AUTOMATED DRIVING – CREATING GAIN OR REDUCING PAIN? AN EMPIRICAL STUDY OF DIRECT AND MEDIATED EFFECTS ON WELLBEING

다국어 초록 (Multilingual Abstract)

Background
Our usage of technology and the continuous adoption of technological innovations has implications for our lives that go beyond traditional marketing questions such as product quality, customer satisfaction, and customer loyalty. The psychological consequences of new technology affect our lives and our well-being as individuals. A current technological advancement is the ongoing development of automated driving capabilities. The recent advances have led to the diffusion of semi-autonomous driving systems, such as Teslas autopilot or Daimlers DISTRONIC PLUS. These commercially available technologies correspond to the second level of SAE international’s J3016 standard for automated driving, which ranges from 0 (“No Automation”) to 5 (“Full Automation”, no human needed). SAE level 2 (“Partial Automation”) is defined as “the driving mode-specific execution by one or more driver assistance systems of both steering and acceleration/deceleration using information about the driving environment and with the expectation that the human driver performs all remaining aspects of the dynamic driving.” (SEA On-Road Automated Driving (ORAD) committee, 2014). First of all, qualities of mobility such as safety and comfort influence well-being directly and autonomous driving has a potential impact on these qualities. Furthermore, advances in autonomous driving have particularly social but also ecological and economic benefits. On the one hand, social benefits result from increased social participation by improving mobility of the non-driving, elderly or people with travel-restrictive medical conditions (Harper, Hendrickson, Mangones, & Samaras, 2016; Wadud, MacKenzie, & Leiby, 2016). On the other hand, social benefits result from increasing road safety, less congestions as well as from a reduced number of accidents (Fagnant & Kockelman, 2015). Individual social or personal benefits such as improved safety and comfort or reduced stress levels are also widely perceived by potential users (Bansal, Kockelman, & Singh, 2016; Karlsson & Pettersson, 2015). Additionally, ecological benefits can be realized due to more efficient driving of vehicles and a smoother traffic flow (Wadud et al., 2016). Economic benefits are mainly a result of decreased travel times and of the fact that at high automation levels driving time could be used more productively. Accordingly, this research tends to answer the following research questions:
• How does automated driving impact consumers’ well-being?
• What qualities of mobility (e.g. safety) mediate the impact of automated driving on consumers’ well-being?
Conceptual Model
As argued above, automated driving potentially has both a direct and an indirect (mediated) impact on well-being. Subjective well-being (SWB) describes one’s well-being through the global evaluation of life satisfaction (LS), positive affect (PA), and negative affect (NA) (Diener, 1984). This tripartite concept of well-being has been widely adopted by researchers, even though the relationship of the three aspects remains in question (Busseri & Sadava, 2011). Busseri and Sadava (2011) provide an overview of five prominent conceptualizations: As three separate components, as a hierarchical construct, as a causal system, as a composite, and as configurations of components. We adhere to Diener’s original model that describes LS, PA, and NA as three separate components. Accordingly, the correlations among the three components are not of primary interest in this model. Therefore, the impact of automated driving on LS, PA, and NA is assessed separately in order to provide a full image of SWB. This leads to the following hypotheses:
H1a. Semi-autonomous driving has a positive impact on the level of SWB by increasing positive affect.
H1a. Semi-autonomous driving has a positive impact on the level of SWB by decreasing negative affect.
H1c. Semi-autonomous driving has a positive impact on the level of SWB by increasing life satisfaction.
Driving influences subjective well-being through different mediators. One potential mediating factor, discussed quite controversially in literature, is fun. While non-automated driving shows a higher level of fun than automated driving, still a large majority is fascinated by automated driving (Kyriakidis, Happee, & Winter, 2015) and also a discharge from the actual driving could be perceived as fun. The most relevant perceived benefit of automated driving studied in literature is safety, followed by stress (e.g. Bansal et al., 2016; Karlsson & Pettersson, 2015; Kyriakidis et al., 2015). Therefore, we formulate the following hypotheses:
H2a. The impact of semi-autonomous driving well-being is mediated by fun.
H2b. The impact of semi-autonomous driving well-being is mediated by stress.
H2c. The impact of semi-autonomous driving well-being is mediated by security.
Design/methodology/approach
Our sample comprises of two groups with a total of 259 respondents. Group 1 contains 111 respondents using automated driving while group 2 contains 148 respondents not using automated driving. In a first step, to test hypotheses H1a to H1c, we use ANOVA to analyse the group’s differences regarding positive affect, negative effect, and life satisfaction. We analyse both the aggregated values and the single items for all three aspects of subjective well-being. ANOVA was conducted using SPSS version 25. In a second step, to test hypotheses H2a to H2c, three multiple parallel mediation models were estimated with ordinary least squares path analysis. Each model consisted of one of the aspects of SWB as the dependent variable as well as the three mediators. The models were estimated using PROCESS version 3.0 (Hayes, 2013) and SPSS version 25.
Findings
ANOVA shows a highly significant interaction between semi-automated driving and well-being for negative affect, but not for positive affect and life satisfaction. Looking at the items that amount to positive affect, we see that drivers with the new technology have more excitement, pride and interest. But these effects are counterweighted by significantly less activity, determination and attention. In the context of semi-autonomous driving, being less active, attentive and determined can be interpreted as a positive thing (driving more relaxed). Therefore, we adjust the positive affect scale and remove these three items to from the scale for further analysis. Cronbach’s alpha for all three aspects of subjective well-being is 0.73 or above, thus indicating adequate convergence. Mediation analysis (process model 4) on positive affect reveals a significant indirect effect of semi-automated driving moderated by fun. There is no significant mediation effect for safety and stress as well as no significant direct effect of semi-automated driving on positive affect. The parallel mediation model using negative affect as the dependent variable, shows a significant direct effect of semi-automated driving as well as significant indirect effects mediated by safety and stress. Semi-automated driving reduces negative affect directly as well as by increasing safety, which reduces negative affect. It also reduces negative affect by decreasing stress, which is positively linked to negative affect. Fun, however, has no significant effect on negative affect. Last, life satisfaction shows a combination of the effects mentioned above: Automated-driving has a significant positive direct effect on life satisfaction. Furthermore, life satisfaction is influenced positively through increased fun, safety, and decreased stress.
Implications
ANOVA and mediation analysis show a positive impact of semi-automated driving on subjective well-being, thus contributing to the investigation of new technologies on consumers’ well-being. Using Diener’s tripartite model of subjective well-being, analyses revealed that the positive effect is especially driven by reducing negative affect. Further research is needed to investigate the transformative impact of (semi-) autonomous driving more deeply and broadly. Especially investigations differentiating between target groups of the new technology might be an interesting path to follow, since the impact on the different aspects of SWB might differ (e.g. increase of positive affect for early adopters vs. decrease of negative affect for people with reduced mobility). Furthermore, our investigation contributes to the conceptual discussion about the structure of the tripartite model of subjective well-being. The fact that life satisfaction as a dependent variable, to some extent, combines the effects observed for positive and negative affect indicates that the three aspects are causally linked instead of being separate. Researchers have promoted a system where positive affect and negative affect are conceptualized as inputs to life satisfaction before (Busseri & Sadava, 2011). Last, our findings give directions to marketing executives for marketing new technologies in general and (semi-)automated driving specifically. First, practitioners need to think about the well-being impact of their technology, i.e. evaluate if it increases well-being by increasing positive affect or by decreasing negative affect. The two paths lead to different marketing measures and ways to promote the technology. More specifically, for semi-autonomous driving a mixed strategy commends itself. It is essential to demonstrate that the new technology reduces the pains of driving while being fun at the same time.