Last week, we welcomed our new Behavioural Scientist, Emily King to the team. No sooner had she said hello than she was off downriver to Woolwich to a workshop Ed Houghton was chairing at the Smart Mobility Living Lab. The subject was consumer barriers to the adoption of CAV (Connected and Autonomous Vehicles). We’ll be a hearing a little more about Emily’s background and experience so far in another piece soon, but first, she breaks down the different factors at play in the application of the COM-B behavioural model to a self-driving future…

In my first week as the new Behavioural Scientist at DG cities, I was fortunate to attend an event on the consumer barriers to commercialisation of connected autonomous vehicles (CAVs) at the Smart Mobility Living Lab (SMLL). The event was attended by a range of industry professionals, researchers, and policymakers and explored the user perspective of self-driving technologies.

The opening presentation for the event highlighted that public trust and acceptance of self-driving technologies need to be in place before CAVs can be commercialised. Public acceptance of CAVs is currently low, as evidenced by findings from project Endeavour that only a quarter of the UK public (27%) would be comfortable using autonomous vehicles tomorrow if it was possible to do so.

Commercialisation is a behavioural challenge

This indicates that commercialisation of CAVs is a primarily behavioural challenge: how can people be encouraged to accept and ultimately to use self-driving technologies? It is clear from the discussions at the event that behavioural science has a crucial role to play in shaping how we communicate with the public about CAVs, and how to design self-driving services in a way that will be accepted by the public.

A key stage in any behavioural science research project is to identify the specific barriers and drivers to the behaviour of interest. In this instance, identifying the barriers to using CAVs amongst different potential user groups is the first step in understanding why this hesitancy to use autonomous vehicles exists. This provides a useful starting point to exploring how policymakers and industry can encourage engagement with this emerging technology and ensure that it works for society.

Discussions at the SMLL event shed light on some potential barriers to consumer adoption of connected autonomous vehicles, which can be summarised through the lens of the COM-B model.

A recap of the COM-B model

The COM-B model is a well-established behaviour change framework which suggests that for an individual’s motivation to engage in a behaviour to translate into actual behaviour change, they need to have both the capability and the opportunity to engage in the behaviour. [1]

Examining the potential underlying capability, opportunity and motivational factors can help to highlight how best to build perceptions of safety and trust, to achieve public acceptance and the opportunity for commercialisation of CAVs.

Diagram showing COM-B model of Capability, Motivation, Opportunity linked to Behaviour

Capability factors

Capability means that an individual has the knowledge, skills, and abilities required to engage in a behaviour.

The SMLL event highlighted a need to continue educating the public about autonomous vehicles, including building knowledge of how the technology works and what the potential benefits of using self-driving vehicles might look like.

Educating about how the technology works and the specifics of existing safety measures is important to help to build perceptions of safety and trust in AVs, which in turn can increase acceptance. One successful method for educating people about AV technology is via conducting trials in person or via virtual reality, which allow individuals to experience riding in a self-driving vehicle first-hand. Discussions at the SMLL event highlighted positive examples of trial participants perceiving AV technology as much safer once they had the opportunity to experience it for themselves.

There are numerous potential benefits of AVs, from improving mobility options for disabled people through to decarbonising the transport system. For autonomous vehicles to be accepted, it is vital that the public are also clearly educated on these specific benefits and how using CAVs can help to achieve them. Individuals tend to (either consciously or unconsciously) weigh up the potential costs and benefits before deciding how to behave, meaning that for people to decide to use CAVs that any perceived costs such as reduced feelings of safety or anxiety about AI need to be outweighed by the perceived benefits.

“Individuals tend to (either consciously or unconsciously) weigh up the potential costs and benefits before deciding how to behave, meaning that for people to decide to use CAVs that any perceived costs, such as reduced feelings of safety or anxiety about AI need to be outweighed by the perceived benefits.”

Opportunity factors

Opportunity factors are the external factors which make a behaviour possible. Opportunity factors encompass all aspects of the CAVs technology and service offer which might influence whether people are willing or able to use them.

The workshop included discussions about the specific use cases and opportunities within the user journey where CAVs could play a useful role. For example, introducing self-driving services in rural areas where there are currently limited transport options could provide more benefit than in major city centres.

Self-driving services also need to be designed so that they are usable for the groups which need them most. As those with disabilities and reduced mobility are a key group expected to benefit from self-driving services, it is vital that they are included in conversations to ensure that CAV technologies are meeting their needs and ensuring they have sufficient opportunity to use CAV services. If these groups are unable to access CAV services in the first place, then this potential benefit of the technology cannot be realised.

Motivation factors

Even when capability and opportunity factors are in place, this does not guarantee that people will be motivated to engage with CAVs.

Motivation is also dependent on factors such as values and emotional states, which can differ vastly between individuals and even within the same individual depending on their current circumstances.

Understanding these more subjective, emotional aspects of CAV acceptance was mentioned at the SMLL event as a necessity going forward. This is an area where behavioural science research can play a useful role. Existing research in the field suggests that acceptance of autonomous vehicles is influenced by an individual’s levels of innovativeness (a general willingness to try new things) and general anxiety about technology, as well as levels of hedonic motivation (valuing enjoyment and sensation seeking) and utilitarian motivation (valuing rationality and effectiveness). These findings point to some potential options for increasing consumers’ motivation to engage with AV technologies, which link to discussions at the SMLL event. [2]

Hedonic motivation was the greatest predictor of intentions to use AVs overall, suggesting that making vehicles fun to use could be a route to increasing adoption of the technology. Workshop discussions at the event highlighted some ideas for increasing the ‘fun’ element of AVs such as giving vehicles faces to ‘anthropomorphise’ them, or including customisable elements so that they could be personalised.

“Hedonic motivation was the greatest predictor of intentions to use AVs overall, suggesting that making electric vehicles fun to use could be a route to increasing adoption of the technology.”

— Emily King, Behavioural Scientist

Meanwhile utilitarian motivation was found to be a predictor of intention to use AVs amongst innovative consumers only. This suggests it is important to educate more innovative consumers on the specific benefits of AV services. For those who are technologically anxious, there is a need to address broader concerns about AI before addressing specific concerns about CAVs.

It is important to note that these are hypotheses based on discussions from the event and existing research in this area. Much more extensive research is needed to identify the full range of behavioural barriers and drivers to build a full understanding of how to support acceptance and use of CAVs.

A behavioural science perspective on consumer barriers to self-driving tech