In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

To achieve complex solutions in the rapidly changing world of e-commerce, it is impossible to go it alone. This explains the latest trend in IT outsourcing---global and partner-based alliances. But where do we go from here?

Relying on social integration theory, this paper develops a conceptual model to explain how college students' online social networking (i.e., using Facebook) influences their social and academic social integration, and learning outcomes. Several rounds of focus group discussions were conducted to explore college students' current online social networking experience and their attitude toward using Facebook for education. Our findings indicate that students' online social networking is straightforwardly influential to their social learning, while its influence on the academic learning might be realised through a gradual and longitudinal process.