Construction of Heterogeneous Conjoint Choice Designs: A New Approach

Extant research on choice designs in marketing focuses on the construction of efficient homogeneous designs where all respondents get the same design. Recently marketing scholars proposed the construction of efficient heterogeneous designs where different respondents or groups of respondents get different subdesigns, and demonstrated substantial efficiency gain when such heterogeneous designs are employed. A significant hurdle in the widespread adoption of heterogeneous designs is the high computation cost, even when the number of subdesigns contained in the heterogeneous design is restricted to be small. In this paper we propose a new approach for the construction of efficient heterogeneous choice designs. In contrast to extant approaches that are based on an exact design framework where it is computationally prohibitive to do an exhaustive search to find a globally optimal design, our proposed approach is based on the continuous design framework where well-established mathematical theories can be leveraged for quick identification of a globally optimal design. The proposed approach makes it feasible to generate a highly efficient choice design that is completely heterogeneous-a unique subdesign for each individual respondent in the choice experiment. The proposed approach is the first in the marketing literature to find a completely heterogeneous choice design with assured high global design efficiency using the continuous design framework. Results from simulation and empirical studies demonstrate superior performance of the proposed approach over extant approaches in constructing efficient heterogeneous choice designs. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2014.0897 .

[1]  Dick R. Wittink,et al.  Forecasting with Conjoint Analysis , 2001 .

[2]  M. Newton.  Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .

[3]  Greg M. Allenby,et al.  Bayesian Designs for Hierarchical Linear Models , 2009 .

[4]  Drew Seils,et al.  Optimal design , 2007 .

[5]  Jie Yu,et al.  Model-Robust Design of Conjoint Choice Experiments , 2008, Commun. Stat. Simul. Comput..

[6]  Joel Huber,et al.  The Importance of Utility Balance in Efficient Choice Designs , 1996 .

[7]  Gary E. Bolton,et al.  INCENTIVE-ALIGNED CONJOINT ANALYSIS , 2004 .

[8]  John R. Hauser,et al.  Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application , 2007 .

[9]  Joel Huber,et al.  Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments , 2001 .

[10]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[11]  Stefanie Biedermann,et al.  On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm , 2013 .

[12]  Kai-Tai Fang,et al.  An efficient method for constructing uniform designs with large size , 2013, Comput. Stat..

[13]  Qing Liu,et al.  Efficient Choice Designs for a Consider-Then-Choose Model , 2011, Mark. Sci..

[14]  Min Ding,et al.  Barter Markets for Conjoint Analysis , 2009, Manag. Sci..

[15]  Eric T. Bradlow,et al.  Beyond conjoint analysis: Advances in preference measurement , 2008 .

[16]  M. Wedel,et al.  Designing Conjoint Choice Experiments Using Managers' Prior Beliefs , 2001 .

[17]  Mark J. Garratt,et al.  Efficient Experimental Design with Marketing Research Applications , 1994 .

[18]  John R. Hauser,et al.  Research Note---On Managerially Efficient Experimental Designs , 2007 .

[19]  Michel Wedel,et al.  Profile Construction in Experimental Choice Designs for Mixed Logit Models , 2002 .

[20]  Paul E. Green,et al.  Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice , 1990 .

[21]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[22]  John R. Hauser,et al.  Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis , 2004 .

[23]  Min Ding.  An Incentive-Aligned Mechanism for Conjoint Analysis , 2007 .

[24]  Michel Wedel,et al.  Heterogeneous Conjoint Choice Designs , 2005 .

[25]  Warren F. Kuhfeld,et al.  Large Factorial Designs for Product Engineering and Marketing Research Applications , 2005, Technometrics.

[26]  Peter Goos,et al.  An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs , 2009 .

[27]  J. Swait,et al.  The Influence of Task Complexity on Consumer Choice: A Latent Class Model of Decision Strategy Switching , 2001 .

[28]  R. K. Meyer,et al.  The Coordinate-Exchange Algorithm for Constructing Exact Optimal Experimental Designs , 1995 .

[29]  Greg M. Allenby,et al.  A Choice Model with Conjunctive, Disjunctive, and Compensatory Screening Rules , 2004 .

[30]  Anthony C. Atkinson,et al.  Optimum Experimental Designs, with SAS , 2007 .

[31]  Michael Jackson,et al.  Optimal Design of Experiments , 1994 .

[32]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[33]  M. Wedel,et al.  The No—Choice Alternative in Conjoint Choice Experiments , 2001 .

[34]  Johann Füller,et al.  Measuring Consumer Preferences Using Conjoint Poker , 2012, Mark. Sci..

[35]  Jie Yu,et al.  Efficient Conjoint Choice Designs in the Presence of Respondent Heterogeneity , 2009, Mark. Sci..

[36]  John Stufken,et al.  SUPPORT POINTS OF LOCALLY OPTIMAL DESIGNS FOR NONLINEAR MODELS WITH TWO PARAMETERS , 2009, 0903.0728.

[37]  Peter Goos,et al.  A Comparison of Criteria to Design Efficient Choice Experiments , 2006 .

[38]  Dennis K. J. Lin,et al.  Ch. 4. Uniform experimental designs and their applications in industry , 2003 .

[39]  J. Kiefer.  General Equivalence Theory for Optimum Designs (Approximate Theory) , 1974 .

[40]  Steven E. Rigdon,et al.  Model-Oriented Design of Experiments , 1997, Technometrics.

[41]  M. Birnbaum.  Decision Making in the Lab and on the Web , 2000 .

[42]  Greg M. Allenby,et al.  Estimating Heterogeneous EBA and Economic Screening Rule Choice Models , 2006 .