GENDER DIFFERENCES IN ONLINE SHOPPERS’ DECISION-
MAKING STYLES
Chyan Yang, Chia Chun Wu
Institute of Business and Management, National Chiao Tung University, Sec. 1, Jhongsiao W. Rd, Taipei, Taiwan(ROC)
Keywords: Internet shopping, Decision-making styles, Gender differences, Exploratory factor analysis, Discriminant
analysis
Abstract: Because of the SARS epidemic in Asia, people chose to the Internet shopping instead of going shopping on
streets. In other words, SARS actually gave the Internet an opportunity to revive from its earlier bubbles.
The purpose of this research is to provide managers of shopping Websites regarding consumer purchasing
decisions based on the CSI (Consumer Styles Inventory) which was proposed by Sproles (1985) and Sproles
& Kendall（1986）. According to the CSI, one can capture the decision-making styles of online shoppers.
Furthermore, this research also discusses the gender differences among online shoppers. Exploratory factor
analysis (EFA) was used to understand the decision-making styles and discriminant analysis was used to
distinguish the differences between female and male shoppers. Managers of Internet shopping Websites can
design a proper marketing mix with the findings that there are differences in purchasing decisions between
genders.
1 INTRODUCTION
Taiwan’s Internet users reached 8.76 million by June
2003, as reported by Institute for Information
Industry ECRC-FIND Center. Compared with last
year, the Internet users only grew by 90 thousands.
This means that Taiwan’s Internet market has
become more mature gradually. In spite of the
mature Internet market, there is seldom successful
E-business and this phenomenon leads to the
Internet bubbles.
Unfortunately the SARS epidemic broke out in
spring 2003 in Asia. However, this crisis did give
the slow Internet market a boost because people
stayed at home whenever possible. In consideration
of the chance to recover the prosperity, this research
attempts to help marketing managers provide
suitable marketing strategies. Therefore, this
research used exploratory factor analysis to find
consumers’ decision-making styles by the CSI,
which was proposed by Sproles （ 1985 ） and
Sproles & Kendall（1986）. By understanding the
consumers’ decision-making styles, managers of
shopping Websites can hold more advantageous
activities to arouse the consumers’ interest and
improve sales
In-store purchases account for the vast majority
of consumer buying. Increased time pressure on
either genders, especially on women, has been cited
as one of the principal advantages of catalogue and
online shopping. It has been broken gradually that
the stereotype of an Internet shopper appears to be a
youngish, well-educated man (Alreck & Settle,
2002). As reported by Nielsen//NetRatings, there are
35 millions of female internet users in Europe,
which is almost 42% of European Internet users.
Moreover, concerning the ranking of the main
countries in the World, the percentage of American
female Internet users is 51%, and the highest and it’s
about 51%. In Sweden and UK, the proportions of
female Internet users are both over 45%. Other
counties such as Netherlands, France, Switzerland,
Spain and German are all over 40%. The report also
shows that shopping, travelling, education, finance,
health, and beauty care Websites are the most
attractive to female Internet users (Institute for
Information Industry, ECRC-FIND).
The same phenomenon can also be found in
Asia-Pacific region. Female Australian Internet
users are 48% of the whole Australian Internet users,
46% of New Zealand, 45% of South Korea, 44% of
Hong Kong, 42% of Singapore, and 41% of Taiwan.
Among these countries, the growth of South Korea
female Internet users is the fastest, which rate
reaches 55%. The rest are Taiwan (27%), Singapore
(16%), Australia (16%), and Hong Kong (11%).
183
Yang C. and Wu C. (2004).
GENDER DIFFERENCES IN ONLINE SHOPPERS’ DECISION-MAKING STYLES.
In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 183-190
DOI: 10.5220/0001396001830190
Copyright
c
 SciTePress

New Zealand is 10%, which is the lowest growth
rate (Institute for Information Industry, ECRC-
FIND).
2 LITERATURE REVIEW
2.1 Decision-Making Style
A consumer decision-making style is defined as a
mental orientation characterizing a consumer’s
approach to making choices. It has cognitive and
affective characteristics (Sproles & Kendall, 1986).
Extant research in this field has identified three
approaches to characterize consumer styles: (1) the
Consumer Typology Approach; (2) the
Psychographics/Lifestyles Approach; and (3) the
Consumer Characteristics Approach. The Consumer
Characteristics Approach is one of the most
promising as it deals with the mental orientation of
consumers in making decisions (Durvasula,
Lysonski, and Andrews, 1993).
The original of this approach was based on an
exploratory study by Sproles (1985) that identified
fifty items related to this mental orientation.
Afterward, Sproles & Kendall (1986) reworked this
inventory and developed a more parsimonious scale
with forty items (Durvasula, Lysonski, and
Andrews, 1993). These items were titled Consumer
Style Inventory. Many studies that discussed
consumer decision-making style refered to Sproles
(1985) and Sproles & Kendall (1986) as the base.
Some relative studies were shown as Table 1.
2.2 Gender Differences in Internet
There have been many studies which contribute to
gender differences in the application of Internet.
Gefen & Straub (1997) extended the Technology
Acceptance Model to IT diffusion and used this
structure to discuss gender differences in the
perception and use of E-Mail. They found that
gender differences indeed influenced the use of E-
Mail. Jackson, Ervin, Gardner & Schmitt(2001) used
path analysis to discuss the use of Internet between
the two genders and found some influential factors
such as motivational, affective and cognitive factors.
The results were shown that women used Internet as
a communication tool while men used it as a search
tool.
Boneva, Kraut & Frohlich(2001) discovered that
women used E-Mail as a personal relationship tool
more than men did. Furthermore, Teo &
Lim(1997）investigated 1370 Singapore residents.
They used Internet to understand the gender gap
about usage patterns and perception of the Internet.
The result has important implication for business
who seeks to sell products targeted at female
consumers via the Internet. The reason is female are
well-educated.
Based the above studies, we added gender
difference in consumer decision-making styles.
There must be some differences while online
shoppers make decisions because Internet shopping
is a kind of application of Internet.
3 METHODOLOGY
3.1 Questionnaire Design
Translation was used to prepare the forty-item CSI
scale for the investigation because of the language
and culture in Taiwan. Slight changes must do
owing to the purpose of this research, for example,
we added such words like “online shopping” in the
items. A five-point scale was used, ranging from
strongly disagree to strongly agree. Moreover, we
used Internet questionnaire instead of traditional
one. The reason was lain on convenience and time-
saving to use this kind of method to delivery
questionnaire.
3.2 Sample Selection
Convenient sampling of 209 Internet users that
consisted of 102 females and 107 males is
conducted. Besides, all these 209 responses were
from those with Internet shopping experiences. For
the sake of deciding online shoppers’ decision-
making styles, this research used exploratory factor
analysis (EFA). Although there were many
researches that discussed CSI, none used CSI to
online shopper. Additionally, we contested that the
gender differences might lead to different decision-
making styles. The method we adopt to recognize
genders differences is discriminant analysis. EFA
and discriminant analysis were tested by using SAS
8.2, and results were shown next section.
4 RESULTS
4.1 Reliability and Validity
In social science research, one of the most widely-
used indices of internal consistent reliability is
Cronbach Alpha (Cronbach, 1951). It can save time
to measure the reliability comparing with test-retest
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reliability and it’s measurement effect is as well as
test-retest reliability. A widely-used rule of the
thumb of 0.7 has been suggested by Nunnally
(1978). Reliability coefficient in this research is
more than 0.7(Cronbach coefficient alpha=0.86), so
the questionnaire we used has internal consistent
reliability. Besides internal consistent reliability, we
should consider the validity of the questionnaire.
The questionnaire possessed content validity
because we adopted from CSI which was suggested
by Sproles (1985) and Sproles & Kendall (1986).
4.2 Results of Exploratory Factor
Analysis
An exploratory factor analysis (EFA) was performed
to categorize online shoppers’ decision-making
styles. Consistent with Sproles & Kendall (1986),
principal components analysis with varimax rotation
was used. Because principal components analysis
didn’t produce a single solution but left the decision
about the right number of factors largely to
researchers, we chose eigenvalue-one as criterion to
decide the number of factors (Kaiser, 1960). The
rule of eigenvalue-one is that the number of factors
is decided when eigenvalue is greater than one. This
research we classified seven factors (Table 2). The
results of EFA were shown in Table 3.
Factor 1: Perfectionism
This kind of online shopper values the quality of
products. When it comes to purchasing products,
they try to get the very best or perfect choice. In
general, they usually try to buy the best overall
quality.
Factor 2: Novel-Fashion Consciousness
This kind of online shopper likes to buy the
fashionable and novel goods. They are the early
adopter. They keep their wardrobe up-to-date with
the changing fashions. Fashionable, attractive styling
is very important to them.
Factor 3: Price Consciousness
This kind of online shopper very considers the
value of money. The lower price products are
usually their choice. They usually take the time to
shop carefully for best buys
Factor 4: Confused by Overchoice
This kind of online shopper is worry about much
information about products. Too much information
will disturb them to make right purchase decisions.
The more they learn about products, the harder it
seems to choose to best. All the information they get
on different products confuses them.
Factor 5: Brand Consciousness
This kind of online shopper values the brand of
products. The well-known national brands are best
for them to choose. They think the more expensive
brands are usually their choice.
Factor 6: Recreational Shopping
This kind of online shopper thinks shopping will
waste time unless it can please him. A product
doesn’t have to be perfect, or the best, to satisfy
them. They enjoy shopping just for the fun of it.
Factor 7: Brand-Loyal Consciousness
This kind of online shopper is brand loyalist.
They have favorite brands they will buy over and
over. Once they find a product or brands they like,
they will stick with it.
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185

Table 1: Relative Research on Consumers’ Decision-Making Styles
Researchers Sample Structure Decision-Making Styles
Sproles（1985） A sample of 111
undergraduate women in two
classes of the School of Family
and Consumer Resources,
University of Arizons
Six Decision-Making Styles:
1. Perfectionism
2. Value conscious
3. Brand consciousness
4. Novelty-fad-Fashion consciousness
5. Shopping Avoider
6. Confused, support-seeker style
Sproles＆Kendal（1986） 482 students in 29 home
economics classes in five high
schools in the Tucson area
Eight Decision-Making Styles:
1.Perfectionistic, high-quality conscious
2.Brand conscious
3.Novel-fashion conscious
4.Recreational, hedonistic consumer
5.Price conscious
6.Impulsive, careless consumer
7.Confused by overchoice consumer
8.Habitual, brand-loyal consumer
Hafstrom, Chae ＆Chung（1992） 310 college students at four
universities in Taegu
Eight Decision-Making Styles:
1. Brand conscious
2.Perfectionistic, high-quality conscious
3. Recreational-shopping consumer
4.Confused by overchoice consumer
5.Time-engery conserving consumer
6. Impulsive, careless consumer
7. Habitual, brand-loyal consumer
8. Price-value conscious
Durvasula, Lysonsk＆Andrews（
1993）
210 undergraduate business
students at a large university in
New Zealand
Eight Decision-Making Styles:
1.Perfectionistic, high-quality conscious
2. Brand conscious
3. Novel-fashion conscious
4. Recreational, hedonistic consumer
5. Price conscious
6. Impulsive, careless consumer
7.Confused by overchoice consumer
8. Habitual, brand-loyal consumer
Jessie X. Fan＆Jing J. XIao（1998） 271 undergraduate students
from Zhongshan University,
South China Normal
University, South China
University of Technology,
Guangdong Commercial
College and Jinan University
Five Decision-Making Styles:
1. Brand consciousness
2. Time consciousness
3. Quality consciousness
4. Price conscious
5.Information utilization
Gianfranco Walsh, Vincent-Wayne
Mitchell & Thorsten Hennig-
Thurau(2001)
455 male and female shoppers
who are entering or leaving a
shop in Lünegurg and
Hamburg
Seven Decision-Making Styles:
1. Brand consciousness
2.Perfectionism
3.Recreational/hedonistic
4. Confused by overchoice
5. Impulsiveness Price conscious
6. Novel-fashion consciousness
7.Varity seeking
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Researchers Sample Structure Decision-Making Styles
Alice S. Y. Hiu, Noel Y. M. Siu,
Chaile C. L. Wang & Ludwig M. K.
Chang(2001)
387 consumer who are in
shopping malls or places
nearby shopping center in
Guangzhou, China
Seven Decision-Making Styles:
1.Perfectionistic, high-quality
2. Brand conscious
3. Novel-fashion conscious
4. Recreational/hedonistic
5. Price conscious
6. Confused by overchoice
7. Habitual, brand-loyal consumer
Cathy Backwell & Vincent-Wayne
Mitchell(2003)
244 female undergraduate
students aged between 18 and
22
Five Decision-Making Styles:
1. Recreational quality seeker
2. Recreational discount seeker
3. Shopping and fashion uninterested
4. Trend setting loyal
5. Confused time/money conserve
4.3 Results of Discriminant Analysis
First, we should test if the means have significant
differences between seven factors in two populations
(female and male) by one-way MANOVA before
discriminant analysis. The result shows that seven
factors’ mean have significant differences between
two populations (Wilks' Lambda=0.86, F=4.52,
p=0.0001, see Table 4).
Second, we chose the factors by stepwise
discriminant analysis that could obviously
discriminant difference between female and male.
The result suggested that only Factor1, Factor 2,
Factor 3 and Factor 5 could differentiate female
from male.
Finally, we used Factor1, Factor 2, Factor 3 and
Factor 5 to implement discriminant analysis. This
research only had two populations, so there was only
one discriminate
function
58142.035004.029435.013104.0 FFFFL ++−−=
. The
standardized canonical coefficients are shown in
Table 5. The total classification error rate is 0.4070,
and the classification results are list in Table 6. This
error rate means that we can classify correctly by
this discriminant function and its correct rate is
about sixty percentages. From the discriminate
function, we can obtain discriminate scores. If the
scores are higher than total mean, then it would be
males’ decision-making. If the scores are lower than
total mean, then it would be females’ decision-
making. In general, it exists differences between
female and male’s decision-making style. Figure 1
shows the differences between two populations.
5 CONCLUSIONS
According to the CSI, online shoppers could be
categorized into seven main decision-making styles:
perfectionism, novel-fashion consciousness, price
consciousness, confused by overchoice, brand
consciousness, recreational shopping and brand-
loyal consciousness. Compared with the findings of
Sproles & Kendal（1986）, online shoppers lack of
the type of “impulsive careless consumer”. This
means that online shoppers are programmed
problem solving while making purchase decisions.
When people adapt online shopping, it means that
they have already thought it carefully and might get
used to shopping through Internet. Therefore,
consumers in cyberspace and reality environment
may act differently to some degrees.
Secondly, this research also discussed the gender
differences among online shoppers. Discriminant
analysis was employed to distinguish the differences
between female and male shoppers. We discovered
that female and male indeed exhibited some
difference on decision-making styles from the
discriminate function. Males are dominated over
price consciousness and brand consciousness and
females are dominated over perfectionism and
novel-fashion consciousness. Meanwhile, these
findings can provide managers of Internet shopping
Websites to design a proper homepage and
marketing mix for males and females.
Third, further researchers can use the seven
online shoppers’ decision-making styles as
segmentation variables to capture more details about
online shoppers. This research can propose some
aspects for both researchers and practitioners who
are interested in consumer behavior in E-Commerce.
GENDER DIFFERENCES IN ONLINE SHOPPERS’ DECISION-MAKING STYLES
187

Table 2: The Criterion to Decide Factor Numbers
Eigenvalue Difference Proportion Cumulative
1 6.50897283 3.20199722 0.3178 0.3178
2 3.30697562 0.91748347 0.1615 0.4793
3 2.38949214 0.45218978 0.1167 0.5960
4 1.93730236 0.31031516 0.0946 0.6906
5 1.62698720 0.40328094 0.0794 0.7701
6 1.22370625 0.18305716 0.0598 0.8298
7 1.04064909 0.10621954 0.0508 0.8806
Table 3: Taiwan Online Shoppers’ Style Characteristics:
Seven-Factor Model (wordings are directly adopted from
Sproles (1985) and Sproles & Kendall (1986) )
Factor Items Factor
Loadings
Factor 1 1Getting very good quality is
very important to me.
2When it comes to
purchasing products, I try to
get the very best or perfect
choice.
3In general, I usually try to
buy the best overall quality.
4I make special effort to
choose the very best quality
products.
6My standards and
expectations for products I
buy are very high.
0.74
0.83
0.86
0.74
0.60
Factor 2 15I usually have one or more
outfits of the very newest
styles.
16I keep my wardrobe up-to-
date with the changing
fashions.
17Fashionable, attractive
styling is very important to
me.
18To get variety, I shop
different stores and choose
different brands.
19It’s fun to buy something
new and exciting.
0.51
0.75
0.79
0.69
0.52
Factor 3 24I make my shopping trips
fast.
25I buy as mush as possible
at sale prices.
26The lower price products
are usually my choice.
0.54
0.54
0.60
Factor Items Factor
Loadings
31I take the time to shop
carefully for best buys.
32I carefully watch how
mush I spend.
0.61
0.55
Factor 4 34Sometimes it’s hard to
choose which stores to shop.
35The more I learn about
products, the harder it seems
to choose to best.
36All the information I get o
n
different products confuses
me.
0.48
0.83
0.82
Factor 5 9The well-known national
brands are best for me.
10The more expensive brands
are usually my choice
11The higher the price of a
product, the better its quality.
0.68
0.75
0.54
Factor 6 5I usually don’t give my
purchases much thought or
care.
7I shop quickly, buying the
first product or brand I find
that seems good enough.
8A product doesn’t have to
be perfect, or the best, to
satisfy me.
23I enjoy shopping just for
the fun of it.
0.48
0.41
0.50
0.49
Factor
7
37I have favorite brands I
buy over and over.
38Once I find a product or
brands I like, I stick with it.
0.76
0.77
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Table 4: Multivariate Analysis Results
Statistic Value F Value Num DF Den DF Pr > F
Wilks' Lambda 0.86407272 4.52 7 201 0.0001
Pillai's Trace 0.13592728 4.52 7 201 0.0001
Hotelling-Lawley
Trace 0.15731001 4.52 7 201 0.0001
Roy's Greatest
Root 0.15731001 4.52 7 201 0.0001
Table 5: Standardized Canonical Coefficients
Variable Can1
F1 -.3104382119
F2 -.9434892732
F3 0.5004005177
F5 0.8141717820
Table 6: Classification Results
Predicted Group
Actual Group
Female Male Total
Female 59
(57.84%)
43
(42.16%)
102
(100%)
Male 42
(39.25%)
65
(60.75%)
107
(100%)
Total 101
(48.33%)
108
(51.67%)
209
(100%)
Figure 1: Gender Differences in Decision-Making Styles (Show by box-and-whisker plot)
GENDER DIFFERENCES IN ONLINE SHOPPERS’ DECISION-MAKING STYLES
189

REFERENCES
Taiwan Regular Internet Users Reached 8.76 Millions By
June 2003, 2003/8/15,
http://www.find.org.tw/0105/howmany/howmany_dis
p.asp?id=57, Institute for Information Industry ECRC-
FIND Center (in Chinese)
American Female Internet Users Are More Than Male and
Female Internet Users Has Grown Rapidly in Asia-
Pacific Region, 2001/8/31,
http://www.find.org.tw/0105/property/0105_property_
disp.asp?board_id=24, Institute for Information
Industry ECRC-FIND Center (in Chinese)
European Female Internet Users Reaches 35 Millions,
2003/7/1,
http://www.find.org.tw/0105/news/0105_news_disp.as
p?news_id=2737, Institute for Information Industry
ECRC-FIND Center (in Chinese)
Alreck, Pamela & Settle, Robert B., 2002, Gender Effects
on Internet, Catalogue and Store Shopping, Journal of
Database Marketing & Customer Strategy
Management, Jan, 9, 2, 150-162
Bakewell, Cathy & Mitchell, Vincent-Wayne, 2003,
Generation Y Female Consumer Decision-Making
Styles, International Journal of Retail & Distribution
Management, 31(2), 95-106.
Boneva, Bonka, Kraut, Robert & Frohlich, David, 2001,
Using E-Mail for Personal Relationships: the
Difference Gender Makes, The American Behavioral
Scientist, Nov, 45, 3, 530-549
Briones, Maricris G., 1998, On-line Retailers Seek Ways
to Close Shopping Gender Gap, Marketing News, Sep
14, 32, 19.
Durvasula, Srinivas, Lysonski, Steven & Andrews, J.
Craig , 1993, Cross-Culture Generalizability of a Scale
for Profiling Consumers’ Decision-Making Styles,
Journal of Consumer Affairs, 27(1), 55-65
Fan, J. X. & Xiao, J. J., 1998, Consumer Decision-Making
Styles of Young-Adult Chinese, Journal of Consumer
Affairs, 32, 275-294.
Gefen, David & Straub, Detmar W., 1997, Gender
Difference in the Perception and Use of E-Mail: An
Extension to the Technology Acceptance Model, MIS
Quarterly, Dec, 21, 4, 389-400
Hafstrom, Jeanne L. ＆ Chae, J. S., 1992, Consumer
Decision-Making Styles: Comparison between United
States and Korean Young Consumers, Journal of
Consumer Affairs, 26(1), 146-158.
Hiu, Alice S.Y., Siu, Noel Y. M., Wang, Charlie C.L., &
Chang Ludwig M. K., 2001, An Investigation of
Decision-Making Styles of Consumers in China,
Journal of Consumer Affairs, 35(2).
Jackson, Linda A., Ervin, Kelly S., Gardner, Philip D. &
Schmitt, Neal, 2001, Gender and the Internet: Women
Communication and Men Searching, Sex Role, Mar,
44, 5/6, 363-379
Kaiser, H. F., 1960, The Application of Electronic
Computers to Factor Analysis, Educational and
Psychological Measurement, 20, 141-151.
Nunnally, J., 1978, Psychometric Theory, New York:
MaGraw-Hill.
Sproles, G. B.＆Kendall, E. L., 1986, A Methodology for
Profiling Consumers’ Decision-Making, Journal of
Consumer Affairs, 20(2), 367-379.
Sproles, G. B., 1985, From Perfectionism to Fadism:
Measuring Consumers’ Decision-Making Styles, in
Proceedings of American Council on Consumer
Interest
, pp. 79-85.
Teo, Thompson S. H. & Lim, Vivien K.G., 1997, Usage
Patterns and Perceptions of the Internet: the Gender
Gap, Equal Opportunities International, 16, 6/7, 1-8
Walsh, G., Mitchell, Vincent-Wayne, & Hennig-Thurasu,
Thorsten, 2001, German Consumer Decision-Making
Styles, Journal of Consumer Affairs, 35, 73-99.
ICETE 2004 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES
190