Unveiling the Nuances: How Fuzzy Set Analysis Illuminates Passenger Preferences for AI and Human Agents in Airline Customer Service

Abstract

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience.