IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt2ns9p8h7.html
   My bibliography  Save this paper

Comprehending Consumption: The Behavioral Basis and Implementation of Driver Feedback for Reducing Vehicle Energy Use

Author

Listed:
  • Stillwater, Tai

Abstract

By easily comprehending their own energy consumption, individuals can make both individually and socially responsible choices. How and when that potential translates into actual choices is explored, first by using mile-per-gallon fuel economy as a metric for driver feedback and finding that the metric is unable to transmit accurate information about the impact of driving style on energy use. An alternative energy economy metric, the theory of planned behavior, is tested against driver responses to an existing feedback system available in the 2008 model Toyota Prius. It appears that driver responses support the use of behavioral theories to both measure and design feedback systems. A novel feedback design based on behavioral theories and drivers' responses to the feedback is presented. The quantitative results of a year long study of fuel economy in response to feedback finds the novel feedback design generates a statistically significant increase in fuel economy overall. The feedback also influences drivers' goals and attitudes. This finding supports the underlying behavioral theory and suggests that energy related behavioral decisions are dependent on the quality and behavioral relevance of information that people have about their choices and the resulting consequences.

Suggested Citation

  • Stillwater, Tai, 2011. "Comprehending Consumption: The Behavioral Basis and Implementation of Driver Feedback for Reducing Vehicle Energy Use," Institute of Transportation Studies, Working Paper Series qt2ns9p8h7, Institute of Transportation Studies, UC Davis.
  • Handle: RePEc:cdl:itsdav:qt2ns9p8h7
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/2ns9p8h7.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lynne, Gary D. & Franklin Casey, C. & Hodges, Alan & Rahmani, Mohammed, 1995. "Conservation technology adoption decisions and the theory of planned behavior," Journal of Economic Psychology, Elsevier, vol. 16(4), pages 581-598, December.
    2. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    3. Rogers, K. J. & Trayford, R. S., 1984. "Grade measurement with an instrumented car," Transportation Research Part B: Methodological, Elsevier, vol. 18(3), pages 247-254, June.
    4. Kurani, Kenneth S. & Turrentine, Thomas S., 2002. "Marketing Clean and Efficient Vehicles: A Review of Social Marketing and Social Science Approaches," Institute of Transportation Studies, Working Paper Series qt2p923054, Institute of Transportation Studies, UC Davis.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stillwater, Tai & Kurani, Kenneth S., 2012. "Preliminary Results from a Field Experiment of Three Fuel Economy Feedback Designs," Institute of Transportation Studies, Working Paper Series qt11r5b3cs, Institute of Transportation Studies, UC Davis.
    2. Stillwater, Tai & Kurani, Kenneth S., 2012. "Goal Setting, Framing, and Anchoring Responses to Ecodriving Feedback," Institute of Transportation Studies, Working Paper Series qt9k86f889, Institute of Transportation Studies, UC Davis.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Munasib, Abdul B.A. & Jordan, Jeffrey L., 2011. "The Effect of Social Capital on the Choice to Use Sustainable Agricultural Practices," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(2), pages 1-15, May.
    2. Cristiano Codagnone & Giuseppe Alessandro Veltri & Francesco Bogliacino & Francisco Lupiáñez-Villanueva & George Gaskell & Andriy Ivchenko & Pietro Ortoleva & Francesco Mureddu, 2016. "Labels as nudges? An experimental study of car eco-labels," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 33(3), pages 403-432, December.
    3. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    4. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    5. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    6. Morrison, Mark, 2005. "Identifying Market Segments for Technology Adoption," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 137937, Australian Agricultural and Resource Economics Society.
    7. Peter Willemé, 2017. "Working Paper 14-17 - Modelling unobserved heterogeneity in distribution - Finite mixtures of the Johnson family of distributions," Working Papers 1714, Federal Planning Bureau, Belgium.
    8. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    9. Fabian Dvorak, 2020. "stratEst: Strategy Estimation in R," TWI Research Paper Series 119, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    10. Willy, Daniel Kyalo & Holm-Müller, Karin, 2013. "Social influence and collective action effects on farm level soil conservation effort in rural Kenya," Ecological Economics, Elsevier, vol. 90(C), pages 94-103.
    11. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    12. Di Falco, Salvatore & Sharma, Sindra, 2018. "Investing in Climate Change Adaptation: Motivations and Green Incentives in the Fiji Islands," Ecological Economics, Elsevier, vol. 154(C), pages 394-408.
    13. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    14. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    15. Xie, Yang & Zilberman, David, 2014. "The Economics of Water Project Capacities and Conservation Technologies," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169820, Agricultural and Applied Economics Association.
    16. Skaggs, Rhonda K., 2000. "Drip Irrigation In The Desert: Adoption, Implications, And Obstacles," 2000 Annual Meeting, June 29-July 1, 2000, Vancouver, British Columbia 36412, Western Agricultural Economics Association.
    17. Frick, Hannah & Strobl, Carolin & Leisch, Friedrich & Zeileis, Achim, 2012. "Flexible Rasch Mixture Models with Package psychomix," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i07).
    18. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    19. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    20. Corradi, Nicola & Priftis, Konstantinos & Jacucci, Giulio & Gamberini, Luciano, 2013. "Oops, I forgot the light on! The cognitive mechanisms supporting the execution of energy saving behaviors," Journal of Economic Psychology, Elsevier, vol. 34(C), pages 88-96.

    More about this item

    Keywords

    Engineering;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:itsdav:qt2ns9p8h7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.