Factors influencing the rollout of robo-advisor in China based on potential investors

This thesis investigates the impact of sociodemographic factors, behavioral factors, and financial and skilled behavioral factors on the intention to use robo-advisors (hereinafter “RAs”) among potential users in China. It comprises three main empirical chapters and one chapter dedicated to robustness analysis. Chapter 1 provides an outline of, and introduction to, the thesis. Thereafter, Chapter 2 sets out the research background for the thesis, and Chapter 3 contains the literature review and hypotheses development. Chapter 4 outlines the editing and publishing process of the questionnaire used in this thesis, details the sample size of the collected data, and presents the main research methodologies employed for analysis, including logit regression and ordinary least squares (OLS). It also includes frequency analysis and preliminary analysis of the data. Chapter 5 analyzes the effect of sociodemographic factors on the intention to use RAs based on 1,250 valid questionnaire responses. The sociodemographic factors considered in this thesis include age, gender, place of residence (urban or rural), marital status, number of financial dependents, employment status, monthly income, residential status, and educational background. Our analysis reveals that males exhibit a greater intention to use RAs compared to females. In addition, married respondents and those with a higher number of financial dependents are found to be more willing to try RAs in the future. Furthermore, both monthly income and educational background significantly and positively influence intention to use RAs. Chapter 6 empirically analyzes the impact of behavioral factors on intention to use RAs. The behavioral factors in this thesis include risk aversion, risk perception, the better-than-average effect (BTAE), illusion of control (IOC), confidence, and trust. Our analysis shows that both risk aversion and risk perception significantly negatively impact the intention to use RAs. Conversely, respondents with a higher degree of IOC and greater trust exhibit a stronger intention to utilize RAs. Chapter 7 analyzes the impact of financial and skilled behavioral factors on the intention to use RAs among respondents, including financial literacy, financial confidence, perception of financial knowledge, experience of using traditional advisors, experience of using RAs, digital literacy, and numeracy skills. The analysis shows that potential users with higher levels of financial literacy are more likely to try out RAs in the future. Moreover, past investment experiences, whether with traditional advisors or RAs, significantly enhance the willingness of future RA usage among potential users. In addition, individuals with higher digital literacy, which indicates a greater acceptance of technology, also demonstrate a heightened intention to try using RAs. Chapter 8 employs propensity score matching using one, three, and five nearest-neighbor matches per observation to conduct a robustness analysis of the main results mentioned in Chapters 5, 6, and 7. The robustness results are similar to our main empirical findings, except for financial confidence, perception of financial knowledge, and numeracy skills. The propensity score matching analysis reveals that potential users with higher levels of financial confidence and a more favorable perception of their own financial knowledge have a stronger intention to use RAs. Similarly, the intention to use RAs also increases with improvement in potential users' numeracy skills. Chapter 9 summarizes the main findings, outlines the limitations encountered during the research process, and identifies audiences potentially audiences interested in this thesis. It also highlights potential avenues for future research.