A Classification of Internet Retail StoresPeter Spiller and Gerald L. Lohse

Department of Operations and Information ManagementThe Wharton School of the University of Pennsylvania

1319 Steinberg Hall - Dietrich HallPhiladelphia, Pennsylvania 19104-6366

office phone: (215) 898-8541FAX 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 histhesis 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 theWharton School of the University of Pennsylvania. His research interests include electronicshopping and on-line information services, cognitive models of decision processes, impact ofdecision support systems on decision processes, human-computer interaction, and visualization ofinformation. 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 thatdynamically adapt to consumer preferences and basic research on how people perceive andprocess information from displays.

Acknowledgment

We are grateful to Dr. Vladimir Zwass, Steven O. Kimbrough and two anonymous reviewersfor their helpful comments and suggestions on earlier drafts of this manuscript.

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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

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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

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

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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’s intention, on-line catalogs focus on a mixture of pure selling, on increasing marketplace

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’s apparel and accessories

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

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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’s store designs.

This paper describes an empirical study of 137 Internet stores selling women’s apparel that

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.

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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’s cover page

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translates into the on-line catalog’s home page. Both the cover page and the home page aim to

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

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customer’s feelings about the store. In our research, we only concentrated on functional qualities

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’s summary [18]. His attribute list is

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’s functional attributes were excluded from our

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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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’s phone number or email, and whether the store

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’s new?” section that can either

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.

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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’s navigation or

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’s apparel for sale were

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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

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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’s retail stores. 22% offered an order form to

request a printed version of the catalog. Almost one third did not provide any information on the

company’s history, policies or background; 80% had less than 10 lines of information. This is a

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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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’s description in the Wall

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

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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’s new” - sections. A great number of sites do not provide any of these

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

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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

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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’s minimum

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’s (1981)

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

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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’s End, Spiegel, Online Sports, J.C. Penney, Shoppers

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

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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’s Scarves, Wickers, Cheyenne

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

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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’s Texas Jeans and Close To You. However, the cluster also contains a few stores, such as

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’s Golf, The Dress Connection, Dance Supplies, Full Swing Golf of Alaska and Fisher

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

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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

20

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’s size selection. In a retail world

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’s new” or

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

21

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

22

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’s purchase history (for example, see http://www.firefly.net/places.fly). Despite all these

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’s size, the quantity of extra information and appetizers, and the interface design

including consistency, page lengths, image sizes and navigation capabilities. We concentrated on a

subset of Internet stores for women’s apparel and did not study non-apparel stores to generalize

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’s apparel

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

23

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

24

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’s design.

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’s marketing strategy. For example, the

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’s clothing boutiques in a

typical shopping mall. Why don’t such stores exist on the Internet? Is it because small retail

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

25

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

26

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.

27

Endnotes1 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 inchesor 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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Retail Store Paper Catalog On-line CatalogSales clerk service Product descriptions, sales clerk

on the phone, information pagesProduct descriptions, information pages, giftservices, special search functions

Store promotion Special offers, lotteries, salecatalogs

Special offers, on-line games and lotteries,links to other sites of interest, appetizers

Store window displays Front and back cover Catalog home pageStore atmosphere Copy quality, graphics, product

arrangement, perceived imageInterface consistency, catalog organization,interface and graphics quality

Aisle products Products on first 2-4 spreads andthe middle spread

Featured products on each hierarchical level ofthe catalog

Store layout Page and product arrangement Screen depth, browse and search functions,indices, image maps

Number of floors in thestore

Catalog organization Hierarchical levels of the catalog

Number of store entrancesand store outlets / branches

Frequency of mailings, number ofunique catalogs mailed

Number of links to a particular catalog

Checkout cashier Order form, 1-800 phone number On-line shopping basket or order formSee and touch of themerchandise

Limited to image quality anddescription

Limited to image quality and description,future potential for sound and video

Table 1 Analogies between retail stores, paper catalogs and on-line catalogs

31

No. Variable* Min MaxSuperStores

PromotionalStores

PlainSales

Stores

OnePage

StoresProductListings

Number Stores 20 30 34 30 231 CATSIZE 1 5 3.35 2.07 2.53 2.10 2.222 HIERARCH 0 6 4.08 2.80 2.78 2.45 2.543 ORDERINF 0 4 1.75 1.00 0.32 0.57 0.174 GIFTSERV 0 1 0.35 0.00 0.00 0.03 0.045 FAQ 0 1 0.15 0.07 0.15 0.03 0.046 COMPINFO 0 4 2.85 2.07 1.32 1.37 1.397 TEXTLENG 0 20 5.10 4.10 3.26 2.92 2.988 SALEREP 0 1 0.65 0.43 0.53 0.00 1.009 FEEDBACK 0 1 0.90 0.77 0.06 0.00 0.00

10 GETKNOW 0 1 0.15 0.03 0.03 0.03 0.0011 XTRAINF 0 1 0.30 0.53 0.00 0.07 0.0912 SIZESELE 0 1 0.55 0.17 0.26 0.13 0.1713 FREQBUY 0 1 0.30 0.00 0.03 0.03 0.0414 LINKS 0 8 1.30 1.33 0.82 0.60 0.8715 BANNER 0 1 0.25 0.00 0.12 0.10 0.0416 APPETS 0 4 1.50 0.73 0.29 0.60 0.3517 WHATNEW 0 1 0.30 0.00 0.06 0.00 0.0418 SHOPMOD 0 1 0.35 0.03 0.00 0.03 0.0019 SITEIDX 0 1 0.10 0.00 0.03 0.10 0.0020 PRODIDX 0 2 1.20 0.40 0.85 0.10 0.0421 PRODSEAR 0 1 0.20 0.03 0.03 0.00 0.0422 BROWSE 0 1 0.20 0.17 0.29 0.30 0.0423 PRODLINK 0 1 0.15 0.10 0.03 0.03 0.0024 PRODSELE 0 3 1.85 1.23 1.18 1.27 1.1725 PRODNUMB 1 25 3.18 3.15 4.43 3.52 5.3726 MENUBARS 0 2 1.55 1.27 1.00 0.90 1.0427 BACKGRO 0 3 1.55 1.03 1.15 1.27 0.7028 HELPINTF 0 4 1.45 0.20 0.12 0.00 0.1329 HPLENGTH 0 11 1.34 1.33 1.83 2.55 1.7230 NAVILENG 0 8 1.84 1.44 1.04 0.51 0.7231 EPROPLEN 0 12 1.64 1.94 2.14 1.70 2.6332 HPIMAG 0 1 0.15 0.12 0.11 0.11 0.0933 PRODIMAG 0 1 0.11 0.08 0.16 0.10 0.0734 IMAGEUSE 0 2 1.95 1.47 1.50 1.30 1.1735 THUMBS 0 1 0.85 0.43 1.00 0.00 0.00

* See Appendix A for a complete variable description

Table 2: Means for each of the five on-line store categories for all 35 variables used in the

cluster analysis

32

Strategy Main Features Examples1. Super Store(20 stores)

• large catalog size• ample extra information and

appetizers (magazines)• navigation tools• pages fit on one screen page

L.L.Bean, Land’s End, Spiegel, OnlineSports, J.C. Penney, Shoppers Advantage,Service Merchandise

2. Promotional Store(30 stores)

• limited product range• extensive company information• ample appetizers and links

AWEAR, Tara Thralls, Madeleine Vionnet’s,Cheyenne Outfitters, Real Bodies, UtilitiesDesign Match, The Old Hide House

3. Plain Sales Catalog(34 stores)

• medium or large catalog size• large images, use of thumbnails• no appetizers or links

Milano, Rhondi, First Lady, Dock of the Bay,Clothes Horse, Leather & Gift Outlet,Skinzwear, Sophie la Nuit

4. One Page Catalog(30 stores)

• limited catalog size• few hierarchical levels• product browse function• one page catalog

Mariam Apparel, Lims, KHLA; AlaskaMountaineering, Australian Coat, Al’s TexasJeans, Close To You

5. Product Listing(23 stores)

• medium catalog size• long product pages / lists• small product images• few hierarchical levels

Rocky Mountain Outfitters, Seabury’s, TheDress Connection, Dance Supplies, FullSwing Golf of Alaska, Fisher HenneyNaturals

Table 3 Classification of five major on-line catalog strategies

33

Aggregate Variable Consists of Variables (simple unweighted sum)Interface Navigation[IDXBROW]

SITEIDX, PRODIDX, BROWSEsite-index, product-index, browse function

Search Tool and Shopping Modes[PSEASHMO]

PRODSEAR, SHOPMODproduct search function, shopping in more than one mode?

Customer Information[CUSTINFO]

WHATNEW, FAQwhat’s new section, FAQ section

Customer Care[CUSTCARE]

GETKNOW, SALEREP, FEEDBACK, GIFTSERVget to know the customer questions, access to sales rep, feedbacksection, gift services

Appetizers[APPETS]

APPET1, APPET2product related appetizers, not product related appetizers

All Links to Other Areas[LINKS]

PRODLKS, COMPLKS, REGIOLKS, INTERLKSproduct, competitor, geographical region or Internet links

Additional Product Information[PRINFO]

XTRAINFO, SIZESELEextra product information, help on product size selection

Basic Variables used in Principal Factor AnalysisCATSIZE COMPINFO IMAGEUSE TEXTLENGHIERARCH MENUBARS HELPINTF ORDERINF

Table 4 Fifteen variables used in the principal factor analysis

34

Factor 1 Factor 2 Factor 3Size Services Interface

QualityCATSIZEHIERARCHCOMPINFOORDERINFOHELPINTFPSEASHMOCUSTCARE

LINKSAPPETSPRINFOTEXTLENG

MENUBARSIMAGEUSECUSTINFOIDXBROW

20.4% 14.0% 11.8%

Table 5 Results of the factor analysis (percentages denote explained variances)

35

Catalog Attribute Descriptive Statistics Consumer Reaction fromJarvenpaa & Todd (1997)

MerchandiseCatalog size: number ofdifferent products

• 62% have less than 50 products• Only 5% had more than 500 products

• 31% were disappointed withproduct variety

Price • 78% had prices comparable with papercatalogs or store prices

• 22% sold at a discount

• 18% felt that prices seem to behigher on the Web

Quality • 33% did not provide any company policyinformation (returns, guarantees, etc).

• Absence of familiar brandname merchandise

Product descriptions, use ofimages, text length

• 50% had less than 3 lines of text perproduct

• Need better descriptions andpictures of the products

ServiceGeneral service, gift services,FAQ on product relatedquestions, and informationabout the company

• Only 19% offered extra product information• 80% had less than 10 lines of text about the

company’s history, reputation, policies, etc.• Only 9% had a FAQ section

• 80% had at least one negativecomment about customerservice on the Web

• Hidden shipping costsSales clerk service, phonenumber, email of sales reps,feedback section, get to knowthe customer questions, helpon product-size selection

• 47% did not offer email for interactiveservice

• 95% did not have links on end productpages to related products

• Only 25% had help for size selection

• Merchants did not anticipateconsumer queries and requestsfor policy information

• Responsiveness determined ifit was a good or bad site

Order information,merchandise return, credit,and payment policies

• Only 30% used a shopping cart metaphor• Most required manual re-entry of product

information onto an order form

• 41% noted a lack ofinformation on returns,delivery time, guarantees, etc.

Fast checkout • 41% did not offer on-line ordering with acredit card

• Some sites had hard to followordering directions

PromotionAdvertising links to othersites, use of banner ads,appetizers to attractcustomers, sales promotion

• 76% did not offer incentives or appetizers toattract and retain customers

• Only 6% offered a “What’s New” section

• “You never get a sale”• “You never get to see head-to-

head competition that you seein a mall”

Interface QualityNavigation indices andbrowsing, ease to find items,number of shopping modes,convenience

• 4% had a site index• 6% had a product search function• Only 6% allowed multiple shopping modes• Only 22% had a product browsing function

• 44% felt goal directedshopping and productcomparisons are difficult onthe Web

Consistency of the interface • 24% did not have consistent menu bars • “This is not for computerilliterate people.”

Providing help • 12% had on-line help for interface usage • “I had places I wanted to goand couldn’t understand how”

Use of images for navigation,size of images, enlargeablethumbnail pictures

• Less than 8% of the total screen areacontained images or graphics

• Over 50% used images for navigation

• Inability to adequatelyvisualize the products

Table 6 Comparison of attributes of on-line catalogs with a survey of consumer reactions to electronic

shopping from Jarvenpaa and Todd (1997)

36

Super Stores

ProductListings

One Page Stores

Plain Sales Stores

Promotional 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)

37

Appendix A Variables used in the Internet survey

Data Coding and examples Data Collection / CommentsMerchandise1. size, number of different

products [CATSIZE]1: 6 - 10 products2: 11-50 products3: 51 - 100 products4: 101 - 500 products5: 501 - products

The number was counted and summedfor all product categories in the on-linestore.

2. number of hierarchiesbetween home page andend product page[HIERARCH]

average number The straightest path through the generaltransition network of the catalog wasevaluated for 6 different products.

3. order information - shipping / ordering / product

quality / return information [ORDERINF]

0: less than 4 lines1: 5-20 lines2: 21 - 40 lines3: 41 - 60 lines4: 61 lines & greater

The total number of lines providinginformation about guarantees and theordering process was counted.

Service4. gift services [GIFTSERV]

0: no1: yes

Wrapping, gift certificates

5. FAQ on product relatedquestions [FAQ]

0: no1: yes

Did the store provide an frequentlyasked questions section on productrelated questions or the company?

6. company information mission statement, history,

policies [COMPINFO]

0: 0 - 11 lines1: 11 - 20 lines2: 21 - 40 lines3: 41 - 80 lines4: 81 lines & greater

The total number of lines providingcompany information was counted.

7. text length on “end productpages”

[TEXTLENG]

average number of lines for 6end product pages

Any full line on the screen was countedas one line, single items of information(e.g. “waterproofed”) were counted as0.5 lines.

8. phone number / email ofsales reps

[SALEREP]

0: none1: yes

Did the on-line store offer any way tocontact a sales representative, by email,phone or fax?

9. feedback section [FEEDBACK]

0: no1: yes

Did the retailer ask for feedback (emailcomments (mailto), blank email form,email questionnaire, phone)?

10. extra get-to-know-the-customer questions[GETKNOW]

0: no1: yes

Any personal questions about the buyeron the order form were rated 1.

11. extra product information [XTRAINF]

0: no1: yes

Any information on care, maintenance,use of products etc. was rated 1.

12. help on product-sizeselection [SIZESELE]

0: no1: yes

Stores presenting tables, figures, etc.with product sizes were rated 1.

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Promotion13. frequent buyer incentives [FREQBUY]

0: no1: yes

Any discounts or gifts associated withhigher sales per customer.

14. links to product relatedinformation sites,competitors, regionalsources or general Internetsites

[LINKS]

Four variables, one for each ofthe link categories (see left),were defined as follows:0: no links1: 1 - 5 links2: 6 or more linksThe variables were summed toform one LINKS variable.

Some sites offered a large number oflinks in one of the categories (e.g., togeneral Internet sites like searchengines). This was considered lessuseful than offering links in differentcategories which was reflected in thecoding scheme. The variable LINKSeventually took values from 0 to 8.

15. extra banner ads on thepages [BANNER]

0: no1: yes

Ads for other companies or the storeitself were rated 1.

16. product-related and non-product related appetizers

[APPETS]

Two variables, one for product-related and one for non-product-related appetizers were coded:0: none1: one closely product relateditem2: more than one item or totalcontent more than 10 pagesThe variables were summed toform one APPETS variable.

Examples for product-related appetizersare: hot product of the day, mailing list,sale items.Examples for non-product-relatedappetizers are: magazines, travelinformation, guest books, games.

17. “What’s new” section [WHATNEW]

0: no1: yes

A section introducing new products,catalog features or news was rated 1.

Other Store Variables18. shopping in different

modes [SHOPMOD]

0: no1: yes

Were there different strategies thecustomer can use to find a product (storeorganization by product category,gender, price level, etc.)?

19. site index [SITEIDX]

0: no1: yes

A site index gives an overview of thestructure and hierarchies of all pages.

20. product index [PRODIDX]

0: no1: yes, text2: yes, with thumbnails

Stores providing comprehensive lists ofall their products, in addition to thedepartmental product organization, wererated 1 or 2.

21. product search function [PRODSEAR]

0: no1: yes

Any inter-catalog search function wasrated 1.

22. product image browsefunction [BROWSE]

0: no1: yes

A browse function that enables shoppersto navigate directly between products,without changing store levels.

23. links between relatedproduct pages

[PRODLINK]

0: no1: yes

Links between related products or linksbetween products and additionalinformation (magazine pages, games,etc.) was rated 1

24. product selection [PRODSELE]

0: off-line1: on-line form to fill manually2: on-line clickable form3: shopping cart

1 refers to an on-line form which has tobe filled out manually; 2 was a formconsisting of pull-down menus listingall product options. An on-lineshopping cart only requires the shopperto hit an order button on end productpages.

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Interface Variables25. number of products on end

product page[PRODNUMB]

average number The number was counted on 6 differentend product pages.

26. menu bars on all pages [MENUBARS]

0: no1: yes, text2: yes, image overlays

Menu bars had a home page button andat least one further navigation button.The buttons had to be placedconsistently on all pages of one level.Buttons using image overlays wereconsidered more sophisticated.

27. background color orpattern

[BACKGRO]

0: no color, white /gray1: one color2: pattern3: texture

In case of changing background colorson different pages, the modal level wasrecorded.

28. help on interface usage [HELPINTF]

0: no1: 1 - 11 lines2: 11 - 20 lines3: 21 - 40 lines4: 41 - lines

Any full line of text on the screen wascounted as one line.

29. page length of home page [HPLENGTH]

length in screen pages Taking the normal screen size asreference6, total page length wasestimated as a multiple of this size byscrolling the page down. If the interfaceused inter-page links, page length wasonly measured down to the line wherethe first link pointed to.

30. page length of navigationpage [NAVILENG]

average length in screen pages average length in screen pages

31. page length of end productpage [EPROPLEN]

average length in screen pages average length in screen pages

32. image size on the homepage

[HPIMAG]

measured as a percentage of thetotal visible screen area (549square cm)

Image size was determined bymeasuring width and height of all homepage images, excluding buttons.

33. image size on “end productpages”

[PRODIMAG]

mean of 6 pictures; measured asa percentage of the total visiblescreen area

Image size was determined bymeasuring width and height of anaverage end product image.

34. use of images [IMAGEUSE]

0: none1: to show products only2: image overlay and productdisplay

Did the catalog use images for productdisplay only, or for graphical buttons,too?

35. use of enlargeable thumb-nail pictures [THUMBS]

0: no1: yes

The use of any enlargeable image wasrated 1.