SHOULD WE EXPECT LESS PRICE RIGIDITY IN THE DIGITAL ECONOMY?
Robert J. Kauffman
Director of MIS Research Center
Professor and Chair
Dongwon Lee
Doctoral Candidate
Information and Decision Sciences
Carlson School of Management, University of Minnesota
Minneapolis, MN 55455
E-mail: {rkauffman, dlee}@csom.umn.edu
Last Revised: March 30, 2004
ABSTRACT
Price rigidity, a phenomenon where prices do not change with the regularity predicted by
standard economic theory, is a topic of long-standing interest in firms, industries and the
economy as a whole. As information technology is increasingly changing the process by
which strategic pricing decisions are made and implemented in business operations, there is a
need to develop a more substantial managerial understanding of firm pricing in “production
function” terms. In the 1990s, technology-driven pricing was largely the domain of the
airlines, hotels and rental car companies, with the practice of revenue yield management.
However, now it is possible for bricks-and-clicks firms, and even traditional retailers, to
implement systems that permit significant adjustments to be made to prices in situations
where menu costs previously made rapid price changes difficult to achieve in an economical
way. The paper draws upon theoretical perspectives that are largely new to the field of
Information Systems, but that offer rich opportunities for theory-building and empirical
research in settings that will be of high interdisciplinary interest.
KEYWORDS: Collusion, contracts, digital economy, e-commerce, market structure,
information asymmetry, menu costs, non-price competition, price rigidity, pricing strategy
ACKNOWLEDGEMENTS: An earlier short version of this paper was presented in the
Electronic Marketing mini-track and appeared in the Proceedings of the 37th Hawaii
International Conference on System Science (HICSS-37), held in January 2004. The authors
especially thank Mark Bergen and Daniel Levi for helpful comments. We also thank Fred
Riggins, Michael Smith, Arnold Kamis and other participants as well as two anonymous
reviewers in the 2004 HICSS conference for helpful suggestions.
1. INTRODUCTION
From our daily lives to commercial transactions between businesses, Internet
technologies have created new forms of and profoundly impacted socio-economic
organizations, and the scope and efficiency of markets (Brynjolfsson and Kahin 2000). The
Internet enhances firm performance by reducing transaction costs necessary to produce and
market goods and services in various ways. They include: increasing managerial efficiencies;
enabling firms to connect their supply chains with suppliers and buyers; offering new means
to collect detailed data about buyers’ purchasing behaviors; making prices and costs more
transparent; lowering technological barriers to entry; and creating more competitive markets
(Baker, et al. 2001; Litan and Rivlin 2001; Sinha 2000). From the customer’s perspective, the
Internet reduces search costs and switching costs between competitive sellers, enabling buyers
to compare products and their prices by using search engines or shopbots (Bakos 1997;
Brynjolfsson and Smith 2000; Daripa and Kapur 2001). These function as price comparison
agents, and are seen on the Internet at such popular Web sites as mySimon.com and
BizRate.com. There are now many electronic marketplaces where consumers and businesses
can benefit from immediate access to products and services and where suppliers can distribute
their products more efficiently (Brynjolfsson and Kahin 2000).
The Digital Economy has also provided traditional “bricks-and-mortar” retailers with
new opportunities to adopt “bricks-and-clicks” retail capabilities, to both complement their
traditional stores as well as take advantage of the Internet channel. Many national retailers,
including such well-known names Best Buy and Barnes & Noble, have rushed to retrieve
customers who switched to pure Internet retailers (e.g., Buy.com and Amazon.com). They
did this by establishing online presences or strategic partnerships with Internet only retailers.
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In March 2000, for example, Best Buy launched a partnership with Micronpc.com in which
Best Buy would establish kiosks in its stores, allowing customers to buy computers directly
from the manufacturer, Micron Electronics (Mainelli 2000). Circuit City and Amazon.com
also joined in a bricks-and-clicks partnership to sell electronics online, and the revenue
sharing looks like a good one for both companies (Junnarkar 2001). Many of the electronics
sold at Amazon are available for pickup at Circuit City locations. Hence, bricks-and-clicks
retailers can provide their customers with “buy online, pick up and return in store” capability
by leveraging logistical and operational expertise with traditional distribution channels as well
as highly developed technology infrastructures with Internet channels. Turnover has
increased due to a real-time inventory system, involving Internet-based electronic shelf
pricing systems (ESPs) that coordinate with the physical stores. As a result, it is becoming
important to seamlessly integrate a firm’s Internet channel with traditional distribution
channels by ensuring product, price and promotion consistency (Gulati and Carino 2000).
1.1. Price-Related Phenomena in the Digital Economy
The new IT capabilities enable firms to better estimate product demand and to make
flexible adjustments in the supply of products or services, thereby increasing market
efficiency. Economists, especially those who work with the theoretical perspectives of
microeconomics, have focused on price as the primary mechanism for efficient resource
allocation (Carlton 1989). Thus, pricing and strategies of firms are essential in most
economic analysis of market performance. Research in IS to date has focused on price
dispersion (e.g., Bailey 1998; Brynjolfsson and Smith 2000; Clay, et al. 2001; Clay, et al.
2002a; Clemons, et al. 2002), price levels (e.g., Bailey 1998; Brynjolfsson and Smith 2000;
Clay, et al. 2001; Clay et al. 2002a; Lee 1998), and price-setting (e.g., Bakos 1997; Clay et al.
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2002b; Kauffman and Wood 2004) to explain the firms’ pricing strategies on the Internet.
Most empirical evidence shows that the results are lower price levels and the failure of the
“one price” Bertrand price competition would predict in electronic markets. Table 1
summarizes some selected empirical research on pricing in e-commerce environments.
Table 1. Previous Empirical Research on Price Dispersion, Price Levels and Price-Setting
RESEARCH
Ancarani and
Shankar (2002)
PERIOD
2002/03–
2002/04
Arbatskaya and
Baye (2002)
Bailey (1998)
1998/04–
1998/07
1997/02–
1997/03
Brown and
Goolsbee (2000)
Brynjolfsson and
Smith (2000)
Clay, et al.
(2001)
Clay et al.
(2002a)
Clemons et al.
(2002)
Kauffman and
Wood (2004)
Lee (1998)
1992–
1997
1998/02–
1999/05
1999/08–
2000/01
1999/08– Prices of books sold online.
2000/01
1997/04
Prices for online airline
tickets.
2000/02– Prices of books and CDs
2000/03
sold online.
1986 –
Prices of used cars in
1995
offline and online auctions.
1999/01– Prices of cars sold online
2000/02
and offline.
2000/11
Prices of books, CDs,
electronics sold online.
2000/07– Prices of DVDs sold online
2000/08
and multi-channel.
Morton, et al.
(2001)
Pan, et al. (2001)
Tang and Xing
(2001)
DATA
Prices of books and CDs
sold in Internet, bricks-andmortar, bricks-and-clicks.
Daily mortgage rate posted
online.
Prices of books, CDs,
software sold online and
offline.
Prices of life insurance
policies.
Prices of books and CDs
sold online and offline.
Prices of books sold online.
FINDINGS
Highest price dispersion in bricks-andclicks retailers. Lowest prices in pure
Internet retailers.
Considerable price dispersion online.
Price dispersion not lower online.
Higher prices online.
Lower price dispersion online. Lower
prices online.
Lower prices online. Lower price
dispersion online by market share.
More competition led to lower prices.
Less price dispersion.
Timing, direction of price changes
correlated across firms.
Higher price dispersion online.
Evidence of follow-the-leader pricing.
Higher prices in online auctions.
Lower price dispersion online. Lower
prices online.
Higher price dispersion online.
Lower price dispersion online. Lower
prices online.
Despite the theoretical and empirical studies on price dispersion, price levels and pricesetting behavior, there are only a few studies on price-changing behaviors— price rigidities—
in e-commerce. Compared to non-Internet markets in which significant costs associated with
changing prices are incurred by retailers, Internet technologies make it possible to more
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accurately control inventory and costs, to sample demand at any given moment, and to have
significant capabilities with respect to price changes and the nature of competition in retail
markets (Brynjolfsson and Smith 2000).
Most observers comment that price adjustment costs are almost entirely absent in the
Digital Economy because they primarily consist of the costs of simple database updates,
which may be easily programmed (Bailey 1998). Thus, the limited number of previous
empirical studies suggests that Internet retailers will have the capability to make more
frequent price changes than traditional retailers (Bailey 1998; Brynjolfsson and Smith 2000).
However, we believe that the issue of price rigidity in the Digital Economy should be
given more scrutiny than the literature has actually provided up until now. There may be
factors that can explain price-changing behaviors that Digital Economy firms demonstrate
other than menu costs. For example, firms may make use of non-price elements, such as
customer service, delivery lags or free shipping instead of price adjustments. In addition,
there may be differences in price-changing behaviors that are observed for different firms
relative to the different products. Similar explanations may also occur at the level of
industries, as well as within or between sales channels. So with this concern about the
pervasive expectation of declining price rigidity on the Internet, we address the following
research questions:
□
Should we expect less price rigidity in e-commerce compared to traditional (nonInternet) channels?
□
Are there any differences in price changing behaviors within and between products,
channels, and industries?
□
In addition to the menu cost explanation, what theories can explain what we observe?
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To answer these questions, we draw upon perspectives that are largely new to the field of
IS that offer rich opportunities for theory-building and empirical research in settings that will
be of high interdisciplinary interest. Such interdisciplinary studies, we believe, have the
potential to provide a distinctive foundation for IS research and can also serve as a guide to
research on other economic phenomena in e-commerce.
1.2. Price Rigidity Theories in Marketing and Economics
Price rigidity is an essential component of new-Keynesian economic and macroeconomic
theory. Economists refer to this as the economics of nominal rigidities (Andersen 1994;
Blinder et al. 1998). Some other terms are also seen: price inertia, price stickiness, and price
inflexibility. Rigid prices occur when prices do not adequately change in response to
underlying cost and demand shocks. Once set, prices often remain unchanged, in spite of
changes in the underlying conditions of supply and demand. Price rigidity has the potential to
prevent Walrasian market clearing that leads to equilibrium in supply and demand and market
inefficiency (Carlton and Perloff 2000).
An early influential study of price rigidity was conducted by Means (1935), who found
that some prices are “administered,” and consequently, are insensitive to the fluctuations of
supply and demand. Subsequently, a wide range of partial equilibrium theories, such as those
based on price adjustment costs, market interactions, asymmetric information, and demand
and contract-based explanations have been proposed to explain why price changes might be
sluggish. They provide a basis for the macroeconomic assumptions of rigid prices (Andersen
1994; Blinder et al. 1998). Table 2 shows brief descriptions on these price rigidity theories.
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Table 2. An Overview of the Multiple Theories of Price Rigidity
THEORIES
Cost of
Price
Adjustment
Market
Structure
Asymmetric
Information
Demandbased
Contractbased
DESCRIPTION
Changing prices is costly, so prices unchanged even with changes in supply and demand.
• Menu Cost: Firms face a lump sum cost whenever they change their prices.
• Convex Adjustment Cost: The costs of changing prices rise at an increasing rate.
• Managerial Cost: Time and attention required by managers for price decision
making slow down price changes.
• Synchronization and Staggering: Stores tend to change the price of different
products either together or independently.
Monopoly power (or a limited number of sellers) as well as coordination failure in
markets are the primary sources of price rigidity
• Industry Concentration: The sluggishness of price changes is a demonstration of
monopoly power.
• Coordination Failure: Absence of an effective coordinating mechanism for market
clearing is the cause of the price rigidity.
The fact that one party to a transaction has more information provides an explanation.
• Price as Signal of Quality: Firms are reluctant to lower prices for fear that their
customers may misinterpret price cuts as reductions in quality.
• Search and Kinked Demand Curve: Customer search costs lead to firms facing a
kinked demand curve.
Firms react to other changes than price changes: inventories, non-price competition.
• Procyclical Elasticity of Demand: Demand curves become less elastic (responsive)
to price changes as they shift in.
• Psychological Pricing Points: Prices have the tendency to be stuck at certain ending
prices.
• Inventories: Inventories are used by firms to buffer demand shocks.
• Non-Price Competition: Instead of price competition, firms use non-price elements
such as delivery lags, service, or product quality.
Prices remain unchanged by nominal or implicit contracts.
• Explicit Contracts: Prices are fixed for limited time periods under nominal contracts.
• Implicit Contracts and Customer Antagonization: As price changes may
antagonize customers, implicit agreements between firms and customers are used to
stabilize prices.
The remainder of this paper is organized as follows. We first develop our thinking by
providing a broad-based review of the literature on the theories of price rigidity from the
perspectives of economics and marketing science. Then, we present new ways of
understanding price rigidity in the Digital Economy based on the interdisciplinary theories.
Finally, we draw conclusions on our assessment of the theory, and discuss a number of
implications of our study for future academic research and pricing strategies in industry.
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2. THEORIES OF PRICE ADJUSTMENT COST
One explanation for price rigidity is based on theories of price adjustment cost: it is
costly for firms to change prices (Blinder et al. 1998; Carlton 1997). A profit-maximizing
firm facing price adjustment costs will change its prices less often than an identical firm
without such costs. They include real costs associated with price changes: printing new
catalogs, new price lists, new packaging, etc; informing sales people and customers; obtaining
sales force cooperation; antagonizing customers resulting in lost future sales; and spending
the time and obtaining the attention required of managers to gather and process the relevant
information and to make and implement decisions (Blinder et al. 1998; Levy et al. 1997). An
important aspect of price adjustment cost is its relationship with the magnitude of the price
change (Zbaracki et al. 2003). The previous literature shows that price adjustment costs are,
in general, modeled in one of two ways: in a menu cost model or using a convex adjustment
cost model (Blinder et al. 1998; Carlson 1992).
2.1. The Theory of Menu Costs
Menu cost theory assumes that the cost of price adjustment is a fixed cost that must be
paid whenever a price is changed, and thus, is independent of the magnitude of the price
change (Andersen 1994; Barro 1972; Blinder et al. 1998; Sheshinski and Weiss 1977). Menu
costs drive firms relatively large and infrequent price changes, and thus make prices less
flexible by deterring recurrent price changes (Blinder et al. 1998; Zbaracki et al. 2003).
Interestingly, however, prior to the publication of articles by Mankiw (1985) and Akerlof and
Yellen (1985), menu costs were thought to be too small to rationalize any substantial effects
of price rigidity. Mankiw (1985) states that in monopoly competition a firm’s increased
return from changing prices is smaller than the increased social welfare, and prices may not
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change even with inefficient allocations. Introducing real rigidities in the face of aggregate
demand shocks, Ball and Romer (1990) argue that even with small costs of nominal price
changes firms do not change their prices and real prices remain unaffected. Fluet and Phaneuf
(1997), using a model where the production technology in the monopolistic firm is
endogenous to the menu cost, claim that price adjustment costs have the same effect as an
increase in the randomness of demand.
It has been hard to directly test menu cost theory due to the lack of cost-related data
(Blinder et al. 1998). So empirical studies have used indirect proxies (e.g., frequency of price
changes, time between changes), to provide evidence supporting this theory (Levy, et al.
2002). Liebermann and Zilberfard (1985), using data obtained from Israeli food retail chains,
examine three possible price adjustment strategies in a highly inflationary situation: cost
minimization (i.e., menu cost), market power (i.e., industry concentration), and institutional
(customer antagonization) approaches. However, they do not find any empirical evidence that
supports these three approaches. Using Stigler-Kindahl data on actual transactions prices,
Carlton (1986) finds that the fixed cost of price adjustment differs across buyers and sellers.
Using the newsstand prices of magazines, Cecchetti (1986) analyzes the frequency and size of
price changes and shows that higher inflation causes more frequent price changes and price
adjustment costs are not fixed; instead they vary with the size of real price changes. Kashyap
(1995) notes that, for twelve selected retail items sold through catalogs over 35 years, there
exists heterogeneity in both frequency and amount of price changes. Wilson (1998) finds
large price rigidities in the short run in a static experimental study of the effects of menu
costs. Slade (1999), in her study on pricing of a single product by groceries, provides some
estimates of the implied price adjustment cost magnitude. Studies involving gasoline
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(Borenstein, et al. 1997) and bank deposit rates (Hannan and Berger 1991; Jackson 1997)
show the evidence of asymmetric price adjustment, in which retail prices respond more
quickly to input price increases than decreases. Using survey data from New Zealand
businesses, Buckle and Carlson (2000) assert that larger firms change price more often than
smaller firms due to the menu costs, which fall systematically as firm size increases. On the
other hand, Powers and Powers (2001)’s analysis on price data from grocery stores shows
symmetric pattern in both frequency and size of price changes for price rises and declines.
To directly measure menu costs, Levy and his colleagues (Dutta, et al. 2002; Dutta et al.
1999; Levy et al. 1997; Levy, et al. 2002; Levy et al. 1998) take into account four components
of menu costs: labor cost of changing grocery shelf prices; cost of printing and delivering new
price tags; cost of mistakes made during the price change process; and cost of in-store
supervision of price changes. They find non-trivial costs of price adjustments for large food
and drugstore chains and point out that prices are less flexible in response to cost shocks that
are smaller, less persistent, and on which market participants have less information.
2.2. Convex Adjustment Cost Theory
Convex adjustment cost theory assumes that the cost of changing prices is convex and
quadratic, increasing in the size of the change. Convex adjustment costs tend to discourage
large price changes and lead to relatively small, frequent partial price adjustments that move
toward a target price level (Andersen 1994; Blinder et al. 1998; Carlson 1992).
Rotemberg (1982a and 1982b) combines the microeconomic model of gradual price
adjustment with a simple macroeconomic model in which the money supply drives aggregate
demand. By introducing a model of monopoly firms with quadratic price adjustment costs,
Rotemberg asserts that it is more expensive to make a large change than several smaller
9
changes of the same total magnitude. Using National Federation of Independent Business
(NFIB) survey data, Carlson (1992) reports that a large percentage of firms leave their prices
unchanged and make larger and more frequent price changes when inflation is higher.
Zbaracki et al. (2003) find that the managerial and customer parts of the price adjustment
costs are convex, while physical costs of price adjustment, such as the costs related to printing
and distributing new catalog and new price lists, and notifying suppliers, are non-convex.
Why? The decision and internal discussion costs, as well as customer negotiation costs, are
higher for larger price changes. Blinder et al. (1998) argue that while the quadratic cost
assumption may be in doubt, it is not unreasonable. However, some empirical studies show
the evidence consistent with the menu cost model rather than convex cost model (Blinder et
al. 1998; Carlson 1992; Levy et al. 1997).
2.3. The Theory of Managerial Costs
Managerial costs, also called decision costs (Sheshinski and Weiss 1992), are defined as
the managerial time and effort dedicated for relevant information gathering and the price
decision-making (Ball and Mankiw 1994; Blinder et al. 1998; Carlton 1997; Levy et al. 1997).
The theory of managerial cost (also called hierarchy theory) assumes that firms cannot change
their prices promptly in response to changes in the firm’s economic situation if many
individuals’ decisions in a hierarchical organization are required to process a price change
(i.e., many levels of authorization) (Bergen et al. 2003; Blinder et al. 1998). Both (physical)
menu costs and managerial costs give rise to price adjustment barriers within a firm (Bergen
et al. 2003; Zbaracki et al. 2003) though some consider managerial cost a special kind of
menu cost (Blinder et al. 1998).
Sheshinski and Weiss (1992) distinguish managerial costs from menu costs as the more
10
critical component by arguing that these costs induce price staggering by firms. Mankiw and
Reis (2001) incorporate managerial costs into a formal macroeconomic model, called the
sticky information model. The model assumes that the costs of information gathering and
processing lead to slow information acquisition and price adjustment. Through the direct
measurement of the size of the managerial and customer costs in a single large manufacturing
firm, Zbaracki et al. (2003) analyze several types of managerial and customer costs, including
costs of information gathering, decision making, negotiation, and communication. They find
that the managerial costs are over six times the menu costs associated with changing prices.
2.4. The Theory of Price Synchronization and Price Staggering
New-Keynesian macroeconomists assume that firms change prices step-by-step over time
(i.e., staggering over time). Not all firms change them simultaneously. In oligopolistic
markets, each firm takes into account the actions of its competitors, and thus pricing policies
will be interdependent, preventing the firm from changing its own products’ prices (Lach and
Tsiddon, 1996; Sheshinski and Weiss 1992; Taylor 1980).
Blanchard (1982) argues that this staggered price-setting leads to inertia in the aggregate
price level. Staggering and synchronization are also proposed by Ball and Cecchetti (1988),
who develop a model in which firms have imperfect information of the current state of the
economy and obtain information by observing the prices set by others. This gives each firm
an incentive to set its price shortly after other firms set their prices. Ball and Romer (1989)
state that the staggered price-setting has the advantage in that it permits rapid adjustment to
firm-specific shocks, but the disadvantages include unwanted fluctuations in relative prices.
By creating price level inertia, staggering price-setting can increase aggregate fluctuations.
Sheshinski and Weiss (1992) study optimal pricing strategy for a multi-product monopolist
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when the timing of price adjustment is endogenous. They argue for a further source for
interdependence, namely, increasing returns in the costs of price adjustment (i.e., economies
of scope). They further state that pricing decisions are influenced by interactions in the profit
function between the prices of the products and the nature of menu or decision costs. So,
synchronization is induced when there are positive interactions between prices in the profit
function and menu costs, where the cost of changing prices is independent of the number of
products. Similarly, the price change process should be staggered over time under negative
interactions in the profit function, and decision costs, where the cost of changing prices is
proportional to the number of products (i.e., constant returns to scale).
There are few empirical studies because much of the work on price rigidities has
primarily concentrated on the single product firms (Fisher and Konieczny 2000). Lach and
Tsiddon (1996), using the multiproduct pricing data from Israeli retail stores, find that price
changes of the same product are staggered across firms (across-stores staggering) while the
price changes of different products are synchronized within the same firm (within-store
synchronization). Fisher and Konieczny (2000), in their empirical work on the data from
Canadian newspapers, present evidence of synchronization within the same firm, as well as
staggering across independent firms in price-setting of newspapers.
3. THEORIES BASED ON MARKET STRUCTURE
In industrial economics, it is commonly said that the sluggishness of prices to demand is
basically a demonstration of market power (Okun 1981). For example, a duopoly with fixed
costs of price adjustment is more flexible in price changes than a monopoly under certain
conditions (Rotemberg and Saloner 1987). In many industries, pricing strategies are
12
interdependent because each firm takes into account the actions of its competitors. Without
an effective coordinating mechanism (e.g., auctioneer) for market clearing, each firm hesitates
to change prices until other firms move first, and thus prices may remain fixed (Blinder et al.
1998).
3.1. The Theory of Industry Concentration
Economists have emphasized monopoly power or a limited number of sellers in markets
as the primary cause of price rigidity or inflexibility (Dixon 1983; Ginsburgh and Michel
1988; Qualls 1979). Because of the difficulty in measuring the competitive conditions in a
given market, most of the studies have focused on a proxy measure for market
competitiveness, i.e., industry (or market) concentration (e.g., Herfindahl index), which
iteratively measures what share of a market is held by the first x-number of largest firms
(Tirole 1988). It provides a rudimentary indicator of the extent of monopoly power.
Numerous studies have investigated the theory of industry concentration and have generated
three different observations on the relationship between the degree of industry concentration
and price rigidity: (1) a positive relationship, (2) a negative relationship, and (3) no significant
relationship.
The notion behind a positive relationship is that highly-concentrated industries behave as
oligopolies with attendant problems of pricing coordination. An oligopolistic firm expects its
competitors to react differently to a price increase and decrease. Although a price decrease
will be followed, a price increase will not be followed (Qualls 1979; Sweezy 1939). Another
explanation for this relationship is limit pricing (i.e., pricing to prevent entry), where firms in
concentrated industries are typically enjoy increasing returns to scale (i.e., economies of
scale), and thus they tend to keep their prices lower than they otherwise would in order to
13
discourage or delay new firm entry (Stiglitz 1984). Rotemberg and Saloner (1987), in a
comparison of monopoly and duopoly markets, theoretically develop the market power
explanation for price rigidity, showing that firms in less competitive markets change price less
often. Ginsburgh and Michel (1988) find a negative link between the degree of industry
concentration and the speed of quantity adjustment in an oligopolistic industry with quadratic
cost functions.
Empirical works showing the evidence of this relationship are as follows. Carlton (1986)
reports an overall positive relationship between rigid prices and the degree of industry
concentration: the more highly concentrated an industry, the less rapidly will cost variations
be transmitted into prices. Hannan and Berger (1991) find evidence from banking industry of
a significant positive relationship between industry concentration and price rigidity, and
limited level of asymmetric behavior. Neumark and Sharpe (1992), using the panel data on
bank deposit, find an asymmetric relationship between industry concentration and price
rigidity, i.e., downward price rigidity and upward price flexibility in concentrated markets.
The justification for the second view (i.e., negative relationship), as claimed by Stigler
(1964), is that it is easier for other competitors to identify secret price cutting when there exist
fewer number of firms in an industry. Thus, firms avoid the secret cutting of prices.
Domberger (1979), in his study on 21 industries using the Herfindahl index of concentration,
shows a negative relationship between price rigidity and industry concentration. Powers and
Powers (2001), analyzing grocery store price data, find that higher degrees of industry
concentration lead to frequent and large price changes.
The third perspective (i.e., no significant relationship) is based on the argument that
industrial concentration is not a key determinant of industrial pricing behavior. Without
14
perfect monopoly or explicit pricing collusion, firms tend to behave as price competitors and
the degree of industrial concentration is inconsequential (Qualls 1979). Starting with a
slightly adapted version of the model from Ginsburg and Michel (1988), Worthington (1989)
demonstrates the possibility of an uncertain relationship between industry concentration and
pricing behavior. Higher industry concentration may result in an increase, a decrease, or no
change in price rigidity. Dixon (1983), using Australian economic data, reports no significant
relationship between the concentration and price rigidity. Jackson (1997), using rational
distributed lag and price adjustment models, provides empirical evidence of a non-monotonic
relationship between industry concentration and price rigidity.
3.2. The Theory of Coordination Failure
Coordination failure theory assumes that the inability of firms to plan and implement
pricing is due to the externalities or a lack of coordination mechanism that may affect firms in
different ways. Potential coordination failures can be explained by many sources of
heterogeneity among firms, such as differences in relative size, cost structure, product
differentiation, and information (Tirole 1988). A recent study by Blinder et al. (1998)
suggests two kinds of solutions to such coordination problems: price leadership and implicit
collusion. He finds this theory to be the most revealing, ranking first in his list of theories.
Several studies on coordination failure have characterized firms’ pricing behavior in
markets of imperfect competition by price leadership (Rotemberg and Saloner 1990; Tyagi
1999). Price leadership occurs when one of the firms in an industry sets the price or
announces a price change, and the other firms then take the price as given. The price
leadership model is solved just like the Stackelberg model (i.e., a leader-follower model),
where one firm, the Stackelberg leader, can commit to its output first, and then, the second
15
mover, the Stackelberg follower, produces its quantity knowing the output of the leader.
Applying the Stackelberg model in pricing patterns of firms, Tyagi (1999) proposes the
Stackelberg pricing model, which shows leader-follower pricing behavior in markets. The
kinked demand curve (Hall and Hitch 1939; Sweezy 1939), the classic explanation for price
rigidity in oligopolistic industries, explains the firm’s reluctance to cut prices because
competitors match price reductions and consequently the first firm cannot gain market share.
In the case of cost increases affecting several rival firms, each individual firm may be
unwilling to remain the price leader out of fear that its competitors will not follow and the
firm will then lose their market share (Blinder et al. 1998; Okun 1981). Without a price
leader to efficiently coordinate price changes, therefore, prices may remain unchanged.
Another explanation for the coordination failures is based on firms’ implicit collusion.
Chamberlin (1929) argued that oligopolistic firms can maintain the monopoly price by
cooperating with their competitors even without a formal agreement to avoid intense price
competition. However, such collusion can be very difficult to sustain in the following
situations: (1) when large business fluctuations occur (e.g., boom and bust cycles), (2) in the
presence of minimal punishment for collusion, (3) when large gains from cheating at possible,
and (4) when detection lags make it difficult to apprehend the transgressor (Blinder et al.
1998; Rotemberg and Saloner 1986; Stiglitz 1984; Tirole 1988). Stiglitz (1984) argues that
firms’ collusive agreements by noncooperative equilibria may lead to price rigidities.
Rotemberg and Saloner (1986), offering a supergame-theoretic model of oligopolistic
behavior during booms, point out that collusion is more difficult to sustain because the gain
from defection increases in current demand, while the loss from punishment increases in
future demand.
16
Cooper and John (1988) propose that strategic complementarity in prices will lead to
multiple equilibria that are superior to low-activity equilibria. So an agent’s optimal level of
price flexibility will depend positively on other agents’ strategies. Ball and Romer (1991), by
combining this notion of strategic complementarity with menu costs in deciding prices under
imperfect competition, show that price flexibility in one firm encourages other firms to make
their prices flexible. Andersen (1994) also argues that strategic complementarity between the
prices charged by different firms can increase the price rigidity. According to the collusion
theory, therefore, prices could be rigid because there may be incentives for firms to sustain
prices at higher levels through implicit agreements.
4. ASYMMETRIC INFORMATION THEORIES
Asymmetric information theory assumes that one party to a transaction (e.g., a firm) is
better informed than the other (e.g., a customer). It gives new insights about why market
failures such as price rigidities occur (Blinder et al. 1998). Stiglitz (1979) argues that, under
asymmetric information, customers tend to transact with firms providing relatively stable
price paths and avoid firms which make frequent and/or large price adjustments. Since the
theory always involves unobserved information, such as product quality and search costs,
there are few empirical studies to explain the causes of price rigidity (Blinder et al. 1998).
4.1. The Theory of Quality Signaling
With many products, it is difficult for customers to observe quality even at the time of
purchase because they are imperfectly informed about the product characteristics (Stiglitz
1984 and 1987). Most people typically believe that higher-priced products are of higher
quality. Thus, firms may have an incentive to sell low quality items at high-quality prices,
17
and the products will be traded at a price which reflects their customers’ beliefs about the
average quality of the products (Riley 1989). The theory of quality signaling assumes adverse
selection (Akerlof 1970), where firms are reluctant to decrease prices in economic recessions
for fear that customers may incorrectly interpret the lowering of prices as a signal that the
product quality has been reduced (Blinder et al. 1998). Cutting one’s price may be interpreted
as a quality reduction, and thus demand may actually decrease rather than increase (Stiglitz
1984). This theory, however, appears only to be relevant for the luxury product market (e.g.,
automobiles), or perhaps certain niche markets for clothes or food, but not of widespread
importance for most products.
Allen (1988) proposes a formal model of price rigidities based on the idea that the
variations of unobservable quality make prices inflexible as long as demand shocks are
sufficiently serially correlated. He shows that prices are inflexible in the products (e.g.,
automobiles) whose quality cannot be easily observed while prices are flexible in the
industries (e.g., petroleum) where the quality is more straightforward. In Blinder et al.’s
survey (1998), this theory is the least significant because the quality differences on which this
theory is based are unobservable.
4.2. The Theory of Search and Kinked Demand Curves
Since Stigler (1961)’s inventive work on search theory, a number of studies have
analyzed the impact of search costs and asymmetric information on price rigidity. He has
argues that search is costly to customers (Stiglitz 1999) and a firm’s price changes are
observed by the firm’s current customers but not by other customers due to the search costs
(Ball and Romer 1990; Stiglitz 1979). If the firm raises its price by more than the customers
expect, it may lose many customers because its regular customers instantaneously recognize
18
the price change and search for other sellers with more attractive prices. If the firm lowers its
price, on the other hand, it sells more to current customers but does not attract new customers
due to search costs (i.e., they do not observe the lower price without search) (Stiglitz 1987 and
1999). Thus, this theory assumes that search costs make the demand curve more inelastic for
price decreases than for price increases. As a result, firms will face kinked demand curves:
the returns from price decreases may be less than the sales loss from price increases.
5. DEMAND-BASED THEORIES
We next consider price rigidity theories, which explain how firms react to demand
fluctuations other than price changes: procyclical elasticity, psychological pricing points,
inventories, and non-price competition.
5.1. Theory of Procyclical Elasticity of Demand
Price changing behavior over the business cycle brings about a discussion on the roles of
demand and supply shocks in the causes of business cycles (Blinder et al. 1998). When
economic fortunes wane, some companies may go out of business. Others may lose their least
loyal customers, but retain their most loyal ones. If the number of companies falls
significantly, this may increase the remaining companies’ ability to coordinate their prices,
reducing price competition. In addition, companies will not reduce prices because the
remaining customers may be insensitive to price changes (Blinder et al. 1998). This
phenomenon is known as procyclical elasticity of demand. It explains why the
responsiveness or elasticity of prices to changes in demand may dampen in a cyclical
downturn.
19
Stiglitz (1984) argues that prices would become invariant across business cycle although
marginal costs of production fell. This is because price markups may increase (i.e., anticyclical markups) even if elasticity of demand decreases (Bils 1987; Blinder et al. 1998).
5.2. The Theory of Psychological Pricing Point
One of the recent additions to the list of theories of price rigidity is Kashyap’s (1995)
psychological pricing point theory, which builds on work in both economics and marketing.
This theory offers an explanation of price rigidity based on the idea of rational inattention
(Sims 2003). This theory has been referred to with different labels, such as thinking costs
(Shugan 1980), reoptimization costs (Roufagalas 1994), information processing costs (Sims
1998), and information gathering costs (Ball and Mankiw 1994).
Kashyap (1995) argues that pricing managers attach great psychological importance to
the price point thresholds. Indeed, he shows from his data that catalog prices tend to have
rigid ending prices. Blinder et al. (1998) also provide evidence on the importance of pricing
points. They find that 88% of firms in the retail industry and 47% of firms in non-retail
industries report some importance of psychological price points in their pricing decisions.
As Blinder et al. (1998) note, however, there are two difficulties with pricing point theory.
First, not much is known on the actual practical importance of psychological threshold pricing.
Second, no satisfactory economic explanation has been offered for pricing point theory. To
fill this gap in the literature, Bergen, et al. (2003) explore the empirical relevance of pricing
point phenomenon for the United States retail food industry. They find that more than 65% of
prices are observed to end in 9 cents (e.g., $19.49), and among those, the most prices end in
99 cents (e.g., $9.99). They argue that it may be rational for consumers to be inattentive to
the rightmost digits. They often face large amounts of costly and hard-to-process information
20
but are constrained by time, resource, and information processing constraints. Since many
consumers ignore the last digit in the price, firms have an incentive to make the last digit as
high as possible (i.e., equal to 9).
5.3. The Theory of Inventories
Achieving the right level of inventory is a key success factor for businesses and their
supply chain management activities. The level of inventory must be sufficient to meet
consumer demand but also be low enough to minimize storage costs. Economists have
considered inventories as buffers (i.e., inter-temporal substitutes) which companies use to
smoothen fluctuations in demand and production (Blinder et al. 1998; Okun 1981). The
theory of inventories asserts that firms use inventories rather than price changes to cushion
demand shocks. When demand falls (or rises), firms increase (or draw down) their
inventories rather than decrease (or increase) prices (Blinder et al. 1998). The degree of price
smoothing caused by inventories depends upon whether the demand shocks are perceived as
short-run or long-run shocks (Blinder et al. 1998). Firms are more likely to use inventory
adjustments for temporary changes of demand, while with permanent changes of demand, real
price changes are inevitable.
Amihud and Mendelson (1983) find that the degree of price flexibility and asymmetric
price responses by firms to economic shocks is explained by the relationship between the cost
of holding positive inventory and negative inventory (i.e., backlog). Irvine (1980) shows that
a short-run inventory-based pricing policy is observed in retail department stores: the price is
above (below) its equilibrium level when the inventory is below (above) its optimal level.
Borenstein et al. (1997) find that, due to production and inventory adjustment lags, there may
be temporary asymmetries in the adjustment of spot gasoline prices to spot crude oil prices.
21
5.4. The Theory of Non-Price Competition
Prices are generally considered the primary means for market clearing and resource
allocation in economics (Carlton and Perloff 2000). Price competition occurs when a seller
emphasizes the lower price of a product and sets a price that matches or beats the competitors’
prices. However, firms hesitate to cut prices in times of reduced demand out of fear that
customers will misinterpret any price cut as a reduction in quality (Blinder et al. 1998).
Carlton (1989) points out that markets may clear through means other than price. He views
price as only one of many dimensions of the terms on which products are exchanged. Nonprice competition, thus, can be used most effectively when a seller can make its product stand
out from the competition by enhancing product quality, setting delivery lags (i.e., the lag
between order placement and shipment), stressing customer service, conducting promotional
efforts, improving packaging, expanding advertising expenditures, and so on.
Carlton (1983) views delivery lags as one of means for market clearing, which
determines demand in market. For example, in response to an increase in demand, prices may
remain relatively unchanged, but consumers may have to wait a little longer for delivery.
Thus, he argues that many markets could be characterized by large fluctuations in delivery
dates and small fluctuations in price. Blinder et al. (1998) also state that firms are willing to
decrease (increase) delivery lags or provide more (less) customer services rather than cut
(raise) prices when demand is low (high). Thus, delivery lags may play the same buffering
role as inventories. Zbaracki et al. (2003) argue that any price change that does not make
sense for the customer can cause customer antagonism, which damages customer perceptions
of the firm’s reputation, integrity, and reliability. Consumers generally are less antagonized
by changing non-price aspects of goods or services than by changing their prices.
22
Non-price competition seems to be especially prevalent where firms perceive implicit
long-term contracts with their customers that stabilize prices, and where firms believe that
their customers judge quality by price (Blinder et al. 1998; Okun 1981). Thus, prices may
appear rigid if other variables are also used to clear markets (Blinder et al. 1998; Carlton
1983).
6. CONTRACT BASED THEORIES
Contract-based theories provide an explanation of rigid prices in the context of
transactions between firms and customers who enter into either explicit or implicit contracts
fixing prices over a given period. Such contracts may provide insurance against uncertainty
or risk in market conditions by delivering stable prices (Blinder et al. 1998). However, there
is relatively little work on the contract-based theories to explain rigid prices because the
explanatory variables are either unobservable or by traditional methods or unobserved in
practice (Renner and Tyran 2003).
6.1. The Theory of Explicit Contracts
Most firms that trade goods and services (especially labor) have nominal contracts that
fix prices for finite periods of time to avoid uncertainties or transaction costs (Carlton 1979).
Explicit contract theory assumes that prices are not free to adjust to either demand or cost
shocks under written contracts. Thus, firms cannot raise prices for existing customers without
any contract renegotiation even with cost shocks or demand shocks.
Carlton (1979) also examines the relationship between price changes and different
duration of contracts (i.e., short-term and long-term) in industrial purchases and finds that the
two prices move in the same direction—but by different magnitudes—in response to supply
23
shocks. Hubbard and Weiner (1992) also analyze the role of contracts in industrial product
markets (i.e., copper) and argue that contracts have different effects on price flexibility,
depending on the relative impacts of demand and supply shocks.
This theory appears to explain the source of price rigidities for firms that do most of their
business with other firms. But it is less useful for firms that make use of implicit contracts
(Blinder et al. 1998).
6.2. The Theory of Implicit Contracts and Customer Antagonization
If the customer and seller trade with one another for long periods of time, they develop
some attachment to making transactions involving the product (Okun 1981). Any price
changes for the product can be a nuisance to customers when they think the changes are
unreasonable (Bergen et al. 2003; Zbaracki et al. 2003). Customer may be antagonized (e.g.,
complain or react) whenever a price change exceeds the expected range or violates established
pricing patterns from past periods (Bergen et al. 2003; Blinder et al. 1998; Okun 1981; Stiglitz
1999; Zbaracki et al. 2003). Customers may be less antagonized if price increases can be
justified by increases in sellers’ costs (Blinder et al. 1998; Kahneman, et al. 1986; Okun 1981;
Renner and Tyran 2003). Thus, the customer and seller rely on established prices, but also on
mutual trust, reciprocal fairness, and “fair play” for efficient allocation of products.
Okun (1981) proposes the concept of the “invisible handshake” as a possible source of
price rigidity in the product market. He argues that both firms and customers in long-term
relationships are stimulated to become involved in implicit agreements that make prices rigid.
This is because customers are antagonized when prices are raised. Customers will switch to
competitors, leading to a loss of sales revenues. Carlton (1986) posits that prices tend to be
more flexible the longer the buyer-seller association. He argues that customers involved in
24
shorter relationships with suppliers are more likely to use fixed-price contracts because of the
fear that they may be exploited by competitors’ price changes. Kahneman et al. (1986) also
find that consumers feel cheated when firms exploit shifts in demand by raising price. Silby
(1996), in his model of customer loss aversion, looks upon demand as a decreasing function
of both price and customer disenchantment, which implies that a decrease in the elasticity of
demand increases the extent of price rigidity. Blinder et al. (1998) directly try to answer the
question of implicit contracts by showing that such contracts exist within two-thirds of the
purchasing economy. Respondents rate implicit contracts a major source of price rigidities.
Powers and Powers (2001), in their study on pricing by grocery stores, find that large firms
lose more customers to their rivals when they change prices. Zbaracki et al. (2003) provide
some qualitative evidence of managers’ fear of “antagonizing” customers. They argue that
price changes call attention to prices and may damage the firm’s reputation, integrity, and
reliability. Bergen et al. (2003) assert that the excessive use of sales promotions can create
customer norms in which sales promotions are expected.
7. PRICE RIGIDITY IN THE E-COMMERCE SECTOR
We now turn to the application of these theoretical perspectives in the context of ecommerce sector issues.
7.1. Price Adjustment Costs in E-Commerce
Compared to the traditional (non-Internet) markets, where significant costs associated
with price adjustment are incurred, the Digital Economy provides a new environment where
physical price adjustment costs are almost absent. This is due to the fact that they are
primarily composed of the costs of simple database updates (Bailey 1998; Brynjolfsson and
25
Smith 2000). Others believe that pure Internet retailers have more price rigidities that stem
from managerial costs (Bergen et al. 2003). The economy lets firms make immediate and
frequent adjustments, so they can profit from small ticks in market demand and supply
(Baker, et al. 2001). Bailey (1998) finds that Internet retailers make significantly more
frequent changes than traditional retailers for homogeneous products, such as books and CDs.
Brynjolfsson and Smith (2000) also observe that online retailers make price changes that were
up to 100 times smaller than those made by bricks-and-mortars sellers.
But what about bricks-and-clicks firms? The Internet does not necessarily reduce the
managerial costs for price changes due to the integration efforts of a firm’s Internet channel
with traditional channels by ensuring product, price and promotion consistency (Bergen et al.
2003). Analyzing the pricing behavior of two leading online bookstores (i.e., Amazon.com
and BarnesandNoble.com), Chakrabarti and Scholnick (2001) find that online retailers exhibit
within-store synchronization in price changes, and argue that price rigidities also exist in
online environments. Tang and Xing (2001), comparing pricing for DVDs between bricksand-clicks and pure Internet retailers find that both types of retailers do not change prices
frequently, in spite of the small menu costs in the online environment. They argue that online
prices may be prone to error, even though they may be easier to change.
Based on the evidence, we believe that price rigidities continue to exist in e-commerce
due to within-store synchronization of prices caused by menu costs, as well as by across-store
staggering of prices caused by managerial costs. Table 3 compares the costs of price
adjustment between different channels.
26
Table 3. Comparison of the Price Adjustment Costs
COST TYPE
Menu Costs
Managerial
Costs
BRICKS-AND-MORTAR
High due to physical
lump-sum costs
High due to hierarchies
for decisions
BRICKS-AND-CLICKS
High due to costs incurred
by traditional channel
High due to the integration
efforts
PURE INTERNET
Low, almost absent
Low due to intensive
use of IT
7.2. Market Structure Effects in E-Commerce
Various observers say that the Digital Economy offers less concentrated markets and
creates more competition by lowering technological barriers to entry due to lower set-up costs,
as well as lower marginal costs of production and distribution (Daripa and Kapur 2001;
Latcovich and Smith 2001). However, in order to survive in such competitive environments,
e-commerce firms require a significant level of investment in advertising and IT infrastructure.
But needed economies of scale raise barriers to entry and may induce industry concentration
in markets (Daripa and Kapur 2001; Shaked and Sutton 1987). Amazon.com’s recent
takeover of the online operations of Toys”R”Us and Borders offers a case in point. Latcovich
and Smith (2001) also find that the online book market, at 93% in the United States, is more
concentrated than the traditional book retail industry, at only 45%. Brynjolfsson and Smith
(2000) report that the four largest Internet retailers account for 99.8% of the total number of
hits for book retailers. Highly-concentrated online markets may allow firms to exploit market
power by reducing the costs of driving traffic to their Web sites. But the true relationship
between industry concentration and price rigidity is not clear. This issue can be resolved
through comprehensive empirical investigation.
No doubt, the Internet makes it easier for firms and buyers to compare products and
prices by using online price comparison sites or shopbots (Bakos 1997; Brynjolfsson and
Smith 2000; Varian 2000). Their diffusion increases the transparency of the cost and pricing
27
strategies of the market participants (Daripa and Kapur 2001). In traditional channels, firms
do not respond instantly to their competitors’ price reductions. It takes time to learn about
price changes and there may be menu costs for making the changes. But the Internet enables
firms to quickly monitor and react to competitors’ price movements, and often creates an
environment for firms to engage in tacit collusion (as the Department of Justice argued about
the online travel agent, Orbitz, in 1999). Knowing that competitors rapidly learn about price
cuts, firms have become cautious about changing prices, and increasingly adopt price
structures that create signals for their competitors, a form of tacit collusion (Daripa and Kapur
2001; Kauffman and Wood 2004). So online prices may be higher than expected. Kauffman
and Wood (2004) find evidence of leader-follower pricing in an empirical analysis of Internet
book and music CD retailers. They argue that the Stackelberg pricing behavior enables firms
to choose diverse pricing strategies—including tacit collusion. We conclude that online
prices also may be rigid due to incentives to sustain higher prices through implicit agreements.
7.3. Information Asymmetry in E-Commerce
According to Stiglitz (1987), information asymmetries between buyers and sellers about
product quality are one of the root causes of price rigidity. Buyers and sellers are
geographically separated and do not interact face-to-face as they transact. Thus, it is difficult
for buyers to inspect product quality. Further, it is doubtful that the firm with a low online
price will be the most reliable if competition is based only on prices. So advertising,
consumer search, and digital intermediaries (e.g., trusted third parties, online reputation
mechanisms) will play a significant role in building trust between buyers and sellers.
However, it seems unreasonable to view quality signaling as a cause of price rigidity. Why?
Because most online transactions primarily deal with homogeneous products (e.g., books,
28
CDs) where quality is rarely in doubt. Rather, online firms compete on price as well as on
non-price elements, such as customer service, promotional activities, and advertising.
Higher search costs tend to induce buyers to remain with their sellers, which may
increase the sellers’ market power and provoke significant level of price rigidity. On the
Internet, buyer search costs are negligible; buyers can locate lower price products or services
easily with the use of search engines or price comparison agents (Bailey 1998; Bakos 1997;
Brynjolfsson and Smith 2000). The reduced information asymmetry gives firms more
incentive to cut prices (Daripa and Kapur 2001). Compared to traditional channels, the
Internet enables firms to reach any individuals that have access to the Internet and to expand
their customer base across regions and national borders. Lower search costs can make the
demand curve more elastic and lead to firms gaining more returns by cutting prices. Thus, the
theory of search and kinked demand curve may not explain price rigidity in e-commerce.
7.4. Consumer Demand in E-Commerce
Blinder et al. (1998) and Okun (1981) point out that firms operating with little inventory
are not able to avoid the impacts of unexpected demand shocks. In today’s environment, a
foundational level of firm inventory is necessary to serve and retain customers. To increase
sales with customers, firms must be responsive to their special needs and requests for supply
chain and logistics support. Compared to traditional channels which require firms to keep the
highest inventories, in e-commerce operations it is possible for firms to obtain more efficient
supply chain benefits. Such benefits extend to both customers and manufacturers due to the
increased use of computer technologies, such as the real-time inventory systems, so firms can
accurately control inventory and send products to market faster. Further, with the growth of
information goods (e.g., electronic books and downloadable music), new retail businesses can
29
be designed with virtually no physical inventory. Therefore, we believe that firm price
changes are more likely to be driven by demand shocks than inventories in the e-commerce
sector.
The Internet gives consumers access to more information about products than has ever
been available to them before. Clay et al. (2002a) point out that it facilitates both price
competition and non-price competition. Online consumers care about other non-price aspects
like seller reputation, delivery locations and times, contract lengths, etc. Moves into nonprice competition are generating new processes of competition. Their basis is not competition
for a transaction, but competition for the consumer and for consumer loyalty. Thus, we
expect that strategic use of non-price elements will be a cause of price rigidity on the Internet.
7.5. Contracts in E-Commerce
Blinder et al. (1998, p. 302) report that “about 85% of all goods and services in the U.S.
non-farm business sector are sold to “regular customers” with whom sellers have an ongoing
relationship. And about 70% of sales are business to business rather than business to
consumers.” Explicit contracts explain rigid prices in supply chain-based procurement, where
market participants may benefit from price rigidities by facilitating risk sharing. However,
transparent prices and costs on the Internet make it difficult to sustain stable prices using
long-term contracts. Why? Fluctuations in cost and price may have an effect on negotiated
contract prices, leading to a shift from a stable contractual environment to greater price
uncertainty. Overall though, we doubt explicit contracts can explain the observed price
rigidities in Internet-based selling.
Unexpected changes in the terms of implicit contracts, in contrast, may antagonize
customers and diminish the firm’s reputation—even in the Digital Economy. For example,
30
Amazon.com experimented with a price discrimination policy to sell the exact same DVD
titles for different amounts to different customers in 2000. However, the outraged responses
from consumers were swift and clear in the message. As a result, the online retailer put its
price experimentation policy on hold, and also refunded money to consumers who paid the
higher prices (Bergen et al. 2003). With reduced search and switching costs for the consumer,
firms may lose more of their customers when they violate consumer expectations about
pricing patterns. So the implicit contract explanation should do well in interpreting price
rigidity in Internet-based selling.
Table 4 summarizes some causes and expected results of price rigidities in e-commerce.
Table 4. Summary of Possible Causes of Price Rigidities in E-Commerce
THEORIES
Price
Adjustment
Costs
Market
Structure
Asymmetric
Information
DemandBased
ContractBased
POSSIBLE CAUSES AND EXPECTED RESULTS
Almost absent price adjustment costs
• Simple database updates, easily programmed
• Real-time inventory system, e.g., Internet-based ESPs
However, prices may remain unchanged.
• Within-store synchronization due to menu costs
• Across-store staggering due to managerial costs
High Industry Concentration
• Especially in book industries
• Economies of scale and limit pricing
Possible Coordination Failures
• Stackelberg pricing: Follow-the-leader
• Tacit collusion: To avoid intense price competition
Unreasonable Quality Signaling
• Mostly homogeneous products (e.g., books, CDs)
Unreasonable Kinked Demand Curve
• Search costs are almost negligible
More likely changes in prices than inventories
• Real-time inventory systems and virtually no physical inventory
Strategic use of non-price competition
• Seller reputation, free shipping, etc.
Doubtable Explicit Contract Explanation
• High price and cost transparency
Well-Explanatory Implicit Contracts
• Customer antagonization, reduced search and switching costs
31
8. CONCLUSION
Despite the growing number of theoretical and empirical studies on price-setting and
dispersion in e-commerce, there are only a few on price changes and price rigidity. Compared
to non-Internet markets, the Internet environment makes it possible to more accurately
monitor and control inventory and costs, and gauge demand nearly in real-time. This, we
believe, is likely to have a profound impact on pricing behavior and the nature of competition
in retail markets. More observers tend to portray the e-commerce sector as one in which price
adjustment costs are almost absent. The limited empirical evidence suggests that e-retailers
make more frequent price changes than traditional retailers by putting their emphasis on the
role of menu costs. As suggested in several previous studies (Bergen et al. 2003; Bailey
1998), firms can flexibly manage and optimize prices by reducing the managerial costs and
menu costs through the intensive use of IT. By combining supply chain management systems
with revenue yield management, for example, firms will further refine pricing decisions that
are in line with both demand and supply.
At the outset of this article, however, we asked: should we expect less price rigidity in ecommerce? Our cautious and early answer is probably not. Price rigidity in e-commerce
should be reconsidered in the appropriate theoretical and practical terms as suggested in the
previous sections: implicit collusion, customer antagonization, non-price elements, market
power, etc. (See Table 4 again.)
We have attempted to draw upon theoretical perspectives from economics and marketing
science to explain the price change behaviors of Internet-based sellers. The suggested
theories are largely new to the IS field, even though they are well known in marketing and
economics, from where they are drawn. Taken together, they offer rich opportunities for new
32
theory-building and empirical research in e-commerce settings that will be of high
interdisciplinary interest.
Currently we are conducting empirical analyses with data collected from price
comparison sites. We are utilizing a data collection software agent to mine price information
for multiple product categories (i.e., books, CDs, DVDs, video games, notebooks, PDAs,
software, digital cameras/camcorders, DVD players, monitors, and hard drives) and more than
1,200 products since the end of March 2003. Such interdisciplinary studies will provide a
new foundation for the development of theory at the crossroads of the academic disciplines of
marketing and IS, and will encourage research on new economic phenomena in the digital
economy.
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