Customer word-of-mouth for generative AI: Innovation and adoption in hospitality and tourism

Abstract

Generative artificial intelligence (AI), such as ChatGPT, is increasingly utilized to facilitate decision-making processes in various aspects of our lives, including travel activities. Despite its growing adoption in the travel service industry, a research gap focusing on the innovation characteristics of ChatGPT, customer adoption, and word-of-mouth (WOM) remains. By utilizing stringent methodologies through variable- and case-based approaches, this study explores the influence of ChatGPT innovation characteristics and customer adoption factors in inducing WOM. The formal set-theoretic approach further explores the intersections between the empirical model, theory, and outcome (WOM). The results provide novel insights into customer WOM for generative AI, examining whether innovation attributes, such as relative benefits, complexity and compatibility, and/or states of customer adoption factors -- particularly in terms of cognitive, affective, and behavioral response individually or in combination -- contribute to WOM, thereby leading to theoretical and practical implications in the hospitality and tourism industry.