International Journal of Electronic Commerce Internet Pricing, Price Satisfaction, and Customer...

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Internet Pricing, Price Satisfaction, and CustomerSatisfactionYong Cao a , Thomas S. Gruca b & Bruce R. Klemz ca College of Business and Public Policy at the University of Alaska--Anchorageb University of Iowa, Tippie College of Businessc College of Business at Winona State UniversityPublished online: 08 Dec 2014.

To cite this article: Yong Cao , Thomas S. Gruca & Bruce R. Klemz (2003) Internet Pricing, Price Satisfaction, and CustomerSatisfaction, International Journal of Electronic Commerce, 8:2, 31-50

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International Journal of Electronic Commerce / Winter 2003–4, Vol. 8, No. 2, pp. 31–50.Copyright © 2004 M.E. Sharpe, Inc. All rights reserved.

1086-4415/2004 $9.50 + 0.00.

Internet Pricing, Price Satisfaction, andCustomer Satisfaction

Yong Cao, Thomas S. Gruca, and Bruce R. Klemz

ABSTRACT: Given the challenges of increasing margins and building a loyal customerbase, the interaction between pricing and customer satisfaction is of great interest to e-tailers. The present research models the relationships between e-tailer pricing, price satis-faction, and satisfaction with the ordering and fulfillment processes. The model is calibratedusing data about book e-tailers from BizRate.com and a database of market-basket prices.By providing a satisfactory ordering process, e-tailers can somewhat ameliorate the nega-tive effects of higher prices and will have higher overall ratings for fulfillment satisfaction.This is critical because fulfillment satisfaction creates loyal customers. As expected, higherprices lead to lower price satisfaction, but the effect of price satisfaction on fulfillmentsatisfaction was negative. This unexpected result has important implications for e-tailersintending to compete based on low prices. Increased levels of price satisfaction due tolow prices do not positively affect satisfaction with the fulfillment process. Therefore, com-peting on price may not be a viable long-term strategy for on-line retailers.

KEY WORDS AND PHRASES: Customer satisfaction, e-commerce, fulfillment, on-line pric-ing, ordering process, Web site.

Pricing and customer satisfaction are critical issues for Internet retailers. Inthe past, many Internet retailers focused on building up a large customer base,using some combination of low prices and high advertising spending to at-tract new buyers. For this approach to succeed, e-tailers had to transform first-time buyers into long-term, loyal customers [35]. The length and depth of acustomer’s relationship with an e-tailer determines whether the e-tailer canrecover its acquisition costs, a prerequisite for profitability [35].

Many Internet retailers used unsustainably low prices to attract a largecustomer base [32]. However, their experiences with this strategy have notbeen very favorable. Consumers came to view such price levels as normaland expected. This resulted in consumer resistance to repurchasing at highernormal (non-promotional) prices [32]. McKinsey and Company conclude thatmost e-tailers “lack a pricing model that provides a solid platform for revenuegrowth” [32].

At the same time, many Internet retailers are failing in other ways to satisfythe customers they have attracted to their sites. A 1999 study by the BostonConsulting Group showed that 28 percent of all on-line purchase attemptsfail, as a result of problems ranging from Web site performance to productdelivery [6]. This is especially important in the conversion of a first-time buyerinto a loyal customer. Satisfied first-time buyers purchase more and purchase

The authors thank Lopo Rego for his invaluable help with this manuscript. Theproject was supported in part by a faculty development grant to Prof. Yong Cao.

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32 CAO, GRUCA, AND KLEMZ

more often than those who are dissatisfied [6]. In all, Internet retailers losemore than $6 billion a year in sales due to dissatisfaction with the on-lineordering process [33].

This paper examines the relationship between pricing, price satisfaction,and satisfaction with the on-line ordering and fulfillment experiences. In con-trast to research using off-line businesses, price and price satisfaction weremeasured separately [28, 44]. The measure of the price level of an e-tailer isbased on a market basket of identical items [3]. Given the importance of ship-ping and handling charges (S&H) for on-line buying behavior [7], price satis-faction was modeled as a function of satisfaction with the product price(s) aswell as the associated delivery charges.

Following current research on brick-and-mortar businesses [10, 21, 28], theresearch described in this article modeled and measured the ordering andfulfillment stages of the on-line shopping experience independently, reflect-ing their separation in time. Prior studies used simultaneous retrospectivemeasures of a consumer’s entire on-line experience [4, 42]. The data used herewere gathered soon after each experience (ordering or fulfillment). This methodis less prone to response-effect biases due to encoding or retrieval problemson the part of respondents [41].

The ultimate focus of the model presented in this paper is to determine thecarry-over effects of price satisfaction and ordering satisfaction on fulfillmentsatisfaction. The experience of Dell and other successful on-line businessesshows that satisfaction with the fulfillment process, including delivering theright product, delivering the product on time, and responding to customerinquiries, is a key driver of customer loyalty [35, p. 112]. Since e-tailers spenda lot to acquire customers [19], transforming customers into loyal buyers is akey component of improved financial performance.

Understanding the effect of prices and price satisfaction on fulfillment sat-isfaction has important strategic implications for an e-tailer’s pricing strategy.If one assumes that lower prices lead to increased price satisfaction, then lowprices should provide an additional boost to fulfillment satisfaction, if thecarry-over effect turns out to be positive. If there is no carry-over effect ofprice satisfaction on fulfillment satisfaction, then low-priced e-tailers will notbe able to reap any benefits from increased fulfillment satisfaction. Therefore,their sacrifice in margin will not result in increased customer loyalty. Clearly,the relationships between price levels, price satisfaction, and fulfillment satis-faction affect the long-term viability of the low-priced e-tailing strategy.

Model Development

The model proposed here arises from several recent developments in the lit-erature on the Internet and customer satisfaction. The first development is theexplicit recognition of the sequential nature of the customer’s experience inmodels of customer satisfaction. This idea began with research that expandedthe definition of quality beyond the traditional focus on product quality. Forexample, a study of the division selling equipment to ATT’s business custom-ers found that overall quality was affected by customers’ perceptions of the

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 33

quality of installation, repair, and billing [27]. Another study of 18 companiesin a variety of manufacturing industries showed that customer satisfactionwith a firm’s product was determined by satisfaction with the order-manage-ment cycle [39]. This included production scheduling, fulfillment, shipping,installation, billing, and returns.

Researchers have recently extended this concept from the services accom-panying the sale of tangible products to the intangible services offered in thehospitality industry. For example, Danaher and Mattsson separately mea-sured conference participants’ satisfaction with their arrival, coffee break,lunch, and conference room [10]. Lemmink, de Ruyter, and Wetzels separateda patron’s restaurant experience into four stages: reception, ordering, meal,and payment [28].

Hoyer, Herrmann, and Huber combined these two streams of research withtheir multifaceted model of satisfaction with an automobile purchase [21]. Intheir study, overall satisfaction is determined by a variety of factors, includ-ing satisfaction with the car itself, satisfaction with the service provided bythe salesperson, and satisfaction with the deal (including the amount receivedfor a trade-in).

In the present paper, following the stream of research described above, theon-line buying process for a typical e-tailer is modeled as a two-step sequence.In the first step, the customer searches the site for products, compares variousfeatures, makes a selection, and places an order. This step is referred to as theordering process. The second step begins after the order is placed. Before theorder arrives, the customer may be able to track its progress on-line. Once theorder arrives, the customer decides whether to keep or return the item. Thesesteps in the encounter with the e-tailer are described as the fulfillment process.

There is an important difference between this model of e-tailer satisfactionand prior studies (e.g., [44, 47]). The model’s measures of satisfaction with theordering process are not expectations. They are measures of the customer’ssatisfaction with the experience during the ordering process (e.g., Web siteperformance and product selection). The same is true with respect to themodel’s measures of the fulfillment process. These satisfaction measures areassociated with customers’ experiences after the sale (e.g., tracking capabili-ties and on-time delivery). Consistent with prior research [10, 11, 21, 28], thedeterminants of customer satisfaction are different for the ordering and ful-fillment stages of the buying process.

The second development incorporated into the present study is a separatemeasurement of price satisfaction. In his model, Gale measures the customer’ssatisfaction with the physical product separately from the customer’s satis-faction with the price paid [16]. This approach is used by well-known compa-nies, such as AT&T [16] and McKinsey & Co. [29]. In a study of two services(communications and entertainment), Bolton and Lemon examine how cus-tomer satisfaction with price affects overall satisfaction [5]. Their construct,labeled “price equity,” is operationalized as “satisfaction with price” in thestudy of the communications service and “value for the money” in the enter-tainment study. In both studies, they find that satisfaction with price has apositive impact on satisfaction with the service provided. Greater satisfac-tion, in turn, leads to increased usage of the service over time.

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In addition to price satisfaction, the present study also measures the buyer’ssatisfaction with S&H. A study by Jupiter Media Metrix found that 63 percentof on-line shoppers consider high S&H a deterrent to purchases on-line [7].Consequently, overall price satisfaction is determined by satisfaction with theWeb site’s S&H as well as with the price paid for the product.

The third development incorporated in the study is a market-basket modelthat captures differences in prices across e-tailers. This approach has beenused by Bailey and others to document the level of price dispersion in Internetmarkets [3]. In earlier studies of the effect of price on satisfaction, the measureof price level was either indirect or restricted to two levels in an experimentaldesign [28, 44].

Figure 1 reflects these developments in the literature and summarizes thenature of the relationships under study.

Path 1: E-tailer Price on Price Satisfaction

The actual price level that an e-tailer charges its customers should have a nega-tive effect on price satisfaction. The Internet makes it easy for customers to com-pare prices from multiple outlets using a price search engine. If customers findthat an e-tailer has consistently higher prices, they may perceive this as unfairbecause identical items are available elsewhere at a lower price [40]. Bolton andLemon suggest that such perceptions of unfairness should lead customers to beless satisfied with the price they pay the higher priced e-tailer [5].

Path 2: Satisfaction with the Ordering Process onPrice Satisfaction

Since Internet shoppers can easily compare prices across various Web sites, itis often assumed that they are more price sensitive. However, Web site fea-tures that allow a customer to find exactly the product desired could reduceprice sensitivity. An experiment in on-line wine shopping by Lynch and Arielyfound that reducing the cost of finding information about different productsreduced price sensitivity [31]. A survey-based study of hotel patrons byShankar, Rangaswamy, and Pusateri confirmed these results [38].

In the present study, satisfaction with the ordering process was determinedby the satisfaction ratings for ease of ordering, product selection, product in-formation, and Web site performance. These aspects of the Web site allow theshopper to quickly and easily find exactly the right product. Therefore, onemay expect that high levels of satisfaction with the ordering process will leadto reduced price sensitivity as reflected in increased price satisfaction.

Path 3: Satisfaction with Ordering Process on Satisfactionwith Fulfillment Process

Research on customer satisfaction with products shows that overall satisfac-tion is determined by several pre- and post-purchase processes, including the

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sales encounter, order fulfillment, and after-delivery follow-up [27, 39]. Sincethis type of research tends to measure overall quality after the fact, there is noopportunity to ascertain carry-over effects from one process to another (e.g.,the impact of satisfaction with the sales process on satisfaction with the fulfill-ment process). This problem is also evident in the research on services. Forexample, in their study of satisfaction with different phases of a hotel serviceencounter, Danaher and Mattsson measured customer satisfaction for a se-quence of service events (check-in, meals, etc.) for hotel guests [11]. However,they did not examine the degree of carry-over in satisfaction scores from onephase of service to another.

This shortcoming was rectified in a more recent study by Lemmink, deRuyter, and Wetzels of customer satisfaction with various aspects of a restau-rant experience [28]. They found that there was generally a significant degreeof carry-over of satisfaction from one experience to the subsequent experi-ence (e.g., from the meal to the checkout process). Halo effects are one pos-sible source of these carry-over effects [20].

In the retail sector, Hoyer, Herrman, and Huber examined how satisfactionwith the various steps in the car-buying process affected overall satisfactionwith the purchase of a new car [21]. Their results show customer satisfactionwith the service received from the salespeople had a positive effect on satis-faction with the condition of the car (e.g., condition of the car at delivery).

As a service, on-line retailing has a level of intangibility comparable to autoretailing and restaurants. Consequently, one expects to observe the same carry-over effects between the ordering and fulfillment processes. Specifically, oneexpects that a customer who is more satisfied with the ordering process wouldalso be more satisfied with the fulfillment process.

Figure 1. Conceptual Model of E-tailer Price, Price Satisfaction, andSatisfaction with Ordering and Fulfillment Processes

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Path 4: Price Satisfaction on Satisfaction with theFulfillment Process

In their study of satisfaction with the sequential phases of restaurant service,Lemmink et al. found that “value for the money” was the most importantdeterminant of satisfaction with the meal for non-business customers [28].Furthermore, overall satisfaction with the meal had a significant carry-overeffect on customer satisfaction with the next stage (i.e., the checkout process).Since the “value for the money” item measured a construct similar to pricesatisfaction, one may argue that the carry-over effect of price satisfaction onsubsequent experiences in a restaurant is significant and positive. Based onthis earlier study, therefore, one may expect price satisfaction to have a posi-tive effect on satisfaction with the fulfillment process in the on-line buyingprocess.

Measuring the Internet ShoppingExperience with BizRate

The present model of the on-line buying process explicitly separates the or-dering and fulfillment processes. This reflects the delay that customers expe-rience between the time they complete their on-line transaction and deliveryof the ordered merchandise. Szymanski and Hise’s model of on-line satisfac-tion measures all aspects of the consumer’s interaction with an e-tailer’s sitesimultaneously [42]. Presumably, the responses provided by this type of ret-rospective survey are retrieved from memory. This introduces potential re-sponse biases due to encoding or retrieval problems [41]. Perhaps therespondents constructed their answers in response to the survey questions[41]. One would expect to obtain better measures near the point in time of theconsumer’s experience. Thus it makes sense to question consumers about theirexperiences with an e-tailer’s ordering process soon after the order is placedrather than some days later when the merchandise arrives.

Measures of customers’ experiences with the ordering and fulfillment pro-cesses, as well as measures of price satisfaction, were obtained fromBizRate.com, an Internet marketing research company that collects data froma panel of more than 200,000 Internet shoppers. Panelists are invited to com-plete a survey whenever they complete an Internet purchase. To encouragepanelist participation, BizRate offers the opportunity to win $50 in a “Spinand Win” game as well as entry in a future drawing for a $1,000 prize. Re-spondents rate the ordering process, price, and S&H using a 10-point scale onwhich 10 indicates a high degree of satisfaction. Respondents are also askedto indicate the expected time of delivery of the ordered merchandise.

A few days after the expected order-delivery date, a follow-up survey issent to the respondent via e-mail. This survey asks respondents to indicatetheir level of satisfaction with the fulfillment process using the same 10-point scale. The survey also asks the respondent’s likelihood to buy fromthe same e-tailer in the future, but at the time of the present study, these data

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were available only to the concerned e-tailer, and not to other e-tailers or toresearchers.

This data-collection method is superior in some ways to other methodsproposed in the literature. Zhang and von Dran examined the determinantsof Web-site quality across a large number of domains, but their study had nomeasures of the quality of the off-line experiences associated with the givenWeb site (i.e., fulfillment) [47]. The authors of the WebQual scale did includemeasures of the fulfillment by on-line booksellers, but almost half of theirrespondents provided site ratings without the benefit of actual experience [4].In contrast, all of the ratings of a Web site available from BizRate are based onactual buyer experiences.

Data to test the model in Figure 1 were collected from aggregated mea-sures of customer satisfaction reported on the BizRate.com Web site. The mea-sures are similar to the market-level indices used in cross-industry researchon customer satisfaction and firm performance by Fornell, Johnson, Ander-son, Cha, and Bryant [15], among others.

BizRate is a marketing research company founded in 1996 [8]. Its revenuecomes from multiple sources. The first is selling detailed reports about theircustomers to some 3,600 clients (as of June 2000). In 1999, data provided byBizRate were chosen by the nonprofit Consumers Union, the publisher of Con-sumer Reports, to provide information to its subscribers about consumers’ on-line experiences [8]. For present purposes, the BizRate data can be consideredanalogous to the quality ratings from Consumer Reports used in earlier studiesof the price/quality relationship [9].

The BizRate site also functions as a shopping portal. BizRate collects a com-mission from each sale initiated through its Web site [45]. The addition of e-commerce capabilities has greatly increased the popularity of the BizRate.comsite. From mid-2000 to early 2001, BizRate.com was one of the most visitedshopping sites on the Web [18].

Measures of Satisfaction with theOrdering Process

BizRate measures customer satisfaction with the ordering process after thecustomer has confirmed the order. The customer provides four measures ofsatisfaction with respect to the ordering process: ease of ordering, productselection, product information, and Web site performance.

The actual question asked of respondents is: “How satisfied are you witheach of the following aspects of this on-line purchase?” The various aspects ofthe ordering experience follow. Customers respond using a 10-point scale onwhich 1 means “not at all satisfied” and 10 means “highly satisfied.”

The aspects of the ordering process measured by BizRate are consistentwith current thinking in the business press, both academic and popular, onproviding convenience or increasing customer satisfaction on-line [25, 30, 38,42]. The individual items and associated citations in the literature are pro-vided in Table 1.

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38 CAO, GRUCA, AND KLEMZC

ons

tru

ctM

easu

reD

efin

itio

nCi

tati

ons

Satis

fact

ion

with

Ease

of u

seC

onve

nien

ce a

nd s

peed

of o

rder

ing

Kauf

man

[25]

, Sei

ders

, Ber

ry, a

nd G

resh

am [3

7],

orde

ring

proc

ess

Szym

ansk

i and

Hise

[42

]

Prod

uct s

elec

tion

Brea

dth/

dept

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pro

duct

s of

fere

dSe

ider

s, B

erry

, and

Gre

sham

[37]

, Szy

man

ski a

ndH

ise [4

2]

Prod

uct i

nfor

mat

ion

Info

rmat

ion

quan

tity,

qua

lity,

and

rele

vanc

eLo

hse

and

Spill

er [3

0], S

eide

rs, B

erry

, and

Gre

sham

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man

ski a

nd H

ise [4

2]

Web

site

per

form

ance

Layo

ut, l

inks

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ture

s, im

ages

, and

spe

edKa

ufm

an [2

5], S

eide

rs, B

erry

, and

Gre

sham

[37]

,Sz

yman

ski a

nd H

ise [4

2]

Pric

e sa

tisfa

ctio

nPr

ice

Pric

es re

lativ

e to

sim

ilar s

tore

sBo

lton

and

Lem

on [5

], G

ale

[16

]

Ship

ping

and

han

dlin

g ch

arge

Cha

rges

and

opt

ions

Cas

sar [

7]

Satis

fact

ion

with

On-

time

deliv

ery

Expe

cted

vs.

act

ual d

eliv

ery

date

Hal

l [17

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ichh

eld

and

Sche

fter [

35],

Seid

ers,

fulfi

llmen

t pro

cess

Berr

y, a

nd G

resh

am [3

7]

Prod

uct r

epre

sent

atio

nPr

oduc

t des

crip

tion/

depi

ctio

n vs

. wha

t you

rece

ived

Reic

hhel

d an

d Sc

hefte

r [35

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Cus

tom

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uppo

rtSt

atus

upd

ates

and

com

plai

nt/q

uest

ion

hand

ling

El S

awy

and

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les

[13]

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se a

nd S

pille

r [30

],Re

ichh

eld

and

Sche

fter [

35]

Ord

er tr

acki

ngA

bilit

y to

effe

ctiv

ely

track

ord

ers

Hal

l [17

]

Pric

eTo

tal p

rice

of m

arke

t bas

ket o

f 9Ba

iley

[3]

iden

tical

item

s

Tab

le 1

. Biz

Ra

te M

easu

res

of

Sati

sfa

ctio

n a

nd C

ita

tio

ns.

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Measures of Price Satisfaction

Two items from the BizRate survey were used to measure price satisfaction onthe whole. The first was satisfaction with the product price, the second, satis-faction with S&H. These were measured using the same 10-point scale as theother elements of the ordering process. These measures are collected at thesame time as those associated with the ordering process.

The second item (satisfaction with S&H) is important. A study by JupiterMedia Metrix found that 63 percent of on-line shoppers considered high S&Ha deterrent to purchases on-line [7].

Measures of Satisfaction with the Fulfillment Process

BizRate collects measures on the fulfillment process after the expected date ofproduct delivery. Satisfaction ratings for four aspects of the fulfillment processare collected from the respondents to the survey about satisfaction with priceand the ordering process: on-time delivery, order tracking, product represen-tation, and customer support. These measures of satisfaction are also based onthe same 10-point scale, with 10 indicating a high degree of satisfaction.

Like the other BizRate measures, these items are consistent with currentthinking in the academic and popular business press regarding building abusiness on-line [17, 30, 35, 37]. For example, as mentioned above, Dell com-puter found that on-time delivery, matching the product delivered to the onerepresented on the Web site, and providing accessible customer service arekey drivers of customer loyalty [35].

Price Measures

Price levels (in dollars) for each e-tailer were measured weekly and aggre-gated using a market basket of identical products. This is the same approachused by Bailey to measure price dispersion in the Internet markets for books,CDs, and software [3].

Empirical Study

The setting for the present study was the on-line book market from Septem-ber 3, 2000, to December 22, 2000. The data for the study came from two sources.The first was the BizRate.com Web site, which provided weekly average rat-ings of customer satisfaction with items corresponding to the ordering andfulfillment processes as well as satisfaction measures for price and S&H. Eachweekly measure was based on a large sample of 500–10,000 respondents.

The second source of data was a longitudinal database of book prices col-lected weekly over the same period from the Web sites of nine e-tailers. Amarket basket of nine titles was constructed that included best sellers, children’sbooks, and reference books. The list of titles is available from the authors.

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The coefficient of variation is a measure of price variation across stores.Bailey’s market basket [3] consisted of 125 titles. The coefficient of variation inthe study’s market basket ranged from 9 to 11 percent, slightly higher thanBailey’s range of 7.5 to 9.75 percent. There are two possible reasons for thisminor discrepancy. First, the study considered more e-tailers (9 vs. 6), includ-ing some with much lower prices than the most popular book e-tailers. Sec-ond, Bailey’s market basket contained many more items. Thus the study’smarket basket fairly represented the variations in prices across the e-tailers inthe study.

Sample Statistics

Table 2 presents the sample means and standard deviations of the measuresacross all nine e-tailers for the entire 11-week period.

The means are all above 8 on a 10-point scale. Some of this ceiling effectmay be due to the selection of respondents from the pool of shoppers whohave completed a transaction. BizRate does not survey Internet shoppers whofail to complete their buying process.1

Note that the variances of the measures of the fulfillment process are largerthan the satisfaction measures for the ordering process. This implies that thereis a much wider variation in satisfaction with the fulfillment process thanwith the ordering process. Since these measures are strongly related to cus-tomer retention, this finding should be of concern to e-tailers seeking to buildlong-term relationships with their customers.

Model Estimation

There are several methods for analyzing the complex multiple dependencerelationships represented by Figure 1. For example, maximum-likelihood es-timation, as used in LISREL [24], is based on a “factor construct” approachthat places large demands on the data. Partial least squares (PLS) is based ona “component construct” approach [46]. Previous research suggests that PLSis better suited to explaining complex relationships than formal theory testing[14]. In short, PLS models are prediction-oriented, whereas LISREL-basedmodels are parameter-oriented.

PLS differs from LISREL in that it estimates parameters via an iterativeestimation procedure. As a consequence of relying on iteration and ordinaryleast squares (OLS), identification is not an issue in PLS estimation. Therefore,researchers may utilize smaller samples than are required by covariance struc-tural techniques such as LISREL. In addition, multivariate normality is not arequirement of PLS. Therefore, PLS places the same demands on the data asfound in OLS estimation.

To estimate the model proposed in Figure 1, four latent variables were speci-fied: price, satisfaction with the ordering process, price satisfaction, and satis-faction with the fulfillment process. The price latent variable is associated withthe manifest variable of the price of the market basket of identical books. There

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are four manifest variables associated with the ordering-process latent vari-able: satisfaction ratings of ease of ordering, product selection, product infor-mation, and Web site performance. The price satisfaction latent variable isassociated with its own manifest variable as well as the S&H satisfaction mani-fest variable. There are also four manifest variables associated with the order-ing-process latent variable: satisfaction ratings of on-time delivery, ordertracking, product representation, and customer support.

The EzPLSTM software was used to estimate the PLS model [26]. All theparameter estimates in this research are expressed in a standardized form tocompare their relative strengths. Parameter significance is assessed using a“jack-knife” technique to construct a distribution of parameter estimates [12].Using these estimates, the mean and standard errors of the parameter esti-mates are used to assess their statistical significance.

PLS Model Results

The results are presented in Figure 2, and inter-construct correlations are pre-sented in Table 3. As can be seen, the manifest variables have strong relation-ships with their associated latent variables. With the exception of the measureof S&H satisfaction, all of the estimated factor loadings are greater than 0.9and are significant at p < 0.01. With a loading of nearly 0.60, satisfaction withS&H has a large (and significant) impact on overall satisfaction with price.

Price on Price Satisfaction

The coefficient for Path 1 is negative, as expected. In the Internet marketplace,customers can easily compare prices across e-tailers. Consequently, on-line

M SD Minimum Maximum

Satisfaction with ordering processEase of use 8.80 0.44 8.0 10.0Product selection 8.65 0.46 7.5 9.2Product information 8.24 0.56 7.3 9.5Web site performance 8.62 0.52 7.6 10.0

Price satisfactionPrice 8.66 0.52 7.7 9.5Shipping and handling charges 8.15 0.73 7.3 9.7

Satisfaction with fulfillment processOn-time delivery 8.18 0.82 6.0 9.1Product representation 8.69 0.48 7.5 9.5Order tracking 8.01 0.92 5.9 9.0Customer support 8.12 0.74 6.3 8.9

Table 2. Sample Statistics for Satisfaction Measures.

Note: Averages across nine e-tailers, September 3, 2000–December 22, 2000.

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shoppers are significantly less satisfied with the prices they pay higher-pricede-tailers.

Although this result may seem obvious, it is important to see the strong ef-fect of pricing levels on price satisfaction in an environment in which custom-ers have ready access to prices across e-tailing outlets. The differences in pricesmeasured across e-tailers have a significant impact on price satisfaction.

Satisfaction with Ordering Process on Price Satisfaction

The sign of this path (Path 2) suggests that e-tailers with a superior orderingprocess are somewhat insulated from the possibly negative effects of higher prices.This result is consistent with Anderson’s finding that higher levels of generalcustomer satisfaction were associated with decreased price sensitivity [1].

The positive effect of satisfaction with the ordering process on price satis-faction (0.33) is smaller, in absolute value, than the negative effect of higherprices on price satisfaction (–0.512). This suggests that on-line buyers are notpure “bargain hunters,” as suggested by Sinha [40], nor are they solely inter-ested in the convenience of Internet shopping without concern about whatthey pay. This result suggests that the sample, as a group, reflects a mixture ofboth of these extreme representations of the “typical” Internet shopper.

Figure 2. Partial Least Squares (PLS) ResultsNote: Path coefficient means and standard errors in parentheses.

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Satisfaction with Ordering Process on Satisfaction with Fulfillment Process

The coefficient for the path between satisfaction with the ordering processand satisfaction with the fulfillment process (Path 3) is positive (0.668). Thisresult is consistent with the positive carry-over observed in studies of satis-faction in the hospitality industry and auto retailing [21, 28].

Price Satisfaction on Satisfaction with Fulfillment Process

The most surprising finding is that the relationship between price satisfactionand satisfaction with the fulfillment process is negative (Path 4: –0.43), theopposite of what was expected.

One possible explanation for this discrepancy is the difference between thedata-collection procedures used in this study and in prior research. Varki andColgate, for example, studied subjects who were evaluating their satisfactionwith a bank—the measures of price perception and overall satisfaction werenearly simultaneous [43]. In the present study, however, the measurements ofprice satisfaction and satisfaction with the fulfillment process were separated byseveral days. The results may reflect the transient nature of price satisfaction.

In prior studies, price satisfaction or perceptions were measured simulta-neously or near the time of the service encounter. In the Internet environment,there is necessarily a time lapse (i.e., shipment limitations for product pur-chases) between the measurement of price satisfaction and the fulfillment pro-cess. In the interim period between the completion of the on-line orderingprocess and the delivery of the product, typical consumers would have mademany purchasing decisions with their attendant evaluations of the price theypaid. This may inhibit the carry-over effect of price satisfaction to their expec-tations for the post-purchase experience.

If price satisfaction is not enduring, it should have little effect on satisfac-tion with the fulfillment process due to a regression to the mean. However, thestudy found a significant negative relationship. This result parallels the find-ings in the Voss et al. study with respect to the relationship between pricesatisfaction measured before the customer’s experience and overall satisfaction[44]. (Recall that the relationship between price satisfaction measured after thesimulated experience and overall satisfaction was positive.) This negative re-

Satisfaction Satisfactionwith with

Price ordering fulfillmentConstruct satisfaction process process

Price satisfaction 0.43* 0.24 –0.13Satisfaction with ordering process 0.84 0.41Satisfaction with fulfillment process 0.93

Table 3. Intra- and Inter-construct Correlation Matrix.

*Average inter-item correlation

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lationship was stronger when there was inconsistency between the price andthe quality of the service encounter (high quality/low price or low quality/high price).

Another possible explanation for the inconsistency between the results ofthis study and of earlier studies is the difference in the way satisfaction ismodeled [28, 44]. In prior research, the central construct was “overall satisfac-tion,” measured after the consumer’s experience. This focus is motivated bythe demonstrated influence of overall customer satisfaction on future con-sumer behavior (e.g., [2, 34]). In the present study, satisfaction with the “ful-fillment process” is considered the key construct because prior researchsuggests that customers whose on-line orders are filled correctly and on timeare more likely to purchase again in the future [35]. Which of these approachesis more appropriate for modeling the behavior of e-tail customers is not wellknown and represents an important area for future research.

Discussion

The results of the present study show that e-tailers benefit in two ways fromproviding a satisfying ordering experience. First, they have higher levels ofprice satisfaction. This effect insulates them somewhat from any negative ef-fects of charging higher prices. Second, the positive experience during theordering process has a significant positive effect on satisfaction with the ful-fillment process. This is important because satisfaction with the fulfillmentprocess is key to building a long-term relationship with an on-line consumer.The present study suggests that providing a satisfying ordering experience isan important factor in long-term success for e-tailers. It helps to increase mar-gins and build a loyal customer base.

The negative effect of price satisfaction on satisfaction with the fulfillmentprocess is large and significant. There are two possible explanations for thisfinding. The first alternative examines how customer self-selection might af-fect expectations for the fulfillment process differently for high- and low-pricede-tailers. The second alternative considers the possibility of systematic varia-tions in performance across high- and low-priced e-tailers. Neither of thesepossibilities bodes well for e-tailers that intend to compete on price as a long-term strategy in the Internet marketplace.

Customer Self-Selection

One way to interpret the negative effect of price satisfaction on satisfactionwith the fulfillment process is by looking at the relationship between satisfac-tion and consumer price tolerance (i.e., the maximum amount a customer iswilling to spend before switching to another product or provider). Considerthe implications of systematic variations in price tolerance for customers ofhigh- and low-priced e-tailers. Since prices are easily compared across e-tailers,customers with a low level of price tolerance would take the time to compari-son-shop using a price search engine. They would self-select to shop at e-

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tailers with lower prices [40]. On the other hand, customers with a high levelof price tolerance may be more convenience-driven [35]. They would choosean e-tailer based on such non-price criteria as off-line advertising and word ofmouth. Customers with low price tolerance would get the low prices theyseek and would have higher levels of price satisfaction than customers withhigh price tolerance, who are more concerned with convenience.

Differences in price tolerance could be based on individual traits. Somepeople are attracted to the Internet because they are bargain hunters, and theconvenience attracts others. However, differences in price tolerance are prob-ably a function of experience with on-line shopping. In a 1999 survey, theBoston Consulting Group found that purchase decisions by first-time on-linebuyers were more likely to be convenience-driven than price-driven [6]. Incontrast, more experienced on-line customers would have a lower price toler-ance and the skills needed to take advantage of the available price searchtools. This is consistent with the analysis of Media Metrix data by Johnson etal., who found that more active on-line shoppers searched more sites beforemaking a purchase [22].

Increased experience with on-line shopping would also raise the expecta-tions of customers with low price tolerance with respect to the fulfillmentprocess. If their earlier experiences were negative, they might not continue toshop on-line. A BCG study found that 28 percent of on-line shoppers whosuffered a failed purchase attempt stopped shopping on-line altogether [6]. Afurther 23 percent stopped purchasing at the site in question. One may pre-sume that veteran on-line shoppers have generally had successful early expe-riences (otherwise, they probably would have stopped shopping on-line).Therefore, successful on-line shopping experience would lead to increasedexpectations regarding the fulfillment process.

Customers with high price tolerance, on the other hand, may not have anyprior experience upon which to generate expectations with respect to the fulfill-ment process [23]. Alternatively, they may feel that most of the convenienceelement of on-line shopping is realized in the ordering process (i.e., wide selec-tion, continuous shopping hours, not having to drive to a mall, etc.), and inconsequence they have relatively lower expectations for the fulfillment process.

If customers with a low level of price tolerance (whether an individual traitor acquired though experience) choose low-priced e-tailers and have higherexpectations for the fulfillment process, then one would observe the negativerelationship seen in the model. Consequently, low-priced e-tailers may be at-tracting more experienced on-line shoppers with lower price tolerances whoalso have very high expectations for the fulfillment process. Keeping such cus-tomers happy and staying profitable may not be a viable long-term strategy.

Not Following Through

An alternative explanation for the negative relationship between price satis-faction and satisfaction with the fulfillment process lays the blame at the feetof low-priced e-tailers. The negative sign of Path 4 in Figure 2 may be causedby a lack of follow-up service by low-priced e-tailers. Some e-tailers reduce

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costs by carrying little inventory and using out-sourced shipping services ornon-FedEx/UPS package delivery companies. Each of these cost-cutting de-cisions could lead to a negative fulfillment experience, thereby resulting inthe observed negative relationship.

In this scenario, it appears that low-priced sites enter the e-tailer game butdo not follow through. Specifically, they do not seem to follow up with after-purchase services. This might be expected, because such activities as orderfulfillment (pick, pack, and ship), delivery, and customer service (via e-mailor phone) have a larger human element than the ordering process, and thusthe service received by customers is more variable. However, only the high-priced e-tailer has the margins to make a large enough investment in the peopleand systems to meet customers’ expectations.

Whether the negative relationship between price satisfaction and satisfac-tion with the fulfillment process is due to differences in expectations or per-formance between high- and low-priced e-tailers, neither interpretation favorsthe strategy of competing on price in the on-line marketplace. If consumersare selecting e-tailers based on their price tolerance, then low-priced e-tailerswill be expected to provide both low prices and excellent after-sale services.However, low-priced e-tailers may not have sufficient margins to supportexpensive and highly human-intensive fulfillment activities. Such a situationdoes not augur well for e-tailers using low prices as their primary competitiveadvantage.

Implications for Future Research

The study described in this article extends our understanding of customersatisfaction in some important ways. It shows that the carry-over effect ofsatisfaction with one stage of a service encounter is persistent over a rela-tively long period. The measurements of satisfaction with the ordering andfulfillment processes used in the study were separated by several days, a farlonger period than in any previous study (e.g., [18, 28]).

As shown above, there are several possible ways to research importantInternet business problems using readily available on-line resources. Pricingdata were collected over time and combined with customer-satisfaction datafrom BizRate. The resulting analysis sheds light on how providing a satisfy-ing ordering process can play an important role in achieving an e-tailer’s goalsof immediate profitability and building a loyal base of customers.

Internet e-tailing is a very different and differentially complex business, ascompared to traditional retailing and many off-line services [36]. Every trans-action involves a number of contact points with the consumer, time lags, andthird parties, such as credit-card clearance firms and delivery companies.Consequently, there are many opportunities for an e-tailer to delight or disap-point customers. An interesting area for future research raised by the presentstudy is the degree of permanence and the direction of influence of price sat-isfaction on satisfaction with the fulfillment process. The study’s results sug-gest that customers do not long remain delighted with lower prices. Whetherthis is a natural decay, a function of customer self-selection, or the fault of the

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e-tailer, the finding raises serious issues for e-tailers hoping to compete onprice as a long-term strategy.

Conclusion

There should be great academic interest in satisfaction measures like those postedon BizRate. Consumers and businesses make important decisions based uponthese data. Consumers choose which sites to visit, and e-tailers monitor thebuying public’s opinion of their business practices. The present exploratorystudy of a single product category provides some interesting preliminary in-sight into how price perceptions are formed on the Internet and how they affectcustomer satisfaction. “Satisfying the customer” is the mantra of most busi-nesses today. Understanding the role of price in this process is critical.

NOTE

1. Several studies have used the panel to determine why some shoppers do notcomplete their purchases. These data are not part of the weekly satisfaction scoresused here.

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YONG CAO ([email protected]) is an assistant professor of marketing in the Col-lege of Business and Public Policy at the University of Alaska—Anchorage. He re-ceived his Ph.D. from the University of Iowa. His research on e-tailer pricing wasrecently published in the Journal of Service Research. His current research topics in-clude e-tailer inventory policy, price competition, and customer free riding of e-tailerservices.

THOMAS S. GRUCA ([email protected]) is the Lloyd J. and Thelma W. PalmerResearch Fellow in Marketing and associate professor of marketing at the Universityof Iowa, Tippie College of Business. He received his Ph.D. in decision and informa-

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tion sciences from the University of Illinois at Urbana–Champaign. His current re-search interests include defensive marketing strategy, health care, pricing, and elec-tronic markets. His research has recently appeared in the Journal of Service Research,European Journal of Operational Research, Information System Frontiers, and Journal of theAcademy of Marketing Science. He is a member of the editorial board of Marketing Let-ters.

BRUCE R. KLEMZ ([email protected]) is an associate professor of marketing in theCollege of Business at Winona State University. He completed his doctoral studies atthe University of Iowa. His research interests include managerial decision-making, ser-vices marketing and AI-based software tools for decision support. His research hasappeared or is forthcoming in Psychology & Marketing, Journal of Services Marketing, Eu-ropean Journal of Operational Research, and International Journal of Research in Marketing.

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