Ben-Elia, E. and Ettema, D. (2011) Changing commuters' behav- ior ...

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for public transport alternatives to support their behavior are more likely to change behavior by not driving ( the coefficients for both these factors are positive). *** Table 3 – about here *** ***Table 4 – about here *** As noted in section 3, this study is compromised of a quasi-experimental design. Based on the recommendation of Shadish et al. (2002), we describe here the plausible threats to the validity of the results. Threats to statistical inferences are not discussed here as we contend that these are likely to be low given the conservative nature of the analysis method applied which guarantees proper statistical identification of the measurable effects. The two most plausible threats in our study are selection and history. Attrition is not an issue since no dropouts occurred. Maturation is also not relevant given the short period of time that the experiment was running. The issue of selection relates to a priori differences in the money and Yeti groups which could compromise the results. In Table 5 we present a statistical comparison of the differences between the self-selected groups by the factors that are associated with the response in the MLR model. Most factors have no significant difference, but gender, chauffeuring children and arrangements with employers regarding flexible working times do. The latter which has the most significant difference was measured in the posterior survey whereas the first two factors relate to pre-measurement. To contend with the threats we estimated the effects moderated by group, specifying the MLR model with group-specific coefficients for these three factors. Regarding gender, it was barely significant in the model (p≈0.1). We tested moderating effects for money and Yeti but this was found not to be significant. We can therefore conclude that gender (woman) has a weak negative association with avoidance behavior. Chauffeuring children is significantly and negatively associated with avoidance behavior (p=0.02). We investigated if this might be moderated by gender but given the small group involved of both men and women who have this constraint we could not identify with confidence any significant moderating effect. Moderating by group also does not reveal significant group differences in the MLR model. Therefore we can suggest that the negative effect identified for chauffeuring on peak avoidance is probable. The threat attributed to pre-arrangements regarding flexible work times requires more attention (p
treatment (pre/post test) remains relatively valid. Practicing also has a weak negative effect on rush-hour driving (p=0.07) which was only found relevant for the money group. 5. Discussion Effectiveness of rewarding ***Table 5 – about here *** The results demonstrate, that rewarding, at least in the short run, is effective as within a short period of time of several weeks, the share of rush-hour avoidance substantially increased. Thus in concordance with motivation theories (e.g. Cameron et al., 2001) rewards do influence the motivation to avoid the rush-hour. Moreover, we also found that the decision how to exercise this change of behavior, whether by driving at other times or by changing transport or work modes seem to be determined by other factors unrelated to the type or level of reward. Nonetheless, it is difficult to conclude from a relatively short longitudinal study about the impacts of rewards in the long run. Motivation theories suggest that if intrinsic motivation kicks in, the change of behavior is more likely to be sustained. However, we observed in the post-test, once rewards ceased, avoidance shares had dropped and participants had returned more or less to their usual behavior of rush-hour driving (as observed in the pretest). In this respect the results are similar to those obtained by Fujii & Kitamura (2003) regarding free bus tickets. Therefore at first glance it seems the change was not sustained for most of the participants. Notwithstanding, in the posterior survey less than 15% of participants stated they had returned to their previous behavior. Unfortunately, we do not have observations to corroborate this subjective evaluation. Further research is being carried out in this respect (see section 7). We also do not posses sufficient (post-test) data to conclude about the affective qualities of the rewards apart for the fact that the vast majority of participants (in both groups) answered affirmatively to the question 'did you like the reward' in the posterior survey. Reward type and levels We found that both types of reward (monetary and in-kind) have a significant and negative effect on rush-hour driving. In the case of a monetary reward, diminishing sensitivity was clearly noted. The 7€ treatment has the largest overall effect on RD; however the largest marginal effect (the derivative) is associated with the 3€ treatment. Therefore, for practical purposes, a moderate monetary reward seems to be sufficient to encourage a relatively substantial change of behavior. In the case of the Yeti reward, the main effect is the credits which had an effect similar to the 7€ reward. In this sense an in-kind reward, likely perceived as an endowment, can be just as useful as the monetary reward. However, for practical reasons, there may be difficulty in implementing an in-kind reward over a long period of time. Though not statistically significant in comparison to pre-test levels, avoidance behavior was also apparent without valid credits. This treatment had no extrinsic reward but travel information was still accessible to Yeti users. Furthermore, it was evident that Yeti users were more likely to drive later compared to participants in the money group. Two possible explanations are possible for this different behavior. On one hand, Yeti users had higher shares regarding support provided from employers. Thus, it is possible that pre-adjustments were involved in choosing to depart later (especially during the pre-test). On the other hand, the main advantage Yeti users had over the other group was 24 hour access to travel information. This leads us to suggest that the decision how to change behavior is also influenced by travel information availability (discussed later on). 12
Page 1 and 2: Ben-Elia, E. and Ettema, D. (2011)
Page 3 and 4: 1. Introduction Congestion on urban
Page 5 and 6: Fourth, do participants’ beliefs
Page 7 and 8: pages. Participants in possession o
Page 9 and 10: epresentative of the total populati
Page 11: Mediators (between-subjects factors
Page 15 and 16: with lower rush-hour frequencies (c
Page 17 and 18: Acknowledgments This study was unde
Page 19 and 20: Kahneman, D., Tversky, A. (1979), "
Page 21 and 22: Table 1: Participants’ characteri
Page 23 and 24: Table 4: Results of mixed logistic
Page 25: Figure 1: Average response shares b

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