The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population

Abstract

The present study investigates how social influence and social interactions can affect the adoption of new technologies, using stated preference (SP) survey data combined with an "accelerated reality" experience of social interaction among the respondents. Specifically, the intention to use a pro-environmental transport mode (the bike sharing) during a public transport strike within a cohort of students has been analysed. Previous studies have modelled social influence effects using SP data by providing a hypothetical scenario with simulated interactions or information about social conformity processes (i.e. social adoption) during the survey. In our paper, in addition to the impact of assumed social norms, the effect of live/real social interactions is included in the survey. SP survey is developed to investigate the effect of Level-of-Service attributes on the hypothetical choices in the scenario of a public transport strike. Besides the pre-defined attributes characterising the alternatives in the SP design, the survey includes techniques to acquire information on conformity and social interactions. Specifically, the interviewees undertake a before and after stated preference experiment (SP1 and SP2), with a period of group discussion in between the two parts. This SP experiment involves different cognitive and interpersonal mechanisms, such as the functional information exchange on benefits and drawbacks of cycling and bike sharing. The aim is to establish whether hypothetical scenarios of social conformity are different from real/live social interactions and whether these social influence processes actually affect the individuals' mode choice. A joint SP1/SP2 mixed logit (ML) model has been estimated to explore the choice behaviour of individuals and allows us to incorporate the inertia/propensity to change behaviour between SP1 and SP2. Moreover, considering the "Reflexive Layers of Influence" (RLI) framework, the processes generated by social interactions (diffusion, translation and reflexivity) are measured and incorporated in the model. We finally show the effect of these social influence variables on the goodness-of-fit of the models and choice simulation for prediction. We also draw conclusions about the value of such enhanced choice models in understanding and predicting the impacts of social interactions on choice behaviour in the context of new transport technologies.

Moderating effect of gender on adoption of digital health consultation: a patient perspective study

“…In developing nations like India, people generally live in joint families and depend on each other in numerous socio-economic contexts (Owens, 1971;Ray et al, 2019). Thus, the adoption of digital health consultation services in this kind of situation will not only become noticeable to their close friends and family members but also their view can inspire them to use the same (Ray et al, 2019;Manca et al, 2019). Hence, it is imperative to examine the relationship between SI and the BI of users towards the adoption of digital health consultation.…”

Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era

“…Yao et al further demonstrated that the credit supervision mechanism was more appealing to attract cyclists to ride shared bikes when a negative credit is introduced [30]. The social interaction influences cyclists' bikesharing choice by inducing family members or close friends to make the same choices [31]. The service quality is a relatively comprehensive factor and is proved to have a positively significant effect on the intention to ride shared bikes [32].…”

Does IoT service orchestration in public services enrich the citizens’ perceived value of digital society?

“…We have associated three constructs as social influence, citizens' empowerment, and facilitating conditions with digital society affinity. The social influence is related to the citizens that get influenced by other citizens in society and develop a negative or positive impact to use IoT services, it generally focuses on the citizens in a society affected by their peers' behaviour (Manca et al, 2019). It is considered to be a powerful construct in many types of research regarding smart government services (Alshehri et al, 2012), which highlights the positive social interaction influence on citizens' intentions to use smart government services.…”

“…In quest of the research objectives, we have conducted a study on the conceptual phenomena of Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Acceptance Model (TAM), and Theory of Planned Behaviour (TPB). The success of IoT service orchestration in the public sector requires the integration of citizens' trust (Bahutair et al, 2019;Nelson & Gorichanaz, 2019) and digital social affinity (Bigné et al, 2007;Manca et al, 2019). We have developed a conceptual model IoT-PVM after formulating the research hypotheses and it was subsequently tested through PLS-SEM.…”