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Service-Product Configurations in Electronic Retailing: A Taxonomic Analysis of Electronic Food Retailers
Gregory R. Heim Wallace E. Carroll School of Management
Boston College Chestnut Hill, MA 02467
Phone: (617) 552-0462; Fax: (617) 552-0433; E-mail: [email protected]
Kingshuk K. Sinha* Operations and Management Science Department
Carlson School of Management University of Minnesota 321 – 19th Avenue South Minneapolis, MN 55455
Phone: (612) 624-7058; Fax: (612) 624-8328; E-mail: [email protected]
WORKING PAPER
Last Revised: October 2, 2000 This study was supported by a grant from the Alfred P. Sloan Foundation by way of The Retail Food Industry Center, University of Minnesota, and by a Grant-in-Aid of Research from the University of Minnesota. Findings of the paper were presented at a workshop on e-business at Indiana University, Bloomington (October 22-23, 1999). The paper has benefited from the comments of the workshop participants, Alan Shocker, Paul Wolfson, the senior editor, and the editor. All errors and omissions are the responsibility of the authors.
* Corresponding author.
Service-Product Configurations in Electronic Retailing: A Taxonomic Analysis of Electronic Food Retailers
Abstract. Service-products of electronic retailers consist of bundles of physical goods, offline services, and digital content. We analyze data on service-product attributes from a sample of 255 electronic food retailers to develop a taxonomy of electronic service-products. The taxonomy is comprised of eight service-product configurations. Digital content and target market segment variables provide the dimensions along which the service-product configurations are differentiated in the taxonomy. We examine and find positive and significant correlation between the ordering of configurations in the taxonomy and (i) customer satisfaction with product information, product selection, and overall service quality, and (ii) customer loyalty. We also find that operations variables such as product availability, timeliness of delivery, and customer support exhibit positive and significant correlations with overall service quality and customer loyalty. We discuss the implications of the study findings for managing electronic food retailing operations.
1. Introduction
Electronic retailers can now deliver service-products to individual customers located
almost anywhere and at any time. Service-products of electronic retailers consist of bundles of
physical goods, offline services, and digital content. Recently, we have witnessed an explosive
growth of electronic retailing and an accompanying proliferation of electronic service-products.
Very little, however, is reported either in the academic or practitioner literature about the typical
configurations of electronic service-products. The premise of this paper is that an understanding
of how electronic service-products can be classified is fundamental to comprehending the
developments and possibilities in electronic retailing, and managing electronic retailing
operations effectively. To this end, we develop a taxonomy of electronic service-products with
data from electronic food retailers. We then examine the association of service-product
configurations in the taxonomy and other relevant variables with (i) customer satisfaction, and
(ii) customer loyalty.
Electronic food retailers bundle a wide variety of complementary (food and non-food)
goods, offline services, and digital content into their service-products. They provide graphical
1
and textual information about their products to serve as a substitute for the usual food shopping
experience involving not only sight, but also smelling, touching, and tasting. These retailers also
serve as an educator about their products, and provide information about food preparation, food
consumption, and nutritional value. The fact that many food items are perishable also makes
service-products of electronic food retailers time-sensitive.
The value propositions emphasized by electronic food retailers include convenience,
price, and delivery. The retailers promise to save customers’ time and effort, promise prices at
least as competitive as supermarket prices, and offer home delivery (Kotha and Euler 2000). So,
at a time when customers are becoming increasingly crunched for time with the number of hours
at work rising and the number of dual-income households increasing, electronic food retailing
provides an attractive alternative to traditional food retailing (PricewaterhouseCoopers 2000).
Results of two recent surveys indicate a surge in the growth of electronic food retailing and high
customer satisfaction with electronic food retailers. A survey of Internet users conducted by PC
Data Online and Goldman Sachs from October 31 to December 26, 1999 found that electronic
purchase of food rose 950 percent1 (Zwiebach 2000). Another survey conducted by BizRate.com
between November 14 and December 4, 1999 concluded that customer satisfaction with
electronic retailing sites for food and wine was rated the highest, followed by the sites for toys,
gifts, home and garden, apparel, computers, entertainment, and consumer goods (Hansell 1999).
The remainder of the paper is organized as follows. In section 2, we discuss the empirical
context of this research. Section 3 contains a discussion on taxonomic analysis of data on
electronic service-products. Section 4 presents the taxonomy of electronic service-products and
describes the service-product configurations in the taxonomy. Section 5 examines the
1 During the same period electronic sales of toys rose 483 percent, sales of flowers and cards rose by 348 percent, and purchases of videos and DVDs rose 308 percent.
2
relationship between the configurations in the taxonomy and other relevant variables with
customer satisfaction, and customer loyalty. Section 6 contains our concluding remarks.
2. The Empirical Context
2.1. The Study Sample
The sample for this study contains 255 electronic food retailers. Data on service-products
from the retailers in the study sample were collected during the months of May 1998 to June
1999. No comprehensive directory of electronic food retailers existed prior to the study. Since
food retailers often sell many non-food goods and services, the basic selection criterion for being
included in our directory was selling at least one item of food. We first pooled addresses of
electronic food retailers from Internet search engines and sites that maintained address lists,
giving us a preliminary list of food-related sites on the World Wide Web. Each site was then
visited and classified as a food-retailing site, a non-retailing site, or non-operational. After
removing the non-retailing and non-operational sites, the sample frame included approximately
650 electronic food retailing sites. Additional food retailers were added to this directory as they
were identified. The study sample of 255 electronic food retailers was chosen randomly from this
list. Prior to the empirical analysis, a single retailer was identified as an outlier and removed
from the data set. Due to the extremely large variety of items sold by the site, it differed radically
from the study sample, and its attributes were judged to be infeasible to be manually downloaded
and tabulated. The retailer was assigned its own configuration, leaving 254 sites in the study
sample. Characteristics of the study sample are summarized in Table 1.
3
Table 1 Characteristics of the Study Sample (n=255) Number in Sample Percent of Sample
Number of Products Offered 1-10 11-50 51-100 101-250 251-500 501-1000 1001-10,000 10,000+
62 82 30 34 23 14 9 1
24.3% 32.2% 11.8% 13.3% 9.0% 5.5% 3.5% 0.4%
Primary Food Category Beverages (not Coffee) Coffee Meat/Seafood Fresh Produce/Vegetables Dairy Broad Grocery Services Dessert/Baked Goods Candy Gift Baskets Gourmet Food Ethnic Food Sauces/Hot Sauce Specialty/Other Foods
35 23 35 14 5 6
24 20 12 27 7
22 25
2.2. Collection of Data on Electronic Service-Products
The collection of data was guided by the conceptual classification scheme of electronic
service-products proposed in a companion paper (Heim and Sinha 2000). As shown in Figure 1,
the conceptual classification scheme is a two-by-two matrix that differentiates electronic service-
products according to their digital content – either static or dynamic, and target market segment
– either unique or broad. Static digital content is downloaded to customers’ service delivery
technology without modification, whereas dynamic digital content is generated when requested
based on the inputs and actions of the customers and the current state (e.g., system settings,
database records) of the electronic service system. Target market segment is the inverse of target
market dichotomies derived from competitive scope (Porter 1985), and represents relative
frequencies of customer needs.
4
Figure 1. Conceptual Classification Scheme for Electronic Service Products (Heim and Sinha 2001, p. 288)
Digital Content
Market Segment Static Dynamic
Unique
Niche Market
Low Volume/Scale Low Variety/Scope
Low Online Customization No Joint Branding
Customized Mega Market
High Volume/Scale High Variety/Scope
High Online Customization High Joint Branding
Broad
Market Extenders
Low-Medium Volume/Scale Low-Medium Variety/Scope
Low-Medium Online Customization Low Joint Branding
Dynamic Mass Market
Medium-High Volume/Scale Medium-High Variety/Scope
Medium/High Online Customization Medium Joint Branding
Digital content and target market segment – the two primary dimensions along which we
differentiate service-products – also describe related customer needs. Demand for static and
dynamic digital content results from non-situational and situational customer needs, respectively
(Shocker and Srinivasan 1979). Non-situational customer needs remain unaffected by time-
specific situations and can be fulfilled through static content designed prior to an electronic
transaction. In contrast, situational customer needs necessitate a real-time service-product
designed for the characteristics of a customer’s situation at a time and place, and they may result
from a high level of complexity in an existing service-product. Thus, situational customer needs
often must be evaluated and fulfilled dynamically. Designing static products for situational
customer needs could provide lower utility and not lead to a purchase (Shocker and Srinivasan
1979).
5
The four cells in Figure 1 correspond to four categories of electronic service-products:
niche market, market extender, dynamic mass market, and customized mega-market. The
ensuing paragraphs describe each of the four categories in the conceptual classification scheme.
Niche Market. The niche market service product category serves small sets of
idiosyncratic customers who generate low demands for service products with unique and static
attributes. In this category, static online content is often packaged with offline services.
Customers are likely to perceive offline services as a core service product dimension.
Dynamically customized online content provides little strategic advantage when customer needs
are unique but not time-critical. Companies are not likely to develop service products with high
online customization when the core attributes of their service product are idiosyncratic physical
goods and offline services.
Market Extender. The market extender category expands the static digital content of its
service product. The digital content is used to describe a broad portfolio of mass market goods
and service attributes. Together they fulfill the common needs of a higher volume of customers
than the niche market category. Large volumes of varied static digital content can facilitate
greater self-customization by customers, but also can create a complex and time consuming task
for customers. Since demands for goods and offline services in this category can rise to levels
that are difficult to customize, market extender service products are characterized by increased
standardization of offline services, and increased online customization.
Dynamic Mass Market. The dynamic mass market service product category meets
situational needs in a time-critical manner for a broader group of customers than can be targeted
by market extender service products. As demand increases, customization of goods and services
becomes increasingly complex and costly. Further, with a greater breadth of service product
6
offerings, the volume of digital content creates a situational need in which customers need online
customization to help them search through service product attributes and filter out those that are
irrelevant. In spite of the variety in dynamic mass market service products, their large target
markets provide incentives for competitors to deliver similar service product attributes,
potentially leading them to become commodities. Providers of dynamic mass market service
products may attempt to differentiate their service products by jointly branding them to create
service products that are more difficult to imitate.
Customized Mega Market. The customized mega market service product category caters
to the situational, idiosyncratic needs of customers that can only be satisfied through online
customization. These providers avoid the problem of imitation in the dynamic mass market
category by offering service products that deliver unique value for these unique needs. This
approach builds significant barriers for other service providers intending to enter the same
market. When the service product of an individual service provider is limited to mass market
attributes they may employ alliances to aggregate their brand with other brands, making it
possible to deliver less substitutable service products to aggregated groups of customers.
3. Taxonomic Analysis
We collected data on proxy variables for the service-product dimensions of digital
content and target market segment breadth. The variables are presented in the first column of
Tables 2 and 3. The data type of the variables in Table 2 was either count or interval, and the
data type of the variables in Table 3 was dichotomous. The data were collected via direct
observation by visiting each of the electronic food retailing sites in the study sample.
7
Data on service-products were analyzed following the guidelines in the taxonomy
development literature (Ein-Dor and Segev 1993, Ketchen and Shook 1996, Miller and Roth
1994). We limited our analysis to variables that could be transformed to approximate normality
(Anderberg 1973), and employed a two-step method for constructing taxonomies suggested by
Ketchen and Shook (1996). The variables were first transformed appropriately to remove the
positive skew often observed in count and non-negative continuous data (Johnson and Wichern
1992). The transformations applied to the variables are presented in Panel A of Table 2. We then
examined each transformed variable for approximate univariate normality using histograms, for
the absence of nonlinear relationships between pairs of variables using scatterplots (Bollen
1989), and for skewness and kurtosis using P-P and Q-Q plots (SPSS 1998). Next, factor analysis
was used to reduce these variables to three factor score variables representing (i) static content,
(ii) dynamic content, and (iii) target market segment breadth. Panel A of Table 2 also presents
the factor loading for each of the variables.
Hierarchical cluster analysis was used to analyze the factor scores to determine a
reasonable number of clusters for a second round of cluster analysis. This two-stage procedure
has been recommended since it appears to improve cluster analysis solutions (Punj and Stewart
1983, Ketchen and Shook 1996). The results of this first stage were used as the initial cluster
centers for subsequent K-means cluster analyses. Lehmann’s rule (Miller and Roth 1994)
suggested the data could support from four to eight clusters. As one cluster already had been
assigned, up to seven clusters could be justified for the remaining observations. We examined the
factor scores using Ward’s clustering method for solutions up through seven clusters and found
that the clusters exhibited large between-cluster distances even for the seven cluster solution,
which we analyzed further.
8
We used the cluster means from the seven-cluster solution as a starting point for a seven-
cluster K-means cluster analysis, the results of which we now discuss. We examined the
reliability of the seven-cluster solution by using a hold-one-out discriminant analysis jackknife
procedure available in SPSS (1998). This analysis showed that 97.6 percent of the retailers
classified by cluster analysis remained in the same clusters when re-classified using discriminant
analysis. This finding provided support for the solution. We also examined scatter plots of the
factor scores and of canonical variates from the discriminant analysis procedure, and found that
each configuration could be differentiated from others along at least one of the three dimensions.
We next analyzed the clusters using the variables reduced into the three factor scores.
Panel B of Table 2 presents the cluster means, and F-statistics for one-way ANOVA performed
on the transformed variables. Panel B of Table 2 also presents non-parametric Kruskal-Wallis
rank tests of the means, which are interpreted similarly. We order the clusters presented in Panel
B of Table 2 (and subsequently in Tables 3, 4, and 6), based on the distances of their means from
Cluster 1. Below each of the cluster means, we present Scheffé multiple comparisons, in which
identical integers indicate insignificantly different sets of means. The numbers in parentheses
below each statistic are the p-values. The analyses indicate differences between the service-
product clusters. The Scheffé procedure also indicates similar patterns of differentiation across
the clusters.
Overall, the service-product variables vary systematically across the clusters. The static
content variables show a service-product with very few graphical or textual components in the
leftmost clusters, but a large number of them in the rightmost clusters that mix static and
dynamic content. The dynamic content variables indicate that not all food retailers shift wholly
from static to dynamic content when the static content in their service-products reaches high
9
levels. Instead, some maintain high levels of static and dynamic content. The target market
segment variables also change as one moves from the static to the dynamic content clusters. The
leftmost clusters have a small number of goods that the retailers ship nationwide, but on average,
they only accept one form of payment and only ship goods in one way. The limitations on
shipping and payment options constrain the customer group these retailers can reach. In contrast,
the rightmost clusters exhibit attributes expected in a service-product targeted at a larger market.
The retailers of such service-products offer hundreds or thousands of goods for delivery
worldwide, and maintain the largest number of methods for payment and shipping.
Following Miller and Roth (1994), we analyzed the clusters using dichotomous variables
not used in building the taxonomy. Table 3 presents the results, including frequencies of the
attributes and chi-square statistics for contingency table tests of independence between the
attributes and the clusters. Within each cell, the top number represents a count of the retailers
that offered the service-product attribute, and the bottom number represents the related
percentage. As in the study by Palmer and Griffith (1998), we find that presently many of the
potential attributes are infrequently included in service-products. However, two patterns are
evident in Table 3. First, the dynamic content for online customization tends to increase as one
moves toward the higher numbered clusters. Second, retailers of service-products in Clusters 1
through 4 tend to target local or national markets, while retailers of service-products in Clusters 5
through 7 tend to target worldwide markets.
10
Table 2 Cluster Analysis Variables, Data Types and Transformations, Factor Loadings, and Cluster Analysis Results PANEL A PANEL B
Cluster Analysis Results
Service-Product Dimension
Data Type
Trans-
formation
Factor
Loading
Clstr. 1 (n=41)
Clstr. 2 (n=43)
Clstr. 3 (n=51)
Clstr. 4 (n=44)
Clstr. 5 (n=34)
Clstr. 6 (n=23)
Clstr. 7 (n=18)
F
(p-value)
Kruskal-Wallis H (p-value)
Digital Content: Static Number of static pages or frame options
Count
ln(Y+1) 0.783 6.80
1,2
4.93
1
13.31
1,2
12.18
1,2
46.50
2
17.35
1,2
138.28
3
24.437 (0.000)
119.087 (0.000)
Kilobytes of HTML files Interval
ln(Y+1) 0.782 21.93
1
23.13
1
90.63
1
58.43
1
372.07
1
85.06
1
2521.83
2
15.630 (0.000)
145.277 (0.000)
Kilobytes of graphics Interval
ln(Y+1) 0.838 190.16
1
109.85
1
529.16
1
349.36
1
1356.09
1
3248.86
2
6983.26
3
33.860 (0.000)
164.628 (0.000)
Number of graphics files Count
ln(Y+1) 0.855 12.1
1
9.1
1
35.1
1
23.9
1
109.6
1
943.0
1
536.0
1
2.935 (0.009)
190.814 (0.000)
Digital Content: Dynamic Number of dynamically generated pages
Count
ln(Y+1) 0.926 0.15
1
1.05
1
7.88
1
1.48
1
19.06
1
513.61
1,2
233.50
2
4.897 (0.000)
131.652 (0.000)
Number of dynamic service attributes
Count
ln(Y+1) 0.926 0.00
1
0.02
1
0.16
1
0.00
1
0.14
1
1.78
2
1.61
2
83.838 (0.000)
180.260 (0.000)
Target Market Segment Breadth
Number of goods offered Count
ln(Y+1) 0.732 11.5
1
46.3
1
63.4
1
108.5
1
328.3
1,2
514.7
2
653.1
2
8.452 (0.000)
144.397 (0.000)
Number of offline services offered
Count
ln(Y+1) 0.371 0.6
1
6.9
1
1.5
1
2.8
1
11.9
1
4.6
1
3.4
1
1.066 (0.384)
233.968 (0.001)
Number of payment options
Count
sqrt(Y) 0.711 0.4
1
2.7
2
2.4
2
3.8
3
4.0
3
4.1
3
4.6
3
49.444 (0.000)
130.527 (0.000)
Number of shipping options
Count
sqrt(Y) 0.727 1.0
1
1.3
1
1.2
1
2.0
1,2
2.8
2
2.9
2
4.2
3
22.648 (0.000)
106.048 (0.000)
11
Table 3 Service-Product Cluster Frequencies of the Dichotomous Variables
Service-Product ClustersService-Product Clstr. 1 Clstr. 2 Clstr. 3 Clstr. 4 Clstr. 5 Clstr. 6 Clstr. 7 χ2
Dimensions and Attributes (n=41) (n=43) (n=51) (n=44) (n=34) (n=23) (n=18) (p-value)Digital Content: Static Online periodical or magazine 1
(2.0%) 2
(5.9%) 3
(13.0%) 8
(44.4%) 63.603b (0.000)
Digital Content: Dynamic Chat facility on site 1
(2.0%) 1
(5.6%) 7.606b (0.268)
Message board on site 1 (2.3%)
1 (2.0%)
2(5.9%)
5 (21.7%)
5 (27.8%)
35.805b (0.000)
Site search system available 6 (11.8%)
3(8.8%)
5 (21.7%)
3 (16.7%)
149.354a (0.000)
Site sort system available 4 (17.4%)
1 (5.6%)
33.827b (0.000)
Expert system available 4(17.4%)
5 (27.8%)
51.661b (0.000)
Email updates and marketing offered
1 (2.4%)
3 (7.0%)
13 (25.5%)
11 (25.0%)
14 (41.2%)
11 (47.8%)
9 (50.0%)
36.253a (0.000)
Periodic email newsletter offered
1 (2.0%)
1 (1.9%)
1 (2.9%)
4 (17.3%)
9 (50.0%)
72.166a (0.000)
Target Market Segment Breadth Shipping targeted at local 6
(14.6%) 2
(4.7%) 3
(5.9%) 8
(18.2%) 6
(17.6%) 1
(4.3%) 2
(11.1%) 8.564a (0.200)
Shipping targeted at nation 24 (58.5%)
25 (58.1%)
29 (43.1%)
30 (68.1%)
21 (61.8%)
18 (78.3%)
9 (50.0%)
5.380 (0.496)
Shipping targeted at several nations
2 (4.9%)
4 (9.3%)
4 (7.8%)
3 (6.8%)
2 (5.9%)
5 (21.7%)
5 (27.8%)
12.630a (0.049)
Shipping targeted at world 11 (26.8%)
12 (27.9%)
18 (35.3%)
12 (27.3%)
19 (55.9%)
11 (47.8%)
10 (55.6%)
14.256 (0.000)
Service includes a club membership
1 (9.1%)
9 (20.9%)
2 (3.9%)
4 (9.1%)
8 (23.5%)
11 (47.8%)
6 (33.3%)
36.017a (0.000)
a One or more cells had expected counts below 5 but not below 1. b One or more cells had expected counts below 1.
12
4. Electronic Service-Product Configurations
The eight configurations in the taxonomy of electronic service-products are presented in
the last column of Table 4. The first seven configurations in the taxonomy correspond to the
clusters identified in Tables 2 and 3. The eighth and last configuration corresponds to the outlier
observation in the study sample. The names of the configurations, discussed below, are assigned
to reflect the associated service-product characteristics, and the relationship with the four
categories of service-products in the conceptual classification scheme depicted in Figure 1 –
namely, niche market, market extender, dynamic mass market, and customized mega market.
Table 4 Conceptual Service-Product Categories, Clusters from the Cluster Analysis, and Service-Product Configurations in the Taxonomy
Conceptual Categories of Cluster No. from the Service-Product Configurations Service-Products Cluster Analysis in the Taxonomy
Cluster 1 Small Segment-Niche Market Niche Market Cluster 2 Medium Variety-Niche Market
Cluster 3 High Variety-Niche Market Cluster 4 Low Content-Market Extender
Market Extender Cluster 5 Medium Content-Market Extender
Cluster 6 High Variety-Dynamic Mass Market Dynamic Mass Market Cluster 7 High Content-Dynamic Mass Market
Customized Mega Market
Outlier observation in the study sample
Customized Mega Market
Small Segment-Niche Market (Cluster 1). The service-products exhibit low static
content, low dynamic content, and a small target market segment. With six static pages and 12
graphic files on average, these service-products consisted of the lowest amount of static content.
The retailers of service-products in this cluster used virtually no dynamic pages and employed
none of the dynamic features that make electronic service-products customizable. They offered
12 goods on average and few offline service variations, one-sixtieth of the average number in
Cluster 7. These retailers give customers few choices for payment or shipping. They also appear
to offer little entertainment intended to draw customers back to the site again.
13
Medium Variety-Niche Market (Cluster 2). The service-products exhibit low static
content, low dynamic content, and a slightly larger target market. Relative to the retailers of
Cluster 1 service-products, these retailers offered a broader product variety. Specifically, they
offered about four times the number of goods offered by retailers of Cluster 1 service-products.
But like Cluster 1 retailers, they provided customers with few options for payment or shipping.
The retailers did not offer any sort of club membership, but were willing to customize and ship
personalized gifts.
High Variety-Niche Market (Cluster 3). The service-products exhibit average static
content, low dynamic content, and a medium target market. On average, the retailers of service-
products in this cluster bundled a mixture of 20 static and dynamic pages, and 35 graphic files to
sell 63 goods. They offered a few offline service variations, and provided shipping options and
payment methods similar to Cluster 2 retailers, leading to a broader target market. A few of these
retailers included simple dynamic content and dynamic online attributes such as message boards.
Low Content-Market Extender (Cluster 4). The service-products exhibit low static
content, low dynamic content, and an above average target market. The retailers of service-
products in this cluster employed a small amount of static content similar to that in Cluster 3 to
sell over 100 goods, and provided a number of shipping and payment options.
Medium Content-Market Extender (Cluster 5). The service-products exhibit above
average static content, average dynamic content, and a large target market. The retailers of
service-products in this cluster offered 328 products on average, and also the highest number of
offline service variations. Even without a large amount of dynamic content, several of these
retailers included dynamic attributes for online customization. They also enhanced their service-
14
products with club memberships and frequent e-mail updates, and by customizing product items
and shipping personalized gifts.
High Variety-Dynamic Mass Market (Cluster 6). The service-products exhibit a high
level of dynamic content, and illustrate the first of two Dynamic Mass Market content strategies,
characterized by high dynamic content and average static content. The retailers of service
products in this cluster sold fifty times as many goods (515 on average) as in Cluster 1. Their
service-products consisted mainly of dynamic content, and offered a number of attributes related
to online customization such as message boards, site search systems, and product information
sort systems. As in Cluster 5, the retailers provided incentives for customers to return to their
site, through club memberships and e-mail updates, customization of product items, and shipping
of personalized gifts.
High Content-Dynamic Mass Market (Cluster 7). The service-products in this cluster
represent the second Dynamic Mass Market content strategy, using high static content and high
dynamic content. The retailers of service-products in this cluster offered the broadest number of
options in the study sample. They employed the highest level of dynamic digital content, used
multiple languages, and were typically operated by several retailers who attached their brands to
the service-product. As with Cluster 6, the retailers of these service-products tended to use
offline services and online customization. For example, they shipped personalized gifts offline,
and employed electronic message boards, search systems, and suggestion systems. The service-
products in this cluster also exhibit attributes designed to retain customers, such as e-mail
notification and online food magazines.
15
Customized Mega Market. The service-products in this configuration correspond to the
single retailer identified as an outlier observation and removed from the study sample prior to
conducting the cluster analysis. The retailer reportedly offered over 800,000 food and non-food
goods, and several-hundred service variations. The service-products were delivered jointly by six
different electronic retailers.
5. Electronic Service-Product Configurations and Customer Satisfaction
We collected publicly reported customer satisfaction survey data to examine the
association between the service-product configurations in the taxonomy and items in the
customer satisfaction survey. The customer satisfaction survey data were reported by
BizRate.com (www.bizrate.com), a marketing research company that surveys customers on their
actual online shopping experience. BizRate’s ratings of electronic retailers are considered to be
among the most credible indicators of online customer satisfaction (Hansell 1999, Weintraub
2000). We collected data from BizRate’s 1998 customer satisfaction survey. The data were
available for 52 retailers in our study sample, and the retailers’ cluster-membership was
distributed from Clusters 2 through 7. Table 5 contains the items in BizRate’s 1998 survey.
Table 6 presents results for our analysis examining the association between the
configurations in the service-product taxonomy and the items in the BizRate customer
satisfaction survey shown in Table 5. We present Spearman correlation statistics, as well as F-
and Kruskal-Wallis statistics for one-way ANOVA. Table 6 indicates that the configurations
exhibit statistically significant associations with several items in the customer satisfaction
survey. Customer satisfaction with both product information and product selection tends to
increase across the taxonomy, and exhibits a significant positive correlation with the ordering of
the service-product configurations in the taxonomy. The mean of the overall service quality
16
index increases across the taxonomy, and exhibits a moderately significant positive correlation
with the ordering of the configurations in the taxonomy. Finally, customer loyalty, which
indicates the likelihood of a customer’s return to the electronic retailer for repeat purchases, and
is fundamental to long term business success of retailers, increases across the taxonomy. There is
a positive and significant correlation between customer loyalty and the ordering of the
configurations in the taxonomy.
These results are intuitive in that the specific items in the customer satisfaction survey
which have significant and positive association with the ordering of configurations in the
taxonomy are customer satisfaction dimensions that service-product attributes can satisfy. The
items in the customer satisfaction survey that do not show significant association with the
ordering of the configurations in the taxonomy are customer satisfaction dimensions that service-
process attributes are likely to satisfy.
We also conducted correlation analyses between the items in the BizRate survey. Tables
7 and 8 present the Pearson and Spearman correlations, respectively. As the last two rows of both
tables indicate, there is positive and significant correlations between overall service quality and
customer loyalty and the other items – namely, product information, product selection, web site
aesthetics, web site navigation, customer support, product availability, ease of
return/cancellation, timeliness of delivery, and price.2
2 The analyses in this section were motivated by the suggestions of the senior editor and the editor. We gratefully acknowledge their suggestions.
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Table 5 Items in BizRate’s 1998 Customer Satisfaction Survey
Product Information Consider the quality, quantity, and relevance of information provided for making your purchase decision an informed one. (1=Poor, 10=Excellent)
Product Selection Consider the breadth of product selection that the merchant has made available, keeping in mind the merchant’s stated area of focus. (1=Poor, 10=Excellent)
Web Site Aesthetics Consider not just how attractive the site was, but how appropriately graphics were used to enhance your shopping experience, not only slow it down. (1=Poor, 10=Excellent)
Web Site Navigation Consider the overall layout/organization, movement around the site, and missing/non-functional links. (1=Not Very Easy, 10=Very Easy)
Customer Support Consider the steps this merchant took to make sure you were informed of your order status and happy with the transaction. Also, consider how available and effective the merchant was in resolving any questions, complaints or problems that you encountered. (Leave blank if “Not Applicable.) (1=Poor, 10=Excellent)
Product Availability Consider how many of the items that you wanted to order were immediately available. Do not include items not yet released by the manufacturer. (Leave blank if “Not Applicable.”) (1=Had no items, 10=Had all items)
Ease of Return/Cancellation If you found it necessary to return/cancel any of the merchandize that you purchased, please rate how easy the return/cancellation process was. (Leave blank if “Not Applicable.”) (1=Very Difficult, 10=Very Easy)
Timeliness of Delivery Consider timeliness in the context of the promised delivery date. (Leave blank if “Not Applicable.”) (1=Poor, 10=Excellent)
Price Consider the price of products relative to other merchants’ prices in this category. (1=Very Expensive, 10=Very Inexpensive)
Overall Service Quality Please consider your TOTAL experience in regards to this purchase. How would you rate the overall quality of service you have received? (1=Poor, 10=Excellent)
Customer Loyalty The next time you are going to buy such products, what is the likelihood that you will purchase from this merchant again? (1=Poor, 10=Very Likely)
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Table 6 Relationship between Service-Product Clusters and Items in the Customer Satisfaction Survey Means of the Indexes Items in the Customer Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Spearman F K-W Satisfaction Survey (n=2) (n=7) (n=5) (n=6) (n=18) (n=14) (p-value) (p-value) (p-value)Product Information 6.00 6.42 5.60 6.00 6.50 7.29 0.340
(0.014) 1.974
(0.100) 9.668
(0.085) Product Selection 6.00 6.42 6.80 6.83 7.11 7.64 0.469
(0.000) 2.517
(0.043) 12.108 (0.033)
Web Site Aesthetics 6.00 5.85 5.60 6.33 6.88 6.42 0.166(0.240)
1.054 (0.398)
5.045 (0.410)
Web Site Navigation 6.50 6.85 6.40 6.33 7.16 6.64 0.087(0.542)
1.177 (0.335)
5.110 (0.403)
Customer Support 7.00 5.42 6.60 5.83 6.38 6.85 0.179(0.204)
1.136 (0.355)
3.414 (0.636)
Product Availability 7.50 6.71 7.00 7.00 7.05 7.14 0.198(0.159)
0.462 (0.802)
4.929 (0.425)
Ease of Return 3.00 5.42 3.60 4.50 5.27 5.14 0.128(0.365)
0.857 (0.517)
4.174 (0.525)
Timeliness of Delivery 5.50 6.00 7.20 7.16 7.00 7.42 0.202(0.150)
1.581 (0.184)
2.817 (0.728)
Price 6.00 6.42 6.80 6.16 6.55 6.92 0.222(0.114)
0.858 (0.516)
4.535 (0.475)
Overall Service Quality 5.90 6.17 6.18 6.25 6.68 6.83 0.353(0.010)
1.775 (0.137)
6.906 (0.228)
Customer Loyalty 5.50 6.14 6.20 6.33 6.83 6.85 0.382(0.005)
2.320 (0.058)
9.793 (0.081)
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Table 7 Pearson Correlations between Items in the BizRate Customer Satisfaction Survey
Price
Product Information
Product Selection
Web Site Aesthetics
Web Site Navigation
Product Availability
Ease of Return/ Cancellation
Timeliness of Delivery
Customer Support
Overall Service Quality
Product Information
0.268 Product Selection
0.391** 0.493**
Web Site Aesthetics
0.160 0.287* 0.550**
Web Site Navigation
0.214 0.426** 0.335* 0.585**
Product Availability
0.193 0.185 -0.030 0.100 0.125Ease of Return/ Cancellation
-0.006 0.343* 0.234 0.137 0.384** -0.081
Timeliness of Delivery
0.245 0.223 0.088 -0.033 0.054 0.537** 0.112Customer Support
0.335* 0.298* 0.269 0.474** 0.481** 0.309* 0.144 0.293*
Overall Service Quality
0.462** 0.674** 0.613** 0.599** 0.688** 0.372** 0.573** 0.479** 0.676**
Customer Loyalty
0.436** 0.612** 0.541** 0.482** 0.618** 0.413** 0.553** 0.552** 0.578** 0.944**
* Significant at the 0.05 level (two-tailed test) ** Significant at the 0.01 level (two-tailed test)
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Table 8 Spearman Correlations between Items in the BizRate Custom Satisfaction Survey
Price
Product Information
Product Selection
Web Site Aesthetics
Web Site Navigation
Product Availability
Ease of Return/ Cancellation
Timeliness of Delivery
Customer Support
Overall Service Quality
Product Information
0.321* Product Selection
0.425** 0.486**
Web Site Aesthetics
0.134 0.257 0.549**
Web Site Navigation
0.227 0.342* 0.403** 0.634**
Product Availability
0.201 0.383** 0.067 0.204 0.240Ease of Return/ Cancellation
0.034 0.343* 0.296* 0.190 0.406** 0.017
Timeliness of Delivery
0.286* 0.211 0.095 0.052 0.081 0.515** 0.086Customer Support
0.416** 0.292* 0.265 0.440** 0.464** 0.415** 0.166 0.369**
Overall Service Quality
0.502** 0.598** 0.659** 0.610** 0.684** 0.443** 0.598** 0.428** 0.702**
Customer Loyalty
0.481** 0.547** 0.587** 0.518** 0.626** 0.421** 0.585** 0.458** 0.610** 0.934**
* Significant at the 0.05 level (two-tailed test) ** Significant at the 0.01 level (two-tailed test)
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6. Conclusion
In this paper, we developed a taxonomy of service-products in electronic retailing. The
taxonomy is characterized by digital content and the target market segment breadth of the
service-products. The empirical context of our research was electronic food retailing. Electronic
food retailers bundle a wide variety of complementary (food and non-food) goods, offline
services, and digital content into their service-products. Electronic food retailing is among the
fastest growing electronic retailing sectors, and has among the highest levels of customer
satisfaction. The sample for this study was comprised of 255 electronic food retailers. For the
retailers in the study sample, we collected data on service-products pertaining to their digital
content and target market segment dimensions. Cluster analysis of the data yielded clusters of
service-products that served as the empirical foundation of the proposed taxonomy. Examination
of the clusters revealed systematic differences across the service-product configurations in the
taxonomy. The results of our empirical analyses demonstrate the value of the proposed taxonomy
in capturing information and variety within and across service-products consisting of physical
goods, offline services, and digital content, in ways that can be related to customer satisfaction
and customer loyalty. To the best of our knowledge, this taxonomy of service-products for
electronic retailers developed from attribute level data on service-products is the first of its kind.
The study findings provide several insights for managing electronic food retailing
operations. One of the insights is that, presently, even food retailers who present customers with
the heaviest levels of dynamic digital content do not include many dynamic, customizable
service-product attributes. The infrequent implementation of these attributes presents
opportunities that electronic food retailers might exploit to differentiate their service-products.
The analysis of customer satisfaction survey data suggests that service-product configurations
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with higher digital content are associated with higher evaluations for customer satisfaction with
product information. Configurations with a larger number of food items are positively associated
with customer satisfaction with product selection. Digital content and product variety of service-
products are positively associated with customer satisfaction with overall service quality and
customer loyalty. Correlation analyses between items in the customer satisfaction survey provide
some insights that are particularly relevant to operations managers. We find support for the
conventional wisdom that the operations variables such as product availability, timeliness of
delivery, and customer support have a significant and positive association with customer
satisfaction with overall service quality and customer loyalty, respectively.
There are two logical extensions to this study. The first study would be to develop a
taxonomy of service-processes for electronic retailers. The second study would be to integrate
the service-product and service-process taxonomies to develop an empirically validated product-
process matrix for electronic retailing operations. Future studies could also expand the scope of
investigations to other electronic retailing sectors, and over time. In closing, we believe this
paper provides a systematic start toward empirically investigating developments at the
intersection of electronic retailing and operations management, and hope that it will motivate
other scholars and practitioners to pursue inquiries at this new and evolving intersection.
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