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What we can learn from five naturalistic field experiments that failed to shift commuter behaviour

Abstract

Across five field experiments with employees of a large organization (n = 68,915), we examined whether standard behavioural interventions (‘nudges’) successfully reduced single-occupancy vehicle commutes. In Studies 1 and 2, we sent letters and emails with nudges designed to increase carpooling. These interventions failed to increase carpool sign-up or usage. In Studies 3a and 4, we examined the efficacy of other well-established behavioural interventions: non-cash incentives and personalized travel plans. Again, we found no positive effect of these interventions. Across studies, effect sizes ranged from Cohen’s d = −0.01 to d = 0.05. Equivalence testing, using study-specific smallest effect sizes of interest, revealed that the treatment effects observed in four out of five of our experiments were statistically equivalent to zero (P < 0.04). The failure of these well-powered experiments designed to nudge commuting behaviour highlights both the difficulty of changing commuter behaviour and the importance of publishing null results to build cumulative knowledge about how to encourage sustainable travel.

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Fig. 1: Forest plot illustrating negligible effects of the five studies by showing the standardized effect size (Cohen’s d) and 95% CI associated with each sample.

Data availability

Coarsened data and modified materials are available at https://osf.io/39rja/. Data and materials have been modified to protect the identity of the partner organization and its employees. Additional materials that do not violate these privacy concerns can be provided on request by the authors.

Code availability

All analyses reported in this study used the statistical software R (v.3.6.1). All R files are available publicly at https://osf.io/39rja/.

References

  1. 1.

    Teter, J., Cazzola, P. & Petropoulos, A. Transport: Tracking Clean Energy Progress https://www.iea.org/tcep/transport (2018).

  2. 2.

    IPCC. Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

  3. 3.

    US Census Bureau. Average One-Way Commuting Time by Metropolitan Areas https://www.census.gov/library/visualizations/interactive/travel-time.html (2017).

  4. 4.

    US Census Bureau. American Community Survey 1-Year Estimate https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk (2017).

  5. 5.

    Hilbrecht, M., Smale, B. & Mock, S. Highway to health? Commute time and well-being among Canadian adults. World Leis. J. 56, 151–163 (2014).

    Article  Google Scholar 

  6. 6.

    Hoehner, C., Barlow, C., Allen, P. & Schootman, M. Commuting distance, cardiorespiratory fitness, and metabolic risk. Am. J. Prev. Med. 42, 571–578 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Martin, A., Panter, J., Suhrcke, M. & Ogilvie, D. Impact of changes in mode of travel to work on changes in body mass index: evidence from the British Household Panel Survey. J. Epidemiol. Community Health 69, 753–761 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Nieuwenhuijsen, M. Influence of urban and transport planning and the city environment on cardiovascular disease. Nat. Rev. Cardiol. 15, 432–438 (2018).

    PubMed  Article  Google Scholar 

  9. 9.

    Ly, K., Sati, S. & Singer, E. A Behavioural Lens on Transportation Systems: The Psychology of Commuter Behaviour and Transportation Choices Research Report Series: Behavioural Economics in Action (Rotman School of Management, University of Toronto, 2017).

  10. 10.

    Applying Behavioural Insights to Transportation Demand Management (Alta Planning and Design and the Behavioural Insights Team, 2018).

  11. 11.

    Thaler, R. & Sunstein, C. Improving Decisions about Health, Wealth, and Happiness (Yale Univ. Press, 2008).

  12. 12.

    Allcott, H. Social norms and energy conservation. J. Public Econ. 95, 1082–1095 (2011).

    Article  Google Scholar 

  13. 13.

    Milkman, K., Beshears, J., Choi, J., Laibson, D. & Madrian, B. Using implementation intentions prompts to enhance influenza vaccination rates. Proc. Natl Acad. Sci. USA 108, 10415–10420 (2011).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Sunstein, C. Nudging: A very short guide. J. Consum. Policy 37, 583–588 (2014).

    Article  Google Scholar 

  15. 15.

    Benartzi, S. et al. Should governments invest more in nudging? Psychol. Sci. 28, 1041–1055 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Organization for Economic Cooperation and Development. Behavioural Insights http://www.oecd.org/gov/regulatory-policy/behavioural-insights.htm (2019).

  17. 17.

    Kenyon, S. & Lyons, G. The value of integrated multimodal traveller information and its potential contribution to modal change. Transp. Res. F 6, 1–21 (2003).

    Article  Google Scholar 

  18. 18.

    Simma, A. & Axhausen, K. Structures of commitment in mode use: a comparison of Switzerland, Germany and Great Britain. Transp. Policy 8, 279–288 (2001).

    Article  Google Scholar 

  19. 19.

    Van Exel, N. & Rietveld, P. Could you also have made this trip by another mode? An investigation of perceived travel possibilities of car and train travellers on the main travel corridors to the city of Amsterdam, The Netherlands. Transp. Res. A 43, 374–385 (2009).

    Google Scholar 

  20. 20.

    Levitt, S. & List, J. Viewpoint: on the generalizability of lab behaviour to the field. Can. J. Econ. 40, 347–370 (2007).

    Article  Google Scholar 

  21. 21.

    Service, O. et al. EAST: Four Simple Ways to Apply Behavioral Insights (The Behavioural Insights Team, 2014).

  22. 22.

    Elgaaied-Gambier, L., Monnot, E. & Reniou, F. Using descriptive norm appeals effectively to promote green behavior. J. Bus. Res. 82, 179–191 (2018).

    Article  Google Scholar 

  23. 23.

    Chorus, C., Molin, E. & Van Wee, B. Use and effects of Advanced Traveller Information Services (ATIS): a review of the literature. Transp. Rev. 26, 127–149 (2006).

    Article  Google Scholar 

  24. 24.

    Bertrand, M., Karlan, D., Mullainathan, S., Shafir, E. & Zinman, J. What’s advertising content worth? Evidence from a consumer credit marketing field experiment. Q. J. Econ. 125, 263–305 (2010).

    Article  Google Scholar 

  25. 25.

    Xia, J., Curtin, K., Li, W. & Zhao, Y. A new model for a carpool matching service. PLoS One 10, e0129257 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    One-in-three U.S. drivers cannot pay for an unexpected car repair bill. AAA NewsRoom https://newsroom.aaa.com/2017/04/one-three-u-s-drivers-cannot-pay-unexpected-car-repair-bill (2017).

  27. 27.

    Frederick, S., Novemsky, N., Wang, J., Dhar, R. & Nowlis, S. Opportunity cost neglect. J. Consum. Res. 36, 553–561 (2009).

    Article  Google Scholar 

  28. 28.

    Fujii, S. & Kitamura, R. What does a one-month free bus ticket do to habitual drivers? An experimental analysis of habit and attitude change. Transportation 30, 81–95 (2003).

    Article  Google Scholar 

  29. 29.

    Thøgersen, J. Promoting public transport as a subscription service: effects of a free month travel card. Transp. Policy 16, 335–343 (2009).

    Article  Google Scholar 

  30. 30.

    Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263 (1979).

    Article  Google Scholar 

  31. 31.

    Homonoff, T. Can small incentives have large effects? The impact of taxes versus bonuses on disposable bag use. Am. Econ. J. Econ. Policy 10, 177–210 (2018).

    Article  Google Scholar 

  32. 32.

    van Essen, M., Thomas, T., van Berkum, E. & Chorus, C. From user equilibrium to system optimum: a literature review on the role of travel information, bounded rationality and non-selfish behaviour at the network and individual levels. Transp. Rev. 36, 527–548 (2016).

    Article  Google Scholar 

  33. 33.

    Lyons, G. The role of information in decision-making with regard to travel. IEE Intell. Transp. Syst. 153, 199 (2006).

    Google Scholar 

  34. 34.

    Chatterjee, K. A comparative evaluation of large-scale personal travel planning projects in England. Transp. Policy 16, 293–305 (2009).

    Article  Google Scholar 

  35. 35.

    Macmillan, A., Hosking, J., Connor, L., Bullen, J. & Ameratunga, C. S. A Cochrane systematic review of the effectiveness of organisational travel plans: improving the evidence base for transport decisions. Transp. Policy 29, 249–256 (2013).

    Article  Google Scholar 

  36. 36.

    Lakens, D., Scheel, A. & Isager, P. Equivalence testing for psychological research: a tutorial. Adv. Methods Pract. Psychol. Sci. 1, 259–269 (2018).

    Article  Google Scholar 

  37. 37.

    Lakens, D. Equivalence tests. Soc. Psychol. Pers. Sci. 8, 355–362 (2017).

    Article  Google Scholar 

  38. 38.

    Funder, D. & Ozer, D. Evaluating effect size in psychological research: sense and nonsense. Adv. Methods Pract. Psychol. Sci. 2, 156–168 (2019).

    Article  Google Scholar 

  39. 39.

    Ajzen, I. From Intentions to actions: a theory of planned behavior. in Action Control (eds Kuhl, J. and Beckmann, J.) 11–39 (Springer, 1985).

  40. 40.

    Heath, Y. & Gifford, R. Extending the theory of planned behavior: predicting the use of public transportation. J. Appl. Soc. Psychol. 32, 2154–2189 (2002).

    Article  Google Scholar 

  41. 41.

    Cheung, S., Chan, D. & Wong, Z. Reexamining the theory of planned behavior in understanding wastepaper recycling. Environ. Behav. 31, 587–612 (1999).

    Article  Google Scholar 

  42. 42.

    Epley, N. & Schroeder, J. Mistakenly seeking solitude. J. Exp. Psychol. Gen. 143, 1980–1999 (2014).

    PubMed  Article  Google Scholar 

  43. 43.

    Zhao, Z. & Zhao, J. Car pride and its behavioral implications: an exploration in Shanghai. Transportation https://doi.org/10.1007/s11116-018-9917-0 (2018).

  44. 44.

    Hagman, O. Mobilizing meanings of mobility: car users’ constructions of the goods and bads of car use. Transp. Res. D 8, 1–9 (2003).

    Article  Google Scholar 

  45. 45.

    Steg, L. Car use: lust and must. Instrumental, symbolic and affective motives for car use. Transp. Res. A 39, 147–162 (2005).

    Google Scholar 

  46. 46.

    Oliver, A. Nudging, shoving, and budging: behavioral economic – informed policy. Public Admin. 93, 700–714 (2015).

    Article  Google Scholar 

  47. 47.

    Furman, J. Applying behavioral sciences in the service of four major economic problems. Behav. Sci. Pol. 2, 1–9 (2016).

    Article  Google Scholar 

  48. 48.

    Wilson, R. Estimating the travel and parking demand effects of employer-paid parking. Reg. Sci. Urban Econ. 22, 133–145 (1992).

    Article  Google Scholar 

  49. 49.

    Washbrook, K., Haider, W. & Jaccard, M. Estimating commuter mode choice: a discrete choice analysis of the impact of road pricing and parking charges. Transportation 33, 621–639 (2006).

    Article  Google Scholar 

  50. 50.

    Zahabi, S., Miranda-Moreno, L., Patterson, Z. & Barla, P. Evaluating the effects of land use and strategies for parking and transit supply on mode choice of downtown commuters. J. Transp. Land Use 5, 103–119 (2012).

    Article  Google Scholar 

  51. 51.

    Bartle, C. & Avineri, E. Personalised travel plans in the workplace: a case study. Proc. Inst. Civ. Eng. Munic. Eng. 167, 183–190 (2014).

    Google Scholar 

  52. 52.

    Carroll, G., Choi, J., Laibson, D., Madrian, B. & Metrick, A. Optimal defaults and active decisions. Q. J. Econ. 124, 1639–1674 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Duflo, E., Gale, W., Liebman, J., Orszag, P. & Saez, E. Savings incentives for low- and moderate-income families in the United States: why is the saver’s credit not more effective? J. Eur. Econ. Assoc. 5, 647–661 (2007).

    Article  Google Scholar 

  54. 54.

    B. Long in College Choices: The Economics of Where to Go, When to Go, and How to Pay for It (ed. Hoxby, C. M.) 101–168 (Univ. of Chicago Press, 2004).

  55. 55.

    Bulman, G. & Hoxby, C. The returns to the federal tax credits for higher education. Tax. Policy Econ. 29, 13–88 (2015).

    Article  Google Scholar 

  56. 56.

    Arimura, T., Li, S., Newell, R. & Palmer, K. Cost-effectiveness of electricity energy efficiency programs. Energy J. 33, 63–99 (2012).

    Article  Google Scholar 

  57. 57.

    Brandon, A. et al. Do the effects of social nudges persist? Theory and evidence from 38 natural field experiments. NBER Working Paper No. 23277 https://www.nber.org/papers/w23277 (NBER, 2017).

  58. 58.

    Munafò, M. & Neill, J. Null is beautiful: on the importance of publishing null results. J. Psychopharmacol. 30, 585–585 (2016).

    PubMed  Article  Google Scholar 

  59. 59.

    Goodchild, L. & Hilten, V. Why it’s time to publish research ‘failures’. Publishing bias favors positive results; now there’s a movement to change that. Elsevier Connect https://www.elsevier.com/connect/scientists-we-want-your-negative-results-too (5 May 2015).

  60. 60.

    Chorus, C. G. A new model of random regret minimization. Eur. J. Transp. Infrastruct. Res. 10, 181–196 (2010).

    Google Scholar 

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Acknowledgements

This project received public funding while one of the researchers was employed at the Behavioral Insights Team, but the authors have not received any funding for the writing and publication of this research. We thank the Behavioral Insights Team for their support in writing this paper. Specifically, we thank M. Sanders and L. Costa. The funders provided input into the design of the study but had no role in data collection and analysis, decision to publish or preparation of this manuscript. We also thank S. O’Flaherty and H. Dystrka for their input, and D. Hagmann, J. Roberts and J. Zlatev. for their comments on earlier drafts of this manuscript.

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Data collection and analysis was performed by A.S.K. A.S.K. drafted the original manuscript. A.V.W. provided critical revisions. Both of the authors approved the final version of the manuscript for submission.

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Correspondence to Ariella S. Kristal.

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Supplementary Results, Supplementary Notes, Supplementary Discussion, Supplementary Tables 1–4 and Supplementary Fig. 1.

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Kristal, A.S., Whillans, A.V. What we can learn from five naturalistic field experiments that failed to shift commuter behaviour. Nat Hum Behav 4, 169–176 (2020). https://doi.org/10.1038/s41562-019-0795-z

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