follow the leader? strategic pricing in e-commerce...
TRANSCRIPT
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
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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.
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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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