Abstract: In recent years, China's e-commerce industry has developed rapidly and the market competition has become increasingly fierce. E-commerce sellers often cut prices to win the market. However, before the implementation of the strategy, it fails to conduct scientific simulation and prediction of its effect, resulting in insufficient preparation of the seller and derailment from the actual market demand. In this paper, Bayesian updating theory is used to construct a dynamic decision-making model for online shoppers by introducing two variables, namely online shopping expectation and risk. The empirical study shows that this model can scientifically fit the implementation effect of different price reduction strategies and provide quantitative basis for e-commerce sellers to choose strategies. The study also found that although risk has a negative impact on the purchase decision, the positive effect of purchase expectation is greater, which means that when sellers provide accurate product information to reduce the purchase risk, they should pay more attention to the product display effect to improve the purchase expectation.

Key words: online shopping, price cut, Bayesian updating theory, purchase expectation, purchase risk