Image Effects and Rational Inattention in
Internet-Based Selling
Dongwon Lee, Robert J. Kauffman, and Mark E. Bergen
ABSTRACT: The frequency of occurrence of certain price points in Internet-based selling
is investigated in order to determine what drives the observed regularities and variations.
Theories based on consumer perceptions of price and quality images, and on rational
inattention to price-endings are explored by specifying and testing empirical models for
price-endings using more than 1.5 million daily observations on multiple product categories
sold by 90 Internet-based retailers collected over a two-year period. The results show
that a firm’s on-line reputation and relative price levels affect the price-endings chosen
in different product categories, and that 9-ending prices increase consumer purchases.
These findings support an image theory of store quality and price. The use of 9-ending
prices varies across Internet selling formats in a way consistent with differences in the
rational attentiveness these channels engender in consumers. This research on the role of
information technology in price-setting provides insights for marketers who wish to optimize
price-setting decisions in the competitive environment of Internet retailing.
KEY WORDS AND PHRASES: Image effects, Internet-based selling, 9-ending prices, price
points, rational inattention, strategic pricing, technology impact.
In the United States, sellers tend to set prices such that the rightmost digit of
the price falls just below a round number.1 These last digits are commonly
called 9-ending prices, and reflect the common use of 9¢, 99¢, $9, and $99, as
in $19.99 or $199 [52]. In marketing and economics, this approach to pricing is also referred to as odd pricing [38], psychological pricing [37], justbelow-the-round-number pricing [50], customary pricing [29], and threshold
pricing [42].2
The use of 9-endings has received considerable attention in public discussions in several European countries as they convert from local currencies to
the euro [26]. In addition to a tendency to use 9-endings, several surveys on
price-endings report that firms use 0¢, 5¢, and 9¢ in posted prices more than
74 percent of the time—and even up to 99 percent of the time [62]. According to Blinder et al., practitioners’ belief in price points “is part of the folklore
of pricing” [12, p. 26]. In a study of 200 large U.S. firms, they found that 88
percent of firms in the retail industry and 47 percent in nonretail industries
reported these kinds of price points in their pricing decisions.
The present study investigates the extent to which certain price points,
especially 9-endings, occur in Internet-based selling environments and seeks
to determine what factors drive e-tailers’ in choosing price endings. With the
new retailing activities on the Internet, one would expect to see changes that
reflect different technological underpinnings of the firm’s production process
for prices [24]. Not only do the new technologies far surpass the capabilities
available in traditional bricks-and-mortar (B&M) stores to adjust their own
prices and track competitors’ prices, they provide the basis for consumers to
make to-the-cent price comparisons [7, 34]. The reduction in search costs for
International Journal of Electronic Commerce / Summer 2009, Vol. 13, No. 4, pp. 127–165.
Copyright © 2009 M.E. Sharpe, Inc. All rights reserved.
1086-4415/2009 $9.50 + 0.00.
DOI 10.2753/JEC1086-4415130406
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LEE, KAUFFMAN, AND BERGEN
Figure 1. Observed Variations for Price-Endings on Internet, Buy.com
and Wal-Mart
Source: Buy.com (www.buy.com) and Walmart.com (www.walmart.com), accessed January 16,
2009.
attractive prices and bargains is accompanied by opportunities for sellers to
implement algorithmic price-discrimination approaches in order to segment
customers based on customer-relationship management systems information
and new data-mining techniques [10]. It is natural, then, that Internet retailers will create new ways to set prices in e-commerce [15, 63].3 However, one
can easily find evidence of regularities in that e-tailers also employ 9-ending
prices for almost every conceivable product, as illustrated, for example, on
Buy.com’s Web site. On the other hand, one also observes some variations for
price-endings on the Internet channel of the retail giant Wal-Mart. It selects
unusual price-endings that are not normally seen in the United States, such as
47, 84, 01, and 88 (see Figure 1). With these changes in Internet-based sellers’
capabilities in strategic pricing and the observed variations for price-endings
on the Internet, the following research questions are proposed:
• What empirical regularities can be observed for firm-selected priceendings in Internet-based selling? Do they differ by product category? By price level? By channels?
• What variables explain the observed empirical regularities and
variations in price-endings in Internet-based selling? How does the
behavior of pure Internet (PI) sellers differ from the behavior of traditional and bricks-and-clicks (B&C) retailers? What theories help to
identify the drivers?
• What new insights do the results provide to enable effective strategic
pricing choices?
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The research presented in this paper is an exploratory effort designed to
provide an initial reading on one aspect of strategic pricing in Internet-based
selling—the question of whether consumer perceptions of price and quality
images for on-line retailers can explain the observation of different empirical
regularities regarding the price-endings or price points that Internet-based
retailers choose to price their goods.
The study finds that the use of 9 as the terminal digit is a robust retail pricing phenomenon on the Internet as in traditional retail channels. It also finds
evidence that on-line reputation has a significant effect on the price-endings
chosen (i.e., 9-endings) and that more 9-endings result in the firm’s being more
popular (i.e., increased demand). These findings support the proposed quality
image effect theory and price image effect theory. The results provide an interesting
commentary on the proposed theoretical explanations for the different price
endings. Unexpected results were obtained for a hypothesis involving consumer rational inattention in terms of the price length. The results suggest that
other behavioral and operations theories may be fruitful to explore. Finally,
the study found that the use of 9-ending prices varies across Internet selling
formats in a way consistent with differences in the rational attentiveness that
these channels engender in the consumers who use them.
Literature Review
The relevant literatures were reviewed to identify the theoretical and empirical
bases for the application of 9-endings and other price-endings in e-commerce.
The prior work is assessed on the basis of the theories examined, the analytical
methods applied, and the dependent variables used.
Evidence of Frequent Use of 9-Endings
The existing empirical evidence is consistent with a varied characterization of
the use of price-endings. For example, Friedman reported that 9- and 5-ending
prices are particularly popular in the U.S. retail food industry [27]. In his data,
9-ending prices are by far the most popular, followed by prices ending with 5.
Together these two digits, which Friedman calls “magic numbers,” accounted
for more than 80 percent of all price-endings in his data.
Most studies explore price-endings in cents, although one exception is Anderson and Simester, who investigate dollar price-endings in women’s clothing
catalogs [3]. They indicate that retail price-setters in the United States tend
to choose the rightmost digits or endings of a price so that the price falls just
below a round number (i.e., 9¢ or $9), accounting for between 11 percent and
68 percent of the rightmost digits observed. Other studies show that 0¢ and
5¢ are used for the rightmost digits of a price. For example, in the food and
restaurant industries, 5-endings show the highest proportions in high-quality
food prices—for example, 71.0 percent in Kreul’s study and 56.5 percent in
Naipaul and Parsa’s [37, 45].
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LEE, KAUFFMAN, AND BERGEN
From the empirical regularities observed in the previous studies in marketing, the review found that prior studies have mostly explored grocery
products, clothing, foods in U.S. print ads, and supermarket scanner data. It
confirmed the frequent use of 9¢ or $9 price-endings as compared to the other
price-endings.
Explanatory Theories and Empirical Studies
A number of theories have been proposed to explain the popularity of prices
with 9-endings. Among them are operations theory [39], perceived gain effect
theory [54], underestimation theory [54], rational inattention theory [40], and
image effect theory [62]. Table 1 reviews these theories to better understand
the 9-ending price phenomenon on the Internet. The theories indicate that
consumers do not consider prices as a whole; instead some price-endings play
a critical role in consumer choices of products.
A number of empirical efforts have been made to support each theory and
to explain why some price-endings, such as 9-endings, are so popular in retail
prices. Studies based on price images that may affect consumer demand show
inconclusive results. Some studies, as demonstrated in Table 2, find that 9-ending prices tend to increase sales due to low price images [3, 18, 52, 53 ].
In other studies, 9-ending prices do not seem to have this effect [22, 29, 33,
62]. The results differ not only across studies, but also within studies across
product categories. Some studies find negative effects of 9-ending prices as
compared to round prices, thus supporting the quality image effect theory,
while others provide inconclusive results [22, 61].
Table 2 summarizes studies that measure the effects of price-endings on
consumer demand and the drivers that cause such price-endings. These studies used different kinds of data (e.g., grocery scanner data, catalog data, field
data, experimental data), methodologies (field experiments, lab experiments,
simulations, panel data analysis, discrete choice models), and dependent
variables (choice of certain price-endings, actual demand, perception of price
and quality, price recall) to corroborate proposed theories.
Hypotheses and Research Design
The next goal is to explore what may be causing the variation in the use of specific price-endings (especially 9-endings) in Internet-based selling. The effort
to identify the variables that may be the drivers was guided by the literature
in marketing and economics. Internet technologies create new capabilities for
pricing strategy. Since this may change the drivers of firm choices of priceendings and of consumer responses to them, it is necessary to find alternative
theories to explain the observed phenomena.
The choice of theories was mostly driven by two considerations.4 The first
focused on the elements that IT changes (i.e., the Internet, and on-line pricediscovery and price-setting tools). There is increasing evidence that a store’s
on-line reputation has a significant effect on consumer purchases [16, 19].
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Theory
Overview
Operations theory
Based on procedural issues internal to firm that reduce monitoring costs.
Odd pricing was developed to control employee theft from cash registers
by requiring change to be given to customer so the sale was recorded.
Perceived gain effect theory
Rounded numbers are more accessible in memory. Firms exploit high cognitive accessibility with 0 and 5 price-endings. These are reference points
in price evaluation. Consumers frame prices as round numbers along with
a small gain. According to prospect theory, perceptions of gain/loss are
disproportionate to the small size of a perceived gain. This enhancement
of the evaluation of a 9-ending price is the perceived gain effect. Pricesetters should favor 9¢ and $9, as seen in the U.S. retail market.
Underestimation theory
Prices that end in 9¢ or $9 are associated with price underestimation by
consumer. Also called level effect theory and dropping-off theory, underestimation theory states that consumers tend to round or truncate price
numbers down or compare price numbers from left to right due to limited
memory capacity.
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. Thus, firms have an incentive to
make the last digits as high as possible at $9 or 9¢.
Image effects theory
Image effects transmit signals that enable consumers to infer something (in
terms of “images”) about a product or store based on the last digits of the
price. Consumers may think a product with a 99¢ or $99 price-ending is
on sale. These price-endings have two effects: a price image effect and a
quality image effect.
Table 1. Brief Overview of Price-Ending Theories.
Similarly, the costs of attention to price information are affected by IT, and
should affect the degree of inattention exhibited by consumers as well as by
firms [7]. The second consideration focused on variables that can be collected
by Internet-based data-collection tools and other related ITs. This directed attention to observable, detectable, and consistently collectible variables, such as
price or other system-based information (e.g., store ratings) related to Internet
Web sites. This also pushed the focus away from variables and theories that
would more naturally be tested using lab experiments or survey methods. In
particular, two areas of theory are explored in depth: image effect theories (i.e.,
consumers’ perceptions of price and quality images of stores) and the theory
of rational inattention.
Image Effect Theories
Image effect theories posit that consumers may infer something (in terms
of “images”) about a product or a store based on the last digits of the price
[62]. Price-endings may transmit signals affecting consumer purchases either
positively or negatively. Price image effect theory argues that product prices
ending in 9¢, 99¢, $9, or $99 signal that products are on sale, prices have been
cut, or the price is the lowest price [50, 53]. A favorable price image signals a
Data
Catalog data
Scanner data
Field data
Experimental
data
Catalog data
Catalog data
Experimental
data
Experimental
data
Catalog data
Research
Anderson and
Simester [3]
Blattberg and
Wisniewski [11]
Dalrymple and
Haines [18]
Dodds and
Monroe [22]
Ginzberg [29]
Huston and
Kamdar [33]
Lambert [38]
Naipaul and
Parsa [45]
Schindler [50]
UEE
PIE, QIE
UEE
PIE, UEE
PIE
PIE, QIE
PIE
PIE, UEE
PIE, UEE
Theories
LEX (RCL, WTB)
LEX (PRP)
LEX (RCL)
DCM (CPE)
FEX (SAL)
LEX (PRP,
QP, WTB)
FEX, PDA (SAL)
PDA (SAL)
FEX, PPM (SAL)
Methods
(dep. var.)
Poorer memory for odd prices than for even prices
Odd prices indicate no recent price increase and low price image
Firms and consumers use price-endings as signal of quality and value of products
No significant underestimation results
Lower price illusions associated with odd price
Number of digits is positively related to 9-endings
Opposing results to price image effects
Inconsistent results in sales effects of odd pricing
No effect of price-ending on perceived quality, perceived value, and willingness
to buy
Odd prices are positively related to sales
Use of 9-ending prices increase consumer demand
Use of 9-ending prices increases consumer demand
9-ending effect is context-dependent
Findings
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LEE, KAUFFMAN, AND BERGEN
PIE, QIE, UEE
QIE
Newspaper
advertisement
data
Experimental
data
Scanner data
Field data
Experimental
data
Schindler and
Kirby [54]
Schindler and
Kibarian [53]
Stiving and
Winer [62]
Stiving [61]
Thomas and
Morwitz [65]
LEX (PRP, QP)
SIM, DCM (CPE)
DCM, PDM (SAL)
LEX (PRP, QP)
FEX (CPE)
FEX (SAL)
LEX (RCL)
Underestimation effect happens when consumers compare two prices and the
prices being compared are close to each other
More round prices for higher-quality products and high priced firms
Left-to-right processing of digits in a price
Opposing implications of price and quality images in 9-ending prices
99-endings signal low price images and discounts
99-endings have negative effects on quality images
9-endings are less frequent in longer prices
More use of 9-endings for price types where relative potential underestimation
is higher
Use of 99-endings leads to increased consumer purchasing
Underestimation on odd prices than even prices
Consumers may process only leftmost price digit
Notes: Theories: PIE (price image effect theory); PGE (perceived gain effect theory); QIE (quality image effect theory); UEE (underestimation effect theory). Methods: DCM (discrete choice
model); FEX (field experiments); LEX (lab experiments); PDA (panel data analysis); PPM (Poisson probability model); SIM (simulation). Dependent variables: CPE (choice of price-ending);
PRP (price perception); QP (quality perception); RCL (price recall); SAL (actual sales); WTB (willingness-to-buy).
Table 2. Previous Empirical Research on Price-Endings in Retailing Contexts.
UEE
PIE, QIE
PGE, UEE
PIE, UEE
Catalog data
Schindler and
Kibarian [52]
UEE
Experimental
data
Schindler and
Wiman [55]
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low price, leading to a sales increase to stores. In quality image effect theory, on
the other hand, odd prices are a sign of low quality, while even prices indicate
high quality [62]. An unfavorable impression of a store’s or a product’s quality
might occur because of the use of 9¢ or $9 price-endings [37, 53].
With many products in the traditional market, it is difficult for consumers
to observe quality even at the time of purchase because they are imperfectly
informed about the product or store characteristics [60]. On the Internet, information asymmetries also prevail. It is rare for buyers to be able to inspect
product quality before they purchase because buyers and sellers are geographically separated and cannot interact face-to-face when they transact. As a result,
the consumer’s assessment of the actual features or true quality of a product
that is to be purchased on-line may be inaccurate. As is known, the Internet
has affected both price competition and nonprice competition between firms
[17]. On-line consumers may care about seller reputation, delivery locations
and times, contract lengths, and other factors. These may lead to different ways
for consumers to assess store quality and affect the role played by threshold
prices. Thus, Internet-based retailers are concerned with effectively positioning their images.
As with the quality image effect theory, Stiving found, in his study of priceendings at 12 department stores, that retailers with a relatively elegant image,
such as Nordstorm and Macy’s, are the most likely to use prices that end in 0
[61].They also tend to avoid prices that end with a 9 so as not to signal lower
quality. Naipaul and Parsa discovered that 0-endings are preferred by highquality restaurants, whereas 9-endings are more frequently used by mid-level
or low-quality restaurants, such as fast-food restaurants [45]. Anderson and
Simester found that some garment retailers, such as J. Crew and Ralph Lauren,
are more likely to use 99-cent endings for discounted items and primarily
use 00-cent endings for regularly priced items [3]. Consumers may infer that
0-ending prices imply high quality and 9-ending prices imply low quality.
With these ideas in mind, one would expect higher-quality Internet retailers
to exhibit similar pricing.
One advantage of the data-collection approach used in this study is that it
was possible to access information that is systematically collected and posted
on the Web. There are sources of information on store reputation or ratings
that can be used as proxies for store quality. Like prices, there are variants
of this information that can be used [64]. As a result, on-line marketers must
understand the importance of consumer reviews or ratings of their stores as
a proxy for perceptions of the quality of the store [19]. Digital intermediaries,
such as trusted third parties or an on-line reputation mechanism (e.g., BizRate.
com, eBay.com), will play a significant role in building trust between buyers
and sellers to “perfect” business processes associated with Internet-based
transaction making. The hypothesis presented below relates to the effects of
quality images on the use of price-endings by Internet-based retailers:
Hypothesis 1 (Store Quality Image Hypothesis for On-line Reputation). In
Internet-based selling, higher-quality firms, based on on-line reputation,
will choose 9-ending prices less often than lower-quality firms to signal their
higher quality.
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Proxies of store quality accessible to the data-collection agent were sought.
In the context of Internet-based selling, it is likely that shopbots and search
engines have created new pressures that diminish information asymmetries
around product prices, descriptions, and quality [30].
A number of studies in marketing and economics suggest that consumers
use prices as a cue for assessing quality if they are imperfectly informed about
store characteristics [47].5 In general, higher-quality stores tend to have higher
product quality, service levels, product assortment, and support. As such they
may face higher costs or charge higher margins for their products. This may
lead to higher prices at higher-quality stores. A favorable impression of a store’s
quality may occur as a result of a high price image [43]. Stiving found empirical evidence that higher-priced department stores use more round prices than
low-priced department stores when price level is used as a signal of quality
[61]. Varian predicted that Internet-based firms would be grouped in two types:
those with low service levels and low prices, and those with high service levels and
high prices [66] In light of these findings, the following hypothesis is proposed
related to relative store price for Internet-based selling:
Hypothesis 2 (Store Quality Image Hypothesis for Relative Store Price). As
in traditional retailing, Internet-based sellers that charge higher prices will
use 9-ending prices less frequently than lower-priced sellers to signal high
quality.
A number of marketing studies suggest that the rightmost digits of prices
are related to consumer demand [11, 29, 52, 53, 62]. According to the price image effect theory, the use of 9-endings creates the impression of a price that is
relatively low, which may affect sales increases [53, 62]. Anderson and Simester
found interesting evidence through three field experiments that 9-ending prices
increase consumer demand while the effect is context-dependent [3]. They
reported a surprising favorable effect of 9-endings on consumer demand—the
demand for a women’s dress was increased by a third when the price of the
dress was raised from $34 to $39, while the demand showed no differences
when the price was changed from $34 to $44. Since consumer interpretations
of the rightmost digits affect the demand curve, firms have an incentive to use
certain price-endings [62]. As the demand for a product increases, Internetbased firms have an incentive or a capability to more frequently use 9-ending
prices to signal favorable low price images.6 The next hypothesis relates to the
effect of store popularity on the use of price-endings.
Hypothesis 3 (Price Image Hypothesis for Store Popularity). In Internetbased selling, due to the positive price image effects of 9-ending prices, firms
that use 9-ending prices will be more popular.
Rational Inattention Theory
According to the theory of rational inattention, it may be rational for consumers
to be inattentive to the rightmost digit because they are constrained by time,
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resources, and information-processing constraints [40, 58]. Thus consumers
may perceive an actual price of $999.99 as $999 or $990—or possibly even
$900—instead of $1,000. Since many consumers appear to ignore the last digit
of the price, firms have an incentive to make it as high as possible at 9 [57].
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 [8]. The rational
inattention theory of pricing suggests that consumers tend to pay less attention
to the rightmost digits of a 9-ending price. The longer the string of digits in a
price, the more important is the leftmost digit relative to the rightmost one.
Huston and Kamdar found that the number of digits in a price is negatively
related to consumers’ attention to price-endings and positively related to
9-endings [33].
The Internet makes more marketing information available to consumers
than before, which may create greater information overload, amplifying this
effect. At the same time, computers and shopbots make the ability to search
and evaluate information easier, decreasing the impact of this effect. However,
empirical regularities are still observed, and these show that Internet-based
retailers also employ 9-ending prices for many products. To explore the role of
rational inattention in this environment, the following hypothesis for Internetbased retailing is proposed:
Hypothesis 4 (Rational Inattention Hypothesis for Price Length). As with
traditional retailing, as the length of a product price increases in terms of the
number of digits in the price, Internet-based sellers will use 9-ending prices
more often.
Variations are also expected in the level of rational inattention across consumers, categories, and buying contexts. As a result, there might be variations
in the popularity of 9-ending prices across different channels. For example,
in channels where consumers have a limited opportunity to be attentive, one
would expect to see more 9-ending prices. On the other hand, in channels
where consumers have a greater opportunity to be attentive, one would expect
to see fewer 9-ending prices.
The world of Internet-based selling is a transformed environment of
mercantile exchange. 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 competition that is readily available to rivals. As a
result, consumers on the Internet can easily compare prices and trace product
information. In short, processing price information is much less costly on the
Internet. With the use of the Internet as a new channel, and the existence of
shopbots that allow consumers to see different prices side-by-side, consumer
search costs are tremendously reduced [7, 66]. This means that consumers who
shop on the Internet should have better opportunities to use all the digits of
a price in making purchase decisions. Because of the increased incentives to
be attentive to all the digits in a price, one would expect the use of 9 in priceendings to be less clear.
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Although there were no matching price data from Internet and bricksand-mortar (B&M) stores, the study data were able to accommodate a test of
9-ending price variations by comparing prices offered by bricks-and-clicks
(B&C) retailers and pure Internet (PI) retailers. The prices of the B&C retailers were expected to behave similarly to those of traditional B&M retailers.
For a B&C retailer, the Internet does not necessarily reduce managerial costs
for price changes, due to system integration efforts between the retailer’s
Internet channel and its traditional channel [10]. In setting their prices, B&C
retailers have the added constraints of ensuring product, price, and promotion
consistency on-line and off-line.7 Since off-line consumers are rationally less
attentive to the rightmost digits of a price due to their processing limitations
[40, 58], and given the constraint of consistent pricing, B&C retailers should
use 9-endings frequently even for their on-line stores. On the other hand, PI
retailers, who only face on-line consumers, should use 9-endings much less
frequently, because shopbots or search engines may actually flatten some of
the potential behaviors that would be operative through the lens of rational
inattention [40]. Therefore,
Hypothesis 5 (Rational Inattention Hypothesis for the Internet Channel).
B&C retailers will use 9-price-endings more often than pure Internet retailers, since on-line consumers are able to give more attention to all the digits in
a price due to Internet technologies.
Empirical Regularities for Price-Endings on the Internet
The discussion in this section considers the data set and the empirical regularity findings obtained from preliminary analysis to characterize the use of cent
and dollar price-endings in Internet-based selling.
New Internet-Based Selling Price-Endings Data Set
A price information-gathering agent was employed for data collection after
identifying the specific quasi-experimental conditions under which selected
data were able to permit exploration and testing of the targeted theory.8 The
tool was able to systematically mine a large amount of price-related information from a popular price-comparison site, BizRate.com, each day from 3:00
a.m. to 5:00 a.m. From a list of products available at price-comparison sites, a
large sample of unique product IDs was generated using stratified proportionate random sampling [68].9
The product categories provided a setting where the products in the pricecomparison samples were all identical. For each product, the tool built a panel
of selling prices that covered products and stores over time, and qualitative
information (e.g., consumer ratings of stores, number of reviews) also collected from BizRate.com. If a product was on sale, the price data reflected
the sale price. The length of an individual product’s price time-series varied
depending on when the data collection for the specific store ended. This was
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because each product might have had a different lifecycle or each store might
have had different inventory policies.
Since the focus was on the extent to which the technological environment
of Internet-based selling affected firm choices about price-setting strategy, it
was natural to explore how on-line reputation appeared to relate to priceending choices (see Table 3).
To support this exploration, data on the sellers’ on-line reputation were
selected from the original data set. The sample identified stores with a large
enough number of reviews (at least 100) to control response biases. In addition, data were selected from stores that had more than two products so as to
control for product biases, and data from products that had more than two
different sellers, to control for seller biases. All told, the data-collection approach yielded 1,538,872 daily observations for 295 different products sold
by 90 Internet-based retailers. The price range was from $3.99 to $3,670.98,
and the mean values of different product categories varied from $13.46 to
$1,798.88.
Previous price-ending studies focused on 9¢ and 99¢ price-endings because
most prices in retail grocery stores are well below $10.00. However, priceending effects may occur with pricier items too, where cents are less often
used [40]. Therefore, the data set for the study had a wider price range and
thus enabled examination of other price-endings in dollars as well as in cents.
Compared to previous research focusing on prices in print media, catalog
data, and grocery scanner data (e.g., [3, 54, 62]), this study used price data
collected from the Internet to establish some empirical patterns of observed
price adjustment and setting behavior across products, firms, and industries.
Some first-order, easy-to-see empirical regularities will now be examined for
product and category price-endings, specifically, the use of cents and dollars
for price points.
Price-Endings in Cents on the Internet
As expected, the rightmost digits of prices were not evenly distributed among
the 10 possibilities (χ2(9) = 1,865,356; p < 0.001). As the “Total” row of Table 4
illustrates, 9¢, 0¢, 8¢, and 5¢ price-endings were overrepresented, with 38.7
percent for 9¢, 16.3 percent for 0¢, 14.3 percent for 8¢, and 13.9 percent for 5¢.
The other endings—1¢, 2¢, 3¢, 4¢, 6¢, and 7¢—were underrepresented; they
accounted for only 16.8 percent. The round amounts of 0¢ and 5¢ were highly
accessible in memory [6], which may explain their large proportions. These
results were consistent with the patterns observed by Schindler and Kirby,
who used U.S. print advertisement price data [54].
Table 4 also shows the frequency distributions for the last digit of the price
data for each product category. The table shows variation by product category,
but 0¢, 5¢, and 9¢ endings are most common. In some categories, such as music
CDs, movie DVDs, and video games, just a few prices ended with 0¢; instead,
more prices ended with 8¢ and 9¢. The price levels of these categories tend to
be much lower than those of other electronics products, based on the observed
mean prices of CDs, DVDs, and video games at $13.46, $27.07, and $29.84.
261,288
364,132
152,420
187,305
61,797
151,648
65,738
108,167
171,818
14,559
1,538,872
Data
points
44
49
37
29
21
23
27
24
30
11
295
No. of
products
10
14
17
32
30
28
21
42
34
11
90
(239)
No. of
stores
13.46
27.07
29.84
238.07
347.19
364.83
387.21
666.63
692.35
1,798.88
248.24
Mean
price ($)
3.44
27.13
12.32
158.09
193.54
621.95
251.56
535.63
733.54
360.40
460.80
Std.
Dev. ($)
3.99
4.95
4.90
5.80
32.99
39.00
58.99
178.95
85.78
869.95
3.99
Min.
price ($)
26.98
144.99
57.99
1,394.99
842.99
3,670.98
1,099.95
2,999.99
3,010.41
2,998.00
3,670.98
Max.
price ($)
Note: The study sampled sellers with more than two different products and at least 100 consumer reviews, and products with more than two different sellers. Internet retailers have many
different categories of products (e.g., Amazon.com sells books, CDs, computer products, and electronics), so the sum of the number of stores in each category (i.e., 239) is not consistent
with the total number of stores in all product categories (i.e., 90).
Table 3. Descriptive Statistics for Internet-Based Sellers’ Price-Ending Data Set.
Music CDs
Movie DVDs
Video games
Software
PDAs
Hard drives
DVD players
Digital cameras
PC monitors
Notebook PCs
Total
Category
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
139
1.9%
2.0%
1.1%
28.7%
35.5%
25.2%
24.1%
38.0%
33.4%
56.4%
16.3%
2.5%
6.2%
28.3%
40.5%
9.9%
33.7%
261,288
364,132
152,420
187,305
61,797
151,648
65,738
108,167
171,818
14,559
1,538,872
95,762
768,117
599,384
75,609
1,129,415
409,457
0¢
1.3%
1.4%
2.7%
2.1%
1.8%
2.2%
1.0%
1.9%
0.1%
2.3%
4.4%
3.5%
0.0%
1.1%
3.2%
1.7%
1.9%
1¢
1.3%
3.2%
2.4%
2.2%
3.0%
2.1%
0.8%
2.1%
9.6%
2.2%
1.6%
4.2%
0.6%
1.5%
2.7%
1.5%
2.8%
2¢
1.7%
2.1%
2.1%
3.0%
2.2%
1.9%
1.2%
3.4%
0.2%
2.7%
2.6%
3.5%
0.2%
0.8%
2.4%
1.4%
2.1%
3¢
5.4%
3.2%
3.9%
3.0%
3.6%
3.6%
2.2%
5.4%
0.7%
3.4%
3.0%
4.1%
6.8%
3.0%
3.8%
0.2%
3.6%
4¢
12.8%
9.1%
20.5%
12.6%
12.2%
18.7%
4.8%
11.5%
6.8%
23.3%
14.0%
19.4%
12.3%
17.1%
23.2%
8.8%
13.9%
5¢
Last digit in cents (%)
1.9%
3.2%
2.4%
2.5%
2.9%
2.3%
1.8%
4.4%
2.2%
2.8%
3.1%
3.7%
0.2%
1.4%
2.3%
0.3%
2.8%
6¢
2.2%
2.9%
4.8%
3.4%
3.4%
4.3%
2.2%
3.6%
1.9%
4.7%
2.7%
3.8%
12.9%
2.5%
3.6%
3.2%
3.6%
7¢
8¢
22.3%
19.2%
7.6%
8.4%
17.0%
7.0%
32.9%
19.1%
8.1%
7.5%
7.7%
4.1%
4.1%
7.2%
8.4%
17.7%
14.3%
Note: * $248.24 is the average price of products including all product categories. Boldface indicates that the percentage of each price-ending is greater than 10%.
Table 4. Price-Endings in Cents by Product Category, Price Level for Last Digit in Price.
Music CDs
Movie DVDs
Video games
Software
PDAs
Hard drives
DVD players
Digital cameras
PC monitors
Notebook PCs
Total
Price level
Price (P) < $10
$10 ≤ P < $100
$100 ≤ P < $1,000
P > $1,000
P ≤ $248.24*
P > $248.24
Category
Data
points
48.7%
49.5%
25.3%
22.3%
44.0%
24.3%
51.3%
46.6%
69.3%
22.3%
25.4%
28.5%
38.8%
27.5%
16.9%
8.8%
38.7%
9¢
140
LEE, KAUFFMAN, AND BERGEN
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
141
Internet-based retailers appear to use more 8¢ or 9¢ price-endings for
lower-priced products. To get a more complete picture of the empirical regularities associated with Internet pricing, the price-endings were also looked
at by price level as a function of the number of price digits. As the number
of digits increased, the proportions of 8¢ and 9¢ price-endings decreased
(i.e., 71.0% for three digits to 30.7% for six digits), while the proportion of
0¢-endings increased (i.e., 2.5% for three digits to 40.5% for six digits), as
shown in Table 4. About 70 percent of prices less than $100 ended with 8¢ or
9¢, whereas less than 10 percent ended with 0¢. In contrast, when a product
was priced above $100, Internet retailers appeared to use 0¢ price-endings
about 30 percent of the time—relatively more than even the 9¢ price-endings.
These results matched the findings in studies by Heeler and Nguyen and by
Bock Interactive [14, 32]. They showed that 26 percent of products sold at the
Yahoo! Store had price endings of 9¢ and $9, while 37 percent of the products
had 0¢ price-endings.
Next considered was the frequency distribution of the last two digits in
cents. The last two digits of prices were not equally distributed among the
100 possibilities (χ2(99) = 9,047,132; p < 0.001). As reported in the rightmost
column (i.e., “Total”) of Table 5, the 99¢, 00¢, and 95¢ price-endings were overrepresented, with 30.0 percent for 99¢, 13.4 percent for 00¢, and 10.1 percent
for 95¢, followed by 7.9 percent for 98¢. The other endings were underrepresented, accounting for 38.6 percent. These results were different from those
obtained by Schindler, who surveyed multiple product prices at a variety of
stores: 56.8 percent ended with 99¢, 6.3 percent with 97¢, 4.8 percent with 49¢,
4.3 percent with 98¢, and 3.3 percent with 00¢ [50]. The price-ending strategies
of the Internet-based sellers and traditional B&M stores in this studies may
be different. This may have resulted because the study data included higherpriced products, such as notebook PCs, than Schindler’s study.
Similar patterns emerged for prices in individual product categories. Except for three categories—CDs, DVDs, and video games—the 00¢, 95¢, and
99¢-endings occurred most frequently. Overall, 99¢ price-endings were more
common for lower-priced product categories, including CDs at 33.8 percent,
DVDs at 30.0 percent, and video games at 65.7 percent of the total. Among
higher-priced products, though, 00¢ price-endings were more often observed,
with notebook PCs at 55.7 percent, digital cameras and camcorders at 35.4
percent, PDAs at 33.0 percent, PC monitors at 30.0 percent, and software at
26.0 percent of the total.
Price-Endings in Dollars on the Internet
Table 6 considers the distribution of the price-endings in dollars was considered
for all the product categories. Just as in the distribution of price-endings in
cents, the last digits of prices in dollars were not evenly distributed among the
10 possibilities (χ2(9) = 892,121; p < 0.001). As illustrated in the “Total” row of
Table 6, $9 price-endings were overrepresented at 33.6 percent, followed by $4
at 10.8 percent and $5 with 8.9 percent. Thus, more than three times more prices
ended in $9 than with the next most frequent price-ending, $4. Only 5.5 percent
99¢
33.8%
98¢
17.6%
48¢
7.7%
49¢
3.6%
89¢
3.6%
18¢
2.3%
19¢
2.2%
29¢
2.0%
59¢
1.6%
79¢
1.5%
$13.46
1
99¢
30.0%
98¢
12.8%
95¢
7.0%
49¢
3.4%
39¢
3.1%
19¢
1.8%
89¢
1.8%
29¢
1.5%
24¢
1.5%
88¢
1.4%
$27.07
DVDs
99¢
65.7%
82¢
8.8%
88¢
6.5%
95¢
3.3%
05¢
2.4%
96¢
1.8%
97¢
1.6%
98¢
1.4%
49¢
1.0%
79¢
0.7%
$29.84
Video
games
00¢
26.0%
95¢
20.8%
99¢
19.6%
98¢
3.9%
97¢
2.3%
94¢
1.3%
58¢
1.3%
89¢
1.2%
78¢
1.0%
50¢
0.9%
$238.07
SW
00¢
33.0%
99¢
23.5%
95¢
8.0%
85¢
4.0%
98¢
3.9%
88¢
2.2%
41¢
1.9%
71¢
1.4%
94¢
1.0%
84¢
0.8%
$347.19
PDAs
99¢
24.2%
00¢
19.9%
95¢
15.4%
90¢
1.2%
50¢
1.2%
98¢
1.1%
79¢
0.9%
55¢
0.9%
49¢
0.8%
15¢
0.8%
$364.83
Hard
drives
Note: Prices ending with 99¢, 95¢, and 00¢ are indicated in boldface.
Table 5. Ten Highest Frequencies of Last Two Digits of Prices in Cents.
Avg. Price
10
9
8
7
6
5
4
3
2
CDs
Rank
99¢
35.7%
00¢
12.5%
95¢
10.5%
97¢
10.5%
90¢
8.1%
94¢
6.1%
88¢
3.0%
50¢
3.0%
77¢
2.4%
89¢
1.5%
$387.21
DVD
players
00¢
35.4%
99¢
26.4%
95¢
12.4%
98¢
3.9%
85¢
1.9%
88¢
1.7%
97¢
1.7%
94¢
0.9%
59¢
0.8%
40¢
0.7%
$666.63
Digital
cameras
00¢
30.0%
95¢
18.9%
99¢
13.8%
98¢
5.1%
75¢
1.1%
50¢
1.1%
97¢
0.7%
94¢
0.7%
67¢
0.7%
96¢
0.6%
$692.75
PC
monitors
00¢
55.7%
98¢
10.6%
95¢
5.9%
99¢
5.9%
48¢
3.6%
18¢
3.5%
61¢
1.6%
87¢
1.6%
97¢
1.5%
09¢
1.4%
$1,798.88
Notebook
PC
99¢
30.0%
00¢
13.4%
95¢
10.1%
98¢
7.9%
48¢
1.8%
49¢
1.7%
88¢
1.6%
97¢
1.4%
89¢
1.3%
82¢
1.1%
$248.24
Total
142
LEE, KAUFFMAN, AND BERGEN
10.9
12.6
0.5
4.6
3.0
6.9
0.2
1.9
4.6
5.9
6.9
0.0
10.2
4.3
4.1
8.4
3.1
0.0
6.7
4.7
5.7
5.8
4.5
$1
5.8
8.7
0.8
4.8
5.3
5.4
0.7
2.2
7.3
2.3
5.5
$0
0.0
8.9
5.5
3.9
7.7
4.4
10.8
8.2
1.8
5.6
4.9
9.1
2.9
2.5
6.5
1.4
6.8
$2
0.5
11.1
4.9
5.1
8.9
4.5
16.7
8.7
2.7
4.4
3.2
7.9
3.4
2.7
6.9
4.0
7.7
$3
1.5
13.5
9.5
4.9
11.7
8.3
16.2
13.3
5.4
7.7
9.3
9.6
12.1
8.7
7.8
7.3
10.8
$4
4.4
11.1
7.1
7.1
9.6
7.0
13.0
12.3
2.3
7.8
8.1
8.7
3.3
5.4
7.9
3.1
8.9
$5
9.7
6.4
5.9
5.6
6.6
5.6
6.4
8.4
2.7
5.8
3.2
8.4
3.9
6.4
6.3
2.1
6.4
$6
Last digit of prices in dollars (%)
17.4
5.8
6.1
3.8
6.9
5.3
4.5
8.6
6.5
7.3
3.7
8.5
5.2
5.0
5.3
1.9
6.5
$7
Notes: *$248.24 is the average price of products including all product categories. Boldface indicates that the percentage of each price-ending is greater than 10%.
Table 6. Price-Endings in Dollars by Product Category.
Music CDs
261,288
Movie DVDs
364,132
Video games
152,420
Software
187,305
PDAs
61,797
Hard drives
151,648
DVD players
65,738
Digital cameras
108,167
PC monitors
171,818
Notebook PCs
14,559
Total
1,538,872
Price Level
Price (P) < $10
95,762
$10 ≤ P < $100
768,117
$100 ≤ P < $1,000
599,384
P > $1,000
75,609
P ≤ $248.24*
1,129.415
P > $248.24
409,457
Category
Data
points
18.0
4.0
8.9
6.3
6.7
7.6
5.0
5.3
5.5
9.5
5.7
9.9
7.9
9.3
8.0
3.8
6.9
$8
48.6
22.4
43.0
53.7
27.7
49.8
10.7
13.9
72.0
42.4
53.6
25.5
60.4
55.9
39.4
68.2
33.6
$9
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
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144
LEE, KAUFFMAN, AND BERGEN
of prices ended with $0, despite the high cognitive accessibility of this round
amount. This is similar to what Levy et al. found in supermarkets [40].
Table 6 also presents the frequency distributions of the last dollar digit of
price by product category. As expected, $9 price-endings were most common
in almost all categories. In some categories (e.g., CDs and DVDs), $1, $2, $3, $4,
and $5 price-endings were common. Why? Since the prices of these product
categories are often between $11 and $15, $9 price-endings don’t make sense.
In some high-priced categories, more than 50 percent of all prices ended with
$9, including notebook PCs at 68.2 percent, DVD players at 60.4 percent, digital
cameras and camcorders at 55.9 percent, and PDAs at 53.6 percent. In addition, for high-priced products whose prices were greater than the mean (i.e.,
$248.24) of all the product categories, price-endings with $9 were observed
almost 50 percent of the time.
From Table 6 it was found that Internet retailers may use more $9 priceendings for high-priced products. As the number of the digits increased, the
proportion of $9 price-endings also increased (i.e., 22.4% for four digits to
53.7% for six digits), although products less than $10 showed a high proportion of $9 price-endings. This occurred because the prices in the data set were
at least $3. It was appropriate to ignore the observations of $9 price-endings.
A better data set would have tracked prices down to $1, which then might
have affected the results.
The frequency distribution of the last two digits in dollars was also analyzed
(see Table 7). The last two digits of prices in dollars were not equally distributed
among the 100 possibilities (χ2(99) = 1,775,631, p < 0.001). As reported in Table
7, most prices had 9-endings, such as $99, $89, or $09. But more prices ended
with $99 than any other 9-endings, and almost 8 percent ended with $99.
Similar price-ending patterns were found from individual product categories. The $99 price-ending was more common than any other two-digit priceending, including the other $9-endings. The exceptions were CDs, DVDs, and
video games, whose prices were overall far less than $100. As reported in the
rightmost column (i.e., W/O 3 Cats.) of Table 7, after these three categories
were removed, all the $9 price-endings were included among the top 10 frequencies of the last two price digits. These findings were in sync with Levy
et al., who found this to be true for cents [40].
Variations in Use of 9-Ending Prices Across Channels
To examine variations in consumers’ rational inattention and firms’ choice of
price-endings in different channels, the data were divided into two categories
of retailers—bricks-and-clicks (B&C) and pure Internet (PI). The resulting count
was 66 PI retailers and 24 B&C retailers among the 90 stores in the data.
Based on this classification, it was found that B&C retailers used 9-endings much more frequently than PI retailers. Specifically, for the cent digit,
although 9 was the most popular price-ending for eight product categories
(except for DVD players and Notebook PCs) and accounted for 76.2 percent
of all prices for the B&C retailers, it was much less popular for the PI retailers. 9-Ending price was also the most popular ending for only four product
$13
16.1%
$14
15.5%
$15
11.8%
$12
9.9%
$11
9.9%
$09
8.2%
$10
5.6%
$16
3.9%
$08
3.2%
$07
3.0%
CDs
$15
9.3%
$11
8.2%
$14
7.2%
$09
6.5%
$10
6.2%
$16
5.2%
$13
5.2%
$22
3.7%
$12
3.4%
$19
3.2%
DVDs
$19
31.9%
$29
18.6%
$49
15.6%
$39
4.5%
$17
3.1%
$23
1.8%
$48
1.8%
$28
1.7%
$14
1.7%
$24
1.6%
Video
Game
$99
17.0%
$89
5.6%
$79
4.7%
$98
3.2%
$49
2.7%
$59
2.7%
$09
2.4%
$19
2.3%
$97
2.2%
$69
2.1%
SW
$99
14.0%
$49
9.7%
$19
6.9%
$79
4.7%
$59
4.4%
$39
4.2%
$69
3.5%
$89
2.8%
$29
2.6%
$34
2.5%
PDA
$99
3.6%
$59
3.4%
$19
2.8%
$29
2.6%
$89
2.5%
$39
2.4%
$69
2.3%
$09
2.1%
$49
2.0%
$79
1.9%
Hard
drive
$99
13.7%
$49
9.5%
$19
6.8%
$69
6.0%
$39
5.5%
$89
5.2%
$29
4.6%
$44
3.3%
$59
3.1%
$79
3.1%
DVD
player
$99
25.1%
$49
8.5%
$89
3.9%
$79
3.9%
$69
3.7%
$29
3.0%
$98
2.4%
$39
2.2%
$09
2.1%
$59
1.9%
Digital
camera
$99
13.7%
$49
4.8%
$29
4.7%
$59
3.1%
$39
3.0%
$19
2.5%
$79
2.2%
$69
2.1%
$44
1.8%
$09
1.7%
PC
monitor
$99
51.8%
$94
5.4%
$29
5.0%
$49
3.6%
$33
3.6%
$61
3.5%
$69
2.0%
$09
1.8%
$79
1.8%
$39
1.6%
Notebook
PC
$99
7.6%
$19
5.8%
$14
4.9%
$15
4.5%
$13
4.2%
$49
4.2%
$09
4.1%
$11
3.8%
$29
3.7%
$12
2.9%
Total
$99
14.8%
$49
5.0%
$89
3.5%
$79
3.3%
$29
3.2%
$19
3.1%
$59
3.0%
$69
2.8%
$39
2.6%
$09
2.0%
W/O 3
Cats.
Note: Prices ending with $9 are indicated in boldface. The rightmost column shows the results after three low-priced product categories (CDs, DVDs, and video games) are left out.
Table 7. Ten Highest Frequencies of the Last Two Digits of Prices in Dollars.
10
9
8
7
6
5
4
3
2
1
Rank
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
145
146
LEE, KAUFFMAN, AND BERGEN
categories, accounting for only 29.8 percent of all prices (76.2% > 29.8%,
p < 0.001). Similarly for the last two digits of a price, although 99¢ was the
most popular price-ending for all but two product categories and accounted
for 74.7 percent of all prices for B&C retailers, it was the most popular ending
for only four product categories and accounted for 19.4 percent of all prices
for PI retailers (74.3% > 19.4%, p < 0.001).
Similar results emerged for the dollar digit. Although $9 was the most
popular price-ending for all but one product category for the B&C retailers and
two for the PI retailers, it accounted for 48.1 percent of all prices among B&C
retailers, but only 30.2 percent among PI retailers (48.1% > 30.2%, p < 0.001).
For the last two dollar digits, $99 was the most popular price-ending for five
product categories for both retailers. It accounted for 9.2 percent of all prices
among B&C retailers, but only for 7.1 percent of all prices among PI retailers
(9.2% > 7.1%, p < 0.001). If the three low-priced product categories (i.e., CDs,
DVDs, and video games) whose prices were mostly less than $50 are excluded,
the $99-endings accounted for 34.9 percent of all prices among B&C retailers
but only for 12.6 percent among PI retailers (34.9% > 12.6%, p < 0.001).
If the last three digits of a price are considered, then $9.99 was the most
popular ending for all but two product categories (CDs and DVD players)
and accounted for 36.2 percent of all prices among the B&C retailers. It was
the most popular for only three product categories, though, and accounted
only for 7.8 percent of all prices among PI retailers (36.2% > 7.8%, p < 0.001).
Finally, for the last four digits, $99.99 was the most popular ending for four
product categories and accounted for 6.4 percent of all prices among B&C
retailers. It was the most popular for only one product category, accounting
for 1.6 percent of all prices among PI retailers. (It was not the most popular
price-ending for the different kinds of retailers. $99.99 ranked behind $19.99
among B&C retailers and behind $99.00 among PI retailers as the second
most popular ending for PI retailers; 6.4% > 1.6%, p < 0.001.) Again, if three
low-priced product categories are excluded, $99.99 was the most popular
price-ending (i.e., 22.1%) among B&C retailers but still ranked behind $99.00
among PI retailers (2.9%). Results at the individual category level showed
similar differences across the two channels.
Summary of Empirical Regularities in Price-Endings
The most popular terminal digit overall was 9, although there were some
cross-category variations in the popularity of 9-endings. The popularity of
9-endings is not limited to the cent digit; it also occurs for the dime, dollar,
and 10-dollar digits. Because the study data included a variety of products
with a wide range of prices and with different retail formats, it is reasonable
to conclude that the use of 9 as the terminal digit is a robust retail pricing
phenomenon on the Internet just as in traditional retail channels.
Use of 9-ending prices was found to vary across Internet selling formats consistently with differences in the rational attentiveness these channels engender
for consumers who use them. The results matched the explanation of 9-ending
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
147
prices based on rational inattention and the argument that the Internet may
offer consumers the opportunity to be more attentive to price-endings.
Research Methods and Empirical Estimation
The variables used in the model, and the empirical estimation and results, are
presented in detail below.
Defining the 9-Ending Variables
Binary coding was used to determine which drivers are the most important in
explaining the variations of price-endings in e-commerce. Each of the prices
collected in the study was coded as either a 9 or a not-9. The prices were the
actual selling prices without any shipping or handling fees, adjusting any
promotions or discounts. Although it was not difficult to figure out what
9-ending prices are supposed to look like, it was difficult to provide a precise
definition of 9-ending prices, as Stiving argued in defining round numbers in
his study [61]. Consumer perceptions of 9-ending prices may be different for
different price levels of the products. For example, $1,999.00 may be a 9-ending
price, just as $10.99 would be. Since the data set had a wide range of prices, an
attempt was made to identify four different categories of 9-endings.
First, the last digits of prices in cents were looked at to classify price endings.
Nine1 was defined as a 1 for 9¢-endings and a 0 for the others. For example,
$1,000.09 was coded as 1, while $99.90 was coded as 0. Nine2 was defined as
a 1 for $9-endings and a 0 for the others. For example, $1,999.00 was coded
as 1, while $10.99 was coded as 0. Then, another price-ending variable was
created, Nine3, depending on the last two digits of prices in cents. Nine3 was
coded as 1 for 99¢-endings and 0 otherwise. Thus, both $1,000.09 and $99.90
were coded as 0 in this classification because they were not 99¢-endings. A
more restricted price-ending variable was also created, Nine4, depending
on the length of prices to check whether a price was a round number or just
below the round number. Nine4 was defined as 1 if each digit of the last n – 1
digits of a price (whose length is n) was composed of 9, and 0 otherwise. For
example, in this classification, $199.99 was a 9-ending price (i.e., just below the
round number). Finally, another price-ending variable was created, Nine5, by
relaxing the definition of Nine4. Nine5 was defined as 1 if the last n – 2 digit(s)
of a price were totally composed of 9, and as 0 otherwise. For example, in this
classification, $2.09 and $20.99 were 9-ending prices, while $9.90 and $29.00
were not.10
Given the definitions of the different 9-endings, two additional types of
variables were also generated—the frequencies (i.e., FNine1, FNine2, FNine3,
FNine4, FNine5) and proportions (i.e., PNine1, PNine2, PNine3, PNine4, PNine5)
of the 9-endings in a given store (see Table 8).
The frequency variables were used as dependent variables for a discrete
choice model—grouped logit to estimate the choices between 0 and not-9
Number of digits in price, excluding decimal point
Control variable indicating price of product at given time
Binary dummy variable indicating store is in category of B&C
channel; coded as 1, and 0 otherwise (i.e., PI channel)
Frequency of Nine1 at given store
Frequency of Nine2 at given store
Frequency of Nine3 at given store
Frequency of Nine4 at given store
Frequency of Nine5 at given store
Proportion (%) of Nine1 at given store
Proportion (%) of Nine2 at given store
Proportion (%) of Nine3 at given store
Proportion (%) of Nine4 at given store
Proportion (%) of Nine5 at given store
Total price observed at given store
PriceLength
PriceLevel
Channel
FNine1
FNine2
FNine3
FNine4
FNine5
PNine1
PNine2
PNine3
PNine4
PNine5
Obs
Rational
inattention
9-ending
Proportion of
9-ending
Observation
30.09
45.60
26.80
12.70
23.49
6,620.50
5,744.96
5,136.32
1,750.97
4,616.11
4.43
248.24
0.28
0.96
9.32
0.00
Mean
17,098
Table 8. Definitions and Summary Statistics of Key Variables in Empirical Model.
RelPrice
Popularity
Latent variable measuring consumers’ perceived store
quality (or satisfaction) when they finish ordering products
Store’s relative price for each store-category combination
Store popularity, based on number of reviews store received
from consumers—ln(Number of reviews)
StoreRating
On-line
reputation
Definition
Variables
Constructs
20,784
35.85
29.63
35.68
23.95
33.33
13,338.58
6,623.60
10,768.45
3,498.63
10,276.93
0.68
460.79
0.45
0.25
2.48
1.00
Std. Dev.
1,008
0.00
0.07
0.00
0.00
0.00
0
1
0
0
0
3
3.99
0
0.27
4.61
–5.50
Min.
102,468
100.00
100.00
100.00
94.08
100.00
61,005
39,267
58,829
19,360
58,182
6
3,670.98
1
1.79
13.82
1.24
Max.
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LEE, KAUFFMAN, AND BERGEN
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
149
price-endings. On the other hand, the proportion variables were used for a
two-stage least squares (2SLS) regression to explore the impact of 9-ending
choices on a store’s Popularity.
Data Aggregation and Defining Variables
To measure the variables (i.e., 9-ending prices) at the store level, each observation was averaged over a group of original panel data observations, resulting
in a proportion. The aggregated variables used in the empirical model are
presented in Table 8. Since the focus was on the extent to which the technological environment of Internet-based selling affects firm choices about pricesetting strategy, it was natural to explore how on-line reputation appeared to
relate to price-ending choices. To measure the quality of the store, the study
used consumer ratings collected from BizRate.com once a week.11 BizRate
provides 15 different kinds of ratings of store quality based on consumers’
purchase experiences. Among the ratings, two overall satisfaction measures
(i.e., Overall rating and Would shop here again) may also contain consumers’
price-related perceptions, so these ratings cannot correctly evaluate the quality of a store due to confounding effects of price and quality measures. BizRate
also provides two different categories of ratings for consumer satisfaction—
pre-ordering satisfaction and post-fulfillment satisfaction. Because pre-order
ratings also include price-related instruments (e.g., Prices relative to other
on-line merchants, Shipping charges, and Variety of shipping options), pre-order
ratings were separated out from these rating instruments.
Measures for store quality were derived by conducting an exploratory
factor analysis for the remaining instruments using principal components
analysis as the extraction technique. The factor analysis found a one-factor
structure with five items loading with an eigenvalue greater than 1.0 (i.e.,
4.362) that accounted for 87.23 percent of the total variance. Consequently,
the instruments selected from the BizRate ratings showed adequate validity
for further analysis. Additional tests were conducted for internal consistency
reliability by applying Cronbach’s alpha test to the individual scales and the
overall measure. As the alpha value of 0.954 for the derived construct was
greater than the guideline of 0.70, it was concluded that the scales could be
applied for the analysis with acceptable reliability. Then the correlationpreserving factor scores were estimated separately for each construct using
the items from the Anderson and Rubin method [4, 5] to derive a latent
explanatory variable—StoreRating.12
In addition, another explanatory variable was defined, RelPrice, as a relative measure of the prices charged by each store. Some retailers sell many
different categories of products, so an average price was determined for
each category-store combination.13 The number of reviews was used as a
proxy for store Popularity because the greater the number of reviews, the
more products the store had sold and the more customers it had served.
In the data set, the values of the number of reviews lay between 100 and
1,000,000. The number of reviews was transformed to reduce outlier effects
with a logarithm. A large number of reviews, as one might expect, should
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LEE, KAUFFMAN, AND BERGEN
StoreRating
RelPrice
Popularity
Channel
StoreRating
RelPrice
Popularity
Channel
1.000
0.026
0.360
0.137
1.000
0.035
0.116
1.000
0.353
1.000
Table 9. Correlations Among Key Variables.
not have had the same proportional effect on price-ending decisions as a
small number of reviews.14
Finally, to measure the extent of consumer rational inattention and differences of rational attentiveness across channels, PriceLength was defined as
the number of price digits excluding the decimal point, PriceLevel as a control
variable indicating the price of a product in a given time period,15 and Channel
as a binary dummy variable indicating whether a store was in the category
of B&C or PI channels. The summary statistics for the variables used in this
study are also provided in Table 8.
Pairwise correlations between the explanatory variables were examined,
as reported in Table 9.16
The highest observed absolute value of any pairwise correlation was 0.360,
which was below the frequently used threshold of 0.6 [31]. Variance inflation factors (VIFs) were also calculated to detect multicollinearity among the
explanatory variables. The highest VIF was 1.301, which was far below the
threshold of 10 [31]. Thus, there was no evidence of these problems.
Empirical Models
Internet-based retailers are assumed to be profit maximizers with respect to
choices between 9 and not-9 price-endings. This decision can be modeled with
a binary choice model. Specifically, the effects of Internet-based sellers’ images
in terms of quality and consumer’s rational inattentiveness across channels
were explored employing a grouped logit model, which is an appropriate model
for analyzing proportions data. This model is similar to the more familiar
techniques (logit, probit, and logistic regression) for binary choice data. One
way to think about the grouped logit is in terms of a method that uses data
representing proportions of observations to estimate individual-level decisions, where the dependent variable is a binary choice and the independent
variables are attributes of the decision-maker [31]. The relationship between
a select group of explanatory variables and the related price-ending choices
was analyzed. The disturbance terms in a logit model may exhibit heteroskedasticity. The study used maximum likelihood estimates (MLE) to correct the
coefficient estimates for this defect.
The probability that a particular store i will choose a 9-ending for the price
of a product was defined as P(Nine = 1) = ez/(1 + ez), where Z is a linear equation of the variables that may affect the firm’s decision:
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
Pi = E ( Ninei = 1 X ) =
1
1
eZ
=
=
,
−Z
− β +β X
1 + eZ
1+ e ( 0 1 ) 1+ e
151
(1)
where
Z = β0 + βStoreRating StoreRatingi + β RelPrice RelPricei + βChannel Channeli .
The odds ratio can be written as Pi /(1 – Pi ), the ratio of the probability that
a firm will choose a 9-ending to the probability that it will not. Applying the
natural log results in the logit, Li:
⎛ P ⎞
Li = ln ⎜ i ⎟ = Zi
⎝ 1 − Pi ⎠
= β0 + βStoreRating StoreRating i + β RelPrice RelPricei + βChannel Channeli + ui .
(2)
To estimate the logit regression, it was necessary to compute the price-change
probability, P{i = FNinei/Obsi , for each store i to obtain Li{ = ln(P{i /(1 – P{i ).
Then a logit model was also employed to explore the effects of consumers’
rational inattention in terms of price length with the control of price levels
across products. This model defines the probability that a firm i will choose a
9-ending for product j on a specific day t as:
(
)
P Ninei , j ,t = 1 = β0 + β PriceLength PriceLengthi , j ,t
+ β PriceLevel PriceLeveli , j ,t + ui , j ,t .
(3)
The explanatory variables—PriceLength and PriceLevel—do not vary across
time by product. As a result, if these variables are used across repeated observations, this may tend to inflate the standard errors that are reported. Thus, to
adjust the standard errors for repeated observations, the data were clustered
at the product level to derive robust standard errors.
Finally, to explore the effects of Internet-based sellers’ images in terms of
price, 2SLS regression was employed at the store level with Popularity as the
dependent variable and the proportion of 9-endings (PNine1, PNine2, PNine3,
PNine4, and PNine5) as the explanatory variable. An instrumental variable
was included because evidence was found for endogeneity in the relationship between the Popularity and 9-endings variables (PNine1, PNine2, PNine3,
PNine4, and PNine5).17
Empirical Estimation and Results
Stata 9.0 (www.stata.com) was used to estimate the empirical models developed in the preceding section. Table 10 shows the coefficients of the variables,
and the odds ratio that measures the simultaneous effects on the other variable
in the grouped logit model. In binary choice regression, however, goodness of
fit is of secondary importance. There is no universally accepted goodness-of-fit
0.980
0.616
4.949
N/A
O/R
1,897,286
0.076
–0.0204***
–0.4839***
1.5992***
–1.3036***
Coeff.
FNine1
O/R
0.921
0.535
2.265
N/A
1,920,753
0.022
–0.0822***
–0.6255***
0.8174***
–1.4911***
Coeff.
FNine2
890,696
0.124
–0.0619***
–0.2246***
1.9637***
–3.1274***
Coeff.
FNine3
0.940
0.799
7.126
N/A
O/R
0.793
0.124
8.991
N/A
O/R
1,592,948
0.153
–0.2318***
–2.0875***
2.1962***
–3.5066***
Coeff.
FNine4
0.831
0.372
9.598
N/A
O/R
1,510,166
0.159
–0.1856***
–0.9881***
2.2615***
–2.6080***
Coeff.
FNine5
Notes: ML estimation. 1,538,872 data points. Dependent variables: FNine1, FNine2, FNine3, FNine4, and FNine5. Significance levels: *** p < 0.01; ** p < 0.05; * p < 0.10.
Table 10. Results of Grouped Logit Models.
–2LL
Pseudo R 2
StoreRating
RelPrice
Channel
Constant
Indep.
Var.
Dep. Var.
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LEE, KAUFFMAN, AND BERGEN
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
153
measure for binary choice models, unlike R2 for linear regression. What matters
is the expected signs of the regression coefficients and their statistical, logical,
and practical significance [31].
Consistent with the Store Quality Image Hypothesis for On-line Reputation
(H1), the effects of StoreRating were significant and negative for all the different 9-endings. The results indicate that Internet sellers with high reputation
use fewer 9-endings than other price-endings.
The relationship between the relative store prices (i.e., RelPrice) and 9-ending
decisions was assessed. The effects of RelPrice on the choice of 9-endings was
also negative and significant in support of the Store Quality Image Hypothesis
for Relative Store Price (H2). This suggests that Internet-based sellers that
charge higher prices will use a 9-ending less frequently than lower-priced
sellers to signal their high quality.
The effects of Channel on the choice of 9-ending prices were positive and significant in each model for the different dependent variables.18 This is consistent
with the Rational Inattention Hypothesis for the Internet Channel (H5), which
states that B&C retailers use 9-ending prices more frequently than PI retailers
because B&C retailers are almost like B&M retailers in setting their prices.
The next step was to assess rational inattention via the estimated coefficients
of PriceLength in each model. Table 11 shows the coefficients and odds ratio
in the models. As predicted by the Rational Inattention Hypothesis for Price
Length (H4), the estimated PriceLength variable should be positive, and this
was true for in $9-ending model (i.e., Nine2). However, PriceLength showed
significant and negative effects on most of the 9-ending price decisions (i.e.,
Nine1, Nine3, Nine4, Nine5), the opposite of the prediction.
Finally, the relationship between 9-ending prices and store popularity was
also examined, as reported in Table 12. As discussed earlier, the 2SLS regression
was conducted to obtain constant and unbiased coefficients using an instrument variable, RelPrice, which was highly correlated with the proportion of
9-endings but was correlated with the dependent variable, Popularity.
A positive effect was found for 9-ending prices on Popularity, as reported
in Table 12. This provided marginal support for the Price Image Hypothesis
for Store Popularity (H3). Retailers on the Internet that use 9-ending prices
more frequently may create an impetus for additional consumer purchases.
Data on actual sales may be needed to nail down whether purchases actually
are observed to increase, though.
Discussion
The study began by examining some first-order, easy-to-see empirical
regularities for product, category, and channel price-endings present in the
Internet-based selling price data. These were compared with other well-known
empirical regularities for price-endings that were observed among traditional
B&M retailers, based on the existing literature. This helped to set up the study
for additional exploration in order to gain an understanding of the observed
variations in price-endings.
O/R
0.484
1.000
N/A
1,974,739
0.039
–0.726***
0.000***
2.720***
Coeff.
Nine1
1,917,140
0.024
0.134***
0.001***
–1.445***
Coeff.
Nine2
1.143
1.001
N/A
O/R
O/R
0.648
1.000
N/A
1,853,954
0.014
–0.435***
0.000***
1.059***
Coeff.
Nine3
938,597
0.078
–1.371***
0.001***
3.492***
Coeff.
Nine4
0.254
1.001
N/A
O/R
O/R
0.313
1.0003
N/A
1,658,308
0.076
–1.162***
0.0003***
3.973***
Coeff.
Nine5
Notes: ML estimation. 1,538,872 data points. Dependent variables: Nine1, Nine2, Nine3, Nine4, and Nine5. Robust standard errors assume clustering at product level. Significance
levels: *** p < 0.01; ** p < 0.05; * p < 0.10.
Table 11. Results of Logit Models.
–2LL
Pseudo R 2
PriceLength
PriceLevel
Constant
Indep.
Var.
Dep. Var.
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LEE, KAUFFMAN, AND BERGEN
3.672***
8.212***
3.98***
0.0904
Popularity
Constant
F(1, 88)
Adjusted R 2
2.545
0.782
S.E.
2.28*
—
5.035***
7.021***
Coeff.
S.E.
6.56***
0.0934
5.919***
8.565***
Coeff.
PNine3
4.504
0.561
S.E.
5.94***
0.0541
4.813***
8.027***
Coeff.
PNine4
3.744
0.997
S.E.
Notes: Model = two-stage least squares (2SLS). Instrument: RelPrice. N = 90. Dependent variable: Popularity. Signif.: *** p < 0.01; ** p < 0.05; * p < 0.10.
17.578
8.090
PNine2
Table 12. Results of 2SLS Regression Models.
Coeff.
PNine1
Indep.
Var.
Indep. Var.
4.56***
0.1043
3.543***
8.485***
Coeff.
PNine5
2.497
0.596
S.E.
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
155
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LEE, KAUFFMAN, AND BERGEN
On the whole, the study found that Internet-based retailers appear to frequently use 9¢ and $9 price-endings, just as traditional B&M stores do. This is an
interesting finding in that firms today are able to flexibly manage and optimize
prices by reducing managerial costs and menu costs through the intensive use
of IT [10]. Consumers on the Internet can easily compare prices, as well as trace
product information through price-comparison sites or search engines. The
technology itself provides a basis for consumers to be able to achieve a higher
level of attention to price, albeit without a concomitant higher level of effort—if
they choose to use the technology. So firms’ price-ending behaviors was further
analyzed across different channels on the Internet—bricks-and-clicks and pure
Internet channels—with the idea that the price-setting behavior of B&C retailers
is quite similar to that of traditional B&M retailers due to the extent of consumers’ rational attentiveness. An implication of rational inattention is that the
popularity of 9-endings may vary across channels depending on the degree of
rational inattention by consumers. Therefore 9-endings should be more popular
in B&C stores than for PI retailers. It was also found that use of 9-ending prices
varied across Internet selling formats consistent with differences in the rational
attentiveness the channels engender in the consumers who use them.
The study also explored and analyzed the drivers of the observed variations
in the use of 9-ending prices across retailers, product categories, and channels.
On the whole, the evidence suggests that 9-ending prices are not set as often
by higher-quality stores in terms of their on-line reputations and relative price
levels. This lends support to the theories suggesting that 9-ending prices are
associated with lower-quality offerings in the marketplace [61]. Although
9-ending prices may be just one element of a firm’s pricing decision, strategicpricing managers at Internet-based retailers should be wary of the signals associated with threshold pricing approaches, especially if they are competing
in the marketplace on the basis of quality. There was also evidence that more
9-ending prices are set by more popular firms, although the proxy for store
popularity, the number of reviews, may have limited information covering
the real consumer demand related to specific stores. Thus, the results of image
effects theories point out that strategic-pricing managers need to balance their
efforts between generating a favorable image in price competition and making
sure to achieve an image of high-enough quality so as to be well positioned for
their core markets [3].
It should be pointed out that this study only obtained mixed support for the
Rational Inattention Hypothesis for Price Length (H4). The price length did not
hold for the prediction with most of the 9-ending prices in the data set. One
may speculate whether this was due to other behavioral considerations that
arise with consumers. For example, Schindler and Kirby argue that with longer
price length, one should expect there to be a decrease in the relative size of the
one-unit differences between a 9-ending price and a 0-ending price [54]. The
latter is likely to be used as a consumer’s reference price for the product. They
provide the following example: A one unit difference between the two-digit
prices of $39 and $40 is 2.5 percent of $40. The one unit difference between the
three-digit prices of $439 and $440 is only 0.23 percent of $440. Kauffman and
Wood make a similar argument in terms of the proportional differences that
occur in follow-the-leader pricing in e-commerce [34]. Consumers may perceive
smaller gains as the number of digits in a price increases, so price-setters may
INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE
157
be less likely to favor the use of 9 price-endings in cents [54]. This may also
be due to the costs of the firm of price adjustment, suggesting that firms also
choose to be rationally inattentive to cents. This rational inattention explanation offers a heuristic to handle the complexity of pricing for the Internet-based
retailer. Exploring these issues in terms of marketing and economic theory is an
interesting direction for IS research, and creates the possibility for obtaining a
blend of results to inform professionals in different disciplines.
Conclusion
The results of this study highlight the opportunities that arise from the new
kinds of data being generated from price information-gathering agents. At one
time, the empirical findings in this area were largely based on small numbers
of prices from carefully constructed data sets [3, 11, 53]. Many of the authors of
the studies actually collected their data by hand; they had no other alternative
[54, 61]. The advent of scanner data sets allowed studies of 9-ending prices
on a larger scale [40, 62].
Today, sophisticated software tools for data collection allow an assessment
of the patterns of price-endings with a scale and scope unimagined by previous generations of researchers [2, 34]. In the present research, the focus was
on the use of 9-ending prices by Internet-based retailers. Clearly, however, the
application of these tools for collecting interesting new data sets extends far
beyond this narrow topic. Indeed, massive quasi-experimental data mining
of the kind implemented in this study, subject to the appropriate analysis,
has the capacity to answer questions that many marketing and operations
analysts have never thought to ask. As a result, the theories of price points,
price rigidity, price dispersion, asymmetric pricing, asynchronization, and
other issues seem especially apropos for analysis with price-gathering software agents [9, 28, 46].
This is not to say that the analysis is without limitations. The data obtained
on prices for consumer electronics, music, videos, and so on, are stunning in
their demonstration of the scale of price information that has become available. These data made it possible to assess the use of 9-endings across a wide
variety of products, product categories, stores, and time periods. However,
data on related issues, such as sales volume, operating costs, and wholesale
prices—unfortunately!—do not become so “magically” available using these
methods. The two theoretical arguments—the image effect theory and the
rational inattention theory—start from rather opposing behavioral assumptions. For example, the image effect theory assumes that consumers do pay
attention to the last digit of the price, while the rational inattention theory
does not. The important question is whether 9-ending prices matter in terms
of sales. Unfortunately, the present study cannot answer this question due
to data limitations. Data on actual sales are needed to nail down whether
purchases actually increase. Nor is there direct information about customer
perceptions, attitudes, and information processing, as Kauffman and Wood
have recognized [35]. This suggests that data-collecting agents (still) are best
suited to test theories that lead to direct implications of pricing patterns across
products, categories, stores, or time.
158
LEE, KAUFFMAN, AND BERGEN
To go substantially beyond these questions, the data available from the
Internet will need to be supported by additional data on the firms that set the
prices, especially their costs and managerial policies with respect to the use of
technology for the production of prices [24]. Another interesting avenue, also
recently discussed in detail by Kauffman and Wood, and demonstrated by
Doong et al., is to conduct a laboratory experiment [23, 35]. This will permit
the development of experimental research designs and treatments that will
enable a theorist to study more precisely how explanatory theory translates
into predictive power for understanding consumer behaviors [41, 67]. Thus,
one may expect that it will be possible to create experimental setups involving
consumers on the Internet with similar demographics, where other factors can
be manipulated. For example, this could involve the same goods at different
prices or with different price-endings, so that it will be possible to observe the
choice process used by the consumer. Stating the position optimistically, there is
a wide range of theories that can be explored (e.g., behavioral and operational
theories) and are likely to give e-commerce researchers and pricing managers
alike more of a grip on the inner workings of consumer decision-making. The
present study is only the beginning of an exploration of the differences between
these theories based on the Internet versus the bricks-and-mortar worlds. In
future work, we hope to design and implement critical tests to understand
which theories work better to explain the observed findings.
ACKNOWLEDGMENTS
The authors contributed equally to this research. An earlier version of this paper was
presented at the 9th INFORMS Conference on Information Systems and Technology,
and portions of it were subsequently presented at the University of Minnesota, Arizona State University, Michigan State University, University of Texas Pan American,
Korea University, and National Sun Yat-Sen University, as well as at the doctoral
consortium of the 2005 American Conference on Information Systems. The authors
thank the editor-in-chief and the anonymous reviewers at the International Journal of
Electronic Commerce for their useful input and suggestions. The authors thank the
anonymous reviewers for CIST 2004, as well as the co-chairs, Chris Forman, Hemant
Bhargava, and D.J. Wu, for their helpful input on an earlier version of this paper. We
further appreciated the comments of Barrie Nault, Sri Narasimhan, Rahul Telang,
and Sunil Mithas for their helpful suggestions. The authors also thank Daniel Levy,
Allan Chen, and Fred Riggins for their helpful comments on related work and their
encouragement. Dongwon Lee’s work was supported by a Research Grant from Korea
University and a Doctoral Support Award from the e-Business Research Center of Penn
State University. Rob Kauffman’s work was supported in part by the MIS Research
Center, the Center for Advancing Business Through Information Technology, and the
W. P. Carey Chair in Information Systems at Arizona State University. All errors of fact
and interpretation are the solely the authors’ responsibility.
NOTES
1. The reason the study focused on the United States is that the price points
phenomenon is known to be affected by cultural and other issues. For example,
Rátfai reports that more than 45 percent of the Hungarian retail prices in his data
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ended with 0; less than 20 percent of the prices ended with 9 [48]. Similarly, Konieczny and Skrzypacz note that 9-ending prices are particularly popular in the United
States and Canada [36]. However, they are scarce in Spain, Italy, Poland, and Hungary, where round prices are common. Fengler and Winter report similar results
for German data, and Mostacci and Sabbatini for Italian data [25, 44]. In contrast,
in Asian countries, including Malaysia, Hong Kong, Singapore, Japan, and China,
Heeler and Nguyen find an unusual overrepresentation of 8-endings [32].
2. See Shapiro and Monroe for reviews of earlier literature, and Anderson and
Simester for a review of recent literature in marketing [3, 42, 56].
3. Bock et al. find that the maturation of Internet markets also leads to lower
prices and price dispersion [13].
4. The reasons why the research model excluded some other theories discussed
in the preceding section are as follows. First, Internet technologies obviate the need
to charge odd prices; transactions now are made with credit cards and on-line payment services (e.g., PayPal, MSN BillPay, iTransact). For this reason, the operation
theory was excluded from the model. Second, in Internet-based selling, customers
use shopbots to locate the cheapest products in 1¢ increments. Instead of round
numbers, consumers may focus on the lowest reported prices. Thus, the perceived
gain effect theory may not explain the phenomenon of 9-ending prices in on-line
markets very well. Third, underestimation theory is similar to rational inattention
theory because the two theories emphasize consumer constraints on memory, time,
and resources in explaining 9-ending prices. However, underestimation theory was
excluded because it cannot explain the level of consumer rational inattentiveness
across channels, one of the study’s focal areas.
5. Previous research on price and perceived quality examined their relationship in two ways: single-cue studies (i.e., price cue) and multicue studies (including brand name, store image, etc.). Both found positive relationships between
price and perceived quality, although these were not always been statistically
significant [43].
6. The original intent was to explore possible factors that may have affected
firms chosen price-endings in a single model, based on the idea that higher demand leads to more use of 9-ending prices. However, the authors came to agree
with the reviewers of the article that this idea was not appropriate in light of the
possibility of endogeneity.
7. It is not necessary that B&C retailers have identical prices on-line and offline. However, most B&C retailers post information about their price-matching
policies [59]. For example, Sears and Best Buy have cross-channel matching policies
to ensure that customers pay the lowest prices available both off-line and on-line.
8. Addressing the concerns of Allen and Wu [1], preliminary consistency sampling was conducted to ensure the quality and consistency of the price data before
implementing the larger analysis. This involved the collection of 100 random
samples of data from the overall data and then comparing them in terms of consistency of price information. Only five inconsistent price-pairs were found across
on-line retailers, on the one hand, and Biz-Rate.com, on the other. The results of
the consistency sampling provide confidence in the quality of the data and that the
approach to data collection did not introduce other unexpected biases. No effort
was made to adjust or correct the inconsistent price-pairs or other data, based on
the view that one can reasonably expect errors in data that are reported by both the
original producers of the prices and the secondary reporters of prices.
9. Sometimes the Internet-based sellers’ Web sites (especially those of small
firms) were inaccessible or the required price information was not available. Some
prices, therefore, are missing in the original data set. The following procedure was
used to handle such missing data. If 10 percent or more observations were missing
for a product, then that series was excluded from the data. If less than 10 percent
of the data were missing, then we examined whether the prices for the day before
and the day after were the same. If they were the same, then the software agent
automatically filled in the missing data with that price. Otherwise, the agent filled
in the missing data with the price for the day after. This was a somewhat arbitrary
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procedure; however, there were only 0.075 percent missing prices in the entire data
set, and therefore, it was unlikely to affect the results significantly.
10. Other 9-ending pricing variables (e.g., Nine6, Nine7) were created by relaxing
the definition of the fourth category (i.e., Nine5), by checking n – 3 digits of a price.
The estimation results were almost the same as when Nine4 was used as a dependent variable. No additional dependent variables were created, though.
11. As for the proxy for the on-line reputation, there are a number of previous
studies in which the degree of satisfaction was used [20, 21, 49]. Think of eBay’s
reputation and feedback mechanism, for example. eBay uses customer satisfaction
for each transaction made to measure the reputation of participating on-line traders.
12. StoreRating can be a negative value (e.g., –5.50) because it is a normalized factor score with a mean of 0 and a standard deviation of 1, as reported in Table 8.
13. RelPricej = (1/N)Σi,j(AvgStoreij/AvgCategoryi), where N is the number of product categories a store j has, AvgCategoryi is the average price of a product category i,
and AvgStoreij is the average price of product category i in store j. So this is a store’s
relative measure of the prices, considering all the different product categories.
14. How convincing is it to use the number of reviews as a proxy for a store’s
popularity? The logic is as follows. The greater the number of reviews, the more
products the store will have sold, and the more customers will have been served.
This is analogous to the number of reviews you see on the Internet for books. Bestsellers seem to have the greatest number of reviews posted on the Web. Thus, the
choice of the number of reviews on products sold by a store is attractive, since it is
easy to acquire in the absence of other information about stores, which is likely to be
inconsistent or more expensive to acquire.
15. Even at the same price-length level, the difference in price levels may vary
across products. For example, for a product at $19 and a product at $99, a $9 difference generates much higher perceived difference for consumers purchasing the
product at $19 than those purchasing the product at $99. Therefore price levels were
controlled across products to generate unbiased estimation results.
16. One might expect a high correlation between StoreRating and RelPrice, but it
was found to be very low (i.e., 0.026). This may have been due to the limitations of
the data set. RelPrice was derived as a relative measure of the prices charged by each
store. However, some retailers (e.g., Amazon.com) sell many different categories
of products, whereas others (e.g., BeachCamera.com) sell only one or two different
product categories. In addition, each store may not have the same products for each
product category, which may create some difficulties for the calculation of relative
prices. However, an effort was made to derive a more correct measure for the relative price by averaging product prices for each product-category combination.
17. The Hausman test was conducted to examine whether the differences between the instrumental variable (IV) estimates and ordinary least squares (OLS)
estimates were large enough to suggest that the OLS estimates ere not consistent.
A significant difference was found between these estimates, indicating that the
OLS estimates would be inconsistent due to the presence of endogeneity. This led
to the use of 2SLS regression as a means to adjust the estimated parameters to deal
with this concern. The suggestions of an anonymous referee were helpful and are
appreciated.
18. The explanatory variables (i.e., StoreRating, RelPrice, Popularity, PriceLength,
etc.) were also analyzed across channels, but no differences were found between the
B&C and PI channels.
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DONGWON LEE ([email protected]) is an assistant professor of management information systems (MIS) at the Business School of Korea University (KUBS). He earned his
Ph.D. degree in MIS from the Curtis L. Carlson School of Management at the University
of Minnesota. He received his B.B.A. and M.B.A. degrees from the College of Business
Administration at Seoul National University and his M.S. degree in MIS from the Eller
College of Management at the University of Arizona. His research interests include
pricing strategies in e-commerce, competition in telecommunications market, adoption
of new technologies, knowledge management and sharing, and on-line/off-line channel analysis. His research papers have appeared in Journal of Management Information
Systems, Information Systems Frontiers, Electronic Markets, Journal of Global Information
Technology Management, ACM Crossroads, and at conferences such as the International
Conference on Information Systems (ICIS), the Hawaii International Conference on
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System Sciences (HICSS), Statistical Challenges in E-Commerce Research Symposium,
Jerusalem Summer School in Economic Theory, INFORMS Conference on IS and Technology (INFORMS CIST), and INFORMS Marketing Science Conference.
ROBERT J. KAUFFMAN ([email protected]) is the W.P. Carey Chair in Information
Systems at the W.P. Carey School of Business, Arizona State University, where he has
joint appointments in information systems, finance, and supply-chain management.
His degrees are from the University of Colorado at Boulder (B.A.), Cornell University
(M.A.), and Carnegie Mellon (M.S., Ph.D.). He has served on the faculties of New
York University, the University of Minnesota, and the University of Rochester, and
worked in international banking and finance in New York City. He is also a past chair
of the Department of Information and Decision Sciences of the Carlson School of
Management, and director of the MIS Research Center at the University of Minnesota.
His research interests span the economics of IS, financial markets, technology adoption, competitive strategy and technology, IT value, strategic pricing and technology,
supply-chain management, and theory development and empirical methods for IS
research. He has won numerous research awards, including the 2006 outstanding
research contribution award for modeling and strategic decision-making research on
embedded standards in technology-based products from the IEEE International Society
for Engineering Management, and the 2007 best research award from the Journal of the
Association of Information Systems for theory-building research in the area of product
and market transparency made possible by information technology. His recent paper
on the diffusion of multiple generations of wireless mobile phones was nominated for
a best research award at the 2008 International Conference on Information Systems.
His publications have appeared in Information Systems Research, Journal of Management
Information Systems, MIS Quarterly, Management Science, Organization Science, and other
leading journals.
MARK E. BERGEN ([email protected]) is the Carolyn I. Anderson Professor of Business Education Excellence at the Carlson School of Management of the University of
Minnesota. He previously taught for eight years in the Graduate School of Business
at the University of Chicago. He has won numerous awards for teaching excellence
at the Carlson School and has been recognized as an outstanding faculty member in
BusinessWeek’s “Guide to the Best Business Schools.” He teaches M.B.A. and executive
courses in pricing strategy, marketing strategy, marketing management, and supplychain management. His research focuses on pricing and channels of distribution, where
he has studied such issues as “pricing as a strategic capability,” price wars, price passthrough, branded variants, dual distribution, gray markets, coop advertising, and quick
response. Dr. Bergen’s research has been published in top scholarly journals, including
Harvard Business Review and Sloan Management Review. In addition, he has extensive
pricing consulting experience with companies in the medical, services, food, retail,
and industrial markets. He holds a B.S. and a Ph.D. in economics.