A Multi-Criteria Decision-Making Approach to Improve Criteria Ranking and Weighting: Integrating SITDE Weighting With PIV and RAM Techniques

Lava Kumar V; Shaik Kamruddin; Venkateswara Kumar K.S; Sripathi Kalvakolanu; Venkateswarlu Nalluri; Jing-Rong Chang

doi:10.54392/ajir2521

Authors

Lava Kumar V School of Business, GITAM Deemed to be University, Bangalore, India. Author https://orcid.org/0000-0002-3539-9280
Shaik Kamruddin Department of Management and Commerce, Maulana Azad National Urdu University (A Central University), Hyderabad, Telangana, India. Author https://orcid.org/0000-0002-0523-8179
Venkateswara Kumar K.S KL Business School, Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh 520002, India. Author https://orcid.org/0000-0001-7291-1880
Sripathi Kalvakolanu Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Pune, India. Author https://orcid.org/0000-0001-8539-4050
Venkateswarlu Nalluri Department of Information Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd., Wufeng District, Taichung City 413310, Taiwan. Author https://orcid.org/0000-0001-5688-8393
Jing-Rong Chang Department of Information Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd., Wufeng District, Taichung City 413310, Taiwan. Author

DOI:

https://doi.org/10.54392/ajir2521

Keywords:

Multi-Criteria Decision Making (MCDM), SITDE Weighting, Preference Index Value (PIV), Range of Alternatives Method (RAM), Global Bank Ranking, Financial Performance Evaluation

Abstract

Skewed data distributions present a major challenge to the accuracy of Multi-Criteria Decision Making (MCDM) methods in financial performance evaluation. Traditional weighting techniques often neglect skewness, leading to biased and unreliable results. This research proposed an sophisticated MCDM approach that enhances the accuracy of the criteria rankings and weighting using Skewness-Insensitive Technique for Data Evaluation (SITDE) weighting combined with Preference Index Value (PIV) and Range of Alternatives Method (RAM) methods. Global bank rankings is obtained using the PIV and RAM MCDM techniques, with final rankings derived through the Borda Count to aggregate results. The findings indicate that DBS Bank, Bank of America, and Societe Generale obtained the highest overall ranking, reflecting superior performance across the applied techniques. By addressing data skewness and enhancing the robustness of the evaluation process, this approach offers a more accurate and adaptive solution for financial decision-making. The integration of SITDE weighting with PIV and RAM represents a meaningful advancement in the application of MCDM methods for global bank performance assessment.

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