Comparing results of ranking conjoint analyses, best–worst scaling and discrete choice experiments in a nonhypothetical context

This study assesses the comparability of discrete choice experiment (DCE), ranking conjoint analysis (RCA) and multiprofile best–worst scaling (BWS) in a nonhypothetical context in terms of estimated partworths, willingness to pay (WTP), response consistency and external validity. Overall, the results suggest that: (i) the conjoint analysis formats that were used in this study provide similar estimated WTP, but different estimated partworths and computed external validityPeer ReviewedPostprint (updated version

Improving the efficiency of individualized designs for the mixed logit choice model by including covariates.

Recent research shows that the inclusion of choice related demo- and sociographics in discrete choice models aids in modeling the choice behavior of consumers substantially. However, the increase in efficiency gained by accounting for covariates in the design of a choice experiment has thus far only been demonstrated for the conditional logit model. Previous findings are extended by using covariates in the construction of individualized Bayesian D-efficient designs for the mixed logit choice model. A simulation study illustrates how incorporating covariates affecting choice behavior yields more efficient designs and more accurate estimates and predictions at the individual level. Moreover, it is shown that the possible loss in design efficiency and therefore in estimation and prediction accuracy from including choice unrelated respondent characteristics is negligible.Covariate; Discrete choice experiment; Mixed logit choice model; Individual efficient design; Hierarchical Bayes estimation;

Venture Capitalists' Evaluations of Start-up Teams: Trade-offs, Knock-out Criteria, and the Impact of VC Experience

The start-up team plays a key role in venture capitalists' evaluations of venture proposals. Our findings go beyond existing research, first by providing a detailed exploration of VCs' team evaluation criteria, and second by investigating the moderator variable of VC experience. Our results reveal utility trade-offs between team characteristics and thus provide answers to questions such as "What strength does it take to compensate for a weakness in characteristic A?" Moreover, our analysis reveals that novice VCs tend to focus on the qualifications of individual team members, while experienced VCs focus more on team cohesion. Data was obtained in a conjoint experiment with 51 professionals in VC firms and analyzed using discrete choice econometric models. (author's abstract

When the Tide is High: Estimating the Welfare Impact of Coastal Erosion Management

A choice experiment was undertaken at Buffalo beach, Whitianga, in order to investigate beach visitors’ preferences for various coastal erosion management options. Constructing rock seawalls is a common response to coastal erosion but seawalls can negatively affect visual amenity, biodiversity and recreational values. The choice experiment results from this study show that the average visitor would be willing to pay $20 per year to remove an existing rock wall at either end of Buffalo beach. Visitors place high value on useable sandy beaches and reserve areas behind the beach. A latent class analysis reveals there are distinct sub-groups with varying preferences for beach characteristics. This paper presents a model with separate classes for residents and visitors and the compensating variation estimates to calculate the overall welfare effect for three coastal management scenarios.Environmental Economics and Policy,

Probabilistic models of set-dependent and attribute-level best-worst choice

We characterize a class of probabilistic choice models where the choice probabilities depend on two scales, one with a value for each available option and the other with a value for the set of available options. Then, we develop similar results for a task in which a person is presented with a profile of attributes, each at a pre-specified level, and chooses the best or the best and the worst of those attribute-levels. The latter design is an important variant on previous designs using best-worst choice to elicit preference information, and there is various evidence that it yields reliable interpretable data. Nonetheless, the data from a single such task cannot yield separate measures of the "importance" of an attribute and the "utility" of an attribute-level. We discuss various empirical designs, involving more than one task of the above general type, that may allow such separation of importance and utility. © 2008 Elsevier Inc. All rights reserved

Image and Reality: the Case of Job Satisfaction

This study makes a distinction between two types of utility. Experienced utility is defined as the job satisfaction derived from the present job, estimated by using a subjective evaluation of job satisfaction. Anticipated utility is defined as the individual’s anticipated job satisfaction before starting the job and it is studied by using a stated preference methodology known as conjoint analysis. The results suggest that the two utility concepts are different. Information about experienced utility is useful for the evaluation of well-being policies and the welfare effects of various employer strategies. Anticipated utility provides knowledge about the job search process.European Commission, Fifth Framework Programme "Improving Human Potential" (contract number: HPSE-CT-2002-00143)

Estimation of a preference based single index from the sexual quality of life questionnaire (SQOL) using ordinal data

There is increasing interest in using ordinal methods to estimate cardinal values for health states to calculate quality adjusted life years. This paper reports the estimation of models of rank data and discrete choice experiment (DCE) data to derive a preference-based index from a condition specific measure relating to sexual health and to compare the results to values generated from time trade-off valuation (TTO). The DCE data were analysed using a random effects probit model and the DCE predicted values were rescaled according to the highest and lowest predicted TTO values corresponding to the best and worst SQOL health states respectively. The rank data were analysed using a rank ordered logit model and re-scaled using two alternative methods. Firstly, re-scaling the rank predicted values using identical methods to those employed for DCE and secondly, re-scaling the rank model coefficients by dividing each level coefficient by the coefficient relating to death. The study raises some important issues about the use of ordinal data to produce cardinal health state valuations

Should Optimal Designers Worry About Consideration?

Consideration set formation using non-compensatory screening rules is a vital component of real purchasing decisions with decades of experimental validation. Marketers have recently developed statistical methods that can estimate quantitative choice models that include consideration set formation via non-compensatory screening rules. But is capturing consideration within models of choice important for design? This paper reports on a simulation study of a vehicle portfolio design when households screen over vehicle body style built to explore the importance of capturing consideration rules for optimal designers. We generate synthetic market share data, fit a variety of discrete choice models to the data, and then optimize design decisions using the estimated models. Model predictive power, design "error", and profitability relative to ideal profits are compared as the amount of market data available increases. We find that even when estimated compensatory models provide relatively good predictive accuracy, they can lead to sub-optimal design decisions when the population uses consideration behavior; convergence of compensatory models to non-compensatory behavior is likely to require unrealistic amounts of data; and modeling heterogeneity in non-compensatory screening is more valuable than heterogeneity in compensatory trade-offs. This supports the claim that designers should carefully identify consideration behaviors before optimizing product portfolios. We also find that higher model predictive power does not necessarily imply better design decisions; that is, different model forms can provide "descriptive" rather than "predictive" information that is useful for design.Comment: 5 figures, 26 pages. In Press at ASME Journal of Mechanical Design (as of 3/17/15