In the ride-hailing industry, effective service recovery strategies following service failures are crucial for passenger satisfaction and platform reputation. However, how the combination of agent type and language style influences passenger satisfaction and the underlying neural mechanisms remains unclear. This study designed an event-related potentials (ERPs) experiment to investigate how two key factors in post-failure remediation -- service agent type and language style -- affect passenger satisfaction and its neural correlates. A 2 (Agent Type: Human/AI) × 2 (Language Style: Humorous/Rational) within-subjects design was employed. Using E-Prime software, participants were presented with five pre-selected, common ride-hailing service failure scenarios. Each scenario was followed by a standardized voucher compensation offer delivered by an agent, with the agent type and language style systematically varied across trials. The procedure involved simultaneous recording of 64-channel electroencephalographic (EEG) signals and collection of behavioral satisfaction ratings. Behavioral data indicated higher satisfaction ratings for human agent and the humorous language style. ERPs results revealed more negative N2 and feedback-related negativity (FRN) amplitudes elicited by the rational language style, whereas human agents and the rational style elicited more positive P300 amplitudes. This study details the standardized preparation of experimental stimuli, precise timing control of the experimental procedure, EEG recording preparation based on the International 10-20 system, and the complete methodological pipeline from data acquisition to preprocessing. This protocol offers high temporal resolution and good reproducibility. It is suitable for research in consumer neuroscience, human-computer interaction, and service management that requires precise quantification of the temporal dynamics underlying social cognition and decision-making processes.
Tang et al. (Fri,) studied this question.