- freely available
Sustainability 2017, 9(4), 622; doi:10.3390/su9040622
1.1. Emerging Research at the Human-Technology Interface
2. The Default Effect
2.1. Defaults in Smart Technologies
2.2. Why Do Defaults Work?
2.3. Moderators of the Default Effect
3. Perceived Control
- Internal, referring to one’s beliefs about his or her abilities to operate technologies in general (i.e., computer self-efficacy); and
- External, or environmental conditions specific to a device.
4. Trust in Automation
- Actual predictability of machine behavior, with greater predictability leading to greater trust;
- User ability to estimate the predictability of machine behavior, which can be enhanced by user experience; and
- Stability of the environment in which the machine operates, where highly variable environments can result in more erratic machine behavior which can be perceived by consumers as less predictable and hence lead to lower levels of trust.
5. Future Directions
- Device specificity. People may not respond uniformly to defaults of different smart home technologies, particularly if levels of knowledge or experience vary across devices. However, the extent to which a smart home ecosystem may foster compliance with defaults for multiple devices is unclear. For instance, if an occupant accepts default thermostat settings, will he be more likely to subsequently accept default demand response settings? This may be driven in part by trust in automation, whereby higher levels of general trust could lead to similar outcomes across devices. Alternatively, a “spillover effect”, whereby trust in one smart home technology may foster trust in another, could occur. Future research should employ behavioral experiments to evaluate spillover effects in the context of smart home technology adoption and use.
- Experience: a double-edged sword? Greater experience with a device or domain can diminish the default effect, while simultaneously increasing perceived control and trust. How these conflicting effects impact acceptance of smart home defaults has yet to be investigated. One possibility is that more experience affects the self-focused aspects of perceived control and trust (e.g., I have the ability to control this device, I know how to get it to do what I want), and increases default overrides. Another possibility is that experience enhances perceived control, which increases trust in the device, such that people are more willing accept defaults. Future behavioral studies should flesh out the ways in which experience may decrease or increase compliance with green defaults.
- Tailoring defaults. Smart home technologies require a nuanced understanding of human behavior in order to meet unique and often shifting occupant preferences . Some studies have found that when occupants are unhappy with learned thermostat programs, they adjust settings until they are comfortable . Hence, a goal of green defaults for smart home technologies is to learn optimal settings for individual occupants, removing the need for the occupants themselves to repeatedly adjust. This is a multi-agent challenge, as one household’s preferred setting may not be the same as another household’s, and within a given household, multiple occupants may have different preferences [54,55,56]. Furthermore, occupant preferences can change over time, and there may be differences between what occupants thought they wanted before installation, and what functionality actually works in practice . For some, this means that devices with automated schedules may be too rigid. Indeed, most households with smart technologies describe an initial period of adjusting settings to customize to occupant needs . Hence, smart home technologies should permit and respond to natural changes in occupant routines . Finally, there is a difference between technologies predicting human behavior versus sensing where an occupant is, what activity s/he is engaged in, where s/he is going, and responding intelligently to these inputs . Because household behavior is often not particularly predictable, intelligent sensing or other occupant inputs can aid in getting settings “right” [3,4]. Although sensors (in the home, mobile, or wearable) are subject to environmental noise, redundancy (i.e., multiple sensors), occupant prompting, and specificity in sensing (e.g., RFID to identify individuals) can help address this ; video and audio sensing have also been proposed but carry privacy concerns . Future work should take an interdisciplinary, multi-modal approach combining sensors and behavioral studies to elucidate occupant preferences around optimal settings.
- Interoperability. Although smart home technologies have arrived, they are not arriving as a seamless, integrated solution. Instead, individual products are becoming available one at a time, such as smart and/or learning thermostats, smart door locks, camera systems, etc. In fact, in the near future, it may be impossible to find devices that are not connected- this is already becoming true for televisions, most of which are now internet-enabled out of the box. People may acquire individual devices, and over time, accumulate several of these, each of which adds some “smartness” to their home, car, on physical person. But if devices do not work together, occupants may become confused or overwhelmed, which would be counterproductive to the smart home enterprise . Imagine a consumer who uses two dozen applications per day to control individual smart devices in her home. This level of effort can take away from her user experience for any of the individual products, and negatively impact perceptions of such products and smart homes as a whole. Thus, as more and more devices become available, they will need to function together as a unified system to maximize value to consumers. Moreover, devices should seamlessly and naturally integrate into home life. Designing smart homes in such a manner will require drawing on behavioral science theory to deepen our understanding of occupant needs, the household social context, and how needs and social interactions shift over time [55,59]. To ensure the long-term viability of the smart and connected home, “future proofing” is needed, whereby the design of current devices considers compatibility with subsequent generations of devices, as well as regulatory frameworks, standards, and policies . Developing such solutions will require interdisciplinary integration across many fields, including the behavioral sciences, public policy, engineering, and computer science to name a few.
- Ethical considerations. In the smart home literature, among the most commonly cited barriers to adoption are concerns over sharing private/sensitive information, “big brother”—type control of home equipment, as well as data security [4,54,55,58]. People have reported that they do not want utility companies having data on their home’s occupancy nor private activities , and in addition are also concerned that such data may be sold or could fall into the wrong hands [3,4]. Addressing these concerns can help promote the uptake of smart home technologies and should be a priority for the smart home research agenda. Policies and standards need to be developed to safeguard consumer privacy and information, and foster greater levels of transparency and accountability for these data on the parts of product manufacturers, energy utilities, and other actors that may have access to occupants’ data [4,55]. Another ethical consideration stems from our above discussion around perceived control, which assumes that occupants also have actual control. However, what range of control should occupants be permitted when the goal is to reduce energy waste ? Should occupants in different settings be permitted different ranges of control? Currently, in many office settings, occupants do not have much if any control over various comfort settings (e.g., thermostat, airflow). Future studies of consumer preferences and behavior can help to shed light on these ethical questions.
6. Conclusions and Recommendations
Conflicts of Interest
- Yang, R.; Newman, M.W.; Forlizzi, J. Making sustainability sustainable: Challenges in the design of eco-interaction technologies. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada, 26 April–1 May 2014; pp. 823–832. [Google Scholar]
- Yang, R.; Newman, M.W. Learning from a learning thermostat: Lessons from intelligent systems for the home. In Proceedings of the 15th International Conference on Ubiquitous Computing, Zurich, Switzerland, 8–12 September 2013; pp. 93–102. [Google Scholar]
- Balta-Ozkan, N.; Davidson, R.; Bicket, M.; Whitmarsh, L. Social barriers to the adoption of smart homes. Energy Policy 2013, 63, 363–374. [Google Scholar] [CrossRef]
- Paetz, A.-G.; Dutschke, E.; Fichtner, W. Smart homes as a means to sustainable energy consumption: A study of consumer perceptions. J. Consum. Policy 2012, 35, 23–41. [Google Scholar] [CrossRef]
- Brush, A.J.B.; Lee, B.; Mahajan, R.; Agarwal, S.; Saroiu, S.; Dixon, C. Home automation in the wild: Challenges and opportunities. In Proceedings of SIGCHI Conference on Human Factors in Computing Systems, Vancouver, BC, Canada, 7–12 May 2011; pp. 2115–2124. [Google Scholar]
- Leijten, F.R.M.; Bolderdijk, J.W.; Keizer, K.; Gorira, M.; van der Werff, E.; Steg, L. Factors that influence consumers’ acceptance of future energy systems: the effects of adjustment type, production level, and price. Energy Effic. 2014, 7, 973–985. [Google Scholar] [CrossRef]
- Peffer, T.; Pritoni, M.; Meier, A.; Aragon, C.; Perry, D. How people use thermostats in homes: A review. Build. Environ. 2011, 46, 2529–2541. [Google Scholar] [CrossRef]
- Peffer, T.; Perry, D.; Pritoni, M.; Aragon, C.; Meier, A. Facilitating energy savings with programmable thermostats: Evaluation and guidelines for thermostat user interface. Ergonomics 2013, 56, 463–479. [Google Scholar] [CrossRef] [PubMed]
- Yang, R.; Newman, M.W. Living with an intelligent thermostat: Advanced control for heating and cooling systems. In Proceedings of the 14th International Conference on Ubiquitous Computing, Pittsburgh, PN, USA, 5–8 September 2012; pp. 1102–1107. [Google Scholar]
- Pichert, D.; Katsikopoulos, K.V. Green defaults: Information presentation and pro-environmental behavior. J. Environ. Psych. 2008, 28, 63–73. [Google Scholar] [CrossRef]
- Hoff, K.A.; Bashir, M. Trust in automation: Integrating empirical evidence on factors that influence trust. Hum. Factors 2015, 57, 407–434. [Google Scholar] [CrossRef] [PubMed]
- Sintov, N.D.; Schultz, P.W. Unlocking the potential of smart grid technologies with behavioral science. Front. Psychol. 2015, 6, 410. [Google Scholar] [CrossRef] [PubMed]
- Brown, C. L.; Krishna, A. The skeptical shopper: A metacognitive account for effects of defaults options on choice. J. Consum. Res. 2004, 31, 529–539. [Google Scholar] [CrossRef]
- Johnson, E. J.; Goldstein, D. Do defaults save lives? Science 2003, 302, 1338–1339. [Google Scholar] [CrossRef] [PubMed]
- Choi, J.J.; Laibson, D.; Madrian, B.; Metrick, A. Optimal defaults. Am. Econ. Rev. 2003, 93, 180–185. [Google Scholar] [CrossRef]
- Cronqvist, H.; Thaler, R.H. Design choices in privatized social-security systems: Learning from the Swedish experience. Am. Econ. Rev. 2004, 94, 424–428. [Google Scholar] [CrossRef]
- Madrian, B.C.; Shea, D.F. The power of suggestion: Inertia in 401(k) participation and savings behavior. Q. J. Econ. 2001, 116, 1149–1187. [Google Scholar] [CrossRef]
- Sunstein, C.R. Behavioral economics, consumption, and environmental protection. Regulatory Policy Program Working Paper RPP-2013-19. In Forthconing in Handbook on Research in Sustainable Consumption; Mossavar-Rahmani Center for Business and Government, Harvard Kennedy School, Harvard University: Cambridge, MA, USA, 2013. [Google Scholar]
- Oullier, O.; Sauneron, S. Green Nudges: New incentives for ecological behavior. Note d’Anal. 2011, 216, 1–10. [Google Scholar]
- Araña, J.E.; Leon, C.J. Can defaults save the climate? Evidence from a field xperiment on carbon ofsetting programs. Environ. Resour. Econ. 2013, 54, 613–626. [Google Scholar] [CrossRef]
- Brown, Z.; Johnston, N.; Hascic, I.; Vong, L.; Barascud, F. Testing the effect of defaults on the thermostat settings of OECD employees. Energy Econ. 2013, 39, 128–134. [Google Scholar] [CrossRef]
- Newsham, G.R.; Bowker, B.G. The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review. Energy Policy 2010, 38, 3289–3296. [Google Scholar] [CrossRef]
- Newsham, G.R.; Mancini, S.; Veitch, J.A.; Marchand, R.G.; Lei, W.; Charles, K.E.; Arsenault, C.D. Control strategies for lighting and ventilation in offices: effects on energy and occupants. Intell. Build. Int. 2009, 1, 101–121. [Google Scholar] [CrossRef]
- Greenberg, D.; Straub, M. Demand Response Delivers Positive Results. Transmission & Distribution World. Available online: http://www.tdworld.com/customer_service/demand_response_delivers_results/index.htmlS (accessed on 15 February 2017).
- Kirby, B.J. Spinning Reserve from Responsive Loads. Oak Ridge National Laboratory Report Number ORNL/TM-2003/19; 2003. Available online: http://certs.lbl.gov/certs-load-pubs.htmlS (accessed on 30 March 2017). [Google Scholar]
- Samuelson, W.; Zeckhauser, R. Status quo bas in decision making. J. Risk Uncertain. 1988, 1, 7–59. [Google Scholar] [CrossRef]
- Camerer, C.F. Prospect theory in the wild: Evidence from the field. In Choices, Values, and Frames; Kahneman, D., Tversky, A., Eds.; Cambridge University Press: New York, NY, USA, 2000; pp. 288–300. [Google Scholar]
- Kahneman, D.; Knetsch, J.L.; Thaler, R.H. Experimental tests of the endowment effect and the Coase Theorem. J. Political Econ. 1990, 98, 1325–1348. [Google Scholar] [CrossRef]
- Thaler, R. The Winner’s Curse: Paradoxes and Anomalies of Economic Life; Simon and Schuster: New York, NY, USA, 2012. [Google Scholar]
- Dinner, I.; Johnson, E.J.; Goldstein, D.G.; Liu, K. Partitioning default effects: Why people choose not to choose. J. Exp. Psychol. Appl. 2011, 17, 332–341. [Google Scholar] [CrossRef] [PubMed]
- Croson, R.; Treich, N. Behavioral environmental economics: Promises and challenges. Environ. Resour. Econ. 2014, 58, 335–351. [Google Scholar] [CrossRef]
- McKenzie, C.R.M.; Liersch, M.J.; Finkelstein, S.R. Recommendations implicit in policy defaults. Pers. Individ. Dif. 2006, 17, 414–420. [Google Scholar] [CrossRef] [PubMed]
- Nolan, J.M.; Schultz, P.W.; Cialdini, R.B.; Goldstein, N.J.; Griskevicius, V. Normative social influences is underdetected. Pers. Soc. Psychol. Bull. 2008, 34, 913–923. [Google Scholar] [CrossRef] [PubMed]
- Sunstein, C.R.; Thaler, R.H. Libertarian paternalism is not an oxymoron. Univ. Chic. Law Rev. 2003, 70, 1159–1201. [Google Scholar] [CrossRef]
- Lofgren, A.; Martinsson, P.; Hennlock, M.; Sterner, T. Are experienced people affected by a pre—Set default option—Results from a field experiment. J. Environ. Econ. Manag. 2012, 63, 66–72. [Google Scholar] [CrossRef]
- Sunstein, C.R.; Reisch, L.A. Automatically Green: Behavioral economics and environmental protection. Harv. Environ. Law Rev. 2014, 38, 2014. [Google Scholar] [CrossRef]
- Loock, C.-M.; Staake, T.; Thiesse, F. Motivating energy-efficient behavior with green IS: An investigation of goal setting and the role of defaults. MIS Q. 2013, 37, 1313–1332. [Google Scholar]
- Kaiser, F.G.; Arnold, O.; Otto, S. Attitudes and Defaults Save Lives and Protect the Environment Jointly and Compensatorily: Understanding the Behavioral Efficacy of Nudges and Other Structural Interventions. Behav. Sci. 2014, 4, 202–212. [Google Scholar] [CrossRef] [PubMed]
- Vetter, M.; Kutzner, F. Nudge me if you can—How defaults and attitude strength interact to change behavior. Compr. Results Soc. Psychol. 2016. [Google Scholar] [CrossRef]
- Beshears, J.; Choi, J.J.; Laibson, D.; Madrian, B.C. The Limitations of Defaults. (Unpublished manuscript).
- Gaffigan, M.E. Advanced Energy Technologies: Budget Trends and Challenges for DOE’s Energy R & D Program; United States Government Accountability Office: Washington, DC, USA, 2008.
- Mathieson, K. Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Inf. Syst. Res. 1991, 2, 173–191. [Google Scholar] [CrossRef]
- Venkatesh, V. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 2000, 11, 342–365. [Google Scholar] [CrossRef]
- Paciuk, P. The Role of Personal Control of the Environment in Thermal Comfort and Satisfaction at the Workplace. Ph.D. Thesis, University of Wisconsin-Milwaukee, Milwaukee, WI, USA, 1989. [Google Scholar]
- Toftum, J. Central automatic control or distributed occupant control for better environment quality in the future. Build. Environ. 2010, 45, 23–28. [Google Scholar] [CrossRef]
- Boerstra, A.; Beuker, T.; Loomans, M.; Hensen, J. Impact of available perceived control on comfort and health in European offices. Archit. Sci. Rev. 2013, 56, 30–41. [Google Scholar] [CrossRef]
- Donnerstein, E.; Wilson, D.W. Effects of noise and perceived control on ongoing subsequent aggressive behavior. J. Pers. Soc. Psychol. 1976, 34, 774–781. [Google Scholar] [CrossRef] [PubMed]
- Weisenberg, M.; Wolf, Y.; Mittwoch, T.; Mikulincer, M.; Aviram, O. Subject versus experimenter control in reaction to pain. Pain 1985, 23, 187–200. [Google Scholar] [CrossRef]
- Bandura, A.; O’Leary, A.; Taylor, C.B.; Gauthier, J.; Gossard, D. Perceived self-efficacy and pain control: Opioid and nonopioid mechanisms. J. Pers. Soc. Psychol. 1987, 53, 563–571. [Google Scholar] [CrossRef] [PubMed]
- Stenner, K.; Frederiks, E.R.; Hobman, E.V.; Cook, S. Willingnes to participate in direct load control: The role of consumer distrust. Appl. Energy 2017, 189, 76–88. [Google Scholar] [CrossRef]
- Muir, B.M. Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 2007, 37, 1905–1922. [Google Scholar] [CrossRef]
- Wang, W.; Benbasat, I. Trust in and adoption of online recommendation agents. J. Assoc. Inf. Syst. 2005, 6, 72–101. [Google Scholar]
- Alam, M.R.; Reaz, M.B.I.; Ali, M.A.M. A Review of Smart Homes—Past, Present, and Future. IEEE Trans. Syst., Man, Cybern. Part C Appl. Rev. 2006, 42, 1190–1203. [Google Scholar] [CrossRef]
- Davidoff, S.; Lee, M.K.; Yiu, C.; Zimmerman, J.; Dey, A.K. Principles of smart home control. In Proceedings of the 8th International Conference on Ubiquitous Computing, Orange County, CA, USA, 17–21 September 2006; pp. 19–34. [Google Scholar]
- Wilson, C.; Hargreaves, T.; Hauxwell-Baldwin, R. Smart homes and their users: A systematic review and key challenges. Persuas. Ubiquitous Comput. 2014, 19, 463–476. [Google Scholar] [CrossRef]
- Cottone, P.; Gaglio, S.; Lo Re, G.; Ortolani, M. User activity recognition for energy savings in smart homes. Pervasive Mob. Comput. 2015, 16, 156–170. [Google Scholar] [CrossRef]
- De Silva, L.C.; Morikawa, C.; Petra, I.M. State of the art of smart homes. Eng. Appl. Artif. Intell. 2012, 25, 1313–1321. [Google Scholar] [CrossRef]
- Eggen, B.; van den Hoven, E.; Terken, J. Human-centered design and smart homes: How to study and design for the home experience? In Handbook of Smart Homes, Health Care, and Well-Being; van Hoof, J., Demiris, G., Wouters, E.J.M., Eds.; Springer: Cham, Switzerland, 2017; pp. 83–92. [Google Scholar]
- Bakar, U.A.B.U.A.; Ghayvat, H.; Hasanm, S.F.; Mukhopadhyay, S.C. Activity and anomaly detection in smart home: A survey. In Next Generation Sensors and Systems; Mukhopadhyay, S.C., Ed.; Springer: Cham, Switzerland, 2016; pp. 191–220. [Google Scholar]
- Siano, P.; Graditi, G.; Atrigna, M.; Piccolo, A. Designing and testing decision support and energy management systems for smart homes. J. Ambient. Intell. Hum. Comput. 2013, 4, 651–661. [Google Scholar] [CrossRef]
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).