AI Adaption Factors in Online Service

Abstract

The scale of digital disruption in business is unprecedented due to Covid19. AI service systems increasingly replace human employees. However, consumers’ adoption and service satisfaction are relatively low, partly due to technology-centric approaches that neglect work practices. According to Task Technology Fit theory, users may not adopt an advanced technology if it does not fit their task requirements. Theoretically, some important factors in extant Technology Adoption theories are not appropriate for investigating service chatbots. Notably, consumers are not required to learn how to use AI systems. This ongoing research draws on technology adoption theories and task technology fit theory to investigate consumers' perception of service chatbots adoption. A conceptual model was proposed to test the key factors: perceived performance efficacy, consumer innovativeness, trust, perceived autonomy, and perceived task technology fit. We used survey to examine and validate the model and conducted structural equation modelling to analysis the data. The initial findings of this ongoing research mostly support the significant effects of these key factors on consumer adoption. The overall findings theoretically contribute to the knowledge of Technology Adoption and Task Technology Fit theories in AI service and practically benefit marketing practitioners in deciding effective service technology strategies for improving consumers’ online service experience