Evaluating Barriers to Blockchain Adoption in the Insurance Sector using Interval-Valued Intuitionistic Fuzzy TOPSIS

In this work, we suggest studying the barriers that prevent from using blockchain technology and smart contracts in the insurance sector. It is possible to improve many services, by introducing ""Fintech"" information technologies which will ensure maximum transparency and speed. The goal of our paper is to answer two main questions: What obstacles stand in the way of the successful use of blockchain technology throughout the insurance sector? Which of them are the most notable obstacles that require decision-makers consideration?. We opt for an analysis of the barriers to blockchain adoption using fuzzy logic for the following reasons. In many realistic situations, it is difficult to gather the exact assessment data; the assessment is based mainly on the decision makers’ knowledge and their experiences using linguistic terms or sentences in a natural or artificial language. The idea is to transform the linguistic variables into fuzzy sets using appropriate membership functions. In other words, fuzzy logic allows a better representation of the uncertainty and subjectivity of decision-makers. In our study, we analyze the answers of twenty experts, - highly skilled professionals with advanced knowledge acquired through education and experience-, about the most significant barriers to blockchain adoption in an interval-valued intuitionistic fuzzy environment. Then, by making use of decision-making tools such as IVIF TOPSIS, we make a ranking of barriers according to their importance to find the most important factors that influence the adoption of blockchain technology. This study’s goal is to propose a model for identifying and tracking the crucial elements that influence managers’ decisions on whether to adopt a financial technology like blockchain in their businesses or not. In the end, we conclude with some recommendations and suggestions to overcome the most important barriers and face future challenges.