Will the growth of multi-channel retailing diminish the pricing efficiency of the web?

Transcription

1 Will the growth of multi-channel retailing diminish the pricing efficiency of the web? (forthcoming: Journal of Retailing) Fang-Fang Tang Xiaolin Xing Nanyang Business School Department of Economics Nanyang Technological University National University of Singapore Singapore Singapore Fax: May 2, 2001 Acknowledgments: This project was undertaken when Xiaolin Xing was visiting University of Pittsburgh and Harvard University in Xiaolin Xing is grateful to the two universities for using their resources and facilities. We thank the seminar participants in Nanyang Technological University and the Chinese University of Hong Kong for comments. We also thank two anonymous referees for helpful suggestions. The editor, L. P. Bucklin, was particularly helpful with his exceptionally detailed and constructive comments that enormously helped improve the exposition. Last, but not least, we thank Shan Xing for data collection assistance.

2 ABSTRACT In this study, we compare the pricing behavior between online branches of traditional retailers and pure Internet retailers. We seek to determine whether the pricing policies of these two types of online organizations differ, possibly because of the different competitive environments in which each operates. While both offer product over the Internet, traditional retailers must also taken into consideration the impact that such offers will have upon the business that they transact in their land-based stores. We anticipate that the constraint of a land-based store will cause such dual-channel operators to charge higher prices than Internet operators not so constrained. Focusing on standardized DVD brands, we gather a data set with of 4896 observations; we find that prices by pure Internet retailers are significantly lower than prices by online multi-channel retailers by an average of $3.27 or 14 percent. The evidence also shows that the price dispersion is sharply lower among the pure Internet retailers than that among the multi-channel retailers online. Price changes by both types are few, but adjustment magnitudes are large. This reveals that both types of online retailers do not change their prices frequently despite the claim that menu cost might be negligible for the online market. The evidence suggests that domination of multi-channel retailing format may diminish the pricing efficiency of the Web. 1

3 1. Introduction The emergence and explosive growth of e-commerce through online trading have ushered in a new era of retail business. 1 Online trading promises the potentials of low barrier of entry, easy access to information, and low transaction costs. These features of online trading imply that the growth of e-commerce has the potential of realizing often stated economic ideals for a truly competitive market: low search costs, fierce price reactions, low margins, and weak market power. Such benefits bring might provide significant welfare benefits to consumers. Empirical studies on online trading efficiency to date do not answer this question. Clay et al. (1999) compared prices of books sold by thirteen online and two physical bookstores. They found that prices in online and traditional stores were the same after controlling for book characteristics. In a more comprehensive study, Brynjolfsson and Smith (2000) examined prices of books and CDs sold through Internet and conventional channels in 1998 and They found that online prices were 9-16 percent lower than that in conventional stores. After weighting the prices by proxies for market share, Brynjolfsson and Smith found price dispersion to be lower among online retailers than conventional stores. Morton, Zettelmeyer, and Risso (2000) compared prices of cars sold in online and conventional channels. They found that, on average, online consumers paid two percent less than offline consumers. Both Bailey (1998a) and Brynjolfsson and Smith (2000) found that online menu costs were lower. Based on comparison between online retailing and traditional retailing, these various results lend support to the hypothesis of superior pricing efficiency in online markets to offline markets. 2

4 This study looks at multi-channel retailers because we observe that with the rapid growth of e-commerce, more and more conventional retailers have initiated sales online. 2 Some traditional retailers have gained ground on the Web while the pure Internet retailers have been encountering economic difficulties and failing widely 3. We seek to derive some insights as to why this may be the case. Partly following the methodology in Tang, Lu and Ho (2000) and Tang and Lu (2001), we explore the different pricing behavior between online-only retailers and online branches of multi-channel retailers. In particular, we seek to contrast the pricing of multi-channel retailers with those of online-only retailers and derive implications. As far as we know, this is the first and only study on the online DVD market from such a perspective. What would happen if the online retail channel were to become increasingly dominated by multi-channel retailers? Since both types of retailers will presumably be exposed to the same set of shopping pressures, one might anticipate that prices would tend to converge. Given that shoppers have typically looked for price savings when shopping on the Internet, retailers offering higher prices may not be viable in this market. In other words, there would be little chance for MCRs to dominate the Internet if they maintained higher prices. However, since a multi-channel retailer s pricing behavior would inevitably have influence upon demand in their conventional stores, given the early findings that traditional stores charge higher prices than online retailers, the consequence is that price differentials between the two will create internal competition and conflict between the two internal channels. Under this condition, it is not clear that multi-channel retailers will compete closely on prices with online-only retailers even though this may leave them 3

5 at a disadvantage. It is consequently not groundless to be concerned about creating a joint retail facility between Internet and land-based establishments for the interchange of customers and a cooperation, rather than competition across the two. 4 It may well be critical to keep prices similar in order to facilitate this synthesis and promote service to consumers. Scrutinizing this hypothesis by empirical evidence can substantially contribute to our understanding of the online pricing efficiency. If there were need for the multi-channel retailers to coordinate prices across their multiple format channels, including the Web, would this dampen the pricing efficiency of the Web? To answer this question, we investigate prices of DVDs sold on the Web by both online-only retailers (henceforth referenced as DotComs) and the online branches of multi-channel retailers (MCR). We test: (a) whether DotComs offer prices lower than MCRs; (b) whether the dispersion of prices exhibited by DotComs is smaller than that by MCRs; and (c) whether the two types of online retailers adjust their prices frequently and whether at similar frequencies or magnitudes. Our hypotheses are discussed in more details in Section 2. Section 3 describes our data collection methodology. Section 4 presents the results of our empirical analysis. Section 5 concludes. 2. Hypotheses According to Smith, Bailey and Brynjolfsson (2000), in retail markets where sellers set prices, efficiency occurs when prices are set equal to the retailer s marginal cost. Marginal cost pricing is the efficient outcome since pricing above marginal cost excludes welfare enhancing trades from consumers who value the product at a level between the price and the marginal cost. Although marginal cost pricing may never prevail on the 4

6 Web, the lower online search costs will lead to lower prices and lower price dispersion for homogeneous goods (see, e.g., Bakos 1997, 1998). As mentioned earlier, empirical studies have shown that online retailers are more efficient than land-based retailers and will charge lower prices (see, e.g., Brynjolfsson and Smith, 2000; and Morton, Zettelmeyer, and Risso, 2000). Since multi-channel retailers may wish to coordinate prices across their different channels to prevent destructive competition among them, they may charge higher prices on the Web than their online-only competitors, although it is not necessary for a multi-channel retailer to charge the same prices online and offline. Thus we make our first hypothesis. H 1 : DotComs charge prices lower than do MCRs. Price dispersion is another efficiency argument. Although the law of one price may never be precisely valid in any retail markets, a relatively small spread between the highest and the lowest prices would provide evidence of pricing efficiency. In a Bertrand model with homogeneous goods, price competition drives all retailing prices down to the marginal cost. However, asymmetric information and search costs may prevent market systems from achieving pricing efficiency. Stigler (1961) analyzed price dispersion across firms for a homogenous good. Because search is costly, an imperfectly informed consumer would only select a limited number of sellers for comparing prices. Higher search cost in a market with imperfectly informed customers would result in a greater dispersion of prices, and greater price dispersion would result in more searches. Following Stigler s seminal paper, both theoretical and empirical studies in the literature have attempted to explain the existence of price dispersion. For example, Nelson (1970) extended Stigler s model by assuming 5

7 that consumer behavior was affected by both price and quality. Pratt, Wise and Zeckhauser (1979) theoretically showed several cases where even small search costs also leaded to substantial price dispersion. Recently, Sorensen (2000) found empirical evidence that price dispersion could be substantial even in a small local retail market for prescription drugs. Several recent studies have shown considerable price dispersion in online markets as well. Clemons, Hann, and Hitt (1998) investigated online markets for airline tickets and found differences in prices across online travel agents as large as 20 percent, even after controlling for observable product heterogeneity. Brynjolfsson and Smith (2000) found that such price dispersion was not lower in Internet markets for books and CDs as compared to conventional markets. However, after weighting these prices by proxies for market share, they reversed their conclusion to the statement that the price dispersion was lower in the Web than in conventional stores. They attributed this phenomenon to the dominance of certain heavily branded retailers, in addition to several other factors such as asymmetric information, search costs, and retailer heterogeneity. Morton, Zettelmeyer, and Risso (2000) investigated the prices of cars sold in California and found that price dispersion declined with online sales. As empirical evidence has shown that price dispersion may be greater among traditional retailers than among online-only retailers, we can expect that when traditional retailers move to online markets, they may charge prices quite differently among them, as what they do in their brick-and-mortar stores. This line of reasoning suggests the following hypothesis. H 2 : Price dispersion is greater across MCRs than across DotComs. 6

8 The third argument for pricing efficiency is frequency of price change, which permits retail systems to adjust more rapidly to variations in supply costs and demand, and therefore enhances market efficiency. Bailey (1998a,b) indicated that online menu costs were lower than that in traditional stores. Brynjolfsson and Smith (2000) concluded Internet retailers price adjustments over time are up to 100 times smaller than conventional retailers price adjustments presumably reflecting lower menu costs in Internet channels. These results lend support to the hypothesis that online retailers change their prices more frequently and that the online market is more efficient. However, even if online menu costs are low, MCRs may not change their prices frequently if they coordinate their online and offline retailing operations. Hence, their online pricing behavior becomes a part of an integrated strategy of doing business in both the online and offline markets. Therefore, we hypothesize H 3 : DotComs change their prices more frequently than do MCRs. 3. Data collection methodology We investigate the online DVD market for testing our hypotheses. This market is chosen for several reasons. First, as far as we know, there does not exist such study on the online DVD market yet. Second, this market is one of the most successful ones that have migrated online and enjoy considerable growth and sales. Third, the fact that branded DVDs are homogeneous makes data collection tractable and price comparison meaningful. The main criterion that a retailer must meet is that it sells a general selection of titles and selling prices are posted on its Web site. Two types of online retailers are 7

9 defined as those that conduct commerce only through the Internet and those that sell also through traditional channels. Following the 100 Hot Shopping Sites by Web21.com, 5 the PowerRanking for Movies by Forrester Research and DVD Talk Online store listing ( six top DotComs selling a general selection of titles were selected (see Table 3-1). Four of six top MCRs were selected according to the authoritative ranking in Darnay and Piwowarski (1999) on the conventional stores in the category of record and prerecorded tapes. Tower and Djangos were selected following the rating by bizrate.com. Together, the market share of these retailers is substantial, insuring that their pricing behavior certainly represents the online DVD market. To minimize selection bias, we did not include the more specialized retailers in specific entertainment niche. Table 3-1 here Next, a selection of titles for comparison was made. A total of 51 titles were examined. 6 Half of them (26 titles) were selected as an even mix of the top bestsellers among the retailers when the study was initiated while the rest were chosen randomly. The reason for such a combination is that if all the titles were selected from a specific bestseller list such as the Amazon's DVD Topsellers, which contains only popular titles by a specific retailer, the results may be biased as these titles are likely to be loss leaders selected by the respective retailer. However, if all titles were selected randomly, one major trend of pricing behavior might be overlooked because the bestsellers occupy a substantial market share in the DVD market and competition in the bestsellers segment is crucial for any market structural analysis. We refer to the first category of titles as popular and the second as random. Further, during data collection process, we took 8

10 care to make sure that the version and other features were exactly the same for a given title. We determined the frequency of data collection as follows. The interval between data collection should not be so long as to miss the point of price change. However, if the interval chosen were too short, data collection costs would become extensive. Thus, we collected data once every five days. We commenced data collection on July 5, 2000 and stopped on August 9, After removing a few titles that had become unavailable in some stores during the period of the data collection (cf. endnote 6), we obtained 4896 price observations in total. The authors and our research assistant carried out the observations. Jointly we accessed the Web sites of the selected retailers and recorded the prices of the selected titles both for retailers. In addition, to ensure the independence of the samples collected, the general price was selected instead of any membership price. Table 3-1 reviews the standard shipping costs by each retailer for normal delivery within the United States. Since the shipping cost structure varies by retailer, we calculated the per item shipping cost based on their shipping cost tariff table for various baskets of typical purchases. Further, Table 3-2 summarizes the mean and median per item shipping costs by the two types of retailer, with the DotComs being lower in both measures although T-test shows that the difference in per item shipping costs by either type of retailer is not significant in any statistical sense. That is, any consumer who shops randomly among these retailers with a random basket of purchase would discover that the difference in per item shipping costs she would pay is insignificant, if not lower, through the DotComs. Since our main hypothesis is that the average price is lower by DotComs 9

11 than by MCRs, adding shipping cost into the following analysis would only enhance such finding. Therefore, we may focus our analysis of the posted prices omitting further consideration of shipping costs. Table 3-2 here 4. Empirical results 4.1. Relative Price Levels We ran several parametric and non-parametric statistical tests to analyze the relative levels of prices between these two types of online retailers. Table 4-1 summarizes the mean prices of our data sample at the most aggregate level. Table 4-1 here These show that posted prices are substantially lower for DotComs than for MCRs by an average of $3.27 or 14.1 percent. We also calculated the percentage of the posted prices by each retailer for each title at each date relative to the list price of each title. The percentage price is more comparable across titles because it avoids any weighting caused by some DVDs being higher priced than others. Here we see that DotComs sampled in our survey offered an average 28 percent discount compared to 16 percent by the MCRs. This again indicates a lower average price level. In the rest of this paper, we will use dollar-price reference the absolute posted prices, and percentage-price for the percentage of the absolute posted prices relative to the list prices for each title. To control for the serial correlation problem, we ran statistical tests for each data set day by day. For both the dollar and percentage prices, t-tests clearly rejected the null hypothesis that the mean DotCom price is equal to the mean MCR price in favor of the 10

12 alternative hypothesis that it is lower by DotCom than by MCR in every case at the significance level below This result also holds when we further divide the data sets by the categorization of popular titles versus random titles. 7 We also tested whether price discounts differed across the two types of retailers for the popular titles and the random tittles. Table 4-2 shows that both types of retailers offer higher discounts for the popular titles than the random titles. However, the discount difference between the two types of retailers is higher for the random titles (13 percent) than the popular titles (10 percent). Statistic tests (T-test and Wilcoxon test) exhibit clear-cut significance below This likely reflects a policy for MCR retailers to approach DotCom prices more closely for items that are more frequently purchased. Table 4-2 here Note that the power-efficiency of T-test decreases when sample sizes decrease, and this problem becomes more severe with smaller sub-samples because of its distribution assumption. Thus, in order to control for the possible bias associated with distribution assumptions, we ran the median test (see Sheskin, 1996: ). This is a non-parametric test that does not require any assumption on sampling distribution properties to ensure that this finding is robust. We ran the median test for each data set, day by day. For both the dollar and percentage prices, all p-values are highly significant (below 0.01), indicating that the median DotCom price is lower than the median MCR price. This result similarly holds when we divide the data sets by the categorization of popular titles versus random titles. In addition to the tests on mean and median prices, we ran a third test on the price difference between these two types of retailers. In this we compared the lowest prices 11

13 found among all the DotComs in our sample for each title with the lowest prices found among all the MCRs sampled. We found that the minimum dollar-price among the DotComs is $0.73 lower on average than the minimum dollar-price charged by MCRs. Further, Table 4-3 shows that during this period the lowest price is found among the DotComs 76.7 percent of the time (with the difference being over half a dollar during 61.7 percent of the time and over one dollar during 27.5 percent of the time). Table 4-3 here Table 4-3 summarizes both the dollar-price and percentage-price cases that coincide in this situation. For each day, if we form the null hypothesis that minimum prices are the same for both DotComs and MCRs, that is, half the time the lowest prices should be found by each type of the retailers, this statistic should follow a binomial distribution. Then, we can run the binomial test against the alternative hypothesis that it is more likely to find minimum prices by the DotComs in each day. All results clearly reject the null hypothesis (p<0.003), in favor of the alternative hypothesis. Besides, it is also interesting to notice that the cases of DotCom minimum price being greater than MCR minimum prices decrease in numbers during the period of this study, from 15 cases (July 5, 2000), 12, 11, 10, 8, 8, 4 to 3 (August 9, 2000), respectively. For the individual retailers, we calculated their mean prices across the period of our study and summarize this information in Table 4-4 (numbers in brackets are rankings). Wilcoxon test clearly rejects the null hypothesis (at p-value of 0.02), in favor of the alternative hypothesis that the individual DotCom s mean prices are lower than the MCR ones during this period. Most MCRs seem to price higher than the DotComs, 12

14 except Musicland which is the only MCR with a mean price below $20, and Tower which has a mean price lower than the highest pricing DotCom, the BigStar. Table 4-4 here Overall, it seems clear from these possible tests of differences (T-test on means, median test, binomial test on minimum prices and Wilcoxon test on individual retailer s mean prices) that the DotComs indeed price lower than the MCRs generally. This supports hypothesis H 1. This result is consistent across each data sub-sample by each date, and by either the popular title category or the random title category for each date Price Dispersion Following Sorensen (2000) and Brynjolfsson and Smith (2000), we use both absolute and relative measures to analyze dispersion in posted prices by the DotComs and MCRs. Absolute price dispersion refers to the range of either the dollar-price or the percentage-price across our sampled DotComs and MCRs, that is, the highest price minus the lowest price for a particular title at each date for the DotComs or the MCRs. While the absolute dispersion statistics for our data show a substantial range of prices for both DotComs and the MCRs for the same DVD title and time period, the MCR case is remarkably larger. The range of dollar-prices across the DotComs averages $3.53 (see Table 4-5). This corresponds to an average percentage-price range of 13 percent, and it is $8.30 or 30 percent across the MCRs; more than double in magnitude. This pattern is also exhibited when we examine the popular and random categories separately. Both the dollar and percentage price ranges are higher for the MCRs with the popular titles while smaller with the random ones, up and down around 2 percent. For the DotComs, the difference between two categories is indistinguishable. 13

15 Table 4-5 here To control for serial correlation, we ran the T-test day by day for each data set for both the popular titles and the random titles. All results remained clearly in favor of the alternative hypothesis (p<0.01), that the price range was significantly smaller for DotComs than for MCRs. Similar results were exhibited for the standard deviation (calculated according to the standard formula). We also applied the median test similarly as in Section 4.1 to control for possible distribution assumption bias. All results remained significant (p<0.01). This indicates that the DotComs exhibit significantly more convergence in pricing than the MCRs. For the relative dispersion in prices across MCRs and DotComs, we compare measures of the price range and the standard deviation by counting the number of titles where a particular measure is larger by the DotComs than by the MCRs, for each date. Here, we cannot find any titles for which both the price range and the standard deviation are larger for the MCRs than for the DotComs at every date. It is unnecessary to apply the binomial test for such a clear-cut result. From these measures and tests, the price dispersion is meaningfully lower for the DotComs than for the MCRs, supporting hypothesis H Incidence of Price Change We first measured price changes by subtracting the previous date data from the current date data (except the first data set), for each title by each retailer. In total, we found 225 price changes out of 4284 observations. In other words, price changes occurred only 5 percent of the time during the measured period. 14

16 As summarized in Table 4-6, price changes by DotComs were much fewer than the price changes by MCRs (65 cases for DotComs vs. 160 cases for MCRs). In addition, DotComs typically reduced prices when making a change, some 60 of their 65 changes, or 92 percent. Contrarily, the MCRs reduced prices in only 82 of their 160 changes 52 percent. In other words, although both types of Internet retailers made relatively few changes, the MCRs were generally more active than the DotComs. This contradicts the hypothesis we had formed above, that DotComs would adjust prices more frequently than MCRs. Table 4-6 here Ancillary findings show that the maximum price increase by DotComs was $7.49 as compared to $10.49 for MCRs. The maximum price decrease cases by the DotComs was $ as compared to the MCRs $ Thus, not only did the MCRs change prices more frequently, but also they made larger changes on average. Larger changes were more characteristic of both retail types, however, as a price change of $1.00 or more represented almost 94 percent out of all the price changes (211 cases) while more than 51 percent of the price changes were over $2.00 (115 cases). In other words, although both the DotComs and MCRs changed their selling prices rather infrequently, it was typically changed rather sharply, if they did. 8 We have also measured the price changes in another way, by subtracting the July 5 data from the August 9 data for each title by each retailer, that is, the price change across the whole period during which all data were collected. A similar pattern to the above can be observed. In total, we find 168 price changes out of 612 observations, but no price change remains the norm in 73 percent of time across the five weeks time span 15

17 (more detailed categorization is summarized in Table 4-7). However, the difference between the retailer types seems rather clear now, in the sense that the mean price change is $0.29 for the DotComs and $0.21 for the MCRs. Both t-test and median test reject the null hypothesis in favor of the alternative hypothesis that the DotCom price increase is larger in magnitude compared to the MCRs (p<0.01). Table 4-7 here More specifically, there are only slightly fewer cases of price increases by DotComs (57, maximum increase of $3.3) than by MCRs (61, maximum of $5.25), but the price decrease cases are much less by DotComs (only 1 at $-0.1) than by MCRs (49, maximum of $ -6.96). Again, all price changes are at least $0.10 in magnitude and further, the price changes of at least $0.49 are over 98.8 percent out of all the price changes (166 cases out of 168). Also, the price changes of at least $1.00 are almost 93 percent out of all the price changes (156 cases), and more than 47.6 percent of the price changes are over $2.00 (80 cases). Note again that both the DotComs and MCRs change their selling prices rather sharply, but the price changes by DotComs (58 cases) are much fewer than the price changes by MCRs (110 cases) across the same time span of five weeks. Across these tests, the empirical evidence does not support hypothesis H Conclusion This study applies the methodology of market price evaluation to online retailer formats that have not been previously compared in the literature. Instead of comparing online prices with those offered in conventional retailers, we investigate how online branches of 16

18 multi-channel retailers compete with their online-only counterparts. Our purpose is to determine whether the online pricing efficiency will decline as a consequence of the presence of multi-channel retailers. Our main findings support such a conclusion. We found that the online branches of the multi-channel DVD retailers sold at higher prices - over 14 percent - than their pure Internet rivals. The difference was strongly significant. In addition, we found differences between the two types of retailers for their popular titles as compared to randomly selected titles. This indicates that online competition is better among best selling titles than slower-moving items. The greater consumer demand for the fastmoving items most likely evokes more comparison-shopping in the aggregate and strong market pressure, much as it does among land-based retailers generally. In conformance with this price difference, we also find price dispersion to be sharply lower less than a half - for the pure Internet retailers than the online branches of the multi-channel retailers. This also is a highly significant difference. Since the extent of price dispersion reflects the power of the market to force sellers to a single price, we see again that such power is weaker with respect to MCRs than the pure Internet operators. Contrary to our first two findings, the evidence is at odds with respect to the role of MCRs on the third. We had hypothesized here that pure online retailers, being more sensitive to the market, would make price changes more frequently than MCRs. This view was not only unsupported, but also contradicted by the presence of a more active price adjustment process by the MCRs. To be sure, neither retail type adjusted prices frequently. The DotCom retailers changed prices only three percent of the time, while 17

19 the MCRs did so only seven percent of the time. Hence, from a price change perspective, the market appears a bit sleepy. While the MCRs were a bit more aggressive, the infrequency of change does not indicate that their purpose was to challenge DotCom prices. Note further that no retailer in the online DVD market adopted the one-cent-pricechange strategy documented in Brynjolfsson and Smith (2000). Online menu management costs may be lower than those in conventional stores, as Brynjolfsson and Smith (2000) conclude. However, a retailer may not change prices frequently for other reasons. For example, online prices may be easier to change, but may be prone to error. A typo of a single digit or a decimal point could cost an online retailer badly. 9 So, even if the online menu cost is small, online retailers seem reluctant to change their prices frequently because the cost of changing prices is not negligible in practice. 10 What are the broader implications of these somewhat mixed findings? Perhaps the most important is that the Internet market is not homogeneous. If this were the case, the prices for both retail types would have been indistinguishable. Rather, this is not the case. Even the difference in frequency of price adjustments suggests this conclusion, although not quite in the manner we had anticipated. The DotComs, who chiefly adjusted prices upward, may have been seeking to add some margin without loss of much volume. The MCRs, who decreased prices about as frequently as they increased them, may have paralleling price promotional practices from their terrestrial stores. This suggests that there be two groups of consumers who are buying. The first are essentially price shoppers who compare prices and purchase from the DotComs. The extent of consumer search by this group forces DotCom prices downward toward the true 18

20 costs of Web based operation. Whether these price pressures provide the DotComs with sufficient margin to survive remains a fascinating issue. Contrarily, consumers buying from the MCRs may be less aggressive and less aware of pure Web based retailers because of the latter s relative lack of visibility. These consumers may also have less trust in pure Internet retailers because of the absence of a local presence. Regardless of the motives for the two segments, this difference in consumer behavior provides the multi-channel retailer with the opportunity to coordinate its pricing strategies across its various operations and avoid internal price competition. The MCR no doubt loses volume as a consequence of this policy, but the higher margins provided and the lesser conflict across its several channels probably make this worthwhile. In this sense, the Web operations of MCRs become ancillary and incremental to their traditional business format. The conclusion we draw is that if the pure online retail format largely fails, and there has been ample evidence suggesting this possibility, our data suggest that consumers will not realize the benefits from the economies inherent in the operation of the Internet. MCRs would come to dominate Web based sales and their policies and interests would cause pricing policies to reflect the costs and promotional programs set forth in their traditional stores. It will be important to assess whether this pattern of market separation is prevalent in other Web markets to substantiate this view. 19

21 REFERENCES Bailey, J. P. (1998a), Intermediation and electronic markets: aggregation and pricing in Internet commerce, Ph.D. Thesis, Technology, Management and Policy, Massachusetts Institute of Technology. (1998b), Electronic commerce: prices and consumer issues for three products: books, compact discs, and software, Organisation for Economic Co-Operation and Development, OCDE/GD(98). 4. Bakos, Y. (1997), Reducing buyer search costs: implications for electronic marketplaces, Management Science, 43 (12) (1998), The emerging role of electronic marketplaces on the Internet, Communications of the ACM, 41(8) Brynjolfsson, E., and Smith, M.D. (2000), Frictionless commerce? A comparison of Internet and conventional retailers, Management Science, 46 (4) Chen, C. (2000), All I want for Christmas is a Pulse, Fortune, 142(13) Clay, K., Krishnan, R., Wolff, E., and Fernandes, D. (1999), Retail strategies on the Web: price and non-price competition in the online book industry, Working Paper, Carnegie-Mellon University. Clemons, E. K., Hann, I. H., and Hitt, L. M. (1998) The nature of competition in electronic markets: an empirical investigation of online travel agent offerings, Working Paper, The Wharton School of the University of Pennsylvania. Darnay, A.J., and Piwowarski, J. (1999), Wholesale and retail trade USA: industry analyses, statistics, and leading companies, (eds) Detroit: Gale. Second edition. 20

22 Messmer, E. (2001), Online retailers plan new projects for coming year, Network World, 18(4) Morton, F.S., Zettelmeyer, F., and Risso, J.S. (2000), Internet car retailing, Mimeo, Yale University, New Haven, CT. Nelson, P. (1970), Information and Consumer Behavior, Journal of Political Economy, 78(2): Pratt, J.W., Wise, D.A., and Zeckhauser, R. (1979), Price differences in almost competitive markets, The Quarterly Journal of Economics, 93(2), Scheraga, D. (2001), Good, but not great, Chain Store Age, 77(2) Sheskin, D. J. (1996), Handbook of parametric and nonparametric statistical procedures, New York: CRC Press. Smith, M. D., Bailey, J. P., and Brynjolfsson, E. (2000), Understanding digital markets: Review and Assessment, in Erik Brynjolfsson and Brian Kahin, eds. Understanding the Digital Economy, Boston: MIT Press. Sorensen, A.T. (2000), Equilibrium price dispersion in retail market for prescription drugs, Journal of Political Economy, 108(4): Stigler, G. (1961), the economics of information, Journal of Political Economy, 69(3): Tang, F. F., Lu, D., and Ho, H.P. (2000), Online trading paradox: evidence and interpretation. Working Paper, Nanyang Technological University, Singapore. Tang, F. F., and Lu, D. (2001), Pricing patterns in the online CD market: an empirical study, Electronic Markets, forthcoming, 11(3). 21

23 Table 3-1. Retailers and Their Per Item Shipping Costs Shipping Rate Number of Items Per Order Per Per item MCRs shipment Borders Musicland Trans World National Record Tower Djangos DotComs Amazon Bigstar Buy.com DVDempire DVDplanet Express All rates are based on standard shipping within the USA. 2. Musicland: $1.50 per item up to 7 items, no more per item charges for 8 items or more. 3. National Record: $2.15 per shipment for 1 item, $3.15 per shipment for 2-3 items, $3.75 for 4-5 items, $4.50 for 6-8 items, $5.50 for 9-11 items, etc. 4. Tower: per shipment - $2.95 for 1 item, $3.95 for 2-3 items and $4.95 for 4 or more items. 5. DVDplanet: $2.50 per shipment without item charges. Table 3-2. Analysis of Per Item Shipping Costs Per item shipping Mean Median cost DotCom MCR T-test Table 4-1. Retailer Type and Mean Prices Type DotCom MCR Dollar-Price Mean $19.92 $23.19 Percentage-Price Mean 72.0 % 83.9 % 22

24 Table 4-2. Retailer Type and Mean Percentage Discounts Type DotCom MCR Popular Titles 71.7 % 82.1 % Random Titles 72.3 % 85.8 % Table 4-3. Retailer Type and Minimum Prices Min DotCom Price < Min DotCom Price = Min DotCom Price > Min MCR Price Min MCR Price Min MCR Price 76.7 % 5.9 % 17.4 % Table 4-4. Mean Prices of Individual Retailers Amazon Bigstar Buy.com DVDempire DVDplanet Express (6) (8) (1) (4) (2) (5) Borders Musicland Transworld NatRecord Tower Djangos (11) (3) (9) (12) (7) (10) Table 4-5. Absolute Price Dispersion Mean (Dollar) DotCom MCR Mean (Percentage) DotCom MCR Price Range Price Range 12.7 % 30.0 % Price STD Price STD 4.7 % 12.0 % Mean-popular DotCom MCR Mean-popular DotCom MCR Price Range Price Range 12.7 % 32.2 % Price STD Price STD 4.6 % 12.9 % Mean-random DotCom MCR Mean-random DotCom MCR Price Range Price Range 12.6 % 27.8 % Price STD Price STD 4.7 % 11.0 % 23

25 Table 4-6. Retailer Type and Five-Day Price Changes Price increase Total DotComs MCRs Price decrease Total DotComs MCRs Table 4-7. Retailer Type and Five-Week Price Changes Price increase Total DotComs MCRs Price decrease Total DotComs MCRs

26 ENDNOTES 1 Despite of the recent financial market adjustment, the long-term prospect of online retailing is still bright. In the 4 th quarter of 2000, online retailing in US has reached $8,686 million, a 67.1 percent growth compared to the 4 th quarter of 1999 and 35.9 percent to the 3 rd quarter of Online sales accounted for 1 percent of US retail totality while it was only 0.6 percent in the 4 th quarter of (Source: unadjusted data by the US Chamber of Commerce, February 16, 2001.) 2 Multi-channel retailers are those with some combination of brick-and-mortar stores, catalogs, websites, and other retailing methods. For this study, we consider those multichannel retailers that run both land-based and online stores. 3 On the basis of average daily unique visitors, Media Metrix listed 10 fastest growing online retailing sites in 2000 holiday season; among them six are from traditional stores. Walmart.com is No. 1 on the list, with a 640 percent growth over one year (Scheraga 2001). 4 Clearly, online and land-based retailing has become more intertwined than ever before. Many customers are using both online and offline channels to search and make their purchases. Chuck Cebuhar, vice president and chief merchant at Sears Online, estimated that about 12 percent of business in Sears land-based stores is due to the result of research customers did online. Some multi-channel retailers, such as Circuit City, now allow shoppers to purchase online and pick up the items at a store. According to George Barr, Circuit City s director of Internet merchandising, about 50 percent of their online shoppers have chosen to pick up their products in their stores (Messmer 2001) 25

27 5 The most rigorous and widely recognized web ranking service - Brynjolfsson and Smith (2000). 6 Our original goal was 50 titles in total. In order to ensure that all retailers in the test carried each title for each data collection point, we selected 58 titles at the beginning (29 bestsellers and 29 random titles). Our complete data set comprises 51 titles. A listing of all titles may be requested from the authors. 7 All data and details of reported statistic test results are available from the authors upon request. 8 For example, Borders increased its price of Fight Club by $10.49 on July 30, but on the next observation day (August 4), it decreased its price of this title by $ A well-known example is buy.com s mispricing. In February 1999, buy.com mispriced a monitor that sold for $565 on the website at $165. Rob Cole made his online purchase and was informed three weeks later that his order could not be honored. When his problem could not be resolved after numerous attempts, Cole created a complaint website called boycottbuy.com. The website became so popular that he received more than s within a week, complaining pricing errors and shipping problems by buy.com. (Cole removed the website after buy.com changed its policy. Chen 2000) 10 Note that the listing prices do not change during the data collection period. So it does not seem to be the case that the large adjustments in prices come from manufacturer or distributor price changes. 26