Modelling the impacts of direct and indirect social influence on travel choice behaviours in the context of new technologies and services
Creators: name: Manca, Francesco
Publisher: name: Imperial College London
Year: 2019

description: In transport demand and travel behaviour research, the decision-making process of individuals has been extensively studied through the help of discrete choice models (DCM). DCMs are a powerful tool which is able to quantify the utility associated to each alternative of a defined choice set (e.g. a set of travel modes, travel routes, etc). For instance, in the specific case of a travel mode choice, this utility generally includes Level-of-Service (LoS) variables characterising each available mode (e.g. times and costs) and socioeconomic variables characterising the individual (e.g. income, age, occupation). These classic DCMs assume that individuals are rational and maximise their utility by always making an independent choice. This assumption appears reductive when investigating the way individuals do in fact behave and make choices. In reality, people influence each other in everyday life. The words and actions of an individual can affect the thoughts, intentions and actions of other individuals in his/her social network. Changes in choice behaviour due to social network effects, mostly 'unobserved' in traditional travel demand models, could have repercussions on the validity of current transport studies, which are used to develop new businesses and services or analyse the impacts of new policies. Thus, when looking for a better understanding of the dynamics behind the travel choices of individuals, especially choices that involve social aspects of transport (such as those in the context of new transport technology or shared service adoption), it is very important to investigate and quantify the effects of social influence. The explanation of the various aspects of social influence is very complex, however. Social influence is a process involving groups or entire societies through different forms of human interactions which are able to change the thoughts, and consequently behaviours, of a group of people that form the social network. It includes both direct influence, which involves interactions within the groups and is present in situations of persuasion, obedience or learning, and indirect influence, which is generated by unconscious mechanisms arising from external manipulations of norms, values and practices. This is linked to different psychological aspects and it is not clear how to collect precise information about it, especially when considering indirect influence, which is not generated from a face-to-face interaction but from the presence of other sources of information. This vague definition of influence may lead to a specification of transport models which does not guarantee reliable estimations and does not reproduce the phenomenon in an appropriate manner. Various processes of social influence on choice behaviour were explored in this PhD project in order to investigate whether the social network has a strong impact on the utility function and, therefore, the final choice behaviour. Different factors (e.g. homophily, norms, etc.) and different processes of social influence (e.g. conformity, real interaction, etc.) were identified through a set of quantitative case studies; these quantitative analyses were performed by defining appropriate mathematical approaches in statistical and econometric models for transportation, such as DCMs. The theoretical and conceptual frameworks of social influence processes proposed in the literature were adapted to investigate purchase preferences in respect to electric vehicles in a workplace and the adoption of bike sharing in a student cohort during a public transport strike. The first case study focused on the analysis of indirect social influence in the context of the electric vehicle (EV) purchase preferences in a workplace; the investigation of this specific context necessitated taking into account a range of information on a) choices regarding the possible purchase of such a vehicle, collected with a classic stated preference survey; b) several psychometric indicators on sociological constructs that can underpin individuals' latent attitudes; c) different descriptors of social influence such as the number of individuals and their relationships in the social network, thus, information on the tie strength. The indirect social influence variable is the combination of the clusters of attitudinal items and the interaction matrix, taking into account the social proximity in the individual's social network, the so-called `Individual's Peer Attitude' variable. This variable were included in the model to evaluate its effect on the vehicle purchase preferences of the individual. The second case study focused on the analysis of direct social influence and social interactions that can affect the adoption of a new technology/service (i.e. bike sharing systems). By administering a targeted survey, data on hypothetical social norms and real social interactions were collected. Measures of social norms were used to incorporate conformity processes. A `live' social interaction experiment was undertaken to capture functional information exchanges of the perception of benefits and drawbacks. Processes of information exchange and internalisation (diffusion, translation and reflexivity) were also investigated to evaluate awareness and assessment of the information and possible revision of the self-concept. All these variables were considered in the model estimation so as to explore their effect on the decision-making process for the adoption of a new technology/service. Finally, since these methodologies are generalisable for different contexts and other types of attitudes, an analytical framework which is capable of including direct and indirect social influence variables was developed and tested in the context of the adoption of bike sharing systems. In both empirical studies, the results show the importance of social influence in explaining individuals' preferences. The ability to include such effects in the utility function makes these models a powerful tool to provide more reliable and precise analyses that can support the decisions of stakeholders and policymakers investing in and campaigning for new transport technology and modes.