A design-of-designs survey framework to analyze information processing strategies in choice experiments

Abstract

This study introduces a novel design-of-designs survey framework tailored to model decision-making processes involving the presence of alternative choice heuristics. Often based solely on a utility maximization framework, traditional choice models fail to account for the diverse heuristics that individuals may employ when faced with varying task complexity and choice contexts. The design-of-designs framework allows capturing potential alternative heuristics by systematically varying task complexity along meta-dimensions such as the number of alternatives, attributes, and choice contexts in different transport-related choices (of route, holiday destination, and residential location). The survey framework incorporates advanced features, including dynamic presentation schemes, perceptual indicators, and cognitive effort metrics, to elucidate how survey complexity may shape heuristic adoption. In particular, residential location alternatives are displayed on interactive maps, enabling respondents to filter alternatives based on selected attribute values and providing insights into selective information processing and the use of choice paths. Additionally, stated non-attendance indicators and response times are registered to evaluate cognitive load and attribute importance. Finally, the survey design also includes the possibility of considering latent constructs, such as engagement, thinking styles and trust in institutions, which have proven important in modelling with alternative heuristics. The paper highlights the importance of integrating non-compensatory behaviour into discrete choice models to enhance behavioural realism by capturing the dynamics of heuristic-driven decision-making. Finally, the paper discusses how the different survey elements can support future research on information processing and heuristic use in stated choice surveys.Keywords: Design-of-designs; Stated choices; Choice heuristics; Task complexity