Energy UX: Leveraging Multiple Methods to See the Big Picture

  • Beth KarlinEmail author
  • Sena Koleva
  • Jason Kaufman
  • Angela Sanguinetti
  • Rebecca Ford
  • Colin Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10290)


Engaging the public to decrease their carbon footprint via energy feedback has become a significant topic of both study and practice and understanding how to best leverage technology for this purpose is an ideal question for the field of HCI to address. One common example is Home Energy Reports (HERs) and Business energy reports (BERs), which are paper or electronic reports that display a consumer’s energy use alongside various benchmarks and “tips” to help (and persuade) them to save energy. While HERs and BERs show great promise, average savings hover around 1–3% with the potential savings in the average home and/or business closer to 15–20%, leaving potential room for improvement. This paper presents a mixed-methods research framework that is being used to improve BER user experience and energy savings. It blends inductive research methods from the fields of design and HCI with deductive methods drawn from psychology and behavioral economics to develop and test hypotheses and translate findings into real-world application. After introducing the framework, a case study is presented in which these steps are followed over two years of research with one BER product across multiple utility pilots. Implications for both energy feedback specifically as well as suggestions on how this framework can be applied across the broader field of usability are discussed.


Energy Feedback Usability Psychology Multi-disciplinary 


  1. 1.
    Karlin, B., Ford, R., Zinger, J.: The effects of feedback on energy conservation: a meta-analysis. Psychol. Bull. 141(6), 1205–1227 (2015)CrossRefGoogle Scholar
  2. 2.
    Froehlich, J., Findlater, L., Landay, J.: The design of eco-feedback technology. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems - CHI 2010 (2010)Google Scholar
  3. 3.
    Bandura, A.: Principles of Behavior Modification. Hold, Rinehart & Winston, New York (1969)Google Scholar
  4. 4.
    Bridgeman, B.: Effects of test score feedback on immediately subsequent test performance. J. Educ. Psychol. 66(1), 62–66 (1974)CrossRefGoogle Scholar
  5. 5.
    Becoña, E., Vázquez, F.L.: Effectiveness of personalized written feedback through a mail intervention for smoking cessation: a randomized-controlled trial in Spanish smokers. J. Consult. Clin. Psychol. 69(1), 33–40 (2001)CrossRefGoogle Scholar
  6. 6.
    Chopra, A.: Modeling a green energy challenge after a blue button. White House Office of Science and Technology Policy (2011).
  7. 7.
    Karlin, B., Ford, R., Squiers, C.: Energy feedback technology: A review and taxonomy of products and platforms. Energy Effi. 7(3), 377–399 (2014)CrossRefGoogle Scholar
  8. 8.
    A Design Thinking Process. ME 113. N.p. (2012). Web, 01 Mar 2017Google Scholar
  9. 9.
    Smith, N., Joffe, H.: How the public engages with global warming: a social representations approach. Publ. Underst. Sci. 22(1), 16–32 (2012)CrossRefGoogle Scholar
  10. 10.
    Fischer, C.: Feedback on household electricity consumption: a tool for saving energy? Energy Effi. 1(1), 79–104 (2008)CrossRefGoogle Scholar
  11. 11.
    O’Neill, S.J., Boykoff, M., Niemeyer, S., Day, S.A.: On the use of imagery for climate change engagement. Glob. Environ. Change 23(2), 413–421 (2013)CrossRefGoogle Scholar
  12. 12.
    Nolan, J.M., Schultz, P.W., Cialdini, R.B., Goldstein, N.J., Griskevicius, V.: Normative social influence is underdetected. Pers. Soc. Psychol. Bull. 34(7), 913–923 (2008). doi: 10.1177/0146167208316691 CrossRefGoogle Scholar
  13. 13.
    Karlin, B., Ford, R.: The usability perception scale (UPscale): a measure for evaluating feedback displays. In: Marcus, A. (ed.) Proceedings of the 2013 Human Computer Interaction (HCII) Conference. Springer, Heidelberg (2013)Google Scholar
  14. 14.
    Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263 (1979)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Beth Karlin
    • 1
    Email author
  • Sena Koleva
    • 1
  • Jason Kaufman
    • 1
  • Angela Sanguinetti
    • 2
  • Rebecca Ford
    • 3
  • Colin Chan
    • 4
  1. 1.See Change InstituteLos AngelesUSA
  2. 2.UC DavisDavisUSA
  3. 3.University of OxfordOxfordUK
  4. 4.Yardi EnergyVancouverCanada

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