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We consider a seller selling a single product in a short period and taking reference price effect into account in the presence of risk preference customers. Customers' demand for the product is closely related to their purchase probability, which is determined by their purchase utility that is contingent on the reference price, selling price, and risk coefficients, through a multinomial logit model. Customers in the market are categorized as three types according to their asymmetry perceptions anchoring on the difference between the reference price and selling price: loss‐averse, gain‐seeking, and loss‐neutral. We first theoretically explore the influences of the reference price, risk coefficients, as well as the numbers of three types of customers on the seller's pricing decisions and profits. Then, we introduce model misspecification and use a computational study to further illustrate the significance of the seller's correct cognition on the customers' risk preference behaviors. We find that customers with higher reference price or lower risk coefficients would urge the seller to increase the optimal price. When there are more loss‐averse customers in the market, markdown is optimal for the seller; whereas markup is optimal for the seller when there are more gain‐seeking customers in the market. In addition, we find that customers' risk preference behaviors are nonnegligible for the seller in making pricing decisions, especially when gain‐seeking customers' risk coefficient is small enough or loss‐averse customers' risk coefficient is large enough.

Because of China's rapid economic development, its freight transportation system has grown to become one of China's high‐pollution‐emission sectors. However, there are few studies that pay close attention to measuring and improving the environmental performance of China's freight transportation system, especially in regard to seaports. In this paper, data envelopment analysis (DEA) is applied to measure the environmental performance of freight transportation seaports. In addition, we also provide benchmarking information to point the way to improving environmental performance effectively. Our proposed DEA model is based on the closest targets, which satisfies the strong monotonicity and can yield the most relevant solution for the inefficient seaports. An empirical study of 21 of China's primary freight transportation seaports shows that most of them have relatively good environmental performance. Among the five coastal port groups, the Bohai‐rim port group had the best environmental performance, whereas the Pearl River port group had the worst. Our data show significant differences between the best and worst performances, indicating that more measures should be taken to balance and coordinate the development between the five coastal port groups.

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