Understanding consumer frustration: analyzing airline revenge, boycott, and avoidance behaviors through PLS-SEM and ANN methods

This study explores how severe airline service failures impact perceived betrayal, helplessness, consumer avoidance, boycott, and revenge among 400 Russian air travelers, analyzed using PLS-SEM and Artificial Neural Networks. Findings reveal that service failure severity influences perceived betrayal and helplessness, which in turn drive avoidance, boycott, and revenge. This research enriches airline service failure literature by examining emotions like betrayal and helplessness and exploring anger-driven responses. The dual-method approach combining machine learning with traditional analysis highlights linear and nonlinear consumer behavior patterns, advancing service management research with a robust, innovative methodology.