Comparing E-Satisfaction Ratings between
Click-and-Brick and Pure-Online E-Tailers
Vishwanath G Hegde • Zinovy Radovilsky • Tobias Gabriel
California State University East Bay, Hayward, CA
Alan S. Khade
California State University, Stanislaus, CA
Online e-satisfaction rating systems have become popular lately and have been serving as a digital
word-of-mouth platform for online customers. This also initiated academic research towards
understanding the link between e-satisfaction ratings and various capabilities of e-tailers. In this
paper, we examine whether the physical infrastructure capabilities of the click-and-brick e-tailers
results in better e-satisfaction ratings than that of pure-online e-tailers. We discuss the implications
of our findings for e-tailing operations, and also provide future research agenda in this area.
I. INTRODUCTION
In recent years, we have witnessed a
proliferation of online rating sites that report the
online or e-satisfaction ratings of e-tailers.
Online ratings sites such as BizRate.com,
Shopzilla.com,
Dealtime.com,
PriceGrabber.com, and Nextag.com, are widely
used by online customers. Many online
intermediaries such as google.com and
shopping.com report e-satisfaction ratings on etailers that are derived from these online rating
sites. The online rating sites are third party
systems that serve as a platform for online
shoppers to share their purchasing experience.
Customers who have made purchases over the
Internet write their e-satisfaction ratings and
comments on the e-tailers they have used. As an
increasing number of online rating systems
become available, more and more online
shoppers post ratings on different systems and
also use these ratings in making purchasing
decisions.
across different systems. Also, the averaged esatisfaction ratings of the same e-tailer inside a
rating system remained consistent over a one-
Thousands of e-tailers volunteer to
participate in the data collection attempts by the
online rating systems because of the significance
of these ratings on their sales. For example,
almost 99,000 e-tailers are listed on
BizRate.com site as off September 2007. Many
of these e-tailers display the BizRate symbol on
their websites in order to notify perspective
customers that these e-tailers have been rated by
a well-established online rating system. In
addition, these online rating systems have been
used by e-tailers to benchmark their
performances against competitors. Many online
rating systems also provide market reports from
their customer database via advanced research
tools and analysis.
This industry development has influenced
academic research as well. Many research
articles have been published lately that examine
the relationship between e-satisfaction ratings
and various aspects of e-tailing. For example,
Wang (2005) showed that the averaged esatisfaction ratings of an e-tailer were consistent
year period. Wang (2005) concluded that the esatisfaction rating systems are reliable and not
misleading to customers. Heim and Sinha (2001)
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California Journal of Operations Management © 2008 CSU-POM
Hegde, Radovilsky, Gabriel and Khade
Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
online rating systems. We investigated whether
there is a significant difference between the esatisfaction ratings of click-and-brick and pureonline e-tailers in the durable consumer products
category. The next section of this paper
describes the e-satisfaction rating system,
consumer durable product segments, comparison
methodology, and research hypothesis.
found that the order procurement variables
(website navigation, product information, price,
etc.) and order fulfillment variables (product
availability, timeliness of delivery, and ease of
return) monitored by BizRate, have significant
impact on customer loyalty. Heim and Sinha
(2002) developed a taxonomy to differentiate
electronic service products according to their
digital content (website features) and target
market segment (relative frequencies of
customer needs). Their empirical analysis based
on 52 electronic food retailers showed that the
electronic service configuration has a significant
impact on the BizRate e-satisfaction ratings.
Thirumalai and Sinha (2005) examined whether
customer expectations of order fulfillment
processes vary across product types. Their
empirical analysis was based on a sample of 256
e-commerce firms rated on BizRate. The authors
found that customer satisfaction with order
fulfillment will decrease moving along a
continuum of product types: convenience goods,
shopping goods, and specialty goods.
It is evident from the literature that
operations management is playing a critical role
in earning the customers loyalty in e-tailing.
However, the relationship between the physical
infrastructure of the e-tailer and the esatisfaction ratings has not been examined.
Specifically, is there a synergy between the
physical and online channels when traditional
retailers embrace hybrid brick-and-click model?
E-commerce researchers have shown that
traditional retailers are increasingly following a
click-and-brick strategy, whereby online and
physical retail channels are becoming more
integrated (Steinfield et al. 2002). The click-andbrick strategy is likely to be more valuable in
case of complex consumer durable products, for
example specialty products like appliances,
computers, electronics, etc. (Thirumalai and
Sinha, 2005).
The objective of this research is to examine
whether the physical infrastructure capability of
the click-and-brick e-tailers have an impact on
their order fulfillment performance as rated in
II. RESEARCH BACKGROUND AND
HYPOTHESES
2.1 BizRate Online e-Satisfaction Ratings
As previously described, a number of online
rating sites post e-satisfaction number on a large
number of e-tailers. Different online ratings
systems track different e-satisfaction measures.
Field et al (2004) listed and compared the
dimensions of e-satisfaction monitored by eight
major online rating systems. They concluded
that e-satisfaction measurements tracked by the
online rating sites match well with those
reported in academic literature. BizRate.com is
one of the most popular and credible ratings sites
that are listed in their research. In fact, BizRate’s
e-satisfaction ratings are used in many of the
research articles cited in this paper. Hence, we
have chosen BizRate as a source to providing esatisfaction ratings in our study. Details on the
BizRate’s data collection procedures and data
property are discussed below.
BizRate surveys B2C online customers and
asks them to evaluate the services provided by
the e-tailer from whom merchandize is bought.
To ensure that the respondents to BizRate’s
survey are representative of the population and
not just fans or detractors, BizRate conducts
validity checks on its non-respondents chosen
randomly. This validity check involves e-mail
follow-up to non-respondents to see whether the
answers by them were any different from those
who had responded earlier (Reibstein, 2002).
The survey results are published on BizRate’s
website. The ratings are not self reported by
firms, but are based on feedback provided by
other customers from their purchase experience.
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Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
2.2 Operational Performance
BizRate
organized
the
e-satisfaction
measurements into three groups which are based
on the data collection process: Pre-Ordering
Satisfaction, Post-Fulfillment Satisfaction and
Detailed Store Ratings. However, these
measurements are grouped differently by the
academic articles based the business processes.
For example, Field et al (2004) groups the
measurements by website design, fulfillment
reliability,
customer
service
and
privacy/security. We have chosen nine esatisfaction measures out of fifteen reported by
BizRate and have organized them into four
categories as shown in Table 1. We removed
website design/technology and privacy/security
measurements since our focus is to study the
operational performances.
2.3 Consumer Durable Product Segment
As mentioned in the introduction section, we
have chosen to compare the performance of
click-and-brick and pure-online e-tailers in
consumer durable product segment. We have
made this decision based on the findings of the
prior research, which indicated the increased
importance of the physical order fulfillment and
service infrastructure in the consumer durable
product segment. For example, Thirumalai and
Sinha (2005) showed that the customer
satisfaction with order fulfillment will decrease
moving along a continuum of product types:
convenience goods, shopping goods and
specialty goods.
TABLE 1: DESCRIPTION OF BIZRATE’S E-SATISFACTION MEASURES USED IN THIS
STUDY
E-satisfaction
Measure/Dimension
Selection of products
Availability of
product you wanted
Prices relative to
other online
merchants
Variety of shipping
options
Shipping charges
Order tracking
On-time delivery
Product met
expectations
Customer support
Description
Types of products available
Product was in stock at time
of expected delivery
Prices relative to other web
sites
Desired shipping options
were available
Shipping charges
Ability to track orders until
delivered
Product arrived when
expected
Correct product was
delivered and it worked as
described/depicted
Availability/Ease of
contacting, courtesy and
knowledge of staff,
resolution of issue
ESatisfaction
Category
Click-and-Brick”
Compared to PureOnline E-tailer
Product
Selection and
Price
Worse
Shipping
Alternatives
and Cost
Better
Order
Fulfillment
Customer
Support
Better
Better
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Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
customers choose the option of local store pickup. We expect that a click-and-brick e-tailer is
better equipped to fulfill the order quickly and
reliably because of the synergy between the
physical and online businesses. This synergy
plays a bigger role in case of consumer durable
products which are heavy and pose larger
logistical challenges. Further, a common order
processing system shared between online and
physical channels is another source of synergy.
Steinfield (2007) indicates that more firms build
their e-commerce capability in conjunction with
an existing IT infrastructure. Hence, the clickand-brick retailers are in a better position to
provide order tracking capability. This is evident
from the inability of the popular pure-online etailers such as Amazon.com to provide order
status online. Based on this analysis, we
established research Hypothesis 3: click-andbrick e-tailers have better e-satisfaction ratings
than those of pure-online e-tailers in the Order
Fulfillment category (see Table 1).
Physical and online channel synergies can
be exploited towards better online customer
service. For example, this may refer to prepurchase services such as helping customer in
assessing needs and selecting appropriate
products, and testing out products. Also,
physical locations can augment purchase
services that include ordering, customization,
and complementary products/services. This
process is likely to result in better match
between the product capabilities and customer
expectations. Post-purchase services such as
installation, repair, extended warranty and
training, can be provided effectively through
physical stores. Many click-and-brick firms
implemented a store-based return policy. Even
products ordered online and shipped from a
central facility can be returned to a local store
rather than requiring customers to return items
via courier. Overall, this analysis led to
establishing research Hypothesis 4: click-andbrick e-tailers have better e-satisfaction ratings
than those of pure-online e-tailers in the
Customer Support category (see Table 1).
2.4 Performance of E-Tailers and Research
Hypothesis
The product selection and pricing is one of the esatisfaction areas where pure-online e-tailers are
reported to have advantages over click-and-brick
e-tailers. Pure-online e-tailers can offer broader
product scope and product depth compared to
physical or hybrid channels. In addition, the
pure-online e-tailer is likely to have much lower
cost structure both in terms of facility and
inventory, which enables pure-online e-tailer to
offer lower prices. Further, the click-and-brick
retailers face channel conflict and online/offline
synchronization issues, and, thus, their online
channel may not be able to cut prices. Based on
this analysis, our research Hypothesis 1 is the
following: the e-satisfaction of click-and-brick
retailers is lower than that of pure-online etailers for the Product Selection and Price
category (see Table 1).
On the other hand, click-and-brick e-tailers
are likely to have a number of potential
advantages versus their pure-online counterparts,
especially in the last three e-satisfaction
categories listed in Table 1. A click-and-brick etailer can leverage the logistical network used
for the in-store retailing business. For example,
online customer can pick up and return
purchases in a local store. We would expect the
click-and-brick e-tailers can offer broader
shipment options. Also, these shipping options
can be offered at a lower cost. All this led us to
establish the research Hypothesis 2: click-andbrick e-tailers have better e-satisfaction ratings
than those of pure-online e-tailers in the
Shipping Alternatives and Cost category (see
Table 1).
Besides, click-and-brick e-tailers have the
ability to fulfill orders quicker and more reliably
compared to pure-online e-tailers. Typically,
click-and-brick firms has large retail network
with multiple inventory locations spread over the
geographical areas served. They are able to
search local store inventories or transfer
inventory from one location to the other using
established logistics infrastructure. Often online
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Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
Musical Instruments and Accessories” that fall
into consumer durables as shown in Table 2.
We selected 18 e-tailers in Appliances
department, 41 e-tailers in Sports Equipment
category and 46 e-tailers for Musical
Instruments category from the large number etailers listed in each product departments. We
employed the following procedure to select the
study sample:
: Step 1: E-Tailers listed in each product
department who is not ranked by customer
yet, were dropped from the dataset.
: Step 2: Each department is too broad and
includes large categories of products. For
example, Appliances department is further
classified into 57 product categories that
included simple appliances such as Toasters
to complex ones such as Dishwashers. Our
objective is to select e-tailers who sell
products with the following attributes: (1)
III. EMPIRICAL ANALYSIS
Performance measurements of e-tailers, obtained
from BizRate.com, are considered the dependent
variables in our empirical analysis. The
independent variables are based upon defining etailers as pure-online or click-and-brick. The
details of the data collection of these two
categories of data are described below. The data
was collected during the months of AugustSeptember 2007.
3.1 BizRate Performance Data Collection
BizRate reports e-satisfaction ratings on more
than 98,687 individual e-tailers, which are
organized by 21 departments. We chose three
product departments representing durable
specialty products: Heavy Home Appliances,
Sports Equipment and Outdoor Gear, and
TABLE 2: DESCRIPTION OF THE STUDY SAMPLE
Product
Department
Products category (used
to select E-Tailers)
Heavy
Home
Appliances
: Dishwasher
: Washer and Dryer
: Ranges
: Freezers
: Ovens
: Refrigerators
: Treadmill
: Trainer
: Exercise Bike
: Home Gym
Sports
Equipment
and Outdoor
Gear
Musical
Instruments
and
Accessories
: Mixers, Amplifiers,
Speakers
: Multi-track recorders
: Microphones
: Guitar, Drums, etc
: DJ
Retailer category
who carry these
products (Binary
variables)
: Home Stores
: Electronics
: General Retailer
: Dedicated Exercise
machines
: Outdoor Products
: Other products
: General retailers
: Dedicated musical
instruments
: CD and DVD
: Electronics
: Computer
: General retailers
Number
of E-Tailers
ClickPureandOnline
Brick
4
14
12
29
6
40
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Hegde, Radovilsky, Gabriel and Khade
Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
:
:
analysis. These variables describe whether the etailer also has both physical and online retailing
or not. This is a binary variable which indicates
“0” if the e-tailer has pure-online model and
indicates “1” if the e-tailer has both online and
the physical retailing models (click-and-brick).
higher purchase price, (2) higher product
weight, (3) higher product complexity, and
(4) higher customer support (during order
life cycle). To achieve this objective, we
selected specific product categories within
each department (as shown in Table 2) based
on these four criteria.
Step 3: We selected e-tailers who are selling
all product categories listed within each
department for our empirical analysis. This
judgment was done by visiting each e-tailer’s
webpage and verifying whether all the
product categories are sold there.
3.4 Multiple Regression Analysis
To determine whether the physical retailing
infrastructure has any influence on the e-tailers’
performance, we used multiple regression
analysis on the three data sets in Table 2 using
SPSS software. The regression results are
presented in Tables 3, 4 and 5. The rows in each
table describe the dependent variables for each
dataset. The columns represent the independent
variables (coefficients of the regression) and
constant in each regression equation. The tables
also contain associated p-values in parentheses.
The overall model fit is measured by the
coefficient of determination R2, which is
presented in the last column.
In this selection process we also noticed that a
number of e-tailers did not sell the product
categories and department even though they are
listed under them. This error was also got
corrected because of our sample selection
method.
3.2 Control of Cross-Department Differences
An important property of the BizRate data was
related to the fact that the aggregated esatisfaction ratings were listed at the e-tailer
level. An individual e-tailer may be listed in
multiple product departments. Hence, the
ranking of a company listed in Appliances
department can be also derived from products of
multiple departments. For example, Best Buy's
ratings can be derived from multiple
departments such as Appliances, Electronics,
Computers and Software, etc. We assumed that
each department could have different esatisfaction rating patterns. We defined variables
to categorize the retailer based on the types of
product departments they are selling. These
retailer categories are binary variables which
would be treated as control (independent)
variables and capture cross-product department
patterns.
IV. DISCUSSIONS OF THE FINDINGS
AND CONCLUSIONS
The significance of the theoretical development
and the model specification is evident from the
R2 values of the regression equations. The R2
value varied from as high as 0.569 to as low as
0.128. These R2 are significant as compared to
the R2 values used in e-tailing research articles
such as Steinfield et al. (2005). The coefficients
that are significant at p-value of 0.05 or lower
are highlighted in bold. The significance of the
product department level differences is evident
from the significance of the coefficients in the
control variables columns for many of the esatisfaction ratings. The base case represents the
pure-online e-tailer with dedicated/niche product
range. The e-satisfaction of General Retailers
and retailers with broader product-line either not
significant or worse compared to the base case in
all the three product segments. The only
exception is the “Electronics” category as shown
by the coefficients in Table 3 and Table 5.
3.3 Data on Pure-Online and Click-and-Brick
Classification
As previously stated, e-tailer classification
variables (brick-and-click and pure-online etailers) are the independent variables in our
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Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
TABLE 3: REGRESSION RESULTS FOR HEAVY HOME APPLIANCES
E-tailer Category **
ClickR2
(Control Variables)
andVariables
Constant
Brick
General
Electronics
Retailer
0.129
0.164
8.700
0.160
Selection of products
0.569
(0.594)
(0.875)
(0.000)
(0.005)
Price relative to other online
0.209
0.027
8.729
-0.526
0.560
merchants
(0.150)
(0.878)
(0.000)
(0.008)
0.253
8.557
0.679
-0.616
Availability of product
0.565
(0.349)
(0.000)
(0.005)
(0.029)
0.347
8.143
0.621
-0.584
Variety of shipping options
0.543
(0.165)
(0.000)
(0.005)
(0.024)
0.610
0.375
-0.659
7.800
0.232
Shipping charges
(0.134)
(0.453)
(0.185)
(0.000)
0.560
-0.510
8.186
1.016
Order tracking
0.541
(0.111)
(0.134)
(0.000)
(0.002)
0.637
-0.480
8.171
1.025
0.464
On-time delivery
(0.117)
(0.215)
(0.000)
(0.004)
0.246
-0.372
8.557
0.522
Product met expectations
0.341
(0.394)
(0.193)
(0.000)
(0.033)
0.563
0.191
-0.095
7.786
0.129
Customer support
(0.183)
(0.713)
(0.851)
(0.000)
** Retail store category “Home products” is dropped from regression; hence it is the base case
TABLE 4: REGRESSION RESULTS FOR SPORT EQUIPMENT AND OUTSIDE GEAR
E-tailer Category **
(Control Variables)
Click-andVariables
Brick
Other
General
Outdoor
Products
Stores
0.033
-0.143
0.083
8.618
-0.354
Selection of products
(0.836)
(0.331)
(0.346)
(0.000)
(0.000)
Price relative to other online
-0.007
-0.096
-0.022
8.696
-0.268
merchants
(0.967)
(0.517)
(0.805)
(0.000)
(0.005)
-0.053
-0.210
8.990
-0.540
-0.318
Availability of product
(0.838)
(0.153)
(0.000)
(0.027)
(0.030)
0.487
0.201
-0.047
0.092
8.149
Variety of shipping options
(0.179)
(0.538)
(0.811)
(0.645)
(0.000)
-0.287
0.109
-0.362
8.341
-0.926
Shipping charges
(0.479)
(0.766)
(0.113)
(0.000)
(0.000)
0.127
-0.186
0.001
8.706
-1.106
Order tracking
(0.688)
(0.285)
(0.996)
(0.000)
(0.000)
0.447
-0.048
0.088
8.657
-0.632
On-time delivery
(0.147)
(0.771)
(0.602)
(0.000)
(0.027)
0.120
-0.275
-0.010
8.750
-0.299
Product met expectations
(0.572)
(0.159)
(0.930)
(0.000)
(0.013)
0.627
-0.072
0.078
8.247
-0.822
Customer support
(0.151)
(0.759)
(0.744)
(0.000)
(0.041)
** Retail store category “Dedicated Exercise Machines” is dropped; it is the base case
Constant
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R2
0.325
0.199
0.206
0.074
0.414
0.323
0.223
0.207
0.196
Hegde, Radovilsky, Gabriel and Khade
Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
TABLE 5: REGRESSION RESULTS FOR MUSICAL INSTRUMENTS AND ACCESSORIES
Variables
Selection of products
Price relative to other
online merchants
Availability of
product
Variety of shipping
options
Shipping charges
Order tracking
On-time delivery
Product met
expectations
Customer support
Constant
8.572
(0.000)
8.895
(0.000)
8.581
(0.000)
8.578
(0.000)
8.316
(0.000)
8.169
(0.000)
8.299
(0.000)
8.760
(0.000)
7.860
(0.000)
CD and
DVD
0.128
(0.685)
-0.245
(0.174)
0.469
(0.139)
0.122
(0.689)
-0.566
(0.322)
0.631
(0.188)
0.701
(0.104)
-0.210
(0.517)
0.940
(0.212)
E-tailer category **
(Control Variables)
General
Electronics
stores
0.134
0.137
(0.372)
(0.434)
0.054
-0.148
(0.527)
(0.141)
0.611
0.365
(0.000)
(0.042)
0.141
-0.167
(0.334)
(0.326)
-0.477
-0.674
(0.084)
(0.038)
0.714
0.665
(0.003)
(0.015)
0.750
0.653
(0.001)
(0.008)
0.257
-0.008
(0.102)
(0.964)
0.462
0.682
(0.199)
(0.106)
Computer
ClickandBrick
-0.372
(0.386)
0.005
(0.983)
0.219
(0.607)
-0.978
(0.022)
-1.916
(0.017)
0.431
(0.505)
0.501
(0.388)
-0.260
(0.556)
-0.760
(0.456)
-0.369
(0.052)
-0.439
(0.000)
-0.667
(0.001)
-0.342
(0.062)
0.014
(0.967)
-0.522
(0.068)
-0.593
(0.022)
-0.325
(0.095)
-0.522
(0.240)
R2
0.128
0.424
0.435
0.284
0.191
0.258
0.331
0.191
0.129
** Retail store category “Dedicated Musicals” is dropped from the regression; it is the base case
Home Appliances) and on-time delivery (in
Musical Instruments and Accessories). The
associated coefficients were either negative or
insignificant. This led us to conclude that the
established Hypotheses 2 and 3 have to be
rejected, i.e., click-and-brick e-tailers did not
produce better e-satisfaction ratings in Shipping
Alternatives and Cost, and Order Fulfillment
categories as compared with those in pure-online
e-tailers.
Surprisingly, click-and-brick e-tailers did
not show a better performance on the customer
support dimensions. The coefficients for these
dimensions were negative but statistically
insignificant in all three product departments
(see Tables 3-5). This means that our
hypothesized comparative advantages of clickand-brick e-tailers in the customer support
dimension are not supported by the regression
results. Thus, we need to reject Hypothesis 4 that
the brick-and-click e-tailers have better e-
According to the results in the tables,
click-and-brick e-tailers performed worse than
pure-online companies on the first three esatisfaction dimensions, i.e., selection of
products, price compared to competition and
product availability. The coefficients for these
dimensions in the “Brick-and-Click” columns of
the Tables 3-5 are either negative (the opposite
relationship) or insignificant.
The only
exception was the Heavy Home Appliances
department where click-and-brick e-tailers
showed better selection of products. Thus, our
Hypothesis 1 of e-satisfaction on the Product
Selection and Price category is supported.
Overall, this means that pure-online e-tailers
provide a better e-satisfaction for this category
and its respective measurements/ dimensions
than those of the brick-and-click counterparts.
The results in Tables 3-5 also demonstrate
that click-and-brick e-tailers did not show better
performance compared to pure-online companies
in terms of variety of shipping options (in Heavy
California Journal of Operations Management, Volume 6, Number 1, February 2008
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Hegde, Radovilsky, Gabriel and Khade
Comparing E-Satisfaction Ratings Between Click-and-Brick and Pure-Online E-Tailers
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Management, 13(4), 2004, 291-307.
Heim, G. R, Sinha, K. K., “Operational Drivers
of Customer Loyalty in Electronic Retailing:
An Empirical Analysis of Electronic Food
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Heim, G. R., Sinha, K. K., “Service Process
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Retailers,” Production and Operations
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Randall, T., Netessine, S., Rudi, N., “An
Empirical Examination of the Decision to
Invest in Fulfillment Capabilities: A Study of
Internet Retailers,” Management Science,
52(4), 2006, 567-580.
Reibstein, D. J., “What Attracts Customers to
Online Stores, and What Keeps Them
Coming Back?” Journal of the Academy of
Marketing Science, 30(4), 2002, 465-473.
Steinfield, C, “Capitalizing on Physical and
Virtual Synergies: The Rise of Click and
Mortar
Models,”
Retrieved
from:
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Steinfield, C. Adelaar, T., Liu, F., “Click and
Mortar Strategies Viewed from the Web: A
Content Analysis of Features Illustrating
Integration Between Retailers’ Online and
Offline Presence,” Electronic Markets, 15(3),
2005, 199–212.
Thirumalai, S., Sinha, K. K., “Customer
Satisfaction with Order Fulfillment in Retail
Supply Chains: Implications of Product Type
in Electronic B2C Transactions,” Journal of
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satisfaction ratings in the Customer Support
category that those of pure-online e-tailers.
The results of this study have significant
implications for academic research and also for
managers of click-and-brick e-tailers. Several
research articles have been proposing the hybrid
click-and-brick model based on the concept of
the synergy between physical and online models
(for example, Steinfield et al., 2005; Steinfield,
2007). There have been discussions on the
impact of the investment in physical/supply
chain infrastructure by e-tailers on the financial
performance (For example, Randall et al., 2006).
However, our findings do not show any
advantage for the click-and-brick e-tailers as far
as online e-satisfaction is concerned. This raises
the following questions: (1) is poor online and
offline channel integration by click-and-brick etailers driving this? (2) is outsourcing supply
chain processes by pure-online e-tailers a better
alternative than developing their own
capabilities. The future research should be able
to find the answer to these questions. Answering
these two questions may be also important for
brick-and-click managers to develop more
efficient e-commerce strategy.
This study was intended to be an
exploratory in nature and has certain limitations.
First, we have examined only three product
departments that fall into consumer durable
product segments. Extension of this inquiry into
other product categories within consumer
product segments is necessary to ascertain our
findings, and also to generalize the results.
V. REFERENCES
Field, J. M., Heim, G. R, Sinha, K. K.,
“Managing Quality in the E-Service System:
Development and Application of a
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