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British Journal of Management, Vol. ***, *-* (.2010) DOI: 10.1111/j.1467-8551.2010.00696.x

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Christina Siehtmatm, Robert Wilken1 and Adamantios DiamantopoiilDS University of Vienna, Chair of International Marketing, Bruenner Strasse 72, A-1210 Vienna, Austria, and

XESCP Europe Business School, International Marketing, Heubnerweg 6, D-14059 Berlin, Germany Email: Christina.sichtmann@univie. ac. at; robert.wilken@escpeurope .de

adamantios.diamaiitopO:[email protected]

Knowing consumers' willingness to pay (WTP) is crucial for making effective pricing decisions. We assess the accuracy of choice-based conjoint analysis (CBCA), a method strongly supported by behavioural theory, in the context of WTP measurement at the individual level. Furthermore, we analyse whether variations in the accuracy of WTP estimates derived by CBCA can be explained by consumers' involvement, brand awareness and the strength of consumer preferences. The results show that CBCA does hot provide accurate WTP estimates and, on average, grossly overestimates the true WTP of consumers. No empirical evidence can be found that consideration of the above-mentioned consumer characteristics results in more accurate WTP values.

Introduction Pricing consliluies "one of the most powerful and effective strategic tools in retailing" (Gauri, Trivedi and Grewal. 2008. p. 256) because of its direct impact on profitability (Grewal and Compeau. 1999; Han. Gupta and Lehmann, 2001). According to empirical evidence, pricing decisions have a disproportionate influence on a firm's revenue (Finch. Becherer and Casavanl, 1998), such that a price increase of 1% leads to an average 11.1% profit improvement, whereas a 1 % increase in sales enhances profit by only 3.?% (Marn and Rosiello, 1992). To make profil-maximizing pricing decisions, firms need to be able to estimate the price sensitivity of consumers with price response functions (Levy et a/., 2004) which in turn are based on consumers' will-ingness-to-pay (WTP), namely the maximum price a consumer is willing to pay for a product (Wang, Venkalesh and Chatlerjee, 2007).

Furthermore, the successful implementation of popular pricing instruments like price differentia* tion? price discrimination, value-based -pricing and price bundling requires knowledge of in-dividual consumers' WTP (Hinterhuber, 2004; Iyengar, Jedidi and Kohli, 2008; Levy et ah, 2004).

Marketing literature proposes a broad range of methods to measure WTP at the individual level (for relevant overviews, see Breidert, Hahsler and Reutterer, 2006; Krishnamufthi, 2001). One prominent approach both in academic research (e.g. Jedidi and Zhang, 2002; Kalish and Nelson, 1991; Popkowski, Peter and Timmermans, 2001) and marketing practice (Wittink and Cattin, 1989; Wittink, Vriens and Burhenne, 1994) is conjoint analysis and, in particular, the choice-based conjoint analysis (CBCA) approach (Lou-viere and Wood worth, 1983).

Although CBCA is particularly attractive from a theoretical point of view (Breidert, Hahsler and

2 C. Sichtmann, R. Wilken and A. Diamantopoulos

Reutterer, 2006; Lusk and Schroeder, 2004), prior research has mainly focused on the accuracy of other conjoint variants in the context of WTP measurement (Backhaus et at., 2005; Volckner, 2006). Furthermore, existing empirical assessments of CBCA do not reveal the extent to which CBCA overestimates or underestimates consumers' 'true' WTP but largely relate to the predictive validity of CBCA given a specified product price. However, such an assessment is particularly relevant for managers who need to evaluate the accuracy of WTP values elicited by CBCA for use in pricing decisions (Allenby et al., 2005).

In addition, the literature suggests that the accuracy of conjoint analysis in general (e.g. Allenby et al., 2005; Wittink and Bergestuen, 2001) and of other conjoint variants such as limit conjoint analysis (LCA) (Backhaus et al., 2005; Sichtmann and Stingel, 2007) in the context of WTP measurement may depend on consumer characteristics. However, there is a lack of empiri-cal research that investigates the impact of such characteristics specifically in the context of CBCA. Such knowledge is also relevant for managers in order to adjust the estimated WTP values for inaccuracy and thus better predict the real WTP of consumers when it is measured with CBCA.

Against this background, our study has two main objectives. First, we assess the accuracy of CBCA in the context of WTP measurement, i.e. the extent to which CBCA reveals 'true' WTP values. As an accuracy criterion we use the Becker, DeGroot and Marschak's (1964) (BDM) lottery approach for which there is wide agree-ment in the literature that it is the most accurate approach for revealing respondent's true WTP (Wang, Venkatesh and Chatterjee, 2007; Werten-broch and Skiera, 2002). Second, we analyse selected consumer characteristics that, according to the literature, can theoretically be expected to impact CBCA accuracy. Specifically, we focus on factors directly linked to the preference formation process: (a) the consumer's involvement with the product category, (b) his/her brand awareness and (c) the strength of preference for product attribute levels. With this second contribution, we attempt to explain variations in the accuracy of WTP estimates derived by CBCA.

In the sections that follow, we first provide some theoretical background on CBCA in the context of WTP measurement and then derive several hypotheses regarding consumer charac-

teristics expected to impact the accuracy of CBCA in predicting the WTP of consumers. Subsequently, we present our study's methodol-ogy and the empirical findings of two comple-mentary empirical studies. We conclude the paper with some suggestions for future research.

Theoretical considerations and hypotheses Choice-based conjoint analysis as a method to measure willingness-to-pay

CBCA combines conjoint analysis and discrete choice analysis and, theoretically, has several advantages (DeSarbo, Ramaswamy and Cohen, 1995; Louviere and Woodworth, 1983; Oppewal, Louviere and Timmermans, 1994). First, in CBCA, respondents indicate their choice of a product profile within a choice set of alternatives. As such, the utility values are estimated from choice data closely resembling a real purchase decision in a retailing context rather than from a ranking or rating of product alternatives. Second, unlike traditional conjoint methods which can be classified as ad hoc procedures, CBCA is sup-ported by random utility theory, a 'long-standing, well-tested, and integrated behavioral theory' (Louviere, Eagle and Cohen, 2005, p. 12). Third, CBCA allows the integration of a 'no choice' option. By using the utility value of the 'no choice' option as a reference point, absolute WTP values can be estimated instead of only relative WTP differences between alternative attribute levels.

Prior empirical research on the general validity of CBCA has shown that consumers system-atically tend to overstate their willingness to buy (e.g. Brazell et al., 2006; Gilbride, Lenk and Brazell, 2008; Louviere and Woodworth, 1983). As the willingness to pay for a stimulus is calculated as the price that equates the alternative to the 'no choice' option (Sapede and Girod, 2002), this overstated willingness to buy could be expected to lead to inaccurate WTP values. However, as shown in Table 1, few studies explicitly address the accuracy of CBCA in the context of WTP measurement.

First, as Table 1 indicates, most prior research focuses on differences between hypothetical and non-hypothetical versions of CBCA, by ran-domly selling to respondents in the holdout sample one of the chosen stimuli included in the

2010 British Academy of Management.

3390271041 3990271041 http://legacy.library.ucsf.edu/tid/svr10j00/pdf

Estimating Willingness- to-pay

Table 1. Prior research on WTP measurement with CBCA

Study

Lusk and Schroeder (2004)

Ding, Grewal and Liechty (2005)

Ding (2007)

Albers et al. (2007)

Natter and Feurstein (2001)

This study

Criterion employed to assess CBCA validity

Hypothetical vs non-hypothetical versions of CBCA (real sale holdout sample)



Real market prices two years after the survey Real market shares

BDM lottery approach

Survey object Estimation method(s)

Beef steaks

Chinese meal/ snack combo

iPod packages

Digital TV services

Mineral water

Chocolate

Heteroscedastic extreme value model Multinomial probit model Random parameters logit model Hierarchical Bayes

Hierarchical Bayes

Latent class

Latent class

Hierarchical Bayes

Key results

Significantly higher WTP values in hypothetical situation

Extent Influence of of consumer accuracy characteristics

Yes No

Price sensitivity in incentive-aligned version is higher than in hypothetical situation

No No

Forecast of real purchases better in the non-hypothetical than in the hypothetical setting No difference with regard to the No price sensitivities of respondents in the two scenarios

Forecast of real purchases better in the non-hypothetical than in the hypothetical setting WTP values similar to real No market prices two years after the survey Significant differences between No CBCA shares-of-preference forecasts to weekly price and sales scanner data But no systematic overestimation or underestimation of forecasts CBCA significantly Yes overestimates the true WTP of respondents

No

No

No

Yes

test design (e.g. Ding, 2007; Ding, Grewal and Liechty, 2005; Lusk and Schroeder, 2004). How-ever, as Ding, Grewal and Liechty (2005) and Ding (2007) indicate, this incentive-aligned ver-sion of CBCA is not necessarily an accurate predictor of real purchase behaviour because the correct prediction of respondents' purchase was still quite low in both studies.

Furthermore, even real market data like market prices (Albers et al., 2007) or market shares (Natter and Feurstein, 2001) as evaluation criteria may not reveal how close the WTP estimates come to the true WTP values of respondents. Real market prices only reflect the pricing decision of

the retailer and, at most, show that the WTP of some consumers lies above the market price when the product is already available on the market. Similarly, market shares as an accuracy criterion can, at best, demonstrate the extent to which CBCA is a good predictor of how many consumers actually buy a specific product at a given price. For example, in Natter and Feurstein's (2001) study, two respondents may have estimated WTPs of respectively 0.59 and 1.18 Euro for a bottle of mineral water that has an actual price of 1. 19 Euro and both actually purchase the product. However, according to the share-of-preference forecast based on CBCA, both respondents are considered as not




buying the product and therefore the forecast is classified as inaccurate, although for the second respondent the inaccuracy of CBCA is clearly very small.

Second, except for the study by Lusk and Schroeder (2004) who calculated dollar metric differences in WTP values for a hypothetical and a non-hypothetical version of CBCA, existing studies do not analyse the extent to which CBCA measures respondents' WTP accurately. From a managerial point of view, this poses a significant question because, if the inaccuracy of WTP estimated with CBCA is only marginal, the method could still be a viable option for generat-ing WTP values as input for pricing decisions.

Third, although the literature indicates that the accuracy of WTP estimates based on conjoint analysis may vary according to consumer char-acteristics, empirical evidence of their influence on CBCA accuracy is scarce. A first indicator that CBCA accuracy is influenced by consumer characteristics is the review of choice models by Allenby et al. (2005) who point out that hetero-geneity in individual-level preferences may have an impact on the accuracy of parameter estimates in such models. Furthermore, Wittink and Bergestuen (2001) discuss conditions under which conjoint analysis should work well. Amongst others, they identify consumers' involvement and familiarity with a product as factors influencing the accuracy of conjoint analysis. Applying LCA, a conjoint variant that similarly to CBCA considers consumers' choice, Backhaus et al. (2005) present an empirical study investigating the accuracy of WTP values and find that this conjoint variant does not suffer from a hypothe-tical bias. As this result contradicts existing studies, they speculate that the appropriateness of LCA in a hypothetical setting may depend on the product category or, more accurately, on the consumer's product involvement. Sichtmann and Stingel (2007) provide first empirical results on the assumption that the accuracy of WTP values elicited with LCA is influenced by consumers' characteristics. They find that, in a low-involve-ment product category, an auction-based method performs better in terms of predictive validity than conjoint analysis. With respect to the high-involvement category, however, the results re-main unclear. Still, the key result is that product involvement may play a significant role in explaining WTP estimates.

In summary, the above studies lead to the assumption that the accuracy of WTP values elicited with CBCA may be influenced by con-sumer characteristics. Yet, none of the studies in Table 1 has empirically examined whether the accuracy of WTP estimates based on CBCA is indeed influenced by consumer characteristics, despite the fact that '[T]his question seems to be the key issue for further research' (Backhaus et al, 2005, p. 559). Empirically based knowledge of factors influencing the accuracy of CBCA in the context of WTP measurement could help research-ers and practitioners to assess whether they can make appropriate adjustments to the estimated WTP values so as to improve their accuracy.

Although there are many possible consumer-related determinants of WTP accuracy, in our study we focus on three such determinants, two of which already appeared in the preceding paragraph: product involvement is one of the most widely discussed constructs in research on consumer behaviour, and preference for particu-lar product attributes is possibly the most obvious influencing factor as it is directly linked to any conjoint model, in the sense that preferences are modelled as utilities in conjoint-based approaches. Additionally, we include brand awareness in our analysis. The reason for this choice is the observation that branding activities belong to the most widely applied marketing activities in order to reduce consu-mers' uncertainty towards products, and WTP accuracy is likely to vary with such consumer uncertainty (Blumenschein et al, 2008). Hence, from both a practical and theoretical point of view, brand awareness seems to be a promising determinant in our context.

To summarize, what appears to be missing in the literature is an analysis that explicitly investigates the extent to which CBCA over-estimates or underestimates the true WTP of respondents, and examines key influencing fac-tors of CBCA accuracy. Our study addresses these research gaps by (1) empirically comparing the WTP values estimated with CBCA and the true WTP of respondents at an individual level (i.e. using a within-subjects design), and (2) analysing whether selected consumer character-istics can explain variations in the accuracy of WTP values estimated by CBCA. In the next section, we address the latter issue by developing hypotheses about consumer characteristics that can



Estimating Willingness- to-pay 5

theoretically be expected to impact the accuracy of CBCA in the context of WTP measurement.

Product involvement

Wittink and Bergestuen (2001, p. 152) argue that conjoint studies should provide accurate results if 'respondents find the conjoint task meaningful and have the motivation to provide valid judge-ments'. This view is empirically supported by Sichtmann and Stingel (2007) who found that the validity of LCA, a variant of traditional conjoint analysis, increased the more interested the re-spondents were in the analysed product category.

Product involvement represents a key construct in consumer behaviour (Celsi and Olson, 1988) which can be designated as a motivational resource (Seiders et al., 2005). It refers to the relevance a person attaches to an object 'based on inherent needs, values and interest' (Zaichkows-ky, 1985, p. 342). Consumers with various levels of product involvement differ in their information processing, such that higher product involvement positively influences the depth, complexity and extensiveness of cognitive efforts devoted to the decision process (Houston and Rothschild, 1978; Laurent and Kapferer, 1985). Involvement also affects the thoroughness and attentiveness of a respondent when answering a survey (Celsi and Olson, 1988). In a choice task situation, more involved consumers dedicate higher efforts to the task (Bettman, Johnson and Payne, 1991; Seiders et al., 2005) and therefore their answering behaviour should represent their real choice behaviour more accurately. Accordingly, we expect more valid WTP results from CBCA when the respondent's involvement level is high.

HI: The higher the level of product involve-ment, the more accurate the WTP estimates obtained from CBCA.

Brand awareness

Brand awareness 'relates to the likelihood that a brand name will come to mind and the ease with which it does so' (Keller, 1993, p. 3). Consumers with various levels of brand awareness vary in their perception of a brand (Baker, Hunt and Scribner, 2002). Brand aware consumers store information associated with the product in their memory (Keller, 1993) and thus can process new information about the brand more easily and are


less prone to information overload (Fiske, Kinder and Larter, 1983). When processing information, they require less cognitive effort and activate relevant knowledge structures automatically (Alba and Hutchinson, 1987). Consequently, brand awareness leads to better-developed and more complex schemata leading to more elabo-rated evaluations of the product and, in the end, better substantiated decisions (Marks and Olson, 1981). Hence, consumers with brand awareness are more confident when comparing brands because their decision criteria are better developed (Alba and Hutchinson, 1987).

Based on these considerations, we expect that brand aware consumers have a more refined and internalized WTP. Consequently, they are more confident about their WTP for a specific brand and can more precisely specify it when asked in a survey. Therefore, and consistent with Wittink and Bergestuen (2001) who state that the accuracy of conjoint-based forecasts is greater when respondents are familiar with the product, we expect WTP values estimated for consumers with brand awareness to be more accurate.

H2: Brand awareness leads to more accurate WTP estimates obtained from CBCA.

Strength of consumer preferences Consumers are often ambiguous about their pre-ferences (Luce, 1959), a phenomenon that is also referred to as choice uncertainty of consumers (Urbany, Dickson and Wilkie, 1989). Ambiguity about preferences implies that in a given choice task consumers indicate that they prefer, say, attribute level k to /, while in another choice task their preference is the other way round. Such behaviour leads to greater response errors in stated preferences (Fischer, Luce and Jia, 2000). Therefore, consumers that do not have a strong preference pattern with regard to attribute levels a consequence of choice uncertainty - should not be that consistent in their choice behaviour; rather, they are likely to be indifferent toward alternative levels of a product attribute (Gilbride and Allenby, 2004). Further-more, they are likely to be less certain about their WTP (Gregory et al, 1995). In contrast, consumers that know their preference pattern make choices more reliably they consistently prefer attribute level k to / in different purchase situations or choice tasks and apply screening rules that more closely resemble marketplace decisions (Allenby et al,


6

2005). Therefore, during a CBCA choice task, utility patterns should be revealed more reliably and precisely for consumers that have a strong pre-ference for a specific attribute level that clearly differs from their preference for other attribute levels. In this case, the WTP values estimated on the basis of respondents' utility values should be more accurate.

H3: The stronger a consumer's preferences for a product attribute level, the more accurate the WTP estimates obtained from CBCA.

Methodology

Although some prior studies indicate that an incentive-aligned conjoint analysis leads to a better prediction of choices (Ding, 2007; Ding, Grewal and Liechty, 2005), we deliberately use a hypothetical conjoint analysis design in this study. The reason for this choice lies in the situations where CBCA is typically applied. In practice, the WTP is particularly relevant for pricing decisions before a product is available on the market. An important advantage of hypothetical conjoint analysis is that alternative options can be tested and compared with regard to consumers' WTP (Lusk and Schroeder, 2004). As such, firms can, for example, decide to produce an option that generates the highest WTP of consumers. In contrast, incentive-aligned conjoint analysis re-quires that the product is already available on the market, or at least in a prototype stage (Ding, 2007). Against the background of practical relevance, therefore, firms are more likely to be interested in the accuracy of WTP values esti-mated from hypothetical CBCA surveys.

As a survey object, we used a bar of chocolate, which also appears in several prior studies in comparable research contexts (e.g. see Bhatia and Fox-Rushby, 2003; Johannesson, Liljas and O'Conor, 1997; Kaas and Ruprecht, 2006; Wang, Venkatesh and Chatterjee, 2007). On the basis of exploratory interviews with potential respondents, we determined that brand and flavour, in addition to price, represent the most salient product attributes for choosing chocolate and therefore included them in the CBCA design. As attribute levels, we employed three different brands and three different flavours. The three brands, Ha-chez, Feodora and Cailler, all fall in the mid-price range (1.19 2.09 Euro) and have relatively low

C. Sichtmann, R. Wilken and A. Diamantopoulos

market shares. Therefore, we expected greater variance with regard to brand awareness as well as less susceptibility to price anchor effects resulting from existing market prices (Kalwani et al., 1990), which could occur with mass market chocolate. Because we hoped to accommodate tastes of as many respondents as possible, we included 'traditional' flavours of milk chocolate, dark chocolate and milk chocolate with hazelnuts in the research design. Even when there are some respondents who dislike one particular attribute level, say dark chocolate, this does not prevent us from including these levels into the design in contrast, such preferences should be revealed in the CBCA parameter estimates. Finally, to preclude framing effects of price levels (Grewal, Monroe and Krishnan, 1998; Ratcliffe, 2000), half the questionnaires included a lower price range, with prices of 1.19, 1.59 and 1.99 Euro, and the other half featured a higher price range of 1.29, 1.69 and 2.09 Euro. Note that both groups together cover the relevant price interval men-tioned previously and that any two adjacent price levels have a difference of 0.40 Euro. An ANOVA subsequently showed that there was no significant effect (p>0.05) of the price range on the WTP values estimated with CBCA. In study 1, the mean WTPs are 1.62 Euro (lower price levels) versus 2.02 Euro (higher price levels), a consider-able yet insignificant difference. In study 2, the mean WTPs are almost the same: 2.29 Euro (lower price levels) versus 2.28 Euro (higher price levels). Additionally, the different price levels used in CBCA did not serve as an anchor for the subsequently stated WTP in the BDM task. In study 1, the mean WTPs are 0.91 Euro (lower price levels in CBCA) and 0.97 Euro (higher price levels in CBCA), respectively, with the difference being insignificant (p = 0.41). In study 2, the mean WTPs are 0.85 Euro (lower price levels in CBCA) and 0.91 Euro (higher price levels in CBCA). Again, this difference is not significant (p = 0.38).

We constructed an orthogonal design with nine choice sets, each composed of three different stimuli and the no-choice option (Huber and Zwerina, 1996). After having completed the CBCA task, respondents answered questions measuring the consumer characteristics that, according to our hypotheses, influence the accu-racy of CBCA, demographic questions (e.g. age) and some questions about their consumption behaviour relating to chocolate (e.g. purchase of




chocolate in the last seven days, price they usually pay for a comparable bar of chocolate). For testing Hypothesis 1, the involvement construct was measured with the well-established Personal Inventory Involvement scale developed by Zaich-kowsky (1994). To assess brand awareness (Hypothesis 2), we asked the respondents whether they knew the brands used in the analysis or not (Keller, 1993). Hypothesis 3 was tested using the preferences for the chocolate flavours measured on a five-point semantic differential with 'do not like it at all' and 'do like it very much' as anchors.

As already noted, we chose the BDM approach to assess the accuracy of WTP values elicited with CBCA because it is 'the "best" method among the available options' (Wang, Venkatesh and Chat-terjee, 2007, p. 203). Within BDM, respondents first indicate their WTP for a given product on sale. Then, they draw a price from an urn, without knowing either the prices or the price levels in the urn (Wertenbroch and Skiera, 2002). If the price drawn is equal to or lower than the stated WTP, the respondents have to buy the product on sale; however, if the price is higher, they do not get a chance to purchase the product. As such, the BDM approach is incentive-compatible for re-spondents as the latter always profit if they tell the truth; not telling the truth (i.e. overstating or understating one's real WTP) leads to an inferior outcome for the respondent (Wang, Venkatesh and Chatterjee, 2007). Consistent with prior research (Kaas and Ruprecht, 2006; Volckner, 2006; Wertenbroch and Skiera, 2002), we con-ducted several validity checks on the BDM procedure which are summarized in the Appendix.

Following Wertenbroch and Skiera (2002), the BDM mechanism was explained to respondents before the actual bidding, highlighting why it would be in the respondent's interest to disclose his or her true WTP. Two out of the nine possible combina-tions of brand/flavour were subsequently randomly chosen to be sold in the BDM lottery (Hachez/milk chocolate or Feodora/dark chocolate).

We conducted two empirical studies to test our hypotheses in which we gathered data during face-to-face interviews. The first study was conducted at the business school of a major

^he item 'fascinating-mundane' was dropped from the original scale as respondents in a pretest of the questionnaire indicated that this item does not relate to chocolate.


German university. A total of 214 participants were recruited. As we cannot obtain any WTP information if a respondent selects the 'no choice' option for all choice sets (Brazell et ah, 2006), we eliminated respondents that never chose one of the stimuli, resulting in a final sample of 197. To examine the stability of our results with a more heterogeneous sample, we conducted a second study at a major German airport. A total of 357 participants was recruited of which 321 remained in the sample after eliminating the respondents who always indicated the 'no choice' option.2

Results WTP measurement with CBCA

Whereas CBCA traditionally estimates utility values at an aggregate level (Louviere and Woodworth, 1983), we apply it in combination with a hierarch-ical Bayes approach, as proposed by Allenby and Ginter (1995) and Arora and Huber (2001). Hierarchical Bayes seems to be the standard estimation procedure for the parameters of a discrete choice model as illustrated by some recent publications by Allenby and colleagues (2005), Ding (2007) and Moore (2004). The notion 'hierarchical' refers to the fact that there are two levels of analysis. On the one hand, individuals' partworths are estimated. On the other hand, aggregate measures as average partworth and partworth heterogeneity are derived. The parameters are estimated in an iterative manner (Allenby and Ginter, 1995). 2As will be explained in the next section, we apply the absolute percentage error (APE) as a measure of accuracy. For some respondents, this measure could not be computed because they stated a WTP of 0 during the BDM lottery. Consequently, the number of respon-dents in study 1 and study 2 was reduced to n = 167 and n = 233, respectively. Additionally, we note that those respondents who never chose any bar of chocolate and were excluded from CBCA estimation also revealed significantly lower WTPs (t-tests; p


The above procedure has the advantage that we can validate CBCA in a within-subjects setting comparing it to the stated WTP values as generated by the BDM approach. In our study, we estimate a multinomial logit model, which establishes an equation for the probability that a particular respondent chooses a particular alternative in a particular choice task (see for example Ding, Grewal and Liechty, 2005).

We used Sawtooth Hierarchical Bayes software v4.4.6 for estimation purposes. In each study, 10,000 preliminary iterations and another 10,000 draws per respondent were used in order to generate parameter estimates. In study 1 (study 2, respectively), the root likelihood was 0.58 (0.62, respectively), meaning that our model is approxi-mately two and a half times better than the 'pure chance' (or 'naive') model with four choice options in each task. The percentage certainty, another predictive measure of the model with values between 0 and 1, indicates how much better a solution is than the 'pure chance' model (a value of 0 means that the model is as poor as the chance model, whereas a value of 1 implies perfect model fit). In our case, we reach values of 0.60 and 0.66 in study 1 and study 2 respectively, which again indicates that our model is much better than the 'naive' model.

The WTP for a particular bar of chocolate can be estimated by determining the price that equals the utility of the 'none option' (Sapede and Girod, 2002). Hereby, the utility function is assumed to be linear; i.e. product attribute levels perceived as inferior by a particular respondent can be

theoretical point of view, the latent class approach is less accurate for estimating the individual utility level of respondents. In fact, the latent class approach yielded more unrealistic WTP values (up to 30.00 Euro)! Comparing the WTP values estimated with the latent class and the hierarchical Bayes approach, we found highly significant differences (t-test; p< 0.001). This result contradicts the findings of Andrews, Ainslie and Currim (2002) and Andrews, Ansari and Currim (2002), which may be due to the fact that our study includes the estimation of WTP values based on utilities for product attributes which provides an additional source of estimation error. In the reduced samples, however, the picture is somewhat mixed, indicating that in study 1 there are no significant differences in WTP values between the two approaches (Wilcoxon test due to non-normality, p = 0.138). Although the individual accuracy of the WTP values mostly differs in the two methods, the main conclusions of this study do not change when using a latent class approach.

compensated, in terms of utility, by other levels of other product attributes that the consumer likes.4 We chose the 'utility model' and not the 'preference model' in order to derive WTPs. While the latter has been reported to reveal better behaved posterior WTP distributions than the former (see Sonnier, Ainslie and Otter, 2007; Train and Weeks, 2005), this result especially holds for small samples and is based on the assumption of a linear utility function. We circumvent this problem by assuming a discrete model for the price attribute in the hierarchical Bayes procedure, reflecting the fact that there might be at least some variation in price sensitivity in the relevant price range. Subse-quently, linear interpolation between two adja-cent price attribute levels can be used to obtain a price utility function for each respondent (Wilken and Sichtmann, 2007). WTP for a particular bar of chocolate is then the price which guarantees that the overall utility of this bar of chocolate equals the utility of the 'none option'. In the case that valid solutions appear in both interpolated sections of the price utility function, the higher price is selected. This is in line with the definition of WTP as the highest price a consumer is willing to pay for a particular product. Note that, in our model, it is possible that a respondent does not have a constant slope of the price utility function in the relevant price interval, i.e. the slopes in the two intervals may differ. In this sense, our discrete model is more flexible than the model used by Sonnier, Ainslie and Otter (2007). Indeed, in both our samples, the slopes differ significantly.5 We interpret this result as an ex post justification for not assuming the price utility function to be linear over the whole relevant price interval.

4We checked for each respondent whether he/she actually chose at least once a bar of chocolate with the same brand and the same flavour as the chocolate that was to be sold via the BDM mechanism. This was the case for every respondent (full sample) in study 1, and for 312 respondents (out of 321, full sample) in study 2. The WTP for the bar of chocolate that was sold later, estimated via CBCA, was lower than the lowest price level in the design for seven out of the nine respondents who actually 'disliked' the offer. Hence, CBCA esti-mated quite high (approximately 2 Euro) WTPs for only two respondents. All these observations demonstrate that the CBCA design was indeed adequate in this study. 5In this context, t-tests were applied in a within-subject setting. Study 1, t = 11.307, p< 0.01; study 2, t = 16.189, p

Estimating Willingness-to-pay 9

WTP estimation: CBCA versus BDM overestimation of WTPs by CBCA (t-test, p

10 C Sichimann, R. Wilken and A. Diamantopoulos

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Figure 2. Individual differences between WTP values measured'with CBCA and with BDM (reduced samples)

In Figure 3 we aggregate the data to price-response functions of the Hachez/milk chocolate and Feodora/dark chocolate offers for both the (full and reduced) university and consumer sam-ples. The demand forecasts based on the WTP values estimated with CBCA always lie far above the real purchase behaviour indicating that CBCA leads to highly exaggerated demand forecasts.

Impact of consumer characteristics Hypotheses 1-3 suggest that the accuracy of WTP estimates generated by CBCA may vary across consumers. We used multiple regression analysis to test the hypotheses related to the impact of involvement (Hypothesis 1), brand awareness (Hypothesis 2) and preference con-cerning attribute levels (Hypothesis 3) on the accuracy of WTP estimated with CBCA. To measure WTP accuracy (our dependent variable), wre employed the absolute percentage error (APE)

that is-well established as a forecast error measure (Mathews and Diamantopoulos, 1994). For each respondent, APE was calculated as follows.

APE WTPBDM - WTPCBCA WTPBDM

x ioo% ;(i;

Regarding the independent variables, product involvement is conceptualized in the literature as a two-dimensional construct with a 'cognitive' and an 'affective' dimension (Zaichkowsky 1985, 1994). In study 1, we thus first conducted a factor analysis that yielded two factors (70.8% variance explained) and calculated factor scores of the items loading strongly on each factor as input variables for the regression analysis. The 'cogni-tive' and 'affective' dimensions yielded highly acceptable reliability values of a = 0.91 and

6We undertook a logarithmic transformation to bring the variable's distribution closer to a normal distribu-tion.



Estimating Willingness-to-pay 11

university sample: Haehez milk chocolate

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university sample: Feodora dark chocolate

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reduced university sample; Faodora dark chocolate

reduced consumer sample: Feodora dark chocolate.

price

FigureS. Price-response functions based on WTP estimates from CBCA and BDM

at Q.84 respectively. Brand awareness was scored as a dichotomous variable (yes/no) ac-cording to the respondent's awareness of the brand sold in the BDM procedure (i.e. Haehez or Feodora). The strength of preference relating to attribute levels was calculated by first computing the respective distances between, the flavour sold in the BDM procedure and the other two flavours

integrated in the CBCA design and then sum-ming up the resulting distances. This measure is meaningful because it yields high yalues only if a respondent has a strong preference for a parti-cular flavour included in the CBCA design, regardless of whether she or he preferred the flavour sold in the BDM procedure, In turn, the measure yields low values only if the respondent

2010 British Academy of Management



is rather indifferent between the flavours included in the CBCA design.7

As Tables 2 and 3 (left panels) show, and contrary to our hypotheses, the above character-istics do not account for the variation in the accuracy of WTP estimates generated with CBCA. Even when eliminating those respondents who never chose the 'no purchase' option who show high WTPs in CBCA the accuracy is still poor. The regression models are not significant (full university sample: R =0.01, F-value = 0.52, p = 0.70; reduced university sample: R2 = 0.04, F-value = 0.88, p = 0.48) and none of the coeffi-cients of the consumer characteristics is signifi-cant either.8

As a further check on the regression results, we also examined the bivariate relationships between the aforementioned consumer characteristics and APE with both Pearson and Spearman correla-tion coefficients. As Tables 4 and 5 (left panels) indicate, even on a bivariate basis, the relevant relationships are extremely weak and non-sig-nificant. We conclude that there is neither a linear nor a monotone relationship between the ana-lysed consumer characteristics and the accuracy of CBCA in WTP measurement.

Similarly to study 1, in study 2 we first conducted a factor analysis of the product involvement items that also yielded two factors with 70.7% variance explained and high relia-bility values (a = 0.91 for the cognitive dimension

7The measure is close to zero when the preference of the flavour sold in the BDM process does not differ much from the preferences for the other two flavours. 8Note that the key assumptions of a linear regression model are fulfilled. First, plotting the residuals and the APE values predicted by the model does not reveal heteroskedasticity; second, the Durbin-Watson test does not detect autocorrelation (full sample: d = 1.95; p


Table 2. Regression results (full samples) Independent variable University sample (n = 166)

Standardized beta Significance

Consumer sample (n = 232)

Standardized beta Significance

Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels

0.01 0.09 0.01

-0.06 R2 = 0.01 F = 0.523 (ns)

0.87 0.26 0.88 0.45

0.02 -0 .11

0.02 0.07

R2 = 0.02 F = 1.058 (ns)

0.72 0.11 0.73 0.27

Note: ns, not significant.

Table 3. Regression results (reduced samples) Independent variable

Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels

University sample (n = 89) Consumer sample (n = 79)

Standardized beta

0.02 0.07

-0 .15 -0 .15 R2 = 0.04 F = 0.880 (ns)

Significance

0.88 0.51 0.17 0.19

Standardized beta

0.10 -0 .07

0.13 0.05

R2 = 0.04 F = 0.712 (ns)

Significance

0.40 0.56 0.27 0.70


Table 4. Correlations between consumer characteristics and APE (full samples)

Pearson correlation Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels Spearman correlation Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels

University sample

Correlation

0.01 0.09 0.04

-0 .13

0.05 0.06 0.05

-0 .06

Significance

0.46 0.11 0.31 0.09

0.51 0.42 0.51 0.46

Consumer

Correlation

0.03 - 0 . 1 1 *

0.03 0.07

0.06 -0 .11

0.01 0.07

sample

Significance

0.32 0.05 0.30 0.30

0.37 0.09 0.92 0.30


dents in both studies (as reflected in APE standard deviations of 153.7% and 217.3% respectively; see also Figures 1 and 2), these results corroborate prior research (e.g. Ding, Grewal and Liechty, 2005; Natter and Feurstein, 2001) that also questions the suitability of CBCA for WTP measurement purposes. Even when eliminating respondents who never chose 'no purchase' in CBCA, we still get an over-estimation of 69.9% in study 1 and 91.3% in study 2, with standard deviations of 78.4% and 80.3%, respectively.

We add to existing knowledge on CBCA by documenting the large extent to which CBCA overestimates the true WTP values of respon-dents, and by showing that the inaccuracy is not systematic across respondents. An explanation for the overestimation of WTP values may be the hypothetical choice situation in the CBCA task. Because the respondents had to hypothetically indicate whether they would buy one of the bars of chocolate offered or not, they may have overestimated their purchase intention. As this information builds the reference point for the




Table 5. Correlations between consumer characteristics and APE (reduced samples) University sample

Correlation Significance

Consumer sample

Correlation Significance

Pearson correlation Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels Spearman correlation Product involvement (cognitive dimension) Product involvement (affective dimension) Brand awareness Strength of preference concerning attribute levels

0.09 0.15 0.05 0.03

0.04 0.05 0.10 0.14

0.42 0.15 0.64 0.77

0.69 0.61 0.36 0.18

0.06 -0 .03

0.03 -0 .02

0.08 -0 .07

0.10 0.01

0.57 0.82 0.82 0.86

0.46 0.50 0.38 0.91

estimation of the WTP values elicited by CBCA, an overestimation of the willingness to buy a product could result in an overestimation of the WTP.

We further tested several hypotheses relating to consumer characteristics that, according to the literature (e.g. Allenby et al., 2005; Backhaus et al., 2005; Sichtmann and Stingel, 2007; Wittink and Bergestuen, 2001), were expected to impact accuracy of WTP estimates generated with CBCA. Our empirical tests indicate that none of the hypothesized factors has a significant influence on accuracy and therefore the perfor-mance of CBCA in the context of WTP measurement cannot be improved by taking into account such characteristics. Although theory suggests that highly involved consumers, con-sumers with strong preference patterns and consumers with high brand awareness provide more accurate responses to choice-based tasks, resulting in more accurate WTP estimates, we show that these assumptions do not hold. This means that, in CBCA-based studies, even the seemingly 'valuable' respondents do not provide accurate information on their WTPs.

An explanation why the hypothesized factors were not influential in our study could be the use of a low-price, hedonic product category. It would therefore be useful to replicate our analysis in other product categories. As prior research indicates that the degree of inaccuracy may depend on the product category (Ding, 2007; Jedidi, Jagpal and Manchanda, 2003), a replica-tion of the study in high-priced product cate-gories would be interesting, although, of course, there are limits on what outlays a respondent may reasonably be expected to make under the

BDM procedure (Wertenbroch and Skiera, 2002). Furthermore, further research could re-plicate the study with a utilitarian product.

Although our findings did not support the research hypotheses, it should be emphasized that 'null outcomes can be meaningful' (Hubbard and Armstrong, 1992, p. 133). The research findings help to show that against theoretical expecta-tions WTP values elicited by CBCA cannot be adjusted by taking into account the consumer characteristics so as to become more accurate. From a managerial perspective, this is important as managers should be alerted against trying to improve the accuracy of WTP values generated by CBCA by focusing on segments that exhibit the consumer characteristics that have been theoretically proposed as accounting for the inaccuracy of CBCA-based WTP values. More-over, from a theoretical perspective, our results may 'help to prevent researchers from reinvesti-gating blind-alleys' (Hubbard and Armstrong, 1992, p. 133). Instead, further research should focus on other explanations of the inaccuracy of WTP values elicited by CBCA.

From a managerial point of view, we cannot recommend to companies to rely on CBCA as a method for measuring WTP, because the WTP values are likely to be very inaccurate. As our results further indicate that such inaccuracy is not systematic, firms cannot make appropriate ad-justment by considering something like a 'correc-tion factor'. Also, the inaccuracy of WTP values estimated with CBCA cannot be improved by controlling for consumer characteristics. In short, the use of CBCA seems particularly problematic in combination with pricing instruments like segmentation-based pricing and value-based


339027105! 3990271053 http://legacy.library.ucsf.edu/tid/svr10j00/pdf


pricing that highly depend on an individual customer's WTP (Hinterhuber, 2004; Levy et al, 2004).

Our study also features some limitations that suggest starting points for further research. Although we varied the price levels in the conjoint profiles to control for differences in WTP values due to design effects, our variation might have been too moderate. Further studies could apply more extreme differences in price levels and/or use asymmetric price differences between three or more price levels.

Another interesting issue for future research involves the comparison of the relative accuracy of CBCA with that of other conjoint variants (e.g. adaptive conjoint analysis or LCA) or contingent valuation in the context of WTP measurement. In addition, we did not use an incentive-aligned approach of CBCA in this study. In this context, future studies could analyse the extent to which an incentive-aligned CBCA approach leads to more accurate WTP values compared to BDM and whether the

consumer characteristics analysed in this study may further reduce inaccuracy. Also, the devel-opment of alternative methods to measure WTP that are incentive-aligned but not associated with the actual sale of the product of interest appears to be a promising area for further research. For example, future research could consider a BDM procedure where a substitute product is sold instead of the product of interest. In this connection, the gain (loss) of the BDM mechan-ism should correspond to the gain (loss) of the substitute product, so that there is actually an incentive to reveal the true WTP for the product of interest.

Finally, our analysis focused on three con-sumer characteristics that were highlighted in the literature as potentially influencing the accuracy of conjoint analyses. Further studies could consider additional factors such as product complexity or innovativeness (Sichtmann and Stingel, 2007) that may be more helpful in explaining variations in WTP values elicited with CBCA.

Appendix: BDM validation

Table Al

Form of validity Validity test Source Results

Volckner, 2006

Volckner, 2006

Wertenbroch and Skiera, 2002

Volckner, 2006

Wertenbroch and Skiera, 2002

Kaas and Ruprecht, 2006

University sample

Rank order corresponds

Rank order corresponds

r = 0.20 (p


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C. Sichtmann, R. Wilken and A. Diamantopoulos

Christina Sichtmann is an Assistant Professor at the Department of International Marketing at the University of Vienna. Her research interests focus on services marketing, international marketing and marketing research issues. Her work has been published in, amongst others, the Journal of International Marketing, European Journal of Marketing, Journal of Strategic Marketing and Review of Managerial Science.

Robert Wilken is an Assistant Professor of International Marketing at ESCP Europe in Berlin. His main research interests are in pricing, negotiation analysis and marketing research methods. His papers have been published in, amongst others, the International Journal of Research in Marketing, Journal of Business-to-Business Marketing and International Journal of Market Research.

Adamantios Diamantopoulos is Professor and Head of the Department of International Marketing at the University of Vienna. His main research interests are in international marketing, marketing research and research methodology and he is the author of some 200 publications in these areas. His work has appeared in, amongst others, the Journal of Marketing Research, Journal of International Business Studies, Journal of the Academy of Marketing Science, International Journal of Research in Marketing, Journal of Retailing, International Journal of Forecasting, Journal of International Marketing and Journal of Business Research.



estimating willingness to pay

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true wtp of consumers

y s i j j

price sensitivity of

price increase

accurate wtp estimates

accuracy of wtp estimates

accurate wtp values

price differentia