Ps4

Effects of Multi-Channel
Consumers’ Perceived Retail Attributes
on Purchase Intentions of Clothing Products
Youn-Kyung Kim
Soo-Hee Park
Sanjukta Pookulangara
ABSTRACT. Consumers are increasingly shopping through multiple
channels, providing opportunities to retailers who can increase profits
by cross-selling products through multi-channels. The objective of the
study was to examine the effects of multi-channel consumers’ perceived
retail attributes on purchase intentions for (a) brick-and-mortar stores,
(b) catalogs, and (c) the Internet. Following a pre-testing, a Computer-
Assisted Telephonic Interview (CATI) was utilized for data collection
with 500 multi-channel consumers. Structural equation modeling re-vealed
that multi-channel consumers perceive important retail attributes
differently across the three channels (i.e., brick-and-mortar stores, cata-logs,
and the Internet). Consumers perceived risks in personal security or
buying private merchandise while shopping in stores. Consumers
shopped clothing items via the Internet due to access to a variety of items
and convenience-related attributes. Additionally, consumers who per-
Youn-Kyung Kim is Associate Professor, Retail and Consumer Sciences, 244A
Jessie Harris Building, University of Tennessee, Knoxville, TN 37996-1911 (E-mail:
ykim13@utk.edu). Soo-Hee Park is Director of Research, Assessment and Evaluation
Division, Tennessee Department of Education, 2730 Island Home Boulevard, Knox-ville,
TN 37920 (E-mail: soo-hee.park@state.tn.us). Sanjukta Pookulangara is Assis-tant
Professor, Dietetics, Fashion Merchandising, & Hospitality, Knoblauch Hall 140,
Western Illinois University, Macomb, IL 61455 (E-mail: SA-Pookulangara@wiu.
edu).
Address correspondence to: Youn-Kyung Kim at the above address.
Journal of Marketing Channels, Vol. 12(4) 2005
Available online at http://www.haworthpress.com/web/JMC
© 2005 by The Haworth Press, Inc. All rights reserved.
doi:10.1300/J049v12n04_03 23

24 JOURNAL OF MARKETING CHANNELS
ceived higher level of cost tended to purchase clothing products through
catalogs and the Internet. Managerial implications are provided for
multi-channel retailers. [Article copies available for a fee from The Haworth
Document Delivery Service: 1-800-HAWORTH. E-mail address: <docdelivery@
haworthpress.com> Website: <http://www.HaworthPress.com> © 2005 by The
Haworth Press, Inc. All rights reserved.]
KEYWORDS. Multi-channel retailing, direct marketing channels,
Internet retailing, apparel shopping
The environment where the retail industry is mature and store expansion
has slowed to a crawl has challenged retailers to widen the range of distri-bution
channels available in the consumer market. Many retailers are drop-ping
single channel approaches such as brick-and-mortar stores or
catalog-only operations and are switching to multi-channel strategies by
linking their store operations with e-commerce and/or catalogs; Internet re-tailers
also are venturing offline (Haydock, 2000; Schoenbachler &
Gordon, 2002). This multi-channel consumer market is driven by factors
such as an increasing number of dual-income families; a lack of consum-ers’
time; technological revolutions; and a myriad of shopping choices–not
only among different products and brands but also among diverse retailer
formats such as brick-and-mortar stores, print catalogs, and online shop-ping
electronic systems (Shim, Eastlick, & Lotz, 2000).
In fact, multi-channel retailing is gaining importance because a multi-channel
approach generates more sales and profit for the multi-channel re-tailer
than a single-channel, single-consumer approach (Hoover, 2001). Al-though
multi-channel retailers face a number of challenges such as channel
conflict, customer-retention issues, and the ability to integrate processes,
they can take advantage of several opportunities including establishing
brand equity, leveraging advertising and marketing expense, leveraging
distribution and supplier networks, driving cross-traffic to multiple chan-nels,
and accessing more customer information (Baker, 1999; Ernst &
Young, 2001; Schoenbachler & Gordon, 2002).
In terms of the spending power, U.S. shoppers who bought through
all three channels of brick-and-mortar stores, catalogs and the Internet
represent 34% of all shoppers and 78% of shoppers purchase from both
Web sites and the brick-and-mortar stores (“The Multi-Channel Retail
Report,” 2001). As such, it appears that the best chance to increase
profit margin per customer lies with retailers with the broadest channel

Kim, Park, and Pookulangara 25
representation (Haydock, 2000; Reda, 2001; Schoenbachler & Gordon,
2002). Already, most revenues in the apparel sector are driven by multi-channel
concepts with a strong brand appeal, such as Victoria’s Secret,
Lands’ End, J. Crew, Liz Claiborne and L.L. Bean (Chevron, 1999; Hill,
2000; Tiernan, 2000).
While retailers recognize that a multi-channel strategy is essential to
long-term viability, the reality is that many lack the knowledge of how
to successfully combine online, catalog, and physical retailing to form a
successful retailing concept (“Integrating Multiple Channels,” 2001).
In order to improve customer loyalty and retention rates, thereby in-creasing
profits, retailers have to ensure that their customers stay with
them irrespective of the channel of shopping. Then, it seems critical for
retailers to assess what retail attributes their customers perceive as im-portant
for each channel and relate these attributes to purchase intention
through the channel. This understanding will offer the promise of more
precise market analysis and marketing strategy development for cloth-ing
products, which in turn will better meet customer expectations on
the multi-channel environment.
In the following sections, we first summarize the multi-channel con-sumer
market for clothing products and then discuss the literature on re-tail
attributes and purchase intentions for clothing products. Next, we
illustrate the procedures and the results we obtained from testing the ef-fects
of multi-channel consumers’ perceived retail attributes on pur-chase
intentions for each channel through structural equation modeling.
Lastly, we present our managerial implications as well as suggestions
for future studies.
MULTI-CHANNEL CONSUMER MARKET
FOR CLOTHING PRODUCTS
Brick-and-mortar retailers still dominate the apparel market, with on-line
retailers making large strides in gaining market share. In 2000, the to-tal
U.S. apparel sales in brick-and-mortar stores accounted for 92.9% of
the total apparel market; catalogs, 3.9%; the Internet, 3.2% (“Retail Ap-parel
Sales Statistics & Trends, 2000”). These figures compare to 88.6%
for brick-and-mortar stores, 9.4% for catalogs, and 0.6% for the Internet
in 1999 (“Retail Apparel Sales Statistics & Trends, 1999-2000”).
The percentage of catalog sales of apparel products seems to continu-ously
have declined from 1980s and early 1990s when they enjoyed
double-digit annual sales growth (Gordon, 1994). Although their de-

clining growth rate can be attributed to competition with the Internet
sales, much attention is still needed to examining attributes of catalogs
that are important to retain and regain their customers.
The range of products sold through the Internet has been widening.
Fast-selling products on the Internet used to be those products about
which the shopper already had sufficient information, such as books,
computer products, travel, health and beauty products (Reda, 2001;
Schaeffer, 2000). As technology improves, items previously thought to
be saleable only in a touch-and-feel environment (e.g., apparel, jewelry)
are enjoying more widespread sales. Online apparel retailers in the
United States and Europe (e.g., Lands’ End, J.C. Penney, and Galleries
Lafayette) have increased profitability by giving consumers access to
interactive try-on sessions such as the “virtual dressing room,” “digital
supply chain” and “online fit prediction” (Abend, 2001; “Lands’ End
Improves,” 2001). Furthermore, the recent integration of apparel manu-facturers
into direct Web selling (e.g., Fabra U Inc., Shawnee Garment
Manufacturing), as well as the continuing incursion of traditional retail-ers
into the online channel, has fueled the clothing surge. In fact, apparel
ranks in the top five product categories sold through the Internet in the
United States (Global Online Retailing Report, 2000). The growing on-line
retailing of clothing products means increased consumer buying
through multi-channels, because brick-and-mortar stores are already
the major media for selling clothing products.
RETAIL ATTRIBUTES
Consumers may patronize or switch channels and/or retailers de-pending
on their perceived attributes (Paulins&Geistfeld, 2003; Wilde,
Kelly,&Scott, 2004). This channel selection may be based on their par-ticular
needs in specific situations. For instance, consumers’ reasons for
the selection of the Internet versus the brick-and-store for their shop-ping
can vary for different consumers and in different situations even
for the same consumer. Some consumers may shop mainly in the
brick-and-mortar stores because they like synchronous human contact
for receiving services in a safe shopping environment, whereas other
consumers or the same consumers may use the Internet for such reasons
as being able to shop in the comfort of home and fast transaction with-out
having to spend time and energy traveling to the store and finding
products wanted and waiting in check-out lines. Thus, multi-channel re-

tailers have to be prepared to address the unique challenges of serving
customers through multiple channels.
Our review of literature suggests that the construct of perceived retail
attributes encompasses benefit and cost components. From a con-sumer’s
point of view, a consumer wants to obtain the greatest possible
satisfaction from a consumption activity, while he or she seeks to mini-mize
costs needed to accomplish a given shopping activity. Thus, we as-sert
that the two components, namely, benefit and cost, should be
viewed as the building blocks of how we conceptualize the retail attrib-utes.
Zeithaml (1988) posited that consumers vary in what they want to
get and what they are willing to give or expend. That is, just as the im-portance
of benefits varies across consumers (i.e., some may want a va-riety
of merchandise, and others low price, convenience, or new
information), the importance of costs also varies (i.e., some are con-cerned
primarily with money, others with time or energy).
A comprehensive analysis of the literature on the benefit component
of retail attributes reveals multifaceted dimensions such as value, as-sortment,
service, convenience, confidentiality (i.e., security, privacy),
atmosphere, and community involvement (Jarvenpaa & Todd, 1997;
Linquist, 1974-1975; Shimet al., 2000).Among these dimensions, value,
assortment, service, convenience, and confidentiality are most relevant
to the three channels (brick-and-mortar stores, catalogs, and the Internet)
that are the scope of this study. Equivalent examples can be provided for
each dimension. For instance, the “Convenience” dimension consists of
layout of the store (or catalogs or the Internet), saving time (e.g., no
lines and no traffic for stores; finding the right product/product category
for catalogs and the Internet), and up-to-date and unique items. The
“Confidentiality” dimension constitutes privacy (e.g., privacy to buy prod-ucts
like lingerie, etc.) and security (e.g., personal security for stores, se-cure
credit card information for catalogs and the Internet). The other
three dimensions–Value, Assortment and Service can be applied to all
three channels. “Value” includes good quality and reasonable price; “As-sortment”
assures access to a variety of the same kind of products (e.g.,
styles, colors, sizes), access to different products, and availability of na-tional
or designer brands; and “Service” refers to good customer service
and easy return of items.
Cost, as another component of retail attributes, includes “money,”
“time,” and “energy” (Downs, 1961;Kim&Kang, 1997). “Money” spent
to acquire a product is a cost that is applied to any channel. However,
catalogs and the Internet involve shipping and handling costs, which are
not present in the case of brick-and-mortar stores that may instead re-

quire transportation cost. “Time” is spent traveling to the store and find-ing
a parking space in the case of brick-and-mortar stores. For shopping
via catalogs or the Internet, time is spent locating products as well as
completing a transaction. “Energy” expended on brick-and-mortar stores
include waiting in checkout lines, finding the product, and fighting with
traffic and parking. While shopping on the Internet, energy is expended
navigating through the Web pages to find products and dealing with
Web site malfunctions (e.g., broken links) as well as electronic check-out.
While shopping through catalogs, energy may be expended finding
the right product (Kim, 2002).
CLOTHING PRODUCTS: PERCEIVED RETAIL
ATTRIBUTES AND PURCHASE INTENTIONS
Consumer attitudes toward the retail attributes influence purchase in-tentions
(Jarvenpaa & Todd, 1997; Kim & Kang, 1997; Shim et al.,
2000). As Hoyer and Alpert (1983) pointed out, “consumers will con-clude
that certain important (and if consciously processed, salient) at-tributes
discriminate well among alternatives while others do not, and it
is the discriminating or determinant attributes which play the major role
in producing a choice” (p. 80).
Studies on store attributes that influence purchasing clothing products
have been limited. Among the limited studies, Shim and Kotsiopulos
(1992) discovered that store attributes of quality/variety and price/re-turn
policies affected patronage behaviour of discount stores; quality/
variety, brand/fashion, price/return policies were important attributes
influencing patronage behaviour of specialty stores. Kim and Kang’s
(1997) study, although not limited to clothing products, examined the
consumers’ perception of shopping costs and its relationship with retail
trends. The study highlighted the retail attributes that include both bene-fit
and cost components in a brick-and-mortar retail format in the con-text
of a shopping mall. They found that all three cost components (i.e.,
money, time, and energy) along with economics, service, institutional
image, convenience/safety, atmosphere, easy return, and selection af-fected
consumer purchase intention. More recently, Paulins and Geistfeld
(2003) reported that store preference was influenced by type of clothing
desired in stock, outside store appearance, shopping hours, and adver-tising.
Several researchers have identified the important attributes that consum-ers
seek from catalogs. In terms of clothing purchases, convenience has re-

peatedly been found to be a principal reason for favoring catalog shopping
over in-store shopping (Eastlick & Feinberg, 1994; Jasper & Lan, 1992;
Kwon, Paek, & Arzeni, 1991; Shim & Bickle, 1994). Other benefits con-sumers
seek from catalog shopping for clothing include wide product as-sortment
(Shim & Drake, 1990), high level of product quality (Eastlick &
Feinberg, 1994), low prices and ease of return (Eastlick & Feinberg, 1994;
Shim & Drake, 1990), and credit availability (Kwon et al., 1991).
Retail attributes also have been linked to online shopping of clothing
products. Kunz (1997) found that online, apparel consumers valued
merchandise quality, merchandise variety, and customer service. Ac-cording
to Taylor and Cosenza (2000), when shopping online for cloth-ing,
consumers rated the functional attributes such as price, ease of
movement and ease of return as important.
In relating perceived important retail attributes to purchase intention,
Then and Delong (1999) suggested that consumers tend to buy more ap-parel
online if they perceive such features as a convenient and secure sys-tem
of ordering, return policy, focus on product display, and the offering
of products that have a range of acceptable fits as opposed to a precise fit.
According to Shim et al. (2000), for sensory experiential products (e.g.,
apparel and accessories), consumers are less likely to be influenced by
functional attributes such as fast transaction service and speedy shopping
than they are for cognitive products (e.g., books, computer software, mu-sic
and videos). This is supported by Verton’s (2001) argument that a per-sonalized
shopping experience via various incentives and virtual image
technology is important to encourage apparel consumers to shop online.
On the other hand, Watchravesringkan and Shim (2003) found that online
purchase intentions for apparel products were predicted by attitudes to-ward
secure transaction (e.g., payment security, consumer information
privacy, return policy, minimal cost and time for return, and product
shopping guarantees) and speedy process. Kim, Kim, and Kumar (2003)
identified product and convenience (e.g., variety of merchandise, conve-nience,
reasonable price, adequate sales information) and service (e.g.,
good customer service, easy of payment options, ease of navigation) as af-fecting
behavioural intention to purchase clothing online.
The aforementioned studies have been limited to channel-specific
analyses, not comparing across channels. Further, cost component was
not fully examined in assessing consumer purchase intention, especially
in the case of catalogs and the Internet. Because studies on multi-channel-ing
have been relatively limited, it is not surprising that there exists no de-tailed
framework for understanding channel choice. Current trends,
however, assert that the reliance on a single channel will probably be an

exception rather than the rule (Black, Lockett, Ennew, Winklhofer, &
McKechnie, 2002). By examining what retail attributes are important to
multi-channel shoppers and relating them to their purchase intention of
each channel, retailers can develop effective strategies for clothing prod-ucts
that will better position them against their competitors.
OBJECTIVES
This study provides an empirical understanding of the retail attributes
marketers should consider when they want to attract and retain the multi-channel
buyer. The objective of the study was to examine the effects of
multi-channel consumers’ perceived retail attributes on purchase inten-tions
to buy clothing products for (a) brick-and-mortar stores, (b) cata-logs,
and (c) the Internet by using a quantitative modeling of primary data
with multi-channel consumers.
METHODS
Pretesting
In order to check content validity and make minor adjustments prior
to main data collection, the survey instrument was pretested with con-sumers
(n = 115) who had shopped through catalogs and the Internet.
These consumers included students, faculty members, and staff of a ma-jor
university in the Southwest. Based on the pretest, items were revised
to ensure readability and a logical flow of questions. The survey instru-ment
was transcribed for the telephonic interview.
Measures
The measures included retail attributes, purchase intention, and de-mographic
information.
Retail attributes. Retail attributes were measured for each of the three
retail channels (i.e., brick-and-mortar store, catalog, and the Internet).
The scale of retail attributes encompassed both benefits and costs. Twelve
items reflecting benefits were selected based on the criteria that the bene-fits
should be able to be applied to all three channels. They were derived
from two studies (Jarvenpaa & Todd, 1997; Shim et al., 2000) and in-cluded
“access to a variety of same kind of products (styles, color, sizes),”
“access to different products,” “availability of national or designer

brands,” “layout,” “good consumer service,” “good quality of product,”
“reasonable price,” “privacy (e.g., privacy to buy products like lingerie,
etc.),” “security,” “saving time,” “up-to-date and unique items,” and “easy
return of items.”
Some of these items were followed by appropriate examples for each
channel. For instance, layout was specified as “layout of the store and
the product,” “layout of the catalog,” or “layout of the web page and
ease of navigation” (e.g., clicking links). Security was exemplified as
“personal security” for stores; “secure credit card information” for cata-logs
and the Internet. Saving time was exemplified as “no lines and no
traffic” for stores; “finding the right product/product category” for cata-logs
and the Internet. Respondents indicated the level of importance for
each item and each channel using a 5-point rating scale: 1 (very unim-portant)
to 5 (very important).
Cost consisting of money, time, and energy also was measured for each
of the three channels based on a 5-point rating scale: 1 (I spend almost
nothing) to 5 (I spend far too much). Consumers responded to how much
money, time, and energy were spent while shopping through each channel.
Appropriate examples were provided for each channel as follows:
Brick-and-Mortar Stores
• The money you spend for product and other shopping related costs
such as gas, parking, and childcare.
• The time you spend traveling to store, parking, checking out at
cash register, etc.
• The energy you spend for the trip to the store, finding a parking
space, and taking care of children while shopping.
Catalogs
such as shipping and handling.
• The time you spend flipping the pages of the catalog placing the or-der,
waiting for the transaction to get through, etc.
• The energy you spend to flip through the pages, finding the right
product.
The Internet
such as shipping and handling.

• The time you spend navigating the web-site, waiting for the web
page to load, waiting for the transaction to get through, etc.
• The energy you spend to find the right web-site, finding the product,
etc.
Purchase intention. Purchase intention for each of the three channels
was measured as the frequency of a consumer’s purchase intentions of
clothing, jewelry, or accessories in the next 6 months on a 7-point rating
scale: 0 (never) to 6 (6 or more times).
Sample and Data Collection
A Computer-Assisted Telephonic Interview (CATI) was utilized for
data collection. Nationwide telephone numbers of 5,000 multi-channel
consumers who had purchased products from the Internet and catalogs
were purchased from a leading marketing firm. Out of randomly se-lected
6,000 numbers by the firm, 4633 numbers were valid numbers
and were contacted. However, 167 consumers were not qualified for the
interview and 800 consumers refused to participate. Five calls were
made to each potential respondent until 500 interviews were completed.
As illustrated in Table 1, a demographic profile of the respondents in-dicated
that approximately 65% of the respondents were female; about
69% of the respondents were between 30 and 59 years of age; 80% of
the respondents were married; and 92% were Caucasian. Fifty four per-cent
of the respondents reported no children living with them, and ap-proximately
27% had 1-2 children. Annual household income had a
fairly even distribution across the categories with 54.2% reporting
income in the range of $30,001-$80,000.
Data Analyses
To establish an initial measurement model, exploratory factor analy-sis
(EFA) was performed. This study adopted maximum likelihood for
estimation method, squared multiple correlation for prior communality,
and an oblique method for rotation. To evaluate measurement models
and to investigate relationships among the latent variables, LISREL 8
(Joreskog&Sorbom, 1993) was utilized. A weighted least squares (WLS)
method with data from polychoric correlation and asymptotic covariance
matrices was used in this analysis. The WLS estimation technique with
polychoric correlations was preferred since this study adopted a Likert-type
scale with five levels to measure retail attributes. Furthermore, the

TABLE 1. Demographic Profile of Respondents
n %a
Gender
Male 173 34.6
Female 327 65.4
Age
under 20 2 0.4
20-29 52 10.6
30-39 86 17.5
40-49 117 23.7
50-59 137 27.9
60-69 67 13.4
70 or over 32 6.5
Marital Status
Married 398 79.6
Single 99 19.8
Children Living at Home
0 271 54.2
1 72 14.4
2 61 12.2
3 16 3.2
4 2 0.4
Annual Income
$10,000 or less 5 1.0
$10,001-$20,000 14 2.8
$20,001-$30,000 43 8.6
$30,001-$40,000 55 11.0
$40,001-$50,000 62 12.4
$50,001-$60,000 53 10.6
$60,001-$70,000 59 11.8
$70,001-$80,000 42 8.4
$80,001-$90,000 27 5.4
$90,001-$100,000 18 3.6
over $100,000 74 14.8
Ethnicity
Caucasian 461 92.2
African American 11 2.2
Hispanic 3 0.6
Asian 5 1.0
Native American 5 1.0
Other 9 1.8
aNumbers do not total 100% due to the missing data.

WLS technique is desirable because it is an asymptotically distribu-tion-
free method and does not require normality in the variables. The in-put
data matrices were generated from a sample of 500 participants.
Also, this study adopted a two-stage approach to structural equation
modeling (Anderson&Gerbing, 1988). That is, the measurement model
was evaluated and established, and then the structural models were esti-mated
and evaluated.
RESULTS
Measurement Model
The EFA revealed a four-factor structure and factors were Value/Ser-vice,
Assortment/Convenience, Confidentiality, and Cost. Based on the
literature review and the EFA result, the final measurement model was
established. The results for the measurement models in Figure 1 are pre-sented
in Table 2. For the measurement model of retail attributes, one
arbitrarily selected observed indicator of each factor was fixed at 1.0 in
order to give the latent variable a referent, while the others were set free.
The overall model was evaluated with the goodness-of-fit index (GFI),
the adjusted goodness-of-fit index (AGFI), the comparative fit index
(CFI), and the root mean square error of approximation (RMSEA). The
resulting goodness-of-fit index for each measurement model was
around .95, indicating a good model fit. Although all RMSEA were
greater than .05 and less than .066, the values indicated acceptable
model fit (because of less than .08). The coefficients for latent con-structs
were above .05. The Cronbach’s alphas for the latent constructs
ranged from .65 to .78, suggesting moderate to high levels of reliability.
Structural Models
Figure 2 illustrates the structural equation models and fit indices for
stores, catalogs, and the Internet. The indices of goodness-of-fit indi-cated
all three models fit the sample data well.
In the store model, the c2 - value of 272.9 was significant (df = 95, p =
0.001), and other fit indices were sufficient to accept the proposed
model (GFI = 0.954, AGFI = 0.935, CFI = .911, and RMSEA = 0.062).
The Confidentiality factor had a negative effect on the purchase inten-tion
of clothing products in stores (g =0.230, p .05). Value/Service,

FIGURE 1. Measurement Model for Store, Catalog, and the Internet
X1
X2
X3
X4
X5
X6
X7
X8
X9
X10
X11
X12
X13
X14
X15
Value/
Service
Cost
Assortment/
Convenience
Confidentiality
X1: Good custom service X2: Good quality of merchandise X3: Reasonable price
X4: Easy return of items X5: Access-same X6: Access-different items
X7: Availability of national-designer brands X8: Layout X9: Savings time
X10: Up-to-date and unique items X11: Privacy X12: Security
X13: Money X14: Time X15: Effort
Assortment/Convenience, and Cost did not predict purchase intention
of clothing products.
In the catalog model, overall fit statistics of the proposed model indi-cated
that the c2 - value of 237.3 was significant (df = 95, p = 0.001), and
that other fit indices suggested a good model fit (i.e., GFI = 0.964, AGFI =
0.949, CFI = .947, and RMSEA = 0.056). The model showed a significant
relationship between the Cost factor and purchasing intention of clothing
products (g = 0.45, p .001). Significant relationships did not exist for the
other three factors: Value/Service, Assortment/Convenience, and Confi-dentiality.
In the Internet model, the c2 - value of 245.2 was significant (df = 95, p =
0.001), and other fit indices were sufficient to accept the proposed model
(GFI = 0.967, AGFI = 0.953, CFI = .957, andRMSEA = 0.059). Both As-sortment/
Convenience (g = .416, p .01) and Cost (g = .259, p .05)
were significantly related to purchasing intention through the Internet,
whereas Value/Service and Confidentiality were not.

TABLE 2. Factor Loadings, Cronbach’s a, and Fit Indices for Measurement Models
Item Description Store Catalog Internet
Value/
Service
Assortment/
Convenience
Confidentiality Cost Value/
Service
Assortment/
Convenience
Confidentaility Cost Value/
Service
Assortment/
Convenience
Confidentiality Cost
1 Customer service 0.848 0.858 0.829
2 Quality of product 0.933 0.892 0.947
3 Reasonable price 0.507 0.724 0.786
4 Easy return 0.694 0.868 0.884
5 Access-same product 0.679 0.848 0.885
6 Access-different product 0.700 0.804 0.905
7 National/designer brands 0.573 0.728 0.735
8 Layout 0.667 0.725 0.732
9 Saving time 0.603 0.611 0.698
10 Up-to-date/unique items 0.658 0.762 0.839
11 Privacy 0.863 0.853 0.822
12 Security 0.829 0.919 0.944
13 Money 0.530 0.542 0.624
14 Time 0.765 0.833 0.907
15 Effort 0.882 0.824 0.804
Cronbach’s a 0.65 0.67 0.67 0.69 0.77 0.77 0.72 0.66 0.75 0.78 0.69 0.73
Model c2 256.6 203.9 214.9
Fit df 84 84 84
Index RMSEA 0.065 0.054 0.057
GFI 0.955 0.968 0.969
AGFI 0.935 0.954 0.956
CFI 0.911 0.953 0.960
Note: RMSEA = Root Mean Square Error of Approximation; GFI = Goodness of Fit Index; AGFI = Adjusted GFI; CFI = Comparative Fit Index.
All factor loadings are statistically significant at p .001.
36

FIGURE 2. Structural Models for Store, Catalog, and the Internet
Assortment/
Convenience
Y1
1.0a
0.135 (.148) .125 (.155)
.040 (.114) –0.230 (.108)*
Cost Confidentiality
Y1
1.0a
Assortment/
Convenience
.054 (.091) .123 (.113)
.432 (.101)*** –.129 (.099)
Cost Confidentiality
Assortment/
Convenience
–.307 (.270) .416 (.190)*
.259 (.094)** –.012 (.159)
Fit indices for store:
= 272.9,
= 95,
RMSEA = .062,
GFI = .954,
AGFI = .935, and
CFI = .911.
c2
df
Note: Values are from the standardized solution.
Values in the parentheses are standard errors.
* p .05, ** p .01, *** p
.001.
a
starting value = 1.0.
Fit indices for catalog:
= 237.3,
= 95,
RMSEA = .056,
GFI = .964,
AGFI = .949, and
CFI = .947.
c2
f
* p .05, ** p .01, *** p
.001.
a
Fit indices for Internet:
= 245.2,
= 95,
RMSEA = .057,
GFI = .967,
AGFI = .953, and
CFI = .957.
c2
df
* p .05, ** p .01, *** p
.001.
a
Confidentiality
DISCUSSIONS
Store
Value/
Service
Purchase
Intention
Catalog
Value/
Service
Purchase
Intention
Internet
Value/
Service
Y1
1.0a
Purchase
Intention
Cost
This study was aimed at identifying retail attributes marketers should
consider when they want to attract and retain the multi-channel buyer,
in an effort to understand consumer channel choice for clothing prod-

ucts. Retail attributes that consumers perceive important and affect their
purchase intention varied by channel.
The confidentiality factor had a negative effect on the future pur-chase
intention of clothing products in stores. This finding suggests that
consumers perceive risks in personal security or buying private mer-chandise
(e.g., intimate clothing products such as lingerie, plus-size
clothing products) while shopping in stores. In terms of catalogs, con-sumers
who perceive higher level of cost from catalog buying tend to
purchase clothing products through catalogs. This seems to contradict
the findings of the previous researchers (e.g., Eastlick Feinberg,
1994; JasperLan, 1992;Kwon et al., 1991; ShimBickle, 1994) who
identified convenience as the principal reason for clothing purchases
through catalogs. It may be that multi-channel consumers of clothing,
jewelry, and accessories do not mind spending money, time, and energy
to find the right product through catalogs, and consider hedonic aspects
(e.g., aesthetics, social impact) as more important than minimizing ex-penditure
of money, time, and energy.
The findings on the Internet indicate that consumers prefer to shop
clothing items via the Internet due to access to a variety of items and
convenience-related attributes. Obviously, consumers prefer access to
variety within the same kind of product classifications in styles, colors,
and sizes, access to different products, availability of national or de-signer
brands, layout of the Internet, saving time (e.g., finding the right
product/product category), and up-to-date and unique items. This result
supports the previous findings on variety of merchandise (Kim et al.,
2003; Kuntz, 1997) and convenience (Kim et al., 2003) as important at-tributes
in purchasing clothing. The reason that confidentiality did not
influence purchase intention to buy clothing products through the
Internet may be related to the fact that security systems are rapidly im-proving,
dispelling the notion that online shopping is a risky business
(Han Maclaurin, 2002).
At the same time, consumers who perceive high levels of cost tend to
purchase clothing products via the Internet channel. This result is surpris-ing
considering the well-established acknowledgment that the Internet
provides a shopping tool to meet consumers’ expectation of minimizing
time and energy expenditure, as demonstrated by the results on several
important attributes in buying clothing products online: ease of move-ment
(Taylor Cosenza, 2000), ease of navigation and payment options
(Kim et al., 2003), and minimal cost and time for return and speedy pro-cess
(Watchravesringkan Shim, 2003). However, it somewhat corre-sponds
to Shim et al.’s (2000) report that, for sensory experiential products

(e.g., apparel and accessories), consumers are less likely to be influenced
by functional attributes such as fast transaction service and speedy shop-ping
than they are for cognitive products (e.g., books, computer soft-ware).
As in the case of catalogs, online consumers of clothing, jewelry,
and accessories may consider emotional or hedonic aspects (e.g., aesthet-ics,
social impact) as more important than functional aspects (e.g., mini-mizing
expenditure of money, time, and energy). In buying these
products, consumers may be willing to spend money, time, and energy in
searching for the right features such as color, size, style, and fit.
MANAGERIAL IMPLICATIONS
This study identified significant effects of multi-channel consumers’
perceived retail attributes on purchase intentions of clothing, jewelry, and
accessories for each of the three channels (i.e., brick-and-mortar stores,
catalogs, and the Internet). The findings indicate that multi-channel con-sumers
perceive important retail attributes differently across the three
channels, which provides salient implications for multi-channel retailers.
For brick-and-mortar store retailing, confidentiality negatively influ-enced
consumers’ purchase intentions. Hence, retailers need to address
this need by ensuring the privacy (e.g., designing a store and creating an
environment for comfortable shopping intimate or plus-size apparel)
and security (e.g., placing security guards) of the consumers in the store.
As more consumers are insulating themselves from world problems
such as crime and violence by staying home as much as possible (Solo-mon
Rabolt, 2004), they may want to be assured of security when
they do shop in brick-and-mortar stores.
For catalogs and the Internet, cost positively affected purchase inten-tion,
suggesting that multi-channel shoppers tend to be active shoppers
and are not concerned about shopping cost (i.e., money, time, and en-ergy).
Interestingly, they are more likely to buy clothing, jewelry, and
accessories when they perceive a higher level of expenditure in money,
time, and energy. Multi-channel shoppers may find products that are not
available from stores (e.g., the Gap company selling maternity clothing
only through the Internet). Also, catalog and online companies may em-phasize
selling exclusive or authentic products that are hard to find in
brick-and-mortar stores.
For the Internet channel, Assortment/Convenience also affected pur-chase
intention of clothing products. This finding suggests that providing
width and depth in products, as well as ease of navigation and convenient

Internet layout (e.g.,merchandise display and transaction) would increase
consumers’ intention to purchase. Due to the lack of interaction with
“live” salespeople and the resulting “do-it-yourself” mentality that results
from having to rely on one’s own abilities to locate and purchase mer-chandise,
adequate (i.e., quantity) and accurate (i.e., quality) amounts of
information are key parts of the service that online retailers must provide
(Janda, Trocchia, Gwinner, 2002).
Given the fact that the multi-channel shopper buys more because of
the channel alternatives, the multi-channel shopper should be able to
cross channels easily for information search, purchases, and post-pur-chases.
According to Buechner and Szczesny (2002), more than 30% of
Sears’ online purchases are made in the store; about one-fifth of these
shoppers end up making unplanned purchases in stores. In this respect,
multi-channel retailers need to use all channels to the best advantage.
This multi-channel advantage can only be achieved through continued
focus on the multi-channel customer. For example, the item purchased
online can be easily returned in the store; the retail store customer ser-vice
issue should be handled online or by telephone.
In conclusion, multi-channel retailers need to formulate a strategy
that enhances multi-channel consumer shopping experiences in all
channels of operations in order to increase consumer purchases. There
is a concern that internal competition among the distribution channels
may potentially cause unnecessary cannibalization in the same com-pany.
One consequence of this concern is that multi-channel retailers
ignore the fact that some channels might be better than others at differ-ent
points in the consumer purchase process. Offline stores, for exam-ple,
provide direct experience of the product, as well as established
logistics systems. On the other hand, catalog and online retailers can of-fer
easier price comparisons, around-the-clock operations, complete
product information, instant inventory status, and effortless communi-cation,
with low cost. Therefore, knowing how to exploit the advantages
of every channel is a basic yet powerful task for multi-channel retailers.
Moreover, accurate customer analysis and development of the corre-sponding
strategies seems to be crucial for successful multi-channel retail-ers.
As mentioned in the report “The Multi-Channel Consumer” by Boston
Consulting Group (2001), 88% of all Internet users are browsers and 42%
of all Internet users are online purchasers from their sample. Most compa-nies,
however, focus only on the latter, overlooking the significant con-sumer
segment that does not purchase online but whose offline purchases
may be influenced by online information (e.g., helping consumers come
close to a final choice or decide on a specific product). Given the fact that

the Internet plays its role in multi-channel environment, not only as a pur-chase
medium but as a guide leading consumers to other channel,
multi-channel retailers should build a contingent strategy based on how
consumers, including segments who are an active purchaser for one chan-nel
yet a tentative purchaser for another, select each channel.
LIMITATIONS AND FUTURE RESEARCH
This study may not be generalized to the population as a whole be-cause
the demographic characteristics of the sample did not follow nor-mal
distribution both in terms of ethnicity (i.e., 93% Caucasians) and age
(i.e., 63% ages 30 to 59 years). It is suggested that any future study be ex-panded
to include ethnic groups as well as other age groups. Including
other product categories/services also warrants comparison studies. Al-though
comparing male and female consumers was beyond the scope of
this study, it might provide rich information to multi-channel retailers in
planning their marketing mix (e.g., product, promotion) for each targeted
gender market. Further, the interaction between different shopping bene-fit
and cost parameters could be studied to facilitate a better understand-ing
of how each parameter eventually affects the purchase intentions.
The findings indicate that the confidentiality factor did not influence
purchase intention of online shopping for clothing products. Although
this is somewhat contrary to previous findings (Bhatnagar Ghose,
2004; MiyazakiFernandez, 2001) that reported security is amajor con-cern
for online shopping, the confidentiality factor in this study was com-posed
of privacy and security. In future research, these constructs may be
separated to see the impact of each construct on purchase intention.
REFERENCES
Abend, J. (2001). Tapping into a virtual world. Bobbin, 42(6), 38-47.
Anderson, J. C., Gerbing, D. W. (1988, May). Structural equation modeling in prac-tice:
A review and recommended two-step approach. Psychological Bulletin, 103,
411-23.
Baker, M. (1999). Multi-channel retailing. ICSC Research Quarterly, 6(3), 13-17.
Bhatnagar, A., Ghose, S. (2004). A latent class segmentation analysis of e-shoppers.
Journal of Business Research, 57(7), 758-767.
Buechner, M. M., Szczesny, J. R. (2002). Recharging Sears. Time Canada, 159(21),
38, 2p, 1c.
Chevron, J. (1999, October). Top of mind: Brick-and-mortar retailers not mass, but
class. Brandweek, 25, 30-32.

Downs, A. (1961). A theory of consumer efficiency. Journal of Retailing, 37, 6-12,
50-51.
Eastlick, M.,Feinberg, R. (1994). Gender differences in mail-catalog patronage mo-tives.
Journal of Direct Marketing, 8(2), 37-44.
Ernst Young. (2001). The multi-channel imperative. Stores, 83(2), 17-21.
Global Online Retailing Report. (2000). Consumer Trends in Online Shopping, pp. 5-9.
Gordon, H. (1994). Retailers are ready for relationship marketing. Direct Marketing,
56(9), 38-44.
Han, P.,Maclaurin, A. (2002). Do consumers really care about online privacy? Mar-keting
Management, 11(1), 35-38.
Haydock, M. (2000, August 21). Multichannel strategies win confidence.DMNews, p. 32.
Hill, S. (2000). Liz Claiborne: Liz Claiborne is one of the ‘in-line’ performers. Apparel
Industry Magazine, 61(6), 44.
Hoover, B. (2001). Multi-channel integration: The future success of e-tailing. Retrieved
March 12, 2001, from www.proxicom.com.
Hoyer, W. D.,Alperts, M. I. (1983). Additional theory and data contrasting measures
of attribute importance. In Patrick Murphy (Ed.), American Marketing Association
Educator’s Conference Proceedings (pp. 78-82).
Integrating Multiple Channels. (2001). Chain Store Age, pp. 24A-25A.
Janda, S., Trocchia, P. J., Gwinner, K. P. (2002). Consumer perceptions of Internet
retail service quality. International Journal of Service Industry Management, 13(5),
412-433.
Jarvenpaa, S. L., Todd, P. A. (1997). Consumer reactions to electronic shopping on
the World Wide Web. International Journal of Electronic Commerce, 1(2), 59-88.
Jasper, C., Lan, P. (1992). Apparel catalog patronage: Demographic, lifestyle and
motivational factors. Psychology Marketing, 9(4), 275-296.
Jöreskog, K., Sörbom, D. (1993). LISREL8: Structural equation modeling with
SIMPLIS command language. Chicago: Scientific Software International, Inc.
Kim, Y-K. (2002). Consumer value: An application to mall and Internet shopping. In-ternational
Journal of Retail and Distribution Management, 30(12), 595-602.
Kim, Y., Kang, J. (1997). Consumer perception of shopping costs and its relation-ship
with retail trends. Journal of Shopping Center Research, 4(2), 27-62.
Kim, Y-K., Kim, E., Kumar, S. (2003). Testing the behavioral intentions model of
online shopping for clothing. Clothing and Textiles Research Journal, 21(1), 32-40.
Kunz, M. (1997), On-line customer: Identifying store, product and consumer attributes
which influence shopping on the Internet. Unpublished dissertation, University of
Tennessee, Knoxville.
Kwon, Y., Paek, S., Arzeni, M. (1991). Catalog vs. non-catalog shoppers of apparel:
Perceived risks, shopping orientations, demographics, and motivations. Clothing and
Textiles Research Journal, 10(1), 13-19.
Lands’ End Improves Online Profitability via My Virtual Model Technology. (2001).
Direct Marketing, 64(2), 11.
Lindquist (1974-1975, Winter). Meaning of image. Journal of Retailing, 50, 29-38.
Miyazaki, A. D., Fernandez, A. (2001). Consumer perceptions of privacy and security
risks for online shopping. Journal of Consumer Affairs, 35(1), 27-44.

The Multichannel Consumer: The Need to Integrate Online and Offline Channels in
Europe (2001, July). The Boston Consulting Group.
The Multi-Channel Retail Report (2001). www.shop.org/research/TMCRR.html.
Paulins, V. A., Loren, V. G. (2003). The effect of consumer perceptions of store at-tributes
on apparel store preference. Journal of Fashion MarketingManagement,
7(4), 371-385.
Reda, S. (2001). VeriFone and Russell Renolds Associates top 100 Internet retailers.
Stores, http://www.store.org.
Retail Apparel Sales StatisticsTrends (1999-2000). http://retailindustry.about.com/
library/weekly/aa022200a.htm.
Retail Apparel Sales Statistics Trends (2000). http://retailindustry.about.com/
library/weekly/01/aa010319a.htm.
Schaeffer, P. N. (2000). Listen to the consumer. Global Online Retailing Report,
10-11.
Schoenbachler, D. D., Gordon, G. L. (2002). Multi-channel shopping: Understand-ing
what drives channel choice. Journal of Consumer Marketing, 19(1), 42-53.
Shim, S., Bickle, M. C. (1994). Benefit segments of the female apparel market:
Psychographics, shopping orientations, and demographics. Clothing and Textiles
Research Journal, 12(2), 1-12.
Shim, S., Drake, M. (1990). Consumer intention to purchase apparel by mail order:
Beliefs, attitude, and decision process variables. Clothing and Textiles Research
Journal, 9(1), 18-26.
Shim, S., Eastlick, M. A., Lotz, S. (2000). Assessing the impact of Internet shopping
on store shopping among mall shoppers and Internet users. Journal of Shopping
Center Research, 7(2), 7-44.
Shim, S., Kotsiopulos, A. (1992). Patronage behavior of apparel shopping: Part II.
Shopping orientations, store attributes, information sources, and personal charac-teristics.
Clothing and Textiles Research Journal, 10(2), 58-64.
Shop.org Report Highlights Multi-Channel “Super Shoppers.” (2001, November).
Stores, 16.
Solomon, M. R., Rabolt, N. J. (2004). Consumer behavior: In fashion. Upper Saddle
River, NJ: Prentice Hall.
Taylor, S. L.,Cosenza, R. M. (2000). The impact of E-commerce on the merchandis-ing
of women’s clothing in traditional shopping centers/mall. Journal of Shopping
Center Research, 7(2), 46-66.
Then, N. K.,Delong, M. R. (1999). Apparel shopping on the Web. Journal of Family
and Consumer Sciences, 91(3), 65-68.
Tiernan, M. (2000). How we catalogers can win on the Web. Catalog Age, 17(6), 95.
Verton, D. (2001). Churn, Computerworld, 35(6), 50.
Watchravesringkan, K., Shim, S. (2003). Information search and shopping intensions
through Internet for apparel products. Clothing and Textiles Research Journal, 21(1), 1-7.
Wilde, S. J., Kelly, S. J.,Scott, D. (2004). An exploratory investigation into e-tail im-age
attributes important to repeat, internet savvy customers. Journal of Retailing
Consumer Services, 11(3), 131-139.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end
model and synthesis of evidence. Journal of Marketing, 52(1), 2-22.

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