1. 1 Risk sensitivity and demand for risk mitigation in transport Torbjørn Rundmo and Bjørg-Elin Moen Norwegian University of Science and Technology (NTNU), Trondheim, Norway Aims Is risk perception a significant determinant of demand for risk mitigation in transport? Lay people, politicians and experts compared Which other factors may be relevant for demands for risk mitigation in transport? (Risk sensitivity, worry, risk tolerance, priority of safety?)

2. 2 Samples Sample 1: A representative sample of the Norwegian population (n = 1716) (response rate: 38%) Sample 2: A group of Norwegian politicians (n=146) (response rate 81%) Sample 3: A representative group of experts on transport safety (n=26) (response rate 100%)

3. 3 Paul Slovic, 1999 An extensive literature.. demonstrates.. (a) that protective behaviours.. as well as demands for risk mitigation.. are influenced by both probability and severity of harm and (b) that perceived risk is a strong predictor of desire for risk reduction. Can the empirical basis for this conclusion be questioned?

4. 4 This conclusion is based on: Expectancy theory (Rotter, 1954; Tolman, 1955) Subjectively expected utility theory (Edwards, 1961) Prospect theory (Kahneman and Tversky, 1979) The health belief model (Janz and Becker, 1984) Empirical evidence: (Fischhoff et a., 1978; Slovic et al., 1979, 1980, 1985, 1986; Borcherding et al., 1986; Kraus and Slovic, 1988, Brun, 1992; Burns et al., 1993; Weinstein and Nicolich, 1993; Colombotos et al., 1994; Rohrman, 1994; Slovic and Monahan, 1995) Problem: Weak empirical evidence

5. 5 Studies carried out previously have given support to the idea that probability is important for perceived risk and consequences are more important for demands for risk mitigation than probability assessments.

6. 6 Demand for mitigation Risk Consequences Probability Summary of findings on risk, consequences, probability and demand for risk mitigation, for hazards above a probability threshold of concern Note: risk and probability are almost synonyms, and have little impact on demand for mitigation, above a probability threshold level pf concern

7. 7 The present research examines the possibility that consequences are important because they associated with worry and that worry relates better to demands for risk mitigation than evaluation of consequences.

8. 8 Table 1. Differences in probability assessment between lay people, politicians and experts Means of transportation Lay people PoliticiansExpertsF 22  3  Plane2.201.854.0521.14***18.19***0.24-1.35-1.76 Train2.852.473.798.13***9.16**0.25-0.66-0.99 Bus3.553.204.165.34**9.42**0.22-0.44-0.69 Ferry2.832.504.3213.58***17.18***0.21-1.06-1.42 Boat3.232.954.216.35***12.05**0.15-0.69-0.87 Own car4.784.47 2.854.680.190.210.01 Motorcycle5.375.224.633.29*3.510.100.540.47 Scooter5.175.004.582.502.070.110.430.32 Bike4.674.594.161.225.630.050.340.30 Pedestrian4.203.974.842.7017.36***0.13-0.39-0.53 Mean – public 2.922.624.1013.02***14.02***0.210.841.15 Mean - private 4.834.634.532.134.480.160.550.32 Wilk’s = 0.95, p <.001, *** = p <.001, ** = p <.01, * = p <.05, 1 = not at all probable, 7 = very probable. The effects sizes labelled (1-2), (1-3) and (2-3) are Cohen’s d-values. Significant effect sizes examined by Bonferroni’s Post Hoc Correction at the.05 level are printed in bold. Table 1. Differences in probability assessment between lay people, politicians and experts Wilk’s = 0.95, p <.001, *** = p <.001, ** = p <.01, * = p <.05, 1 = not at all probable, 7 = very probable. The effects sizes labelled (1-2), (1-3) and (2-3) are Cohen’s d-values

9. 9 Lay people Politician s Expert s F 22  Plane6.416.304.2631.83***17.36***0.101.951.55 Train4.954.713.957.98***14.79***0.180.780.62 Bus4.394.214.112.063.030.150.250.08 Ferry4.293.984.003.69*8.46*0.220.21-0.01 Boat4.654.253.6810.70***21.94***0.301.860.48 Own car4.254.063.842.566.13*0.150.330.19 Motorcycle5.335.183.5321.68***23.33***0.121.491.48 Scooter4.894.863.638.65***11.45**0.020.961.06 Bike4.01 4.160.090.480.000.10-0.09 Pedestrian4.184.173.890.361.160.010.190.20 Mean – public 4.934.674.0012.52***23.38***0.191.010.55 Mean - private 4,534.443.815.37**10.35**0.060.680.60 Table 2. Differences in assessment of consequences of accidents between lay people, politicians and experts Wilk’s = 0.93, p <.001, *** = p <.001, ** = p <.01, * = p <.05. 1 = trivial consequences, 7 = certain to be a lethal accident. The effects sizes labelled (1-2), (1-3) and (2-3) are Cohen’s d-values Lay people and politicians perceived the severity of consequences to be greater compared to the group of experts

10. 10 Lay people Poli- ticians Ex- perts F 22  Plane3.132.564.5810.28***19.57***0.27-1.70-1.06 Train2.592.294.009.68***9.87**0.18-0.88-1.14 Bus2.632.384.2112.77***13.03***0.17-1.07-1.37 Ferry2.612.274.3214.82***16.71***0.22-1.10-1.46 Boat2.932.574.057.99***11.68**0.22-0.69-1.07 Own car3.203.283.791.302.56-0.04-0.35-0.31 Motorcycle4.304.473.582.013.37-0.09-0.390.51 Scooter3.934.233.422.675.21-0.17-0.280.46 Bike3.113.423.683.09*6.12*-0.18-0.34-0.15 Pedestrian2.752.863.683.14*4.73-0.07-0.55-0.48 Mean – public2.772.404.2314.11***17.10***0.211.081.32 Mean - private3.453.653.631.362.270.110.38 Table 3.Differences in worry between lay people, politicians and experts Wilk’s = 0.95. p <.001, *** = p <.001, ** = p <.01, * = p <.05. 1 = not at all worried, 7 = very worried. The effects sizes labelled (1-2), (1-3) and (2-3) are Cohen’s d-values. Experts were more worried when thinking about the risks compared to lay people and politicians

11. 11 Lay people PoliticiansExpert s F 22  Plane5.245.034.422.283.230.100.420.28 Train5.615.564.167.27**9.97**0.030.880.82 Bus5.395.314.373.80*5.96*0.040.620.55 Ferry5.254.964.632.88*5.94*0.160.350.19 Boat5.405.254.682.16*5.170.090.430.33 Own car5.545.564.424.478.73*-0.010.690.76 Motorcycle5.755.794.328.34****10.60**-0.030.941.07 Scooter5.555.654.534.24*4.66-0.060.650.78 Bike5.295.344.681.142.48-0.020.340.37 Pedestrian5.345.414.631.543.46-0.030.390.42 Mean – public 5.385.224.453.82*6.21*0.080.540.43 Mean - private 5.495.544.514.35*6.63*0.030.600.68 Table 4. Demand for risk mitigation Wilk’s = 0.97. p <.01, *** = p <.001, ** = p <.01, * = p <.05. 1 = low demand for risk mitigation, 7 = high demand for risk mitigation. The effects sizes labelled (1-2), (1-3) and (2-3) are Cohen’s d- values Lay people and politicians demanded more risk reduction measures compared to the group of experts

12. 12 Probability public transp. Probability private transp. Conseq. private transp. Worry public transp. Worry private transp. Demand for risk mitig. public transp. Demand for risk mitig. private transp..20.31.37.31.26.51.18.26.49.20.09.12.24 R 2 =.35 e 6 =.65 R 2 =.42 e 2 =.58 R 2 =.15 e 3 =.85 R 2 =.18 e 4 =.82 Root Mean Square of Approximation (RMSEA) = 0.07, Comparative Fit Index (CFI) = 0.97, Critical N (CN) = 366.12, Goodness of Fit Index (GFI) = 0.98, Adjusted GFI = 0.95.62.11.13.39 Conseq. public transp. Figure 1: Predictors of demands for risk mitigation in transport R 2 =.38 e 5 =.62

13. 13 Fit statistics – invariant and non-variant model ModelRMSEACFICNGFI Invariant.16.93549.60.98 Non variant.07.97632.50.98

14. 14 Risk Sensitivity, Risk Perception, Worry, and Risk Tolerance as Predictors of Priority of Safety and Demand for Risk Mitigation Torbjørn Rundmo and Bjørg-Elin Moen Norwegian University of Science and Technology (NTNU), Trondheim, Norway Is risk perception a significant determinant of demand for risk mitigation in transport? (Lay people, politicians and experts compared) Which other factors may be relevant for demands for risk mitigation in transport? (Risk sensitivity, worry, risk tolerance, priority of safety?)

15. 15 Sample A representative sample of the Norwegian population (n = 510). The response rate was 51 per cent. The sample was fairly representative for the Norwegian public with regard to gender, age and education. The respondents were on average 43 years of age and 43 per cent were educated at a college or university. A total of 47 per cent had a work-related or senior high school degree and the remaining 10 per cent had junior high school education. A total of 51.8 per cent of the respondents were women.

16. 16  1 =.80  2 =.62  3 =.20  4 =.44  5 =.16 e 9 =.38e 10 =.41

17. 17 Risk mitigation in transport: Preliminary conclusions – two studies Worry was a stronger and more significant predictor of demands for risk mitigation compared to consequences (both the studies) Probability assessment was a totally insignificant predictor (study 1) Risk perception (including probability assessment as well as judgement of severity of consequences) was found to be an insignificant predictor of demand for risk mitigation (study 2) It is a fair chance that the assumption that perceived risk is important for behaviour and decisions under uncertainty (Slovic) may be wrong

18. 18 PRIORITY OF SAFETY One general question: When you choose means of transportation, how high do you prioritize safety? –Plain –Train –Bus –Ferry –Boat –Own car –Motorcycle –Scooter –Bike –As pedestrian

19. 19 PRIORITY OF SAFETY MEASUREMENT When I choose a mean of transportation I prioritize safety above all else My friends and acquaintances prioritize safety as high as I do I would choose a risky mean of transportation if there was no other ways of getting there I don’t want to risk my life and health by riding with unsafe means of transportation It is my responsibility to say something when I see something unsafe I will exit at the first possibility if safety regulations are violated when I use public means of transportation like bus or railroad I always say something when others break rules and regulations for proper safety I follow safety rules when I use means of transportation It is important to emphasize safety I take risk to get somewhere if others do To choose safe means of transportation is important to me