Citation

Jang S, Rasouli S, Timmermans H. Transp. Res. E Logist. Transp. Rev. 2022; 163: e102744.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.tre.2022.102744

PMID

unavailable

Abstract

There is ample evidence that individuals' evaluation and choice of transportation-related alternatives in stated choice experiments vary by task complexity. Previous research has analyzed experimental error primarily as a function of the number of choice alternatives, the number of attribute (levels) and response time. It has ignored the fact that the cognitive ability of individuals may be another important moderating variable. Another limitation of previous research is that experimental error has been studied exclusively for utility-maximizing models, not for the more recently introduced regret-rejoice models. To augment this body of research, this study therefore proposes two advanced flexible reference-dependence models, based on the concepts of regret and rejoice, and argues that information processing depends on task complexity, which is affected not only by information load and response time but also by subjects' cognitive ability. We apply an integrated approach examining both varying the variance of error term and simplifying task complexity by considering only partial information. Furthermore, we explore how the effect of information load differs between long-term and short-term decision problems. To that end, two stated choice experiments with varying information load are designed, one related to a short-term decision context (route choice) and one about a long-term decision context (residential choice). Cognitive ability is explicitly measured using a validated psychological test.

RESULTS show subjects' inclination to consider full information in arriving at a choice increases with increasing cognitive ability. Response time and age appeared to be other significant determinants for the magnitude of error variance of choices. The proposed models outperform the conventional utility model and conventional regret- rejoice models and result in less error in the prediction of market share. The improvement in the prediction performance is more pronounced for more complex choice tasks.

Language: en

Keywords

Cognitive ability; Regret-Rejoice choice model; Task complexity