Addressing consumer needs: Effects of firms remediation strategies on satisfaction and brand usage intent in AI-powered voice assistant service failures

Abstract

With the accelerating integration of AI-powered voice assistants into daily consumer interactions, effectively managing AI-driven service failures has become paramount for maintaining consumer trust, satisfaction, and sustained brand engagement. Despite extensive research on traditional service recovery mechanisms, existing frameworks fall short in addressing the distinct and complex nature of AI-driven failures. Motivated by the lack of systematic frameworks tailored explicitly to AI voice assistant contexts, this study introduces an integrated analytical framework grounded in Consistency Theory and Situational Crisis Communication Theory (SCCT) to address customer needs through strategic service failure remediation. Utilizing a mixed-methods research design and analyzing 5894 consumer reviews, the study establishes a consistency relationship between service failures and remediation strategies. It delineates three innovative approaches to service recovery and examines their impact on consumer satisfaction and brand usage intent. The empirical findings reveal significant positive effects of strategically aligned recovery efforts on consumer satisfaction and brand usage intentions. By addressing the specialized nature of AI-driven failures, this research not only advances theoretical knowledge in AI service management but also provides actionable strategic guidance for businesses seeking to optimize consumer experiences and foster enduring customer relationships.