Experimental Design
We conduct a randomized field experiment with a targeted sample of 3000 Swiss citizens, combined with survey experiments. The Field Experiment is conducted over a period of 6 months, the main treatment of the experiment is the use of the Swiss Climate Challenge App through which participants receive information about their personal mobility-related CO2 emissions and incentives to reduce these emissions. The experiments, seek to answer the fol-lowing questions:
How does a gamified mobility tracker application combined with integrated reduction incen-tives affect individuals…
a. ... mobility-related emission attitudes?
b. … perceived behavioural control to change their mobility behaviour?
c. … perceived social norms to change their mobility behaviour?
d. ... behavioural intentions to change their mobility behaviour?
e. … actual mobility behaviour?
f. … support for different mobility policies?
g. … support for different emission reduction policies?
What impact has belief updating on available subsidies and the availability of charging Stations for Electric Vehicles (EV) on individuals…
h. … perception of EV charging infrastructure?
i. … perception of tax incentives for EV’s?
j. … support for different mobility climate-mitigation policies?
k. … perception of EV?
In the four Survey waves over the course of the experiment, we collect self-stated mobility attitudes, perceived norms, perceived control, and behavioural intentions, also, policy support for different mobility policies is elicited as well as socio-demographic control variables. In the first survey a belief-updating vignette experiment is integrated. Further, in a conjoint experiment, in the third survey, the participants are presented with randomly varied climate mitigation policy packages and choose between these differently designed packages.
Hypotheses
The setup of the overall experiment allows to test different hypotheses and expectations, mainly through the different dependent variables introduced above and through the use of two additional survey experiments. For each section a brief overview of the theoretical foundation is given. Generally, the hypotheses can be found on two levels, firstly on the individual level in the form of attitudes, perceptions, and intentions, and secondly on a policy level in the form of policy support. As all hypotheses are formulated in the context of experiments, they are mainly concerned with the effect of the respective treatment on the dependent variable.
We use the Theory of Planned Behaviour (TPB) (Ajzen, 1991) as our main theoretical foundation for the experiment. The theory of planned behaviour is a widely used theory in the research of mobility behaviour (Anable, 2005; Bamberg et al., 2003; Chen & Chao, 2011; Daniel et al., 2022; Daramy-Williams et al., 2019; Dütschke et al., 2022). The Theory of Planned Behaviour states that attitudes, subjective norms, and perceived behavioural control all play a role in determining human behaviour.
We formulate the following hypotheses:
H1: The provision of Gamified Information and Incentives leads to a relatively higher importance of climate impacts of mobility choices compared to other mobility-related attributes such as speed, reliability, price, comfort, personal health, safety, and air pollution.
H2: The provision of Gamified Information and Incentives leads to more positive attitudes towards a reduction of mobility-related emissions.
H3: The provision of Gamified Information and Incentives leads to a higher level of perceived behavioural control to reduce mobility-related emissions.
H4: The provision of Gamified Information and Incentives leads to a perception of stronger subjective norms towards the reduction of mobility-related emissions.
H5: The provision of Gamified Information and Incentives leads to a stronger intention to reduce emissions from mobility.
H6: The provision of Gamified Information and Incentives leads to a reduction of mobility-related CO2 emissions.
However, evidence form earlier studies (Bamberg & Schmidt, 2001; Chen & Chao, 2011; Daniel et al., 2022) points to several factors limiting such voluntary treatment effects. From the results of these studies, it can be deduced that the strength on an intervention plays an important role in individual changes in attitudes, intentions, and behaviours. Not surprisingly the strongest effects are typically found, if substantial monetary incentives for transport mode-switching existed (Bamberg & Schmidt, 2001). Moreover, Daramy-Williams et al. (2019) argue that habits are changed if the treatment leads to a revaluation of the behaviour. Also nudging interventions that change mobility choice architecture and defaults can be very effective (Berger et al., 2022), even without strong incentives (Liebe et al., 2021). However, in this experiment it is questionable if personalised gamified information and green coin incentives lead to a substantial re-evaluation of the behaviour. The voluntary treatment in this ex-periment also does not involve default switching. Moreover, in contrast to structural factors (e.g., available mobility infrastructure), individual factors (e.g., attitudes) have a limited influence on transportation mode choices (Javaid et al., 2020). This is in line with the findings of an earlier experiment using the same mobile application as we employ and the statistical power to detect effect sizes exceeding Cohen’s d 0.14, where no significant effect of emission reduction was found (Goetz et al., 2022). As a result of these limitations, we assume only small standardized effect sizes of the provision of gamified information and incentives, especially for Hypotheses 5 and 6.
Research in the field of Behavioural spillover gives insight into how personal attitudes and behaviours relate to policy support. The literature in this field, emphasizes that individuals are driven in their behaviour by their perceived identity and the aim for consistency (Cornelissen et al., 2013; Freedman & Fraser, 1966; Steg & de Groot, 2018), thus exhibiting one pro-environmental behaviour will impact likelihood of other pro-environmental behaviours. Based on this literature we formulate the following hypotheses:
H7: The provision of Gamified Information and Incentives leads to higher support of policy instruments aiming to reduce mobility emissions.
H8: The provision of Gamified Information and Incentives leads to generally higher support of pro-environmental policies.
Regarding the belief-updating vignette experiment in the first survey wave, we base our first set of hypotheses on a combination of the TBP with the Technology Acceptance model (TAM) (Davis, 1989) to explain changes in attitudes and behavioural intentions. The technol-ogy acceptance model states that the acceptance of a new technology is influenced by its perceived usefulness and its ease of use. Perceived usefulness relates to an individual’s beliefs about how a technology will increase his or her performance (Davis, 1989). In the context of mobility mode choices this relates to aspects of costs, time, and convenience (Chen & Chao, 2011). Perceived ease of use refers to the perception of how much effort would be needed to use a technology (Davis, 1989) , in this context a specific mode of transportation (Chen & Chao, 2011).
It is the assumption that the perceptions derived from the TAM influence the behavioural intentions formulated in the TPB through a change in attitudes (Chen & Chao, 2011; Daniel et al., 2022). Through our survey experiment we provide respondents with information about the availability of EV charging stations close to their place of residence and available cantonal subsidies in the form of motor vehicle tax reductions, assuming a change in the perceived usefulness and perceived ease of use of EV’s. For participants where the update provides a more positive assessment (i.e., people are informed that the real availability of EV charging station is higher as initially believed and that cantonal subsides are higher as believed), this update will lead to a more positive assessment of EV’s:
H9: A positive belief update leads to a higher EV use intention.
H10: A positive belief update leads to a lower use intention for cars with combustion motor.
H11: A positive belief update leads to a higher intention to buy an EV.
Moreover, with respect to potential belief updating effects on policy support, we develop an argument based on the policy sequencing and feedback literature (Béland et al., 2022; Béland & Schlager, 2019; Fesenfeld et al., 2022; Meckling et al., 2017; Pahle et al., 2018). First, we argue that citizens’ perceptions of previous policy-induced benefits are an important but insufficiently studied factor that shapes public support for the subsequent adoption of higher carbon prices. Thus, the nature of feedback from previous policies on support for later policies critically hinges on how citizens subjectively notice and interpret material and immaterial utility gains from pre-existing policies. Second, we propose that previous policies that create novel opportunity structures for citizens to shift to low-emission alternatives, such as a higher density of EV charging stations, can positively affect public support for the later adoption of more stringent measures like higher carbon prices on fossil fuels. Through the perception of EV’s as a viable alternative to conventional cars people might hold less objections towards policies reducing conventional car usage as they see less threat to their self-interest, a factor proven to play an important role in the field of transportation policies (Börjesson et al., 2015; Tan et al., 2022). On the level of policy support, we thus formulate the following hypotheses.
H12: A positive belief update leads to a higher support of higher CO2 levies on fossil fuel.
H13: A positive belief update leads to a higher support of policy instruments aiming to reduce mobility emissions.