Designing Conversational Agents for Energy Feedback

  • Ulrich GnewuchEmail author
  • Stefan Morana
  • Carl Heckmann
  • Alexander Maedche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)


Reducing and shifting energy consumption could contribute significantly to a more sustainable use of energy in households. Studies have shown that the provision of feedback can encourage consumers to use energy more sustainably. While there is wide variety of energy feedback solutions ranging from in-home displays to mobile applications, there is a lack of research on whether and how conversational agents can provide energy feedback to promote sustainable energy use. As conversational agents, such as chatbots, promise a natural and intuitive user interface, they may have great potential for energy feedback. This paper explores how to design conversational agents for energy feedback and proposes design principles based on existing literature. The design principles are instantiated in a text-based conversational agent and evaluated in a focus group session with industry experts. We contribute with valuable design knowledge that extends previous research on the design of energy feedback solutions.


Conversational agent Chatbot Energy feedback Focus group Design science research 


  1. 1.
    International Energy Agency: World Energy Investment (2017). Accessed 28 Jan 2018
  2. 2.
    European Commission: 2050 Low-Carbon Economy Roadmap (2017). Accessed 15 Jan 2018
  3. 3.
    Kobus, C.B.A., Mugge, R., Schoormans, J.P.L.: Washing when the sun is shining! How users interact with a household energy management system. Ergonomics 56, 451–462 (2013)CrossRefGoogle Scholar
  4. 4.
    Karlin, B., Zinger, J.F., Ford, R.: The effects of feedback on energy conservation: a meta-analysis. Psychol. Bull. 141, 1205–1227 (2015)CrossRefGoogle Scholar
  5. 5.
    Karlin, B., Ford, R., Squiers, C.: Energy feedback technology: a review and taxonomy of products and platforms. Energy Effi. 7, 377–399 (2014)CrossRefGoogle Scholar
  6. 6.
    Weiss, M., Helfenstein, A., Mattern, F., Staake, T.: Leveraging smart meter data to recognize home appliances. In: 2012 IEEE International Conference on Pervasive Computing and Communications, pp. 190–197. IEEE (2012)Google Scholar
  7. 7.
    Pullinger, M., Lovell, H., Webb, J.: Influencing household energy practices: a critical review of UK smart metering standards and commercial feedback devices. Technol. Anal. Strateg. Manag. 26, 1144–1162 (2014)CrossRefGoogle Scholar
  8. 8.
    McTear, M., Callejas, Z., Griol, D.: The Conversational Interface: Talking to Smart Devices. Springer, Heidelberg (2016). Scholar
  9. 9.
    Dale, R.: The return of the chatbots. Nat. Lang. Eng. 22, 811–817 (2016)CrossRefGoogle Scholar
  10. 10.
    Gartner: Top Trends in the Gartner Hype Cycle for Emerging Technologies (2017). Accessed 20 Dec 2017
  11. 11.
    Bourgeois, J., Van Der Linden, J., Kortuem, G., Price, B.A., Rimmer, C.: Conversations with my washing machine: an in-the-wild study of demand-shifting with self-generated energy. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 459–470 (2014)Google Scholar
  12. 12.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28, 75–105 (2004)CrossRefGoogle Scholar
  13. 13.
    Tremblay, M.C., Hevner, A.R., Berndt, D.J.: Focus groups for artifact refinement and evaluation in design research. Commun. Assoc. Inf. Syst. 26, 599–618 (2010)Google Scholar
  14. 14.
    Kluger, A.N., DeNisi, A.: The effects of feedback interventions on performance: a historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychol. Bull. 119, 254–284 (1996)CrossRefGoogle Scholar
  15. 15.
    Sanguinetti, A., Dombrovski, K., Sikand, S.: Information, timing, and display: a design-behavior framework for improving the effectiveness of eco-feedback. Energy Res. Soc. Sci. 39, 55–68 (2018)CrossRefGoogle Scholar
  16. 16.
    Dalén, A., Krämer, J.: Towards a user-centered feedback design for smart meter interfaces to support efficient energy-use choices. Bus. Inf. Syst. Eng. 59, 361–373 (2017)CrossRefGoogle Scholar
  17. 17.
    Froehlich, J.: Promoting energy efficient behaviors in the home through feedback: the role of human-computer interaction. In: Proceedings of the HCIC Workshop (2009)Google Scholar
  18. 18.
    Weiss, M., Staake, T., Mattern, F., Fleisch, E.: Powerpedia - changing energy usage with the help of a smartphone application. Pers. Ubiquit. Comput. 16, 655–664 (2012)CrossRefGoogle Scholar
  19. 19.
    Gnewuch, U., Morana, S., Maedche, A.: Towards designing cooperative and social conversational agents for customer service. In: Proceedings of the 38th International Conference on Information Systems (ICIS), Seoul, South Korea (2017)Google Scholar
  20. 20.
    Weizenbaum, J.: ELIZA - a computer program for the study of natural language communication between man and machine. Commun. ACM 9, 36–45 (1966)CrossRefGoogle Scholar
  21. 21.
    Maedche, A., Morana, S., Schacht, S., Werth, D., Krumeich, J.: Advanced user assistance systems. Bus. Inf. Syst. Eng. 58, 367–370 (2016)CrossRefGoogle Scholar
  22. 22.
    Beale, R., Creed, C.: Affective interaction: how emotional agents affect users. Int. J. Hum. Comput. Stud. 67, 755–776 (2009)CrossRefGoogle Scholar
  23. 23.
    Bickmore, T., Cassell, J.: Relational agents: a model and implementation of building user trust. In: Proceedings of the 2001 SIGCHI Conference on Human Factors in Computing Systems (2001)Google Scholar
  24. 24.
    Kuechler, B., Vaishnavi, V.: Theory development in design science research: anatomy of a research project. Eur. J. Inf. Syst. 17, 489–504 (2008)CrossRefGoogle Scholar
  25. 25.
    Gall, M.: (2018). Accessed 28 Jan 2018
  26. 26.
    Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25, 77–89 (2016)CrossRefGoogle Scholar
  27. 27.
    Miller, W., Senadeera, M.: Social transition from energy consumers to prosumers: rethinking the purpose and functionality of eco-feedback technologies. Sustain. Cities Soc. 35, 615–625 (2017)CrossRefGoogle Scholar
  28. 28.
    Watson, A., Viney, H., Schomaker, P.: Consumer attitudes to utility products: a consumer behaviour perspective. Mark. Intell. Plan. 20, 394–404 (2002)CrossRefGoogle Scholar
  29. 29.
    Morana, S., Schacht, S., Scherp, A., Maedche, A.: A review of the nature and effects of guidance design features. Decis. Support Syst. 97, 31–42 (2017)CrossRefGoogle Scholar
  30. 30.
    Sarikaya, R.: The technology behind personal digital assistants: an overview of the system architecture and key components. IEEE Sig. Process. Mag. 34, 67–81 (2017)CrossRefGoogle Scholar
  31. 31.
    Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., Staake, T.: Overcoming salience bias: how real-time feedback fosters resource conservation. Manage. Sci. 64(3), 1458–1476 (2018). Scholar
  32. 32.
    Fogg, B.J.: Computers as persuasive social actors. In: Persuasive Technology: Using Computers to Change What We Think and Do, pp. 89–120. Morgan Kaufmann Publishers, San Francisco (2002)CrossRefGoogle Scholar
  33. 33.
    Nass, C., Steuer, J., Tauber, E.R.: Computers are social actors. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, pp. 72–78 (1994)Google Scholar
  34. 34.
    Ham, J., Midden, C.J.H.: A persuasive robot to stimulate energy conservation: the influence of positive and negative social feedback and task similarity on energy-consumption behavior. Int. J. Soc. Robot. 6, 163–171 (2014)CrossRefGoogle Scholar
  35. 35.
    Statista Number of mobile phone messaging app users worldwide from 2016 to 2021 (2018). Accessed 28 Jan 2018
  36. 36.
    Easwara Moorthy, A., Vu, K.P.L.: Privacy concerns for use of voice activated personal assistant in the public space. Int. J. Hum. Comput. Interact. 31, 307–335 (2015)CrossRefGoogle Scholar
  37. 37.
    Appel, J., von der Pütten, A., Krämer, N.C., Gratch, J.: Does humanity matter? Analyzing the importance of social cues and perceived agency of a computer system for the emergence of social reactions during human-computer interaction. Adv. Hum.-Comput. Interact. 2012, 1–10 (2012)CrossRefGoogle Scholar
  38. 38.
    Klopfenstein, L.C., Delpriori, S., Malatini, S., Bogliolo, A.: The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 2017 Conference on Designing Interactive Systems, pp. 555–565 (2017)Google Scholar
  39. 39.
    Tremblay, M.C., Hevner, A.R., Berndt, D.J.: The use of focus groups in design science research. In: Hevner, A., Chatterjee, S. (eds.) Integrated Series in Information Systems. Design Research in Information Systems, pp. 121–143. Springer, Boston (2010). Scholar
  40. 40.
    Morana, S., Schacht, S., Scherp, A., Maedche, A.: Designing a process guidance system to support user’s business process compliance. In: ICIS 2014 Proceedings, pp. 1–19 (2014)Google Scholar
  41. 41.
    Zheng, G., Vaishnavi, V.K.: A multidimensional perceptual map approach to project prioritization and selection. AIS Trans. Hum.-Comput. Interact. 3, 82–103 (2011)CrossRefGoogle Scholar
  42. 42.
    Snow, S., Buys, L., Roe, P., Brereton, M.: Curiosity to cupboard: self reported disengagement with energy use feedback over time. In: Proceedings of the 25th Australian Computer-Human Interaction Conference, pp. 245–254 (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ulrich Gnewuch
    • 1
    • 2
    Email author
  • Stefan Morana
    • 1
  • Carl Heckmann
    • 2
  • Alexander Maedche
    • 1
  1. 1.Institute of Information Systems and Marketing (IISM), Karlsruhe Institute of Technology (KIT)KarlsruheGermany
  2. 2.hsag Heidelberger Service AGHeidelbergGermany

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