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What users want in e-commerce design: effects of age, education
andincomeNancy J. Lightneraa The Moore School of Business,
University of South Carolina, 1705 College Street, Columbia,
SC29208, USA.
To cite this Article Lightner, Nancy J.(2003) 'What users want
in e-commerce design: effects of age, education and
income',Ergonomics, 46: 1, 153 168To link to this Article: DOI:
10.1080/00140130303530URL:
http://dx.doi.org/10.1080/00140130303530
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What users want in e-commerce design: eects of age,education and
income
NANCY J. LIGHTNER*
The Moore School of Business, University of South Carolina, 1705
CollegeStreet, Columbia, SC 29208, USA,
Keywords: E-commerce; Satisfaction; Demographics; Survey.
Preferences for certain characteristics of an online shopping
experience may berelated to demographic data. This paper discusses
the characteristics of thatexperience, demographic data and
preferences by demographic group. The resultsof an online survey of
488 individuals in the United States indicate thatrespondents are
generally satisfied with their online shopping experiences,
withsecurity, information quality and information quantity ranking
first inimportance overall. The sensory impact of a site ranked
last overall of the sevencharacteristics measured. Preferences for
these characteristics in e-commerce siteswere dierentiated by age,
education and income. The sensory impact of sitesbecame less
important as respondents increased in age, income or education.
Asthe income of respondents increased, the importance of the
reputation of thevendor rose. Web site designers may incorporate
these findings into the design ofe-commerce sites in an attempt to
increase the shopping satisfaction of their users.Results from the
customer relationship management portion of the survey suggestthat
current push technologies and site personalization are not an
eective meansof achieving user satisfaction.
1. IntroductionElectronic commerce, or e-commerce, is the
selling of goods and services via anelectronic media, using
technology to facilitate rapid exchange of detailedinformation
between buyers and sellers. While prominent e-commerce sites
havefailed, examples of successful firms have emerged. For example,
Amazon.com posteda profit for the first time for the fourth quarter
of 2001 and eBay has posted quarterlyearnings since 2000. Clearly,
the Internet as a marketplace has potential for success.However,
defining what elements lead to success is a monumental task,
involving allaspects of a business. While an e-commerce system
consists of suppliers anddistributors as well as the business
itself, the interaction is supported by informationsystems, with
the computer screen acting as the interface among all
businessfunctions and between the business and its customers and
potential customers. Sinceweb sites are currently the only
interface to e-commerce, designing those sites toaccommodate target
market preferences should enhance consumers shoppingexperience, and
perhaps consequently, motivate them to purchase and repurchasefrom
the web site. Presently it is unknown what site characteristics
determine onlinesatisfaction and what characteristics of the
consumer impact those preferences. Thisresearch investigates
various aspects of the online shopping experience in order to
*e-mail: [email protected]
ERGONOMICS, 2003, VOL. 46, NO. 1 3, 153 168
Ergonomics ISSN 0014-0139 print/ISSN 1366-5847 online # 2003
Taylor & Francis Ltdhttp://www.tandf.co.uk/journals
DOI: 10.1080/0014013021000035280
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make design recommendations for e-commerce companies. Since
e-commerce isrelatively new, an exploratory study was undertaken as
a means of identifyingaspects of design that warrant additional
research. A survey of those who hadconducted business online,
either for personal or business purposes, was undertakento identify
what web site characteristics were important to their purchase
decisions.
2. Conceptual background and research propositionsOne proposed
research model of the consumer in an e-commerce environmentincludes
the Web Environment, Customer and Web Technology as
independentsubsystems (Helander and Khalid 2000), with issues
within each subsystem identifiedas possible areas of interest. This
research uses the Helander model as a basis for aninvestigative
study into the elements of a web site that create user satisfaction
andconsequently generate repeat business. It tests specific
elements for their impact onthe overall model. The following
propositions indicate general areas of interestdeemed important to
e-commerce satisfaction and the impact of site and
personalcharacteristics on the importance. They are:
Proposition I: Online shoppers prefer e-commerce web sites that
contain specificcharacteristics.
Proposition II: Demographic characteristics of online shoppers
aect overallsatisfaction with e-commerce sites.
Proposition III: Demographic characteristics of online shoppers
aect preferencesin web sites.
Proposition IV: Demographic characteristics aect online shoppers
preferences formethods of retaining customers.
3. Research methodology3.1. Research variablesThe model of the
consumer in electronic commerce proposed by Helander andKhalid
(2000) consists of three subsystems; the Web Environment, the
Customer andWeb Technology. These subsystems relate in a circular
fashion. The WebEnvironment impacts the Customer by what is
presented, the Customer in turncontrols the Web Technology, in
part, through their interaction with search agentsand other
features. The Web Technology determines what is available for use
in theWeb Environment, depending on what technology is available
and implemented.The Helander model suggests several dependent
variables for use in research,including outcome measures such as
productivity and satisfaction measuresincluding enjoyment. Research
variables for this study were identified followingthe subsystems
suggested in the Helander model.
The Web Environment subsystem includes the areas of Merchandize,
Naviga-tion, Easy to Purchase, Promotions and Feedback. The
research variables developedfor the Merchandize area are
Information Quality, Information Quantity,Comparison and Price.
Delone and McLean (1992) identified Information Qualityas a measure
of system output used in determining the success of any
informationsystem. Characteristics that comprise Information
Quality suggested are accuracy,precision, currency, output
timeliness, reliability, completeness, conciseness, formatand
relevance (Bailey and Pearson 1983). Understandability (Srinivasan
1985),report usefulness (Mahmood and Medewitz 1985), suciency,
freedom from bias,
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comparability and quantitativeness (King and Epstein 1983) are
additional criteriathat have been used as measures. According to
these criteria, we might consider ourvariables of Information
Quantity (completeness) and Comparison (comparability)as indicators
of Information Quality as well. For purposes of this study,
InformationQuality was defined as the perception of whether the
information contained in thesite was true, corresponding to the
reliability characteristic identified by Bailey andPearson (1983).
Information Quantity was defined as the amount of
informationprovided about the product under consideration.
Comparison was included as aMerchandize variable, although it
refers to the ability to comparison shop based onproduct
specifications and price.
The Navigation variable of speed was included in this study and
defined asNavigation Speed. Since the commercialization of the
Internet, the speed of access isof concern to users (Bartholomew
2001, Liao and Cheung 2002, Lightner et al.1996). Speed to purchase
and inclusion of a shopping basket were included as Easyto Purchase
variables in the Web Environment subsystem. The variable of Email
wasincluded as a measure of the Promotions area. Contacting
customers throughelectronic mail is used as a means of generating
additional site visits and possiblepurchases. The Personalization
variable was a measure of the Feedback area. Sitepersonalization is
using technology to deliver dynamically generated content
(Luedi1997), such as suggesting products based on past buying
behaviour. Luedi (1997)claims that personalization is the only way
to create customer loyalty and generaterepeat visits to your Web
sites. (p. 22). This claim warrants an investigation intohow
personalization impacts buying behaviour.
Demographic data was selected as a surrogate to the modulating
variables ofNeeds, Attitudes, Purchasing Power, Competence,
Addiction, Motivation, Age andTrust identified by Helander as
important to the Customer subsystem. Bellman et al.(1999) concluded
that lifestyle predicts Internet usage, not demographics. This
studyinvestigated whether demographics would predict e-commerce
preferences once theyentered the online shopping arena. In addition
to demographic data, the variables ofSecurity, Price, Reputation
and Repeat Business were included to investigateattitudes toward
the buying process. The concern over security continues to
plaguethe online world. Despite evidence that online credit card
transactions are as secureas those involving waiters or waitresses,
security consistently ranks as the numberone concern of those that
shop online (Salisbury et al. 2001, Luo 2002, Wilson andAbel 2002)
and as the reason why those that do not shop online do not (Luo
2002).The research variable of Security reflects trust in the
online system and the variableReputation reflects trust in the
specific vendor. Discovering whether vendors receiverepeat business
reflects the overall buying attitude of consumers.
The Web Technology subsystem is represented by the Sensory
Impact variable.Sensory Impact indicates whether the aesthetic
properties of a site influence thebuying behaviour or preferences
for a site. The discipline of Marketing focuses oncapturing and
holding the attention of consumers so that they eventually
purchaseproducts. The Handbook of Marketing Scales (Beardon and
Netmeyer 1999)contains a compilation of measures to use to predict
consumer behaviour, includingmeasures of Optimal Stimulation levels
and other characteristics. These measures arereported to relate to
a wide range of consumer-related behaviours. As a composite,some of
the measures that address stimulus needs are categorized as Sensory
Impactin our study. Sensory Impact is accomplished with stylistic
elements such as colourand movement and exhibit Displays within the
Web Technology subsystem.
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Online shopping satisfaction is included as a variable to
measure the degree towhich web sites meet expectations.
3.2. Survey instrumentsAn online survey was developed containing
23 questions. Questions one through tenasked for basic demographic
information such as age, sex, education, family size,income and
amount of online purchases made. Age, education, income
andfrequency and amount of purchases were collected as categorical
variables. Question11 asked respondents to rate their general
satisfaction with making purchases online.The remaining questions
asked respondents to rate the importance of web sitecharacteristics
according to a 7-point Likert scale ranging from very
stronglydisagree to very strongly agree. Some of the research
variables (IQL, IQN, SEC,SIMP) were represented by two questions
apiece. Other variables (COMP, BSP,NSP, SCART, PRICE, REP) were
surveyed using one question each. See table 1 forthe survey
questions pertaining to buying preferences and how they relate to
theresearch variables examined in this study.
The original survey was made available to students at three
universities who wereasked to participate for class credit. In
addition, several listserv groups consisting ofInformation Systems
and Human Computer Interaction academics and profes-sionals were
solicited for participation. Data was collected from a total of
327participants for this survey. Data was collected over 2 weeks,
from 20 February to 7March 2001. This survey was administered in
both paper-based and on line fashion.Students in one of the three
universities filled out a printed copy of the survey andsubmitted
it to the instructor who manually entered the results into a
spreadsheet.Students in the other two universities submitted their
responses via a web site. Anelectronic mail message to several
listserv discussion groups generated additionalresponses to the web
site, which entered the responses into a file for analysis.
Sincethe subject of the survey is e-commerce, it was expected that
collecting data onlinedid not introduce bias to the sample.
A second survey was created and administered online from 15
November to 13December 2001. This survey was identical to the first
one, except three questionsconcerning the impact of customer
relationship management (CRM) were added tothe end. These questions
asked about the propensity towards loyalty for particularvendors
(RPEAT), whether site personalization contributed to loyalty (PERS)
andwhether e-mailing announcements increased sales (EMAIL). A total
of 161participants responded to the second survey. Participants of
the second survey wereuniversity students who participated for
class credit and their friends that theyrecruited for additional
credit.
4. ResultsThree-hundred and twenty-seven sets of responses were
collected from the firstadministration of the survey in early 2001.
An additional 161 responses werecollected in late 2001. A set of
t-test analyses showed that participant characteristicswere
significantly dierent in education level and preference for
information quality,security and sensory impact and in overall
e-commerce satisfaction between the twosamples. The first set of
responses was from individuals with higher levels ofeducation and
lower preferences for sensory impact and security. Overall
satisfactionwith shopping online was reported by the second set of
respondents. As shown intable 2, the mean value for a preference
for security increased a half-point between
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the first and second survey (sample 1 m=6.06, sample 2 m=6.40,
t=73.83,p=0.0002). Since the data were collected before and after
the 11 September 2001terrorist attacks, an increase in the
preference for security may have reflected anoverall national
concern for security. Both values ranked highest in their
respectivesamples, however, indicating that security was the top
concern even before theattacks.
Table 1. Survey questions as related to research variables by
subsystem.
Variable # Question
Web Environment subsystemIQL 13 When I buy from an online
vendor, verification that the information
on the site is true is most important to me.IQL 20 When I buy
from an online vendor, knowing that the information on
the site is true is most important to me.IQN 11 When I buy from
an online vendor, having all product details available
to me is most important.IQN 19 When I buy from an online vendor,
finding product specifications in
great detail is most important to me.COMP 22 When I buy from an
online vendor, whether the site provides an
eective means of product and price comparison is most important
tome.
BSP 12 When I buy from an online vendor, a site that is
organized in such away as to minimize the buying time will get my
business.
NSP 16 When I buy from an online vendor, finding the information
that I wantquickly is most important to me.
SCART 23 When I buy from an online vendor, the eective use of
the shoppingcart mechanism is most important to me.
EMAIL 25 When vendors send me product or sale announcements via
e-mail, Ialways visit the site and usually buy something there.
PERS 26 I prefer online vendors that personalize their site to
my past buyingbehaviour.
Customer subsystemSAT 9 Your level of satisfaction with your
online purchase experiences: 1 7
with 1 indicating very dissatisfied and 7 indicating highly
satisfied.SEC 14 When I buy from an online vendor, seeing evidence
that the
transaction is secure before I buy is most important to me.SEC
18 When I buy from an online vendor, verifying that the transaction
is
secure during the buying process is most important to me.PRICE
17 When I buy from an online vendor, price is most important to
me.REP 21 When I buy from an online vendor, the reputation of the
vendor is
most important to me.RPEAT 24 When shopping online, I mostly use
the same vendor(s) over and over
again.
Web Technology subsystemSIMP 10 When I am searching for an
online vendor, liking how the site looks is
most important to me.SIMP 15 When I buy from an online vendor,
one that attracts my attention will
most likely get my business.
IQL=Information Quality, IQN=Information Quantity,
COMP=comparison shopping,BSP=buying speed, NSP=navigation speed,
SCART=shopping cart, EMAIL=use of e-mail for advertising, PERS=site
personalization, SAT=satisfaction, SEC=security,PRICE=price,
REP=vendor reputation, RPEAT=repeat use, SIMP=Sensory Impact.
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Table 3 contains the descriptive statistics for the combined
dataset. The averagerespondent was male, young, with at least some
college credits and fairly auent.Most of them (72%) purchase
infrequently for the home and spend less than $100 amonth (74%) or
between $100 and $500 a month (24%). Business purchases,
whileperformed by fewer respondents (209 or 43%), follow the same
pattern of purchases.These statistics reflect an online population
that have the potential to purchasefrequently on line, but are not
active buyers currently.
Further analysis was conducted on a combination of the data,
since they bothreflect e-commerce preferences. Respondents of the
second survey represent adierent population in terms of education,
and we consider the increase ineducational range an enhancement to
the data. See table 4 for the statistics on webcharacteristics for
the combined data set. The overall Cronbachs alpha of 0.82indicates
that satisfactory internal reliability was achieved. Since the data
for thecharacteristics were derived from measures consisting of two
items, the Cronbachsalpha for each characteristic was also
calculated. All achieved the acceptable value of0.70, the desired
minimum proposed by Nunnally and Bernstein (1994).
4.1. Proposition I: e-commerce site preferencesAs table 4
indicates, security, information quality and information quantity
rankedhighest in overall preference for e-commerce shoppers. Paired
t-tests indicate that themean values for these three variables were
not significantly dierent (a50.05). Nextin importance for these
respondents were price, navigation and buying speed and
thereputation of a vendor. The ability to shop by comparing
products, the presence of ashopping cart mechanism and a need for
sensory impact in a site ranked last. Overallsatisfaction measured
5.39 out of 7, with a rating of 5 meaning Agree and a 6 rating
Table 2. Comparison of sample responses.
Sample 1 Sample 2Characteristic (early 2001) (late 2001) t-value
p-value
Age 4.26 4.16 0.99 0.32Gender 0.37 0.43 71.30 0.19Education
level 3.52 2.37 11.14 50.0001*Family size 2.46 2.59 71.39
0.17Family income 2.99 2.80 1.46 0.15Frequency of purchaseshome
1.31 1.34 70.40 0.69Amount of purchaseshome 1.28 1.33 70.95
0.34Frequency of purchasesbusiness 1.26 1.38 71.39 0.17Amount
purchasesbusiness 1.38 1.56 71.97 0.05Overall satisfaction 5.26
5.65 73.49 0.0005*Preference for sensory impact 4.73 5.17 73.69
0.0003*Preference for information quantity 6.03 6.18 71.60
0.11Preference for speed 5.73 5.84 71.12 0.26Preference for
information quality 6.03 6.23 72.29 0.02*Preference for security
6.06 6.40 73.83 0.0002*Importance of vendor reputation 5.67 5.86
71.70 0.09Preference for product comparison 5.16 5.34 71.38
0.17Preference for price 5.79 5.90 70.89 0.37Preference for
shopping cart 5.06 4.78 1.95 0.05
* indicates significance at a50.05.
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Table 3. Frequency counts and descriptive statistics of
respondent data.
Characteristic N
Age in years (m=2.17, std. dev.=0.91) 48820 and under 10521 30
25731 40 63440 63
Gender (m=0.39, std. dev.=0.49) 485Male=0 294Female=1 191
Education level (m=3.14, std. dev.=1.38) 488High school
80Technical school diploma 90Some college 105College degree
108Graduate degree 105
Family size (m=2.50, std. dev.=0.96) 4861 1132 663 5 25645
51
Family income in US$ (m=2.39, std. dev.=1.07) 4885$30 000 95$30
000 $60 000 224$61 000 $100 000 904$100 000 79
Frequency of purchase home (m=1.32, std. dev.=0.56) 488Once a
month or less 3522 5 times a month 1236 10 times a month 8410 times
a month 5
Average amount of purchasefor home use in US$(m=1.29, std.
dev.=0.52)
488
5$100 362$101 $500 118$501 $1000 54$1000 3
Frequency of purchasebusiness (m=1.30, std. dev.=0.63) 209Once a
month or less 1602 5 times a month 406 10 times a month 4410 times
a month 5
Amount of purchasebusiness (m=1.44, std. dev.=0.62) 2025$100
125$101 $1,000 67$1001 $5000 84$5000 2
Overall satisfaction (m=5.39, std. dev.=1.15) 4881=very
dissatisfied to 7=highly satisfied
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meaning Strongly agree. These findings support Proposition I,
which states thatonline shoppers prefer e-commerce web sites that
have certain characteristics.
4.2. Proposition II: impact of demographics on preferences and
e-commercesatisfactionAnalysis of variance (ANOVA) using e-commerce
satisfaction as the dependentvariable and demographic data gathered
in both surveys as independent variablesshowed that demographic
values impact satisfaction (R2=0.35, F=2.43,p=0.0001), supporting
Proposition II. As shown in table 5, family income(F=6.70,
p=0.0002), age (F=2.62, p=0.02) and education level
(F=4.20,p50.0001) all significantly impact e-commerce
satisfaction.
Table 6 contains the intercorrelations for all of the study
variables. The analysisindicates that age, education and income are
all positively and significantlycorrelated with satisfaction. These
demographic variables were strongly andpositively intercorrelated
as were family size and education. An ANOVA using e-commerce
satisfaction score as the dependent variable and the web
characteristicstested as independent variables also showed a
significant impact of web sitepreferences on satisfaction (R2=0.28,
F=2.23, p50.0001).
Table 7 contains the results of the ANOVA, indicating the
importance of sensoryimpact (F=3.06, p=0.0004), security (F=2.49,
p=.007), both types of speed
Table 4. Descriptive statistics of web characteristic data.
Combination of survey results(N=488; Cronbachs a=0.82).
Characteristic Mean Std. dev. a Survey question(s)
Security 76.17 1.04 0.86 14, 18Information Quality 76.10 0.99
0.82 13, 20Information Quantity 76.08 0.97 0.76 11, 19Price 75.83
1.25 N/A 17Speed 75.76 1.02 0.70 12, 16Navigation 75.78 1.16 N/A
12Buying 75.75 1.16 N/A 16Vendor reputation 75.73 1.12 N/A
21Comparison shopping 5.22 1.36 N/A 22Shopping cart 4.97 1.46 N/A
23Sensory impact 4.88 1.27 0.76 10, 15
N/A indicates only one question formed the measure.Means
connected by lines are not dierent at a 40.05.
Table 5. ANOVA of demographics to overall satisfaction (N=377;
F=3.23, p50.0001).
Demographic measure F-value p-value
Education level 4.20 0.0001*Family income 6.70 0.0002*Age 2.62
0.02*Gender 3.69 0.06Frequency of online purchaseshome 0.86
0.46Family size 0.41 0.75
* indicates significance at a 40.05.
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Table 6. Intercorrelations for the survey questions.
Age 1.0Gender 0.00 1.0Education 0.53 0.00 1.0 Demographic
dataFamily size 0.04 0.01 0.09 1.0Income 0.28 0.06 0.33 0.33
1.0Frequency home 0.05 0.08 0.01 0.02 0.07 1.0$-home 0.04 0.10 0.01
0.04 0.06 0.34 1.0Frequency business 0.09 0.06 0.01 0.08 0.12 0.34
0.25 1.0 Web characteristics$-business 0.16 0.05 0.09 0.14 0.10
0.17 0.37 0.47 1.0
Sensory Impact 0.30 0.07 0.28 0.04 0.17 0.03 0.02 0.00 0.03
1.0Information Quantity 0.15 0.07 0.11 0.04 0.11 0.08 0.02 0.06
0.04 0.36 1.0Navigation speed 0.00 0.05 0.08 0.06 0.00 0.03 0.04
0.04 0.00 0.34 0.50 1.0Buying speed 0.03 0.09 0.05 0.03 0.04 0.03
0.02 0.01 0.08 0.33 0.44 0.54 1.0Information Quality 0.08 0.03 0.12
0.03 0.08 0.03 0.01 0.10 0.13 0.32 0.64 0.37 0.36 1.0Security 0.06
0.01 0.11 0.05 0.06 0.08 0.01 0.10 0.05 0.26 0.48 0.38 0.34 0.62
1.0Price 0.19 0.05 0.11 0.02 0.13 0.03 0.02 0.06 0.09 0.18 0.39
0.37 0.31 0.33 0.29 1.0Reputation 0.02 0.06 0.01 0.08 0.01 0.07
0.01 0.09 0.04 0.24 0.35 0.31 0.32 0.44 0.42 0.18 1.0Comparison
0.13 0.02 0.13 0.11 0.11 0.01 0.00 0.02 0.08 0.37 0.33 0.35 0.41
0.38 0.26 0.39 0.41 1.0 CRM dataShopping cart 0.07 0.05 0.03 0.05
0.05 0.03 0.03 0.05 0.10 0.31 0.20 0.31 0.32 0.30 0.23 0.18 0.29
0.45 1.0
Repeat use 0.01 0.11 0.17 0.04 0.08 0.12 0.07 0.03 0.23 0.32
0.02 0.19 0.23 0.03 0.04 0.05 0.21 0.22 0.28 1.0E-mail 0.18 0.02
0.03 0.32 0.18 0.22 0.14 0.03 0.15 0.40 0.16 0.05 0.09 0.07 0.01
0.14 0.03 0.20 0.45 0.42 1.0Personlization 0.18 0.04 0.04 0.23 0.10
0.23 0.22 0.01 0.12 0.48 0.01 0.18 0.19 0.10 0.17 0.01 0.12 0.33
0.30 0.29 0.56 1.0
Satisfaction 0.15 0.07 0.17 0.01 0.27 0.06 0.03 0.03 0.12 0.10
0.14 0.15 0.17 0.15 0.13 0.08 0.05 0.01 0.01 0.23 0.18 0.13 1.0
*Values with shaded backgrounds are significant at a 40.05.
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(NSP F=2.07, p=0.05, BSP F=2.64, p=0.02), price (F=2.18,
p=0.04),information quantity (F=2.13, p=0.02) and information
quality (F=2.15,p=0.02) on overall satisfaction. The factors of
information quality, security,sensory impact, information quantity
and both speed types are also positively andsignificantly
correlated (a50.05) with overall e-commerce satisfaction. As shown
intable 6, the web characteristic variables showed highly
significant and positiveintercorrelation.
4.3. Proposition III: relationship between demographics and
preferencesDetailed analysis of how demographic variables aect web
site preferences for e-commerce was conducted on the factors that
significantly impacted overallsatisfaction. A series of ANOVAs were
performed using demographic data asindependent variables and web
site characteristics as dependent variables. The resultsindicate
that age, education and income impact a preference for sensory
impact,price, information quantity and comparison shopping. Age and
education aectedthe preference for security, while gender alone
aected the preference for buyingspeed (table 8), with results
showing that women prefer buying speed more than men.Figure 1 shows
how income level aects preferences for web site characteristics
aswell as the overall preferences. The trend is clear that as
income increases, thepreference for sensory impact, price and to a
lesser degree, information quantity,decreases. Figure 2 contains
the same information concerning age and Figure 3contains
information concerning education levels. In all three figures,
trends indicatethat as age, income and education levels increase,
the preference for many of thecharacteristics diminishes.
4.4. Proposition IV: relationship between demographic
preferences for methods ofretaining customersTable 9 contains the
results of the ANOVA with the three CRM questions asindependent
variables and overall e-commerce satisfaction as the dependent
variable(N=161, F=2.36, p=0.0033). These results indicate that
supporting online vendorsthrough repeat business impacts
satisfaction, while site personalization and e-mailmessages
announcing sales do not. A series of ANOVA was conducted,
usingdemographic values as independent variables and the three CRM
questions asdependent variables (see table 10). These results
indicate that as the age of respondents
Table 7. ANOVA of Web characteristics to overall
satisfaction(N=488; F=2.23, p50.0001).
Site preferences F-value p-value
Sensory Impact 3.06 0.0004*Security 2.49 0.007*Navigation speed
2.07 0.05*Buying speed 2.64 0.02*Information Quality 2.15
0.02*Information Quantity 2.13 0.02*Price 2.18 0.04Comparison
shopping 1.00 0.42Reputation of vendor 0.70 0.65
* indicates significance at a 40.05.
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Table 8. Impact of demographics on web characteristic
preferences.
Web characteristics
SensoryImpact Price
InformationQuantity Security
Comparisonshopping
Navigationspeed Buying speed
Demographic F p F p F p F p F p F p F p
Age 8.27 ( ) 50.0001* 3.42 ( ) 0.003* 3.80 ( ) 0.001* 3.44 ( )
0.002* 2.93 ( ) 0.01* 2.05 0.06 1.90 0.08
Education 9.12 ( ) 50.0001* 2.52 ( ) 0.01* 2.63 ( ) 0.01* 2.86 (
) 0.004* 2.61 ( ) 0.01* 3.17 0.002* 0.90 0.52
Income 5.22 ( ) 0.002* 3.96 ( ) 0.01* 2.66 (+) 0.05* 1.07 0.36
3.42 ( ) 0.02* 1.40 0.24 0.55 0.65
Gender 2.65 0.10 1.40 0.24 2.38 0.12 0.10 0.75 0.14 0.71 1.11
0.29 4.13 0.04*
Direction of impact indicated by +/7 in parentheses.* indicates
significance at a 40.05.
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increased, the preference for site personalization decreased. As
family size increased,the preference for site personalization
increased, and sales generated by e-mail promptsalso increased.
These findingsmarginally support Proposition IV, which proposed
thatdemographic characteristics impact the success of CRM
techniques.
Figure 1. Impact of income on web site preferences.
Figure 2. Impact of age on web site preferences.
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5. DiscussionThe strong correlation between the frequency and
amount spent on home purchasesand business purchases reinforces the
notion that a wired lifestyle indicates apropensity toward online
shopping (Bellman et al. 1999). These findings aid in
Figure 3. Impact of education on web site preferences.
Table 9. CRM components to satisfaction ANOVA results (N=161;
F=2.36, p=0.003).
CRM component F-value p-value Mean Std. dev.
Repeat business 6.50 50.0001* 5.08 1.35Site personalization 0.43
0.86 4.72 1.55E-mail prompted sales 0.83 0.55 3.61 1.77
* indicates significance at a 40.05.
Table 10. Impact of demographics on Customer Relationship
Management (CRM)preferences.
CRM preferences
Repeat business E-mail prompted sales Site
personalization(F=1.16, p=0.30) (F=2.02, p=0.008*) (F=2.34,
p=0.002*)
Demographic data F p F p F p
Age 1.15 0.34 1.74 0.13 2.42 (7) 0.04*Education 1.65 0.13 1.26
0.28 1.71 0.11Income 0.39 0.76 1.54 0.21 1.69 0.17Gender 1.54 0.22
0.22 0.64 0.02 0.88Family size 0.96 0.41 5.04 (+) 0.002* 5.26 (+)
0.002*Frequency ofpurchase home
0.87 0.46 2.33 0.08 2.20 0.09
* indicates significance at a 40.05.
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determining how to accommodate those that do venture online to
conduct e-commerce. Results from the overall satisfaction analysis
indicate that on average,strong preferences for security,
truthfulness and completeness of site contents exist.Evidence also
suggests that once buyers are comfortable with an online vendor,
theyreward them with repeat business. These findings are not
dependent on anydemographic data and reflect an overall preference
by the buying population. Resultsof the detailed analysis indicate
that preferences for other site characteristics aredependent on
age, education and income levels, with older, highly educated and
highincome respondents emerging as a group with clearly defined
preferences for e-commerce sites. We refer to individuals in this
group as mature, auent. Thepreferences of the mature, auent may
reflect a dierence in the manner in which e-commerce sites are
used. According to Simons framework of decision making
(1960),gathering intelligence is the first step in the decision
making process. The second stepinvolves taking the intelligence
gathered and formulating possible courses of actionbased on it. The
third step is choosing an alternative from those available. Based
onthe results of these surveys, it appears as though the mature,
auent buyer possiblyuses the Web as a tool during the actual
purchase of goods, once the decision to buy ismade using other
sources, such as friends and print media. This is evidenced by
thedecrease in importance of almost all site characteristics for
the mature, auentsegment. Of special interest is the phenomenon the
drop in importance of price for themature, auent group. This may
indicate that once the purchase decision is made,price is not a
factor in what site to use to make purchases. Younger, less
educated andthose with less income have preferences that seem to
indicate that they use the Webfor intelligence gathering and
formulating alternative products to purchase.Explanations as to why
this may occur can be found in the wired lifestyle suggestedby
Bellman et al. (1999). Younger people have been more exposed to the
Internet fora larger portion of their lives and may be more
comfortable with using it and trustingit for information gathering
and as a decision aid.
5.1. LimitationsThis study was limited in that it surveyed on
line consumers about their preferences,but does not present
evidence that the stated preferences are reflected in actual
onlineconsumption. Since actual buying behaviour is dicult to
capture, the patternsreported may be an artifact of the collection
method. The use of two surveysconducted almost a year apart also
introduces diculties with interpretation, sincedesign and
operational aspects of e-commerce sites may have changed over
thesurvey time. The use of categorical variables for the
demographic informationintroduces diculties in performing
statistical analysis for highly reliable results.This study
investigates whether demographic characteristics are predictors of
onlinepreferences. More precise and thorough investigation is
needed to explain anyphenomena shown here.
5.2. Implications for research and practiceCreating a model for
e-commerce success is a daunting task, since it incorporatesmany
aspects of the system. This study confirms that characteristics of
the WebEnvironment and Customer subsystems presented by Helander
and Khalid (2000)impact e-commerce satisfaction. However, the
characteristics of security, informa-tion quality and quantity were
ranked highest, indicating that the usefulness of thesystem is more
important than its usability.
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We translate these findings into practical recommendations on
what to include inan e-commerce site and how to alter the site to
accommodate dierent targetmarkets. Overall recommendations are that
regardless of the target market, evidenceof a secure site is most
important to the average buyer. Conveying detailedinformation about
products and services and evidence that the information is true
isalso extremely important satisfying buyers and generating repeat
business. Whilevendor reputation seems important, once buyers
discover a vendor that suits theirneeds, they tend to buy from them
over and over again. Site personalization is notcurrently a
preference, nor is solicitation via e-mail messages. Since the
supportsystem to provide these enhancements adds expense to the
overall system, a costbenefit analysis will probably show that the
added expense does not produce repeatvisits as expected. Since the
preferences are clear for the mature, auent consumer, ifa business
has those customers as its target market, we recommend removing
designelements with the purpose of providing sensory impact, such
as complicatedwallpaper or animation. This group seems more intent
on deciding which productfits their needs, regardless of price, and
is not as interested in elements that dissuadethem from that task.
Younger, less auent consumers, on the other hand, are
moreinterested in having information about products available to
them using a methodthat invokes the senses. These consumers are
concerned about price as well, but notto a large extent.
6. ConclusionsThis study shows that the individual as a consumer
remains an important concernfor e-commerce research. We identified
areas of most interest in e-commerce(security, information quality
and quantity), as well as dierences in preferences bydemographic
group. One area that emerged for further study is in task
dierences.Browsing for product information may be considered as
separate from the actualbuying process. In addition, more research
into customer service and customerrelations is warranted. This
study showed that eorts to attract consumers viaelectronic means
are not yet eective.
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