Compensation request to customer: Comparing the perceived fairness of AI vs. human representatives and the influence of solitude and companionship
Janelle Chan
Abstract
Hotels face a dilemma when customers cause damage: requesting compensation can hurt relationships, while covering repair costs strains budgets. With AI chatbots becoming prevalent in service roles, this research examines the comparative effectiveness of AI and human representatives in soliciting compensation following customer property damage. It also explores how customers’ fairness perceptions and responses to compensation requests vary when they are alone versus accompanied. Grounded in interpersonal justice theory, the research involved three scenario-based empirical studies with participants recruited from Prolific. Simulations of realistic situations involving accidental property damage with perceived control as a moderator show that solo customers favor human representatives for fairness and emotional responses. Meanwhile, accompanied customers prefer AI. This research addresses gaps in the literature by examining reactive customer behavior, their perceptions, and reactions during the decision-making process. It offers practical guidance for the hospitality industry to balance cost considerations while maintaining positive customer relationships.