IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v35y2025i1d10.1007_s12525-025-00763-1.html
   My bibliography  Save this article

Can feedback nudges enhance user satisfaction? Kano analysis for different eco-feedback nudge features in a smart home app

Author

Listed:
  • Michelle Berger

    (FIM Research Center for Information Management
    Branch Business & Information Systems Engineering of the Fraunhofer FIT
    University of Hohenheim, Digital Management)

  • Henner Gimpel

    (FIM Research Center for Information Management
    Branch Business & Information Systems Engineering of the Fraunhofer FIT
    University of Hohenheim, Digital Management)

  • Feline Schnaak

    (FIM Research Center for Information Management
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Linda Wolf

    (FIM Research Center for Information Management
    Branch Business & Information Systems Engineering of the Fraunhofer FIT
    University of Bayreuth)

Abstract

Digital nudging in smart home apps promotes energy conservation behavior in everyday life, helping to mitigate climate change. Prior research demonstrates the promising effect of the digital nudging element eco-feedback supporting behavioral change. However, the effect depends on adopting and using smart home apps with eco-feedback integrated. Hence, investigating user preferences concerning eco-feedback nudges is crucial in developing smart home apps that satisfy users. Considering the eco-feedback nudge features derived from a structured literature review, we conducted two user surveys approximately one year apart and assessed user satisfaction using the Kano model. The Kano model categorizes these features according to whether the user expects the feature or not, and whether the feature has a positive effect on user satisfaction when implemented or a negative effect when not implemented. As a result, we examine the impact of different eco-feedback nudge features on user satisfaction. Our study evaluates the robustness of user satisfaction over time and thereby adds another perspective to the traditional focus on the effectiveness of these nudges. Combining both perspectives – effectiveness and user satisfaction – is valuable for developers and providers of smart home apps to suggest which eco-feedback nudge features to incorporate.

Suggested Citation

  • Michelle Berger & Henner Gimpel & Feline Schnaak & Linda Wolf, 2025. "Can feedback nudges enhance user satisfaction? Kano analysis for different eco-feedback nudge features in a smart home app," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-21, December.
  • Handle: RePEc:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00763-1
    DOI: 10.1007/s12525-025-00763-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-025-00763-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12525-025-00763-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bradley S. Jorgensen & Sarah Fumei & Graeme Byrne, 2021. "Reducing Peak Energy Demand among Residents Who Are Not Billed for Their Electricity Consumption: Experimental Evaluation of Behaviour Change Interventions in a University Setting," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    2. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(1), pages 35-41, January.
    3. Crago, Christine L. & Spraggon, John M. & Hunter, Elizabeth, 2020. "Motivating non-ratepaying households with feedback and social nudges: A cautionary tale," Energy Policy, Elsevier, vol. 145(C).
    4. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    5. Myers, Erica & Souza, Mateus, 2020. "Social comparison nudges without monetary incentives: Evidence from home energy reports," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    6. Bidwell, David, 2013. "The role of values in public beliefs and attitudes towards commercial wind energy," Energy Policy, Elsevier, vol. 58(C), pages 189-199.
    7. Annette Wenninger & Daniel Rau & Maximilian Röglinger, 2022. "Improving customer satisfaction in proactive service design," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1399-1418, September.
    8. Henner Gimpel & Dominikus Kleindienst & Niclas Nüske & Daniel Rau & Fabian Schmied, 2018. "The upside of data privacy – delighting customers by implementing data privacy measures," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 437-452, November.
    9. Sorrell, Steve, 2015. "Reducing energy demand: A review of issues, challenges and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 74-82.
    10. Kim, Jin Han & Kaemingk, Michael, 2021. "Persisting effects of social norm feedback letters in reducing household electricity usage in Post-Soviet Eastern Europe: A randomized controlled trial," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 153-161.
    11. Nemati, Mehdi & Penn, Jerrod, 2020. "The impact of information-based interventions on conservation behavior: A meta-analysis," Resource and Energy Economics, Elsevier, vol. 62(C).
    12. Brülisauer, Marcel & Goette, Lorenz & Jiang, Zhengyi & Schmitz, Jan & Schubert, Renate, 2020. "Appliance-specific feedback and social comparisons: Evidence from a field experiment on energy conservation," Energy Policy, Elsevier, vol. 145(C).
    13. Asmare, Fissha & Jaraitė, Jūratė & Kažukauskas, Andrius, 2021. "The effect of descriptive information provision on electricity consumption: Experimental evidence from Lithuania," Energy Economics, Elsevier, vol. 104(C).
    14. Wei Gu & Peng Bao & Wenyuan Hao & Jaewoong Kim, 2019. "Empirical Examination of Intention to Continue to Use Smart Home Services," Sustainability, MDPI, vol. 11(19), pages 1-12, September.
    15. Bhati, Abhishek & Hansen, Michael & Chan, Ching Man, 2017. "Energy conservation through smart homes in a smart city: A lesson for Singapore households," Energy Policy, Elsevier, vol. 104(C), pages 230-239.
    16. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Publisher Correction: Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(10), pages 891-891, October.
    17. Henner Gimpel & Tobias Manner-Romberg & Fabian Schmied & Till J. Winkler, 2021. "Understanding the evaluation of mHealth app features based on a cross-country Kano analysis," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 765-794, December.
    18. Cellina, Francesca & Fraternali, Piero & Herrera Gonzalez, Sergio Luis & Novak, Jasminko & Gui, Marco & Rizzoli, Andrea Emilio, 2024. "Significant but transient: The impact of an energy saving app targeting Swiss households," Applied Energy, Elsevier, vol. 355(C).
    19. Verena Tiefenbeck & Anselma Wörner & Samuel Schöb & Elgar Fleisch & Thorsten Staake, 2019. "Publisher Correction: Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives," Nature Energy, Nature, vol. 4(4), pages 346-346, April.
    20. Carlos Tam & Diogo Santos & Tiago Oliveira, 2020. "Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model," Information Systems Frontiers, Springer, vol. 22(1), pages 243-257, February.
    21. Brandsma, Jeroen S. & Blasch, Julia E., 2019. "One for all? – The impact of different types of energy feedback and goal setting on individuals’ motivation to conserve electricity," Energy Policy, Elsevier, vol. 135(C).
    22. Diego Casado-Mansilla & Apostolos C. Tsolakis & Cruz E. Borges & Oihane Kamara-Esteban & Stelios Krinidis & Jose Manuel Avila & Dimitrios Tzovaras & Diego López-de-Ipiña, 2020. "Socio-Economic Effect on ICT-Based Persuasive Interventions Towards Energy Efficiency in Tertiary Buildings," Energies, MDPI, vol. 13(7), pages 1-26, April.
    23. Ruokamo, Enni & Meriläinen, Teemu & Karhinen, Santtu & Räihä, Jouni & Suur-Uski, Päivi & Timonen, Leila & Svento, Rauli, 2022. "The effect of information nudges on energy saving: Observations from a randomized field experiment in Finland," Energy Policy, Elsevier, vol. 161(C).
    24. Tussyadiah, Iis & Miller, Graham, 2019. "Nudged by a robot: Responses to agency and feedback," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    25. Cass Sunstein, 2014. "Nudging: A Very Short Guide," Journal of Consumer Policy, Springer, vol. 37(4), pages 583-588, December.
    26. Tarun M. Khanna & Giovanni Baiocchi & Max Callaghan & Felix Creutzig & Horia Guias & Neal R. Haddaway & Lion Hirth & Aneeque Javaid & Nicolas Koch & Sonja Laukemper & Andreas Löschel & Maria del Mar Z, 2021. "A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings," Nature Energy, Nature, vol. 6(9), pages 925-932, September.
    27. A. M. M. Sharif Ullah & Jun'ichi Tamaki, 2011. "Analysis of Kano‐model‐based customer needs for product development," Systems Engineering, John Wiley & Sons, vol. 14(2), pages 154-172, June.
    28. Fanghella, Valeria & Ploner, Matteo & Tavoni, Massimo, 2021. "Energy saving in a simulated environment: An online experiment of the interplay between nudges and financial incentives," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    Full references (including those not matched with items on IDEAS)

    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. Fang, Ximeng & Goette, Lorenz & Rockenbach, Bettina & Sutter, Matthias & Tiefenbeck, Verena & Schoeb, Samuel & Staake, Thorsten, 2023. "Complementarities in behavioral interventions: Evidence from a field experiment on resource conservation," Journal of Public Economics, Elsevier, vol. 228(C).
    2. Bonan, J. & Cattaneo, C. & d’Adda, G. & Galliera, A. & Tavoni, M., 2024. "Widening the scope: The direct and spillover effects of nudging water efficiency in the presence of other behavioral interventions," Journal of Environmental Economics and Management, Elsevier, vol. 127(C).
    3. Adélaïde Fadhuile & Daniel Llerena & Béatrice Roussillon, 2023. "Intrinsic motivation to promote the development of renewable energy: a field experiment from household demand," Working Papers 2023-01, Grenoble Applied Economics Laboratory (GAEL).
    4. Raman, Gururaghav & Zhao, Bo & Peng, Jimmy Chih-Hsien & Weidlich, Matthias, 2022. "Adaptive incentive-based demand response with distributed non-compliance assessment," Applied Energy, Elsevier, vol. 326(C).
    5. Jianling Jiao & Nuonuo Chen & Ranran Yang, 2024. "How to promote green travel effectively: a study of niche information interventions based on meta-analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(4), pages 8267-8301, April.
    6. Al-Ubaydli, Omar & Cassidy, Alecia & Chatterjee, Anomitro & Khalifa, Ahmed & Price, Michael, 2023. "The power to conserve: a field experiment on electricity use in Qatar," LSE Research Online Documents on Economics 121048, London School of Economics and Political Science, LSE Library.
    7. Dirk Leffrang & Oliver Müller, 2025. "The Sustainability-Performance Trade-off in AI: The Role of Sustainability Information and Unmet Performance Goals in Sustainable AI Decisions," Working Papers Dissertations 135, Paderborn University, Faculty of Business Administration and Economics.
    8. Dominik Bär & Stefan Feuerriegel & Ting Li & Markus Weinmann, 2023. "Message framing to promote solar panels," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Andreas Gerster & Mark A. Andor & Lorenz Götte, 2020. "Disaggregate Consumption Feedback and Energy Conservation," CRC TR 224 Discussion Paper Series crctr224_2020_182, University of Bonn and University of Mannheim, Germany.
    10. Rita Abdel Sater, 2021. "Essays on the application of behavioural insights to environmental policy [Essais sur l’application des connaissances comportementales aux politiques environnementales]," SciencePo Working papers tel-03450909, HAL.
    11. Zhang, Chaoqun & Zha, Donglan & Jiang, Pansong & Wang, Fu & Yang, Guanglei & Salman, Muhammad & Wu, Qing, 2023. "The effect of customized information feedback on individual electricity saving behavior: Evidence from a field experiment in China," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    12. Julia Talbot-Jones & Sophie Hale & Suzie Greenhalgh, 2020. "Review of policy instruments for freshwater management," Working Papers 20_10, Motu Economic and Public Policy Research.
    13. Zha, Donglan & Zhang, Chaoqun & Jiang, Pansong & Wang, Fu, 2024. "What makes energy consumption behavior visible? Conceptualization, scale development and validation of customized information feedback," Journal of Business Research, Elsevier, vol. 182(C).
    14. Spandagos, Constantine & Baark, Erik & Ng, Tze Ling & Yarime, Masaru, 2021. "Social influence and economic intervention policies to save energy at home: Critical questions for the new decade and evidence from air-condition use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    15. Mark A. Andor & Lorenz Goette & Michael K. Price & Anna Schulze-Tilling & Lukas Tomberg, 2025. "Real-Time Feedback and Social Comparison Reports Impact Resource Use and Welfare: Evidence From a Field Experiment," CRC TR 224 Discussion Paper Series crctr224_2025_651, University of Bonn and University of Mannheim, Germany.
    16. Pratik Mochi & Kartik Pandya & Joao Soares & Zita Vale, 2023. "Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community," Mathematics, MDPI, vol. 11(10), pages 1-15, May.
    17. Giorgos Meramveliotakis & Manolis Manioudis, 2024. "Default Nudge and Street Lightning Conservation: Towards a Policy Proposal for the Current Energy Crisis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9228-9237, June.
    18. Botao Qin & Haoyan Chen, 2022. "Does the nudge effect persist? Evidence from a field experiment using social comparison message in China," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 689-703, July.
    19. Crago, Christine L. & Spraggon, John M. & Hunter, Elizabeth, 2020. "Motivating non-ratepaying households with feedback and social nudges: A cautionary tale," Energy Policy, Elsevier, vol. 145(C).
    20. Liebe, Ulf & Gewinner, Jennifer & Diekmann, Andreas, 2018. "What is missing in research on non-monetary incentives in the household energy sector?," Energy Policy, Elsevier, vol. 123(C), pages 180-183.

    More about this item

    Keywords

    Digital nudging; Feedback; Smart home app; Energy conservation behavior; User satisfaction;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    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:spr:elmark:v:35:y:2025:i:1:d:10.1007_s12525-025-00763-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.