Money doesn’t matter! Householders’ intentions to reduce standby power are unaffected by personalised pecuniary feedback

Autoři: Kathryn Buchanan aff001;  Riccardo Russo aff001
Působiště autorů: Department of Psychology, University of Essex, Essex, United Kingdom aff001;  Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223727


Many researchers have examined whether giving people feedback about their energy use can lead them to decrease it. However, to date no consensus has been reached about which type of eco-feedback is the most effective. We aim to test the efficacy of different feedback techniques by providing participants with personalised information about the annual monetary costs of their home’s standby power usage (i.e., appliances that consume electricity despite not being actively used). Using a sample of 708 participants we tested the following feedback strategies: advice, disaggregation, loss vs gain framing, social norms, and collective information. We measured the impact of each of these feedback conditions on knowledge and intention to change behaviour, and compared them to a control condition. Using both frequentist and Bayesian analyses, we found that relative to the control condition all the feedback strategies led participants to report significant gains in knowledge. Yet, neither the additional knowledge gains, nor the feedback approach used significantly affected behavioural intentions. Consequently, the results suggest that while a wide range of feedback strategies emphasizing the financial impact of standby power consumption can effectively improve knowledge, this approach alone is insufficient in inciting intentions to change energy consumption behaviours.

Klíčová slova:

Behavior – Conservation of energy – Control theory – Experimental design – Finance – Literacy – Metaanalysis – Research design


1. Buchanan K, Russo R, Anderson B. The question of energy reduction: The problem (s) with feedback. Energy Policy. 2015 Feb 1;77:89–96.

2. Abrahamse W, Steg L, Vlek C, Rothengatter T. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology. 2007 Dec 1;27(4):265–76.

3. Brandon G, Lewis A. Reducing household energy consumption: a qualitative and quantitative field study. Journal of Environmental Psychology. 1999 Mar 1;19(1):75–85.

4. Karlin B, Zinger JF, Ford R. The effects of feedback on energy conservation: A meta-analysis. Psychological Bulletin. 2015 Nov;141(6):1205. doi: 10.1037/a0039650 26390265

5. Delmas MA, Fischlein M, Asensio OI. Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012. Energy Policy. 2013 Oct 1;61:729–39.

6. Katzev RD, Johnson TR. Promoting energy conservation: An analysis of behavioral research. Westview Press. Boulder; 1987.

7. Fischer C. Feedback on household electricity consumption: a tool for saving energy?. Energy efficiency. 2008 Feb 1;1(1):79–104.

8. Darby S. Making it obvious: designing feedback into energy consumption. InEnergy efficiency in household appliances and lighting 2001 (pp. 685–696). Springer, Berlin, Heidelberg.

9. Froehlich J, Findlater L, Landay J. The design of eco-feedback technology. InProceedings of the SIGCHI conference on human factors in computing systems 2010 Apr 10 (pp. 1999–2008). ACM.

10. Abrahamse W, Steg L, Vlek C, Rothengatter T. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology. 2005 Sep 1;25(3):273–91.

11. Mogles N, Walker I, Ramallo-González AP, Lee J, Natarajan S, Padget J, Gabe-Thomas E, Lovett T, Ren G, Hyniewska S, O'Neill E. How smart do smart meters need to be?. Building and Environment. 2017 Nov 15;125:439–50.

12. Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V. The constructive, destructive, and reconstructive power of social norms. Psychological Science. 2007 May;18(5):429–34. doi: 10.1111/j.1467-9280.2007.01917.x 17576283

13. Raw GJ, Ross DI. Energy demand research project: Final analysis.Ofgem. 2011 June. C2019 [cited 2019 Oct 01]. Available from

14. Schultz PW, Estrada M, Schmitt J, Sokoloski R, Silva-Send N. Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms. Energy. 2015 Oct 1;90:351–8.

15. YouGov plc. YouGov Survey Results. Available from:

16. Bator RJ, Phelps K, Tabanico J, Schultz PW, Walton ML. When it is not about the money: Social comparison and energy conservation among residents who do not pay for electricity. Energy Research & Social Science. 2019 Oct 1;56:101198.

17. Spence A, Leygue C, Bedwell B, O'Malley C. Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour?. Journal of Environmental Psychology. 2014 Jun 1;38:17–28.

18. Steinhorst J, Klöckner CA. Effects of monetary versus environmental information framing: Implications for long-term pro-environmental behavior and intrinsic motivation. Environment and Behavior. 2018 Nov;50(9):997–1031.

19. McGuire WJ. Attitudes and attitude change. The Handbook of Social Psychology. 1985:233–346.

20. Boomsma C, Goodhew J, Goodhew S, Pahl S. Improving the visibility of energy use in home heating in England: Thermal images and the role of visual tailoring. Energy Research & Social Science. 2016 Apr 1;14:111–21.

21. Kelly J, Knottenbelt W. Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature. [Preprint] arXiv:1605.00962. 2016 May 3 [cited 2019 October 01]. Available from

22. Energy Star. What are energy vampires and what can I do about them?; c2019 [cited 2019 Oct 01] Available from

23. Tversky A, Kahneman D. Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics. 1991 Nov 1;106(4):1039–61.

24. Sorrell S. Reducing energy demand: A review of issues, challenges and approaches. Renewable and Sustainable Energy Reviews. 2015 Jul 1;47:74–82.

25. Yates SM. Using prospect theory to create persuasive communications about solar water heaters and energy conservation [dissertation]. University of California; 1982.

26. Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V. The constructive, destructive, and reconstructive power of social norms: Reprise. Perspectives on Psychological Science. 2018 Mar;13(2):249–54. doi: 10.1177/1745691617693325 29592653

27. Harries T, Rettie R, Studley M, Burchell K, Chambers S. Is social norms marketing effective? A case study in domestic electricity consumption. European Journal of Marketing. 2013 Sep 20;47(9):1458–75.

28. Olausson U. Global warming—global responsibility? Media frames of collective action and scientific certainty. Public Understanding of Science. 2009 Jul;18(4):421–36.

29. Buchanan K, Russo R. Going the extra green mile: When others' actions fall short of their responsibility. Journal of Environmental Psychology. 2015 Jun 1;42:82–93.

30. Nolan JM, Schultz PW, Cialdini RB, Goldstein NJ, Griskevicius V. Normative social influence is underdetected. Personality and Social Psychology Bulletin. 2008 Jul;34(7):913–23. doi: 10.1177/0146167208316691 18550863

31. Is your energy bill scary? Slaying energy vampires can save Americans millions. c2019 [cited 2019 Oct 01] Available from

32. NStar. Energy Vampire Calculator. c2013 [cited 2013 Aug ]. Available from

33. George D, Mallery P. SPSS for Windows step by step: A simple guide and reference. 11.0 update. wps. ablongman. com/wps/media/objects/385. George 4answers pdf. 2003.

34. Cortina JM. What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology. 1993 Feb;78(1):98.

35. Hoekstra R, Monden R, van Ravenzwaaij D, Wagenmakers EJ. Bayesian reanalysis of null results reported in medicine: Strong yet variable evidence for the absence of treatment effects. PloS one. 2018 Apr 25;13(4):e0195474. doi: 10.1371/journal.pone.0195474 29694370

36. Bayes Factor (Dienes) Calculator. c2019 [cited 2019 Oct 01] Available from

37. Hayes AF, Preacher KJ. Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology. 2014 Nov;67(3):451–70. doi: 10.1111/bmsp.12028 24188158

38. Frederiks ER, Stenner K, Hobman EV. Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour. Renewable and Sustainable Energy Reviews. 2015 Jan 1;41:1385–94.

39. Buchanan K, Russo R, Anderson B. Feeding back about eco-feedback: How do consumers use and respond to energy monitors?. Energy Policy. 2014 Oct 1;73:138–46.

40. Lawrence W, Keyte J, Tinati T, Haslam C, Baird J, Margetts B, Swift J, Cooper C, Barker M. A mixed-methods investigation to explore how women living in disadvantaged areas might be supported to improve their diets. Journal of health psychology. 2012 Sep;17(6):785–98. doi: 10.1177/1359105311425271 22044913

41. Hargreaves T. Beyond energy feedback. Building Research & Information. 2018 Apr 3;46(3):332–42.

42. Strengers Y. Smart energy technologies in everyday life: Smart Utopia?. Springer: L; 2013 Sep 25.

43. Whittle R, Ellis R, Marshall I, Alcock P, Hutchison D, Mauthe A. From responsibility to accountability: Working creatively with distributed agency in office energy metering and management. Energy Research & Social Science. 2015 Nov 1;10:240–9.

Článek vyšel v časopise


2019 Číslo 10