SavedAdd your journal

Journals / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Sayı: 63

BIG DATA ANALYTICS SOFTWARE SELECTION WITH MULTI-CRITERIA DECISION-MAKING METHODS FOR DIGITAL TRANSFORMATION

DİJİTAL DÖNÜŞÜM İÇİN ÇOK KRİTERLİ KARAR VERME YÖNTEMLERİ İLE BÜYÜK VERİ ANALİTİĞİ YAZILIM SEÇİMİ

Tayfun Öztaş(Pamukkale Üniversitesi)0000-0001-8224-5092
Pages
297–317
Publication Date
2024-07-22
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, the proposed method's applicability is validated through comparative analysis with five different MCDM methods, reinforcing the reliability of the obtained results.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the process of transitioning to digital businesses, managers are faced with numerous decision-making challenges across various domains.
📊
Key Findings & Data:This complexity poses a significant hurdle for traditional businesses seeking to embrace digital transformation.
💡
Conclusion & Impact:Furthermore, the proposed method's applicability is validated through comparative analysis with five different MCDM methods, reinforcing the reliability of the obtained results.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Dijital İşletme · Dijital Dönüşüm · Büyük Veri Analitiği

Abstract

In the process of transitioning to digital businesses, managers are faced with numerous decision-making challenges across various domains. This complexity poses a significant hurdle for traditional businesses seeking to embrace digital transformation. To address this challenge, the Preference Selection Index (PSI) and Additive Ratio Assessment (ARAS) methods are utilized for selecting Big Data Analytics (BDA) software, employing multi-criteria decision-making (MCDM) approaches. With a scenario involving 8 alternatives and 7 criteria, the PSI method is employed to establish the weights of the criteria. Subsequently, the ARAS method is utilized to rank the alternatives. The analysis identifies "Ease of Use" as the criterion with the highest importance weight (0.1464), while "Data Workflow" emerges as the least significant criterion (0.1378). Based on the highest utility degree (0.9548), the fifth alternative was identified as the most suitable big data analytics software for this scenario. Furthermore, the proposed method's applicability is validated through comparative analysis with five different MCDM methods, reinforcing the reliability of the obtained results.

Özet

Dijital işletmelere geçiş sürecinde, yöneticiler çeşitli alanlarda çok sayıda karar verme zorluğuyla karşı karşıya kalmaktadır. Bu karmaşıklık, dijital dönüşümü benimsemek isteyen geleneksel işletmeler için önemli bir engel teşkil etmektedir. Bu zorluğun üstesinden gelmek için, çalışmada Çok Kriterli Karar Verme (ÇKKV) yaklaşımlarından faydalanılarak Büyük Veri Analitiği (BVA) yazılımı seçmek için Tercih Seçim Endeksi (PSI) ve Eklemeli Oran Değerlendirme (ARAS) yöntemleri kullanılmıştır. Sekiz alternatif ve yedi kriter içeren bir senaryoda, kriterlerin ağırlıklarını belirlemek için PSI yöntemi kullanılmıştır. Daha sonra, alternatifleri sıralamak için ARAS yöntemi kullanılmıştır. Analiz sonucunda "Kullanım Kolaylığı" en yüksek önem ağırlığına (0.1464) sahip kriter olarak belirlenirken, "Veri İş Akışı" en az öneme sahip kriter (0.1378) olarak ortaya çıkmıştır. En yüksek fayda derecesine (0.9548) göre, beşinci alternatif bu senaryo için en uygun büyük veri analitiği yazılımı olarak belirlenmiştir. Ayrıca, önerilen yöntemin uygulanabilirliği beş farklı ÇKKV yöntemi ile karşılaştırmalı analiz yoluyla doğrulanarak elde edilen sonuçların güvenilirliği desteklenmiştir.

🔖

Cite This Article

Öztaş, T. (2024). BIG DATA ANALYTICS SOFTWARE SELECTION WITH MULTI-CRITERIA DECISION-MAKING METHODS FOR DIGITAL TRANSFORMATION. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Sayı: 63, 297–317. https://doi.org/10.30794/pausbed.1398830

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (84)

  1. Abbasianjahromi, H., Rajaie, H., & Shakeri, E. (2013). “A Framework for Subcontractor Selection in the Construction Industry”, Journal of Civil Engineering and Management, 19/2, 158–168. https://doi.org/10.3846/13923730.2012.743922
  2. Aksoy, S., & Yetkin Ozbuk, M. (2017). “Multiple Criteria Decision Making in Hotel Location: Does It Relate to Postpurchase Consumer Evaluations?”, Tourism Management Perspectives, 22, 73–81. https://doi.org/10.1016/j.tmp.2017.02.001
  3. Albawab, M., Ghenai, C., Bettayeb, M., & Janajreh, I. (2020). “Sustainability Performance Index for Ranking Energy Storage Technologies using Multi-Criteria Decision-Making Model and Hybrid Computational Method”, Journal of Energy Storage, 32, 101820. https://doi.org/10.1016/j.est.2020.101820
  4. Alkan, N., & Kahraman, C. (2024). “CODAS Extension Using Novel Decomposed Pythagorean Fuzzy Sets: Strategy Selection for IOT Based Sustainable Supply Chain System”, Expert Systems with Applications, 237, 121534. https://doi.org/10.1016/J.ESWA.2023.121534
  5. Almomani, M. A., Aladeemy, M., Abdelhadi, A., & Mumani, A. (2013). “A Proposed Approach for Setup Time Reduction Through Integrating Conventional SMED Method With Multiple Criteria Decision-Making Techniques”, Computers & Industrial Engineering, 66/2, 461–469. https://doi.org/10.1016/j.cie.2013.07.011
  6. Ampaw, E. M., Chai, J., Jiang, Y., Darko, A. P., & Ofori, K. S. (2024). “Rethinking Small-Scale Gold Mining in Ghana: A Holy Grail for Environmental Stewardship and Sustainability”, Journal of Cleaner Production, 437, 140683. https://doi.org/10.1016/j.jclepro.2024.140683
  7. Asemi, A., Asemi, A., Ko, A., & Alibeigi, A. (2022). “An Integrated Model for Evaluation of Big Data Challenges and Analytical Methods in Recommender Systems”, Journal of Big Data, 9/1, 13. https://doi.org/10.1186/s40537-022-00560-z
  8. Attri, R., & Grover, S. (2015). “Application of Preference Selection Index Method for Decision Making Over the Design Stage of Production System Life Cycle”, Journal of King Saud University - Engineering Sciences, 27/2, 207–216. https://doi.org/10.1016/j.jksues.2013.06.003
  9. Aytaç Adalı, E., Öztaş, T., Özçil, A., Öztaş, G. Z., & Tuş, A. (2023). “A New Multi-Criteria Decision-Making Method Under Neutrosophic Environment: ARAS Method With Single-Valued Neutrosophic Numbers”, International Journal of Information Technology & Decision Making, 22/01, 57–87. https://doi.org/10.1142/S0219622022500456
  10. Ayyıldız, T. E., & Ekinci, E. B. M. (2023). “Selection of Six Sigma Projects Based on Integrated Multi-Criteria Decision-Making Methods: The Case of the Software Development Industry”, The Journal of Supercomputing, 79/13, 14981–15003. https://doi.org/10.1007/s11227-023-05250-y
  11. Badi, I., Stević, Ž., Radović, D., Ristić, B., Cakić, A., & Sremac, S. (2023). “A New Methodology for Treating Problems in The Field Of Traffic Safety: Case Study Of Libyan Cities”, Transport, 38/4, 190–203. https://doi.org/10.3846/transport.2023.20609
  12. Berger, R. (2015). The digital transformation of industry. Roland Berger Strategy Consultants, A European Study Commissioned by the Federation of German Industries (BDI), Munich.
  13. Biswas, A. K., Islam, Md. R., & Habib, Md. A. (2023). “An Analytical Investigation of Critical Factors to Prioritize Coalfields for Underground Coal Gasification – Bangladesh Case”, Heliyon, 9/7, e18416. https://doi.org/10.1016/j.heliyon.2023.e18416
  14. Chen, F., Li, Y., Feng, Q., Dong, Z., Qian, Y., Yan, Y., Ho, M. S., Ma, Q., Zhang, D., & Jin, Y. (2023). “Road Safety Performance Rating Through PSI-PRIDIT: A Planning Tool for Designing Policies and Identifying Best Practices for EAS Countries”, Socio-Economic Planning Sciences, 85, 101438. https://doi.org/10.1016/j.seps.2022.101438
  15. Chen, M., Mao, S., & Liu, Y. (2014). “Big data: A Survey”, Mobile Networks and Applications, 19, 171-209. Dehshiri, S. S. H., & Firoozabadi, B. (2024). “Sustainable Solar Energy in Urban Areas: A Novel Framework for Uncertainty, Reliability, And 7E Analysis”, Journal of Cleaner Production, 444, 141136. https://doi.org/10.1016/j.jclepro.2024.141136
  16. Duc Trung, D. (2022). “Multi-Criteria Decision Making of Turning Operation Based on PEG, PSI and CURLI Methods”, Manufacturing Review, 9, 9. https://doi.org/10.1051/mfreview/2022007 Ebert, C., & Duarte, C. H. C. (2018). “Digital Transformation”, IEEE Software, 35/4, 16-21.
  17. Ghadikolaei, A. S., & Esbouei, S. K. (2014). “Integrating Fuzzy AHP and Fuzzy ARAS for Evaluating Financial Performance”, Boletim Da Sociedade Paranaense de Matemática, 32/2, 163. https://doi.org/10.5269/bspm.v32i2.21378 Ghenai, C., Albawab, M., & Bettayeb, M. (2020). “Sustainability Indicators for Renewable Energy Systems Using Multi-Criteria Decision-Making Model and Extended SWARA/ARAS Hybrid Method”, Renewable Energy, 146, 580–597. https://doi.org/10.1016/j.renene.2019.06.157
  18. Gopal, P. R. C., Rana, N. P., Krishna, T. V., & Ramkumar, M. (2022). “Impact of Big Data Analytics on Supply Chain Performance: An Analysis of Influencing Factors”, Annals of Operations Research. https://doi.org/10.1007/s10479-022-04749-6
  19. Görçün, Ö. F., & Küçükönder, H. (2021). “A Novel Performance Evaluation Technique Based on Integrated Weighting Approach: A Case Study in The Field of Sport Management”, Decision Science Letters, 10/4, 511–524. https://doi.org/10.5267/j.dsl.2021.5.004
  20. Goswami, S. S., Behera, D. K., Afzal, A., Razak Kaladgi, A., Khan, S. A., Rajendran, P., Subbiah, R., & Asif, M. (2021). “Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS”, Symmetry, 13/8, 1331. https://doi.org/10.3390/sym13081331
  21. Hanine, M., Boutkhoum, O., Tikniouine, A., & Agouti, T. (2016). “Application of an Integrated Multi-Criteria Decision Making AHP-TOPSIS Methodology for ETL Software Selection”, SpringerPlus, 5/1, 263. https://doi.org/10.1186/s40064-016-1888-z
  22. Helmy, S. E., Eladl, G. H., & Eisa, M. (2021). “Fuzzy Analytical Hierarchy Process (FAHP) Using Geometric Mean Method to Select Best Processing Framework Adequate to Big Data”, Journal of Theoretical and Applied Information Technology, 99/1, 207–226.
  23. Hoan, P. Van, & Ha, Y. (2021). “ARAS-FUCOM Approach for VPAF Fighter Aircraft Selection”, Decision Science Letters, 10, 53–62. https://doi.org/10.5267/j.dsl.2020.10.004
  24. Ighravwe, D. E., & Oke, S. A. (2019). “A Multi-Criteria Decision-Making Framework for Selecting a Suitable Maintenance Strategy for Public Buildings Using Sustainability Criteria”, Journal of Building Engineering, 24, 100753. https://doi.org/10.1016/j.jobe.2019.100753
  25. Iordache, M., Schitea, D., Deveci, M., Akyurt, İ. Z., & Iordache, I. (2019). “An Integrated ARAS And Interval Type-2 Hesitant Fuzzy Sets Method for Underground Site Selection: Seasonal Hydrogen Storage in Salt Caverns”, Journal of Petroleum Science and Engineering, 175, 1088–1098. https://doi.org/10.1016/j.petrol.2019.01.051
  26. Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016). “The Framework for the Selection of Personnel Based on the SWARA and ARAS Methods Under Uncertainties”, Informatica, 27/1, 49–65. https://doi.org/10.15388/Informatica.2016.76
  27. Keršulienė, V., & Turskis, Z. (2012). “Integrated Fuzzy Multiple Criteria Decision Making Model For Architect Selection”, Technological and Economic Development of Economy, 17/4, 645–666. https://doi.org/10.3846/20294913.2011.635718
  28. 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. https://doi.org/10.3390/sym13040525
  29. Kim, T., Oh, Y., Koo, J., & Yoo, D. (2022). “Evaluation of Priority Control District Metered Area for Water Distribution Networks Using Water Quality-Related Big Data”, Sustainability, 14/12, 7282. https://doi.org/10.3390/su14127282
  30. Lamba, K., & Singh, S. P. (2018). “Modeling Big Data Enablers for Operations and Supply Chain Management”, The International Journal of Logistics Management, 29/2, 629–658. https://doi.org/10.1108/IJLM-07-2017-0183
  31. Lamrini, L., Abounaima, M. C., & Talibi Alaoui, M. (2023). “New Distributed-TOPSIS Approach for Multi-Criteria Decision-Making Problems in a Big Data Context”, Journal of Big Data, 10/1, 97. https://doi.org/10.1186/s40537-023-00788-3
  32. Madić, M., Antucheviciene, J., Radovanović, M., & Petković, D. (2017). “Determination of Laser Cutting Process Conditions Using the Preference Selection Index Method”, Optics & Laser Technology, 89, 214–220. https://doi.org/10.1016/j.optlastec.2016.10.005
  33. Magableh, G. M., & Mumani, A. A. (2022). “Simulation Based-MCDM Approach for Evaluating Traffic Solutions”, Promet - Traffic&Transportation, 34/1, 117–133. https://doi.org/10.7307/ptt.v34i1.3842
  34. Maghsoodi, A. I. (2023). “Cryptocurrency Portfolio Allocation Using a Novel Hybrid and Predictive Big Data Decision Support System”, Omega, 115, 102787. https://doi.org/10.1016/j.omega.2022.102787
  35. Maheshwari, N., Choudhary, J., Rath, A., Shinde, D., & Kalita, K. (2021). “Finite Element Analysis and Multi-criteria Decision-Making (MCDM)-Based Optimal Design Parameter Selection of Solid Ventilated Brake Disc”, Journal of The Institution of Engineers (India): Series C, 102/2, 349–359. https://doi.org/10.1007/s40032-020-00650-y
  36. Mahmoodi, E., Azari, M., & 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. https://doi.org/10.1111/jfr3.12894
  37. Maniya, K. D., & Bhatt, M. G. (2011). “An Alternative Multiple Attribute Decision Making Methodology for Solving Optimal Facility Layout Design Selection Problems”, Computers & Industrial Engineering, 61/3, 542–549. https://doi.org/10.1016/J.CIE.2011.04.009
  38. Maniya, K., & Bhatt, M. G. (2010). “A Selection of Material Using a Novel Type Decision-Making Method: Preference Selection Index Method”, Materials & Design, 31/4, 1785–1789. https://doi.org/10.1016/j.matdes.2009.11.020
  39. Medineckiene, M., Zavadskas, E. K., Björk, F., & Turskis, Z. (2015). “Multi-Criteria Decision-Making System for Sustainable Building Assessment/Certification”, Archives of Civil and Mechanical Engineering, 15/1, 11–18. https://doi.org/10.1016/j.acme.2014.09.001
  40. Mergel, I., Edelmann, N., & Haug, N. (2019). “Defining Digital Transformation: Results from Expert Interviews”, Government Information Quarterly, 36/4, 101385.
  41. Mostafaeipour, A., & Le, T. (2024). “Evaluating Strategies for Developing Renewable Energies Considering Economic, Social, and Environmental Aspects: A Case Study”, Environmental Science and Pollution Research, 31, 23697–23718. https://doi.org/10.1007/s11356-024-32612-7
  42. Obeidat, M. S., Ababneh, W., & Al Theeb, N. (2023). “The Preference Selection Index Performance in Large Alternatives’ Decisions to Support the AHP: The Case of a University Selection”, Journal of Applied Research and Technology, 21/1, 56–72. https://doi.org/10.22201/icat.24486736e.2023.21.1.1423
  43. Ozcalici, M. (2022). “Allocation with Multi Criteria Decision Making Techniques”, Decision Making: Applications in Management and Engineering, 5/2, 78–119. https://doi.org/10.31181/dmame0305102022o
  44. 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
  45. Pamucar, D., Ulutaş, A., Topal, A., Karamaşa, Ç., & Ecer, F. (2024). “Fermatean Fuzzy Framework Based on Preference Selection Index and Combined Compromise Solution Methods for Green Supplier Selection in Textile Industry”, International Journal of Systems Science: Operations & Logistics, 11/1, 2319786. https://doi.org/10.1080/23302674.2024.2319786
  46. Pamučar, D., Vasin, L., & 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, https://doi.org/10.13140/2.1.2707.6807
  47. Pathak, V. K., Singh, R., & Gangwar, S. (2019). “Optimization of Three-Dimensional Scanning Process Conditions Using Preference Selection Index and Metaheuristic Method”, Measurement, 146, 653–667. https://doi.org/10.1016/j.measurement.2019.07.013
  48. Pour, P. D., Ahmed, A. A., Nazzal, M. A., & Darras, B. M. (2023). “An Industry 4.0 Technology Selection Framework for Manufacturing Systems and Firms Using Fuzzy AHP and Fuzzy TOPSIS Methods”, Systems, 11/4, 192.
  49. Sachdeva, N., Singh, O., Kapur, P. K., & Galar, D. (2016). “Multi-Criteria Intuitionistic Fuzzy Group Decision Analysis with TOPSIS Method For Selecting Appropriate Cloud Solution to Manage Big Data Projects”, International Journal of System Assurance Engineering and Management, 7/3, 316–324. https://doi.org/10.1007/s13198-016-0455-x
  50. Sagiroglu, S., & Sinanc, D. (2013). “Big Data: A Review”, 2013 International Conference on Collaboration Technologies and Systems (CTS), IEEE, 42-47, Doi: 10.1109/CTS.2013.6567202.
  51. Samant, M. R., Kishore Krisna, S., Raaj Khishorre, K. R., & Sreeharan, B. N. (2022). “A Systematic Way of using Preference Selection Index Methodology for Selecting Suspension Coil Spring Material”, Materials Today: Proceedings, 68, 2249–2257. https://doi.org/10.1016/j.matpr.2022.08.443
  52. Schober, P., Boer, C., & Schwarte, L. A. (2018). “Correlation Coefficients: Appropriate Use and Interpretation”, Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864
  53. Sharma, M., Gupta, R., Sehrawat, R., Jain, K., & Dhir, A. (2023). “The Assessment of Factors Influencing Big Data Adoption and Firm Performance: Evidences From Emerging Economy”, Enterprise Information Systems, 17/12, 12. https://doi.org/10.1080/17517575.2023.2218160
  54. Sivalingam, V., Ganesh Kumar, P., Prabakaran, R., Sun, J., Velraj, R., & Kim, S. C. (2022). “An Automotive Radiator with Multi-Walled Carbon-Based Nanofluids: A Study on Heat Transfer Optimization Using MCDM Techniques”, Case Studies in Thermal Engineering, 29, 101724. https://doi.org/10.1016/j.csite.2021.101724
  55. Sivalingam, V., Poogavanam, G., Natarajan, Y., & Sun, J. (2022). “Optimization Of Atomized Spray Cutting Fluid Eco-Friendly Turning of Inconel 718 Alloy Using ARAS And CODAS Methods”, The International Journal of Advanced Manufacturing Technology, 120/7–8, 4551–4564. https://doi.org/10.1007/s00170-022-09047-w
  56. Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). “Critical Analysis of Big Data Challenges and Analytical Methods”, Journal of Business Research, 70, 263-286.
  57. Son, N. H., & Hieu, T. T. (2023). “Selection Of Welding Robot by Multi-Criteria Decision-Making Method”, Eastern-European Journal of Enterprise Technologies, 1/3(121), 66–72. https://doi.org/10.15587/1729-4061.2023.269026
  58. Stark, J. (2020). Digital transformation of industry: Continuing Change. Springer Cham.
  59. 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. https://doi.org/10.1016/j.cie.2019.106231
  60. TechAmerica Foundation. (2012). Demystifying Big Data: A Practical Guide to Transforming the Business of Government. (28.11.2023) https://breakinggov.sites.breakingmedia.com/wp-content/uploads/sites/4/2012/10/TechAmericaBigDataReport.pdf.
  61. Tian, C., Peng, J., Long, Q., Wang, J., & Goh, M. (2022). “Extended Picture Fuzzy MULTIMOORA Method Based on Prospect Theory for Medical Institution Selection”, Cognitive Computation, 14(4), 1446–1463. https://doi.org/10.1007/s12559-022-10006-6
  62. Toslak, M., Ulutaş, A., Ürea, S., & Stević, Ž. (2023). “Selection Of Peanut Butter Machine by the Integrated PSI-SV-MARCOS Method”, International Journal of Knowledge-Based and Intelligent Engineering Systems, 27/1, 73–86. https://doi.org/10.3233/KES-230044
  63. Turskis, Z., & Juodagalvienė, B. (2016). “A Novel Hybrid Multi-Criteria Decision-Making Model to Assess a Stairs Shape for Dwelling Houses”, Journal of Civil Engineering and Management, 22/8, 1078–1087. https://doi.org/10.3846/13923730.2016.1259179
  64. Turskis, Z., & Zavadskas, E. K. (2010). “A New Fuzzy Additive Ratio Assessment Method (ARAS‐F). Case Study: The Analysis of Fuzzy Multiple Criteria in Order to Select the Logistic Centers Location”, Transport, 25/4, 423–432. https://doi.org/10.3846/transport.2010.52
  65. Tuş, A., & Aytaç Adalı, E. (2018). “CODAS ve PSI Yöntemleri ile Personel Değerlendirmesi”, Alphanumeric Journal, 6/2, 243–256. https://doi.org/10.17093/alphanumeric.432843
  66. Tuş, A., & Aytaç Adalı, E. (2019). “The New Combination with CRITIC And WASPAS Methods for the Time and Attendance Software Selection Problem”, OPSEARCH, 56/2, 528–538. https://doi.org/10.1007/s12597-019-00371-6
  67. Ul Haq, R. S., Saeed, M., Mateen, N., Siddiqui, F., & Ahmed, S. (2023). “An Interval-Valued Neutrosophic Based MAIRCA Method For Sustainable Material Selection”, Engineering Applications of Artificial Intelligence, 123, 106177. https://doi.org/10.1016/J.ENGAPPAI.2023.106177
  68. Ullah, S., Awan, M. D., & Sikander Hayat Khiyal, M. (2018). “Big Data in Cloud Computing: A Resource Management Perspective”, Scientific Programming, 2018, 5418679.
  69. Ulutaş, A., & Topal, A. (2022). “A New Hybrid Model Based on Rough Step-Wise Weight Assessment Ratio Analysis for Third-Party Logistics Selection”, Soft Computing, 26/4, 2021–2032. https://doi.org/10.1007/s00500-021-06374-0
  70. Ulutaş, A., Balo, F., & 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. https://doi.org/10.3390/polym15061500
  71. Ulutaş, A., Balo, F., Sua, L., Demir, E., Topal, A., & Jakovljević, V. (2021). “A New Integrated Grey MCDM Model: Case of Warehouse Location Selection”, Facta Universitatis, Series: Mechanical Engineering, 19/3, 515. https://doi.org/10.22190/FUME210424060U
  72. Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). “A New Hybrid Fuzzy PSI-PIPRECIA-COCOSO MCDM Based Approach to Solving the Transportation Company Selection Problem”, Technological and Economic Development of Economy, 27/5, 1227–1249. https://doi.org/10.3846/TEDE.2021.15058
  73. Ulutaş, A., Topal, A., Görçün, Ö. F., & Ecer, F. (2024). “Evaluation Of Third-Party Logistics Service Providers for Car Manufacturing Firms Using a Novel Integrated Grey LOPCOW-PSI-MACONT Model”, Expert Systems with Applications, 241, 122680. https://doi.org/10.1016/j.eswa.2023.122680
  74. Vahdani, B., Zandieh, M., & Tavakkoli-Moghaddam, R. (2011). “Two Novel FMCDM Methods for Alternative-Fuel Buses Selection”, Applied Mathematical Modelling, 35/3, 1396–1412. https://doi.org/10.1016/j.apm.2010.09.018
  75. Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). “Digital Transformation: A Multidisciplinary Reflection and Research Agenda”, Journal of Business Research, 122, 889-901.
  76. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). “Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities”, Journal of Business Research, 70, 356-365.
  77. Yadav, R. (2022). “Fabrication, Characterization, and Optimization Selection of Ceramic Particulate Reinforced Dental Restorative Composite Materials”, Polymers and Polymer Composites, 30. https://doi.org/10.1177/09673911211062755
  78. Yapıcı Pehlivan, N., Şahin, A., Zavadskas, E. K., & Turskis, Z. (2018). “A Comparative Study of Integrated FMCDM Methods For Evaluation of Organizational Strategy Development”, Journal of Business Economics and Management, 19/2, 360–381. https://doi.org/10.3846/jbem.2018.5683
  79. Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). “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
  80. Yilmaz, I., Adem, A., & Dağdeviren, M. (2023). “A Machine Learning-Integrated Multi-Criteria Decision-Making Approach Based on Consensus for Selection of Energy Storage Locations”, Journal of Energy Storage, 69, 107941. https://doi.org/10.1016/j.est.2023.107941
  81. Yilmaz, K., & Burdurlu, E. (2023). “Selection Of Wooden Furniture Joints with Multi-Criteria Decision-Making Techniques”, Wood Material Science & Engineering, 1–16. https://doi.org/10.1080/17480272.2023.2242329
  82. Zagorskas, J., & Turskis, Z. (2020a). “Location Preferences of New Pedestrian Bridges Based on Multi-Criteria Decision-Making and GIS-Based Estimation”, The Baltic Journal of Road and Bridge Engineering, 15/2, 158–181. https://doi.org/10.7250/bjrbe.2020-15.478
  83. Zagorskas, J., & Turskis, Z. (2020b). “Setting Priority List for Construction Works of Bicycle Path Segments Based on Eckenrode Rating And ARAS-F Decision Support Method Integrated in GIS”, Transport, 35/2, 179–192. https://doi.org/10.3846/TRANSPORT.2020.12478
  84. Zavadskas, E. K., & Turskis, Z. (2010). “A New Additive Ratio Assessment (ARAS) Method in Multicriteria Decision‐Making”, Technological and Economic Development of Economy, 16/2, 159–172. https://doi.org/10.3846/tede.2010.10