Incorporating Latent Variables into Discrete Choice Models — A Simultaneous Estimation Approach Using SEM Software

BuR - Business Research Offidal Open Aa:ess Joumal of VHB Verband der Hochsdlullehrer fiir Bemebswirtschaft e.V. Volume 1 I Issue 2 I December 2008 I 220·237 Incorporating Latent Variables into Discrete Choice Models -A Simultaneous Estimation Approach Using SEM Software Dirk Temme, Institute ofMcll'ketiny, Ilumboldt University ofBerlin, Gemwny, E-j\,Juil: Marcel Paulssen, IIEC I /autes Etudes Commerciales, Universite de Genet'€, Switzerland, E-Mail: Till Dannewald, Tnfas ITR Fmnkfurt, Gemwny, P:-Mail: till.dannewald@ infas-ttr.de Abstract Integrated choice and latent variable (ICL V) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICL V applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field. Keywords: Hybrid choice models, Mode choice, Mplus, Value-attitude hierarchy Manuscript received June 13, 2008, accepted by Adamantios Diamantopoulos (Marketing) November 11, 2008. 1. Introduction Discrete choice models are extensively used in various academic fields to analyze a huge range of choices between mutually exclusive alternatives (e.g., brands, service providers, travel modes, financial investments, residences, political parties, or strategies). Traditionally, these models have directly mapped observed features of alternatives and observed characteristics of decision makers to overt choice behavior. For instance, in order to explain travel mode choice for daily work trips, modal attributes (e.g., travel time and cost) as well as commuter socio-demographics (e.g., household income and number of drivers in a household) have been considered (e.g., Train 1978 ). The decision maker's internal processes during preference formation and notably the role of factors that are not directly ob- 220 servable, such as attitudes or lifestyle preferences, remain unexplained in a so-called "black box" in traditional discrete choice analysis. Meanwhile, researchers have increasingly recognized that decision makers differ significantly in psychological constructs such as attitudes, perceptions, values, or lifestyle preferences and that these factors affect an alternative's utility in a systematic way (Ben-Akiva, McFadden, Train, Walker, Bhat, Bierlaire, Bolduc, Boersch-Supan, Brownstone, Bunch, Daly, De Palma, Gopinath, Karlstrom, and Munizaga 2 002; Walker and Ben-Akiva 2002). Mode choice decisions, for example, might not only depend on objective criteria (e.g., time, income) but also on commuters' preferences for convenience, safety, or flexibility (e.g., Vredin Johansson, Heldt, and Johansson 2 006). Two otherwise identical commuters differing in their desire for flexibility BuR - Business Research Offidal Open Aa:ess Joumal of VHB Verband der Hochsdlullehrer fiir Bemebswirtschaft e.V. Volume 1 I Issue 2 I December 2008 I 220·237 might thus choose different travel modes. Extending choice models with latent variables representing attitudes or values can therefore lead to a deeper understanding of the choice processes taking place in the consumer's ''black box" and at the same time should provide greater explanatory power. Therefore, integrated choice and latent variable OCLV) models which merge classic choice analysis with the structural equation approach (SEM) for latent variables represent a promising new class of models. Recently, some encouraging applications of ICLV models have appeared in the literature: Explaining prototype choice in conjoint analysis by incorporating subjective product characteristics (i.e., perceptions) (Luo, Kannan, and Ratchford 2008) ; analyzing private asset investments taking into account factors such as individual risk attitude and impatience (Eyman, Borsch-Supan, and Euwals 2002); and modeling the impact of lifestyle preferences on residence choice (Walker and Li 2007). Despite their conceptual appeal, there are still relatively few applications of ICLV models in marketing and related fields. The major reason for their lack of popularity is most likely the fact that full information estimation of these models is rather involved and hitherto it was been required that researchers develop their own programs (Ben-Akiva, McFadden, Train, Walker, Bhat, Bierlaire, Bolduc, BorschSupan, Brownstone, Bunch, Daly, De Palma, Gopinath, Karlstrom, and Munizaga 2002).1 Most of the rare current applications are restricted to binary choice and, with the noticeable exception of the paper by Dellaert and Stremersch (2005), only consider direct effects oflatent variables on choice (e.g., Ben-Akiva, Walker, Bernardino, Gopinath, Morikawa, and Polydoropoulou 2002; Ashok, Dillon, and Yuan 2002) . Thus, causal relationships between latent variables commonly investigated in structural equation modeling are neglected. In contrast, we test a behavioral theory, the value-attitude hierarchy, which proposes hierarchical relationships between latent variables in a discrete choice analysis. Furthermore, by applying the program Mplus (Muthen and Muthen 1998-2007), one of the most comprehensive software packages for SEM, we present a powerful and very flexible option for estimat- ing ICLV models which has not been considered so far. To sum up, our paper primarily provides a methodical contribution. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. Thus, unlike previous applications of the ICLV model, we do not only include latent variables as an additional set of predictors (e.g., Ben-Akiva, Walker, Bernardino, Gopinath, Morikawa, and Polydoropoulou 2002; Ashok, Dillon, and Yuan 2002). Furthermore, our paper extends the transportation choice literature by testing a value-attitude hierarchy with the impact of commuters' personal values on "soft" choice criteria and on subsequent mode choice. The remaining part of the paper is structured as follows: First, we introduce the general structure of ICLV models and discuss their estimation with the Mplus software. Then, we illustrate the applicability of Mplus in an empirical study on travel mode choice. A hierarchical be (...truncated)