research-article

Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System

Authors:

Martijn Willemsen,

Chris SnijdersAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 65 - 73

https://doi.org/10.1145/3109859.3109902

Published: 27 August 2017 Publication History

Abstract

People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.

References

[1]

Abrahamse, W., Steg, L., Vlek, C. and Rothengatter, T. 2005. A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology. 25, 3 (Sep. 2005), 273--291.

[2]

Abrahamse, W., Steg, L., Vlek, C. and Rothengatter, T. 2007. The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents. Journal of Environmental Psychology. 27, 4 (2007), 265--276.

[3]

Benders, R. M. J., Kok, R., Moll, H. C., Wiersma, G. and Noorman, K. J. 2006. New approaches for household energy conservation - In search of personal household energy budgets and energy reduction options. Energy Policy. 34, 18 (Dec. 2006), 3612--3622.

[4]

Berkovsky, S., Freyne, J. and Oinas-Kukkonen, H. eds. 2012. Influencing Individually: Fusing Personalization and Persuasion. ACM Trans. Interact. Intell. Syst. 2, 2 (Jun. 2012), 9:1--9:8.

Digital Library

[5]

Bond, T. G. and Fox, C. M. 2006. Applying the Rasch Model: Fundamental Measurement in the Human Sciences, Second Edition. Psychology Press.

[6]

Boudet, H. S., Flora, J. A. and Armel, K. C. 2016. Clustering household energy-saving behaviours by behavioural attribute. Energy Policy. 92, (May 2016), 444--454.

[7]

Cialdini, R. B. 2007. Influence: the psychology of persuasion. Collins.

[8]

Cialdini, R. B. and Trost, M. R. 1998. Social influence: Social norms, conformity and compliance. The handbook of social psychology, Vols. 1 and 2 (4th ed.). D. T. Gilbert, S. T. Fiske, and G. Lindzey, eds. McGraw-Hill. 151--192.

[9]

Cosley, D., Lam, S. K., Albert, I., Konstan, J. A. and Riedl, J. 2003. Is Seeing Believing? How Recommender System Interfaces Affect Users' Opinions. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2003), 585--592.

Digital Library

[10]

Costanzo, M., Archer, D., Aronson, E. and Pettigrew, T. 1986. Energy conservation behavior: The difficult path from information to action. American Psychologist. 41, 5 (1986), 521--528.

[11]

Darby, S. 1999. Energy advice - what is it worth. Proceedings, European Council for an Energy-Efficient Economy Summer Study, paper III. 5, (1999), 3--5.

[12]

Dietz, T., Gardner, G. T., Gilligan, J., Stern, P. C. and Vandenbergh, M. P. 2009. Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions. Proceedings of the National Academy of Sciences. 106, 44 (Nov. 2009), 18452--18456.

[13]

Ekstrand, M. D., Harper, F. M., Willemsen, M. C. and Konstan, J. A. 2014. User Perception of Differences in Recommender Algorithms. Proceedings of the 8th ACM Conference on Recommender Systems (New York, NY, USA, 2014), 161--168.

Digital Library

[14]

Ekstrand, M. D. and Willemsen, M. C. 2016. Behaviorism is Not Enough: Better Recommendations Through Listening to Users. Proceedings of the 10th ACM Conference on Recommender Systems (New York, NY, USA, 2016), 221--224.

Digital Library

[15]

Embretson, S. E. and Reise, S. P. 2000. Item Response Theory for Psychologists. Psychology Press.

[16]

Fogg, B. J. 2002. Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann.

Digital Library

[17]

Gardner, G. T. and Stern, P. C. 2008. The short list: The most effective actions US households can take to curb climate change. Environment: science and policy for sustainable development. 50, 5 (2008), 12--25.

[18]

Harper, F. M., Li, X., Chen, Y. and Konstan, J. A. 2005. An Economic Model of User Rating in an Online Recommender System. SpringerLink (Jul. 2005), 307--316.

Digital Library

[19]

Kaiser, F. G., Byrka, K. and Hartig, T. 2010. Reviving Campbell's Paradigm for Attitude Research. Personality and Social Psychology Review. 14, 4 (Nov. 2010), 351--367.

[20]

Kaiser, F. G. and Wilson, M. 2004. Goal-directed conservation behavior: the specific composition of a general performance. Personality and Individual Differences. 36, 7 (May 2004), 1531--1544.

[21]

Kaptein, M., Markopoulos, P., de Ruyter, B. and Aarts, E. 2015. Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. International Journal of Human-Computer Studies. 77, (May 2015), 38--51.

Digital Library

[22]

Karlin, B., Davis, N., Sanguinetti, A., Gamble, K., Kirkby, D. and Stokols, D. 2014. Dimensions of Conservation Exploring Differences Among Energy Behaviors. Environment and Behavior. 46, 4 (May 2014), 423--452.

[23]

Knijnenburg, B. P., Sivakumar, S. and Wilkinson, D. 2016. Recommender Systems for Self-Actualization. Proceedings of the 10th ACM Conference on Recommender Systems (New York, NY, USA, 2016), 11--14.

Digital Library

[24]

Knijnenburg, B. P., Willemsen, M. and Broeders, R. 2014. Smart sustainability through system satisfaction: Tailored preference elicitation for energy-saving recommenders. Twentieth Americas Conference on Information Systems (AMCIS) (Georgia, 2014).

[25]

Knijnenburg, B. P. and Willemsen, M. C. 2015. Evaluating recommender systems with user experiments. Recommender Systems Handbook. Springer. 309--352.

[26]

Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H. and Newell, C. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction. 22, 4--5 (2012), 441--504.

Digital Library

[27]

Mankoff, J., Matthews, D., Fussell, S. R. and Johnson, M. 2007. Leveraging Social Networks To Motivate Individuals to Reduce Their Ecological Footprints. Proceedings of the 40th Annual Hawaii International Conference on System Sciences (Washington, DC, USA, 2007), 87--.

Digital Library

[28]

McKenzie-Mohr, D. 2000. Fostering sustainable behavior through community-based social marketing. American Psychologist. 55, 5 (2000), 531--537.

[29]

McMakin, A. H., Malone, E. L. and Lundgren, R. E. 2002. Motivating Residents to Conserve Energy without Financial Incentives. Environment and Behavior. 34, 6 (Nov. 2002), 848--863.

[30]

Petkov, P., Goswami, S., Köbler, F. and Krcmar, H. 2012. Personalised Eco-feedback As a Design Technique for Motivating Energy Saving Behaviour at Home. Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design (New York, NY, USA, 2012), 587--596.

Digital Library

[31]

Petkov, P., Köbler, F., Foth, M., Medland, R. and Krcmar, H. 2011. Engaging energy saving through motivation-specific social comparison. CHI'11 Extended Abstracts on Human Factors in Computing Systems (2011), 1945--1950.

Digital Library

[32]

Radha, M., Willemsen, M. C., Boerhof, M. and IJsselsteijn, W. A. 2016. Lifestyle Recommendations for Hypertension Through Rasch-based Feasibility Modeling. Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (New York, NY, USA, 2016), 239--247.

Digital Library

[33]

Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A. and Riedl, J. 2002. Getting to Know You: Learning New User Preferences in Recommender Systems. Proceedings of the 7th International Conference on Intelligent User Interfaces (New York, NY, USA, 2002), 127--134.

Digital Library

[34]

Schultz, P. W. 2014. Strategies for promoting proenvironmental behavior: Lots of tools but few instructions. European Psychologist. 19, 2 (2014), 107--117.

[35]

Starke, A., Willemsen, M. C. and Snijders, C. 2015. Saving Energy in 1-D: Tailoring Energy-saving Advice Using a Rasch-based Energy Recommender System. Proc. 2nd Int. Workshop on Decision Making and Rec. Sys. (DMRS) (Bolzano, Italy, 2015), 5--8.

[36]

Steg, L. 2008. Promoting household energy conservation. Energy policy. 36, 12 (2008), 4449--4453.

[37]

Tintarev, N. and Masthoff, J. 2007. A Survey of Explanations in Recommender Systems. 2007 IEEE 23rd International Conference on Data Engineering Workshop (Apr. 2007), 801--810.

Digital Library

[38]

Urban, J. and Ščasny, M. 2014. Structure of Domestic Energy Saving: How Many Dimensions? Environment and Behavior. (2014), 13916514547081.

[39]

Wilson, C. and Dowlatabadi, H. 2007. Models of Decision Making and Residential Energy Use. Annual Review of Environment and Resources. 32, 1 (2007), 169--203.

[40]

Winett, R. A., Love, S. Q. and Kidd, C. 1982. Effectiveness of an energy specialist and extension agents in promoting summer energy conservation by home visits. J. Environ. Syst.; (United States). 12, 1 (1982).

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Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System

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cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 27 August 2017

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Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

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RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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https://dl.acm.org/doi/10.1145/3673791.3698430
Geninatti Cossatin ABoratto LGena CMarras MGermanakos PPopescus E(2024)Promoting Green Fashion Consumption in Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664922(50-54)Online publication date: 27-Jun-2024
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