Bibliometric Analysis of the MCDM Methods in the Last Decade: WASPAS, MABAC, EDAS, CODAS, COCOSO, and MARCOS

Öz

In recent years, multi-criteria decision-making (MCDM) techniques have expanded the corpus of existing techniques and demonstrated their effectiveness with applications in various fields. In this study, bibliometric analysis was conducted to evaluate the research trend on new ranking-based MCDM methods in the last decade, namely WASPAS, MABAC, EDAS, CODAS, COCOSO, and MARCOS. The various keyword combinations are searched on the Web of Science and the Scopus databases. Bibliometric analysis is carried out in R with the Biblioshiny app for the bibliometrix package. In total, 1,215 related publications are analyzed. The sources, authors, countries, and publications are examined in terms of production and total citation, and the most frequent keywords with trend topics are obtained. The summaries of the findings are as follows: The number of publications has increased over the years for all the methods. The most cited studies belong to the authors of the methods and fuzzy implementations related to the methods. For the author's impact and productivity, Zavadskas and Pamučar stand out. Turkey and India rank in the top five in terms of the number of publications produced on all methods. China is the most cited country for the three methods. According to keyword analysis, different research topics such as sustainability, renewable energy, optimization, supplier selection, hydrogen production and transport are investigated through these methods and other techniques are utilized such as SWARA, AHP, TOPSIS, Best-Worst, DEMATEL, MAIRCA, and CRITIC.

Anahtar Kelimeler

• Bibliometric Analysis
• WASPAS
• MABAC
• EDAS
• CODAS
• COCOSO
• MARCOS
• Bibliyometrik Analiz WASPAS
• Bibliyometrik Analiz

Son On Yıldaki ÇKKV Yöntemlerinin Bibliyometrik Analizi: WASPAS, MABAC, EDAS, CODAS, COCOSO ve MARCOS

Öz

Son yıllarda, çok kriterli karar verme (ÇKKV) teknikleri mevcut tekniklerin korpusunu genişletmiş ve çeşitli alanlardaki uygulamaları ile etkinliklerini göstermiştir. Bu çalışmada, son on yılda WASPAS, MABAC, EDAS, CODAS, COCOSO ve MARCOS olmak üzere yeni sıralama tabanlı ÇKKV yöntemlerine ilişkin araştırma eğilimini değerlendirmek için bibliyometrik analiz yapılmıştır. Çeşitli anahtar kelime kombinasyonları, Web of Science ve Scopus veritabanlarında aranmıştır. Bibliyometrik analiz, bibliometrix paketine ait Biblioshiny uygulamasıyla R programında gerçekleştirilmiştir. Toplamda 1.215 ilgili yayın analiz edilmiştir. Kaynaklar, yazarlar, ülkeler ve yayınlar üretim ve toplam atıf açısından incelenmiş ve trend konuları ile en sık kullanılan anahtar kelimeler elde edilmiştir. Bulguların özetleri şu şekildedir: Tüm yöntemler için yayın sayısı yıllar içinde artmıştır. En çok atıf alan çalışmalar, yöntemlerin ve yöntemlerle ilgili bulanık uygulamaların yazarlarına aittir. Yazar etkisi ve üretkenliği incelendiğinde Zavadskas ve Pamučar öne çıkmaktadır. Türkiye ve Hindistan, tüm yöntemlerde üretilen yayın sayısı bakımından ilk beşte yer almaktadır. Çin, üç yöntem için en çok alıntı yapılan ülkedir. Anahtar kelime analizine göre bu yöntemlerle sürdürülebilirlik, yenilenebilir enerji, optimizasyon, tedarikçi seçimi, hidrojen üretimi ve nakliye gibi farklı araştırma konuları araştırılmakta ve SWARA, AHP, TOPSIS, Best-Worst, DEMATEL, MAIRCA ve CRITIC gibi diğer yöntemler de bu çalışmalarda kullanılmaktadır.

Anahtar Kelimeler

• Bibliyometrik Analiz
• WASPAS
• MABAC
• EDAS
• CODAS
• COCOSO
• MARCOS

Kaynakça

1. Abdullah, M. F., Siraj, S., & Hodgett, R. E. (2021). An overview of multi-criteria decision analysis (MCDA) application in managing water-related disaster events: analyzing 20 years of literature for flood and drought events. Water, 13(10), 1358. https://doi.org/10.3390/w13101358
2. Adunlin, G., Diaby, V., Montero, A. J., & Xiao, H. (2015). Multicriteria decision analysis in oncology. Health Expectations, 18(6), 1812–1826. https://doi.org/10.1111/hex.12178
3. Almeida-Filho, A. T. D., de Lima Silva, D. F., & Ferreira, L. (2021). Financial modelling with multiple criteria decision making: A systematic literature review. Journal of the Operational Research Society, 72(10), 2161–2179. https://doi.org/10.1080/01605682.2020.1772021
4. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
5. Aria, M. & Cuccurullo, C. (2022), “Package ‘bibliometrix’”. https://cran.utstat.utoronto.ca/web/packages/bibliometrix/bibliometrix.pdf
6. Asadabadi, M. R. (2018). The stratified multi-criteria decision-making method. Knowledge-Based Systems, 162, 115–123. https://doi.org/10.1016/j.knosys.2018.07.002
7. Basílio, M., Pereira, V., Costa, H. G., dos Santos, M., & Ghosh, A. (2022). Bibliometric analysis of scientific production on methods to aid decision making in the last 40 years. In SciELO Preprints. https://doi.org/10.1590/SciELOPreprints.3576
8. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069. https://doi.org/10.1016/j.eswa.2012.05.056

9. Bouzembrak, Y., Klüche, M., Gavai, A., & Marvin, H. J. (2019). Internet of Things in food safety: Literature review and a bibliometric analysis. Trends in Food Science & Technology, 94, 54–64. https://doi.org/10.1016/j.tifs.2019.11.002
10. Chahrour, M., Assi, S., Bejjani, M., Nasrallah, A. A., Salhab, H., Fares, M., & Khachfe, H. H. (2020). A bibliometric analysis of COVID-19 research activity: a call for increased output. Cureus, 12(3), 12:e7357. https://doi.org/10.7759/cureus.7357
11. Chejarla, K. C., Vaidya, O. S., & Kumar, S. (2022). MCDM applications in logistics performance evaluation: A literature review. Journal of Multi‐Criteria Decision Analysis, 29(3–4), 274–297. https://doi.org/10.1002/mcda.1774
12. Chen, Y., Jin, Q., Fang, H., Lei, H., Hu, J., Wu, Y., Chen, J., Wang, C., & Wan, Y. (2019). Analytic network process: Academic insights and perspectives analysis. Journal of Cleaner Production, 235, 1276–1294.
13. Chowdhury, P., & Paul, S. K. (2020). Applications of MCDM methods in research on corporate sustainability: A systematic literature review. Management of Environmental Quality: An International Journal, 31(2), 385–405. https://doi.org/10.1108/MEQ-12-2019-0284
14. Costa, I.P.A.; Basilio, M.P.; Maeda, S.M.N.; Rodrigues, M.V.G.; Moreira, M.A.L.; Gomes, C.F.S.; Santos, M. Bibliometric Studies on Multi-Criteria Decision Analysis (MCDA) Applied in Personnel Selection. In Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III—Proceedings of MMBD 2021 and MLIS 2021; Tallón-Ballesteros, A.J., Ed.; IOS Press: Amsterdam, The Netherlands, 2021; 341, 119–125.
15. Das, M. C., Sarkar, B., & Ray, S. (2012). Decision making under conflicting environment: a new MCDM method. International Journal of Applied Decision Sciences, 5(2), 142-162. https://doi.org/ 10.1504/ijads.2012.046505
16. de Souza, D. G. B., dos Santos, E. A., Soma, N. Y., & da Silva, C. E. S. (2021). MCDM-based R&D project selection: A systematic literature review. Sustainability, 13(21), 11626. https://doi.org/10.3390/su132111626
17. Donthu, N., Kumar, S., & Pattnaik, D. (2020). Forty-five years of Journal of Business Research: A bibliometric analysis. Journal of Business Research, 109, 1-14. https://doi.org/10.1016/j.jbusres.2019.10.039
18. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
19. Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266, 121981. https://doi.org/10.1016/j.jclepro.2020.121981
20. Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact?. Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
21. Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
22. Ferreira, F. A., & Santos, S. P. (2021). Two decades on the MACBETH approach: a bibliometric analysis. Annals of Operations Research, 296(1), 901–925. https://doi.org/10.1007/s10479-018-3083-9
23. Francik, S., Pedrycz, N., Knapczyk, A., Wójcik, A., Francik, R., & Łapczyńska-Kordon, B. (2017). Bibliometric analysis of multiple criteria decision making in agriculture. Technical Sciences/University of Warmia and Mazury in Olsztyn.
24. Gaviria-Marin, M., Merigo, J. M., & Popa, S. (2018). Twenty years of the Journal of Knowledge Management: A bibliometric analysis. Journal of Knowledge Management, 22(8), 1655–1687. https://doi.org/10.1108/JKM-10-2017-0497
25. Gaviria-Marin, M., Merigó, J. M., & Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting and Social Change, 140, 194–220. https://doi.org/10.1016/j.techfore.2018.07.006
26. Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1–29. https://doi.org/10.1016/j.ejor.2015.07.019
27. Guo, Y. M., Huang, Z. L., Guo, J., Li, H., Guo, X. R., & Nkeli, M. J. (2019). Bibliometric analysis on smart cities research. Sustainability, 11(13), 3606. https://doi.org/10.3390/su11133606
28. Guo, Y., Hao, Z., Zhao, S., Gong, J., & Yang, F. (2020). Artificial intelligence in health care: bibliometric analysis. Journal of Medical Internet Research, 22(7), e18228. https://doi.org/10.2196/18228
29. Guerrero-Baena, M. D., Gómez-Limón, J. A., & Fruet Cardozo, J. V. (2014). Are multi-criteria decision making techniques useful for solving corporate finance problems? A bibliometric analysis. Revista de Métodos Cuantitativos Para La Economía y La Empresa, 17, 60–79.
30. Gul, M., Celik, E., Aydin, N., Gumus, A. T., & Guneri, A. F. (2016). A state of the art literature review of VIKOR and its fuzzy extensions on applications. Applied Soft Computing, 46, 60–89. https://doi.org/10.1016/j.asoc.2016.04.040
31. Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1–12. https://doi.org/10.3846/16486897.2017.1281139
32. Kaya, İ., Çolak, M., & Terzi, F. (2018). Use of MCDM techniques for energy policy and decision‐making problems: A review. International Journal of Energy Research, 42(7), 2344–2372. https://doi.org/10.1002/er.4016
33. Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
34. Keshavarz Ghorabaee, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2016a). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research, 50(3), 25–44.
35. Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Esmaeili, A. (2016b). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. Journal of Cleaner Production, 137, 213-229. https://doi.org/10.1016/j.jclepro.2016.07.031
36. Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016c). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International journal of computers communications & control, 11(3), 358–371. https://doi.org/10.15837/ijccc.2016.3.2557
37. Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Hooshmand, R., & Antuchevičienė, J. (2017). Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. Journal of Business Economics and Management, 18(1), 1– 19. https://doi.org/10.3846/16111699.2016.1278559
38. Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). Simultaneous evaluation of criteria and alternatives (SECA) for multi-criteria decision-making. Informatica, 29(2), 265–280. https://doi.org/10.15388/Informatica.2018.167
39. Khan, I., Pintelon, L., & Martin, H. (2022). The application of multicriteria decision analysis methods in health care: a literature review. Medical Decision Making, 42(2), 262–274. https://doi.org/10.1177/0272989X211019040
40. Koca, G., & Yıldırım, S. (2021). Bibliometric analysis of DEMATEL method. Decision Making: Applications in Management and Engineering, 4(1), 85–103. https://doi.org/10.31181/dmame2104085g
41. Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609. https://doi.org/10.1016/j.rser.2016.11.191
42. Kumar, S., Pandey, N., Lim, W. M., Chatterjee, A. N., & Pandey, N. (2021). What do we know about transfer pricing? Insights from bibliometric analysis. Journal of Business Research, 134, 275–287. https://doi.org/10.1016/j.jbusres.2021.05.041
43. Liao, H., Tang, M., Luo, L., Li, C., Chiclana, F., & Zeng, X. J. (2018). A bibliometric analysis and visualization of medical big data research. Sustainability, 10(1), 166. https://doi.org/10.3390/su10010166
44. Liao, H., & Wu, X. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94, 102058. https://doi.org/10.1016/j.omega.2019.04.001
45. Liu, W., & Liao, H. (2017). A bibliometric analysis of fuzzy decision research during 1970–2015. International Journal of Fuzzy Systems, 19(1), 1–14. https://doi.org/10.1007/s40815-016-0272-z
46. Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M. Z. M., & Ibrahim, O. (2017). A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265–292. https://doi.org/10.1016/j.asoc.2017.03.045
47. Martínez-López, F. J., Merigó, J. M., Valenzuela-Fernández, L., & Nicolás, C. (2018). Fifty years of the European Journal of Marketing: a bibliometric analysis. European Journal of Marketing, 52(1–2)., 439–468. https://doi.org/10.1108/EJM-11-2017-0853
48. Massam, B. H. (1988). Multi-criteria decision making (MCDM) techniques in planning. Progress in Planning, 30, 1–84. https://doi.org/10.1016/0305-9006(88)90012-8
49. Merigó, J. M., & Yang, J. B. (2017). A bibliometric analysis of operations research and management science. Omega, 73, 37–48. https://doi.org/10.1016/j.omega.2016.12.004
50. Minhas, M. R., & Potdar, V. (2020). Decision support systems in construction: A bibliometric analysis. Buildings, 10(6), 108. https://doi.org/10.3390/buildings10060108
51. Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información, 29(1).
52. Morkūnaitė, Ž., Kalibatas, D., & Kalibatienė, D. (2019). A bibliometric data analysis of multi-criteria decision making methods in heritage buildings. Journal of Civil Engineering and Management, 25(2), 76–99. https://doi.org/10.3846/jcem.2019.8315
53. Mukhametzyanov, I., & Pamučar, D. (2018). A sensitivity analysis in MCDM problems: A statistical approach. Decision making: Applications in Management and Engineering, 1(2), 51–80. https://doi.org/10.31181/dmame1802050m
54. Nobanee, H., Al Hamadi, F.Y., Abdulaziz, F.A., Abukarsh, L.S., Alqahtani, A.F., AlSubaey, S.K., Alqahtani, S.M., Almansoori, H.A. (2021). A bibliometric analysis of sustainability and risk management. Sustainability, 13(6), 3277. https://doi.org/10.3390/su13063277
55. Ogrodnik, K. (2019). Multi-criteria analysis of design solutions in architecture and engineering: Review of applications and a case study. Buildings, 9(12), 244. https://doi.org/10.3390/buildings9120244
56. Pamučar, D., Vasin, L., & Lukovac, L. (2014, October). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. In XVI international scientific-expert conference on railway, railcon (pp. 89-92).
57. 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. https://doi.org/10.1016/j.eswa.2014.11.057
58. Pamučar, D., Petrović, I., & Ćirović, G. (2018a). Modification of the Best–Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert systems with applications, 91, 89–106. https://doi.org/10.1016/j.eswa.2017.08.042
59. Pamučar, D., Stević, Ž., & Sremac, S. (2018b). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
60. Peng, X., & Yang, Y. (2016). Pythagorean fuzzy Choquet integral based MABAC method for multiple attribute group decision making. International Journal of Intelligent Systems, 31(10), 989-1020. https://doi.org/10.1002/int.21814
61. Peng, X., Zhang, X., & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53(5), 3813–3847. https://doi.org/10.1007/s10462-019-09780-x
62. Rey-Martí, A., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2016). A bibliometric analysis of social entrepreneurship. Journal of Business Research, 69(5), 1651–1655. https://doi.org/10.1016/j.jbusres.2015.10.033
63. Sotoudeh-Anvari, A. (2022). The applications of MCDM methods in COVID-19 pandemic: A state of the art review. Applied Soft Computing, 109238. https://doi.org/10.1016/j.asoc.2022.109238
64. Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457. https://doi.org/10.3390/math8030457
65. Stefano, N. M., Casarotto Filho, N., Vergara, L. G. L., & da Rocha, R. U. G. (2015). COPRAS (Complex Proportional Assessment): state of the art research and its applications. IEEE Latin America Transactions, 13(12), 3899–3906. https://doi.org/10.1109/TLA.2015.7404925
66. Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020a). 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. https://doi.org/10.1016/j.cie.2019.106231
67. Stević, Ž., & Brković, N. (2020b). A novel integrated FUCOM-MARCOS model for evaluation of human resources in a transport company. Logistics, 4(1), 4. https://doi.org/10.3390/logistics4010004
68. Stojčić, M., Zavadskas, E. K., Pamučar, D., Stević, Ž., & Mardani, A. (2019). Application of MCDM methods in sustainability engineering: A literature review 2008–2018. Symmetry, 11(3), 350. https://doi.org/10.3390/sym11030350
69. Tandon, A., Kaur, P., Mäntymäki, M., & Dhir, A. (2021). Blockchain applications in management: A bibliometric analysis and literature review. Technological Forecasting and Social Change, 166, 120649. https://doi.org/10.1016/j.techfore.2021.120649
70. Toloie-Eshlaghy, A., & Homayonfar, M. (2011). MCDM methodologies and applications: a literature review from 1999 to 2009. Research Journal of International Studies, 21, 86–137.
71. Tramarico, C. L., Mizuno, D., Salomon, V. A. P., & Marins, F. A. S. (2015). Analytic hierarchy process and supply chain management: A bibliometric study. Procedia Computer Science, 55, 441–450. http://doi.org/10.1016/j.procs.2015.07.005
72. Vafaeipour, M., Zolfani, S. H., Varzandeh, M. H. M., Derakhti, A., & Eshkalag, M. K. (2014). Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach. Energy Conversion and Management, 86, 653–663. https://doi.org/10.1016/j.enconman.2014.05.083
73. van Nunen, K., Li, J., Reniers, G., & Ponnet, K. (2018). Bibliometric analysis of safety culture research. Safety Science, 108, 248–258. https://doi.org/10.1016/j.ssci.2017.08.011
74. Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2018). A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458
75. Yildiz, A., & Yayla, A. Y. (2015). Multi-criteria decision-making methods for supplier selection: A literature review. South African Journal of Industrial Engineering, 26(2), 158–177. https://doi.org/10.7166/26-2-1010
76. Yu, D., Wang, W., Zhang, W., & Zhang, S. (2018). A bibliometric analysis of research on multiple criteria decision making. Current Science, 114(4), 747–758. https://doi.org/10.18520/cs/v114/i04/747-758
77. Yu, D., Xu, Z., & Wang, W. (2019). A bibliometric analysis of Fuzzy Optimization and Decision Making (2002–2017). Fuzzy Optimization and Decision Making, 18(3), 371-397. https://doi.org/10.1007/s10700-018-9301-8
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. https://doi.org/10.5755/j01.eee.122.6.1810
79. Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), 165–179. https://doi.org/10.3846/20294913.2014.892037
80. Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335.
81. Zhu, X., Meng, X., & Zhang, M. (2021). Application of multiple criteria decision making methods in construction: A systematic literature review. Journal of Civil Engineering and Management, 27(6), 372–403. https://doi.org/10.3846/jcem.2021.15260
82. Žižović, M., & Pamučar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126–137. https://doi.org/10.31181/dmame1902102z
83. Zopounidis, C., Galariotis, E., Doumpos, M., Sarri, S., & AndriosopouloS, K. (2015). Multiple criteria decision aiding for finance: An updated bibliographic survey. European Journal of Operational Research, 247(2), 339–348. https://doi.org/10.1016/j.ejor.2015.05.032
84. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
85. Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158–181. https://doi.org/10.1016/j.eswa.2017.02.016