Revealing Differences in Willingness to Pay due to the Dimensionality of Stated Choice Designs: An Initial Assessment

Stated choice (SC) methods are now a widely accepted data paradigm in the study of the choice responses of agents. Their popularity has spawned an industry of applications in fields as diverse as transportation, environmental science, health economics and policy, marketing, political science and econometrics. With rare exception, empirical studies have used a single SC design, in which the numbers of attributes, alternatives, choice sets, attribute levels and ranges have been fixed across the entire design. As a consequence the opportunity to investigate the influence of design dimensionality on behavioural response has been denied. Accumulated wisdom has promoted a large number of positions on what design features are specifically challenging for respondents; and although a number of studies have assessed the influence of subsets of design dimensions, there exists no single study (that we are aware of) that has systematically varied all of the main dimensions of SC experiments. This paper reports some initial findings on what influences, in aggregate, specific design configurations have on the mean willingness to pay for specific attributes using a Design of Designs (DoD) SC experiment in which the ‘attributes’ of the design are the design dimensions themselves. The design dimensions that are varied are the number of choice sets presented, the number of alternatives in each choice set, the number of attributes per alternative, the number of levels of each attribute and the range of attribute levels. The empirical evidence, using a sample of respondents in Sydney choosing amongst trip attribute bundles for their car commuting trip, suggests that, within the boundaries of design dimensionality investigated, mean estimates of WTP for travel time savings in the aggregate cover a range that is appropriate for reporting a global mean and a set of meaningful values for sensitivity testing in project appraisal and demand prediction. When these aggregated mean estimates are conditioned on all design dimensions we do not find any systematic differences due to specific design dimensions; however when each design dimension is assessed without controlling for the other dimensions we find evidence to support differences in aggregate mean WTP attributable to the number of attributes per alternative and the number of alternatives in a choice set.

[1]  Peter Martinsson,et al.  Design techniques for stated preference methods in health economics. , 2003, Health economics.

[2]  Jordan J. Louviere,et al.  Attribute Range Effects in Binary Response Tasks , 2000 .

[3]  Joan L. Walker,et al.  Hybrid Choice Models: Progress and Challenges , 2002 .

[4]  K. Train,et al.  Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles , 1999, Controlling Automobile Air Pollution.

[5]  Arthur Koestler,et al.  Janus: A Summing Up , 1978 .

[6]  D. Hensher,et al.  Assessing the influence of design dimensions on stated choice experiment estimates , 2005 .

[7]  D. Hensher,et al.  Using stated response choice data to enrich revealed preference discrete choice models , 1993 .

[8]  N. Malhotra.  Information Load and Consumer Decision Making , 1982 .

[9]  André de Palma,et al.  Rational Choice under an Imperfect Ability to Choose , 1994 .

[10]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[11]  N. Klein,et al.  Context Effects on Effort and Accuracy in Choice: An Enquiry into Adaptive Decision Making , 1989 .

[12]  David A. Hensher,et al.  The Mixed Logit Model: the State of Practice and Warnings for the Unwary , 2001 .

[13]  David A. Hensher,et al.  Measurement of the Valuation of Travel Time Savings , 2000 .

[14]  John M. Rose,et al.  Applied Choice Analysis: A Primer , 2005 .

[15]  Jordan J. Louviere,et al.  Combining sources of preference data , 1998 .

[16]  D. Hensher.  The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications , 2001 .

[17]  Jordan J. Louviere,et al.  Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data , 1983 .

[18]  J. R. DeShazo,et al.  Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency , 2002 .

[19]  Joffre Swait,et al.  Choice Environment, Market Complexity, and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice , 2001 .

[20]  J. Shanteau,et al.  The perceived strength of an implied contract: Can it withstand financial temptation? , 1991 .

[21]  J. Swait,et al.  CONOMY The Effect of Choice Environment and Task Demands on Consumer Behavior : Discriminating Between Contribution and Confusion Joffre Swait and , 1996 .

[22]  Robert Kohn,et al.  Dissecting the Random Component of Utility , 2002 .

[23]  Kathryn Roeder,et al.  Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology , 1999 .

[24]  Jordan J. Louviere,et al.  The Effect of Attribute Variation on Consumer Choice Consistency , 1999 .

[25]  Jordan J. Louviere,et al.  An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application , 2001 .

[26]  Kenneth E. Train,et al.  Discrete Choice Methods with Simulation , 2016 .

[27]  K. Train,et al.  Forecasting new product penetration with flexible substitution patterns , 1998 .

[28]  Joffre Swait,et al.  The Effect of Choice Environment and Task Demands on Consumer Behavior: Discriminating Between Contribution and Confusion , 1996 .

[29]  D Brownstone.  DISCRETE CHOICE MODELLING FOR TRANSPORTATION. IN: TRAVEL BEHAVIOUR RESEARCH. THE LEADING EDGE , 2001 .

[30]  Chandra R. Bhat,et al.  Flexible Model Structures for Discrete Choice Analysis , 2007 .

[31]  W. Greene,et al.  Fixed and Random Effects in Nonlinear Models , 2001 .

[32]  D Hensher,et al.  COMBINING DATA SOURCES. IN: TRAVEL BEHAVIOUR RESEARCH. THE LEADING EDGE , 2001 .

[33]  R. Heiner.  The Origin of Predictable Behavior , 1983 .

[34]  David A. Hensher,et al.  A latent class model for discrete choice analysis: contrasts with mixed logit , 2003 .

[35]  John M. Rose,et al.  Applied Choice Analysis: List of tables , 2005 .

[36]  John M. Rose,et al.  Applied Choice Analysis: List of tables , 2005 .

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

[38]  Jordan J. Louviere,et al.  Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event , 1983 .

[39]  Frank S. Koppelman,et al.  CLOSED-FORM DISCRETE-CHOICE MODELS* , 2007 .

[40]  W. Kamakura,et al.  Modeling Preference and Structural Heterogeneity in Consumer Choice , 1996 .

[41]  David Brownstone,et al.  Discrete Choice Modelling for Transportation , 2001 .

[42]  Marisa J. Mazzotta,et al.  Decision Making When Choices Are Complex: A Test of Heiner's Hypothesis , 1995 .

[43]  J. Deshazo,et al.  Test of Choice Set Misspecification for Discrete Models of Consumer Choice , 2001 .