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

Yıl 2024, PRODUCTIVITY FOR INNOVATION, 1 - 20, 15.01.2024
https://doi.org/10.51551/verimlilik.1320794

Öz

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.

Kaynakça

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  • Alnafrah, I. (2021). “Efficiency Evaluation of BRICS’s National Innovation Systems Based on Bias-Corrected Network Data Envelopment Analysis”, Journal of Innovation and Entrepreneurship, 10, 26, DOI: 10.1186/S13731-021-00159-3.
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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.
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  • Chen, Y., Li, W. and Yi, P. (2020). “Evaluation of City Innovation Capability Using the TOPSIS-Based Order Relation Method: The Case of Liaoning Province, China”, Technology in Society, 63, 101330.
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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.
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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.
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Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period

Yıl 2024, PRODUCTIVITY FOR INNOVATION, 1 - 20, 15.01.2024
https://doi.org/10.51551/verimlilik.1320794

Öz

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.

Kaynakça

  • Adar, T. and Delice, E.K. (2019). “New Integrated Approaches Based on MC-HFLTS for Healthcare Waste Treatment Technology Selection”, Journal of Enterprise Information Management, 32(4), 688-711, DOI: 10.1108/JEIM-10-2018-0235.
  • 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.
  • Alnafrah, I. (2021). “Efficiency Evaluation of BRICS’s National Innovation Systems Based on Bias-Corrected Network Data Envelopment Analysis”, Journal of Innovation and Entrepreneurship, 10, 26, DOI: 10.1186/S13731-021-00159-3.
  • Ayan, B., Abacıoğlu, S. and Basilio, M.P. (2023). “A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making”, Information, 14(5), 285, DOI: 10.3390/INFO14050285.
  • 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.
  • Chen, Y., Li, W. and Yi, P. (2020). “Evaluation of City Innovation Capability Using the TOPSIS-Based Order Relation Method: The Case of Liaoning Province, China”, Technology in Society, 63, 101330.
  • Cornell University, INSEAD and WIPO (2018). “The Global Innovation Index 2018: Energizing the World with Innovation”.
  • 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.
  • Ecer, F. (2021). “A Consolidated MCDM Framework for Performance Assessment of Battery Electric Vehicles Based on Ranking Strategies”, Renewable and Sustainable Energy Reviews, 143, 110916, DOI: 10.1016/J.RSER.2021.110916.
  • 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.
  • Ecer, F., Böyükaslan, A. and Hashemkhani Zolfani, S. (2022). “Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology”, Axioms, 11(8), 404, DOI: 10.3390/AXIOMS11080404.
  • Ecer, F., Küçükönder, H., Kayapınar Kaya, S. and Görçün, Ö.F. (2023a). “Sustainability Performance Analysis of Micro-Mobility Solutions in Urban Transportation with A Novel IVFNN-Delphi-LOPCOW-CoCoSo Framework”, Transportation Research Part A: Policy and Practice, 172, 103667, DOI: 10.1016/J.TRA.2023.103667.
  • Ecer, F., Ögel, İ.Y., Krishankumar, R. and Tirkolaee, E.B. (2023b). “The Q-Rung Fuzzy LOPCOW-VIKOR Model to Assess the Role of Unmanned Aerial Vehicles for Precision Agriculture Realization in the Agri-Food 4.0 Era”, Artificial Intelligence Review, 56, 13373-13406, DOI: 10.1007/S10462-023-10476-6.
  • Erdin, C. and Çağlar, M. (2023). “National Innovation Efficiency: A DEA-Based Measurement of OECD Countries”, International Journal of Innovation Science, 15(3), 427-456.
  • Ersoy, N. (2023). “BIST Perakende Ticaret Sektöründe LOPCOW-RSMVC Modeli ile Performans Ölçümü”, Sosyoekonomi, 31(57), 419-436.
  • Fetanat, A. and Tayebi, M. (2023). “Industrial Filtration Technologies Selection for Contamination Control in Natural Gas Processing Plants: A Sustainability and Maintainability-Based Decision Support System Under Q- Rung Orthopair Fuzzy Set”, Process Safety and Environmental Protection, 170, 310-327, DOI: 10.1016/J.PSEP.2022.12.014.
  • G20. (2023). “G20 - Background Brief”, https://www.g20.org/content/dam/gtwenty/gtwenty_new/about_g20/G20_Background_Brief.pdf (Accessed: 19.06.2023)
  • Garcia-Bernabeu, A., Cabello, J.M. and Ruiz, F. (2020). “A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain”, Mathematics, 8(5), 797.
  • Gigović, L., Pamučar, D., Bajić, Z. and Milićević, M. (2016). “The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots”, Sustainability, 8(4), 372, DOI: 10.3390/SU8040372.
  • Görçün, Ö.F., Pamucar, D. and Biswas, S. (2023). “The Blockchain Technology Selection in the Logistics Industry Using a Novel MCDM Framework Based on Fermatean Fuzzy Sets and Dombi Aggregation”, Information Sciences, 635, 345-374.
  • Gul, M. and Ak, M.F. (2020). “Assessment of Occupational Risks from Human Health and Environmental Perspectives: A New Integrated Approach and Its Application Using Fuzzy BWM and Fuzzy MAIRCA”, Stochastic Environmental Research and Risk Assessment, 34(8), 1231-1262, DOI: 10.1007/S00477-020-01816-X.
  • Hájek, P., Stříteská, M. and Prokop, V. (2018). “Integrating Balanced Scorecard and Fuzzy TOPSIS for Innovation Performance Evaluation”, Twenty-Second Pacific Asia Conference on Information Systems (PACIS 2018) Proceedings, Jokohama, Japan.
  • Halkos, G.E. and Tzeremes, N.G. (2013). “Modelling the Effect of National Culture on Countries’ Innovation Performances: A Conditional Full Frontier Approach”, International Review of Applied Economics, 27(5), 656-678.
  • Hezam, I.M., Vedala, N.R.D., Kumar, B.R., Mishra, A.R. and Cavallaro, F. (2023). “Assessment of Biofuel Industry Sustainability Factors Based on the Intuitionistic Fuzzy Symmetry Point of Criterion and Rank-Sum-Based MAIRCA Method”, Sustainability, 15(8), 6749.
  • Huang, X. (2023). “The Roles of Competition on Innovation Efficiency and Firm Performance: Evidence from the Chinese Manufacturing Industry”, European Research on Management and Business Economics, 29(1), 100201.
  • Işık, Ö. (2022). “Covid-19 Salgınının Katılım Bankacılığı Sektörünün Performansına Etkisinin MEREC-PSI-MAIRCA Modeliyle İncelenmesi”, Nişantaşı Üniversitesi Sosyal Bilimler Dergisi, 10(2) 363-385.
  • Jewell, C. (2021). “Global Innovation Index 2021: Tracking Innovation through the COVID-19 Crisis”, Wipo Magazine, September 2021(3), 9-15.
  • Kahreman, Y. (2023). “G20 Ülkelerinin Ekonomik Performanslarının 2008 Krizi Döneminde LOPCOW-COCOSO Yöntemi ile Değerlendirilmesi”, İzmir İktisat Dergisi, 38(3), 786-803.
  • Kaynak, S., Altuntas, S. and Dereli, T. (2017). “Comparing the Innovation Performance of EU Candidate Countries: An Entropy-Based TOPSIS Approach”, Economic Research-Ekonomska Istraživanja, 30(1), 31-54.
  • Keleş, N. (2023). “Lopcow ve Cradis Yöntemleriyle G7 Ülkelerinin ve Türkiye’nin Yaşanabilir Güç Merkezi Şehirlerinin Değerlendirilmesi”, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(3), 727- 747.
  • Keskin, Z.B. and Kılıç Delice, E. (2022). “Nesnel, Öznel ve Bütünleşik Kriter Ağırlıklandırma Yöntemlerinin Karşılaştırılması: COVID-19 Uygulaması”, European Journal of Science and Technology, 34, 579-584, DOI: 10.31590/EJOSAT.1083549.
  • Lu, M.T., Tzeng, G.H. and Tang, L.L. (2013). “Environmental Strategic Orientations for Improving Green Innovation Performance in Fuzzy Environment-Using New Fuzzy Hybrid MCDM Model”, International Journal of Fuzzy Systems, 15(3), 297-316.
  • Mahmoodi, E., Azari, M. and Dastorani, M.T. (2023). “Comparison of Different Objective Weighting Methods in a Multi‐Criteria Model for Watershed Prioritization for Flood Risk Assessment Using Morphometric Analysis”, Journal of Flood Risk Management, 16(2), e12894, DOI: 10.1111/JFR3.12894.
  • Murat, D. (2020). “The Measurement of Innovation Performance in OECD Countries”, Journal of Management & Economics Research, 18(4), 209-226.
  • Namazi, M. and Mohammadi, E. (2018). “Natural Resource Dependence and Economic Growth: A TOPSIS/DEA Analysis of Innovation Efficiency”, Resources Policy, 59, 544-552.
  • Narayanamoorthy, S., Brainy, J.V., Shalwala, R.A., Alsenani, T.R., Ahmadian, A. and Kang, D. (2023). “An Enhanced Fuzzy Decision Making Approach for the Assessment of Sustainable Energy Storage Systems”, Sustainable Energy, Grids and Networks, 33, 100962, DOI: 10.1016/J.SEGAN.2022.100962.
  • Narayanan, E., Ismail, W.R. and Mustafa, Z. (2022). “A Data-Envelopment Analysis-Based Systematic Review of the Literature on Innovation Performance”, Heliyon, e11925.
  • Nila, B. and Roy, J. (2023). “A New Hybrid MCDM Framework for Third-Party Logistic Provider Selection Under Sustainability Perspectives”, Expert Systems with Applications, 234, 121009.
  • Niu, W., Rong, Y., Yu, L. and Huang, L. (2022). “A Novel Hybrid Group Decision Making Approach Based on EDAS and Regret Theory Under a Fermatean Cubic Fuzzy Environment”, Mathematics, 10(17), 3116, DOI: 10.3390/MATH10173116.
  • Oturakci, M. (2021). “Comprehensive Analysis of the Global Innovation Index: Statistical and Strategic Approach”, Technology Analysis & Strategic Management, 35(6), 676-688, DOI: 10.1080/09537325.2021.1980209.
  • Pamučar, D., Deveci, M., Schitea, D., Erişkin, L., Iordache, M. and Iordache, I. (2020). “Developing a Novel Fuzzy Neutrosophic Numbers Based Decision Making Analysis for Prioritizing the Energy Storage Technologies”, International Journal of Hydrogen Energy, 45(43), 23027–23047, DOI: 10.1016/J.IJHYDENE.2020.06.016.
  • Pamučar, D., Gigović, L., Bajić, Z. and Janošević, M. (2017). “Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers”, Sustainability, 9(8), 1315, DOI: 10.3390/SU9081315.
  • Pamučar, D., Vasin, L. and Lukovac, V. (2014). “Selection of Railway Level Crossings for Investing in Security Equipment Using Hybrid DEMATEL-MARICA Model: Application of a New Method of Multi-Criteria Decision-Making”, XVI International Scientific-Expert Conference on Railways, Niš, Serbia, 89-92, DOI: 10.13140/2.1.2707.6807.
  • Robertson, J., Caruana, A. and Ferreira, C. (2021). “Innovation Performance: The Effect of Knowledge-Based Dynamic Capabilities in Cross-Country Innovation Ecosystems”, International Business Review, 101866.
  • Roszko-Wójtowicz, E. and Białek, J. (2016). “A Multivariate Approach in Measuring Innovation Performance”, Zbornik Radova Ekonomskog Fakulteta U Rijeci: Časopis Za Ekonomsku Teoriju I Praksu, 34(2), 443-479, DOI: 10.18045/ZBEFRI.2016.2.443.
  • Şahin Macit, N. (2023). “Tedarikçi Seçimi Probleminin AHP Temelli MAIRCA Yöntemi ile Çözümü”, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 37,42-63.
  • Simic, V., Dabic-Miletic, S., Tirkolaee, E.B., Stević, Ž., Ala, A. and Amirteimoori, A. (2023). “Neutrosophic Lopcow-ARAS Model for Prioritizing Industry 4.0-Based Material Handling Technologies in Smart and Sustainable Warehouse Management Systems”, Applied Soft Computing, 143, 110400, DOI: 10.1016/J.ASOC.2023.110400.
  • Ul Haq, R.S., Saeed, M., Mateen, N., Siddiqui, F. and Ahmed, S. (2023). “An Interval-Valued Neutrosophic Based MAIRCA Method for Sustainable Material Selection”, Engineering Applications of Artificial Intelligence, 123, 106177, DOI: 10.1016/J.ENGAPPAI.2023.106177.
  • Ulutaş, A., Balo, F. and Topal, A. (2023). “Identifying the Most Efficient Natural Fibre for Common Commercial Building Insulation Materials with an Integrated PSI, MEREC, LOPCOW and MCRAT Model”, Polymers, 15(6), 1500, DOI: 10.3390/POLYM15061500.
  • WIPO. (2021). “Global Innovation Index 2021: Tracking Innovation through the COVID-19 Crisis”, DOI: 10.34667/TIND.44315.
  • WIPO. (2022). “Global Innovation Index 2022: What Is the Future of Innovation-Driven Growth?”, DOI: 10.34667/TIND.46596.
  • Xu, K., Mei, R., Sun, W., Zhang, H. and Liang, L. (2023). “Estimation of Sustainable Innovation Performance in European Union Countries: Based on the Perspective of Energy and Environmental Constraints”, Energy Reports, 9, 1919-1925.
  • Yin, S., Zhang, N. and Li, B. (2020). “Improving the Effectiveness of Multi-Agent Cooperation for Green Manufacturing in China: A Theoretical Framework to Measure the Performance of Green Technology Innovation”, International Journal of Environmental Research and Public Health, 17(9), 3211.
  • Yontar, E. (2023). “Critical Success Factor Analysis of Blockchain Technology in Agri-Food Supply Chain Management: A Circular Economy Perspective”, Journal of Environmental Management, 330, 117173, DOI: 10.1016/J.JENVMAN.2022.117173.
  • Yu, A., Shi, Y., You, J. and Zhu, J. (2021). “Innovation Performance Evaluation for High-Tech Companies Using a Dynamic Network Data Envelopment Analysis Approach”, European Journal of Operational Research, 292(1), 199-212.
Toplam 67 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çok Ölçütlü Karar Verme
Bölüm Makaleler
Yazarlar

Tayfun Öztaş 0000-0001-8224-5092

Gülin Zeynep Öztaş 0000-0002-6901-6559

Yayımlanma Tarihi 15 Ocak 2024
Gönderilme Tarihi 28 Haziran 2023
Yayımlandığı Sayı Yıl 2024 PRODUCTIVITY FOR INNOVATION

Kaynak Göster

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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