This paper studies the assortment optimization problem in online retailing by using a multinomial logit model in order to take consumer choice behavior into account. We focus on two unique features of online purchase behavior: first, there exists increased amount of uncertainty (e.g., size and color of merchandize) in online shopping as customers cannot experience merchandize directly. This uncertainty is captured by the scale parameter of a Gumbel distribution; second, online shopping entails unique shopping-related disutility (e.g., waiting time for delivery and security concerns) compared to offline shopping. This disutility is controlled by the changes in the observed part of utility function in our model. The impact of changes in uncertainty and disutility on the expected profit does not exhibit obvious structure: the expected profit may increase or decrease depending on the assortment. However, by analyzing the structure of the optimal assortment based on convexity property of the profit function, we show that the cardinality of the optimal assortment decreases and the maximum expected profit increases as uncertainty or disutility decreases. Therefore, our study suggests that it is important for managers of online retailing to reduce uncertainty and disutility involved in online purchase process.