A Novel Method to Investigate the Effect of Normalization Techniques on Fuzzy Multi-Criteria Decision-Making in Web Service Quality Assessments

Fuzzy multi-criteria decision-making (MCDM) methods remain popular for addressing decision-making problems involving uncertainty and explainability. However, decisions are usually made using data with different dimensions or even modalities. Therefore, existing MCDM methods incorporate various normalization techniques in order to transform attribute values into dimensionless quantities, ensuring the robustness and reliability of the decision-making results. Nevertheless, these normalization techniques may affect the ranking of alternatives. This study therefore proposes a novel method to investigate the effect of various normalization techniques on fuzzy MCDM methods. The study introduces a novel method for creating a fuzzy decision-making matrix using Tukey’s fences method, enabling the evaluation of alternatives using attributes under uncertain conditions. This method was evaluated in the context of web service quality assessments involving multi-dimensional and random variable attributes. The study demonstrated that Vector and Linear normalization techniques yield similar alternative rankings when using fuzzy MCDM methods, whereas rankings differ when Non-linear normalization techniques are applied. We believe that the current study will allow researchers and practitioners to address various practical uncertain decision-making problems with multi-dimensional attributes, thus promoting the digital transformation of complex, real-world decision-making issues.