Chatbots in Airlines: Are They Really Assisting?

Abstract

This study investigates how value co-destruction processes in airline chatbot interactions contribute to customer dissatisfaction, despite airlines’ increasing implementation of these technologies. The study employs a dual-method approach to analyze 6800 records, comprising 1500 user-generated reviews and 5300 texts, and identifies recurring concerns. The results indicate that four predominant themes emerge: poor service quality, inadequate technology, lack of empathy, and unreliable information support. These findings inform the Artificial Intelligence-Mediated Value Disconfirmation Model, which integrates Expectation Confirmation Theory, Service-Dominant Logic, the Unified Theory of Acceptance and Use of Technology, and Behavioral Reasoning Theory to explain how algorithmic opacity, emotional deficits, and interaction rigidity drive customer dissatisfaction. Theoretically, the Artificial Intelligence-Mediated Value Disconfirmation Model contributes to understanding airline-specific paradoxes in Artificial Intelligence-human complementarity. Practically, the study proposes three design principles: Emotion Alignment, Human Escalation, and Context Continuity, to design robust, emotion-aware chatbots that lead to higher customer satisfaction and faster issue resolution.