Novel comprehensive MEREC weighting-based score aggregation model for measuring innovation performance: the case of G7 countries. (English) Zbl 1519.91190
Keywords:
innovation; MCDM; innovation measurement; Borda rule; G7 countries; score aggregation modelReferences:
| [1] | 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 Turk-ish). |
| [2] | 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. |
| [3] | Banu, G.S. (2018). Measuring innovation using key performance indicators. Procedia Manufacturing, 22, 906-911. |
| [4] | 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. |
| [5] | 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. |
| [6] | Bloch, C., Bugge, M.M. (2013). Public sector innovation-from theory to measurement. Structural Change and Economic Dynamics, 27, 133-145. |
| [7] | 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. |
| [8] | Brenner, T., Broekel, T. (2011). Methodological issues in measuring innovation performance of spatial units. Industry and Innovation, 18(1), 7-37. |
| [9] | 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. |
| [10] | Chakraborty, S., Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica, 25(1), 1-20. |
| [11] | 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. |
| [12] | Cirera, X., Muzi, S. (2020). Measuring innovation using firm-level surveys: evidence from developing countries. Research Policy, 49(3), 103912. |
| [13] | Cooper, R.G. (1999). From experience: the invisible success factors in product innovation. Journal of Product Innovation Management, 16(2), 115-133. |
| [14] | De Borda (1781). Mémoire sur les élections au scrutin. Histoire de L’Académie Royale des Sciences, 102, 657-665. |
| [15] | 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 dimen-sions. Soft Computing, 23(16), 7117-7133. |
| [16] | 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. |
| [17] | 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. |
| [18] | Dutta, S., Lanvin, B., Wunsch-Vincent, S. (Eds.) (2020). Global Innovation Index 2020: Who Will Finance Innovation? Cornell University. |
| [19] | Dziallas, M., Blind, K. (2019). Innovation indicators throughout the innovation process: an extensive literature analysis. Technovation, 80, 3-29. |
| [20] | 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. |
| [21] | 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. |
| [22] | 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. |
| [23] | 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 Com-puting, 104, 107199. |
| [24] | 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. |
| [25] | 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. |
| [26] | 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. |
| [27] | Ecer, F., Böyükaslan, A., Hashemkhani Zolfani, S. (2022). Evaluation of cryptocurrencies for investment de-cisions 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. |
| [28] | European Innovation Scoreboard (2021). https://ec.europa.eu/growth/industry/policy/innovation/scoreboards_en. |
| [29] | Feeny, S., Rogers, M. (2003). Innovation and performance: benchmarking Australian firms. Australian Economic Review, 36(3), 253-264. |
| [30] | 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. · Zbl 07538890 |
| [31] | Garcia-Bernabeu, A., Cabello, J.M., Ruiz, F. (2020). A multi-criteria reference point based approach for assess-ing regional innovation performance in Spain. Mathematics, 8(5), 797. |
| [32] | 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. |
| [33] | 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. |
| [34] | Global Innovation Index (2020). https://www.globalinnovationindex.org/home. |
| [35] | Grupp, H., Schubert, T. (2010). Review and new evidence on composite innovation indicators for evaluating national performance. Research Policy, 39(1), 67-78. |
| [36] | 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. · Zbl 1489.91080 |
| [37] | 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. |
| [38] | Hagedoorn, J., Cloodt, M. (2003). Measuring innovative performance: is there an advantage in using multiple indicators. Research Policy, 32, 1365-1379. |
| [39] | 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). |
| [40] | Huang, H., Wang, F., Song, M., Balezentis, T., Streimikiene, D. (2021). Green innovations for sustainable de-velopment of China: analysis based on the nested spatial panel models. Technology in Society, 65, 101593. |
| [41] | Ivanov, C.I., Avasilcăi, S. (2014). Performance measurement models: an analysis for measuring innovation pro-cesses performance. Procedia-Social and Behavioral Sciences, 124, 397-404. |
| [42] | 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. |
| [43] | Jungmittag, A. (2004). Innovations, technological specialisation and economic growth in the EU. International, Economics and Economic Policy, 1, 247-273. |
| [44] | 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. |
| [45] | 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. |
| [46] | 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. |
| [47] | 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. |
| [48] | 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. |
| [49] | 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. |
| [50] | Kumar, V., Kalita, K., Chatterjee, P., Zavadskas, E.K., Chakraborty, S. (2022). A SWARA-CoCoSo-based ap-proach for spray painting robot selection. Informatica, 33(1), 35-54. |
| [51] | Lai, H., Liao, H.C., Wen, Z., Zavadskas, E.K., Al-Barakati, A. (2020). An improved CoCoSo method with a max-imum variance optimization model for cloud service provider selection. Inzinerine Ekonomika-Engineering Economics, 31(4), 411-424. |
| [52] | Ligardo-Herrera, I., Gómez-Navarro, T., Gonzalez-Urango, H. (2019). Application of the ANP to the prioritiza-tion of project stakeholders in the context of responsible research and innovation. Central European Journal of Operations Research, 27(3), 679-701. · Zbl 07073953 |
| [53] | 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. |
| [54] | 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. |
| [55] | Mokarrari, K.R., Torabi, S.A. (2021). Ranking cities based on their smartness level using MADM methods. Sustainable Cities and Society, 72, 103030. |
| [56] | Navarro, M., Gibaja, J.J., Bilbao-Osorio, B., Aguado, R. (2009). Patterns of innovation in EU-25 regions: a ty-pology and policy recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. |
| [57] | OECD (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data (No. 4). |
| [58] | 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. |
| [59] | 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. |
| [60] | 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. |
| [61] | Pamucar, D., Ecer, F., Deveci, M. (2021). Assessment of alternative fuel vehicles for sustainable road trans-portation of United States using integrated fuzzy FUCOM and neutrosophic fuzzy MARCOS methodology. Science of The Total Environment, 788, 147763. |
| [62] | 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. |
| [63] | 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. |
| [64] | 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. |
| [65] | Schumpeter, J. (1965). Les dynamiques du capitalisme, Geneva, Droz. |
| [66] | 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. |
| [67] | 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. |
| [68] | Thompson, V.A. (1965). Bureaucracy and innovation. Administrative Science Quarterly, 10(1), 1-20. |
| [69] | Tokumasu, S., Watanabe, C. (2008). Institutional structure leading to the similarity and disparity in innova-tion inducement in EU 15 countries-finnish conspicuous achievement triggered by Nokia’s IT driven global business. Journal of Services Research, 8, 1-38. |
| [70] | 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. |
| [71] | Varmazyar, M., Dehghanbaghi, M., Afkhami, M. (2016). A novel hybrid MCDM model for performance evalu-ation of research and technology organizations based on BSC approach. Evaluation and Program Planning, 58, 125-140. |
| [72] | 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. |
| [73] | 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. |
| [74] | 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. |
| [75] | Wolfe, R.A. (1994). Organizational innovation: review, critique and suggested research directions. Journal of Management Studies, 31(3), 405-431. |
| [76] | 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. |
| [77] | 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. · Zbl 1487.90401 |
| [78] | Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. |
| [79] | 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. |
| [80] | 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. |
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