DERI L´ıon - Stephen Kinsella · This study will present three interesting and interlocking facets of this problem: a description of the forces at work on the consumers, producers

DERI Lı́on

D17.01 Survey of Salience of Extant Approaches toDigital Enterprises from Evolutionary Economics,Bounded Rationality and Satisficing Approaches

1st July 2004

Document version:https://lion.deri.org/PDFdeliverables/D17.01/0.09.pdfLatest version:https://lion.deri.org/PDFdeliverables/D17.01/0.09.pdfPrevious version:https://lion.deri.org/PDFdeliverables/D17.01/0.07.pdf

Authors:S. Kinsella

Private and Confidential

Acknowledgement

This material is based upon works supported by the Science Foundation Ire-land under Grant No. 02/CE1/I313.

DisclaimerThe opinions, findings and conclusions or recommendations expressed in thismaterial are those of the author(s) and do not necessarily reflect the viewsof the Science Foundation Ireland, NUI, Galway or the Hewlett-Packard Com-pany.

Contents

1 Overview and Motivation 61.1 Recent approaches to modeling digital enterprises—the case of

shopbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 What is a shopbot? 92.1 How do shopbots actually work? . . . . . . . . . . . . . . . . . . 92.2 Shopbots and the Semantic Web . . . . . . . . . . . . . . . . . . 11

3 Institutional Analysis 123.1 What Factors Limit Consumer Adoption of Shopbots? . . . . . . 14

3.1.1 Consumer Characteristics . . . . . . . . . . . . . . . . . . 143.1.2 Market Characteristics . . . . . . . . . . . . . . . . . . . . 18

4 Descriptive Statistics 194.1 Data Collection Methodology . . . . . . . . . . . . . . . . . . . . 194.2 Comparison of one vendor across shopbots and through time . . 204.3 Comparison of Price Variation Across Shopbot . . . . . . . . . . 21

4.3.1 Across Time . . . . . . . . . . . . . . . . . . . . . . . . . 214.4 Price Variation across shopbots . . . . . . . . . . . . . . . . . . . 224.5 Percentage price differentials across shopbots . . . . . . . . . . 25

5 Model 275.1 Determinants of µ . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6 Conclusion and Further Work 32

7 Appendix 337.1 Evolutionary Economics . . . . . . . . . . . . . . . . . . . . . . . 33

7.1.1 Development of evolutionary economics . . . . . . . . . . 337.1.2 Evolutionary Economics: The Nelson and Winter Tradition 35

7.2 Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Private & Confidential

List of Figures

1 Decision Processes within an Idealised Shopbot . . . . . . . . . 102 Shopbot Decision Processes-More Complexity 6= More Efficiency. 103 Strategic Link Placement may Drive Price Wedges . . . . . . . . 134 Top sellers show much more variation in prices over time. . . . . 215 Random Titles show more variation . . . . . . . . . . . . . . . . . 216 Here we see much lower standard deviations, suggesting fewer

price changes, less demand and less competition on these items. 227 Price Variation across time for the Shopbot Dealtime from 1987-

2000 shows less variation than from 2001+ . . . . . . . . . . . . 228 More Variation is Shown after 200 because the sample was taken

then, highlighting the importance of consumer attention in dictat-ing price variation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

9 The same patterns as above can be seen for the shopbot Froogle 2310 Again, more price variation is seen closer to the sample date,

2000-2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2311 Top Sellers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2412 Top selling and Random DVD Titles sold by the Shopbot CDUni-

verse. A contradiction? . . . . . . . . . . . . . . . . . . . . . . . . 2513 Percentage Price Differentials are quite large amongst DVD titles 2614 When split into top selling and random for the shopbot Deal-

time.com, the overall level of dispersion by sales volume is re-vealed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

15 Percentage Price dispersion is almost always negative in thevideo game markets, suggesting they are a loss-leader for mostonline stores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

16 Quantity Demanded Over Time . . . . . . . . . . . . . . . . . . . 2817 Thresholf Effects to being Locked-In to a Vendor . . . . . . . . . 31

4 of 39 Copyright c© NUI, Galway 2004


Abstract

Previous approaches to Digital Enterprises are surveyed, and their relevanceto current approaches to modeling the Law of One Price, which holds that foridentical goods in frictionless markets, the price for those goods should be thesame. We find evidence based on a large new dataset of prices for homoge-neous goods that this is not so. We also compare the data gathered here withdata from other sources to obtain a more reliable indicator. A model is builtto explain the variation in the prices of different goods in terms of informationflows. This study is carried out within the context of the nascent Semantic Web.



1 Overview and Motivation

[N]othing is more fundamental in setting our research agenda andinforming our research methods than our view of the nature of thehuman beings whose behavior we are studying.

—[Simon(1985), pg. 303]

The emergence and explosive growth of online markets and in particular onlineretailing, has generated a large amount of academic research on online pricedispersion. Online price dispersion is defined as the distribution of prices ofa product (such as range and standard deviation) with the same measurablecharacteristics across sellers at a given point in time [Darmon(2002), pg.3].Since online price dispersion has significant implications for modeling con-sumer and producer behavior and has the potential to have important policyimplications, it is a worthwhile field of study.

This study will present three interesting and interlocking facets of this problem:a description of the forces at work on the consumers, producers and institu-tional structures in the online retail ‘space’; an empirical analysis of two subsetsof these-the markets for DVDs and video games searched and compared byshopbots and finally a model that explains the findings of this analysis in termsof the informational constraints imposed on the consumer by themselves.

The viewpoint taken here is that, to understand shopbot behavior, one mustunderstand the nature of the product they are selling as well as the type ofconsumer that is purchasing from a store they were guided to by a shopbot.The products themselves are usually physically homogeneous—DVDs, videogames, electronic products—but informationally heterogeneous. One wants tobuy ‘Titanic’ the DVD more than one wants to buy ‘Last Action Hero’ for informa-tional reasons related to one’s expectations of the product. The lever I use toexplore these changing expectations is a piecewise demand function, whosedeterminants are modeled as a sequential game. The result is a correlationwith the data observed in nature.

1.1 Recent approaches to modeling digital enterprises—thecase of shopbots

Online price dispersion has been found to be persistent and substantial in re-cent work, suggesting that a frictionless economy on the internet may be a longtime coming (See [Baye and Morgan(2001)]). Smith and Brynolfsson ([Brynjolf-sson and Smith(2000)], [Smith and Brynjolfsson(2003)]) described and evalu-ated shopbot behavior in the online book market, finding that branding andretailer credibility were still significant influences in the decision to buy at onesite rather than another. They also found in contrast to Tang et al [Xing andTang(2004)] that consumers were sensitive to total price changes when ship-ping and tax charges were taken into account. They go on to suggest (pg. 1)that shopbots are



conducive to rational, objective decision-making by shoppers. In-deed, it closely approximates the idealized setting implicit in logitmodels of consumer choice: consumers have most of the relevantinformation about each alternative and can compare them side byside.

Varian [Varian(1995)], [Varian(2001)], [Varian and Shapiro(1999)] describesdigital markets as representing the apogee of the neoclassical stance on mar-kets and market structure. In a series of articles and a book he has attachedgreat importance to the reduction of costs by the internet—the cost of searchfor the consumer and the costs of advertising and fixed costs related to shopfronts, etc for the producer—and the effects this tandem reduction will have onmarket structure. He argues that traditional economic thinking is still valid forthe online market place1, and suggests that claims that there is a ‘New Econ-omy’ being built using the internet as its base are premature at best, falsehope at worst. How right he and Shapiro were. Nevertheless, all of Var-ian’s articles describe the internet in terms of drastically lowering the costsof search for the consumer—which invites more direct comparison of closelyrelated alternatives—and lowered brand loyalty for the producer, which impliesa stronger focus on price competition. Thus prices must be driven lower andlower until they reach either marginal cost of below marginal cost for a time.Either way, the dispersion of prices for a single good at a single point in timeshould be much less than other bricks and mortar retailers, and this price dis-persion should be in equal amounts across different products [Varian(1995)]. Itseems that nothing could be further from the truth.

In a series of papers examining the actual empirical relationship between on-line price dispersion and seller behavior, Xing and Tang [Tang and Xing(2001)],[Xing and Tang(2004)] describe the markets for CDs and DVDs as being drivenby two factors: the division in most large companies between their bricks andmortar operations and their online operations, if they are ‘multi-channel retail-ers’ like Barnes and Noble, and the cohesion simply being an online seller likeAmazon.com gives a business. They ascribe a significant amount of the pricedispersion they observe over time to the institutional effects of multi-channelretailers trying to stop competition from happening within their business as op-posed to without it, in the marketplace. They observe larger and more frequentprice changes in the digital-only retailers although accompanied (obviously) byhigher price variation, while observing less price changes and less price vari-ation in the multi-channel retailers [Tang and Xing(2001)]. The predictions thatVarian made about lower search and supply costs leading to a more efficientand transparent market have not, it seems, come to pass. And there is moreevidence to this fact.

Shankar et al [Shankar(2002)] find that the posited drivers of online price dis-persion have not changed over the period of time that they have been identifiedand studied. These drivers are: product heterogeneity, convenience of shop-ping online, consumer awareness of seller’s existence, e-tailers branding, trust,lock-in due to switching costs and price discrimination. I discuss all of these

1Granted, his major work in this area was done with Shapiro in 1999, so there are reasons forboth his cautious tone and the warnings he and Shapiro sound about the ineffectiveness of manyof the new business models.



characteristics below in detail in the Institutional Analysis section, but sufficeit to say for the moment that these authors have found significant evidencethat not only is there price dispersion online, but this price dispersion is per-sistent and the factors affecting it have not changed. As an example, theycompare online-only retailers like Amazon.com to Barnes and Noble and findthat list prices have changed significantly over time on the more popular prod-ucts, but observe less price movement on less popular products, which is to beexpected—more consumer interest in, and therefore competition for a productshould necessitate price convergence. But this is not what they find. All Xingand Tang observe is slightly more variation, and certainly not convergence ofany kind among the more popular products.

Clay et al [Clay et al.(2001)Clay, Krishnan, and Wolff] found, using a sampleof over 24,000 price and quantity observations from the shopbot Dealtime, thatthe factors that significantly induced the consumer to buy were, in order: ship-ping costs, brand of store, price of good, time to delivery and convenience ofshopping. Thus it makes sense for a shopbot to bundle shipping costs as aloss-leader with large orders over a certain threshold, as consumers are lesslikely to be affected by them in that case. This is exactly what Amazon andmost large retailers do, in fact.

Finally, experiments run on consumers have shown that, indeed, branding andconsumer lock in does matter and can influence the amount of price disper-sion in a market [Baye and Morgan(2001)], while simulations of different typesof shopbot—straight comparison sites versus shopbots with other search andmembership capabilities—reveal the same bias toward lock-in features likemembership deals and discounts for bulk buying [Darmon(2002)]. There arealso significant features of the shopbot’s design that could and should be al-tered to take account of the consumers’ decision processes in the search andshopping process itself, and [Montgomery et al.(2004)Montgomery, Hosana-gar, Krishnan, and Clay] have looked at a utility model of these decision pro-cesses to see where the consumer experience can be enhanced. Now I turnto the actual mechanics of the shopbot itself and begin a description of theinternal and external systems that make up the shopbot.



2 What is a shopbot?

Shopbot is short for shopping robot. A shopbot is a comparison shopping en-gine that automatically and efficiently searches a large number of online stores,providing a consumer with a large list of prices and other attributes of a productat a given time from a given and usually large number of sellers. Comparedwith traditional sequential search processes [McCall(1970)] this represents oneof the key consumer innovations in consumer choice and search online. Shop-bots alter the process of product information acquisition online in positive andnegative ways.

Obviously the flow of potential benefits that an idealised shopbot might realisefor the customer stem from their ability to truncate the search process into aone-stop, two-click process. Commentators have posited that a set of webservices providing an efficient and unbiased comparison of different vendors’wares allows, facilitates and perhaps even forces a more competitive marketstructure on the vendors, and this is always good for the consumer, isn’t it?

Not always. There are several costs associated with shopbots as well. Thetime it takes for the consumer to use the shopbot, the additional “cognitiveeffort” [Montgomery et al.(2004)Montgomery, Hosanagar, Krishnan, and Clay,pg. 189] that the shopbot places on the consumer in a is not a trivial thing–up to100 prices and therefore 100 different stores can be returned in a single query–and waiting for that query to execute on a slow connection imposes a furthertime cost. Indeed, Montgomery et al estimate that even for the larger shopbotsites, on a T1 connection the query can take up to 45 seconds [Montgomeryet al.(2004)Montgomery, Hosanagar, Krishnan, and Clay, pg. 191]. But most ofall, if a consumer uses a shopbot to compare prices using a shopbot and endsup choosing a mainstream e-tailer like Amazon.com, for example, for reasonsother than straight price comparison, then two problems present themselves.Firstly, the use of the shopbot web service has been a waste of time and sec-ond, we do not know our consumers as well as we thought. We either failedto specify their choice functions accurately enough, or our consumers are notrational [Markillie(2004)], [Shugan(1980)]. But before we can talk about theconsumers, producers and institutions that make up the market, it is instructiveto delve a little into the processes underlying the shopbot mechanism itself togain some understanding of both the benefits to and the limitations of theseprocesses.

2.1 How do shopbots actually work?

A description of the actual processes underlying the shopbot system may beappropriate, in order to properly model the effects such a system has on theconsumer.

A shopbot compares certain aspects (price, shipping costs, quantity ordered/available,time to delivery, etc) of the same or similar products from several different ven-dors. At the decision process level, the layout of the decision process by whicha shopbot takes the query sent it by the prospective consumer is given in figure



Figure 1: Decision Processes within an Idealised Shopbot

Figure 2: Shopbot Decision Processes-More Complexity 6= More Efficiency.

1. Once the consumer has decided to use a shopbot to make their search andis online and at the site, the query is typed in, sent in wrappers (see below)to the individual vendor, where a database is accessed and a set of resultsobtained from that database. All of the results are sent back for the perusal ofthe consumer. At the conclusion of this process, the consumer is faced with alarge list of product attributes. These attributes (price, quantity available, ship-ping costs, region-specific taxes, etc) are the basis on which most of the sitesare ranked. I say ‘most’, because there are some sites that top the list everytime for certain products. For example, on Dealtime.com, one will always findproducts from DeepDiscountDVD.com at the top of a list of search results forDVDs. There are ‘preferred retailers’ at the top of every list on every shopbotwho have paid for that position with either advertising revenue or preferentialaccess to their databases. More on these affiliations in the institutional analysissection below.

At the operational level the mechanism the shopbot uses is called a wrapper.The wrapper is simply a piece of code which is combined with another pieceof code to determine how that code is executed2. The wrapper acts as aninterface between its ‘caller’ and the wrapped code. This may be done for com-patibility, e.g. if the wrapped code is in a different programming language oruses different calling conventions, or for security reasons. Here the intentionis simply to provide an interface between one database with another via a webbrowser [deBruijn(2003), pg. 13]. This wrapper uses “pre-specified heuristics”[deBruijn(2003), pg. 16] to extract text from the various product pages on eachvendor’s website. For each website and vendor, a new wrapper must be cre-ated, and when the vendor changes anything about the structure of their dataor the layout of their site, the wrapper specific to them must also be updated.There is therefore a trade off to be made between the level of effort the code-writer is willing to put into each individual wrapper, and the quality of timelyinformation that the specific wrapper affords the user [Fensel(2000), pg.4].

A shopbot thus compares many of the characteristics of different websites ina fairly dynamic way. I use the word ‘fairly’ deliberately here. When prices

2See http://www.free-definition.com/Wrapper.html for a more complete definition anduseful links.


http://www.free-definition.com/Wrapper.html


are changed by the individual vendor, the shopbot wrapper will not need to beupdated, as these prices are simply hat get updated each time a query is sentfrom one shopbot to each site. The Semantic Web, through the developmentof ontologies designed to be re-used by each vendor and each shopbot, will beable to affect changes in the level of efficiency the shopbots currently possessin product comparison.

2.2 Shopbots and the Semantic Web

Through the use of an ontology, which for our purposes may be defined asa formal, explicit specification of a shared conceptualisation [deBruijn(2003)],where an explicit conceptualisation means simply a worldview with the relation-ships between the concepts made definite. This is a gross simplification of thefield of ontology modeling, and its practitioners are asked for their forbearance.However, the above will suit my purpose. If an ontology is simply a sharedworld view with explicit relationships between concepts which is intended tobe shared, how would the creation of, say, reusable ontology for book salesaffect the efficiency of a shopbot? The shopbot, using either the ontology ofa retailer or “a mapping from its own ontology to the vendor’s”[deBruijn(2003),pg.44], can send and receive product information in a more structured and for-mal way. So, each individual database can be integrated automatically by theagent working for the shopbot before any results are displayed for the consumerto process, thus cutting down on the cognitive overload the consumer experi-ences when presented with too many choices, most of which are irrelevantfor them. This is the promise of the Semantic Web for electronic consumers.But before the Semantic Web becomes a reality for the majority of online con-sumers, a description of the institutional context in which the shopbots of todayoperate may be in order.


file:nt-appn.comp.nus.edu.sg/fm/alloy/introduction.htm #instances.t


3 Institutional Analysis

Institutions are the rules of a game in a society or, more formally...thehumanly devised constraints that shape human interaction. In con-sequence they structure incentives in human exchange, whetherpolitical, social or economic.

[North(1990), pp. 3–5]

The above quote from Douglass North, a pioneer in institutional analysis setsthe tone for this section. Here I will look at the underlying rules and rela-tionships of the shopbot web service in a purely descriptive way in order tounderstand more fully what the forces are that shape the behavior and thedevelopment of the shopbot web service. I also take a look at the possibledevelopment of the shopbot service through the creation of product purchaseontologies being developed as the backbone technology of the Semantic Web.

Shopbots provide a search facility. They are accessed via the web using abrowser from anywhere in the world and allow the consumer to look at variousattributes of the same or similar products being sold by many different ven-dors. The shopbots display the information inside a web browser window in atop-down fashion, with the sponsored links to preferred retailers at the top ofevery list, and then a listing of all the other vendors’ prices and links to theirsites. It should be noted that the consumer can change the order of this sortingprocess from price to shipping cost to edition of the DVD, so the shopbot canreflect the consumers’ sensitivity to factors other than price. Indeed, [Xing andTang(2004)] from time series data collected on DVDs which they have gen-erously allowed me to analyse, found that customers were more than twice assensitive to proportionate changes in shipping cost than proportionate changesin product price, suggesting that an optimal strategy for online retailers in gen-eral would be to reduce explicit shipping costs and bundle any increases in costinto the price of the product.

Some shopbots have direct access to price information from certain vendorsbecause of special marketing agreements. In this circumstances, query timemay be much lower because a lookup is performed on a local database ratherthan a query to an online store with all the concomitant computations that re-quires of the service. As we have seen above, one of the most important deter-minants of whether a web service will be used repeatedly by consumers in theirregular shopping is the amount of time they must spend waiting for a query tobe processed. More than ten seconds and a consumer’s attention will begin todrift and they will click away according to [Nielsen(2000)], and as reported in[Montgomery et al.(2004)Montgomery, Hosanagar, Krishnan, and Clay]. Thismakes the case for collusion or strategic agreements between shopbots andvendors more and more a necessity—consumers will place premia on fastersites with faster load times and faster download times for digital products suchas videos, programs or music. If this is the case, then the a priori assump-tion of a shopbot collecting prices in an unbiased way and returning them toa consumer must be abandoned. The shopbot business model is predicatedupon one thing—advertising revenue streams from vendors whose businesssites are frequented by shopbot users. Seen in this light the shopbot is not a



Figure 3: Strategic Link Placement may Drive Price Wedges

catalyst for a more transparent and competitive market structure. The shopbotis simply another intermediary, offering value-added services through its prior-ity access to information channels, and gaining revenue for its shareholders isits raison d’etre.

So it seems that the underlying business model of the shopbot is simply a moretargeted version of that of larger search facilities like Google or Yahoo!, wherea web service is offered to prospective consumers in return for advertising rev-enue and preferred product placement within the search results of the shopbot.The shopbots return a search result on, say, a DVD, with the top 3 results beingfrom the shopbot’s ‘preferred retailers’. This observation is at the heart of theshopbot paradox.

Shopbots themselves are in an awkward strategic situation. On one hand theymust make their searches efficient, timely and easy to encourage consumers touse their services; on the other hand, they damage their business model if theydrive all consumers to the lowest priced retailer or drive price dispersion to zeroby being too efficient. In this context, shopbots may be able to maximize theirrevenue by intelligently designing their interfaces to improve consumer searchwithout eliminating retailer differentiation. And this is what they seem to do. Forexample, when one searches using the shopbot at Cnet.com and looks for thefilm Titanic on DVD, one finds an error page that the product is not available onCnet.com, but the error mesage contains a link to mysimon.com, a supposedlyrival shopbot. Again, when one searches using buypath.com, one gets virtuallythe same results as when using the Dealtime.com web service. This is becausefor DVDs and video games, the services use the same database in many cases.When one searches at mysimon.com for, say, the DVD Top Gun, one finds firsta page of sponsored links and then a smaller link, below eye level, to the 23other search results on offer, another page away. Figure 3 below shows ascreen shot of this deliberate obfuscation. Thus the sponsors of the shopbotare not simply allowed preferential space next to other searches; they are in factallowed preferential positioning within the shopbot site, and this allows them totake advantage of the finite ammount of ‘cognitive load’ the consumer can bear,thus increasing the likelihood that the consumer will click through and buy fromthem.



Thus the institutions underlying the shopbot web service are: the same busi-ness models as those underlying larger search engines like Google and Ya-hoo!, where the search facility is offered in the hopes of attracting advertisingrevenue from vendors, the presence of formal and informal linkages acrossdifferent shopbots, and the problem of being ‘too good’, where by being too ef-ficient the shopbot may drive all price competition to zero and thus eliminate theneed for itself. Thus it is in the individual shopbot’s interests to allow and evenfacilitate price dispersion through the use of prominent placement of sponsoredlinks and the intelligent design of their web interfaces to make it slightly moredifficult to access search results other than those being sponsored. The nextsection extends this one and asks a question of the remarkable statistic quotedin [Montgomery et al.(2004)Montgomery, Hosanagar, Krishnan, and Clay, pg.202] that only 6% of online consumers are aware of and use shopbots. I askthe question, what is limiting the adoption of shopbots?

3.1 What Factors Limit Consumer Adoption of Shopbots?

Using the timescales of internet entrepreneurs, shopbots are not new innovations—they have been around since at least 1995 with www.searchprice.com, nowdefunct. The academic literature on shopbots, which for obvious reasons lagstheir development, goes as far back3 as 1997 with [Doorenbos et al.(1997)Doorenbos,Etzioni, and Weld], cited in [Montgomery et al.(2004)Montgomery, Hosanagar,Krishnan, and Clay]. Given that Google started up late February 1999, andthe growth of the web has been exponential, why do only 6% of internet userstake advantage of the existence of shopbots? I have grouped the possible re-sponses to this question into 2 sections, consumer characteristics and marketcharachteristics.hprice.com, now defunct. The academic literature on shop-bots, which for obvious reasons lags their development, goes as far back4

as 1997 with [Doorenbos et al.(1997)Doorenbos, Etzioni, and Weld], cited in[Montgomery et al.(2004)Montgomery, Hosanagar, Krishnan, and Clay]. Giventhat Google started up late February 1999, and the growth of the web has beenexponential, why do only 6% of internet users take advantage of the existenceof shopbots? I have grouped the possible responses to this question into 2sections, consumer characteristics and market charachteristics.

3.1.1 Consumer Characteristics

Education In the United States, among people with a bachelor’s degree orhigher, more than 80 % had access to the internet (NTIA, September2001). The proportion falls to 40 % among high school graduates, andto 12.8 % among those not having graduated from high school. At thesame time access to the internet has grown fastest among people withlower levels of education. Descriptive statistics cannot give an adequateanswer to the question why a certain group of individuals make more orless use of the internet. The reason is that demographic characteristicsare often mutually dependent. For instance, people with a higher income

3As far as I am aware, of course.4As far as I am aware, of course.



often have a higher level of education. The statistical relation betweeninternet access and level of education could only be an indication thatpeople with a higher level of education often have a higher income. Oncloser analysis it appears that income and level of education have inde-pendent effects on internet access. People with a lower educational levelwho live in households with a relatively high income, have less access tothe internet than people with a higher educational level who live in house-holds with a lower income. Thus, since the use of the internet is becom-ing more widespread and consumers are turing to online stores for moreroutine goods like clothing [Markillie(2004)], it should be expected that,at least using this metric, the use of shopbots will increase as consumerdemography pushes internet use from academia to ‘the masses’.

Age The access to computers and the internet is strongly connected with age.Computers and internet are most used by children and teenagers, fol-lowed by people between 26 and 55. Older people are using computersand internet least of all. The increase of internet use is distributed over allage groups. This general upward shift in the age distribution is caused bytwo factors. The first one is the absolute increase on the internet use andthe second is a cohort effect. People who were 55 years in 1995 nowbelong to the category of 60+. It is assumed thereby that people whoused the internet when they were younger, probably will keep using theinternet. Indeed, coupled with the fact that internet use will probably notstop with aging, those cohort effects are moving with a double amount offorce—not only are the older cohorts ‘becoming’ more internet aware, buttheir younger counterparts will have been educated using the internet,thus it is more likely that the use of shopbots will increase in absolute ifnot relative terms.

Attention Consumers no longer need to search stores geographically and in-stead search for products online, where the constraint is their attention,according to [Smith(2001)]. Here consumers face what is known as acognitive constraint- they simply cannot compute all the various prices,quantities, etc that are presented to them, and instead evolve heuristicsto avoid this happening. ‘Eye level is buy level’ is an old adage of the re-tailing industry, and it applies to the internet as well, where [Montgomeryet al.(2004)Montgomery, Hosanagar, Krishnan, and Clay, pg. 202] foundthat consumers would purchase goods on the first page of the set of re-sults returned by the shopbot with a probability of 90%.

Consumer’s Awareness of Seller’s Existence As remarked above in the re-cent literature section, it is highly unlikely that a naive consumer will knowof the existence of many of the fringe sites that are selling their desiredproducts. This creates a problem for both the fringe retailers and theincumbent and dominant vendors. Consumers know that the dominantfirms are dominant, and so expect a positive price differential on populargoods. [Smith(2001), pg.9] found that on popular products the larger anddominant retailers charged from 7–12% above the marginal cost of theseproducts. Knowing this, fringe retailers may succeed by undercutting theirlarger competitors on price in certain product ranges with a lot of demandfor them. For example, the list price of Tony Hawk Challenge, one of the



highest selling and most highly rated video games of the last few yearswas $19.99 for Walmart.com, a very large retailer, while for theNerds.net,it was $17.33 across three of the five shopbots (Nextag, Buypath andFroogle). So by undercutting the larger firms on popular items, the fringefirms may gaina small toehold in the market. The problem with this strat-egy is that it eats into the cash reserves of a company that can ill affordit, and asks a cash-rich business like Amazon to dig into its pockets formore advertising/branding revenue also, which it can do without difficultythanks to cheap per-click online advertising. Of course, all of this anaylsisabstracts from the two principle issues the consumers have a priori withonline shopping- setting up accounts which lock them in to a particularretailer, and giving out their credit card details to an unknown company:the problems of lock-in and trust.

Lock-in Consumers have become accustomed to using sites such as Amazonor portals such as Yahoo! to search for their goods. Indeed [Montgomeryet al.(2004)Montgomery, Hosanagar, Krishnan, and Clay], using a simu-lated model of consumer utility at different types of shopbot found that,even being aware of the existence of a shopbot, these was a 64% prob-ability that consumers would still prefer to use their favourite online storerather than use a shopbot. Having set up an account with a an onlineretailer, and provided them with one’s credit card details, one is loath togo through the same set up procedure again to save 50 cent. It seemsthat a significant price differential must be offered to induce the consumerto shift. Thus an efficient incentive-based strategy would be for the fringecompany to provide that comparison by linkng to Amazon’s website andchecking what the price difference (should it exist) is, and making theconsumer aware of just how much it is. Of course, practically this isimpossible- the likelihood of Amazon allowing its databases to be usedin such a fashion asymptotically approaches zero the more one thinksabout the prospect.

Trust Consumers are more likely to give their credit card and billing details toa large and well known online retailer with a credible return policy andinsurance against fraud of any kind. Every Amazon.com web page an-nounces that they will refund the consumer if through Amazon’s negli-gence they are somehow discommoded. This creates the basis for trustonline. The fact that other customers can rate an individual seller withinamazon, both positively and negatively, is a significant reinforcing factor inthe generation of online trust. [Darmon(2002)] found that of all the elec-tronic reputation mechanisms available in 2002, the presence of a ‘ref-eree’ in the form of another consumer or a trusted supplier was the onlyone that correlated positively and significantly with higher sales acrossthose items. Smaller or newer retailers do not have this trust buildingmechanism unless they create it themselves through some subterfuge,and they are not likely to garner much of this type of review unless theysell more of a particular type of good, and even then only in that specificcompetence will the trust-building mechanism accrue to them: few peopletrust Amazon.com to sell them new cars [Markillie(2004)] or new make-up products—there are limits to the range of goods that are sufficientlymore convenient that they will be purchased online. But consumers are



more likely to trust Amazon than theNerds.net, as they have an estab-lished line of credit and an account with the leader in the market. Thatthere might be better deal out there is outweighed by the convenience ofone click consumerism, free shipping and handling costs above a certainlevel of purchase as well as the reasonable expectation that the productyou have purchased will arrive in timely fashion. The obstacles the fringefirm must overcome through simple price competition are rather large in-deed. The reason these obstacles exist in the first place is the fact thatAmazon.com was the first to bring a convenient and reliable service tothe online marketplace.

First to Market In traditional industries, the price premia and market sharesassociated with being the first to recognise and exploit an emerging busi-ness model have been exhaustively documented [Shankar et al.(1999)Shankar,Carpenter, and Krishnamurthi],[Schmalensee(1982)] and it seems thatthe online sales industry is no exception. Here we see the results of aconcerted effort over a number of years by industry leaders like Ama-zon.com to improve the customer experience and shipping/handling diffi-culties that dogged it at the start to become the book vendor of choice onthe internet today. There are significant advantages to having the forwardmomentum in a market exhibiting exponential growth.

The impact of these figures have become lessened through overuse5, asthey have been used and much abused by commentators on the inter-net over the last 2 years, but their content is unmistakable—the num-ber of users going online to shop has been growing at logistic ratesfor over 3 years. In such a market the first to market absorbs all ofthe trust/reputation/recognition advantages discussed above, while beingforgiven its failings due to teething problems, and becoming the bene-ficiary of increasing returns to advertising revenues on search engineslike Google as more and yet more users come online, clicking through tosites like Amazon and spending there. shopbots, as aggregators acrossboth fringe and dominant retailers, have a double edged sword to grasp:if they search the larger and more successful sites more efficiently dueto some contractual agreement or if it places the larger site’s price ina prominent position because of advertising revenue, then the shopbotdooms the smaller vendor while becoming an extension of the larger. Oralternatively, if the shopbot reverses the above description and contractsto many smaller sites, it faces the problem of dwindling revenues fromclick-through advertising on the larger sites choking off its cash flow andstarving it into bankruptcy. The middle way is, of course, to be impartialand simply rank the sites in terms of prices, as froogle.com, a sub-siteof google, does. But froogle does have the advantage of being tetheredto the largest search engine in the world, and that makes a world of dif-ference as there are economies of scale, especially in the realm of clickthrough advertising.

Thus the problem of consumer characteristics is multi-dimensional, al-5I will not add my name to the list of those who trailed out the exponential growth graph to make

their point. Suffice it to say I am aware of its existence. See http://www.niaa.org/socio for allthe details.


http://www.niaa.org/socio


though signs point to an increase in absolute shopbot usage, if not arelative increase.

3.1.2 Market Characteristics

Immature Market Searching for goods online is still not a mature market, andfar from it. One measure of this is the demography of those using theinternet as we have seen above, but another metric of how immature themarket is is the types of goods sold online. These goods have tradition-ally been homogeneous information goods like documents, DVDs, musicor video games. These products all appeal to a small subsection of theeconomy and are themselves immature products in terms of their overalllife cycle. Clothing, however is a mature product— one of the most ma-ture, and for the first time last Christmas, North Americans bought moreclothing online than any other type of product [Markillie(2004)]. Now weare seeing a move from informational goods to low information physicalgoods being purchased online. Interestingly, this was not predicted in1997 by [Choi(1997)] who suggested that the products themselves wouldchange rather than the type of sale made.

Cognitive Load There is a case to be made that shopbots are simply not de-veloped enough as a shopping process— the cognitive load is still toomuch for the consumer to bear [Shugan(1980)], and indeed [Montgomeryet al.(2004)Montgomery, Hosanagar, Krishnan, and Clay] find that theconsumer, when faced with too many or too difficult choices, will revert tosuing simple heuristics to choose (‘I’ll take the first one I see’) or goingback to what they know (‘Amazon have never steered me wrong before,I’ll go with them again’). Indeed, if and when ontologies are introduced asa mechanism for coping with this informational deluge, one of the mea-sures of their success will be in how often a consumer chooses a producton the basis of an ontology-driven search, or how often the consumerreverts back the their original heuristic/experience model.

An interesting way of judging this would be to set up and experimentwith three groups of computer literate and web-literate subjects: a controlgroup searching, say Amazon, an ontology driven group searching withontology-enabled shopbots, and a standard search engine driven groupsearching with Google for a pre-specified, pre-researched product. Theexperiment is likely to conclude that, while the ontology-driven group out-performs the basic search group in terms of product price and quality,the heuristic-driven group may find that they escape the search processmuch more quickly.

In the next section I move from a general overview to focus on a particular setof products and shopbots, specifically the markets for DVDs and Video Gamesacross 5 shopbots.



4 Descriptive Statistics

Here I present the results of analysis performed on an original dataset gatheredin February and March of 2004 from five shobots, the details of which can befound in the appendix to this section, and on a time series dataset from [Xingand Tang(2004)]6.

4.1 Data Collection Methodology

Price data were collected from 5 shopbots using their standard search fields.There are observations overall across 2 categories- video games and DVDs.There are 26 video game titles in all7, the top 10 as rated by nd 16 randomly se-lected titles across genre, age rating and difficulty rating. Great care was takento ensure product homogeneity, that is, each observation was checked individ-ually to make sure that the edition/version/etc was exactly the same, so like isdefinitely compared with like. Strict physical homogeneity is very important inthis analysis. Many of the shopbots listed in table 4 in the appendix were dis-qualified because they have either the same owner and therefore were poweredby the same database—for example pricevariety.com and aimlower.com— orhad too narrow a focus on one type of product, for example The branded DVDsand video games are physically homogeneous, which makes data collectiontractable and therefore price comparison meaningful.

In the [Xing and Tang(2004)] dataset, their data was obtained from November16, 2001 to January 11, 2002, covering the Christmas and New Year period. Intotal, there are 5508 price observations. As the data were collected for the USmarket, all prices are in US dollars.

Other data gathered by one of the authors (SK) in the month of March, 2004,based on shopbot data8 across the same 52 DVDs and 26 Video Games, typedby 5 different shopbots. In total 1,612 individual price data were collected forDVDs, and 804 entries for video games. All prices are in purchasing powerparity adjusted (2001) US dollars.

Half of the DVDs (26 titles) were bestsellers while the rest were chosen ran-domly. The bestsellers were selected as an even mix of the top bestsellersamong the retailers when the study was initiated. The same methodology wascarried through to the video games. I refer to the first category of titles in bothvideos and dvds as ‘top ten and the second as ‘random, and the details ofthese titles are laid out in table 5 in the appendix.

This dataset is unique in that not only were all observations individually checkedfor product homogeneity, but also these data contain details of the individualvendors’ list prices by shopbot and across products, and their deviations froma computed average. These data are directly comparable with another largetime series dataset of prices, generously provided by Tang and Xing (2004)described above, and represent a cross section of data entries for 52 different

6Both are available upon request from the corresponding author.7Please see table 3 the appendix for a full list of the titles8See table 5the appendix for details of products and procedures used in gathering this data.


http://www.biztalk.com#biztalk.com.ahttp://www.dvdsearch.com#dvdsearch.com..


products across vendors, across shopbots and through time. It is thus a uniqueresource in the literature to date.

4.2 Comparison of one vendor across shopbots and throughtime

Here I take the case of CDUniverse, a large retailer with a presence in boththe DVD and video game markets that employs several shopbots and indeedsponsors links in one of them for DVDs (Buypath). Here I look at the standarddeviation of prices for the ‘top ten’ DVDs sold by CD Universe, both throughtime and across shopbot and compare them to the standard deviations of therandomly chosen DVDs. The results are shown in figures reffig:CDU:Shopbotand 4 below. It can be readily seen from figure 4 that there is more varia-tion in the prices of the top selling DVD titles over time, a trend which mightbe partially explained by the time of year the sample was taken—November2001 to January 2000 sees a seasonal surge in demand that has not beenaccounted for here—but another factor tends to be overlooked in this analysis,that these titles were not just the highest selling but also the most hyped. Ofthe ten titles listed in table 5, all were distributed by one of the top four dis-tributors in the USA, and all had ‘a star’ in them. Devany and Walls [?] foundthat star-power was in fact fictitious, that the probability of failure or success inopening a motion picture in a cinema was dependent on the level of positiveor negative information a series of tailored audiences provided others wishingto see the film. The same information flows matter here— they are just not aspronounced. All of the top ten sellers had been high earners at the box office.Indeed, ‘Titanic’ took more box office revenue than any film previous to it. Ofthe random titles, few were big sellers (as rated by www.biztalk.com) in thebox office. However certain titles, like ‘The Matrix’ were so-called slow burners;the film opened at a few cinemas to a tailored audience, and built up a positive‘information cascade’, as Devany and Walls would term it, and went on to makea large amount of money on foot of this. This testifies to the role of informationflows in determining the success or failure of a title.

The level of price variation in the top sellers may be because of this positiveinformation cascade: some DVDs are released with a fanfare and a large ad-vertising budget, others are just released. Most of the top sellers can expectthis type of treatment. So sellers, knowing that the consumer is already awareof the DVD title and is coming to them looking for it, may be tempted to placea premium on the prices of the top sellers for a few weeks until the first swellof demand dies down. As it is virtually costless to observe the pricing behaviorof other sellers, this type of pricing strategy could be termed ‘uncoordinatedcartelisation’, in the sense that everyone knows what everyone else is doing,and knows they could undercut them, but the short term buoyancy of the sud-denly popular DVD prevents them from dropping their margins for a few weeks.This is what tends to be the case in the market for newly released CDs also[Xing and Tang(2004)].


www.biztalk.com


Figure 4: Top sellers show much more variation in prices over time.

4.3 Comparison of Price Variation Across Shopbot

Figures 5 and 6 show the relationships that exist between the shopbots for bothtop selling titles and randomly chosen titles in the video game market. We cansee that there is much more variance in the price of the top-selling productsthan the bottom selling products.

There do seem to be trends that carry through all of the data, although they doseem to contradict in places. For example, figure 5 shows the reverse of thistrend.

Figure 5: Random Titles show more variation

4.3.1 Across Time

Figures 7 and 8 show the difference between the price variation products re-leased in 2001 and after and those released before that period. For the DVDmarket and for 2 shopbots, Dealtime and Froogle, and I show in figures 9 and10 that there is much more variation in price for those titles released closerto the period the sample as taken-late 2000 and early 2001. As mentionedabove, the main driver of price variation is the amount of information a con-sumer and producer have about each other, in that their price/quantity combi-nations have more variation than those titles released before that period. The



Figure 6: Here we see much lower standard deviations, suggesting fewer pricechanges, less demand and less competition on these items.

later releases were put in both as comparison and because some editions hadchanged, but only in terms of re-release date and not product quality. Thereare also a number of discrepancies that are difficult to clear up. For example,there are significant price variations across 2 products for whom there was lit-tle demand—Ico and NHL2001, two badly performing titles that neverthelessexhibit a large ammount of variation.

Figure 7: Price Variation across time for the Shopbot Dealtime from 1987-2000shows less variation than from 2001+

Across shopbots, the standard deviations of the top sellers and random titlesfollow much the same pattern—there is more variation in the prices of the topsellers across each of the shopbots with few exceptions. We can see this infigure 10 for the video game market quite clearly.

4.4 Price Variation across shopbots

Table 1 shows the results of t-tests performed on the different shopbots testingthe null hypothesis that the mean variation between the top-selling shopbotsand the randomly-chosen shopbots was equal. With 95% confidence, one cansay that the test was unable to reject this hypothesis, meaning that the mean



Figure 8: More Variation is Shown after 200 because the sample was takenthen, highlighting the importance of consumer attention in dictating price vari-ation

Figure 9: The same patterns as above can be seen for the shopbot Froogle

Figure 10: Again, more price variation is seen closer to the sample date, 2000-2001



Shopbot Homoskedastic HeteroskedasticDealTime 0.3457 0.3496MySimon 0.3822 0.3880cnet 0.2162 0.3268Nextag 0.1560 0.2923Pricegrabber 0.10461 0.0328Buypath 0.8899 0.9518froogle 0.2475 0.2503

Table 1: T-tests for equal means between Top-Selling and Random Titles

Shopbot H0 Equal VariancesDealTime 0.0858MySimon 0.0101Nexta 1.102Pricegrabber 0.0046Buypath 0.5252froogle 0.2077

Table 2: F-test for equal variance between Top-Selling and Random Titles

variance between the two samples was not, in fact, that different. So the whilethe absolute level of variation is quite high and is indeed significant, as mea-sured by the F-tests reported in table 2 there are no level affects between theranges within the shopbots. This means that the shopbots are not, for somereason, deliberately raising and lowering prices in response to differential de-mand patterns.

Figure 11: Top Sellers

Figure 11 shows the standard deviations of the top selling DVDs across thefive different shopbots. It is clear that there is substantially more variation inthe prices of the randomly selected titles, as shown in figure 5, and standardF-tests reported in table 2 appear to conclude the same—there is less pricevariation in the less popular and less traded titles.

Within one shopbot, however, there is a different story to be told. Figure 4shows that there is more variation among the randomly sellers than the top



sellers, which would seem to contradict the previous findings. Here we seethat the variation in prices for the top titles is higher than the random titles,within the shopbot itself. A standard F-test with 95% confidence confirms thatthis variation in variances is statistically significant. Why does this happen? Amodel is developed below to attempt to explain this.

Figure 12: Top selling and Random DVD Titles sold by the Shopbot CDUni-verse. A contradiction?

4.5 Percentage price differentials across shopbots

Across the different shopbots and across the two types of product—randomand top-selling—there are several points that should be discussed here.

The first is that, across shopbot, it appears from figures 5 and 12 that thereis more variation in the top selling titles. In this section I show more data thatappears to contradict this. Figure 13 below shows the standard deviations ofthe top and bottom sellers within one shopbot-pricegrabber.com. Here we seethat the random DVD titles have much less negative price variation relative tothe list price than video games, where almost all of the percentage differencesfrom the list prices were negative. These price differentials were calculatedaccording to the following formula

Pd = Pl − Pi/1− Pl ∗ 100

Where Pl is the list price, and Pi is the individual price listing. This gives ameasure of the variation in the product from the standpoint of its listed price.We can thus see inter-product line price variation with this measure.-

I take another view: because of the existence of persistent price differentialseven on shopbot sites, where vendors are directly comparable, even with re-gard to shipping and taxes, there must be an explanation for the fact that therestill appears to be significant price differentials on homogeneous goods in anearly ‘perfect’ marketplace. I try to provide a model, a convincing story, toexplain these price differentials.



Figure 13: Percentage Price Differentials are quite large amongst DVD titles

Figure 14: When split into top selling and random for the shopbot Deal-time.com, the overall level of dispersion by sales volume is revealed

Figure 15: Percentage Price dispersion is almost always negative in the videogame markets, suggesting they are a loss-leader for most online stores



5 Model

The central problem that all of the preceding sections have identified is theseparation of the physical product from its informational content. Thus I havebuilt a model that tries to describe the demand for those products in termsof their informational content and the consumers’ awareness of that content.The physical products are simply that—a means to an end. Their utility liesin the conveyance of information and nothing else. One can think of them ascontainers; magnetic media for the video game, CD or DVD demanded by theconsumer.

In this model there is only one type of consumer who has every reasonableexpectation that there will be a complete information retrieval from both theshopbot and the individual online vendor. The difference between the twotypes of informational agent is that the individual is ‘locked-in’ to the individ-ual website through some kind of account or loyalty scheme, and this doesnot exist for the shopbot. The shopbot, however, displays far more prices andother attributes about the demanded product, and so has the potential to of-fer different prices/etc from different sellers. The trade off the consumer mustmake is in terms of the cost of switching from one locked-in seller to a seriesof open sellers that may or may not be trust worthy. More generally, there ismore uncertainty surrounding previously unknown online retailers. There isalso the problem that the consumer themselves, although they know that theonline seller can be contacted with an order and have that order shipped, mayneed to open another account and so forth.

Thus it is the task of this section to model the dynamics of a market for informa-tionally heterogeneous products encased in physically homogeneous material.The story I want to tell begins around the quantity of the good that is demanded.We have seen that Video Games, CDs and DVDs are released with high levelsof advertising and marketing exercises, and so the consumer is very aware ofthem if the advertising is doing its job. Then, after a certain time, call it t̄, theproduct becomes less popular, and the demand for the good slopes down toa steady state until the end of the period of observation, T . What matters forus is what proportion of the goods are bought when they are just released andtherefore popular, and what proportion is bought after the demand for the goodsubsides into a steady state. Denoting this proportion by µ, where µ ≤ 1 thequestion of interest is then: what proportion of the total overall sales are real-ized when the product is popular, and what proportion is achieved when it isnot popular?

What I would expect to see in a market like this is an initial period of pricevariation, followed by a period of much lower variation. If we denote the quantityof the good i demanded in the market as qid, then the inverse demand functionfor the product would look like equation 1 below9.

qid ={

µt̄D 0 ≤ t̄ ≤ TµTD t̄ ≥ T

(1)

9This specification was originally used in [?, pp.250–252] and was used again by [Smith(2001),pp.5–9].



Equation 1 says that, following an initial bout of popularity, demand for the goodsubsides to a steady level. This is illustrated in figure 16 below.

Figure 16: Quantity Demanded Over Time

So much for the demand side. What about the suppliers in this idealized mar-ketplace? Will the producers of good i want to be in the top half of this distribu-tion or the bottom? The obvious answer is the top, but these producers are alsoaware that time is a factor, and to take a crossection of sales at a time t for ourrepresentative good, i, we would find that the producers total profits are simplytheir total sales at a price Pt, times the fraction of consumers Θ that bought atthat price, Θ(Pit), minus whatever costs they had to pay for the production anddistribution of the good, c. This relationship is given below in equation 2.

Πit(Pit) = q(Pit− c)Θ(Pit) (2)

This is a cross-sectional relationship for an individual producer. Aggregatingover the total proportions of sales in the entire market for the good µ, we have

µ≤1∑µ≥0

ΘPt − nq1∑0

c (3)

And this is simply total revenue minus total cost in the market .

This market relationship is a reasonably standard one, where the demand forthe good is given by the interactions of consumers and producers looking fortheir ideal price and quantities. We know that this is a fiction—c.f. Prof. Velupil-lai’s accompanying report D17.02-0.03 for a complete analysis of just how fic-titious it is—but for the moment it is a pleasant fiction. The reason to put upwith an unrealistic abstraction is that it gives you something that is not obvious



and also useful. What is useful about this particular arrangement of the ‘facts’of the market is that it allows me to ask questions about the distribution of µ, aparameter in the model that is not free but rather distributed according to theamount of information the consumer has about our representative product, i.

5.1 Determinants of µ

This section draws heavily on [DeVany and Walls(1996)] in terms of the spec-ification of the model. If µt̄D is the proportion of people buying a product whenit is popular, then we must ask what determines the distribution of µt̄D. Sup-pose that this proportion is distributed according to the numbers of people thatmight or might not buy the product online. Consumers choose in a random se-quence initially whether to search for the product or not. If a consumer comesacross a rating by another consumer that is in favor of a film, or they know fromsome other source that they are likely to enjoy the video game, then there is acommon probability p that a randomly chosen searcher will choose to buy theproduct. We know that the proportion of people that want to choose the productis given by µ, and that there are different subjective evaluations of this prod-uct. If this is the case, then µ is distributed with a binomial random varianceaccording to

P [µ = m|p] = µm

pn(1− p)n−m,m = 0, 1, . . . , n.(4)

If all consumers held a common opinion about the quality of i, then the rev-enues the producers derive from the product’s sale would follow the binomialdistribution, but that is the trivial case. What is more interesting is when theconsumers hold different subjective beliefs about the quality of an unknownproduct like a film or video game. If this is the case, then by conditioning on pand integrating over the binomial distribution, we have

P [µ = m] =∫ 10

P [µ = m|p]f(p)dp

=∫ 10(

µp

)pk(1− p)n−mdp = 11+n , m = 0, 1, . . . , n

And here we can see that each of the possible n + 1 outcomes is equally likely,in the sense that each purchase is independent of any other purchase at thetime of purchase, but before that, previous consumers’ experiences will havean impact on the present consumer’s decision to buy from an online retailer,and so we can tell our story in terms of sequential decision processes. Theproblem of inexactness of information is dealt with neatly in the Devany/Wallsspecification—the fact that information is not exact leads to a stochastic com-ponent Φbeing added to the benefit each consumer receives from their experi-ence of consuming this product, so that the benefit to an individual j from usingproduct i will be

bji = Φ + �ji

(5)



where Φ can be seen as a common, unobserved quality measure of the prod-uct i and �j is the individual benefit to experiencing the product as measuredin deviations from the product. The amount of information available to the con-sumer can vary according to what type of service they are using- a shopbot ora regular vendor. The vendor contains the ratings and reviews of many differ-ent previous users. We will denote this information set by the vector mathbfRwhere in l previous experiences are recorded for the consumer’s perusal. Eachelement of this vector is the experienced quality of a previous consumer, andso the vector takes the form

R = (Φ + �l,Φ + �l−1, . . . ,Φ + �0)

The shopbot contains, in principle, the ratings of a finite but large number x ∈ Xof different vendors, and so the shopbot can proffer a potential

x≤X∑x=1

(R)

experiences to the consumer if they use the shopbot service.

For the individual consumer j, the expected return to consuming a product ifrom an individual vendor x is conditional on R, according to

E(bji|Rj) = E(Φ|Rj) (6)

And if the cost of experiencing the product is c, where c is the vector of the listprice, shipping costs, delivery costs and discounts received, the representativeconsumer j will purchase the product from the vendor if

E(bj+1Rj) > c(7)

The interesting part of this formulation is that the differential impact of the shop-bot upon the information gathering process can now be discussed. But beforethat, one final piece of the model is needed.

It has been noted in several of the sections above that ‘lock-in’ is prevalentin online markets. That is, one will go to Amazon.com if one has an accountthere and not at another vendor. A model that seeks to explain price dispersiononline must take account of this and my model does this by assuming that ifone has an account at or voucher with a particular vendor, one will discount anyperceived price premia one encounters on that site by a factor δ so, for example,if a consumer feels for whatever reason that the price for a product i, Pi is higherthan it should be relative either to a perceived or real price differential ∆P theywill discount this by a ‘convenience factor’ of

Pi + ∆P(1 + δ)

= 1

, where 1 is the ‘true’ price of the good, or the best price that could be gottenelsewhere at that time.

So, for example, if our consumer has a ‘convenience’ factor of δ = 0.05 andthe perceived price differential ∆P is +0.10, on a product with a total cost c



Figure 17: Thresholf Effects to being Locked-In to a Vendor

of 100, then the consumer will calculate a threshold price of 104.76% that hasto be exceeded before they can be persuaded to shop elsewhere than theirchosen vendor. This is not a maximising consumer in the neo-classical sense.Rather this consumer simply wants the product that will satsfy them to a givenlevel. Thus the metaphor of ‘thresholds’ is appropriate in several senses—theconsumer will not cross the door of another vendor unless the price premiumthat they are offered there is well below the price listed on a site which they are‘locked-in’. Figure 17 below illustrates this example.

Here we see that a shopbot or a vendor using a shopbot would have to offera significant price differential and have the awareness of the consumer thatthe product is being sold in this shopbot before the consumer will switch to us-ing the shopbot from the ordinary vendor. This is the paradox of the reportedstatistic by Montgomery [?, montgomery:2004]hat only 6% of online shoppingis done using shopbots. Though a shopbot offers a significantly larger expe-rience set R, there is a countervailing force in the level of lock-in a consumermight experience online to stop adoption of the shopbot as a dominant service.

Price differentials are not high enough across the shopbots to achieve this asfigure 4 illustrates for the case of three shopbots for the highly popular DVDTitanic. They are thus stuck in a limbo between different sets of priorities—theconsumer’s search parameters given their level of involvement with one or moreonline vendors, and the requirements of the individual vendors that advertisethemselves on the shopbots.



6 Conclusion and Further Work

This report has reached one conclusion—that the types of questions that havepreviously been asked of the price dispersion data have not been the correctones. Each paper cited in this survey has paid lip-service to the role that in-formation plays in online purchasing and sales, while few have actually triedto model the diffusion of information across different products. The findings ofthis report are:

The market for physically homogeneous products is not the same as the marketfor informationally hetergeneous products.

Demand for these products is based around a relatively large number of factorssuch as consumer attention, search costs, advertising and market characteris-tics.

Prior to the release of these products there is much greater uncertainty withregard to overall demand and the initial popularity of the product, much moreso than for traditional goods.

The role of positive and negative information-cascades cannot be understatedin determining the level of overall demand for a product.

The overall use of shopbots by consumers will rise in the future as more con-sumers come online, but the relative use of shopbots may not increase. Thisis because of the twin problems of online trust and consumer’s lock-in to onelarge site.

These lock-in effects are a cause of a large ammount of price dispersion, ac-cording to [Xing and Tang(2004)] and the data surveyed here.

Shopbot search patterns can be made much more efficient by the generationand use of shared e-commerce ontologies.

The benefits to the consumer for using shopbots are at present not sufficientto outweigh the ease of use a large site with consumer lock-in has in terms ofconvenience and price premia.

Consumers are much more sensitive to changes in the cost of shipping anitem rather than changes in the component cost of the item itself, and so aninteresting strategy for shopbots to follow would be to incorporatesome of thosecosts into their business model and so attract more customers.

Consumer awareness is a key factor in determining online success, and sothe existence of Froogle, a shopbot tied to Google, the World’s largest searchengine, may be a factor in helping more consumers to use shopbots in thefuture.

In future work, non-equilibrium models will be developed using this data as astarting point, and simulations of these models will be carried out.



7 Appendix

This appendix contains details of the the types of titles searched in the study, aswell as details of a different, more enlightened approach to modeling economicbehaviour called Evolutionary Economics.

7.1 Evolutionary Economics

Evolutionary economics is a relatively new economic methodology that is mod-eled on biology. It stresses complex interdependencies, competition, growth,and resource constraints.

The first 200 years of economic theory was modeled primarily on physics eco-nomic terminology like “labour force“, “equilibrium“, “elasticity“, and “velocity ofmoney“, are no accident. Conventional economic reasoning begins with thedefinition of scarcity, then assumes the existence of a “rational agent“ bentsolely on the attainment of one goal the maximization of her/his welfare asdefined by that agent.

All relevant information is assumed to be held in common (“perfect informa-tion“), and the scheme of valuation (“preferences“ or “tastes“) used by thedecision-maker is also assumed to be constant and native to the agent (“nonenvy“or “independent preferences“). Given the foregoing stipulations, the determi-nation of the “rational choice“ for any agent becomes a straightforward exercisein the differential calculus.

Evolutionary economics derives from a more modern tradition of inquiry, whichdoes not take the characteristics of either the objects of choice or of the decision-maker as fixed.

7.1.1 Development of evolutionary economics

Karl Marx began in the mid-19th century with his schema of stages of historicaldevelopment, by introducing the notion that “human nature“ was not constantand was not determinative of the nature of the social system; on the contrary,he made it a principle that human behavior was a function of the social andeconomic system in which it occurred.

At approximately the same time, Darwin developed a general framework forcomprehending any process whereby small, random variations could be accu-mulated over time and under the urgings of economic forces into large-scalechanges that resulted in the emergence of wholly novel forms (“speciation“).

This was followed shortly after by the work of the American pragmatic philoso-phers (James, Peirce, Dewey) and the founding of two new disciplines, psy-chology and anthropology, both of which were oriented toward cataloging anddeveloping explanatory frameworks for the variety of behavior patterns (bothindividual and collective) that were becoming increasingly obvious to all sys-tematic observers. The state of the world converged with the state of the evi-



dence to make almost inevitable the development of a more modern frameworkfor the analysis of substantive economic issues.

Thorstein Veblen began his career in the midst of this period of intellectual fer-ment, and as a young scholar came into direct contact with some of the leadingfigures of the various movements that were to shape the style and substanceof the newly-minted social sciences into the next century and beyond. Veblensaw the need for taking account of cultural variation in his approach; no univer-sal “human nature“ could possibly be invoked to explain the variety of normsand behaviors that the new science of anthropology showed to be the rule,rather than the exception. His singular analytical contribution was what cameto be known as the “ceremonial / instrumental dichotomy“; Veblen saw that ev-ery culture is materially-based and dependent on tools and skills to support the“life process“, while at the same time, every culture appeared to have a strati-fied structure of status (“invidious distinctions“) that ran entirely contrary to theimperatives of the “instrumental“ (read: “technological“) aspects of group life.The “ceremonial“ was related to the past, and conformed to and supported thetribal legends; “instrumental“ was oriented toward the technological imperativeto judge value by the ability to control future consequences. The “Vebleniandichotomy“ was a specialized variant of the “instrumental theory of value“ dueto John Dewey, with whom Veblen was to make contact briefly at the Universityof Chicago.

The most important works by Veblen include, but are not restricted to, his mostfamous works (“Theory of the Leisure Class“; “Theory of Business Enterprise“),but his monograph “Imperial Germany and the Industrial Revolution“ and theessay entitled “Why Economics is not an Evolutionary Science“ have both beeninfluential in shaping the research agenda for following generations of socialscientists. TOLC and TOBE together constitute an alternative construction onthe neoclassical marginalist theories of consumption and production, respec-tively. Both are clearly founded on the application of the “Veblenian dichotomy“to cultural patterns of behavior, and are therefore implicitly but unavoidablybound to a critical stance; it is not possible to read Veblen with any under-standing while failing to grasp that the dichotomy is a valuational principle atits core. The ceremonial patterns of activity are not bound to just any past, butrather to the one that generated a specific set of advantages and prejudicesthat underly the current structure of rewards and power. Instrumental judg-ments create benefits according to an entirely separate criterion, and thereforeare inherently subversive. This line of analysis was more fully and explicitlydeveloped by Clarence E. Ayres of the University of Texas from the 1920s.

Joseph Schumpeter, who lived in the first half of 20th century, was the authorof the book “The Theory of Economic Development“. In this book he took the,at that time very radical, evolutionary perspective. He based his theory onthe assumption of usual macroeconomic equilibrium, which is something like“the normal mode of economic affairs“. This equilibrium is being perpetuallydestroyed by entreprenuers who try to introduce innovations. A successfulintroduction of an innovation disturbs the normal flow of economic life, becauseit forces some of the already existing technologies and means of production tolose their positions within the economy.



7.1.2 Evolutionary Economics: The Nelson and Winter Tradition

Through their work Nelson and Winter demonstrate the possibility of overcom-ing the basic difficulty in studying evolutionary processes, namely the needto combine elements which are normally considered as belonging to quitedifferent areas of investigation. These elements are the processes of trans-mission, variety creation, and selection; or more specifically: Simons workon rule-based behaviour (Nelson and Winter, 1982, chs. 4-5), Nelsons andother Schumpeter-related work on invention and innovation (Nelson and Win-ter, 1982, ch. 11), and Alchians and Winters work on natural selection (Nelsonand Winter, 1982, ch. 6). Such a combination presupposes two opposing ca-pabilities: an ability to cope with a wide diversity of elements, and an ability tocut out the details and integrate the elements into an initially crude conceptionof an evolutionary process. The computer helps to organise this synthesis-ing exercise to the very last steps since the simulation format does imposeits own constructive discipline in the modeling of dynamic systems: the pro-gram must contain a complete specification of how the system state at t + 1depends on that at t and on exogenous factors, or it will not run. (Nelson andWinter, 1982, 208 f.) By taking this process to a preliminary conclusion, Nelsonand Winter provide a constructive proof of the existence of relatively interestingevolutionary-economic models. At the same time they give an explanation ofthe weaknesses of the informal approaches to evolutionary processes: theseprocesses are normally so complex that it is nearly impossible to master themintellectually by means of the methods of the old evolutionary modes of think-ing.

7.2 Tables and Figures

Here I attach details of the titles and shopbots searched, as well as a shortpiece on experimental economics.

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Video Game Titles for Playstation 2

Tony Hawk’s Pro Skater 3Dave Mirra Freestyle BMX 2James Bond 007: Agent Under FireAce Combat 4: Shattered SkiesATV Offroad FuryFord Racing 2Time SplittersHalf LifeMuseum NamcoSuper Bust A MoveFantaVisionThe adventures of Cookie and CreamNFL 2K3NHL 2002Socom: US Navy SealsRatchet and ClankJak And DaxterJak IISly Cooper and Thievious RacoonusRise to HonorWar of the monstersIcoPrimalExterminationGran Turismo 4Kinetica

Table 3: Video Games Searched, Ten Popular, 16 Random

Shopbots Searched Shopbots Searched

http://www.dealtime.com/ http://www.mysimon.com/http://shopper.cnet.com http://www.nextag.com/http://www.pricewatch.com/ http://www.pricegrabber.comhttp://www.bizrate.com/ http://www.americanexpress.com/shoppinghttp://www.bestwebbuys.com http://www.dvdpricesearch.com/http://www.AimLower.com http://www.streetprices.com/http://www.PriceMix.com http://www.metaprices.com/http://www.buypath.com/ http://price-rx.com/http://www.findall.com/ http://www.storerunner.com/http://froogle.google.com/froogle/ http://www.pricevariety.comhttp://www.citibay.com/ http://www.directtextbook.com/

Table 4: List of Shopbots Searched, Most were discarded as being too Specific



DVDs DVDs DVDs

Shrek Star Wars: Episode I SwordfishLegally Blonde Pearl Harbor Planet of the ApesJurassic Park III Snow White and the Seven Dwarfs Apocalypse Now ReduxFinal Fantasy America’s Sweethearts Star Trek: the Motion PictureDoctor Zhivago Bridget Jones’s Diary The MatrixRush Hour 2 Dr. Dolittle 2 DumboCitizen Kane Cast Away Brother, Where Art Thou?Memento Empire of the Sun Office SpaceGladiator Spy Kids Leaving Las VegasDeep Impact Funny Games SLC PunkCon Air I Love Trouble Eyes wide shutPalmetto Object of Beauty Small SoldiersThe Three Musketeers last Action Hero Fierce CreaturesJingle All the Way U-Turn The AssociateGood Will Hunting Top Gun TitanicHunt for Red October Rainmaker Made in AmericaPandora Project Babar Rogue Trader

Table 5: DVDs Searched

Seller/Bot

dealtime.commysimon.comShopper.cnet.comNextag.combuytalk.comFroogle.com

Table 6: Shopbots actually Searched with Data Collected From Them



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Overview and MotivationRecent approaches to modeling digital enterprises---the case of shopbots

What is a shopbot?How do shopbots act

Documents

DERI L´ıon - Stephen Kinsella · This study will present three interesting and interlocking facets of this problem: a description of the forces at work on the consumers, producers