Agarwal dan chatterjee

Complexity, uniqueness, and
similarity in between-bundle
choice
Manoj K. Agarwal
Associate Professor of Marketing, School of Management,
Binghamton University, State University of New York,
Binghamton, New York, USA
Subimal Chatterjee
Associate Professor of Marketing, School of Management,
Binghamton University, State University of New York,
Binghamton, New York, USA
Keywords Brand management, Decision making, Product costs, Services,
Marketing concepts, Market forces
Abstract When offering product/service bundles to customers, marketers must decide
how best to configure the bundles such that consumers do not find the bundle-choice
particularly difficult. This paper examines perceived decision difficulty in selecting from
a menu of bundles, where the bundles vary on the number of component services, the
number of unique services between competing bundles, and their perceived similarity. It
is found that larger bundles make decisions more difficult, more unique services between
the competing bundles increases decision difficulty for small, but not large, bundles and
similar bundles pose greater choice difficulty than dissimilar bundles. Implications of the
results are discussed.
Introduction
A new strategy for marketers seeking to differentiate their offers from the
competition is to bundle products and services together. Bundling poses a
new challenge to the product/brand manager as they now have to think
beyond a single brand or a single product category and visualize markets
where disparate products and services need to be combined as a single entity
and sold as a package. Indeed, bundled services appear to be rapidly shaping
the marketing landscape of the twenty-first century, with consumers, in
general, slowly getting used to the idea of bundles instead of single services
(Marketing News, 2000). Consumers can now avail of bundles in
telecommunications (e.g. phone, cable television and Internet service
(Denver Post, 2001), utility (e.g. electricity/gas services with heating and air
conditioning repair and maintenance, home appliance repair and electrician's
services), (Public Utilities Fortnightly, 1999, p. 62), or some mixture of the
two (e.g. local and long distance telephone with electric/gas services (Utility
Business, 1999, p. 72).
Keeping pace with the practice community, academic research in bundling
has examined a wide range of issues ranging from the sellers' strategic
handling of bundling (i.e. when to bundle and when not to bundle) to buyers'
evaluation of bundle value (for a comprehensive review, see Stremersch and
The Emerald Research Register for this journal is available at
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The current issue and full text archive of this journal is available at
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This research was supported by a grant from the Marketing Science Institute. A more
detailed working paper is available in MSI Report No. 02-103 (2002).
New strategy
358 JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 12 NO. 6 2003, pp. 358-376, # MCB UP LIMITED, 1061-0421, DOI 10.1108/10610420310498795
An executive summary for
managers and executive
readers can be found at the
end of this article

Tellis, 2002). For example, Guiltinan (1987) presents a normative
framework to help sellers choose services for different mixed-bundling
discount forms; Eppen et al. (1991) offer sellers several guidelines to create
competitive bundles; Venkatesh and Mahajan (1993) demonstrate how a
mixed bundling strategy can be more profitable to sellers than pure
components or pure bundling strategies; Bakos and Brynjolsson (1999, 2000)
show how sellers can create ``economies of aggregation'' by bundling
information goods; Koschat and Putsis (2002) advise magazines to
``unbundle'' their readers (i.e. publish different special editions targeted to
specific geographic segments or customized electronic editions to individual
readers) so that they can extract price premiums from advertising agencies.
From the consumers' perspective, Yadav and his colleagues (Yadav and
Monroe, 1993; Yadav, 1994, 1995) study consumers' bundle-evaluation
process and find, among other things, that bundles generate greater perceived
savings than discounts on the bundle's individual items, and consumers'
bundle evaluations vary significantly depending on which item is featured as
the price leader. Recently, Gourville and Soman (2001) caution against
excessive bundling that can dampen consumption by masking the sunk costs
of the individual items within the bundle (i.e. consumers feel less sunk-cost
pressure to consume the individual items within the bundle than if the items
were purchased separately).
As bundles proliferate and consumers get better accustomed to buying
packages instead of individual products and services, research on bundling
needs to change its focus from evaluating bundles in a monadic context to
evaluating bundles in a competitive context (i.e. selecting between multiple
bundles). In a study of 100 firms that use bundling as a major component of
their selling strategy, Mercer Management Consulting identifies the ability
of bundles to ``simplify consumers' lives'' as one of the keys to successful
bundling (Ovans, 1997). Our paper, which focuses on decision difficulty in
choosing between multiple bundles, is, to the best of our knowledge, the first
to examine the psychology of choice simplification in a competitive context.
Since selecting between bundles is relatively unfamiliar to consumers
(compared to selecting between alternatives), they may be tempted to avoid
the decision altogether and fall back to the status quo (i.e. select individual
items instead of bundles) (Luce, 1998). Hence, by investigating the sources
of decision difficulty in bundle-choice, we are better suited to understand the
reason for such choice deferrals and suggest remedies to managers.
While there is a rich history of research in decision difficulty, both from the
cognitive as well as the emotional perspective (see, for example, Luce et al.,
2001), the current thinking is confined to analyzing choices between multiple
alternatives, or a single alternative (choice versus no choice), where each
alternative is a collection of attributes. In the next section, we introduce the
concept of decision difficulty in choosing between bundles, where each
bundle is now a collection of alternatives, and consumers are forced to trade
off alternatives across competing bundles
Theory and hypotheses
Perceived complexity of the bundles
Research on decision difficulty in a multi-alternative, multi-attribute setting
has generally focused on the information load or the computational difficulty
arising from processing many alternatives and/or attributes (see, for example,
Helgeson and Ursic, 1993; Payne, 1982; Payne et al., 1988). A typical
example of this type of research is a choice between two alternatives (e.g.
Consumers' perspective
History of research
JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 12 NO. 6 2003 359

automobiles), with each alternative described by multiple attributes (e.g.
price, horsepower, gas-mileage, ride comfort). Faced with increasing
information about alternatives and attributes, consumers select decision
strategies to minimize computational effort (simple heuristics or shortcuts)
(Payne et al., 1990).
Consider Scenario 1 below where the consumer faces a choice between
Bundles A and B:
Scenario 1
(1) Bundle A:
. residential telephone;
. cellular telephone;
. Internet services.
(2) Bundle B:
. television programming;
. electricity and gas services;
. residential telephone.
Scenario 1 is similar to a multi-alternative and multi-attribute decision
if the bundles (bundles A and B) are construed as the alternatives, and
the services (e.g. television, telephone, and electricity and gas in
Bundle B) are construed as the attributes. As the number of services
in each bundle increases (i.e. as the bundles become more complex),
consumers face an increasing computational challenge requiring more
comparisons of services within, as well as between, bundles. The increasing
computational challenge may, in turn, make the decision more difficult and
tempt the consumer to defer the choice (Dhar, 1996). Thus:
H1. Decisions will be perceived to be more difficult, and there is a tendency
to defer decisions higher, when there are more (compared to fewer)
services in the competing bundles.
Number of unique services between bundles
The premiss that computational difficulty increases with the number of
services holds as long as all services are unique to the two bundles. In
Scenario 1, bundles A and B have one common component (residential
telephone) and two unique services, and thus require four inter-bundle
comparisons (TV-cell phone, TV-Internet, electricity and gas-cell phone,
electricity and gas-Internet).
Each inter-bundle comparison may lead to an ``alternative conflict''
(e.g. how to trade off between the benefits of television with cell phone)
leading to preference uncertainty, ambivalence, and longer decision times
(Fischer et al., 2000). With fewer unique components, the number of
comparisons, and thereby such alternative conflicts, should be lessened. Hence:
H2. Decisions will be perceived to be more difficult, and there is a tendency
to defer decisions higher, when there are more (compared to fewer)
unique services between the competing bundles.
Similarity/dissimilarity between competing bundles
Past research suggests that similarity between two alternatives increases with
the number of attributes common to (or shared by) the alternatives, and
Computational challenge
Common component
Similar alternatives
360 JOURNAL OF PRODUCT & BRAND MANAGEMENT, VOL. 12 NO. 6 2003

decreases with the number of features unique to each attribute (Tversky,
1977). Choosing between similar alternatives is difficult, as consumers need
to expend more cognitive effort to determine differences between the
alternatives (Shugan, 1980; Cooper-Martin, 1993). Alternatively, when
alternatives share the same features, they are typically compared at the
feature levels (e.g. comparing two microwave ovens on brand name, wattage,
size and warranty), whereas when alternatives do not share common features,
they are compared at an abstract level (e.g. comparing the overall value of a
microwave and a vacation). Abstract-level comparisons (e.g. overall value of
the alternative) are easier, and preferable to feature-level comparisons
(Johnson, 1984; Corfman, 1991).
The above analysis, when translated to a bundle setting, implies that
selecting between similar bundles (involving feature level comparisons)
will be more difficult than selecting between dissimilar bundles
(involving abstract level comparisons). Notice that there are two distinct
issues of interest. First, what type of components (similar or dissimilar)
would consumers like to see within a bundle? Second, are decisions easier
or difficult when the consumers select between two similar bundles
relative to selecting between two dissimilar bundles? In answering the
first question, research suggests that consumers would like to see
``similar'' services bundled together. For example, Hermann et al. (1997)
find that bundles whose components are functionally related (e.g. a
centralized lock system and an alarm system) generate greater purchase
intentions than bundles with functionally unrelated components (e.g. a
centralized lock system and a sunroof). Similarly, using a conjoint
framework, Goett et al. (2000) find that consumers' willingness to pay is
highest when their electricity service is bundled with other fuels (e.g.
natural gas, propane, and oil), and lowest when their electricity service is
bundled with financing services for new electrical equipment. The focus
of our third hypothesis is not on what components consumers would like
to see within a bundle, but on the second question, i.e. the differential
decision difficulty stemming across choices between two similar bundles
and choices between two dissimilar bundles.
H3. Decisions will be perceived to be more difficult, and the tendency will
be to defer decisions higher, when choosing between two similar
bundles than between two dissimilar bundles.
Forming similarity judgments
What makes consumers view two bundles as similar or dissimilar to each
other? We suggest that there are two drivers of bundle similarity:
(1) the number of common components between the bundles, with more
common services (fewer unique services) increasing their perceived
similarity (Tversky, 1977); and
(2) the perceived similarity of the components across the bundles.
To illustrate, consider Scenario 1. There are two unique services across the two
bundles, and our contention is that their global similarity assessment will be
based on the local similarity assessments of the unique service-pairs between
them (e.g. TV-cell phone, TV-Internet, electricity and gas-cell phone,
electricity and gas-Internet; see the feature sharing model in Tversky (1977).
H4. The perceived similarity between competing bundles (global similarity)
is negatively related to the number of unique components between the
Issues of interest
Unique service pairs

bundles, and positively related to the similarities of the unique
service-pairs between the bundles (aggregated local similarities).
Methodology
We conduct our research in three phases:
(1) a focus group phase;
(2) a pre-test phase; and
(3) a survey phase.
The inputs from the first two phases gave us qualitative insights into the
problem faced by consumers in choosing from multiple bundles, insights we
use to create and refine the final survey instrument.
Focus groups
Participants. In the first phase, we ran two focus groups, with eight
participants in the first focus group and six participants in the second. The
participants came from local area residences and received $20 compensation
for about 45 minutes of their time. An experienced moderator conducted the
focus groups and kept video as well as audio records of the discussion.
Procedure. The focus groups were organized in three stages. In the first stage,
the participants were introduced to nine different residential services. The
services, selected after researching the popular press on the types of service
bundles offered by telecommunications, cable, and utility companies, were:
. cellular telephone;
. television programming;
. Internet connection;
. electricity and gas;
. home appliance repair and service;
. lawn and garden service;
. heating and cooling maintenance service; and
. plumbing services.
In the second stage, the moderator asked each participant to construct his/her
``ideal'' bundle, i.e. the services that they would like to see offered together
as a package. Thereafter, each participant discussed why she/he had selected
the specific services to put together. In the third and final stage, the
moderator described two ``similar'' bundles of telecommunications services
(bundles A and B in Scenario 2) and asked participants to imagine that they
had to choose one out of the two. The participants discussed which of the two
bundles they would select and why that decision was particularly easy or
difficult. The same procedure was followed using two dissimilar bundles
(bundles C and D in Scenario 3).
Scenario 2 (similar bundles)
(1) Bundle A:
. cellular phone;
. Internet.
Local area residences
``Ideal bundle''

(2) Bundle B:
. cellular phone;
. television.
Scenario 3 (dissimilar bundles)
(1) Bundle C:
. electricity and gas;
. lawn and garden.
(3) Bundle D:
. cellular phone;
. appliance repair;
. lawn and garden.
Results. The results of the focus groups are discussed around two issues:
(1) the participants' idea of the ideal bundle; and
(2) their assessment of perceived decision difficulty when selecting between
two similar bundles and selecting between two dissimilar bundles.
When asked to construct their `ìdeal'' bundle, the popular bundles
constructed by the focus group participants involved some combination of
either residential telephone, cellular, and Internet, or appliance repair,
heating and cooling, and plumbing. In combining the first group of services,
one participant remarked ``. . . a natural good combination, if at a better value
than separate'', another participant stated ``currently, my Internet access is
through the phone line'', while a third expressed ``they're both telephones,
having one bill is more convenient, only write one check per month''. In
combining the second group of services, one participant observed, ``they fit
together . . . especially as a home owner, it's easier to deal with one
company'', while another stated `ìf I can rely on just one company, it's
much easier, and you create a rapport with the company, you can depend on
them''. Not all participants, however, desired service bundles. One
participant stated, `Ì'm skeptical about the bundling of services, I want to
see that there are real benefits to me . . . I don't mind writing four checks
. . . you'll have to prove to me that it's a better deal, cost-wise and
service-wise'', while another said `Ì don't want bundles at all . . . I don't
need a cell phone, don't need satellite TV with 300 channels, no Internet
. . . for repairs, I go to the local guy, I do the garden myself''. It appears that
our focus group consumers would like to see similar services within bundles,
services that are related or fit together.
Given a choice between two ``similar'' bundles (bundles A and B in Scenario
2), the majority agreed that Bundle A appeared more logical, with related
services, and would probably be their choice. One participant stated, ``Bundle
A seems to match better my desires/environment . . . as for Bundle B, I'm not
interested because I'm working with a satellite provider, and I don't see the
combination between that provider and my phone services at this point . . . I
don't see the tie''. A second participant stated, ``Bundle A . . . the services
relate better . . . they're all phone services . . . Internet is mostly through the
Popular bundles
Similar bundles

phone at this time. It groups better''. The participants, however, did not think
that making a choice between the two bundles would be easy, citing reasons
such as missing information about prices, customer services, and the ability of
the supplier to handle multiple services. Thus, one participant stated,
``Difficult . . . I would need to research friends and family, company, customer
service, type of contract, how to get in and out'', while another said ``Difficult
. . . I'm very picky . . . customer service is important . . . I want to receive
service in return for what I pay, I want the option to switch''.
Given the choice between two ``dissimilar'' bundles (bundles C and D in
Scenario 3), most participants expressed concern that the services did not seem
to fit together and they would not pick any one of the two. One participant
stated, ``None . . . very disparate . . . I can't imagine a company being efficient
with both phones and electric and gas, they're too different''; a second
participant observed, ``Neither . . . no relation between gas and phone'', while
a third participant expressed, ``Neither . . . how can you have a company with
expertise in all these diverse things?'' The majority of the participants agreed
that if asked to make such a choice, the decision would be very easy for them
in that they would not select any package (i.e. defer the decision). Thus, one
participant observed, ``I would stick to individual . . . they [the bundles] are not
a natural fit or combination'', while another stated, ``Easy choice . . . I
wouldn't choose either . . . no need to bundle anything''.
The results of the focus groups appear to conform to our predictions. For
example, deciding between two similar bundles appears to be difficult for
our participants. Conversely, when it comes to choosing between two
dissimilar bundles, participants seem to be adopting a global criterion (e.g.
bundles do not fit together) and easily decide not to select any bundle.
Pre-test
The focus groups identified similarity within and across competing bundles
as an important factor in consumers' bundle choice. We next conducted a
pre-test to determine the extent to which the nine services used in the focus
groups are perceived to be similar or dissimilar to each other.
Procedure. A total of 177 student participants read a brief description of the
nine residential services identified in the focus groups. The participants then
rated the similarity or dissimilarity of each of the 9
C2 or 36 pairs of service
on a nine-point scale ranging from one (very similar to each other) to nine
(very dissimilar to each other). To avoid patterned responses to a series of
similarity ratings, we interspersed filler tasks after every ten rating questions.
Analysis and results. The ALSCAL procedure in SPSS created a
two-dimensional similarity map (Figure 1). The data were treated as ordinal
matrix conditional. Both two- and three-dimensional maps were examined.
The average stress (Kruskal Formula 1) was between 0.14 and 0.18. We use
the two-dimensional map due to its ease of understandability. From Figure 1,
we see a separation of the four telecommunications services (cellular,
Internet, residential telephone, and television programming) from the other
five services that are dispersed across the two-dimensional map. In that
respect, the similarity map appears to confirm the findings of the focus group
as far as the grouping of similar services are concerned.
Survey
Creating similar versus dissimilar bundles
From Figure 1, we see a separation of the four information-related services
(cellular, Internet, residential telephone, and television programming) from the
Global criterion
ALSCAL procedure

other five services. Our original intention was to develop the similar and
dissimilar bundles based on the clusters on the similarity map. However, given
that that the services appear to be dispersed across the map (i.e. multiple tight
clusters are not apparent), this is not possible. Instead, as shown shortly, we
created our own versions of similar and dissimilar bundles and, in the survey,
asked participants their perception of the similarity or dissimilarity of the
competing bundles. Thus, bundle similarity/dissimilarity is a measured
variable (instead of a manipulated variable) in our final experiment.
Varying number and uniqueness of services
Our study requires creating service bundle pairs with two, three, or four services,
and one or two unique services between them, for a total of 3 Â 2 or six decision
scenarios. Table I describes different versions of the six scenarios used in our
survey. Due to space constraints, the full details of the procedure to generate the
bundles are not discussed here and are available from the authors. Briefly, we
used eight services in a master design (appliance repair was randomly dropped
from the original set) and a mix of information and non-information related
services to generate the decision scenarios.
To illustrate, consider the first row and last column of Table I (Version 1 and
choice of four-service bundles). Participants in this condition had to select
between two competing bundles where each bundle had four services. For some
participants, the choice entailed competing bundles, each with one unique
service, i.e. selecting between a bundle of phone, cellular, plumbing, and lawn
and garden, or a bundle of television, cellular, plumbing, and lawn and garden.
Thus, phone is unique to the first bundle, and television is unique to the second
bundle. For other participants, the choice entailed two competing bundles, each
with two unique services, i.e. selecting between a bundle of phone, cellular,
plumbing, and lawn and garden, or a bundle of television, Internet, plumbing,
and lawn and garden. Thus, phone and cellular are unique to the first bundle and
television and Internet are unique to the second bundle.
Base design
Objects design, manipulating the number of components in the competing
bundles (two, three, and four), and the number of unique elements between
the competing bundles (one and two). The similarity/dissimilarity of the
competing bundles is treated as a measured variable. In an attempt to include
as many different combinations of services as possible, we created six
different versions of survey. Each participant was exposed to only one of the
Figure 1. Similarity map of nine services in pre-test
Service bundle pairs
Competing bundles
Six scenarios

Choiceoftwo-servicebundlesChoiceofthree-servicebundlesChoiceoffour-servicebundles
One-uniqueTwo-uniqueOne-uniqueTwo-uniqueOne-uniqueTwo-unique
Version1
Television+phone
or
Cellular+phone
Phone+cellular
or
Television+Internet
Cellular+television+Internet
or
Plumbing+television
+Internet
Phone+television+cellular
or
Internet+plumbing+cellular
Phone+cellular+plumbing
+lawnandgarden
or
Television+cellular
+plumbing+lawnandgarden
Phone+cellular+plumbing
+lawnandgarden
or
Television+Internet+
plumbing+lawnandgarden
Version2
Cellular+television
or
Internet+television
Phone+Internet
or
Television+cellular
Television+phone+Internet
or
Plumbing+phone+Internet
Phone+plumbing+television
or
Phone+Internet+television
Television+phone+Internet
+plumbing
or
Cellular+phone+Internet
+plumbing
Television+plumbing+phone
+cellular
or
Internet+lawnandgarden
+phone+cellular
Version3
Phone+Internet
or
Cellular+Internet
Phone+television
or
Cellular+Internet
Television+phone+cellular
or
Plumbing+phone+cellular
Television+Internet+phone
or
Plumbing+cellular+phone
Phone+television+Internet
+lawnandgarden
or
Cellular+television+Internet
+lawnandgarden
Phone+lawnandgarden
+Internet+television
or
Plumbing+cellular+Internet
+television
Version4
Lawnandgarden
+phone
or
Plumbing+phone
Phone+plumbing
or
Lawnandgarden
+Internet
Electricityandgas+lawnand
garden+Internet
or
Plumbing+lawnandgarden
+Internet
Phone+lawnandgarden
+electricityandgas
or
Plumbing+Internet
+electricityandgas
Phone+heatingandcooling
or
Electricityandgas+heating
andcooling+plumbing
+lawnandgarden
Phone+heatingandcooling
or
Electricityandgas+Internet
TableI.Experimentalstimuliusedinthesixsurveyversions
(continued)

Choiceoftwo-servicebundlesChoiceofthree-servicebundlesChoiceoffour-servicebundles
One-uniqueTwo-uniqueOne-uniqueTwo-uniqueOne-uniqueTwo-unique
Version5
Plumbing+lawnand
garden
or
Internet+lawnand
garden
Phone+Internet
or
Lawnandgarden
+plumbing
Lawnandgarden+phone
+Internet
or
Plumbing+phone+Internet
Phone+plumbing+lawnand
garden
or
Internet+electricityandgas
+lawnandgarden
Electricityandgas+phone
+Internet+plumbing
or
Heatingandcooling+phone
+Internet+plumbing
Plumbing+electricityandgas
+heatingandcooling+phone
or
+heatingandcooling+phone
Version6
Phone+Internet
or
Plumbing+Internet
Phone+lawnandgarden
or
Plumbing+Internet
Lawnandgarden+electricity
andgas+phone
or
+phone
+phone
or
+phone
Phone+electricityandgas
+Internet+lawnandgarden
or
Heatingandcooling+
electricityandgas+Internet
+lawnandgarden
Phone+lawnandgarden
+Internet+electricityandgas
or
Plumbing+heatingandcooling
+Internet+electricityandgas
TableI.

six versions (any row of Table I). In order to reduce the transparency of the
``uniqueness'' manipulation, all participants rated the six scenarios in the
following order:
(1) two-component bundles with two unique components;
(2) three-component bundles with one-unique component;
(3) four-component bundles with two unique components;
(4) two-component bundles with one unique component;
(5) three-component bundles with two unique components; and
(6) four-component bundles with one unique component.
Stimuli and measures. The survey booklet comprised an introduction and six
sections (sections A through F). In the introductory page, the participants
were asked to imagine that they had recently purchased a house and
described different residential services. Thereafter, participants were asked
to read through each of the next six sections and complete the task asked of
them in each section, as described below.
Section A asked participants if they use each of the six services at present
and/or if they have used the services in the past. This section served as a
check for the perceived familiarity of the different services used in the
survey, i.e. we did not wish any particular service to be novel and unfamiliar
to the participants.
Section B required participants to rate the similarity or dissimilarity of the 15
possible pairs of the six services on a nine-point scale, ranging from one
(very dissimilar) to nine (very similar). Note that in any version the
participants were exposed to six services (and not all eight; see Table I). The
similarity ratings were used to generate similarity maps for a separate study
and thus the results are not discussed here.
Section C introduced the participants to seven different decision scenarios.
The first decision scenario served as a practice task to familiarize the
participants with the choice task. The next six decision scenarios are derived
from our design (any row of Table I). For each decision scenario, participants
imagined that their ``preferred'' firm was offering different pairs of competing
service packages. The two bundles in any pair were labeled as Package 1 and
Package 2. The participants read through the description of each pair of
packages and then performed a choice task and a ratings task. In the choice
task, participants circled one of three options: choose Package 1; choose
Package 2; or choose neither. In the ratings task, participants responded to a
nine-point scale ranging from four (disagree completely) to + four (agree
completely) measuring perceived decision difficulty (I found the decision very
difficult). Thus Section C introduces the two manipulations (number of
services in the bundle, and the number of unique services across competing
bundles) and the key dependent variables of the study (choice and ratings).
Section D of the booklet described the seven pairs of bundles one more time
and asked participants to rate the similarity/dissimilarity of the pairs on a nine-
point scale, ranging from one (very dissimilar) to nine (very similar). The
inputs from Section D are used as a measure of the perceived similarity or
dissimilarity of the competing bundles, our measured independent variable.
Finally, sections E and F of the booklet contained questions about the
participants' shopping habits and basic demographic information for sample
description purposes.
Nine-point scale
Independent variable

Procedure
A list of 1,200 Broome County, NY households was obtained from
Marketing Systems Group, Fort Washington, PA. Initially, 1,020
questionnaires were mailed in March 2000, with each household randomly
receiving one of the six versions of the questionnaire. The survey included a
cover letter, which introduced the authors and the funding agency and
discussed the practical significance of the research. After a month, we made
telephone calls to the remaining 180 households on the list, informing them
of the research and soliciting their participation in the survey. In all, we
received 159 completed surveys, a response rate of about 13 per cent. Since
about 20 per cent of the addresses on the list were either incorrect or the
residents had moved, the true response rate is closer to 17 per cent. The
number of respondents in the six versions of the questionnaire ranged
between 24 and 30.
Sample characteristics
Our sample is 59 per cent male, a mean age of 53 years, a median household
income between $40,000 and $55,000, and 79 per cent are house owners
(mean ownership of 20 years). About 48 per cent of the sample use a dial up
modem, 19 per cent use a cable modem, and a very small percentage a DSL
line. All have residential telephone, cable television, and use electricity and
gas services, 80 per cent have used heating and cooling services, 56 per cent
have used a plumbing service, 37 per cent use a cellular phone and 4 per cent
have a satellite dish.
Analysis and results
Base model
We ran an ANOVA with varying numbers of components (two, three and
four), numbers of unique components (one and two) and perceived similarity
of the bundles (measured on a one to nine similarity/dissimilarity scale) as
the within-subjects factors. The dependent variables in the ANOVAs were:
. perceived difficulty measured on a nine-point agree-disagree scale (``I
found the decision to be very difficult''); and
. decision deferral, a dichotomous (0, 1) variable that was assigned a value
of one if participants indicated that they wished to defer the decision by
not choosing any of the two packages described in the decision scenario.
Number of services. H1 suggests that decisions will be more difficult (and
the tendency to defer decisions stronger) when there are more (compared to
fewer) services in the competing bundles. As predicted, there was a
significant main effect of the number of services on decision difficulty
(F2,275 = 5.08, p < 0.01; XTwo Services = 2.87, XThree Services = 2.97,
XFour Services = 3.38), as well as decision deferral (F2,283 = 19.19, p = 0.00;
XTwo Services = 0.24, XThree Services = 0.28, XFour Services = 0.45).
Number of unique services. H2 suggests that decisions will be more difficult
(and the tendency to defer decisions stronger) when there are more
(compared to fewer) unique services across the two competing bundles. H2,
however, was not supported. The main effect of uniqueness for decision
difficulty was not significant (XOne Unique Service = 3.06,
XTwo Unique Services = 3.08; F1,37 < 1, ns). The impact of uniqueness on
decision deferral approached statistical significance, but in a direction
opposite to that predicted. The tendency to defer a decision was stronger
when there was one unique service between the competing bundles
Household income
Main effect

(XOne Unique Service = 0.34) compared to two unique services
(XTwo Unique Services = 0.31; F1,141 = 3.81, p = 0.05).
There was a significant interaction between uniqueness and total number of
services on decision difficulty (F2,275 = 3.49, p = 0.032). Uniqueness increased
decision difficulty for small (two-service) bundles (XOne Unique Service = 2.66,
XTwo Unique Services = 3.07; F1,143 = 3.81, p = 0.053), but not for large bundles
(for three-service bundles: XOne Unique Service = 3.09, XTwo Unique Services = 2.86;
F1,142 = 1.87, ns; for four-service bundles: XOne Unique Service = 3.44,
XTwo Unique Services = 3.32; F1,140 < 1, ns). Notice that in two-service bundles,
the ``two-unique'' condition results in bundles with no common services
between them, a feature that never holds in the case of the bigger bundles. One
speculation, therefore, is that uniqueness, in general, will not affect decision
difficulty unless it results in competing bundles that have no common services
between them. A second speculation is that respondents give up on item-by-
item comparison, a necessary condition for identifying unique services, and
just assess the overall value of bigger bundles.
The main effect for decision deferral is harder to explain. Why should the
tendency to delay the decision be more when there are fewer unique services
between bundles? After all, fewer unique services should make the bundles
easier to compare. One speculation is that decision deferral in our case may
not be perfectly symptomatic of decision difficulty. For example, in focus
groups with participants, one finding was that choosing to defer a decision
was often not equated to a difficult choice, but rather a very easy choice
where the participants knew that they did not want either option.
Similarity between bundles. H3 suggests that decisions will be more difficult
(and the tendency to defer decisions stronger) when choosing between two
similar bundles (compared to two dissimilar bundles). Participants'
self-reports of decision difficulty were positively correlated with the
perceived similarity of the competing bundles (r = 0.086, p = 0.01), indicating
that the more similar the two bundles, the more difficult the decision. The
low R2
suggests that there are other variables besides similarity that drive
decision difficulty. The correlation between decision deferral and perceived
similarity, however, was not significant (r = ±0.031, p = 0.344).
Similarity judgments. H4 suggests that perceived similarity between competing
bundles is negatively related to the number of unique components between the
bundles and positively related to the local similarities of the components
between bundles. To test our hypothesis, we ran a regression where
participants' reported similarity score of the competing bundles served as the
dependent variable, with the number of services in each bundle (NUMBER),
the number of unique services between the two bundles (UNIQUE), the
average of the perceived similarities of all the unique service-pairs between the
two bundles (PAIRSIM), and the two interaction terms involving PAIRSIM
(PAIRSIM*NUMBER, and PAIRSIM*UNIQUE) as the predictors.
The fit of this model is reasonable (R2
= 0.15). As predicted, more unique
components decrease the perceived bundle similarity (

= ±0.83, p < ±
0.01). Consistent with our hypothesis, the variable PAIRSIM is positively
related to bundle similarity (

= 0.62, p < 0.01 ), and suggests that
similarities of the services between the bundles (local similarities) affects
the perceived similarity of the bundles (global similarity). Unexpectedly,
bigger bundles were judged to be more similar than smaller bundles
(

= 0.69, p < 0.01). One reason could be that, for bigger bundles,
consumers give up on item-by-item comparisons and evaluate the bundles
Significant interaction
Decision deferral
Unique components

as a whole (e.g. the two bundles are quite similar to the extent that they
both serve my needs well).
Discussion
Summary and implications
As bundles proliferate and consumers get better accustomed to the concept
of buying packages instead of individual products and services, bundle
flexibility, or the ability for consumers to choose their preferred bundle will
become the key issue (Wireless Review, 2000). This flexibility can take
two forms:
(1) sellers offer an aÁ-la-carte selection, buyers estimate their reservation
price and for that price select the products they wish to bundle together
(i.e. the buyer, not the seller, construct the bundle); or
(2) sellers offer several bundles, similar in spirit to Macdonald's value meals
(e.g. order ``Number One'' if you want a Big Mac, fries, and a soft
drink), and consumers select their most preferred bundle from this menu.
In this paper we investigated the second form of bundle flexibility,
contending that the best menu of bundles, from the consumers' perspective,
is one that facilitates an easy selection of their preferred bundle. Thus, our
focus is not so much on whether or not a specific bundle is selected. Rather,
our focus is to recognize the features of the decision task that facilitates
decision making. Based on the literature on multi-alternative, multi-attribute
decision making, as well as the inputs from two focus groups, we
investigated the impact of three factors on the consumers' perception of
decision difficulty in bundle choice: the number of services within each
bundle, the number of unique services between the competing bundles, and
the perceived similarity between competing bundles.
The first factor, the number of services in competing bundles, not
surprisingly, increases consumers' perception of decision difficulty. The
finding poses an important challenge for the service managers. To
differentiate their bundles from their competition, managers may be
tempted to pack as many products or services in their bundle as possible.
However, as our results show, if all managers follow the same strategy by
offering ``large'' bundles, the consumers will find the selection process
very difficult and may end up not selecting any bundle at all. The reason
for this reluctance could stem from the intractability of making multiple
comparisons of the alternatives between the competing bundles.
Alternatively, it could stem from the consumers' unwillingness to trust a
single service provider to efficiently provide the many disparate services,
or the sticker shock that they anticipate experiencing when the time
comes to pay for one big bundle.
The second factor, the number of unique services between bundles, appears
to affect the consumers' perceived decision difficulty in choosing between
competing bundles only for small (two-service) bundles. However, it is not
clear whether it is uniqueness in general, or one particular form of
uniqueness (that leads to zero overlapping components across the competing
bundles) that contributes to decision difficulty.
The third and final factor, perceived similarity between competing
bundles, is found to have a weak (but significant) relationship with
decision difficulty. Choosing between similar bundles is more difficult
than choosing between dissimilar bundles. Although the survey results
point to a weak relationship, the focus groups appear to think that
Bundle flexibility
Perceived difficulty

similarity or ``relatedness'' is an important issue driving consumers'
comparison between competing bundles. A related question, then, is how
are these similarity judgments formed in the first place. Our results
suggest that such judgments are probably formed when consumers
compare the unique services across the competing bundles one pair at a
time. In addition, bigger bundles are perceived to be more similar to each
other compared to smaller bundles, making the choice decision difficult.
Service providers, therefore, need to offer small bundles that are not
perceived to be similar to one another, or to present them in a way that
highlights differences between them.
Some caution and future research
In focusing our attention on decision difficulty, we need to be cautious of
two things. First, creating menus that provide an easy decision
environment for consumers may not always be the most profitable
strategy for the marketer. For example, the surest way of facilitating a
decision is to offer a dominating bundle (i.e. a bundle that contains all the
consumers' preferred services at a discount), but the service provider may
lose money on every bundle that is sold. Second, just making the decision
less difficult does not necessarily mean that the consumer is better off
than before. For example, selecting between two bundles, one that is
undesirable in all respects and the other only slightly less so, may be quite
easy, but that does not mean that the consumer will be happy with the
final choice. However, as we have shown, decision features such as task
complexity and similarity between the choice options, found to affect
decision difficulty in multi-attribute and multi-alternative choices, can be
readily adapted to the bundling context as well. In that respect, our work
should help managers present choices that simplify the consumers'
decision-making process without necessarily sacrificing the seller's
profitability, or reducing consumer welfare.
Although we have considered service bundles, our framework is sufficiently
broad to encompass bundles in general. For example, common bundles that
consumers encounter almost routinely in their lives include fast-food meal
bundles (main meal, side orders, and a drink), photographic equipment
bundles (camera body, one or more lenses, and in some cases accessories and
supplies such as flashes, tripods, and film) and personal computer bundles (a
processor, a monitor, and occasionally peripherals such as printers and
scanners). Future research needs to address the generalizability of our
findings to such common bundles as well.
A second consideration for future research will be to assess how consumers
respond to different bundling contexts. For example, in this paper we
considered bundles where the separate services were quite easy to identify. In
some cases, bundles are more subtle (e.g. programs that come bundled together
on the computer's hard drive, or functions such as streaming, downloading and
burning that are bundled with online music sharing services). In other cases,
bundling entails significant strategic implications. For example, should
personal TV (e.g. TiVo), which allows you to play, pause, and re-play live
television broadcasts, be marketed as a standalone service, or should it be
bundled together into a set-top box that provides cable television or satellite
television? Understanding the context effects on bundle evaluation should
significantly increase our knowledge in this area.
A third challenge for marketers will be to develop personalized bundles of
products and services, if the assumption is that the market for service bundles
Caution
Future research

will be niche segments requiring highly tailored services. For example, for
energy related bundles, residential consumers are more likely to be receptive
to a package that might consist of heating and air conditioning repair and
maintenance, home appliance repair and electrician's services. Small business
customers, on the other hand, would probably like power quality consulting
and energy audits built into their bundles. Future research may wish to address
how best personalization can be made to bear on the number and variety of
bundles that service providers are going to offer in the marketplace.
References
Bakos, Y. and Brynjolfsson, E. (1999), ``Bundling information goods: pricing, profits and
efficiency'', Management Science, Vol. 45 No. 4, pp. 1613-30.
Bakos, Y. and Brynjolfsson, E. (2000), ``Bundling and competition on the Internet'', Marketing
Science, Vol. 19 No. 4, pp. 63-82.
Cooper-Martin, E. (1993), `Èffects of information format and similarity among alternatives on
consumer choice processes'', Journal of the Academy of Marketing Science, Vol. 21 No. 3,
pp. 239-46.
Corfman, K.P. (1991), ``Comparability and comparison levels used in choices among
consumer products'', Journal of Marketing Research, Vol. 28 No. 3, pp. 368-74.
Denver Post (2001), 14 February.
Dhar, R. (1996), ``The effect of decision strategy on deciding to defer choice'', Journal of
Behavioral Decision Making, Vol. 9 No. 4, pp. 265-81.
Eppen, G.D., Hanson, W.A. and Martin, R.K. (1991), ``Bundling ± new products, new markets,
low risk'', Sloan Management Review, Vol. 32 No. 4, pp. 7-14.
Fischer, G.W., Luce, M.F. and Jia, J. (2000), `Àttribute conflict and preference uncertainty:
effects on judgment time and error'', Management Science, Vol. 46 No. 1, pp. 88-103.
Goett, A.A., Hudson, K. and Train, K.E. (2000), ``Customers' choice among retail energy
suppliers: the willingness-to-pay for service attributes'', The Energy Journal, Vol. 21
No. 4, pp. 1-28.
Gourville, J.T. and Soman, D. (2001), ``The potential downside of bundling: how packaging
services can hurt consumption'', Cornell Hotel and Restaurant Administration Quarterly,
Vol. 42 No. 3, pp. 29-37.
Guiltinan, J.P. (1987), ``The price bundling of services: a normative framework'', Journal of
Marketing, Vol. 51 No. 2, pp. 74-85.
Helgeson, J.G. and Ursic, M.L. (1993), `Ìnformation load, cost/benefit assessment and
decision strategy variability'', Journal of the Academy of Marketing Science, Vol. 1 No. 21,
pp. 13-20.
Herrmann, A., Huber, F. and Coulter, R.H. (1997), ``Product and service bundling decisions
and their effects on purchase intention'', Pricing Strategy and Practice, Vol. 5 No. 3,
pp. 99-107.
Johnson, M. (1984), ``Consumer choice strategies for comparing noncomparable alternatives'',
Journal of Consumer Research, Vol. 11 No. 1, pp. 741-53.
Koschat, M.A. and Putsis, W.P. Jr (2002), `Àudience characteristics and bundling: a hedonic
analysis of magazine advertising rates'', Journal of Marketing Research, Vol. 39 No. 2,
pp. 262-73.
Luce, M.F. (1998), ``Choosing to avoid: coping with negatively emotion laden consumer
decisions'', Journal of Consumer Research, Vol. 24 No. 4, pp. 409-33.
Luce, M.F., Bettman, J.R. and Payne, J.W. (2001), `Èmotional decisions: tradeoff difficulty
and coping in consumer choice'', in John, D.R. (Ed.), Monograph of The Journal of
Consumer Research, No. 1, University of Chicago Press, Chicago, IL.
Marketing News (2000), 22 May.
Ovans, A. (1997), ``Make a bundle bundling'', Harvard Business Review, Vol. 75 No. 6,
pp. 18-20.
Payne, J.W. (1982), ``Contingent decision behavior'', Psychological Bulletin, Vol. 92 No. 2,
pp. 382-402.
Payne, J.W., Bettman, J.R. and Johnson, E.J. (1988), `Àdaptive strategy selection in decision
making'', Journal of Experimental Psychology: Learning, Memory, and Cognition,
Vol. 14, July, pp. 534-52.

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