Abstract
This paper describes our initial work towards a segmentation-based approach to personalized digital behavior change interventions in the domain of sustainable, multi-modal urban transport. Segmentation is a key concept in market research, and within the transport domain, Anable has argued that there are segments of travelers that are relatively homogenous in terms of their mobility attitudes and behaviors. We describe an approach aimed at tailoring behavior change notifications by using segmentation-based techniques for user profiling. We report results from a Mechanical Turk study in which we obtained a crowd-sourced categorization of motivational messages. This is a first step towards understanding how to better deliver persuasive messages to relevant users profiles and situational contexts in the urban mobility domain. We conclude by discussing future steps of our work that should inform the deployment of persuasion profiling techniques to achieve sustainable mobility goals.
Highlights
We are investigating how to maximize the persuasion potential for nudging citizens towards more sustainable transport choices via an urban mobility platform based on mobile and web interfaces
To decide on which messages to put forward to the phase and potentially use as motivational messages during our project trial, we selected those that had the highest kappa values and that had at least a kappa ≥ 0.4
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Summary
We are investigating how to maximize the persuasion potential for nudging citizens towards more sustainable transport choices via an urban mobility platform based on mobile and web interfaces. We discussed how behavior change theories can be integrated into a sustainable urban mobility platform [1,2]. Building on the theories and techniques of Michie et al [3,4,5] we were inspired by the segmentation work of Anable [6,7]. We aim to combine the profiling tools of Anable’s segmentation analysis with digital intervention techniques to deploy targeted digital interventions that prompt people to use more sustainable transport modes. Participants are prompted at appropriate times to change their behavior, for example to provide a lift or use public transport
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