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Promoting Eco-Driving Habits: A Randomised Controlled Trial Dimitrios Xenias Lorraine Whitmarsh Paul Haggar Cardiff University Steve Skippon Shell.

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Presentation on theme: "Promoting Eco-Driving Habits: A Randomised Controlled Trial Dimitrios Xenias Lorraine Whitmarsh Paul Haggar Cardiff University Steve Skippon Shell."— Presentation transcript:

1 Promoting Eco-Driving Habits: A Randomised Controlled Trial Dimitrios Xenias Lorraine Whitmarsh Paul Haggar Cardiff University Steve Skippon Shell

2 Habits Much (most?) of what we do is habitual (contra. most soc. psych. models) 3 ingredients to habit:...frequency...automaticity...cued by stable contexts (i.e. spatial, social and temporal environment) Verplanken & Wood (2006)

3 Habituation...attenuates attention to new information (Verplanken et al., 1997)...attenuates attention to changing conditions (Horeni et al., 2007; Neal et al., 2011 )

4 Habit discontinuity Habit Context Information about alternative choices (e.g., bus travel) tends to be ignored when we have strong habits (e.g., to drive ) But when habits are disrupted by events/decisions (e.g., relocation, new job) behaviour-relevant information becomes more salient and influential = Habit discontinuity hypothesis (Verplanken & Wood, 2006) Tailored public transport info and 1-day bus pass given 6-weeks post-relocation was significantly more effective (increase from 18% to 47%) than when given to those not relocating (18% to 25%, n.s.; Bamberg, 2006)

5 Context change Relocation Family circumstances Change of employment Change of vehicle … = Window of opportunity Thompson et al (2011); Schäfer et al. (2012)

6 Change of vehicle - interventions 1) Information provision: ▫ Shown to reduce energy use by up to 9% (Maibach, 2008) ▫ More likely to be effective if situated where action occurs (Whitmarsh et al., 2011) 2) Feedback provision: ▫ Drivers save up to 10% fuel, esp. under little stress (Dogan et al., 2011) ▫ Interventions more likely to work when real time (Stillwater & Kurani,2012) 3) Social influence: ▫ Talking to people we identify with (Ellmers et al., 2002) helps behaviour change towards a stated norm (Rabinovich et al., 2010)

7 Design & Hypotheses InformationFeedbackSocial influence Control Existing vehicle ABC New vehicle DEF D,E,F > A,B,C - B,E > A,D - C,F > B,E,A,D

8 Interventions 1. Information 2. Feedback3. Social influence

9 Interventions 1. Information 2. Feedback3. Social influence

10 Measures Eco-driving habit strength (e.g. Verplanken & Orbell, 2003) Personality measures (e.g. TIPI: Gosling, Rentfrow & Swann, 2003) Driving style (e.g. MDSI; Taubman-Ben-Ari et al., 2004) Goals when travelling (Skippon et al., 2013) Vehicle and personal information Fuel consumption (receipts and mileage)

11 Sample size InformationFeedbackSocial influenceControl Existing vehicle 62 (50) New vehicle 62 (50) ▫ Assuming medium effect size (e.g., 6% improvement in mpg = 0.25 f; Boriboonsomsin et al., 2010 ) required sub-group sizes of 50 (i.e., total N = 400). ▫ Target N= 500 (400 after attrition)

12 Recruitment strategy One-off infoFeedbackSocial influenceControl Existing vehicle 62 (50) New vehicle 62 (50) ▫ >700 members of the Cardiff Community Panel (personalised s) ▫ Advertisement in two local newspapers ▫ Advertisement on the University Intranets (Cardiff and Bath) ▫ Flyers in >20 garages in Cardiff and Bristol ▫ TRL panel = substantial help, mainly for car changers (hardest to get!) ▫ Google AdWords (an expensive idea!) ▫ Responding participants were directed to a vetting survey

13 Attrition... One-off infoFeedbackSocial influenceControl Existing vehicle 61 (27)85 (41)64 (26)66 (33) New vehicle 03 (03)36 (12)07 (04)29 (19) ▫ Recruitment strategies brought 670 participants to the vetting survey (bias: younger + female) ▫ 383 passed vetting – 55 quit immediately after ▫ 328 began study (cut off mid-July 2013) ▫ 165 completed study ’000s reached670 vetted383 passed328 started165 completed

14 ♂♀ Sample ( ♂ = 84, ♀ =81) StartedCompleted AgeFrequency%Cumulative %Frequency%Cumulative % Total

15 No differences between finishers and starters Item (examples) NRangeMeanNRangeMean When I am driving I try to save fuel (DrivingGoals5item_BL_1) When I am driving I try to get to my destination as quickly as possible (DrivingGoals5item_BL_2) If I drive in the next week, I intend to drive in a fuel-efficient way (Intention_ecodriving_BL_1) At the moment, how easy would you find it to drive in a / fuel-efficient way? (PerceivedBehabiouralControl_BL_1) Do any of your friends or family drive in a fuel-efficient way? (Social Norm (present)_BL_1) Quicker than alternatives (ReasonsForDriving_BL_1) Inadequate alternatives / no other option (ReasonsForDriving_BL_2) Cheaper than alternatives (ReasonsForDriving_BL_4) Gives me a sense of prestige (ReasonsForDriving_BL_10) How much time pressure are you usually under / when you are driving to work / studies? (Time Pressure_BL)

16 Distribution of fuel efficiency

17 Fuel efficiency (car change) T-test for efficiency calculated as cost (t(1,163)=.48, p=.63) T-test for efficiency calculated as fuel volume (t(1,163)=.31, p=.76). (Error bars represent 95% Confidence Intervals.)

18 Fuel efficiency (intervention) T-test for efficiency calculated as cost (t(1,163)=1.13, p=.26) T-test for efficiency calculated as fuel volume (t(1,163)=.90, p=.33). (Error bars represent 95% Confidence Intervals.)

19 Fuel efficiency (intervention) F-test for efficiency calculated as cost (F(3,158)=.50, p=.68) F-test for efficiency calculated as fuel volume (F(3,158)=.34, p=.77). (Error bars represent 95% Confidence Intervals.)

20 F(2,318)=28.093, p<.001 Habit (SRHI) seems to increase, regardless of car change

21 All t(163).483 (Error bars represent 95% Confidence Intervals.) Habit (SRHI) seems to increase, regardless of car change

22 Although ANOVA showed overall trend was not sig. across conditions (F(3,158)=1.91, p=.13, partial η 2 =.04, observed power =.49), specific condition contrasts revealed sig. difference between information and control conditions (contrast estimate=.44, p=.02). CIs suggest lack of an overall significant trend is likely due to issues with sample sizes. Habit (SRBAI) increases most in information condition

23 Increase in careful driving for feedback condition ‘Careful’ dimension of MDSI showed a marginal change after the study, compared to before (F(3,154)=2.39, p=.07, partial η 2 =.04, observed power=.59). This change only sig. for in-car feedback intervention (condition 2), compared to control (Dunnett’s t=.25, p=.03)

24 Fuel efficiency does not really correlate with anything, except these trends Pearson Correlation Sig. (2-tailed) N Emotional Stability (baseline) Agreeableness (halfway) Meteorological conditions also did not affect fuel efficiency

25 Habit (SRHI) is related to driving style (MDSI) MeasureStatistic SRHI Baseline SRHI Halfway SRHI Final MDSI Reckless at Baseline Pearson’s r-.27 ** -.29 ** -.27 ** MDSI Careful at Baseline Pearson’s r.35 **.32 **.27 ** ** indicates p <.001

26 Some conclusions Type of intervention did not lead to change in fuel consumption, when measured using means available to drivers in real world (mileage, fuel purchases) Did find eco-driving habit strength increased over the duration of the study, particularly for condition 1 (information provision), whereas condition 2 (in-car feedback) was associated with increase in careful driving style Our RCT design allows confidence in our findings and suggests real-world interventions to change driving style may be more problematic than previously thought Thus, may be hard to make effective real-world eco-driving interventions Working with real-world samples introduces issues with fuel data and mileage reporting accuracy, which may have added significant measurement error. Error could be mitigated in future studies by using in-car fuel monitors, this could compromise external validity: if an intervention does not lead to changes the drivers themselves can perceive/measure, it is rather unlikely to succeed

27 Thank you

28 Some considerations... Measures 1: Fuel data (took a lot of debugging!) : >1,300 fuel receipts, 117 of which (8.4%) with cost only (quantity had to be estimated). £57,156 represented in fuel receipts 36,159 litres represented in fuel receipts £7,206 (12.5%) does not correspond to fuel quantity, as 8.4% of receipts report cost only – therefore missing fuel had to be estimated. Fuel efficiency calculated fortnightly: data miss 13% - 20% of mileage data Fuel efficiency calculated 6-weekely: data miss around 3% of mileage data 1-Week interval data cannot be computed (most 1 week windows don’t have fuel receipts  artificial consumption data. The narrower the timeslots, the less fuel efficient participants appear to be. 6-Weekly = much more accurate Truly Unknown = fuel remaining in tank Generally, a lot of missing fuel data 

29 Concerns about efficiency data trustworthiness

30 SRHI is unrelated to fuel efficiency Driving style is unrelated to fuel efficiency SRHI Baseline SRHI HalfwaySRHI FinalHabitChange Net Cost per Distance Pearson Correlation Sig. (2-tailed) N Net Fuel per Distance Pearson Correlation Sig. (2-tailed) N This is very similar to MDSI driving style, too 

31 Pre-post efficiency change? (Car change x intervention) F(3,165)=.487, p=.692 Cost Volume F(3,165)=.340, p=.769

32 Pre-post efficiency change? (Car change x intervention) F(7,165)=.978, p=.449 Cost Volume F(7,165)=.756, p=.625


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