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Big data analytics (BDA) and artificial intelligence (AI) may provide both bright and dark sides that may affect user participation in ridesharing. We do not know whether the juxtaposed sides of these IT artifacts influence users' cognitive appraisals, and if so, to what extent will their participative behavior be affected. This paper contributes to the IS research by uncovering the interplay between the dark and bright sides of BDA and AI and the underlying mechanisms of cognitive appraisals for user behavior in ridesharing. We performed two phases of the study using mixed-methods. In the first study, we conduct 21 semi-structured interviews to develop the research model. The second study empirically validated the research model using survey data of 332 passengers. We find that the usage of BDA and AI on ridesharing platforms have a bright side (usefulness, "the good") but also a dark side (uncertainty and invasion of privacy, "the bad and the ugly"). The bright side generates perceived benefits, and the dark side shape perceived risks in users, which discount the risks from the benefits of using the ridesharing platform. Privacy control exerts a positive effect on the perceived benefits to encourage individuals to use the ridesharing platform.

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Cross-border e-commerce has developed rapidly integrating the global economy. Research has presented some solutions for the challenges and barriers in crossborder 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.

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