Combining revealed and stated preferences data

Our objective is to develop a unifying framework for the incorporation of different types of survey data in individual choice models. We describe statistical methodologies that combine multiple sources of data in the estimation of individual choice models and summarize the current state of the art of data combination methods that have been used with market research data. The most successful applications so far have combined revealed and stated preference data. We discuss different types of market and survey data and provide examples of research contexts in which one might wish to combine them. Although these methods show a great deal of promise and have already been used successfully in a number of applications, several important research issues remain. A discussion of these issues and directions for further research conclude the paper.     

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

Recent Progress on Endogeneity in Choice Modeling

We describe recent progress in several areas related to endogeneity, including: choice set formation and attention to attributes; interactions among decision-makers; respondents' strategic behavior in answering stated preference choices; models of multiple discrete/continuous choice; distributions of willingness-to-pay; and methods for handling traditionally endogenous explanatory variables.     

Consideration set influences on consumer decision-making and choice: Issues,models, and suggestions

This paper affords a stylized view of individual consumer choice decision-making appropriate to the study of many marketing decisions. It summarizes issues relating to consideration set effects on consumer judgment and choice. It discusses whether consideration sets really exist and, if so, the factors that affect their composition, structure, and role in decision-making. It examines some new developments in the measurement and modeling of consideration set effects on decision-making. The paper concludes with suggestions for needed research. The authors wish to acknowledge the numerous ideas and perspectives contributed by the other members of the Banff Symposium workshop:Mukesh Bhargava (University of Alberta),Bill Black (Louisiana State University),Gary Gaeth (University of Iowa),Hotaka Katahira (University of Tokyo, Japan),Gilles Laurent (Centre HEC-ISA, France),Irwin Levin (University of Iowa),David Midgley (Australian Graduate School of Management),Thomas Novak (Southern Methodist University), andJames Wiley (University of Alberta). This paper has benefited greatly from their contributions.     

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.     

Extended Framework for Modeling Choice Behavior

We review the case against the standard model of rational behavior and discuss the consequences of various anomalies of preference elicitation. A general theoretical framework that attempts to disentangle the various psychological elements in the decision-making process is presented. We then present a rigorous and general methodology to model the theoretical framework, explicitly incorporating psychological factors and their influences on choices. This theme has long been deemed necessary by behavioral researchers, but is often ignored in demand models. The methodology requires the estimation of an integrated multi-equation model consisting of a discrete choice model and the latent variable model system. We conclude with a research agenda to bring the theoretical framework into fruition.     

Identification of parameters in normal error component logit-mixture (NECLM) models

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the ‘equality condition’, an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data. Copyright © 2007 John Wiley & Sons, Ltd.     

Happiness and travel mode switching: Findings from a Swiss public transportation experiment

We present results from a recent experiment conducted in Switzerland that studies the effects of a temporary change in the mode of travel to work on travel happiness and mode switching. The primary aim is to study the dynamics in travel satisfaction ratings obtained before and after the intervention. Two behavioral mechanisms driving the change in satisfaction ratings are analyzed. The first one is that satisfaction ratings are influenced by reference points. The second mechanism is that satisfaction ratings are affected by cognitive awareness, whereby the disruption of routine travel conditions makes people think more fully about their travel happiness with different modes of transportation.It is found that the measure of satisfaction with the commute by car obtained right after the temporary intervention is significantly different from the measure obtained before the intervention, and both behavioral hypotheses are supported by the pattern of change in satisfaction ratings. The policy and modeling relevance of different well-being measures obtained at different points in time is discussed.As to mode switching, none of the 30 participants switched completely to public transportation after the intervention but a number of them continued to commute occasionally by public transportation. The relationship between mode switching and satisfaction and the implications of this intervention for public transportation agencies and other organizations interested in behavioral modification are discussed.