Do Critical Reviews Affect Box Office Revenues Through Community Engagement and User Reviews?

With advances in technology and the popularity of the Internet, consumers increasingly rely on various sources of electronic word-of-mouth (eWOM), such as online user reviews and critical reviews, in their decision-making processes. Despite general consensus on the importance of eWOM and the ability of critical reviews to influence product sales, very little is known about the mediation between critical reviews and user reviews. Therefore, we used path analysis to examine how critical reviews and user reviews simultaneously affect box office revenues using eWOM data collected from Metacritic.com and IMDb.com, and box office revenue information collected from BoxOfficeMojo.com. The results showed that critical reviews valence not only directly affects box office revenues but also increases active postings in the community and user reviews volume, thus indirectly leading to greater box office revenues. The study provides strategic guidance and practical implications for eWOM communication management.

A decision support model for investment on P2P lending platform.

Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace-Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.