Ex-Ante and Ex-Post Evaluation of a New Transit Information App: Modeling Use Intentions and Actual Use

Abstract

This study investigates the behavioral drivers underlying the adoption of a multimodal travel information mobile app. The hypothesized framework is validated empirically through the case-study of Madrid. Madrid’s Public Transport real-time information app (“Mi Transporte”) allows users to obtain customized and automated information. A three-wave survey containing questions aligned with the Theory of Planned Behavior was conducted in 2015 and 2016 with a representative sample of transit users. Data analysis includes a factor analysis and a structural equation model to validate the hypotheses. The model assumes that the intention to use the app can be explained as a function of attitudinal factors and respondent characteristics. Results show that the app adoption is correlated with the intention of the users to adopt it and with their willingness-to-pay; the users’ intentions can be explained by various factors like user’s expectations on the app, affinity for technology (technophilia) and the previous use of other transport apps. The roles of search functionalities, side-mode information, time saving skills and the importance of the Level of Service (LOS) are also analyzed in the model. Relations between user characteristics and latent variables are subsequently explained as well as the ex-post satisfaction and change in travel patterns to measure the impact on the transport behavior of the app users. The study provides a better understanding of app adoption based on traveler characteristics, the attributes of the app and the perception of its capabilities.