There is no prominent reference in the extant literature of the degree to which .... g x x t if x α α α α. â
. > â§. âª. = ⨠+.
The Information Deficit in Electronic Markets Panos M. Markopoulos and Ravi Aron and Lyle H. Ungar University of Pennsylvania
Abstract Electronic markets for information-intensive products are characterized by less than complete information about some product attributes even though these attributes influence the buyer’s purchase decision. We term this as an “Information Deficit” and investigate this phenomenon through an analytical model and through analysis of an extensive collection of data from currently functional online markets. We present an analytical model of an electronic market where sellers can control the extent of product attribute information that they release and explore the conditions under which sellers make product information available to potential customers. We model the seller’s decision to furnish product attribute information as a three stage game and analyze the resulting equilibria and welfare implications from a policy standpoint. We demonstrate that under fairly general conditions, in the presence of even very small cost associated with the dissemination of information, vendors will choose to provide just enough information to distinguish their products from their competitors. We investigate how sellers’ strategies differ when buyers’ indulge in parallel search for product information aided by shopbots as opposed to sequential search for price. We thus demonstrate the existence of an inefficiency in Internet markets that causes considerable uncertainty to the buyers that are located in the “switching threshold” of products. We also furnish data from currently functional electronic markets and analyze a data set consisting of nearly two thousand products in the PC-Game industry and show extensive empirical evidence in support of our model’s predictions.
1. Introduction Several articles appearing in the trade and business press have noted that while electronic markets could potentially feature fine grained information about all attributes of a product or service, they rarely do. For instance, very few Realtors offer three dimensional views
of the interior of homes or the school district's performance statistics on their web sites. Similarly on-line travel agents do not offer information about airline movie schedules on long haul flights to even first class travelers just as on-line retailers of high end stereo systems do not offer detailed information about the distortion in sound and loss of fidelity induced by different types of connection cables and wires. Yet, to many buyers of these services and products such information is indeed valuable and furthermore, such information is available with the producer of the product (service). Thus, there is a widely observed phenomenon of less than complete product information being furnished in these markets which we term as the “Information Deficit” of the market. In extant literature the information that pertains to aspects of consumer taste is termed as “horizontal product information” and the product attributes themselves as “horizontal product attributes”. We shall follow the same convention here. We explore the conditions under which sellers disseminate horizontal information to buyers and analyze the phenomenon of Information Deficit in Internet-based markets. Further, we examine if there exists a critical cost threshold (of providing granular information) which when exceeded will result in the phenomenon of sub-optimal levels of information being furnished. We find that, indeed, such a critical cost threshold exists and what is more, it is infinitesimally small. We furnish rich empirical evidence to demonstrate the phenomenon. It is reasonable to claim that missing or imperfect product information in Internetbased markets results from the existence of costs associated with providing information of a fine grain about product attributes: a seller will not offer detailed product information, unless the benefits outweigh the costs. Additionally, there is strong theoretical support for the argument that more information can lead to increased seller revenues, for example see the models by Nelson (Nelson 1970) and Kihlstrom (Kihlstrom 1974). There is also some empirical evidence that supports this view as was shown by
Gu, Hitt and Clemons (Gu 2002), in markets for experience goods, such as books and music CDs. Therefore, one would expect that information that is very cheap to disseminate, such as the type of software that OEMs bundle with their PCs, would always be available in electronic markets. This is clearly not the case and indeed a very wide range of product attributes, from the exact dimensions of microwave ovens to the Internet bandwidth that hotels provide to business travelers in their rooms, are often not furnished to the potential customers. We demonstrate through an analytical model, that, when there exists any positive cost, howsoever small, of disseminating product information, sellers choose not to disseminate any more detailed product information than what is necessary to differentiate them from their competitors. There exist horizontal product attributes about which information is always given to potential consumers. Examples include airline flight times and refrigerator dimensions. These are clearly horizontal attributes, as a flight that leaves in the morning is no better or worse than a flight that leaves in the afternoon1. Similarly making a refrigerator shorter but wider does not necessarily make it better or worse. Yet, there is always information available about these product attributes. Our model is able to explain both positive and neutral seller incentives to provide more information, by establishing the existence of a threshold in consumers “sensitivity” about different product attributes. Only if the threshold is exceeded by sufficiently many consumers, do sellers have incentives to provide information about the attribute. The paper is organized as follows. Section 2 provides a review of the related literature and we develop our model in Section 3. We analyze our model under Oligopoly and Monopoly in Section 4, where we show our main result, that sellers will provide just enough horizontal information for their products to appear “distinct” to the buyers. We 1
While consumers may have different preferences, their valuation is strictly determined by taste.
also observe that in most functional internet markets, today, some horizontal product attributes tend to always be disseminated and explain this phenomenon by arguing that these are attributes for which people have “strong preferences”, as measured by our fit cost parameter. In Section 5, we provide empirical evidence in support of our theoretical results and conclude in Section 6.
2. Related Literature In 1929, Hotelling explained spatial product differentiation (Hotelling 1929), with his “linear city” model. Samuelson introduced the “unit circle” model (Samuelson 1967) which was refined and further developed with Salop's treatment in (Salop 1979)2. Takahashi and de Palma developed a unified treatment of both models and showed that, under fairly general conditions, these are to a large degree equivalent (Takahashi 1993). The most important result of the spatial product differentiation literature is that, when market entry is free, vendors pursue strategies of maximum differentiation, that is, they try to locate their products as far from competitors' products, as it is possible. Following Stigler's theory of buyer search in product markets (Stigler 1961), an extensive body of literature has been developed in economics, that investigates the effects of imperfect information and buyer search, on price dispersion, product differentiation and market structure. See for example the articles by Diamond (Diamond 1971), Nelson (Nelson 1970) and Akerlof (Akerlof 1970). Optimal sequential consumer search in uncertain environments has also been studied extensively by Lippman and McCall (Lippman 1982), Weitzman (Weitzman 1979) and others3. The effects of the reduction of consumers' search costs, with the advent of Internet, have been studied as well, for example see (Bakos 1997). Bakos, synthesized 2
For good map to the spatial differentiation literature see Greenhut, Norman and Hung (Greenhut 1987). A good literature review on the impact of imperfect information is provided by Stiglitz in (Stiglitz 1989). The literature on optimal search strategies, is reviewed by Rothchild in (Rothschild 1973).
3
prior work with the dynamics of the then emerging phenomenon of Internet markets. We adapt Bakos's model to focus on the flow of information in the market. Our two models are similar but applied to fundamentally different questions. First, reflecting recent developments in shopbot technology and product comparison software, we consider parallel, shopbot-aided search, as opposed to “fast” sequential search. Furthermore, unlike Bakos, we observe that consumers may still be left with a degree of unresolved uncertainty about the products, even after having examined the products returned by a shopbot, due to the incomplete nature of the information that the shopbot can access and furnish. This observation is part of our model's mathematical formulation. There is no prominent reference in the extant literature of the degree to which available product information in the market still leaves consumers uncertain about many product attributes. Indirect evidence is provided in the literature that documents persistent price dispersion in both commodity and commodity-like Internet markets. For example, two papers, by Brynjolfsson and Smith (Brynjolfsson 2000), and Clay, Krishnan and Wolf (Clay 2000) report significant price dispersion in the online book industry. Another report by Clemons, Hann and Hitt (Clemons 2001) documents significant price dispersion among different online travel agents, even after accounting for differences in airfare qualities. The price dispersion that was measured in this body of work cannot be explained unless we accept that there exists sufficient information uncertainty about product attributes in those markets4. The effect of consumer demand for product information on the market, is usually studied in the Von Neumann expected utility framework which asserts that consumers always prefer products of certain value to those of uncertain value, with the same 4
It is widely accepted that equilibrium price dispersion models with perfect information cannot account for all observed product price dispersion and that, at least to some extend, price dispersion exists because consumers are imperfectly informed about product offerings.
expected payoff. Nelson and Kihlstrom (Nelson 1970; Kihlstrom 1974) argue that the uncertainty about product information is determined by consumers who can acquire costly product information to reduce this uncertainty. Gu, Hitt and Clemons (Gu 2002) posit that the seller that reduces buyers' uncertainty about her products will experience increased product demand and provide empirical support for their claim. This body of literature follows the neoclassical consumer theory, where consumers choose the quantity for each good, to maximize their utility, subject to budget constraints. For example see Varian's treatment (Varian 1992). Models that follow the neoclassical consumer theory are thus “predisposed” to produce incentives for sellers to provide more accurate product information. Our model is robust enough to reproduce this result5, even though we do not assume that consumers demand multiple units of the good6.
3. A Model of an Internet Market We employ Salop's classical model of spatial differentiation (Salop 1979), with uniformly distributed buyer preferences, altered to account for uncertainty about the sellers' exact positions in product space. Our market is an Oligopoly with N sellers and M buyers for a good that is spatially differentiated along a unit circle (see Figure 1). We assume that the number of buyers, M, is a very large number, close to infinity. Each buyer has a most-preferred product location l * and incurs a “fit” cost U (l , l * ) when he purchases a good at location l . We model the fit cost to be a linear function t l − l * of the magnitude of the distance between l and l * . Buyers demand one unit of the good, subject to a reservation utility v . Depending on the value of the v parameter (or alternatively, the value of the t parameter), the sellers can be monopolists, with portions
5
This is true for sufficiently high values for the buyer fit cost parameter. It will be clear in our analysis in Section 3 that the results hold good independent of the quantity demanded. 6
of the buyer population not served by any of the sellers, or the market can be such that all buyers are served7. We consider both cases in our treatment. All players are risk neutral and sellers supply a single good with constant marginal cost (normalized for convenience to be zero). Thus, the utility that Buyer j, with ideal product location l *j , receives from buying a product from Seller i, located at li and priced at pi is B j = v − pi − t li − l *j . Seller Seller
Seller 2a Seller
2a
2a 2a
2a
2a
2a Seller
2a Seller
Seller
Seller
2a Seller
Figure 1: Our model of product differentiation
We model this as a game where sellers move first by simultaneously choosing a location for their product and choose their prices having observed each other's locations. We introduce the idea of a parallel search which is a mechanism that is analogous to a search using an electronic search engine except that it returns the approximate location of a good (and therefore, the seller's position). The central idea here is that in the absence of detailed and exhaustive product information, the buyer can only know an interval within which the product is located and not the exact location of the product, which happens to be a composite of all product attributes, not just the ones that are available to the search device. Thus, the buyer can only learn the sellers' approximate location within an interval of size 2 ⋅ α such that the seller's true location is equiprobable anywhere Note that when v/t is sufficiently large, even when all sellers are maximally separated in product space, the market will be fully served. 7
within the interval. We term this interval as the seller's “uncertainty interval”. The buyer is assumed to use a free shopbot for the parallel search, incurring no search cost. Each seller can then choose to provide more detailed product information and reduce his uncertainty interval to α = 0 , an action for which the seller incurs a “small” cost c.8 Since the sellers assume positions simultaneously, without knowing each other's location in the product space, the extent of differentiation can vary from zero to maximal differentiation, or in other words, maximum separation in the product space. While it is possible that due to entirely fortuitous chance events, sellers will achieve a position of maximum differentiation, this is in fact a zero probability event.9 Therefore, the expected fit costs for a buyer that purchases a product from a seller whose exact location is unknown, but his expected location is at distance x from the x+a
buyer's location (see Figure 2.) is
∫
x−a
α −x
given by
∫ 0
⎛ 1 ⎞ yt ⎟ dy + ⎜ ⎝ 2α ⎠
α +x
∫ 0
y ⋅t dy = x ⋅ t , if x>α. If xα x 2, if the uncertainty intervals of two sellers overlap11, at least one of the sellers will provide more information by setting his uncertainty interval to zero. Furthermore, as a result of this, the buyer that is indifferent between the two sellers (marginal buyer) will be located outside sellers' uncertainty intervals. Where N is the number of sellers in the market. The proof or this and all other propositions is given in the Appendix of Mathematical Proofs. For N=2 (duopoly) there exists a case that can occur with non-zero probability, where a pure strategy equilibrium when the two sellers have overlapping uncertainty intervals, does not exist12. In the mixed strategy equilibrium both sellers may decide to maintain their overlapping uncertainty intervals. However, it can be shown that the probability of both sellers choosing to maintain their uncertainty intervals is proportional to the cost of providing information and thus it is for all practical purposes very close to zero. This outcome is theoretically possible for N>2 sellers, but unlike the duopoly case, it can be shown to be a zero probability event, that requires a specific product positioning in the game's first stage. The intuition behind the proof is that from Equation 1 and Figure 3 the seller can decrease the fit costs of buyers whose ideal product locations are closer than the span of
10
While it is smaller than any other positive quantity in our model, it is not smaller than any positive quantity, for this would imply that in the limit α → 0 . 11 According to the definition of our game, the overlap may occur after the first two stages of the game. 12 As can be seen in the Proposition's proof in the Appendix of Mathematical proofs this occurs when the marginal buyer (the buyer that is indifferent between the two sellers in the duopoly) is initially inside both sellers' uncertainty intervals, but because of their actual (a-priori unknown) location of the sellers, it so happens that if each of the sellers unilaterally provides more information the location of the new marginal buyer happens to be outside the competitor's uncertainty interval.
the seller's uncertainty interval. Thus by providing perfect product information the seller is able to capture some of the buyers that would otherwise prefer one of the two neighboring products offered by competitors. Depending on where exactly the marginal buyer (the buyer that is indifferent between two sellers) is situated before any information dissemination takes place and also depending on where the new marginal buyer would be situated if one of the two vendors were to release detailed information, either one or the other of the two vendors with overlapping uncertainty intervals will reveal detailed product information. The exact rules that determine which one of the two sellers releases information are given in the proposition's proof. It is also important to note that the result is not an artifact of the information dissemination stage being the last stage of the game. The result still holds if we change the timing of the game so that sellers decide on information on the second stage and set their prices on the third stage, having observed each other's choice regarding information dissemination. Proposition 1 can be intuitively understood if we consider two sellers whose uncertainty intervals overlap. This translates to two products, whose differences are highly “blurred” because of very high consumer uncertainties. With their products' differences blurred, the sellers will tend to compete more on price, as none of them has a sizable market segment on which to exert monopolistic influence, by virtue of the competitor being in disadvantage with this segment. INSERT FIGURE 5 HERE In Figure 5, we plot two sellers' profit13 when they withhold detailed product information, while their uncertainty intervals overlap. The profit is given as a percentage
13
The figure corresponds to Case 3 in the proof of Proposition 1, in the Appendix of Mathematical Proofs.
of the profit attainable when both of the sellers release detailed product information, which is independent of d and α. Figure 5 reveals that for a given distance between two sellers, the greater their uncertainty ranges (and hence, the greater the overlap), the less is the sellers' profit. We now turn our attention to the case where the uncertainty intervals of the sellers do not overlap. As can be seen from Equation 1, it may now be the case that the marginal buyer (the buyer that is indifferent between two product offerings) is outside vendors' uncertainty intervals and her fit costs vary linearly with distance. We establish our second result: Proposition 2 In an oligopolistic market, if the marginal buyer between two sellers is located outside the sellers’ uncertainty intervals14, then for any positive cost, howsoever small, associated with disseminating detailed information, the sellers will choose not to provide detailed product information. INSERT FIGURE 6 HERE Intuitively, from Figure 6, if Seller 2 withholds detailed information, he will sell to buyers in AKB , and if he provides it, he will sell to buyers in A ' KB ' , with A ' KB ' = AKB . So the seller will be indifferent between withholding or providing his
detailed product information. Then, for any cost associated with setting α=0, he will chose to withhold his detailed information. There are costs associated with keeping track of detailed product information and being able to disseminate it. In our airline example an investment is needed for the airlines to keep track of service parameters such as in-flight entertainment, leg room between seats in different classes etc. Likewise, computer retailers find that it is costly to furnish detailed information about all product features, and archive it in such a way as to 14
This implies that the uncertainty intervals of the sellers do not overlap.
make parallel search feasible. For instance, keeping track of every bundled software piece and every component manufacturer's features is costly and therefore, the consumer often finds out about these features after purchase on the point of consumption. Proposition 2 suggests that one of the main reasons for the Information Deficit phenomenon is that seller and buyer incentives to disseminate and acquire, respectively, more information, are asymmetric. Sellers do not always care to provide more detailed and focused information about their products, while there will always be at least some buyers that require more and better information before their purchase decision. These are buyers that are in the “switching threshold” of products, i.e. buyers that have identified a best product candidate, but may change their minds in light of better product information. Graphically, these would be buyers whose ideal product location, falls about half way between two different product offerings. For example, one such situation can be imagined in the case of a traveler that has identified two potential transatlantic flights that fit her schedule equally well, at the same cost. At first, it appears that both flights “diverge equally” from her ideal flight15. However, in light of more detailed information, for example detailed in-flight movie schedule, she may decide that in reality, one of the two flights is closer to her ideal flight, than the other one. Proposition 2 and Proposition 1 imply that sellers have incentives to provide just enough information to make their products appear “distinct” (no overlapping uncertainty intervals) to the marginal buyer, but no more information than that. This is the boundary that defines the market's Information Deficit. There is just enough information for the products to appear distinct but not enough information for the buyers to identify product locations with certainty.
15
In the context of our model, both flights have the same product space distance from the traveler's ideal product location.
For example, in the case of many home appliances, Internet retailers often furnish photographs from which the appliance color can be approximately, but not exactly understood, due to differences in the capturing and displaying of color of different films and cameras. Consumers are usually able to tell, for example, if an appliance is significantly more white than another, but the exact shade of color is not known, unless the retailers provide perfect color samples by specifying the RGB color properties, either explicitly, or by a sample color image that exactly matches the color properties.16 Proposition 1 also implies that increased competition can lead to more product information: As we increase the number of sellers in the market, each seller's market share shrinks as competitors move closer and closer. The seller will thus need to provide more information to avoid uncertainty interval overlap with its neighbors17, which of course implies that the marginal buyer will be located inside the sellers’ uncertainty intervals. We validate this prediction by providing evidence for the PC-Game market in Section 5.
4.1.2. The Product Location and Pricing Stages in an Oligopolistic Market COROLLARY 1 At the end of the third stage all the buyers that are indifferent between two different product offerings (marginal buyers), will be outside vendors' uncertainty intervals. The result follows directly from Proposition 1 and Proposition 2. Thus, sellers will choose product locations and prices in the light of this knowledge. By backward induction, sellers know that competition for the marginal buyer 16
The fact that some appliances are more likely than others to include such color samples is the topic of Section 4.2.1. 17 As we discuss below, product price equilibria break down in the unit circle model, when the products approach “too close” to each other in the product space. Thus, our model yields no predictions about information availability in commodity markets.
will occur with the marginal buyer having linear fit costs for all different products. In other words, the marginal buyer's fit costs are given by the first branch of Equation 1 which is the same as the fit cost function used in the classic literature that assumes that buyers are perfectly informed about products. This means that the outcomes of the product location and pricing stages are the same as in the classic literature18, where perfect information is assumed. We proceed to summarize the results available from the classic economics literature that are relevant to our model. It is well understood that not all product location configurations allow for the existence of pure strategy equilibria: for a fixed number N of vendors in the market, some product location configurations support pure strategy price equilibria and others do not. Furthermore, in the first stage of the game the sellers will choose locations in such a way that buyers will be uncertain about exact product locations, unless the sellers choose to provide better information. That is, the sellers will not position their products in any predictable manner (for example equidistantly) along the unit circle. This is because the sellers have considerable flexibility when deciding where to place their products as their revenues remain unaffected for a wide range of choices. Thus, our assumption that buyers are uncertain about product details, unless the vendors choose to reveal detailed information, is well justified19. In the Appendix of Mathematical Proofs we expand on these remarks and discuss in detail how the results of the classic literature apply to our model. We now proceed to calculate the product prices in the second stage of the game. Consider three consecutive sellers Si −1 , Si , Si +1 on the circle, charging pi −1 , pi , pi +1 , respectively, and let li −1 and li be the distances between them. More precisely, li −1 is the 18 19
We refer to literature that does not permit free market entry. We repeat our note that the “maximum differentiation principle” applies only under free market entry.
distance between Si and Si −1 and li is the distance between Si and Si +1 . The market share of Si on the side neighboring with Si +1 is determined by the position of the marginal buyer, the buyer that is indifferent between the two vendors. Let x be the distance
of
the
marginal
buyer
v − pi − xt = v − pi +1 − (li − x)t ⇔ x =
Si −1 is
Si ,
from
then
x
is
such
that
li pi − pi +1 − . Thus, Si 's market share on the side of 2 2t
li −1 pi − pi −1 l p − pi +1 , for a total market share of − , and on the side of Si +1 is i − i 2 2t 2 2t
2 p − pi +1 − pi −1 ⎞ li + li −1 2 pi − pi +1 − pi −1 ⎛l +l − . Si 's revenues are therefore, pi ⎜ i i −1 − i ⎟ and 2t 2 2t ⎝ 2 ⎠ taking FOC we find pi =
pi −1 + pi +1 + t (li −1 + li ) . 4
The system of equations that describes the price equilibrium is given in Table 1. N
An additional equation is obtained by requiring that
∑l i =1
4 pi
− p2
− p1
4 p2
− p3
− p2
4 p3
− p4
= 1. =
t ( l1 + lN )
…
=
t ( l2 + l1 )
…
=
t ( l3 + l2 )
…
…
− pN
…
…
− p1
i
…
− pN − 2
4 pN −1
− pN
=
t ( lN −1 + lN − 2 )
− pN −1
4 pN
=
t ( lN + lN −1 )
Table 1 The system of equations that describes the price equilibrium
What allows a seller to be flexible regarding his exact product placement, is the fact that the neighboring intervals always appear with the same coefficient in the equation that characterizes a vendors equilibrium product price. For example the solution for N=3 is p1 =
t (1 + l1 + l3 ) , where the coefficient of l1 and l3 is the same (unit). If S1 chooses to 5
move x units closer to S2 then l1 would reduce by x and l3 would increase by x, leaving
S1 with the same optimal price. Furthermore, S2 's price would reduce by xt/5, while S3 's price would increase by xt/5, leaving S1 with the same market share. S1 's revenues would thus be unaffected by the move. In the Appendix of Mathematical Proofs we show that this is true for N sellers. We thus show that vendors always have considerable flexibility regarding their exact product location: any change in market share and the neighbor's price change on one side, is completely offset by the change in market share and the neighbor's price change on the other side. The seller cannot, however, be located too close to a neighbor, as that neighbor would price low enough to capture the seller's entire market share.
4.2.
A Monopolistic Market
For high enough values of the consumers fit cost parameter t (or alternatively, for low enough values for the consumers product valuation parameter v), sellers become monopolists with captive market shares, while there exist some buyers that cannot be served by any of the sellers for any positive product price. As we gradually increase the buyers fit cost parameter t the change from an oligopolistic market to a monopolistic market does not in general occur for a specific value of t, but, depending on the sellers' locations, for a range of values for t the market includes both monopolistic sellers and segments where sellers still compete as oligopolists. It is as if there exist “islands” of competition on the unit circle, interrupted by buyer segments that are not being served by any vendor. These cases are beyond the scope of this paper as such “islands” are handled more naturally by a Hotelling-type model where the market is modeled as a unit segment, instead of the unit circle that we consider here.
This section considers the monopolist's problem. Since each monopolist maximizes profits in isolation, the strictly defined sequence of the game stages is no longer important.
4.2.1. Analysis of the Monopolist’s Problem By solving v − p − g ( x) = 0 (see Equation 1) for x, we derive the monopolistic seller's
demand function:
v− p ⎧ 2 ⎪⎪ t D( p) = ⎨ ⎪ 2 2a (v − p ) − a 2 ⎪⎩ t
if
p < v − at
if
p > v − at
Equation 2
accounting for the fact that the seller will sell to buyers on both sides of its product location. The seller’s profit function is given by Π ( p ) = pD( p ) . Notice that the vendor has two ways of determining which branch of the equation describes his profit. First, the vendor can choose a price above or below the v − at threshold. Second, and more relevant to information dissemination, the vendor can choose to provide detailed product information by setting the uncertainty interval α to zero and thus eliminate the second branch of the equation ∀p < v .20 The condition under which the vendor would prefer to incur the cost of providing detailed product information is given by the following Proposition: Proposition 3 If the buyers' fit cost parameter, t, exceeds
v , sellers benefit from 2a
providing their detailed product information, by setting their a → 0 .
20
Of course the vendor can never charge a price higher than v, the buyers' reservation utility.
A summary of the analysis performed in the Appendix of Mathematical Proofs is as follows: For t < p* =
v the second branch is preferable for a profit maximizing price of 2a
2v − at with the vendor capturing D( p* ) buyers, which yields an area larger than 3
the vendor's uncertainty interval. However, for t >
v the first branch is preferred, with a 2a
profit maximizing price of v/2, which captures D(v/2) of the market, an area smaller than the size of the vendor's uncertainty interval. However the vendor must reveal detailed product information and set α to zero for the first branch Equation 2 to be applicable. We thus see that the vendor provides detailed product information and reduces the uncertainty interval to zero size only if the profit maximizing price is such that the marginal buyer is inside the vendors uncertainty interval. The intuition for this is that the buyers' fit costs do not reduce linearly as the buyer's location approaches the seller's expected location. The fit costs actually “bend upwards” for distances below α, as can be seen in Figure 3. The seller can counter the deterioration of buyers' fit costs by reducing the uncertainty area α, for example by setting α=0, by giving away full detailed product information, or presenting existing information in a way that is better understood by consumers. If the buyers' fit cost parameter is high enough that the seller can only hope to sell to buyers that are very close (closer than α), then the buyer would want to reduce buyer fit costs, in order to be able to charge a higher price. The explanation above underlines an important facet of horizontal product information dissemination: Increased information about a product raises the willingness to pay of only those customers whose ideal location is “close” to the product, in our model, closer than the uncertainty interval, α. For example, for
tailored/customized goods, where the final product is bound to be “very close” to the customer's ideal product, more information and/or a higher degree of customization which reduces uncertainty, would raise the customer's willingness to pay. This finding is supported by Gu and Clemons (Gu 2003) in a model that considers non-linear consumer fit costs. One interesting way to view the effects of increasing the fit cost parameter from zero to a very large number, is the following: first, with very low fit costs all sellers can potentially reach all buyers in the market and intense competition leads to prices where the buyers' valuation v of the product is independent of product prices. Indeed, under these conditions, product prices are independent of the consumers' willingness to pay v as can be seen in Table 1. Above a certain threshold, sellers abandon competition for overlapping market segments and focus on being monopolists to such of those buyers that the competitors cannot capture. Now, v enters the optimal price function as sellers are able to charge based on willingness to pay. However, α, the uncertainty parameter, still does not enter the price function, as the marginal buyer is located outside the uncertainty interval and reducing uncertainty would have no effect on the price that the marginal buyer can be charged. As we increase t even more, above the threshold specified in Proposition 3, the marginal buyer enters the seller's uncertainty interval and α enters the optimal price function. Only then do sellers have incentives to reduce uncertainty and increase the marginal buyer's willingness to pay. This can be clearly seen in Figure 7 that depicts the vendor's profits from the two different branches of Equation 2. INSERT FIGURE 7 HERE
An alternative way to view Proposition 3, is that sellers have incentives to provide their detailed information if a >
v . This is seen in Figure 8. Together with Proposition 1 2t
our model provides two reasons why sellers may wish to make horizontal product information available. Proposition 1 suggests that competition is a reason, as sellers want their product offerings to appear “distinct” from their competitors, or in other words, “do not wish their uncertainty intervals to overlap”. Proposition 3 suggests that very strong consumer preferences can be another reason for making more detailed product
information available. The implication of this statement is that the information availability about different product attributes in a given market (for example in different vendors' web sites or shopbots), can be conceived as a proxy for the importance of those attributes to the consumers. Firms can use this proxy for the early detection of shifts in consumers' preferences or of emerging taste related trends, and identify opportunities for product differentiation and/or product line extension. This relatively inexpensive early warning mechanism certainly deserves a closer look in future research. INSERT FIGURE 8 HERE
4.3.
Discussion of the Monopoly Results
It is often the case, that all product attributes are not equally important to buyers and there exist product parameters of taste that are almost always reported by sellers. For example, the dimensions of refrigerators is usually a horizontal attribute, since making a refrigerator wider but shorter does not make it any better or worst. However, people can have very strong preferences that depend on the space availability in their kitchen. The dimensions of a refrigerator is a product attribute that is almost always included in online product descriptions from refrigerator manufacturers. The authors searched for this information on the refrigerators presented in the ConsumerReports.org web site. From 37 refrigerators for which any product information was available in manufacturers or retailers web sites, all of them included information about refrigerator dimensions.
There are many examples such as these for which the analysis in Section 4.2.1 provides an elegant explanation. We term these attributes, for which full information is made available, as “critical” attributes. The common characteristic of these horizontal attributes is that usually consumers have “very strong” preferences about them. A flight that is scheduled for 3pm, instead of 1pm, or a refrigerator that is two inches wider might be completely worthless to consumers that wish to fit these products with a business meeting or a specific kitchen space, respectively. To account for these attributes we must use a very high fit cost parameter t, as we did in our analysis, so that many buyers are not served by the sellers' offerings and only buyers very close to the sellers' offerings are willing to buy their products. An issue that requires clarification is whether our choice of modeling strong consumer preferences with linear fit costs is as valid as treating weaker preferences the same manner. As we discuss in the Appendix of Mathematical Proofs, Proposition 3 holds for a variety of fit cost function that describe well our intuitive notion of “strong consumer preferences”.
5. Empirical Evidence and Observations We present empirical evidence that support our theoretical results.
5.1.
Empirical Evidence on the Information Deficit
In the introduction of this paper, we provided several examples of information that is readily available to the product manufacturer or service provider, but is not being provided to the consumers. In order to get an estimate of the extent of the information deficit phenomenon in the home appliance market, we measured information availability for microwave ovens and refrigerators. We obtained the list of product attributes that, according to Consumer
Reports, consumers should be looking for in these products. Obviously, not every consumer cares for all product attributes in this list, but, for every such parameter it is very likely that a large number of consumers will require relevant information. Furthermore, these parameters are readily available to the home appliance manufacturer and it is relatively very cheap to inform the consumers about them via the Web. From the list of refrigerators and microwave ovens in the ConsumerReports.org web site, we searched for the manufacturer's web page with the product information. We were able to obtain the product related web page for 36 refrigerators and 22 microwave ovens. For each product, we randomly selected five attributes21 from the Consumer Reports list of “important” product attributes, and checked for their availability in the manufacturer's web site. We, thus, checked for information availability for a total of 290 product parameters. The attributes' true values were known to us from the consumer report and this allowed us to classify all attributes as either positive (scoring higher than the average in the sample), neutral (scoring close to average in the sample, or a taste related parameter) and negative (scoring lower than the average in the sample). The results are summarized in Table 2 and Table 3. Information about refrigerators Attributes Found Attributes Not Found
Positive 94 10
Neutral 33 6
Negative 8 29
Total 135 45
Negative 2 27
Total 61 49
Table 2 Information dissemination about refrigerators
Information about microwave ovens Attributes Found Attributes Not Found
Positive 26 14
Neutral 33 8
Table 3 Information dissemination about microwave ovens.
Any form of information dissemination, direct or indirect, was counted as a “hit”. For example some product features could be observed in product photos, inferred from
21
We have thus created an unbiased sample of product attributes, for each of the products.
other relevant information, or provided in the appliance's manual, if such a manual was available. What the tables do not capture is our observed “low quality” of information available to consumers that made each observation much more time consuming that was originally anticipated. An “intelligent” software agent would probably be able to extract only a small portion of this information, as the state of the art in computer vision and semi-structured document language recognition is still far from the level of sophistication required for these tasks. Two things become clear about the availability of product information in those two classes of products. First, there is rich evidence in support of the wide prevalence of the Information Deficit phenomenon. A significant percentage (25%) of refrigerator attributes were not reported in manufacturers' web sites in any form, textual or visual. The same percentage for microwave ovens was even higher, close to 46%. The second observation that can be made about the data, is that there is no obvious or even simple explanation for the Information Deficit phenomenon. For example the simplistic hypothesis that “positive information is always reported and that all missing information is negative”, is immediately rejected. A significant percentage of the missing information about product attributes (35% and 45% for refrigerators and microwave ovens, respectively) was not of negative nature. Another simplistic explanation: that vendors do not supply all information in order to avoid a cognitive overload on the part of the buyer, is also not valid since the cognitive overload can simply be avoided by “layering” the information and letting consumers access more details by clicking into links deeper in the vendor’s website. The information that we report missing was also missing from these links We believe that the observations made above can be safely deduced from the data, even taking into account the highly subjective nature of the measurements.
5.2.
Competition and Information Availability
The combination of the first two propositions of product information dissemination predicts that, other things being equal, more information will be furnished in markets where the distances between product offerings are smaller. Here, we present the results of our statistical analysis of data on information availability in the entertainment software industry – a multi-billion industry with record growth in the last few years (IDSA 2003). The entertainment software industry, and PC Games especially, provide an excellent opportunity to test our hypothesis on product information availability. PCGame developers have a 0-1 choice regarding the dissemination of their product information: they either release a demo version of the game or not. This binary choice allows us to unambiguously assess the information availability in the PC-Game market and correlate it with the structure of the product space. We collected data on 1825 games from GameSpot (www.gamespot.com). The GameSpot web site employs reviewers that rate PC games from as far back as 1993. Games are rated from 0 to 10, according to five attributes. Following the industry's standard terminology, the attributes were: “Gameplay” --- interface, control and how “fun” the game is to play,--- “Graphics”, “Sound”, “Value” --- the game's longevity,--and “Reviewer's Tilt”, which allows the reviewers to include other product attributes, such as a compelling “story line” for example, or express their opinion in a more subjective manner as to how successfully the aforementioned attributes were combined. In order to capture the structure of the product space and the developers' tradeoffs as objectively as possible, we also considered dropping the “Tilt” parameter from our data set. The results are largely unaffected in both cases.
We choose to treat this product space as if a game placement decision is horizontal, involving tradeoffs between a number of vertical parameters. The GameSpot web site also provides PC-game demo versions. We consider the availability of a demo version for a game, as a positive indicator that the game developer had incentives to release more detailed product information.22 We assume that the availability of game demos in GameSpot reflects the overall game demo availability of the market and that GameSpot does not intentionally correlate demo availability with the average distances in the product space. Games were first divided into eight genres, according to GameSpot, plus the “all games” category which accounts for the fact that games also compete for sales with games from other genres. The games were further divided into three periods: “1993998”, “1999-2000” and “2001-2003” according to their official release date. In order to control for changes in the reviewers' leniency towards games or decisions on part of GameSpot to carry a smaller or larger proportion of game demos we considered the correlation between the change in the average distance of games in the product space and the change of the proportion of games that had a demo version available for download. Finally, to control for the fact that different game genres tend to display differences in graphics and sound (for example sports games tend to score much higher in these parameters vis-à-vis puzzle games), we measured average distance changes in percentiles, from the previous period.
22
Not all game demos are available in the web site and a missing game demo does not necessarily mean that the developer did not release a demo. It means that either a demo does not exist, or the developer did not make it available through Gamespot, an action which too carries information about the developer's incentives to release product information.
We measure the average distance D between any two games of a certain genre and period as the average squared Euclidean distance:23 D=
G 1 1 ⎛ T ⎞ (rik − rjk ) 2 ⎟ ∑ ∑ ⎜ G (G − 1) i , j =1 T − 1 ⎝ k =1 ⎠
where G is the number of games in the particular genre and period, T is the number of game attributes and rik is the rating of game i on attribute k. The data is summarized in Table 4 and Table 5. Figure 9 displays our data graphically, with and without the “Tilt” parameter. Number of games and average distance
Number of demos
’93-’97
’98-’00
’01-’03
’93-’97
’98-’00
’01-’03
A
B
C
A
B
C
Action
152
8.24
136
7.37
119
10.83
104
98
65
Adventure
108
9.07
45
6.79
34
8.48
26
25
13
Driving
36
5.74
79
6.09
46
9.55
29
58
21
Puzzle
58
6.63
57
7.56
27
4.65
26
22
3
Role Playing
20
7.57
43
7.92
42
6.93
8
17
14
Simulation
66
6.12
67
5.64
31
8.55
34
44
12
Sports
72
6.77
77
8.32
35
10.72
39
50
12
Strategy
139
8.97
171
7.48
165
7.05
92
104
103
All
651
8.19
675
7.49
499
8.99
358
418
243
Table 4 Number of Games, Average Distance and Number of Demos in each time period.
Including all five game parameters the linear regression negative slope coefficient is statistically significant with a p-value of 2.3% which further improves at 1.4% if we use weighted regression, using the reciprocal of the standard errors of the demo availability changes, as weights. INSERT FIGURE 9 HERE
23
It is obvious that, even though the differentiation in this space is not entirely horizontal, the average Euclidean distance between two products does capture some of their true underlying horizontal differentiation. The results that we report are still valid, even after we account for vertical differentiation with regression analysis.
Genre & Time Periods Action Adventure Driving Puzzle Role Playing Simulation Sports Strategy All Action Adventure Driving Puzzle Role Playing Simulation Sports Strategy All
→B A→B A→B A→B A→B A→B A→B A→B A→B B→C B→C B→C B→C B→C B→C B→C B→C B →C A
Change in av. dist.
Change in demo avail.
(-10.57%)
3.64%
Demo change std. err. 5.41%
(-25.13%)
31.48%
8.56%
6.09%
( -7.14%)
8.53%
14.05%
( -6.23%)
9.26%
4.62%
( -0.47%)
13.54%
( -7.82%)
14.16%
8.52%
22.90%
10.77%
8.06%
(-16.60%)
( -5.37%)
5.50%
( -8.63%)
6.93%
2.70%
46.87%
(-17.44%)
5.99%
24.94%
(-17.32%)
11.30% 8.95%
56.82%
(-27.77%)
(-38.50%)
(-27.49%)
8.96%
(-12.41%)
( -6.20%)
10.54%
51.47%
(-26.96%)
10.64%
28.86%
(-30.65%)
9.81%
( -5.78%)
1.61%
5.32%
20.09%
(-13.23%)
2.92%
Table 5 Change in average distance between games versus demo availability.
A simple correlation test, using Kendall's method, rejects the null hypothesis that the two series are uncorrelated with a p-value of 0.67% for all five game attributes, or 0.39%, if we exclude the “Tilt” parameter.24 A second test provides overwhelming evidence for our prediction that game developers “feel pressured” to provide product information and clearly demonstrate their product's differences, when competitors offer products that appear similar. Our hypothesis is that the games that did provide a demo version are on average closer to their neighbors than the games that did not provide a demo version. The data are displayed in Table 6. A paired two sample t-test for means, rejects the null hypothesis that demo and no-demo games have on average the same distance from their neighbors, with a
p-value of 0.000187% (1.87 parts in a million).
24
This fact reinforces our note of caution that the “Tilt” parameter is the most subjective one, and probably not a very good measure of product space location.
Average Distance: Demo vs. No-Demo games ’93-’97
’98-’00
’01-’03
A
B
C
Demo
No-Demo
Demo
No-Demo
Demo
No-Demo
Action
7.97
8.83
6.97
8.43
10.43
11.31
Adventure
9.02
9.08
6.94
6.60
6.51
9.70
Driving
5.25
7.75
5.43
7.92
8.50
10.44
Puzzle
5.52
7.53
6.06
8.51
3.84
4.75
Role Playing
8.28
7.09
7.45
8.22
6.54
7.13
Simulation
5.89
6.37
5.15
6.59
6.47
9.86
Sports
6.69
6.86
7.20
10.37
9.38
11.41
Strategy
8.72
9.46
7.13
8.03
6.92
7.26
All
7.77
8.72
6.87
8.49
8.15
9.79
Table 6 Average distance to other games, for games with and without a demo version.
Finally, we have carefully examined our data searching for potential confound variables that provide alternative explanations for the observed results. One such variable was total game score. Correcting for the effect of total game score on game availability we find that whereas average distance from competitors is not a statistically significant predictor for demo availability for “high-quality” games (games that scored more than approximately 7 out of 10), it is highly statistically significant for lower quality games, in fact, more so than total game score.25 We believe that the results provide strong support for our prediction that information availability increases, as the product space becomes more densely populated and vice-versa: The more “similar” games existed in a particular genre and
time period, the more the game developers released game demo versions to underline and highlight their products differences, and this is especially true for non top-quality games.
25
For more details see (Markopoulos 2004).
5.3.
Information and Strength of Consumers’ Preferences
Our model predicts that horizontal attributes for which consumers have strong preferences will be reported more often than attributes that carry smaller fit costs for the consumers. This is intuitively what most of us observe in every day purchases as more often than not we are able to find information about the product attributes that matter the most to us. In online product comparison web sites, the product database can be queried along a fixed number of attributes, per product. The product attributes which searches can be based-on, correspond to both the sensitivity of consumers preferences towards these attributes, and the overall information availability about these attributes on the product manufacturers' own web sites. For example, at shopping.yahoo.com, refrigerators can be searched by specifying the desired refrigerators dimensions, while microwave ovens cannot. This corresponds to both stronger consumer preferences for refrigerator dimensions compared to microwave oven dimensions, as well as more information being furnished in the Internet about the former attribute versus the latter. An example of how the importance of product parameter corresponds to information availability, comes from GoldStar Electronics, a home appliance manufacturer. The company's web site26, as of spring 2003, provided information about exact dimensions on all of the manufacturer's refrigerators but no dimension information for any of the manufacturer's microwave ovens27. Arguably, people can fit a microwave oven in their kitchen much easier than they can fit a refrigerator and thus preferences for microwave dimensions are much “softer” and carry a much smaller fit cost.
26
http://www.lgeus.com/LG\_website/Goldstar/appliance\_main.asp The same was true about the parent company's website, featuring the LG line of refrigerators, until at least December 2002.
27
An example that relates to the “quality” of information28 being furnished comes from ASKO, a manufacturer of dishwashers, washers and dryers. The company's web site (www.askousa.com), as of fall 2002, provides color samples on all seven of the manufacturer's dishwashers but only to two out of five dryers and one out of three washers. The appliances for which no exact color samples are given, can still be seen in photos, where consumers can only obtain an approximate idea of the true appliance color. Again, people are much more likely to have strong preferences about the color of an appliance that is placed in their kitchen and has to be matched with kitchen furniture, than the color of an appliance that is placed in the boiler room.
6. Conclusions and Extensions We presented a model of endogenous product information availability in a spatially differentiated market. The model is simple enough to be mathematically tractable, yet versatile enough to illuminate a variety of vendor incentives towards better product information. More precisely, we believe that our choice of modeling consumer uncertainty about exact product parameters as an “interval” of uncertainty is justified by the intuitiveness of the results that it produces. We hope that this model will be a starting point for future research on important managerial problems. This is because we believe that if the model can correctly capture endogenous vendor incentives towards better product information, it can also be used when information availability is given exogenously, for example when vendors operate in markets where consumers obtain much of the information that drives their product selection process by third party sources, such as online communities or “infomediaries”. A number of interesting questions falls into this category, such as how should information availability in a market be an input in the product design process? Or, how should vendors respond to changes in market 28
Not to be confused with information about product quality.
availability caused by third parties, or by the advent of new information and distribution channels, such as the Internet? In order to test the validity of our model, we studied the conditions under which sellers make horizontal information available to buyers and found that sellers have incentives to provide horizontal information, only when consumers' sensitivity to fit costs exceeds a certain threshold. We demonstrated that, in the presence of even very small costs associated with the dissemination of information, the sellers will choose to make only enough information available to the market, to separate them from their competitors. Our model, thus, explains the underlying causes for the Information Deficit that is seen in Internet-based markets. Our results shed light on the effect of consumers' information endowment on vendor profitability and product placement and differentiation. As an extension of this research we intend to investigate these implications further. More precisely we are interested in modeling niche market strategies, enabled by increased information endowment on the part of the consumers, that try to take advantage of high consumer fit costs, in markets where consumers are heterogeneous in respect to their fit cost parameter. The increased importance of such vendor strategies is due to the new ability of vendors to better communicate their exact product placement, using the Internet. Furthermore, we intend to incorporate the distinction between horizontal and vertical product information, providing as a consequence a more complete treatment of product information dissemination in Internet markets.
Acknowledgments The authors would like to thank the participants of WISE 2002 for their insightful comments and suggestions during an initial presentation of our ideas. This work was supported in part by NSF grants DMI0121395 and SBR96-02053.
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Varian, H. R. (1992). Microeconomic Analysis. New York, W.W. Norton & Co.: 144156. Weitzman, M. (1979). "Optimal search for the best alternative." Econometrica 47(3): 641–654.
x
Buyer
a
a x
a-x
a
Seller
a
Buyer
Buyer fit costs
Figure 2 Fit costs for a buyer at distance x from expected location of seller.
g(x)
at 2 0
x a Buyer’s distance from the center of seller’s uncertainty interval
Figure 3 Buyer expected fit costs as a function of the expected distance from the center of the seller’s uncertainty interval.
Seller 1 chooses location
0 < l1< 1
0 < l2< 1
Stage 1
Seller 2 chooses location
Sellers choose location
Seller N chooses location
0 < l N< 1 Seller 1 chooses price
p1 > 0 Seller 2 chooses price
p2 > 0
Stage 2 Sellers choose price
Seller N chooses price
pN > 0 Seller 1 chooses uncertainty
a
a
a
0
Seller 2 chooses uncertainty
Stage 3 a
Sellers choose how much information to provice
a
a
a
0
a
a
0
Seller N chooses uncertainty
a
a
a
0
a
a
a
0
a
Figure 4 The extended form of the game.
a
a
0
Seller Profit
100
90
80
70 d= 1 4
60
d= 1 3
d= 1 2
0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Uncertainty Parameter: a Figure 5 Sellers' profits in duopoly when neither of them provides detailed product information and their uncertainty intervals overlap, as a percentage of the profit attainable when both sellers provide detailed product information. Parameter d is the distance between the two sellers.
Seller k
djk
x/2
B' djk 2
rj
dij
B
Se
lle
K a x K' a
Seller i
x/2 A'
A
dij 2
pj-pi 2t
Figure 6 Seller j's product sales are the same whether he provides or withholds detailed product information.
pj-pk 2t
Seller profit
0.4 0.3
v=3
0.2 0.1
v=2 v=1
info
info info
noinfo
10
noinfo
noinfo
20 30 40 50 Fit cost parameter: t
60
Figure 7 The effect of increasing buyers' fit cost parameter t on the monopolistic seller's profitability, when the seller withholds (no info) and when the seller provides (info) full detailed product information. In the first case, we set α=0.1.
Seller profit
t=30
info
0.06
0.04
t=50
t=90
info
info
no info
0.02 no info 0.02
no info
0.04
0.06
0.08
0.1
0.12
Uncertainty parameter: a Figure 8 The effect of increasing the buyers' uncertainty about the monopolistic seller's exact product location on the seller's profitability. We contrast the results with the seller's profitability when he provides detailed product information, reducing buyers' uncertainty to zero. We have set v=2.
60%
R 2 = 0.2823
Demo Availability
All game attributes included
30%
Average Game Distance
0 -60%
-30%
30%
60%
90%
-30%
y = -0.33x - 0.0376
-60%
60%
R2
Demo Availability
"Tilt" attribute excluded
30%
0 -60%
-30%
= 0.2529
Average Game Distance
30%
60%
90%
-30%
-60%
y = -0.2906x - 0.0423
Figure 9 Change in demo availability versus change in average pair wise game distance. Error bars represent 95% confidence intervals.
