Abstract
The previous chapters described several approaches employed for determining partworths of attributes and tradeoffs among them. The chapters dealt with various methods for both of ratings-based and choice-based conjoint methods. In addition, we described several applications of conjoint methodology to different marketing problems such as product design, product positioning, pricing, market segmentation, and several miscellaneous problems. During the last thirty plus years since these methods were introduced to marketing research, researchers have tackled various problems that are encountered in applying these methods in practice. As Hauser and Rao (2006) have noted, conjoint analysis is alive and well. In fact there have been several developments in the last 5–10 years that place this methodology as one of the most vibrant techniques in marketing research.
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Notes
- 1.
See also Raghavarao and Wiley (2009).
- 2.
This material is based on Ding et al. (2009).
- 3.
For expensive products, a lottery mechanism may be used to determine which participant will end up receiving the final product and cash.
- 4.
Specifically, the probability that the i-th subject chooses the k-th alternative from the j-th choice set is given by
$$ p_{ij}^k=\frac{{\exp \left\{ {\beta_i^Tx_{ij}^k} \right\}}}{{\sum\limits_l {\exp \left\{ {\beta_i^Tx_{ij}^l} \right\}} }} $$where \( x_{ij}^k \) describes the k-th alternative evaluated by the i-th subject from the j-th choice set, and βi is a vector of partworths for the i-th subject. They assume a hierarchical shrinkage specification for the individual partworths, where a priori, \( {\beta_i}\sim N\left( {{\beta},\Lambda } \right) \).
- 5.
The hand strength is determined in a manner similar to that of the poker. The authors consider six types of hands. The weakest hand will be a pair, with two cards having the same level on an attribute and the strongest hand having all three cards have the same level on two attributes (called double flush). Probabilities of hand strength are computed from a random set of cards (possibly four) drawn without replacement. These probabilities are used to determine the probability of winning with each hand against the computer.
- 6.
This method is part of the Sawtooth Software under the name MAXDiff. It offers several features such as the MAXDiff Experimental Designer for developing questions and MAXDiff Analyzer for analyzing the data collected.
- 7.
A recent paper (Miller et al. 2011) compares four separate measures for measuring willing-to-pay for an attribute. The main result in this paper is that incentive-aligned methods pass statistical and decision-oriented tests.
- 8.
These measures are obtained as compensating variation in price for a change in the attribute levels so as to keep the utility the same. For a utility function U(A, P) = a0 +a1XA1 + a2XA2 + a3XA3 – bP, for one attribute, A with four levels, A1, A2, A3, and A4 (coded as dummy variables XA1, XA2, XA3) and price (P), the willingness-to-pay for a change in attribute level from A2 to A1 will be (a2-a1)/b.
- 9.
In this model for the pre-influence stage, the individual i’s utility for the j-th profile for the p-th choice set in the first stage (pre-influence) is specified as: \( U_{ijp}^I={X_{jp }}\beta_i^I+\varepsilon_{ijp}^I \) where \( {X_{jp }} \) is the K-dimensional vector of attributes (suitably coded and including brand dummy variables) for profile p (p = 1, …, P) in choice set j (j = 1, …, J); \( \beta_i^I \) is the K-dimensional vector of initial attribute importance weights of individual i; and \( \varepsilon_{ijp}^I \) follows an IID standard normal distribution. Each choice set contains P profiles. Under the assumption that the individual chooses one out of P profiles by maximizing one’s utility, i.e. \( Y_{ijp}^I=1 \) if \( U_{ijp}^I=\max [U_{ij1}^I,..,U_{ijP}^I] \); otherwise \( Y_{ijp}^I=0 \), where \( Y_{ijp}^I \) is the choice in the first stage, the implied choice model will be the multinomial probit model. The model is similar for the post-influence stage.
- 10.
There is growing evidence in the behavioral research that consumers construct preferences when the need arises via context-sensitive processes (Bettman et al. 1998; Simonson 2005).
- 11.
- 12.
This section draws from Rao (2008).
- 13.
The adaptive conjoint analysis (ACA) approach involves presenting two profiles that are as nearly equal as possible in estimated utility measured on a metric scale and developing new pairs of profiles sequentially as a respondent provides response to previous questions. There has been considerable amount of research on this approach. In a recent paper, Hauser and Toubia (2005) found that the result of the metric utility balance used in ACA leads to partworth estimates to be biased due to endogeneity. The authors also found that these biases are of the order of response errors and suggest alternatives to metric utility balance to deal with this issue. See also, Liu, Otter, and Allenby (2007) who suggest using the likelihood principle in estimation to deal with the endogeneity bias in general.
- 14.
A study that looks at the dynamics of partworths during the data collection process for conjoint data is due to Liechty et al. (2005).
- 15.
Eric Bradlow (2005) presents a wish list for conjoint analysis such as within task learning/variation, embedded prices, massive number of attributes, non-compensatory decision rules, Integration of conjoint data with other sources, experimental design (from education literature), getting the right attributes and levels, mix and match, and product-bundle conjoint. There is a considerable overlap between this list and mine described below. Recently, Agarwal et al. (2012) have developed a review of the current state of conjoint research.
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Rao, V.R. (2014). Recent Developments and Future Outlook. In: Applied Conjoint Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87753-0_10
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