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Supporting digital nomads’ city choice using AHP-Gaussian and MABAC
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Orientador(es)
Resumo(s)
The phenomenon of digital nomadism has been gaining prominence worldwide, driven by advances in communication technologies and the growth of remote work. Choosing an ideal city to live in is a complex decision, involving multiple quantitative and qualitative factors. This study applies a hybrid multicriteria decision-making approach, combining the Analytic Hierarchy Process—Gaussian (AHP-Gaussian) for determining criteria weights and the Multi-Attributive Border Approximation Area Comparison (MABAC) method for ranking alternatives. Data were obtained from the Kaggle platform, covering 15 cost-of-living criteria for 50 cities worldwide. The AHP-Gaussian method reduces subjectivity in weight assignment, while MABAC ensures stable and reliable rankings even with variations in the dataset. The combined application of these methods allows digital nomads to make better-informed choices aligned with their personal and professional priorities. The results point to Seattle (USA) as the most suitable city among those evaluated, followed by Atlanta and Washington, both also in the USA. The study demonstrates that the hybrid approach is an effective and replicable decision support tool in contexts with similar decision-making needs.
Descrição
Palavras-chave
Digital nomad Multicriteria Analytic hierarchy process—Gaussian (AHP-Gaussian) Multi-attributive border approximation area comparison (MABAC)
Contexto Educativo
Citação
Editora
Springer
Licença CC
Sem licença CC