Artificial intellingence based conversational agents in service recovery

This study focuses on artificial intelligence-based conversational agents in service recovery. The objective of this research is to investigate the factors that affect the service recovery satisfaction provided by AI-based conversational agents. Service recovery is an important topic that has been investigated for years. However, there are limited studies that emphasize the role of artificial intelligence-based conversational agents' role in solving and answering service failures. This study explores the factors that affect service recovery satisfaction provided by artificial intelligence-based conversational agents. The study uses an online survey as a data collection tool, and the sample size is n=250. Quantitative data analyses have been made by using regression and ANOVA. The regression analysis shows that anthropomorphism, the need for human interaction, distributive, procedural, interactional justice, apology, and compensation are positively related to service recovery satisfaction. The strongest relationship is between service recovery satisfaction and distributive justice. Extra analyses indicate that service recovery methods and perceived justice are related. Hence, service failure severity and the need for human interaction are more important for the ones who need to contact human employees because of getting an inadequate answer. This study has theoretical and managerial contributions by addressing the gap in literature. Firms should focus on the outcome since distributive justice is important for service recovery satisfaction