Journal of Business Research 57 (2004) 748 – 757
A typology of online shoppers based on shopping motivations
Andrew J. Rohma,*, Vanitha Swaminathanb,1
a
b
Department of Marketing, Northeastern University, Boston, MA 02115, USA
Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abstract
This paper develops a typology based upon motivations for shopping online. An analysis of these motives, including online convenience,
physical store orientation (e.g., immediate possession and social contact), information use in planning and shopping, and variety seeking in
the online shopping context, suggests the existence of four shopping types. These four types are labeled convenience shoppers, variety
seekers, balanced buyers, and store-oriented shoppers. The convenience shopper is more motivated by convenience. The variety seeker is
substantially more motivated by variety seeking across retail alternatives and product types and brands than any other shopping type.
Balanced buyers are moderately motivated by convenience and variety seeking. The store-oriented shoppers are more motivated by physical
store orientation (e.g., the desire for immediate possession of goods and social interaction). Shopping types are profiled in terms of
background variables and the propensity to shop online. The results are contrasted with a matched sample of off-line shoppers. Implications
of this typology for theory and practice are discussed.
D 2002 Elsevier Inc. All rights reserved.
1. Introduction
Revenues from online retailing continue to grow. A
recent Forrester Research report forecast that online retail
sales will reach US$269 billion in 2005, from US$45 billion
in 2000 (Dykema, 2000). The growth of online shopping
has generated considerable interest among academic
researchers. In particular, researchers have begun examining
the impact of online shopping environments on consumer
choice (Swaminathan et al., 1999), the role of Internet
shopping as a channel of distribution (Alba et al., 1997),
factors influencing shopping online (Swaminathan et al.,
1999), and the impact of online shopping on price sensitivity (Shankar et al., 1999).
Given the significant growth in online retailing, the
online retailer needs to understand the particular reasons
why consumers choose to shop online. This need is particularly relevant for the increasingly competitive online
grocery retail market, in which numerous national and
regional firms compete among themselves as well as
bricks-and-mortar stores within a relatively static market.
* Corresponding author. Tel.: +1-413-545-5665; fax: +1-413-5453858.
E-mail addresses: [email protected] (A.J. Rohm),
[email protected] (V. Swaminathan).
1
Tel.: + 1-413-545-5665.
0148-2963/$ – see front matter D 2002 Elsevier Inc. All rights reserved.
doi:10.1016/S0148-2963(02)00351-X
The objective of this research is to develop a typology of
online shoppers based on shopping motives. While there is a
rich tradition of shopping typologies developed for store or
catalog settings (Stone, 1954; Stephenson and Willett, 1969;
Darden and Ashton, 1975; Williams et al., 1978; Bellenger
and Korgaonkar, 1980; Westbrook and Black, 1985; Gehrt
and Shim, 1998), there is a paucity of research examining
typologies in the online context. This research makes an
important contribution to the current literature by extending
our knowledge of consumer typologies to the online channel.
From a managerial perspective, online shopping typologies or classification schemes provide the basis for understanding and targeting different groups of consumers. Given
that online retailing has tremendous growth, a typology
specific to this channel will enable us to identify distinct
segments of consumers, thereby enabling retailers to effectively tailor their offerings to these customer types.
The shopping typology developed here is based on the
grocery-shopping context. The grocery-shopping context is
an effective one in which to study consumers and their
shopping motivations for various reasons. First, previous
research (e.g., Darden and Ashton, 1975; Williams et al.,
1978) examines shopping motivations in the grocery context. Therefore, this context allows us to contrast results
obtained in this study with previous research findings.
Second, the purchase cycle for groceries is frequent and a
wide array of goods. Third, although numerous online
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
grocery retailers have struggled to reach profitability, the
potential for growth in the online replenishment channel
remains significant. Anderson Consulting (Buss, 1999) predicts that by the year 2007 almost 20 million people will buy
their groceries and other household goods online, compared
with fewer than 200,000 currently.
Based upon Bunn (1993), we employ a five-step procedure for empirical typology development. The resulting
cluster solution supports and extends current shopping
typologies by differentiating between online grocery consumer types. In order to gain a broader understanding of
shopping motives across retail settings, we conducted a
parallel study of grocery shoppers in the offline, or bricksand-mortar, setting. A cluster analysis of offline shoppers
reveals a unique to the bricks-and-mortar setting.
The paper is structured as follows. First, we review the
literature on shopping motives. Second, we discuss the
sampling frame and data collection procedures. Third, we
present analyses and results. Fourth, we discuss the implications of this research as well as future research directions.
2. Conceptual background
Past shopping typologies have primarily been based on
consumer motives for shopping (e.g., Tauber, 1972; Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985).
Motivation theory (e.g., McGuire, 1974)—which suggests
that human motives, whether cognitive or affective, are
749
primarily geared towards individual gratification and satisfaction—provides the theoretical basis for examining the
underlying reasons for why people shop. Consumers may be
motivated by the ability to implicitly derive a certain set of
utilities by patronizing a given type of shopping setting
(Sarkar et al., 1996). These utilities may include location
(place utility), expanded store hours and quick, efficient
checkout (time utility), and an efficient inventory and
distribution system that enables consumers immediate possession (possession utility) of the goods purchased. The
motivations that underlie extant shopping typologies are
summarized in Table 1.
As can be seen in Table 1, several motives may be used
to classify the online shopper: shopping convenience,
including time savings (e.g., Bellenger and Korgaonkar,
1980; Darden and Ashton, 1975; Eastlick and Feinberg,
1999; Stephenson and Willett, 1969; Westbrook and Black,
1985; Williams et al., 1978); information seeking (e.g.,
Bellenger and Korgaonkar, 1980), social interaction gained
from shopping (e.g., Bellenger and Korgaonkar, 1980;
Westbrook and Black, 1985), and shopping as a recreational experience itself (e.g., Bellenger and Korgaonkar,
1980; Gehrt and Shim, 1998). Additionally, the literature
suggests that the tendency to seek variety (e.g., Raju, 1980;
McAlister and Pessemier, 1982; Menon and Kahn, 1995)
and the desirability of immediate possession (e.g., Alba et
al., 1997) may also be motives for shopping. These six
motives, that help to classify the online shopper, are
examined in greater detail next.
Table 1
Review of the shopping typology literature
Author(s)
Gehrt and
Shim (1998)
Westbrook and
Black (1985)
Bellenger and
Korgaonkar (1980)
Williams et al. (1978)
Darden and
Ashton (1975)
Darden and
Reynolds (1971)
Stephenson and
Willett (1969)
Stone (1954)
Shopping context
mail-order catalogs
urban retail department
stores
mall and shopping
center
retailers
mall-based
shopping centers
nonmall
retail grocery stores
supermarkets
large and small
urban
stores
consumer products
(e.g.,
apparel, shoes)
department stores
Sample and data collection
catalog shoppers
French
surveys
written
adult female shoppers
203
structured personal
interviews
adult shoppers
324
questionnaires
intercept
298 grocery shoppers
interviews
personal
middle-class suburban
116housewives
personal interviews and
written
surveys
167 middle to upper
class
suburban housewives
surveys
written
actual store patronage
and
buying behavior
depth interviews of
female
shoppers
Primary shopping motives
Overall
convenience/
time savings
The shopping
experience
B
B
B
B
B
B
B
B
Information
seeking
B
B
B
B
B
B
B
Social
interaction
B
750
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
2.1. Shopping convenience
Numerous shopping motive studies (Stephenson and
Willett, 1969; Darden and Ashton, 1975; Williams et al.,
1978; Bellenger and Korgaonkar, 1980; Eastlick and Feinberg, 1999) have identified convenience as a distinct motive
for store choice in the offline setting. Bellenger and Korgaonkar (1980) characterized the convenience shopper as
selecting stores based upon time or effort savings. Recent
research (Swaminathan et al., 1999) suggests that convenience is an important factor, particularly because location
becomes irrelevant in the online shopping context. The
online shopper may be motivated by the convenience of
placing orders online at home or at the office any time of
day. Consistent with past research regarding time and effort
savings (Bellenger and Korgaonkar, 1980; Eastlick and
Feinberg, 1999), we consider time and effort savings as a
part of the overall shopping convenience construct.
2.2. Information seeking
Bellenger and Korgaonkar (1980) propose that the ability
to seek and gather information in a retail setting is a
shopping motive in the offline context. Online shopping
offers an infrastructure by which the consumer is able to
search, compare, and access information much more easily
and at deeper levels than within the bricks-and-mortar retail
structure (Alba et al., 1997; Lynch and Ariely, 2000). This
concept of information as adding value to the retail experience is supported by Hoffman and Novak (1996), who
suggest that the Internet offers not only a wide variety of
information, it offers the capability to deliver specific
information tailored to the needs of the consumer.
2.3. Immediate possession
Sheth (1983) and Shaw (1994) discuss the utility derived
from the possession of goods or services. Certain consumers
will demand instantaneous delivery of products or services
and will patronize those retailers able to provide immediate
possession. In an analysis of competition between direct
marketers and conventional retailers, Balasubramanian
(1998) suggests that direct marketers can reduce consumer
resistance to catalog or Internet purchases by reducing
delivery time. For these reasons, consumers motivated by
immediate possession may choose to shop within a conventional retail store format rather than in the online context.
2.4. Social interaction
The concept of retail social interaction as a source of
shopping motivation stems from work by Tauber (1972)
positing that numerous social motives help to influence
shopping behavior. These motives include social interaction,
reference group affiliation, and communicating with others
having similar interests. Alba et al. (1997) suggest that
desire for social interaction plays a role in determining the
choice of retail format, e.g., the store, catalog, or online
setting. Past research suggests that consumers motivated by
social interaction may choose to shop within a conventional
retail store format as opposed to the online context.
2.5. The retail shopping experience
The retail shopping experience is often considered a
shopping motive unto itself (e.g., Bellenger and Korgaonkar, 1980; Dawson et al., 1990; Bendapudi and Berry,
1997). The recreational shopper has been defined in the
literature as one who enjoys shopping as a leisure-based
activity, spends more time per shopping trip on average,
considers store décor an important patronage decision, and
is more impulsive, e.g., tends to make unplanned purchases
(Bellenger and Korgaonkar, 1980). Tauber (1972) identified
a variety of psychosocial needs related to shopping behavior, one of which was that certain shoppers received sensory
stimulation from the retail environment. According to Bellenger and Korgaonkar (1980), this type of shopper is
motivated by the process and enjoyment of the shopping
experience itself, independent of product-specific or other
task-directed objectives.
Online retailers, in general, may find it difficult to
replicate the sensory effects and product-trial experiences
available to the consumer in a physical store setting.
Therefore, similar to the catalog setting, online retailers
may find it more challenging to attract recreational shoppers
who may be less predisposed to shopping online.
2.6. Variety seeking
Although variety-seeking research is limited in the online
setting, previous research has suggested that variety-seeking
or varied behavior stems from intrapersonal or interpersonal
motives (McAlister and Pessemier, 1982). Consumer behavior research (Raju, 1980; Menon and Kahn, 1995) has
linked variety seeking to the presence of an ideal level of
stimulation (e.g., an intrapersonal motive for novelty, complexity, or change), whereby a consumer’s optimal stimulation level determines their degree of exploratory and
variety-seeking behavior in situations such as shopping.
The ability to comparison shop may increase variety-seeking behavior in the online context; therefore, variety seeking
is likely to be a significant motive in the online context.
In summary, a typology based upon these items will thus
capture the mix of motives influencing the various types of
online consumers. The sampling frame, data collection
procedure, and construct measures are described next.
3. Method
This section describes a study undertaken to better
understand the online shopper. It also describes an associ-
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
ated study of offline shopping motives conducted in the
grocery context in order to compare online and offline
shopping motives and consumer types.
3.1. Online sample
The research sample employed in this study consists of
both active and lapsed customers of an online grocery
retailer. Although the respondents are limited to a groceryshopping context, the random sample employed in this
study includes consumers across various purchase frequencies. Further, the scale items examine general shopping
motivations and utilities and thus apply to general shopping
contexts across retail channels. These items seek to help
better illustrate customer motivations for online shopping
in general, as well as within the online grocery setting.
Appendix A lists these items.
The unit of analysis in this study is the individual
online grocery consumer. A random sample of 1000
potential respondents was drawn from the customer database of an online grocery retailer based in the northeast
United States. To ensure appropriate response rates, a twostep process was followed. First, prenotification cards
introducing the study and emphasizing the importance of
the respondent’s involvement preceded the mailing of the
written surveys by one week. Second, the surveys were
mailed with a cover letter thanking the consumer for their
participation along with preaddressed and postage-paid
envelopes. This step also included an incentive in the
form of credit towards their next grocery order placed
with the focal online grocer upon return of the completed
survey.
Of the 1000 mailed surveys, a total of 429 responses
were received from online shoppers. Of these, 17 were
eliminated based on incomplete responses (i.e., several
responses had missing values). This resulted in a usable
sample of 412 responses. Eleven percent of this usable
online sample (n = 46) were considered lapsed customers
since they had not shopped with the focal online grocery
retailer during their last 10 shopping trips.
3.2. Offline sample
To rule out the possibility that the specific characteristics of the sample were responsible for the results
obtained, and to gain a better understanding of motivations
such as time savings and recreation in the offline context,
another survey of a matched sample of grocery customers
within the bricks-and-mortar setting was undertaken. This
survey was mailed to approximately 350 grocery customers. Respondents were randomly chosen from a mailing list
representing the zip codes from which the original online
respondents were drawn. One hundred and three completed
questionnaires were returned from this mailing, resulting in
a 29% response rate. In order to ensure that the respondents were comparable to the online sample, the respond-
751
ents were matched in terms of age, education, and income.
Chi-square tests indicated no significant differences among
age, income, and education distributions between the offline and online samples at the 5% significance level. Table
2 outlines the online and offline respondent demographics
by gender, age, education, income, and household size, as
well as the propensity to shop online within the online
sample.
In order to test for nonresponse bias, the early and late
responses were contrasted in terms of demographic variables and responses to the key variables of interest. None of
the differences in terms of demographic variables and
responses to key variables of interest emerged significant.
Therefore, the responses were pooled.
Table 2
Respondent demographics online and offline shoppers
Demographic profile
Percentage of sample
Online shoppers
Offline shoppers
Gender
Female
Male
72
28
75
25
Age
Less than 30 years old
Between 30 and 49 years
50 years and over
27
63
10
19
59
21
Education
Less than high school graduate
High school graduate or equivalent
Some college, no degree
College graduate
Postgraduate
0
2
7
33
58
3
5
16
41
35
Income
Less than US$15,000
US$15,000 – 29,999
US$30,000 – 49,999
US$50,000 – 74,999
US$75,000 – 99,999
US$100,000 +
Do not know
3
5
13
19
15
35
10
4
9
15
14
10
36
12
Household size (including all adults and children)
1 person
19
2 persons
37
3 persons
20
4 – 5 persons
21
6 or more persons
2
31
34
19
16
0
Propensity to shop for groceries online
High propensitya
31
Moderate propensityb
34
35
Low Propensityc
a
70% or more of respondent’s online grocery shopping is with focal
online retailer within last 10 shopping trips.
b
Between 30% and 60% of online grocery shopping is with focal
online retailer within last 10 shopping trips.
c
20% or less of online grocery shopping is with focal online retailer
within last 10 shopping trips.
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A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
3.3. Measures
Measures for the online study were developed following
standard scale development procedures (Churchill, 1979).
Multi-item scales were generated based upon previous
measures, a review of the relevant literature, and preliminary interviews. The written survey contained 31 items on a
seven-point Likert scale anchored by strongly agree and
strongly disagree. These items examined shopping motives
that were considered salient to the online context, including
convenience, information seeking, immediate possession,
social interaction, the retail shopping experience, and variety seeking. The measures for this study were developed as
follows.
3.3.1. Shopping convenience
Overall shopping convenience is defined as time and
effort savings in shopping. The scale for shopping convenience was developed based on Hawes and Lumpkin (1984)
and Gehrt and Shim (1998). Five items were generated that
were intended to tap into general aspects of convenience,
including time savings and effort reduction associated with
shopping over the Internet.
3.3.2. Information seeking
Information seeking is defined as searching, comparing,
and accessing information in a shopping context. Three
information-seeking items were selected, based on Arora
(1985), to measure information seeking.
3.3.3. Immediate possession
Immediate possession refers to the instantaneous delivery
of products or services. In the absence of existing scales to
measure immediate possession, four immediate possession
items were generated that tap into the desire for immediate
possession versus delayed delivery.
3.3.4. Social interaction
Social interaction refers to consumers’ desire to seek out
social contacts in retail and service settings. A three-item
social interaction scale was developed based upon previous
work by Hawes and Lumpkin (1984) and Westbrook and
Black (1985).
3.3.5. Retail shopping experience
Retail shopping experience refers to the enjoyment of
shopping as a leisure-based activity and taps into aspects of
the enjoyment of shopping for its own sake. Three scale
items based on Bellenger and Korgaonkar (1980) and
Stephenson and Willett (1969) were used to measure retail
shopping experience.
3.3.6. Variety seeking
Variety seeking is defined as the need for varied behavior
or the need to vary choices of stores, brands, or products.
Five variety-seeking items were generated based on previous work by Raju (1980).
The psychometric properties of the final measures were
assessed using exploratory factor analysis with varimax
rotation, coefficient alpha, and adjusted item-to-total correlations. Scale items were evaluated for possible deletion
based upon standard procedures (Hair et al., 1995). First,
scale items with loadings less than .30 (Hair et al., 1995),
significant mixed factor loadings, and communality indices
less than .40 were deleted. Second, scale items that increased
adjusted item-to-total correlations when removed were also
deleted. The revised factor solution was derived based upon
the remaining items. The resulting factor analysis yielded
four underlying dimensions of shopping motives: overall
convenience, physical store orientation, information use in
the planning and shopping task, and variety seeking.
Appendix A lists the scale items, factor loadings, Cronbach’s alpha, and item –total correlations for each of the four
factors. Each factor contained between four and five items.
The resulting scale scores were determined by taking the
average of the individual scale items.
For the offline study, multi-item measures were used to
represent the six shopping motives identified in the earlier
online study. These motives, including convenience,
information-use in planning and shopping, immediate possession, retail shopping experience, social interaction, and
variety seeking, were captured using measures adapted from
the online survey to the offline shopping context. For
instance, the survey item ‘‘I find the Internet provides me
with a lot of information about products and services’’ was
adapted for the offline setting to ‘‘In general, I find stores
provide me with a lot of information about products.’’
4. Results
4.1. Online grocery shopping types
Based upon the set of measurement items, factor analysis, and resulting scale scores, subsequent cluster analysis
identified a four-group typology of online grocery shopping
types. Initial cluster analysis employed Ward’s minimum
variance method to obtain a hierarchical cluster solution.
No outlying observations were found, supporting the sample’s representativeness. Examination of the dendogram
and cubic cluster criterion plots as well as the pseudo-F
statistic (Johnson, 1998) suggested a four-to-five-cluster
solution. Based upon the relationships found between
cluster solutions and the background variables (described
more fully in the next section) as well as the supporting
literature, a four-cluster solution was found the most
meaningful and interpretable.
Consistent with previous research (e.g., Bunn and Clopton, 1993), a variation of Punj and Stewart’s (1983) crossvalidation procedure was followed to check the reliability of
the four-cluster solution. This is primarily used in order to
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
ensure that the sample size used in the study is not reduced
during cross-validation.
Means and standard deviations for each cluster and pairwise contrasts of shopping motives across clusters are
reported in Table 3. The four factors, consisting of overall
convenience ( F = 73.60, P < .01), physical store orientation
( F = 5.92, P < .05), information use in planning and shopping
( F = 10.42, P < .01), and variety seeking ( F = 299.46,
P < .001), differed significantly across clusters. An analysis
of the four clusters reveals the following four shopping types:
The convenience shopper (Cluster 1). This group, which
comprises 11% of the online grocery sample, is the smallest
online shopping classification. This shopping type is motivated more than the other three types by the prospects of
overall online shopping convenience. This segment also
exhibits less of a physical store orientation (e.g., is motivated less by the prospect of immediate possession of goods
or services purchased and social interaction) as well as less
variety-seeking behavior across retail channels.
The variety seeker (Cluster 2). The largest group in the
sample (41%) is moderately motivated by online shopping
convenience, yet substantially more so by variety seeking
across retail alternatives and product types and brands. The
variety seeker exhibits scores close to the mean on physical
store orientation as well as a tendency to plan purchases and
shopping trips.
The balanced buyer (Cluster 3). This online shopping
type (33% of the sample) is similar to the variety seeker in
his or her desire for convenience and lowest in his or her
tendency to plan the shopping task or seek information. The
balanced buyer is moderately motivated by the desire to
seek variety and exhibits a score near the mean in terms of
physical store orientation.
The store-oriented shopper (Cluster 4). This online
shopping type (15% of the sample) is characterized by the
lowest level of online shopping convenience. He or she rates
highest overall on physical store orientation (i.e., desire for
immediate possession of goods and social interaction),
753
below the mean for tendency to plan purchases, and
relatively low for variety-seeking behavior.
A significantly greater percentage of store-oriented shoppers, measured by the question ‘‘How long have you been
shopping for products and services over the Internet,’’ were
found to have shopped online less frequently as compared to
the other three shopping types. Deeper distinctions among
clusters are made in the following sections, yet several
initial observations can be made. First, as seen in Table 3,
post hoc pairwise contrasts using Duncan’s multiple-range
procedure suggest that Cluster 1 (convenience shoppers)
differs significantly from Cluster 2 (variety seekers), and
Cluster 4 (store-oriented shoppers) with regard to overall
online shopping convenience. Convenience shoppers and
store-oriented shoppers differ across overall online shopping
convenience, physical store orientation, use of information
in planning and shopping, and variety seeking. Except for
variety seeking, variety seekers, and balanced buyers follow
similar patterns, differing only in intensity. Multiple comparisons indicate that the store-oriented shopper differs from
the other three shopping types across the information use
and planning motive.
The propensity to grocery shop online was examined
across shopping types as well (see Table 3). A comparison
of propensity to shop online for groceries revealed differences across the four shopping types ( P < .05). The storeoriented shoppers (as can be seen in Table 4) also have the
lowest online purchase frequencies across shopping types
for all of the specific product categories examined. These
results that compare online shopping propensities across
cluster types enhance the validity of the four online shopping types and proposed typology.
4.2. Relationship between background variables and online
shopping types
A more thorough understanding of the online consumer
can be gained by relating the background variables to the
Table 3
Underlying shopping motives: means and standard deviations by online shopping type
Shopping motive
Factor 1: overall convenience
Factor 2: physical store orientation
Factor 3: information use in
planning and shopping
Factor 4: variety seeking
Propensity to shop online***
Number of observations
Percentage of observations
Online shopping type
Total sample
F value * ( P)
Pairwise contrasts**
Convenience
shopper
Variety
seeker
Balanced
buyer
Store-oriented
shopper
6.02 (0.91)
4.96 (0.85)
4.53 (0.65)
5.21 (0.93)
4.64 (0.81)
4.61 (0.79)
5.78 (0.85)
5.06 (0.81)
4.38 (0.80)
3.38 (0.95)
5.60 (0.75)
3.59 (0.68)
5.52 (0.83)
4.92 (1.12)
4.46 (0.82)
73.60 ( P < .01)
5.92 ( P < .05)
10.42 ( P < .01)
A > B,D; C>B,D
D>A,B,C
A,B,C>D
3.58 (0.39)
6.61 (0.51)
45
11
5.34 (0.53)
5.87 (0.86)
169
41
4.59 (0.38)
6.81 (0.70)
136
33
3.88 (0.95)
3.59 (0.91)
62
15
4.56 (1.91)
4.45 (0.23)
412
100
299.46 ( P < .01)
52.73 ( P < .01)
B>A,C,D; C>A,D; D>A
A>B,D; C>B,D
A = convenience shopper; B = variety seeker; C = balanced buyer; D = store-oriented shopper.
* df = 3.
** Duncan’s post hoc multiple-range test (a=.05).
*** Means (and standard deviations) of propensity to shop online, based upon the percentage of times a respondent had shopped at the focal online retailer
during their last 10 shopping trips.
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A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
Table 4
Product types purchased online by online shopping type
Product type purchased online base (n = 412)
Books/magazines
Clothing
Toys
Music CDs
Computer hardware
Computer software
Travel
Home electronics/appliances
Flowers
Financial services
Number of respondents that purchased within online shopping typea
Convenience
shoppers
Variety
seekers
Balanced
buyers
Store-oriented
shoppers
29
16
9
16
15
19
22
8
13
12
91
51
20
59
30
77
96
18
38
38
77
44
18
40
42
41
58
12
22
27
11
12
3
11
6
3
25
3
6
11
Chi-square* *
P
32.91
4.38
5.87
6.92
16.50
35.83
8.18
5.17
8.33
1.55
< .01
NS
NS
NS
< .01
< .01
.05
NS
.05
NS
a
What types of products or services have you purchased online during the past 6 months?
* * df = 3.
four online shopping types illustrated previously. Several
variables (e.g., age, household size, per capita income,
gender, and product type) that potentially influence retail
shopping and online purchasing behavior are examined.
The results of these background variables compared across
the four clusters are reported next. First, univariate
ANOVA and chi-square tests revealed no differences in
age, income, and household size across the four online
shopping types. This suggests a homogeneous online
shopping sample with respect to demographics. Second,
the percentage of members within each of the four shopping types having bought certain product types was
examined. The results reported in Table 4 indicate significant differences in purchase behavior across clusters
for the following product classes: books and magazines
(c2 = 32.91, P < .01), computer hardware (c2 = 16.50,
P < .01), computer software (c2 = 35.83, P < .01), travel
(c2 = 8.18, P=.05), and flowers (c2 = 8.33, P=.05). Variety
seekers and balanced buyers exhibit purchase frequencies
similar to those of the convenience shopper in several
product classes (e.g., books and magazines, music CDs,
computer software, travel, and flowers). This suggests that
although the convenience shopper might be more motivated
by the convenience of shopping online, the variety seeker
and balanced buyer may constitute relatively active online
shopping types as well.
4.3. Offline grocery shopping types
The procedure for selecting factors and determining
cluster membership was similar to that of the online sample.
The analysis for the bricks-and-mortar consumers identified
three distinct clusters of offline shoppers: the time-conscious
shopper, the functional shopper, and the recreational shopper. These clusters represent 20%, 32%, and 48% of the
offline sample, respectively. Four offline shopping motives
(factors), consisting of offline or physical store orientation
( F = 16.95, P < .01), shopping adventure and experience
( F = 223.32, P < .01), impulse shopping ( F = 9.26, P < .01),
and time savings ( F = 12.77, P < .01), differed significantly
across clusters.
5. Discussion
The empirical findings suggest that there are distinct
online grocery shopping types. These shopping types are
named convenience shoppers, variety seekers, balanced
buyers, and store-oriented shoppers. The mean scores of
underlying shopping dimensions across clusters directionally support the convenience shoppers as being most motivated by convenience, a key factor influencing the growth
of online shopping. Conversely, these directional results
also support the profile of store-oriented shoppers as being
more motivated by offline store characteristics such as
immediate possession and social contact. Variety seekers
are characterized by those who seek variety in retail alternatives or products and brands. Balanced buyers exhibit
scores relatively close to the mean for all four shopping
dimensions except for a lower propensity to plan purchases,
therefore suggesting a segment that makes more impulsive
purchases online.
Chi-square tests of differences in purchase frequencies
across the clusters indicate that significant differences exist
for the following product classes: books and magazines,
computer hardware, computer software, travel services, and
flowers (see Table 4). The convenience shopper exhibits the
highest purchase frequency for the majority of these product
classes, excluding only computer software, in which variety
seekers exhibited the highest purchase frequency. This
suggests that the variety seeker, although noted for his or
her variety-seeking behavior and tendency to seek out
alternative retail types, is an important consumer type for
the online retailer to target due to this shopping type’s online
purchasing activity. Further, the store-oriented shopper
exhibits the lowest purchase frequency for all of the product
classes, suggesting this group might be less of an immediate
priority to the online retailer.
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
This research enhances our understanding of shopping
motives that are salient to the online context. Similar to
previous typologies (e.g., Bellenger and Korgaonkar, 1980)
conducted in traditional shopping contexts, this study identified overall shopping convenience as a motive for shopping online, particularly among convenience shoppers.
Additionally, as in previous typologies (e.g., Bellenger
and Korgaonkar, 1980; Stephenson and Willett, 1969), the
desire for social interaction was identified as a shopping
motive, particularly among store-oriented shoppers. These
similarities suggest that certain underlying motives for
shopping, such as desire for convenience and social interaction, have not changed due to the online context.
Unlike previous shopping typologies, variety seeking
was identified as an online shopping motive for a certain
consumer type. This finding suggests that variety-seeking
behavior is an important construct, particularly as emerging
shopping channels, such as the Internet, offer the consumer
more choice and ease of access.
Unexpectedly, this research failed to support two factors
commonly attributed to reasons why consumers shop
online: (1) time savings and (2) recreation and enjoyment.
Scale items measuring these two constructs were dropped
from the exploratory factor analysis for poor or mixed
loadings. Perhaps the notion of time savings was subsumed
within the overall shopping convenience construct. Additionally, it is possible that the use of the Internet for online
shopping appeals to more functional as opposed to recreational shoppers.
One implication of this research is that some of the
underlying motivations such as convenience remain important in both online and offline settings. Time savings and
recreation and enjoyment, which emerged as key motivations in previous research in offline settings, did not appear
to be significant within the context of this online study. One
possible explanation maybe that while the Internet saves
time during the purchasing of goods, and eliminates the time
needed to travel to the physical store, it also increases the
time taken to receive goods. Therefore, time savings may
not be perceived as a significant advantage while shopping
online.
The analysis for the offline sample identified three
distinct clusters of offline shoppers: time-conscious, functional, and recreational shoppers. The results from this
matched offline sample suggest that time savings, functional
shopping, and shopping as recreation are significant factors
in the bricks-and-mortar context. These results approximate
those of previous offline shopping typologies (e.g., Bellenger and Korgaonkar, 1980) that identified two distinct
shopping types: the recreational and the economic shopper.
The significance of the time-savings factor in the offline
results is particularly interesting. This is in contrast to our
findings regarding time savings in the online context.
However, because offline shopping is generally thought to
be more time-consuming than in the online context, time
savings becomes a significant differentiator in the offline
755
context. In addition, comparing the offline and online
results, variety seeking and convenience were found to be
significant factors in the online but not the offline setting.
The offline results discussed here add validity to the online
results in that the factors of recreational shopping and time
savings in the offline setting and convenience and variety
seeking in the online setting seem to differentiate the two
shopping contexts.
5.1. Theoretical implications
This study represents an important first step in extending
the general shopping literature to the context of online
shopping. Variety seeking and convenience were found to
be significant motivating factors in the online shopping
context. Time savings and recreational motives were significant motives in the offline but not the online context,
suggesting that these factors may serve to differentiate the
shopping types across these channels. By illustrating shopping motives that are salient to the online setting (e.g.,
convenience and variety seeking) versus the offline context
(e.g., time savings and recreational motives), we begin to
build a theory of online shopping motivation and behavior.
This study also contributes to our current knowledge of
marketing on the Internet. In a broad sense, previous work
in Internet marketing has examined a wide array of consumer, retailer, and producer phenomenon within various
types of shopping channels, including interactive home
shopping (e.g., Alba et al., 1997), the application of network
theory (Iacobucci, 1998), the concept of flow in helping to
describe consumer navigation behavior (Hoffman and
Novak, 1996), consumer perception of risk in Internet
shopping (Swaminathan et al., 1999), and personal privacy
issues related to online shopping (Rohm and Milne, 1998).
However, to the authors’ knowledge, scant research that
investigates differences across consumers on the basis of
online shopping motives exists.
5.2. Managerial implications
Retail and marketing managers may benefit from the
results reported here. The findings suggest that consumers
who are motivated by convenience are likely to shop online
for specific types of products and services, e.g., books and
magazines and travel. An online retailer seeking to market
explicitly to this segment may want to develop strategic
alliances with retailers specializing in these product or service
areas. The convenience shopper, balanced buyer, and variety
seeker exhibit a high propensity to shop in various product
classes. Given that the balanced buyer and variety seeker
exhibit greater propensity to seek variety in their shopping, it
is possible that these groups are attracted by the Internet’s
convenience, as well as its search and comparison capabilities
that enable consumers to seek alternatives based upon various
attributes, such as price. In addition, these groups are moderately motivated by immediate possession, suggesting that
756
Appendix A. Factor analysis of shopping motives and utilities
Scale items
Factor loadings
1. Internet ordering convenience
2. The Internet is a convenient way of
shopping
3. The Internet is often frustrating a
4. I save a lot of time by shopping on the
Internet
5. Shopping over the Internet is a pleasant
experience
6. I would rather buy at store than wait for
delivery
7. I like to shop where people know me
8. While shopping on the Internet, I miss the
experience of interacting with people
9. I like browsing for the social experience
10. I like to have a great deal of information
before I buy
11. I always compare prices
12. I carefully plan my purchases
13. I buy things I had not planned to purchase
14. I am cautious in trying new products a
15. I enjoy exploring alternative stores
16. Investigating new stores is generally a waste
of time a
17. I like to try new products and brands for fun
18. I like to buy the same brand a
a
Physical store
characteristics
Information use in
planning and shopping
Variety seeking
.74
.84
.16
.31
.10
.01
.06
.03
.71
.71
.52
.78
.05
.05
.19
.12
.09
.03
.57
.74
.81
.02
.06
.01
.72
.37
.57
.21
.09
.64
.08
.18
.68
.82
.12
.01
.09
.04
.47
.50
.03
.03
.75
.29
.15
.65
.09
.20
.43
.34
.14
.16
.05
.12
.05
.12
.07
.01
.17
.11
.17
.09
.64
.68
.41
.29
.27
.13
.11
.04
.26
.42
.70
.70
.23
.34
.33
.47
.50
.45
.05
.22
.07
.20
.07
.06
.65
.50
.40
.22
Item is reverse coded. The cumulative variance is explained by four factors: 53%. Scale items are anchored by 1 = strongly disagree and 7 = strongly agree.
Coefficient
alpha
.80
.71
.52
.60
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
Overall
convenience
Item-to-total
correlation
A.J. Rohm, V. Swaminathan / Journal of Business Research 57 (2004) 748–757
their demand for digital products such as music CDs and
software is likely to be relatively high.
On the other hand, the store-oriented shopper is highly
motivated by immediate possession. Marketers may need to
examine ways in which they can enhance the ability to
deliver within a shorter period, e.g., same-day delivery, so
that a greater proportion of segments such as store-oriented
shoppers can be attracted to online shopping.
5.3. Future research
Some limitations of this research, that also provide a
basis for future research, should be noted. Online shopping
is a relatively new phenomenon. The results presented in
this study are based on a sample of consumers who may be
perceived as early adopters and innovators in the context of
online shopping. One potential limitation is that the characteristics of the sample may change once more consumers
begin shopping online. Another limitation is that respondents were customers of a single online retailer within a
single industry. Future research should focus on extending
these findings to other industries. Future research in this
area should also examine how shopping typologies might
vary with bricks-and-clicks shoppers, e.g., people who shop
for specific items in both the online and online settings.
Acknowledgements
The authors thank George Milne, Rajdeep Grewal,
Easwar Iyer, Thomas Brashear, and Marc Weinberger for
their constructive comments on earlier versions of this paper.
References
Alba J, Lynch J, Weitz B, Janiszewski C, Lutz R, Wood S. Interactive home
shopping: consumer, retailer, and manufacturer incentives to participate
in electronic marketplaces. J Mark 1997;61:38 – 53 (July).
Arora R. Involvement: its measurement for retail store research. J Acad
Mark Sci 1985;229 – 41 (Spring).
Balasubramanian S. Mail versus mall: a strategic analysis of competition
between direct marketers and conventional retailers. Mark Sci 1998;
17:181 – 95.
Bellenger DN, Korgaonkar PK. Profiling the recreational shopper. J Retail
1980;56:77 – 92 (Fall).
Bendapudi N, Berry LL. Customers’ motivations for maintaining relationships with service providers. J Retail 1997;73:15 – 37 (Spring).
Bunn MD. Taxonomy of buying decision approaches. J Mark 1993;57:
38 – 56 (January).
Bunn MD, Clopton SW. Patterns of information source use across industrial
purchase situations. Decis Sci 1993;24:457 – 78 (March – April).
Buss DD. Damaged goods: online grocers still struggle to deliver. Business
2.0 1999;74 – 8 (April).
Churchill GA. A paradigm for developing better measures of marketing
constructs. J Mark Res 1979;16:64 – 73 (February).
Darden WR, Ashton D. Psychographic profiles of patronage preference
groups. J Retail 1975;50:99 – 112 (Winter).
Darden WR, Reynolds FD. Shopping orientations and product usage rates.
J Mark Res 1971;505 – 8 (November).
757
Dawson S, Bloch PH, Ridgway NM. Shopping motives, emotional states,
and retail outcomes. J Retail 1990;66:408 – 27 (Winter).
Dykema EB. Online retail’s ripple effect. Forrester Report, 2000 [September]. Available at: http://www.forrester.com/ER/Research/Report/
0,1338,10723,00.html. Accessed March 21, 2001.
Eastlick MA, Feinberg RA. Shopping motives for mail catalog shopping.
J Bus Res 1999;45:281 – 90 (July).
Gehrt K, Shim S. A shopping orientation segmentation of French consumers: implications for catalog marketing. J Interact Market 1998;12:
34 – 46 (Autumn).
Hair JF, Anderson RE, Tatham RL, Black WC. Multivariate data analysis.
4th ed. Upper Saddle River (NJ): Prentice-Hall, 1995.
Hawes JM, Lumpkin JR. Understanding the outshopper. J Acad Mark Sci
1984;200 – 18 (Fall).
Hoffman DL, Novak TP. Marketing in hypermedia computer-mediated environments: conceptual foundations. J Mark 1996;60:50 – 68 (July).
Iacobucci D. Interactive marketing and the Meganet: network of networks.
J Interact Market 1998;5 – 16 (Winter).
Johnson DE. Applied multivariate methods for data analysts. Boston (MA):
Duxbury Press, 1998.
Lynch JG, Ariely D. Wine online: search costs affect competition on price,
quality, and distribution. Mark Sci 2000;19:83 – 103 (Winter).
McAlister L, Pessemier E. Variety seeking behavior: an interdisciplinary
review. J Consum Res 1982;9:311 – 21 (December).
McGuire W. Psychological motives and communication gratification. In:
Blumler JF, Katz J, editors. The uses of mass communications: current
perspectives on gratification research. Beverly Hills (CA): Sage, 1974.
p. 106 – 67.
Menon S, Kahn BE. The impact of context on variety seeking in product
choices. J Consum Res 1995;285 – 95 (December).
Punj G, Stewart DW. Cluster analysis in marketing research: review and
suggestions for application. J Mark Res 1983;134 – 48 (May).
Raju PS. Optimum stimulation level: its relationship to personality, demographics, and exploratory behavior. J Consum Res 1980;272 – 82
(December).
Rohm AJ, Milne GR. Emerging marketing and policy issues in electronic
commerce: attitudes and beliefs of Internet users. In: Andreason A,
Simonson A, Craig Smith N, editors. 1998 Marketing and Public Policy
Conference Proceedings, vol. 8. Chicago, IL: American Marketing
Association, 1998. p. 73 – 9.
Sarkar MB, Butler B, Steinfield C. Intermediaries and cybermediaries: a
continuing role for mediating players in the electronic marketplace.
J Comput-Mediat Commun 1996;1. Available at: http://jcmc.huji.ac.i1/
vol1/issue3/.
Shankar V, Rangaswamy A, Pusateri M. Customer price sensitivity and the
online medium. Working paper, 1999.
Shaw EH. The utility of the four utilities concept. Sheth RA, Fullerton JN,
editors. Res Mark. 1994:(Suppl 6);47 – 63.
Sheth JN. An integrative theory of patronage preference and behavior. In:
Darden WF, Lusch RF, editors. Patronage behavior and retail management. New York: North-Holland, 1983. p. 9 – 28.
Stephenson PR, Willett RP. Analysis of consumers’ retail patronage strategies. In: McDonald PR, editor. Marketing involvement in society and
the economy. Chicago (IL): American Marketing Association, 1969.
p. 316 – 22.
Stone GP. City shoppers and urban identification: observation on the social
psychology of city life. Am J Sociol 1954;60:36 – 45 (July).
Swaminathan V, Lepkowska-White E, Rao BP. Browsers or buyers in
cyberspace? An investigation of factors influencing likelihood of electronic exchange. J Comput-Mediat Commun 1999;5 [December].
Available at: http://www.ascusc.org/jcmc/vol5/issue2/swaminathan.
htm.
Tauber EM. Why do people shop? J Mark 1972;36:46 – 9 (October).
Westbrook RA, Black WC. A motivation-based shopper typology. J Retail
1985;61:78 – 103 (Spring).
Williams RH, Painter JJ, Nicholas HR. A policy-oriented typology of
grocery shoppers. J Retail 1978;54:27 – 43 (Spring).