Capturing the effect of multiple social influence sources on the adoption of new transport technologies and services

This paper presents a conceptual and modelling framework that makes it possible to disentangle and quantify multiple social influence effects affecting the individual’s choice behaviour. The proposed structure simultaneously accounts for live social interaction effects, social influence processes of diffusion, translation and reflexivity, conformity processes related to social norms, such as the hypothetical adoption rate within a social network, and correlated effects related to psychometric attitudinal characteristics of peers. The modelling framework is applied to investigate the adoption of bike-sharing in a student cohort during a public transport strike. A joint hybrid choice model is estimated using a two-wave stated preference dataset and incorporating latent variables, social influence measures and live social interactions with dynamic ‘inertia’ processes. In this empirical context, results and sensitivity analyses show that the social influence variables are highly significant and explain part of the heterogeneity in choosing bike-sharing. A greater utility for this travel mode is associated with a greater hypothetical bike-sharing adoption and with live social interactions improving the understanding of the bike-sharing benefits. The results also suggest that conformity processes and social interaction effects can have a higher impact on the choice than correlated effects related to the attitudinal ‘propensity towards cars’ in the social network. This study, therefore, provides further evidence that, in the context of new technology adoptions, the choice is not only driven by explanatory variables that can be generally observed but may well be affected by social factors facilitating the exchange of information and the understanding of the individuals.