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Journal of Retailing 83 (3, 2007) 309324

Do market characteristics impact the relationshipbetween retailer characteristics and online prices?

Rajkumar Venkatesan a,, Kumar Mehta b,1, Ravi Bapna c,2

a 100 Darden Blvd., Darden Graduate School of Business, Charlottesville, VA 22903, United Statesb Management Information Systems, 4400 University Drive, MSN 5F4, School of Management,

George Mason University, Fairfax, VA 22030, United Statesc Information Systems, Indian School of Business, Gachibowli, Hyderabad, 500 03 2, India

Received in revised form 30 April 2006; accepted 30 April 2006

Abstract

We propose that market characteristics interact with retailer characteristics to determine online prices. The retailer characteristics examined

includeservice quality of a retailer, channels of transaction provided by a retailer and the size of a retailer. The market characteristics capture

the level and nature of competition, and the price level of a product. We utilize a Hierarchical Linear Model (HLM) framework for capturing

and testing the proposed interactions. The better fit between the model and the online market structure is reflected by a twenty-five percent

increase in explainable price dispersion over results from comparable studies. Our study demonstrates that while retailer characteristics do

impact online prices, this influence is significantly enhanced or diminished by the accompanying market characteristics.

2007 New York University. Published by Elsevier Inc. All rights reserved.

Keywords: Internet markets; Retailer pricing strategy; Retailer service quality; Competition

Introduction

Industry surveys show that online firms have transitioned

from the initial phase of investment and brand-building to

the profit maximization phase. The published profitability

and sales reports from 2001 to 20033 indicate that Internet-

based retailingis now a firmly established andan increasingly

profitable market. In light of this maturation, it is imperative

to ask whether the extant understanding of retailer pricing

strategies comprehensively explains the online retailerspric-

ing behavior. Shankar and Bolton (2004) have suggested

that descriptive research on the determinants of retailer

Corresponding author. Tel.: +1 434 924 6916; fax: +1 434 243 7676.

E-mail addresses: [email protected] (R. Venkatesan),

[email protected] (K. Mehta), ravi [email protected] (R. Bapna).1 Tel.: +1 703 993 9412; fax: +1 703 993 1809.2 Tel.: +1 860 486 8398; fax: +1 860 486 4839.3 eMarketer (2004) indicates 79 percent online retailers were profitable in

2003. Further, a Mullaney (2003) indicates that 40 percent of the publicly

traded Internet companies reported profits in 2002, with the key areas being

online retailing and finance.

pricing strategies aids managers in profiling their pricing

practices and predicting competitors behavior. However, it

is important to note that the Internet as a retailing channel

is significantly different from the traditional marketplace.

In contrast to the traditional channels, Internet is character-

ized by vastly increased availability of information regarding

product attributes, prices and retailer service quality metrics.

A key challenge has been to obtain an understanding of the

retailers continued ability to price differentiate in the face of

lowered search costs in Internet markets. The main findings

to date have been that(a) price dispersion in Internet mar-

kets is persistent with time, (b) in some, but not all, instancesretailer service quality explainssome of thevariancein online

prices, and (c) the transaction channels provided by a retailer

and level of competition matter (see Pan et al. 2004 for a

detailed review).

Economic theories of consumer demand and retailer pric-

ing suggest that product prices are a function of boththe

characteristics of the retailers and the competition (Pakes

2003; Betancourt and Gautschi 1993). Studies of pricing by

grocery retailers in offline markets have found that retail-

0022-4359/$ see front matter 2007 New York University. Published by Elsevier Inc. All rights reserved.

doi:10.1016/j.jretai.2006.04.002
mailto:[email protected]:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jretai.2006.04.002http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.jretai.2006.04.002mailto:[email protected]:[email protected]:[email protected]


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310 R. Venkatesan et al. / Journal of Retailing 83 (3, 2007) 309324

ers customize their prices at the brand store level. They

do so based on their intimate knowledge about their cus-

tomers, product categories, store characteristics, and most

importantly in response to market competition (Shankar

and Bolton 2004). Empirical evidence also indicates that

when setting prices the retailers consider multiple objec-

tives such asreaction to competitor actions, maximizingstore brand share, and manufacturer incentives (Chintagunta

2002). These observations from offline markets establish

that retailers pricing decisions not only reflect their inter-

nal resources and objectives, but also point to the fact that

the retailers pay close attention to characteristics of the mar-

ket in which they operate. We are motivated to investigate

existence of a similar effect in online markets where these

characteristics are easily observable by the retailers.

A distinct feature of this study is that we explicitly incor-

porate interactions between retailer and market level factors.We believe that by examining the moderating influence of

market level factors, we can provide a more comprehensive

picture of the relationship between prices and retailer char-

Table 1

Comparing proposed framework with existing studies

Representative research Model includes characteristics of Major findings

Shoppers Product Retailer Competition Competition and

retailer Interaction

Analytical models

Lal and Sarvary (1999) Ya Y N N N Price sensitivity of online shoppers decreases when (1)

there is a large pool of online shoppers, (2) nondigitalproduct attributes are important, (3) consumers have a

favorable loyalty to the brand they currently own, and

(4) the fixed cost of a shopping trip is higher than the

cost of visiting an additional store.

Chen and Hitt (2003) Y N Y N N Better known retailer has higher price on average than a

lesser known retailer, but lower price on some products

and/or some of the time resulting in price dispersion.

Smith (2001) Y N Y N N Focus of consumer search on few well known retailers

leads to interdependence among these and also higher

prices. Lesser known retailers however adopt random

pricing strategy in equilibrium.

Empirical models

Tang and Xing (2001) N H Y N N Traditional retailers charge the highest prices, followed

by brick-and-click retailers, and pure-play retailers havethe lowest prices.

Ancarani and Shankar

(2004)

N H Y N N With regard to posted prices, traditional retailers charge

the highest prices, followed by multichannel retailers

and pure play retailers. However, when shipping costs

are included, multichannel retailers charge the highest,

followed by pure-play retailers and traditional retailers.

Baye and Morgan

(2001)

N Y N Y N Price dispersion is persistent with time. However,

product life cycle leads to changes in number of

competing firms and the range of price dispersion.

Baye and Morgan

(2003)

N Y Y Y N Use certified versus noncertified merchants as a

measure of good and poor service quality. Certified

merchant enjoys a premium but vanishes with increased

number of certified competing firms.

Carlton and Chevalier

(2001)

N H Y Y N Manufacturers charge higher prices on their websites

and limit availability to avoid some retailers from freeriding on the sales efforts of other retailers.

Clay et al. (2001, 2002) N H N Y N Price dispersion is persistent with time, and decreases

with increase in competition

Baylis and Perloff

(2002)

N H Y N N Observed online price dispersion is not due to service

differentiation.

Pan et al. (2002, 2003b) N H Y Y N Significant portion of price dispersion is due to

nonretailer characteristics. Increase in level of

competition reduces prices charged by retailers at a

diminishing rate. Influence of service quality is not

consistent across product categories.

Present study N H Y Y Y Do market characteristics interact with retailer

characteristics to determine price levels?

a Y: yes, N: no, H: homogenous products.


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R. Venkatesan et al. / Journal of Retailing 83 (3, 2007) 309 324 311

acteristics. Especially since existing empirical evidence from

analyses of online prices indicate only a weak influence of

service qualityan important asset of retailers (Zeithaml et

al. 2002). Considering the persistent online price dispersion,

coupled with firms choice of strategic and tactical decisions

based on the competitive forces that exist in their markets

(Porter 2001), we examine whether this supposed anomalycan be explained by considering the interaction between the

retailer and market characteristics.

Research question

The main objective of our paper is to study online retail-

ers pricing strategies in a single framework that accounts for

the influence of(a) retailer characteristics - service qual-

ity and channels of transaction, (b) market characteristics -

level and nature of competition, and the price of a product,

and (c) interactions among these factors. Table 1 provides a

comparison of our study with relevant literature. It is evident

that the interaction between retailer and market characteris-tics and its effect on online prices has never been explicitly

examined. This is the main focus of our study.

In remainder of the paper we use retailer in reference

to retailers with a web presence. They can be multichannel

retailers, that is they mayhave a physical presence and/or also

use a catalog channel. Our hypotheses and analyses focus on

the online pricing strategies of these retailers.

Expected contributions

Our study intends to make two primary contributions.

First, we develop an integrated framework for studying theinteractions between market and retailer characteristics. For

retailers selling nondifferentiable products, market competi-

tion can vary widely across products and product categories.

The differences in competitive forces make it feasible for the

retailer to make a strategicchoice (level of service quality, and

transaction channels) across all products, and vary the tacti-

cal decision (price level) in each product-market depending

on what competitive forces exist. Pricing is thus determined

by the interaction between the retailers strategic choices and

the market forces. Substantively, study of the interaction also

provides a better understanding of the competitive effects of

service quality, a relevant but sparsely researched issue in

marketing.

Second, we highlight the fact that the determinants of

retailer pricing decisions exist at two different levels of

abstraction. Retailer characteristics such as service qual-

ity and transaction channels vary across individual retailers.

Market characteristicssuch as level and natureof competition

vary across product-markets and are common to all retail-

ers selling the particular product. Traditional linear models

cannot accommodate simultaneous inclusion of the retailer

and market characteristics and the interactions between them.

We utilize a Hierarchical Linear Modeling (HLM) frame-

work which explicitly incorporatesthe main effects of retailer

and market level characteristics at different levels, and also

accounts for the interactions between them (Raudenbush and

Bryk 2002). This allows us to test not only whether, but also

when and how in the context of various market forces, do ser-

vice quality and channels of transaction influence a retailers

online pricing decisions.

We first present the conceptual model for our analysesand integrate the relevant literature to formulate five testable

hypotheses. We then describe our data collection from het-

erogeneous sources reflecting information widely available

to the consumers and retailers alike and explain operational-

ization of our constructs. Subsequently, we present the HLM

framework which incorporates the factors at different levels

of abstraction, and follow it up with discussion of the results

from the analyses. We derive the managerial implications

from our results, and conclude with a discussion on potential

directions for future research.

Conceptual model and hypotheses

Main effects: influence of Internet retailer factors

Service quality

Varian (2000) predicted that over time two classes of retail-

ers will emerge-low service/low price and high service/high

price. A study of the online retail market by Pan et al. (2002)

finds that service quality does explain a retailers ability to

pricedifferentiate, albeit verylittle. In contrast, the theoretical

expectations indicate service quality as an important factor

in consumers choice of a retailer (Wolfinbarger and Gilly

2003). Betancourt and Gautschi (1993) suggest that retail-ers provide services in order to reduce the customers total

cost of consumption and that these services play an impor-

tant role in customers selection of a retailer. They present an

analytical model which shows that between two retailers sell-

ing the same product, a consumer would be willing to pay a

higher price for the higher service quality retailer, but only as

long as the consumers valuation of the difference in service

quality is more than the difference in prices between the two

retailers. Based on theoretical expectations we hypothesize

that:

H1. The higher the service quality of a retailer the higher

the prices charged.

Channels of transactions

Empirical evidence from several years of data indicates

that multichannel retailers have higher price levels than pure-

play (click only) retailers and brick-only retailers (Ancarani

and Shankar 2004; Pan et al. 2003a; Ratchford et al. 2003).

Due to their wide geographic presence, National Brick-and-

Click retailers can provide customers value added services

such as the ability to order products online and pick up

(or in some cases return) the products in an offline store.

Such services save time and provide added ease of use to


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consumers. These facilities, which we term as fulfillment

and post-fulfillment facilities, lead to greater consumer trust

thereby reducing the price sensitivity of consumers. On the

other hand, the single distribution channel for a pure play

(Internet or click only) retailer, lowers the inventory costs

and can be potentially leveraged to price differentiate by cost

leadership. Consistent with prior studies, we expect NationalBrick-and-Click retailers to price higher because of better

brand recognition, greater trust, and provision of better ful-

fillment and post-fulfillment facilities. Therefore,

H2a. National Brick-and-Click retailers charge higher

prices than pure-play retailers.

Local Brick-and-Click retailers can provide better fulfill-

ment and post-fulfillment facilities albeit in a local area and

thus have the potential to charge higher prices relative to

pure play retailers. Since they are expected to have lower

brand recognition than National Brick-and-Click retailers,

we expect their price levels to be lower than those of retail-ers with national presence. Local Brick-and-Click retailers

aim to utilize their online presence to extend their geo-

graphic reach beyond that of the traditional store. This would

attract additional consumers without incurring significant

additional costs. Based on valid expectations for both higher

and lower prices, we do not specify any directional hypothe-

ses related to the pricing strategy of Local Brick-and-Click

retailers.

Online retailers that provide direct mail catalogs bene-

fit from lower costs because direct mail catalog companies

need little modification to include online ordering capabili-

ties into their existing business models (Doolin et al. 2003).This addition of online ordering improves the convenience

they provide to customer and can help the direct mail catalog

retailer to differentiate and charge higher prices. Similarly,

pure play retailers are adding direct mail catalogs to their

channel portfolio because(a) they can leverage existing

infrastructure (distribution centers, customer service and tele-

phone ordering) to support their direct mail catalog channel

(Quick 2000), and (b) online retail managers believe that

direct mail catalogs enables them to build brand recognition

and increase trust among consumers (Quick 2000), thereby

leading to higher prices. Hence, we hypothesize that:

H2b. Online retailers that provide direct mail catalogs

charge higher prices than pure-play retailers.

Moderating effects

The influence of retailer characteristics (service quality

and channels of transaction) on online prices is expected

to be moderated by market characteristics such aslevel

of competition, nature of competition. The main effects of

retailer characteristics explain how, within a product market,

the prices vary across retailers. The moderating effects of

market characteristics capture the differences in the influence

of the retailer characteristics on online prices across product

markets.

Interaction of level of competition and service quality

Cohen (2000a) presents the concept of Distortion in Infor-

mation Function (DIF-ness) to measure consumers tendency

to use heuristics when faced with large amount of informa-tion. DIF-ness suggests that till a certain threshold consumers

scan the increasing number of choices and make a choice

that maximizes their utility. Beyond this threshold, increase

in alternatives can lead the consumers to use heuristics based

on brand name and service quality, to make their selection.

Research in signaling theory suggests that consumers often

use price as a proxy for quality of product and/or retailer

(Kirmani and Rao 2000).

Considering these two aspects, we expect that when faced

with small number of competitors in a product-market, retail-

ers with high service quality can easily differentiate their

services and charge a higher price. Increase in the number

of competitors (DIF-ness) would initially force the retail-ers (especially higher service quality retailers) to lower their

prices in order to avoid losing consumers to the competitors.

When the number of competitors exceeds a certain threshold,

both DIF-ness and signaling theory suggest that the con-

sumers use of heuristics would counteract the downward

pressure on prices created by increased competition. Thus,

we propose that the number of competitors in a market would

diminish the positive influence of service quality on prices;

and the moderating influence of the number of competitors

would be nonlinear in nature. With increase in competition

high service quality retailers would charge higher prices than

low service quality retailers. However, the difference in theprices of high and low service quality retailers is expected to

reduce at a diminishing rate. We expect the level of competi-

tion to capture the differences in the extent to which service

quality influences the prices charged by retailers across prod-

uct markets. We hypothesize that:

H3. The positive influence of service quality on prices

charged by a retailer is diminished, albeit at a decreasing

rate, as the number of competitors increase.

Interaction of price level and service quality

The price of a given product implicitly denotes the per-

ceived risk and is an important determinant of consumers

involvement in a product (Cohen and Areni 1991). Accord-

ing to Cohen (2000b), the consumers selection is based on

risk averseness particularly when the price of a product is

high. This would lead them to prefer retailers with high ser-

vice quality, who benefit from the consumers risk averseness

(or willingness to pay higher for service quality) by charging

higher prices. The empirical evidence does indicate increase

in price dispersion with increase in average price of the prod-

uct (Pratt et al. 1979). This increase in dispersion could stem

from theconsumers willingness to payhigher pricesto retail-

ers who are able to reduce the perceived risk by inducing trust


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(Ba and Pavlou 2002). In product-markets at higher price

levels, retailers who are able to foster trust by way of better

service quality are afforded scope for pricedifferentiation and

would charge relatively higher. Though for all products, the

retailers with high service quality will charge higher prices

than retailers with low service quality; their price premium

would increase with the price of a product (Betancourt andGautschi 1993). We expect the price level of a product to

reflect the differences in the influence of service quality on

prices charged by retailers across different product markets,

and propose:

H4. The positive influence of service quality on prices

charged by retailers is enhanced further as the price level

of the product increases.

Interaction of nature of competition and retailer

characteristics

A differentiation strategy can be successful if(1) the

benefits provided by the differentiation are valued by the

consumer, and (2) the differentiation is unique or not eas-

ily replicated by the competitors (Barney 1991). In a market

for nondifferentiable products, a retailer canoffer higher con-

sumer benefits by providing high service quality. The ability

to chargea price premium forthe higherservice quality would

then depend on competition from retailers who provide sim-

ilar or higher levels of service to consumers (Betancourt and

Gautschi 1993). We define one aspect of the nature of compe-

tition in a particular product market as the variation in service

quality of the retailers selling that product.

As mentioned earlier, in general (for all products) the high

service quality retailers are expected to charge higher pricesthan the low service quality retailers. However, the differ-

ence in the prices is expected to be higher in markets with

a larger variation in service quality among retailers selling a

product (where possessing high service quality is a unique

asset) compared to markets with lower variation in service

quality. We expect the variation in service quality to capture

the extent of difference in the influence of service quality

on prices charged by retailers across product markets. We

hypothesize that:

H5a. The positive influence of service quality on prices

charged by retailers is further enhanced when there is a high

variation in service quality among the retailers selling the

product.

We use number of National Brick-n-Click retailers as a

proportion of total number retailers selling a particular prod-

uct to capture a second aspect of nature of competition. 4

4 Our hypotheses regarding the moderating influence of market charac-

teristics are drawn on the basis that online retailers use the easily available

competitive information to formulate their pricing strategies. We do not

characterize competition in terms of the proportion of local brick and click

retailers and retailers with catalog because information on whether an online

Fig. 1. Joint influence of retailer and market characteristics.

National Brick-n-Click retailers have a higher trust with

consumers, and provide unique channel alternatives for ful-

fillment and post-fulfillment processes, and thus can be

expected to charge higher prices. However, this ability of

a national retailer to price higher is dependent on the compe-

tition from other National Brick-n-Click retailers. Increase

in the number of National Brick-n-Click retailers selling a

product would result in reduced ability to differentiate from

each other in terms of multichannel benefits and result in

diminishing their price premiums. We hypothesize that:

H5b. The premium charged by a National Brick-n-Click

retailer is diminished with an increase in the proportion of

National Brick-n-Click retailers that sell a product.

Data collection

The criteria used for identifying data to collect was: (a)

product categories that include nondifferentiable products to

avoid price variation because of product differentiation, (b)

availability of at least 20 products in each of the categories to

indicate substantial penetration product in the online retail

market, and (c) availability of a minimum of seven price

quotes for each of the selected products to ensure a base level

of competitive intensity. Using these criteria, eight product

categories were selected-Books, Camcorders, DVDs, DVD

players, PDAs, Printers, Scanners and Video Games. Table 2provides the summary information of the data collected for

operationalizing the constructs presented earlier in Fig. 1.

For each retailer we collected data for the service qual-

ity ratings and the competitive intensity from Bizrate.com

(similar to Pan et al. 2003b). The data for size of the retailer

was obtained from Alexa.com which provides rank of the

retailer is a localbrickand clickretailer orif a retailer hasalsohas a catalog is

noteasilyevidentonline. However,we would expect consumers andNational

Brick-and-Clickretailers to be aware of theidentityof other National Brick-

and-Click retailers through the competitive intelligence surveys that are

usually undertaken in firms of this size.


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Table 2

Constructs operationalized from multiple sources

Construct Data collected Source Related research using similar measures

Price Posted price for a product. Used to calculate

the price index.

Bizrate.com Pan et al. (2002), Clay et al. (2002)

Service quality Survey ratings obtained by Bizrate from

online consumers over ten items on a

10-point scale (1 = Poor, 10 = Outstanding

Bizrate.com Pan et al. (2002)

Transactional channels Dummy coded variables for characterizing

the channels through which the retailer

offers the products. The channels are first

classified as one of the following: pure play

(online only), national chain with online

presence, and local store(s) with online

presence. In addition, we also distinguish

retailers that offer mail-order catalog.

Inspection of Retailer

Websites

Tang and Xing (2002)

Size Rank of retailers based on number of unique

visitors to the online.

Alexa.com Pan et al. (2003b)

Competitive intensity Number of retailers offering an identical

product and with service quality ratings

available from Bizrate.

Bizrate.com Pan et al. (2003b)

Price level Average posted price for a product. Bizrate.com Cohen and Areni (1991), Pan et al. (2003b)

Variance in service quality Coefficient of variation of the service qualityamong retailers Selling a product

Bizrate.com

Proportion of National

Brick-n-Click relations

Number of National Brick-n-Click retailers

selling a product.

Inspection of Retailer

Websites

retailer website based on the number of unique visitors. The

retailers transaction channels were coded based on inspec-

tion of retailer websites cross-verified with their description

at Bizrate.com and web21.com5 (Tangand Xing 2002).Using

location and geographical spread of physical store(s) the

retailers were classified as:

National Brick-n-Click: Presence of physical stores inmore than one state.6 (Example: Best Buy with presence

BestBuy.com)

Local Brick-n-Click: physical store locations within one

state. (Example: 17th Street Photo from New York with

online presence as 17photo.com)

Pure-Play: no physical stores present. (Example: buy.com

and amazon.com)

Catalog provider: if mail-order catalog services are

offered. (Example: Sears)

Bias reduction

We collected 22,209 price quotes, for 1,880 products

from 233 retailers. For our analyses we only used prices

quoted for items identified as new by retailers other than

refurb/discounters. We excluded retailers with missing ser-

vice quality ratings.7 Though most retailers provide the price

5 web21.com provides a comprehensive directory listing of retailers and

their physical store locations (if any).6 In rare cases where the website had insufficient information, the retailer

was directly contact for the information.7 Bizrate requires a minimum of 30customersurveys over last ninetydays

to publish ratings information. The exclusion of retailers without service

Table 3

Bias reduction reduces analyzable data

Product

category

# Posted-prices # Retailers # Products

Collected Analyzed Collected Analyzed

Books 5750 2752 19 9 685

Camcorder 1386 882 86 57 57

DVD 9242 5738 30 15 799DVD Player 547 446 66 51 40

PDA 568 479 77 57 32

Printer 1087 906 75 54 36

Scanner 708 574 77 53 31

Video games 2921 1616 74 42 200

search engines with product informationsuchasnew, refur-

bished, the retailers primarily offering refurbished items tend

not to do so. Refurbished and used products are generally

sold at lower prices since they differ from new products in

terms of product quality; thereby leading to a bias in the price

dispersion measure. We conducted a manual verification of

each retailers website to identify refurb/discounters,8

andremoved them from our analyses. Table 3 shows the impact

of the bias reduction on the size of the final data set, yielding

a total of 13,393 price points suitable for analysis. To assess

the qualitative impact of the bias reduction process we exam-

ratings is methodologically necessary because service rating is a critical

independent variable in our analysis.8 The classification of refurb/discount retailers was also independently

verified. Note that the presence of refurbishers can be a significant source of

bias in the price information. It can certainly increase price dispersion since

refurbishers are expected to price lower. Yet, to the best of our knowledge,

no prior study has explicitly accounted for their presence in the data.


7/16


ined the dispersion of prices in the collected data and the

analyzed data and found no significant difference. Although

no significant difference was observed between the data, we

believe that the bias reduction provides for more reliable and

credible parameter estimates in our analyses.

Calculation of price index

To account for differences in price levels across different

products within a category, we index the price charged by a

retailer for a product.9 The price index is calculated as the

ratio of the difference between the price charged by a retailer

and the minimum price for the product to the minimum price

for the product (Eq. (1)):

PINDi,j=PINDi,jMinPricej

MinPricej(1)

where, PINDi,j = Price index for retailer i for product mar-

ket j; MinPricej = Minimum price charged by any retailer forproduct market j.

Measuring service quality

Bizrate.com provides consumers with fourteen measures

for service quality of a retailer. These are single-item mea-

sures on a scale of 1 to 10, where 1 is very poor and 10

is outstanding. Ten of these items measure the level of ser-

vice quality of a retailer and includeEase of Finding a

Product, Product Selection, Clarity of Product Information,

Look and Design of the website, Shipping Options, Charges

Stated Clearly, Product Availability,OrderTracking, On-time

Delivery, and Customer Support. We exclude four items that

measure the intentions of theshopper to revisit the website, an

overall rating of the retailer, whether the product met expec-

tations, and shipping charges, since these four items do not

directly measure the service quality of a retailer. The service

quality measure for a retailer i (Servindi) was computed as

the average of the ten items from Bizrate.com. Such a simple

composite measure of individual items that measure differ-

ent aspects of a construct has been widely used for measuring

the market orientation of an organization (Jaworski and Kohli

1993), and the service orientation of a retailer (Homburg et

al. 2002).

We conducted a principal component factor analysis withthe ten items and found that all the ten items loaded into

one factor that explained approximately ninety percent of the

9 We do not include shipping and handling as part of the prices charged

by the retailer for three reasons: (1) for the product categories used in our

study, shipping and handling represents only 2 percent of the total price, and

the correlation between the prices charged by a retailer with and without the

charges is greater than 0.98 for all the product categories, (2) each retailer

charges differently for different shipping options and thereby making direct

comparison difficult, additionally the default shipping options provided in

Bizrate.com vary from retailer to retailer, and (3) approximately 20 percent

of the retailers do not report shipping and handling charges on Bizrate.com. Table4

Descriptivestatistics

*

Productcategoryconstruct

Camcorder

DVD

player

PDA

Printer

Sc

anner

Books

DVD

Videogame

Pricelevel

0.2

3(0.2

1)

0.2

7(0.2

5)

0.2

0(0.2

0)

0.2

2(0.1

8)

0

.25(0.2

4)

0.2

5(0.2

7)

0.2

2(0.

25)

0.5

0(0.9

6)

Servicequality

7.8

(1.4

0)

8.1

(0.9

0)

8.3

(0.6

9)

8.3

(0.7

4)

8

.3(0.6

7)

8.2

(0.9

4)

8.4

(0.7

5)

8.2

(0.7

1)

Size

**

277(465)

334(531)

108(315)

139(340)

154

(379)

24(148)

44(106)

19(45)

Pricelevel

963(671)

290(242)

354(170)

262(154)

340

(462)

15(11)

30(30)

32(11)

Numberofretailers

15(9.8

)

11(7.6

)

15(9.2

)

25(9.3

)

1

9(7.9

)

4(0.9

)

7(1.4

)

8(4.5

)

Variationinservicequality

0.2

6(0.2

2)

0.1

1(0.0

5)

0.1

2(0.0

3)

0.1

0(0.0

2)

0

.13(0.0

6)

0.1

9(0.0

3)

0.2

0(0.

10)

0.1

0(0.0

5)

Retailertype

National

4

2

4

4

4

1

3

21

Local

10

12

7

8

7

2

2

4

Click

43

37

46

42

42

6

10

17

Total

57

51

57

54

53

9

15

42

*Valuesinparenthesesrepresentstandardde

viation;

**valuesarein1,0

00s,andrepresenttherankofaretailer.Soalowerrankimpliesalargerretailer.


8/16


Fig. 2. Data structure used for the Hierarchical Linear Model.

variance.10 We also observed very high correlation between

the service quality obtained from the factor analysis and the

measure obtained as the average of the ten individual items.

The substantive inferences of the study did not change with

either of the service quality measure. To maintain parsimony

in our model, we thus use one measure for service quality of

the retailer as the average of the ten items.

Market description

Thedescriptive statistics for the variables used in our anal-

yses are provided in Table 4. From Table 4 we note that(1)

the mean of number of retailersin individual product-markets

is lesser for Books, DVDs, and Videos Games as compared to

other product categories. While the number of retailers offer-

ing books, DVDs, and video games is lower, these retailers

are on average substantially larger than the retailers in other

product categories, (2) the price dispersion varies from 18

percent (for printers) to 96 percent (for video games). These

price dispersion levels are comparable to those reported by

Pan et al. (2002).

Hierarchical linear model framework

Fig. 2 illustrates the structure of the data used in this

study. As represented in Fig. 2, the variables used to test

our hypotheses correspond to different levels of aggregation.

Specifically, retailer characteristics, that is the main effects,

are at the lowest level of aggregation and are measured for

each retailer. The market characteristics, that is the moder-

ating effects, are at a higher level of aggregation and are

measured for each product market. Such data are designated

as multilevel data (Raudenbush and Bryk 2002).

The above data structure suggests that a Hierarchical Lin-ear Modeling (Raudenbush and Bryk 2002; Steenkamp et al.

1999) framework should naturally accommodate our concep-

tual model. In HLM, a linear regression model is specified

at the lowest level of aggregation (Level 1). The intercept

and slope coefficients for the model at Level 1 are modeled

as a function of the variables at the next level of aggrega-

tion (Level 2). We are interested in explaining variation in

10 Thecronbach alpha measure of scale reliabilityfor theten itemswas also

found to be 0.92 which is widely acknowledged as an acceptable reliability

coefficient (Nunnaly 1978).

prices charged by different retailers for a particular prod-

uct. The prices charged by the retailer are a function of(a)

the characteristics of each retailer such as service quality,

the channels of transaction that are available to the retailer,

and the size of the retailer, (b) the characteristics of each

product-market such as number of competitors, average price

levels and nature of competition, and (c) the interactions of

the retailer and the market characteristics. The retailer prices

and characteristics that we are interested in are at the lowest

level of aggregation and hence form the basis for the Level 1

regression model. The intercept and slope coefficients of the

Level 1 model (the retailer level) are modeled as a function

of the Level 2 variables (market characteristics).

Proposed HLM model

In the Level 1 model, the price index of a retailer is influ-

enced by the various hypothesized retailer characteristics and

is given by;

PINDij= 0j+ ij SQi + 2jNBCi + 3

LBCi

+4Ci+5

Sizei+rij (2)

where SQi = service quality of retailer I; NBCi = 1 if retailer

i is National Brick-n-Click, 0 otherwise; LBCi = 1 if retailer

i is Local Brick-n-Click, 0 otherwise; Ci = 1 if online retailer

i provides a mail-order catalog, 0 otherwise.

In the Level 1 model, the intercept term (0j), the slope

coefficients of service quality (1j), and the slope coeffi-

cient for the indicator of National Brick-n-Click retailer (2j)

vary across product markets. The Level 2 models explain the

variation across product markets in the intercept and slope

coefficients of the Level 1 model. The intercept term is mod-

eled as,

0j = 00 + 01 LCMj+ 02 VSQj+ 03

PLj+ 04 PNBCj+ u0j (3)

where LCMj = Log of Number of competitors in product-

market j, PLj = Price level of product-market j, and

VSQj = Variance in the service quality among the retailers

in product-market j.

The slope of service index is modeled as;

1j= 10 + 11 LCMj+ 12 VSQj

+13 PLj+ u1j (4)


9/16


The slope of National Brick-n-Click is modeled as;

2j= 20 + 21 PNBCj+ u2j (5)

where, PNBCj = Proportion of National Brick and Click

retailers in product-market j.

Substituting (3)(5) in (2) we get,

PINDij= 00 + 01 LCMj+ 02 VSQj+ 03

PLj+ 10 SQi + 20 NBCi

+3 LBCi + 4 Ci + 5 Sizei + 11

[LCMi SQi]+ 12 [VSQj SQi]

+13 [PLj SQi]+ 21 [PNBCjNBCi]

+[u1j SQi + u2jNBCi]+ [u0j]+ rij (6)

To test the proposed nonlinear relationship between level

of competition and price premiums, we use a log conversion

of number of competitors. 00 represents the average pricecharged by the retailers. rij is a homoscedastic error term

with mean 0 and variance 2r and represents an estimate of

the unexplained variance at the retailer level. u0j is a ran-

dom parameter with mean 0 and variance 2uj and measures

the deviation of product market j from the mean, that is the

unexplained variance in prices at the product-market level.11

A Full Information Maximum Likelihood (FIML)12

methodology is used to estimate the model parameters (s,

s, and us) with an iterative algorithm (details on the esti-

mation are provided by Raudenbush and Bryk 2002). Using

Ordinary Least Squares (OLS) regression on multilevel data

presents statistical problems because(a) the multilevel dataviolates the assumption of independence (or homogeneity)

of observations required in OLS, (b) it has been shown that

the resulting estimates are biased, and (c) the estimated stan-

dard errors of the effects are too small (Raudenbush and Bryk

2002). These smaller estimatederrorsin fact lead to improved

R-square values and also increase the significance of the esti-

mated coefficients. It is important to note that by adopting the

HLM approach we are not only estimating a more complex

model, but also raising the requirements on robustness of the

estimates from our model.

Analysis of pricing strategies

In addition to testing the hypotheses, we are also interested

in evaluating if the interactions between retailer and market

factors provide a significant improvement in explaining price

11 One canalso interpretthe random effects parameteruj, as a heteroscedas-

ticerrorterm in themixed heteroscdastic models used by Wittink(1977) and

Shankar and Krishnamurthi (1996).12 We also estimated our models using a Restricted Maximum Likelihood

(REML) method because when either the number of retailers per product

market or the number of total prices per product category is small, the FIML

estimates can have smaller standard errors. We did not find any substantive

differences in the REML and FIML estimates in our data.

dispersion. We accomplish this objective by estimating three

benchmark HLM Null models that are explained below.

HLM Null Model I

In HLM Null Model I, the price index of a retailer i in

product-market j (PINDij), is specified as a function of onlyan intercept term that varies across product markets. Using

Eq. (6) as the basis, HLM Null Model I is given by:

PINDij= 00 + u0j+ rij. (7)

HLM Null Model II

The HLM Null Model II assumes that the price index of

a retailer is impacted only by the main effects of retailer

characteristics. Using Eq. (6) as the basis, HLM Null Model

II is provided by:

PINDij = 00 + 10 SQi + 20 NBCi + 3 LBCi

+4 Ci + 5 Sizei + [u1j SQi + u2j

NBCi]+ [u0j]+ rij. (8)

HLM Null Model III

The HLM Null Model III assumes that the main effects

of both retailer and market characteristics influence the price

index of a retailer. Even though it considers only the main

effects of retailer and market characteristics, the HLM Null

Model III differs from extant models in the literature because

it accommodates for the retailer and market characteristics at

different levels of aggregation. Using Eq. (6) as the basis, the

HLM Null Model III is represented as;

PINDij = 00 + 01 LCMj+ 02 VSQj+ 03

PLj+ 04 PNBCj+ 10 SQi + 20

NBCi + 3 LBCi + 4 Ci + 5 Sizei

+ [u1j SQi + u2jNBCi]+ [u0j]+ rij (9)

Results and discussion

Model comparison

When FIML is used formodelestimation, a likelihood ratio (LR)

test is appropriate for evaluating the significance of multiple fixed

effects in the nested HLM models (Raudenbush and Bryk 2002).

We evaluate the significance of the main effects of retailer factors

with a LR test that compares HLM Null Models I and II. The signif-

icance of the main effects of market factors is established by the LR

test between HLM Null Models II and III. Finally, the significance

of the interactions between retailer and market factors is obtained

through a LR test between HLM Null Model III and the Proposed

HLM Model. We assess the explanatory power of a model by com-

puting the observed variance in prices (Vp) with the variance of


10/16


Table 5a

Comparison of reduction in price dispersion

Camcorder

(n = 882)

DVD player

(n = 547)

PDA

(n = 479)

Printer

(n = 906)

Scanner

(n = 574)

Books

(n = 2752)

DVDs

(n = 5738)

Video Games

(n = 1616)

Likelihood values

Proposed HLM Model 503 25 430 525 92 141 2318 2935

HLM Null Model 3 493 16 424 498 83 134 2270 2925

HLM Null Model 2 484 13 419 481 72 97 2243 2877HLM Null Model 1 400 4 405 425 60 55 2152 2840

Likelihood ratio

Interaction effectsa 19 18 12 54 18 14 96 20

Main effects-market factorsb 19 6* 10** 34 22 82 54 96

Main effects-retailer factorsc 167 18 28 112 24 84 182 74

R-square

Proposed HLM Model 0.42 0.42 0.56 0.35 0.44 0.67 0.59 0.70

HLM Null Model 3 0.34 0.35 0.48 0.21 0.37 0.62 0.54 0.62

HLM Null Model 2 0.34 0.32 0.41 0.18 0.35 0.49 0.41 0.61

HLM Null Model 1 0.18 0.29 0.33 0.16 0.23 0.40 0.36 0.63

Simple Linear Model 0.27 0.29 0.05 0.11 0.08 0.34 0.36 0.62

*Significant at < 0.10, **significant at < 0.05, and all other cells for the likelihood ratio tests are significant at < 0.01.a

LR test between HLM Full Model and Null Model III.b LR test between HLM Null Models III and II.c LR test between HLM Null Models II and I.

the residuals from the corresponding model (Vr) for each product

category. The explanatory power is assessed by an R2 measure cal-

culated as [1 (Vr/Vp)]. In addition to the HLM Null Models (I, II,

& III) we also compare the Proposed Model with a Simple Linear

Model incorporating only retailer characteristics (similar to Pan et

al. 2002). The variables in Simple Linear Model are service qual-

ity, channels of transactionNational Brick-n-Click retailer, Local

Brick-n-Click retailer, existence of mail-order catalog, and size of a

retailer.13

The LR test between the Proposed HLM Model and the HLM

Null Model III is significant at < 0.01 for all the eight productcategories (Table 5a); providing strong support for the influence

of the interactions between retailer and market factors on retailer

prices. For six of the eight product categories we find strong sup-

port ( < 0.01) for the main effects of market factors on retailer

prices. The main effects of market factors are only marginally sig-

nificant for the DVD Player (< 0.10) and PDA product categories

(< 0.05). Finally, for all the eight product categories, the main

effects of retailer factors have a significant influence on retailer

prices. We see that the Proposed HLM Model of price dispersion

provides a higher explanatory than all of the Null Models. Specifi-

cally, on an average the Proposed Model explains 52 percent of the

variation in retailer prices. In comparison, on average theHLM Null

Models I, II, and III approximately explain 32, 39 and 44 percent of

the variation in retailer prices, respectively. The R2 for the Proposed

HLM Model ranges from 21 percent (printers) to 70 percent (video

games).

Overall, the better fit between the model and the online market

structure is reflected by average improvement of 25 percent in R2

over the empirical evidence in existing literature. The suitability of

HLM structure is also evident in ability of the HLM Null Model 1

to explain greater degree of price dispersion than the Simple Linear

Model.

13 The Simple Linear Model is provided by PINDij =0 +1 SQi +2 NBCi +3 LBCi +4 Ci +5 Size + rij.

Main effects-influence of retailer characteristics

Table 5b provides the results from the estimation of the HLM

model with all covariates (labeled as Proposed HLM Model in

Table 5a).

Service quality

For all the eight product categories the statistically significant

positive coefficient for service quality indicates that retailers with

higher service quality are also charging higher prices. Across prod-uct categories we find consistent evidence that retailers consider

service quality as a means for differentiating and thereby charg-

ing higher prices (supporting H1). We believe that this consistently

significant influence of service quality is observed because our

framework accounts for retailer and market characteristics at two

different levels, and also incorporates their interactions.

Channels of transaction

In our analyses, a retailer is classified as pure play, National

brick-n-click, Local Brick-n-Click, and catalog. All of the chan-

nel classifications are used in the analyses with the exception of

pure play, which is used as the base. The coefficients for each

of the retailer classifications are thus interpreted as the average

increase/decrease in the prices relative to a pure play retailer. Over-

all, we observe that the retailer channel structure has a significant

influence on the prices charged by retailers.

Our results support H2a. We find that National Brick-n-Click

retailers charge significantly higher prices than the pure play retail-

ers in all of the eight product categories. The increase in price levels

for National Brick-n-Click retailers ranges from 6 percent (printers)

to 39 percent (DVD players). If the National Brick-n-Click retailers

were to become cost leaders and price their products in relation to

their economies of scale, one would expect to observe lower prices

and not the higher prices that our results indicate. We believe that

the National Brick-n-Click retailers are able to charge higher prices

because(1) they are able to engender trust among online shoppers


11/16


Table 5b

Results from estimation of hierarchical linear model

Camcorder

(n = 882)

DVD

player

(n = 547)

PDA

(n = 479)

Printer

(n = 906)

Scanner

(n = 574)

Books

(n = 2752)

DVDs

(n = 5738)

Video

games

(n = 1616)

Product-market factors

Log of number of competitors 0.015 0.60** 0.13** 0.16* 0.03 0.07** 0.40** 1.65***

Variance in service quality 0.03* 0.03 0.41 0.13 0.36* 0.46* 1.53** 0.43Price level 3.3E-5 5.8E-3 7.9E-6 1.2E-4 8E-6* 2E-3** 0.015*** 0.11

Proportion-National Brick-n-Click 1.3E-05 1.70*** 0.24 0.58 3.0E-6 0.33*** 0.062 0.50

Retailer factors

Service quality 0.01** 0.20** 0.02* 0.04*** 0.08*** 0.008* 0.030*** 0.15***

National Brick-n-Click 0.16*** 0.39*** 0.09*** 0.06* 0.08* 0.33*** 0.062*** 0.17*

Local Brick-n-Click 0.02 0.03 0.05*** 0.021 0.02 0.25*** 0.09 0.07

Catalog 0.10*** 0.12 0.04** 0.048*** 0.02 0.05*** 0.02* 0.08

Size 33.7*** 34.1*** 5.7* 3.1 2.3 5.8** 2.4*** 1.3**

Interaction effects

Price level service quality 8.2E-6 4.2E-5 1E-4 9.2E-4 2.0E-5 2E-3* 1.7E-3*** 0.012

Log of number of

competitors service quality

6.7E-3 0.07*** 0.03* 0.025** 0.02*** 0.03*** 0.041*** 0.22***

Variance in service

quality service quality

0.04*** 0.27*** 0.29*** 1.81** 0.22** 0.07 0.43*** 1.41**

Proportion of National

Brick-n-ClickNational

Brick-n-Click

1.03** 4.71*** 0.48*** 0.35 0.15 0.13*** 0.59** 0.26*

*Significant at < 0.10, **significant at < 0.05, ***significant at < 0.01.

given their national presence and brand recognition, and (2) appro-

priate use of technology enables these retailers to provide shoppers

additional convenience in terms of being able to switch channels of

transaction frompre-ordering to post-fulfillment, for exampleorder-

ing online and taking delivery offline at a nearby store. Arguably,

such conveniences are part of a higher level of service quality that

a retailer offers and the national retailers are clearly able to charge

a premium for these additional services.14

These findings provideadditional empirical support to previous findings that multichannel

retailers charge higher prices than pure-play retailers (Ancarani and

Shankar 2004).

The Local Brick-n-Click retailers are observed to be(a) charg-

ing significantly lower prices (than pure play retailers) in the PDA

(5 percent lower) product category, (b) significantly higher prices in

the books product category (25 percent higher), and (c) no signifi-

cant difference in their price levels for the remaining 6 categories.

In summary, we find that for a majority of product categories ana-

lyzed, Local Brick-n-Click retailers are not charging a premium for

the facilities that their multichannel operation provides. The Local

Brick-n-Click retailersseem to be using their Internet operations for

bettergeographic reach,as there areminimal marginalcostsin oper-

ating an Internet retail store. In this regard, it should also be noted

that for customers outside of the geographic reach of the physical

store, the Local Brick-and-Click retailer is equivalent to a pure-play

retailer because none of the advantages of multichannel transactions

are available.

Finally, retailers providing mail-order catalogs in addition to

their website are found to be charging higher prices for three prod-

uct categories (camcorder - 10 percent higher, printers and books -

14 Note that in addition to the price levels all National Brick-and-Click

retailers also charge the sales taxes in almost all states. Consequently, con-

sumers are de-factopayinga premiumsignificantlyhigher thanthat indicated

in our results.

5 percent higher). However, for two product categories they charge

lower prices than pure play retailers (PDA - 4 percent lower, and

DVDs - 2 percent lower). These results indicate that providing an

additional channel of transaction (such as a catalog) does influence

retailer pricing strategies, but the effect of providing a catalog in

addition to a website varies by product category. Therefore, we do

not find support for H2b. In summary, our results provide conclu-

sive evidence that National Brick-n-Click retailers charge higherprices than pure-play retailers, while the same is not true for Local

Brick-n-Click retailers and online retailers with mail-order cata-

logs. Our results imply that while previous research has classified

retailers in the online markets as either pure-play or brick-and-click

(Pan et al. 2002; Tang and Xing 2002), there is finer distinction

among brick-and-click retailers, with only the national brick-and-

click retailers consistently charging higher prices than pure-play

retailers.

Moderating influence of market characteristics

Given that the interaction between retailer characteristics and

market characteristics are significant for a majority of the productcategories, we do not discuss the individual main effects of the

influence of market characteristics on the pricing strategies of the

retailers.

Level of competition and service quality

In seven of the eight product categories (all product categories

except camcorders) the coefficient of the interaction between com-

petitive intensity and service quality is negative and significant,

hencesupporting H3. The significance of coefficient indicatesthat an

increase in the level of competition in a product market diminishes

the positive impact that service quality hason the price charged by a

retailer. However, the negative influence of level of competition has


12/16


diminishing returns, indicating a weakening strength of the decrease

in the price charged with increasing number of competitors.

Price level and service quality

We hypothesized that the consumers selection of a retailer is

based on risk averseness particularly when the price of a product is

high. This would lead consumersto preferretailers with high service

quality, who would in turn benefit from the consumers risk averse-

ness (or willingness to pay higher for service quality) by charging

higher prices. The results indicate a positive and significant rela-

tionship between price level and service quality for only Books and

DVDs. Therefore, for six of the eight product categories we do not

find support for H4. Similar to Pan et al. (2003b) we do not find a

strong support for the moderating influence of price levels on the

relationship between service quality and prices. The weak influence

of price levels on the premium charged by retailers could be that

for products with higher price levels, consumers can search online

for product information with ease. Once the consumers decide on a

product to purchase, they make a choice of the retailer based on the

various characteristics of the retailers offering the product. There-

fore, we are unlikely to observe a moderating influence of pricelevels in our analyses.

Nature of competition: variance in service quality and service

quality

We find significant support for a positive interaction effect

between variance in service quality of retailers selling the prod-

uct and the service quality of a retailer (H5a) for seven product

categories (all product categories except books). The retailers

who provide a high level of service quality further increase the

price levels with increase in scope for service differentiation,

which is typically marked by presence of low quality retailers.

In such product-markets with high variation in service qual-

ity it is easier for high service quality retailers to justify their

premiums.

Nature of competition: proportion of national retailers and

national retailer

For six of the eight product categories (camcorders, DVD play-

ers, PDA, Books, DVDs, and Video Games), national retailers

reduce their prices as the proportion of national retailers in the

product-market increases (H5b). Our results show that while the

national retailers charge higher prices than others, they do so

only as long as competition from other national retailers in the

product-market is low. An increase in the number of national

retailers in the product-market diminishes the scope for ser-

vice differentiation for national retailers in terms of multichannel

characteristics, thereby exerting a downward pressure on theirprices.

Total effect of service quality

An important objective of our study is to evaluate the effect

of service quality of a retailer on prices charged. We propose that

after accounting for market characteristics service quality would

have a positive influence on the prices charged. In order to make

inferences regarding the influence of service quality we investi-

gate the total impact of service quality that includes both the main

and the moderating effects. Evaluation of the total effects is par-

ticularly important because the moderating influences of market

characteristics are found to have opposing forces - level of com-

petition is observed to negatively impact the relationship, whereas

the nature of competition is found to positively impact the rela-

tionship between retailers service quality and prices charged. The

total effects of service quality measures the impact of a change in

service quality of a retailer on the prices charged, and is obtained

by taking the first derivative of Eq. (6) with respect to service

quality;15

PIND

ServQi= 10 + 11 [Log(#of competitors)i]+ 12

[VServindj]+ [u1j] (11)

Foreach product category, we plotted thevalues of thederivative

given by (11) for all the observations, that is for all the observed

values of level of competition and the nature of competition. The

plots indicate that the sign of the derivative is positive across the

range of the data used in our study. We can safely conclude that for

all product categories, and over a wide range of values of level of

competition and nature of competition, service quality has a positiveinfluence of the prices charged. The change in the derivative, over

the various values of level and nature of competition are shown in

Fig. 3 for two representative product categoriesPDAs and DVD

Players.

Fig. 3 shows that for both DVD Players and PDAs, the prices

charged by a retailer increases with the variation in service quality

(nature of competition) and decreases at a diminishing rate with the

number of retailers (level of competition. Specifically, we observe

that as the number of retailers offering a product increases beyond

11 for DVD Players, and 20 for PDAs,the diminishing impact of the

level of competition on prices charged is minimized. Overall, the

change in prices charged by the retailer over the range of values for

level and nature of competition is positive for an increase in service

quality.

Independent analyses of pure-play and brick-and-click retailers

It can be argued that brick-and-click retailers have a larger set

of constraints, such as price consistency between the offline stores

and Internet stores that would impact their pricing strategies. These

factors are not accounted for in our analyses and can potentially

confound the observed results. To eliminate any possibility of such

confounding effects, we separately estimated the model coefficients

using only the pure-play retailers and only brick-and-click retailers

(both national and local). The results of our analyses areprovided in

Appendix A. TableA1 shows thatthe results fromthe separateanaly-

ses aresimilarto theresults obtained by pooling both pure-playonlyand brick-and-click retailers. We conducted a pooling test (Kumar

and Leone 1988) to examine whether data for pure-play retailers

and brick-n-click retailers can be combined. We could not reject the

null hypothesis that the pooling is appropriate (for = 0.01). We

find that the coefficients of pure-play only retailers are similar to

the brick-n-click retailers, which implies that the factors outlined

in our study impact the pricing decisions of retailers irrespective of

their channel structure.

15 For Books and DVD players we find a significant positive influence of

pricelevels, which is alsoincluded in equation10 forcalculating total effects.


13/16


Fig. 3. Total effects of service quality on online prices.

Implications, limitations and future research

Theoretical implications

The results from this study contribute significantly to the

current understanding of the sources of price differentiation

by online retailers. Our study extends previous findings on

how retailer characteristics such as service quality and chan-

nels of transaction (Pan et al. 2002; Ancarani and Shankar

2004) impact the prices. We also show how market char-

acteristics such as number of competitors influence price

dispersion in online markets. In particular,our proposed com-prehensive model explains greater amount of variance in

prices and provides empirical evidence that retailer and mar-

ket characteristics interact to determine the prices charged by

a retailer. Our key findings include: (1) a positive influence of

service quality on prices across severalproduct categories; (2)

similar to prior findings (Pan et al. 2002), an increase in num-

ber of competitors induces a downward pressure in prices for

all retailers, albeit at a decreasing rate; (3) for the first time in

the literature, we show how the increase in scope for service

differentiation is leveraged by high service quality retailers to

increase their prices particularly in face of high competition;

(4) interestingly we find that low service quality retailers in

fact seek higher prices in markets characterized by either a

large numberof competitors andlow variation in service qual-

ity or in markets with a large variance in service quality and

few competitors, and (5) National Brick-and-Click retailers

reducetheirpricesinfaceofincreasingcompetitioninformof

other National Brick-and-Click retailers. The findings from

our study pertain to retailer characteristics, market charac-

teristics and their interactive effects, thereby highlighting the

importance of incorporating the hierarchical structure of the

market in a unified empirical model when studying pricing

strategies of online retailers.

Managerial implications

We expect that in a wide variety of product-markets our

findings will serve as benchmarks for retailers choices of

service quality and channels of transaction. Such bench-

marks are especially useful for established retailers who are

in the midst of adding new products to their portfolio, and

for new entrants jointly deciding market entry, service qual-

ity and channel related investments and prices. To provide

an illustration of such a benchmark analysis, we conducted

a post-hoc analysis with comparison of price levels across

eight product-market segments.

Table 6

Comparison of premiums over segments


14/16


Table A1

Results from estimation of Hierarchical Linear Model

Camcorder DVD player PDA Printer Scanner Books DVDs Video games

Pure-play


Log (number of competitors) 0.012 0.43*** 0.18*** 0.14*** 0.07*** 0.10 0.24* 1.03***

Variance in service quality 0.001 0.05*** 0.57** 0.29 0.49 0.35* 1.21** 0.41

Price level 4.5E-6 5.4E-3 1.8E-6 3.2E-4 4.1E-6 6E-6* 0.007** 8.0E-2**

Retailer factors

Service quality 0.07** 0.018** 0.015* 0.19*** 0.14** 0.006*** 0.09* 0.23*

Size 31.4** 35.1*** 2.6* 3.8 3.2 4.2 2.2*** 1.9***

Interaction effects

Price level service quality 4.8E-6 5.5E-5 2.0E-4 1.0E-5 7.2E-6 1E-4 2E-4** 0.008

Log (number of

competitors) service quality

5.1E-3 0.04*** 0.03** 0.06*** 0.04*** 0.01*** 0.015* 0.11**

Variance in service

quality service quality

0.05* 0.34*** 0.31*** 0.04* 0.13** 0.01 0.09* 1.11**

Brick-n-Click only


Log (number of competitors) 0.14 0.96*** 0.04** 0.02** 0.14 0.18 0.02*** 0.06

Variance in service quality 0.009 0.02 0.29 0.59 0.24 0.02 1.9 0.62

Price level 2.2E-5* 4.5E-4 2.8E-6 3.1E-4 5E-6 5.7E-3 0.005 9.2E-3**

Retailer factors

Service Quality 0.05 0.31*** 0.07* 0.01*** 0.08** 0.01** 0.08*** 0.10**

Size 37.0 36.1*** 6.7*** 2.7 2.1 6.1*** 1.2*** 1.1

Interaction effects

Price level service quality 7.1E-6 3.7E-5 9E-5 3.7E-5 2.2E-5 1.3E-3 8.5E-3 3.6E-3

Log (number of competitors)

service quality

7E-3 0.10*** 0.028 0.009* 0.01*** 0.06* 0.058** 0.28**

Variance in service quality

service quality

0.03* 0.21** 0.28*** 0.03 0.01 0.009 0.32** 1.96**

*Significant at < 0.10, **significant at < 0.05, ***significant at < 0.01.

We performed a median split of service quality, level ofcompetition and variance in service quality to obtain two lev-

els (High and Low) for each of the three factors resulting in

eight segments. We then compared the mean price premi-

ums charged by retailers across these segments. The results

of this comparison (based on a MANOVA) are provided in

Table 6.

From Table 6 we obtain the following insights on how the

premium of a retailer with a certain level of service quality

(high, low) varies across market conditions (level and nature

of competition).

1. Low service quality retailer charges lowest premium in

markets with low competition and little scope for service

differentiation (cell 4). Any change in intensity of compe-

tition and/or availability for service differentiation (cells

1, 2, and 3), yield increased premiums for the retailer.

It needs to be emphasized that though the premiums

increase, they are not different from each other, that is

level of premium is not significantly different across cells

1, 2, and 3.

2. In contrast, a high service quality retailer charges high-

est premium in markets characterized by low competition

and low scope for differentiation (cell 8). Additionally,

the high service quality retailer is able to seek similar

high premiums in highly competitive markets with a highpotential for service differentiation (cell 5).

We obtain the following insights on how the premiums

charged between the high and low service quality retailers

varies within same market conditions,

1. High service quality retailer charges significantly higher

than the low service quality retailer when(a) level

of competition and scope for differentiation are high

(cell 5 vs. cell 1), and (b) both level of competition

and scope for service differentiation are low (cell 8 vs.

cell4).

2. Most importantly, under no market condition is the lowservice quality retailer able to charge significantly higher

premium than the higher service quality retailers.

The above analyses, clearly identifies the market condi-

tions that aresuitable for retailers with a given level of service

quality and transaction channels for charging higher prices.

Such an analysis maps out the decision space for retailers,

giving them quantifiable measures of tradeoffs that are crit-

ical for making informed entry in a product market, pricing

decisions and investment levels for strategic positioning in

terms of service quality. Finally, we note that these insights

arebasedon post-hoc analyses of theresults from ourconcep-


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tual model and serve as guidelines for detailed examination

by future research.

Limitations and future research

Current price dispersion research, which includes this

study, is limited by not having access to supply side costdata. Future research needs to address revenue measures that

consider the actual transacted prices. It is possible for the

dispersion of transacted prices are made is different from

the dispersion in retailer prices. Further, we believe that fac-

tors such as product life cycle and timing of market entry

need to be analyzed (Pan et al. 2002, 2003b), implying the

necessity of longitudinal data. Note that pricing is one of the

many strategic decision criteria that a retailer has to manage

in order to optimize profits. For instance, Internet retailers

are also interested in building a market share, and in ensur-

ing consumer retention. Future studies should compare the

influence of service quality across these different decisioncriteria and investigate the process through which they lead

to optimal profits for retailers. Our results indicate channel

choice affects the pricing strategy of the online retailer. In

fact we find that retailers providing multiple channels of

transaction also charge higher prices. Future research should

investigate whether providing multiple channels of transac-

tion also increases the revenues of the retailers, and why

providing multiple channels of transaction affects consumer

choice and willingness to pay. Given that price dispersion

as a metric intrinsically signals the confluence of consumer

search, retailer pricing strategies and informational efficiency

of markets, we hope that our integrated approach will be use-

ful to other researchers examining the interactions between

these competing forces in electronic markets.

Acknowledgements

The authors thank V. Kumar, Jim Marsden, and Alok

Gupta for their helpful comments.

Appendix A. Separate Analysis of Pure-Play and

Brick-And-Click Retailers

(See Table A1).

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