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Recommendation Systems and Consumer Satisfaction Online: Moderating Effects of Consumer Product Awareness

Recommender systems are popular tools in e-commerce websites for helping people find the products that fit them. Many algorithms have been proposed, mostly to improve their accuracy. The underlying assumption is that accuracy will increase user satisfaction and ultimately lead to high purchase intention. However, past research has suggested that accuracy does not necessarily lead to satisfaction. As consumers usually being aware of at least some products when using recommenders, could this assumption depend on different types of recommender systems and consumer product awareness? This study investigates the effects of popular recommenders such as collaborative-filtering and content-based systems to see if they have different effects on user satisfaction and willingness to purchase for consumers with different types of product awareness.