JOURNAL OF CONSUMER PSYCHOLOGY, 12(3), 215–229 Copyright © 2002, Lawrence Erlbaum Associates, Inc.
MUL TI COMP ONENT CHA KRBUNDLE AVART I PRICES E T AL .
Partitioned Presentation of Multicomponent Bundle Prices: Evaluation, Choice and Underlying Processing Effects Dipankar Chakravarti Leeds School of Business University of Colorado, Boulder
Rajan Krish Kraft Foods Inc.
Pallab Paul Daniels College of Business University of Denver
Joydeep Srivastava Robert Smith School of Business College Park, Maryland
Firms may choose to present the price of a multicomponent product bundle in partitioned (separate price for each mandatory component) or consolidated (single, equivalent price) fashion. In this article, we report on 2 experiments that examined the effects of such presentations on evaluations and choices as well as the underlying processing effects. In Experiment 1, consistent with a mental accounting analysis, a multicomponent product bundle was evaluated more favorably and chosen more often when its components were presented with partitioned (vs. consolidated) prices. The effects were, however, moderated by the component partitioned. In particular, it appeared that partitioning prices altered attention paid to the components partitioned and related product features. In Experiment 2, we found that different splits of the bundle price influenced evaluations and choices depending on how the focal product price related to that of a comparison option. These price-split effects were also moderated by the component partitioned, suggesting attention effects similar to Experiment 1. The findings show that although the effects of price partitioning were consistent with mental accounting principles, they were moderated by information processing effects related to the partitioned component.
Consumers often purchase multicomponent product and service bundles. For example, a vacation purchase may include an airline ticket, hotel accommodations, and a rental car. Similarly, an appliance purchase may involve the focal product (e.g., a refrigerator), some accessories (e.g., an icemaker), and services (e.g., an extended warranty). Manufacturers (and retailers) often tie or bundle component products or services under a single price tag (Guiltinan, 1987). Alternatively, they may assign price tags for each component, Requests for reprints should be sent to Dipankar Chakravarti, Leeds School of Business, Division of Marketing, University of Colorado, Boulder, CO 80309–0419. E-mail:
[email protected] u
whether it is mandatory (pure bundling, as in a package deal) or also sold separately (mixed bundling). Thus, the refrigerator, the icemaker, and the warranty may each have a separate price tag (partitioned prices) or a single price tag (consolidated price). If the bundle components and the total price are exactly equivalent in the two presentations, will consumer evaluations and choices vary across them? Economists have argued that price bundling is an effective segmentation and discrimination tool in markets with heterogeneous consumer preferences. Depending on the distribution of consumers’ reservation prices for the individual components, bundling may lead to higher profits compared to when firms offers unbundled components (Adams & Yellen,
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1976; Guiltinan, 1987; Schmalensee, 1982). Indeed, bundling complementary components may even add value and fetch a price premium (Telser, 1979). Several articles have discussed optimal bundling strategies and the associated pricing of the bundle and its components (e.g., Fuerderer, 1999; Matutes & Regibeau, 1992; McAfee, McMillan, & Whinston, 1989; Venkatesh & Mahajan, 1993). However, the consumer behavior analyses in this literature are rooted in economic principles and have largely ignored the psychological ramifications of price bundling and related decisions on price presentation. Recent studies have reported that partitioned (vs. consolidated) pricing of bundle components can influence evaluations and choices (e.g., Drumwright, 1992; Mazumdar & Jun, 1993; Nagle, 1987; Yadav & Monroe, 1993). Reference price concepts central to the prospect theory value function (Kahneman & Tversky, 1979) and mental accounting propositions on segregating gains and integrating losses (Thaler, 1985) have been used to theorize about these effects. However, empirical tests have often produced conflicting results (e.g., Drumwright, 1992; Heath, Chatterjee, & France, 1995; Johnson, Herrmann, & Bauer, 1999; Wang, 1996). The findings suggest that consumers code the presented prices and benefits quite flexibly and often edit the frames that are presented (Thaler & Johnson, 1990). Moreover, the effort and accuracy characteristics of the judgmental heuristic used (e.g., anchoring and adjustment) may influence decision outcomes (Morwitz, Greenleaf, & Johnson, 1998). Thus, a variety of task and context factors (e.g., prior purchase intentions for the bundle components) may influence how bundle components are processed (e.g., Suri & Monroe, 1999), affecting how consumers evaluate and choose among partitioned or consolidated presentations of multicomponent bundles. In this article, we propose that partitioned (vs. consolidated) prices for multicomponent product bundles induce evaluations and choices consistent with specific mental accounting principles. However, partitioning also makes aspects of the bundle differentially salient. These processing effects moderate the mental accounting effects on evaluation and choice. Thus, in an appliance purchase context, partitioned pricing may draw attention from (or to) specific features of the focal appliance. Partitioning the price of a consumption-related feature (e.g., an icemaker) may focus attention on add-on consumption benefits and reduce scrutiny of the performance (reliability) ratings of the refrigerator. In contrast, partitioning the price of a performance-related feature (e.g., a warranty) may make product failure a salient concern and focus attention on performance data. Such mechanisms that draw attention from (or to) component features may influence how consumers weight specific bundle features, affecting their evaluations and choices. When a multicomponent bundle is presented with a consolidated (overall) price, component level reference price comparisons play only a limited role. However, if the bundle price is partitioned into component prices, reference compar-
isons may change as consumers may then attend selectively to the focal product, the partitioned component, or the entire bundle. Alternative price splits may then be coded differently, and consumers may weight different bundle features (e.g., price, performance risk, or add-on consumption value) depending on the component partitioned. We propose that such differential coding as a function of the component partitioned can lead to different evaluations and choice outcomes for partitioned versus consolidated prices. In this article, we examine how partitioned versus consolidated pricing of a pure product bundle (i.e., mandatory components) influences evaluations and choice. We propose a baseline mental accounting analysis and track the underlying effects that moderate evaluations and choice. In the next section, we develop the mental accounting analysis, specifying how consumers may edit and code the benefits and prices of bundle components when prices are partitioned or consolidated. We then describe how associated information processing effects may moderate the outcomes. Two empirical studies were designed to test these propositions. Experiment 1 showed that the evaluations and choice proportions for a multicomponent product bundle (e.g., a refrigerator, an icemaker, and a warranty) were higher when component prices were partitioned (vs. consolidated with a single tag). However, the effects varied depending on whether the component partitioned was consumption related (e.g., icemaker) or performance related (e.g., warranty), and we tracked the underlying processes using a set of framing manipulations. Experiment 2 replicated the evaluation and choice effects for partitioned prices in a similar context. Further, it showed that the effects depended on how the total bundle price was split among the components and on the component that was partitioned. We conclude that although the observed price partitioning effects were consistent with the baseline mental accounting analysis, they were also moderated by associated processing effects. Partitioned (consolidated) pricing of a bundle component raised (lowered) its salience and drew attention to related bundle features, altering evaluations and choices. We discuss the implications of the findings and conclude with suggestions for future research. CONCEPTUAL BACKGROUND Our baseline analysis of the evaluation and choice effects of partitioned versus consolidated pricing of bundle components rests on prospect theory (Kahneman & Tversky, 1979). This descriptive model explains preference shifts induced by differential framing of decision problems. According to prospect theory, people code decision outcomes as gains or losses against a reference point. Evaluations are based on these gains and losses rather than on total assets. The coded outcomes are mapped to subjective worth via a value function that is concave in gains and convex in losses (i.e., there is a greater subjective difference between gains and losses that
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are closer to the reference point than those that are further away). The value function is also steeper in losses than in gains. This loss aversion (Kahneman & Tversky, 1984) property implies that a loss of a given size is more aversive than a gain of equal size is attractive. Framing may shift reference points, altering how the benefits and costs of a proposition are subjectively represented (e.g., Fischhoff, 1983; Tversky & Kahneman, 1986). The model explains many evaluations and choices that appear anomalous under traditional rationality tenets (Tversky & Kahneman, 1991). Thaler (1985) extended prospect theory to account for how people encode compound (joint) outcomes. The component outcomes may be evaluated together (consolidated) or separately (partitioned) with associated differences in perceived value. Specifically, for a value function concave in gains (i.e., marginal gains are perceived as higher for gains closer to the reference point), partitioned (vs. consolidated) evaluations of a compound event involving multiple gains should result in a higher perceived value. Similarly, for a value function convex in losses (i.e., marginal losses are perceived as higher for losses closer to the reference point), consolidated evaluation of a compound event involving multiple losses should also raise its perceived value (i.e., make it less negative). For mixed gains (a gain compounded with a smaller loss), consolidation cancels the loss against part of the gain. Because the value function is steeper in losses than in gains, the perceived value of the compound event is higher if the gain and loss are consolidated and the net gain is evaluated, versus if the gain and the loss are partitioned and evaluated separately, and a net value is computed.1 Tests of these mental accounting propositions have yielded conflicting results, and the underlying information processing is not understood clearly (Wang, 1996). Some studies that did not find support for the mental accounting propositions suggest that partitioned price information may be processed with heuristics such as anchoring and adjustment (Johnson et al., 1999; Morwitz et al., 1998) and that the anchors and processing heuristics used may depend on prior purchase intentions for the partitioned components (Suri & Monroe, 1999). Others (e.g., Fischhoff, 1983) have argued that the specific reference points used in a decision problem are difficult to predict (Fischhoff, 1983) particularly because consumers often “edit” (i.e., reframe) events and outcomes for cognitive or hedonic reasons (Thaler & Johnson, 1990). There is evidence of asymmetric loss aversion in quality versus price contexts (Hardie, Johnson, & Fader, 1993), and mental accounting predictions have received little support when the supplied decision frames were percentage based and induced different reference comparisons (Heath et al.,
1However, see Luce (2000, p. 243) for a technical qualification resting on the relative sizes of the scaling parameters for the gain and loss sides of polynomial-additive utility functions (and correspondin g additive value functions).
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1995). Thus, contextual changes and processing effects that accompany partitioned or consolidated pricing of bundle components may moderate the mental accounting effects. In this article, we propose that a partitioned price may raise the salience of a component and its related features, increasing its weight in evaluation and choice. Moreover, partitioned (vs. consolidated) prices, as well as different partitions of a total price, may induce different reference comparisons and affect evaluations and choice. Mental Accounting of Partitioned–Consolidated Prices Our baseline predictions for partitioned and consolidated pricing of a multicomponent product bundle rest on Thaler’s (1985) analysis of the mental accounting of compound events. We consider both the component benefits (the gains side) and the component prices (the loss side) of the bundle and propose how consumers may edit and combine the components (Thaler & Johnson, 1990). We build our discussion using a bundle consisting of a refrigerator, an icemaker, and a warranty. There is either a separate price tag for each component or a single consolidated price tag for the bundle. Drumwright (1992) analyzed such purchases as instances of mixed gains, where the bundle components are coded as separate benefits (gains) in both the partitioned and the consolidated presentation. However, presentation influences how prices are coded. With a consolidated tag, the total price is coded as a single loss but when the total price is partitioned with separate tags, each component price is coded as a separate loss. Given a value function that is convex in losses, the total price is evaluated less negatively when consolidated (vs. partitioned). Because the bundle benefits have equal value in both presentations, the bundle is evaluated more favorably when its prices are consolidated (see also Johnson et al., 1999, for a similar analysis). Both Drumwright and Johnson et al. reported mixed empirical support for the idea that bundling can integrate multiple losses (i.e., component prices) and raise evaluations. The aforementioned mixed results, along with findings that consumers may edit supplied frames (Thaler & Johnson, 1990), suggest alternative accounts of the mental accounting that may stem from price partitioning. Thus, even if prices are partitioned by component, consumers can easily add them to determine the total price of the bundle and then evaluate the associated loss. Such editing leaves the mental account on the price (loss) side identical, regardless of whether the presentation is partitioned or consolidated. However, the generally incommensurate benefits of partitioned components may be harder to combine directly. Hence, their benefits (gains) may be easier to code and evaluate separately (perhaps relative to component-specific referents). The total value is then determined by adding the component values. In contrast, consolidated presentations may encourage holistic evaluation of the
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component benefits, and the total value may be mapped only after the benefits are added. With a value function concave in gains, the component benefits would then have a higher total perceived value when partitioned (vs. consolidated). This analysis of the decision (as a mixed gain with differential editing) suggests that evaluations will be more favorable for partitioned presentations. Kahneman and Tversky (1984) cautioned that the payments consumers make in routine economic exchanges should not be viewed as uncompensated losses. Rather, they suggest that prices are “legitimate exchange for value received” and should be treated as proxies for the goods and services acquired. Thus, prices may simply serve as value tags for the component benefits, focusing mental accounting entirely on the gains side, where the value function is concave. A consolidated price then directly integrates the component gains. In contrast, partitioned prices frame each component as a part gain that is evaluated separately. Hence, the total gain is valued more when the bundle is presented with partitioned (vs. consolidated) prices. In summary, a product bundle will be valued more with partitioned price presentations if prices and benefits are differentially edited in a mixed-gain frame or if prices are processed as value tags for the component benefits in a multiple-gain frame (H = hypothesis): H1: Evaluations of a product bundle will be more positive and choice proportions higher when its price is presented in partitioned versus consolidated fashion. Moderating Effect of the Partitioned Component Partitioned versus consolidated prices may not only drive mental accounting effects but may also influence which aspects of the bundle are attended to during decisions. Partitioned (vs. consolidated) presentation of a consumption-related accessory or a performance-related feature can raise its salience (Gaeth, Levin, Chakraborty, & Levin, 1991) and draw attention from (or to) other bundle features. Partitioning the price of a consumption-related accessory (e.g., an icemaker) may thus direct attention to the additional consumption value of the accessory and raise the overall evaluation and choice proportions of the bundle. In contrast, partitioning an equally priced performance-related feature (e.g., a warranty) may draw attention to the performance of the focal product and make salient the risk of product failure. This negative aspect may lower the bundle evaluation when the partitioned component is performance related compared to when the component is consumption related (with equal price tags): H2: Evaluations and choice proportions of a product bundle with partitioned prices will depend on the component that is partitioned. Partitioning the price of a consumption-related accessory will produce more
favorable evaluation and choice effects than partitioning an equally priced performance-related feature. Tracking the Attentional Effects of Partitioned Prices We propose that differential attention to specific bundle features as a function of the component partitioned drives the moderating effects on bundle attractiveness. Partitioning a performance-related feature (e.g., a warranty) encourages more processing of the focal product performance (or reliability) data. In contrast, partitioning a consumption-related accessory (e.g., an icemaker) makes salient consumption value but induces no special consideration of performance data. The proposition may be explored directly by assessing respondents’ relative focus on the performance data versus other product features with concurrent or retrospective verbal protocols. Another (more indirect) approach may test postdecision memory for the performance data. Alternatively, one might indirectly track processing by manipulating the performance data and examining whether evaluations and choices are affected under relevant price presentations. We used two such indirect tracking approaches, manipulating the performance level data and how they were framed (positive or negative) to test our account of the attentional effects of price partitioning. The framing of product performance data influences evaluations and choices. Levin and Johnson (1984) showed that estimates of price change per unit of (ground beef) quality vary if quality is described positively (percentage lean) or negatively (percentage fat). Levin and Gaeth (1988) found similar results, although the effect was attenuated when respondents actually tasted the meat. Wiener, Gentry, and Miller (1986) found that flood insurance purchases were less likely if framed as losses versus gains. Consistent with these results, a bundle should receive a higher (lower) evaluation if the focal product performance data are framed positively (reliability ratings) versus negatively (failure ratings): H3: Evaluations will be more positive and choice proportions higher when performance data for the focal product are framed positively versus negatively. We suggested an underlying mechanism whereby partitioning a performance-related feature (e.g., a warranty) should draw more attention to performance data than partitioning a consumption-related accessory (e.g., an icemaker) or consolidating the bundle price. Hence, the framing effects in H3 should be accentuated in this condition. Thus, a partitioned icemaker is unlikely to direct attention to the focal product performance data regardless of its framing. However, a partitioned warranty draws attention to the performance data so that evaluation and choice proportion differences between positively and negatively framed data should be larger:
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H4: The influence of positive versus negative framing on evaluation and choice will be greater when the warranty price is partitioned relative to when the icemaker price is partitioned or when the bundle price is consolidated. Note that if partitioning influences consumers to attend to specific bundle features, they should be more sensitive to changes in these features. Thus, if consumer evaluations and choices are differentially sensitive to changing performance levels of the focal product as a function of whether prices are consolidated or different component prices are partitioned, one may attribute these variations to differential attention to the performance data. In our example, there should be greater sensitivity to the performance levels when the partitioned component is performance-related, particularly when the performance data are framed negatively. However, evaluations and choice should be less sensitive to the performance data when the partitioned component is consumption related or when the bundle price is consolidated. EXPERIMENT 1 Method
Design. The previous hypotheses were tested in Experiment 1 with a scenario where respondents were shopping for a good quality, medium-sized refrigerator (see Appendix A). The options were narrowed down to two models, A and B. Model B (the comparison option) was fixed across all study conditions, whereas the description of Model A (the target) was varied. Among its features, Model A had three components: the focal product being a refrigerator, an icemaker being the consumption-related accessory, and a service warranty representing the performance-related feature. These were presented in one of three ways. The consolidated presentation had a single price tag of $499.95 for all three components. The partitioned presentation had two versions. In one version, the refrigerator and the icemaker were priced together for $399.95 with the warranty having a partitioned price of $100. In the other version, the refrigerator and warranty were priced together for $399.95, and the icemaker had a partitioned price of $100. The total bundle price was $499.95 in all three conditions. The task instructions suggested that purchase of all three components was mandatory (i.e., a pure bundle) in all presentation conditions. The performance of Model A was manipulated at four levels, and the data were framed either negatively (failure rates < 1, 5, 10, and 15%) or positively (reliability rates > 99, 95, 90, and 85%). The performance of Model B was always stated positively (95%). Descriptions of other aspects (e.g., the refrigerator size and energy efficiency) completed the scenario. The descriptions and the prices corresponded to refrigerators actually available in the market at the time. The stimuli were
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calibrated in pretests to avoid ceiling or floor effects. In summary, the core manipulations in Experiment 1 constituted a 3 × 4 × 2 between-subject design. The bundle was presented in three ways (refrigerator, warranty, and partitioned icemaker; refrigerator, icemaker, and partitioned warranty; or a consolidated bundle with a single equivalent price). In addition, the manipulations included four different performance levels and positive (reliability rate) and negative (failure rate) framing.
Respondents and procedure. The 480 respondents (undergraduates at a southwestern public university, M age = 22.7 years, 55% female) were randomly assigned to the study cells. After carefully reading the one-page scenario that embedded the manipulations, respondents completed the three dependent measures. They then answered questions about task realism, manipulation checks, and selected shopping behavior covariates. Respondents were told that there were no right or wrong answers. They completed the tasks in 10–15 min. Data from 7 respondents were incomplete and were discarded. Data from 29 additional respondents (partitioned conditions) were also excluded because they did not realize that purchasing the partitioned components was mandatory. The results reported are based on data from the remaining 444 respondents. Measures. Three dependent measures were collected. The first was a 7-point scale ranging from –3 (quite undesirable) to 3 (quite desirable) of the desirability of Model A relative to Model B. The second asked respondents to divide 100 points between A and B to reflect relative choice likelihoods. Analyses of variance (ANOVAs) were performed on these two evaluation measures as functions of the manipulated factors and their interactions. The findings were very similar for the desirability and choice likelihood measures. We, therefore, report only the latter. Respondents also stated their choice between A and B. Target (A) choice proportions were analyzed with a log-linear model as a function of the manipulated factors and their interactions. Results
Basic checks. Respondents thought that the refrigerator descriptions were quite realistic, and the ratings did not vary across study conditions (M = 3.93; 1 = quite unrealistic, 5 = quite realistic). Their assessment (1 = quite likely, 5 = quite unlikely) that Model A would perform trouble-free for 5 years dropped with declining performance (Ms = 2.50, 2.53, 2.87, and 2.88 for failure rates levels of < 1, 5, 10, and 15% respectively), F(3, 420) = 4.02, p < .01. Note that the assessments differed significantly (p < .05) only for failure rates above and below 5%. Also, framing the performance data as reliabilities versus failure rates led to a better assessment (Ms
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= 2.57 vs. 2.82), F(1, 420) = 6.33, p < .02. The pattern of means shows that the manipulations were encoded as intended. However, consistent with our hypotheses, the small effect sizes may reflect differential attention to performance data across study conditions.
Consolidated–partitioned presentation. H1 predicts that bundle evaluations will be more positive and choice proportions will be higher when the price of a bundle is partitioned versus consolidated. The data in Table 1 show significant differences on both choice likelihood (Ms = 61.5 vs. 43.4), F(2, 420) = 20.79, p < .0001, and choice (66.4 vs. 39.2%), c2(2, N = 444) = 33.40, p < .0001. Relative to the consolidated presentation, the bundle was evaluated more favorably and chosen more frequently when the icemaker price was partitioned (choice likelihood Ms = 66.2 vs. 43.4, p < .0001; choice = 71.7 vs. 39.2%, p < .0001). This was also true when warranty price was partitioned (choice likelihood M = 56.7, p < .0002; choice = 61.1%, p < .0005). Consistent with H1, respondents preferred both partitioned presentations relative to the consolidated presentation. Moderating effect of the partitioned component. H2 predicts that the evaluations and choice proportions of a product bundle with partitioned prices will depend on the component that is partitioned. Bundle evaluations were more positive and choice proportions were higher when a consumption-related accessory (i.e., icemaker) was partitioned relative to when a performance-related feature (i.e., warranty) was partitioned (choice likelihood Ms = 66.2 vs. 56.7, p < .0002; choice = 71.7 vs. 61.1%), c2(1, N = XX) = 3.81, p = .07. Although the choice data are only marginally significant, these differences were generally consistent with H2. Tracking the attentional effects of partitioned prices. H3 predicts that bundle evaluations will be more positive and choice proportions will be higher when performance data for the focal product are framed positively versus negatively. Contrary to expectations (and prior findings in the literature), there were no differences in preference as a function of positive (reliability rate) versus negative (failure rate) framing when the data are pooled over the three bundle presentation conditions (choice likelihood Ms = 56.6 and 54.8, p > .55; choice = 58.5 and 56.8%, p > .70). Thus, H3 was not supported. Attention mechanisms. The premise of H4 is that partitioning a performance-related component (the warranty) draws attention to the performance data of the focal product. This should accentuate the effects of positive–negative framing of the performance data relative to when a consumption-related component (the icemaker) was partitioned or
when the bundle price was consolidated. The implied interaction between positive–negative framing and price presentation was such that the framing effect was larger when the warranty price was partitioned versus when the icemaker price was partitioned or the bundle price was consolidated. The data are in Table 1. Choice likelihood ratings showed a significant interaction, F(2, 420) = 4.36, p < .02. Positive–negative framing had no effect when the icemaker price was partitioned (p > .15) or when the bundle price was consolidated (p > .65). However, with a partitioned warranty price, the ratings were higher for the positive frame as predicted (Ms = 63.4 vs. 50.4, p < .01). Choice proportions also showed a significant interaction, c2(2, N = 444) = 10.39, p < . 01. Positive–negative framing did not influence choice when the icemaker price was partitioned or when the bundle price was consolidated (ps > .32 and .24, respectively). However, when the warranty price was partitioned, choice proportions were higher for positive versus negative framing (72.2 vs. 50.7%, p < .01). These data supported H4 and the proposed attentional mechanism. The simple effects of partitioning the icemaker and warranty price for positive versus negative performance frames showed an interesting pattern. For the positive (reliability) frame, the component partitioned did not influence choice likelihood (p > .90) or choice frequency (p > .58). Thus, the bundles were equally liked whether the icemaker or the warranty price was partitioned. However, significant differences emerged for the negative (failure) frame. Relative to when the warranty price was partitioned, the bundle was preferred when the icemaker price was partitioned (choice likelihood Ms = 69.3 vs. 50.4, p < .001; choice = 75.3 vs. 50.7%, p < .002). Consistent with the reasoning that a partitioned performance-related component draws particular attention to the performance data of the focal product, the effect of partitioning the warranty price was clearly asymmetric for positive versus negative frames. There was a strong negative impact in the negative performance frame (failure rate) but no significant impact in the positive performance (reliability) frame. We argued earlier that if partitioning influences the bundle features that consumers attend to, they should become more sensitive to these features. In this study, there should have been greater sensitivity to the performance levels when the warranty price was partitioned relative to when the icemaker was partitioned or the bundle price was consolidated. Table 2 (Panel A) displays the choice likelihood and the choice proportion for each of the four performance levels in the positive frame. Panel B shows the corresponding data for the negative frame. Both panels show (in parentheses) the p values for tests of differences between scores for the > 99% reliability (< 1% failure) and the other performance levels (reliabilities of 95, 90, and 85% or corresponding failure rates of 5, 10, and 15%) for each bundle presentation condition. Recall that the comparison option (Model B) had a fixed 95% reliability rating. In the positive frame (Panel A), we focus on the tests for
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TABLE 1 Experiment 1: Evaluation and Choice of Refrigerator A (Means by Bundle Presentation and Performance Frame) Partitioned Presentation Performance Frame Cell sizes Positive (reliability rate) Negative (failure rate) Total Choice likelihood ratings Positive (reliability rate) Negative (failure rate) M Choice percentages Positive (reliability rate) Negative (failure rate) M
Consolidated Presentation 70 73 143
Icemaker 75 77 152
Warranty 72 77 149
Total (M) 147 154 301
42.6 44.1 43.4
63.0 69.3 66.2
63.4 50.4 56.7
63.2 59.9 61.5
34.3 43.8 39.2
68.0 75.3 71.7
72.2 50.7 61.1
70.1 63.0 66.4
the 95 and 90% reliability rates. The data show no sensitivity to decreasing reliability ratings in any of the bundle presentation conditions (all ps > .36, .64, and .25 for the consolidated price, the partitioned icemaker price, and the partitioned warranty price, respectively). The data pattern was different in the negative frame (Panel B of Table 2). We focus on the tests associated with the 5 and 10% failure rates. There was an insensitivity to increasing failure rates in the consolidated price condition (all ps > .11) as well as when the icemaker price was partitioned (all ps > .15). However, with a partitioned warranty price, the measures dropped sharply as soon as the target option exceeded the comparison alternative failure rate of 5% (choice likelihood p < .02; choice p < .03). Thus, partitioning the warranty price raised sensitivity to performance levels when the data were negatively framed (failure rates). This sensitivity was, however, unchanged when the icemaker price was partitioned or when the bundle price was consolidated. These data are consistent with the idea that partitioning prices may alter the salience of various bundle features (with corresponding effects on evaluation and choice).
Discussion Experiment 1 showed that partitioned versus consolidated pricing of multicomponent product bundles influences evaluations and choice. The target bundle was evaluated more favorably and chosen more often when its components were partitioned with separate price tags versus consolidated with a single, equivalent price tag. The findings were consistent with mental accounting propositions based on the prospect theory value function. However, they did not conform to the simple mixed-gain analysis suggested by Drumwright (1992) or Johnson et al. (1999). Rather, the data supported an alternative mixed-gain analysis where frames were differentially edited for prices and benefits (Thaler & Johnson, 1990) or a
multiple-gains analysis with the prices serving as value tags for the component benefits. Experiment 1 also showed that the preference for partitioned presentations is moderated by the nature of the partitioned component. Partitioning the icemaker price produced higher evaluations and choice proportions relative to partitioning an equally priced warranty. This was consistent with the idea that partitioning the price of a consumption-related accessory focuses attention on add-on consumption value and raises the attractiveness of the bundle. Partitioning the price of a performance-related feature focused attention on the focal product performance data. However, this also made salient the risk of product failure, reducing the overall attractiveness of the bundle in some conditions. Thus, the processing (attention) effects induced by the nature of the partitioned component can moderate the baseline mental accounting effects on evaluations and choice. Our results imply that a partitioned warranty price should increase attention to the performance data of the refrigerator relative to a partitioned icemaker price or a consolidated bundle price. We tested this by tracking how price partitioning influenced respondents’ sensitivity to manipulations of the performance data of the refrigerator. One manipulation involved positive versus negative framing of the performance data. Surprisingly (cf. Levin & Gaeth, 1988; Levin & Johnson, 1984; Wiener et al., 1986), evaluations and choices showed no main effects of positive versus negative framing.2 However, the framing effects did vary by how price was presented. As predicted, a partitioned warranty price (vs. a partitioned icemaker price or consolidated pricing) accentuated the effects of performance framing on evaluation and choice. With performance framed as reliabilities, the effects did not vary whether prices were consolidated or partitioned or by 2Respondent s may have edited the failure frame for the target (Model A) performance data to simplify comparisons with Model B, for which performance was always stated as reliabilities. Thus, consumers may edit the provided positive–negative frames such that this well-documente d effect sometimes vanishes.
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TABLE 2 Experiment 1: Evaluation and Choice of Refrigerator A (Means by Bundle Presentation, Performance Frame, and Performance Level) Partitioned Presentation Performance Frame–Level A. Positive (reliability rate) Choice likelihood ratings >99% 95% 90% 85% Choice percentages >99% 95% 90% 85% B. Negative (failure rate) Choice likelihood ratings 99, 95, and 90%, respectively, p > .85). This suggests that the positively framed performance data received relatively low attention.
Replication. As a start, we sought to replicate the relevant findings of Experiment 1. Experiment 2 permitted tests of H1 and H2. H3 and H4 could not be tested because the performance frame was not manipulated. The data (Table 3) showed that evaluations and choices differed significantly, depending on how the bundle price was presented, choice likelihood F(2, 497) = 14.82, p < .0001; choice c2(2, N = 512) = 24.95, p < .0001. Relative to the consolidated presentation, evaluations and choice proportions were higher with the partitioned icemaker price (choice likelihood Ms = 58.9 and 46.7, p < .0005; choice = 62.8 and 43.6, p < .0005) and with the partitioned warranty price (choice likelihood Ms = 64.3 and 46.7, p < .0001; choice = 69.1 and 43.6, p < .0005). These data supported H1 and were consistent with the findings of Experiment 1. The findings for H2 corresponded to those for the positive (reliability) frame in Experiment 1. The component partitioned did not significantly influence evaluations or choices (choice likelihood p = .10; choice p > .15). The data (Table 3) show that evaluations improved similarly whether the icemaker or the warranty price was partitioned. These results reflect the increase in preference predicted by mental accounting propositions (i.e., analyses as mixed gain with differential editing or multiple gains with prices as value tags). The negative effects of warranty partitioning in Experiment 1 appeared to be driven by the failure rate frame.
Focal product price effect. H5 predicts that evaluations and choice proportions will depend on how the total bundle price is partitioned between the focal product and the partitioned component. When the focal product price is unfavorable
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(vs. favorable) relative to the comparison option, evaluations and choices will be lower (vs. higher). Table 3 shows the relevant data. When the price of the focal product was unfavorable (higher) versus favorable (lower) relative to the price of the comparison option, the bundle was evaluated lower and chosen less often—choice likelihood Ms = 58.2 vs. 64.9, F(1, 497) = 3.95, p < .05; choice = 59.3 vs. 72.7%, c2(1, N = XX) = 6.18, p < .02. These data provided strong support for H5.
Moderating effect of the partitioned component. H6 predicts that evaluations and choice effects of unfavorable versus favorable focal product prices (relative to a fixed comparison option) will depend on the component partitioned. Partitioning the icemaker price should accentuate the price-split effect relative to partitioning the warranty price. The data in Table 3 show a two-way interaction such that the bundle price split had an impact when the icemaker (but not the warranty) was partitioned. With the icemaker partitioned, the bundle was evaluated lower and chosen less often when the focal product price was unfavorable (higher) relative to that of the comparison option (choice likelihood Ms = 53.7 and 64.0, p < .05; choice = 54.7 and 70.9%, p < .05). These effects vanished for both measures when the warranty was partitioned (all ps > .05). Thus, the identity of the partitioned component influenced the salience of the bundle price splits. Discussion The results of Experiment 2 were generally consistent with those of Experiment 1. We replicated the higher evaluations and choice frequencies for partitioned presentations. Also, as in Experiment 1 (reliability frame), the effects were similar regardless of whether the icemaker or warranty price was partitioned. These results were consistent with mental accounting analyses, assuming either mixed gain with differential ed-
iting or multiple gains with prices as value tags. Experiment 2 also showed that an unfavorable focal product price (relative to the comparison option) lowers evaluations and choices compared to when the comparison is favorable. This suggests that the focal product price may anchor bundle evaluations and choice and that the preference for partitioned presentations may be enhanced or offset by how the total bundle price is split (Morwitz et al., 1998). As in Experiment 1 (reliability frame), the preference for partitioned presentations was preserved over performance level changes, irrespective of the component partitioned. Performance data in the reliability frame did not trigger the negative evaluation or choice effects that they did in the failure rate frame (Experiment 1). Also, the price-split effect appeared only in the partitioned icemaker condition but not in the partitioned warranty condition. Thus, the partitioned icemaker price seemed to direct more attention to the focal product price. Perhaps the partitioned warranty drew attention away from the price split to the performance data. Because the component partitioned moderated the focal product price-split effect, it seems that the partitioned component was neither ignored nor treated equivalently, irrespective of its identity (cf. Morwitz et al., 1998). GENERAL DISCUSSION Experiments 1 and 2 had corroborative results. Partitioned presentation of multicomponent bundle prices raised evaluations and choice proportions relative to consolidated presentation of an equivalent total price. These effects were consistent with mental accounting analyses involving either mixed gains with differential editing of prices and benefits or multiple gains with prices serving as value tags for component benefits. However, both experiments showed that the mental accounting effects were moderated by information processing
TABLE 3 Experiment 2: Evaluation and Choice of Refrigerator A (Means by Bundle Presentation and Base Price of Focal Product) Partitioned Presentation Focal Product Price Cell sizes Unfavorable (high) Favorable (low) Total (high and low) Choice likelihood ratings Unfavorable (high) Favorable (low) Mean (high and low) Choice percentages Unfavorable (high) Favorable (low) Mean (high and low)
Consolidated Presentation
170
Icemaker 86 86 172
Warranty 80 90 170
Total (M) 166 176 342
46.7
53.7 64.0 58.9
62.7 65.8 64.3
58.2 64.9 61.6
43.6
54.7 70.9 62.8
63.8 74.4 69.1
59.3 72.7 66.1
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effects that accompanied the partitioned presentation of component prices. Experiment 1 showed that the evaluation and choice effects of partitioned prices were moderated by the component that was partitioned. We tracked the locus of attention as a function of the partitioned component by manipulating the framing (reliability–failure rates) and the levels of the performance data. Partitioning raised the salience of add-on benefits provided by the component (e.g., consumption value for an icemaker, insurance value for a warranty). However, it may have also induced consideration of other related aspects of the product. Thus, a partitioned warranty may have raised the perceived risk of product failure and directed attention to performance data. This reduced evaluations and choice proportions dramatically when performance data were framed as failure rates and performance levels fell below that of a referent comparison option (Experiment 1). However, when the data were framed as reliability rates, the component partitioned had no differential effect in either study. Thus, consistent with mental accounting propositions, partitioning prices may raise the attractiveness of a multicomponent product bundle. However, partitioning may also change the locus of consumer attention and influence the salience of other bundle features. These attentional effects may cancel or reinforce the mental accounting benefits. Partitioning some components may heighten the salience of other negative associations, perhaps making the bundle even less attractive than if it were consolidated. Partitioning other components may highlight positive associations or deflect attention from disadvantageous features of the focal product. The second-order performance-framing effects observed in these studies are important to consider in real-world price presentation decisions.4 In Experiment 2, bundle evaluations varied as a function of how the price was split between the focal product and the components. First, evaluation and choices were higher if the price split made the focal product price lower than that of a comparison option. Thus, the focal product price was an important anchor in evaluation (Morwitz et al., 1998; Yadav, 1994) and the attractiveness of the bundle may have been raised if the price split was engineered to make the focal product price favorable relative to competitive prices. Second, the price-split effect was moderated by the component partitioned. Evaluations and choice proportions were lower when the icemaker (but not the warranty) was partitioned so that the focal product price was higher than that of the comparison option. Thus, the partitioned components were neither ignored nor treated equivalently (cf. Morwitz et al., 4These influences may be contingent on situational or individual factors and may not always be obvious. For example, more experienced or involved consumers may infer greater insurance value for the warranty as focal product performance fails. These decisions must be informed by careful customer analysis.
1998). The partitioned icemaker may have directed attention to price or add-on consumption value, whereas the partitioned warranty deflected attention from price to the performance data. With differently partitioned comparison options, the attentional effects could involve multiple comparisons in absolute and/or relative (percentage) terms (Heath et al., 1993). Theory-based empirical work on these phenomena would, therefore, have considerable practical significance. Among limitations, the paper-and-pencil tasks and the student respondents should temper extrapolation of the results. As with other results from between-subject framing studies, the effects are more likely when preferences are constructed online, and heuristic analysis inhibits discovery of the equivalence of partitioned and consolidated presentations (Morwitz et al., 1998; Payne, Bettman, & Johnson, 1992). The correspondence of the judgment and choice findings may stem from contiguous elicitation and may be dissociated if elicited further apart in time. Finally, one may ask if the results generalize beyond the product category and purchase situations that were used in these studies. We note that a replication study with stimuli describing a computer and a printer bundle (with consolidated or partitioned prices) provided a similar pattern of results. Partitioning the printer price produced higher evaluations and choices relative to a consolidated presentation.5 Future Research These studies focused mainly on contexts involving gain frames and positively framed performance data. At first glance, loss frames and negative presentations may appear less relevant in marketing contexts. However, many purchases are triggered by prior (or anticipated) product failure and may occur in negative mind sets. Hence, there is conceptual and substantive interest in consumer reactions in explicit loss frames. Mental accounting results in loss frames may help discriminate between the mixed gain with differential editing and multiple-gains analyses of price partitioning effects. Other analyses of preferences for separating or combining events (e.g., Linville & Fischer, 1991) drive different predictions on the basis of competing arguments and data about behavior in loss domains. Partitioned component prices allow additive versus subtractive framing of choices (Park, Jun, & MacInnis, 2000). Allowing consumers to choose the components they want or to reject those they do not want produces different choices. The respondents chose more components (with a higher total price) in a subtractive mode (working down from a fully loaded product) than in an additive mode (building from a base product), and the effect varies by the relative importance of the components. More research on the underlying perceptual and evaluative pro5Details
of this study are available from the authors.
MULTICOMPONENT BUNDLE PRICES
cesses may add insights on whether price partitioning induces consumers to conform to or transcend budget constraints. This issue has significant conceptual, managerial, and policy implications. Our focus here has been on the bundling of complementary products and services. With technologically complex products, bundle presentation may influence subjective perceptions of the costs and benefits of integration versus modularity (Wilson, Weiss, & John, 1990) and affect how competitive systems are evaluated. Bundles may also be formed with substitute products (to cater to variety seekers) or with unrelated or even incongruous products. A mental accounting analysis alone may provide limited insights into such situations. Rather, explanations invoking categorization and schematic processes that depend on consumer expertise (Alba & Hutchinson, 1987; Henderson & Peterson, 1992) are likely to provide more enduring direction for managerial and public policy decisions. Recent research (Johnson et al., 1999; Yadav & Monroe, 1993) has shown that buyer evaluations may depend on whether marketers offer discounts on the bundle price or on individual components. We show that decision outcomes vary with different splits of the same total bundle price. More generally, marketers may develop price tags for bundle components in different ways. For example, each component may be priced consistent with an accepted reference price. Alternatively, a component may be priced very low to signal a discount or priced unusually high to signal value. Despite equivalent total prices, such framing may influence perceptions of fairness (Kahneman, Knetsch, & Thaler, 1986), depending on the relative prominence of bundle components. These phenomena are relevant for designing tie-in promotions and pricing strategies in situations where transactions cluster naturally (e.g., buying a new car and trading in an old car). See Purohit (1995) for an initial conceptual and empirical analysis of the latter issue. Finally, we focus exclusively on price as the primary consolidation or partitioning tool. Products may also be bundled or partitioned with other marketing mix elements. Approaches such as umbrella and associative branding (Park, McCarthy, & Milberg, 1992), advertising tie-ins, and distribution alliances may be evaluated as tools for bundling or partitioning products in consumers’ minds. Broadening the concept of bundling in this way opens up many new research opportunities. ACKNOWLEDGMENTS The authors contributed equally to the article and are listed alphabetically. They thank C. W. Park, Mike Houston, and Kent Nakamoto for their comments on earlier drafts of the article. The constructive suggestions of the JCP reviewers and the Editor, Dawn Iacobucci, are also gratefully acknowledged. REFERENCES Adams, William J., & Yellen, Janet L. (1976). Commodity bundling and the burden of monopoly. Quarterly Journal of Economics, 90, 475–498.
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APPENDIX A STIMULI FOR EXPERIMENT 1 Consolidated Presentation (Negative Framing and 99% Reliability) You and a roommate have found and leased a conveniently located rental house close to campus. The rent ($500 per month) is just right, the house is well furnished and the furniture and appliances are quite new. However, when you move in, you find that the refrigerator is not working. The repairman tells you that the compressor has failed and the refrigerator needs replacement. You complain to your landlord, who apologizes profusely and suggests that you buy and install a refrigerator of your own. For your inconvenience, he offers to waive the first month’s rent. You and your roommate want a refrigerator that is always well stocked with food, soda and beer for those late night cram sessions and great parties that you like to throw. The landlord’s offer is a lucky break and you decide to buy a good quality, medium-sized refrigerator (around 15 cu. ft.) with an icemaker and a good warranty. Student budgets are tight though, and you would not mind spending a little less than $500 without compromising much quality. You have shopped carefully at major appliance stores and have narrowed the choice down to two models that you feel offer good value for money. Refrigerator Model A is priced at $489.95. The price includes a high quality icemaker and a manufacturer’s warranty of 2 years on all parts and labor. The refrigerator has a capacity of 14.8 cu. ft. which is about the right size. It comes in the color of your choice and has convenient temperature controls. The glass interior and freezer shelves are easy-to-adjust and there are large butter and utility compartments in the door. The refrigerator has good-sized meat and dairy compartments and a vegetable crisper. Consumer Re-
Refrigerator Model A (Partitioned Icemaker, Focal Product Price Favorable) Refrigerator Model A is priced at $424.95. The price includes a manufacturer’s warranty of 2 years on all parts and labor. The refrigerator has a capacity … vegetable crisper. Consumer Reports shows … a better than 99 percent reliability rate over a 3-year period. As a limited time special, for an additional $65 only, the manufacturer offers a high quality icemaker with the purchase of Model A.
Refrigerator Model A (Partitioned Warranty, Focal Product Price Unfavorable) Refrigerator Model A is priced at $424.95. The price includes a high quality icemaker. The refrigerator has a capacity … vegetable crisper. Consumer Reports shows … a better than 99 percent reliability rate over a 3-year period. As a limited time special, for an additional $65 only, the manufacturer offers a warranty of 2 years on all parts and labor with the purchase of Model A.
Other Notes The focal product and add-on prices were $389.95 and $100 respectively in the low-base-price condition (Model B was priced at $419.95). Two other reliability levels (95 and 90%) were used. Model B had a 95% reliability. Other scenario aspects and product descriptions were held fixed across conditions.
