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Journal of Retailing 86 (1, 2010) 106–115
Online Shopper Motivations, and e-Store Attributes: An Examination of
Online Patronage Behavior and Shopper Typologies
Jaishankar Ganesh a, Kristy E. Reynoldsb,∗, Michael Luckettc, Nadia Pomirleanu d
aDepartment of Marketing, University of Central Florida, United States
bDepartment of Management and Marketing, University of Alabama, P.O. Box 870225, Tuscaloosa, AL 35487-0225, United States
cDepartment of Marketing, University of South Florida, United States
dDepartment of Marketing, University of Nevada – Las Vegas, United States
Abstract
e-Stores and online shopping have become important aspects of a retailer’s strategy. Previous research suggests that online shoppers are
fundamentally different from traditional offline shoppers. However, based on the Big Middle Theory (Levy et al. 2005), the authors believe that
there are segments of online shoppers that are very similar to regular shopper groups. To determine this, online shopping motivations and e-store
attribute importance measures are separately used as the basis to develop online shopper typologies. Results reveal that there are more similarities
than differences among traditional and online store shoppers. However, there are a few unique shopper types present at online stores, attracted by
the distinctive characteristics and attributes of the online retail environment. The findings offer interesting implications for online retail strategy.
Published by Elsevier Inc on behalf of New York University.
Keywords: Online shopping; e-Store; Shoppers
Introduction
The development of shopper typologies is a well-established
stream of research in retailing with over 40 studies investigat-
ing retail patronage behavior using a variety of bases, such
as retail attribute importance, shopping motivations, attitude
toward shopping, shopping frequency, and store loyalty. Most
of these studies have concentrated on understanding consumer
patronage behavior in traditional retail formats. However, more
recently, research in online retailing has suggested that those
who shop online behave in fundamentally different ways com-
pared to traditional retail shoppers (e.g., Alba et al. 1997;
Evanschitzky et al. 2004; Rohm and Swaminathan 2004; Shim et
al. 2001; Srinivasan, Anderson, and Ponnavolu 2002; Wallace,
Giese, and Johnson 2004; Winer et al. 1997; and Wolfinbarger
and Gilly 2003).
According to past research, online shoppers are thought to
be more concerned with convenience, are willing to pay extra to
save time (Burke 1997; Li, Ko, and Russell 1999; Morganosky
and Cude 2000; Syzmanski and Hise 2000), and may also dislike
regular shopping (Burke 1997; Morganosky and Cude 2000).
∗Corresponding author. Tel.: +1 205 348 0050.
E-mail address: kreynold@cba.ua.edu (K.E. Reynolds).
In addition, past research contends that online shoppers may
demand more product information, more product variety, and
more personalized or specialized products compared to regular
shoppers (Burke 1997; Syzmanski and Hise 2000). Finally, it
is believed that online shoppers are not strongly motivated to
shop for fun or recreation (Li, Ko, and Russell 1999; Mathwick,
Malhotra, and Rigdon 2001).
Although past research contends that online shoppers are very
different from regular shoppers, based on the Big Middle Theory
(Levy et al. 2005), we believe that many of today’s online shop-
pers are in fact quite similar to regular shoppers in terms of their
shopping motivations and store attribute importance. This is the
basic premise that is postulated and tested in the current research.
Further, the current study addresses some unanswered questions
relating to how online shoppers compare with traditional retail
shoppers in terms of the shopper’s level of knowledge of the
product sought, and on other interesting online variables such
as flow and tele-presence.
In essence, the main objectives of this study are as follows:
(a) Test the Big Middle Theory in an online context by devel-
oping a typology of online shoppers based on shopping
motivations and a typology based on e-store attribute impor-
tance.
0022-4359/$ – see front matter. Published by Elsevier Inc on behalf of New York University.
doi:10.1016/j.jretai.2010.01.003
J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115 107
(b) To compare these online shopper typologies with those iden-
tified in traditional retail formats using similar measures
(shopping motivations and store attribute importance).
(c) To profile these online shopper segments on patronage
behavior.
The theoretical rationale behind using shopping motivations
and store attribute importance is well documented in the litera-
ture (e.g., Bellenger, Robertson, and Greenberg 1977; Bellenger
and Korgaonkar 1980; Ganesh, Reynolds, and Luckett 2007;
Westbrook and Black 1985). The online shopper segments found
here are compared to the shopper subgroups found in the litera-
ture dealing with traditional retail formats.
Theoretical framework and research propositions
Based on the Big Middle Theory, we believe that an examina-
tion of online patronage behavior and a comparison of shopper
typologies would reveal shopper segments that are similar to
those found in traditional store formats. Levy et al. (2005)
define the Big Middle as “the marketspace in which the largest
retailers compete in the long run, because this is where the
largest number of potential customers reside” (p. 85). Exam-
ples include mass-market discount stores such as Wal-Mart
and Target. While short-term success can be found outside
of the Big Middle, the authors argue that over the long-term
most successful niche or segment player retailers will migrate
toward the largest market segment by expanding their mer-
chandising mix, lowering product margins, increasing inventory
turnover rates, and eliminating certain customer service ele-
ments.
According to this theory, there are two ways that suc-
cessful retailers can occupy the Big Middle position in the
retail landscape—as either “Low-price” or “Innovative” play-
ers. Innovative players target quality-conscious consumers
seeking high-end products while Low-price retailers target
price-conscious shoppers. Over time, Innovative players move
toward the Big Middle by expanding product lines, driving
down margins, and increasing volume, while Low-price play-
ers upgrade product lines at slightly higher margins. Essentially,
both types of retailers reinvent themselves by stretching their
brand upward, in the case of Low-price players, or downward in
the case of Innovators. Ultimately, both retailers hybridize them-
selves in order to appeal to the largest portion of the market (Levy
et al. 2005).
Since Big Middle retailers excel at innovating, offering low
prices, or often both, consumers from all parts of the retailing
spectrum gravitate to them. In other words, consumer segments
that traditionally patronized specific retail formats for their dis-
tinct core positioning appeal now move toward these successful
Big Middle retailers since they get best of all worlds. This sug-
gests that, irrespective of the retail format structure, successful
Big Middle retailers are bound to see common shopper sub-
groups within the consumer base patronizing their stores.
In the current context, online stores have entered the retail
arena as “Innovators.” With increased consumer acceptance for
online purchasing and continued advances in technology, some
online retailers will seek to move toward the Big Middle mar-
ketspace. Amazon.com, Overstock.com, and Ebay Stores are
among many good examples of this process in action. Having
begun with relatively small, focused product assortments, these
online retailers have moved to the Big Middle by expanding
into numerous product lines, lowering margins, and expanding
their concepts (i.e., online affiliate marketing programs, blogs,
cross-selling push technology, diversification into higher priced
offerings).
Since retailers are constantly adapting to an ever-changing
retail consumer, the Big Middle theory suggests the existence of
a core group of shoppers seeking a relatively consistent and more
demanding bundle of retail attributes: broad and deep product
mixes with consistently low prices. Recent research offers ten-
tative support for the existence of Big Middle shoppers within
traditional retail format settings (Ganesh, Reynolds, and Luckett
2007). However, no similar examination includes online retailers
as a point of comparison. Given the growth of the online shopper
segment, virtually every type of retail format has shifted toward
attracting these shoppers, including department stores, discount
stores, factory outlet malls, category killers, traditional malls,
and arguably online retailers. Thus, based on the Big Middle
Theory, we should expect common types across traditional and
online formats.
Therefore, although previous research has shown that online
shoppers are very different from regular shoppers, based on the
Big Middle Theory, we believe that a majority of today’s online
shoppers are in fact very similar to regular shoppers. We thus
propose the following research proposition:
RP: The similarities across retail formats and the competitive structure
of the retail marketplace suggest the presence of common shopper
subgroups across the customer bases of traditional and online retail
formats.
Research method
Instrument design and pre-tests
An initial qualitative investigation was undertaken to identify
online consumers’ attitudes toward shopping on the internet,
their shopping motivations, and e-store attribute importance
issues relevant to shopping online. Results from the qualitative
study, in combination with a review of the existing literature
(i.e., Alba et al. 1997; Burke 1997; Lynch and Ariely 2000;
Novak, Hoffman, and Yung 2000; Peterson, Balasubramanian,
and Bronnenberg 1997; Syzmanski and Hise 2000), were pre-
tested, ultimately resulting in the final instrument used in this
study.
First, in order to gain a deeper understanding of the online
shopping phenomenon from the customer’s perspective, depth
interviews were conducted with consumers who had actually
shopped for and purchased items online. This phase of the instru-
ment design process helped validate shopping motivations and
attribute importance items suggested by previous research while
also revealing new items unique to the online environment.
Respondents were recruited on a referral basis—
undergraduate students were asked to provide names of
108 J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115
friends and family members who would be willing to partici-
pate in an in-depth interview. An attempt was made to interview
a diverse sample of shoppers with regards to age, occupation,
gender, income, and reasons for shopping online. The final
sample included 64 men and 41 women, ranging from 18 to
66 years of age. A variety of occupations and income levels
were represented. The interviews began with an explanation
of the study’s objectives. The participants were then asked to
think about online shopping in general and to describe reasons
why they shop online, the benefits they received from online
shopping, and e-store attributes that they considered important
when shopping online.
The qualitative data were analyzed following the guidelines
suggested by Lincoln and Guba (1985). After the interviews
were completed, they were transcribed and the transcriptions
were read thoroughly many times. Recurring themes, ideas, and
reasons were identified, and a categorizing process developed by
Lincoln and Guba (1985), which involved sorting “units” into
categories based on similar characteristics, was then employed.
After several iterations, the recurring themes, ideas, and reasons
were grouped into categories of motivations for online shopping
and e-store attribute importance.
The overall themes that emerged from the analysis of the
depth interview data relating to online shopping motivations
included the ability to easily search, increased product selection
and product availability, overall shopping convenience, price
and bargain hunting, shopping for entertainment and escape,
social shopping, trend shopping (keeping up with trends and
seeing what is new), and avoiding regular shopping. The over-
all themes that emerged relating to online attribute importance
included the importance of an online store’s product selection,
the website’s convenience, prices at the online store, site features
such as entertainment offerings, security, and customer service.
The items generated from the interviews were then combined
with those identified by past research studies. The online shop-
ping motivation scale items were derived from both the themes
from the qualitative research and past research on traditional
and online shopping. Similarly, the attribute importance items
were generated from the themes uncovered during the depth
interviews and a review of relevant literature. All items were
measured on a seven-point scale. Responses for the shopping
motivation items ranged from “provides me no satisfaction at
all” (1) to “provides me a great deal of satisfaction” (7). The
attribute items were measured on a scale ranging from “not at
all important” (1) to “extremely important” (7).
The combined items were then evaluated for content and
face validity by the authors as well as several marketing fac-
ulty members and redundancies were eliminated. The resulting
instrument was subsequently subjected to three rounds of pre-
testing for item reduction and scale purification purposes. This
process consisted of an initial empirical reduction of the item
pool using exploratory factor analysis. In addition, substantive
as well as empirical considerations were employed throughout
the scale purification process.
A total of 602 respondents (identified employing a snowball
approach) participated in the pre-tests that resulted in the final
instrument, which contained items that measured the follow-
ing constructs: (a) e-store attribute importance (23 items) and
(b) online shopping motivations (33 items). A list of the main
constructs and the measures used in this study along with the
coefficient alphas are provided in Appendix A.
Online data collection
Data was collected for this study using an online survey
administered to a web panel. The design and formatting of the
online survey was done based on the advice of the project man-
agers at Greenfield Online, a recognized pioneer in online mar-
keting research with a national online consumer panel composed
of over 1.7 million households. Prizes and contests are typically
used as incentives for participation; a $500 lottery incentive was
offered for this study. Members of the Greenfield Online panel
can be invited to participate in two ways: through targeted emails
based upon desired customer profiles or by accessing the open
“Take a Survey” page where all non-targeted surveys are listed.
In an effort to reach a broad cross-section of the online consumer
panel, the second option was utilized for this research.
An online consumer panel was chosen for this study over
broadcast email, mail survey, mall intercept, or random digit
dialing for several reasons. First, the most appropriate and log-
ical manner in which to identify and study online consumers is
through an online approach (Szymanski and Hise 2000). Second,
just as the data for many of the better shopper typology studies
focusing on traditional retail formats were gathered on site, to
be consistent, data concerning online shoppers were similarly
gathered in an online environment. Finally, online surveys offer
the ability to reach a large number of respondents, while also
allowing the researcher to avoid interviewer bias. In addition,
because the surveys are voluntarily completed during leisure
time, non-cooperation problems are minimized.
The survey was posted on a Thursday and was made avail-
able for respondents to participate until the following Tuesday.
This gave potential participants ample opportunity to log-in and
take the survey at their convenience during both weekday and
weekend times, and clear instructions as to the format of the sur-
vey and the process of responding to the survey were provided.
Respondents were informed that the survey was for academic
research purposes and were assured of confidentiality. Since
it was an “open-area study,” (i.e., the survey was posted in a
pre-specified area accessible to all registered members of the
Greenfield Online web panel) users were then able to volun-
tarily opt-in to take the survey. Therefore, it is not possible to
obtain a response rate. A total of 3,161 completed responses
were obtained at the end of the data collection period, and data
cleaning revealed 3,059 usable responses.
Data analysis and results
A preliminary analysis of the data revealed ample variance
in the responses for all items measured. The demographic pro-
file of the respondents to this study suggested that participants
were predominantly women (66.6%), with nearly 30% between
the ages of 25 and 34 years. However, other age groups were
also well-represented with 16% between 18 and 24 years, 24%
J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115 109
between 35 and 44 years, 17% between 45 and 54 years, and
10% being 55 or older. The ranges of income levels were 24%
under $30,000 per year, 28% between $30,000 and $49,999,
21% between $50,000 and $74,999, 9% between $75,000 and
$99,999, and some 6% making $100,000 per year or more.
Online shopping motivation dimensions
The 33 items used to measure online shopping motiva-
tions were used in exploratory factor analysis to assess their
dimensionality, factor structure, and measurement properties.
A scree-plot of the eigenvalues indicated a seven-factor solu-
tion (the results of the factor analyses can be obtained from
the authors). A confirmatory factor analysis using LISREL
(Anderson and Gerbing 1988; Fornell and Larcker 1981)was
employed to further assess the factor structure. All items
loaded highly on their intended construct. Although the chi-
square statistic was significant (χ2= 4428.92, df = 474, p< .001),
other fit statistics indicated an acceptable measurement model
(GFI = .90, AGFI = .89, CFI = .93, RMSEA = .05).
Items loading on the first factor, “Web Shopping Conve-
nience,” refer to attributes such as shopping from home, one-stop
shopping, completing shopping tasks quickly, avoiding regu-
lar shopping (i.e., having to deal with salespeople, standing in
line, traveling from store to store, and crowds). The second fac-
tor, “Online Bidding/Haggling,” relates to bargaining on price
in an online auction, being the winner in an auction, haggling
over price, and submitting bids. Items loading on the third fac-
tor, “Role Enactment,” involve looking for deals, hunting for
bargains, and comparison-shopping. The fourth factor, “Avant-
gardism” refers to keeping up with trends, shopping for new
products, and creating a new image. The fifth factor, “Affilia-
tion,” relates to interacting with other online shoppers. The sixth
factor, “Stimulation,” involves interacting with interesting web-
sites. Finally, the seventh factor, “Personalized Services,” refers
to being personally notified of new products or special deals.
e-Store attribute importance dimensions
Likewise, the 23 items used to measure e-store attribute
importance were used in an exploratory factor analysis to assess
their dimensionality, factor structure, and measurement prop-
erties. A scree-plot of the eigenvalues from the first sample
indicated a six-factor solution (the results of the factor analy-
sis can be obtained by request from the authors). Once again,
a confirmatory factor analysis using LISREL was employed to
further assess the factor structure and all items loaded highly on
their intended construct. The chi-square statistic was significant
(χ2= 1867.52, df = 215, p< .001). However, other fit statis-
tics indicated an acceptable measurement model (GFI = .94,
AGFI = .93, CFI = .95, RMSEA = .05).
The first factor, “e-Store Essentials,” relates to site attributes
such as safety, security, order confirmation, shipping costs,
timely deliveries, as well as the ease of ordering, paying for,
and returning merchandise purchased online. The second fac-
tor is labeled “Offline Presence” because the items loading on
it refer to whether the website also has a physical “bricks and
mortar” location and the ability to return merchandise purchased
online to a physical store. The third factor, “Price Orientation,”
comprises items that involve the availability of special deals,
the frequency of sales and specials, and notification of sales or
specials. The fourth factor, “Website Attractiveness,” relates to
attributes such as the appearance and design of the website. Items
loading on the fifth factor, “Merchandise Variety,” relate to the
variety of products offered by the site, brand names available,
and the availability of new products. Finally, the sixth factor,
which refers to the certification of the website by watchdog
organizations or the Better Business Bureau, is termed “Web
Security/Certification.”
Online shopping motivation and e-store attribute
importance based shopper typologies
Two sets of shopper subgroups were formed using respon-
dents’ ratings of the seven motivation dimensions and their
ratings of the six attribute importance factors. In each case, shop-
per clusters were formed based on standardized factor scores
using a multi-step cluster analysis using Ward’s method. In
the case of shopper segments formed using online motivation
dimensions, an examination of the changes in the root-mean-
square standard deviation (RMSSTD), semipartial R-squared
(SPR), R-squares (RS) and distance between two clusters indi-
cated a seven-cluster solution. Then, a K-means clustering
procedure with the initial seeds provided by the hierarchical
analysis solution was performed to obtain the final clusters.
The seven shopper subgroups obtained from the current study
using online shopping motivation dimensions were termed as:
(a) interactive, (b) destination, (c) apathetic, (d) e-window shop-
per, (e) basic, (f) bargain seekers, and (g) shopping enthusiast,
and are presented in Table 1.
Likewise, using similar procedure as above, the respondents
were classified into shopper subgroups based on their ratings of
the six attribute importance factors. In this case, a six-cluster
solution was found to be most acceptable that includes: (a) des-
tination, (b) basic, (c) risk averse, (d) apathetic, (e) shopping
enthusiast, and (f) bargain seekers. Table 2 shows the results of
this analysis.
It is important to note that the pairwise cluster validation
(Dant and Gundlach 1998) performed to investigate the
uniqueness and stability of the clusters revealed that all seven
of the motivation factors and all six of the attribute factors
were significant in distinguishing among the clusters. This is
evidence of the validity of the findings. A pairwise analysis of
the differences between the clusters performed to determine the
number and percentage of significantly different pairs revealed
that, for both motivation and attribute based cluster profiles,
on average, 85% of the individual pairwise differences were
significant at p< .05. This suggests that the clusters obtained
using the seven online shopping motivational dimensions and
six e-store attribute importance dimensions are both unique and
stable.
Interestingly, five of the shopper subgroups (apathetic, basic,
bargain seekers, destination, and shopping enthusiast) were
common to both online shopping motivation and e-store attribute
110 J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115
Table 1
Cluster centroids based on online shopping motivation dimensions.
MotivationaCluster meansbF-value Sig.
Interactive Destination Apathetic e-Window
shopper
Basic Bargain
seekers
Shopping
enthusiast
Role enactmenti–ws .53 .07 −.72 .48 −1.41 .67 .17 284.79 .0001
Online bidding/hagglingi–bs,i–d,d–a,a–bs .19 .05 −.03 −.57 −.20 .07 .39 28.50 .0001
Web shopping conveniencebs–se,bs–d,and se–d .10 .24 −1.41 −.20 .83 .39 .27 209.46 .0001
Avant-gardismws–b −.64 1.47 .00 −.46 −.51 −.29 .36 312.70 .0001
Affiliationbs–b,bs–ws −.52 −.79 .23 −.26 −.27 −.18 1.62 419.78 .0001
Stimulationi–a,i–b,d–se −.21 .24 −.35 1.09 −.14 −1.00 .33 190.88 .0001
Personalized servicesd–a,a–b 1.10 .11 −.07 −.61 −.16 −.93 .29 219.50 .0001
Cluster size 431 382 414 376 321 356 419
Percentage of respondents 16.58% 14.70% 15.93% 14.47% 12.35% 13.70% 16.12%
aAll cluster means are significant at the .001 level. All pairwise comparisons are significant at the .05 level, except those mentioned. For example, with respect to
the role enactment factor, all seven clusters are significantly different from each other except for the “Interactive–E-window shopper” (i–ws) cluster pair.
bThe values represent mean factor scores. The original items were measured on a 1–7 scale (1 – provides me no satisfaction at all; 7 – provides me a great deal of
satisfaction).
importance based typologies. Further, these same five shopper
subgroups are remarkably consistent to the traditional shopper
subgroups found by past research (see Table 3).
Two and one additional online shopper clusters were also
found using online shopping motivations and e-store attribute
importance dimensions, respectively. The two motivation based
clusters that are unique to the online format are interactive
shoppers and e-window shoppers.Interactive shoppers scored
highest on both the personalized services and online bid-
ding/haggling dimensions. In addition, these shoppers exhibited
a high degree of individualism although they are not necessar-
ily the trendiest consumers (i.e., low scores on Affiliation and
Avant-gardism). Interactive shoppers do not feel a strong need
to share information with other shoppers and appear to be more
mature (with the highest percentage of respondents in the 35–44
years age range). The second unique cluster is e-window shop-
pers. These shoppers are predominantly driven by Stimulation
and are motivated to visit interesting web sites or to simply surf
the internet. E-window shoppers exhibit the lowest score on the
online bidding/haggling factor, supporting the profile of a curi-
ous shopper more interested in seeing what is out there than
negotiating to obtain the lowest possible price.
The one shopper subgroup, obtained using e-store attribute
importance factors, that is unique to the online environment
exhibits the highest score on the offline presence and web
security/certification factors. In addition, this group holds
price orientation as the least important—evidence of a more
skeptical consumer. Therefore, this group is termed risk
averse—reflecting the subgroup’s preference for physical stores
and concern regarding security issues.
Table 4 presents a comparison of the shopper subgroups
obtained in this study to those found in past research addressing
online shopping. Interestingly, when compared to the traditional
formats in Table 3, we find more differences than similarities
among online shopper typologies.
Although our knowledge of online retail patronage behavior
has grown immensely due to these studies, a lack of common
measures applied across online shopping studies has led to a
vast array of shopper typologies that are neither comparable nor
generalizable. As can be seen from the table, the current study
Table 2
Cluster centroids based on e-store attribute importance dimensions.
Attribute importanceaCluster meansbF-Value Sig.
Destination Basic Risk averse Apathetic Shopping
enthusiast
Bargain
seekers
e-store essentialsse–bs .16 .50 .39 −2.83 .02 .04 237.78 .0001
Offline presencera–se,bs–a −1.12 −.52 .76 −.17 .65 −.11 409.10 .0001
Price orientationse–b −.57 .43 −1.19 −.49 .39 .67 318.78 .0001
Website attractivenessb–se, a–b .40 .14 −.53 −.19 .62 −1.17 327.78 .0001
Merchandise varietyd–bs,ra–a .54 −1.49 −.25 −.53 .29 .49 377.01 .0001
Web security/certificationd–ra,d–se,b–se,b–a,ra–se .10 −.19 .16 −.10 .05 −.14 8.06 .0001
Cluster size 520 399 396 156 881 486
Percentage of respondents 18.32% 14.06% 13.95% 5.50% 31.04% 17.12%
aAll cluster means are significant at the .001 level. All pairwise comparisons are significant at the .05 level, except those mentioned. For example, with respect to
the e-store essentials factor, all six clusters are significantly different from each other except for the “Shopping Enthusiasts–Bargain Seeker” (se-bs) cluster pair.
bThe values represent mean factor scores. The original items were measured on a 1–7 scale (1 – provides me no satisfaction at all; 7 – provides me a great deal of
satisfaction).
J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115 111
Table 3
A comparison of shopper typologies common between current study and extant literature.
Current study findings (obtained using online
shopping motivations and e-store attribute
importance)
Equivalent typologies from past research on traditional retail formats
Mall/store attributes Motivations AIO statements Shopping behaviors
Apathetic shoppers
Characterized by a lack of strong motivation on
any shopping dimension and consistent low
ratings on attribute importance.
Apathetic:Apathetic:Apathetic:Minimalists:
Darden and Ashton (1975) Stone (1954) Darden and Reynolds (1971) Bloch et al. (1994)
Williams et al. (1978) Westbrook and Black (1985)
Lumkin (1985) Ganesh, Reynolds, and Luckett (2007) Reluctant shopper:
Reynolds, Ganesh, and Luckett (2002) Stoltman (1995)
Ganesh, Reynolds, and Luckett (2007)
Shopping enthusiasts:
Characterized by high values/ratings on all
motivational dimensions and attribute
importance measures.
Recreational:Recreational:Personalizing:Recreational:
Bellenger, Robertson, and Greenberg
(1977)
Bellenger and Korgaonkar (1980) Darden and Reynolds (1971) Stephenson and Willet (1969)
Karande and Ganesh (2000) Process involved:
Williams et al. (1978) Westbrook and Black (1985) Enthusiasts:
Enthusiasts:Ethusiasts:Bloch et al. (1994)
Reynolds, Ganesh, and Luckett (2002) Arnold and Reynolds (2003)
Ganesh, Reynolds, and Luckett (2007) Ganesh, Reynolds, and Luckett (2007)
Destination shoppers
Motivated to keep up with trends and to create a
new image (Avant-gardism motivation
dimension) and by merchandise variety and
website attractiveness.
Pliable store loyalists:Brand conscious:Store loyal:Store loyal:
Rothe and Lamont (1973) Korgaonkar (1984) Moschis (1976) Stephenson and Willet (1969)
Destination:Destination:
Reynolds, Ganesh, and Luckett (2002) Ganesh, Reynolds, and Luckett (2007)
Ganesh, Reynolds, and Luckett (2007)
Basic shoppers
Task oriented shoppers motivated by web
shopping convenience dimension and e-store
essentials. Not interested in merchandise variety.
Basic/convenience:Economic/convenience:Convenience:
Darden and Ashton (1975) Bellenger and Korgaonkar (1980) Stephenson and Willet (1969)
Williams et al. (1978) Korgaonkar (1984) Traditionalist:
Bellenger, Robertson, and Greenberg
(1977)
Ganesh, Reynolds, and Luckett (2007) Bloch et al. (1994)
Reynolds, Ganesh, and Luckett (2002) Choice optimizing:
Ganesh, Reynolds, and Luckett (2007) Westbrook and Black (1985)
Bargain seekers
Price-oriented shoppers who enjoy hunting for
and finding bargains. Seem to be more proactive
in search and less interested in waiting to being
informed about alternatives on the Internet.
Low price:Economic:Economic:Price/bargain conscious:
Williams et al. (1978) Stone (1954) Darden and Reynolds (1971) Stephenson and Willet (1969)
Economic/convenience:Westbrook and Black (1985)
Bellenger, Robertson, and Greenberg
(1977)
Price oriented:Specials shopper:
Lumpkin (1985) Korgaonkar (1984) Moschis (1976)
Deal prone:Providers:
Karande and Ganesh (2000) Arnold and Reynolds (2003)
Bargain seekers:Bargain seekers:
Ganesh, Reynolds, and Luckett (2007) Ganesh, Reynolds, and Luckett (2007)
112 J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115
Table 4
A comparison of online shopper typologies.
Research study Online context Typology base Methodology and
# of segments
Cluster names
Current study Online shoppers Online shopping motivations Cluster analysis
7 segments
Interactive, destination, apathetic,
e-window shopper, basic, bargain
seekers, and shopping enthusiasts
Current study Online shoppers e-Store attribute importance Cluster analysis
6 segments
Destination, apathetic, basic, bargain
seekers, shopping enthusiasts, and
risk averse
Kau, Tang, and Ghose
(2003)
Internet users Behavioral segmentation Cluster analysis
6 segments
On–off, comparative, traditional,
dual, e-laggard, and information
surfer
Swinyard and Smith
(2003)
Online shoppers Internet lifestyles Cluster analysis
4 segments
Shopping lovers, adventuresome
explorers, suspicious learners, and
business users
Bhatnagar and Ghose
(2004)
Electronics, legal
service and music
shoppers
Benefit segmentation Latent class
analysis
4 segments
S1 (Hi product risk, Hi security risk),
S2 (moderate product risk), and S3
(low product risk)
Rohm and Swaminathan
(2004)
Online grocery
shopping
Shopping motivations Cluster analysis
4 segments
Convenience, variety seeker,
balanced, and store-oriented buyer
Brengman et al. (2005) Internet users Web usage related lifestyles Cluster analysis
4 segments
Tentative shoppers, suspicious
learners, shopping lovers, and
business users
Barnes et al. (2007) Online shoppers Psychographic profile Cluster analysis
3 segments
Risk averse, open minded, and
reserved information seekers
Jayawardhena, Wright,
and Dennis (2007)
Internet users Purchase orientation Cluster analysis
5 segments
Active, price sensitives, discerning,
loyal, and convenience shoppers
is the first to use two most widely used measures – online shop-
ping motivations and e-store attribute importance – to develop
and compare online shopper typologies. Also the current study
results provide a comprehensive comparison of shopper sub-
groups between traditional and online formats.
Discussion and implications
The results of this study suggest the following:
(a) Using shopping motivation measures, seven shopper seg-
ments are found for online stores.
(b) Using e-store attribute importance measures, six segments
are found for online stores.
(c) The five common shopper types found in previous research
that focused on traditional retail formats are also present in
the online environment.
(d) There are three shopper subgroups that are unique to the
online shopping environment—e-window shoppers, inter-
active shoppers, and risk averse shoppers.
Overall, the findings support the study’s research proposition
and offer some interesting implications to academics and prac-
titioners. The current study advances our knowledge in the area
of online retailing by: (a) testing the Big Middle Theory in an
online context; (b) designing, testing, and validating a scale of
online shopping motivations, and e-store attribute importance;
(c) identifying the shopper subgroups that are present in an
online context; and (d) providing a means of comparing tra-
ditional shopper types with online shopper types. In total, based
on both attribute importance and online shopping motivations,
eight shopper types were identified at e-stores, five of which are
similar to those found at traditional formats, and three of them
that are unique to the web.
Most of the existing research on online shopping has predom-
inantly focused on differences in behavior exhibited by online
shoppers when compared to traditional retail shoppers (e.g.,
Alba et al. 1997; Li, Ko, and Russell 1999; Mathwick, Malhotra,
and Rigdon 2001; Winer et al. 1997) and on reasons why con-
sumers prefer to shop online (e.g., Burke 1997; Li, Ko, and
Russell 1999; Morganosky and Cude 2000; Syzmanski and Hise
2000). Primary factors identified by past research as important
discriminators of online and traditional retail shopping include:
(a) convenience (Chiang and Dholakai, 2003; Donthu and Garcia
1999), (b) perceived risk (Forsythe and Shi 2003; Garbarino and
Strahilevitz 2004; Lee and Tan 2003), (c) ability to search for
information and products (Chiang and Dholakai 2003), and (d)
price (Donthu and Garcia 1999).
The current study, however, shows that online shoppers are
in fact more similar to traditional shoppers than they are dif-
ferent. The prediction of the Big Middle Theory regarding the
existence of a core group of shoppers seeking a consistent set of
retail attributes is supported by the findings here. One implica-
tion of present research is that it identifies factors that serve as
a common denominator and not a differentiator between online
and offline shoppers. Businesses with both online and offline
presence are thus provided with parsimonious tools to address
common bases of shoppers. This will help maintain and pro-
mote a unified brand image while undertaking more efficient
segment strategies. The presence of similar shopping motivation
dimensions and attribute importance factors in both online and
traditional formats suggests that these are the core factors that
J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115 113
influence shopping irrespective of formats. Any new or exist-
ing competition in the retail landscape needs to address these
common issues in sufficient depth so as to be able to attract and
retain the common shopper types.
Another interesting finding of this study is the presence of
motivation dimensions, attribute importance factors, and the
associated shopper types that are unique to the online format.
These factors redefine the competitive landscape and add an
element of sustainable advantage to online stores. For exam-
ple, although earlier we drew a parallel between web shopping
convenience and Choice Optimization, the items that comprise
the web shopping convenience dimension (such as “Shopping
from home,” “Shopping any time of day or night,” “Avoiding
crowds,” “Not having to travel from store to store”) seem to
redefine the concept of convenience and shopping optimiza-
tion. These are items that the traditional formats cannot easily
replicate.
The findings of this study suggest that, among possible others,
points of distinction for e-stores are (a) interactivity and the
ability to offer personalized services; (b) their ability to redefine
convenience; and (c) their ability to control their website content.
For example, the online unit of Ritz Cameras, Inc. and the online
retailer Horchow both use live online chat to give customers
immediate advice and feedback regarding products featured at
their online stores. Nike and Nintendo offer several interesting
and captivating games and challenges at their website that can
potentially induce flow. Pottery Barn offers a “floor plan” layout
on its Web site that allows shoppers to virtually browse from
room to room to view products.
From a theoretical point of view it is important to offer
an explanation for the discovery of differences in shopper
typologies between consumers of traditional versus online retail
formats. The theory of disruptive innovation offers a strong
foundation for this. A disruptive innovation is defined as an
advancement (e.g., social, technological or both) which allows
Innovative newcomers to leverage technology to alter tradi-
tional business models. These disruptive innovations offer new
business models that fundamentally alter the economics of an
industry. When applied to the retailing industry, the Internet
has been characterized as the most recent disruptive innovation
(Christensen and Tedlow 2000).
Given the ever-present tradeoffs between content, speed, and
ease of navigation, the findings here provide some preliminary
indications as to (a) which type of products and which type
of customers would benefit from a content-rich site; (b) use of
personalization services (e.g., Amazon.com) real time customer
service agents (e.g., Lands End, Ritz Camera, Horchow) or both
as a competitive tool; and (c) the ability to attract and keep first
time buyers and other Risk Averse shoppers by addressing safety
and security concerns and providing physical store contacts for
returns.
Limitations and directions for future research
This study has attempted to shed some light on the attitudes
and motivations of online shoppers and to offer guidelines for
retail managers. However, one must take caution in that there
are several limitations associated with this study. The first limi-
tation of the current study is its cross-sectional nature. We know
very little about how the motivations of a single shopper change.
It would be advantageous to see how an online shopper’s moti-
vations change over time, and across situations. Although only
longitudinal studies can yield this level of knowledge, this sort
of endeavor would be extremely useful and enlightening in both
a theoretical and a practical sense.
Another direction for future research could trace the evolution
of the three shopper subgroups that are distinct to the online
environment to examine how they change characteristics over
time. Most importantly, consumers’ patronage behavior needs
to be examined from a multidimensional perspective. Although
this study supports the findings of other recent research regarding
the emergence of common shopper subgroups across formats,
future research should attempt to examine this issue based on
responses from shoppers who shop both at traditional and online
formats.
The Apathetic shoppers found in this study would also be an
interesting topic for future research. According to our findings,
these shoppers score low on Convenience. Perhaps these con-
sumers are very active traditional shoppers who like to touch and
feel merchandise and do not value the convenience provided by
online stores. This idea was not investigated further in the cur-
rent study and we hope that others might explore this notion in
future studies.
Finally, it would be interesting to study the shopping behav-
ior and patterns of consumers’ who shop at a particular retailer’s
brick and mortar and online stores to understand better the com-
petitive dynamics and channel aspects of these outlets.
Acknowledgements
The authors thank Dr. Ronald Michaels and the Department
of Marketing at UCF for their research support, Greenfield
Online for their assistance in data collection, and Bill Black,
Raj Echambadi, and Chris White for their insightful comments
and suggestions.
Appendix A. Measures of constructs
Measures Coefficient Alpha
Shopping motivation (All items were measured on a
scale where: 1 – provides me no satisfaction at all; 7 –
provides me a great deal of satisfaction)
Web shopping convenience .93
Shopping from my home
Avoiding regular shopping
Avoiding having to deal with salespeople
Having products delivered right to my home
Shopping any time of day or night
Avoiding standing in line
One-stop shopping
Avoiding crowds
Completing my shopping tasks quickly
Not having to travel from store to store
Finding exactly what I want in the least amount of
time
114 J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115
Measures Coefficient Alpha
Online bidding/haggling .89
Bargaining over the price of an item through an
online auction
Being the winning bidder in an online auction
Haggling over the price of a product
Submitting online bids for products
Bargaining with a website on the price of a product
Role enactment .88
Looking for great deals
Hunting for and finding a real bargain
Comparison-shopping to find the best product for my
money
Avant-gardism .88
Keeping up with new trends
Getting to create a new “image” for myself or my
home
Being one of the first to have the latest in new
fashions or new products
Keeping up with the newest fashions
Affiliation .90
Chatting with other consumers who share my own
interests
Finding other consumers who are interested in the
same product as I am
Interacting with other Web shoppers
Stimulation .84
Interacting with websites that I am interested in
Seeing interesting websites while shopping
Just looking around at interesting websites
Finding entertaining websites
Personalized services .87
Being notified of new products that interest me
Being alerted to special deals or sales
Having emails sent to me about new products,
upcoming sales events or both
e-Store attributes (All items were measured on a 1–7 scale where:1–not
at all important;7–extremely important)
e-Store essentials .92
Safety/security of site
Confirmation of order/delivery
Ease of ordering
Ease of payment
Ease of returning merchandise
Quality of information
Ease of contacting company
Low-cost shipping and delivery charges
Deliveries are made in a timely manner
Offline presence .89
Website company also has physical store
Physical store for website located nearby
Ability to return purchases to a physical store
Price orientation .80
Special deals
Notices about sales or new products
Frequency of sales or special deals
Website attractiveness .78
Attractiveness of website
Cutting-edge site
Well-designed website
Measures Coefficient Alpha
Merchandise variety .74
Availability of a wide variety of products
Availability of brand-name products
Availability of latest products
Web security/certification .78
Website is certified by an online watchdog
organization
Website is certified by the Better Business Bureau
References
Alba, Joseph, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan
Sawyer and Stacy Wood (1997), “Interactive Home Shopping: Consumer,
Retailer, and Manufacturer Incentives to Participate in Electronic Market-
places,” Journal of Marketing, 61 (July), 38–53.
Anderson, James C. and David W. Gerbing (1988), “Structural Equation
Modeling in Practice: A Review and Recommended Two-step Approach,”
Psychological Bulletin, 103 (3), 411–23.
Arnold, Mark J. and Kristy E. Reynolds (2003), “Hedonic Shopping Motiva-
tions,” Journal of Retailing, 79 (2), 77–96.
Bellenger, N. Danny, Dan H. Robertson and Barnett A. Greenberg (1977),
“Shopping Center Patronage Motives,” Journal of Retailing, 53 (2), 29–38.
Bellenger, N. Danny and Predeep K. Korgaonkar (1980), “Profiling the Recre-
ational Shopper,” Journal of Retailing, 56 (Fall), 77–91.
Bloch, H. Peter, Nancy M. Ridgwayand Scott A. Dawson (1994), “The Shopping
Mall as Consumer Habitat,” Journal of Retailing, 70 (1), 23–42.
Brengman, Malaika, Geuens Maggie, WeijtersBert, Scott M. Smith and William
R. Swinyard (2005), “Segmenting Internet Shoppers Based on their Web-
Usage-Related Lifestyle: A Cross-Cultural Validation,” Journal of Business
Research, 58 (1), 79–88.
Burke, Raymond R. (1997), “Do you see what I see? The Future of Vir-
tual Shopping,” Journal of the Academy of Marketing Science, 25 (Fall),
352–60.
Chiang, Kuan-Pin and Ruby Roy Dholakai (2003), “Factors Driving Consumer
Intention to Shop Online: An Empirical Investigation,” Journal of Consumer
Psychology, 13 (1/2), 177–83.
Christensen, Clayton and Richard S. Tedlow (2000), “Patterns of Disruption in
Retailing,” Harvard Business Review, 78 (1), 42–5.
Dant, Rajiv P. and Gregory T. Gundlach (1998), “The Challenge of Auton-
omy and Dependence in Franchised Channels of Distribution,” Journal of
Business Venturing, 14 (1), 35–67.
Darden, William R. and Fred D. Reynolds (1971), “Shopping Orientations and
Product Usage Rates,” Journal of Marketing Research, 8 (4), 505–8.
Donthu, Naveen and Adriana Garcia (1999), “The Internet Shopper,” Journal of
Advertising Research, (May–June), 52–8.
Evanschitzky, Heiner, Gopalkrishnan R. Iyer, Josef Hesse and Dieter Ahlert
(2004), “E-Satisfaction: A Re-examination,” Journal of Retailing, 80 (3),
239–47.
Fornell, Claes and David F. Larcker (1981), “Evaluating Structural Equation
Models with Unobservable Variables and Measurement Error,” Journal of
Marketing Research, 18 (February), 39–50.
Forsythe, Sandra M. and Bo Shi (2003), “Consumer Patronage and Risk Percep-
tions in Internet Shopping,” Journal of Business Research, 56 (11), 867–75.
Ganesh, Jaishankar, Kristy E. Reynolds and Michael Luckett (2007), “Retail
Patronage Behavior and Shopper Typologies: A Replication and Extension
Using a Multi-Format, Multi-Method Study,” Journal of the Academy of
Marketing Science, 35 (3), 369–81.
Garbarino, Ellen and Michal Strahilevitz (2004), “Gender Differences in the
Perceived Risk of Buying Online and the Effects of Receiving a Site Rec-
ommendation,” Journal of Business Research, 57 (7), 768–74.
J. Ganesh et al. / Journal of Retailing 86 (1, 2010) 106–115 115
Greenfield Online (2006). www.greenfield.com.
Jayawardhena, C., L.T. Wright and C. Dennis (2007), “Consumers Online: Inten-
tions, Orientations and Segmentation,” International Journal of Retail &
Distribution Management, 35 (6), 515–26.
Karande, Kiran and Jaishankar Ganesh (2000), “Who Shops at Factory Out-
lets and Why?: An Exploratory Study,” Journal of Marketing Theory and
Practice, 8 (4), 29–43.
Kau, A.K., Y.E. Tang and S. Ghose (2003), “Typology of Online Shoppers,”
Journal of Consumer Marketing., 20 (2), 139–56.
Korgaonkar, Pradeep K. (1984), “Consumer Shopping Orientations, Non-Store
Retailers, and Consumers’ Patronage Intentions: A Multivariate Investiga-
tion,” Journal of the Academy of Marketing Science, 12 (1), 11–22.
Lee, Khai Sheang and Soo Jiuan Tan(2003), “E-retailing Versus Physical Retail-
ing: A Theoretical Model and Empirical Test of Consumer Choice,” Journal
of Business Research, 56 (11), 877–85.
Levy, Michael, Dhruv Grewal, Peterson S Robert A. and Bob Connolly (2005),
“The Concept of the ‘Big Middle’,” Journal of Retailing, 81 (2), 83–8.
Li, Hairong, Cheng Ko and Martha G. Russell (1999), “The Impact of Per-
ceived Channel Utilities, Shopping Orientations, and Demographics on the
Consumer’s Online Buying Behavior,” Journal of Computer Mediated Com-
munication, (December), 5.
Lincoln, Yvonna S. and Egon G. Guba (1985), in Handbook of Qualitative
Research, Denzin Norman K. and Lincoln Yvonna S., eds. Thousand Oaks,
CA: Sage Publications
Lumpkin, James R. (1985), “Shopping Orientation Segmentation of the Elderly
Consumer,” Journal of the Academy of Marketing Science, 13 (2), 271–89.
Lynch, John G. Jr. and Dan Ariely (2000), “Wine Online: Search Costs Affect
Competition on Price, Quality, and Distribution,” Marketing Science, 19
(Winter), 83–103.
Mathwick, Charla, Naresh Malhotra and Edward Rigdon (2001), “Experiential
Value: Conceptualization Measurement and Application in the Catalog and
Internet Shopping Environment,” Journal of Retailing, 77 (Spring), 39–56.
Morganosky, Michelle A. and Brenda J. Cude (2000), “Consumer Response to
Online Grocery Shopping,” International Journal of Retail and Distribution
Management, 28 (1), 17–26.
Moschis, George P. (1976), “Shopping Orientations and Consumer Uses of
Information,” Journal of Retailing, 52 (Summer), 61–70.
Peterson, Robert A., Sridhar Balasubramanian and Bart J. Bronnenberg (1997),
“Exploring the Implications of the Internet for Consumer Marketing,” Jour-
nal of the Academy of Marketing Science, 25 (Fall), 329–46.
Reynolds, E. Kristy, Jaishankar Ganesh and Michael Luckett (2002), “Tradi-
tional Malls Versus Factory Outlets: A Comparison of Shopper Typologies,”
Journal of Business Research, 55 (9), 687–96.
Rohm, Andrew and Vanitha Swaminathan (2004), “A Typology of Online Shop-
pers Based on Shopping Motivations,” Journal of Business Research, 57 (7),
748–57.
Rothe, James T. and Lawrence M. Lamont (1973), “Purchase Behavior and
Brand Choice Determinants: For National and Private Brand Major Appli-
ances,” Journal of Retailing, 49 (Fall),, 19–33.
Shim, Soyeon, Mary Ann Eastlick, Sherry L. Lotz and Patricia Warrington
(2001), “An Online Prepurchase Intentions Model: The Role of Intention
to Search,” Journal of Retailing, 77 (Fall), 397–416.
Srinivasan, Srini S., Rolph Anderson and Kishore Ponnavolu(2002), “Customer
Loyalty in e-commerce: An Exploration of Its Antecedents and Conse-
quences,” Journal of Retailing, 78 (Spring), 41–50.
Stephenson, Ronald P. and Ronald P. Willet (1969), “Analysis of Consumers’
Retail Patronage Strategies,” in Marketing Involvement in Society and the
Economy, McDonald P. R. ed. Chicago, IL: American Marketing Associa-
tion, 316–22.
Stone, P. Gregory (1954), “City Shoppers and Urban Identification: Observation
on the Social Psychology of City Life,” American Journal of Sociology, 60
(July), 36–45.
Stoltman, Jeffrey (1995), “Shopping at Retail Centers: Forms, Contexts, Moti-
vations and Strategies,” Journal of Shopping Center Research, 2 (2),
113–45.
Swinyard, W.R. and S.M. Smith (2003), “Why People (Don’t) Shop Online: A
Lifestyle Study of the Internet Consumer,” Psychology & Marketing, 20 (7),
567–9.
Syzmanski, David M. and Richard T. Hise (2000), “e-Satisfaction: An Initial
Examination,” Journal of Retailing, 76 (3), 309–22.
Wallace, David W., Joan L. Giese and Jean L. Johnson (2004), “Customer
Retailer Loyalty in the Context of Multiple Channel Strategies,” Journal
of Retailing, 80 (4), 249–63.
Westbrook, Robert A. and William C. Black (1985), “AMotivation-Based Shop-
per Typology,” Journal of Retailing, 61 (Spring), 78–103.
Williams, Robert H., John J. Painter and Herbert R. Nichols (1978), “A Policy-
Oriented Typology of Grocery Shoppers,” Journal of Retailing, 54 (Spring),
27–43.
Winer, Russell, John Deighton, Sunil Gupta, Eric Johnson, Barbara Mellers,
Vicki Morwitz, Thomas O’Gwinn, Arvid Rangaswamy and Alan G. Sawyer
(1997), “Choice in Computer-Mediated Environments,” Marketing Letters,
8 (3), 287–96.
Wolfinbarger, Mary and Mary C. Gilly (2003), “eTailQ: Dimensionalizing,
Measuring and Predicting Retail Quality,” Journal of Retailing, 79 (3),
183–98.
