Shun Cai

The Many Faces of Green IT

The importance of sustainable development has been widely recognized by people, society, governments, and the business world. Green information technology IT has therefore become one of the latest considerations to improve the environmental sustainability of a business. Yet, as important as this term is, it is being used in so many different ways that it might lose meaning altogether. It is important to clarify the different meanings of green IT to understand how to pursue it better as a business, economic and social objective. Four different perspectives on green IT, and the benefits and barriers of green IT strategy adoption are reviewed and discussed in this article.

Predicting microblog users' lifetime activities - A user-based analysis

Pareto/NBD model and BG/NBD model showed effective ability to fit and predict SNS interaction behavior.We discovered that user base analysis models are more adapted to modeling intrinsic motivated behavior.User segmentation can improve the prediction accuracy by distinguishing currently active and inactive users. With the rapid development of online social media, social networking services have become an important research area in recent years. In particular, microblogging as a new social media platform draws much attention from both researchers and practitioners. Although most current studies focus on the effect of social networks on the diffusion of services or information, most are descriptions or explanations of what has already happened. This study focuses on future activity by employing probability models such as the Pareto/NBD and BG/NBD models to predict user lifetime vitality. Three experiments were implemented to test the two models. Our results showed that both the Pareto/NBD model and the BG/NBD model were effective in predicting SNS user usage behavior on microblogging websites. It was found that tweeting behavior is more suitable for such probability models than retweeting behavior and user segmentation can improve prediction accuracy by distinguishing between currently active and inactive users.

Judging online peer-to-peer lending behavior

The past decade has witnessed a growing number of business models that facilitate economic exchanges between individuals with limited institutional mediation. One of the important innovative business models is online peer-to-peer lending, which has received wide attention from government, industry, and researchers. Using the signaling theory, we compare the effects of various signals on the likelihood of successful funding in three models (i.e., first-time borrowing, repeated borrowing without prior lending, and repeated borrowing with prior lending). Using data collected from PPDAI.com, we verify the three proposed models by employing logistic regression. Results and implications are analyzed and discussed.