طراحی یک سیستم تصمیم یار برای قیمت گذاری تصادفی در تجارت الکترونیک
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی|
|3444||2013||10 صفحه PDF||36 صفحه WORD|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Available online 16 January 2013
3.مدل ترفیعی به همراه عدم قطعیت قیمت
1.3.مدل قیمت گذاری ثابت
2.3.مدل قیمت گذاری تصادفی
3.2.3.تصمیم بهینه خرده فروش
4.استراتژی قیمت گذاری بهینه
1.0.4.آثار ضریب تخفیف بر استراتژی قیمت گذاری
2.0.4.آثار انتظار مشتریانِ خواهان قیمت پایین بر استراتژی قیمت گذاری
5.آثار عدم تقارن اطلاعات
The Internet has provided great convenience for online shoppers and has presented unprecedented opportunities for online retailers to understand their customers. Getting the pricing right has emerged as one of the ultimate keys to the success of electronic commerce. Although some online retailers have tried some personalized pricing strategies for perishable capacity or inventory in some industries, consumers' resistance to price discrimination is still a great concern. Can we develop other price discrimination strategies for online sellers to sell standard durable products without giving the impression that they are treating their customers unfairly? Randomized pricing, which is proposed in this paper, belongs to this kind of strategy. In this paper, we present a framework that can be used to study the randomized pricing strategy by incorporating some new features into electronic commerce. For example, information asymmetry about the prices of products does not exist across internet users because of easy access to price information and very low searching cost. Consumers' reneging behavior is also considered. Online consumers usually wait up to a certain period of time for deals. Specifically, we model online retailers' price variation as a Markov process in which the price randomly switches between high level and low level. Strategic consumers make a tradeoff between buying immediately at a high price with instant utility or buying later at a low price with a probability and discounted utility. We show in this paper that randomized pricing strategy can always generate more profit than flat pricing strategy. The effects of consumers' patience and discount factor on optimal prices and promotion probability are studied. Finally, we show that the optimal benefit that the retailer can obtain from hiding promotion probability depends on the value of the discount factor.
With the development of electronic commerce and the fast growing number of Internet users, Internet has become a vital distribution channel in many industries. In recent years, emerging online stores have become another great source of retailing. Although consumers still purchase nondurable goods in traditional retail stores, e.g., Walmart, many intense Internet users have become accustomed to buying durable products online, such as computers, camcorders, and MP3 players, among others. However, most online retailers still struggle to make money out of the Web after making a huge investment in online business. Therefore, searching for ways to run an online business successfully is a great challenge. According to Baker et al. , getting the pricing right has emerged as one of the ultimate keys to success in managing online businesses. They observe two widely disparate approaches to pricing that dominate the online business. Many start-ups offer untenably low prices to capture first-mover advantage. By contrast, many incumbents largely neglect online pricing and simply apply their offline prices to the Internet. The purpose of this research is to explore some online pricing strategies in electronic commerce. By breaking the barriers of geography and time, Internet has provided great convenience for online shoppers and unprecedented opportunities for online retailers to understand their customers. Through the Internet, consumers can instantaneously obtain all the information they need about the products they intend to buy without incurring a searching cost. Recently, with the development of 3G and 4G telecommunication technology, online retailers have provided more applications based on new operating systems (e.g., Apple OS and Android systems) for cell phones and other mobile devices (e.g., iPad). Users with wireless-connected mobile devices can access real-time commercial environments wherever they are. However, the Internet gives online companies opportunities to test customers' price sensitivity, change prices instantly, and segment customers. In the last decade, electronic commerce provided online sellers a field for experimenting with different alternatives for pricing. For example, Amazon.com experimented with a pricing strategy in which different customers were charged different prices for the same DVD movies. By using the information gathered from the customers' profile, Amazon.com adjusted the price of identical goods to make them correspond to the customers' willingness to pay. Although Amazon.com claimed that the price variations were part of a random “price test,” many customers responded negatively to the strategy; hence, Amazon.com stopped the pricing tests (Streitfeld ). Hotware.com and Priceline.com are two Internet success stories, each of which used a business model based on variations of opaque pricing. Through Hotware.com, customers can buy last-minute unsold seats and hotel rooms at listed prices but with opaque quality. By contrast, Princeline.com offers customers a self-pricing alternative called Name-Your-Own-Price (NYOP) (see Hinz et al. ). In this setting, a buyer first places an initial offer. If it is rejected, the buyer updates the offer until it is accepted. Thus, the final price depends on the individual buyer's willingness to pay, which is opaque to the public. The opaque pricing strategy helps hotels and airlines cut losses by offering unsold products at discounted prices without revealing the published fares they promoted. In fact, some empirical research reported that sellers benefit from obfuscated pricing strategies in the electronic marketplace (see Ellison and Ellison ). However, designing opaque pricing is tricky, considering the strong buyer's resistance to one-to-one price discrimination. Currently, opaque pricing strategies are usually applied to sell products with perishable capacity and that entail the personal perception of quality, such as hotel rooms and flight seats. Can we develop other price discrimination strategies for online sellers to sell durable products with standard quality without giving the impression that they are unfairly treating their customers? The randomized pricing strategy that we propose in this paper belongs to this kind of strategy. In this paper, we construct a randomized pricing strategy for online retailers by borrowing long-standing promotion methods from traditional retailing and incorporating some new characteristics into electronic commerce. Under this promotional pricing strategy, the online retailer can randomly provide promotions by reducing the price temporally over an infinite horizon. The temporary price reduction or promotion is a common strategy in brick-and-mortar stores. Sellers can provide price discount on selected packages of goods or seasonal products for a short period of time. Promotion generates a price discrimination effect because of the information asymmetry on promotions and the differentiation in searching and transportation costs across consumers. However, the Internet has brought double-edged effects on traditional promotion strategies. On the one hand, without advertising on traditional media, online retailers can instantly change posted prices on websites. This type of advertisement gives online retailers more flexibility to launch promotions (e.g., promotion frequency and duration) at low cost. On the other hand, because of low transportation cost in electronic commerce (e.g., free-shipping policy offered by online sellers), potential consumers are more likely to wait before they make final purchases. Moreover, they are unlikely to miss deal chances during their waiting period because of the low cost that searching incurs. Some online retailers even email promotional newsletters or send SMS to registered users regularly. In other words, online product and price information visibility are equal to all potential consumers; therefore, information asymmetry across consumers does not exist. Given the fact that online sellers encounter more sophisticated consumers who are more patient and are well-informed, designing new promotion strategies oriented toward electronic commerce elicits some interesting research questions. We now summarize our research model and questions. Using a randomized pricing strategy, we focus our study on the online retail selling of durable products over an infinite horizon. In view of this pricing strategy, the retailer randomly switches the price between regular level and low level; thus, we assume that customers are heterogeneous in terms of reservation price and patience. When the current price is high, consumers evaluate the tradeoff between buying at high price with an instant utility and buying later at low price with a probability and a discounted utility. The questions we raise and answer in this study are as follows. First, what are the optimal promotion probabilities and high/low prices in this pricing strategy? Second, how is optimal pricing strategy affected by consumers' discount factor and patience? Finally, how can the retailer benefit from hiding the pricing pattern? The remainder of the paper is organized as follows. In Section 2, we briefly review the related literature and identify the contributions of our work. Then, we present the pricing model and derive optimal solutions in Section 3. In Section 4, we analyze the effects of information asymmetry on the retailers' pricing strategy. Section 5 concludes the paper with a brief summary and suggestions for further research.
نتیجه گیری انگلیسی
Promotion, a commonly used strategy in the retail and service industries, is currently popular among both researchers and practitioners. In previous literature, information asymmetry is identified as a main driver of price reduction as a promotion strategy. However, research on promotion strategy in electronic commerce is absent. Different from traditional retailing, online retailing possesses some unique features. For example, information asymmetry on product prices does not exist among potential online consumers because of the low cost of online searching. No transportation cost is spent to visit online stores, and thus consumers may withhold their intention to purchase to wait for deals. Information technology has enhanced the capacity of online sellers to track and analyze the purchase behavior of consumers. Considering these factors in electronic commerce, we propose and develop a randomized pricing strategy for an online retailer who sells durable goods. Specifically, we model the price variation of the retailer as a Markov process and derive the optimal promotion frequency and depth for the retailer. The reneging behavior of strategic consumers is also included in the model. In this framework, consumers are categorized according to their reservation prices and levels of patience. They make a tradeoff between buying at the current high price with instant utility and buying later at a low price with probability and discounted utility. We study the effect of patience and discount factor on the optimal pricing strategy. Furthermore, we check the incentives for online retailers to hide their promotion probability. Our model offers several interesting managerial insights for online retailers applying the randomized pricing strategy. First, we show that, compared with the flat price strategy, the randomized pricing strategy always increases the profit of the retailer, which can be improved by up to 33.3%. This result encourages online retailers to use more intelligent randomized pricing strategies, which are not fully explored in the current electronic commerce context. Second, our research results provide some guidelines for implementing randomized pricing according to the characteristics of consumers. Our analysis suggests that the retailer should maintain the promotional price for only one period and then return to the regular price. When low-type consumers are more patient, the retailer should decrease promotion frequency and the low price and increase the high price simultaneously. By contrast, when the discount factor is higher, the retailer should decrease the promotion frequency and the high price and increase the low price to induce high-type consumers to purchase at the high price. Third, by checking the effect of information asymmetry on pricing strategy in the background, we show that hiding promotion probability only when the discount factor is low is beneficial. However, when the discount factor is larger than some threshold values, the retailer should maintain the pricing strategy for consumers. These results show the importance of information on the profile of consumers to implement the appropriate randomized pricing strategy. Our model has a few limitations that provide avenues for future research. One limitation is that we only model the randomized pricing strategy of a monopolist. If competitors are present in the market, consumers may be redirected to other retailers when the current price is high. Then some other factors, such as product substitutability, searching cost, and consumer loyalty, can be included in the model to study the equilibrium outcome under competition. This model can be extended to analyze the pricing strategy for a brick-and-mortar company that opens a new online channel. Another limitation of our model is that to maintain the tractability we only consider the case with Th = 1. However, we infer that some results still hold in more generic situations. For example, one of key results is that the retailer should not provide the promotion in two consecutive periods (i.e., β* = 1). We predict that even if high-type consumers are more patient (i.e., Th > 1), the retailer should still follow this strategy. The essence of this randomized pricing strategy is to make price discrimination through occasional promotions. If the retailer immediately returns the price to the regular level after the promotional period, some high-type consumers may buy the product at the high price without loss of any other consumers. This is better than the strategy with offering two consecutive promotions, under which all high-type consumers arriving in the second promotional period will pay the low price.