FOLLOW THE LEADER? STRATEGIC PRICING IN E-COMMERCE

Robert J. Kauffman

Associate Professor of Information and Decision Sciences

Charles A. Wood Doctoral Program in Information and Decision Sciences

Carlson School of Management

University of Minnesota {rkauffman;cwood}@csom.umn.edu

Last Revised: November 26, 2000

_____________________________________________________________________________________

ABSTRACT

Conventional wisdom and current research suggest that the Internet will lower electronic commerce (EC) product prices by causing intense competition among vendors. However, this does not seem to be happening. This research presents a multi-industry investigation of pricing behavior using a customized data-collecting Internet agent that we call the Time Series Agent Retriever (TSAR). We use theories of information asymmetry and Stackelberg pricing to show how Internet technology increases the ability of firms to tacitly collude to keep prices higher than expected in the presence of intense competition. Our results are developed using an econometric technique called vector autoregression (VAR). They show that Internet technology creates the potential to lower information asymmetry among Internet-based sellers. Thus, it allows rapid reaction between competitors, thereby allowing firms to avoid the intense competition predicted by current theory. We find that fast competitor reaction to the price promotions of a firm minimizes any profit derived from increased market share that the firm hopes to achieve from the lower price. This short reaction time allows Stackelberg pricing, in contrast with Bertrand-Nash pricing, which is often discussed in research on pricing in Internet-based selling.

_____________________________________________________________________________________

KEYWORDS: Bertrand competition, collusion, competition, econometric analysis, electronic

commerce, information asymmetry, Nash competition, Stackelberg competition, vector autoregression.

_____________________________________________________________________________________ An earlier version of this paper in extended abstract form was accepted for presentation at the 2000 International Conference on Information Systems (ICIS-2000), Brisbane, Australia, December 12-15, 2000. We thank Mark Bergen, Baba Prasad, George John, three anonymous reviewers from ICIS, and the participants in the Information and Decision Sciences Research Workshop at the Carlson School of Management for their useful input on this work.

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INTRODUCTION

Researchers have contended that the Internet will result in intense competition among Internet-

based sellers in electronic commerce (EC) (e.g., Bakos, 1997). Surprisingly, however, the predicted

intense competition has not yet materialized. In fact, Sager and Green (1998) quote a Business Week

article that asks: “So where are all the bargains?” The article notes that although it is more convenient to

shop online, Internet-based sellers are more interested in matching rather than beating their competitors’

prices. The Internet permits Internet-based sellers to easily view and automatically respond to the actions

of their competitors via sophisticated software agent technologies or simple Web browsers. Internet-based

sellers can retrieve their competitor’s prices using the same technology that is used in “shopbots” by

consumers to find the best prices for a product (Varian, 2000). In fact, Smith, Bailey, and Brynjolfsson

(2000) describe how Buy.com (www.buy.com) has millions of computers each running customized

shopbots to retrieve millions of prices so that Buy.com can promise "the lowest cost on earth". EC

technology reduces information asymmetry among Internet sellers by allowing information to flow more

easily among competitors, and thus, EC technology opens up a whole new spectrum of competitive

possibilities, including price signaling games, rapid price change reactions, and even strategic price

tracking. As a result, new businesses are still are coming to grips with the strategies required for EC. The

popular press already documents that some of the new strategies that Internet-based sellers are beginning

to employ are different from the strategies they used before entering the competitive environment of the

Internet (e.g., Cortese, 1998).

Information systems (IS) (Bakos, 1997; Brynjolfsson and Smith, 1999), marketing science (Lal

and Sarvary, 1999; Alba et al., 1997; Bailey, 1998) and economics researchers (Varian, 2000) have

recently examined the dynamics of product pricing in the EC environment. Some researchers indicate

how increases in competition among Internet-based sellers will cause prices to converge to a single price

at or near marginal costs (Bakos, 1997). This, they reason, will result in intense competition because

consumers can easily compare products and prices (Choudhury, Hartzel and Konsynski, 1998). Other

authors have shown how the process of selling on the Internet contains enough market friction to cause

price differentiation (Brynjolfsson and Smith, 1999; Lynch and Ariely, 1999).

These explanations offer useful insights into price setting by Internet-based sellers. However,

certain phenomena, such as the competitive interactions between market leaders and followers, need to be

explored in greater depth. In this article, we will examine pricing strategies and competitive interactions

among Internet-based sellers in multiple industries. We address the following research questions:

• How can researchers empirically evaluate industry-wide competitive pricing reactions with

micro-level data from the Internet?

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• What theoretical evidence, if any, describes the motivation for price changes in Internet-based

selling?

• What empirical evidence, if any, indicates that firms are using EC technology to quickly respond

to competitive actions in their industry, and what are the strategic results of this quick response?

We will emphasize the role of the reduction in information asymmetry among Internet-based

sellers. Our research has shown that, in comparison to traditional environments, Internet-based sellers are

utilizing pricing strategies that heretofore have been infeasible. To answer the research questions that we

have posed, we develop a conceptual model of price competition among sellers of commodity products on

the Internet, and then empirically test a number of related hypotheses. Using an econometric technique

called vector autoregression (VAR) (Sims, 1980 and 1986) that was developed in the rational

expectations economics literature, we examine competitive strategies for pricing for different classes of

identical goods across firms and industries to determine whether the effects are industry-specific or more

general. We find that EC technology allows Internet-based sellers to respond very quickly to price

promotions. This increase in responsiveness reduces the effectiveness of any price promotion on the

Internet because sellers can easily detect and respond to their competitors' price changes. In addition, the

short reaction time allows firms to implement price change strategies other than those that are consistent

with intense Bertrand-Nash competition. Instead, we find evidence of Stackelberg pricing involving a

leader-follower dynamic that allows competitive reaction or tacit collusion.

LITERATURE

In this literature review, we examine how various research disciplines view price competition.

This provides us with a means for modeling price leadership and price following behavior in Internet-

based selling. IS and marketing research already have described the dynamics of pricing on the Internet

(e.g., Bakos 1997; Lal and Sarvary, 1999; Brynjolfsson and Smith, 1999). We add to this research by

showing how selling on the Internet allows a reduction in information asymmetry for pricing. Firms can

more easily detect and respond to the promotional pricing of their competitors than in a traditional

environment. In economics, tacit collusion exists when competitors follow each other's prices in an effort

to avoid competition (Chamberlin, 1929). Tacit collusion is a form of Stackelberg pricing where

competitors respond to a market leader's price, and is in sharp contract to Bertrand-Nash competition,

where low prices are set simultaneously to avoid a competitor's stealing of the market. We also examine

how Stackelberg pricing leads to asymmetric competition, in which the price promotions of larger firms

(e.g., market leaders) affect smaller firms, but the actions of smaller firms have little effect on larger firms

(Carpenter et al., 1988).

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Bertrand-Nash Pricing and Stackelberg Pricing

Tyagi (1999) describes two economic theories that describe price interaction: Bertrand-Nash

competition (i.e., Nash competition or Bertrand competition) and Stackelberg pricing (i.e. von

Stackelberg pricing). Bertrand-Nash competition involves simultaneous price-setting choices among

competitors. Competitors arrive at their price during a period by estimating what their competitors will

charge. Bertrand (1883) noted that, when selling commodities (e.g., identical items, such as books, music

CDs, gold, etc.), competitors will charge a price that is identical to where marginal costs meet average

costs. This is because competitors will be worried that if they charge higher than average costs for a

product, they will not sell any product because their competitors will undercut their price. Once prices

are reduced to match average costs, a supplier no longer will wish to sell any items. Bakos (1997) and

Bailey (1998) describe how EC competition is subject to Bertrand's one price rule, and argue that it

eventually causes prices to decrease to a low minimum, where every supplier of a commodity is forced to

charge a single, low price for a commodity good for fear of being undercut by a competitor. However,

Bailey also noted how the price levels for EC products are significantly higher than the identical goods

sold in traditional markets.

There already is some evidence of Bertrand-Nash competition in some markets outside the

Internet. For example, Nijs, et al. (2001) reports on evidence of Bertrand-Nash pricing in grocery stores

in the Netherlands. In addition, Iwata (1974) found evidence of Bertrand-Nash competition in the flat

glass industry. However, most economists note that pure Bertrand-Nash competition does not often occur

in traditional competitive environments; indeed, they observe that competitors often price commodities at

different levels. As a result, they often refer to Bertrand’s one price rule as the Bertrand Paradox.

Tirole (1998) provides a useful clarification. He shows that Bertrand described a duopoly that

occurs at a single moment in time. If firms want to sell their products only once, in Tirole’s view, then the

Bertrand-Nash analysis may accurately predict the outcome. However, if a temporal dimension is

considered, and firms continue selling products over multiple periods, it is no longer clear that they will

benefit by reducing their prices to marginal cost. Launching a price war is not usually a rational strategy

for a firm that wishes to maximize its profits over time. Instead, firms benefit by charging higher prices as

long as the net present value (NPV) of the profit of future high-priced transactions is greater than the

NPV of future marginal-cost priced transactions. Rational managers will not engage in a price war that

reduces profit without gaining them market share.

Von Stackelberg (1934) expanded the competitive interaction literature by describing how firms

can react to each other, rather than setting prices simultaneously. Stackelberg pricing involves prices

being set by a market leader and then market followers react in a follow-the-leader fashion. Roy,

Hanssens and Raju (1994) show Stackelberg pricing by showing how the price of Chrysler's New Yorker

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followed the price of Ford's Thunderbird. In addition, Kadiyali, Vilcassim and Chintagunta (1996) find

that Unilever's Wisk detergent acted as a price leader to Procter and Gamble's Era Plus, while Procter and

Gamble's Tide acted as a price leader to Unilever's Surf. Scherer and Ross (1990) report that Kellogg

acted as a price leader in the cereal industry and led 12 out of 15 price changes. The authors also show

how U.S. Steel acted as a price leader for the rest of the steel industry for decades, and report evidence of

follow-the-leader behavior in the gasoline and turbo-generator industries in the United States.

Stackelberg pricing allows diverse pricing strategies to exist, including the possibility of tacit collusion.

Stackelberg pricing also allows intense competition, but with an important difference from Bertrand-Nash

competition. When reacting to competitor price changes, the simultaneous instead of sequential pricing

decisions associated with Bertrand-Nash competition forces vendors to initially set their prices to a low

point, where demand intersects with marginal costs. Conversely, Stackelberg competition allows vendors

to charge prices that are higher than the Bertrand one-price point, and then to react to the prices set by

their competitors.

Based upon our review of existing research, Stackelberg pricing appears to be difficult to achieve

in traditional markets when sellers offer a wide variety of products, as one can intuitively expect.

Consider our preceding examples of Stackelberg pricing. Firms monitored competitor prices with a

single car model, a single detergent, a handful of cereals, the single price of a turbo-generator, or for

single-product commodities, such as steel or gasoline. Stackelberg pricing is difficult in traditional

industries, where a firm needs to monitor prices on hundreds or thousands of items from several

competitors. For example, Nijs, et al. (2001) fail to find competitive price reactions using scanner data

from 560 different products from more than 350 different supermarkets. Following a leader's prices

becomes extremely complicated and costly when reviewing prices for numerous items on a continuous

basis, such as in common retail settings such as bookstores, music CD shops, or grocery stores. In

traditional markets, typical retail sellers are forced to use Nash pricing to try to predict their competitor's

actions without the ability of continuously monitoring competitor promotional activity.

In this research, our view is that Stackelberg pricing strategies are not feasible unless a firm can

react to a competitor's actions before the market significantly responds to these actions. Varian (2000)

echoes this view and postulates that Internet sellers can easily monitor each other just as Internet buyers

can easily compare prices. Internet-based sellers can write software agents that monitor competitor prices

and even instantly alter prices to match competitors' prices. They can also do this on an algorithmic basis,

establishing rules as to when to match prices and when to avoid matching prices. (As an example, we

built this kind of software to collect data for this study from multiple competitors.) As a result, EC

technology potentially can facilitate competitive pricing strategies that go beyond Bertrand-Nash

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competition. This monitoring of competitors can change the dynamics of retail pricing, facilitating

Stackelberg pricing strategies, such as price collusion, to a greater extent than was possible before.

Product Pricing in Internet-Based Selling

IS researchers propose several theories that predict the effects of the Internet on price competition

and price-setting. Bakos et al. (1999) shows how lower search costs due to the technologies of the Internet

can lead to intense Bertrand-Nash competition between firms. This, in turn, leads to price reductions that

will cause product prices in Internet-based selling to converge to marginal cost. Alba et al. (1997)

describe how some vendors have avoided moving to the Web because they fear what will happen if

intense Bertrand-Nash competition occurs. This reluctance to engage in Internet-based selling allowed

for the entry of new players in the marketplace, such as Amazon.com (www.amazon.com) in the

bookselling business and e*Trade (www.etrade.com) in the stockbrokerage industry, albeit through the

single channel of the Internet. Lynch and Ariely (2000) find evidence of increased price sensitivity for

wine among consumers when the Internet began to more effectively support their online price search

activities.

However, the intense competition that has been predicted does not seem to have yet materialized.

Smith, Bailey and Brynjolfsson (2000) empirically demonstrate that price dispersion still exists online,

and additional evidence is presented in Bailey (1998) and Brynjolfsson and Smith (2000). Lal and

Sarvary (1999) explain how the customers of Internet-based sellers tend to prefer some firms to others,

especially if the product being sold has relevant, but not overwhelming non-digital attributes. Choudhury,

Hartzel and Konsynski (1998) examine the online aircraft parts industry and do not find lower prices for

parts, except for non-emergency parts for small airlines. Their research shows that buyers typically are

unwilling to change trusted suppliers just to save money. Brynjolfsson and Smith (1999) show that buyers

tend to prefer market leaders in Internet-based selling and will pay a premium for the market leader’s

goods (e.g., Amazon). Clemons, Hann, and Hitt (1998) describe how online travel agents charge different

prices when they are presented with the same customer request. Furthermore, the same seller is prone to

establish different online "storefronts," each charging a different price and each offering different levels

of user-facilitating functions. Thus, they point out that online travel is not best characterized by

undifferentiated Bertrand-Nash competition.

Lately, collusion in the Internet-based has received much attention from researchers, who used

analytical modeling and illustrations to make their point. For example, Campbell, Ray and Muhanna

(1999) develop an analytical model that shows how short reaction times in Internet-based selling can

cause an upward pressure on prices. The authors suggest that this is due to collusion among Internet

sellers, which is facilitated by the increased information flow. Dillard (1999) tracked the price of a single

best-selling book from four different Internet-based booksellers (also reported in Varian 2000). He shows

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how Barnes and Noble (www.bn.com or BN.com) and Amazon respond to each other’s price. The

resulting prices were somewhat higher than the same prices charged by smaller bookstores, but smaller

bookstores also reacted to changes in price by Amazon. In Dillard’s study, prices moved upward, not

downward, indicating that a move toward marginal cost-based price was not occurring. Bailey (1998) also

demonstrates evidence for price convergence in the bookselling industry. The market leaders, Amazon

and BN.com, appears to follow one another’s prices after BN.com entered the market: within four months

after entry, the average difference between prices charged for a “market basket” of best-selling books

between these two sellers was almost zero. Clearly, there is some confusion regarding the impact of EC

on competition and pricing.

Tacit Collusion

Chamberlin (1929) introduces tacit collusion to show how competitors will cooperate with each

other without a formal agreement in order to avoid intense competition and maximize profits. According

to Chamberlain, the market price will approach the monopoly price when competitors tacitly collude.

Other authors discuss limitations of tacit collusion, specifically that colluding players will “cheat,” if

possible, and charge a little less to capture a larger market share and profit at the expense of their

colluding partners (Tirole 1998). This “cheating” has been observed in the international oil industry,

where colluding OPEC partners exceeded agreed-upon maximum production by a total of 1.2 million

barrels per day, hoping they might benefit at the expense of other producing countries that followed the

prescribed limit (Georgy 2000). There is an incentive to cheat unless detection of cheating is swift,

especially in the case of tacit collusion where no formal contract exists.

The Internet makes it possible for the information asymmetries among competitors to be reduced.

It also allows for immediate evaluation of competitors’ pricing through browsers and Internet software

agents, thus facilitating tacit collusion by reducing a competitor’s ability to cheat. Furthermore, such

reactions may act as a disincentive to Bertrand-Nash competition, especially if some competitors can

respond to a price promotion before the market at-large can. For example, if a competitor knows that any

price cuts will be immediately detected and responded to, then a price-cutting strategy can result in less

revenue for a product without any increase in market share. If market leaders constantly review, detect

and match or beat prices with their competitors, the various technologies that are available will tend to

minimize the benefits (e.g., larger lead times in price promotion and increased market share) of Internet

sellers' price promotions.

Thus, the possibility exists that Internet technologies will allow Internet sellers to respond

immediately to the price promotions of their competitors. The likely result will be that the sellers no

longer need to charge a “Bertrand low price” to be on the safe side in price competition. Instead, Internet

technologies will enable an Internet seller to set an initial price, and then wait and see what reactions its

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competitors make in response. By thinking about price competition in Stackelberg terms, rather than in

Bertrand-Nash terms, rich pricing strategies, such as tacit collusion or reactive competition, become more

viable for Internet-based sellers.

Asymmetric Competition

Stackelberg pricing can lead to asymmetric competition. Market leaders' actions, such as price

promotion and advertising, affect the entire market, while smaller firms' actions do not affect larger firms

(Carpenter, et al. 1988). Blattberg and Wisniewski (1989) describe how price tiers form as market leaders

compete among them themselves, while market followers both compete among themselves and with the

market leaders. Sethuraman, Srinivasan and Doyle (1999) examine 1,060 studies of cross-price effects.

Cross-price effects occur when firms discount prices to gain market share at the expense of competitors.

These studies confirm an asymmetric cross-pricing effect: a firm’s price changes affect competitors in the

same or in a lower price tier, but do not have much impact on higher price tier firms.

Research that involves pricing among Internet-based retailers should take into account the

potential for the effects of asymmetric competition. When asymmetric competition is possible, firms that

sell on the Internet can leverage various technologies to immediately respond to competitor promotional

pricing and to avoid losing market share to competitors with more market power. This research uses the

concept of asymmetric competition to illustrate how price promotions of competitors with more market

power will have a greater impact on an industry than competitors with little market power.

THEORY

Up until now, most IS researchers have concentrated on Bertrand-Nash competition as the major

explanation for the pricing strategies that are observed in Internet-based selling (e.g., Bakos 1997; Bailey

1998; Choudhury, Hartzel and Konsynski 1998). They reason that in traditional (non-Internet) markets,

friction exists that will increase search costs so that it is difficult for a consumer to know if a better deal

for a product exists. For example, it will take time and some costs for a book shopper who needs several

books to compare prices for these books at several different bookstores. Rather, the shopper will be likely

to stop at a nearby bookstore and do her shopping at this one location. With the Internet, however, there

can be a dramatic search cost reduction, and allowing buyers to easily compare prices by searching

through several Web sites from their home computer or by using shopbots (e.g., BottomDollar.com or

MySimon.com)

Equation 1 shows this by illustrating the single-period profit from a price promotion.

( ) ( )∑∑==

−−−=hl Q

qqh

Q

qql MCpMCp

11π (1)

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In this expression, the profit resulting from changing price from a higher price, ph, to a lower price, pl,

results in more products being sold, Ql, at the lower price, as opposed to the quantity sold at a higher

price, Qh, less marginal costs MCq of each product. In addition, firms must worry about the price charged

by existing competitors and new entrants, each of whom will try to price products at a point below other

competitors to maximize market share and resulting in a reduced quantity being sold at the higher price

Qh. As Qh approaches zero, there is greater motivation to reduce prices to the lower price. As downward

price pressure continues, firms take less and less unit profit from each item being sold, hoping to increase

quantity sold, until the profit from operations approaches zero.

In the absence of immediate detection and responses to competitor prices, Bertrand-Nash

competition results, causing prices to approach average costs for an item. We agree with other IS

researchers that, with all else held constant, reductions in search costs and search time result in more

intense Bertrand-Nash competition, as competitors each attempt to decrease prices until profit from such

competition is zero. However, the same technologies on the Internet that reduce search costs for

consumers also reduce monitoring costs for sellers. If a firm's competitors respond to a price promotion

after the market responds, Equation 1 holds true, and a firm's market share increases because of the lower

price. However, if a firm's competitors are able to react to price changes before the market responds to a

price promotion, then any reduction by one seller will be instantly matched by another competitor’s price

change. Thus, if competitors are able to respond before the market does, then the price promotion of the

firm results in the same quantity sold as before, but at a lower price, resulting in reduced profits, as shown

by Equation 2.

( ) ( )

( )∑ −=

∑ −−∑ −=

=

==

h

hh

Q

qhl

Q

qqh

Q

qql

pp

MCpMCp

1

11π

(2)

Equations 1 and 2 illustrate two possibilities that can occur if, for simplicity, we assume constant

quantity demanded. If competitors respond to a firm's price promotion, then Equation 1 still holds and the

profit is equal to the lower price multiplied by the additional market share captured by the lower price.

But, if competitors respond by matching the lower price, then the firms in the industry will split the

market as before, and thus sell the same quantity (Qh) as they would if they each maintain the higher

price. However, while the quantity sold will be the same, the price will be lower, resulting in less profit

for both the firm and its competitors.

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Equation 3 shows a combination of Equations 1 and 2 by considering the possibility that there is

some probability or expectation that key competitors will respond to a price change by a firm before the

market does. However, there is no certainty that this will occur.

( ) ( ) ( ) ( )∑∑∑===

−−

−−−−=

hhl Q

qlh

Q

qqh

Q

qql ppMCpMCp

1111 λλπ (3)

In Equation 3, λ is the probability that specific competitors will respond to a firm’s price change

before the market as a whole adjusts its prices. If there is a low probability that key competitors will

respond with an identical price, then Equation 1 still will hold true, and market share is increased.

However, if there is a high probability that key competitors will respond with an identical price, then the

reduced price results in the same quantity sold as if there was no price promotion. The same quantity sold

at a lower price results in a reduction of firm profit. Obviously, fast competitor reaction results in an

unattractive outcome for a firm initiating a price promotion.

Bertrand competition may dominate in markets where large orders are made infrequently, or

when competitors cannot observe each other's prices or cannot respond until after consumers have

responded to industry prices. However, we contend that in most EC markets, such as books, music CDs,

software, toys, pet supplies, clothes, etc., orders are for the most part steady over a period of time and the

same shopbots that allow Internet buyers to search for the best price can be used by Internet sellers to

monitor and quickly respond to competitor prices. Furthermore, these equations illustrate how, since

Internet technology allows competitors to respond to each other almost immediately, price promotions

used solely to increase market share are likely to be unsuccessful. Competitive pricing may still be

implemented for other reasons, such as to create barriers to entry or when an Internet-based seller has a

cost advantage over its competitors. But such strategies are better implemented by immediate reaction to

competitors allowed by Internet technology (e.g., Stackelberg competition) rather than by assumptions

about what a competitor will price (e.g., Bertrand competition). Thus, Internet-based sellers will avoid

intense Bertrand-Nash competition in favor of richer and more varied pricing strategies that follow a

Stackelberg pricing model, such as tacit collusion, reactive pricing or price tiers.

FROM THEORY TO EMPIRICAL MODEL

Based on our theoretical argument, if we can show that Internet-based sellers respond quickly

with “follow-the-leader”-motivated price changes (i.e., competitors have a high λ in Equation 3), then

Stackelberg pricing strategies should be facilitated. If not (i.e., competitors have a low λ in Equation 3),

then Bertrand-Nash strategies should prevail in Internet-based selling. In this section, we empirically test

for near-immediate price reactions to explore whether the “follow-the-leader” picture that emerges from

the theory that discuss can be sustained. Vector autoregression (VAR) (Sims 1980; Enders 1995), an

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econometric technique that is most closely associated with rational expectations economics and studies of

the macro economy, is appropriate to frame our follow-the-leader analysis for two reasons. First, VAR

models can be specified to include immediate endogenous effects, where one vendor immediately

responds to another vendor within a single time period. Since the immediate reactions that are possible

with Stackelberg competition in Internet-based selling is an important facet of our argument, we require a

technique that includes endogenous effects in its analysis. Second, VAR, like other autoregression

techniques, allows us to measure the effect of shocks to a system of equations. For this research, a

“shock” is defined as an unanticipated price change. Using VAR, we are able to statistically examine the

immediate industry-wide effect of one firm's price change on that firm's competitors.

We hypothesize that new technologies allow firms to easily detect competitor price changes in

Internet-based selling, leading to the existence of Stackelberg price competition. We further hypothesize

that a firm’s price changes create a shock that causes competitors to respond to this price change. Based

on our theoretical model, we begin with the linear autoregression model described in Equation 4. This

equation illustrates how Internet sellers' price changes are induced by reactions to competitors' previous

price changes as well as industry effects. We consider the error term (ε i j t) in Equation 4 to be a "shock",

or an unanticipated price change, that is not explained by reactions to competitor price promotions or

price increases, but is reacted to by other vendors in future time periods.

ijtk

K

kk

J

cticjcjijt Industrypriceprice εωγα ++∆+=∆ ∑∑

==−

111, (4)

Table 1 includes the definitions of the variables in this model.

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Table 1. Linear Autoregression Model

VARIABLE DESCRIPTION

∆pricei jt Percentage change in Price for Product i {i=1 to I} sold by Firm j {j=1 to J} at Time t {t=1 to T}.

αj Intercept that captures individual firm effects for Firm j. This vector of intercepts describes price changes made by a firm that are not attributable to a change in the competitor’s price.

γ jc Coefficient indicating the effect on Firm j's price change in the current period of Competitor c's price change in the previous period {c=1 to J}.

ω k Coefficient of the industry effects for the Industries k studied {k = 1 to K}. It represents industry-wide effects on every product selling in an industry. The bookselling industry is the base case and is omitted to eliminate perfect collinearity among the explanatory variables.

Industryk A dummy variable to capture industry effects for the industries studied. It is equal to 1 if Firm j sells Product i in Industry k and 0 otherwise. The bookselling industry is the base case.

ε ijt Error term for the estimated price for Product i sold by Firm j's price for at Time t.

In Equation 4, we only consider one time lag. This decision results from our attempt to determine

if there is a significant immediate reaction to competitors. By only considering a single day's lag, we

remain true to our theory describing how Internet technology can enable near-immediate competitor

reactions to firm actions. Thus, in our research, we are not trying to determine the overall effect of a price

change as it "ripples" through an industry (as we might, if we were thinking more in the vein that

macroeconomists do). Instead, we rather purposefully limit our investigation to examine near-immediate

industry-wide reaction to a firm's price promotions (i.e., a single ripple) to determine if there is significant

immediate competitive reaction to a firm's price promotions. This compares well with traditional

marketing research, which typically measures lags in terms of weeks, not days (e.g., Nijs, et al. 2001;

Dutta et al. 1999).

In the rest of this section, we explain how we handle the complexities of information structure

and defects in our data that require some changes in the basic model, thus changing the basic linear model

described in Equation 4 into a transformed VAR model.

Heteroskedasticity. In keeping with traditional VAR research (Sims, 1980), we initially examine

a linear VAR model. However, a linear assumption can lead to heteroskedasticity: large competitor price

changes will not have the same proportional effect on firms within an industry as small competitor price

changes. Furthermore, the percentage change confounds the results. For example, consider a price

promotion that changes a product’s price to one-fifth the previous price (∆price = -80%). If the same firm

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then changes the price back, the value of ∆price is 400% (e.g., five times the discount price). Thus, order

of magnitude problems can exist in the current model.

Logarithmic transformation can lead to greater predictive power than the typical VAR linear

model. However, VAR impulse response functions predict reactions to shocks and cannot be used to

predict stability. In our research, stability occurs when there is no price change (i.e., ∆price=0). Hence,

any transformation must preserve all zero values of ∆price, indicating stable prices, so that the accuracy

of the predictors is preserved, and the sum of squares of the regression, R2, and p-values are not inflated

inappropriately. To deal with these issues, we also consider a natural logarithm transformation of prices in

our model, as shown in Equation 5.

)1ln( +∆=′∆ ijtijt priceepric (5)

To illustrate this transformation, consider our previous example where a price promotion results

in an 80% decrease in price followed by a 400% increase to restore the price to its original value after the

promotion expires. Using our transformation, ∆price = -80% (e.g., discount price = one-fifth the usual

price) is transformed to ln (-.80% + 1) = ln (.2) = -1.61. Conversely, when the price is restored to its

original value, ∆price = 400% (e.g., usual price = five times discount price) is transformed to

ln (400% + 1) = ln (5) = +1.61. Thus, our logarithmic transformation adjusts our VAR model for non-

linearity, preserves stable prices (e.g, ∆price = 0 � ∆price' = 0), preserves the sign of the price change,

and gives the same order of magnitude to price increases and decreases. Finally, the log transformation

reduces the effects of outliers in the system. Equation 6 shows the linear transformed model:

ijtk

K

kk

J

ctijjcjijt Industryepricepric εωγα ++′∆+=′∆ ∑∑

==−

111, (6)

Endogeneity. One statistical issue in our model is that price change variables may be

endogenous: firm price changes can be dependent upon competitor price changes in the current period.

Firms can use methods similar to the data collection methodology used in this research to respond to price

changes within the same period. Although the analyst can add exogenous variables to increase

explanatory power, much of the explanatory power in a VAR model is based on the interaction among

endogenous variables.

To account for endogeneity, VAR adjusts the dependent variable by a coefficient, β, derived from

the endogenous effects that other variables have on the dependent variable in the same time period

(Enders 1995). The impulse response function depicted in Equation 7 models how one firm’s price change

for a product in the current period is determined by its competitors’ price changes for that product in

current and previous periods. More formally, the price change for product i sold by firm j at time t is

13

adjusted by a series of coefficients, βjc, determined by the effect of every competitor c’s price change on

firm j's price change in the current period (i.e., βjc = 1 for j = c):

ijtk

K

kk

J

ctijjcj

J

cijtjc Industryepricepric εωγαβ ++′∆+=′∆ ∑∑∑

==−

= 111,

1

(7)

Unlike other regression models, VAR models include a parameter, β, on the left hand side of the

equation. This parameter is used to adjust the dependent variables for endogenous effects (Enders 1995).

If we ignore endogeneity and assume exogeneity, then a VAR model need not be used, because there is

an implicit assumption of βjc = 1. Sims (1980) also notes how assuming complete exogeneity often places

what he calls “unreasonable restrictions” on the econometric model, and advocates the use of VAR to

account for endogenous effects when the possibility of endogeneity exists.

Correlation. Like other regression methods, VAR is susceptible to errors when high correlations

exist between explanatory variables. Whenever a ∆price correlation between two firms was above 70%,

we removed the firm with the lowest number of unique Web users, reflecting the lowest competitor

influence, as defined by data from PCData Online (www.pcdataonline.com). Firms that did not change

prices throughout the testing period, and therefore have no price reactions, were also removed from our

data set.

Using this methodology to check our data set for these kinds of problems, we learned that, in the

bookselling industry, NoWalking.com is perfectly correlated with 10base.com. So we removed

NoWalking.com from our study. Similarly, BookBuyers Outlet was highly correlated with Amazon.com

at 83.8%, and we also removed this observation from the study. Amazon and 10base.com remained in

our study. After removing these two firms with highly correlated observed prices, the remaining firms

that had the highest correlation were Amazon and BN.com at 38.9%. Thus, no other firm was removed

from our study.

Underidentification. VAR β coefficients are always underidentified. As a result, it is necessary

to apply additional technique to make estimation possible. VAR analysis typically uses a technique

called Cholesky decomposition, described in Chess, et al. (1992) and Enders (1995), to restrict the values

of the β coefficients so that a solution can be found to Equation 7. This requires us to rank firms in

accordance with our theory used to determine the variables in the VAR system of equations.

We ranked firms by their competitor influence, proxied by the number of unique visitors to each

firm’s web pages using data is provided by PCData Online. Any firm whose web site was not listed was

ranked at the bottom of the list. This technique allows us to examine the effect of larger firms on smaller

firms in each industry and places competitor influence within the VAR model in accordance with

asymmetric competition theory.

14

Hypotheses and Data Collection

We next discuss our hypotheses. We also discuss the data set that we used, and the software agent

tool that made our complex data collection possible.

Hypotheses. Using the models defined in Equations 6 and 7, we test two hypotheses. We expect

a significant reaction by EC firms to price changes by their competitors when using only a one-period lag.

Reaction will occur as price increases and decreases, as predicted by Stackelberg price competition

theory.

Follow-the-Leader Hypothesis: Internet sellers will exhibit significant Stackelberg reactions to their competitors’ price increases and decreases within a single day.

Contribution margins in terms of average markups for products are not directly observable.

However, for the items in this research (i.e., books and CDs), markups are usually made as a percentage

of selling price. Hence, high-priced items will have a greater markup, and price is a reasonable proxy for

markup. As prices level increases, firms that do not respond to the price promotions of their competitors

will risk larger amounts of revenues.

Price Effect Hypothesis: Firms will exhibit stronger immediate Stackelberg reactions to price changes with expensive items (e.g., items with a list price in the top 25% of all items) when compared to immediate reactions to inexpensive items (e.g., items not in the top 25% of all items), as defined in Equations 6 and 7.

Data Collection. Haltiwanger and Jarmin (2000) note that it is difficult to collect electronic

commerce data and that traditional data collection techniques are often inadequate when measuring the

digital economy. For our data collection, we developed a customized Internet data-collecting agent called

the Time Series Agent Retriever (TSAR). TSAR first retrieves the top-selling items for three different

industries from various websites: Billboard Magazine’s site for the top CDs

(http://www.billboard.com/charts/bb200.asp) and USA Today’s website for books

(http://www.usatoday.com/life/enter/books/leb1.htm). We used vendor-neutral sources for the bestsellers

in each category to avoid bias. TSAR then retrieves data from two shopbots, MySimon.com

(www.mysimon.com) and Deal Pilot (www.dealpilot.com), by querying on the bestsellers and storing

data into a database. Based on our own anecdotal observations, the shopbots covered the markets quite

effectively. Figure 1 shows TSAR’s data collection functionality.

15

Figure 1. TSAR Data Collection Functionality

…

Borders

CDNow

BN.COM

DealPilot

Task Scheduler TSAR

MySimon

Shopping Bots

Billboard

USA Today

Best Seller Lists

Amazon

EC Vendors

We ran TSAR at 4:00 a.m. every day from February 21 to March 29, 2000. The resulting dataset

contains 70,552 daily prices with 1,793 price changes from 169 products and 53 firms. We removed any

firm who had less than 300 recorded prices in our dataset, resulting in 1,674 price changes that were used

for analysis. In addition to TSAR data, PCData Online provided contemporaneous web usage statistics

for the 70 most often visited book, CD music and video sales sites. A typical drawback of VAR research

is that VAR requires large data sets to achieve sufficient degrees of freedom, but our data collection

approach enables us to overcome the related concerns. Our TSAR data sets are very large and offer a

good match with our VAR analysis methods.

Results

Technology allows firms to respond to competitor promotional pricing in a Stackelberg manner

within the same time period as defined by this study (e.g., one day). In this research, an exogenous

assumption would imply that the data collection method used is always more efficient than any firm’s

ability to respond to its competitors. If endogeneity is present, ignoring endogenous effects reduces the

strength of the reported relationships and the predictive power of the econometric model. We show in this

section that firms have the capability to respond within one day to each other’s promotional pricing.

Thus, an exogeneity assumption is inappropriate for our data, and endogenous effects on the dependent

variable need to be considered.

Our VAR analysis is done at the product/firm level, with each product that a firm sells is analyzed

to see if that product’s prices are significantly (and almost immediately) affected by changes in price by

other firms for the same product. Then we consider the entire set of a firm’s prices for products in

aggregate, and test if these prices can be predicted by changes in these products by other vendors. If a

firm is a “Follower”, there will be a significant relationship. With “Non-Followers,” the relationship will

16

be insignificant. Some firms (e.g., Amazon) sell products in both industries, but we consider company

reactions in each industry to be separate companies, as we believe that the same firm can act differently

when competing in different industries. Thus, Amazon would be considered two companies in this study

(e.g., Amazon-books and Amazon-CDs).

As stated earlier in this paper, VAR is used to include the effects of endogeneity. If EC retailers

are able to monitor each other's prices in real-time, it is unreasonable to expect followers to always take a

full day to respond to competitor price changes. Collecting data on a continuous basis is beyond our

ability to store and analyze, and thus we use a one-day increment to track prices. VAR adjusts the

dependent variable for the endogenous reactions that occur within our data collection time frame before

any regression analysis takes place, and we can therefore account rapid leader-follower behavior in our

data.

In keeping with the VAR model shown in Equation 7, the dependent variable ∆priceijt (e.g., the

price change for product i sold by firm j in current period t) is adjusted by a value, βjc, which is derived

using the VAR methodology and reflects the endogenous effect of all competitor c’s current period price

change on a firm j’s current period price change. Thus, the results presented in the section reflect a VAR

adjustment for endogenous effects. In this section, we show some anecdotal evidence of endogeneity that

led us to use the VAR methodology as well as the results of our VAR analysis.

Table 2. Results for the Linear and Transformed, and VAR Models

DATA SET SUB-SAMPLE

# OBS. PRICE CHANGES

FOL-LOWERS

NON-FOL-LOWERS

F-STAT.

R2

Follow-the-Leader Hypothesis – Transformed Linear Model (Equation 6) – Supported Books 29,992 364 11 12 5.5*** 9.0%Music CDs 93,688 1,310 13 15 15.2*** 11.4%Both Industries 123,680 1,674 26 42 11.0*** 10.6%

Follow-the-Leader Hypothesis – VAR Model (Equation 7) – Supported Books 29,992 364 12 13 7.5*** 16.8%Music CDs 93,688 1,310 21 7 22.8*** 22.3%Both Industries 123,680 1,674 33 20 16.1*** 20.5%

Price Effect Hypothesis – Transformed Linear Model (Equation 6) – Not Supported Inexpensive Books 13,024 226 10 8 5.6*** 10.1%Inexpensive CDs 74,144 1,088 14 14 13.2*** 12.3%Expensive Books 9,800 138 7 16 2.1*** 8.4%Expensive CDs 14,658 222 8 13 8.2*** 20.3%

Price Effect Hypothesis –VAR Model (Equation 7) – Not Supported Inexpensive Books 13,024 226 10 8 7.3*** 17.9%Inexpensive CDs 74,144 1,088 22 6 22.1*** 26.1%Expensive Books 9,800 138 10 13 4.4*** 21.8%Expensive CDs 14,658 222 12 9 7.2*** 25.2%Note: *** means p < .01. F-statistics provide an indication of the statistical significance of the

hypothesis for the sample set.

17

Table 2 shows the results of the analyses for the Follow-the-Leader Hypothesis and the Price

Effect Hypothesis. For each hypothesis, the results of both the transformed linear model and the VAR

model are described. The Followers column shows the number of firms that exhibited follow-the-leader

behavior. The Non-followers column shows the number of firms that did not exhibit significant follow-

the-leader behavior in our study. The F-statistic column shows that, overall, firms tended to exhibit some

immediate follow-the-leader behavior in pricing, thus supporting the Follow-the-Leader Hypothesis.

Our results show that Internet sellers have a near-immediate significant reaction to their

competitors. As our theory states, rational managers will raise or lower prices because of many

circumstances (e.g., cost increases, overstocks, etc.), but firms will avoid starting "price wars" that will

ultimately hurt their own profits without gains in market share.

We could not conclusively show support for the Price Effect Hypothesis. The observed levels of

R2 suggest that Internet booksellers seem to react marginally more quickly to more expensive items, as

predicted by our hypothesis, but that CD makers seem to react more quickly to less expensive items,

which runs contrary to our hypothesis. We propose a new premise that there are industrial characteristics

that determine whether managers either react quickly to changes in prices of expensive items or managers

want to more carefully consider what they will do before reacting to a competitor when a competitor price

change occurs on an expensive item. With more per-item revenue at stake, firms in certain industries may

not be willing to immediately follow the actions of a competing firm without careful consideration.

Conversely, immediate reaction would increase revenue for these expensive items, or reduce the effects of

competitor price promotions when a lot of revenue is at stake. We feel that more investigation is needed

in this area.

DISCUSSION

In our dataset, 1,793 price changes were detected: 871 positive price changes (where the price

increased) and 922 negative price changes (where the price decreased). In general, firms react to both

positive and negative competitor price changes, as can be intuitively expected.

With VAR models, there are numerous coefficients. For example, in this research, we examine 23

booksellers and 28 music CD sellers. With 23 different book sellers in this study, we would have 23

different sets of coefficients, each set containing up to 45 coefficients describing each firm's reaction up to

22 endogenous reactions and 23 lagged period reactions to competitor price changes. With 28 different

music CD sellers, we would have 28 different sets of coefficients, each set containing up to 55

coefficients. This would be difficult to place inside a single paper. Since a large number of coefficients

are not unusual with VAR analysis, the typical representation of VAR analysis is a graph showing an

impulse response function, which shows what the price should be based on the function derived for it, and

18

what the price actually is, for, in this case, a series of firm products. The impulse response function is

often represented in graphical format (e.g., Sims 1980; Enders 1995; Nijs, et al., 2001). For example,

Figure 2 shows the graphical representation of the impulse response function for BN.com's book,

Sullivan's Island, a book sold by BN.com. In Figure 2, BN.com's price for the Sullivan's Island book is

tracked throughout our period, and is mapped against the price predicted by the impulse response

function.1

Figure 2. BN.com Price and Impulse Response Function the Estimates Price of Sullivan's Island

The impulse response function for all books sold by BN.com predicts that Amazon's price

changes in the current and previous periods, and Border's previous period price changes all significantly

affect BN.com's propensity to change it's price is the current period. When a competitor changes the price

for a product, an industry shock is created that firms may or may not respond to. In Figure 2, Borders

lowers the price it is charging for Sullivan's Island. This results in a shock that is responded to by

BN.com. Amazon causes another shock by raising its price, causing BN.com to follow and raise it's price

as well. Since BN.com responds to both Amazon and Borders, any price change made by these two

competitors causes a change in the BN.com's impulse response function, reflecting BN.com's propensity

to react to these two competitors.

1 In actuality, a true graphical representation of an impulse response function in this research would concentrate on predicted price changes, not nominal prices, with the ∆p prediction usually at zero. For illustration purposes, the

BN.Com Price forSullivan's Island by Dorothea Benton Frank

$3

$4

$5

$6

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Day Number

BN P

rice

BN.COM Price Impulse Response Function

Borders lowers price

Amazon lowers price

Amazon raises price

Borders raises price

19

For this research, showing graphical representations of all the impulse response functions is

impractical. Developing an impulse response function chart for each product sold by each vendor is

impractical in this research because of the quantity of items and sellers that are examined. In this

research, we examine 23 booksellers selling 105 products and 28 music CD sellers selling 154 products.

This would result in 6,727 impulse response function charts included in this paper. However, in this

research, we are interested in firms' price reactivity to their competitors at the industry level, rather than

the specific competitors reactions to specific products denoted by an impulse response function. Impulse

response functions that are developed for this research are done at the firm level, not the product level,

thus giving us a better picture of aggregate reaction to competitors rather than reactions for each product.

We have analyzed the how well all of firm-level impulse response functions fit competitive interaction

strategies in Tables 3 and 4. Table 3 shows the VAR analysis of companies in the book industry, ordered

by unique Web site visits, and how these companies reacted to their competitors.

Table 3. VAR Analysis--Reaction to Competitor Price Changes by Company (Book Industry)

COMPANY R2 F-STAT. P-VALUE Amazon.com 0.133 8.519 0.000*** BN.com 0.434 40.925 0.000*** Borders.com 0.102 5.831 0.000*** Big Words 0.000 0.004 1.000 Books A Million 0.009 0.446 0.994 varsitybooks.com 0.090 4.508 0.000*** eCampus 0.031 1.403 0.077* Powell's Books 0.000 0.000 1.000 eFollet 0.000 0.005 1.000 buy.com 0.145 6.749 0.000*** 10base.com 0.000 0.008 1.000 1bookstreet.com 0.208 9.825 0.000*** a1Books 0.008 0.302 1.000 AllDirect.com 0.000 0.006 1.000 AlphaCraze.com 0.000 0.006 1.000 Books Now 0.170 6.833 0.000*** BookVariety.com 0.002 0.052 1.000 Harvard Book Store 0.322 15.010 0.000*** Kingbooks 0.000 0.006 1.000 Page One Bookstore 0.085 2.789 0.000*** Rainy Day Books 0.282 11.483 0.000*** Rutherford's Bookshop 0.270 10.602 0.000*** Word's Worth 0.106 3.319 0.000*** Note: *** means p < .01; ** means p < .05; * means p < .1

chart in this section has been transformed to show the predicted price from the impulse response function over an extended period of time.

20

To be considered a statistical "follower" in our study, a company had to follow competitors' price

changes within a 5% p-value. For "non-followers," we could not find a good-fitting impulse response

function that describes a firm's price changes based on competitor reaction.

We were surprised at the results from the book industry. We found it interesting that companies

in the book industry seemed to gravitated either toward extreme price reaction, with an p-value of zero,

taken out to three digits, or extreme ambivalence, completely ignoring the actions of their competitors,

with a p-value of one, taken out to three digits. Only eCampus showed a slight following, with a p-value

of 7.7%. These results show a clear division of pricing strategy, where companies select between either

monitoring and reacting to competitors or virtually ignoring competitors. Table 4 shows the results of our

VAR analysis for the CD industry, ordered by unique Web site visits.

Table 4. VAR Analysis--Reaction to Competitor Price Changes by Company (Music CD Industry)

COMPANY R2 F-STAT. P-VALUE Amazon 0.010 1.207 0.209 CDnow 0.010 1.173 0.240 BN.com 0.093 11.389 0.000*** Best Buy 0.015 1.577 0.022** 800.com 0.262 36.679 0.000*** UBL 0.010 0.976 0.507 Borders.com 0.621 159.852 0.000*** CD Universe 0.184 21.305 0.000*** CheckOut.com 0.032 3.031 0.000*** Tower Records 0.019 1.745 0.004*** TWEC.com 0.002 0.138 1.000 CD World 0.552 104.593 0.000*** AltaVista Shopping 0.045 3.878 0.000*** buy.com 0.097 8.621 0.000*** 10base.com 0.005 0.366 1.000 CD Quest 0.249 25.506 0.000*** CDconnection.com 0.379 45.690 0.000*** eUniverse 0.052 4.025 0.000*** Insound 0.704 170.571 0.000*** K-Tel 0.816 312.215 0.000*** MuzicDepot 0.068 5.002 0.000*** mymusic.com 0.179 14.666 0.000*** Quickmusic 0.000 0.002 1.000 Rock.com 0.554 80.218 0.000*** Song Search 0.203 16.145 0.000*** TheTop5 0.023 1.447 0.019** Total E's 0.021 1.292 0.075* World Party Music 0.028 1.737 0.001*** Note: *** means p < .01; ** means p < .05; * means p < .1

While the CD industry is not as given to as many extremes as the bookselling industry, there are

some CD sellers that ignore the actions of their competitors. The CD industry analysis, when compared to

21

the book industry analysis, also contains some surprises. While the book industry shows strong price

reaction in the top tier (e.g., Amazon, BN.com, and Borders), the CD industry shows that, in the top tier

(e.g., CDNow, Amazon, and BN.com), only BN.com significantly reacts to competitors. This indicates

that the same companies have different pricing strategies when competing in different industries. In

addition, while there are a similar number of followers and non-followers in both industries, many of the

followers in the CD industry tend to have their strategy dominated by reaction to price competition (e.g.,

Borders R2=62.1%, CD World R2=55.2%, Insound R2=70.4%, K-Tel R2=81.6%, Rock.com R2=55.4%) .

While firms in the book industry, tend to show they consistently react to competitors' price changes,

competitive reaction seems to be only a part of pricing strategy (e.g., the largest bookseller reaction,

BN.com, shows an R2=43.4%). This causes the R2 for the CD industry (R2=22.3%) to be larger than the

R2 for the Book industry (R2=16.8%).

Our empirical analysis shows industrial trends toward Stackelberg competition and away from

Bertrand-Nash competition in that we show significant and relatively immediate price reaction by many

firms in the two EC industries we examined. In Bertrand competition, prices would be more stable as

vendors set their prices to an initial low price and only react, simultaneously and identically, to changes in

production costs.

Implications for Business Processes and System Design. In traditional markets, the cost of

monitoring thousands of items may exceed the profit of fast reactions, especially if high consumer search

costs are considered. Internet sellers, on the other hand, must consider detecting and responding to

competitor price promotions, especially since EC technology can reduce customer search costs. This

research points out that, for Internet sellers, processes need to be put in place that allow fast reaction to

competitor price changes, both increases and decreases. Competitors' price increases reduce the

downward pressure of price, while competitors' price decreases must be responded to in order to retain

market share and to give competitors an incentive to avoid price promotions that will reduce profits and

possibly result in a price war or necessitate Bertrand-like behavior.

Additional business processes are needed, and the actual design of processes that automatically

detect and respond to competitor price changes need to be considered by businesses. Stackelberg

competition, in contrast with Bertrand competition, allows many different pricing strategies, including

reactive competitive pricing or tacit collusion. Systems that can allow companies to quickly respond to

competitor price changes will expand the competitive interaction possibilities, all of which are more

profitable than charging the Bertrand one price.

Implications for Other Industries. We have examined two EC industries in this study. In this

examination, we have discovered that the same seller can act differently when competing in different

industries, even if those industries are similar. Thus, our results may not be generalizable a priori to other

22

industries. However, our research brings up some important points for Internet sellers in other industries

to consider. First, in both industries, many firms have developed the capability to respond to competitors.

The ability to quickly respond to competitors can give an advantage to EC firms, and thus EC firms

should consider whether the benefits of developing this capability could lead to a short run competitive

advantage or a long run competitive parity where such capabilities are needed for EC firm survival.

Second, as our analytical model shows, firms that can respond quickly end up discouraging price

discounts from their competitors, because such a response will have only minimal impact. Thus,

developing the capability to quickly respond to other firms can result in reduced competition. Finally, as

argued in this research, Stackelberg pricing can lead to a wider variety of competitive choices, thus giving

EC firms a wider variety of competitive strategies to choose from. This is especially appealing if the only

alternative strategy is Bertrand competition, where revenues are drastically decreased to only cover

average costs.

CONCLUSION

This research makes several contributions. First, it is one of the first multi-industry empirical

studies of EC firms’ reactive pricing behavior. As such, this article gives EC researchers better insight

into the effects that technology has on the potential actions of EC firms. Second, this study incorporates

Stackelberg pricing theory and tacit collusion theory to explain that, even though firms observe

competitors’ prices, it is irrational to offer price promotions with the sole intention of capturing market

share if competitors are likely to immediately respond to your price promotion. Their immediate response

will make your price promotion result in less profit for all firms with no increase in market share. The

empirical evidence shown in this study shows fast competitor reaction in the EC environment, thus

diminishing any benefits from a price promotion. Firms tend to match competitor price changes, and

prices tend to go both up and down (as opposed to just down). Furthermore, we did not observe

convergence to single, stables price as one would expect with Bertrand competition. Third, the firms

included in this study do not always have an increased tendency to follow competitors’ price changes

when an item is expensive. This runs counter to intuition and to our theory, and supports a premise that

industry and firm characteristics determine whether a firm is more interested in following either

expensive or inexpensive items. This needs to be investigated more thoroughly in future research.

Fourth, this research shows how vector autoregression can be effectively used to empirically test an

industry-wide response to a firm's actions. While the VAR technique is used somewhat widely in

economics literature (e.g., Sims, 1980) and in marketing science literature (e.g., Nijs et. al. 2001), it is

relatively new to IS literature. We feel the VAR technique is very appropriate when researching

competitive interaction between multiple firms, especially when immediate effects need to be considered.

23

For academic researchers, it is important to model pricing dynamics at a micro-level of detail to

achieve a better understanding of how the EC market is different from traditional markets. For managers,

this research engenders a better understanding of EC product pricing dynamics. Managers need to

understand how to deploy EC pricing strategies in relation to strategies of their competitors. Future

research should include more in-depth analysis of EC firm behavior, and dynamic interplay between EC

consumers and EC firms. While our study supports the premise that price level affects follow-the-leader

behavior, the effects are interesting and surprising in that the least expensive items show greater price

reactivity. The exact effect of price level on competitive price reaction needs to be further investigated.

This study has three limitations. First, the VAR methodology forces the researcher to rank

endogenous effects of variables to form β coefficients. Thus, the researcher must apply the appropriate

theory to describe variables that are likely to have an endogenous effect on other variables. We addressed

this limitation by using asymmetric competition theory and PCData Online data to rank the firms via web

usage statistics. Second, we assumed that any non-detected price changes were zero. Several times,

especially with small companies, web sites were unreachable or the required price information was not

available. Assuming price stability in these cases, in our view, is valid and conservative. Third, only the

top sellers were considered for this list. It is impractical to search through every existent book, CD and

software title. We feel that the top 100 books and the top 100 CDs represent typical behavior that

represents the majority of sales in these industries. It could be argued that this behavior does not apply to

low-sales items, but we contend that both the larger market and vendor capabilities are represented by our

study.

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