introduction: Entrepreneurship, Innovation and Growth. Ind Innov 11(3):161–165

    Article  Google Scholar 

  • Westland JC, Hao JX, Xiao X, Shan S (2016) Substitutes, Complements and Network Effects in Instant Messaging Services. Netw Spat Econ 16(2):525–543

    Article  Google Scholar 

  • Zoltan A, Audretsch D (1989) Patents as a Measure of Innovative Activity. Kyklos 42:171–180. https://doi.org/10.1111/j.1467-6435.1989.tb00186.x

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, University of Nis, Nis, 18000, Serbia

    Dušan Marković

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  1. Dušan Marković
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Correspondence to Dušan Marković.

Additional information

The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of authorship manipulation. The author does not agree to this retraction.

References

1. Marković, D. Netw Spat Econ (2017). https://doi.org/10.1007/s11067-017-9373-y

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Marković, D. RETRACTED ARTICLE: Appraisal of Science and Economic Factors on Total Number of Granted Patents. Netw Spat Econ 18, 1019–1026 (2018). https://doi.org/10.1007/s11067-017-9373-y

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  • DOI: https://doi.org/10.1007/s11067-017-9373-y

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