SHOULD WE EXPECT LESS PRICE RIGIDITY
IN INTERNET-BASED SELLING?
Robert J. Kauffman
Director, MIS Research Center, and
Professor and Chair
Dongwon Lee
Doctoral Program
Information and Decision Sciences
Carlson School of Management, University of Minnesota
Minneapolis, MN 55455
{rkauffman, dlee}@csom.umn.edu
Last revised: August 26, 2005
ABSTRACT
Price rigidity involves the observation of prices that do not change with the regularity predicted
by standard economic theory. It is a topic of long-standing interest for firms, industries and the
economy as a whole. As information technology (IT) changes 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 process 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. So, we believe that the issue of
price rigidity in the digital economy should be considered with a broader, more deeply insightful
perspective. This paper draws upon theoretical perspectives that are largely new to the field of
Information Systems (IS), but that offer rich opportunities for theory building and empirical
research in settings that will be of high interdisciplinary interest. Such interdisciplinary studies,
we believe, will provide a distinctive foundation for IS research and serve as a guide to research
for a variety of new economic phenomena in economies that are influenced by firm investments
in Internet technology. In our analysis of the relevant theories, we identify those that are most
likely to enhance our understanding of pricing in Internet-based selling, and discuss the logical
contradictions among competing theories that must be reconciled in order to identify what is
most relevant for the Internet retailing context.
KEYWORDS: Competing theories, economic analysis, electronic commerce, inter-firm
competition, IT, Internet-based selling, price rigidity, pricing strategy, theory-building research.
1
1.
INTRODUCTION
Price rigidity, the inability of firms to adjust prices in the presence of forces that suggest
they should change, is important enough to occupy a central stage in the research agenda of new
Keynesian Macroeconomics (Ball and Mankiw, 1994; Blinder et al., 1998; Rotemberg and
Saloner, 1987) and Industrial Organization (Carlton, 1989; Stiglitz, 1984). This is in stark
contrast to the traditional assumption that firms flexibly adjust prices, which lies at the heart of
classical economic models used in Microeconomics, and in many areas of business such as
Finance, Marketing, and Strategy. Price rigidity is of particular interest in the context of
Internet-based selling, and the capabilities that firms now have to set and adjust prices. For the
purposes of this research on Internet-based selling and price rigidity, it is important for us to
establish the scope of our investigation. By “the Internet” in “the Internet-based selling context,”
we are referring to the set of technological, organizational and managerial capabilities that
permit a firm’s prices to be accessible to consumers and businesses via Web sites that sell
information, goods and services, and are associated with electronic commerce. In contrast,
“traditional retailing” does not involve the presentation of prices to consumers and businesses via
the Internet. Instead, prices in traditional retailing are typically made available through in-store
price tags, promotions and advertisements in the print, television and radio media, and by mail
and phone, among other non-technological means.
1.1.
Price Rigidity and the Production of Prices with IT
Today, the Internet offers opportunities to inform our understanding of price rigidity and
the nature of price adjustment in three fundamental ways. First, the Internet provides a unique
technological context for the micro-level study of price-setting behavior and strategies. As of the
late 1990s, when the hype around electronic commerce crested, it was commonplace in
discussions among academic researchers to hear observations about how information technology
(IT) was changing industry practices in strategic pricing. In fact, some of the most influential
papers of that time, including Bakos (1997, 1998), and Brynjolfsson and Smith (2000), adopted
this general perspective—that pricing is likely to be frictionless on the Internet. Others pointed
to the technology-enabled practices associated with revenue yield management and financial risk
management (Clemons, et al. 1999). The “financification” of the strategic pricing function in the
firm was well under way.
2
Second, firms may adjust prices differently on the Internet than in traditional commercial
settings, and being able to detect this kind of behavior may change our conventional wisdom
about price rigidity. IT has changed the process by which strategic pricing decisions are made
and implemented in business operations, so there is a need to develop substantial managerial
understanding of these new firm pricing approaches. In the 1990s and earlier, technology-driven
pricing was largely the domain of the airlines, and other hotels and rental car companies which
implemented revenue yield management practices. The technologies of that pre-Internet era,
combined with powerful underlying data capture and online real-time capabilities, took
advantage of advanced knowledge in economic modeling and marginal value-based price
discrimination approaches to effectively implement pricing structures (e.g., the number of
consumer segments and advance purchase requirements), as well as to make day-to-day
adjustments in “demand bucket” prices in response to shifting demand patterns and shocks.
Today, however, we have the same kinds of technologies available in many other price-setting
contexts. As a result, 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 uneconomical.
Third, the ability to access transaction price data using software agents now allows
researchers to explore pricing and price adjustment patterns at a previously unimaginable level of
microeconomic detail (Allen and Wu, 2002; Kauffman and Wood 2003a). Although this kind of
data has been used to study other aspects of pricing, such as price dispersion (e.g., Bailey, 1998;
Clay et al., 2001; Clemons et al., 2002; Lee, 1998), price levels (e.g., Brynjolfsson and Smith,
2000; Clay et al., 2002a) and price-setting behavior (e.g., Clay et al., 2002b; Kauffman and
Wood, 2005), there are still only a few studies that have focused on price adjustment and price
rigidity in e-commerce (Bergen et al., 2005a, 2005b, 2005c; Kauffman and Lee, 2004a, 2004b;
Oh and Lucas, 2004). Most of the empirical evidence shows lower price levels and the failure of
the “one price” Bertrand price competition that is predicted in electronic markets. Table 1
summarizes selected research on strategic pricing in e-commerce environments.
3
Table 1. Empirical Research on Pricing in Internet-Based Selling
RESEARCH
PERIOD DATA
Ancarani and
2002/03– Prices of books and CDs sold in
Shankar (2004) 2002/04 Internet, bricks-and-mortar,
bricks-and-clicks
Arbatskaya and 1998/04– Daily mortgage rates posted
Baye (2004)
1998/07 online
Bailey (1998)
1997/02– Prices of books, CDs, and
1997/03 software sold online and offline
Baye and
2001/03 Prices of consumer electronics
Morgan (2004)
sold online
Baye, et al.
2000/08- Prices of consumer electronics
(2004)
2001/03 sold online
Baylis and
1999/09- Prices of digital cameras and
Perloff (2002) 1999/12 scanners sold online
FINDINGS
□ Highest price dispersion in bricks-andclicks retailers
□ Lowest prices in pure Internet retailers
□ Considerable price dispersion online
□ Price dispersion not less online
□ Higher prices online
□ High price dispersion online
□ Higher price dispersion and price level in
the small number of firms
□ Firms that offer good service charge lower
prices
□ High-priced firms remain high-priced over
long periods
Brown and
Goolsbee
(2002)
Brynjolfsson
and Smith
(2000)
Carlton and
Chevalier
(2001)
Clay et al.
(2002a)
Clay et al.
(2002b)
Clemons et al.
(2002)
Kauffman and
Wood (2005)
Lee (1998)
1992–
1997
Prices of life insurance policies
□ Less price dispersion online
□ Lower prices online
1998/02– Prices of books and CDs sold
1999/05 online and offline
□ Less price dispersion online
□ Lower prices online
2000/062000/10
□ High price dispersion online
□ Less price dispersion across authorized
Prices of fragrances, DVD
players, and refrigerators
1999/08– Prices of books sold online
2000/01
1999/08– Prices of books sold online
2000/01
1997/04 Prices for online airline tickets
2000/02–
2000/03
1986–
1995
Morton et al.
1999/01–
(2001)
2000/02
Pan et al. (2001) 2000/11
Smith (2001)
Prices of books and CDs sold
online
Prices of used cars in offline and
online auctions
Prices of cars sold online and
offline
Prices of books, CDs, electronics
sold online
Late 1999 Prices of books sold online
Tang and Xing
(2001)
2000/07–
2000/08
□
□
□
□
dealers
Less price dispersion online
Competition led to lower prices
Timing and direction of price changes
correlated across firms
More price dispersion online
□ Evidence of follow-the-leader pricing
online
□ Higher prices in online auctions
□ Less price dispersion online
□ Lower prices online
□ More price dispersion online
□
□
Prices of DVDs sold online and in □
other channels
□
More price dispersion in IT-related goods
Higher prices for well-known retailers
Less price dispersion online
Lower prices online
However, we believe that the issue of price rigidity on the Internet should be given more
scrutiny than the literature has provided to date. There may be factors that can explain the price-
4
changing behaviors that Internet-based firms demonstrate other than menu costs. For example,
there still seems to be a role for market forces to play in price adjustment—even for Internetbased firms (Bergen et al, 2005a). Firms may also make use of non-price elements, such as
customer service, product information, reputation, or free shipping, instead of price adjustments
(Kauffman and Lee, 2004b). In addition, certain price points may be observed (e.g., 99¢)—even
on the Internet—due to consumers’ rational inattention to the last digit of the price, and sellers’
interest to exploit their decision-making behavior (Bergen et al., 2004; Levy et al., 2005).
Furthermore, there may be differences in price adjustment tactics that are observed for different
firms relative to the different products (Bergen et al. 2005a). Similar explanations may also
occur at the level of industries, as well as within or between sales channels.
1.2.
Research Questions and Our Process for Developing New Theory
With this concern about the pervasive expectation of declining price rigidity on the
Internet, Information Systems (IS) researchers such as ourselves have an important opportunity
to address research questions from the unique perspective of our academic field:
□
Should we expect less price rigidity in e-commerce, specifically Internet-based selling,
compared to traditional, non-Internet channels? What theoretical perspectives would
prompt such a conclusion? Is new theory required to establish new knowledge in this
area?
□
Are there any differences in price changing-behaviors within and between products,
channels, and industries? What theoretical knowledge would provide a basis for
observing them? What contingencies might be operative?
□
How important is the menu cost explanation in the context of Internet-based selling?
Will other theories explain what we observe? Why? Or why not?
To answer these questions, we draw upon perspectives that are largely new to the IS field,
but offer rich opportunities for theory-building and empirical research in settings that will be
closely-related to IS research. We believe that the previous IS literature has failed to identify the
central role of IT in the marketing function to explain the rigidity of prices on the Internet.
Instead, it has offered only very limited explanations, such as the menu cost explanation
(Brynjolfsson and Smith, 2000; Bailey, 1998) and tacit collusion (Kauffman and Wood, 2005;
Campbell, et al., 2005). Although this work has been commendable and innovative in its efforts
5
to open up a meaningful dialogue between Marketing and IS, from a theoretical perspective it
also has been limited in its scope. We have been making an effort to achieve progress and
deepen this dialogue by developing our thinking in the context of IS and e-commerce. We have
been pursuing a number of theories of price rigidity drawn from IS, Economics, Psychology and
Marketing. Also, from an empirical perspective and likely due to the difficulty of measuring the
extent of price rigidity directly, only a few studies have attempted to provide empirical evidence
for explanatory theories from Marketing and Economics. However, data collection software
agents now offer new capabilities to explore price-related information on the Internet, since
online retail prices can be sampled in a manner that yields almost identical information as
scanner data analysis on transaction prices which consumers pay at the cash register in traditional
channels.
To answer these questions convincingly and effectively—and to set the stage for the
development of new theory—requires that we simultaneously establish a process that takes
advantage of what is known about the analysis of problems involving competing theories. Just
as we know that psychological theories of “primacy” and “recency” involving first memories and
recent memories have significant standing cognitive psychologists, we recognize that much of
the literature on price rigidity presents us with the same dilemmas, logical argumentation and
paradoxes of competing theories. The “nature vs. nurture” debate offers similar competition for
a definitive explanation of how children develop into intelligent and highly-capable adults. And
so we believe that our argumentation process for establishing results in this research must draw
upon established methodology in the philosophy of science for resolving some of the paradoxes
relative to price rigidity in Internet-based selling.
Throughout this paper, we will return to a number of specific ideas that have guided our
overall exploration, helped us sort out the many different and conflicting ideas that are available,
aided our determination of paramount theoretical explanations, informed our taste for presenting
new theoretical knowledge, and enabled us to present a more convincing synthesis of current
thinking in this area. For example, some arguments about the sources of price rigidities are
based on consumer behaviors that have been interpreted in terms of theories from psychology
(e.g., customer antagonization, psychological pricing points). Some relate to more micro-level
economic explanations, where a firm’s production process for prices occurs (e.g., menu costs,
6
managerial costs). Some other explanations may stem from more macro-level economic
explanations, market structure and competition (e.g., industry concentration, tacit collusion).
To resolve possible conflicting and competing explanations, we developed our analysis
framework as a means to identify the role of IT at different levels of analysis where price rigidity
may be affected: the consumer, firm, and market levels. So, our analysis will move from the
micro level to macro level, and from Psychology and Marketing theories to Economics theories.
In addition, we have attempted to explore more microeconomic approaches to explain why price
rigidities occur in Internet-based selling. Unlike some of the Marketing and Economics literature,
we don’t intend to achieve as broad coverage of macroeconomic theories that analyze the
consequences of price rigidities. Related to this, we reduced the coverage of price-setting
decision making in response to macro factors like inflation. We also provide a brief discussion
of how to match our theory-building efforts with additional new empirical evidence through a
new empirical research approaches. We discuss this in the context of price adjustment and price
rigidity in Internet-based selling, and other related phenomena that are of interest in IS research
that involves the collection of large data sets.
With this research, we establish a new perspective for IS research: the newly-important
central role of IT in terms of strategic pricing on the Internet, and the need for IS researchers to
seize the opportunity to create new knowledge that pertains to it. We also point to a number of
existing theories that we believe will be especially actionable for future research and enhanced
managerial understanding in this area. Taken together, we believe that such interdisciplinary
studies 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.
2.
RECONCEPTUALIZING PRICE RIGIDITY ON THE INTERNET:
NEW THEORETICAL PERSPECTIVES FOR IS RESEARCH
In this section, we survey interdisciplinary perspectives that help to position this work
with respect to strategic pricing in Marketing as an important new direction for IS research.
Then, we present new ways for IS researchers to understand price rigidity and price adjustment
on the Internet based on the interdisciplinary theories. We propose a research framework to
examine different theories on price rigidity and develop a new theory to explain the observed
empirical regularities of firms’ price-changing strategies on the Internet. Our assessment of
7
competing theories is guided by the recognition that conflicting and paradoxical predictions
create the necessary tension in the theory-building and assessment process to motivate their
resolution with new ideas.
2.1.
PRICE RIGIDITY THEORIES IN ECONOMICS AND MARKETING
For IS researchers to appreciate the new opportunities that we have with respect to the
study of IT-driven phenomena in strategic pricing in the marketing function, it is necessary for us
to accept the fact that we need to master the vast, theoretically complex and methodologically
mature literature on price rigidity and price adjustment in Economics and Marketing. Moreover,
we need to come to grips with the overlaps, contradictions with the respect to theoretical
predictions, and the range of ideas that are offered for when the different theories are likely to be
most operative. First though, we need to establish some basic definitions, to ensure that the IS
reader becomes familiar with the conceptual underpinnings of the area.
Price rigidity is an essential component of new-Keynesian economic and macroeconomic
theory. Economists refer to this as the economics of nominal rigidities (Blinder et al., 1998;
Carlton, 1989). Some other terms are also used, including price inertia, price stickiness, and
price inflexibility. Rigid prices occur when prices do not adequately change in response to
underlying cost and demand shocks (Carlton, 1986; Blinder et al., 1998). 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 efficiency (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 theories, such as those based on price
adjustment costs, market interactions, asymmetric information, and demand and contract-based
explanations have been proposed to obtain a reading on why price changes might be sluggish.
They provide a basis for the macroeconomic assumptions of rigid prices (Andersen, 1994;
Blinder et al., 1998). There are a number of micro-level settings that have been of special
interest to researchers, who have revealed rich and varied theoretical knowledge about price
adjustment. Some of the contexts that have been studied in terms of price rigidity include:
product quality (Allen, 1988), inventory policies (Amihud and Mendelson, 1983), crude oil price
changes and gasoline prices at the service station pump (Borenstein, et al., 1997), wholesaler
8
contracts (Carlton, 1979), banking and financial services (Hannan and Berger, 1991), and
grocery stores (Powers and Powers, 2001). The weight of these studies suggests that there has
been no single reason for either price adjustment or price rigidity, but rather a variety of useful
theories to explain what has been observed. Table 2 shows brief descriptions on these price
rigidity theories.
Table 2. An Overview of the Multiple Theories of Price Rigidity
THEORIES
Cost of Price
Adjustment
DESCRIPTION
Changing prices is costly; prices unchanged even with changes in supply and demand.
□ Menu Cost: Firms face a lump sum cost whenever they change their prices (Carlton,
1986; Kashyap, 1995; Levy et al., 1997).
□ Managerial Cost: Time and attention required by managers for price decision may
slow down price changes (Mankiw and Reis, 2002; Zbaracki et al., 2004).
□ Synchronization and Staggering: Stores tend to change the price of different
products either together or independently (Lach and Tsiddon, 1996; Sheshinski and
Weiss, 1992).
Market
Monopoly power (or a limited number of sellers) as well as coordination failure in
Structure
markets are the primary sources of price rigidity.
□ Industry Concentration: The sluggishness of price changes is a demonstration of
monopoly power (Carlton, 1986; Powers and Powers, 2001).
□ Coordination Failure: Absence of an effective coordinating mechanism for market
clearing is the cause of the price rigidity (Andersen, 1994; Rotemberg and Saloner,
1986, 1990).
Asymmetric
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
Information
customers may misinterpret price cuts as reductions in quality (Allen, 1988; Stiglitz,
1987).
□ Search and Kinked Demand Curve: Customer search costs lead to firms facing a
kinked demand curve (Ball and Romer, 1990; Stiglitz, 1999).
Demand-Based 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 (Rotemberg and Saloner, 1986; Warner and Barsky,
1995).
□ Inventories: Inventories are used by firms to buffer demand shocks (Amihud and
Mendelson, 1983; Borenstein et al., 1997).
□ Psychological Pricing: Prices have the tendency to be stuck at certain ending prices
(Kashyap, 1995; Sims, 2003).
□ Non-Price Competition: Instead of price competition, firms use non-price elements
such as delivery lags, service, or product quality (Carlton, 1983; Okun, 1981).
ContractPrices remain unchanged by nominal or implicit contracts.
□ Explicit Contracts: Prices are fixed for limited time periods under nominal contracts
Based
(Carlton, 1979; Hubbard and Weiner, 1992).
□ Implicit Contracts: As price changes may antagonize customers, implicit
agreements between firms and customers are used to stabilize prices (Bergen et al.,
2003; Okun, 1981; Zbaracki et al., 2004).
9
2.2.
A FRAMEWORK FOR PRICE RIGIDITY IN INTERNET-BASED SELLING
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. From the firm’s perspective, the Internet enables firms
to adjust prices differently, which may change our conventional wisdom about price rigidity.
Many observers have commented that physical price adjustment costs are almost entirely absent
in e-commerce because they primarily consist of the costs of simple database updates, which
may be easily programmed (Bailey, 1998; Brynjolfsson and Smith, 2000). This suggests that
Internet-based retailers have the capability to adjust prices more flexibly than traditional retailers,
like financial markets (e.g., foreign exchange and equity trading—all pure supply and demand
plays) (Bergen et al., 2005a).
Sometime in 2001 and 2002, the weight of the discussion and debate seemed to shift to
pricing frictions in e-commerce—in spite of the revolutionary, powerful and new capabilities of
IT in support of the firm to adjust prices. Paralleling this new direction for the discussion of
pricing on the Internet, we also saw another debate move in an unexpected direction: from
disintermediation of traditional firms by Internet firms in e-commerce to reintermediation by
traditional firms, ultimately leading to the demise of many Internet firms (Kauffman and Wang,
2004). As a result of these developments, some observers of e-business and the changing digital
economy have called for renewed efforts to discover useful theoretical knowledge and
established normative guidance for senior managers.
With these observations about the new economy in mind, we now turn to the application
of the new theoretical perspectives for price rigidity and price adjustment in the context of IS and
e-commerce sector issues. Based on the interdisciplinary theories in Table 2, we propose a
research framework to examine different theories on price rigidity that will be observed in
Internet-based selling environments at the different levels of analysis covering the consumer, the
firm and the market. (See Figure 1.)
10
Figure 1. Research Framework for Price Rigidity in Internet-Based Selling
FIRM LEVEL
Price Adjustment Costs
Non-Price Competition
Demand and Inventory
Holiday Pricing
PRICE RIGIDITY IN
INTERNET-BASED
SELLING
CONSUMER LEVEL
Contracts and Customer
Antagonization
Information Asymmetry
Price Points
MARKET LEVEL
Industry Concentration
Coordination Failure
and Tacit Collusion
Note: Price rigidity theories treat different levels of analysis, and some may have overlaps.
We construct our framework from micro level of the consumer and the firm to the macro
level of the market to help the readers organize their thinking by grasping different theories from
Psychology, Marketing, Economics and IS. 1 First, at the consumer level, we focus on buyers’
psychological behaviors impacting firms’ price- changing strategies to explain price rigidities in
Internet-based selling. These include firms’ incentives to avoid customer antagonization
incurred by violations of implicit contracts, taking advantage of customers’ psychological
misinterpretation of price cuts as quality reduction, and responding to consumers’ rational
inattention to certain price endings (i.e., psychological pricing points). At the firm level, we
emphasize Internet-based sellers’ strategic capabilities. This especially includes the firm’s
production process for prices. This production process is driven by intensive use of IT and
Internet technologies to evaluate the effectiveness of changing prices relative to other traditional
competing retailers. In particular, we consider Internet-based sellers’ enhanced organizational
capabilities (in terms of menu costs and managerial costs) and their impacts on real price
changes. Finally, at the market level, we examine issues of market structure (e.g., level of
competition) and the possible collusive actions that may be occurring in Internet-based selling.
1
Our focus is on the understanding of how the Internet influences firm price changing strategies. So we primarily
rely on microeconomic theories instead of macroeconomic theories (e.g., inflation) to explain why price rigidity
occurs in Internet-based selling. The authors thank a reviewer for pointing out this new insight.
11
However, the theories explaining price rigidity may overlap across these different levels. We
will further explain each level of analysis in the following sections.
3.
CONSUMER-LEVEL ANALYSIS: CONTRACTS, INFORMATION
ASYMMETRIES, AND PSYCHOLOGICAL PRICING POINTS
We first consider sellers’ responses to consumers’ behavior and psychology to explain
why price changes may be sluggish in Internet-based selling. Due to our focus on consumers at
this level, it is appropriate for us to cover a broad range of theories from Marketing, Psychology,
Economics and IS to identify the necessary issues and derive the related propositions. In
particular, we will focus on contracts, information asymmetries, and psychological pricing points
at this level of analysis. We try to resolve possible conflicts of competing theories by rigorously
examining the available evidence.
3.1.
Contracts and Price Rigidity
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 that
fix 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). So, the theories have been
used as one of the most important tools to understand price setting and adjustment strategies.
For traditional retailing, there is relatively little empirical work on the contract-based explanation
because the explanatory variables are either unobservable by traditional methods or unobserved
in practice (Blinder, 1998; Renner and Tyran, 2003). To what extent will they be operative in
the Internet-based selling context?
Explicit Contracts. Most firms that trade goods and services (e.g., labor) have nominal
contracts that fix prices for finite periods of time to avoid uncertainties or transaction costs
(Carlton, 1979). The theory of explicit contracts assumes that prices are not free to adjust to
either demand or cost shocks under written contracts. 2 Thus, from the point of view of this
2
Carlton (1979) examines the relationship between price changes and contracts of different durations (i.e., shortterm 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 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. 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.”
12
theory, Internet-based sellers may not be able to raise prices for existing customers without any
contract renegotiation, even with cost shocks or demand shocks. Explicit contracts explain rigid
prices in supply chain-based procurement, where IT plays an important role as well. Buyers and
sellers 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 costs and prices create pressures on pre-negotiated contract prices, leading
to a shift from a stable contractual environment to greater price uncertainty. So overall, we
doubt that explicit contracts can explain price rigidity and adjustment in Internet-based selling.
Implicit Contracts and Customer Antagonization. If customers and sellers trade with
one another for long periods of time, they develop some attachment to making transactions
involving the product (Okun, 1981). This is referred to as the theory of implicit contracts. Any
price changes for the product, as a result, can be a nuisance to customers when they think the
changes are unreasonable (Bergen et al., 2003; Zbaracki et al., 2004). Customer antagonization
theory suggests that customers may be antagonized, and complain or react negatively 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.,
2004). 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 not only on established prices, but also on mutual trust,
reciprocal fairness, and “fair play” for efficient allocation of products—all of which are elements
of implicit contracts. 3
But what about Internet-based selling? Are the same forces likely to be at work?
According to Kauffman and Lee (2004a), unexpected price changes in the terms of implicit
3
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. Carlton (1986) posits that prices tend to be more flexible the longer the
buyer-seller association. He argues that customers involved in 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 prices. 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) show evidence that implicit contracts exist within two-thirds of the purchasing economy.
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. And finally, Zbaracki et al. (2004) provide additional 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.
13
contracts may antagonize customers and diminish the firm’s reputation—even in Internet-based
selling. The classic example occurred in 2000 when Amazon.com experimented with a price
discrimination policy to sell the exact same DVD titles for different amounts to different
customers. However, the outraged responses were swift and clear in their message to Amazon:
don’t do this to your best customers! As a result, the online retailer put its price experimentation
policy on hold—permanently apparently—and also refunded money to consumers who paid the
higher prices (Bergen et al., 2003). With reduced search and switching costs for the consumer in
e-commerce, firms may lose more of their customers when they violate consumer expectations
about pricing patterns. So explanations for price rigidity in Internet-based selling based on the
theory of implicit contracts should do well to interpret what is observed in the marketplace—and
be more predictive of the outcomes than the theory of explicit contracts. Still, there may be
circumstances under which some contingency comes into play that overrides this general
conclusion. Empirical research is needed to ascertain whether this will be the case. We will
further discuss this theory in e-commerce context when we develop some propositions related to
other theories of price rigidity.
3.2. Information Asymmetry and Price Rigidity
The world of Internet-based selling is a transformed environment of mercantile exchange.
More than any other conclusion that observers have drawn about e-commerce, the common
wisdom is that the technologies of the Internet have led to greater transparency, more widespread
dissemination of information, and increasingly similar information about many elements of
product, price and market competitive that are readily available to archrivals. This has been to
the great benefit of consumers, who are now better informed about the opportunities they have to
make value-maximizing purchases in the marketplace than ever before. In this context, it makes
sense to undertake a careful assessment of the extent to which information symmetries and
asymmetries are operative in determining the observed price rigidity and adjustment outcomes.
Asymmetric information theory assumes that one party to a transaction (e.g., a firm) is
better informed than the other (e.g., a consumer). It gives new insights about why market
failures occur (Blinder et al., 1998). Stiglitz (1979) argues that under asymmetric information
customers tend to transact with firms with relatively stable prices and avoid firms which make
14
frequent or large price adjustments. 4 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 in these theoretical terms (Blinder et al., 1998). In the context of Internet-based
selling, it is likely that price bots and search engines have creates new pressures that diminish
information asymmetries around product prices, descriptions and quality. A countervailing force
is also present, however, and this may give rise to a paradox with respect to the observed
outcomes. Recent research by Granados et al. (2005a, 2005b) suggests that market transparency,
the selling mechanism design capability of firms to make various dimensions of their products
(especially product prices, descriptional completeness, quality level and information accuracy)
more visible or more opaque to consumers, will play a role here. Just as the new technologies
can be used strategically by firms to reveal information in Internet-based selling, so can they also
be used to strategically mask information and create induced informational asymmetries. Here
again, empirical research will be necessary to sort out the conditions under which each
explanation will be paramount in interpreting the outcomes.
Search Costs and Kinked Demand Curves. Since Stigler (1961)’s inventive work on
search theory—which is now critical to our understanding of the Internet-based selling context—
a number of studies have analyzed the impact of search costs and asymmetric information on
price rigidity. The general argument is that search is costly to customers (Stiglitz, 1999) and a
firm’s price changes will be 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 its customers expect, it may lose customers. Why? Because its regular customers
instantaneously recognize the price change and search for other sellers with more attractive
prices. If the firm lowers its price, it will sell more to current customers but will be unable to
attract new customers due to search costs involved: potential new customers will not be able to
observe the lower price without search (Stiglitz, 1987 and 1999). Thus, this theory suggests that
search costs will make the demand curve more inelastic for price decreases than for price
increases: kinked demand curves will occur.
4
Blinder et al. (1998) assert that this theory appears only to be relevant for the luxury product market (e.g.,
automobiles), or perhaps certain niche markets for clothes or food. The signaling of quality, however, applies to
every type of goods. Indeed, the providers of luxury goods can afford promotion expenses to inform the consumers
about quality and the consumers have strong incentives to invest in gathering information about quality. For
standard goods, pricing is an efficient means for both side of the market to provide and get information about quality
at a low cost. The authors thank a reviewer for sharing these insightful ideas.
15
On the Internet, buyer search costs are negligible. Buyers can locate lower prices for
products or services easily with the use of search engines or price comparison agents (Bakos,
1997; Varian, 2000). The reduced information asymmetry increases firm incentives to cut prices
(Daripa and Kapur, 2001). Compared to retailing via the 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 from price cuts than might otherwise
be predicted. Therefore, we believe that the existing theory of search and kinked demand curves
may not be as operative in Internet-based selling as in traditional retailing.
Quality Signaling. Many consumers recognize that it is hard to know exactly what you
are buying when you make a purchase over the Internet. With so many products available, it is
difficult for customers to observe quality in traditional retailing 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. Inevitably, 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). But if cutting prices is interpreted as a quality reduction, then demand
may actually decrease rather than increase (Stiglitz, 1984). 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 for products (e.g., automobiles) whose quality cannot be easily observed, while prices
are flexible in industries (e.g., petroleum) where the quality is easier to gauge. Will this be the
same in Internet-based selling? We next will discuss reasons why this may or may not be true,
as a means to begin to form a variance theory of price rigidity in Internet-base selling.
Quality is hard to gauge on the Internet under some circumstances. A number of
Marketing studies suggest that consumers may use prices as a cue for assessing quality, if they
are imperfectly informed about the product or store characteristics (Rao and Monroe, 1989;
Monroe, 2003). Another interpretation is due to Stiving and Winer (1997), who argue that image
16
effects transmit signals that enable consumers to infer something (in terms of “images”) about a
product or store based on price. So, a favorable impression of a product’s or a store’s quality
might occur as a result of high price image (Monroe, 2003). So, prices tend to be less flexible
for high-priced products to sustain and signal their high quality images.
Just as in the traditional market, online consumers also have difficulties with examining
the quality of products or product and service delivery capabilities of stores. At the time of
purchase of some good from an Internet-based seller, the consumer is unable to see or feel the
actual product. As a result, the consumer’s assessment of the actual features or true quality of a
product that is to be purchased online may be inaccurate. Also, it is doubtful that Internet
retailers with low online prices will be the most reliable (Kauffman and Lee, 2004a). So, digital
intermediaries, such as trusted third parties or an online reputation mechanism, will play a
significant role in building trust between buyers and sellers to “perfect” business processes
associated with Internet-based transaction-making (Dai and Kauffman, 2005). In some online
products (e.g., books, CDs) where quality is homogenous and rarely in doubt, it seems
unreasonable to view quality signaling as a cause of price rigidity. However, consumers on the
Internet also have difficulties in detecting the quality of heterogeneous products (e.g., computers,
electronics) even if buyer search costs are negligible. So, quality is more certain, and Internetbased sellers compete more on prices. This leads us to:
Proposition 1 (The Information Quality Proposition): If consumers have more
information on the quality of a product sold on the Internet, then Internet-based sellers
will change product prices more frequently, resulting in less price rigidity.
An empirical result has been found to support this theoretical assertion. Bergen et al.
(2005b) report on evidence that prices of products that have more consumers’ reviews change
more frequently (i.e., the more information on the quality of a product that consumers have, the
more often the price will be changed by the online seller). However, due to limited evidence
about the effects of information quality on price adjustments, additional empirical efforts are
called for to confirm this proposition.
The Internet gives consumers access to different information about products and firms
than has ever been available before. Moreover, it has changed the composition of firms in the
marketplace, and the ways that customers can interact with Internet-based firms, as well. In
general, higher quality firms tend to have higher product quality, service levels, product
17
assortments and support. As such they may face higher costs or charge higher margins for their
products. This leads to higher prices for higher-quality firms. Varian (2000) predicted that
Internet-based firms would be grouped into two types: those with low service levels and low
prices, and those with high service levels and high prices. With reduced search and switching
costs for online consumers, they can easily detect the violation of implicit rules for price changes
unlike in traditional channels. So, firms may lose more of their profits when they go against
consumer expectations about pricing patterns for high-priced products. In addition, higherquality firms that signal the market with their higher prices are more sensitive to consumers’
responses to the unexpected price changes. This leads us to assert another proposition on quality
signaling in terms of relative prices of firms:
Proposition 2 (The Quality Signaling Proposition): To signal high-quality store images
successfully, higher-quality, higher-priced Internet-based firms change the prices of
products less frequently than lower-quality, lower-priced Internet-based sellers.
A number of empirical research efforts have been made that tend to support this
theoretical argument. Baylis and Perloff (2002) find evidence that the price ranking of Internetbased firms selling electronics products (e.g., digital cameras and scanners) does not change
frequently: high-price firms usually keep their prices high over long periods. Kauffman and Lee
(2004b) and Bergen et al. (2005b) also find, in their analysis of homogenous product industries
such as books, CDs, and DVDs, that high-priced online stores change prices less frequently than
low-price online stores do. Therefore, managers in Internet-based selling should be wary of the
signals associated with their pricing approaches, especially if they are competing on quality.
This also lends support to suggestions that firms are competing on quality on the Internet, and
not just on price.
3.3.
Psychological Pricing Points and Price Rigidity
One of the recent additions to the list of theories to explain price rigidity is Kashyap’s
(1995) psychological pricing point theory, which builds on work in Economics and Marketing.
This theoretical perspective appears to be very useful in order to interpret the extent of price
rigidity that has been observed in Internet-based selling to date (Bergen et al., 2005c; Kauffman
and Lee, 2004a; Levy et al., 2005). Kashyap (1995) argues that pricing managers attach great
psychological importance to the price point thresholds, such as $9 or 9¢, which consumers may
misperceive, not round up etc., for different cognitive reasons. He shows that catalog prices tend
18
to have rigid ending prices. 5 Price rigidity in both traditional retailing and Internet-based selling
may occur due to rational inattention on the part of consumers (Sims, 2003).
Rational inattention theory posits that it may be rational for consumers to be inattentive
to the rightmost digit(s) in a price because they are constrained by time, resources, and
information processing capacity (Levy et al., 2005; Sims, 2003). 6 Since many consumers
appear to ignore the last digit of the price, firms will have an incentive to make it as high as
possible at $9 or 9¢ (Basu, 1997). Given the firm’s reaction to its customers’ inattention to the
last digit of the price, rational consumers expect that firms will set it equal to 9. Thus, 9-endings
may be a rational expectations equilibrium outcome (Basu, 1997). So, firms will tend to stick to
prices with “9”-endings even if they face cost shocks to change prices in small amounts (e.g., a
1¢ increase or decrease in cost). This is because when the price-setter is facing a price change
decision that would require, say, a price increase from $1.79 to $1.80, the increase may not be
optimal if the customers ignore the last digit and perceive the change to be bigger than it actually
is (i.e., as a 10¢ increase). Such a misperception may decrease demand more than what is
justified by the actual price increase (i.e., of 1¢).
Similarly, a price decrease from $1.79 to $1.78 may not have the desired effect on
demand if consumers ignore the last digit. On the other hand, prices that do not end with a 9
may become more flexible compared to when consumers are rationally attentive. For example,
an increase from $1.78 to $1.79 may not decrease demand at all if customers ignore the last digit.
Similarly, a decrease from $1.80 to $1.79 may be perceived to be bigger (like a 10¢ decrease)
than it actually is, and thus may boost demand more than what is expected. So overall, 9-ending
prices may lead to more price rigidity when customers are rationally inattentive to the last digit.
What about Internet-based selling? Consumers on the Internet can easily compare prices
and trace product information, as we have discussed so far. Technology provides a basis for the
consumer being able to achieve a higher level of attention to price—if they use it. So, in spite of
the arguments in favor of rational inattention to price endings, shopbots may actually flatten
5
For example, consumers may perceive an actual price of $999.99 as $999 or $990—or possibly even $900—
instead of $1,000. 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.
6
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).
19
some of the potential behaviors that would be operative through this theoretical lens. However,
we still observe empirical regularities which show that Internet-based retailers also employ “9”ending prices for almost every conceivable product as illustrated in Buy.com’s Web site. (See
Figure 2.)
Figure 2. Observed Regularities for Price Endings in Buy.com’s Web Site
So we suggest the following proposition:
Proposition 3 (The Price Points Proposition): Similar to traditional retailers, Internetbased sellers change their prices with 9-endings less frequently than those with other priceendings because they have an incentive to make the price endings equal to $9 or 9¢.
As Blinder et al. (1998) note, however, there are two difficulties with pricing point theory.
First, not much is known about the practical importance of psychological threshold-based pricing.
Second, no satisfactory economic explanation has been offered for pricing point theory. To fill
this gap in the literature, Levy et al. (2005) explore the empirical relevance of pricing points in
the United States retail food industry. They find that more than 65% of prices are observed to
end in 9¢, and among those, the most prices end in 99¢ (e.g., $9.99). In addition, they find that
prices are between 25% and 40% less likely to change when their terminal digits are 9. There
are also a number of empirical studies that corroborate this proposition in the Internet-based
selling context. For example, in their recent study on the Internet-based bookselling industry,
20
Kauffman and Lee (2004b) offer evidence that online consumers may be also rationally
inattentive to the last digit of the price, and so sellers may take advantage of this and make the
price ending as high as possible to achieve the highest level of profit. In addition, Bergen et al.
(2005c), using almost 5 million daily observations on multiple categories of products sold by
hundreds of Internet-based retailers, find robust and consistent results indicating that Internetbased sellers are likely to change prices less frequently to make the price-ending equal to “9.”
Despite the lack of more general evidence, we expect that rational inattention still will be a
source of price rigidity in e-commerce since Internet-based sellers have an incentive to sustain
price endings at a higher level to maximize profits. Exploring these issues in terms of theory
from Marketing and Economics promises to be an interesting direction for future IS research.
4.
FIRM-LEVEL ANALYSIS: PRICE ADJUSTMENT COSTS, NONPRICE COMPETITION, DEMAND, HOLIDAY PRICING, AND
PRICE RIGIDITY
With the new retailing activities on the Internet, we expect to see changes that reflect the
different technological underpinnings of the firm’s production process for prices (Dutta et al.,
2003). Not only do the new technologies far surpass the capabilities that are available in
traditional bricks-and-mortar stores to adjust prices (Kauffman and Lee, 2004a) and track
competitors’ prices (Kauffman and Wood, 2005), they provide the basis for consumers to make
to-the-cent price comparisons (Bakos, 1997). The reduction in search costs for attractive prices
and bargains is accompanied by opportunities for sellers to implement algorithmic price
discrimination approaches, to segment customers based on customer relationship management
(CRM) systems information and new data mining techniques. It is natural, then, that Internetbased firms will create new ways to set and adjust prices in e-commerce. So, in this section, we
consider the role of IT in terms of the strategic pricing of Internet-based sellers. We will look at
new technological capabilities of firms in adjusting prices responding to the shocks of costs and
demand in Internet-based selling.
4.1.
Price Adjustment Costs and Price Rigidity
One explanation for price rigidity is based on theories of price adjustment costs: it is
costly for firms to change prices (Blinder et al., 1998; Carlton, 1989). A profit-maximizing firm
21
facing price adjustment costs will change its prices less often than an identical firm without such
costs. They include the 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). So, these theories
explain the rigidity of prices based on two important costs generated by firms: menu costs and
managerial costs.
Menu Costs. Menu costs 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 to make relatively large and infrequent price changes, and thus make
prices less flexible by deterring recurrent price changes (Blinder et al., 1998; Zbaracki et al.,
2004). 7 Interestingly, prior to the studies by Mankiw (1985) and Akerlof and Yellen (1985),
menu costs were thought to be too small to rationalize any substantial effects of price rigidity.
Testing menu cost theory directly has been hard due to the lack of cost-related data (Blinder et al.,
1998). In many settings, the costs of collecting such data would be prohibitive. So, empirical
studies have used indirect proxies (e.g., frequency of price changes and time between changes)
to provide evidence supporting this theory (Levy et al., 2002).
8
7
Mankiw (1985) states that in monopoly competition a firm’s increased return from changing prices is smaller than
the increased social welfare. So prices may not 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.
8
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 data on actual transactions prices, Carlton (1986)
finds that the fixed cost of price adjustment differs across buyers and sellers. Cecchetti (1986) analyzes the
frequency and size of price changes in the newsstand magazines 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, heterogeneity exists
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 grocery
stores, provides some estimates of the implied price adjustment cost magnitude. Studies involving gasoline
(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.
Based on survey data from New Zealand businesses, Buckle and Carlson (2000) assert that larger firms change price
22
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 price decision-making (Ball and Mankiw, 1994; Blinder et al., 1998; Carlton, 1997; Levy et
al., 1997). The theory of managerial costs (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). 9 Both (physical)
menu costs and managerial costs give rise to price adjustment barriers within a firm (Bergen et
al., 2003; Zbaracki et al., 2004) though some consider managerial cost a special kind of menu
cost (Blinder et al., 1998).
Price Synchronization and Staggering. New-Keynesian macroeconomists assume that
firms change prices step-by-step over time in a process that is called price staggering. Not all
firms change them simultaneously. For example, some used price synchronization, the process
of aligning strategic price changes in time, instead. 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). 10 Sheshinski and Weiss (1992) study optimal pricing strategy
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) 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: the labor cost of changing grocery shelf prices; the cost of printing and
delivering new price tags; the cost of mistakes made during the price change process; and the 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 and less persistent, and for cost
shocks about which market participants have less information.
9
Sheshinski and Weiss (1992) distinguish managerial costs from menu costs as the more critical component by
arguing that these costs induce price staggering by firms. Mankiw and Reis (2002) 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 slower 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. (2004) 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 greater
than the menu costs associated with changing prices.
10
Blanchard (1982) argues that 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 theirs. Ball and Romer (1989)
state that staggered price-setting has the advantage in that it permits rapid adjustment to firm-specific shocks, but the
23
for a multiproduct monopolist 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 observe 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). 11
Compared to traditional non-Internet markets, where significant costs associated with
price adjustment are incurred, the Internet provides a new environment where physical price
adjustment costs (i.e., menu costs) are almost absent (Bailey, 1998; Brynjolfsson and Smith,
2000). Changes in underlying technologies that support the production of price changes at the
level of the firm lead to changes in costs of adjustment. This flexibility also suggests that
Internet markets have the capacity to be more fluid, with price changes more spread out over
time to match market demand. So, we also expect that the new technologies will offer firms the
possibility to be more flexible and efficient in the application of pricing strategy. This suggests
applying pricing models that are financial market-like—the strategic equivalent of Clemons et
al.’s (1999) “market technostructure”—all pure supply and demand plays (Bergen et al., 2005a).
So, we propose the following proposition related to menu costs in Internet-based selling:
Proposition 4 (The Flexible Price Adjustment Proposition): Due to very low menu costs,
Internet-based sellers have the capability to change prices flexibly by any amount (e.g.,
1¢) at any time (e.g., twice a day). 12
There are a number of empirical findings that corroborate this proposition in Internet-
disadvantages include unwanted fluctuations in relative prices. By creating price-level inertia, staggering pricesetting can increase aggregate fluctuations.
11
There are few empirical studies because much of the work on price rigidity has primarily concentrated on single
product firms (Fisher and Konieczny, 2000). Lach and Tsiddon (1996), using multiproduct pricing data from Israeli
retail stores, find that price changes of the same product are staggered across firms (across-store 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 Canadian newspapers, present evidence of synchronization within
the same firm, as well as staggering across independent firms in price-setting of newspapers.
12
This proposition can be also applied to physical stores with electronic shelf pricing (ESP) systems since it is also
easy and costless for the stores to change prices due to low menu costs.
24
based selling. Bailey (1998) finds that Internet retailers make significantly more frequent
changes than traditional retailers for homogeneous products, such as books and CDs, due to low
menu costs. Brynjolfsson and Smith (2000) observe that online retailers make price changes that
are up to 100 times smaller than those made by bricks-and-mortars sellers. Bergen et al. (2005a)
also find evidence from their study of online bookselling industry that Internet retailers adjust
prices any day of the week throughout the year. This is in contrast to grocery stores or
traditional retailers, who adjust prices with sale fliers and print and radio advertisements to take
advantage of greater demand that occurs around the weekend. So, we believe that Internet-based
sellers—perhaps due to their high technology prowess—are capable of being more flexible than
traditional bricks and mortar retailers.
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).
Zbaracki et al. (2004) uncovered evidence from industrial markets that shows the managerial
costs are significantly greater (i.e., more than six times) than the menu costs associated with
price changes. So, Internet retailers may have more price rigidities that stem from managerial
costs (Bergen et al., 2003). Table 3 compares the costs of price adjustment in terms of menu
costs and managerial costs between the different channels.
Table 3. A Comparison of the Price Adjustment Costs by Channel
COST TYPE
Menu Costs
Managerial
Costs
BRICKS-AND-MORTARS
High due to physical
lump-sum costs
High due to hierarchies
for decision-making
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
With these observations and ideas in minds, we suggest the following propositions
related to price adjustment costs:
Proposition 5 (The Across-Store Price Staggering Proposition): Internet-based sellers
stagger price changes of the same product across firms due to managerial costs and the
interdependence on the actions of their competitors.
There are also a number of studies confirming this proposition in the context of Internetbased selling. Tang and Xing (2001), comparing pricing for DVDs between bricks-and-clicks
and pure Internet retailers, find that both types of retailers do not change prices frequently, in
25
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. Bergen, et al. (2005) explore daily
patterns of Internet pricing, and find that Amazon.com, with a 222-day price change interval for
the study period, appears to change prices less frequently than the Barnes&Noble.com, with a
56-day interval. This finding is hard to explain with the reduced physical costs of price
adjustment that have been the primary focus in the literature. However, this might be explained
by strategic pricing and managerial capabilities, and different underlying costs. 13 Kauffman
and Wood (2005) also suggest the possibility of follow-the-leader strategic pricing dynamics,
where one of the players adopts a deliberately aggressive price change leader style, and others
mimic the changes. Analyzing the pricing behavior of two leading online bookstores (i.e.,
Amazon.com and Barnes&Noble.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. Although there may be contradictory evidence on price adjustment
costs, we believe that price rigidity continues to exist in e-commerce due to managerial costs for
a firm’s channel integration efforts. Managerial costs are likely, as well, to lead to across-store
staggering of prices—even in the presence of the most advanced and new ITs.
4.2.
Non-Price Competition and Price Rigidity
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. Carlton (1989) points out that markets often clear through means other than price. He
views price as only one of many dimensions of the terms on which products are exchanged.
Non-price 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
13
The logic might go something like this: BN, as a later entrant to Internet-based selling, may have more strategic
pricing expertise from its traditional store operations—allowing them to pursue a strategy of changing prices more
frequently. An alternative explanation may lie in the negative media reports that resulted from Amazon’s brief foray
into computer-based price discrimination for its existing customers. The firestorm that erupted around that incident
may have caused management to temper its approach to strategic pricing, to ensure that frequent price changes
would not create additional impressions that the firm was shifting prices for its own advantage.
26
efforts, expanding advertising expenditures, etc. 14 Non-price competition seems to be especially
prevalent where firms perceive that there are implicit long-term contracts with their customers
that help to 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). 15
What about Internet-based selling? Online consumers also care about other non-price
aspects, such as seller reputation, delivery locations and times, contract lengths, and so on. So,
the Internet has impacted both price competition and non-price competition between firms (Clay
et al., 2002a). For example, Internet-based sellers, as a result, may use free-shipping options (or
delivery lags) instead of lowering (or raising) product prices, even if the underlying market
conditions have changed. Such adjustments to non-price elements are likely to offer new ways
to compete and will require firms to formulate new business rules, too. We also observe that the
competition that motivates the use of non-price elements may not focus on obtaining specific
transactions or purchases. Instead, the more important emphasis may be the competition to
obtain new customers and to maintain high customer loyalty. Kauffman and Lee (2004b)
provide case study and empirical evidence that an online retailer, Buy.com, makes use of
shipping costs (e.g., free shipping), instead of direct price adjustments in its approach to strategic
pricing. (See Figure 2.) However, Bergen et al. (2005b) find evidence that Internet-based sellers
are also concerned about the fact that consumers may perceive high shipping costs to be unfair.
So shipping costs are a part of the total price and appear to cause price choices to become
endogenous. Therefore, Internet-based sellers will make use of both price and non-price
promotions during a period of intense competition. Although the use of non-price promotions
does not necessarily indicate that online retailers adjust prices less frequently, we conjecture that
non-price elements causing price rigidity can be also used effectively by the retailer in the ecommerce context. So, the true relationship between non-price promotions and price rigidity
14
Carlton (1983) views delivery lags as one of means for market clearing, which determines market demand. 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 may have instability in delivery dates but only
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).
15
Zbaracki et al. (2004) 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.
27
should be explored through further comprehensive empirical investigation. We will further
discuss this theory when we develop a proposition related to holiday pricing in Internet-based
selling context.
Figure 2: Evidence of Non-Price Competition for a Bestselling Book
Price without Shipping Cost
Total Price with Shipping Cost
15
20
14.5
14
18
13.5
16
Price
Price
13
12.5
14
12
11.5
12
11
10.5
10
10
1
31
61
91
121
151
181
211
241
271
1
301
31
61
91
121
151
181
211
241
271
301
Days
Days
Non-Price Competition?
Note: The figure depicts the aggregate price trajectories over time of seven competing booksellers on
the Internet for one bestselling book. By comparing the price without shipping cost to the price
with shipping cost for each bookseller, it is possible to see the extent to which one competitor,
Buy.com, is manipulating shipping cost while it simultaneously adjusts the price of the book
(without the shipping cost). This provides an illustration of non-price competition.
4.3.
Demand, Inventory and Price Rigidity
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. So, 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 or inter-temporal
substitutes which companies use to smooth 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 raise) 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
28
adjustments for temporary changes of demand. With permanent changes of demand though, real
price changes are inevitable. 16
What has been changed in today’s environment where new technologies have impacts on
the process of inventory logistics? There is no doubt that a foundational level of firm inventory
is also necessary to serve and retain customers in Internet-based selling. To increase sales with
customers, firms must be responsive to their special needs and requests for supply chain and
logistics support. From an economic perspective, firms want to be able to adjust prices to react
to changes in demand. Yet this is often difficult to do in a traditional bricks and mortar retail
setting. The flow of data, and the processes and costs associated with changing prices can limit a
traditional brick and mortar store’s ability to react to these changes. So, compared to traditional
channels which require firms to keep the highest inventories, Internet-based retailers are now
able to more accurately control inventory and costs, and sample demand any time they need to.
They also possess significant price-changing capabilities. The new technologies associated with
the Internet also provide traditional bricks-and-mortar retailers with opportunities to adopt
bricks-and-clicks retail capabilities, such as leveraging logistical and operational expertise with
traditional distribution channels, as well as connecting their technology infrastructures with the
Internet. As a result, it is now possible to integrate a firm’s Internet channel with traditional
distribution channels while ensuring product, price and promotion consistency (Gulati and
Carino, 2000). And, with the move to the sale of information goods (e.g., e-books or MP3
music), retail businesses can be designed with virtually no physical inventory. So, price changes
are more likely to be driven by demand considerations than inventory levels. As the demand (i.e.,
popularity) of the product increases, firms have an incentive or a capability to more frequently
change prices to maximize their profits by attracting their customers. With these ideas in mind,
we present the next proposition:
Proposition 6 (The Product Demand Proposition): Internet-based sellers have an
incentive to frequently change the prices of highly-demanded products due to consumers’
higher price sensitivity caused by lower search costs.
16
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 there may be temporary asymmetries in the adjustment of spot gasoline
prices to spot crude oil prices due to production and inventory adjustment lags.
29
There are also a number of empirical results that lend support for this proposition.
Kauffman and Wood (2005) find, in their study of online bookselling industry, that the price of
bestselling books may be dependent on booksellers’ business rules for selling books. For
example, books that are selling very well may be discounted more frequently compared to other
book categories. This is because bestsellers face markets with more unstable, changing demand
due to the effects of announcements and the buzz in the media (e.g., the New York Times
bestsellers list, the many columns of leading book critics, etc.). Bergen et al. (2005b), in their
analysis of over three million daily observations of prices for books, CDs and DVDs find that
product popularity, in terms of sales ranks from Amazon.com and BN.com, positively affects the
frequency of observed price changes. In addition, they also find that the bestseller category
shows the highest percentage of price changes compared to other categories (e.g., computer
books, new books, and steadysellers). Therefore, we believe that price changes in Internet-based
selling are more likely to be driven by demand than inventories.
4.4.
Holiday Pricing and Price Rigidity
Firm-level price-changing behavior over the business cycle necessitates that
consideration be given to 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 trend is
known as procyclical elasticity of demand. It explains why the responsiveness or elasticity of
prices to changes in demand may be dampened in a cyclical downturn. 17 Warner and Barsky
(1995) find strong empirical evidence of procyclical elasticity of demand just prior to the
Christmas holidays and also on three-day weekend holidays. These are times when consumers
are engaged in more intense shopping or search activities. This phenomenon is also called as the
thick market effect. The idea is that firms have a somewhat counterintuitive tendency to charge
lower prices during periods of peak demand (which contradicts the high demand Æ high price
rule). This illustrates the will of firms to compete more aggressively on the basis of prices in
17
Stiglitz (1984) argues that prices become invariant across the business cycle even in the presence of a decline in
the marginal costs of production. This is because anti-cyclical price markups may increase even if the elasticity of
demand decreases (Bils, 1987; Blinder et al., 1998).
30
markets with high demand, when there are also significant opportunities to generate revenues
and earn a profit based on a high volume of sales. 18
What about Internet-based selling in the business cycle context? Rapid growth of online
sales during the holiday period provides Internet-based sellers with huge opportunities and great
incentives to attract potential consumers to their stores by applying the appropriate strategic
pricing models. 19 So, new technologies will offer the possibility to be more flexible and
efficient in the application of pricing strategy, letting Internet-based sellers make immediate and
frequent adjustments (Baker et al., 2001). They can further profit from small ticks in market
demand and supply, by applying pricing models that are financial market-like. They will provide
functionality that is analogous to what dealers and middlemen can utilize in settings which
involve dynamic trading. So, one might also expect to see more differences in price adjustment
patterns on the Internet between holiday periods and non-holiday periods. Why? We think it
may be because Internet-based sellers have the capabilities to deal with unexpected changes in
demand, such as during the holiday periods, through the use of technologies (e.g., CRM and data
mining techniques) and with almost absent menu costs. So, they can change (more likely lower)
prices more frequently to attract consumers in high demand periods. Another alternative
explanation is possible though too: one might expect fewer differences because online consumers
may be able to see the changes more easily due to their extensive use of search engines and price
comparison agents (Bakos, 1997; Warner and Barsky, 1995). Bergen et al. (2005a) find that
none of the top ten price change days in Amazon and BN occurred in the Christmas holiday
shopping season, or around other holidays. So, we doubt that prices will be less rigid on the
Internet during the holiday season—though price discounts are a widely-observed phenomenon
in traditional selling channels.
Seybold (2005) reports that the biggest concerns that consumers have during the holiday
season typically are shipping delays. Indeed, many Internet-based sellers have experienced their
18
Chevalier et al. (2003) provide contradicting evidence that prices do not fall during Thanksgiving or Christmas
holiday seasons, however. Instead, they find significant procyclical pricing patterns over seasonal cycles due to
retail margin changes, which is consistent with retailers’ “loss- leader” strategies for competition. Loss-leader
advertising, for example, is a strategy in which retailers offer a big discount in order to attract customers for future
profits.
19
During the 2004 winter holiday season, online shoppers in the United States spent $23.2 billion excluding travel,
which accounts for more than 10% of the total for holiday retail sales and a 25% increase from the same time frame
in 2003 (Seybold, 2005). In addition, comScore Networks (2005) also reported that in 2004 online retail spending
including travel grew by 26% compared to the previous year to a record $117 billion in sales.
31
customers’ anger due to delayed shipments and orders during the holiday season, and have had to
endure the publication of scathing articles in national newspapers and magazines and Internet
blogs decrying their fulfillment capabilities. So we wonder: in the presence of the new
technologies that support the production of prices for Internet-based sellers, will the holiday
period be different due to the potential problems associated with the delivery of purchased
goods? We expect that, instead of lowering prices, Internet-based sellers may offer free shipping
with possible delivery lags in holiday seasons, as suggested by Carlton (1983) for traditional
sellers. With these ideas in mind, we suggest the following propositions related to holiday
pricing and shipping delays:
Proposition 7 (The Holiday Season Delivery Lag Proposition): During the holiday
season, Internet-based sellers will offer free shipping more often to avoid antagonizing
their customers due to delivery lags instead of direct price changes.
5.
MARKET-LEVEL ANALYSIS: INDUSTRY CONCENTRATION
AND COORDINATION FAILURES
In Industrial Economics, it is commonly noted that the sluggishness of prices in response
to demand shifts 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 interdependent because each firm takes into account the actions of its competitors. Without
an effective coordinating mechanism (e.g., an intermediary) for market clearing, each firm
hesitates to change prices until other firms move first, and thus prices may remain fixed (Blinder
et al., 1998).
What about Internet-based selling markets though? Will the same dynamics obtain?
From our daily lives to commercial transactions between businesses, Internet technologies have
created new forms of socio-economic organizations, and have profoundly impacted the scope
and efficiency of markets (Brynjolfsson and Kahin, 2000). The Internet enhances the
performance of markets 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
32
data about consumers’ 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). So, market forces in Internet-based selling may also play quite
different roles in firms’ strategic pricing and price adjustment decisions compared to in
traditional environments. In this section, thus, we consider more macro-level of analysis in
terms of market structures including the level of competition and coordination failures.
5.1.
Industry Concentration and Price Rigidity
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). 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: a positive relationship, a negative relationship, and no
significant relationship. 20
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 able to 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 discourage or
delay new firm entry (Stiglitz, 1984). 21 The justification for a negative relationship between
industry concentration and price rigidity, as claimed by Stigler (1964), is that it is easier for other
20
Due to the difficulty in measuring the competitive conditions in a given market, most of the studies have focused
on a proxy measure for market competitiveness, especially industry (or market) concentration (e.g., Herfindahl
index), which iteratively measures what share of a market is held by the first x-number of the largest firms (Tirole,
1988). It provides a rudimentary indicator of the extent of monopoly power.
21
Rotemberg and Saloner (1987), in a comparison of monopoly and duopoly markets, develop a theoretical
explanation of market power for price rigidity, showing that firms in less competitive markets change prices 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. Carlton (1986) reports a 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 the
banking industry of a significant positive relationship between industry concentration and price rigidity, and limited
level of asymmetric behavior. Neumark and Sharpe (1992), using panel data on bank deposit behavior, find an
asymmetric relationship between industry concentration and price rigidity: downward price rigidity and upward
price flexibility in concentrated markets.
33
competitors to identify secret price cutting when there exist fewer number of firms in an industry.
Thus, firms avoid the secret cutting of prices. 22 The third perspective of no significant
relationship between industry concentration and price rigidity is based on the argument that
industrial concentration is not a key determinant of industrial pricing behavior. Without perfect
monopoly or explicit pricing collusion, firms tend to behave as price competitors and the degree
of industrial concentration is inconsequential (Qualls, 1979). 23
What are the impacts of technologies on Internet-based markets? Do they create more
competitive markets compared to traditional channels? Various observers say that the Internet
environment offers less concentrated markets but nevertheless creates more competition by
lowering technological barriers to entry due to lower set-up costs, as well as lowering the
marginal costs of production and distribution (Daripa and Kapur, 2001; Latcovich and Smith,
2001). However, to survive in such competitive environments, e-commerce firms require a
significant level of investment in advertising and IT infrastructure. But the necessary economies
of scale for these kinds of investments raise barriers to entry and may induce greater industry
concentration in markets (Daripa and Kapur, 2001; Shaked and Sutton, 1987). Amazon.com’s
takeover of the online operations of Toys“R”Us and Borders offers a case in point. Latcovich
and Smith (2001) also report that the online book market has become more concentrated than the
traditional book retailing industry in the United States. They calculated the top four-firm
aggregate market share for online booksellers at 93%, while the same metric for the traditional
book retailing industry was only 45%. Other research reported that the four largest Internet
retailers accounted for 99.8% of the total number of Internet hits for book retailers in 2000
(Brynjolfsson and Smith, 2000). Highly-concentrated online markets may allow firms to exploit
market power by reducing the costs of driving traffic to their Web sites. In such highlyconcentrated markets, consumers bear fewer costs to get information about the prices proposed
by each of the competitors. In addition, they can easily detect price changes through the use of
22
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.
23
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 industry concentration and price rigidity.
Jackson (1997), using rational distributed lag and price adjustment models, provides empirical evidence of a nonmonotonic relationship between industry concentration and price rigidity.
34
search engines or price comparison agents if there are fewer sellers in a market. So, firms are
reluctant to change prices due to such difficulties of secret price changes. With these ideas in
mind, we propose:
Proposition 8 (The Internet Market Concentration Proposition): On the Internet, high
industry concentration leads to greater price rigidity due to consumers’ enhanced search
capabilities; the more highly concentrated an industry is, the less rapidly will the
industry’s firms adjust prices in response to changes in market conditions.
There exist relative few empirical studies to corroborate this proposition in Internet-based
selling context. Bergen et al. (2005b), in their study of homogenous online products (e.g., books,
CDs, and DVDs), find that high industry concentration leads to high price rigidity. In addition,
they show that books—for which the industry is the most concentrated—change prices the least
frequently (i.e., over 2 times less than CDs and 3.5 times less than DVDs). Although the true
relationship between industry concentration and price rigidity is not clearly defined in prior
research, we still expect that highly-concentrated industries on the Internet (e.g., books, CDs)
will behave as oligopolies with the corresponding price coordination problems, resulting in
greater price rigidity.
5.2.
Coordination Failure and Price Rigidity
Coordination failure theory states that the inability of firms to plan and implement
pricing is due to 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 coordination failure 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 mover, the Stackelberg follower,
produces its quantity knowing the output of the leader. The kinked demand curve (Hall and
Hitch, 1939; Sweezy, 1939), the classic explanation for price rigidity in oligopolistic industries,
35
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:
when large business fluctuations occur (e.g., boom and bust cycles), in the presence of minimal
punishment for collusion, when large gains from cheating at possible, and when detection lags
make it difficult to apprehend the transgressor (Rotemberg and Saloner, 1986; Stiglitz, 1984;
Tirole, 1988). 24 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.
Is Internet-based selling affected by some of these issues? No doubt, the Internet makes
it easier for sellers 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 strategies of the market participants (Daripa
and Kapur, 2001; Granados, et al., 2005a). 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 sellers to quickly monitor and
react to their 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). Being more transparent, the Internet-based markets become more competitive,
24
Stiglitz (1984) argues that firms’ collusive agreements are non-cooperative equilibria which 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. 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.
36
beating competitor prices in a competitive move or matching competitor prices in a tacitly
collusive move (Kauffman and Wood 2003b). Knowing that competitors rapidly learn about
price cuts, however, Internet-based sellers 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, 2005). Although, we expect that
online prices also may be rigid due to incentives to sustain higher prices through implicit
agreements. With these ideas, we suggest the following proposition:
Proposition 9 (The Tacit Collusion Proposition): To avoid intense price competition
caused by enhanced search capabilities which both consumers and sellers can use,
Internet-based sellers will tend to tacitly collude with each other, leading to rigid
(higher) prices on the Internet.
Internet technologies appear to enable firms to tacitly collude to keep their prices higher. A
number of researchers have studied this theory and provided interesting results to support this
proposition. Bailey (1998) provides evidence for convergence of book prices that prices between
Amazon.com and BN.com were almost the same four months after BN entered into the Internet
bookselling industry. Varian (2000) also demonstrates how collusion is occurring in Internet
bookselling industry by tracking a book price. Kauffman and Wood (2003b, 2005) also find
evidence from bookselling and CD industries that how Internet retailers tacitly collude each
other by allowing swift reactions among competitors. Finally, Campbell et al. (2005) show that
reduced consumer search costs to locate lower prices and firms’ enhanced capability to detect
cheating on collusive price arrangement facilitate the firms’ incentives to sustain collusion and
result in higher prices.
6.
Discussion
“Whether or not price rigidity is efficient, one common conclusion emerging from
models with price rigidity is that markets with rigid prices behave very differently
than markets with flexible prices. Therefore, an important unanswered question
is: Just how rigid are prices? Despite the great interest in this question, there
have been virtually no attempts to answer it with data on individual transaction
prices.” (Carlton, 1986, p. 637)
As we have discussed so far, price rigidity is a topic of long-standing interest for
marketing science researchers and economists, as well as for senior managers in firms in
different industries, and for those who seek to understand the economy as a whole. We have
37
attempted to provide the IS audience with an overview of the bare bones of the competing
theories of price rigidity and price adjustment from the point of view of Economics, Psychology,
and Marketing. We have argued that this large body of knowledge positioned across three
different academic disciplines outside our own discipline of IS is absolutely “must read” material.
The existing theoretical knowledge will enable IS researchers to make contributions to our fuller
understanding of the dynamics of strategic pricing in marketing function of the firm, especially
as it relates to Internet-based selling and brick-and-clicks firms. Moreover, we believe that
broader awareness of this literature will help IS researchers to begin to tackle some of the more
difficult problems that relate to technology as an implementation engine for pricing strategy
within the firm and in the competitive marketplace. Table 4 summarizes the possible causes and
expected results that the different interdisciplinary theories suggest for price rigidity in Internetbased selling. We also identify the propositions from among the nine that we presented that
relate to each of the theories. (See Table 4.) As we also have discussed in the previous sections,
there might exist theoretical conflicts or paradoxes to explain price rigidity in the context of
Internet-based selling. So, we attempted to reflect on the various arguments and competing
theories based on the available theoretical and empirical evidence.
Yet, due to the difficulty of measuring the extent of price rigidity directly, only a few
studies have attempted to provide empirical evidence for explanatory theories from Marketing
and Economics. Many authors such as Carlton (1986), Cecchetti (1986), Kashyap (1995),
Warner and Barsky (1995), and Dutta, et al. (2002) echo the need for more micro-level studies of
price adjustment using actual retail transaction prices of firms. Their calls for additional research
on these topics applies equally well to the Internet-based selling context, only it will be necessary
for people to have an intimate knowledge of IT and the Internet.
Today, the Internet provides unprecedented opportunities to collect data involving more
observations (e.g., of human decisions, prices, products, etc.) with lower costs, fewer strict
assumptions, and more realism for empirical research involving technology and marketingrelated phenomena. So, quasi-experimental designs in IS and e-commerce can take advantage of
natural experiments in the real world and enable a researcher to distinguish between outcomes
associated with different levels of impacts, access or use of IT. A quasi-experiment, also called a
natural experiment, is a type of quantitative research design conducted to explain relationships
and clarify why certain events happen (Campbell, 1988). The quasi-experimental method puts
38
an emphasis on the understanding of the source of variation used to estimate key parameters
(Meyer, 1995). We call this approach the massive quasi-experimental (MQE) data mining
method (Kauffman and Wood, 2003a).
Table 4. Summary of Possible Causes of Price Rigidity in E-Commerce (EC)
THEORIES
POSSIBLE CAUSES AND EXPECTED RESULTS
Price
Adjustment
Costs
Menu costs are rapidly diminishing within firms.
□ Simple database updates are possible, easily programmed
□ Real-time inventory systems, e.g., Internet-connected ESP systems, up-tonow managerial information
□ Within-store synchronization may not exist due to reduced menu costs
However, prices may still remain unchanged for other reasons.
□ Across-store staggering still exists due to managerial costs
High industry concentration leads to greater price rigidity.
□ Likely to occur especially in online bookselling industry
□ Economies of scale and limit pricing support this outcome
But coordination failures are also likely.
□ Stackelberg pricing and follow-the-leader is possible, but may lead to
problems with profitability
□ So tacit collusion may be used to avoid intense price competition
Too much price adjustment may signal lower quality products.
□ Mostly homogeneous products (e.g., books, CDs) should have less flexible
prices, as a result
But if lower prices signal lower quality, the demand curve will kink,
making it more difficult for firms to maximize profit through price
adjustments.
□ IT makes rapid price adjustments possible, but still they may not desirable
in terms of overall firm value
□ However, search costs on the Internet are almost negligible, and this will
make it possible for consumers to take advantage of sudden price
adjustments
Point to greater likelihood of changes in prices than inventories.
□ Real-time inventory systems and virtually no physical inventory
Uncertain psychological pricing points may diminish the effects of rapid
price adjustments.
□ Reduced information processing costs occur due to new technologies, but
people still may be rationally inattentive to prices
May call for strategic use of non-price elements in competition.
□ Seller reputation, free shipping, etc., can be leveraged for gains
Explicit contracting is not sufficient to produce rigid prices in Internetbased selling.
□ High prices and cost transparency may motivate supply chain buyers to
renegotiate contracts.
But implicit contracts may have the opposite effect.
□ Customer antagonization and undesirable switching costs create inertia for
price adjustment
□ Lower search costs may not overcome the implicit contract costs
Market
Structure
Asymmetric
Information
DemandBased
Effects
ContractBased
Effects
RELATED APPLICABLE
PROP.
TO EC?
P4, P7
No
P5
Yes
P8
Yes
P9
Yes
P1, P2
Yes
N/A
No
P6, P7
No
P3
Yes
P7
Yes
N/A
No
P1, P2,
P7
Yes
Note: Propositions Î P1 (Information Quality); P2 (Quality Signaling); P3 (Price Points); P4 (Flexible Price Adjustment);
P5 (Across-Store Staggering); P6 (Product Demand); P7 (Holiday Delivery Lags);
P8 (Market Concentration); P9 (Tacit Collusion)
39
We are currently conducting empirical analyses with data collected from price
comparison sites that permit us to explore the efficacy of a number of propositions and related
price rigidity theories, such as menu cost theory, managerial cost theory, industry concentration
theory, psychological pricing points theory, etc. To do this research, we are employing a
sophisticated software agent for data collection. The tool is able to systematically mine pricerelated information (e.g., list prices, selling prices, shipping costs, release or publication date), as
well as some qualitative information (e.g., consumers’ ratings of the products and stores, the
number of reviews, sales rank) 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. As of early September
2005, we have collected almost 7 million data points to examine empirical regularities and
variations of Internet-based retailers’ price setting behaviors and strategies.
We analyze the price-related data that we obtained as a means to establish the empirical
regularities or patterns of observed price adjustment and setting behavior across products, firms
and industries. This approach is common in economic studies involving very large data sets (e.g.,
financial market research, economic forecasting, and auction market performance), as a
precursor to the exploration of existing theory and the development of new theory. Examples of
such work include: studies on cross-industrial heterogeneity in price adjustments (Carlton, 1986;
Kashyap, 1995); the measurement of menu costs using scanner data (Levy, et al., 1997); the
empirical regularities in eBay auctions (Bajari and Hortacsu, 2003) and strategic pricing for
Internet-based travel services (Clemons, et al., 2002). Note that this view of methodology is not
only common in other fields, such as Marketing and Economics, it is also well accepted. The
reason for this is that progress in research with respect to some of the leading problems (e.g.,
how wholesale and consumer prices move, how price discriminated is best achieved, etc.) in
these fields has involved the application of multiple competing and complementary theories that
have been individually well accepted. The accompanying empirical research efforts, as a result,
are not always intended to isolate one theory as “the one and only truth,” but instead to gauge the
extent of the application of multiple theories as “a collection of truths” that support a much richer,
fuller understanding of the phenomenon of interest.
So, MQE data mining of the kind we have been implementing, with the appropriate
statistical and econometric methods, has the capacity to answer questions that many researchers
40
in Marketing and Economics never have been able to answer or even contemplate asking. (See
Figure 4.)
Figure 4. The Massive Quasi-Experimental Data Mining Method
Price-Related
Information
Existing Theories
Theory
Development
$$ $
Empirical
Regularities
Unstructured
Data
Empirical
Test & Results
Step 1 (Problem Identification and Data Collection): Running the price information gathering agent,
PIGA, to parse the unstructured data from multiple Internet sites, including BizRate, BestWebBuy,
Amazon, and BN.com.
Step 2 (Data Transformation and Preparation): Cleaning and preprocessing the data including
preliminary consistency sampling to ensure the quality and reliability of the collected price data.
Step 3 (Discovering Empirical Regularities): Examining some first order, easy-to-see empirical
regularities and variations of price adjustment patterns across products, categories, and retailers.
Step 4 (Empirical Model Development): Developing empirical models from existing theories to explain
what drives the observed regularities and variations.
Step 5 (Empirical Estimation and Testing): Operationalizing potential variables from the collected data
(e.g., number of reviews, ratings, sales rank, etc.) and estimating coefficients in developed econometric
models.
Step 6 (Theory Development): Developing new theories to explain the newly-observed phenomena
based on the results of empirical models.
Due to imperfect randomization and lack of control, however, the methods may suffer
from a lack of internal validity in that there may be several competing hypotheses with the
experimental manipulation that can act as explanations for the observed results (Campbell, 1988;
Meyer, 1995). In addition, data on related issues, such as sales volume, operating costs,
wholesale prices, and so on do not become “magically” available using this method. Nor is
direct information about customer perceptions and information processing. This suggests that
data-collecting agents are best suited to test theories that lead to direct implications of pricing
patterns across products, categories, stores or time. To go substantially beyond these questions,
the data available from the Internet will need to be subsidized by additional data on firms (e.g.,
their costs, policies with respect to the use of technology for the production of prices, etc.) or a
laboratory experiment (e.g., consumers with similar demographics and other factors presented
with the same goods at different prices and price adjustments and to see what their reactions are),
to be most effective in the future. Perhaps a next step beyond the “all-Internet” focus of data
collection that we have done is to build hybrid data sets, involving the Internet with matched
41
sources of data from the non-electronic channel (e.g., newspapers, in-store prices and their costs,
etc.). Indeed, as a next step, work that involves this kind of data collection would be helpful in
establishing a more systemic and a more holistic understanding of the inner workings of strategic
pricing in competitive markets. Also, we believe that the interviews with people in real business
environments allow us to gain insights and learn how they price their products. The follow-up
interviews with the sources of the data results in insights beyond what we can get from just
theory and the literature.
7.
CONCLUSION
At the beginning of this article, we asked a key question. Should we expect less price
rigidity in e-commerce? Our cautious and early answer is probably not. Price rigidity in ecommerce should be reconsidered in the appropriate theoretical and practical terms as suggested
in the previous sections: in terms of psychological pricing points, customer antagonization, and
non-price elements, as well as other competitive considerations. These include managerial
capabilities, the sophistication of the competition, and a firm’s chosen price/quality/service
profile in the market, among other considerations—all of which may provide a basis for a
variance theory of Internet-based price rigidity. (See Table 4 again.) These ideas are
representative of a range of theory-based explanations that support an argument against the
likelihood of observing greater price flexibility in the digital economy when technology is at the
heart of the production of the firm’s strategic prices.
In this research, we have drawn upon theoretical perspectives from Economics,
Psychology and Marketing Science to explain the price change behaviors of Internet-based
sellers—players in the digital economy who are among the most effective users of new
technologies. The theories that we have explored are largely new to the IS field, even though
they are well known within Marketing and Economics. We believe that they offer rich
opportunities for new theory-building and empirical research in settings that involve investments
in IT and Internet technologies related to the Marketing function. We believe that such
interdisciplinary studies based on massive quasi-experimental data mining methods will provide
a foundation for the development of new theories at the crossroads of the academic disciplines of
Marketing, Economics and IS, and will encourage research that is able to probe for a deeper
42
understanding of new economic phenomena associated with the expansion of the digital
economy.
ACKNOWLEDGEMENTS: An earlier short version of this paper appeared in the Proceedings of the
37th Hawaii International Conference on System Sciences (HICSS-37), held in January 2004. We also
thank the conference participants, as well as two anonymous reviewers from HICSS-37 for helpful
suggestions. Fred Riggins, Michael Smith, Arnold Kamis, Eric Wang and Allen Chen have offered useful
input. Portions of this and related empirical work have been presented at: the 2004 INFORMS Annual
Meeting and the 2004 INFORMS Conference on Information Systems and Technology in Denver,
Colorado; the 2004 International Conference on Systems Science in Washington, DC; the 2004
Knowledge and E-Business Research Symposium in Kenting, Taiwan; the 2004 International Conference
on Electronic Business in Beijing, China; the 2005 IS Research Symposium; the 38th Hawaii
International Conference on System Sciences; the 2005 Maryland Symposium on Statistical Challenges in
E-Commerce; the 2005 AMCIS Doctoral Consortium; the 2005 INFORMS Marketing Science
Conference; and the 2005 International Conference on Electronic Commerce in Xi’an, China. We also
have presented this work in various university seminars, including Queens University (Canada), the
University of Illinois, Chicago; Arizona State University; National Sun Yat-sen University (Taiwan),
National Central University (Taiwan); National Taiwan University. The authors thank Mark Bergen,
Daniel Levy and Sirkka Jarvenpaa for their helpful comments and encouragement. We also appreciate
the critical comments received when we have presented in the Information and Decision Sciences
Workshop at the Carlson School of Management of the University of Minnesota, our home institution.
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