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
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