ABSTRACT:
We study an online environment where a firm provides strategic product recommendations to consumers. We develop an analytical framework to integrate recommendations into the consumer search process. The firm sells two imperfectly substitutable products with different profit margins and makes a personalized product recommendation to each consumer based on its uncertainty (lack of knowledge) of his preferences. We define recommendation bias as the firm’s deliberate decision to recommend a product to a consumer that does not minimize expected misfit cost of the consumer. Consumers can accept the product recommendation, search for the nonrecommended product, or leave the website. We identify five consumer segments based on consumers’ responses to the firm’s recommendations. We show that the recommendation bias, profit, and consumer surplus depend on the interaction between the firm’s uncertainty regarding consumer preferences and consumer search costs. A reduction in its uncertainty about consumers leads to a corresponding increase in the firm’s profit but does not necessarily result in a reduction in consumer surplus. An increase in search costs can lead to nonmonotonic changes in the firm’s recommendation strategy, causing an increase or decrease in recommendation bias when the firm’s uncertainty about consumers is low. Furthermore, the firm’s profit can behave non-monotonically with respect to search costs: the firm benefits from an increase in search costs when these costs are small and uncertainty about consumers is low, but it can be adversely affected when search costs are moderate. Interestingly, consumer surplus may increase when search costs increase.
Key words and phrases: Recommendation bias, consumer search, digital commerce, firm uncertainty, product recommendation, recommendation strategy, search cost, online recommenders, online commerce