A CONJOINT ANALYSIS OF ONLINE CONSUMER SATISFACTION

Journal of Electronic Commerce Research, VOL. 6, NO.2, 2005

A CONJOINT ANALYSIS OF ONLINE CONSUMER SATISFACTION1


L. Christian Schaupp
Cameron School of Business
University of North Carolina – Wilmington
cschaupp@vt.edu

France Bélanger
Pamplin College of Business
Virginia Polytechnic and State University
belanger@vt.edu


ABSTRACT

The ability to measure the level of customer satisfaction with online shopping is essential in gauging the success
and failure of e-commerce. To do so, Internet businesses must be able to determine and understand the values of
their existing and potential customers. Hence, it is important for IS researchers to develop and validate a diverse
array of metrics to comprehensively capture the attitudes and feelings of online customers. What factors make on-
line shopping appealing to customers? What customer values take priority over others? This study’s purpose is to
answer these questions, examining the role of several technology, shopping, and product factors on online customer
satisfaction. This is done using a conjoint analysis of consumer preferences based on data collected from 188 young
consumers. Results indicate that the three most important attributes to consumers for online satisfaction are privacy
(technology factor), merchandising (product factor), and convenience (shopping factor). These are followed by
trust, delivery, usability, product customization, product quality, and security. Implications of these findings are
discussed and suggestions for future research are provided.

Keywords: Online customer satisfaction; E-satisfaction; Conjoint analysis; E-commerce; E-commerce metrics

1. Introduction
Internet commerce involves the sale and purchase of products and services over the Internet [Keeney 1999]. It
was touted to have massive sales potential, with previous expectations of over $1 trillion by 2002 [Burke 1997;
Mehler et al. 1997]. Yet, these expectations have fallen well short of the $1 trillion estimate, with the U.S. Census
Bureau reporting that U.S. e-commerce sales in 2002 equaled only $43.5 billion and $70 billion in 2003. However,
online spending is on the rise. Retail e-commerce sales in the second quarter of 2004 were approximately $15.7
billion, an increase of 23.1 percent from the second quarter of 2003 (U.S. Census Bureau 2003). E-commerce sales
in the second quarter of 2004 accounted for 1.7 percent of total sales, while in the second quarter of 2003 e-
commerce sales were 1.5 percent of total sales (U.S. Census Bureau 2003).
Given the continual rise in online spending and its increasing influence on total retail sales in the U.S. further
exploration of per person spending patterns is warranted. Clearly, consumers must be satisfied with their e-
commerce shopping experience to acquire more goods and services on-line. Given the need to understand what
users want in a Web site [Straub & Watson 2001], it is important for IS researchers to develop and validate a diverse
array of metrics to comprehensively capture the attitudes and feelings of online customers. What factors make on-
line shopping appealing to customers? What customer values take priority over others? This study’s purpose is to
answer these questions, examining the role of several technology, shopping, and product factors using a
conjoint analysis of consumer preferences to measure online customer satisfaction (e-satisfaction).
Metrics for assessing the customer satisfaction level of online shopping are essential in gauging the ultimate
success or failure of e-commerce. Different customers may disagree about their perceptions and satisfaction level
for a particular Web site. An experienced online customer may find his or her experience to be very enjoyable and
fulfilling. The experienced online shopper is more likely to have an easier time navigating the site, searching for
information on particular products, as well as ordering on-line. Shopping via the Internet is salient in this
experienced customer’s mind because of past experiences with the use of technology. This experienced shopper

1 A preliminary discussion of concepts included in this paper was presented at AMCIS, Tampa, FL, Aug. 4, 2003.

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may be more likely to leave the Website with a feeling of satisfaction, granted the purchases arrive in a timely
manner, and receiving the product is hassle free. Another shopper, new to the process of shopping online, may find
it difficult and impersonal. Different customers will consider different costs and benefits in appraising the net value
of prospective Internet purchases [Keeney 1999]. As a result, it is imperative that the beliefs and expectations of the
consumer are noticed and met by Internet businesses. In order to measure customer satisfaction level, accurate
metrics must be in place.

2. Background
Many articles on business-to-consumer (B2C) e-commerce focus on experiences of one or a couple of
organizations, describing organizational experiences in deploying B2C websites [Chen et al. 2003; Dahle 1997; El
Sawy et al. 1999; Starne 1997; Stuart 1999]. There has also been extensive research focused on reporting the
existing status of B2C e-commerce [Fruhling & Digman 2000; Ho 1997; U.S. Department of Commerce 1999], as
well as research done to forecast future trends and providing general guidelines for designing and managing B2C
websites [Calkins et al. 2000; Gogan 1996-97; Morris & Hinrichs 1996]. These studies have touted various
characteristics as being important factors to an effective B2C e-commerce website, but there is presently no unified
view of how this affects online customer satisfaction.
Studies of the use of the Internet for commercial purposes are varied. Liu et al. [1997] examined the web sites
of Fortune 500 companies to identify how they are using the web for interacting with their customers. Ho [1997]
examined 1800 websites from various industries across several countries. Hoffman et al. [1996] created six
categories for classifying commercial web sites: online storefront, Internet presence, content, mall, incentive site,
and search agent. Others classify online shopping stores as superstores, promotional stores, sales stores, one-page
stores, and product listings [Spiller & Lohse, 1997]. Liu and Arnett [1998] proposed a framework for designing
quality B2C websites. Hoffman et al. [1996] analyzed case studies to recommend several measures for improving
B2C Websites. All of these aforementioned studies have taken the organization’s perspective and offered guidelines
for conducting B2C e-commerce. Jarvenpaa and Todd [1997] suggested that Internet merchants focus on factors
affecting human behavior: product perceptions, shopping experience, and customer service. Szymanski and Hise
[2000] examined e-satisfaction from the consumer’s perspective and found that convenience, site design, and
financial security displayed the greatest effect on e-satisfaction. Ranganathan and Ganapathy [2002] took the
consumers’ perspective and offered guidelines for online merchants to have an effective site based on four
dimensions: information content, design, security, and privacy. There have also been numerous studies of e-
commerce adoption, described in later paragraphs.
This study extends the e-satisfaction literature by building on previous work to develop a comprehensive
conceptual model of e-satisfaction. In contrast to other studies, this research takes a more comprehensive look at the
determinants of e-satisfaction as a whole by forcing the consumer to assess Internet commerce as a whole. It also
provides a more realistic view of the consumer’s online decision making process by having the respondent make an
overall evaluation of several measures of online shopping attributes concurrently, taking into consideration that
choices cannot all be maximized.

3. Conceptual Model
Literature on online consumer satisfaction and values reveals several antecedents to online customer
satisfaction. For example, convenience, site design, and financial security affect e-satisfaction [Szymanski & Hise
2000]. Of the many other features discussed [Buskin 1998; Ernst & Young 1999], we selected three categories of
factors as key to influencing e-satisfaction: technology, shopping, and individual product factors. While there are
other factors, conjoint analysis requires us to keep a relatively parsimonious model, and these three categories are
most often mentioned in the literature, and are directly related to consumers’ interactions with Internet businesses.
The technology factors deal with the website qualities that ensure functionality of the site acknowledging that the
consumer must be able to access the site, and be able to use it in order to purchase. Shopping factors deal with
aspects of the consumer’s feelings during and after the shopping experience. Product factors pertain to the qualities
of the product or service for sale. These three categories, which comprehensively capture consumers’ interaction
with the technology, the online shopping experience, as well as the actual product (or service) purchased, are
depicted in Figure 1. Variables in each category, identified in a pilot study, are described further below.
Online Consumer Satisfaction (E-satisfaction)
Customer satisfaction is critical for establishing long-term client relationships [Patterson et al. 1997] and,
consequently, is significant in sustaining profitability. As a result, a fundamental understanding of factors impacting
online customer satisfaction is of great importance to e-commerce [McKinney et al. 2002]. Customer satisfaction is
the consequence of experiences during various purchasing stages: (1) needing something, (2) gathering information

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about it, (3) evaluating purchasing alternatives, (4) actual purchasing decision, and (5) post purchasing behavior
[Kotler 1997]. During information gathering, the Internet offers consumers extensive benefits, because it reduces
search costs, increases convenience, vendor choices, and product options [Alba et al. 1997; Bakos 1998]. However,
online consumers are dependent upon the Website information as a replacement for physical contact with
salespersons [McKinney et al. 2002]. As a result, consumers make inferences about the attractiveness of a product
based on: (1) information provided by retailers, and (2) design elements of the Website such as ease and fun of
navigation [Wolfinbarger & Gilly 2001].

Technology Factors:
Security
Usability and Site Design
Privacy
Shopping Factors:
Convenience
Online customer satifaction
Trust and Trustworthiness
Delivery
Product Factors:
Merchandising
Product Value
Product Customization
Figure 1: Conceptual Model

Technology Factors
Technology factors include the qualities of a website that ensure functionality of the site, including: security,
privacy, and usability/site design [Jarvenpaa & Todd 1997; Keeney 1999; Palmer & Griffith 1998; Rasmussen 1996;
Torkzadeh & Dhillon 2002]. Technology factors deal with the consumer’s perceptions of their interaction with the
B2C website and the Internet merchant responsible for that website. Three features of each attribute (security,
privacy, and usability/site design) will be evaluated using a conjoint analysis to get a preferred feature within each
attribute as well as determining an overall ranking of each attribute, including an overall importance score (see
Table 1).
Security
Kalakota and Whinston [1996] define a security threat as a circumstance, condition, or event with the potential
to cause economic hardship to data or network resources in the form of destruction, disclosure, modification of data,
denial of service, and/or fraud, waste, and abuse. Despite the fact that security positively influences intention to
purchase online [Ranganathan & Ganapathy 2002; Salisbury et al. 1998], it remains one of the major concerns
[Kiely 1997; Mardesich 1999; Mayer et al. 1995]. Many consumers are still reluctant to release payment card
information to online merchants, fearing a loss of control over their accounts. Merchants and financial institutions,
in turn, are concerned about the costs associated with online chargebacks and fraud. To alleviate customers’ fears,
many B2C Websites offer alternate forms of payment (e.g. telephone ordering) and/or accounts with ID’s and
passwords [Ranganathan & Ganapathy 2002]. Bélanger et al. [2002] found that the presence of security features on
an e-commerce site was important to consumers, and discuss how consumers’ security concerns may be addressed
by similar technology protections as those of the business, such as encryption and authentication. In this study, the
features evaluated within the attribute of security include: (1) whether the site provides encryption, (2) whether the
site requires the user to set up an account with an ID and password, and (3) whether a confirmation screen is
displayed after the completion of the purchase to ensure accuracy.

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Privacy
Privacy in e-commerce is defined as the willingness to share information over the Internet that allows for the
conclusion of purchases [Bélanger et al. 2002]. B2C Web sites gather information about visitors via explicit modes
(e.g. surveys) and implicit means (e.g. cookies) [Patterson et al. 1997], providing the necessary data for decision
making on marketing, advertising, and products. However, many users have concerns over potential misuse of
personal information [Brown & Muchira 2004; Hair et al. 1995; Ranganathan & Ganapathy 2002; Torkzadeh &
Dhillon 2002]. For example, a Business Week/Harris poll of 999 consumers in 1998 revealed that privacy was the
biggest obstacle preventing them from using Websites, above the issue of cost, ease of use, and unsolicited
marketing [Green et al. 1998]. An IBM Multi-National Consumer Privacy survey in 1999 showed that 80% of the
U.S. respondents felt that they had lost all control over how personal information is collected and used by
companies. Seventy-eight percent had refused to give information because they thought it was inappropriate in the
circumstance, and 54% had decided not to purchase because of concerns over the use of their information collected
during the transaction [Bélanger et al. 2002]. A study by Forrester Research supports these findings, showing that
two-thirds of consumers are worried about protecting personal information online [Branscum 2000]. To address
issues of privacy, many Websites display privacy policies [McGinity 2000]. Also, independent companies (e.g.
TRUSTe) can verify, audit, and certify privacy policies [Ranganathan & Ganapathy 2002]. In this study, the
features evaluated within the attribute of privacy are: (1) the use of a privacy statement, (2) the merchant’s policy
on selling customer information to third parties, and (3) the use of cookies to collect personal information.
Usability/Site Design
Navigation, product information, and site design are critical to e-satisfaction [Szymanski & Hise 2000]. Thus, a
key to building a usable Website is to create good links and navigation mechanisms Mannix 1999; Radosevich
1997). An advantage of the Internet is its capacity to support interactivity for users [Palmer 2002], and online
consumers are influenced by the interactivity of the Website [Alba et al. 1997; Jarvenpaa & Todd 1997]. Fast,
interactive, uncluttered, and easy-to-navigate sites with quality searching capabilities should be perceived more
favorably by consumers. The features evaluated within the attribute of usability and site design are: (1) providing a
user-friendly interface, (2) an interactive site, and (3) possessing adequate searching capabilities.
Shopping Factors
Shopping factors focus on customers’ feelings and perceptions during and after the shopping experience.
Factors determining this include convenience, trust and trustworthiness of Web merchants, and delivery time
[Bélanger et al. 2002; Keeney 1999; Nielsen 2000 Patterson et al. 1997; Torkzadeh and Dhillon 2002]. The
prototypical online consumer leads a wired lifestyle and is time starved, suggesting that online shoppers may do so
to save time [Bellman et al. 1999]. This indicates that the overall convenience of the shopping experience is very
important as well as the amount of time it takes for the product to be received. Trust is of importance during the
actual shopping experience because if the consumer does not trust the merchant to make good on their purchase a
transaction will not take place. Three features of each attribute (convenience, trust, and delivery) will be evaluated
using a conjoint analysis to get a preferred feature within each attribute as well as determining an overall ranking of
each attribute, including an overall importance score (see Table 1).
Convenience
Convenience is often found to be the most important determinant in retail store patronage and many forms of
shopping such as catalog and Internet shopping [Berkowitz et al. 1999; Cox & Rich 1964; Ernst & Young 1999;
Gillett 1970; Kalakota & Whinston 1996]. E-commerce gives an individual the opportunity to economize on time
and effort by making it easy to locate merchants, find items, and procure offerings [Balasubramanian 1997].
Researchers identify convenience as a ‘fundamental objective’ related to online shopping [Keeney 1999; Torkzadeh
& Dhillon 2002). B2C sites should be designed so that consumers minimize time finding the product or information
(Ranganathan & Ganapathy 2002]. Web sites should therefore make it more convenient to buy standard or repeat
purchase items (such as Amazon’s one-click-to-purchase approach). Convenience includes the overall ease of
finding a product, time spent on shopping, post purchase service, complete contact information, and minimization of
overall shopping effort. The features evaluated for convenience will include: (1) overall ease and fun of the
shopping experience, (2) post purchase customer service, and (3) ability to look up detailed product information and
to make price comparisons.
Trust and Trustworthiness
Trustworthiness is the perception of confidence in the e-marketer’s reliability and integrity [Bélanger et al.
2002]. Buying decisions are partly based on trust in the product, salesperson, or company [Hosmer 1995]. Internet
shopping decisions involve trust between customers and merchants, and their computer systems [Lee & Turban
2001]. Prior research has identified and validated many elements of trustworthiness, such as ability, benevolence,
and integrity [Lee & Turban 2001; Manes 1997; Van Slyke et al. 2004]. The ability of a merchant is reflected in its

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ability to handle sales transactions, and the expertise to generally conduct business over the Internet [Bélanger et al.
2002]. The consumer must have faith in the ability of the merchant and their system. Integrity is evidence of the
Internet business’s honesty and sincerity. For trust to exist, the online consumer must perceive the Internet business
as being reliable and as having integrity. In this study, the features evaluated within the attribute of
trust/trustworthiness are: (1) the customer’s faith in the merchant and their computer system, (2) the Internet
merchant’s perceived reliability and integrity, and (3) the overall minimization of the customer’s worries and
regrets.
Delivery Time
Delivery time is the total time between order placement and delivery, which includes: dispatch, shipping, and
delivery. Dispatch is the amount of time necessary for an order to go from initial order placement to being shipped
out. During shipping the purchase is in transit from the merchant’s warehouse to the shipping company’s
distribution facility. Delivery is the amount of time necessary for the package to go from the distribution center to
the customer’s door. Customers must be made aware of delays to minimize disappointment when the delivery date
isn’t met. Satisfaction is partially dependant upon expectations being met. The features of the attribute delivery
time to be evaluated are: (1) overall minimization of delivery time, (2) the customer being made aware of any
potential delays in shipping, and (3) providing customers a tracking number for their shipment.
Product Factors
Product factors pertain to the qualities of the product or service for sale. Often, products purchased online are
no different than those purchased at brick and mortar stores. Customers choose between competing products
depending upon which offer the best value [Keeney 1999]. Factors determining this include merchandising, overall
product value, and availability of product customization [Jarvenpaa & Todd 1997; Keeney 1999; Szymanski & Hise
2000; Torkzadeh & Dhillon 2002; Zhu & Kraemer 2002]. Product factors deal with consumers’ perceptions of the
actual product being purchased. Three features of each attribute (merchandising, product value, and customization)
will be evaluated using a conjoint analysis to get a preferred feature within each attribute as well as determining an
overall ranking of each attribute, including an overall importance score (see Table 1).
Merchandising
Merchandising is defined as the factors associated with selling offerings online separate from site design and
shopping convenience [Szymanski & Hise 2000]. Jarvenpaa and Todd [1997] found that consumers were impressed
by the breadth of stores on the Internet, but were disappointed with the depth of a merchant’s offerings. Merchants
who have offered a wide variety of products and selections seem to be more successful (e.g., The Internet Shopping
Network with 27,000 different computer and software items; CDNow with 112,000 different titles) [Jarvenpaa &
Todd 1997]. Even beyond offering a broad product selection, Kalakota and Whinston [1996] argue that e-commerce
should offer consumers the opportunity to make requests for products that are difficult to get via traditional
channels. Jarvenpaa and Todd [1997] claim that it may be that consumers expect e-commerce to offer a wider
product variety because of the reach of the Internet and the potential to track down specialty goods and services.
Superior product assortment results in positive perceptions of customer satisfaction [Szymanski & Hise 2000],
especially if the customer wants an item that isn’t widely available. The features of the merchandising attribute to
be evaluated are: (1) offering extensive product assortment and variety, (2) offering exclusive and specialty
products, and (3) offering seasonal products.
Product Value
Minimizing product cost and maximizing product quality are major factors in e-commerce success [Keeney
1999]. Total cost includes product cost, taxes, shipping, Internet, and travel costs [Keeney 1999]. Quality is an
intrinsic property of a product. Product quality is the expected standard of product or service excellence [Jarvenpaa
& Todd 1997]. Brands and retailers that are well known and well regarded from the traditional channels may
translate to quality on an online channel. The question becomes how consumers will assess product quality when
they are unfamiliar with the retailer or the product brand [Jarvenpaa & Todd 1997]. Kalakota and Whinston [1996]
stress the need to provide independent evaluations of goods and services to convince consumers of the quality of the
merchandise sold by the Internet merchant on the web. Thus, the end result for the consumer should be a feeling of
gratification with the purchase once completed. Torkzadeh and Dhillon [2002] combine these two objectives
(product cost and product quality) into an Internet product value measure (used in this study). The features of the
product value attribute to be evaluated are: (1) post purchase feeling of customer gratification, (2) perceived product
quality, and (3) overall product cost.
Product Customization
Product customization is the users’ ability to customize products according to personal preferences [Zhu &
Kraemer 2002]. For example, configuring a computer and related product features directly on a merchant’s
Website. Customization is one of the great advantages of online shopping [Van Slyke et al. 2004], allowing what

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some have termed a segment of one, where each customer is unique in his or her tastes, choices, and acquisitions.
The features of the product customization attribute to be evaluated in this study are: (1) offering a customizable
product, (2) offering online configuration capabilities, and (3) the number of options that are available for the
product.

Table 1. Summary of Research Questions
Category
Research Questions
Technology
1. Which security feature among encryption, ID’s and passwords, and order confirmation screens is

perceived as more important by consumers in affecting their satisfaction?
2. Which privacy feature among a privacy statement, the merchant’s policy on selling customer
information, and the use of cookies is perceived as more important by consumers in affecting their
satisfaction?
3. Which usability/site design feature among a user-friendly interface, an interactive site and
adequate searching capabilities is perceived as more important by consumers in affecting their
satisfaction?
Shopping
4. Which convenience feature among the overall ease and fun of the shopping experience, post

purchase service, and price comparisons/ product information available from the site is perceived as
more important by consumers in affecting their satisfaction?
5. Which trust/trustworthiness design feature among the customer’s faith in the merchant and their
system, the e-marketer’s perceived reliability and integrity, and the minimization of the customer’s
worries and regrets is perceived as more important by consumers in affecting their satisfaction?
6. Which delivery feature among the minimization of delivery time, the customer being made aware
of delays, and providing the customer with a tracking number is perceived as more important by
consumers in affecting their satisfaction with online shopping?
Product
7. Which merchandising feature among offering an extensive product assortment and variety,

offering exclusive and specialty products and offering seasonal products is perceived as more
important by consumers in affecting their satisfaction?
8. Which product value feature among providing a post purchase feeling of customer gratification,
perceived product quality, and overall cost of the product is perceived as more important by
consumers in affecting their satisfaction?
9. Which product customization feature among offering a customizable product, offering online
configuration capabilities, and the number of options available for a product is perceived as more
important by consumers in affecting their satisfaction?

Engaging in e-commerce involves a complex decision making process. Consumers have to take into
consideration various factors, which are summarized in Table 2. Yet, they can’t maximize all factors. For example,
will a consumer be satisfied with a Web merchant that provides no security through encryption but has a nice
privacy policy? Conversely, will the consumer be satisfied with a merchant that has a strict security system that logs
everything customers do, infringing on their privacy?
One methodology useful to analyze such trade-offs in consumer decision-making is Conjoint Analysis (CA)
[AMA 2000] which is fairly new to e-commerce research. Conjoint analysis is a multivariate technique used to
estimate or determine how respondents develop preferences [Hair et al. 1998]. The technique has been used in IS
research before. Bajaj [1998] presents a conjoint analysis that views competing architectures as a product class, and
compares the effects of various attributes in the decision models of senior IS managers when evaluating these
alternative architectures. The basic requirement for using the conjoint analysis methodology for an IS research
question is that a product class be created for the IS that is under question. The advantage of conjoint analysis is that
it provides information about bundles of attributes. It therefore enables IS researchers to evaluate online consumer
preferences by examining the examining the attributes that are the most or least important. In this study, the
decision is the prioritization of e-satisfaction values and the product classes that are created, which include three
underlying e-satisfaction categories: technology, shopping, and product factors. Within these three categories there
are three variables in each, and each individual variable has three levels (measures).
Methodology
Conjoint Analysis is a research technique used to estimate or determine how respondents develop preferences
for products or services, and to measure the trade-offs people make when making a decision [Hair et al. 1995].
Conjoint analysis is based on the premise that subjects evaluate the value or utility of a product/service/idea (real or

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hypothetical) by combining the separate amounts of utility provided by each attribute, in this study e-satisfaction
values. Conjoint analysis is a decompositional technique, because a subject’s overall evaluation (preference) is
decomposed to give utilities for each predictor variable, and for each level of a predictor variable. Conjoint analysis
is commonly found in behavioral studies [Green & Srinivasan 1978] and in marketing studies [Green & Rao 1971]
where the predictor variables are called attributes, and the dependent variable is often an overall evaluation of a
product. A conjoint analysis study has two primary objectives. The first is to determine the contributions of
various predictor variables and their respective values (or levels) to the dependant variable (usually overall
evaluation). The second objective is to establish a predictive model for new combinations of values taken from the
predictor variables [Bajaj 1998].

Table 2. Summary of Constructs – Determinants of E-Satisfaction
Category
Question
Feature
Attributes
Technology
1 Security
Encryption
Accounts with IDs
Confirmation


and passwords
screen
2 Privacy
Privacy
statement
Policy on
Use of cookies

information selling
3 Usability
User-friendly Adequate search
Interactive site
interface
capability
Shopping
4
Convenience
Ease and fun of
Post purchase service Price/product

shopping
comparisons
5
Trust
Faith in merchant
Reliability and
Minimization of
and system
integrity
worries & regrets
6 Delivery
Minimization
of Awareness of
Tracking number
delivery time
potential delays
Product
7 Merchandising
Extensive
Exclusive and
Seasonal products

assortment
specialty products
8 Product
Value
Customer
Product quality
Overall cost
gratification
9 Customization
Customizability Online
Number of options
configurations

The conjoint analysis methodology has several advantages. The first advantage is it focuses on the
measurement of consumer preferences for attribute level variables. In our study the product is e-commerce. The
second advantage is that conjoint analysis allows for a more realistic decision model for a population, because it
forces subjects to evaluate the products as a whole (as in real life); it forms individual decision models for each
subject and it allows the formation of an aggregate decision model across all the subjects, and permits the statistical
testing of the null hypothesis that all the attributes have an equal utility in the aggregate decision model [Bajaj
1998]. The third advantage is the fact that the methodology makes no assumptions about the nature of the
relationships between the attributes and the dependent variable. This makes it very useful when exploring unknown
variables as potential predictor variables [Bajaj 1998]. Finally, other advantages of conjoint analysis include its
ability to accommodate metric or non-metric dependent variables, its ability to use non-metric variables as
predictors, and the flexible assumptions about the relationships of the independent variables with the dependent
variable (ex. assumptions of linearity are not made) [Hair 1992]. Metric variables refer to an interval or ratio scale
while non-metric variables refer to a nominal or ordinal scale.
Conjoint Analysis is related to experimentation in the traditional sense, in that the effects of levels of
independent variables are determined on a dependent variable. In the case of e-commerce, where there is human
behavior involved, it is necessary to also determine the effects of levels of certain variables (equivalent to
independent variables) on the dependent variable, which in most cases (including this study) is an overall rating or a
purchase decision or an adoption decision [Bajaj 1998].

The basic model in a conjoint analysis is: Y = b1 + b2 + b3+…. + bn + constant + ε

where:
Y = respondent’s preference for the product concept (metric or non-metric)




bi = beta weights (utilities) for the features (non-metric)





ε = an error term
In our study, respondents’ ratings for the various customer values form the dependent variable. The measures
of each customer value (the attribute levels) are the independent (predictor) variables. The estimated betas

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associated with the independent variables are the utilities (preference scores) for the levels. There are six steps
involved in a Conjoint Analysis study [Green & Srinivasan 1978], summarized in Table 2.
1. Selection of a model of preference: Green and Srinivasan [1978] considered three main preference models:
vector model (linear), ideal point model (linear plus quadratic), and the part-worth function model (piecewise
linear). The vector model estimates the fewest parameters by assuming the linear functional format. The part-worth
model estimates the largest number of parameters because it allows for the most general functional form and the
ideal point model is between these two extremes [Green & Srinivasan 1978]. In this particular study the part-worth
function model was chosen because of the advantages listed prior. The part-worth function model is also the most
commonly used in practice.
2. Data collection method: Data collection procedures in conjoint analysis studies have primarily involved
variations on two basic methods: (1) the two-factor-at-a-time procedure and (2) the full profile approach [Green &
Srinivasan 1978]. The two-factor-at-a-time approach considers factors (attributes) on a pair wise basis. The
respondent ranks the various combinations of each pair of factor levels from the most to least preferred. In this
study the full-profile approach was used. The full profile approach utilizes the complete set of factors for the subject
to evaluate. It has been argued that the full-profile approach gives a more realistic description of stimuli by defining
the levels of each of the factors and possibly taking into account the potential environmental correlations between
factors in real stimuli [Green & Srinivasan 1978]. Another advantage of the full-profile method is the ability to
measure overall preference judgments directly using behaviorally oriented constructs such as intention to buy
[Green & Srinivasan 1978]. In this study, where the environmental correlation between factors is large and the
number of factors on the stimulus cards is small (but greater than two), the full profile approach is likely to have
more predictive validity.
3. Stimulus set construction for the full-profile method: The additive compensatory model assumed in conjoint
analysis is likely to predict well even if the decision process is more complex [Green & Srinivasan 1978; Huber
1987]. In this study, respondents considered three specific features of each attribute that was to be analyzed and
gave each feature a preference rating based on a ten-point scale ranging from “completely unacceptable”
combination of customer values to the “perfect” combination of customer values.
4. Stimulus presentation: This study employed a paragraph description of the task as well as a written example
of what needed to be done with the items that followed. The actual survey was set up in spreadsheet format with
rows and columns substituting for profile cards. Sample instructions are provided in Appendix A.
5. Measurement scale for the dependent variable: There are two basic alternatives for defining a measurement
scale for the dependent variable: metric (ratio scales, assuming approximately interval scale properties) or non-
metric (paired comparisons, rank order). The measurement in this study uses the metric method. The main
advantage of the metric method is the increased information content potentially present in the scales [Green &
Srinivasan 1978].
6. Estimation methods: The parameter estimation method used in this study was ordinary least squares (OLS)
regression. Johnston [1972] explains that the OLS procedure is the most appropriate when a study includes a
dependent variable that is interval scaled. The OLS procedure also has the advantage of providing standard errors
for the estimated parameters [Gogan 1996-97].

Table 3. Steps in Conjoint Analysis
Step This
study
1. Select a model of preference
Part-worth
2. Data collection method
Full profile
3. Stimulus set construction (full-profile method)
Additive compensatory model
4. Stimulus presentation
Written instructions
5. Measurement scale for the dependent variable
Metric
6. Estimation method
Ordinary Leased Square

In our study, numerous potential attributes for each factor were identified from previous literature and scored
through a pilot study. The three strongest items from each attribute were selected for inclusion in the study (see
Table 2). They were then pre-tested prior to final item selection. Modifications were made to reduce concerns of
participants with wording and to clarify instructions.
Sample
The sample consisted of 188 undergraduate students (average age of 22 years old). Respondents included 60%
males and 40% females. Almost all (99.5%) of the respondents had convenient access to a computer and to a credit
card (92%), and 85% of the respondents had more than five years of computer experience. 89% of the respondents

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Journal of Electronic Commerce Research, VOL. 6, NO.2, 2005

had access to a broadband connection to the Internet. 99% of the respondents reported using the Internet in some
capacity everyday and 57% stated gathering information about a product or service via the web at least several times
a week. Over 90% reported shopping online several times a year and 22.3% reported that they hopped online at
least several times a month. This sample is very appropriate for this study given the subjects’ high level of Internet
usage and online shopping experience.
Data Analysis
After the data were entered into Excel spreadsheets and cleaned for missing data, they were analyzed using
SPSS 11.5. Conjoint utilities (part worths) are scaled to an arbitrary additive constant within each attribute. The
arbitrary origin on the scaling within each attribute results from dummy coding in the design matrix. In this study,
the part worth of one level within each attribute was arbitrarily set to zero to then estimate the remaining levels as
contrasts with respect to zero. For example, for the security attribute of the technology factor we set encryption to
zero. Confirmation screens and accounts/IDs/Passwords were then analyzed in comparison to encryption.

4. Results
Ranking of Technology Factors
The results of technology factor rankings are presented in Table 4 and Figure 2. Within the attribute of security,
the use of a confirmation screen was found to be the most important “level” in determining whether the online
consumer would be satisfied. However, the three preference ratings were very closely grouped (very small
differences between part worths) indicating that perhaps online consumers today expect sites to provide security that
offers a combination of these features.

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Figure 2. Average Technology Factor Utility Scores


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Schaupp & Bélanger: A Conjoint Analysis of Online Consumer Satisfaction
Table 4. Results for Technology Factors
Factor Attribute
Part
Worth
Security
Confirmation screen
0.0609
Encryption
0.0000
Accounts w/ ID and passwords
-0.0006
Privacy
Use of a privacy statement
0.0000
Selling of customer info.
-1.4320
Cookies to collect personal info.
-2.6974
Usability
Searching capabilities
0.0000
User friendly interface
-0.1814
Interactive site
-0.3186

Within the privacy attribute, the use of a privacy statement was the most important feature. Searching
capabilities were found to be the most important feature within the attribute of usability. User-friendly interfaces
and having an interactive site were deemed less influential in the purchasing decision. A plausible explanation for
this finding would be that online consumers want to be able to locate things of interest. User-friendly interfaces and
offering an interactive site will add to the site, but if the customer cannot locate what they are looking for, it is hard
to make a purchase. It could also simply be that friendly interfaces and interactive sites have become standard.
Shopping Factors
Within the attribute of convenience, overall ease of shopping was found to be the most important as compared
to the ability to price compare and gather information as well as the post-purchase service that the e-commerce site
provides (Table 5; Figure 3). The overall ease of shopping is undoubtedly a major advantage of e-commerce.
Within the attribute of trust, the customer’s faith in the merchant and the merchant’s computer system was found to
be of the most importance. In reality, an e-commerce site’s ability to minimize customers’ worries and regrets as
well as their perceived reliability and integrity of the system is going to be dependent upon the customer’s faith in
the merchant and their computer system. If the consumer does not have faith in the merchant and their system it is
highly unlikely that they will perceive the e-marketer to be reliable and of integrity. Without this positive perception
0
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E-marketer’s
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Figure 3. Average Shopping Factor Utility Scores

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