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Yıl 2024, Cilt: 11 Sayı: 3, 419 - 430, 30.09.2024

Furkan Göktaş

https://doi.org/10.54287/gujsa.1474940

Öz

Kaynakça

  • Acuña-Soto, C., Liern, V., & Pérez-Gladish, B. (2021). Normalization in TOPSIS-based approaches with data of different nature: application to the ranking of mathematical videos. Annals of Operations Research, 296(1), 541-569. https://doi.org/10.1007/s10479-018-2945-5
  • Anderson, H. J., & Stejskal, J. (2019). Diffusion efficiency of innovation among EU member states: a data envelopment analysis. Economies, 7(2), 34. https://doi.org/10.3390/economies7020034
  • Aytekin, A., Ecer, F., Korucuk, S., & Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/j.techsoc.2022.101896
  • Beck, A., & Sabach, S. (2014). A first order method for finding minimal norm-like solutions of convex optimization problems. Mathematical Programming, 147(1), 25-46. https://doi.org/10.1007/s10107-013-0708-2
  • Bouslah, K., Liern, V., Ouenniche, J., & Pérez‐Gladish, B. (2023). Ranking firms based on their financial and diversity performance using multiple‐stage unweighted TOPSIS. International Transactions in Operational Research, 30(5), 2485-2505. https://doi.org/10.1111/itor.13143
  • Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
  • Brodny, J., Tutak, M., Grebski, W., & Bindzár, P. (2023). Assessing the level of innovativeness of EU-27 countries and its relationship to economic, environmental, energy and social parameters. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100073. https://doi.org/10.1016/j.joitmc.2023.100073
  • Chen, C.-P., Hu, J.-L., & Yang, C.-H. (2011). An international comparison of R&D efficiency of multiple innovative outputs: The role of the national innovation system. Innovation, 13(3), 341-360. https://doi.org/10.5172/impp.2011.13.3.341
  • Do Carmo Silva, M., Gavião, L. O., Gomes, C. F. S., & Lima, G. B. A. (2020). Global Innovation Indicators analysed by multicriteria decision. Brazilian Journal of Operations & Production Management, 17(4), 1-17. https://doi.org/10.14488/BJOPM.2020.040
  • Ecer, F., & 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. https://doi.org/10.15388/22-INFOR494
  • EIS (2023). European Innovation Scoreboard 2023, the Summary Innovation Index. (Accessed:07/03/2024) https://projects.research-and-innovation.ec.europa.eu/en/statistics/performance-indicators/european-innovation-scoreboard/eis
  • Erdin, C., & Çağlar, M. (2023). National innovation efficiency: A DEA-based measurement of OECD countries. International Journal of Innovation Science, 15(3), 427-456. https://doi.org/10.1108/IJIS-07-2021-0118
  • European Commission (2023). European Innovation Scoreboard 2023 methodology report. (Accessed:07/03/2024) https://research-and-innovation.ec.europa.eu/statistics/performance-indicators/european-innovation-scoreboard_en
  • Eurostat (2023). Glossary: Innovation. (Accessed:07/03/2024) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Innovation
  • Grant, M. C., & Boyd, S. P. (2008). Graph implementations for nonsmooth convex programs. In Recent Advances in Learning and Control, 95-110. Springer, London. https://doi.org/10.1007/978-1-84800-155-8_7
  • Jeon, J., Geetha, S., Kang, D., & Narayanamoorthy, S. (2022). Development of the evaluation model for national innovation capability. Technology Analysis & Strategic Management, 34(3), 335-348. https://doi.org/10.1080/09537325.2021.1900561
  • Jurickova, E., Pilik, M., & Kwarteng, M. A. (2019). Efficiency measurement of national innovation systems of the European Union countries: DEA model application. Journal of International Studies, 12(4), 286-299. https://doi.org/10.14254/2071-8330.2019/12-4/19
  • Kabadurmuş, Ö., & Karaman Kabadurmuş, F. N. (2019). Innovation in Eastern Europe & Central Asia: A Multi-Criteria Decision-Making Approach. Business & Management Studies: An International Journal, 7(3), 98-121. https://doi.org/10.15295/bmij.v7i3.1234
  • Kaynak, S., Altuntas, S., & Dereli, T. (2017). Comparing the innovation performance of EU candidate countries: an entropy-based TOPSIS approach. Economic Research-Ekonomska Istraživanja, 30(1), 31-54. http://doi.org/10.1080/1331677X.2016.1265895
  • Murat, D. (2020). The measurement of innovation performance in OECD countries. Journal of Management & Economics Research, 18(4), 209-2026. http://doi.org/10.11611/yead.822303
  • Namazi, M., & Mohammadi, E. (2018). Natural resource dependence and economic growth: A TOPSIS/DEA analysis of innovation efficiency. Resources Policy, 59, 544-552. https://doi.org/10.1016/j.resourpol.2018.09.015
  • Ozkaya, G., Timor, M., & Erdin, C. (2021). Science, technology and innovation policy indicators and comparisons of countries through a hybrid model of data mining and MCDM methods. Sustainability, 13(2), 694. https://doi.org/10.3390/su13020694
  • Satı, Z. E. (2024). Comparison of the criteria affecting the digital innovation performance of the European Union (EU) member and candidate countries with the entropy weight-TOPSIS method and investigation of its importance for SMEs. Technological Forecasting and Social Change, 200, 123094. https://doi.org/10.1016/j.techfore.2023.123094
  • Selvaraj, G., & Jeon, J. (2021). Assessment of national innovation capabilities of OECD countries using trapezoidal interval type-2 fuzzy ELECTRE III method. Data Technologies and Applications, 55(3), 400-429. https://doi.org/10.1108/DTA-07-2020-0154
  • Taherdoost, H. (2023). Analysis of simple additive weighting method (SAW) as a multi-attribute decision-making technique: A step-by-step. Journal of Management Science & Engineering Research, 6(1), 21-24. https://doi.org/10.30564/jmser.v6i1.5400
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2022). Assessing normalization techniques for simple additive weighting method. Procedia Computer Science, 199, 1229-1236. https://doi.org/10.1016/j.procs.2022.01.156

A Hybrid MADM Approach Based on Simple Additive Weighting and TOPSIS: An Application on Comparison of Innovation Performances of the EU Countries

Yıl 2024, Cilt: 11 Sayı: 3, 419 - 430, 30.09.2024

Furkan Göktaş

https://doi.org/10.54287/gujsa.1474940

Öz

This study aims to objectively compare the long-term innovation performances of the EU countries. In this context, we propose a hybrid multi-attribute decision-making (MADM) approach combining Simple Additive Weighting (SAW) and TOPSIS. The proposed approach (CST) uses the alternatives’ quadratic utility functions considering the weighted sum value and distance to the positive ideal solution. It also uniquely determines the criteria weight vector using a strictly concave maximization problem. Using the Summary Innovation Index (SII) data for the 2016-2023 period, CST reveals that Sweden, Denmark, and the Netherlands are in the first three ranks. In contrast, Romania, Bulgaria, and Latvia are in the last three ranks.

Anahtar Kelimeler

Convex Optimization Innovation Multi-Attribute Decision-Making Simple Additive Weighting TOPSIS

Kaynakça

  • Acuña-Soto, C., Liern, V., & Pérez-Gladish, B. (2021). Normalization in TOPSIS-based approaches with data of different nature: application to the ranking of mathematical videos. Annals of Operations Research, 296(1), 541-569. https://doi.org/10.1007/s10479-018-2945-5
  • Anderson, H. J., & Stejskal, J. (2019). Diffusion efficiency of innovation among EU member states: a data envelopment analysis. Economies, 7(2), 34. https://doi.org/10.3390/economies7020034
  • Aytekin, A., Ecer, F., Korucuk, S., & Karamaşa, Ç. (2022). Global innovation efficiency assessment of EU member and candidate countries via DEA-EATWIOS multi-criteria methodology. Technology in Society, 68, 101896. https://doi.org/10.1016/j.techsoc.2022.101896
  • Beck, A., & Sabach, S. (2014). A first order method for finding minimal norm-like solutions of convex optimization problems. Mathematical Programming, 147(1), 25-46. https://doi.org/10.1007/s10107-013-0708-2
  • Bouslah, K., Liern, V., Ouenniche, J., & Pérez‐Gladish, B. (2023). Ranking firms based on their financial and diversity performance using multiple‐stage unweighted TOPSIS. International Transactions in Operational Research, 30(5), 2485-2505. https://doi.org/10.1111/itor.13143
  • Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
  • Brodny, J., Tutak, M., Grebski, W., & Bindzár, P. (2023). Assessing the level of innovativeness of EU-27 countries and its relationship to economic, environmental, energy and social parameters. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100073. https://doi.org/10.1016/j.joitmc.2023.100073
  • Chen, C.-P., Hu, J.-L., & Yang, C.-H. (2011). An international comparison of R&D efficiency of multiple innovative outputs: The role of the national innovation system. Innovation, 13(3), 341-360. https://doi.org/10.5172/impp.2011.13.3.341
  • Do Carmo Silva, M., Gavião, L. O., Gomes, C. F. S., & Lima, G. B. A. (2020). Global Innovation Indicators analysed by multicriteria decision. Brazilian Journal of Operations & Production Management, 17(4), 1-17. https://doi.org/10.14488/BJOPM.2020.040
  • Ecer, F., & 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. https://doi.org/10.15388/22-INFOR494
  • EIS (2023). European Innovation Scoreboard 2023, the Summary Innovation Index. (Accessed:07/03/2024) https://projects.research-and-innovation.ec.europa.eu/en/statistics/performance-indicators/european-innovation-scoreboard/eis
  • Erdin, C., & Çağlar, M. (2023). National innovation efficiency: A DEA-based measurement of OECD countries. International Journal of Innovation Science, 15(3), 427-456. https://doi.org/10.1108/IJIS-07-2021-0118
  • European Commission (2023). European Innovation Scoreboard 2023 methodology report. (Accessed:07/03/2024) https://research-and-innovation.ec.europa.eu/statistics/performance-indicators/european-innovation-scoreboard_en
  • Eurostat (2023). Glossary: Innovation. (Accessed:07/03/2024) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Innovation
  • Grant, M. C., & Boyd, S. P. (2008). Graph implementations for nonsmooth convex programs. In Recent Advances in Learning and Control, 95-110. Springer, London. https://doi.org/10.1007/978-1-84800-155-8_7
  • Jeon, J., Geetha, S., Kang, D., & Narayanamoorthy, S. (2022). Development of the evaluation model for national innovation capability. Technology Analysis & Strategic Management, 34(3), 335-348. https://doi.org/10.1080/09537325.2021.1900561
  • Jurickova, E., Pilik, M., & Kwarteng, M. A. (2019). Efficiency measurement of national innovation systems of the European Union countries: DEA model application. Journal of International Studies, 12(4), 286-299. https://doi.org/10.14254/2071-8330.2019/12-4/19
  • Kabadurmuş, Ö., & Karaman Kabadurmuş, F. N. (2019). Innovation in Eastern Europe & Central Asia: A Multi-Criteria Decision-Making Approach. Business & Management Studies: An International Journal, 7(3), 98-121. https://doi.org/10.15295/bmij.v7i3.1234
  • Kaynak, S., Altuntas, S., & Dereli, T. (2017). Comparing the innovation performance of EU candidate countries: an entropy-based TOPSIS approach. Economic Research-Ekonomska Istraživanja, 30(1), 31-54. http://doi.org/10.1080/1331677X.2016.1265895
  • Murat, D. (2020). The measurement of innovation performance in OECD countries. Journal of Management & Economics Research, 18(4), 209-2026. http://doi.org/10.11611/yead.822303
  • Namazi, M., & Mohammadi, E. (2018). Natural resource dependence and economic growth: A TOPSIS/DEA analysis of innovation efficiency. Resources Policy, 59, 544-552. https://doi.org/10.1016/j.resourpol.2018.09.015
  • Ozkaya, G., Timor, M., & Erdin, C. (2021). Science, technology and innovation policy indicators and comparisons of countries through a hybrid model of data mining and MCDM methods. Sustainability, 13(2), 694. https://doi.org/10.3390/su13020694
  • Satı, Z. E. (2024). Comparison of the criteria affecting the digital innovation performance of the European Union (EU) member and candidate countries with the entropy weight-TOPSIS method and investigation of its importance for SMEs. Technological Forecasting and Social Change, 200, 123094. https://doi.org/10.1016/j.techfore.2023.123094
  • Selvaraj, G., & Jeon, J. (2021). Assessment of national innovation capabilities of OECD countries using trapezoidal interval type-2 fuzzy ELECTRE III method. Data Technologies and Applications, 55(3), 400-429. https://doi.org/10.1108/DTA-07-2020-0154
  • Taherdoost, H. (2023). Analysis of simple additive weighting method (SAW) as a multi-attribute decision-making technique: A step-by-step. Journal of Management Science & Engineering Research, 6(1), 21-24. https://doi.org/10.30564/jmser.v6i1.5400
  • Vafaei, N., Ribeiro, R. A., & Camarinha-Matos, L. M. (2022). Assessing normalization techniques for simple additive weighting method. Procedia Computer Science, 199, 1229-1236. https://doi.org/10.1016/j.procs.2022.01.156
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Nicel Karar Yöntemleri
Bölüm İstatistik
Yazarlar

Furkan Göktaş 0000-0001-9291-3912

Erken Görünüm Tarihi 4 Eylül 2024
Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 28 Nisan 2024
Kabul Tarihi 4 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 11 Sayı: 3

Kaynak Göster

APA Göktaş, F. (2024). A Hybrid MADM Approach Based on Simple Additive Weighting and TOPSIS: An Application on Comparison of Innovation Performances of the EU Countries. Gazi University Journal of Science Part A: Engineering and Innovation, 11(3), 419-430. https://doi.org/10.54287/gujsa.1474940
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