Comparative Analysis of Objective Weighting Approaches in Multi-Criteria Decision Making Using WASPAS
Selecting the best-performing honorary employee is a multi-criteria decision-making problem because employee performance is influenced by several criteria with different levels of importance. This study aims to conduct a comparative analysis of objective weighting approaches in the Weighted Aggregated Sum Product Assessment (WASPAS) method for evaluating honorary employee performance and examining the stability of the resulting rankings. Eight alternatives, namely HE-A to HE-H, were evaluated using five criteria: Work Performance, Discipline, Cooperative Ability, Initiatives and Problem Solving, and Communication Skills. Twelve objective weighting methods, namely Entropy, G2M, RECA, LOPCOW, CRITIC, MEREC, WENSLO, LOGSTA, LODECI, ITARA, SITDE, and DAM, were independently applied to determine criterion weights. The resulting weights were then integrated into WASPAS to generate alternative rankings under different weighting scenarios. The results show that HE-F consistently achieved the first rank across all objective weighting approaches, while HE-E consistently occupied the eighth position. The ranking comparison further indicates that most methods produce ranking patterns close to the original ranking, although MEREC introduces greater changes in several alternative positions. Spearman rank correlation analysis confirms a strong consistency of the resulting rankings, with the highest correlation obtained by LODECI, SITDE, and DAM at 0.9524, followed by G2M and CRITIC at 0.9286 and MEREC at 0.9048. Overall, the findings demonstrate that the combination of objective weighting approaches and WASPAS provides a reliable framework for employee performance evaluation, while comparative correlation analysis can be used to assess ranking stability and robustness across different weighting perspectives.
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References
[1] S. Jovčić and P. Průša, “A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection,” Mathematics, vol. 9, no. 21. p. 2729, 2021. doi: 10.3390/math9212729.
[2] F. Mizrak, K. C. Mizrak, and G. R. Akkartal, “Developing a strategic framework for airline destination selection: A multi-criteria decision-making approach applied to Turkish airlines,” Transp. Res. Interdiscip. Perspect., vol. 29, p. 101322, 2025, doi: 10.1016/j.trip.2025.101322.
[3] A. Ulutaş, A. Topal, and F. Ecer, “The Alternative Prioritization and Assessment System (ALPAS) Method for Environmental Performance Evaluation,” Mathematics, vol. 13, no. 20. p. 3333, 2025. doi: 10.3390/math13203333.
[4] D. Pamucar, V. Simic, Ö. F. Görçün, and H. Küçükönder, “Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals,” Expert Syst. Appl., vol. 239, p. 122312, 2024, doi: 10.1016/j.eswa.2023.122312.
[5] S. Dhruva, R. Krishankumar, E. K. Zavadskas, K. S. Ravichandran, and A. H. Gandomi, “Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo,” Informatica, vol. 35, no. 1, pp. 65–98, Nov. 2024, doi: 10.15388/23-INFOR537.
[6] H. Hariyanto, A. Christian, M. S. Nurhayati, and B. Sudarsono, “Modification of Multi-Attributive Border Approximation Area Comparison (MABAC) to Improve Multi-Criteria Assessment,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 6, no. 1 SE-Articles, pp. 50–60, Mar. 2025, doi: 10.33365/jatika.v6i1.15.
[7] Y. Liu, “A novel decision making approach based on Picture Hesitant Fuzzy Environment for optimizing art education strategies,” Sci. Rep., 2026, doi: 10.1038/s41598-026-48827-2.
[8] S. Soyguder and G. G. Dinler, “Comparative Analysis of Robotic and Automatic Warehousing Systems with Fuzzy-Based Multi-Criteria Decision Making,” Int. J. Fuzzy Syst., 2026, doi: 10.1007/s40815-026-02249-4.
[9] S. Bektaş, “Financial Performance Ranking With A New Hybrid Decision Making Model: Application Of The CRISUS-RAWEC Hybrid Model For The Reinsurance Industry,” Gümüşhane Univ. J. Soc. Sci., vol. 17, no. 1, pp. 233–249, 2026.
[10] A. Köseoğlu, F. Altun, and R. Şahin, “A novel q-ROHFS prospect theory based MABAC method for failure mode risk prioritization in aircraft landing systems,” Sci. Rep., vol. 15, no. 1, p. 42389, 2025, doi: 10.1038/s41598-025-26585-x.
[11] J. Barman, B. Biswas, and K. S. Rao, “A hybrid integration of analytical hierarchy process (AHP) and the multiobjective optimization on the basis of ratio analysis (MOORA) for landslide susceptibility zonation of Aizawl, India,” Nat. Hazards, vol. 120, no. 9, pp. 8571–8596, 2024, doi: 10.1007/s11069-024-06538-9.
[12] P. Rani, A. R. Mishra, D. Pamucar, A. M. Alshamrani, and A. F. Alrasheedi, “Assessment of digital transformation indicators to prioritize sustainable financial services using q-rung orthopair fuzzy rough decision-making model,” Appl. Soft Comput., vol. 170, p. 112715, 2025, doi: 10.1016/j.asoc.2025.112715.
[13] C.-H. Chen, “A Hybrid Multi-Criteria Decision-Making Approach Based on ANP-Entropy TOPSIS for Building Materials Supplier Selection,” Entropy, vol. 23, no. 12. 2021. doi: 10.3390/e23121597.
[14] A. M. Correa Machado, P. I. Ekel, and M. P. Libório, “Goal-based participatory weighting scheme: balancing objectivity and subjectivity in the construction of composite indicators,” Qual. Quant., vol. 57, no. 5, pp. 4387–4407, 2023, doi: 10.1007/s11135-022-01546-y.
[15] A. Yudhistira, S. Setiawansyah, T. Ardiansah, S. Maryana, Y. Yadin, and R. Oktaviani, “Development of Multi-Attribute Utility Theory Methods in Dynamic Decision Models Using Change-Data Driven,” Evergreen, vol. 11, no. 4, pp. 3279–3289, Dec. 2024, doi: 10.5109/7326962.
[16] S. Sumanto, R. R. Suryono, R. Aryanti, H. Rohayani, S. Setiawansyah, and D. Desyanti, “Comprehensive Evaluation of Distance-Based MCDM Methods Using Response to Criteria Weighting: A Ranking Stability and Sensitivity Analysis,” J. Inf. Technol. Softw. Eng. Comput. Sci., vol. 4, no. 4, pp. 119–138, Oct. 2026, doi: 10.58602/itsecs.v4i4.377.
[17] M. Bharadwaj. L, R. Sankargupta, R. Madurai Elavarasan, E. Devaraj, and A. Nandagopal, “Multi-Criteria Decision Analysis framework: Transitioning from coal to Large-Scale Photovoltaics in Australia for achieving SDG 7,” Appl. Energy, vol. 405, p. 127208, 2026, doi: 10.1016/j.apenergy.2025.127208.
[18] A. Ajithkumar and P. GaneshKumar, “A greener approach for selecting organic PCM in solar thermal collectors using the MCDM framework,” Sol. Energy, vol. 305, p. 114283, 2026, doi: 10.1016/j.solener.2025.114283.
[19] D. Desyanti, I. Ahmad, and S. Sanusi, “Assessing Microcredit Eligibility Using LOGSTA Weighting and Comprehensive Distance-Based Ranking: A Multi-Criteria Decision-Making Approach,” J. Comput. Data Sci., vol. 1, no. 1, pp. 1–31, doi: 10.67449/jcoda.v1i1.1.
[20] I. Tronnebati, F. Jawab, Y. Frichi, and J. Arif, “Green Supplier Selection Using Fuzzy AHP, Fuzzy TOSIS, and Fuzzy WASPAS: A Case Study of the Moroccan Automotive Industry,” Sustainability, vol. 16, no. 11. pp. 1–22, 2024. doi: 10.3390/su16114580.
[21] K. Rudnik, G. Bocewicz, A. Kucińska-Landwójtowicz, and I. D. Czabak-Górska, “Ordered fuzzy WASPAS method for selection of improvement projects,” Expert Syst. Appl., vol. 169, p. 114471, 2021, doi: 10.1016/j.eswa.2020.114471.
[22] Z. Turskis and V. Keršulienė, “SHARDA–ARAS: A Methodology for Prioritising Project Managers in Sustainable Development,” Mathematics, vol. 12, no. 2, p. 219, Jan. 2024, doi: 10.3390/math12020219.
[23] M. I. Takaendengan, “Performance Comparison of Multi-Criteria Decision-Making Methods in Decision Support Systems,” J. Comput. Data Sci., vol. 1, no. 1, pp. 88–108, 2026, doi: 10.67449/jcoda.v1i1.5.
[24] S. Dündar, “Prioritizing the Regional Preferences of Turkish Investors Regarding Foreign Direct Investment by ARLON Method,” Konya J. Eng. Sci., vol. 13, no. 3, pp. 927–946, 2025, doi: 10.36306/konjes.1648279.
[25] T. Van Dua, D. Van Duc, N. C. Bao, and D. D. Trung, “Integration of objective weighting methods for criteria and MCDM methods: application in material selection,” EUREKA Phys. Eng., no. 2, pp. 131–148, Mar. 2024, doi: 10.21303/2461-4262.2024.003171.
[26] J. Wang, S. Setiawansyah, F. Ulum, A. Yudhistira, and A. D. Wahyudi, “Optimization of Production Operator Performance Assessment with Grey Geometric Mean Weighting and Combinative Distance-based Assessment,” Komputika J. Sist. Komput., vol. 14, no. 2, pp. 211–220, Nov. 2025, doi: 10.34010/komputika.v14i2.15977.
[27] N. Hendrastuty, S. Setiawansyah, M. G. An’ars, F. A. Rahmadianti, V. H. Saputra, and M. Rahman, “G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation),” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 1, pp. 403–416, 2025, doi: 10.11591/ijeecs.v38.i1.pp403-416.
[28] D. A. Megawaty, D. Damayanti, S. Sumanto, P. Permata, D. Setiawan, and S. Setiawansyah, “Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection,” JOIV Int. J. Informatics Vis., vol. 9, no. 1, pp. 314–323, 2025, doi: 10.62527/joiv.9.1.2744.
[29] A. Asistyasari, M. W. Arshad, I. Chandra, Y. Nuryaman, and V. H. Saputra, “Integration of RECA Weighting and MARCOS Methods in Decision Support Systems for the Selection of the Best Customer Recommendations,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 6, no. 2, pp. 122–136, Jun. 2025, doi: 10.33365/jatika.v6i2.219.
[30] Z. Guo et al., “An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization,” Knowledge-Based Syst., vol. 301, p. 112286, 2024, doi: 10.1016/j.knosys.2024.112286.
[31] Ö. Işık, M. Shabir, and S. Moslem, “A hybrid MCDM framework for assessing urban competitiveness: A case study of European cities,” Socioecon. Plann. Sci., vol. 96, p. 102109, 2024, doi: 10.1016/j.seps.2024.102109.
[32] J. Wang, S. Setiawansyah, and S. Sumanto, “Comparison of Objective Weighting Methods in SAW and Their Effect on Alternative Ranking Results,” J. Masy. Inform., vol. 17, no. 1, pp. 89–112, Feb. 2026, doi: 10.14710/jmasif.17.1.78414.
[33] G. Hoendarto, A. Saikhu, and R. V. Hari Ginardi, “Bridging IoT devices and machine learning for predicting power consumption: case study universitas Widya Dharma Pontianak,” Energy Informatics, vol. 8, no. 1, p. 87, 2025, doi: 10.1186/s42162-025-00540-6.
[34] P. Liu and J. Shen, “GHF-COPRAS Multiple Attribute Decision-Making Method Based on Cumulative Prospect Theory and Its Application to Enterprise Digital Asset Valuation,” Axioms, vol. 13, no. 5. 2024. doi: 10.3390/axioms13050297.
[35] S. Dündar and İ. Karadağ, “Selection of a Facility Location for a Cosmetics Company by Integrated F-LBWA and I-RAWEC Methods,” Int. J. Fuzzy Syst., 2025, doi: 10.1007/s40815-024-01960-4.
[36] A. L. Olteanu (Burcă) et al., “Prioritizing the European Investment Sectors Based on Different Economic, Social, and Governance Factors Using a Fuzzy-MEREC-AROMAN Decision-Making Model,” Sustainability, vol. 16, no. 17. 2024. doi: 10.3390/su16177790.
[37] G. Demir, “Selection of Biomass Resources with Fuzzy Multi-Criteria Decision-Making Methods: Alternatives for Sustainable Energy Production,” J. Oper. Intell., vol. 3, no. 1, pp. 161–179, Apr. 2025, doi: 10.31181/jopi31202537.
[38] K. Kara, G. C. Yalçın, E. Akagün Ergin, V. Simic, and D. Pamucar, “A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance,” Socioecon. Plann. Sci., vol. 94, p. 101918, 2024, doi: 10.1016/j.seps.2024.101918.
[39] Z. Stevic, A. Ulutas, A. Topal, D. Marinkovic, and S. Cavoski, “A New Objective Method for Determining Criteria Weights in MCDM Models–Logsta,” Int. J. Simul. Model., vol. 24, no. 4, 2025, doi: 10.2507/IJSIMM24-4-738.
[40] O. Pala, “Assessment of the social progress on European Union by logarithmic decomposition of criteria importance,” Expert Syst. Appl., vol. 238, p. 121846, 2024, doi: 10.1016/j.eswa.2023.121846.
[41] G. C. Yalçın, K. Kara, and T. Senapati, “A hybrid spherical fuzzy logarithmic decomposition of criteria importance and alternative ranking technique based on Adaptive Standardized Intervals model with application,” Decis. Anal. J., vol. 11, p. 100441, 2024, doi: 10.1016/j.dajour.2024.100441.
[42] S. Setiawansyah, “Comparative Analysis of CODAS, TOPSIS, and COCOSO Methods Using Objective Weighting in Multi-Criteria Decision Support Systems,” J. Ilm. Inform. dan Ilmu Komput., vol. 5, no. 1 SE-Articles, pp. 25–45, Mar. 2026, doi: 10.58602/jima-ilkom.v5i1.78.
[43] M. A. Hatefi, “Indifference threshold-based attribute ratio analysis: A method for assigning the weights to the attributes in multiple attribute decision making,” Appl. Soft Comput., vol. 74, pp. 643–651, 2019, doi: 10.1016/j.asoc.2018.10.050.
[44] T. Ahmadi Pargo and S. Hashemkhani Zolfani, “Developing an Expert System for Hardware Selection in Internet of Things-Based System Design: Grey ITARA-COBRA Approach (With an Example in the Agricultural Domain),” Information, vol. 16, no. 6. 2025. doi: 10.3390/info16060425.
[45] L. K. V, S. Kamruddin, V. K. K.S, S. Kalvakolanu, V. Nalluri, and J.-R. Chang, “A Multi-Criteria Decision-Making Approach to Improve Criteria Ranking and Weighting: Integrating SITDE Weighting With PIV and RAM Techniques,” Asian J. Interdiscip. Res., vol. 8, no. 2 SE-Articles, pp. 1–20, Apr. 2025, doi: 10.54392/ajir2521.
[46] Y. B. Gopisetty and H. R. Sama, “Skewness impact through distributional evaluation (SITDE) method: a new method in multi-criteria decision making,” J. Oper. Res. Soc., vol. 76, no. 6, pp. 1204–1224, Jun. 2025, doi: 10.1080/01605682.2024.2416910.
[47] D. A. Megawaty et al., “DAM Weighting: A New Approach to Determining Criteria Weighting in Multi-Criteria Decision Making,” Evergreen, vol. 13, no. 2, pp. 788–800, 2026, doi: 10.5109/7432655.
[48] S. Kanchan et al., “Developing a model for waste plastic biofuels in CRDi diesel engines using FTIR, GCMS, and WASPAS synchronisations for engine analysis,” Energy Explor. Exploit., vol. 42, no. 2, pp. 648–684, Dec. 2023, doi: 10.1177/01445987231216762.
[49] G. N. Yücenur and A. Maden, “Location selection for a photovoltaic agricultural with f-PIPRECIA and WASPAS methods: A case study,” Energy, vol. 314, p. 134179, 2025, doi: 10.1016/j.energy.2024.134179.
[50] S. Setiawansyah and Y. Rahmanto, “Implementation of the Geometric Mean Multi-Attribute Utility Theory (G-MAUT) in Determining the Best Honorary Employees,” J. Ilm. Comput. Sci., vol. 3, no. 2, pp. 111–119, Jan. 2025, doi: 10.58602/jics.v3i2.50.
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