Price dispersion in the online book market


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Price dispersion in the online book market

  1. 1. Alternative Explanations for Price Dispersion in the Online Book Market:Loss Leader Marketing, Branding and Switching CostsByBradley Aaron MorganAdvisor: Maria ArbatskayaA thesis submitted to the Faculty of Emory Collegeof Emory University in partial fulfillmentof the requirements of the degree ofBachelor of Arts with HonorsDepartment of Economics2000
  2. 2. Table of ContentsForward 11. Introduction 22. Testable Hypotheses 43. Literature Review 93.1 Frictionless Commerce 93.2 Price Dispersion 10Asymmetrically Informed Consumers and Search Costs 11Product and Retailer Heterogeneity 13Shipping & Handling Costs 144. Data Collection and Methodology 154.1 Why Books? 154.2 The Data Collection Process 174.3 Data Organization and Variable Creation 194.4 Biases in Data 205. Branding and Loss Leader Pricing Theory 225.1 The Internet and Branding 22Coupons 235.2 Loss Leader Pricing Theory 246. Switching Costs 266.1 Switching Costs and the Borenstein Model 266.2 Switching Costs and Price Discrimination 286.3 Switching Costs and Intermediation 297. Econometric Framework and Modeling 327.1 Descriptive Statistics 327.2 Table of Variables 337.3 Formal Regression Model For All Books (Regression 1) 347.4 Formal Regression Model For All Paperbacks (Regression 2) 368. Econometric Analysis and Conclusions 388.1 The Relationship between Shipping & Handling and thePosted Price8.2 Market Power, Branding, and Pricing 408.3 The Posted Price, Availability, and Shipping Time 428.4 Amazon, Barnes & Noble, and Bestseller Loss Leader 43Marketing StrategyWorks Cited and References 69
  3. 3. Index of Tables and AppendicesTablesDescriptive Statistics 32Table of Variables 33Output of Regression 1 35Output of Regression 2 37Statistical Summaries 45Table 1: All Books 45Table 2: All Bestsellers 46Table 3: All Paperbacks 47Table 4: All Hardcovers 48Table 5: Paperback Bestsellers 49Table 6: Hardcover Bestsellers 50Table 7: Total Bestsellers (Fiction) 51Table 8: Total Bestsellers (Non-Fiction) 52Table 9: Fiction Bestseller Paperbacks 53Table 10: Non-Fiction Bestseller Paperbacks 54Table 11: Fiction Bestseller Hardcover 55Table 12: Non-Fiction Bestseller Hardcover 56Output of Regression 3 66Output of Regression 4 68AppendicesAppendix A: Statistical Summaries of Posted Price in 51Varying CategoriesAppendix B: Visual Examples of Loss Leader Pricing Strategy 57Amazon 57Barnes & Noble 60Appendix C: Personalized Product 63Appendix D: Visual Examples of Consumer Choice and Market 64Segmentation at AmazonAppendix E: Intercept-Free Regression Modeling 65
  4. 4. Product pricing [for books] should be competitive on the Web, in partbecause of the ease with which consumers will be able to compare prices.Shoppers can compare prices within seconds by switching from Web siteto Web site. Provided that shipping costs are equal, in many instancesthere should be little incentive for customers to order from higher-pricedproviders.-Morgan Stanley Dean Witter
  5. 5. 1. IntroductionIn their comparison of conventional and electronic book markets, Brynjolfssonand Smith vehemently contest the notion and possibility of the Internet as a“frictionless” marketplace (Brynjolfsson and Smith 1999). The authors concludethat while total prices for the majority of books are lower on the Internetcompared to conventional retailers, Internet prices exhibit a dramatically higherdegree of price variation. They attribute high price variation primarily toasymmetric consumer awareness, heterogeneity in retailer trust, and brandmarket power. While their results are both relevant and significant, they fail toclosely examine other important variables that may produce the observedvariations in price. I extend Brynjolfsson’s and Smith’s analysis, focusing only onthe Internet sector, and emphasize what I argue are both critical and measurableexplanatory factors. I argue that the Internet bookseller’s leaders, Amazon andBarnes & Noble, both employ similar marketing strategies which render theirprices strikingly similar, but different from their competitors. Specifically, I showthat both Amazon and Barnes & Noble attempt to capture market share by lossleader pricing New York Times Bestsellers. I believe that the successfuldeployment of this strategy, coupled with other variables allowing these twoleader firms to charge price premiums, substantially increases overall marketprice variance.The two critical variables I believe Brynjolfsson and Smith omit from their modelare the shipping & handling price and the total time between order placement
  6. 6. and order delivery. I argue in Chapter 2 that incorporating these variables intomy econometric model will significantly help explain how varying segments ofInternet consumers place different premiums on endogenous variables (e.g.,posted price, shipping price, time, trust, etc.). Chapter 3 reviews and assessesBrynjolfsson’s and Smith’s article, giving critical attention to their methodologyand explanations for price dispersion. A discussion of my data collectionprocess and methodology follows in chapter 4. Chapters 5 and 6 explore issuesBrynjolfsson and Smith fail to investigate in depth. In chapter 5 I argue brandingis key on the Internet because it allows market-leading firms to place pricepremiums on their books and enables them to peruse marketing strategies thatproliferate their market shares. Chapter 6 examines both traditional and morecontemporary switching cost theories and discusses their applications inelectronic commerce. In chapter 7 I develop an econometric framework thattests my hypotheses. Chapter 8 presents an examination of my econometricresults and conclusions.My study is important given its assessment of the level of competition likely toexist in the online book market over time. I argue that the barriers imposed byAmazon, the Internet’s first mover in this market, and Barnes & Noble, thesecond largest firm, will prevent the emergence of any significant competition.This information is not only useful to consumers, but also to Amazon’s andBarnes and Noble’s competitors, investment banks, capital marketsprofessionals, and marketing firms. While I do not claim the observed level of
  7. 7. price dispersion will continue indefinitely, I do hold that it will continue muchlonger than most leading capital markets analysts predict.
  8. 8. 2. Testable HypothesesEconomic intuition coupled with my own observations from buying books online, leadme to expect the following:Hypothesis A: Amazon and Barnes & Noble draw consumers totheir site by employing a similar loss leader marketing strategy.Most books sold by Amazon and Barnes & Noble are discounted twenty to thirty percentoff the retail (i.e., conventional) price. However, both firms offer all New York TimesBestsellers at fifty percent off their retail price. Both firms, on and off the Internet,heavily advertise New York Times Bestsellers. Nevertheless, while both sites heavilyemphasize this discount via advertising, neither site overtly advertises this discount oncethe user visits the site1. I believe that both firms use this strategy to send a signal toconsumers that all their books, not just the New York Times Bestsellers, are relativelycheap. In turn, consumers visit these two sites and theoretically purchase books theyhave not originally intended to purchase because of the perceived discount. Moreover, Iargue loss leader pricing contributes to price dispersion, as firms engaging in this strategyare able to charge price premiums on non-New York Times Bestsellers partly because ofthe signal these loss leader books send to the consumer. Small sites, while they mayprice their bestsellers competitively, do not have the level of advertising necessary toeffectively render New York Times Bestsellers loss leader products. Therefore, theymust follow the leaders in pricing for these bestsellers, and still charge a discount onbooks the leaders enjoy successfully charging at a higher price. Consequently, lossleader pricing not only allows these firms to capture a higher market share, but also gives1See Appendix B
  9. 9. them a comparative advantage over competitors in that competitors are forced to markdown New York Times Bestsellers and maintain comparatively higher discounts on otherbooks. They lose because despite offering the identical discount on the New York TimesBestsellers, they do not draw in any further market share.Hypothesis B: Books will be significantly more expensive at highlybranded, more trusted, and better known web sites.Branding is a signal of quality that induces consumers to purchase a higher pricedproduct even if it is identical to another lesser-known product.2Branding also signalstrust and establishes reputation. Amazon boasts the comparative advantage of being thefirst mover in the Internet book business. They are therefore able to accumulate a loyalcustomer base and establish a means to induce repeat purchases from retained customers.Barnes & Noble, the Internet’s second largest book retailer, through its large chain ofconventional bookstores, also enjoys the great benefits of signaling trust and establishingloyalty on the Internet. I believe the smaller, and lesser-known firms, must offer lowerprices to induce customers to switch from the branded sites or entice new price elasticconsumers who are privy to switching but only for striking price breaks.Hypothesis C: Ceteris parabis, the sites offering a lower posted price willhave a higher availability and shipping time3, thus indicating competitivemarket equilibrium previously overlooked in the literature.Many firms price the majority of their books below Amazon’s and Barnes &Noble’s prices yet continue to operate without being forced to shutdown. Either acontinual stream of venture capital allows for the prolongation of their operations,2The power of branding induces consumers to buy brand name products over identical generics.3See chapter 4.2 or 7.2 (Table of Variables) for definitions of each of these variables.
  10. 10. or they are generating revenue from a source other than books themselves4. Ibelieve, therefore, that many of the firms forced to compete with market leadersby offering lower prices - and theoretically incurring continual losses -compensate for lower posted prices by:A. Not incurring the same costs realized by market leaders.B. Using a lower grade of shipping, and consequently creating an expectedlylonger shipping period.It follows from assumption (A) that leader firms incur costs not realized by smallerfirms since the latter do not have the same sophisticated and expensiveinventory systems. Consequently, I believe that the procedure of processing anorder, obtaining the book, and delivering it to the shipper, is more inefficient andconsequently more time intensive at smaller sites. I therefore expect theavailability time, the time between a customer’s order and the firm’s delivery ofthat order to the shipping agent, to be significantly higher.In accord with assumption (B), I expect more price sensitive consumers topatronize the smaller, or cheaper sites, as they are most apt to engage in lowestprice searches or use a third-party search intermediary. Since these consumersare relatively price sensitive, they are likely to be insensitive to the certainty ofdelivery time, as they are mostly concerned with price. Most users find leader4Despite their high market capitalization, both Amazon’s and Barnes & Noble’s Internet business havecontinually reported losses every quarter since each of their initial public offerings. It is puzzling that thesetwo Internet behemoths with tremendous brand name recognition, market capitalization, venture capital,economies of scale, amongst other competitive advantages, have competitors that continue to operate.
  11. 11. firms “convenient” and “easy” and are willing to pay a price premium in exchangefor “certainty.” Uncertainty in delivery time comes not only from a wide range inshipping time, but also a wider range in availability. Therefore, more pricesensitive consumers are most likely to tolerate a longer period of total time.This phenomenon should not appear strange to those believing in frictionlessInternet markets. The presence of these endogenous variables should allow formarket segmentation and consequently motivate firms that cater to differentcategories of tastes and preferences to enter the market. This type of marketsegmentation benefits both consumers and firms, not by the presence of a singleprice, but rather by variable pricing that fluctuates based on consumerpreferences.Hypothesis D: When the posted price is higher, shipping cost will belower, indicating another form of price equilibrium.Firms often market their books as having a lower cost than their competitors.Often this claim is true. However, in most cases this claim does not refer to thetotal cost of the books, but only their posted prices. It appears that many firmstouting relatively low prices compensate via comparatively high shipping &handling prices. This, in essence, renders the firm not a purveyor of books, butrather shipping & handling! Furthermore, one would expect that the firmscharging higher shipping prices are precisely those whose books have a highermean shipping time. The logic is that a longer shipping time should beaccompanied by a lower shipping cost to the firm. Higher than average shipping
  12. 12. prices and a lower grade of standard shipping may allow firms with comparativelylower posted prices to compensate for losses.I feel one internal mechanism allowing firms to market their books as such is thetime lag between the realization between the posted price and the shipping &handling price. Most sites have a subtle hyperlink to information about shipping& handling costs as well as shipping policies; however, none actively advertisesshipping prices alongside posted prices5. Moreover, consumers are not assensitive to the shipping & handling prices as much as they are to the postedprice, as the consumers may perceive these charges, similarly to taxes, as given.It is not only a lack of sensitivity to shipping prices that allows for this ostensiblynegative relationship between posted and shipping prices. At most sites, once aconsumer selects his basket of goods and proceeds to “check out,” he is requiredto register. Registration not only involves entry of personal information (e.g.,email address, name, home address, etc.) but also usually includes optionalquestions about consumption patterns that firms often use for marketingpurposes. Sometimes registration also involves inputting credit card, billing, andshipping information. The consumer realizes shipping and handling costs, andtherefore the total cost, only after this lengthy input process is finished. Even ifconsumers are upset about what they believe to be high or unreasonableshipping costs, they are not apt to switch to another firm. For most consumers,unless they are hyper price sensitive, the cost expended during “registration”
  13. 13. often outweighs the cost of attempting to find a bookseller offering a lower totalcost. Therefore, most consumers proceed to confirm their orders.5See Appendix B
  14. 14. 3. Review of Literature3.1 Frictionless CommerceThe majority of my work expands upon studies conducted by Erik Brynjolfssonand Michael D. Smith in their article Frictionless Commerce? Comparison ofInternet and Conventional Retailers. As per the title, this article dealspredominantly with testing for a frictionless market via a comparison ofconventional and Internet book and CD retailers. The authors first define thestate of “Internet efficiency” as an Internet market in which (i) location is irrelevantand (ii) consumers are fully informed of both product selection and prices.Brynjolfsson and Smith use an online bookseller list generated by Yahoo togenerate a list of Internet booksellers. They then track 10 bestsellers and 10random books over time and compare prices to those at the conventionalretailers. The authors use Yahoo’s list because they believe it is bothcomprehensive and unbiased. While I agree, they fail to consider the integrity ofthe booksellers they include in their dataset. Yahoo lists all known onlinebooksellers, not necessarily those booksellers verified legitimate. This isproblematic because many of the booksellers Yahoo lists may not give accurateprice information or may not change their posted prices when actual priceschange. Finally, it is possible that many of the widely unknown booksellersoperate merely to steal credit card information. I argue that unbiased third-partysearch intermediaries that monitor and evaluate web sites generate moreaccurate “lists” of online booksellers.
  15. 15. To test for “Internet efficiency” Brynjolfsson and Smith conduct comparative t-tests6on mean store posted prices and measure the percentage of time Internetprices are less than or equal to conventional prices. Variables such astransportation costs (conventional stores only), shipping & handling prices, andtaxes are then incorporated and the t-tests are repeated. They ultimatelyconclude that prices are lower on the Internet compared to brick and mortarcounterparts whether just prices alone are accounted for and when the t-tests arerepeated incorporating the other variables. In addition to lower prices, they find:1) Internet prices are more dispersed.2) Menu costs online are significantly lower and therefore prices changemore frequently and in smaller increments.Clearly, this leads to the conclusion that frictionless commerce does not exist. Inthe following sub-chapters, I give an overview of the conclusions reached by theauthors. My work builds upon these results.3.2 Price DispersionA portion of price dispersion arising in online book markets is explained throughexogenous heterogeneous factors that are not part of the product per se, but arerather complementary. Retailers use these service factors in an attempt to6A “t” random variable is formed by dividing a standard normal, Z ~ N(0,1), random variable by thesquare root of an independent chi-square random variable, V ~ χ2(m), that has been divided by its degreesof freedom, m.
  16. 16. differentiate their product via a bundled package. Price dispersion online forbook and CD markets may be attributed to:1) Differences in search costs2) Asymmetrically informed consumersIncorporating market share data into their model, they discover price isconcentrated around the market leader’s price at companies very close in marketshare to the market leader. However, on average, the price spread betweeneach book is extremely low. Only when compared to companies holdingnegligible market power does the price spread between books becomesignificant. They also find that despite setting prices significantly below themarket leader’s price, many firms, specifically those smaller retailers, do notreceive any significant portion of total sales per book. This leads Brynjolfssonand Smith to a key conclusion:Ironically, as we discuss below, a high market share may, in and ofitself, be an important “feature” that supports a price premiumbecause of the importance of network externalities in word-of-mouth marketing and trust-building (21).Asymmetrically Informed Consumers and Search CostsThe authors contend that producers will set prices above marginal cost whenpositive search costs exists. They note that the ostensibly lower search costs onthe Internet will force book prices toward Bertrand marginal cost pricing7.Although search costs on the Internet appear to be trivially small due to searchintermediaries and other channels that allow for easy comparison of information,7The Bertrand model holds that a price war between firms ultimately results in P1=P2=MC. The presenceof search costs violates a primary assumption of the Bertrand model. However, if the Internet doesfacilitate a movement toward lower or negligible search costs, a Bertrand price war should ensue.
  17. 17. they argue consumer ignorance ultimately increases search costs. Internetconsumers are figured as comparatively wealthier and therefore more priceinsensitive. As a result, they are less apt to engage in costly searches.The authors cite a number of different landmark search cost articles, which theyuse to explain price dispersion via heterogeneous search costs.Contrary to economic models of search costs (Salop and Stiglitz 1977) whichcontend that assuming (1) consumers are imperfectly informed, (2) information iscostly to obtain, and (3) firms set prices to leverage consumer heterogeneity ininformation and search costs, the authors do not find that the firm with the lowestprices captures most sales. The firm with the lowest price should theoreticallycapture all sales from informed consumers and some sales from uninformedconsumers. Despite Book’s8consistently lower prices, Amazon continues to holda dramatically higher market share. This violation of the Salop and Stiglitz modelis attributed to asymmetric and heterogeneous consumer information coupledwith search costs greater than zero. They note that this explains why companieswith high market shares are able to charge higher price premiums but does notexplain the reason successful bookstores (e.g., Powell’s)9are able to charge8Books is now Barnes & Noble9Powell’s, an online/conventional hybrid based out of Seattle, specializes in used, new, rare books. Thecompany distinguishes itself from Amazon and Barnes & Noble by boasting as large as a collection and theability to choose between different types of the same book (i.e., hardcover, paperback, used, new). Onereason Powell’s may continue to attract and retain customers despite comparatively higher prices is itsspecialization in rare books. See discussion on switching costs for possible reasons consumers do not buyrare books from Powell’s and then switch to cheaper sites for other, less expensive books. Finally, thecompany distinguishes itself through its West coast eclecticism, and therefore tastes and preferences maybe the cause.
  18. 18. higher prices and yet retain a strong customer base. To explain thisphenomenon, they turn to heterogeneity in products and firms.Product and Retailer HeterogeneityAnalysis is shifted to explain heterogeneous factors of service characteristics thatmight account for price dispersion. Brynjolfsson and Smith use hedonicregression10analysis to test for price variation caused by service heterogeneity.They discover that service factors do not vary across firms or are often negativelycorrelated with price. This renders their hedonic regression model fruitless inexplaining residual price dispersion.They also realize that product heterogeneity is fallible in explaining pricedifferences in that services are “informational” and therefore separable from thehomogenous product. Consumers are capable of employing a strategy in whichthey use an information source from one web site and then switch to anotheroffering a lower posted price.Unobservable variables, such as heterogeneous consumer measures of trust,may play a key role in price dispersion. Studies have argued trust is an integralcomponent of any successful Internet marketing strategy (Urban 1999). Trust,they contend, is important given the spatial and temporal demarcations between10A regression model that attempts to explain variation in the dependent variable via heterogeneity inservice characteristics and conveniences offered by firms.
  19. 19. the firm and the consumer. Trust is signaled via reliability, word of mouthadvertising, conventional channels of advertising, links from reputable sites, andpresence in non-Internet markets.Shipping & Handling CostsBrynjolfsson and Smith modify their regression model to include factors such asshipping & handling costs.11They note the number of items shipped and thespeed of delivery are determinant factors in shipping & handling charges. Thetype of shipping is also important. For standard shipping, many firms, such asAmazon charge a fixed, and relatively high standard fee for the first book and alower fee for each additional book ordered. This creates variable shipping pricesacross companies and significantly contributes to price dispersion.While the authors then proceed to merely state the necessity of branding alongwith trust in Internet markets, they do not elaborate on the reasons for itsimportance. In Chapter 5 I will pick up where Brynjolfsson and Smith leave offand attempt to explain the reasons branding is crucial in electronic commerce. Iwill discuss the importance of brand market power and its allowance for keymarketing strategies at market leading firms such as loss leader pricing. In
  20. 20. chapter 6 I will demonstrate how the imposition of switching costs allows for thesuccessful deployment of these strategies as market leading firms in the onlinebook industry are able to act monopolistically. Finally I will develop and analyzemy econometric models for evidence of loss leader pricing and test for thepossible variations of market equilibrium via testing hypotheses B, C, and D..11See discussion of Hypothesis D for further information regarding shipping & handling. Additionally, Idiscuss shipping and handling costs at length in chapter 8.2.
  21. 21. 4. Data Collection & Methodology4.1 Why Books?The decision to exclusively examine books is made for several reasons. Firstly,books are homogenous goods and therefore do not vary in productcharacteristics across retailers. This is a fundamental assumption in the modelof perfect competition - a state of zero market friction. Importantly, economiststraditionally tout homogenous goods as resilient to marketing forces because ofconsumer indifference. Although consumers often receive utility in browsingthrough a neighborhood bookstore or examining a book by flipping through itspages, most consumers do not need a brick-and-mortar experience to decidewhich book to purchase. Internet technology enables consumers to virtuallyreplicate many key components of the conventional book shopping experiencevia the online medium. For example, many sites post mainstream critical reviewsof new books alongside reviews customers contribute. Moreover, manybooksellers allow consumers to read or listen to parts of a text.This differs considerably from the shopping experience for heterogeneous, orbranded goods, requiring consumers to “feel,” “taste,” “experience,” or “sample,”a product. Many consumers are reluctant to purchase goods such as clothesand shoes from the Internet because they cannot test them for proper fit ordesired look before purchasing. This reluctance can be attributed, for example,to heterogeneity in product sizes across brands. The cost of uncertainty forsome consumers to purchase goods online does not outweigh the utility from
  22. 22. savings. Moreover, many consumers enjoy shopping for heterogeneousproducts for reasons such as stress relief and socialization (Landro 1999). Theproblem of “testing” is also evident in shopping for furniture and bedding(Fletcher 1999).Another quintessential characteristic of the Internet book market is its relativeInternet maturity. Although the Internet is a new technology, bookselling is oneof the oldest and largest business to consumer industries on the web andcontains numerous companies selling the same book in the same market.Additionally, there have been a myriad of recent legal disputes between theleaders of the online book market (Amazon and Barnes & Noble). Their fiercecompetitive battle to obtain market share is also indicative of an established andcompetitive market.Books are also very conducive to Internet retailing. In addition to theaforementioned characteristics of books, they possess inherent qualities thatrender their sale over the Internet relatively simple. Morgan Stanley notes thefour key properties of books that allow for their ease of sale:1. Books are well-known commodities.2. Books are inexpensive.3. Huge variety translates into mass appeal.4. Books are easy to ship (Morgan Stanley 1997).I will also add that since books are ubiquitously sold, they should also be readilyavailable. Theoretically, given an efficient supply chain, this will allow online
  23. 23. firms to utilize the ability to not hold as much inventory and therefore reducecosts.12It is also important to note that books are only one of many commodities sold onthe Internet that are rated by a third party13. While these rating sites are notutilized by most users, they are readily available, easy to use, and plentiful innumber. In fact, Yahoo, a popular Internet site, has a shopping sub-categorypage that allows users to compare prices. Furthermore, an entire Yahoodirectory is devoted to listing the fifteen third party web sites that rank books.The ubiquitous presence of these third party book-ranking sites should increasecompetitive behavior for books on the Internet.4.2 The Data Collection ProcessTo collect data for bestsellers, I use the New York Times Bestsellers list, asthese are the most comprehensive and relevant to my study. The New YorkTimes Bestseller list is regarded as the most inclusive and exact measure of abook’s popularity. I feel bestsellers are the best category of books to examine inmy study since they are the most likely to be sold by any given retailer becausethey are the most widely purchased. I collect information from each of the fourcategories of New York Times Bestsellers: paperback fiction, paperback non-12Traditional economic models of inventory state that inventory is beneficial to firms for reasons ofproduct smoothing, factors of production, stock-out avoidance, and work in process. When a firm holdsinventory, it forgoes the real interest it could have earned.13Of course many products on the Internet are “rated” or “reviewed.” However, along with otherhomogenous commodities such as CD’s and software, books are unique in that they systematically ratedaccording to price, shipping costs, etc.
  24. 24. fiction, hardcover fiction, and hardcover non-fiction. The New York Times liststhe top fifteen books in each category.Next, I use a “shop bot,” a web site that automatically compares prices, as the primarymechanism through which I collect my data.14Information is collected for each of theobservations posted price, availability15, shipping cost, and shipping time16. When datafor availability and shipping time are in range form,17I take the average value. The unitscharacterizing both variables are entered as days.Excluded from my dataset are observations that are either classified as “out of stock” or“on order.” The former observations cannot be converted into any definite value andinclusion of the latter will cause my data to become skewed. “On-order” times typicallyrange from six to eight weeks and have been statistically calculated as an outlier due totheir abnormally high residuals and interference in the accuracy of my regressionresults.18I also exclude from my dataset used books, as they are not the concern of this14The specific "shop bot" I use for the collection of data is bestbookbuys. I feel this is the mostcomprehensive, unbiased, and dependable bot on the web. Currently, the search capabilities ofbestbookbuys spans over 28 retailers and returns information about availability, price, shipping & handlingprice and time, as well as relevant tax information. Furthermore, results are tabled and delivered inascending order of price. See also chapter 6.3.15Refers to the time between order processing and the time it takes the firm to ship order:AVAIL=t(shipped out to delivery agent)-t(order processed) [measured in days]16Refers to the time between shipment and receipt of package:SHTIME=t(receipt of order)-t(shipped out to delivery agent) [measured in days]17A range listing the lowest possible value through the highest possible value.18Although I cannot include these observations in my regression model, they are very interesting. Amazonclassifies many books and bestsellers as out of stock. The implications of this lack of availability arestriking. See Appendix A for a full discussion.
  25. 25. study. Finally, I exclude any retailer offering a special “club price” to paying membersonly.19Data is gathered for each of the twenty-eight retailers searched by bestbookbuys20Observations from every site are not usually available for each book. Moreover, when asearch for a particular book returns less than seven observations, it is excluded from thedataset and an alternate random book is chosen. I believe any such book is “rare” in thatit is not readily available and therefore violates my selection criteria21and may skew mydataset. In addition to the sixty bestsellers, data is collected for forty-five randomlychosen titles, both paperback and hardcover.22No special effort is made to gather aproportionate amount of paperbacks and hardcover. Since I randomly generate ISBNnumbers, I assume that statistically, given my large sample size, I should finish with anumber of paperbacks relative to hardcovers equal to the true population proportion.4.3 Data Organization and Variable CreationAll of the data is next appropriately organized in a Microsoft Excel file. Variables arecreated for each of the characteristics relevant to the subsequent econometric analysis.The non-dummy explanatory variables I examine are posted price, shipping & handling19The only retailer currently offering “club prices” is For a 5-dollar annual fee,members receive discounts on select books. These observations are excluded, as access to these prices islimited to paying members. This may be argued as a form of second-degree price discrimination. SeeChapter 6.2 on switching costs and price discrimination.20Since the time of data collection thebigstore was added to bestbookbuys’ list.21See Chapter 4.1 Why Books?22I generate 10 random numbers in Microsoft Excel. If the number begins with a 1 or 0, a potentiallyrandom ISBN, I feed it into the search engine of bestbookbuys. Potentially valid numbers are continuouslyattempted until data from 45 valid ISBN’s are obtained.
  26. 26. price, shipping time, availability, and total time.23Dummy variables24are created foreach of the 105 observed books and the 14 retailers.25Dummies are also created forbestsellers and their different categories. Finally, a dummy variable is created for each“type” of book. Specifically, I create a dummy for each of the 4 types of New YorkTimes Bestsellers26: bestsellers (overall), paperbacks (overall) and hardcovers (overall).There are a total of 136 dummy variables. I find, after summarizing overall statistics,alllbooks4less, bookpool, classbook, and hamiltonbooks27do not contain a significantamount of observations and are therefore excluded from my regression model.4.4 Biases in DataAlthough I specifically use bestbookbuys to prevent biases in my data, numerousvariables cannot be determined until the book is actually ordered, processed, andthen shipped. As I mention in the footnote during my discussion of the returnedvalues associated with availability and shipping times, the range of the valuereturned are only estimated ranges. While I take the arithmetic mean to bestestimate availability and shipping time, I have no way of determining the data’strue statistical distribution. Consequently, I feel that precise knowledge of thedistribution of availability and shipping time will significantly alter my results asmy two most important explanatory variables are estimated in this manner.23See Table of Variables for descriptions of each variable.24Explanatory variables that are qualitative in nature. This study utilizes both intercept and slopedummies.25For most books, returns data on the same 10-14 top retailers. claims to be comprehensive, it never returns extensive observations. 9 of the 28retailers it searches returned no data in any of the searches.26Paperback Fiction, Paperback Non-Fiction, Hardcover Fiction, Hardcover Non-fiction.27These companies specialize, respectively, in children’s books, technical books, college textbooks, andsurplus books. Their lack of overall coverage accounts for their small number of observations (<ten).
  27. 27. Nevertheless, these precise measures are unattainable unless each individualbook is ordered from each retailer - a process that is prohibitively expensive.An additional problem arises when multiple books are purchased in a singleorder. Unlike the “premium” sites that instantly convey to the user the availabilityof each book, many smaller sites do not provide this information. Moreover,smaller sites appear to have more variation in availability ranges for their books.Consequently, the total range of availability for books on these sites mayincrease with each additional book ordered. However, this is not applicablewhen books are shipped gratis as they become available, which is the case atmany retailers.
  28. 28. 5. Branding and Loss Leader Pricing TheoryThe recent explosion in capital valuations of Internet related companies hasprompted many academics to explain the reasons for this tremendous growthand its economic consequences. Additionally, much attention is spent attemptingto explain pricing and consumer behavior on the Internet.5.1 The Internet and BrandingOne factor prohibiting many consumers from utilizing electronic markets is ageneral lack of trust. Fortunately for both firms and consumers, consumer lack oftrust is dissipating over time as consumers become more comfortable withelectronic commerce. Moreover, companies such as bizrate that systematicallyrate sites according to trustworthiness strengthen consumer trust. Nevertheless,consumer trust is still an issue and partly responsible for the millions of dollarsexpended on advertising. However, trust is only one of many reasons a firmneeds to establish a strong brand name.According to Morgan Stanley, studies show once users become familiar andcomfortable with the web, they limit themselves to the pages they are familiarwith. The consequence, according to Morgan Stanley, is a few leading brands ineach retail category (Morgan Stanley 1997). The obvious outcome of thisbehavior is a lack of motivation to search, even if lower prices and better servicescan be obtained. The importance of consumer familiarity furthers the need forfirms to aggressively brand themselves against their competitors. Aggressive
  29. 29. branding is evident on the web. Not only are a large percentage of firm revenuesreinvested in adverting out of the Internet realm, but also most Internet sites arepeppered with banners assertively promoting goods and services.Morgan Stanley also argues that Amazons strong brand name and customerloyalty will create barriers to entry within the online book industry despite therelative ease with which a company may enter the industry28. Amazon hasbecome a “buzz word” that users have come to know and trust (Morgan Stanley1997). Their ability, through branding and market power to exercise monopolisticbarriers to entry further debunks recent figurations of the Internet as frictionlessmarketplace since free entry and exit are key assumptions in the economicmodel for perfect competition.CouponsThe striking quality of Internet coupon codes is their relatively large redeemablevalues. For example, offers fifteen dollars off any purchase offifteen dollars or more and free shipping. Moreover, nascent firms circulatecoupon codes redeemable for thirty to fifty percent off any purchase.29Aninteresting characteristic of Internet coupon codes is their lack of specificity.Many third party sites, such as hotdeals, esmarts, and cleverclicksters, gathercoupon codes marketed to specific users via email marketing, newspapers, andmagazines, and make them available, at no cost, to visitors of their websites.28Legal or natural impediments protecting a firm from competition from potential new entrants.
  30. 30. Recently, many firms have been only accepting coupon codes accompanied by aspecific email address that may only be used only once. Nevertheless, despitethe drastic discounts these coupon codes offer consumers and the lossestheoretically incurred by the firms offering them, branding is paramount on theweb and firms, to avoid shutdown, must establish a brand name at any cost.When non-discriminate30coupon codes are considered, price variation increasesdrastically. However, only savvy and price sensitive consumers are aware of theease and universality of coupon codes31. Notwithstanding, the extremediscounts offered by coupon codes contribute to online price dispersion.5.2 Loss leader Pricing TheoryWhile loss leader pricing is most often observed in grocery stores, I believe it isalso evident in the online book industry. Loss leader pricing is a promotionalstrategy but markedly different from others in this category. Firstly, retailers donot price loss leader products to yield a profit. On the other hand, retailers oftenprice loss leader goods at or below marginal cost and therefore sustain a loss.Secondly, loss leader goods are heavily advertised and intended to bringconsumers into a store [or a website] (Nagle 1987). Once consumers enter thestore [or the website] they purchase additional goods other than the loss leaderproduct. The firm therefore compensates for the loss on the loss leader productvia sales of other products that are priced well above average cost:29The magnitude of these savings can be more appreciated in light of Brynjolfsson’s and Smith’s workdemonstrating prices are usually less expensive on the Internet even without coupons.30Coupon codes that are not user specific.31I should note that at rating intermediaries, such as bestbookbuys, coupon codes for books are listed.
  31. 31. P>AC → π>0Customers drawn in by the loss leader product also purchase complementaryproducts that are priced well above retail price. Companies are able to inducethe purchase of complementary products as they exercise monopoly power onceconsumers enter the store due to the presence of high search costs (Gerntnerand Hess 1991). While high search costs are not characteristic of the Internet,most consumers are not privy to searching for lower cost goods due to thepresence of high switching costs32.I believe the heavy discounts given to consumers by online book market leaderson New York Times bestsellers and the large amount of advertising spentpromoting these books is evidence of a loss leader promotional strategy. Therelative cheapness of bestsellers is heavily advertised on the Internet, especiallyby the firms leading the market. It appears that they lure customers to their website and entice them to purchase not only bestsellers but also other books andproducts33. This is evident as once consumers visit the site, they realize allbooks are not nearly as heavily discounted as bestsellers. Furthermore, NewYork Times Bestsellers are barely advertised within the web site. On the otherhand, once companies lure consumers in, they heavily advertise new or selectedtitles34. I believe this is precisely the marketing strategy employed by these twofirms in an aggressive attempt to encourage consumers to purchase other higherpriced books and products in addition to bestsellers. In effect, the figuration of32See Chapter 6. Switching Costs
  32. 32. bestsellers as a loss leader priced commodity acts a signal that all books the siteoffers (and perhaps other commodities) are cheap.33Both Amazon and Barnes & Noble see products other than books.34See Appendix B for examples.
  33. 33. 6. Switching Costs6.1 Switching Costs and the Borenstein ModelIt has been suggested that price discrimination can occur between customersmore apt to switch firms and those not likely to switch (Borenstein 1991).Although Borenstein’s study focuses on the gasoline markets, its implicationsextend far into the online book industry. According to Borenstein:If most of a station’s marginal buyers are deciding between buyingfrom that station or reducing total purchases of gasoline, then theseller is a monopolist for most of its buyers and faces a demandelasticity close to or equal to the buyers elasticity of demand for thegood. Alternatively, if most of a station’s marginal customers aredeciding between buying from one station or switching to anotherthen the seller is competing with other stations for its marginalcustomer and faces a demand elasticity that reflects the buyers’cross elasticity of demand among sellers (Borenstein 1991).I believe Borenstein’s model offers and alternative explanation for theunexplained price variation found in Brynjolfsson and Smith’s (1999) model.When consumers are unaware of market size, or unwilling to switch from oneInternet bookseller to another, which potentially may offer them books at a lowercost, the firm to which they are loyal essentially becomes a monopoly. Thesemonopolistic capabilities arise because of the consumer’s unequivocal loyalty tothe site. However, in the latter case of Borenstein’s assertion, in whichconsumers contemplate switching to another seller, the demand elasticity of bothsellers changes. Unlike Borenstein’s model, this does not necessarily reflectupon the consumer’s cross-price elasticity of demand35when considering online35Ceteris parabis, the responsiveness of the demand for a good relative to the price of a substitute orcomplement. Calculated as the percentage change in the quantity demanded of the good divided by thepercentage change in the price of the substitute or complement.
  34. 34. markets because Internet consumers are not necessarily only concerned withprice. Numerous other factors play a key role in consumers consumptiondecisions36. The Internet offers its users a personalized bundled product37unparalleled by any homogenous counterpart in the conventional sector. Studieshave shown that the Internet’s unique service features, specifically those offeredby firms like Amazon, involve and engage their customers and retain them forreasons not necessarily related to price (Morgan Stanley 1997). Customers alsoreturn to utilize customer editorials, recommendations, and other services. Thisviolation of Borenstein’s model problematizes the actual costs a consumer faceswhen contemplating switching from one online bookstore to another. Switchingcosts are therefore not only a function of time, but also of the opportunity cost offorgoing a relationship with a firm. One of the unique features of certain onlinebooksellers is their ability to guide tastes and preferences. Once a consumer isregistered with a firm (e.g., Amazon) and has explicated his tastes andpreferences via purchase history from that firm, he is subsequently givenrecommendations for other titles in accord with his preferences. To switch toanother online bookseller he faces the opportunity cost of forgoing valuable bookrecommendations for titles he may not otherwise have been aware of. This costmust be weighed against the benefit of a lower cost.38The consequence of theswitch is not only detrimental to the consumer but to the original seller as well.The opportunity cost of switching becomes higher as more books are ordered36The factors being those discussed throughout this thesis.37See Appendix C.38Assuming a duopolistic model, the lower the cost at the other bookstore, the more apt the consumer is toswitch.
  35. 35. from a firm employing such a strategy. Therefore, when leaving a firm thatguides tastes and preferences:Switching Costs=ƒ(time, price, buying pattern monitoring, δZ)Where Z is all other service variables either lost or gained in the switch.It is important to realize that “buying pattern monitoring” is not always a desirableservice. Many Internet consumers are concerned with privacy and may not trustan online firm that closely monitors buying patterns and behavior and thenaggressively markets products. Observed marketing may actually cause aconsumer to switch to another site offering higher prices. Trust is of paramountimportance in Internet markets and any monitoring, whether it benefits theconsumer, may trigger switching (Brynjolfsson and Smith 1999).6.2 Switching Costs and Price DiscriminationJoseph Bailey aptly notes that an essential requirement of price discrimination ismarket power. However, on the Internet firms may not be able to charge higherprices since consumers can easily choose another site. If a site’s prices arenoticeably higher than competitor prices, consumers are apt to switch (Bailey1998). In accord with Bailey’s price discrimination model, we should expect tosee those firms with a higher market share possessing the capacity to pricediscriminate. Therefore, based on his assumptions, firms with higher marketshares should be price makers and those with little market shares, price takers.My econometric analysis loosely supports these assertions. Firstly, while thedependent variable (post) increases when Amazon and Barnes & Noble are
  36. 36. present in my model, these two variables are not statistically significant.Nevertheless, the smaller, and unbranded firms with significant p-values aresubstantially lower in price than Amazon and Barnes & Noble and cause thedependent variable (post) to fall. I believe issues of branding accounts for thedisparity Bailey seems unable to adequately explain. Moreover, Internet pricediscrimination is theoretically possible given some firm’s abilities to exercisemonopoly power once a consumer has opted not to switch.I have already discussed the advantages of buying pattern monitoring withrelation to product suggestion and awareness to otherwise product oblivion.However, it may not always be in the consumer’s best interest to allow themonitoring of purchase patterns and site navigation behavior as it may allowfirms to price discriminate against the consumer. Bailey notes that many firmsoffer superior search services that direct consumers toward particular productswhile forcing the customer to reveal invaluable marketing information to the firm.Bailey argues the optimal consumer strategy is to gather the firm assistedinformation and then anonymously switch to another firm offering a lower price.He argues that if the consumer does not switch the firm will follow their optimalstrategy of discrimination against the consumer. He nevertheless notes thatprice discrimination may harm a firm’s reputation and lead to mistrust.6.3 Switching Cost and Intermediation
  37. 37. The myriad of online booksellers has given rise to intermediaries that search andfind queried books at the lowest cost. The presence of online intermediaries bothcomplicates and facilitates the ease of switching. An OECD study measures theimplications of financial intermediaries in the online book, CD, and softwareindustries (OECD 1998). The authors discover that while software intermediarieshave the capacity to help consumers obtain a lower price, some third-party ratingsites may be biased as some firms block pricing information. Although thisinformation is not observed for book or software intermediaries, the capacity forprice blocking exists. Price blocking probably does not exist because any firmblocking prices from a third-party query might be rendered suspicious anduntrustworthy.A second issue arising from financial intermediaries is the reliability of reportedinformation. Unless the intermediary derives prices directly from the company’sweb page, prices may be unreliable and the consumer may become vulnerableto a “bait-and-switch” tactic (OECD 1998). According to this theory, thecustomer is shown the low price for one good, and then, once the consumervisits the web site, he is shown another higher priced substitute. A bait-and-switch practice involves the purchase of substitute products when advertised, butlow-stocked brands, are unavailable. Their article states many reasons this tacticis more effective in brick and mortar retailers and how it may be renderedineffective on the Internet for reasons of difficulty in execution. I partiallydisagree with the OECD since sites offering their users book recommendations
  38. 38. may suggest products similar to the bait-and-switch product the consumer mayhave been previously unaware of and privy toward purchasing.Although a bait-and-switch strategy is similar to loss leader pricing I do notbelieve it is practiced by market leading firms. At the time of data collection,Barnes & Noble had every New York Time Bestseller “in stock.” Amazon,however, had only ninety-five percent of its bestsellers “in stock” despite heavyadvertising. It is possible to argue Amazon uses a bait and switch tactic althoughhighly unlikely since any site engaging in such behavior might be perceived asuntrustworthy. It is interesting to note, however, that all the other non-bestsellerbooks I examine are in stock at Amazon. Furthermore, given the presence offifteen book intermediary search sites, those companies reporting unreliableinformation or creating fallacious links should theoretically not survive in the longrun especially because search intermediaries are ranked by branded web sitesas well as popular Internet magazines and publications.
  39. 39. 7. Econometric Framework and Modeling7.1 Descriptive StatisticsDescriptive Statistics for all 1,406 ObservationsVariableMeanPriceStandardDeviationMinimumValueMaximumValueAVAIL (Availability) 2.029 1.567 1 12POST (Posted Price) 13.897 14.181 1.95 178.98SHTIME (Shipping Time) 5.494 2.699 1.5 12.5SHCOST (Shipping & Handling Cost) 3.637 1.255 0 4.95TC (Total Cost) 17.526 14.138 2.59 183.93TTIME (Total Time) 7.523 3.176 2.5 15
  40. 40. 7.2 Table Of VariablesTable of VariablesExplanatory Variables (Non-Dummy)avail The average time, measured in days, between the placement oforder and delivery to shipping provider.shtime The average time, measured in days, between shipment anddelivery to customer.ttime Total Time=avail+shtimepost The posted, or raw price of specified book (excludes shipping &handling, taxes, and any other applicable charges).shcost The cost of a firm’s standard/economy shipping for one Total Cost=post + shcostDummy Variables39Firm Dummies (14 Total)amzn ( bandn (Barnes & Noble)bigwrd ( bkamil (Books A Million)bkstrt (1 Book Street) border (Borders)buycom ( ecamp ( ( kngbok ( ( powell ( ( tbac ( amzn+bandnBooks Dummies (105 Total)book001-105 Each book examined in studybook001-030 New York Times Bestsellers Paperbackbook031-075 Randomly selected booksbook076-105 New York Times Bestsellers Hardcoverpaperback Paperback bookcompanies Group of all companies examinedcompanieslead Group of all companies examined lessleaders39All dummy variables assume a value of 1 if True and 0 if False
  41. 41. 7.3 Formal Regression ModelsFormal Regression Model For All Books (Regression 1)POST = α + β1SHCOST + β2TTIME + β3AMZN + β4BANDN +β5(NYTPF+NYTPNF) + β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF)+ β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) +β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD +β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP +β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + εWhere z is the vector of all other dummies, including 104 book dummies,and δ is the corresponding vector of coefficients.4040I exclude the last book dummy (Book105) to prevent collinearity. Also, I exclude varsit for the samereason.
  42. 42. Dependent Variable: POST (in dollars) N=1406 observationsExplanatory Variables Coefficient Std. Error t-Statistic Prob.Intercept 33.057 3.887 8.505 0.000SHCOST 0.473 0.180 2.630 0.009TTIME 0.264 0.074 3.581 0.000AMZN 0.168 0.432 0.389 0.698BANDN -0.024 0.420 -0.057 0.955NYTPF+NYTPNF -9.222 3.244 -2.842 0.005NYTHCF+NYTHCNF -18.924 3.735 -5.066 0.000AMZN*(NYTPF+NYTPNF)-2.641 0.640 -4.126 0.000AMZN*(NYTHCF+NYTHCNF)-3.797 0.633 -6.001 0.000BANDN*(NYTPF+NYTPNF)-2.279 0.622 -3.666 0.000BANDN*(NYTHCF+NYTHCNF)-3.110 0.621 -5.010 0.000PAPERBACK -21.543 2.663 -8.090 0.000BIGWRD -0.068 0.341 -0.200 0.841BKAMIL -2.369 0.315 -7.531 0.000BKSTRT 0.023 0.704 0.033 0.974BORDER -2.540 0.310 -8.190 0.000BUYCOM -2.668 0.333 -8.017 0.000ECAMP -0.201 0.837 -0.241 0.810FATBRA -0.294 0.335 -0.879 0.380KNGBOK -0.387 0.536 -0.721 0.471POWELL 1.037 0.734 1.412 0.158TBAC -2.318 0.464 -4.992 0.000R-squared 0.971270 Mean dependent var 13.89772Adjusted R-squared 0.968464 S.D. dependent var 14.18615S.E. of regression 2.519221 Akaike info criterion 4.771119Sum squared resid 8123.485 Schwarz criterion 5.241469Log likelihood -3228.097 F-statistic 346.1805Durbin-Watson stat 1.163796 Prob (F-statistic) 0.000000
  43. 43. Formal Regression Model For All Paperbacks41(Regression 2)POST = α + β1SHCOST + β2TTIME + β3AMZN +β4BANDN +β5(NYTPF+NYTPNF) + β6AMZN*(NYTPF+NYTPNF) +β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD + β9BKAMIL + β10BKSTRT +β11BORDER + β12BUYCOM + β13ECAMP + β14FATBRA + β15KNGBOK +β16POWELL + β17TBAC + δZ + εWhere z is the vector of all other dummies, including 71 book dummies,and δ is the corresponding vector of coefficients.4241Includes paperback bestsellers.42I exclude the last book dummy (Book075) to prevent collinearity. Also, I exclude varsit for the samereason.
  44. 44. Dependent Variable: POST (in dollars) N=1406 observationsExplanatory Variables Coefficient Std. Error t-Statistic Prob.Intercept 23.370 2.006 11.650 0.000SHCOST -1.106 0.359 -3.080 0.002TTIME 0.860 0.209 4.119 0.000AMZN -1.635 0.683 -2.394 0.017BANDN -1.364 0.652 -2.092 0.037NYTPF+NYTPNF -13.111 3.471 -3.778 0.000AMZN*(NYTPF+NYTPNF) -0.856 1.132 -0.756 0.450BANDN*(NYTPF+NYTPNF) -1.225 1.099 -1.114 0.265BIGWRD -2.826 0.612 -4.615 0.000BKAMIL -4.607 1.360 -3.386 0.001BKSTRT -2.370 0.615 -3.854 0.000BORDER -3.123 0.645 -4.841 0.000BUYCOM -7.493 1.738 -4.312 0.000ECAMP -0.352 0.641 -0.548 0.584FATBRA -0.934 1.154 -0.810 0.418KNGBOK 2.684 1.688 1.590 0.112POWELL -4.407 1.195 -3.689 0.000TBAC 23.370 2.006 11.650 0.000R-squared 0.889701 Mean dependent var 13.8977Adjusted R-squared 0.882152 S.D. dependent var 14.1862S.E. of regression 4.869964 Akaike info criterion 6.06658Sum squared resid 31187.26 Schwarz criterion 6.40628Log likelihood -4173.808 F-statistic 117.857Durbin-Watson stat 0.455668 Prob (F-statistic) 0
  45. 45. 8. Econometric Analysis and Conclusions8.1 The relationship between shipping & handling and the posted priceThe econometric analysis supports some of my theoretical ideas. The dataindicates that as the posted price increases, the shipping & handling priceincreases. Therefore, with great confidence (p-value<.001), 43I cannot reject thenull hypothesis that there is a negative correlation between the posted andshipping & handling price of a given book (Hypothesis D). This demonstratesthat firms offering a lower posted price do not, on average, employ a strategy inwhich they markedly increase the total price via imposing on the consumer acomparatively higher shipping price during “checkout.” This may indicate that theprice retailers charge consumers for shipping & handling reflects firm costs anddoes not reflect any firm’s profit strategy.The correlation is problematic because the time in-between the realization of theposted and shipping & handling price is never recorded. Asymmetry in consumerawareness of components of total costs may potentially affect consumer pricesensitivity and therefore render my results somewhat problematic. Although Ibelieve incorporating such a measure will prove invaluable to this study,accuracy and objectivity will be impossible to incorporate. Firstly, the actual“time” lag between realizations is based upon connection technology as well as43The p-value or observed significance level of a statistical test is the smallest value of α for which H0 canbe rejected. It is the actual risk of committing a Type I error, if H0 is rejected based on the observed value fthe test statistic. The p-value measures the strength of the evidence against H0.
  46. 46. Internet congestion.44Furthermore, realization of shipping & handling prices,unless the site’s prices are overtly clandestine, (i.e., there is no way to knowthem until checkout) are also difficult to measure because of subjectivity inconsumer awareness and sensitivity.Unlike my analysis, which assumes a single book per order, Brynjolfsson andSmith assume that customers purchase, on average, three titles per order. Theirquantity is derived via a study that finds consumers of Amazon purchase anaverage of 2.8 titles per order. I believe that because the marginal shipping costapproaches the variable shipping cost as the quantity of books orderedincreases, consumers should be enticed to purchase more books in the sameorder:SHCOST=FC(X) + (VC(Y))(QTY(Z))As Z → ∞ , MCSHCOST → $0.99I only assume one book per order because I am interested in the raw relationshipbetween the posted price and shipping & handling price. Furthermore, I test therelationship between the posted price and availability and shipping time. Addingmultiple books to an order basket complicates my objective as:1) Consumers may not necessarily be able to calculate the spread ofavailability and shipping time at the time of order execution.2) Dynamic shipping and availability times may result in multipleshipments per order and bias any measure, assumption, oranalysis of consumer time sensitivities.44For example, those consumers with a modem will experience a greater time lag between realizations vs. aconsumer with a T1 or T3 network connection. In addition, those consumers “surfing” during businesshours, when Internet congestion is high, are also more apt to encounter longer lag times.
  47. 47. Therefore, a dynamic regression model that continually recounts for anincreasing number of books ordered, might yield different results. Each firm thatengages in two price shipping strategies charges a different amount for the fixedcost and additional cost. Furthermore, a number of firms charge only a fixedamount independent of quantity. A further study measuring consumer sensitivityto different shipping pricing policies and their effects on total quantity may helpgive a refined and more precise measure of the correlation. Regression 2, whichonly includes paperbacks (including bestsellers), yields dramatically differentresults. The strong negative coefficient (-1.06) and significance (p-value<. 001)allows me to reject Hypothesis D. The results are somewhat enigmatic but dosupport my theoretical ideas put forth in Hypothesis D. I posit that firms that aremore likely to sell a given hardcover, relative to paperbacks, are more apt tocharge a higher shipping price. Therefore, the exclusion of hardcovers from mymodel yields the hypothesized relationship.8.2 Market Power, Branding, and PricingNeither Amazon nor Barnes & Noble significantly contributes to a change in theposted price in my model. Both variables are insignificant (p-value >.10) andBarnes & Noble’s coefficient is negative, albeit only slightly. Given the marketshare of the market leaders, which Brynjolfsson and Smith estimate is overeighty percent for Amazon and fourteen percent for Barnes & Noble as of August1999, (Brynjolfsson and Smith 1999) it is peculiar that prices are not significantlyhigher on these two sites. Despite these high market shares, the data may
  48. 48. suggest both companies feel vulnerable to competition from each other and fromsmaller retailers. Nonetheless, given their significantly lower prices onbestsellers (p-value=0) coupled with every smaller site yielding negativecoefficients, it is arguable whether Amazon & Barnes & Noble do not, in fact,charge higher than average prices.I modify my regression model to include only paperbacks (Regression 2) to testfor any significant price differences. Both Amazon and Barnes & Noble aresignificantly more expensive than all other retailers exhibiting significant results.Therefore, I reject Hypothesis B and conclude, that for all paperbacks, books aresignificantly more expensive at Amazon and Barnes & Noble. I believe theseresults derive from the number of bestsellers in Regression 1 vs. Regression 2.Regression 1 contains 60 bestsellers. Regression 2 only contains 30. As theoutcome of Regression 1 indicates, New York Times Bestsellers are pricedconsiderably cheaper at market leader’s sites. Consequently, I believe it is thehigh percentage of bestsellers in my first model (57.14%) vs. (20.82%) that givesthe illusion books are no less expensive at Amazon and Barnes & Noble.Another significant result arising from Regression 2 is the relationship betweenpaperback prices at Amazon and Barnes & Noble and other retailers. I contendmy econometric results indicate paperbacks are notably cheaper thanhardcovers at market leading firms when both Regressions 1 and 2 are runwithout the inclusion of intercept terms. This observation indicates another form
  49. 49. of market segmentation as both firms discriminate between consumers preferringdifferent types of the same book. This is exemplified at both Amazon and Barnes& Noble as both firms offer on option to purchase a hardcover version instead ofpaperback or visa versa when a given book is selected (see Appendix D).The second intercept-free regression (Regression 4 in Appendix E) demonstratesthat prices for paperbacks are significantly lower at Amazon versus Barnes &Noble. In this sense, Amazon is relatively more discriminate between prices ofhardcovers and paperbacks. I argue these results indicate Amazon moreaggressively price discriminates between consumers preferring paperbacks andthose favoring hardcovers.8.3 The Posted Price, Availability, and Shipping TimeGiven the positive relationship between the posted price and total time in theresults, and their significance, I cannot reject the null hypothesis that books witha higher posted price have longer processing and delivery times (Hypothesis C).Although I find these results surprising, they are not without explanation.Availability and shipping times are not usually posted on a retailer’s web site andare not on all book search intermediary sites. Furthermore, my theoreticalassumption is inextricably tied to the idea that the market leader’s price should,on average, be higher (Hypothesis B). This higher than average price shouldhave been, in part, the reason for the speedy processing and delivery timecharacterizing books sold by Amazon and Barnes & Noble. However, since I
  50. 50. cannot reject Hypothesis B, my posited relationship between the posted priceand total time has been affected via a “domino effect” as the primary assumption(Hypothesis C) is inextricably tied to not rejecting Hypothesis B.Furthermore, consumers that are insensitive to price may also be insensitive totime. A likely strategy adopted by retailers placing a price premium on theposted price of their books may be tied to a realization that the averageconsumer purchasing goods are time insensitive in addition to prince insensitive.Additionally, sites affixing time premiums to their books, with the exception ofAmazon, are perhaps those that do not retain customers well and will be forcedto shutdown in the long run as customers realize that not only are pricescomparatively high, but service is also slow.Loss leader pricing is not observed in Regression 2. The nature of paperbacks isthat they are intrinsically priced lower. This problematizes the results I discuss inthe next section.8.4 Amazon, Barnes & Noble, and Bestseller Loss Leader Marketing StrategyThe strong negative and both statistically and economically significantcoefficients on bestseller variables for both leader firms in Regression 1 allowsme to reject the null hypothesis that bestsellers act as a loss leader product forboth leader firms (Hypothesis D). However, isolating my model to onlypaperbacks, I observe different results. I find that for paperbacks alone, no
  51. 51. significant results exist. I believe these results stem from the nature ofpaperbacks. Comparing hardcovers and paperbacks for bestsellers only, weobserve a comparatively significant price difference verses the differencebetween hardcovers and paperbacks for all books. Since all bestsellers areoffered in either form, I claim retailers price discriminate between price sensitiveconsumers. This is further evident as paperbacks are not released until afterhardcovers.Loss leader pricing is evident given the web images presented in Appendix B.Appendix B first shows examples of heavily advertised New York TimesBestsellers. These advertisements appear on popular sites, including Yahoo,which is one of the web’s most popular sites. In accord with loss leader pricingtheory and supported by my empirical results, once the consumer clicks on thesebanner advertisements, they are transported to either Amazon’s or Barnes &Noble’s web page where the New York Times Bestsellers luring the consumer tothe site are not immediately evident. The consumer must first click on thebestseller hyperlink and is then transported to the “bestseller” page. Still, NewYork Times Bestsellers do not “pop out” at the consumer. Rather, the firm’s ownbestsellers are marketed to the consumer and only upon careful scrutiny of thepage does the consumer find links to pages containing New York TimesBestsellers. Nevertheless, a simple search upon transport from the banner to thefirm’s home page will forgo this obstacle of links. A savvy consumer will forgoclicking on banners and shop via a reputable search site such as bestbookbuys.
  52. 52. While it is empirically evident that these leader firms incur a loss on this lossleader product, the idea that bestsellers are a loss leader product is madeproblematical by the fact that neither Amazon nor Barnes & Noble realize a profiton the non loss leader books they sell. In the last fiscal year (1999), net loss atAmazon totaled $720 million, up from $124.5 million (Yahoo Finance 2000). ForBarnes & Noble, net loss fell 42% to $48.2 million in the 1999 fiscal year.
  53. 53. Appendix A: Statistical Summaries of Posted Price inVarying CategoriesTable 1 – All Books (105)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn45100 95.24 13.05 18.26 0.71bandn 105 100.00 13.24 18.24 0.73bigwrd 86 81.90 14.47 17.23 0.84bkamil 97 92.38 10.05 6.37 1.58bkstrt 95 90.48 12.87 9.49 1.36border 95 90.48 10.08 6.33 1.59buycom 84 80.00 10.09 6.47 1.56ecamp 66 62.86 15.71 17.95 0.88fatbra 91 86.67 11.92 7.76 1.54kngbok 96 91.43 11.40 9.07 1.26pagone 76 72.38 14.94 8.14 1.83powell 100 95.24 16.04 9.57 1.68spree46105 100.00 13.24 18.24 0.73tbac 105 100.00 12.60 18.36 0.69varsit 85 80.95 11.22 8.82 1.27TOTAL 1406 88.00 12.67 12.13 1.21545I find Amazon classifies five percent of New York Times Bestsellers as “out of stock” or “on order.”This is ironic given their aggressive campaign pushing this genre of books. Furthermore, a market leaderboasting efficiency and speed should ideally hold an ample number of bestsellers in its inventory or haveefficient distribution channels that make them readily available. An interesting question this questionablemarket failure raises is: do consumers shopping for bestsellers not available at Amazon wait for availability,or buy from another retailer? I have demonstrated that it is dangerous to allow existing customers to switchto another site as studies have shown customer retention is key on the Internet as consumers are particularlychoosy. Therefore, it is in Amazon’s best interest to hold an extra supply of its loss leader pricingcommodity to prevent such consequences. On the other hand, New York Times Bestsellers as a loss leaderproduct may act as a bait-and-switch good in which Amazon recommends alternative books “like” the on-order bestseller that are priced higher. This tactic, albeit unethical, would greatly increase Amazon’s profitmargins.46Actually, Spree, as you may notice, has an identical value in every summary statistic table as Barnes &Noble. Spree is a reward intermediary site that promotes brand loyalty. Registered users of Spree earnfinancial rewards for shopping on branded web sites linked through their site. Although prices are identicalfor a book purchased via spree or Barnes & Noble, registered users of spree receive identical benefits tousers of Barnes & Noble and also receive additional utility enjoyed via the free membership benefits ofSpree’s web site.
  54. 54. Table 2 – All Bestsellers (60)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 57 95.00 9.71 5.37 1.81bandn 60 100.00 9.79 5.57 1.76bigwrd 53 88.33 12.53 7.42 1.69bkamil 59 98.33 9.38 4.94 1.90bkstrt 56 93.33 11.83 6.32 1.87border 58 96.67 9.29 5.28 1.76buycom 56 93.33 9.08 5.09 1.79ecamp 23 38.33 13.90 5.36 2.59fatbra 55 91.67 11.33 6.27 1.81kngbok 55 91.67 10.18 6.88 1.48pagone 55 91.67 13.99 6.47 2.16powell4766 110.00 15.56 8.39 1.85spree 60 100.00 9.79 5.57 1.76tbac 60 100.00 11.82 5.60 2.11varsit 53 88.33 10.56 7.22 1.4647This ostensibly erroneous figure stems from Powell’s multiple book pricing strategy. Bestbookbuyssometimes reports multiple observations per book for Powell’s. These are usually categorized into “specialnew” or “special.” I opt to include these observations since, unlike Books A Million, prices are not limitedto paying members only.
  55. 55. Table 3 – All Paperbacks (72)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 69 95.83 10.00 8.85 1.13bandn 72 100.00 9.98 9.32 1.07bigwrd 57 79.17 9.88 9.00 1.10bkamil 66 91.67 8.04 6.10 1.32bkstrt 67 93.06 10.63 9.77 1.09border 63 87.50 8.02 5.83 1.37buycom 56 77.78 7.99 6.25 1.28ecamp 40 55.56 13.05 10.37 1.26fatbra 63 87.50 8.78 6.24 1.41kngbok 69 95.83 9.57 8.95 1.07pagone 47 65.28 12.67 8.10 1.57powell 64 88.89 11.94 8.22 1.45spree 72 100.00 9.98 9.32 1.07tbac 72 100.00 8.53 8.71 0.98varsit 57 79.17 8.88 7.52 1.18TOTAL 934 86.48 9.75 8.167 1.19
  56. 56. Table 4 – All Hardcover (33)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 31 93.94 19.83 29.21 0.68bandn 33 100.00 20.36 28.51 0.71bigwrd 29 87.88 23.49 24.73 0.95bkamil 31 93.94 14.34 4.61 3.11bkstrt 28 84.85 18.23 6.17 2.96border 32 96.97 14.15 5.25 2.70buycom 28 84.85 14.31 4.63 3.09ecamp 26 78.79 19.81 25.32 0.78fatbra 28 84.85 18.97 6.07 3.12kngbok 27 81.82 16.09 7.70 2.09pagone 29 87.88 18.60 6.88 2.70powell 36 109.09 23.32 7.25 3.22spree 33 100.00 20.36 28.51 0.71tbac 33 100.00 21.48 28.44 0.76varsit 28 84.85 15.96 9.49 1.68
  57. 57. Table 5 – Paperback Bestsellers (30)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 28 93.33 5.19 1.51 3.45bandn 30 100.00 5.09 1.50 3.38bigwrd 27 90.00 7.06 2.84 2.48bkamil 30 100.00 5.26 1.43 3.68bkstrt 30 100.00 7.06 2.10 3.36border 29 96.67 5.14 1.51 3.42buycom 30 100.00 5.11 1.50 3.41ecamp 0 0.00 N/A N/A N/Afatbra 30 100.00 6.20 3.20 1.94kngbok 30 100.00 6.05 3.32 1.82pagone 28 93.33 10.09 3.38 2.99powell 34 113.33 9.15 3.80 2.41spree 30 100.00 5.09 1.50 3.38tbac 30 100.00 6.67 2.19 3.05varsit 27 90.00 6.50 2.33 2.79
  58. 58. Table 6 – Hardcover Bestsellers (30)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 29 96.67 14.07 3.91 3.60bandn 30 100.00 14.48 3.89 3.72bigwrd 26 86.67 18.20 6.36 2.86bkamil 29 96.67 13.64 3.38 4.04bkstrt 26 86.67 17.34 4.88 3.55border 29 96.67 13.45 4.34 3.10buycom 26 86.67 13.66 3.69 3.70ecamp 23 76.67 13.90 5.36 2.59fatbra 25 83.33 17.48 1.98 8.83kngbok 25 83.33 15.14 6.79 2.23pagone 27 90.00 18.03 6.47 2.79powell 32 106.67 22.37 6.26 3.57spree 30 100.00 14.48 3.89 3.72tbac 30 100.00 16.97 2.05 8.27varsit 26 86.67 14.78 8.16 1.81
  59. 59. Table 7 – Total Bestsellers (Fiction) (30)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 27 90.00 9.32 5.14 1.81bandn 29 96.67 9.07 5.32 1.70bigwrd 25 83.33 12.03 8.17 1.47bkamil 28 93.33 8.82 4.98 1.77bkstrt 27 90.00 10.81 5.40 2.00border 28 93.33 8.48 4.92 1.72buycom 26 86.67 8.26 4.85 1.70ecamp 11 36.67 14.27 4.86 2.94fatbra 25 83.33 10.50 6.37 1.65kngbok 26 86.67 9.17 6.51 1.41pagone 27 90.00 13.59 7.16 1.90powell 32 106.67 15.40 8.43 1.83spree 29 96.67 9.07 5.32 1.70tbac 29 96.67 11.40 5.96 1.91varsit 24 80.00 9.89 7.57 1.31
  60. 60. Table 8 – Total Bestsellers (Non-Fiction) (30)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 30 100.00 10.07 5.63 1.79bandn 30 100.00 10.07 5.63 1.79bigwrd 28 93.33 12.97 6.81 1.90bkamil 31 103.33 9.89 4.92 2.01bkstrt 29 96.67 12.78 7.03 1.82border 30 100.00 10.05 5.58 1.80buycom 30 100.00 9.79 5.26 1.86ecamp 12 40.00 13.57 5.98 2.27fatbra 30 100.00 12.02 6.21 1.94kngbok 29 96.67 11.09 7.19 1.54pagone 28 93.33 14.38 5.84 2.46powell 34 113.33 15.71 8.47 1.85spree 30 100.00 10.07 5.63 1.79tbac 30 100.00 12.21 5.31 2.30varsit 29 96.67 11.11 7.00 1.59
  61. 61. Table 9 – Fiction Bestseller Paperbacks (15)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 13 86.67 4.38 1.33 3.28bandn 15 100.00 4.29 1.26 3.40bigwrd 13 86.67 6.28 2.06 3.05bkamil 15 100.00 4.49 1.30 3.47bkstrt 15 100.00 6.35 1.55 4.10border 14 93.33 4.34 1.30 3.34buycom 15 100.00 4.35 1.31 3.33fatbra 15 100.00 5.61 2.25 2.50kngbok 15 100.00 5.67 2.25 2.52pagone 13 86.67 8.84 2.63 3.36powell 15 100.00 8.05 2.79 2.89spree 15 100.00 4.29 1.26 3.40tbac 15 100.00 5.90 1.68 3.52varsit 12 80.00 5.78 1.61 3.58
  62. 62. Table 10 – Non-Fiction Bestseller Paperbacks (15)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 15 100.00 5.89 1.31 4.50bandn 15 100.00 5.89 1.31 4.48bigwrd 14 93.33 7.79 3.33 2.34bkamil 15 100.00 6.03 1.14 5.30bkstrt 15 100.00 7.77 2.38 3.26border 15 100.00 5.89 1.31 4.50buycom 15 100.00 5.87 1.31 4.47ecamp 0 0.00 N/A N/A N/Afatbra 15 100.00 6.79 3.92 1.73kngbok 15 100.00 6.43 4.18 1.54pagone 15 100.00 11.18 3.66 3.06powell 19 126.67 10.02 4.32 2.32spree 15 100.00 5.89 1.31 4.48tbac 15 100.00 7.43 2.42 3.08varsit 15 100.00 7.07 2.68 2.64
  63. 63. Table 11 – Fiction Bestseller Hardcover (15)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 14 46.67 13.90 2.06 6.75bandn 14 46.67 14.20 2.16 6.59bigwrd 12 40.00 18.26 7.71 2.37bkamil 13 43.33 13.81 1.86 7.44bkstrt 12 40.00 16.39 2.22 7.38border 14 46.67 12.62 3.40 3.71buycom 11 36.67 13.59 1.52 8.94ecamp 11 36.67 14.27 4.86 2.94fatbra 10 33.33 17.83 1.00 17.89kngbok 11 36.67 13.95 7.43 1.88pagone 14 46.67 18.00 7.26 2.48powell 17 56.67 21.89 5.96 3.67spree 14 46.67 14.20 2.16 6.59tbac 14 46.67 17.29 1.18 14.68varsit 12 40.00 14.00 8.97 1.56
  64. 64. Table 12 – Non-Fiction Bestseller Hardcover (15)Firm Obs.% TotalAvailabilityMeanPriceStandardDeviationCoefficientOfVariationamzn 15 100.00 14.24 5.15 2.76bandn 10 100.00 14.24 5.15 2.76bigwrd 14 93.33 18.15 5.23 3.47bkamil 15 100.00 13.50 4.30 3.14bkstrt 14 93.33 18.16 6.33 2.87border 15 100.00 14.21 5.07 2.81buycom 15 100.00 13.71 4.76 2.88ecamp 12 80.00 13.57 5.98 2.27fatbra 15 100.00 17.25 2.44 7.08kngbok 14 93.33 16.08 6.37 2.52pagone 13 86.67 18.07 5.80 3.12powell 15 100.00 22.91 6.75 3.39spree 15 100.00 14.73 5.01 3.14tbac 10 100.00 16.70 2.60 6.42varsit 14 93.33 15.44 7.67 2.01
  65. 65. Appendix B: Visual Examples of Loss Leader PricingStrategy1) AmazonAdvertisements for Amazon
  66. 66. Amazons Home Page
  67. 67. Amazons Bestseller Page
  68. 68. 2) Barnes & NobleAdvertisements For Barnes and Noble
  69. 69. Barnes & Noble’s Home Page
  70. 70. Barnes & Noble’s Bestseller Page
  71. 71. Appendix C: Personalized Product
  72. 72. Appendix D: Visual Example of Consumer Choice andMarket Segmentation at Amazon
  73. 73. Appendix E: Intercept-Free Regression ModelingModified Regression Model For All Books (Regression 3)POST = β1SHCOST + β2TTIME + β3AMZN + β4BANDN + β5(NYTPF+NYTPNF) +β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF) +β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) +β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD +β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP +β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + εWhere z is the vector of all other dummies, including 104 book dummies,and δ is the corresponding vector of coefficients.
  74. 74. Dependent Variable: POST (in dollars) N=1406 observationsExplanatory Variables Coefficient Std. Error t-Statistic Prob.SHCOST 3.050 0.431 7.079 0.000TTIME 0.946 0.183 5.160 0.000AMZN -0.472 1.144 -0.412 0.680BANDN 1.053 1.095 0.961 0.337NYTPF+NYTPNF 38.564 5.487 7.028 0.000NYTHCF+NYTHCNF -3.665 2.745 -1.335 0.182AMZN*(NYTPF+NYTPNF) -1.444 1.680 -0.860 0.390AMZN*(NYTHCF+NYTHCNF) -2.958 1.662 -1.780 0.075BANDN*(NYTPF+NYTPNF) -2.352 1.622 -1.450 0.147BANDN*(NYTHCF+NYTHCNF) -3.590 1.621 -2.215 0.027PAPERBACK -52.807 2.639 -20.010 0.000BIGWRD -1.734 0.890 -1.948 0.052BKAMIL -2.222 0.821 -2.706 0.007BKSTRT 2.585 1.835 1.409 0.159BORDER -2.008 0.808 -2.483 0.013BUYCOM -2.654 0.869 -3.055 0.002ECAMP 12.209 2.008 6.079 0.000FATBRA -0.021 0.872 -0.024 0.981KNGBOK 0.319 1.401 0.228 0.820POWELL -4.066 1.882 -2.161 0.031TBAC -5.051 1.174 -4.303 0.000R-squared 0.804151 Mean dependent var 13.89273Adjusted R-squared 0.785178 S.D. dependent var 14.18828S.E. of regression 6.576119 Akaike info criterion 6.689525Sum squared resid 55354.03 Schwarz criterion 7.156411Log likelihood -4574.39 F-statistic 1.532244Durbin-Watson stat 0.804151 Prob (F-statistic) 13.89273
  75. 75. Modified Regression Model For All Paperbacks48(Regression 4)POST = β1SHCOST + β2TTIME + β3AMZN +β4BANDN + β5(NYTPF+NYTPNF) +β6AMZN*(NYTPF+NYTPNF) + β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD +β9BKAMIL + β10BKSTRT + β11BORDER + β12BUYCOM + β13ECAMP +β14FATBRA + β15KNGBOK + β16POWELL + β17TBAC + δZ + εWhere z is the vector of all other dummies, including 71 book dummies,and δ is the corresponding vector of coefficients.48Includes paperback bestsellers.
  76. 76. Dependent Variable: POST (in dollars) N=1406 observationsExplanatory Variables Coefficient Std. Error t-Statistic Prob.SHCOST 2.635 0.242 10.892 0.000TTIME 0.548 0.363 1.512 0.131AMZN -3.660 1.193 -3.069 0.002BANDN -0.954 1.133 -0.842 0.400NYTPF+NYTPNF -14.814 6.043 -2.452 0.014AMZN*(NYTPF+NYTPNF) 0.588 1.977 0.297 0.766BANDN*(NYTPF+NYTPNF) -1.132 1.915 -0.591 0.554BIGWRD -0.674 1.132 -0.595 0.552BKAMIL -3.522 1.065 -3.306 0.001BKSTRT -0.876 2.319 -0.378 0.706BORDER -2.944 1.069 -2.754 0.006BUYCOM -3.747 1.123 -3.337 0.001ECAMP 10.761 1.405 7.657 0.000FATBRA -1.057 1.118 -0.946 0.344KNGBOK -4.576 1.984 -2.306 0.021POWELL -10.164 2.450 -4.149 0.000TBAC -2.088 2.066 -1.011 0.312R-squared 0.665019 Mean dependent var 13.89273Adjusted R-squared 0.642347 S.D. dependent var 14.18828S.E. of regression 8.485175 Akaike info criterion 7.176431Sum squared resid 94677.64 Schwarz criterion 7.512589Log likelihood -4951.44 F-statistic 1.247258Durbin-Watson stat 0.665019 Prob (F-statistic) 13.89273
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