An Empirical Analysis of the Intention to Use Level-5 Driverless Cars and their Possible Evolution in the Market

The evaluation of the variable Attitude towards Using Driverless Cars has been performed by asking participants three different questions (Table 22). The first one assessed whether people believe that driverless cars could be a good idea; The second one asked participants whether they think that driverless cars could make the act of driving more interesting; The last question asked to respondents whether using driverless cars would be fun for them. For the first question, participants were provided with a 5 Point Likert Scale as answer options, ranging from 1 (Definitely not) to 5 (Definitely yes). While for the other two questions, participants were provided with a 5 Point Likert Scale ranging from 1 (Extremely unlikely) to 5 (Extremely likely). A preliminary analysis has shown that, on one hand, respondents believe that driverless cars would be a good idea and that would make driving more interesting (means of, respectively, 3.62 and 3.73, and standard deviations of 1.00 and 1.31). On the other hand, they also believe that it is quite unlikely that autonomous cars would be fun for them (mean of 2.45 and standard deviation of 1.19).

Considering the first question, a distinction of the responses based on gender has revealed that both men and women are prone to believe that driverless cars would be a good idea. However, women have shown a slightly lower agreement (56 women versus 34 men answered "Definitely not" and "Probably not", with a mean of 3.52 for female responses and 3.74 for male ones). Regarding age, all the groups mostly believe that this technology would be a good idea. In particular, those who believe this the most are people aged 45+, with a mean of about 3.92. Considering continents, participants from Oceania are the most negative about the idea of self-driving cars (mean of 3.33), followed by America (3.45), Europe (3.70), and Asia that, instead, appears to be the most positive continent (mean of 3.84). To the second question, a reversed situation occurs compared to the first one. Indeed, more women than men believe that driverless cars would make driving more interesting (201 women versus 144 men answered "Extremely likely" and "Somewhat likely", with a mean of, respectively, 3.86 and 3.62). With a mean of 2.84, people aged under 18 years are those who least believe that the act of driving would be more interesting. While participants belonging to the remaining age groups are unanimous about this question with a mean of about 3.60. The analysis of responses per continent revealed that responses are quite homogeneous indifferently from the continent of origin (mean of about 3.70). The same considerations could be done for the last question that, as said before, is the only one that among all demonstrates a negative attitude toward driverless cars. Responses to this question are homogeneous considering distinctions for gender and age groups. Indeed, both male and female responses have a mean of about 2.46, while age groups all have a mean of about 2.40. One exception is represented by the distinction for continents, which have revealed that, among all, participants from Oceania are those who mostly believe that autonomous cars would be fun to use (mean of 3.11). Respondents from the other continents are, instead, less convinced about this (Asia mean of 2.49, Europe 2.39, America 2.26).
As reported by the correlation matrix (Table 23) correlations among all the variables are positive and significant. As expected, the highest correlation is between ATTITUDE2 and ATTITUDE3, as both variables explain more or less the same thing.

Again, an additional variable has been included in the model (ATTITUDE), which has been derived from the average of the responses to the three questions. ATTITUDE has a mean of 3.25 and a standard deviation of 1.26. Moreover, the included variable is strongly correlated with the resulted variables ATTITUDE1, ATTITUDE2, and ATTITUDE3. Therefore, it can be used as a valid proxy for further computations.
In the next sections, the variables explained will be used to perform a multiple regression model and a cluster analysis to verify the hypotheses and derive insights.