A Behavioral Design Approach for Improving Safety on E-Scooters

Our behavioral practice at the BVA Nudge Consulting consists of using insights from behavioral science to obtain an in-depth understanding of the forces at play in a given problem, diagnosing the root causes of those problems and designing and experimenting with solutions that directly address the barriers and levers identified through the behavioral diagnosis. This approach allows us to produce a fresh set of design solutions, or nudges, specifically customized to address the behavioral problem at hand. This ensures that the solutions we design are much better suited for resolving those specific behavioral issues than solutions derived from “best practices” borrowed from other contexts.

By way of example, consider how we can improve e-scooter safety by addressing the helmet problem. Most of us agree that wearing a helmet while riding an e-scooter, e-bike or electric skateboard can reduce the severity of accidents and, in the most extreme cases, even save our life. And yet, in spite of laws, rules, incentives and widely-available information oriented to promoting the use of helmets, most e-riders do not wear one. This fact attests to the strength of the behavioral barriers at play as we make the decision to wear a helmet or not:

• Cognitive biases and emotions: riders often overestimate their riding skills (overconfidence effect); they can be excessively confident in their ability to control or avoid external circumstances (illusion of control); and they might believe they are less likely than others to have negative experiences (optimism bias)
• Social cues and influences: riders may buy into narratives that challenge the desirability or utility of using helmets (social narratives); riders might perceive that not using a helmet is socially acceptable (social norms); riders might see few people wearing helmets (lack of social proof); and riders who feel strongly against using helmets can feel socially validated (confirmation bias/false consensus effect)
• Contextual features: the need for using a shared e-scooter might arise at a moment when access to a helmet is difficult (time inconsistencies); people about to ride an e-scooter might forget to take a helmet with them (cognitive bandwidth constraints/poor prospective memory); riders might find it unflattering, uncomfortable or inconvenient to wear or carry around a helmet to ride their e-scooter (inconvenience/aesthetic concerns)

This is not a comprehensive list of barriers to wearing a helmet, of course, and there can never be an exhaustive single list of obstacles, as these will vary from one context to another (most of us in the US have access to decent helmets, for instance, but in some developing countries, even obtaining a helmet could be a key barrier). Systematically uncovering these behavioral barriers in a given context, however, affords us with a good basis from which we can begin to experiment with behavioral solutions for nudging people to wear a helmet.

Currently, companies such as Bird, Lime and now even Lyft that offer shared electronic scooters through mobile apps are employing traditional levers to encourage helmet use: sending riders free helmets (incentives) and occasionally exhorting them to use them through the app (information). But, even with these strategies, riders are on the whole not using helmets. This is ultimately because these “solutions” are incomplete and are not fully addressing the broad array of underlying barriers. A better investment of time and effort would be to take advantage of the available touchpoints (namely the apps and the e-scooters themselves) to create solutions that directly address the behavioral barriers to wearing a helmet identified above.

Consider the following sample nudges: to override cognitive biases and emotions, we can create an in-app game that rewards riders (“helmet points”) for uploading a picture of them wearing a helmet right before riding; to reorient social cues and influences, create super-short videos that play automatically as the app starts with a compelling messenger (e.g. celebrity) reminding us that “not wearing a helmet” on e-scooters is an uncool, socially frowned-upon behavior; to address contextual features, in addition to giving users helmets, or in its stead, give them stylish, multi-purpose “helmet bags” or helmet attachment clips for conventional bags and backpacks.

Ultimately, these sample nudges are only a starting point. To fully solve the helmet problem and to promote e-scooter safety over all, companies like Lime, Skip, Bird, Lyft, Spin and others that are promoting the use of shared e-scooters need to engage with behavioral designers to perform in-depth diagnoses that allow them to fully understand the forces at play influencing decisions that affect safety. Moreover, this needs to be done in a variety of contexts, taking into account differences in rider demographics and preferences, urban design, infrastructure, traffic laws and transit patterns, among other contextual considerations. Carrying out this process will allow these companies to determine what are the most relevant and prevalent drivers of behavior and, subsequently, to create and experiment with behavioral solution bundles that target all of the resulting key barriers and levers.