Adding e-Tailing Quality to the Mix
Rose Sebastianelli and Nabil Tamimi
Kania School of Management, University of Scranton, Scranton, Pennsylvania 18510-4602, U.S.A.,
Keywords: e-tailing quality, Online shopping frequency, Logistic model.
Abstract: A logistic regression model is developed to predict the frequency with which consumers make online
purchases. Using survey responses from a national sample of U.S. online consumers, we categorize
respondents as high and low frequency Internet shoppers. We include as predictor variables demographics
(age, gender, education level and income), frequency of Internet browsing, product type (search versus
experience), and seven empirically derived dimensions of e-tailing quality (reliability, accessibility,
ordering services, convenience, product content, assurance and credibility).
A growing body of literature has been devoted to
understanding the factors that motivate consumers to
shop online. Empirical studies have examined the
impact of demographics, external environment,
personal characteristics, vendor/service/product
characteristics and website quality on the attitudes
and behavior of online consumers (Li and Zhang,
2002). Another stream of research has focused on
defining e-tailing (electronic retailing) quality more
broadly. Acknowledging that consumers’ experience
with online retailers goes beyond the website
interface, some argue that e-tailing quality includes
every aspect of purchasing via the Internet (e.g.,
Wolfinbarger and Gilly, 2003). Given the likely link
between e-tailing quality and online shopping
satisfaction, we suggest that all dimensions of e-
tailing quality be included when examining the
factors that motivate consumers to purchase online.
1.1 e-Quality Dimensions
Early work identifying the dimensions associated
with e-quality (electronic quality) often used service
quality as a reference point (e.g., Cox and Dale,
2001). Some (e.g., Long and McMellon, 2004),
found that quality aspects perceived to be important
in website design, website use, and online retailing
corresponded directly to SERVQUAL (Berry and
Parasumaran, 1991). Others, such as Madu and
Madu (2002), borrowed from both service and
product quality to propose a more comprehensive
conceptual e-quality framework. For instance, they
identify the following 15 dimensions: performance;
features; structure; aesthetics; reliability; storage
capability; serviceability; security and system
integrity; trust; responsiveness; product/service
differentiation and customization; web store
policies; reputation; assurance; and empathy.
Some researchers also included factors typically
associated with traditional retailing. For example,
Cho and Park (2002) included customer service,
purchase and delivery results in developing an e-
commerce user-consumer satisfaction index.
Similarly, in a study measuring perceived
satisfaction with online retail shopping, Kim and
Eom (2002) included scale items related to physical
retailing. They found that issues such as guaranteed
on-time delivery and hassle-free return affected
online consumer satisfaction.
A number of empirical studies focused on the
underlying constructs (or dimensions) of e-quality.
Yoo and Donthu (2001), in developing SITEQUAL,
identified the following four e-quality dimensions:
(1) ease of use; (2) aesthetic design; (3) processing
speed; and (4) security of personal and financial
information. Wolfinbarger and Gilly (2003), in
developing their scale eTailQ, reduced 40 attributes
to four underlying dimensions: (1) fulfillment/
reliability; (2) website design; (3) customer service;
and (4) security/privacy. Equally comprehensive in
their approach is the work of Parasumaran, Zeithaml
and Malhotra (2005) in developing E-S-QUAL. The
22 items of E-S-QUAL resulted in the following
four dimensions: (1) efficiency (ease of using the
site); (2) fulfillment (extent to which the site
promises are fulfilled); (3) system availability
(correct technical functioning); and (4) privacy
(degree of protection).
1.2 e-Tailing Quality Framework
Our goal in this study is to include the potential
impact of e-tailing quality dimensions to predict the
frequency with which consumers purchase via the
Internet. The e-tailing dimensions we use in our
model are derived empirically from a set of
attributes that comprise our own e-tailing quality
framework. Previously used to benchmark real
online transactions for a sample of online retailers
(Tamimi, Rajan and Sebastianelli, 2003), these
attributes are organized along the four phases of a
consumer’s online shopping experience: (1)
encountering the online retailer’s homepage; (2)
selecting a product from the online catalog; (3)
completing the order form; and (4) customer service
and support.
To represent quality in the first phase,
encountering an online retailer’s homepage, we
identified the following twelve attributes: (1) Meta
tags – website easily found by search engines; (2)
Home page title – easily recognizable; (3) Domain
name – unique; (4) Speed of loading – time it takes
to download; (5) Links – number of bad links; (6)
Contact information – visible on homepage; (7)
Timeliness of information – recently updated; (8)
Privacy policies – explicit on homepage; (9) Search
engines – available on homepage; (10) Translation
to multiple languages – ability to translate content
into multiple languages; (11) Navigational bars or
site maps – present on homepage for ease of use;
(12) Value added extra content – such as product
Since an online retailer must provide sufficient
realism to its customers in the online catalog, we
identified the following seven attributes for this
phase: (1) Presence of product search engine
allow search by category or price range; (2) Price
adjacent to the product in the catalog; (3) Images
clear color product images; (4) Comprehensive
product descriptions – include size, dimension,
weight, etc.; (5) Labeling of out of stock items – easy
to find; (6) Brands and models – wide variety
offered; (7) Special offers – coupons and discounts.
After product selection, an online shopper
encounters an order form, typically integrated with
an online shopping cart. Security and trust are key
issues in this phase. Consequently, we identified the
following eight attributes: (1) Breakdown of overall
costs – includes all extra charges; (2) Multiple
payment options – available; (3) Shopping cart
editing – ability to add and remove items from the
cart; (4) Security – presence of seals of approval
logos or encryption technologies; (5) Shipping
options – several available; (6) Instructions – helpful
in completing the order form; (7) Ease of
transaction – minimum number of clicks required to
complete; (8) Price calculation – correct and
Finally, customer service and support are
critical determinants of satisfaction. We identified
the following ten attributes: (1) Instant merchant
notification – instant automated notification of order
receipt; (2) Order tracking – issuance of order
tracking number; (3) On-time delivery – actual
matches promised delivery date; (4) Honest product
representation – product received matches online
representation; (5) Explicit return policy – clear
explanation of return policy and restocking charges;
(6) Order cancellation – option to cancel submitted
orders; (7) Order changes – option to change
submitted orders; (8) Product return – hassle free;
(9) Customer help – available online or toll free
number; (10) Accurate billing – bill is accurate.
2.1 Sample
Our sample consisted of Internet shoppers defined as
those who are engaged in buying products or
services online. The sampling frame, comprised of
opt-in e-mails, was obtained from Martin
Worldwide, a provider for direct mail and
telemarketing leads. The link to the web survey was
sent via e-mail to 6666 online consumers. Only one
e-mail was sent to each consumer. In order to
increase study participation, an incentive lottery was
2.2 Survey Instrument
The online questionnaire consisted of three sections,
two of which are relevant for this paper. The first
gathered background information on online
shopping behaviors and preferences (e.g., types of
products and/or services purchased) and
demographics. The second section consisted of
statements representing all of the attributes in our e-
tailing quality framework. These statements were
randomly ordered on the questionnaire (not grouped
according to phase). Respondents were asked to rate
how important each factor was in determining the
quality of an online retailer using a five-point scale
(1 = not important, 2 = slightly important, 3 =
somewhat important, 4 = important, 5 = very
3.1 Respondent Profile
A total of 422 respondents completed the online
survey for a response rate of 6.3%. With regard to
gender, 59% of the respondents are female. The
average age is 44. Of those responding, the majority
is Caucasian (73%), employed full time (62%), and
married (57%). The majority (59%) has an annual
household income of less than $50,000 with 21%
earning more than $75,000. In terms of online
behaviors, 23% indicate that they have made at least
10 purchases online during the last six month period
and 43% report browsing the Internet daily.
3.2 Factor Analysis
Principal components factor analysis was used to
extract the factors (dimensions) from the set of e-
tailing quality attributes. Varimax rotation of the
solution was employed to improve interpretability.
The results, published elsewhere (Sebastianelli,
Tamimi, and Rajan, 2008), are shown here in Figure
1. The seven e-tailing quality dimensions extracted
from the attribute importance ratings are reliability,
accessibility, ordering services, convenience,
product content, assurance and credibility.
Cronbach’s alpha (a measure of reliability) as well
as the specific attributes loading strongly on each are
also provided for each dimension.
3.3 Binary Logistic Regression
Respondents were asked to indicate their number of
online purchases during the last 6 months. Response
categories were none, 1-3, 4-6, 7-9, or more than 10.
We eliminated the middle category (4-6) and defined
3 or fewer purchases to be “low” and 7 or more to be
“high” for the binary dependent variable.
We included demographics (age, gender,
education level and income) as well as frequency of
Internet browsing and product type as potential
independent variables. We categorized respondents
Factor I – “Reliability” (Cronbach’s alpha = .857)
Accurate calculation of total price when ordering.
Accurate billing.
Ability to add / remove items from the shopping cart.
Clear breakdown of overall cost.
Price displayed adjacent to the product in the catalog.
Labeling of items that are out of stock.
Issuance of order tracking numbers.
On time delivery of order.
Factor II – “Accessibility” (Cronbach’s alpha = .843)
Ability to translate website into multiple languages.
Unique trademark.
A meaningful homepage title.
Search engines present on homepage.
Retailer website easily found by search engines.
Presence of navigational bars or site maps.
Timely information updates on homepage.
Factor III – “Ordering Services” (Cronbachs alpha = .825)
Options for canceling an order.
Ability to change a submitted order.
Availability of different shipping options.
Issuance of instant notification of order received.
Presence of instructions for completing the order
Availability of several payment options.
Factor IV – “Convenience” (Cronbach’s alpha = .783)
Special offers such as coupons in the online catalog.
Value added extras on the homepage.
Presence of search engines in the online catalog.
Speed of page downloading.
Order form completed with minimum clicks.
Factor V – “Product Content” (Cronbach’s alpha = .670)
Display of color images of products.
Providing complete product descriptions.
A wide variety of brands and models offered.
Factor VI – “Assurance” (Cronbach’s alpha = .659)
Inclusion of privacy policies on the homepage.
Availability of online help or toll free number.
No bad links.
Security of orders (e.g., presence of seals or logos).
Factor VII – “Credibility” (Cronbach’s alpha = .766)
Information on return policy and restocking charges.
Presence of contact information on homepage.
Accuracy of online product representations.
Hassle free product return.
Figure 1: Factor analysis results.
answers to the question “which product are you most
likely to purchase online?” as being either search or
experience based on the scheme provided by Girard,
Korgaonkar, and Silverblatt (2003). Finally, we
included all seven e-tailing quality dimensions using
the factor scores as independent variables.
A stepwise model building procedure was used
to develop the binary logistic model. Specifically,
Forward Selection (Wald), a stepwise method for
which independent variables are entered into the
model based on the significance of the score statistic
and removed based on the probability of the Wald
statistic, was employed. The resulting model is
shown in Table 1. Its overall accuracy in predicting
correct group membership is 71.6%.
Table 1: Binary Logistic Regression Model.
Variable B S.E. Wald Sig.
Convenience .322 .188 2.929 .087
roduct Content .585 .197 8.834 .003
rowsing .871 .206 17.854 .000
ge -.031 .017 3.226 .072
ncome .815 .179 20.666 .000
Constant -4.260 1.147 13.804 .000
Our results indicate that two e-tailing quality
dimensions, product content and convenience, along
with income, age, and frequency of Internet
browsing are significant predictors of online
shopping frequency. Not surprisingly, consumers
who frequently purchase products online tend to be
younger, with higher incomes and spend more time
browsing than less frequent online shoppers. While
online retailers cannot control these predictors, they
can control e-tailing quality dimensions. Our
findings suggest that online retailers wishing to
increase purchases from their Internet sites should
focus on improving the quality of e-tailing attributes
associated with product content and convenience.
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