International Choice Modelling Conference, International Choice Modelling Conference 2015

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Effects of task complexity and time pressure on (activity-travel) choice behavior: new models and empirical evidence
Chao Chen, Caspar Chorus, Eric Molin, Bert van Wee

Last modified: 11 May 2015

Abstract


Short abstractThis paper derives, estimates and applies a heteroscedastic discrete choice model that accommodates potential effects of task complexity and time pressure on decision-making. To the best of our knowledge, this is the first time that both factors are jointly captured in a discrete choice model. We collect data using a novel activity-travel simulator experiment that has been specifically designed with the aim of testing our model. Results are in line with expectations, in that higher levels of task complexity and time pressure are found to result in a smaller scale of utility and more random choice behaviour. An empirical illustration suggests that choice probability-differences between models that do and those that do not capture these effects, can be very substantial; this in turn suggests that failing to capture the effects of task complexity and time pressure in discrete choice models might lead to serious forecasting bias.Extended abstractChoice modelers from various domains have long since acknowledged, that the choices that people make are often highly complex. In response, various ways have been proposed to incorporate task complexity in discrete choice models, usually by means of making the scale of utility a function of task complexity which leads to heteroscedastic logit specifications (e.g., Swait and Adamowicz, 2001; DeShazo & Fermo, 2002; Arentze et al., 2003; Caussade et al., 2005; Dellaert et al., 2011).Much less attention has been paid to the notion that choices are often made under considerable time pressure, and this too may influence choice beavior. Although relevant to various domains outside Transportation, the importance of time-pressure is particularly obvious for activity-travel decision-making contexts, pre-, but especially en-route. In many such activity-travel situations, time pressure  is a potentially important factor influencing decision-making processes: consuming too much decision-time comes with the risk of late arrival at the activity location due to, for example, missing a bus or a highway-exit. However, we know of no attempts to explicitly capture, in a discrete choice model, the influence of time pressure on the making of operational activity-travel choices. Also in fields adjacent to transportation, we were unable to find studies that aim to capture time pressure in a discrete choice model.This paper contributes to the choice modelling literature i) by being the first to explicitly and jointly model task complexity and time pressure in a discrete choice model; and ii) by estimating and testing the resulting model using data from a novel activity-travel simulator experiment that has been specifically designed for that purpose. Our modelling approach is inspired by previous studies (see references above) that have built and estimated heteroscedastic logit models; in our case, the scale of utility is conceived to be a function of task complexity as well as time pressure. Results are in line with expectations, in that higher levels of task complexity and time pressure are found to result in a smaller scale of utility. In other words, higher levels of task complexity and time pressure lead to more random choice behaviour and as a consequence to less pronounced differences in choice probabilities between alternatives. In addition, we observe a subtle distinction between on the hand an engagement effect (i.e., the willingness of the individual to spend some time to consider the available alternatives before making her choice) and on the other hand a time pressure effect (which limits the amount of time that can be spent on such considerations). Our modeling approach is able to distinguish these two diverging effects using a non-linear functional form.An empirical illustration suggests that choice probability-differences between models that do and those that do not capture these effects, can be very substantial; this in turn suggests that failing to capture the effects of task complexity and time pressure in discrete choice models might lead to serious forecasting bias.ReferencesArentze, T., Borgers, A., Timmermans, H. and DelMistro, R.: Transport stated choice responses: effects of task complexity, presentation format and literacy. Transportation Research Part E: Logistics and Transportation Review 39(3), 229-244 (2003)Caussade, S., Ortúzar, J.d.D., Rizzi, L.I. and Hensher, D.A.: Assessing the influence of design dimensions on stated choice experiment estimates. Transportation Research Part B: Methodological 39(7), 621-640 (2005)Dellaert, B.G.C., Donkers, B. and Soest, A.V.: Complexity Effects in Choice Experiment–Based Models. Journal of Marketing Research 49(3), 424-434 (2011)DeShazo, J.R. and Fermo, G.: Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency. Journal of Environmental Economics and Management 44(1), 123-143 (2002)Swait, J. and Adamowicz, W.: The influence of task complexity on consumer choice: a latent class model of decision strategy switching. Journal of Consumer Research 28(1), 135-148 (2001)

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