Online knowledge exchange platforms have become an important information technology (IT) artifact that empowers online learning for the Internet users. A key challenge for knowledge exchange platforms is how to motivate the desirable user engagement behaviors. Based on the motivation theory and the equity theory, we propose a set of hypotheses regarding the spillover effects of financial incentives on three types of desirable yet non-incentivized user engagement, namely, voluntary knowledge sharing, knowledge seeking, and social engagement. We obtain an archival data set from a major online knowledge exchange platform (Zhihu.com) to evaluate our hypotheses. Leveraging a quasi-natural experiment wherein the platform implemented a paid knowledge sharing feature, we employ difference-in-differences models in tandem with propensity score matching to evaluate the spillover effects of financial incentives on the abovementioned types of engagements. Our results show that the initial financial incentives on the paid knowledge sharing activities further motivate users to voluntarily share more knowledge and increase their social engagement in the platform. However, the financial incentives have no significant impact on users’ knowledge seeking behavior. Our study suggests that the financial incentives not just have an effect on incentivized engagement, but they spillover to users’ desirable non-incentivized online engagement behaviors. Therefore, the overall positive effect of financial incentives to a platform is likely under-estimated in prior research. Our research offers implications to practice that financial incentives can be an effective strategy to nurture users, to seed content, and to enhance sociality of a platform.

Purpose: Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach: This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings: The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value: In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet