Abstract
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.
Highlights
In a world in which people are constantly exposed to others’ opinions and actions, whether in person or through social media, social influence may produce changes in attitudes and behaviours towards or against a specific mode of transport (Dugundji and Gulyás, 2012)
This phenomenon, which might arise at different levels of social interaction (Maness et al, 2015), is generally described as a manifestation of compliance when behaving in response to a direct or implicit request, and conformity when behaving to conform to the response of others (Cialdini and Trost, 1998; Cialdini and Goldstein, 2004), and the majority of transport studies trying to measure social influence effects focused their attention on these
The aim is to develop conceptual and modelling frameworks that can simultaneously represent live social interaction effects such as awareness of the benefits and drawbacks of a new technology/service (Manca et al, 2019), social influence processes of diffusion, translation and reflexivity (Axsen and Kurani, 2014), conformity processes related to social norms such as the hypothetical adoption rate in the individual social network (Kim et al, 2014; Cherchi, 2017), and more implicit correlated effects (Manski, 1993) related to psychometric factors such as attitudes, norms and perceived behavioural control of the peers within the individual’s social network, which are measured using the individual’s peer attitude (IP A) variable (Manca et al, 2020)
Summary
In a world in which people are constantly exposed to others’ opinions and actions, whether in person or through social media, social influence may produce changes in attitudes and behaviours towards or against a specific mode of transport (Dugundji and Gulyás, 2012). Previous research extensively analysed how individuals connect and interact with each other through social networks (Carrasco and Miller, 2006), discussed the general definition of social influence drawing upon the social science literature and tried to quantify part of the effects of social influence on travel behaviour
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