MIS 373 Lecture Notes - Lecture 8: Ethereum, Bitcoin, User-Generated Content

• School: UT-Austin
• Department: Management Information Systems
• Course: MIS 373
• Professor: A Barua

Document Summary

Useful for predicting sales of new products. A product gets more visibility if seen with preferred products. Enabled by web 2. 0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (wom) or consumer reviews. It has become increasingly important to understand how wom content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study. The results demonstrate significant linkage between network-based measures and based on a digital camera dataset from amazon. com. product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research.