Consumers acceptance of artificially intelligent (AI) device use in service delivery. (December 2019)

• Record Type: Journal Article
• Authors: Gursoy, Dogan; Chi, Oscar Hengxuan; Lu, Lu; Nunkoo, Robin
• Is Part Of: International journal of information management. Volume 49(2019)
• Page Start: 157
• Page End: 169
• Publication Date: 2019-12
• Subjects: Artificial intelligence -- AI devices -- Technology -- Intention -- Adoption -- Services; Social sciences -- Information services -- Periodicals; Social sciences -- Research -- Periodicals; Information science -- Periodicals; Management information systems -- Periodicals; Knowledge management -- Periodicals; Sciences sociales -- Documentation, Services de -- Périodiques; Sciences sociales -- Recherche -- Périodiques; Sciences de l'information -- Périodiques; Systèmes d'information de gestion -- Périodiques; Information science; Management information systems; Social sciences -- Information services; Social sciences -- Research; Periodicals; Electronic journals; 025.52068
• DOI: 10.1016/j.ijinfomgt.2019.03.008
• Languages: English
• ISSNs: 0268-4012

Abstract

Highlights: This study tests an artificial intelligent device use acceptance model. The model provides insights on consumer acceptance/rejection of AI device. Level of anthropomorphism enhances customers' effort expectancy. Emotion determines acceptance and rejection of AI devices. A multi-stage appraisal is necessary to understand behavioral outcomes. Abstract: This study develops and empirically tests a theoretical model of artificially intelligent (AI) device use acceptance (AIDUA) that aims to explain customers' willingness to accept AI device use in service encounters. The proposed model incorporates three acceptance generation stages (primary appraisal, secondary appraisal, and outcome stage) and six antecedents (social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, and emotion). Utilizing data collected from potential customers, the proposed AIDUA model is tested. Findings suggest that customers go through a three-step acceptance generation process in determining whether to accept the use of AI devices during their service interactions. Findings indicate that social influence and hedonic motivation are positively related to performance expectancy while anthropomorphism is positively related to effort expectancy. Both performance and effort expectancy are significant antecedents of customer emotions, which determines customers' acceptance of AI device use in service encounters. This study provides a conceptual AI device acceptance framework that can be used by other researchers to better investigate AI related topics in the service context.