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A Model Experiment on Effective Factors in Artificial Intelligence Technologies: A Panel Data Analysis with the Most Successful Countries

Yıl 2022, Cilt 17, Sayı 2, 368 - 386, 01.08.2022

Öz

The aim of this study is to investigate the effective factors in the success of the most successful countries in AI technologies. For this purpose, a dynamic model has been established in order to determine the relationship between AI patents, R&D expenditures, number of researchers and scientific publications, based on the 2005-2017 period data on the most successful countries in AI technologies. This model was estimated by the S-GMM method and the relationship of these factors was investigated. As a result of the econometric analysis, the positive relationship between R&D expenditures, the number of scientific publications and researchers in AI technologies has been empirically revealed.

Kaynakça

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Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi

Yıl 2022, Cilt 17, Sayı 2, 368 - 386, 01.08.2022

Öz

Bu çalışmanın amacı, yapay zekâ teknolojilerinde en başarılı ülkelerin bu başarılarındaki etkili faktörleri araştırmaktır. Bu amaçla yapay zekâ teknolojilerinde en başarılı ülkelere ilişkin 2005-2017 dönemi verileri esas alınarak yapay zekâ patentlerinin, Ar-Ge harcamaları, araştırmacı sayıları ve bilimsel yayın sayıları ile ilişkisini tespit etmek maksadıyla dinamik bir model kurulmuştur. Bu model S-GMM yöntemiyle tahmin edilerek söz konusu faktörlerin ilişkisi araştırılmıştır. Ekonometrik analiz sonucunda, yapay zekâ teknolojilerinde Ar-Ge harcamalarının, bilimsel yayın sayılarının ve araştırmacı sayısının pozitif yönlü ilişkisi ampirik olarak ortaya konulmuştur.

Kaynakça

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  • Acemoğlu, D. (2020), IMF Conference, Sixth Richard Goode Lecture: Remaking the Post-COVID World, December 4, 2020, https://www.imf.org/en/News/ Seminars/Conferences/2020/11/18/sixth-richard-goode-lecture-remaking-thepost-covid-world (Erişim: 10.11.2021).
  • Acemoğlu, D; Restrepo, P. (2019), “The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand”, IZA Discussion Paper No: 12704, Institute of Labor Economics.
  • Aguilera, A.; Barrera, M. G. R. (2016), “Technological Unemployment: an Approximation to the Latin American Case”, AD-Minister, No. 29: 59-78.
  • Ali, A.; Sinha, K. (2014), “Exploring the opportunities and challenges in nanotechnology innovation in India”, J. Soc. Sci. Poli. Imp, Vol. 2, No. 2: 227-251.
  • Altuzarra, A. (2019), “R&D and patents: Is it a two way street?”, Economics of Innovation and New Technology, Vol. 28, No.2: 180–196.
  • Anderson, T. W.; Hsiao, C. (1981), “Estimation of Dynamic Models with Error Components”, Journal of the American Statistical Association, No. 76: 598-606.
  • Anderson, T. W.; Hsiao, C. (1982), “Formulation and Estimation of Dynamic Models u-Using Panel Data”, Journal of Econometrics, No. 18: 47-82.
  • Arellano, M. (2003), Panel Data Econometrics, Oxford: Oxford University Press.
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  • Arellano, M.; Bover, O. (1995), “Another Look at the Instrumental Variables Estimation of Error Component Models”, Journal of Econometrics, No. 68: 29-51.
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  • Baltagi, B. H. (2005), Econometric Analysis of Panel Data, 3rd Edition, New York: John Wiley & Sons Inc.
  • Baruffaldi, S.; van Beuzekom, B.; Dernis, H.; Harhoff, D.; Rao, N.; Rosenfeld, D.; Squicciarini, M. (2020), “Identifying and measuring developments in artificial intelligence: Making the impossible possible”, OECD Science, Technology and Industry Working Papers, No. 2020/05, OECD Publishing, Paris.
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  • Becker, G. S.; Murphy, K. M.; Tamura, R. (1990), “Human Capital, Fertility and Economic Growth”, The Journal of Political Economy, Vol. 98, No. 5: 12-37.
  • Bloom, N.; Van Reenen, J. (2002), “Patents, real options and firm performance”, The Economic Journal, No. 112: 97-116.
  • Blundell, R.; Bond, S. (1998), “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models”, Journal of Econometrics, No. 87: 115-143.
  • Bond, S.; Bowsher, C.; Windmeijer. F. (2001), “Criterion-based Inference for GMM in Autoregressive Panel Data Models”, Economic Letters, No. 73: 379-388.
  • Brouwer, E.; Kleinknecht, A.; Reijnen, J. O. N. (1993), “Employment Growth and Innovation at the Firm Level”, Journal of Evolutionary Economics, No. 3: 153-159.
  • Chen, Z.; Zhang, J. (2019), “Types of patents and driving forces behind the patent growth in China”, Economic Modelling, No. 80: 294-302.
  • Chen, Z.; Zhang, J.; Zi, Y. (2021), “A cost-benefit analysis of R&D and patents: Firm-level evidence from China”, European Economic Review, Vol. 133, No. 103633: 1-28.
  • Cohen, W. M.; Nelson, R. R.; Walsh, J. P. (2002), “Links and impacts: the influence of public research on industrial R&D”, Management Science, Vol. 48, No.1: 1-23.
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Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal
Bölüm Makaleler
Yazarlar

İbrahim DAĞLI> (Sorumlu Yazar)
Kıbrıs Batı Üniversitesi
0000-0001-8199-821X
Türkiye

Yayımlanma Tarihi 1 Ağustos 2022
Başvuru Tarihi 22 Aralık 2021
Kabul Tarihi 2 Mart 2022
Yayınlandığı Sayı Yıl 2022, Cilt 17, Sayı 2

Kaynak Göster

APA Dağlı, İ. (2022). Yapay Zekâ Teknolojilerinde Etkili Faktörler Üzerine Bir Model Denemesi: En Başarılı Ülkelerle Panel Veri Analizi . Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi , 17 (2) , 368-386 . Retrieved from https://dergipark.org.tr/tr/pub/oguiibf/issue/70614/1039958