service, i.e. selecting between a bundle of phone, cellular, plumbing, and lawn ...... can provide our water plus services such as emergency plumbing and ...
An executive sum m ary for m anagers and executive readers can be found at the end of this article
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.
New strategy
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 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). The Emerald Research Register for this journal is available at http://www.emeraldinsight.com/researchregister The current issue and full text archive of this journal is available at http://www.emeraldinsight.com/1061-0421.htm
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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. Consum ers’ perspective
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.
History of research
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.
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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.
Com putational challenge
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:
Com m on component
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:
Sim ilar alternatives
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
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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). Issues of interest
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.
Unique service pairs
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
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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. Local area residences
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:
‘‘Ideal bundle’’
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residential telephone;
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cellular telephone;
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television programming;
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Internet connection;
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electricity and gas;
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home appliance repair and service;
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lawn and garden service;
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heating and cooling maintenance service; and
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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:
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residential telephone;
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cellular phone;
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(2) Bundle B: .
residential telephone;
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cellular phone;
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television.
Scenario 3 (dissimilar bundles) (1) Bundle C: .
residential telephone;
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electricity and gas;
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lawn and garden.
(3) Bundle D: .
cellular phone;
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appliance repair;
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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. Popular bundles
When asked to construct their ``ideal’’ 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 ``if 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, ``I’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 ``I 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.
Sim ilar bundles
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
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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’’. Global criterion
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 9C2 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.
ALSCAL procedure
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
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Figure 1. Similarity map of nine services in pre-test
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. Service bundle pairs
Com peting bundles
Six scenarios
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 £ 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
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Phone + plumbing or Lawn and garden + Internet
Version 4 Lawn and garden + phone or Plumbing + phone
Table I. Experimental stimuli used in the six survey versions
Electricity and gas + lawn and garden + Internet or Plumbing + lawn and garden + Internet
Television + phone + cellular or Plumbing + phone + cellular
Phone + television or Cellular + Internet
Version 3 Phone + Internet or Cellular + Internet
Phone + lawn and garden + electricity and gas or Plumbing + Internet + electricity and gas
Television + Internet + phone or Plumbing + cellular + phone
Phone + plumbing + television or Phone + Internet + television
Cellular + television + Internet Phone + television + cellular or or Plumbing + television Internet + plumbing + cellular + Internet
Choice of three-service bundles Two-unique
Television + phone + Internet or Plumbing + phone + Internet
Phone + cellular or Television + Internet
One-unique
Version 2 Cellular + television Phone + Internet or or Internet + television Television + cellular
Version 1 Television + phone or Cellular + phone
Choice of two-service bundles One-unique Two-unique
Phone + heating and cooling + plumbing + lawn and garden or Electricity and gas + heating and cooling + plumbing + lawn and garden
Phone + television + Internet + lawn and garden or Cellular + television + Internet + lawn and garden
Television + phone + Internet + plumbing or Cellular + phone + Internet + plumbing
(continued)
Phone + heating and cooling + plumbing + lawn and garden or Electricity and gas + Internet + plumbing + lawn and garden
Phone + lawn and garden + Internet + television or Plumbing + cellular + Internet + television
Television + plumbing + phone + cellular or Internet + lawn and garden + phone + cellular
Phone + cellular + plumbing + lawn and garden or Television + Internet + plumbing + lawn and garden
Choice of four-service bundles Two-unique
Phone + cellular + plumbing + lawn and garden or Television + cellular + plumbing + lawn and garden
One-unique
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Table I.
Version 6 Phone + Internet or Plumbing + Internet
Version 5 Plumbing + lawn and garden or Internet + lawn and garden
Phone + lawn and garden or Plumbing + Internet
Phone + Internet or Lawn and garden + plumbing
Choice of two-service bundles One-unique Two-unique
Lawn and garden + electricity and gas + phone or Plumbing + electricity and gas + phone
Internet + lawn and garden + phone or Plumbing + electricity and gas + phone
Phone + plumbing + lawn and garden or Internet + electricity and gas + lawn and garden
Choice of three-service bundles Two-unique
Lawn and garden + phone + Internet or Plumbing + phone + Internet
One-unique
Phone + electricity and gas + Internet + lawn and garden or Heating and cooling + electricity and gas + Internet + lawn and garden
Phone + lawn and garden + Internet + electricity and gas or Plumbing + heating and cooling + Internet + electricity and gas
Plumbing + electricity and gas + heating and cooling + phone or Internet + lawn and garden + heating and cooling + phone
Choice of four-service bundles Two-unique Electricity and gas + phone + Internet + plumbing or Heating and cooling + phone + Internet + plumbing
One-unique
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. Nine-point scale
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).
Independent variable
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 ninepoint 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.
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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. Household incom e
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:
M ain effect
.
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
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(XOne Unique Service = 0.34) compared to two unique services (XTwo Unique Services = 0.31; F1,141 = 3.81, p = 0.05). Significant interaction
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-byitem comparison, a necessary condition for identifying unique services, and just assess the overall value of bigger bundles.
Decision deferral
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).
Unique com ponents
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
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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. Bundle flexibility
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.
Perceived difficulty
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
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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. Caution
Future research
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
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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), ``Effects 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), ``Attribute 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), ``Information 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), ``Audience 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), ``Emotional 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), ``Adaptive strategy selection in decision making’’, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 14, July, pp. 534-52. JO U R N A L O F P R O D U C T & B R A N D M A N A G E M E N T , V O L . 12 N O . 6 20 03
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Payne, J.W., Johnson, E.J., Bettman, J.R. and Coupey, E. (1990), ``Understanding contingent choice: a computer simulation approach’’, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, March-April, pp. 296-309. Public Utilities Fortnightly (1999), 15 January, p. 62. Shugan, S.M. (1980), ``The cost of thinking’’, Journal of Consumer Research, Vol. 7 No. 1, pp. 99-111. Stremersch, S. and Tellis, G.R. (2002), ``Strategic bundling of products and prices: a new synthesis for marketing’’, Journal of Marketing, Vol. 66 No. 1, pp. 55-72. Tversky, A. (1977), ``Features of similarity’’, Psychological Review, Vol. 84 No. 4, pp. 327-52. Utility Business (1999), February, p. 72. Venkatesh, R. and Mahajan, V. (1993), ``A probabilistic approach to pricing a bundle of products or services’’, Journal of Marketing Research, Vol. 30 No. 4, pp. 494-508. Wireless Review (2000), 15 May, p. 62. Yadav, M.S. (1994), ``How buyers evaluate product bundles: a model of anchoring and adjustment’’, Journal of Consumer Research, Vol. 21 No. 2, pp. 342-53. Yadav, M.S. (1995), ``Bundle evaluation in different market segments: the effects of discount framing and buyers’ preference heterogeneity’’, Journal of the Academy of Marketing Science, Vol. 23 No. 3, pp. 206-15. Yadav, M.S. and Monroe, K.B. (1993), ``How buyers perceive savings in a bundle price: an examination of a bundle’s transaction value’’, Journal of Marketing Research, Vol. 30 No. 3, pp. 350-8.
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This summ ary has been provided to allow managers and executives a rapid appreciation of the content of this article. Those with a particular interest in the topic covered m ay then read the article in toto to take advantage of the more comprehensive description of the research undertaken and its results to get the full benefit of the material present
Executive summary and implications for managers and executives To bundle or not to bundle? Imagine some firm makes you an offer to bundle together all the products and services you need to run your home ± utilities like gas, electricity and water; telecommunications, entertainment, maintenance, gardening, etc. Seem like a good offer? Well think about it for a few seconds and you will appreciate the difficulties. You have a swimming pool ± does the offer include its cleaning and maintenance? Can you vary the bundle to include or exclude particular services ± you like to do your own garden? And how does the offer of everything on a single bill stack up in terms of cost and service quality? The bundling of services makes superficial sense to the consumer with the appeal of one supplier and the promise that this will provide savings in time and money. But the reality is more complicated since we are no longer judging between suppliers of the individual services but between varied bundles of services. As consumers we face a more complicated choice ± instead of a supplier for the phone and a suppliers for the television we face a choice between mixes of suppliers. The electricity company wants to sell us telephony services as does the cable TV firm. And the gas company can provide our water plus services such as emergency plumbing and garden maintenance. Since we would not buy telephony from more than one supplier, we face a problem choosing between bundled services that include telephony plus a range of other services. For many, the temptation may be to sack it all and buy the phone service from a specialist company. Agarwal and Chatterjee suggest that this may well be the case since, when faced with ``. . . increasing information about alternatives and attributes . . .’’, consumers ``. . . select decision strategies to minimize computational effort’’. Working out whether the bundled service provides value-for-money is too much like hard work so we opt for the simple choice and stick with single suppliers for services. This situation is real but it does not stop organizations seeking to increase revenues and market penetration by adding services to create bundles. And, the breaking down of competitive barriers between utilities through deregulation and privatization has accelerated this process. The challenge for the designers of product and service bundles is to create groups that do not put off consumers. There must be sense to the bundle The first rule of bundling must be to ask whether there is any real connection between the core product (e.g. gas) and the products or services with which we intend to bundle this core product. The link between gas and electricity, for example, is well established and makes some sense to the consumer (especially where there is an extensive mains gas system). And, consumers are able to make a judgment in choosing between alternative suppliers of gas and electricity together as well as between these suppliers and those supplying only one or the other. For the supplier of gas and electricity the challenge is now to add further services or products to the existing offer. And, given this choice, do we opt for supplying more things that enter the home through pipes or wires (telephones, television, water, etc.), things that run on gas and electricity (appliances and financing for appliances) or services that maintain the
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property (e.g. plumbing and electrical work)? Each option has some logic, but where the consumer is faced with choices from different organizations presenting each of these options the decision becomes harder. And we may want some but not all of the services in a given bundle. Agarwal and Chatterjee suggest that firms should begin to look at flexibility in bundles. The authors give two options here ± aÁ-la-carte selection or multiple bundles. In the first case the consumer selects a combination from a menu of options ± a self-created bundle, while in the second case the range of bundles is set up and the consumer selects one option from many. It seems that the only constraints preventing aÁ-la-carte approaches lie on the firm’s operation efficiency. And, the more products and services we include in the potential bundle the more different bundles are bought and the more difficult it is for the firm to manage a complex system. Despite these problems it is likely that there is ability to present the consumer with a menu from which to select. And this change presents a problem in terms of positioning. Since we can imagine a situation where an electricity distributor has customers who buy gas and telephones but not electricity, that firm ceases to be an electricity company and becomes a supplier of domestic services. At present this is not the case (I buy my gas from Southern Electricity), but there is a chance that firms will begin to focus more on the totality of service to the household rather than on the efficient supply of a commodity product. We also need to ask whether consumers will continue to seek out the cheapest supplier or will opt for good service and a minimum of hassle. At all levels the bundling of services will become increasingly important for many firms, either because of new competition or else through the demands of consumers for products and services to be presented in a form that allows the consumer to create (or simply to buy) a bundle of these products and services. Agarwal and Chatterjee have, to some extent, opened up Pandora’s box by arguing for a more flexible approach to the bundling of goods and services ± firms would be wise to examine the challenge this presents before they find market share drifting off to competitors they never anticipated. (A preÂcis of the article ``Complexity, uniqueness, and similarity in between-bundle choice’’. Supplied by Marketing Consultants for Emerald.)
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