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Increasing evidence suggests that choice behaviour in real world may be guided by principles of bounded rationality as opposed to typically assumed fully rational…
Increasing evidence suggests that choice behaviour in real world may be guided by principles of bounded rationality as opposed to typically assumed fully rational behaviour, based on the principle of utility-maximization. Under such circumstances, conventional rational choice models cannot capture the decision processes. The purpose of the chapter is to propose a modeling framework that can capture both decision outcome and decision process.
The modeling framework incorporates a discrete cognitive representation structure and implies several decision heuristics, such as conjunctive, disjunctive and lexicographic rules. This allows modeling unobserved decision heterogeneity involved in a single decision, for example, in the form of a latent-class specification, taking into account mental effort, risk perception and expected outcome as explanatory factors.
Two models based on this framework are applied to decision problems underlying pedestrian shopping behaviour and compared with conventional multinomial logit models. The results show that the proposed models may not be superior to logit models in terms of model selection criteria due to the extra complexity in selecting heuristics, but suggest more interesting insights to the underlying decision mechanisms.
Understanding decision processes additional to outcomes is a promising research direction. A more developed model should take into account more contextual and socio-demographic factors in the heuristic selection part. The assumptions of information processing must be subject to empirical tests to validate the model.
The proposed modeling framework bridges the long-existing contradicting approaches in the field of decision modeling, namely the rational approach and the bounded rational approach, by proving that non-compensatory decision heuristics can be inferred from compensatory model formulations with discretized information representations and decision criteria assumed. It also incorporates a heuristic choice part into the decision processes in the form of latent-class specifications and shows the viability of the new modeling framework.
Many consumer-focused corporate social responsibility (CSR) studies suggest a positive link between the responsibility demonstrated by a company and consumers’ intention…
Many consumer-focused corporate social responsibility (CSR) studies suggest a positive link between the responsibility demonstrated by a company and consumers’ intention to favor the company in their purchases. Yet an analogous causal effect between corporate social and financial performances is not evident. This chapter conceptualizes how social desirability and cynicism contribute to the discrepancy between consumers’ attitudes and their actual purchase behavior, and analyzes why consumer choices indicated in surveys do not consistently convert into actions.
We develop a conceptual framework based on hybrid choice modeling to estimate the impact of two new variables, Corporate Social Desirability and Corporate Social Cynicism, on CSR research. The model presented synthesizes research findings from the fields of CSR and psychology with a discrete choice methodology that allows inclusion of psychological aspects as latent variables.
The goal of the framework is to bridge the gap between choices stated by consumers in CSR surveys and their actual choices by quantifying and extracting the effects of biases that otherwise threaten the validity of such survey results. As the next step, the practical value of the model must be evaluated through empirical research combining a CSR choice study with social desirability and cynicism measurement.
The framework proposes a novel way of controlling CSR surveys for potential biases created by social desirability and cynicism and enables quantification of this impact, with potential application to other fields where psychological aspects may distort research results. Future empirical evidence based on the framework may also offer new insights into the mechanisms by which the two biases distort findings.
An assumption made in many applications of stated preference modeling is that preferences remain stable over time and over multiple exposures to information about choice…
An assumption made in many applications of stated preference modeling is that preferences remain stable over time and over multiple exposures to information about choice alternatives. However, there are many domains where this assumption can be challenged. One of these is where individuals learn about new products. This paper aims to test how attribute preferences as measured in an experimental choice task shift when respondents are exposed to new product information. The paper presents results from a study investigating consumer preferences for a new consumer electronics product conducted among 400 respondents from a large consumer panel. All respondents received several choice tasks and were then able to read additional information about the new product. After this they completed an additional set of choice tasks. All choices were from pairs of new product alternatives that varied across eight attributes designed according to an orthogonal plan. Using heteroscedastic logit modeling, the paper analyses the shifts in attribute utilities and scale variances that result from the exposure to product information. Results show that as respondents become better informed about a new attribute the attribute has a greater influence on their choices. In addition a significant shift in scale variance is observed, suggesting an increase in preference heterogeneity after information exposure.
Purpose: This chapter introduces a choice modeling framework that explicitly represents the planning and action stages of the choice process.Methodology: A discussion of…
Purpose: This chapter introduces a choice modeling framework that explicitly represents the planning and action stages of the choice process.
Methodology: A discussion of evidence from behavioral research is followed by the development of a discrete choice modeling framework with explicit planning and action submodels. The plan/action choice model is formulated for both static and dynamic contexts; where the latter is based on the Hidden Markov Model. Plans are often unobservable and are treated as latent variables in model estimation using observed actions.
Implications: By modeling the interactions between the planning and action stages, we are able to incorporate richer specifications in choice models with better predictive and policy analysis capabilities. The applications of this research in areas such as driving behavior, route choice, and mode choice demonstrate the advantages of the plan/action model in comparison to a “black box” choice model in terms of improved microsimulations of behaviors that better represent real-life situations. As such, the outcomes of this chapter are relevant to researchers and policy analysts.
This paper introduces a behavioral framework to model residential relocation decision in island areas, at which the decision in question is influenced by the…
This paper introduces a behavioral framework to model residential relocation decision in island areas, at which the decision in question is influenced by the characteristics of island regions, policy variables related to accessibility measures, and housing prices at the proposed island area, as well as personal, household (HH), job, and latent characteristics of the decision makers.
The model framework corresponds to an integrated choice and latent variable (ICLV) setting where the discrete choice model includes latent variables that capture attitudes and perceptions of the decision makers. The latent variable model is composed of a group of structural equations describing the latent variables as a function of observable exogenous variables and a group of measurement equations, linking the latent variables to observable indicators.
An empirical study has been developed for the Greek Aegean island area. Data were collected from 900 HHs in Greece contacted via telephone. The HHs were presented hypothetical scenarios involving policy variables, where 2010 was the reference year. ICLV binary logit (BL) and mixed binary logit (MBL) relocation choice models were estimated sequentially. Findings suggest that MBL models are superior to BL models, while both the policy and the latent variables significantly affect the relocation decision and improve considerably the models' goodness of fit. Sample enumeration method is finally used to aggregate the results over the Greek population.
In previous research (Abou-Zeid et al., 2008), we postulated that people report different levels of travel happiness under routine and nonroutine conditions and supported…
In previous research (Abou-Zeid et al., 2008), we postulated that people report different levels of travel happiness under routine and nonroutine conditions and supported this hypothesis through an experiment requiring habitual car drivers to switch temporarily to public transportation. This chapter develops a general modeling framework that extends random utility models by using happiness measures as indicators of utility in addition to the standard choice indicators, and applies the framework to modeling happiness and travel mode switching using the data collected in the experiment. The model consists of structural equations for pretreatment (remembered) and posttreatment (decision) utilities and explicitly represents their correlations, and measurement equations expressing the choice and the pretreatment and posttreatment happiness measures as a function of the corresponding utilities. The results of the empirical model are preliminary but support the premise that the extended modeling framework, which includes happiness, will potentially enhance behavioral models based on random utility theory by making them more efficient.
Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household…
Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.
Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.
Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.
Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.
The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL…
The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL) model. That flexibility search lead to discrete choice hybrid choice models (HCMs) formulations that explicitly incorporate psychological factors affecting decision making in order to enhance the behavioral representation of the choice process. It expands on standard choice models by including attitudes, opinions, and perceptions as psychometric latent variables.
In this paper we describe the classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM. To show its feasibility, we apply it to data of stated personal vehicle choices made by Canadian consumers when faced with technological innovations.
We then go beyond classical methods, and estimate the HCM using a hierarchical Bayesian approach that exploits HCM Gibbs sampling considering both a probit and a MMNL discrete choice kernel. We then carry out a Monte Carlo experiment to test how the HCM Gibbs sampler works in practice. To our knowledge, this is the first practical application of HCM Bayesian estimation.
We show that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented. The HCM framework not only proves to be capable of introducing latent variables, but also makes it possible to tackle the problem of measurement errors in variables in a very natural way. We also show that working with Bayesian methods has the potential to break down the complexity of classical estimation.