Modelling live social interactions and indirect social influence affecting the adoption of transport technologies and services: a bike sharing case study

Social influence is an important factor in the decision-making process of 2 an individual [1, 2]. Especially in contexts of adoption of new technologies 3 and services, it appears reductive to investigate individual’s behaviours and 4 responses without considering the inherent social network influence, which 5 can help for a better understanding of the dynamics behind their choices. 6 The processes generated by social influence have been extensively analysed 7 in other disciplines, such as sociology, social psychology and economics. Re8 vising and resuming these studies, Axsen and Kurani [3] conceptualised the 9 dynamics of social interaction processes on intentions and adoptions of new 10 technologies. Another important social influence process discussed in their 11 previous review paper, Axsen and Kurani [4] is the conformity process. In12 dividuals perceive how other people behave and tend to conform under the 13 pressure of subjective and social norms [5]. In fact, norms can influence 14 cognitive processes which generate the intention to a certain behaviour and 15 can also affect the decision making process of the individuals. Besides social 16 interaction and conformity processes, Ng [6] defined an additional level of 17 indirect social influence which is characterised by elusive information and 18 absence of resistance and reinforcement. This influence is very difficult to ex19 plain as the individual are not even aware of it (i.e. when a person develops 20 similar attitudes to most of his/her peers). 21 In transport research, previous methodologies have mainly focused on 22 the inclusion of conformity in quantitative models, i.e. discrete choice mod23 els, and have emphasised the analyses of user behaviours at social level, i.e. 24