Diffusion of Feedback: Perceptions and Adoption of Devices in the Residential Market

Abstract

Providing households with energy feedback is widely promoted as a conservation strategy and its effectiveness has been established in field studies. However, such studies actively recruit participants and little is known about naturalistic consumers. Despite hundreds of products emerging, few have taken hold in the market. Diffusion of innovation is a theory of technology adoption that details both the general process by which innovation spreads as well as the individual process of technology adoption. The current study analyses survey data from 836 individuals through a diffusion framework to assess the current and potential market of energy feedback. Questions related to knowledge and perceptions of feedback reveal important insights about customer acceptance and statistical comparison of adopters and non-adopters identify key characteristics related to adoption. Implications for the design and marketing of feedback technologies are discussed.

Categories and functionality of smart home technology for energy management

“…Despite the wealth of research into energy feedback (e.g. Darby, 2006, Ehrhardt-Martinez et al, 2010, little is known about how and for whom feedback works best, largely due to methodological design issues and use of non-naturalistic settings (Karlin et al, 2015a, Karlin et al, 2015c. A better understanding of how users might interact with these devices in the context of a smart home setting would drive further insight into how they can support energy savings and load shifting.…”

“…The rapidly evolving market means that the functionality of smart home technology isn't entirely clear. There has been a lack of demonstration of energy and other user benefits in naturalistic settings, and the ability to deliver flexibility to the grid through demand side management has yet to be proven at scale in the residential sector (Karlin et al, 2015a;Balta-Ozkan et al, 2013, Klaassen et al, 2016Oliver & Sovacool, 2017). This work therefore aims to explore the types and combinations of energy focused smart home products that exist on the market, and show how may they work to support user and grid needs.…”

Understanding the path to smart home adoption: Segmenting and describing consumers across the innovation-decision process

“…For example, Karlin et al [27] surveyed naturalistic adopters and non-adopters of home energy feedback technology, and found that adopters were more likely to be male, older, married, homeowners, with a higher income, more liberal political ideology, more environmental concern, and more conscious of their energy bill; barriers for non-adopters featured lack of knowledge, including awareness of the technology, where to buy, and how to install/set-up products. More recently, Parag and Butbul [28] surveyed prospective SHT adopters (defined as non-technophobic consumers) in Israel and assessed predictors of interest in SHT adoption and interest in demand flexibility through SHT.…”

“…A Demographics and housing characteristics: Higher income, being male, homeownership, and larger home size will predict membership in any cluster that has high knowledge, interest, or owns smart hardware (as suggested by [27,13,28]). B Technology adoption and use: Adoption of other innovative energyrelated household technologies (solar PV and plug-in electric vehicles) and higher use of personal technologies will predict membership in any cluster that has high interest or owns smart hardware (as suggested by [15,19,[26][27][28]).…”

“…C Perceived smart home benefits and barriers: Perception of more salient SHT benefits, especially non-energy benefits, and less concern with risks will predict membership in any cluster that has high interest or owns a smart hardware product; the converse, perception of fewer potential SHT benefits and more risks will predict membership in any cluster with low interest (as suggested by [1,28]). Finally, informational barriers, such as not knowing where to find information on or purchase SHT, and concerns about hassles of installation and setup will predict membership in any cluster with low awareness (as suggested by [27]).…”