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G20 Ülkelerinin İnovasyon Performans Analizi: COVID-19 Dönemini İçeren Yeni Bütünleşik LOPCOW-MAIRCA ÇKKV Yaklaşımı

Year 2024, PRODUCTIVITY FOR INNOVATION, 1 - 20, 15.01.2024

Tayfun Öztaş Gülin Zeynep Öztaş

https://doi.org/10.51551/verimlilik.1320794

Abstract

Amaç: Bu çalışmada G20 ülkelerinin 2018-2022 yılları içerisindeki inovasyon performanslarının çok kriterli karar verme yöntemleri ile ele alınması amaçlanmaktadır. Ayrıca ülkelerin 5 yıllık performansları incelenerek COVID-19 salgınının inovasyon performanslarına bir etkisinin olup olmadığı da irdelenmektedir.
Yöntem: Çalışmada bütünleşik bir LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) yöntemi uygulanmıştır. İlk olarak inovasyon performansını temsil eden göstergeler (kurumlar, beşerî sermaye ve araştırma, altyapı, pazar gelişmişliği, iş gelişmişliği, bilgi ve teknoloji çıktıları, yaratıcı çıktılar) LOPCOW yöntemi ile objektif olarak ağırlıklandırılmıştır. Daha sonra G20 ülkelerinin inovasyon performansları MAIRCA yöntemi ile hesaplanmıştır. Son olarak, elde edilen bulguları desteklemek için karşılaştırmalı bir analiz de sunulmuştur.
Bulgular: Çok kriterli karar verme yöntemleriyle ele alınan inovasyon performans analizi sonucunda, beşerî sermaye ve araştırma en önemli gösterge, Birleşik Devletler de en iyi inovasyon performansına sahip ülke olarak elde edilmiştir. Duyarlılık ve karşılaştırmalı analiz sonucunda ise, bütünleşik LOPCOW-MAIRCA yönteminin güçlü ve güvenilir çıktılar sunduğu sonucuna varılmıştır.
Özgünlük: Bu çalışma 2018-2022 dönemini göz önünde bulundurarak COVID-19 salgınının ülkelerin inovasyon performansı üzerindeki etkisini incelemesi ve kullandığı bütünleşik çok kriterli karar verme yöntemlerinin literatürde henüz uygulanmamış olması nedenleriyle özgün katkılar sunmaktadır.

Keywords

İnovasyon verimliliği Performans analizi ÇKKV LOPCOW MAIRCA

References

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Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period

Year 2024, PRODUCTIVITY FOR INNOVATION, 1 - 20, 15.01.2024

Tayfun Öztaş Gülin Zeynep Öztaş

https://doi.org/10.51551/verimlilik.1320794

Abstract

Purpose: This study aims to examine the innovation performance of G20 countries in 2018-2022 with multi criteria decision making methods. When the 5-year performance was analyzed, it was also revealed whether the COVID-19 outbreak has an impact on the innovation performance of the countries.
Methodology: An integrated LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) method was applied in the study. First, the indicators representing innovation performance (institutions, human capital, and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs) was objectively weighted by the LOPCOW method. Then, the innovation performance of G20 countries was calculated with the MAIRCA method. Finally, a comparative analysis was also presented to support the findings.
Findings: As a result of the innovation performance analysis using multi criteria decision making methods, human capital, and research were found to be the most important indicators, and the United States was found to be the country with the best innovation performance. In the sensitivity and comparative analysis, it was concluded that the integrated LOPCOW-MAIRCA method provides robust outputs.
Originality: This study makes original contributions by analyzing the impact of the COVID-19 pandemic on the innovation performance of countries considering the 2018-2022 period and the integrated multi criteria decision making methods it uses that have not yet been applied in the literature.

Keywords

Innovation productivity Performance analysis MCDM LOPCOW MAIRCA

References

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  • Ali, M.A., Hussin, N., Haddad, H., Al-Araj, R. and Abed, I.A. (2021). “A Multidimensional View of Intellectual Capital: The Impact on Innovation Performance”, Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 216.
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  • Aytekin, A., Ecer, F., Korucuk, S. and Karamaşa, Ç. (2022). “Global Innovation Efficiency Assessment of EU Member and Candidate Countries via DEA-EATWIOS Multi-Criteria Methodology”, Technology in Society, 68, 101896.
  • Bączkiewicz, A., Kizielewicz, B., Shekhovtsov, A., Wątróbski, J. and Sałabun, W. (2021). “Methodical Aspects of MCDM Based E-Commerce Recommender System”, Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2192-2229, DOI: 10.3390/JTAER16060122.
  • Bektaş, S. and Baykuş, O. (2023). “CRITIC ve MAIRCA Yöntemleriyle Türk Dünyası Ülkeleri, Türkiye ve Rusya’nın 2010-2020 Dönemi için Makroekonomik Performanslarının Analizi”, Uluslararası İktisadi ve İdari İncelemeler Dergisi, 39, 107-122.
  • Biswas, S., Bandyopadhyay, G. and Mukhopadhyaya, J.N. (2022). “A Multi-Criteria Framework for Comparing Dividend Pay Capabilities: Evidence from Indian FMCG and Consumer Durable Sector”, Decision Making: Applications in Management and Engineering, 5(2), 140-175, DOI: 10.31181/DMAME0306102022B.
  • Broekel, T., Rogge, N. and Brenner, T. (2018). “The Innovation Efficiency of German Regions–A Shared-Input DEA Approach”, Review of Regional Research, 38, 77-109.
  • Chang, H.F. and Tzeng, G.H. (2010). “A Causal Decision Making Model for Knowledge Management Capabilities to Innovation Performance in Taiwan's High-Tech Industry”, Journal of Technology Management & Innovation, 5(4), 137-146.
  • Chatterjee, K., Pamucar, D. and Zavadskas, E.K. (2018). “Evaluating the Performance of Suppliers Based on Using the R’AMATEL-MAIRCA Method for Green Supply Chain Implementation in Electronics Industry”, Journal of Cleaner Production, 184, 101-129, DOI: 10.1016/J.JCLEPRO.2018.02.186.
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  • Cornell University, INSEAD and WIPO (2019). “The Global Innovation Index 2019: Creating Healthy Lives-The Future of Medical Innovation”.
  • Cornell University, INSEAD, and WIPO (2020). “The Global Innovation Index 2020: Who Will Finance Innovation?”
  • Demir, G., Riaz, M. and Almalki, Y. (2023). “Multi-Criteria Decision Making in Evaluation of Open Government Data Indicators: An Application in G20 Countries”, AIMS Mathematics, 8(8), 18408-18434, DOI: 10.3934/MATH.2023936.
  • Deng, J., Zhang, N., Ahmad, F. and Draz, M.U. (2019). “Local Government Competition, Environmental Regulation Intensity and Regional Innovation Performance: An Empirical Investigation of Chinese Provinces”, International Journal of Environmental Research and Public Health, 16(12), 2130.
  • Durmuş, M. and Tayyar, N. (2017). “AHP ve TOPSIS ile Farklı Kriter Ağırlıklandırma Yöntemlerinin Kullanılması ve Karar Verici Görüşleriyle Karşılaştırılması”, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 12(3), 65-80, DOI: 10.17153/OGUIIBF.303330.
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  • Ecer, F. (2022). “An Extended MAIRCA Method Using Intuitionistic Fuzzy Sets for Coronavirus Vaccine Selection in the Age Of COVID-19”, Neural Computing and Applications, 34(7), 5603-5623, DOI: 10.1007/S00521-021-06728-7.
  • Ecer, F. and Aycin, E. (2023). “Novel Comprehensive MEREC Weighting-Based Score Aggregation Model for Measuring Innovation Performance: The Case of G7 Countries”, Informatica, 34(1), 53-83.
  • Ecer, F. and Pamucar, D. (2022). “A Novel LOPCOW‐DOBI Multi‐Criteria Sustainability Performance Assessment Methodology: An Application in Developing Country Banking Sector”, Omega, 112, 102690, DOI: 10.1016/J.OMEGA.2022.102690.
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There are 67 citations in total.

Details

Primary Language English
Subjects Multiple Criteria Decision Making
Journal Section Articles
Authors

Tayfun Öztaş 0000-0001-8224-5092

Gülin Zeynep Öztaş 0000-0002-6901-6559

Publication Date January 15, 2024
Submission Date June 28, 2023
Published in Issue Year 2024 PRODUCTIVITY FOR INNOVATION

Cite

APA Öztaş, T., & Öztaş, G. Z. (2024). Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period. Verimlilik Dergisi1-20. https://doi.org/10.51551/verimlilik.1320794
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