Discovering Causal Structure from Attitudinal Data: What Motives People to Use Mobility-Management Travel Apps

Abstract

In recent years, one of the solutions that has received much attention with a view to motivating change toward sustainable urban mobility is information dissemination and persuasion delivered through mobilitymanagement travel apps. However, their influence to promote sustainability highly depends on understanding the underlying mechanisms and processes of behavior change. This paper aims to uncover causal structure in travel app users’ behavior, in connection with behavioral theories. This study investigates the applicability of causal discovery methods to establish the associations between the constructs of a theoretical framework. The case-study focuses on the new travel information system in Copenhagen (Denmark). 822 Danish citizens participated in a technology-use preference survey distributed online. The results show the capability of the Max-Min Hill-Climbing (MMHC) algorithm to learn the causal structure of a theoretical framework, and accordingly interpret established associations. The proposed decision framework incorporates Alderfer’s ERG theory of human needs and Bandura’s triadic reciprocal determinism to explain the adoption intention of mobility-management travel apps.