A model validation of robo-advisers for stock investment

The study examines the intention of stock investors to adopt robo-advisers (also known as automated investing services) in financial investment decisions. Using an adapted questionnaire, we analyze data from 637 usable surveys. The study uses variance-based partial least squares structural equation modeling to test our hypotheses. The study reveals that the critical drivers in determining the attitude of stock investors toward the use of robo-advisers for decisions about investment in stocks are data security and perceived vulnerability. Further, prediction and, judgment and behavioral biases are the performance constructs that are considered necessary by stock investors towards the use of robo advisers. Our study has implications for financial advisers, brokerages, and practitioners in understanding the behavior of retail investors in adopting robo-advisers.