Consumers today have access to far more information when making purchase decisions than ever before, thanks to the proliferation of consumer-oriented websites and apps. We show that they make two systematic errors that are in offsetting directions in utilizing advice to make a “probabilistic affective forecasting”. People underestimate the degree of “preference matching” with reviewers, whereas overweigh their advice, thus make eventual forecasts that are surprisingly accurate compared to a Bayesian criterion. We extend the literature on advice taking and WOM to better understand advice taking in the social media era, and demonstrate to the online review sites the importance of keeping negative reviews and displaying reviews from users' social network.
Henry Shen is an Assistant Professor in Data-driven Marketing at IÉSEG School of Management in Paris, France. He obtained a Ph.D. specializing in Consumer Behavior and Judgment and Decision Making from University of California, Riverside. His research focuses on consumer purchase decision in online context, and has appeared at many top conferences such as ACR North America, BDRM, SJDM, and AoM conference. He is also the founder and CEO of GinG, a data-driven consulting firm dedicated to consumer insight and innovative marketing approaches in Chinese movie industry.