1. Home
  2. Issues
  3. Volume 34, Issue 1 (2023)
  4. Novel Comprehensive MEREC Weighting-Base ...

Informatica


Novel Comprehensive MEREC Weighting-Based Score Aggregation Model for Measuring Innovation Performance: The Case of G7 Countries
Volume 34, Issue 1 (2023), pp. 53–83
Pub. online: 5 September 2022      Type: Research Article      Open accessOpen Access
Received
1 February 2022
Accepted
1 August 2022
Published
5 September 2022

Abstract

Innovation can be the greatest hope of overcoming economic challenges. This paper aims to evaluate countries concerning their innovation performances. We introduce an innovation performance evaluation methodology by considering objective factors and applying seven reliable MCDM methods: MEREC, CODAS, MABAC, MARCOS, CoCoSo, WASPAS, and MAIRCA. MEREC calculates the relative weights of indicators considered, while the other techniques decide the ranking order of G7 countries. The Borda rule is then employed to gain an aggregated ranking order. “Business sophistication” is the most critical indicator, whereas the US has the best position regarding the overall ranking. Sensitivity control is as well conducted.

References

 
Ayçin, E., Çakin, E. (2019). Ülkelerin inovasyon performanslarının ölçümünde Entropi ve MABAC çok kriterli karar verme yöntemlerinin bütünleşik olarak kullanılması. Akdeniz İİ BF Dergisi, 19(2), 326–351 (in Turkish).
 
Badi, I., Ballem, M., Shetwan, A. (2018). Site selection of desalination plant in Libya by using combinative distance-based assessment (CODAS) method. International Journal for Quality Research, 12(3), 609–624.
 
Banu, G.S. (2018). Measuring innovation using key performance indicators. Procedia Manufacturing, 22, 906–911.
 
Becheikh, N., Landry, R., Amara, N. (2006). Lessons from innovation empirical studies in the manufacturing sector: a systematic review of the literature from 1993–2003. Technovation, 26(5–6), 644–664.
 
Blažek, J., Kadlec, V. (2019). Knowledge bases, R&D structure and socio-economic and innovation performance of European regions. Innovation: The European Journal of Social Science Research, 32(1), 26–47.
 
Bloch, C., Bugge, M.M. (2013). Public sector innovation—from theory to measurement. Structural Change and Economic Dynamics, 27, 133–145.
 
Böyükaslan, A., Ecer, F. (2021). Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework. Technology in Society, 67, 101745.
 
Brenner, T., Broekel, T. (2011). Methodological issues in measuring innovation performance of spatial units. Industry and Innovation, 18(1), 7–37.
 
Capello, R., Lenzi, C. (2013). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119–154.
 
Chakraborty, S., Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1–20.
 
Chakraborty, S., Zavadskas, E.K., Antucheviciene, J. (2015). Applications of WASPAS method as a multi-criteria decision-making tool. Economic Computation and Economic Cybernetics Studies and Research, 49(1), 5–22.
 
Cirera, X., Muzi, S. (2020). Measuring innovation using firm-level surveys: evidence from developing countries. Research Policy, 49(3), 103912.
 
Cooper, R.G. (1999). From experience: the invisible success factors in product innovation. Journal of Product Innovation Management, 16(2), 115–133.
 
De Borda (1781). Mémoire sur les élections au scrutin. Histoire de L’Académie Royale des Sciences, 102, 657–665.
 
do Carmo Silva, M., Gavião, L.O., Gomes, C.F.S., Lima, G.B.A. (2017). A proposal for the application of multicriteria analysis to rank countries according to innovation using the indicators provided by the World Intellectual Property Organization. RAI Revista de Administração e Inovação, 14(3), 188–198.
 
do Carmo Silva, M., Lima, G.B.A., Gomes, C.F.S., Rangel, L.A.D., Caiado, R.G.G. (2019). A SMARTS-Choquet’s approach for multicriteria decision aid applied to the innovation indexes in sustainability dimensions. Soft Computing, 23(16), 7117–7133.
 
Dorfeshan, Y., Mousavi, S.M. (2020). A novel interval type-2 fuzzy decision model based on two new versions of relative preference relation-based MABAC and WASPAS methods with an application in aircraft maintenance planning. Neural Computing and Applications, 32(8), 3367–3385.
 
Dortaj, A., Maghsoudy, S., Ardejani, F.D., Eskandari, Z. (2020). A hybrid multi-criteria decision making method for site selection of subsurface dams in semi-arid region of Iran. Groundwater for Sustainable Development, 10, 100284.
 
Dutta, S., Lanvin, B., Wunsch-Vincent, S. (Eds.) (2020). Global Innovation Index 2020: Who Will Finance Innovation? Cornell University.
 
Dziallas, M., Blind, K. (2019). Innovation indicators throughout the innovation process: an extensive literature analysis. Technovation, 80, 3–29.
 
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.
 
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.
 
Ecer, F., Hashemkhani Zolfani, S. (2022). Evaluating economic freedom via multi-criteria MEREC-DNMA model: case of OPEC countries. Technological and Economic Development of Economy, 28(4), 1158–1181.
 
Ecer, F., Pamucar, D. (2021). MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services. Applied Soft Computing, 104, 107199.
 
Ecer, F., Pamucar, D. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: an application in developing country banking sector. Omega, 112, 102690.
 
Ecer, F., Pamucar, D., Zolfani, S.H., Eshkalag, M.K. (2019). Sustainability assessment of OPEC countries: application of a multiple attribute decision making tool. Journal of Cleaner Production, 241, 118324.
 
Ecer, F., Pamucar, D., Mardani, A., Alrasheedi, M. (2021). Assessment of renewable energy resources using new interval rough number extension of the level based weight assessment and combinative distance-based assessment. Renewable Energy, 170, 1156–1177.
 
Ecer, F., Böyükaslan, A., 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.
 
European Innovation Scoreboard (2021). https://ec.europa.eu/growth/industry/policy/innovation/scoreboards_en.
 
Feeny, S., Rogers, M. (2003). Innovation and performance: benchmarking Australian firms. Australian Economic Review, 36(3), 253–264.
 
Furková, A. (2022). Implementation of MGWR-SAR models for investigating a local particularity of European regional innovation processes. Central European Journal of Operations Research, 30, 733–755.
 
Garcia-Bernabeu, A., Cabello, J.M., 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., Milićević, M. (2016). The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depot. Sustainability, 8(4), 1–30.
 
Gigović, L., Pamučar, D., Božanić, D., Ljubojević, S. (2017). Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: a case study of Vojvodina, Serbia. Renewable Energy, 103, 501–521.
 
Global Innovation Index (2020). https://www.globalinnovationindex.org/home.
 
Grupp, H., Schubert, T. (2010). Review and new evidence on composite innovation indicators for evaluating national performance. Research Policy, 39(1), 67–78.
 
Gül, S., Aydoğdu, A. (2021). Novel entropy measure definitions and their uses in a modified combinative distance-based assessment (CODAS) method under picture fuzzy environment. Informatica, 32(4), 759–794.
 
Haefner, N., Wincent, J., Parida, V., Gassmann, O. (2021). Artificial intelligence and innovation management: a review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392.
 
Hagedoorn, J., Cloodt, M. (2003). Measuring innovative performance: is there an advantage in using multiple indicators. Research Policy, 32, 1365–1379.
 
Hájek, P., Stříteská, M., Prokop, V. (2018). Integrating balanced scorecard and fuzzy TOPSIS for innovation performance evaluation. In: Twenty-Second Pacific Asia Conference on Information Systems, Japan 2018. https://www.worldbank.org/en/home. (Accessed at 31.01.2022).
 
Huang, H., Wang, F., Song, M., Balezentis, T., Streimikiene, D. (2021). Green innovations for sustainable development of China: analysis based on the nested spatial panel models. Technology in Society, 65, 101593.
 
Ivanov, C.I., Avasilcăi, S. (2014). Performance measurement models: an analysis for measuring innovation processes performance. Procedia-Social and Behavioral Sciences, 124, 397–404.
 
İnel, M.N., Türker, M.V. (2016). Ulusal inovasyon performansının ölçümü için çok nitelikli karar verme teknikleri ile bir model denemesi. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 38(2), 147–166.
 
Jungmittag, A. (2004). Innovations, technological specialisation and economic growth in the EU. International, Economics and Economic Policy, 1, 247–273.
 
Kayapınar Kaya, S., Ayçin, E., Pamucar, D. (2022). Evaluation of social factors within the circular economy concept for European countries. Central European Journal of Operations Research, 1–36.
 
Keshavarz Ghorabaee, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J. (2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research, 50(3), 25–44.
 
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
 
Kim, M.J., Lee, C.K., Preis, M.W. (2020). The impact of innovation and gratification on authentic experience, subjective well-being, and behavioral intention in tourism virtual reality: the moderating role of technology readiness. Telematics and Informatics, 49, 101349.
 
Konings, R., Louw, E. (2014). Innovation performance of the transport sector at regional level. In: International Conference on Traffic and Transport Engineering, 27–28 November, Belgrade.
 
Kumar, S., Haleem, A., Sushil (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823–1844.
 
Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E.K., Chakraborty, S. (2022). A SWARA-CoCoSo-based approach for spray painting robot selection. Informatica, 33(1), 35–54.
 
Lai, H., Liao, H.C., Wen, Z., Zavadskas, E.K., Al-Barakati, A. (2020). An improved CoCoSo method with a maximum variance optimization model for cloud service provider selection. Inzinerine Ekonomika-Engineering Economics, 31(4), 411–424.
 
Ligardo-Herrera, I., Gómez-Navarro, T., Gonzalez-Urango, H. (2019). Application of the ANP to the prioritization of project stakeholders in the context of responsible research and innovation. Central European Journal of Operations Research, 27(3), 679–701.
 
Lu, M.T., Tzeng, G.H., 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.
 
Mathew, M., Sahu, S. (2018). Comparison of new multi-criteria decision making methods for material handling equipment selection. Management Science Letters, 8(3), 139–150.
 
Mokarrari, K.R., Torabi, S.A. (2021). Ranking cities based on their smartness level using MADM methods. Sustainable Cities and Society, 72, 103030.
 
Navarro, M., Gibaja, J.J., Bilbao-Osorio, B., Aguado, R. (2009). Patterns of innovation in EU-25 regions: a typology and policy recommendations. Environment and Planning C: Government and Policy, 27(5), 815–840.
 
OECD (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data (No. 4).
 
Özkaya, 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.
 
Pamučar, D., Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028.
 
Pamučar, D., Mihajlović, M., Obradović, R., Atanasković, P. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Systems with Applications, 88, 58–80.
 
Pamucar, D., Ecer, F., Deveci, M. (2021). Assessment of alternative fuel vehicles for sustainable road transportation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology. Science of The Total Environment, 788, 147763.
 
Paredes-Frigolett, H., Pyka, A., Leoneti, A.B. (2021). On the performance and strategy of innovation systems: a multicriteria group decision analysis approach. Technology in Society, 67, 101632.
 
Peng, X., Zhang, X., Luo, Z. (2019). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53, 3813–3847.
 
Regional Innovation Scoreboard (2021). https://ec.europa.eu/growth/industry/policy/innovation/regional_en.
 
Roszko-Wójtowicz, E., Białek, J. (2016). A multivariate approach in measuring innovation performance. Journal of Economics and Business, 34(2), 443–479.
 
Schumpeter, J. (1965). Les dynamiques du capitalisme, Geneva, Droz.
 
Smith, K. (2005). Measuring innovation. In: Fagerberg, J., Mowery, D.C., Nelson, R.R. (Eds.), The Oxford Handbook of Innovation. Oxford University Press, Oxford, pp. 148–177.
 
Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.
 
Thompson, V.A. (1965). Bureaucracy and innovation. Administrative Science Quarterly, 10(1), 1–20.
 
Tokumasu, S., Watanabe, C. (2008). Institutional structure leading to the similarity and disparity in innovation inducement in EU 15 countries-finnish conspicuous achievement triggered by Nokia’s IT driven global business. Journal of Services Research, 8, 1–38.
 
Torkayesh, A.E., Ecer, F., Pamucar, D., Karamaşa, Ç. (2021). Comparative assessment of social sustainability performance: integrated data-driven weighting system and CoCoSo model. Sustainable Cities and Society, 71, 102975.
 
Varmazyar, M., Dehghanbaghi, M., Afkhami, M. (2016). A novel hybrid MCDM model for performance evaluation of research and technology organizations based on BSC approach. Evaluation and Program Planning, 58, 125–140.
 
Vrontis, D., Christofi, M. (2021). R&D internationalization and innovation: a systematic review, integrative framework and future research directions. Journal of Business Research, 128, 812–823.
 
Walker, R.M., Jeanes, E., Rowlands, R. (2002). Measuring innovation–applying the literature-based innovation output indicator to public services. Public Administration, 80(1), 201–214.
 
Wang, P., Wang, J., Wei, G., Wei, C., Wei, Y. (2019). The multi-attributive border approximation area comparison (MABAC) for multiple attribute group decision making under 2-tuple linguistic neutrosophic environment. Informatica, 30(4), 799–818.
 
Wolfe, R.A. (1994). Organizational innovation: review, critique and suggested research directions. Journal of Management Studies, 31(3), 405–431.
 
Yazdani, M., Zarate, P., Zavadskas, E.K., Turskis, Z. (2019). A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519.
 
Yu, A., Shi, Y., You, J., 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.
 
Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3–6.
 
Zhuparova, A.S. (2012). Problems of measuring effectiveness of innovation performance. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 6(6), 1601–1608.
 
Zolfani, S.H., Ecer, F., Pamučar, D., Raslanas, S. (2020). Neighborhood selection for a newcomer via a novel BWM-based revised MAIRCA integrated model: a case from the Coquimbo-La Serena conurbation, Chile. International Journal of Strategic Property Management, 24(2), 102–118.

Biographies

Ecer Fatih
https://orcid.org/0000-0002-6174-3241
fecer@aku.edu.tr

F. Ecer is a professor at the Afyon Kocatepe University, the Department of Business, Turkey. His current research interests are decision analysis, multiple criteria decision making (MCDM), optimization methods, artificial intelligence, artificial neural networks (ANNs), fuzzy set theory, grey set theory, soft computing, sustainability, renewable energy, transportation, and data mining. His work has been published, or is forthcoming, in high-quality international journals. As of 2022, he has an h-index of 17 (Scopus), 17 (Web of Science), and 30 (Google Scholar). Dr. Ecer has also been serving on the Review Board and Editorial Board for a number of SSCI/SCI/SCI-E/ESCI indexed journals in the world. According to Scopus and Stanford University, he is among the World’s top 2% of scientists as of 2020.

Aycin Ejder
https://orcid.org/0000-0002-0153-8430
ejder.aycin@kocaeli.edu.tr

E. Aycin is an associate professor in the Faculty of Business at the Kocaeli University, Turkey. He completed his PhD at Dokuz Eylul University, Turkey. His research interests lie in the areas of operation research, decision sciences, and multi-criteria decision making. He has collaborated actively with researchers in several other disciplines of computer science, finance, and industrial engineering.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2023 Vilnius University
Open access article under the CC BY license.

Keywords
innovation MCDM innovation measurement Borda rule G7 countries score aggregation model

Metrics
since January 2020
1184

Article info
views

751

Full article
views

725

PDF
downloads

107

XML
downloads


Share


RSS