To Save or Not to Save? Let Me Help You Out: Persuasive Effects of Smart Agent in Promoting Energy Conservation

  • Guo Yu
  • Pei-Luen Patrick RauEmail author
  • Na Sun
  • Xiang Ji
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9741)


In public places, people’s energy conservation decisions and behaviors are easily suppressed by contextual and/or personal factors. To perform and maintain energy-saving behaviors, people need to be empowered both externally and internally. This research explored how a smart agent could help. The first study revealed that when a smart agent empowered people externally by offering help, people would be more active and resolute in decision-making and more likely to save energy, while some would be unaffected and decide to use energy. The second study found that the acknowledgement of behavioral impact could significantly facilitate people’s evaluation processes and enhance their self-efficacy, but such effects would be moderated by the time cost of a task, which was proved positively correlated with the perceived task difficulty. Both theoretical and practical implications for energy conservation were discussed, and six guidelines for smart agent design were proposed.


Energy conservation Self-efficacy Persuasive agent Empowerment Behavioral impact 



The authors would like to acknowledge the sponsorship provided by the National Natural Science Foundation China grant 71188001.


  1. Anker-Nilssen, P.: Household energy use and the environment: a conflicting issue. Appl. Energy 76(1), 189–196 (2003)CrossRefGoogle Scholar
  2. Bandura, A.: Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191 (1977)CrossRefGoogle Scholar
  3. Bandura, A.: Self-referent thought: a developmental analysis of self-efficacy. In: Social Cognitive Development: Frontiers and Possible Futures, pp. 200–239 (1981)Google Scholar
  4. Bandura, A.: Self-efficacy mechanism in human agency. Am. Psychol. 37(2), 122–148 (1982)CrossRefGoogle Scholar
  5. Bandura, A., Reese, L., Adams, N.E.: Microanalysis of action and fear arousal as a function of differential levels of perceived self-efficacy. J. Pers. Soc. Psychol. 43(1), 5–21 (1982)CrossRefGoogle Scholar
  6. Coyle, D., Moore, J., Kristensson, P.O., Fletcher, P., Blackwell, A.: I did that!: measuring users’ experience of agency in their own actions. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 2025–2034. ACM (2012)Google Scholar
  7. Fischer, C.: Feedback on household electricity consumption: a tool for saving energy? Energy Effi. 1(1), 79–104 (2008)CrossRefGoogle Scholar
  8. Griskevicius, V., Tybur, J.M., Van den Bergh, B.: Going green to be seen: status, reputation, and conspicuous conservation. J. Pers. Soc. Psychol. 98(3), 392–404 (2010)CrossRefGoogle Scholar
  9. H’Mida, S., Chávez, E., Guindon, C.: Determinant of pro-environmental behaviors within individual consumers. J. Econ. Lit. Classif. M 31, 1–12 (2008)Google Scholar
  10. Hargreaves, T., Nye, M., Burgess, J.: Making energy visible: a qualitative field study of how householders interact with feedback from smart energy monitors. Socio-Econ. Trans. Towards Hydrogen Econ. – Find. Eur. Res. Regul. Pap. 38(10), 6111–6119 (2010). doi: 10.1016/j.enpol.2010.05.068 Google Scholar
  11. Kirby, R., Forlizzi, J., Simmons, R.: Affective social robots. Robot. Auton. Syst. 58(3), 322–332 (2010)CrossRefGoogle Scholar
  12. Kollmuss, A., Agyeman, J.: Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior? Environ. Educ. Res. 8(3), 239–260 (2002)CrossRefGoogle Scholar
  13. Moore, J., Haggard, P.: Awareness of action: inference and prediction. Conscious. Cogn. 17(1), 136–144 (2008)CrossRefGoogle Scholar
  14. Pierce, J., Paulos, E.: Beyond energy monitors: interaction, energy, and emerging energy systems. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems, pp. 665–674. ACM (2012)Google Scholar
  15. Roalter, L., Kranz, M., Möller, A.: A middleware for intelligent environments and the internet of things. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 267–281. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. Sahakian, M.D., Steinberger, J.K.: Energy reduction through a deeper understanding of household consumption. J. Ind. Ecol. 15(1), 31–48 (2011)CrossRefGoogle Scholar
  17. Van Raaij, W.F., Verhallen, T.M.: A behavioral model of residential energy use. J. Econ. Psychol. 3(1), 39–63 (1983)CrossRefGoogle Scholar
  18. Xiao, B., Benbasat, I.: E-commerce product recommendation agents: use, characteristics, and impact. MIS Q. 31(2), 137–209 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guo Yu
    • 1
  • Pei-Luen Patrick Rau
    • 1
    Email author
  • Na Sun
    • 1
  • Xiang Ji
    • 1
  1. 1.Department of Industrial Engineering, Institute of Human Factors and ErgonomicsTsinghua UniversityBeijing China

Personalised recommendations