Changing commuters’ behavior using rewards: A study of rush-hour avoidance
Eran Ben-Elia*,
Centre for Transport and Society
Faculty of Environment and Technology
University of the West of England
Frenchay Campus, Bristol, BS16 1QY, United Kingdom
Dick Ettema
Urban and Regional research centre Utrecht
Faculty of Geosciences
Utrecht University
P.O. Box 80115
3508 TC, Utrecht, The Netherlands
* (corresponding author)
Key Words
Attitudes, behavior-change, congestion, habitual behavior, information, motivation, reward.
Abstract
In a 13-week field study conducted in The Netherlands, participants were provided with daily rewards – monetary and non-monetary, in order to encourage them to avoid driving during the morning rush-hour. Participants could earn a reward (money or credits to keep a Smartphone handset), by driving to work earlier or later, by switching to another mode or by teleworking. The collected data, complemented with pre and post measurement surveys, were analyzed using longitudinal techniques and mixed logistic regression. The results assert that the reward is the main extrinsic motivation for discouraging rush-hour driving. The monetary reward exhibits diminishing sensitivity, whereas the Smartphone has endowment qualities. Although the reward influences the motivation to avoid the rush-hour, the choice how to change behavior is influenced by additional factors including gender and education, scheduling considerations, habitual behavior, and cognitive factors regarding attitudes and perceptions, as well as travel information availability factors.
1. Introduction
Congestion on urban roads throughout the European Union is increasing and is expected to worsen as the demand for trip making increases and supply of road infrastructure remains limited (European Commission, 2006a, 2006b). Loading of excess demand on the transportation system has considerable external costs such as pollution, noise and road user safety (Mayeres et al., 1996). Road overloading disrupts vehicle flow, increases the frequency of incidents and magnifies the uncertainty of travel schedules (Lomax & Schrank, 2003). Congestion is a collective, synchronic phenomenon: massive commuting at a more or less common time-frame (e.g. the morning rush-hour). Thus, shifting of commuters’ departure times to less congested times, before or after the rush-hour, change of transport mode (from car to public transport) or change of work mode (working from home), should, in theory, lead to considerable time savings, greater travel certainty and lower external costs of congestion.
Transportation demand-based solutions (e.g. road pricing, promoting modal alternatives, parking policy and land use planning policy) have been suggested to reduce congestion (Shiftan & Golani, 2005). In this respect, transport economists have been arguing for the implementation of road pricing as a first-best solution to efficiently alleviate congestion externalities (Nijkamp & Shefer, 1998; Rouwendal & Verhoef, 2006; Small & Verhoef, 2007). However, road pricing is controversial and its behavioral implications are not well understood. As suggested initially by Vickrey (1969), optimal pricing requires the design of variable tolls, making them quite complex for drivers’ comprehension (Bonsall et al., 2007; Verhoef, 2008). In addition, road pricing raises questions regarding social equity (Giuliano, 1994), fairness and public acceptability (Eriksson et al., 2006) as well as economic efficiency (Banister, 1994; Viegas, 2001).
Second-best schemes have been suggested to circumvent the difficulties in implementing first-best pricing solutions (Small & Verhoef, 2007). In The Netherlands the notion of using rewards to achieve desired outcomes in travelers’ behavior has been recently implemented in the context of the Spitsmijden[1] program (Ettema et al., 2010; Knockaert et al., 2007), thus far, the largest systematic effort to analyze the potential of rewards in the field as a policy mean for changing commuter behavior. A pilot study (see section 3 for further details), involving 340 participants and lasting over 13 weeks, was organized in the second half of 2006. Its objective was to investigate, in an empirical field study, the potential impacts of rewards on commuters’ behavior during the morning rush-hour. Participants could earn a reward (money or credits to keep a Smartphone handset which also provided real-time traffic information), by driving to work earlier or later, by switching to another travel mode or by teleworking. Initial results provided evidence of substantial behavior change in response to the rewards, with commuters shifting to earlier and later departure times and more use of public transport and alternative modes or working from home (Ettema et al., 2010).
The effectiveness of rewards to reinforce a desirable behavior (e.g. identification and loyalty, work effort) is supported by a large volume of empirical evidence (Kreps, 1997; Berridge, 2001). However, in the context of travel and traffic behavior, rewards are poorly represented. Punishments and enforcement (such as policing, felony detectors, fines etc.), have been more widely documented than rewards (e.g. Rothengatter, 1992; Perry et al., 2002; Schuitema, 2003). The relative salience of negative motivational means reflects, to a large extent, a disciplinary bias. Given that travel behavior has been to the most part subjected and influenced by microeconomic theories (McFadden, 2007), it is not surprising that the behavioral rationale of many demand based strategies to manage traffic congestion is based on negative incentives that associate, through learning, the act of driving with punishments (such as tolls or increased parking costs).
The few examples where rewards have been applied in a travel context are short term studies involving the use of a temporary free bus ticket as an incentive to reduce car driving. To most parts, the results of these studies are inconclusive. For example, (Fujii et al., 2001; Fujii & Kitamura, 2003) found that an incentive did encourage a change towards reducing car driving; however the level of car driving returned to previous levels once the incentive was stopped. In contrast, (Bamberg et al., 2002; Bamberg et al., 2003), found that habitual behavior prevented substantial reductions in car use. It is not the scope of this paper to debate which policy (pricing or rewards) is more effective. However there is substantial evidence that people respond more favorably and are more motivated when rewarded rather than punished (Kahneman & Tversky, 1984; Geller, 1989). Thus, the potential of rewards as a base for traffic management policy is well worth considering if based on robust behavioral foundations.
The main aim of this paper is to comprehensively analyze and explore the changes in behavior during the course of the aforementioned pilot study and identify key factors that influenced the response to the rewards. The rest of the paper is organized as follows: Section 2 sets a number of theoretically driven research questions and hypotheses. Section 3 describes the experimental setup and methods. Results, based on a mixed logistic regression analysis are presented in section 4. A discussion is presented in section 5, followed by summary and conclusions in section 6.
2. Research questions & hypotheses
Several key questions are postulated: First, how effective are rewards as a means for motivating travel behavior change? The literature does not provide a clear indication. One view suggests that satisfying rewards contribute to higher rates of motivation (Cameron et al., 2001; 1994). The other view propounds that rewards interfere and undermine intrinsic motivation, deflecting motivation from internal to external causes and reducing the amount of effort devoted to participate in activities (Deci, 1971; 1975; Lepper & Green, 1978). Theory of Cognitive Evaluation (TCE) further asserts that the effect of reward will depend on how it affects perceived self-determination and competence (Deci & Ryan, 1985).
Second, does the nature of the reward (monetary, in-kind) affect the willingness to change travel behavior and its tenacity? People seem more receptive to large monetary rewards compared to small ones (Gneezy & Rustichini, 2000; Gneezy, 2003). Moreover, a monetary reward might be framed as a prospective gain. According to Prospect Theory (Kahneman & Tversky, 1979), diminishing sensitivity to money can affect the perseverance of change. Participants’ apparently have greater satisfaction and motivation is higher with gifts compared to monetary rewards; however when asked, most people prefer receiving money (Shaffer & Arkes, in press). In-kind rewards may therefore encourage behavior change through a different cognitive path: the endowment effect. A Smartphone handset granted to some participants may be regarded as an uncertain endowment. An endowment is not easily relinquished, once given (Kahneman et al., 1991). The endowment effect may well motivate to change behavior just in order to avoid the loss associated with the possibility to give up a valued object. In this respect, the in-kind reward, unlike the monetary one may have affective as well as motivational properties.
Third, to what extent do personal and social characteristics (e.g. gender, education level, personal income, or household composition) sustain or diminish the potential impact of rewards? The connection between socio-economic characteristics and travel choices is well documented (e.g. Harris & Tanner, 1974; Ben-Akiva & Lerman, 1985; Axhausen & Gärling, 1992) In this respect income may well affect motivation in the case of the monetary reward. Diminishing sensitivity could suggest that participants with higher incomes might be less motivated to change behavior for a rather marginal monetary gain.
Fourth, do participants’ beliefs attitudes and norms influence their responsiveness to change behavior? Several studies (e.g. Gärling et al., 1998; Gärling et al., 2001) suggest attitudes towards travel alternatives, affect the choice of travel modes. The Theory of Planned Behavior (TPB) (Fishbein & Ajzen, 1975; Ajzen, 1991) suggests a positive attitude towards a certain behavior will influence a person’s intention to consciously engage in it. Rewards which create a positive attitude with a certain behavior, will contribute to this behavior being repeated. Another issue is that of personal norms that are self expectations or specific actions in specific situations (Schwartz, 1977). They refer to feelings of moral obligations to behave in a certain way (e.g. environmental friendly behavior). If a reward scheme is regarded as congruent with the personal norms and expectations, it is more likely to encourage behavior-change.
Fifth, are there situational factors (home and work-related) that affect the relative salience of rewards as means for travel behavior change (here, rush-hour avoidance)? TBP stresses the role of others’ attitudes, and the perceived situational control on influencing intentional behavior-change. If a person perceives behavior changes as difficult, the probability of repeating this action is relatively low. Scheduling constraints such as household obligations (e.g. child care, children chauffeuring) and work organization have been found to influence individuals’ responses to pricing schemes and limit their perceived effectiveness (Gärling & Fujii, 2006). Participants with child care or children chauffeuring responsibilities on one hand, or participants with inflexible working times, on the other hand, might have a limited ability to change behavior even when motivated by the reward. Conversely, the support a person gets from the household, workplace and from colleagues or friends that are also participating in a reward based scheme may well contribute to one’s own participation.
Sixth, to what extent options chosen to avoid the peak are determined by habits? In the long run habitual travel behavior, as asserted by Gärling et al. (2001) and Gärling & Axhausen (2003), is quite relevant for promoting or discouraging a behavior change different from the usual travel behavior. Theory of Interpersonal Behavior (TIB) (Triandis, 1977, 1980) stresses the role of habit in behavior. With habitual behavior, decisions are made with a lesser degree of consciousness which decreases the likelihood behavior will change in response to a change in context. Habitual behavior is less intentional more automated and script based (Ronis et al., 1989; Gärling & Garvill, 1993). Travel decisions (e.g. the drive to work) are an example of habitual behavior as repeated decisions which loose intention and become gradually routinized (Verplanken et al., 1997; Gärling et al., 1998).
Last, what is the role travel information plays in changing commuters’ behavior? Several studies point out that availability of information has significant effects on travelers’ behavior in the lab (Avineri & Prashker, 2006; Ben-Elia et al., 2008). For example in the case of route-choice, Ben-Elia & Shiftan, (2010) found real-time travel information expedites learning in unfamiliar environments and reduces initial exploration. At the same time, exposure to information is also associated with more heterogeneity in choice behavior and in risk attitudes. In this respect the Smartphone reward could well have instrumental value as it also provides access to real-time traffic information. Information might motivate change of behavior by facilitating the travel decision process and by reducing subjective effort and difficulty increasing the perceived situational control.
3. Method
3.1 Participants
Using license plate recognition cameras, 2,300 cars, both privately owned and leased company vehicles and traveling at least three times a week during the morning rush hour on the busy stretch of the A12 motorway (about 15 km connecting Zoetermeer to The Hague). The Dutch Department of Road Transport provided the names and addresses of the car owners and they were approached by mail with an invitation to participate in the experiment. A total of 341 commuters - 221 men and 120 women – chose to participate in the experiment. Upon registration, the participants self selected one out of two types of reward. The first type of reward was an amount of money (3-7 Euros and see next subsection) for each day that the participant avoided driving during the morning rush-hour. In this case, participants were provided with a realistic estimate of how much they could earn in the course of the study. The second type comprised credits towards ultimately keeping a Smartphone (called Yeti) at the end of the experiment. 232 participants (60% men), selected a monetary reward (‘money’) and 109 (74% men) the Yeti reward. The Yeti’s market value was around € 500 at the time. All the participants were inhabitants of the town of Zoetermeer and the vast majority was working at the time in The Hague or its vicinities. They are characterized by relatively high percentage of higher education, moderate to high incomes and mostly families with children. Table 1 presents the descriptives of the participants by group.
***Table 1 about here***
3.2 Procedure
The task and rules were communicated to the participants through the project's back office: Participation had to be voluntary. The participants were to commute at least three times a week from home to work. They had to have access to e-mail and the Internet. They were requested to complete surveys completely and timely. They were made aware that their movements by car would be recorded and had to agree to the installation of an on-board transponder in their car. In addition it was explained that only the car in which a transponder had been previously installed could be eligible for the reward. A travel log (i.e. logbook) was to be filled in daily on a personal webpage on the projects’ internet site. Participants that opted for the Yeti reward were also instructed to switch on the Smartphone during every car trip, in order to get full and easy access to real-time travel information. All communication was to be conducted via the project’s back-office which dealt with complaints or operational problems. A weekly newsletter was also sent to participants’ homes providing further information and clarifications. Participants’ earnings were shown on their personal webpage. The earnings were updated once a week according to the relevant treatment schemes. The monetary rewards were directly paid to participants’ bank accounts at the end of the working week by bank transfer.