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1. A Classification of Internet Retail Stores
Peter Spiller and Gerald L. Lohse
Department of Operations and Information Management
The Wharton School of the University of Pennsylvania
1319 Steinberg Hall - Dietrich Hall
Philadelphia, Pennsylvania 19104-6366
office phone: (215) 898-8541
FAX number: (215) 898-3664
e-mail: lohse@ wharton.upenn.edu
Peter Spiller was a visiting scholar at the Wharton School of the University of Pennsylvania,
completing his master of industrial engineering from the Technical University of Berlin, Germany.
He studied the implications of Internet marketing and electronic commerce. After finishing his
thesis in spring 1997, he became a consultant with McKinsey and Co. in Duesseldorf, Germany.
Gerald L. Lohse is an Assistant Professor of Operations and Information Management at the
Wharton School of the University of Pennsylvania. His research interests include electronic
shopping and on-line information services, cognitive models of decision processes, impact of
decision support systems on decision processes, human-computer interaction, and visualization of
information. He has published over 30 articles, book chapters, and reports on these topics.
Current projects include Internet yellow pages directories, personalized on-line retail catalogs that
dynamically adapt to consumer preferences and basic research on how people perceive and
process information from displays.
Acknowledgment
We are grateful to Dr. Vladimir Zwass, Steven O. Kimbrough and two anonymous reviewers
for their helpful comments and suggestions on earlier drafts of this manuscript.
2. A Classification of Internet Retail Stores
Abstract
We describe an empirical method for classifying Internet retail sites for electronic commerce.
The technique used 35 observable Internet retail site attributes and features of on-line stores as
the raw data to classify the on-line stores into meaningful groups. This paper presents a
classification of on-line retail stores based upon an August 1996 convenience sample of 137
Internet retail stores. Descriptive statistics for 44 variables provide a snapshot of key attributes
and features of on-line stores. Subsequent cluster and factor analysis identified five distinct Web
catalog interface categories: super stores, promotional stores, plain sales stores, one page stores
and product listings. On-line stores differ primarily on the three dimensions: size, service
offerings, and interface quality. A preponderance of the stores in the study had limited product
selection, few service features, and poor interfaces. This categorization provides a better
understanding of the strategies pursued in Internet-based marketing and will be helpful for
Internet retail store designers as well as for researchers to structure and target further analyses in
this domain.
Keywords: Internet marketing; on-line catalog; electronic store fronts; cyberstore; cybermall;
electronic shopping; classification; cluster analysis; factor analysis
1
3. Introduction
According to Hoffman, Novak, and Chatterjee [15] on-line retail stores ‘offer direct sales
through an electronic channel via an electronic catalog or other more innovative format’ As with
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the physical store and paper catalogs, on-line stores differ in terms of the variety and types of
products offered, price, advertising and promotional efforts and service. On-line stores also differ
in their catalog or store design and layout. Table 1 summarizes some of the analogies between
retail stores, paper catalogs and on-line catalogs that aided the variable definition.
Insert Table 1 here
To help understand the different strategies adopted by retail Web sites, this exploratory
research structures and classifies on-line retail stores into meaningful groups. A classification of
retail Web sites will help retail Web site developers understand their design alternatives as well as
the functional requirements of a site. For example, what are potential design alternatives for on-
line stores? Is there a set of factors or dimensions (e.g., size or service) that can characterize a
retail Web site? How do Internet retail sites differentiate their retail store from the competition?
By classifying Internet retail stores, we hope to (1) structure this domain for further systematic
inquiry, (2) provide concepts for developing theories, (3) identify anomalies, and (4) predict future
research directions [19].
Hoffman, Novak, and Chatterjee [15] and Cappel and Myerscough [7] have suggested broad
classifications of Internet sites for electronic commerce. They classified Internet sites on the
highest possible level that can be summarized as (1) on-line store fronts or catalogs selling
products or establishing marketplace awareness, (2) content sites providing information and
support, and (3) Web traffic control sites, such as malls and search engines. However, both
2
4. classification schemes are not based on empirical analysis, but upon a functional analysis of broad
characteristics of commercial Web sites [7, 15].
Our research builds on the classifications outlined above [7, 15] and concentrates on an
empirically-based classification of retail on-line stores. A great amount of variation in on-line
stores can be observed either on the Internet or on proprietary on-line services. Depending on the
retailer’ intention, on-line catalogs focus on a mixture of pure selling, on increasing marketplace
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awareness or providing how-to information, or just on providing some incentives to order a paper
catalog. Some retailers see their on-line presence as an opportunity for a more personal
communication with the customer, others use it mainly to learn about the medium and to
determine its possibilities (for a discussion of different catalogers’ goals see [30]). A classification
of on-line store interfaces provides a better understanding of how the electronic stores embody
many of the retail store attributes that customers perceive, such as the atmosphere, convenience,
friendliness, tidiness, organization, and product display.
Some product categories, such as software, financial services or travel, are widely viewed as
being successful on the Internet today [23, 29]. To grow on-line sales, the Internet must attract a
critical mass of mainstream shoppers, not just addicted specialists shopping for particular
products; therefore, we chose to focus on on-line retail stores selling women's apparel. According
to a Simmons survey of almost 200,000 adults, clothing accounts for the greatest proportion of
catalog merchandise purchased in the US (34% in 1995) [40]. Women’ apparel and accessories
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hold the largest share of consumer spending, accounting for 19% of all dollars spent on catalog
purchases. Thus, on-line women's apparel stores are an important segment of retail catalog sales.
This paper contributes to a growing literature on electronic commerce. The statistical
summary of on-line store features and attributes support many of the findings of the survey of
3
5. consumer reactions to electronic shopping by Jarvenpaa and Todd [16]. Many of the problems
associated with on-line shopping (e.g., finding products in the store and ordering the product) [9]
originate in the retail Web site design. The statistical methodology provides an objective,
empirical technique for clustering stores into meaningful groups. The technique also provides
developers of commercial Web sites a methodology for eliciting functional requirements for on-
line stores by 1) identifying major store design features, 2) organizing on-line stores into
meaningful clusters that invite comparison of various design alternatives and 3) helping clients
understand the competitor’ store designs.
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This paper describes an empirical study of 137 Internet stores selling women’ apparel that
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categorized different on-line sales approaches, user interface styles, and services used in on-line
stores. First, the study surveys 44 Internet apparel Web site features. Descriptive statistics
summarize key features of the Web sites surveyed. We measured interface functionality, the
ordering and payment possibilities, customer service offerings and additional features that attract
customers to the site. Second, we categorized some general approaches for Internet store designs
by analyzing patterns in descriptive attributes of the on-line stores surveyed. Using the survey of
store features and subsequent cluster and factor analysis, we identify five distinct strategies that
characterize Web retail sites.
The following section defines on-line store variables used in the study. Next, we describe the
sampling method and statistical analyses. After presenting descriptive statistics on characteristics
of the on-line stores in the survey, we discuss the results of our classification of Internet stores.
Finally, we raise a set of future design issues for Internet stores.
4
6. Identification of Characteristic On-line Store Attributes
First, we defined a set of variables that describe and differentiate on-line stores. Analogies from
three domains helped define the variables. The most obvious source for on-line store design
variables is the literature on the design and evaluation of paper-based catalogs. We also measured
attributes shoppers consider when selecting a retail store. Shopping an on-line store is certainly
much more like shopping a paper catalog than shopping a retail outlet because both involve mail
delivery of the purchases and in both cases customers can not touch and smell the items. On the
other hand, on-line shopping can incorporate new interactive features to catalog shopping, such as
email inquiries to sales representatives, discussion forums for customers or voice and video
applications. In this sense, on-line stores are somewhere in between paper catalogs and retail
stores, but also add completely new dimensions to shopping like automated price comparisons,
unlimited “shelf” spaces, and so on. We recorded these “extra” features but did not use them to
describe or categorize the on-line stores because they were too rare in the stores we surveyed. In
addition to these attributes, we examined attributes of human computer interfaces such as the
interface clarity and navigation.
Paper Catalog Design Variables
Research on paper catalog design and evaluation primarily emphasizes the importance of
layout factors to distinguish selling and not selling catalogs. Several authors suggest guidelines
for design and evaluation [17 (pp. 243-250), 35, 36]. Most of these concentrate on page layout,
typography, copy, art-design and reader involvement of the catalogs. However, we did not try to
operationalize any subjective attributes of the layout quality into descriptive variables. Another
feature is the “look and feel” of the catalog. This includes picture placements, merchandise
grouping, exclamations, and front and back covers [14, 37]. The paper catalog’ cover page
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5
7. translates into the on-line catalog’ home page. Both the cover page and the home page aim to
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excite shoppers about the catalog and to generate interest to flip through the pages. Thus, we
describe home page features, such as the use of images, the home page image size and page
length.
Page lengths were measured in screen pages. The browser was expanded to fill the full screen.
We defined a screen page as the browser1 screen area on a 1024x768 17” monitor that can be
looked at without scrolling the page2. Pages were scrolled and the number of screen pages fitting
on the Web-page was counted. Image sizes represent the sum of all images on one screen page,
not including graphical buttons. We did not measure the size of image files in bytes because file
sizes largely depend on image resolutions and file formats (gif, jpg, etc.). Further, screen area
allows comparisons between image use in paper media to on-line media. We also measured
whether images in the catalog were used for product display and menu buttons (image overlay),
only for product display, or not at all. We also coded whether the catalog used thumbnail images.
This captures the level of catalog sophistication in terms of the image use.
Attributes Considered by Shoppers in Retail Store Selection
Electronic shopping incorporates many of the same characteristics as normal shopping, such as
departmental product organization and browsing possibilities. Thus, we measured attributes that
shoppers consider when patronizing a retail store.
A great amount of research was done on the evaluation of department stores by consumers.
Berry [6] empirically identified a number of attributes using a mail survey. May [22] and
Lindquist [18] emphasized the importance of the retail stores’ image. Lindquist distinguished
store image components in functional qualities, such as the merchandise selection, price ranges,
credit policy and the store layout, as well as psychological attributes that were associated with the
6
8. customer’ feelings about the store. In our research, we only concentrated on functional qualities
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because we did not survey actual customers.
More recent research developed models predicting customer store choice behavior. Several
authors apply consideration set theory to store choice modeling [11, 24, 39]. Others use
hierarchical models [1] or threshold models [20] for predicting customers’ store choice behavior.
Yet, most authors use store attributes described by Lindquist’ summary [18]. His attribute list is
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a compilation from 26 researchers in this field. We adopt these store attribute groups from
Lindquist. Merchandise includes product variety, quality, guarantees, and price. Service
examines general and sales clerk service, merchandise return, and credit and payment policies.
Promotion explores sales, advertising, and promotion displays. Convenience includes general
convenience in the store, like store organization or store hours. Arnold et al. [2, 3] suggested a
few additional store attributes associated with store convenience: ease of navigating through the
store and a fast checkout. A number of Lindquist’ functional attributes were excluded from our
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research (clientele of the store, physical facilities, store atmosphere, institutional factors). They
are either irrelevant in the on-line shopping environment or were not related to our survey of on-
line catalog features.
Merchandise The overall selection of the merchandise can be characterized by the total
number of products and the number of product categories. The number of product categories is
not easy to define across different stores since stores offering different types of products will have
very different levels of product categories. In one store, men's, women's and children's clothing
might represent three high-level product categories; in the next case, it might be business attire,
casual and sporting goods. Thus, in order to assess the selection, or broadness of the stores, we
used the number of hierarchical store levels as a proxy variable for the number of product
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9. categories. Information about the quality of the goods includes guarantees and order information
such as information on shipping and returns.
Service Interactive customer services are very important for on-line stores [34]. Service
information included whether the store featured any gift services, a frequently asked questions
section, company information, the information content provided for the average product (this can
be considered as the information that is accessible to the customer when talking to a sales
representative in the store), a sales representative’ phone number or email, and whether the store
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featured a feedback section, offered help on the product-size selection or featured extra questions
in the order form to get to know the customer.
Promotion Different store promotions were observed. Next to frequent buyer schemes, we
considered appetizer information like magazines or lotteries and also links to other sites as
promotion. This strategy of magazine catalogs or “Magalogs” is also increasingly used in paper
catalogs [26]. Another variable recorded the existence of a “What’ new?” section that can either
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be seen as promotion or general service.
Navigation Stores featuring a deep hierarchy of levels had to provide tools to easily navigate
the store. To shop, consumers navigate from the home page to end product pages containing
images and descriptions of the products. Organization is a prerequisite for navigability [21]. The
interface structure, its different hierarchical levels, and their organization should be apparent to
the user and easy to understand. In on-line stores, navigability can be supported by features such
as search or browse functions. Providing product indices or a site index also greatly enhances the
feeling of “knowing where you are” in an interface. Browsing and navigation capabilities of an
Internet retail store also include the number of modes to shop the store (by brand, price,
department), and convenience during checkout in an on-line store.
8
10. User Interface Consistency is considered as one of the most basic usability principles of
computer interfaces [28, 31, 38]. Many authors distinguish three types of consistency: internal
consistency, external consistency with other interfaces familiar to the user, and consistency with
real world features [13]. In our research, we surveyed whether consistent menu bars were in
place on all pages.
The use of different colors to guide users of a computer interface has received a lot of attention
(for color in HCI see [21, 27]; for color in on-line marketing see [10]). We surveyed the
background color, texture, or pattern of the home page. Texture and color have been shown to
influence consumer choice behavior3.
Help functions are most often related to helping users recover from errors or find a particular
function in the documentation [28, p. 148]. We analyzed whether stores offered any initial help
for customers to shop their store. This included help information about the store’ navigation or
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the use of ordering features like a shopping cart function.
Altogether, 44 attributes were used to evaluate each on-line stores. Appendix A contains the
checklist that describes all 35 variables used in the cluster and factor analyses. Nine additional
variables were collected for descriptive statistics only.
Methods
We used a convenience sample of on-line stores in our survey. Internet search engines such as
Yahoo, Alta Vista and Infoseek were used to find single stores or comprehensive lists of stores.
Two thousand stores were screened by looking either at the description provided by the search
engine or at the store itself. All stores selling more than five items of any kind of clothing were
bookmarked for further consideration. Stores selling just one product in different variations (e.g.,
T shirt stores) were not considered. Finally, 137 stores offering women’ apparel for sale were
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9
11. selected for comparison. This convenience sample certainly does not claim to cover all Internet
apparel stores, though it should be quite representative for the type of apparel stores currently on-
line. The on-line stores were selected from July 15th to July 28th, 19964.
The first author coded all store variables using a standardized checklist. Given the rapidly
changing nature of the sites, the reliability of the coding was tested post hoc using a random
sample of 42 of the Web sites 137 sites. The first author and a research assistant recoded all
measures for each of these 42 Web sites. Using Cronbach alpha, inter-rater reliability over this
subsample was 0.818.
Due to resource limitations, we did not count individual words associated with each interface
feature. Instead, we counted the total number of screen pages and full text lines that contained
related information. Full text lines were adjusted to reflect different font sizes and frames. A post
hoc analysis of a random sample of 14 of the 137 Web sites surveyed found a high correlation (r =
0.98) between line count and word count. Thus, line count is a reasonable approximation for
word count.
Data analysis for the survey is twofold. The first analyses provide descriptive statistics on the
data collected. The second analyses classify and group the surveyed on-line stores using cluster
and factor analysis. Principal factor analysis was used to reduce the number of variables
describing the on-line stores to a few components. This analysis identified three factors that
characterize the on-line stores in the survey.
The cluster and factor analysis excluded descriptive variables not considered useful to
characterize an on-line store. These excluded variables are those coding membership
requirements, store locators, payment methods, price policies, and whether an order form for print
catalogs was available. This data was only recorded for descriptive reasons, not to classify the
10
12. on-line stores. The four variables coding different links of the on-line stores were summarized in
a single variable (LINKS). Product and non-product related appetizers were summarized in the
variable APPETS.
Results
Descriptive features of on-line apparel stores
Store size may influence consumer satisfaction of the on-line shopping experience. Most on-
line stores surveyed were reasonably small. 62% have less than 50 products for sale. Only 5%
had more than 500 products, including L.L. Bean with approximately 1,600 and Shoppers
Advantage with nearly 250,000 products offered in the on-line store.
78% of the on-line stores offered goods at prices on par with paper catalogs or store prices; no
on-line stores appeared to charge a premium for on-line sales. The remaining 22% sold at a
discount on the Internet. These mostly included firms relying solely on the Internet for generating
sales.
Company information presented in the on-line stores varied greatly. 15% of the on-line stores
provided a “store locator” to find the company’ retail stores. 22% offered an order form to
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request a printed version of the catalog. Almost one third did not provide any information on the
company’ history, policies or background; 80% had less than 10 lines of information. This is a
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surprising number since customers want to know who they are dealing with and sending the credit
card information to [12]. For companies with an established reputation, this is not an issue.
However, for new “virtual companies” solely operating on the Internet, this is one important way
to establish credibility of the business.
Only a few of the on-line stores did not make use of images. About half of the on-line stores
not only used images for product display, but also used them for navigation with image overlay
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13. links. This is especially true for home pages and menu bars. Image size averaged about 60 cm2
on home and end product pages each which is equivalent to 11% of screen area. The median
image size lies between 5 and 8% of total screen area. While we measured the quantity of the
images, we did not assess the quality of the images. For example, Ridgon’ description in the Wall
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Street Journal [33] emphasizes the problem of image quality, “. . . after waiting 5 minutes for
some fuzzy pictures to appear, I soon realized that the technology has not caught up with my
imagination.” Thus, there are tradeoffs among image size, image quality, and downloading time.
Page length, measured in multiples of a normal screen size, was one screen page in most cases.
Lists of products on end product pages or lists of product indices on navigation pages were
longer than one screen page. More sophisticated on-line stores tended to fit pages on single
screens. Most retailers also tried to display only single products on end product pages. In
contrast, mail-order catalogs often group clothing outfits on the same page, making it easier to
coordinate a multi-item purchase. While most on-line stores displayed only one product per
image, some on-line stores displayed sets of products on one picture. Text information
accompanying the product images varied depending on the product type and on-line store. While
some products such as trekking goods or clothing that claimed to be made of special fibers had a
lot more text information, 50% of the on-line stores surveyed averaged less than three lines of text
per product. This small amount is surprising given the ability of an on-line Web site to make a lot
of product information easily available to consumers.
Due to the limited product range of most on-line stores, the number of hierarchies was three or
less for 75% of the on-line stores. Few sites made use of thumbnail images. Thumbnails can be
found in on-line stores where customers can be characterized as not being very sure about the
12
14. actual good which they intend to buy (e.g., gift or lingerie catalogs). Background color was white
or gray for 42% of the sites. 42% used patterns or texture.
Interface navigation is supported by consistent menu bars, search functions and indices.
However, only a few stores had product search functions, site or product indices. This is certainly
due to the small size of most stores. Additionally, 24% of the sites surveyed did not have
consistent menus to navigate through the pages.
Ordering is done using a shopping cart metaphor in 30% of cases; only one third accepted off-
line orders. Five of all 137 on-line stores required a membership to shop. Already, 29% offered
on-line ordering with credit card data submission as the only way to pay. 59% offered this as an
option. The next most popular payment method was phone ordering, with 12% of the on-line
stores relying solely on this alternative. Very few sites offered frequent buyer incentives or used
additional personal questions in their order form to learn more about their customers. This
information is readily available to on-line service providers such as America Online and
CompuServe. It is also becoming available on the Internet as the use of cookie technology
becomes more widespread.
Additional customer service information includes help on the interface usage or product size
selection, FAQ- and “What’ new” - sections. A great number of sites do not provide any of these
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services. These results are very similar to those for interactive service offerings like contact
phone numbers or email addresses of sales representatives or feedback sections. 47% of the sites
do not offer an email address for interactive service.
Customers will probably return to an on-line store site if it offers incentives. These appetizers,
which attract customers to come back to the site, consist of built in features like magazines,
lotteries and links to other interesting sites. 76% of the sites do not offer any appetizers. Those
13
15. found most often include magazines with product related articles, glossaries, travel or other
product related tips, and lottery games.
Interestingly, very few stores made use of links between appetizer and product pages or among
different product pages. By not displaying multiple products on the same screen, Internet stores
restrict the comparison of products or the purchase of associated items [5]. Links in Internet
stores can overcome this problem by directing the customer to related products. For example, the
Spiegel site (www.spiegel.com) features some very elaborate magazine pages with many links to
associated products elsewhere in the store.
55% of the stores do not have any links to other areas and only 8% provide links to more than
two of the following areas: product related sites, affiliates, regional sites and Internet sources. A
few sites had links to competitors (malls, related Internet shops) but they were not comprehensive
enough to be useful for product and price comparisons. Of the 20% of the sites having some
links, most had links to search engines, Netscape or MS Explorer, or to the consulting company
that designed the Web site.
Cluster analysis of store attributes
Prior studies by Day and Heeler [8] and Finn and Louviere [11] used cluster analysis to
segment stores or shopping centers. Day and Heeler used variables like the number of employees,
the number of store checkouts, customer demographics, in-store promotions, individual product
sales and total store sales to cluster regional retail stores. They also used subsequent principal
factor analysis to reduce the number of variables to a few dimensions. Finn and Louviere
considered the variables proposed in Arnold et al. [3] to arrive at a segmentation of local
shopping centers. Two important store attributes identified by Finn and Louviere were the quality
of service and a wide selection of products. Cluster analysis is a purely empirical method of
14
16. classification but it is widely employed in marketing for market, product, and customer
segmentation. Guidelines for the application of cluster analysis in marketing can be found in Punj
and Steward [32].
Before computing the cluster analysis with the SYSTAT Software Package [41], all interval
and ordinal level data were standardized to an interval from zero to one. Standardization makes
overall level and variation comparable across variables [41]. The standardized variables were
transformed into normalized Euclidean distances. Hierarchical clusters were generated using the
combined set of Euclidean distances. In the calculation, we first used the single linkage method
that adds extreme cases only at the end. However, this procedure did not identify any extreme
cases for deletion from the data set.
It is essential to realize that each clustering technique is biased towards finding clusters
possessing certain characteristics related to cluster size or dispersion (variance). Ward’ minimum
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variance technique (as well as the k-means method) tends to find clusters with roughly the same
number of observations per cluster. Average linkage tends to find clusters of equal variance.
Single linkage is notorious for creating groups with many singletons or outliers. Milligan’ (1981)
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review of clustering methods found that the Ward method had the best overall performance. It
tends to produce groups of similar size and usually provides very good results [4, p. 298]. Thus,
the actual clustering was conducted using Ward's method.
A tree diagram of the cluster data shows five distinct clusters with sizes ranging from 20 to 34
cases. These initial major branches of the tree are the focus of our discussion. We did not
describe distinct subgroups within these five major clusters. For each of the five clusters, Table 2
shows the means for each of the 35 variables used in the clustering. We make no claim that there
are only five categories of on-line storefronts or that they are optimal in any sense. We merely
15
17. state that the five categories we have identified are meaningful groups with similar
characteristics5. Next, we describe each of the five clusters in more detail and give some specific
examples from each cluster.
Insert Table 2 here
Super Stores The first cluster contains most of the largest stores in terms of total product
number and number of pages. The score for the average store size is 54% higher than that of the
second ranked cluster. This is also reflected in the number of levels between home page and
product pages, where the score is 70% higher. Average information for the customer is most
extensive, including information about the company, about ordering, gift services and “What’s
new?” - sections. The number of extra appetizers and customer-care features such as feedback or
access to sales representatives are also more extensive.
Most stores in this cluster have a product index or a search function. This is certainly
correlated with the stores’ size. Small sites selling only a few products would not need a search
function or a product index. Super Stores also provide the most text information for each
product. Number of products on product pages is small with most sites displaying only one
product per page. The corresponding page length is one screen page in most cases.
The L.L. Bean on-line store, Land’ End, Spiegel, Online Sports, J.C. Penney, Shoppers
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Advantage and Service Merchandise can be found in this cluster. They all try to attract customers
with a broad supply of extra information and offer a sophisticated interface with many short pages
and navigation tools.
Promotional Store Front Stores in this cluster also score high on customer information and
appetizer variables. Most provide information pages about the products, as well as on unrelated
16
18. topics like the environment (AWEAR) or lifestyle issues (Tara Thralls). The numbers of products
are small. There are no striking features that characterize the user interface.
The cluster includes AWEAR, Tara Thralls, Madeleine Vionnet’ Scarves, Wickers, Cheyenne
s
Outfitters, Real Bodies, Utilities Design Match and The Old Hide House. Most sell very few
products, but offer a lot of information on the company or other issues. The focus of the store is
more on promoting general company awareness than on generating on-line sales.
Plain Sales Stores The third cluster comprises a number of relatively large stores with no or
little extra product information. While company information content is still average, most stores
of this cluster offer no appetizers, links or additional customer services.
On the other hand, interface navigation is quite elaborate for most “Plain Sales” stores. They
all use thumbnail images. Average size of a product image is largest in this category. Many stores
feature a product index or a browse function. Each product page tends to be long and shows
more than one product in most cases.
Stores in this cluster make abundant use of graphics for product display. Products do not
require explanations, extra information on usage or other additional information. Special WWW
features like links are hardly used. This cluster includes many boutiques like Milano, Rhondi,
First Lady, Dock of the Bay, Clothes Horse and Leather & Gift Outlet, as well as other stores that
emphasize a product image in their marketing. The latter group includes for example swimwear
and lingerie sites, such as Skinzwear or Sophie la Nuit.
One Page Stores The two variables distinguishing this cluster from others are the number of
levels between home page and product pages, and the home page length. The number of levels is
very small across most sites in this cluster. Home page length is very large for half of the on-line
17
19. stores. The on-line stores are all relatively small and feature little extra information, links or
appetizers.
The cluster actually consists of two different subgroups. The first one is stores relying heavily
on browse functions to navigate among products. For this reason, numbers of hierarchical levels
are low. This group includes boutiques such as Mariam Apparel, Lims or KHLA. The second
group contains stores only consisting of one long home page, displaying a list of products below
an introductory statement. Examples for this group are Alaska Mountaineering, Australian Coat,
Al’ Texas Jeans and Close To You. However, the cluster also contains a few stores, such as
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Marissa Fabiani, that are very extensive and have several levels.
Product Listings The number of levels is below average for most stores in this cluster. Little
additional information and few appetizers are available. Store size is average in comparison to
other categories. Product listings have large numbers of products on end product pages and a
greater page length. Average product image size is smaller than in any other cluster. Almost none
of the stores feature navigation tools like indices or a search function. Products are found in lists
on product pages.
The average store in this cluster contains only the home page with links to certain product
categories and additionally one long page for each category, displaying a list or directory of small
product images and their description. Images are not enlargeable. The page also contains
ordering information. Stores in this cluster include Rocky Mountain Outfitters, Seabury’s
Women’ Golf, The Dress Connection, Dance Supplies, Full Swing Golf of Alaska and Fisher
s
Henney Naturals.
The example stores mentioned above represent their groups fairly well. Of course, any cluster
includes some cases that might appear to fit better into a different category. Thus, our results
18
20. require a few caveats as noted above. Table 3 summarizes the main features of the five apparel
store groups identified by our analysis.
Insert Table 3 here
Store dimensions from principal factor analysis
While cluster analysis identified five broad categories of apparel on-line stores, principal factor
analysis identified a few high level dimensions characterizing the stores. The goal of this analysis
is to summarize the data set from the survey as accurately as possible using a few factors. In
addition to the variables used in the cluster analysis, aggregate variables have been defined by
adding the values of binary variables describing a similar category. For example, the three
variables coding the different navigation tools (PRODIDX, SITEIDX, BROWSE) were grouped
into one variable reflecting the stores’ navigation capabilities. The advantage of this procedure is
that it reduces variance in the data, thereby yielding clearer identifiable factors. Not all variables
collected in the survey were used in the factor analysis because variables that do not contribute to
basic factors tend to deteriorate results. For this reason, the variables describing page lengths and
picture sizes were excluded. Table 4 lists the basic variables and all aggregate variables used in the
principal factor analysis. The factors were rotated automatically using the varimax algorithm. The
Scree Test suggests three factors. Results for the three factor model is shown in Table 5.
Insert Table 4 here
Insert Table 5 here
Seven variables load particularly high on the first factor. They describe the total number of
products, the number of levels, the amount of company information presented, the amount of
information regarding ordering and using the interface, whether there is a search function or
19
21. different modes to shop in and the number of customer care features like gift services, access to
sales representatives and feedback possibilities. The first six variables are related to the store’s
size. Customer care is related to store size. Larger stores provide more customer care features.
We refer to the first factor as Store Size. This factor explains 20.4% of the total variance.
The second factor, Service, includes the variables LINKS, APPETS, PRINFO and
TEXTLENGTH. LINKS counts the number of hyperlinks to related information. APPETS
measures appetizers such as magazines, travel information, sale items and games. TEXTLENG
describes the information content available for individual products, PRINFO stands for general
product information and services such as help on the product’ size selection. In a retail world
s
store, this information is available from the sales clerk. They are all associated with appetizers
and information supplied for the customers’convenience and attraction.
The third factor comprises variables reflecting the interface design. The variable MENUBARS
describes whether the store has consistent text or image menu bars on all pages while
IMAGEUSE scores higher if images are not only used for product display but also as navigation
and orientation tools in the store. IDXBROW reflects the availability of site and product indices
as well as browsing functions to locate products and information in the store. A “What’ new” or
s
a FAQ section, summarized in the variable CUSTINFO, also provide the customer with
information about the store and its features. These variables measure interface quality and
consistency, hence we call the factor Interface Quality.
In effect, the three factors Store Size, Customer Services and Interface Quality capture a great
share of the variance in the data explaining 46.2% of total variance. As there are only three
components, we plot the stores in the three dimensional space spanned by the factors (Figure 1).
Insert Table 6 here
20
22. Insert Figure 1 here
The five categories found in the cluster analysis can be roughly identified. The Super Stores
are clearly segregated from the other stores by their size. The stores within this cluster still differ
in terms of services and interface appearance. Shoppers Advantage for example is weak on the
Services scale as it does not offer many additional features which are not product related. Land’s
End and Spiegel with their extensive magazines score high on this dimension. L.L. Bean and J.C.
Penney have the highest interface quality due to very good menu bars and navigation capabilities.
The Service Merchandise Store is quite far away from this group, mainly due to its smaller size
with only 31 products.
We described Promotional Stores as having few products and lots of additional information.
Thus, this cluster can be best identified in the Size versus Services projection. Tara Thralls and
AWEAR score especially high on the Services dimension while all stores in this cluster have less
than the average size. Plain Sales Stores can be found below average on the Services dimension.
They also score medium to low on the Size dimension as they are still small in comparison to the
Super Stores cluster. The interface is better than average.
The One Page Store cluster can be found on the negative axis of all three dimensions.
Services, Size and Interface Quality are below average for most stores. Product Listings are
bigger in size and offer more services than One Page Stores. Still, the Interface Quality is also
below average as can be seen from the graph.
Discussion and Summary
The Internet and the WWW are changing rapidly. Stores will become more sophisticated and
incorporate additional features in the future. Today, video, audio and instant access to high
quality images are limited basically by low bandwidths. Future on-line stores can become much
21
23. more interactive, involving, for example, voice mail inquiries to sales representatives. Intelligent
on-line stores might direct consumers automatically to products complementary to a specific
person’ purchase history (for example, see http://www.firefly.net/places.fly). Despite all these
s
possible changes, retailers will still have different intentions. There is no simple on-line store
design to accommodate everyone and each retailer will follow a different marketing strategy.
This research identified five different on-line store design strategies: Super Stores, Promotional
Stores, Plain Sales Stores, One Page Stores and Product Listings. They differ mainly in terms of
the store’ size, the quantity of extra information and appetizers, and the interface design
s
including consistency, page lengths, image sizes and navigation capabilities. We concentrated on a
subset of Internet stores for women’ apparel and did not study non-apparel stores to generalize
s
our findings. We assume that the basic approaches concerning the stores’ design will not differ
very much for other types of stores. Further research is needed to justify any generalizations
empirically. We also did not determine the percentage of on-line retail stores for women’ apparel
s
that would be classified as super stores, promotional stores, plain sales stores, one page stores and
product listings. Additional research is needed to estimate those values.
This research makes several contributions. First, it provides an interesting snapshot (July
1996) of Internet store features. The descriptive statistics of these on-line store attributes
suggests many opportunities to improve the experience for consumers. As noted in Table 6, many
of the descriptive statistics of on-line store attributes resonate with the findings by Jarvenpaa and
Todd [16] in their survey of consumer reactions to electronic shopping on the World Wide Web.
We also found few products in a typical store, a low overall quality of the pictures in the on-line
store, a dearth of product information in the on-line stores as compared to traditional paper
22
24. catalogs, only a small amount of store policy information available from an on-line store, and few
interface features to help find goods and services during goal directed shopping.
Second, the paper documents a useful statistical methodology for clustering on-line stores into
meaningful groups. This exploratory data analysis structured the on-line stores into meaningful
groups using observable attributes and features of on-line stores as the raw data. While different
clustering techniques for analyzing data can and do produce different taxonomies, the goal is to
identify groups that are more similar within a cluster than between clusters. We believe that
clustering provides an objective, empirical basis for classification of Internet sites for electronic
commerce.
Third, the technique provides developers of Web sites a methodology for eliciting functional
requirements from their clients. Our technique will help developers characterize attributes and
features of alternative Web sites to 1) facilitate the definition of requirements, 2) identify major
design features, 3) structure the design space into meaningful clusters to facilitate the
consideration of various design alternatives, and 4) help clients position their Web site
strategically by understanding the competing on-line store designs.
The classification has important implications for on-line store designers. Retailers can compare
their own catalogs with those in the survey. Measures for each of the 35 variables shown in
Appendix A can be added to a data file containing the means shown in Table 2. Using a statistical
package like Systat [41], a matrix of Euclidean distances can be computed and the data can be
clustered for comparison. Retailers then could compare their catalog to the categories we
identified and determine whether it has key features associated with particular categories.
Our list of features and attributes in Table 2 and Appendix A also serves as a check-list for
Internet store designers. The check-list can be used to survey peer group on-line stores. Do
23
25. these on-line stores incorporate new features that appear useful for the consumer? Do
competitors emphasize particular features like special services or high quality images? Comparing
the on-line store with similar competitors’ on-line stores generates ideas about how to improve
the store’ design.
s
The classification provides a better understanding of the design space in which the stores
operate. It reveals promising Internet store models that are not yet existent. Retailers might not
want to follow one of the standard strategies, but may want to differentiate themselves from the
competition. The classification can help envision new strategies that combine elements of known
approaches to uniquely position their on-line store’ marketing strategy. For example, the
s
classification did not find any One Page Stores that provided high service levels or a high quality
interface. This is an anomaly given the large number of niche women’ clothing boutiques in a
s
typical shopping mall. Why don’ such stores exist on the Internet? Is it because small retail
t
stores lack the expertise needed to develop high quality on-line stores with good service? Or are
the costs of developing such a Web site prohibitive to small retail stores?
Such questions lead to a larger research agenda exploring the effectiveness of different types of
stores identified in our classification. This might involve measuring effectiveness as a function of
sales per unit of traffic, total units of traffic, and total sales for the various types of stores. In their
survey of consumer reactions to electronic shopping, Jarvenpaa and Todd [16] did not examine
reactions to different types of store separately. It is not clear whether consumers prefer to shop at
large stores or small stores; at discount stores or department stores or how the type of on-line
store impacts consumer behavior. According to surveys of retail stores [17], large stores should
attract more customers than small stores. From the perspective of an on-line retailer, it would be
important to know whether large stores are more effective than small stores in converting store
24
26. traffic into sales. More generally, this raises the question about the relationship between
effectiveness and the categories of stores identified in the research (Super Stores, Promotional
Storefronts, Plain Sales Stores, One Page Stores and Product Listings). By structuring this
domain of inquiry, the classification delineates on-line stores into similar groups. Effectiveness
can be examined within and between groups. For example, are Super Stores more effective than
Plain Sales Stores? Within stores identified as Super Stores, what determines the effectiveness of
a particular Super Store compared to other Super Stores? In Super Stores, a search feature may
improve sales. It is not at all clear that a search feature would impact sales in One Page Stores.
The store categories permit a more detailed comparison of effectiveness measures among stores.
Thus, the classification will aid future research in this domain.
Analyzing the evolution of Web stores can lead to insights about what makes a successful on-
line store. The three store dimensions we identified raise some questions for future research.
Service In electronic commerce, the physical store is replaced by an on-line store. The sales
clerk is now a help button or a product index on the screen display. What services can increase
customer loyalty to an on-line store? Are consumers more likely to make a purchase from an on-
line store if it offers more than one mode of shopping? What type of product information attracts
consumers? Table 6 suggests a general lack of attention to customer service in existing on-line
stores. Better presentation of customer service information will be essential in the redesign and
revisions of existing on-line stores.
Size How important is product variety? Are niche boutiques better than Super Stores? Many
of the stores surveyed were small, but it is not clear whether this is a good or bad characteristic.
Consumers are more likely to find specific products in a small on-line store. In large on-line
stores, it is not clear how best to facilitate browsing and navigation. More importantly, a better
25
27. use of links among products in a on-line store may enhance the shopping experience. Consumers
might be more likely to make a purchase when a related product is linked.
Interface The user interface design is essential for conducting business online. How easy is it
for the consumer to find what they want in the store? Is there an optimal number of screen levels
consumers are willing to navigate before giving up and leaving? If they find a product, how easy is
it to buy the product online? Clearly there are interface problems associated with navigation,
search, and the ordering process in many existing on-line stores.
Since sales can be adversely influenced by poor on-line store design, it is essential to quantify
the effects of different layout, organizational, browsing and navigation features on Internet store
traffic and sales. Managers, production staff and merchant partners should not assume customers
do not want an item if it is not selling. Nor should they conclude that a poor response to a given
store design is because of the merchandising mix. It is important to take a harder look at the
possible relationship between poor selling items and store design and layout. This is especially
important given the replacement of retail store personnel by various user interface features in on-
line stores. Could customers be having a tough time wading through the screens? Can customers
find what they want in the stores? Are customers aware of what products are in the stores? After
all, diligence in browsing a store is not a virtue on-line retailers should expect from their
customers. Because of the cost involved with launching a new store or changing an existing store
design, it is important to allocate product development resources to on-line store features that
improve store traffic and sales. In our future research, we hope to prioritize store redesign efforts
to those features with the greatest impact on Internet store traffic and sales by quantifying the
benefits of specific design features.
26
28. Endnotes
1 The browser Netscape Explorer 2.02 showed both Toolbar and Location in the window.
2 In the USA, 23.8% of the monitors are 17 inch; 33.4% are 14 inch; 39.9% are 15 inch; and 2.8% are 19 inches
or larger (June 24, 1997 PC Magazine; p. 173). The long term trend is towards larger monitors.
3 [Mandel and Johnson 1997 http://www-marketing.wharton.upenn.edu/ec/project.html]
4 A complete list of all the catalogs used in the study is available from the authors.
5 To address concerns about the distance measure, we re-examined the data using two other distance measures.
Gamma distances were computed using 1 minus the Goodman-Kruskal gamma correlation coefficient. This is
used typically for rank order or ordinal scales. Pearson distances were computed using 1 minus the Pearson
product-moment correlation coefficient. This is used typically for any quantitative data. As a basis for
comparison we specified 5 clusters (using this option in Systat) and saved the cluster number in a Systat file. We
compared the cluster of each on-line store using each new distance metric to the clusters produced using
Euclidean distances. Using the Gamma distance measure, 93 stores out of 137 (68%) were in the same cluster
as those found using Euclidean distances. Using the Pearson distance measure, 95 stores out of 137 (69%) were
in the same cluster as those found using Euclidean distances. More importantly, the interpretation and nature of
each cluster (on-line store category) remains substantively intact.
6 The browser Netscape Explorer 2.02 showed toolbar and location at a resolution of 1024x768.
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31. Retail Store Paper Catalog On-line Catalog
Sales clerk service Product descriptions, sales clerk Product descriptions, information pages, gift
on the phone, information pages services, special search functions
Store promotion Special offers, lotteries, sale Special offers, on-line games and lotteries,
catalogs links to other sites of interest, appetizers
Store window displays Front and back cover Catalog home page
Store atmosphere Copy quality, graphics, product Interface consistency, catalog organization,
arrangement, perceived image interface and graphics quality
Aisle products Products on first 2-4 spreads and Featured products on each hierarchical level of
the middle spread the catalog
Store layout Page and product arrangement Screen depth, browse and search functions,
indices, image maps
Number of floors in the Catalog organization Hierarchical levels of the catalog
store
Number of store entrances Frequency of mailings, number of Number of links to a particular catalog
and store outlets / branches unique catalogs mailed
Checkout cashier Order form, 1-800 phone number On-line shopping basket or order form
See and touch of the Limited to image quality and Limited to image quality and description,
merchandise description future potential for sound and video
Table 1 Analogies between retail stores, paper catalogs and on-line catalogs
30
33. Strategy Main Features Examples
1. Super Store •large catalog size L.L.Bean, Land’ End, Spiegel, Online
s
(20 stores) •ample extra information and Sports, J.C. Penney, Shoppers Advantage,
appetizers (magazines) Service Merchandise
•navigation tools
•pages fit on one screen page
2. Promotional Store •limited product range AWEAR, Tara Thralls, Madeleine Vionnet’ s,
(30 stores) •extensive company information Cheyenne Outfitters, Real Bodies, Utilities
•ample appetizers and links Design Match, The Old Hide House
3. Plain Sales Catalog •medium or large catalog size Milano, Rhondi, First Lady, Dock of the Bay,
(34 stores) •large images, use of thumbnails Clothes Horse, Leather & Gift Outlet,
• appetizers or links
no Skinzwear, Sophie la Nuit
4. One Page Catalog •limited catalog size Mariam Apparel, Lims, KHLA; Alaska
(30 stores) •few hierarchical levels Mountaineering, Australian Coat, Al’ Texas
s
•product browse function Jeans, Close To You
• page catalog
one
5. Product Listing •medium catalog size Rocky Mountain Outfitters, Seabury’ The
s,
(23 stores) •long product pages / lists Dress Connection, Dance Supplies, Full
•small product images Swing Golf of Alaska, Fisher Henney
•few hierarchical levels Naturals
Table 3 Classification of five major on-line catalog strategies
32
34. Aggregate Variable Consists of Variables (simple unweighted sum)
Interface Navigation SITEIDX, PRODIDX, BROWSE
[IDXBROW] site-index, product-index, browse function
Search Tool and Shopping Modes PRODSEAR, SHOPMOD
[PSEASHMO] product search function, shopping in more than one mode?
Customer Information WHATNEW, FAQ
[CUSTINFO] what’ new section, FAQ section
s
Customer Care GETKNOW, SALEREP, FEEDBACK, GIFTSERV
[CUSTCARE] get to know the customer questions, access to sales rep, feedback
section, gift services
Appetizers APPET1, APPET2
[APPETS] product related appetizers, not product related appetizers
All Links to Other Areas PRODLKS, COMPLKS, REGIOLKS, INTERLKS
[LINKS] product, competitor, geographical region or Internet links
Additional Product Information XTRAINFO, SIZESELE
[PRINFO] extra product information, help on product size selection
Basic Variables used in Principal Factor Analysis
CATSIZE COMPINFO IMAGEUSE TEXTLENG
HIERARCH MENUBARS HELPINTF ORDERINF
Table 4 Fifteen variables used in the principal factor analysis
33
36. Catalog Attribute Descriptive Statistics Consumer Reaction from
Jarvenpaa & Todd (1997)
Merchandise
Catalog size: number of •62% have less than 50 products •31% were disappointed with
different products •Only 5% had more than 500 products product variety
Price •78% had prices comparable with paper •18% felt that prices seem to be
catalogs or store prices higher on the Web
•22% sold at a discount
Quality •33% did not provide any company policy •Absence of familiar brand
information (returns, guarantees, etc). name merchandise
Product descriptions, use of •50% had less than 3 lines of text per •Need better descriptions and
images, text length product pictures of the products
Service
General service, gift services, •Only 19% offered extra product information •80% had at least one negative
FAQ on product related •80% had less than 10 lines of text about the comment about customer
questions, and information company’ history, reputation, policies, etc.
s service on the Web
about the company •Only 9% had a FAQ section •Hidden shipping costs
Sales clerk service, phone •47% did not offer email for interactive •Merchants did not anticipate
number, email of sales reps, service consumer queries and requests
feedback section, get to know •95% did not have links on end product for policy information
the customer questions, help pages to related products •Responsiveness determined if
on product-size selection •Only 25% had help for size selection it was a good or bad site
Order information, •Only 30% used a shopping cart metaphor •41% noted a lack of
merchandise return, credit, •Most required manual re-entry of product information on returns,
and payment policies information onto an order form delivery time, guarantees, etc.
Fast checkout •41% did not offer on-line ordering with a •Some sites had hard to follow
credit card ordering directions
Promotion
Advertising links to other •76% did not offer incentives or appetizers to •“You never get a sale”
sites, use of banner ads, attract and retain customers •“You never get to see head-to-
appetizers to attract •Only 6% offered a “What’ New” section
s head competition that you see
customers, sales promotion in a mall”
Interface Quality
Navigation indices and •4% had a site index •44% felt goal directed
browsing, ease to find items, •6% had a product search function shopping and product
number of shopping modes, •Only 6% allowed multiple shopping modes comparisons are difficult on
convenience •Only 22% had a product browsing function the Web
Consistency of the interface •24% did not have consistent menu bars •“This is not for computer
illiterate people.”
Providing help •12% had on-line help for interface usage •“I had places I wanted to go
and couldn’ understand how”
t
Use of images for navigation, •Less than 8% of the total screen area •Inability to adequately
size of images, enlargeable contained images or graphics visualize the products
thumbnail pictures •Over 50% used images for navigation
Table 6 Comparison of attributes of on-line catalogs with a survey of consumer reactions to electronic
shopping from Jarvenpaa and Todd (1997)
35
37. Super Stores
Promotional Stores
Product
Listings Plain Sales Stores
One Page Stores
Figure 1 Apparel Internet catalogs classified by Size, Services and Interface
(To accommodate store names, only a subset of the 137 stores are displayed)
36
38. Appendix A Variables used in the Internet survey
Data Coding and examples Data Collection / Comments
Merchandise
1. size, number of different 1: 6 - 10 products The number was counted and summed
products [CATSIZE] 2: 11-50 products for all product categories in the on-line
3: 51 - 100 products store.
4: 101 - 500 products
5: 501 - products
2. number of hierarchies average number The straightest path through the general
between home page and transition network of the catalog was
end product page evaluated for 6 different products.
[HIERARCH]
3. order information - 0: less than 4 lines The total number of lines providing
shipping / ordering / product 1: 5-20 lines information about guarantees and the
quality / return information 2: 21 - 40 lines ordering process was counted.
[ORDERINF] 3: 41 - 60 lines
4: 61 lines & greater
Service
4. gift services 0: no Wrapping, gift certificates
[GIFTSERV] 1: yes
5. FAQ on product related 0: no Did the store provide an frequently
questions [FAQ] 1: yes asked questions section on product
related questions or the company?
6. company information 0: 0 - 11 lines The total number of lines providing
mission statement, history, 1: 11 - 20 lines company information was counted.
policies 2: 21 - 40 lines
3: 41 - 80 lines
[COMPINFO] 4: 81 lines & greater
7. text length on “end product average number of lines for 6 Any full line on the screen was counted
pages” end product pages as one line, single items of information
[TEXTLENG] (e.g. “waterproofed”) were counted as
0.5 lines.
8. phone number / email of 0: none Did the on-line store offer any way to
sales reps 1: yes contact a sales representative, by email,
[SALEREP] phone or fax?
9. feedback section 0: no Did the retailer ask for feedback (email
[FEEDBACK] 1: yes comments (mailto), blank email form,
email questionnaire, phone)?
10. extra get-to-know-the- 0: no Any personal questions about the buyer
customer questions 1: yes on the order form were rated 1.
[GETKNOW]
11. extra product information 0: no Any information on care, maintenance,
[XTRAINF] 1: yes use of products etc. was rated 1.
12. help on product-size 0: no Stores presenting tables, figures, etc.
selection [SIZESELE] 1: yes with product sizes were rated 1.
37
39. Promotion
13. frequent buyer incentives 0: no Any discounts or gifts associated with
[FREQBUY] 1: yes higher sales per customer.
14. links to product related Four variables, one for each of Some sites offered a large number of
information sites, the link categories (see left), links in one of the categories (e.g., to
competitors, regional were defined as follows: general Internet sites like search
sources or general Internet 0: no links engines). This was considered less
sites 1: 1 - 5 links useful than offering links in different
[LINKS] 2: 6 or more links categories which was reflected in the
The variables were summed to coding scheme. The variable LINKS
form one LINKS variable. eventually took values from 0 to 8.
15. extra banner ads on the 0: no Ads for other companies or the store
pages [BANNER] 1: yes itself were rated 1.
16. product-related and non- Two variables, one for product- Examples for product-related appetizers
product related appetizers related and one for non-product- are: hot product of the day, mailing list,
related appetizers were coded: sale items.
[APPETS] 0: none Examples for non-product-related
1: one closely product related appetizers are: magazines, travel
item information, guest books, games.
2: more than one item or total
content more than 10 pages
The variables were summed to
form one APPETS variable.
17. “What’ new” section
s 0: no A section introducing new products,
[WHATNEW] 1: yes catalog features or news was rated 1.
Other Store Variables
18. shopping in different 0: no Were there different strategies the
modes 1: yes customer can use to find a product (store
[SHOPMOD] organization by product category,
gender, price level, etc.)?
19. site index 0: no A site index gives an overview of the
[SITEIDX] 1: yes structure and hierarchies of all pages.
20. product index 0: no Stores providing comprehensive lists of
1: yes, text all their products, in addition to the
[PRODIDX] 2: yes, with thumbnails departmental product organization, were
rated 1 or 2.
21. product search function 0: no Any inter-catalog search function was
[PRODSEAR] 1: yes rated 1.
22. product image browse 0: no A browse function that enables shoppers
function [BROWSE] 1: yes to navigate directly between products,
without changing store levels.
23. links between related 0: no Links between related products or links
product pages 1: yes between products and additional
[PRODLINK] information (magazine pages, games,
etc.) was rated 1
24. product selection 0: off-line 1 refers to an on-line form which has to
1: on-line form to fill manually be filled out manually; 2 was a form
[PRODSELE] 2: on-line clickable form consisting of pull-down menus listing
3: shopping cart all product options. An on-line
shopping cart only requires the shopper
to hit an order button on end product
pages.
38
40. Interface Variables
25. number of products on end average number The number was counted on 6 different
product page end product pages.
[PRODNUMB]
26. menu bars on all pages 0: no Menu bars had a home page button and
1: yes, text at least one further navigation button.
[MENUBARS] 2: yes, image overlays The buttons had to be placed
consistently on all pages of one level.
Buttons using image overlays were
considered more sophisticated.
27. background color or 0: no color, white /gray In case of changing background colors
pattern 1: one color on different pages, the modal level was
2: pattern recorded.
[BACKGRO] 3: texture
28. help on interface usage 0: no Any full line of text on the screen was
1: 1 - 11 lines counted as one line.
[HELPINTF] 2: 11 - 20 lines
3: 21 - 40 lines
4: 41 - lines
29. page length of home page length in screen pages Taking the normal screen size as
reference6, total page length was
[HPLENGTH] estimated as a multiple of this size by
scrolling the page down. If the interface
used inter-page links, page length was
only measured down to the line where
the first link pointed to.
30. page length of navigation average length in screen pages average length in screen pages
page [NAVILENG]
31. page length of end product average length in screen pages average length in screen pages
page [EPROPLEN]
32. image size on the home measured as a percentage of the Image size was determined by
page total visible screen area (549 measuring width and height of all home
square cm) page images, excluding buttons.
[HPIMAG]
33. image size on “end product mean of 6 pictures; measured as Image size was determined by
pages” a percentage of the total visible measuring width and height of an
[PRODIMAG] screen area average end product image.
34. use of images 0: none Did the catalog use images for product
1: to show products only display only, or for graphical buttons,
[IMAGEUSE] 2: image overlay and product too?
display
35. use of enlargeable thumb- 0: no The use of any enlargeable image was
nail pictures [THUMBS] 1: yes rated 1.
39
