Understanding the Factors Influencing Adoption of Robo-advisor: Integration Model of TAM and TTF

Abstract

Robo-advisor services are being used more and more widely in the field of financial investment. However, research into their adoption and use has rarely considered from the customer perspective. This study proposes a unified model that integrates the technology acceptance model (TAM) and the task-technology fit (TTF) model to investigate intention to use Robo-advisor services. The samples in this study included 316 clients with experience using Robo-advisor services. LISREL 8.80 is used to conduct SEM and to verify the research hypothesis. The results show that: (1) perceived usefulness, perceived ease of use and perceived risk have an impact on the attitude of customers towards using Robo-advisor services; (2) technical characteristics and task characteristics significantly predict task-technology fit; (3) task-technology fit serves as an important predictor of attitudes towards using Robo-advisor services; (4) the relative advantages and information transparency of Robo-advisor services significantly affect the perceived usefulness and perceived risk respectively; (5) the quality of information does not significantly affect the perceived usefulness and perceived risk unexpectedly. This paper contributes to the consumer research of Robo-advisor services and provide management implications for Robo-advisor and fin-tech.