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Online product pricing is an important research field in the platform economy, and a two‐stage mixed presale is a mainstream mode of online sales. In the process of online consumption, the payment method and presale mode have an important impact on consumer behaviour and product pricing. Based on the two‐stage mixed presale mode, this study considers the impact of anchoring and the payment pain reduction effect on consumer behaviour, establishes the corresponding product pricing model and obtains the optimal presale price, sales and profit. It sheds light on the influence mechanism of anchoring and the payment pain reduction effect on consumer behaviour and online product pricing. Theoretical and numerical analyses reveal that the payment pain reduction effect can help to raise the product price, stimulate market vitality and bring higher profits for online retailers, and the anchoring effect and consumers' loss aversion psychology can enhance the profits and sales of online retailers, but they will discourage setting high prices for products. The research results will further enrich the relevant research on customer behaviour and product pricing in the platform economy and will provide decision bases for platform enterprises and online retailers to participate in presale activities and formulate product pricing strategies. [ABSTRACT FROM AUTHOR]

With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a "showroom" effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers' optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. [ABSTRACT FROM AUTHOR]

Incorporating consumer choice behavior into a product line design optimization model enhances the understanding of consumer choices and improves the opportunities to increase profit. Most product line optimization problems assume that parameters are precisely known in consumer choice model. However, the decision maker does not precisely know the model parameters because of insufficient sample data, measurement problems, and other factors. We investigate the problem of establishing robust product line pricing under a multinomial logit model to account for the uncertainty of the valuation parameter. First, we present a nominal product line model to maximize profit. We then establish a robust product line model to maximize the worst-case expected profit, where the valuation parameter lies in an uncertainty set. We consider both single and multiple products development and derive the optimal prices' closed-form expressions. Through numerical experiments, we illustrate the benefit of robust product line pricing to address parameter uncertainty. We demonstrate that the difference between the expected nominal profit and the worst-case profit increases with the increase of the interval of the uncertainty set, and the robust profit relative to the worst-case nominal profit improves. The robust product line design can ensure steadier, even higher profit. [ABSTRACT FROM AUTHOR]

This paper investigates the product-positioning problem for a monopolist developing of a new product. The majority of the literature on product positioning assumes that the consumer choice model and parameters are both precisely known. In reality, however, the marketer may be uncertain about the model structure or the parameters due to incomplete information, small sample data size, etc. In this paper, we investigate a robust product-positioning problem based on the multinomial logit model to account for parameter uncertainty. We first establish a nominal product-positioning model, in which valuation parameters are assumed to be known. Then, we investigate a static robust product-positioning problem with the multinomial logit model to obtain the maximin revenue. We also propose a two-period dynamic robust model. For each setting, we consider both homogeneous and heterogeneous customers' preferences. Finally, numerical experiments are presented to compare the nominal model with the static robust model, and to compare the static robust model with the dynamic robust model. The experiments show that when facing uncertain parameters, the robust model obtains more stable revenue than the nominal model; the dynamic robust model obtains higher revenue than the static robust model. [ABSTRACT FROM AUTHOR]