An analytical framework for tourism application selection using neutrosophic decision techniques

In multicriteria decision-making (MCDM), selecting the best option among a set of alternatives by a committee of decision-makers based on given criteria is crucial. Usually, this task is accomplished by using linguistic terms. Researchers apply fuzzy sets in conjunction with a multi-criteria decision-making (MCDM) approach to translate linguistic terms into equivalent fuzzy numbers. However, different degrees of uncertainty and ambiguity arise during this process, which affects the decision-making results. In this context, a complex neutrosophic fuzzy set (CNFS) is employed due to its notable ability to resolve fuzziness and ambiguity in complex environments. Given the practical features of CNFS, this paper integrates and extends two MCDM methods. The research methodology has two phases. The first method is for development, which involves the weighting approach using the developed complex neutrosophic fuzzy-weighted zero-inconsistency (CN-FWZIC) method. This is followed by the second method, called the complex neutrosophic fuzzy decision by opinion score method (CN-FDOSM), which was developed and integrated with CN-FWZIC to prioritize the alternatives. The following main phase included a real-life case study of evaluating and benchmarking tourism data management applications. The results are as follows: (i) The CN-FWZIC method successfully weighs all the smart e-tourism criteria with complete consistency, showing that the highest weight is attributed to the 'recommender system' criterion (0.2148540), and the 'internet of things' criterion is attributed to the lowest weight, with a score of 0.1057198. (ii) The CN-FDOSM method comprehensively ranks all applications of tourism data management by category. For example, in the 'tourism marketing' category, A2 is assigned the first rank with a score of 0.541232539, and A1 is assigned the last rank with a score of 0.314564076. Finally, a robust evaluation was conducted through systematic ranking, sensitivity analysis, complexity analysis, and comparative analysis.