Electronic Business: Concepts, Methodologies, Tools, and Applications (4-Volumes) P157 pps

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How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
of date, and some URLs represented replications
of those that were already considered for the
study. Finally, the data set consisted of complete
REVHUYDWLRQVIRUEXVLQHVV¿UPVRSHUDWLQJ
on the Web. Out of the 375 Web sites, 310 were
retail Web sites and 65 were service Web sites.
The retail industry contained Web sites on mer-
chandize stores; apparel and accessory stores;
furniture; household appliances; electronics; and
so forth, where as the service industry consisted
of Web sites on hotels and motels; rooming and
boarding houses; sporting and recreational camps;
RV parks; software services; and so forth. The
collected data provided rich description of the typi-
cal features, their level of support for consumers’
non-compensatory strategies, and their level of
support for consumers’ compensatory strategies
and preferences.
To give further assurance of accuracy and
validity of data collection, a second author ran-
domly gathered data about some companies in
the sample to compare to the other author’s data
collection. There was almost perfect agreement
between the two authors.
Results
2XU RYHUDOO ¿QGLQJV DUH GLVSOD\HG JUDSKLFDOO\
in Figure 1. Typical Web site features are shown
¿UVW2IRX UVDPSOHRI:HEVLWHVDOOJLYHJHQ-
eral company information and about two-thirds

(68.5%) support online purchasing of products or
services. Most of the Web sites that support online
purchases display the privacy policy and inform
that cookies can be loaded to the consumer’s
computer. Most of the Web sites that support
Figure 1. The percentage of web-retailers’ web sites investigated (375 total) having various web site
features, including features that would support consumers’ decision strategies and preferences
100
68.5
68.5
68.5
48
39.2
63.2
43.5
30.7
4
51.2
28
16
12.3
14.7
0
0
0
3.7
0.5
0
0 102030405060708090100
Provides company information

Provides product information
Allows online purchase
Provides price information
Website com municates privacy policy
Privacy policy informs that cookies can be loaded
Home page is organized by category
Seller recommends products
User is shown related products
Other customer's ratings are show n
User can enter text for search
User can choose from list of keywords
User can provide or select a single search criterion
User can sort products by attributes
User can provide or select multiple search criteria
User preferences betw een attributes are elicited
User can indicate the weighting of each attribute
User can specify which attributes are important
User can create side-by-side comparison
External ratings are shown
Products are scored, screened, or ranked based on user-specified model
Typical web site features
Supports non-
compensatory
strategies
Supports compensatory
strategiesand user
preferences
1495
How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
RQOLQHSXUFKDVHVDOVRHQDEOHFRQVXPHUVWR¿QG

VSHFL¿FFDWHJRULHVZKLFKIDFLOLWDWHVFRQVXPHUV¶
search. About half of the Web sites recommend
products in some way, about a third show related
products. Only 4% of the Web sites surveyed show
other customers’ ratings.
In the middle of Figure 1, the results are shown
for features that would be helpful to consumers
Table 4. Survey of WebDSS attributes
Attributes
All
(N=375)
Industry Sales Volume
Retail
(N=310)
Service
(N=65)
Above
(N=188)
Below
(N=187)
Typical Web Site Features
Provides company information
100.0
(%)
100.0(%) 100.0(%) 100.0(%) 100.0(%)
Provides product information 68.5 72.3 50.8 69.7 67.4
Allows online purchase 68.5 72.3 50.8 69.7 67.4
Provides price information 68.5 72.3 50.8 69.7 67.4
Website communicates privacy policy 48.0 51.7 29.2 59.6 36.4
Privacy policy informs that cookies can be

loaded
39.2 42.6 23.1 53.7 24.6
Home page is organized by category 63.2 66.1 49.2 63.8 62.6
Seller recommends products 43.5 47.7 23.1 50.0 36.9
User is shown related products 30.7 36.8 1.5 39.9 21.4
Other customers’ ratings are shown 4.0 4.8 0.0 7.4 0.5
Web Site Features Supportive of Non-Compensatory Decision Strategies
User can enter text for search 51.2 60.0 9.2 51.6 50.8
User can choose from list of keywords 28.0 25.5 40.0 30.9 25.1
User can provide or select a single search
criterion
19.2 15.2 38.5 20.7 17.6
User can sort products by attributes 12.3 13.5 6.2 16.0 8.6
Web Site Features Supportive of Compensatory Decision Strategies or User Preferences
User can provide or select a multiple search
criterion
14.7 10.0 36.9 13.8 15.5
User preferences between attributes are
elicited
0.0 0.0 0.0 0.0 0.0
User can indicate the weighting to each at-
tribute
0.0 0.0 0.0 0.0 0.0
User can specify which attributes are impor-
tant
0.0 0.0 0.0 0.0 0.0
User can create side-by-side comparison 3.7 4.5 0.0 5.3 2.1
External ratings are shown 0.5 0.6 0.0 0.5 0.5
Products are scored, screened, and ranked
EDVHGRQXVHUVSHFL¿HGPRGHO

0.0 0.0 0.0 0.0 0.0
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How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
desiring to execute non-compensatory strategies.
Most of the Web sites that supported selling had at
least one feature that would enable the consumer
WR ¿QG SURGXFWV EDVHG RQ D FHUWDLQ FULWHULRQ
such as entering text for a search, choosing from
a list of keywords, or providing a single search
criterion. Nonetheless, only 12.3% of the Web
sites enable the sorting of products based on an
attribute value.
At the bottom of Figure 1, the results are shown
for features that would be helpful to consumers
desiring to execute compensatory strategies.
When we considered the support for compensa-
tory strategies that incorporated consumer pref-
erences, we found almost no support. Just 14.7%
of the Web sites supported searches based on
multiple criteria. Only 3.7% displayed side-by-side
comparison. Only .5% showed external ratings
of products or services. NONE of the Web sites
assisted the consumers by allowing the users to
give weights of attributes or specify which weights
are important. NONE of the Web sites provided
IRUVFRULQJEDVHGRQXVHUVSHFL¿HGPRGHOV
To gain further insight into the breakdown of
the Web sites in our sample, we subdivided our
sample two ways: retail versus service, and sales
volume above or below average. These results are

shown in Table 4. Inspection of these breakdowns
reveals several patterns. First, the typical Web
site features are provided more often for retail
products than for services.
Service industry Web sites are more prone
to just give company information and not try to
sell directly on the Web site. On the other hand,
company size did not appear to affect the extent
of online selling, perhaps because there are few
¿ Q DQFLDORUWHFK Q RORJ LF D O E D U U LHUVWRDVPDOOEXVL-
ness that wants to begin selling on the Internet.
The larger companies appear to attempt to market
their products somewhat more by recommending
products, showing related products, and showing
other customer ratings.
Retailers of products more frequently allowed
users to enter text for a search, while service
companies more frequently allowed a choice of
keywords or provision of a single search criterion.
Since these features are merely different ways
of achieving the same objective, we do not see
sellers of products or services as dominating in
supporting ways of specifying criteria. For the
few Web sites that supported sorting of products
by attributes, this feature was more frequently
provided by retailers of products than by service
¿UPV7KHVRUWIHDWXUHZDVDOVRPRUHIUHTXHQWO\
SURYLGHGE\ODUJH¿UPVWKDQVPDOO¿UPV
For compensatory strategies, the main result
is that Web sites gave little support at all. For

VRPHUHDVRQVHUYLFH¿UPVJDYHPRUHVXSSRUWLQ
searching multiple criteria than sellers of prod-
ucts. Of the few Web sites showing side-by-side
comparisons, all were retailers of products (rather
than services) and most were large companies.
External ratings were all of products rather than
services. This may be due to a lack of available
external ratings of services.
MANAGERIAL IMPLICATIONS
7KHPDLQ¿QGLQJRIRXULQYHVWLJDWLRQRIHFRP-
merce Web sites is a complete absence of support
for consumers’ compensatory strategies based
on their own preferences. Given the results of
academic research that compensatory WebDSS
provide better decision quality, satisfaction, and
FRQ¿GHQFHWRFRQVXPHUDQGUHGXFHHIIRUWDQRS-
portunity is waiting for managers to start looking
for ways to implement such tools.
The purpose of a DSS is to help a customer
pick the best possible choice in all situations. The
use of non-compensatory DSS is not associated
with better decision quality (Fasolo et al., 2005).
However, managers have to make sure that com-
pensatory WebDSS are easy to use. Most of the
compensatory WebDSS implemented in research
experiments typically have two screens. In the real
world, as the number of screens used to capture
consumer preferences increases, the longer it takes
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How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies

for customers to make a decision. Such design
may discourage users. Therefore, to the extent
that compensatory WebDSS are easy to use, they
are likely to be used by consumers.
The execution of compensatory strategies
requires users to submit weights to attributes and
then the DSS recommends products with high-
est expected values. But, how does a user know
what algorithm is being used to come up with
the results? Therefore, it is recommended that
managers provide information concerning how
WKH¿QDOVFRUHVH[SHFWHGYDOXHVDUHFDOFXODWHG
from the user supplied weights.
It is also possible that the lack of expertise
DQGGHYHORSPHQWDOFRVWVPD\LQÀXHQFHPDQDJHUV
not to implement compensatory WebDSS. We
EHOLHYHWKDWWKHH[WHQWWRZKLFKWKHEHQH¿WVRI
implementing such WebDSS outweigh the costs
implies that it would be a worthwhile proposition
for managers to consider developing compensa-
tory based decision support tools.
Directions for Future Research
While our study results showed absence of support
for executing compensatory strategies in e-com-
merce Web sites based on consumer preferences,
with some additional research, we were surprised
WR¿QGVRPHWKLUGSD U W \:HEVLWHVSURYLGLQJVXFK
support. Examples of such third party sites include
My product advisor (roductadvi-
sor.com), Select smart (ectsmart.

com), and Yahoo! shopping smart sort computer
and electronic recommendations (http://shopping.
yahoo.com/smartsort). Future research could
investigate two research questions. First, what
are the factors that inhibit e-commerce Web sites
from providing support for compensatory-based
strategies based on consumer preferences? Sec-
ond, what are the implications for e-commerce
Web sites with third party Web sites providing
such support when consumers expect such support
from the Web retailers themselves?
A second area of research could look into the
issues surrounding consumers’ adoption of deci-
sion technology implemented to support individu-
als’ decision-making processes. Research shows
that less than 10% of home users visit shopbots
(Montgomery, Hosanagar, Krishnan, & Clay,
2004). Therefore, future research could look into
various factors that would improve the consumer
adoption of decision technology. Furthermore,
additional research is needed to understand how
individual differences in decision makers affect
adoption and usage of decision technology on
e-commerce Web sites.
The present survey considers only compensa-
tory and non-compensatory based systems, and
the results suggest that an important gap exists
between theory and practice. Future studies could
conduct similar kinds of studies to investigate
how well e-commerce Web sites provide support

concerning content, collaborative, and hybrid
WebDSS as well as the feature- and need-based
WebDSS. It is our hope that as with our study, im-
portant insights could be brought out by conduct-
ing studies that investigate the extent of Web site
support concerning other types of WebDSS.
Compensatory decision tools that are imple-
mented in the experiments may face challenges
when extended to the real world. For example,
most of the compensatory WebDSS designed
in experiments contain all the attribute values
for a given alternative set. However, in the real
world, attributes values may be missing for some
alternatives, and therefore computing expected
values for such alternatives could be problematic.
Therefore, future research could look at the effects
of missing information on consumer choices in
online decision support environments.
Future research could also look at measuring
WKHPRQHWDU\EHQH¿WWRDQRUJDQL]DWLRQLPSOH-
menting a Web-based decision support tool on its
Web site. The existing research so far has focused
on decision outcome variables such as satisfac-
tion, decision quality, effort, and so forth. Of
1498
How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
interest to managers could be whether improved
WebDSS tools augment the user’s willingness
to purchase.
CONCLUSION

Research conducted by decision scientists over
the last few decades has examined the normative
way of decision making (how decisions must be
PDGHDQGLGHQWL¿HGVHYHUDOGHFLVLRQVWUDWHJLHV
individuals use to make a decision. These decision
strategies are compensatory and non-compensa-
tory in nature. After the advent of the Internet
and the subsequent growth of the e-commerce
market, most Web sites are implementing Web-
based decision support tools to help consumer
make their choices. One category of Web-based
decision tools uses decision strategies to provide
consu mer support. I n this st udy, we focus on Web
site support for executing consumers’ compensa-
tory and non-compensatory strategies.
The study makes two contributions. By syn-
thesizing the existing literature concerning the
effectiveness of implementing compensatory
versus non-compensatory WebDSS, we found
that a majority of the evidence favors implement-
ing compensatory WebDSS. If compensatory
WebDSS are so effective, one would expect to
observe e-commerce Web sites increasing the level
of support for executing consumers’ compensatory
strategies. Based on a study of 375 U.S. company
Web sites, we found that very little support exists
for features that support compensatory strategies
(such as side-by-side comparison of alternatives)
and no support exists for executing compensatory
strategies based on consumer preferences.

We also note several limitations of our study.
As far as we are aware, there is no study that
explored how well Web sites provide support
for compensatory and non-compensatory based
strategies. Though it is problematic to generalize
WKH¿QGLQJVRI86EDVHGFRPSDQLHVWRFRPSDQLHV
worldwide, a future study could look into how
well such strategies are supported in Web sites
worldwide. Secondly, choosing 25% of U.S based
companies is purely arbitrary. However, we believe
that the results of our study are representative of
the current situation on e-commerce Web sites.
)RUH[DPSOH)DVRORHWDOVWDWHWKDW³DO-
though we have no precise data to support it, we
are under the impression that real World Wide
Web compensatory sites are having rougher and
shorter lives than non-compensatory sites….We
have anecdotal evidence that transparency and
length might be a reason for the lack of success
of compensatory ones” (p. 341).
The results of this study open up an opportu-
nity for managers to start providing more support
for compensatory-based decision strategies, and
at the same time begs the question of the lack of
popularity of such tools. A number of potential
reasons have been presented and a host of research
questions have been raised. It is our hope this
attempt fuels further research in improving the
GHVLJQRI:HE'66DQG¿QGLQJIDFWRUVWKDWDIIHFW
the adoption of WebDSS, ultimately contributing

WRWKHEHQH¿WRIERWKWKH:HEVLWHVDQGXVHUV
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ENDNOTES
1
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2
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database
3
The questionnaire captures general details,
support for user to locate a product, evalu-
ate individual products, support in terms of
others ratings, support to compare products,
support for multi-attribute models, and infor-
mation about cookies. The only place where
the researcher’s perceptions could bias the
results is the section on support provided to
XVHUWRVHOHFWDVSHFL¿FSURGXFW7KLVSDUW
is not used in the analysis. The rest of the
variables are binary in nature. For example,
a Web site can provide a keyword-based
search or not. Similarly, a Webs ite can let
the users pick important attributes or not,
weight the attributes or not. Therefore, we
believe that what is needed from a data col-
lector is general observation skills and since

perceptions are not recorded, we believe that
use of one of the authors to collect data is
reasonable.
1501
How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
APPENDIX A.
URL: __________________________________ SIC Code: __________________
Preparer __________
Name of Business ___________________________________________________________
Date__________
Types of Products Offered _____________________________________________________
Circle all that apply:
shows company info, shows product info, shows prices, allows online purchase
Support that Helps User Locate a Product:
Y N Home page is organized by category to assist with product search
Y N User can enter text for search
Y N User can choose from list of keywords for search
Y N User can provide or select a single search criterion (e.g., homes with 3 bedrooms, < $200,000)
Y N User can provide or select multiple search criteria
Y N User is shown related products
Support that Helps User Evaluate Individual Products:
BA A AA Products are described in detail (Below average, average, above average)
BA A AA Products are shown in high quality pictures
Special features (pictures): ____________________________________________________
6XSSRUWWKDW3URYLGHV8VHUZLWK2WKHUV¶5DWLQJVRID6SHFL¿F3URGXFW
Y N Other customers’ ratings or comments are shown for products
Y N External ratings (e.g. Consumer Reports ratings) are shown for products
Source: _________________________________________________
Y N 6HOOHUUHFRPPHQGVVRPHSURGXFWVHJ³EHVWYDOXH´
Verbiage: _________________________________________________

Support that Helps User Compare Products:
Y N User can sort products by an attribute: _______________________________________
Y N User can create side-by-side comparison of products on a single web page
Support that Creates Multi-Attribute Model of Elicited User Preferences:
Y N User can specify which attributes are important and system picks products for user to review
Explain: ______________________________________________________________
__
Y N User preferences between attributes are elicted by system (e.g., providing user with pairs of
product attributes and asking user which is more important).
Y N User can indicate how much weight should be given to each attribute.
1502
How Well E-Commerce Web Sites Support Compensatory and Non-Compensatory Decision Strategies
Y N Products are scored, screened, or ranked (indicate which) based on multi-attribute model of user
preferences
Explain: ______________________________________________________________
___
System Informs of Cookies in Privacy Policy:
Y N Website communicates a privacy policy
Y N Privacy policy informs that cookies might be loaded onto user’s computer
Other Type of Support:
Please describe in detail any other type of decision support provided for the consumer
________________________________________________________________________
_________________________________________________________________________
________________________________________________________________________
This work was previously published in the International Journal of E-Business Research, edited by I. Lee, Volume 4, Issue 4,
pp. 43-57, copyright 2008 by IGI Publishing (an imprint of IGI Global).
1503
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 5.8
The Human Face of E-Business:

Engendering Consumer Initial Trust
Through the Use of Images of Sales
Personnel on E-Commerce Web Sites
Khalid Aldiri
University of Bradford, UK
Dave Hobbs
University of Bradford, UK
Rami Qahwaji
University of Bradford, UK
ABSTRACT
Business-to-consumer (B2C) e-commerce suf-
fers from consumers’ lack of trust. This may be
partly attributable to the lack of face-to-face in-
terpersonal exchanges that provide trust behavior
in conventional commerce. It was proposed that
initial trust may be built by simulating face-to-
face interaction. To test this, an extensive labora-
tory-based experiment was conducted to assess
the initial trust in consumers using four online
vendors’ Web sites with a variety of still and video
images of sales personnel, both Western and Saudi
Arabian. Initial trust was found to be enhanced
for Web sites employing photographs and video
clips compared to control Web sites lacking such
images; also the effect of culture was stronger
in the Saudi Arabian setting when using Saudi
photos rather than Western photos.
INTRODUCTION
The beginning of the 21st century brought rapid
G H YH O RSP H QWW RW K H¿HOG RI H  FR P P H UF H D Q GP D Q\

enterprises in Western developed countries found
success in this area. According to emarketer.com,
total online retail sales for 2005 were $144,613
million. In 2001 Internet sales to households from
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