قیمت گذاری برای خدمات حمل و نقل خرده فروشان آنلاین : روش های تحلیلی و تجربی

We develop an analytical model and conduct subsequent empirical analyses using data collected from the online retailers of digital cameras and video games. We find that (1) Internet retailers' base prices are positively associated with on-time delivery probability but shipping charges are negatively associated with on-time delivery probability; (2) Internet retailers will increase base prices when they offer free shipping; and (3) Internet retailers' shipping price difference between the standard and expedited shipping modes increases with the shipping time of standard shipping, but decreases with the shipping time of expedited shipping and also the Internet retailer's on-time probability. Our findings suggest that Internet retailers, to maximize profits, can strategically determine base price and shipping prices, and they can also strategically pace menu pricing for different shipping options.

The advent of Internet technology has given rise to electronic commerce. A key feature of electronic commerce is the spatial separation between buyers and sellers. As a result, the buyers rely on shipping to fulfill their orders. Hence, how e-tailers should price their shipping services, in addition to their product prices, has invoked a lot of discussion among managers and researchers alike [1] and [16]. Although economics theory has predicted that prices on the Internet are likely to converge due to reduced search cost [4], especially for homogenous goods, empirical studies have nonetheless found significant price dispersion on the Internet [6], [7], [8], [9], [12], [14] and [29]. One source of price dispersion comes from the variation in retailers' shipping and handling charges (henceforth referred to as simply shipping charges as in Pan et al. [24]) [3]. Such variation often exists for strategic reasons; that is, how a retailer prices shipping services is a valuable method to differentiate itself from competitors. Appropriate shipping fees are also essential to winning business, given that shipping charges are considered a main reason why online shoppers abandon their shopping carts and discontinue the purchase process [10]. Retailers have several options for designing shipping prices: they can charge nothing (i.e., free-shipping) by subsidizing the shipping cost, they can share some of the costs with customers [12] and [15], or they can charge shipping fees that are higher than the actual shipping cost in order to make a profit (i.e., profit-shipping) [27]. Anecdotal evidence has shown that Internet retailers use all of these options. For example, Amazon.com, BN.com, and Buy.com have been practicing a free-shipping policy with a minimum order amount. Even Wal-Mart.com and ebay.com have decided to offer free shipping as their competitive strategies [15] and [16]. In contrast, CDNow.com exercises a profit-shipping policy. At the time of Tedeschi's [30] research, CDNow.com charged a price that was higher than the average cost of shipping a CD: $3 for the first item shipped and $1 for each additional item. These shipping charges yielded a profit margin (15–20%) that was similar to the actual CD sale. There are, of course, other retailers, such as Ashford.com, who charge exactly the cost of shipping in order to gain customers' trust and loyalty [10]. In some cases, when retailers charge shipping fees that are higher than actual shipping costs, they are doing so not to increase profits but rather to offset a reduced product price that was designed to help win business. In other words, a retailer may simply transfer part of its profit margin from the product price to the shipping charges by charging a shipping fee higher than actual shipping costs [10]. Research has indeed shown that strategically allocating the total price between the product price and its shipping charges can be an effective marketing strategy for Internet retailers [22], [23] and [21]. Therefore, how to determine prices for products and shipping services is a clear challenge for Internet retailers [5] and [15], especially given that these retailers have limited knowledge about customers' price and time sensitivity. Given this information asymmetry between Internet retailers and customers, whether or not to bundle the pricing of product and shipping together (i.e., the free shipping model), and if it is profitable not to bundle, how to set prices for product and shipping separately (i.e., the profit shipping model)? The objective of this paper is to answer these questions through a game-theoretic model and empirical analysis of data collected from Internet retailers selling digital cameras and video games. In particular, we first built an analytical model that studied the equilibrium pricing decisions of the base product and shipping schedules of Internet retailers and the purchasing and shipping choices of customers. Considering heterogeneous customers' self-selection in terms of retailers and shipping options as well as the competitor's response, each retailer sets a base price plus a menu price for shipping services of different levels to maximize his own profit. We then performed empirical analyses to validate the results from the analytical model, using the data collected from major Internet retailers. Our major findings include: (1) Internet retailers' base prices are positively associated with on-time probability, but shipping charges are negatively associated with on-time probability; (2) Internet retailers will increase base prices when they offer free shipping; and (3) Internet retailers' shipping price premium of expedited shipping over standard shipping increases in the time of the standard shipping mode, but decreases in the time of the expedited shipping mode and the Internet retailer's on-time probability. All of these results are supported by subsequent empirical analysis using data collected from the Internet on the retailers selling digital cameras and video games. Our findings suggest that Internet retailers, to maximize profits, may strategically determine base price and shipping prices, and they also may strategically pace menu pricing for different shipping options. There is a growing body of literature on Internet retailers' pricing issues [13]. Smith and Brynjolfsson [29] and Brynjolfsson and Smith [9], in analyzing price dispersion online, found that customers are very sensitive to shipping fees and shipping times. Similarly, Lewis et al. [19], using data collected from an online grocery store, showed that shipping fees significantly impact order incidence as well as basket size. Pan et al. [24] found that the shipping options adopted by retailers contribute to the variation in firms' prices. Dinlersoz and Li [17], using data collected from the Internet book retailing industry, found that firms with lower product prices offer higher shipping quality (measured by delay in shipping) and lower shipping fees than firms with higher product prices. Schindler et al. [27] conducted an experiment to show that customers skeptical of shipping charges prefer a bundled-price format, whereas customers not skeptical of shipping charges prefer a base price and shipping charge format, when an external reference price is available. Also, Kauffman and Lee [22] tracked price changes for 387 books online for 309 days, and found empirical evidence that some Internet retailers use shipping price changes, instead of product price changes, for competitive advantage. However, the pricing of shipping services remains under-studied. Our paper is different from previous works and makes important contributions in a number of ways. First, we are one of first studies that comprehensively capture the dynamics of purchase, shipping, and return between Internet retailers and customers, whereas most of prior paper focused on a segment of the purchase processes. Second, our paper builds a theoretical foundation for Internet retailers' optimal choices of shipping strategy, and provides supporting empirical evidence on how a shipping price scheme can be designed. The findings from both the analytical model and the empirical analysis reveal that product prices and shipping charges can be strategically manipulated by Internet retailers to their benefit. Overall, to our best knowledge, our research is one of very few in the literature that studies pricing issues with respect to Internet retailers' product prices and shipping services.

Strategically pricing for product and shipping service has become a critical and effective tool for an Internet retailer to differentiate himself from the others, which is necessary due to reduced transaction cost and heightened competition on the Internet [4] and [11]. In this paper, we present an analytical model and subsequent empirical analyses that shed light on how an Internet retailer can design an optimal combination of base price and shipping price plus an optimal menu scheme of shipping charges and shipping options to gain a competitive edge. We further examine the relationships between optimal shipping charge and various driving factors affecting a retailer's pricing decision for shipping services (e.g., on-time probability, free shipping offering, etc.), as well as the interactions between shipping charges and shipping delays for different shipping options. We find that Internet retailers' base prices are positively associated with on-time probability, shipping charges are negatively associated with on-time probability, and that Internet retailers' base prices are positively associated with free shipping offerings. We also find that Internet retailers' shipping price gap between expedited and standard shipping modes increases with the shipping time of the standard shipping mode, but decreases with the shipping time for expedited shipping. The shipping price gap also decreases with the Internet retailer's on-time probability. All of these results are supported by our empirical analysis using data collected from the Internet retailers of digital camera and video game products. Our findings suggest that Internet retailers, to maximize profits, may strategically choose a combination of base price and shipping prices, and may also strategically pace menu pricing for different shipping options. Our research contributes to the electronic business literature on Internet pricing in the following ways: (1) by developing an analytical model that comprehensively captures the dynamics of purchase, shipping, and return between Internet retailers and customers, and (2) by empirically validating those results from the analytical model using data collected from Internet retailers selling digital cameras and video games. Our paper builds a theoretical framework for Internet retailers making decisions on the optimal shipping strategy, and provides supporting empirical evidence that Internet retailers strategically design different shipping price schemes. To our best knowledge, our research is one of the few in the literature that study pricing issues with respect to Internet retailers' product prices and shipping services. The research also provides managerial insights into Internet retailers' strategic behavior on determining different shipping strategies (e.g., free shipping vs. profit shipping) and on designing their optimal pricing scheme for shipping. “Good” Internet retailers are more likely to offer a combination of high product price and low shipping charges than are “bad” Internet retailers. When offering free shipping, Internet retailers may be able to subsidize their free shipping service by charging higher prices for the product itself than those when free shipping is not offered. Furthermore, Internet retailers, dependent on their on-time probability, can strategically pace their different menu of shipping time and charges to maximize profits. Finally, our research has several limitations that can be addressed in future research. Our model captures the mechanism by which an Internet retailer interacts with heterogeneous customers. Future research can extend our modeling frame to a multi-period dynamic model that can capture the negative impact of shipping delays on future sales. Empirical analysis can also be extended from our cross sectional analysis to longitudinal analysis in order to validate the dynamic model. Another limitation of our research is that the data used to measure Internet retailers' on-time probability comes from customer feedback. More objective variables, such as percentage of on-time shipping, will enrich the analysis. Finally, the shipping practice may be country specific. Our study is focused on the practice in the U.S. Future research may expand our models to other countries and examine if our findings still hold.