As a typical application of fintech, the robo-advisor has increasingly gained attention over the last decade. However, most research regarding the robo-advisor has focused on its development issues such as performance improvement and regulation, while limited research has paid attention to trust. This research extends the literature by investigating the trust influencing mechanism of robo-advisors by a mixed method approach. Specifically, we identified six salient trust influencing factors by qualitative interviews and proposed the research model based on trust transfer theory. This model was tested via a survey of 230 investors. Our study finds the significant influencing role of supervisory control and validates the relationships among trust influencing factors, trust in technologies, trust in vendor and trust in robo-advisor. Moreover, several differences between junior investors and senior investors are also found in our research. This study examines trust transfer theory in the new context of the robo-advisor and contributes to further development of this increasingly utilized service.

Cross-border e-commerce has developed rapidly integrating the global economy. Research has presented some solutions for the challenges and barriers in cross-border e-commerce from the perspective of the enterprise. However, little is known about the requirements of cross-border e-commerce talents and how to train them. In this paper, we firstly conducted semi-structured interviews to acquire the requirements of cross-border e-commerce talents. Business and market knowledge, technical skills, analytical ability and business practical ability were found to be the four core requirements. Then, we integrated problem-based learning and social media to design a talents training model for cross-border e-commerce and did a program to evaluate effectiveness of the model. Finally, its effectiveness was evaluated from the four evaluation dimensions of attitude, perceived enjoyment, concentration and work intention. The talents training model was improved according to the suggestions.

(2017). Exploring the emerging research topics on information technology-enabled collaboration for development. Information Technology for Development: Vol. 23, Information Technology Enabled Collaboration for Development, pp. 403-414.

Open innovative crowdsourcing has received increasing attention. This study sets out to investigate idea convergence and generation in open innovative crowdsourcing communities from a cognitive load perspective to explore aspects of cognitive idea processing. We have conducted a laboratory experiment to investigate the effects of three manipulations (task complexity, idea presentation, and procedural guidance) on three types of cognitive load and the following idea convergence and generation quality. We have also examined the influencing mechanisms of cognitive loads on satisfaction with process and satisfaction with outcome. Our results show that the three cognitive loads have significant effects: Higher intrinsic cognitive load significantly leads to lower satisfaction with process and outcome. Higher extraneous cognitive load significantly leads to satisfaction with process. Higher germane cognitive load significantly leads to higher convergence quality and lower new idea generation quality.

Predicted price-to-book value ratios (P/BV) are widely used for the valuation of listed common stocks. However, with the application of automated trading system (ATS), the existing indicators that are applied in the method are losing their effectiveness in the Chinese market. Combining qualitative research with the text mining method, this study explores and validates those ignored factors to improve the accuracy of the stock valuation. On the basis of the principal of the existing valuation method, we clarify the scope of the factors that affects the P/BV ratio prediction. Through semi-structured interviews that are designed with six first-level factors which are taken from the literature, we then excavate some second-level factors. After that, with three corpuses including samples form Sina.com.cn, Xueqiu.com, and CSDN.net, four first-level factors and thirteen second-level factors have been verified step by step through the Latent Dirichlet Allocation (LDA) model. In the process, two other new factors and three sub-factors are also found. Furthermore, based on the factor correlation that was found in a data analysis, a factor relationship model was built. The results can be used in a stock valuation in future work as the basis of the indicator system for the prediction of P/BV ratio.

Trust is playing an important role in the sharing economy-driven business. However, little research has been conducted measuring trust as a dynamic process in the sharing economy era. This research is a preliminary exploration investigating trust antecedents and the dynamic development of clients' trust in ridesharing platforms. We used 102 two-stage interviews collected from clients of a major Chinese ridesharing platform. Then we conducted a qualitative content analysis and proposed a theoretical model based on literature and data analysis. We found that antecedents of trust in two stages are different. Personality-based beliefs and cognition-based beliefs are the main antecedents of initial trust, while knowledge-based beliefs, institution-based beliefs, and calculus-based beliefs are the main antecedents of ongoing trust. These findings could help us understand how trust evolves over time and enable us to explore several viewpoints on ridesharing platforms.