Personalized recommendation, behavior-based pricing, or both? Examining privacy concerns from a cost perspective
研究隐私成本如何影响个性化推荐和基于行为定价策略的效果，发现两者结合能提高零售商利润，而仅用推荐可能降低利润。
In the era of the big data, e-commerce increasingly adopts personalized recommendation and behavior-based pricing (BBP) strategies to enhance consumer experience, while also raising concerns about privacy. This study examines the impact of privacy costs on the effectiveness of those strategies using a two-period Hotelling model. The results indicate that retailers who combine personalized recommendation with BBP strategies can achieve higher prices and profits compared to those who do not employ these strategies, particularly when there are significant differences in privacy costs. Our study further reveals that relying solely on personalized recommendation without incorporating BBP may lead to decreases profit. Moreover, the accuracy of recommendations and variations in privacy costs significantly influence retailers’ strategy choices, emphasizing the importance of these factors in gaining a competitive advantage. This research provides valuable insights for online retailers on how to effectively position themselves in the market while addressing consumer privacy concerns, offering a new perspective on the comprehensive impacts of personalized recommendation and BBP strategies in the business landscape. • Impacts of privacy costs on the effectiveness of strategies are studied. • Retailers who combine personalized recommendation with BBP achieve higher profits. • Relying on personalized recommendation without BBP may lead to decrease profit. • The accuracy of recommendations and privacy costs affect strategy choices. • The expanding gap in privacy cost intensifies market competition.