Unlimited shelf space in Internet supply chains: Treasure trove or wasteland?

Journal of Operations Management 29 (2011) 305–317

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Unlimited shelf space in Internet supply chains: Treasure trove or wasteland? Elliot Rabinovich ∗ , Rajiv Sinha, Timothy Laseter Arizona State University, W.P. Carey School of Business, PO Box 874706, Tempe, AZ 85287-4706, United States

a r t i c l e

i n f o

Article history: Received 25 June 2009 Received in revised form 2 July 2010 Accepted 6 July 2010 Available online 14 July 2010 Keywords: E-commerce Marketing-operations interface Empirical study

a b s t r a c t Internet retailing offers merchants limitless shelf space. This has led experts to highlight the existence of a “long tail” of offerings on the web and assert that the future of online business is “selling less of more.” However, it is difficult for Internet retailers of physical goods to sell a large scope of products without having to handle potentially large amounts of product returns from customers. This is due to the fact that customers can and do get overwhelmed by excessive product variety and often make erroneous purchasing decisions. We shed light on this issue through an assessment of theoretical predictions based on data from sales and returns of almost 7000 products in a particular product category. While retailers can benefit from expanding the scope of their inventories to generate Internet sales, the success of this strategy will depend on the control of unjustified product returns by consumers and the management of recurrent execution errors and product fit failures in transactions with customers. Furthermore, from our results, the gains that this strategy will bring to retailers will be bound by the amount of time products have been available on the Internet retailer’s site, as well as by other attributes such as product price and size. © 2010 Elsevier B.V. All rights reserved.

1. Introduction In the retail industry, merchandise assortments can increase inventory carrying costs and expenditures caused by product returns. To control these costs, many retailers have maintained relatively few stock-keeping units (SKUs), yielding a pattern of concentration in sales commonly known as the 80/20 rule and described by the Pareto Principle. This pattern of concentration has led authors to underscore the drawbacks of inactive SKU assortments in the supply chain (Fisher et al., 2000). Accordingly, many studies have highlighted the importance of balancing additional sales from a wider range of SKUs with their associated costs (Van Ryzin and Mahajan, 1999; Smith and Agrawal, 2000; Chong et al., 2001). Some have claimed that the surge of Internet retail activity can dilute this concentrated pattern of sales by lowering consumer search costs (Brynjolfsson et al., 2003). As a result, they have argued that Internet commerce may contribute to an expansion in the share of sales by niche products, thereby creating a longer tail in the distribution of SKU sales (Brynjolfsson et al., 2007). This phenomenon may ultimately make it more attractive to sell a greater variety of SKUs without incurring excessive carrying and product return costs (Cachon et al., 2005).

∗ Corresponding author. Tel.: +1 480 965 5398; fax: +1 480 965 8629. E-mail address: [email protected] (E. Rabinovich). 0272-6963/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jom.2010.07.002

While the Internet may improve customers’ ability to search for and find products best suited for their needs, research has yet to document a discernible market preference for product variety, as reflected in the distribution of actual sales across many different SKUs offered to consumers. It is unclear, in particular, whether such a preference would be hindered by limitations faced by consumers when purchasing certain products remotely or by Internet retailer failures in executing the operations necessary to complete those purchases. Difficulties such as these will inevitably translate into greater product returns, which, in the end, will hamper the shift towards greater product variety in Internet sales—especially if those sales depend on the availability of products in inventory exclusively owned and stocked by an Internet retailer. The Internet retailing industry includes many merchants that sell most of their products from stock they themselves own and carry at a facility. For these retailers, returns – which typically range from nearly 1% to well over 20% of sales depending on category (Brohan, 2005) – can create product obsolescence risks and transportation and warehousing costs in excess of $20 per return, according to Gartner Research. To limit these returns, Internet retailing managers may reduce the scope of products they offer by eliminating items that are frequently returned (Baird, 2008). However, they may also consider whether these returns occur independently of these products and, instead, are caused by customers’ difficulties in effectively searching, evaluating, and purchasing these products on the Internet (Gardner, 2008).

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Fig. 1. Examples of sales and return concentrations.

A study of market preferences that examines the distribution of sales across SKUs in juxtaposition with the distribution of product returns across the same SKUs will help Internet retailers define a more appropriate breadth of product assortment. Therefore, we seek to evaluate customer preferences for product variety when purchasing from an Internet retailer in relation to customer tendencies to rely on product returns after they have received their purchases. If, as illustrated by the examples in Fig. 1, returns prove to be less concentrated than sales (e.g., 35% of items account for 65% of returns while 20% of items account for 80% of sales), it suggests that a broad offering exposes the Internet retailer to costly risk because returns are distributed across a relatively wider product assortment. In contrast, a more concentrated returns pattern (e.g., 5% of items account for 95% of returns) highlights a more manageable risk relative to the product breadth. Either way, further insight into systemic drivers of customer returns would help an Internet retailer grapple with the question of which specific products should be included in the assortment. Simply put, customers would ideally like an assortment that provides sufficient choice but does not impose needless effort to identify the optimal selection during the shopping process. Should the process prove overwhelming or if the product cannot be adequately assessed in an online forum, the customer may simply buy the product with an expectation of returning it if it proves insufficient upon physical inspection. Prior research (Mollenkopf et al., 2007) highlighted the incidence of “Devil Customers” and anecdotal evidence from Zappos.com, the leading shoe retailer, underscores how some customers purposely buy multiple sizes and/or colors of the same product with an expectation of keeping only the one which fits or matches best while returning the others. However, to our knowledge, the broader issue of the optimal product breadth in light of these behaviors has not been examined theoretically or empirically. To investigate this issue rigorously, we consider a formulation comprising all-inclusive transactions between online retailers and customers (Kohn and Shavell, 1974). These transactions involve customer purchases (generating forward material flows) as well as purchases that are returned (triggering reverse material flows). Under this formulation, customers expressly state their preferences for different SKUs by purchasing them once they have had a chance to evaluate, compare, and find those items that appear to meet their needs (Bailey, 1998). However, customers will also have the chance to ratify or reverse their decisions by choosing whether to return those products that prove inadequate (Wood, 2001).

Moreover, we examine how different product characteristics and product return reasons by customers shape the purchases and the returns of different SKUs through an evaluation of the distribution of sales across SKUs and in juxtaposition with the distribution of product returns across the same SKUs. While researchers have examined how product characteristics affect consumers’ ability to find goods for purchase on the Internet (Heim and Sinha, 2001) and have explored customers’ motivations for returning products in the Internet retailing context (Boyer and Hult, 2006), the effect of these factors on product sales and returns is far from obvious and has received little research attention. To address our research objectives, Section 2 first reviews the relevant literature and develops several hypotheses that are empirically assessed in Sections 3 and 4. We then present a discussion of these results and our conclusions in Section 5. 2. Theoretical framework The study of customer preferences for different products is rooted in the work on stability in competition based upon products’ physical locations (Hotelling, 1929). Because the advent of the Internet has made product locations a less dominant operating factor in differentiation (Boyer et al., 2002), research in operations management has shifted the focus to the role of services and prices in the study of competitive advantages and customer preferences in electronic commerce. The work on services in Internet retailing has mainly considered measures of customers’ stated preferences about implicit elements of quality embedded in retailing sites. Through this work, authors have conceptualized and empirically assessed linkages between the quality of service elements, such as order processing, picking, and delivery, and perceptions of performance, satisfaction, and loyalty among customers (Heim and Sinha, 2001; Thirumalai and Sinha, 2005; Boyer and Hult, 2006). Moreover, work in this stream of literature has captured subjective evidence regarding the role that the quality of services plays in generating competitive advantages and profits for Internet retailers. Service elements such as order fulfillment and the interactivity, design, and functionality of Internet sites have been part of this research (Hallowell, 2001; Boyer et al., 2002; Piccoli et al., 2004; Olson et al., 2005). Research on pricing in electronic commerce has added to this stream of literature by considering the connections that exist between policies on retail pricing and Internet services within

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supply chain management. This work has provided insights into the relationships that exist between pricing policies and inventory ownership decisions in Internet retail supply chains (Rabinovich et al., 2008) as well as into the relationship between pricing policies and delivery strategies in Internet commerce (Rabinovich, 2007). Moreover, studies in this area have delved into the role played by prices in the execution of fulfillment services in Internet retailing (Rabinovich and Bailey, 2004). Missing in this field of research is an understanding of the role played by customer preferences for product variety in Internet retail operations. While studies have documented different customer preferences for services and prices on the Internet, they have not considered how extensively customers will avail themselves of the wide product selection that is potentially available on the Web and of the drivers of product returns. The oft-cited “limitless shelf space” of Internet retailers does not necessarily reflect widespread preferences for an endless variety of SKUs by customers. These preferences are a function of individual customer decisions to buy as well as to keep different SKUs (Bonifield et al., forthcoming). Past research has shown that Internet retailers do offer a wider variety of SKUs than their offline counterparts (Brynjolfsson et al., 2003) and that sales for these products have the potential to spread out broadly across SKUs (Brynjolfsson et al., 2009, 2007). However, it is yet not known how well these sales actually match individual customer preferences. That is, it is not clear whether customers will end up upholding or reversing their purchasing decisions by returning the various products they have bought on the Web. In the long run, the effectiveness of inventory variety as a competitive advantage for retail differentiation will depend on whether customer preferences are actually met (Kekre and Srinivasan, 1990). And this is reflected in the ratification or the reversal of customer purchases, through the occurrence of product returns (Anderson et al., 2009a). In the operations management field, early researchers made somewhat restrictive assumptions about the occurrence of returns. They assumed, for instance, that the variety of product sales carries no implications for the distribution of product returns and vice versa (Schrady, 1967). However, theorists have recently pointed to the existence of an incremental demand phenomenon whereby the variety of sales across SKUs can be accompanied by broadly distributed product returns (Kiesmüller and van der Laan, 2001). Some have even proposed that expansions in the scope of sales may be driven by shoppers, who on one hand, are more likely to experiment and buy a greater variety of SKUs, but on the other hand, are just as likely to indiscriminately return these SKUs without delay (Clemons et al., 2006). Could this phenomenon have equal applicability in an Internetcommerce setting? Information Economics Theory points to the possibility that this may not be the case. As we expand upon below, Information Economics Theory suggests that, through the use of the Internet, customers are in a position to make informed purchasing decisions about increasingly expansive arrays of products that should not generate a large scope of product returns. 2.1. Information economics and Internet retailing The theory of Information Economics predicts that the Internet’s interoperability and open standards for the transfer of data will contribute to lowering the asymmetries in information availability stacked against customers as they make their purchasing decisions and transact with retailers (Rabinovich, 2007). As a result, customers on the Internet are not hindered by the excessive data collection outlays that are common in offline retail settings where information is scattered across physical product locations (Stigler, 1961). In making purchasing decisions on the Internet, customers

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do not face the need to engage in many of the activities and resource expenditures (time and monetary) that are required to overcome different physical location boundaries in order to find and purchase a wide variety of products that matches their preferences (Bakos, 1997). Furthermore, on the Internet, customers are in a position to access increasingly rich, up to date, and accurate information about many products (not just the best selling ones) because of the low menu costs involved in maintaining the integrity, relevance, and currency of these data through this medium (Levy et al., 1997). Consequently, purchasing decisions about different kinds of individual products are not typically subject to incomplete and imprecise product information as well as other data imperfections that could degenerate in failed purchasing decisions and product returns (Stiglitz, 1989). Because, on the Internet, the customer does not leave a store with a product in hand but, instead, must await delivery by the retailer, product returns are likely to be the result of explicit shortfalls that occur after the initial purchasing decisions by customers have been made (Forbes et al., 2005). As a result, return occurrences are likely to cluster around a narrow scope of SKUs that are frequently exposed to these risks. For instance, returns on the Internet are likely to concentrate around SKUs that are subject to execution errors that occur between the time of purchase and the actual receipt of the products. Also, returns are likely to occur more frequently when SKUs require physical inspection by customers to ensure their fit with customer needs. These theoretical arguments suggest that, on the Internet, product sales will be more broadly distributed across SKUs than product returns. This leads to our main hypothesis regarding the distribution of sales and returns across SKUs in an Internet commerce setting. Hypothesis 1. On the Internet, product sales will be more broadly diversified across SKUs than product returns. Though we anticipate that the relative distribution of sales versus returns across SKUs postulated in Hypothesis 1 will hold broadly, we also anticipate that the relative distribution of sales versus returns will vary for a number of reasons. Specifically, the distribution of sales and returns will be shaped by potential risks of loss for consumers that depend on specific SKU characteristics, such as product size, product price, and the length of time that products have been available for sale at retail sites. In the next two sections, we present theoretical arguments regarding the role of risk in Internet retailing transactions. Our arguments build on two determinants of risk exposure (transaction expenditures and ambiguity) and the product characteristics that contribute to the importance of these determinants. 2.2. Expenditures and risk in Internet retailing Customers’ exposure to risk of loss is proportional to the expenditures they incur in their transactions with Internet retailers (Finch, 2007). These expenditures will be influenced in turn by the size and the price of the products in these transactions. Increases in the size of products (as a function of volume and weight) will increase transaction expenditures for online buyers because they will boost the shipping and handling (S&H) fees that buyers will pay for order delivery (Laseter et al., 2006). Since Internet buyers are particularly averse to paying S&H fees, they will find it especially justifiable to buy large items and pay excessive S&H fees only for a narrow segment of popular SKUs that they know well (Smith and Brynjolfsson, 2001). Customers are likely to avoid paying high S&H fees for little known large items located at the tail end of the sales distribution in order to control their exposure to the risk of losing these expenditures if the products fail to

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meet their preferences (Rabinovich, 2004). Consequently, we posit that: Hypothesis 2. The distribution of Internet sales for large products will be more skewed towards best-selling SKUs than the distribution for small products. We also expect to see increasingly limited returns for larger products. This is because customers will be less likely to incur the expenditures of time and effort that are needed to return heavy and/or large volume SKUs. Although these expenditures are nonmonetary in nature, they have been found to carry a great deal of importance for customers on the Internet (Mollenkopf et al., 2007). Customers will be careful to invest the time and effort required to return large-sized items only when these items have markedly failed to meet their preferences. Consequently, we expect large, heavy products to exhibit a greater concentration of returns across SKUs relative to the distribution of returns for smaller, lighter products. This leads to our third hypothesis. Hypothesis 3. On the Internet, large products will exhibit a higher concentration in the distribution of returns across SKUs than small products. Since product prices account for much of the transaction expenditures for customers on the Internet, customers will engage in extensively searching for high-priced items in order to minimize their exposure to a risk of loss in their retail transactions on the Web (Johnson et al., 2004). This potential loss may be reflected in the payment of overcharges that are increasingly common for higherpriced, more obscure products sold on the Internet (Clay et al., 2001; Ratchford, 2009). Consequently, customers will not find it as advantageous to use the Internet in order to buy obscure products that carry relatively high prices. Rather, Internet retail customers will tend to buy more popular products when seeking expensive items. On the other hand, customers, will be more likely to return a poorly fitting product upon receipt when the price for the product is high (Petersen and Kumar, 2009). This is because customers will have a stronger motivation to recoup their expenditure after a purchase failure if the product they purchased carries a high value (Anderson et al., 2009b). These arguments lead to our fourth hypothesis: Hypothesis 4. On the Internet, high-priced goods will exhibit a higher concentration in sales but a wider distribution of returns across SKUs than low-priced goods. 2.3. Ambiguity and risk in Internet retailing Customers’ ability to make more knowledgeable product assessments will also determine their exposure to the risk of loss from making ill-informed purchases on the Internet (Finch, 2007). The trajectory that products have accumulated on Internet sites will lower this exposure. This is because as the amount of time a product has been available online increases, so will the opportunity to access information about the product available for customers (Rabinovich et al., 2003). This information will include not only data about the different attributes of the product, but also reviews by customers and experts who have vetted the product, as well as other user-generated content such as related product recommendations, pictures, etc. (Chevalier and Mayzlin, 2006). With greater opportunity to have information such as this at their fingertips, customers will be able to make more knowledgeable assessments about products located at the head through the tail-end of the sales distribution (Clemons et al., 2006). Thus, the amount of time products have been available on an Internet site will help customers gather more product information and make more effective and unambiguous purchasing

decisions, irrespective of how mainstream or popular the products are. Furthermore, because Internet retailers and their customers will gain exposure and become more familiar with products as they accumulate more time on retailing sites, we expect returns for products with a longer trajectory on these sites to occur more sporadically across different SKUs. Product returns involving long-established items are not likely to spread out and occur indiscriminately across SKUs. Rather, they are likely to cluster around a few SKUs involved in purchases subject to systemic ambiguities, misrepresentations, or erroneous assessments by Internet retailers and their customers. Therefore, our last hypothesis states that: Hypothesis 5. Products that have long been available at Internet sites will exhibit a wider distribution of sales but a higher concentration of returns across SKUs than more recently available products. 3. Empirical design 3.1. Assessment framework A strategy of broad product offering will be advantageous for an Internet retailer when demand (as measured by units sold) is broadly dispersed across products, relative to the distribution of returns (in terms of units sent back by customers) across products. To empirically assess this phenomenon, we use transactional data from an Internet retailer to directly compare the distribution of sales and returns. The decision to consider a single retailer is consistent with our theoretical framework and with prior empirical research on Internet retailing (Heim and Sinha, 2001; Olson and Boyer, 2003). Focusing on one retailer ensures uniformity across exogenous dimensions that might bias demand and return activity. The retailer provides all customers with consistent information formats pertaining to its SKUs and upholds uniform return policies across SKUs. Thus, limiting our empirical evaluation to this single Internet retailer reduces inconsistencies in the way product information contents are displayed and products are allowed to be returned, which might influence customers’ buying preferences and product return behavior (Esper et al., 2003). Also, by focusing on one Internet retailer, we can study sales and returns that include the same product range, originate from and go back to inventory that is owned by the retailer, and is held at a single retailer facility. This setting is consistent with the motivation for our study, as discussed in Section 1. Moreover, as discussed below, it facilitates the comparison between the distributions of sales and returns while controlling for differences across the range of products sold and returned as well as logistical considerations that could affect the volume of sales and returns observed. Because we examine transactions over 10 months (September 1, 2005 to June 30, 2006), we can also account for varying demand levels in the retailer’s distribution of sales and returns due to their seasonality over this time period. While our data set includes all SKUs, regardless of whether they recorded any sales or returns during the period, our information allows us to identify and eliminate discontinued items that could not have registered returns. The Internet retailer in our empirical setting sells kitchen appliances and houseware items to customers throughout the U.S. At the time of this study, the retailer had been in operation for more than 5 years, ranked among the top ten in this segment of the e-commerce industry, and was among the largest 500 Internet retailers in terms of total sales. By collecting data on this specific retailer’s transactions with customers, we focus on a setting involving items that – according to the typology developed by Murphy and Enis (1986) and

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extended to Internet commerce by Colarelli-O’Connor and O’Keeffe (2000) – belong to a category of goods that are subject to significant deliberation, comparison, and substitution among different alternatives by customers before purchase. Moreover, because at the time of our study there were many different firms in the industry segment where the retailer competes, customers had fairly unrestricted access to compare and substitute products during their purchases, as well as to return unwanted products and exchange them with other items from different retailers. Furthermore, unlike Internet settings involving hedonic goods such as books, music, and other items commonly sold on the Web, the SKUs in our setting are utilitarian–their demand is driven by the benefits that customers experience from different bundles of features in the products (Moe and Fader, 2001). As a result, when buying these products, customers are likely to narrow down their preferences through the comparison of specific product alternatives based on common bundles of product attributes. This will drive customers to engage in a more targeted evaluation and comparison across specific SKUs prior to their purchase decision. At the same time, customers of these products are not as likely to be subject to the kind of affective experiences commonly associated with books, music, and other products that are usually purchased more spontaneously, when deciding whether to return a recently acquired item (Dhar and Wertenbroch, 2000). Therefore, customers will be able to make product return decisions more objectively, based on tractable reasons regarding the nature of the products and the execution of their transactions, in relation to their needs and expectations. The ability to track these return reasons objectively is relevant in our evaluation of the distribution of returns in our study. Moreover, customers in our study incur the lowest possible out-of-pocket costs for returns in the market, as they do not pay shipping and handling (S&H) or restocking charges involved in returning previously purchased items. As a result, customers in this context do not face this disincentive which occurs in other Internet retail settings. These conditions create a unique contrast in our setting between easily tractable, less demanding, low-cost returns and more elaborate and targeted purchasing deliberations by buyers across products available for sale on the Internet. This contrast is conducive to the juxtaposition of more broadly distributed occurrences of returns and narrower, more focused purchases of products by customers across SKUs available on the Internet. Therefore, our study tests our theoretical expectation in Hypothesis 1 that Internet sales are broadly distributed across products while returns are clustered around a few SKUs, within an empirical setting that is most likely to favor conditions that defy this expectation. Finally, a focus on the particular products that we chose for our study contributed to the internal validity of our assessment of Hypotheses 2–5. In our study’s setting, product prices, sizes, and longevity at the retail site exhibit a great deal of variation across SKUs. This variation is much greater than that among other items sold widely on the Web (e.g., books, CDs, DVDs), for which price and size are fairly uniform and product shelf lives are generally very short (Waldeck, 2005). This makes product price, size, and longevity at the retail site more relevant attributes in the assessment of sales and return patterns in our study (Johnson et al., 2004). 3.2. Empirical operationalization To evaluate the distribution of sales and returns, we rank the SKUs by sales and returns (in units) recorded during the 10-month period of our data collection and then allocate to each SKU its volume of sales and returns observed during the same data collection period. Overall, our data include the sale and the return of 785,886 units and 15,869 units, respectively. The full database comprises

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Table 1 Descriptive information in empirical operationalization. Total number of units sold Total number of units returned Number of SKUs Timeframe of analysis Average distance from retailer’s only warehouse to delivery Average (standard deviation) SKU retail price Average (standard deviation) SKU time at retail site Average (standard deviation) SKU weight Average (standard deviation) SKU volume Range of reasons for returns: Product fit failure – reasons include: “changed my mind,” “wrong item ordered,” “not as shown,” and “quality not as expected” Execution error – reasons include: “arrived damaged,” “product defective,” “wrong item sent,” and “arrived late” Unspecified – reasons include: “gift return,” “no reason for return,” and “other”

785,886 15,869 6725 September 1, 2005 to June 30, 2006 1600 miles 72.96 (123.83) Dollars 1040 (823.25) days 5.1 (6.72) lbs. 0.45 (0.71) ft3 Percentage of observations: 40.0%

16.2%

43.8%

Note: SKUs and sale orders and return claims in our sample did not differ systematically from data found in the broader kitchen and houseware segment of e-commerce, according to Internet Retailer Magazine estimates and the Home World Housewares Census. In a first test, the price average of the 6725 SKUs in our dataset was compared against the price average of SKUs available from other firms in the kitchen and houseware segment of e-commerce. An independent-sample t-test showed no statistically significant difference between the two price averages (t-value = −1.248, p-value = 0.212). In a second test, we compared the average distance to deliver the SKUs in our dataset against the average delivery distance reported for its orders by one of the retailer’s competitors in the Internet commerce industry (1342 miles). Again, a t-test showed no statistically significant disparity in the average distances (t-value = 0.239, p-value = 0.811). Finally, a third test compared the average time lag between the placement of orders by customers and the arrival of returns back to the retailer against the average available from other firms in the broader e-commerce industry. Again, the test showed no statistically significant differences in the average lags (t-value = −1.404, p-value = 0.160).

a total of 6725 SKUs available to consumers through the retailer’s website during the study period (Table 1). The empirical analysis of these data is subject to several factors that could influence its validity. One of those factors is customers’ saturation with the Internet retailer’s site and the product listings in it during our study. Since this saturation may reflect a diminishing interest in the market for the products offered at the retailer’s site, it may skew our results. Therefore, we examined the proportion of Internet users that visited the retailer’s website from 2003 to 2009 to determine whether this proportion had peaked and started to decline during this period of time. We gathered these data from Alexa.com, an organization that has been collecting data on Internet sites through Web crawls since 1996. We found that, for every million Internet users, there was an average of 250 daily visitors to the retailer’s website from 2003 to 2006 and that this number increased to 800 daily visitors between 2006 and 2009. Therefore, the proportion of visitors to the retailer’s site appears to have grown substantially after the time we performed our study (in 2006). This growth suggests that customers had not reached a point of saturation at the time we conducted our research and that our assessment of the distribution of sales and returns is not biased by a diminishing interest among consumers for the retailer’s products. As we noted in the development of Hypothesis 1, the distribution of product returns also depends on explicit shortfalls that occur after initial purchasing decisions by customers have been made. To that end, we focus on these return reasons as a key outcome of

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Fig. 2. Lorenz curves.

service experiences that customers can assess only after making their purchases on the Internet and being exposed to the service operations that lead to the delivery of the product and its physical inspection by the recipient(s) (Rabinovich and Bailey, 2004). As part of our data collection, we obtained customers’ selfreported reasons for product returns. Customers were asked to provide the retailer with a reason for their return by selecting one of eleven options included on a printed card sent by the retailer. In Table 1, we expand on customers’ explanations for their product returns and categorize these returns into three groups. In the first group of returns, customers stated no specific motivation for sending back their products. In the second and third groups, customers offered two types of reasons for their returns: products’ poor fit with customer preferences, and retailer errors in the execution of the transactions. As discussed in Section 2.1, we expect that the returns in the latter two groups will occur less indiscriminately, and their distribution may expand less broadly across SKUs. We will empirically verify this expectation as part of our testing of Hypothesis 1. Finally, the evaluation of Hypotheses 2–5 requires us to consider additional factors that account for different characteristics of the items transacted between the customers and the retailer. We consider four product characteristics. The first two characteristics, product volume (in cubic feet) and weight (in pounds), account for SKUs’ size. In our study, each of the sale orders and return claims averaged one item, and each item had on average a volume of 0.45 ft3 and a weight of 5.1 lbs. The other two product characteristics that can influence sales and returns distributions are the retail price and the amount of time the product has been available for sale by the Internet retailer. In our study, the average price of each item sold was $72.96, and a typical product had been part of the retailer catalog for an average of 1040 days by the end of our data collection period (June 30, 2006). 4. Statistical results We first determine the extent to which customers exhibit a preference for product variety, as reflected in their purchases of a wide diversity of items from an Internet retailer. If customers do, in fact, buy a broad scope of SKUs online, we should see a low concentration of sales and a long tail in the distribution of sales. At the theoretical extreme, a perfectly uniform distribution of product sales across the SKUs would suggest broad preferences of all SKU options, assum-

ing every product has an equal probability of meeting customer needs. Though this extreme condition is unlikely, a sales distribution concentrated on fewer SKUs suggests that customers have a diminishing preference for product variety and, instead, converge to a cluster of products that have mainstream market appeal. To test Hypothesis 1, we must compare the sales distribution to the degree of concentration in returns. If the results show that the level of concentration in product sales is higher than that in returns, they would disaffirm our information economics based hypothesis. 4.1. Lorenz curve and Gini coefficients To describe at a basic level the relationship between the distribution of product sales and returns, we used the Lorenz Curve and the Gini Coefficient (Lorenz, 1905; Gini, 1912). In our setting, the Lorenz Curve is drawn using an x-axis measuring the cumulative percentage of products and a y-axis measuring the cumulative percentage of units sold (returned). SKUs are sorted so that the items with the fewest sales (returns) are closest to the upper right hand corner (0%, 0%) and the SKUs with the highest sales (returns) are closest to the bottom left hand corner in Fig. 2. The Gini Coefficient is the ratio of the area between a Lorenz Curve and a 45◦ line to the total area over a 45◦ line. Accordingly, it offers a way to assess descriptively the relative concentration of our distributions of sales and returns. In our context, it is defined as  =1−

2



=1

[( + 1 − )ı ]



( + 1)

ı =1 

,

(1)

where the demand for  products is denoted by (ı1 , ı2 , ı3 , . . ., ı ) and ı1 ≤ ı2 ≤ ı3 ≤ . . . ≤ ı . In the theoretically extreme case of evenly distributed sales or returns, the Lorenz Curve coincides with the 45◦ line and the Gini Coefficient equals zero. As the distribution becomes more concentrated, the curve moves away from the 45◦ line and the coefficient increases towards a limit of 1. Customer preferences for purchasing a wide variety of products as opposed to having to return them would be reflected in a lower concentration in the distribution of sales relative to the distribution of returns. In this case, the Lorenz Curve involving product sales would be closer to the 45◦ line than would the Lorenz Curve for product returns. In turn, the Gini Coefficient from the distribution of product sales would be lower than that obtained from the distribution of product returns.

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Table 2a Loglinear regressions of sales onto sales rank and of returns onto returns rank. Independent variables [and coefficients]

Dependent variables Ln (Salesϕ )

Ln (Returnsϕ )

*

Constant [ˇ0 ]

16.400 (0.165)

13.601* (0.235)

ln (Sales Rankϕ ) [ˇ1 ]

−1.994* (0.027) [−2.046, −1.942]

–

ln (Returns Rankϕ ) [ˇ1 ]

–

−3.207* (0.060) [−3.324, −3.090]

Model summary

R = 0.66 [R2 ] = 0.443 Adj R2 = 0.443 Std. error of the estimate: 0.663

R = 0.716 [R2 ] = 0.512 Adj R2 = 0.512 Std. error of the estimate: 0.906

Standard errors are in parentheses and the 95% confidence interval range for each ˇ1 coefficient is in square brackets. * Significantly different from zero, p < 0.01.

The initial results in Fig. 2 reveal a small difference in the concentration in the distribution of sales relative to the distribution of returns and show that the curves are both a bit fatter than the Pareto 80/20 rule since 20% of the SKUs account for only about 76% of sales or returns. In turn, the Gini Coefficient based on the distribution of product sales (0.74) is lower than, but very close to, the Gini Coefficient from the distribution of product returns (0.75). To examine whether our results are subject to bias from the inclusion of items that registered neither sales nor returns during the 10-month analysis period, we exclude these items and then analyzed the sales and returns distributions separately. The Gini Coefficient for the distribution of sales is 0.69 and the coefficient for the returns distribution is 0.71, suggesting that any bias that might be present in the initial analysis does not alter our results. 4.2. Regression models A simple comparison of the Lorenz Curves and Gini Coefficients does not allow us to determine whether a statistical difference exists between the sales and returns distributions. To address this issue, we fit the data to the following log-linear regressions: Ln (Salesϕ ) = ˇ0 + ˇ1 ln (Sales Rankϕ ) + εϕ

(2)

Ln (Returnsϕ ) = ˇ0 + ˇ1 ln (Returns Rankϕ ) + εϕ ,

(3)

where the volume of sales and returns (in units) for each SKU (ϕ) is denoted by Sales and Returns. Each SKU’s level of sales and returns in relation to other products is denoted by Sales Rank and Returns Rank. A decrease in the values of both of these

rank variables reflects an increase in the SKU’s level of sales and returns. We then compare the coefficients of product sales rank and product returns rank from Eqs. (2) and (3). Given this specification, these coefficients respectively measure how quickly the share of sales or the share of returns attributed to each product falls as either the Sales Rank or the return rank for the product increases. This approach is consistent with prior research that has used log-linear models to analyze the distribution of product sales on the Internet (e.g., Chevalier and Goldsbee, 2003; Brynjolfsson et al., 2007). Both ˇ1 coefficients are statistically different from 0, and, based on their 95% confidence intervals, the coefficient for ˇ1 is statistically higher (less negative) in the sales regression than in the returns regression (Table 2a). This result is consistent with Hypothesis 1. It suggests that, relative to product returns, product sales are significantly more uniform across SKUs. Because the log-linear analysis cannot take into account observations in which the volumes of SKU sales and SKU returns equal zero, we complemented the analyses in Table 2a with negative binomial distribution (NBD) models. This type of model has been used extensively with count data (Hausman et al., 1984) such as the return and sales volumes we collected. The NBD regression results (Table 2b) show a negative and significant relationship between Sales Rank and Sales and between Returns Rank and Returns. Moreover, the coefficient for ˇ1 is statistically higher (less negative) in the sales regression than in the returns regression. This evidence is consistent with that of the log-linear regression results and supports Hypothesis 1 because it shows that, relative to product returns, product sales are significantly more uniform across SKUs.

Table 2b Negative binomial regressions of sales onto sales rank and of returns onto returns rank. Independent variables [and coefficients]

Dependent variables Salesϕ

Returnsϕ

Constant [ˇ0 ]

9.955 (0.087)

13.987* (0.249)

Sales Rankϕ [ˇ1 ]

−0.011* (0.000) [−0.012, −0.010]

–

Returns Rankϕ [ˇ1 ]

–

−0.257* (0.005) [−0.266, −0.247]

Likelihood-ratio test of alpha = 0  ¯ 2 = 7.8e + 05 LR of 2 = 22469.02 Prob ≥  ¯ 2 = 0.000 and Prob ≥ 2 = 0.000 Pseudo R2 = 0.104

Likelihood-ratio test of alpha = 0  ¯ 2 = 5.6e + 03 LR of 2 = 5752.04 Prob ≥  ¯ 2 = 0.000 and Prob ≥ 2 = 0.000 Pseudo R2 = 0.222

Model summary

*

Standard errors are in parentheses and the 95% confidence interval range for each ˇ1 coefficient is in square brackets. * Significantly different from zero, p < 0.01.

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Table 3 Pair-wise comparison across return reasons of the negative binomial regression of returns onto returns rank. Independent variables [and coefficients]

Return reason comparison Reason Y = product fit failure versus reason Z = execution error

Reason Y = Product fit failure versus reason Z = unspecified

Reason Y = execution error versus reason Z = unspecified

Constant [ˇ0 ]

16.838* (0.447)

17.226* (0.374)

17.752* (0.201)

Returns RankReason Y and Reason Z, ϕ [ˇ1 ]

−1.005* (0.025) [−1.054, −0.956]

−0.498* (0.011) [−0.519, −0.478]

−0.513* (0.011) [−0.536, −0.491]

DummyReason Y, ϕ [ˇ2 ]

1.082** (0.532) [0.0392, 2.124]

0.402 (0.481) [−0.541, 1.345]

−0.667 (0.539) [−1.722, 0.390]

Returns RankReason Y and Reason Z, ϕ × DummyReason Y, ϕ [ˇ3 ]

0.439* (0.025) [0.389, 0.488]

−0.059* (0.014) [−0.087, −0.031]

−0.506* (0.025) [−0.556, −0.456]

LR 2 = 8470.37 Prob > 2 = 0.000 Pseudo R2 = 0.312

LR 2 = 9187.19 Prob > 2 = 0.000 Pseudo R2 = 0.268

LR 2 = 8548.21 Prob > 2 = 0.000 Pseudo R2 = 0.305

Model summary

Standard errors are in parentheses and the 95% confidence interval range for each coefficient is in square brackets. * Significantly different from zero, p < 0.01. ** Significantly different from zero, p < 0.05.

4.3. Controlling for customer return reasons Differences among customer return reasons may explain the distribution of the number of returned items across SKUs. As a result, statistical differences may exist in the relationship between Returns Rank and Returns based on customers’ reasons for returns. Given the consistency of the results of the log-linear regression function (Table 2a) and of the NBD regression (Table 2b), along with the possibility of considering SKUs with zero returns in the NBD regression, we use the NBD approach to evaluate the variation of SKUs’ return volume with their return rank across the three categories of return reasons listed in Table 1. We ran independent NBD regression functions based on return activity triggered by these three categories. An objective of this analysis is to verify whether the distribution of sales is statistically significantly broader than the distribution of returns after controlling for customers’ reasons for returns. Another goal is to determine whether statistically significant differences exist in the distribution of returns across SKUs for the three types of reasons for product returns. In particular, we verify whether there is a statistically higher concentration of returns across SKUs when returns are due to explicit post-purchase execution errors or fit failures versus unspecified reasons. When statistical differences do exist, we also identify the category of return reason that yielded the highest concentration of returns across SKUs. In Table 3, we present results from three regression functions based on the relationship in Eq. (4). The ˇ1 coefficients for product returns rank at each of the regressions are negative and statistically different from zero. Moreover, in line with our results in Section 4.2, each of these coefficients is statistically lower (more negative) than the ˇ1 coefficient for product sales rank in the sales regression in Table 2b. Specifically, a statistical comparison using 95% confidence intervals between the ˇ1 coefficient for product sales rank in Table 2b (−0.011) and each of the ˇ1 coefficients for Returns Rank in Table 3 (−1.005, −0.498, and −0.513) shows that there is a higher concentration in the distribution of product returns relative to the distribution of sales across the different return reasons. This disparity in concentration is at its highest when returns occur due to fit failures that are realized upon inspection by customers or when returns are due to errors in the execution of the transactions. When returns with unspecified causes are considered, the disparity in concentration between sales and returns is lower but still persists, as reflected in the corresponding confidence intervals for the ˇ1 coefficients for Returns Rank in Table 3 and the ˇ1 coefficient for product sales rank in Table 2b. These results offer

support for Hypothesis 1: they provide evidence to suggest that, relative to product returns, product sales are significantly broader across SKUs, after controlling for customer reasons for returning the products ReturnsReason RankReason

Y and Reason Z, ϕ

Y and Reason Z,ϕ

Returns RankReason

= ˇ0 + ˇ1 Returns

+ ˇ2 DummyReason

Y and Reason Z, ϕ

Y, ϕ

+ ˇ3

× DummyReason

Y, j+εϕ

(4) Each regression in Table 3 juxtaposes the coefficient in the relationship between product return rank (Returns Rank) and return volume (Returns) across each pair of return reason categories (Reason Y and Reason Z), as reported by customers when returning each SKU (ϕ). This comparison is captured by the ˇ3 coefficient in Table 3. The ˇ3 coefficient represents the difference in the magnitude of the coefficient in the rank–volume link when we contrasted it – in a pair-wise fashion – across the results obtained for returns due to fit failures, returns due to execution errors, and returns with no clearly specified motivation. The ˇ3 coefficient is positive and statistically different from 0 in the regression contrasting returns caused by fit problems against returns generated by execution errors. Thus, there is a higher concentration in the volume of returns across products when returns follow execution errors than when the returns are triggered by a poor fit of the products with customer preferences. The ˇ3 coefficient is negative and statistically different from 0 in the other two regression functions. One of these functions contrasts the magnitude of the coefficient under returns due to fit failures versus under unspecified returns; the other contrasts the magnitude of the ˇ3 coefficient under returns caused by transaction execution errors against the ˇ3 coefficient under returns with no specified motivation. The values of these coefficients point to a higher concentration in the volume of returns across products when returns result either from a poor fit upon customer inspection or from errors in the execution of the transactions, relative to returns with unspecified causes. This is consistent with our expectation that retailer execution errors and the inadequate matching between products and customer preferences after order fulfillment account for more targeted SKU returns on the Internet. These types of returns may originate from product characteristics that are difficult for customers to assess until they receive and examine the products

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Table 4 Pair-wise comparison across attribute categories of the negative binomial regression of sales onto sales rank. Independent variables [and coefficients]

Product characteristic comparison High price (expensive SKUs) versus low price (inexpensive SKUs)

High volume (large SKUs) versus low volume (small SKUs)

High weight (heavy SKUs) versus low weight (light SKUs)

High time (older SKUs at retail site) versus low time (newer SKUs at retail site)

Constant [ˇ0 ]

8.265* (0.038) [8.190, 8.341]

8.517* (0.045) [8.427, 8.607]

8.498* (0.044) [8.410, 8.586]

9.364* (0.0736) [9.217, 9.511]

Sales RankAttribute H and Attribute L, ϕ [ˇ1 ]

−0.007* (7.5e−05) [−0.072, −0.0069]

−0.008* (8.5e−05) [−0.0082, −0.0078]

−0.008* (8.4e−05) [−0.0082, −0.0078]

−0.010* (1.3e−04) [−0.0103, −0.0097]

DummyAttribute H, ϕ [ˇ2 ]

2.068* (0.097) [1.874, 2.262]

0.823* (0.083) [0.657,0.989]

0.898* (0.084) [0.730, 1.066]

−0.907* (0.086) [−1.079, −0.735]

Sales Rankattribute H and Attribute L, ϕ × DummyAttribute H, ϕ [ˇ3 ]

−0.004* (1.7e−04) [−0.0037, −0.0043]

−0.002* (1.5e−04) [−0.0023, −0.0017]

−0.002* (1.6e−04) [−0.0023, −0.0017]

0.002* (1.6e−04) [0.0017, 0.0023]

LR 2 = 11320.12 Prob > 2 = 0.000 Pseudo R2 = 0.183

LR 2 = 11035.43 Prob > 2 = 0.000 Pseudo R2 = 0.173

LR 2 = 11060.80 Prob > 2 = 0.000 Pseudo R2 = 0.174

LR 2 = 11154.26 Prob > 2 = 0.000 Pseudo R2 = 0.175

Model summary

Standard errors are in parentheses and the 95% confidence interval range for each coefficient is in brackets ** Significantly different from zero, p < 0.05. * Significantly different from zero, p < 0.01.

personally or they may result from execution difficulties caused by the fulfillment of particular products. 4.4. Assessing the role of product attributes We use an NBD regression to assess, for different levels of product weight, volume, price, and time available for sale at the retail site, how the share of sales (share of returns) attributed to each product changes as the product’s sales rank (return rank) increases. In our regression, we consider two levels or categories (high [H] and low [L]) of SKU weights, volumes, prices, and times available for sale. We use the median for the distribution of SKUs to define the high and low groups and assigned the SKUs to categories accordingly. This ensures that the SKU sample is evenly split across each pair of categories for the different attributes and that asymmetrical statistical power will not bias the results of the statistical analyses. In Tables 4 and 5, we present results from eight regressions based on the relationships in Eqs. (5) and (6). The ˇ1 coefficients

for product sales rank at each of the four regressions in Table 4 are negative and statistically different from zero. The same is true for the ˇ1 coefficients for product returns rank at each of the four regressions in Table 5. Moreover, based on their corresponding confidence intervals, each of the ˇ1 coefficients for product sales rank in the regressions in Table 4 are statistically higher (less negative) than their counterpart coefficients in the regressions in Table 5. These results are in line with our results in Sections 4.2 and 4.3 and offer additional support for Hypothesis 1 as they provide further evidence to suggest that, relative to product returns, product sales are significantly more uniform across SKUs. SalesAttribute

H and Attribute L, ϕ

RankAttribute

H and Attribute L,ϕ

RankAttribute

H and Attribute L, ϕ

= ˇ0 + ˇ1 Sales

+ ˇ2 DummyAttribute

H, ϕ

+ ˇ3 Sales

× DummyAttribute

H, ϕ

+ εϕ (5)

Table 5 Pair-wise comparison across attribute categories of the negative binomial regression of returns onto returns rank. Independent variables [and coefficients]

Constant [ˇ0 ]

Returns RankAttribute H and Attribute L, ␸ [ˇ1 ]

DummyAttribute H, ␸ [ˇ2 ]

Returns RankAttribute H and attribute L, ␸ × DummyAttribute H, ␸ [ˇ3 ]

Model summary

Product characteristic comparison High price (expensive SKUs) versus low price (inexpensive SKUs)

High volume (large SKUs) versus low volume (small SKUs)

High weight (heavy SKUs) versus low weight (light SKUs)

High time (older SKUs at retail site) versus low time (newer SKUs at retail site)

10.495* (0.237) [10.021, 10.969] −0.187* (0.005) [−0.197, −0.177] −0.120* (0.036) [−0.192, −0.048] 0.031* (0.006) [0.019, 0.043]

10.688* (0.254) [10.180, 11.196] −0.192* (0.005) [−0.202, −0.182] 0.184* (0.035) [0.114, 0.254]

10.773* (0.244) [10.285, 11.261] −0.194* (0.004) [−0.202, −0.186] 0.163* (0.035) [0.093, 0.233]

11.501* (0.213) [11.075, 11.927]

−0.001 (0.006) [−0.013, 0.011] LR 2 = 5357.22 Prob > 2 = 0.000 Pseudo R2 = 0.228

−0.002 (0.006) [−0.014, 0.010] LR 2 = 5345.72 Prob > 2 = 0.000 Pseudo R2 = 0.227

−0.027* (0.002) [−0.031, −0.023]

LR 2 = 5163.54 Prob> 2 = 0.000 Pseudo R2 = 0.228

Standard errors are in parentheses and the 95% confidence interval range for each coefficient is in square brackets. **Significantly different from zero, p < 0.05. * Significantly different from zero, p < 0.01.

−0.208* (0.004) [−0.216, −0.200] 0.329* (0.026) [0.277, 0.381]

LR 2 = 5496.56 Prob > 2 = 0.000 Pseudo R2 = 0.234

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ReturnsAttribute RankAttribute

H and Attribute L, ϕ

H and Attribute L, ϕ

Returns RankAttribute +εϕ

Table 6 Summary of results.

= ˇ0 + ˇ1 Returns

+ ˇ2 DummyAttribute

H and Attribute L, ϕ

H, ϕ

× DummyAttribute

+ ˇ3 H, ϕ

(6)

Each of the first four regressions (Table 4) juxtaposes the link between product sales rank and sales volume across the high and low category pairs for the four product attributes. In turn, each of the second set of four regressions (Table 5) juxtaposes the link between product return rank and return volume across the high and low category pairs for these same attributes. These comparisons are captured by ˇ3 in Tables 4 and 5. ˇ3 reflects the difference in magnitude of the coefficient in the rank–volume relationship when contrasted – in a pair-wise fashion – for sales (Eq. (5)) and returns (Eq. (6)) between the high and low groups of SKU characteristics. Values for the ˇ3 coefficient that are statistically different from zero point to a statistical difference in the distribution of sales and returns across the high and low categories of product weight, volume, price, and time at retail site. We first consider the sales and the returns regressions contrasting the high and low categories of SKU weight and volume. The results from the sales regressions (Table 4) show the ˇ3 coefficients to be negative and statistically different from 0. In the returns regressions (Table 5), the ˇ3 coefficients are also negative but not statistically different from 0. These results offer empirical evidence to support Hypothesis 2 but not Hypothesis 3. They only confirm our expectation that sales for heavy/large items are skewed toward best-selling SKUs, relative to other lighter/smaller SKUs available online. Such a bias for buying more popular SKUs as product size increases highlights specific customer preferences that are consistent with our expectations regarding the role that SKU weight and volume play in limiting buyers’ Internet purchases. Since S&H expenses are proportional to SKU volume and weight, the prospect of incurring these expenditures when buying large/heavy items on the Internet appears to drive customers away from purchasing lesser-known items. Customers may perceive that obscure items carry a greater risk of return, in which case they would lose the S&H fees paid for the initial delivery of the items and face the more onerous task of returning a large item. We now examine the results from the sales and the returns regressions comparing high and low categories of SKUs in terms of their price (Tables 4 and 5). The ˇ3 coefficient for the sales regression comparing SKUs with high and low prices is negative and statistically significant (Table 4). This result provides confirmation for Hypothesis 4 because it shows that customers will cluster their purchases around best-selling SKUs when buying more expensive items on the Internet. This reflects customers’ aversion to using the Internet to buy a broad range of obscure products at relatively high prices. As prices increase, the potential for losses during customers’ transactions with Internet retailers becomes more onerous, thereby skewing Internet purchases for expensive products towards more popular (or away from more obscure) SKUs. We obtained a positive and statistically significant ˇ3 coefficient for the returns regression comparing high-priced SKUs against lowpriced SKUs (Table 5). This suggests that, relative to the distribution of the volume of returns for less costly items, the distribution of returns for high-priced SKUs will stretch further down from SKUs with high return volumes to SKUs with fewer returns. This finding is consistent with Hypothesis 4. Finally, we consider the sales and the returns regressions contrasting the high and low categories of SKUs’ time available for sale at the retail site (Tables 4 and 5). We obtained a positive and sta-

Hypothesis

Results

Hypothesis 1

Empirical results show that product sales are more broadly diversified across SKUs than product returns Empirical results show that the distribution of Internet sales for large products will be more skewed towards best-selling SKUs than the distribution for small products Empirical results failed to show that, on the Internet, large products will exhibit a higher concentration in the distribution of returns across SKUs than small products Empirical results show that high-priced goods will exhibit a higher concentration in sales but a wider distribution of returns across SKUs than low-priced goods Empirical results show that products that have long been available at Internet sites will exhibit a wider distribution of sales but a higher concentration of returns across SKUs than more recently available products

Hypothesis 2

Hypothesis 3

Hypothesis 4

Hypothesis 5

tistically significant ˇ3 coefficient for the sales regression (Table 4) juxtaposing SKUs with longer shelf lives on the Internet site against newer SKUs. This result is in line with Hypothesis 5: online purchases for products with greater retail site longevity will spread out to include more obscure SKUs, which will result in a lower concentration in the distribution of sales across SKUs. On the other hand, the ˇ3 coefficient for the returns regression (Table 5) comparing SKUs with greater longevity against newer SKUs is negative and statistically significantly different from zero, showing that the distribution of the volume of returns for products with a longer Internet retailing presence is skewed towards fewer SKUs. This result is in line with Hypothesis 5. It suggests that the distribution of returns for SKUs that have been available for sale over longer periods of time at the Internet retailing site is concentrated among fewer problematic SKUs. Table 6 provides a summary of our results and the validation they provided for our five hypotheses. Our results offer support for all of our hypotheses, except Hypothesis 3. 5. Conclusion Because Internet retailers compete in an environment in which consumers can easily evaluate and compare their product offerings, they must strike a balance in the distribution of product sales and product returns that optimally matches customers’ preferences for product variety. Only then will retailers be able to compete under the low search costs that current and potential customers are exposed to when evaluating and comparing available products before purchasing and prevent the return of products that fail to meet their needs and expectations. As shown in our research, online retailers can compete for demand more effectively by broadening their inventory sales in a way that will address how consumers use the Web to evaluate products prior to purchase rather than incurring the costs of returning the products. While Internet retailers do face product returns as part of their normal operations, our results show that returns tend to occur around a narrow group of products that markedly fail to meet market demand. We identify two different causes for failure behind the concentration in the distribution of returns across a narrow group of SKUs: product inadequacies in matching preferences upon customers’ inspection and errors in the execution of customer–retailer transactions involving particular products. SKU attributes also play a role in defining the distribution of sales and returns across products in online retailing. First, higher weight and volume drive demand to cluster around best-selling SKUs. This suggests that, for large items, buyers may be averse to broadening the scope of their purchases down the long tail of SKUs in order to avoid the risk of paying the

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high S&H fees necessary to receive obscure items that may not meet their expectations given the personal effort and cost to return them. Similarly, for expensive items, demand flocks to best-selling SKUs as a safety measure taken by shoppers unwilling to accept the risks associated with buying high-priced, little known products on the Internet. In contrast, we find that the amount of time SKUs have been available for sale on a website flattens their sales distribution down the long tail of obscure products. This is likely the result of gains in consumers’ exposure and increased information with long-standing products at retailing sites. Taken together, these findings underscore an advantage for diversified retailers selling long-standing products that customers can recognize and evaluate. Such a merchandising approach will cater to consumers’ affinity to use the Web to evaluate products prior to purchasing them. The advantage is especially significant when high S&H fees are not imposed for the delivery of heavy or bulky items and/or when relatively lower SKU prices do not bring about large shortfall risks for shoppers concerned about buying on the Internet. When those risks do materialize, we found that customers actively return their products in a pattern that reflects a special sensitivity about high prices. In particular, customers are more likely to return high-priced items and carry out these returns more arbitrarily. This is reflected in our results, which show that relative to the distribution of returns for less costly items, the distribution of returns for high-priced SKUs stretches further down from SKUs with high volumes of returns toward SKUs with fewer returns. Our results also show that the patterns of product returns depend on the amount of time that products have been available for sale at a retail site. According to our results, product returns for long-established items occur more sporadically and cluster around a limited amount of items than those for more recently introduced items. This finding is consistent with theoretical arguments about product ambiguity and customer risk in Internet retailing transactions: it suggests that the trajectory that products have accumulated at a retailing site will lower the exposure by customers to making erroneous product selections in their purchases that may ultimately result in product returns. This may be because retailers and customers will have a greater opportunity to learn about the products as they accumulate more time on the websites. 5.1. Contributions to the literature This paper integrates information economics theory and conceptual arguments on the role of customer risk in Internet retailing transactions into the operations management literature in order to explain why and how retailers’ effectiveness in selling their inventories depends on having a product scope that matches market preferences for product variety and product return activity. To do this, the paper develops a theoretical model of preferences for product variety in sales and diversity in product returns for Internet markets. Furthermore, the paper offers empirical evidence to test the model through the use of objective data collected from retailer–consumer transactions on the Internet. This approach to the study of Internet retailing extends operation management research beyond a typical focus on surveys investigating customers’ stated preferences about Internet retail services and products (e.g., Thirumalai and Sinha, 2005) or on other subjective evidence about Internet retailers’ conduct (e.g., Hallowell, 2001). Moreover, because we use archival data for our empirical analysis, our results offer a nonintrusive assessment of routine encounters of retailers with customers over the Web. The use of this data-collection methodology allows for an assessment of our research questions over time (almost a year) and space (multiple customer locations) without relying on contrived observations that could create bias.

315

Our study also advances the existing literature on the management of information services, which thus far has mainly considered how commercial uses of the Internet by retailers contribute to the lowering of consumers’ search costs at the time of purchase and the resulting decrease in prices (Brynjolfsson and Smith, 2000) and the broadening of the variety of products available to consumers (Brynjolfsson et al., 2003). We expand on these effects by going past the initial pre-purchasing events to include returns. This allows us first to discern the ways that customers use the Web to broaden their access to purchasing obscure items across the shelf spaces offered by Internet retailers. Subsequently, we are able to compare this accessibility against the hindrances customers face when making returns to correct for unsuccessful purchasing decisions. 5.2. Managerial implications Our results underscore the role that product variety plays in the management of Internet retailing operations. In this context, long tails are not as constrained by limitations in storage and location commonly found in traditional supply chains and, thus, they can provide a lot of choices for consumers. However, they can also provide a wide range of product quality (from good to bad) and a great deal of room for customers to make errors in the selection of the items they buy. As customers browse down the long tail of items available at Internet retailers, more choices are available to them, but they also become increasingly exposed to the risk of selecting products that do not match their needs. Practitioners need to balance these forces. Operations managers have a deep understanding of how to manage inventory levels including factors such as the importance of forecasting accuracy and pooling uncertainty in safety stocks. Different rates of returns in Internet retailing however, elevate the need to develop an equally deep understanding of the drivers of returns and to integrate this insight into inventory management practices. Specifically managers must decide how beneficial it would be to broaden their inventory assortments and generate sales from a wider product variety, given the potential that such scope of SKUs has for generating excessive product returns. Consumers can easily seek inventory variety in their purchases through the Internet. As our results show, this preference for inventory variety is reflected in sales that are not only broadly distributed across products but also not offset by widespread product returns that may result from failures in matching demand with inventory comprising a broader range of SKUs. Managers will benefit from understanding how these inherent consumer preferences can shape the scope of their inventory assortments so that they can ultimately anticipate how their product selections, their execution of customer orders, and other service strategies will impact the tradeoffs between the operations necessary to sell their products and the operations necessary to process their product returns. According to our results, customers will generate product returns for a delimited array of SKUs. Because a narrow scope of returns across SKUs can help identify problem items, retailers should use the occurrence of these returns as an opportunity to focus their efforts on improving the management of their product returns around those problem items. Also, they should target for removal from inventory those items that exhibit excessively frequent returns. This is especially important if these products’ quality levels are so low or their deliveries are so frequently subject to product spoilage or breakage that they have a significantly negative effect on the tradeoff between the retailer revenues obtained from these products and the operating costs of inventorying these items and processing their returns. For those SKUs for which returns are consistently caused by a poor fit with customer preferences, managers should examine

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whether these occurrences follow from customer limitations in evaluating these goods remotely via the Internet. It is possible that improvements in the richness and accuracy of the content and information offered online about these products will help curtail these return occurrences. Some retailers, such as Amazon.com, have succeeded in expanding the depth of information about their SKUs on the Internet by posting more detailed product pictures and user guidelines contributed by their own customers. Other retailers, such as eBags.com, have opted to highlight in their product pages those customer reviews that have been considered to be most helpful in evaluating and describing the products by other customers. Finally, grocery merchants such as FreshDirect, which sell specialized food-items, have chosen to rely on external sources to offer expert, independent, and current information about their products and any possible safety recalls. This approach ensures that customers are able to make their purchasing decisions without running the risk of using product information that may not be as sophisticated, credible, and up to date as it should be. Managers should also bear in mind that while our results lend general support to the admonition for firms to seek ways to expand the scope of their inventories to generate Internet sales from a broad diversity of products, they also show that some conditions are more favorable than others to the implementation of this retail strategy. Our findings suggest that retailers that sell small, lowpriced items that do not require customers to spend heavily on S&H or other product fees will be in a better position to benefit from a strategy that offers customers the opportunity to buy niche products without incurring the risk of significant monetary loss in the case of a return. On the other hand, retailers of expensive, bulky products should be more discriminating in adding product variety. Moreover, these firms should develop capabilities to capture and process for resale product returns quickly and efficiently. An awareness of these issues may help shoe retailers such as Zappos.com succeed in broadening the long tail of their sales distribution. Zappos.com has become an industry leader in the management of product returns: most of the products sent back by customers are processed and credited back to customer accounts within hours of being received by the retailer. Zappos.com sees these return occurrences as opportunities to provide customers with valuable service experiences. Furthermore, most of the products Zappos.com carries in inventory are priced very competitively. Over 80% of the 68,000 shoe-items that Zappos.com sells are low priced (below $120) and are heavily discounted relative to prices found elsewhere. Moreover, virtually all of the items that Zappos.com sells are small sized, with volumes and weights that do not require high S&H expenditures. This has made it possible for Zappos.com to become a price leader in SKU prices as well as in S&H fees in the Internet retailing industry. But, as importantly, this pricing strategy could help Zappos.com promote sales across a wide variety of SKUs by maintaining a low customer exposure to perceived risks associated with the disbursement of high expenditures in the purchase of lesser-known SKUs. Managers at retailers like Overstock.com and other merchants selling more expensive products across various high-end categories (e.g., home appliances and electronics) need to be particularly thoughtful in their assortment decisions. This is because, as our results show, higher product prices will skew the distribution of Internet sales towards best selling SKUs while expanding the distribution of returns across more SKUs. These retailers need to rely on a specialized set of strategies to promote sales among their less popular items while ensuring that those sales do in fact meet their customer preferences. Some of those strategies could involve offering price-matching or product quality guarantees to shoppers. Product quality guarantees, in particular, can also be effective in the sale of items that have been recently introduced at retailing

sites. Our results show that customers are less inclined to buy these products—online purchases for products with short life spans at retail sites tend not to include obscure SKUs. By offering quality guarantees for these goods, these retailers may reverse this tendency. Moreover, our results show that products that have been introduced recently by Internet retailers can exhibit a more indiscriminate pattern of returns. Therefore, it would behoove retailers to be prepared to collect and process those returns so that the items can be available for resale or for disposal, if necessary, as quickly and as inexpensively as possible. These issues apply within a retailer’s product line and not just across product categories. For example, Best Buy offers products ranging from large-screen HDTVs running thousands of dollars to DVDs and CDs running a fraction of the cost in small packages. Our results suggest that customers will avail themselves of a wide range of DVDs and CDs but stick to the more popular items in buying a large-screen TV. As such, adding a long tail of less popular large screen TVs may not generate as much incremental sales as anticipated. It is also possible that such an array of TV sets will generate returns that will be expensive to process by the retailer, given the value of the products and the transportation costs necessary to take these items back to their inventory locations. This is likely to make it unprofitable for the retailer to offer a wide variety of HDTVs for customers via the Internet. Given the critical role of returns and this new insight into the drivers, operations managers can no longer accept product variety as a purely merchandising decision. Effective operations managers should actively influence product offering decisions based upon the full product economics—including returns. Simultaneously, operations managers should continuously build the capability to cost effectively manage returns to allow Internet retailers to compete on the basis of variety when needed. 5.3. Study limitations and future research opportunities Our results serve as a potential starting point for future studies on the role of the Internet in managing product flows directed downstream as well as upstream in the supply chain. To the best of our knowledge, our research is the first to theoretically and empirically examine these flows in Internet-based supply chains. However, additional work is needed to understand how the management of these flows could be improved so as to yield the greatest retailer benefits. For example, future research can extend the exchanges that we considered between an Internet retailer and its customers to account for the management of triadic or other multi-party settings. These extensions can help evaluate whether sales of wide inventory assortments are limited by competition among Internet retailers and whether product returns are likely to involve products that are available to different degrees across various Internet retailer competitors. We also see potential benefits from extending our research through the integration of objective data like the one in our study and customer perceptions regarding the fulfillment and the quality of the products in their purchases and the processing of their returns. Through this integration, researchers will be in a position to understand the extent to which inventory variety and service operations bundled in the sale of that inventory contribute to customer satisfaction and even to product dissatisfaction that customers may not have acted upon by returning the product. Finally, the longitudinal or cross-sectional investigation of sales and return variations by products as a result of changes in return policies by retailers can add further insights to our results by contributing to a better understanding of how return policies may alter the balance in the distribution of sales and the distribution of product returns across SKUs. The study of Internet retail strategies

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