As research into automation gains momentum, and increasing amounts of money are invested into adopting
innovative automated transportation solutions, it is very important that we gain an understanding of the factors that
will impact their adoption. This is something which has rarely been explored in the literature to date. The purpose of
this study was to use UTAUT to learn more about the levels of importance placed by potential ARTS customers on
performance expectancy, effort expectancy, and social influence in two locations (La Rochelle in France, and
Lausanne in Switzerland). This was the first study to explore how user acceptance variables might influence the use
of a public automated transport system, and a particular strength of the study was that data was collected on-site
during the demonstrations, thus ensuring that first-hand experience was measured.
The results indicate that all three UTAUT constructs impact on intention to use ARTS. Performance expectancy
is the strongest predictor, suggesting that the most important factor that people will consider in deciding whether or
not to use an ARTS is how well they believe it will perform in comparison to other public transport systems. Social
Influence and Effort Expectancy also had an impact on behavioural intentions, indicating that the influence of other
people, and perceptions of how difficult the system is to use will also both influence the decision to use an ARTS.
These results show that the UTAUT framework can be applied to increase understanding of user’s behavioural
intentions around automated vehicles. However, similar to Adell’s (2010) investigation of a driver support system,
the explanatory power of the research model was only 22% percent. This suggests that the current manifestation of
UTAUT is not capturing all of the factors which influence individual’s behavioural intentions to use automated
transport systems. It is also possible that behavioural intentions to use an ARTS are strongly influenced by variables
such as on-board comfort, and distance travelled (see Delle Site et al., 2011), and that the inclusion of such vehicle
characteristic variables in future research models may increase the power of the model. Indeed, Venkatesh et al.
(2012) suggest that hedonic motivation is a critical determinant of behavioural intention in consumer-based
contexts. Another issue which is likely to be of particular relevance in the transport context is how safe consumers
feel while using the ARTS, and this is something which could be considered in future research with automated
vehicles.
While there was a difference between the two demonstration sites in terms of age distribution and car and public
transport usage, these factors did not have any impact on the UTAUT variables. Previous research using the
UTAUT model had found that gender, age, and experience all moderated the relationships between the predictor
variables and behavioural intentions. However, this relationship did not emerge in the present study. Given all of the
participants would have had limited experience with the ARTS vehicle, and there were no differences between the