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This paper constructs a risk reduction model based on signalling theory to investigate how the platform- and user-oriented signals affect perceived risks, which in turn affect users' actual engagement and purchase behaviours in social commerce. The moderating effects of perceived effectiveness of social commerce institutional mechanisms (PESIM) on the relationships between signals and perceived risks are also considered. Longitudinal data from 226 users of Xiaohongshu were collected to test the proposed hypotheses. We identify signals oriented from platforms and users significantly influence users' perceptions of commerce and participation risks. We also demonstrate that commerce and participation risks influence users' actual engagement and purchase behaviours, respectively. Furthermore, we also verify that PESIM negatively moderates the relationship between signals and perceived risk. [ABSTRACT FROM AUTHOR]

This paper examines creators' contributions and the incentive effects of users' feedback, including upvoting, sharing, commenting, following, viewing comments, and clicking on the creators' homepage. We build a negative binomial and ordinary least squares (OLS) regression model using data from a Chinese music streaming company NetEase Cloud Music. Results show that creators' number of content contributions is positively affected by the number of users' commenting, viewing comments, sharing, and clicking on homepage behaviors. On the other hand, users' upvotes and sharing behaviors can significantly influence the quality of content published by creators. Moreover, we find that the intensity level of creators has moderate effects on such influence. These findings help the researchers better understand creators' behaviors and community managers to build user-generated content (UGC) communities. [ABSTRACT FROM AUTHOR]

With the increased popularity of mobile learning, there is a growing demand on the understanding of how the scalable technology, such as mobile devices, influences individual learning behaviours as well as their learning outcome. A theoretical model was built based on the adaptive structuration theory (AST) and the knowledge spiral theory. Using this model, we examined the relationship between structural sources, individuals' adaptive structural behaviours, and their perceived learning. A Structural Equation Modeling method was employed in our empirical study. Findings indicate that users' task adaptation had a positive influence on their perceived learning, In addition, their exploitive technology adaptation influenced the ultimate perceived learning, but the impact of users' exploratory technology adaptation on learning was mediated by their task adaptation. Contrary to expectations, the effect of computer self-efficacy on exploitive and exploratory technology adaptation was negative, and exploratory technology adaptation negatively affected exploitive task adaptation. A detailed discussion of the findings and implications are provided in this paper. [ABSTRACT FROM AUTHOR]