An incentive compatible conjoint ranking mechanism.

Estimates in table 2 further reveal willingness-to-pay values for no-hormone use in the base-line treatment of $1.78 for ground beef and $3.34 for steak. These statistics are much lower than those found by Lusk, Roosen, and Fox (2003) who estimated the value of nonhormone use in beef steaks at $8.12/lb for U.S. consumers using a hypothetical choice experiment. However, the values are similar to those obtained by Lusk and Schroeder (2004) who found that people in a choice experiment were willing to pay between $1.36 and $3.75 (depending on the estimated model and whether the decision task was real or hypothetical) to have a "natural" steak rather than a "generic steak."

The next several rows of results relate to the primary hypotheses of interest: whether behavior in the new IC ranking mechanism differed from that in the traditional hypothetical ranking approach. For ground beef, moving from hypothetical to IC rankings had no significant impact on any of the utility coefficients or on the error variance. Interestingly, preferences for ground beef were also unaffected by information about pasture grazed beef. The lack of sensitivity to changes in both the information and elicitation mechanism could be a reflection of the fact that people often do not appear to behave rationally when dealing with low-valued goods. For example, List and Lucking-Reiley (2002) found people's behavior in auctions appeared significantly more rational when bidding on expensive sports cards as compared to sports cards that retailed for only $2. They argue such behavior arises because the opportunity costs of irrational behavior increases with the value of the good. As such, we would expect people to be more sensitive to treatment effects when ranking higher-valued steaks. As shown in table 2, this is exactly what we find.

In the steak model, moving from hypothetical to nonhypothetical rankings has a number of effects. In the no information treatments, moving from non-IC to the IC mechanism decreases the marginal utility of pasture-raised beef, decreases "price sensitivity," and increases the utility of "no meat." It is telling that the two parameters, Cash and None, were significantly affected by the hypothetical nature of the mechanism because it is these two parameters that people would attempt to manipulate if they desired to strategically respond to the survey. For example, Wertenbroch and Skiera (2002) argue, (p. 230), "[i]f subjects believe that their responses will be used to set long-term market prices, they have an incentive to under-state their [willingness-to-pay]. If they believe that their responses will determine the introduction of a desirable new product, they may perceive reasons to overstate their [willingness-to-pay]."

That people were less sensitive to changes in Cash when the steak decision task was nonhypothetical means, holding the magnitude of other parameters constant, that the computed amount of money required to make an individual indifferent between two options that differ in the level of an attribute (e.g., marginal willingness-to-pay) would be larger in the IC mechanism than the traditional hypothetical approach. This finding may initially seem counter-intuitive, as most studies find higher willingness-to-pay in hypothetical versus real treatments. Note, first, however, that whether price sensitivity should increase or decrease when a task is made IC is theoretically ambiguous. On one hand, people might be expected to become more price sensitive with an IC mechanism as they will actually receive the cash. On the other hand, making a decision task IC might force people to more carefully consider other product attributes when completing the ranking, since they will now be taking a meat product home, rather than simply picking the options with the highest cash offer. Second, it is important to note that the marginal utility of other attributes is not held constant when a decision task is made IC. In particular, the disutility associated with the "no meat" option significantly increases when the task is IC. This is the same as saying that the utility from having a steak significantly decreases when the task is IC. This latter result is exactly what studies such as Lusk and Schroeder (2004) found: the utility of having a steak falls when the task is real instead of hypothetical. That the marginal utilities of most of the other steak attributes (hormone, traceability, and size) are uninfluenced by whether the decision task is IC is also consistent with Carlsson and Martinsson (2001) and Lusk and Schroeder (2004), who found little difference in real and hypothetical marginal utilities for product attributes.

Results in table 2 also indicate that information significantly affected preference parameters. Providing information about pasture-grazed beef significantly increased the marginal utility of the pasture attribute as expected. Interestingly, the marginal effect of information interacted with the IC-treatment effect for the Size, Cash, and None attributes. To see this, note that the utility of None can be written as: -11.873 + 1.289 x Info + 8.082 x Non-Hyp - 5.698 x Info x Non-Hyp. This implies that when information was provided in the hypothetical mechanism, the value of having a steak decreased; however, providing information in the IC mechanism increased the utility of having a steak.

Turning to the scale function, results reveal that moving from hypothetical to nonhypothetical rankings increased the scale in the steak model, which implies that error variance decreased (the p-value from a two-tailed t-test is only 0.08, but the hypothesis that the scale factor is greater than zero is rejected at the p = 0.04 level according to a one-tailed test). This result is consistent with the findings of Haab, Huang, and Whitehead (1999) and suggests that, holding all else equal, predicted market shares will be more uniform using hypothetical responses as compared to IC responses. Providing information in the steak rankings was associated with an increased error variance. Finally, for both the steak and ground beef models, we find that the error variance significantly increased for choices associated with higher ranks. Apparently, people were more consistent in determining which options were among the few most desirable as compared to determining the relative ranking among options of medium to low desirability.

To further investigate the implications of the results, market share simulations were conducted. The estimates in table 2 were used to predict the market share that a new pasture-raised product would garner relative to a traditional beef product. To carry out the simulation, parameters in table 2 were substituted into the logit formula, assuming the only products in the choice set were a new pasture-raised product and a traditional nonpasture raised product. Table 3 reports results from the market share simulations assuming conventional and pasture-fed steak (ground beef) were available for sale at $8.00/lb ($2.25/lb) and $10.00/lb ($4.25/lb), respectively. The first column of results shows the predicted market share using estimates from the IC, nonhypothetical mechanism. Results indicate that the pasture-fed product would be expected to achieve about 52% market share in the steak market and 56% in the ground beef market. These results contrast sharply with the market share estimates from the hypothetical treatment, which predicts that pasture-fed beef would only achieve about 39% market share in the steak market and 42% market share in the ground beef market. The 95% confidence intervals suggest that this is a statistically significant difference in predicted market shares for pasture-raised steaks, but not for ground beef. Obviously, pasture-raised products do not currently enjoy market shares as high as those estimated in table 3, and it is important to recognize that table 3 reports estimates of demand at given prices and that a host of supply-side factors need be considered to project an equilibrium quantity sold.

Conclusions

Although conjoint analysis is one of the most popular marketing research tools, it suffers from a potentially serious shortcoming: the method is not incentive compatible. This paper introduces a conjoint ranking mechanism that overcomes this shortcoming. The mechanism requires people to rank a set of product profiles, where a profile that is assigned a lower rank is more likely to be purchased. The method of implementation used in this article involved subjects placing cards containing descriptions of the product profiles on various sized slices of a wheel, which was subsequently spun to determine the product that was ultimately received.