Nowadays, global uncertainty has consequences for financial investors. Some of them consider sustainable investment as a refuge from instability. Environmental, social, and governance (ESG) scores are fundamental tools for assessing a company's sustainability and other ethical and social aspects. Due to this challenge, several rating agencies provide ESG grades and scores for companies, investors, and stakeholders. However, the uncertainty associated with the linguistic descriptions of the rating scales, along with partial information regarding their methodology, can complicate the decision‐making process for investors. For this reason, this paper proposes a new approach to constructing ESG scores by integrating two methodologies: ordinal proximity measures and the extended best–worst method. The former considers the different perceptions that investors may have concerning the ratings on the scale. At the same time, the extended best–worst method seeks to define criteria weight coefficients using fuzzy set theory to manage ambiguity and imprecision. These procedures are applied to obtain linguistic assessments for 115 companies worldwide in the energy utilities sector, considering a specific investor's preferences. The dataset was obtained from Refinitiv Datastream and encompasses the period from 2018 to 2021. The findings demonstrate that the proposed procedures complement and enrich the rating methodology used by Refinitiv and can enhance the investment decision‐making processes under uncertain situations.

Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions.

Generation Z have become hugely important travellers in recent years. They are transforming the way accommodation is chosen through Online Travel Agencies (OTAs), which means it is important to understand their preferences. As a result, this paper focuses on the scoring systems of the OTAs and examines the priorities of this generation, with the view to improving the rating procedures and facilitating the selection of accommodation. For this purpose, the paper presents a new approach that combines decision-making procedures and compositional data analysis to examine the preferences of this type of traveller. Likewise, this contribution explores the environmental awareness of young people through Expedia’s eco-friendly criterion, it being the first OTA to incorporate environmental aspects into its scoring system.