Reduction in car use is one of the most effective ways to tackle congestion-related problems. One possibility to do this is by using positive incentives to stimulate the use of other transport modes. There is evidence that this may be more effective than punishing travelers for undesirable behavior.
Many apps that promote cycling already exist, such as Strava, CycleMaps, BetterPoints, and SMART. In urban areas, cycling is a promising alternative to automobiles as its infrastructure uses little space and is cheap. After walking, cycling is the most sustainable mode of transport, and it is healthy. The question thus arises: which positive incentives are most effective in stimulating cycling? Should travelers, for example, be rewarded, and if so, should the reward be money, in-kind gifts or another type of reward? In other words, it is not easy to select the best incentives.
Part of the problem is that apps or other types of incentive schemes have been tested in real-world situations. As a result, it is difficult to measure to what extent behavioral change can be attributed to these incentives or external factors. The effectiveness of positive incentives and how they should be provided has been researched within the European Empower project. One of those studies, conducted at the University of Twente in the Netherlands, focused on the effectiveness of various positive incentives on cycling. The results are summarized in this article.
We used an on-line survey and mock-up apps to test the effectiveness of incentives. Although mock-ups can only be used to measure intentional rather than real behavioral change, it is a cheap way to compare the potential success of smartphone apps in a controlled environment. Big employers in the region of Twente were asked to participate. Six of them have actively participated, resulting in a substantial response rate of 1800 employees in total. In addition to questions about actual commute trips to work, personal characteristics, and reasons for (not) cycling to work, participants also got the opportunity to play with mock-up apps. An illustration is shown in Figure 1.
There were five apps with different incentives to cycle more to work. Those incentives were 1) a monetary reward, 2) points that can be redeemed for products in a webshop 3) encouragement by badges, 4) a ranking in which the cyclist who cycles most can win a prize, and 5) a cooperative group app in which participants can choose challenges with the objective to cycle as much as possible as a group.
Participants were randomly assigned to one of the five mock-ups. Once assigned, the participants could interact by clicking on the smartphone screen as if it was a real smartphone app. Moreover, this part was designed to be personalized. When participants clicked to join a challenge, the challenge was related to their commuting distance and frequency of cycling to work.
Participants could choose a challenge they thought was suitable for them and receive a corresponding reward based on the level of the challenge and the commuting distance. The amount of the rewards was based on a real budget for cycling promotion derived from an analysis of an existing local scheme.
After interacting with the mock-ups, the respondents were asked whether they would feel motivated to cycle more to work based on the personalized schemes offered to them. They were also asked whether they would consider using the app in real life. A multinomial logit model was used to estimate how potential cycling frequency and app use depends on personal characteristics and the type of incentive.
Figure 1: Illustration of screens in a mock-up app. From left to right: cycling stats (twice per week commute per bike); challenges (commute 3, 4 or 5 times per week by bike) with corresponding rewards; the total amount of rewards cumulated over time; and webshop with in-kind gifts.
Cycling frequency and rewards
Several important results were found. Not surprisingly, cycling is popular among Dutch commuters. However, more surprising is that many employees cycle occasionally when commute distances are more than 10km. Thus, although the share of cycling rapidly declines with commute distance, the decline of the share of so-called occasional cyclists is much less steep. Bad weather is an important reason for them not to cycle on a particular day. The fact that many commuters are occasional cyclists is encouraging because they are much more positive about being rewarded than respondents that never cycle.
Figure 2 shows which rewards respondents prefer. According to the figure, 38% of employees that never cycle do not want to be rewarded at all. This percentage is only 16% among the occasional cyclists. Figure 2 also shows that most respondents prefer money as a reward. The results from figure 2 confirm the idea that people that never cycle cannot be easily stimulated to cycle.
Although it is important to stress that respondents could only choose challenges to cycle more than they currently do, the results may not be that surprising. Indeed, it is more difficult to increase the cycling frequency from zero to once a week than for example from twice to three times a week.
Interestingly, the logit model showed there are some differences between demographic groups. The respondents who choose money or in-kind gift instead of no rewards tend to be female, young, care about travel time and travel costs, and tend to have a low income. Interestingly, females are more inclined to choose in-kind gifts, whereas males opt for money or no reward at all.
Figure 2: which rewards would commuters prefer when they cycle more?
Use of apps
So far, we only looked at the type of reward. However, it does not really make a difference if commuters would hypothetically accept rewards to cycle more, but are not prepared to use apps by which this can be done. In other words, are commuters prepared to use cycling apps, and if so, which type of app would they prefer? At first sight, results are not very encouraging.
Although almost every respondent owns a smartphone, only 27% of respondents use apps that stimulate the use of active modes. And in almost all cases health is the mean reason to use such an app. This result is not unexpected given that 82% of cyclists (occasional or always cycling) indicated that health is an important reason for taking the bike.
This poses the question whether health can be used as a theme to encourage commuters that never cycle to start using the bike. Although this question is hard to answer, it is becoming clearer that health factors could play a crucial role in getting people to use active modes or smartphone apps that encourage the use of active modes.
The mock-up effect
Does the limited use of cycling apps mean commuters’ attitude to a cycling app is negative? The mock-up analyses were used to answer this question and to identify the most promising cycling app. Figure 3 shows the results.
Surprisingly, when respondents could play with the app, the app with in-kind gifts performed better than the app with money. Although this result appears to contradict the results from the question about the rewards (Figure 2), it is important to stress that a mock-up app is not the same as the reward itself. The in-kind mockup also provides a web-based shop, which contributes to the fun factor. This can be seen as a double reward, as after gaining points, you will also be able to shop with your points.
However, when offered as an alternative to money, the in-kind gifts suddenly is much less attractive. It is therefore not useful to use both of them at the same time. The attractiveness of the in-kind mockup app is encouraging. More than 70% of the occasional cyclists that played with the in-kind gifts mock-up were not negative about the app, and more than 40% were actually very positive and said they would definitely consider using the app.
Commuters that never cycle are less positive. This is consistent with results shown in Figure 2. Only 20% were very positive and said they would definitely consider using the app, however more than 60% of the respondents were not negative (they were either positive or neutral in their response). It seems that this group has not rejected the app straight away, but is more skeptical.
We also looked at the effectiveness. If respondents are positive, would they actually also be prepared to cycle more? As mentioned before, respondents were challenged to cycle more compared to their current situation. For example, a commuter that cycles twice a week could choose to cycle three, four or five times a week to work. Respondents always choose a challenge, but tend to be conservative. Most of them are only prepared to cycle one or two extra times per week.
Figure 3: Attitude toward the five mock-up apps for commuters that occasionally (left) and never cycle (right)
If we look back what we have learned, one of the most important things is that segmentation is crucial. Occasional cyclists are more open toward apps that encourage them to cycle more and health is the most important factor for them to cycle to work. In contrast, commuters that never cycle are much less positive toward using such apps. One important future research question is whether it is possible to convince car users to use the bike because of health reasons.
However, at the moment, these cycling apps should mainly target occasional cyclists. Fortunately, this group is large in the Netherlands. As a result, there is much room for car use reduction, especially by stimulating bike use during bad weather. The analysis of the mock-ups shows that the app with in-kind gifts is preferred. Respondents are also prepared to cycle more, but only slightly more than they do now. It is important to provide challenges that are not too difficult, but just difficult enough to be stimulating.
In that sense, we expect these apps to be only successful when they stimulate gradual change where people over time, and step by step, cycle more and in the process reduce their car use. Of course, this study only looked at the intentions of people. At this moment, we are implementing our findings in the real-world SMART app, testing whether users actually cycle more in the real world. Results will hopefully be available soon.
T Thomas University of Twente, Enschede, the Netherlands, firstname.lastname@example.org
B Huang University of Twente, Enschede, the Netherlands, email@example.com
T Fioreze University of Twente, Enschede, the Netherlands, firstname.lastname@example.org
This research is funded within Empower as part of a EU Horizon 2020 program.
May 31, 2018