The effect of social influence and social interactions on the adoption of a new technology: The use of bike sharing in a student population
File(s)
Accepted version
Author(s)
Manca, Francesco
Sivakumar, Aruna
Polak, John
Type
Journal Article
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.
Date Issued
2019-08-01
Date Acceptance
2019-02-14
Citation
Transportation Research Part C: Emerging Technologies, 2019, 105, pp.611-625
ISSN
0968-090X
Publisher
Elsevier
Start Page
611
End Page
625
Journal / Book Title
Transportation Research Part C: Emerging Technologies
Volume
105
Copyright Statement
© 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.
Subjects
Science & Technology
Technology
Transportation Science & Technology
Transportation
Social influence
Live social interactions
Bike sharing adoption
Inertia effect
Joint SP1/SP2 mixed logit
DISCRETE-CHOICE
MODELS
PREFERENCE
BEHAVIOR
IDENTIFICATION
INERTIA
MIXTURE
LOGIT
Logistics & Transportation
09 Engineering
08 Information and Computing Sciences
15 Commerce, Management, Tourism and Services
Publication Status
Published
Date Publish Online
2019-02-22