Empowering householders: Identifying predictors of intentions to use a home energy management system in the United Kingdom

Abstract

Trials of technologies designed to promote residential demand-side energy management (DSM) have found aggregate levels of load-shifting behaviour and curtailment in energy use. These aggregate data, however, mask considerable differences in people's engagement in DSM at an individual household level. We present the findings of a quantitative exploration of people's intentions to use a home energy management system (HEMS) for residential DSM in the United Kingdom. The technology acceptance model (TAM) was used in conjunction with constructs measuring psychological empowerment and environmental attitudes to explore participants' acceptance of a HEMS to facilitate load-shifting. Findings from a mediation analysis showed perceptions of the usefulness of the HEMS and its ease of use were important predictors of people's intentions to use one. They also highlight a potential conflict between an individual's home energy consumption goals and national DSM goals. The implications of these findings for understanding end-user acceptance of HEMS are discussed. We conclude that seeking opportunities to promote shared, internalised goals for residential DSM may be an avenue for increasing the uptake and use of technologies designed to enable load-shifting (and other energy conservation behaviours) among endusers.

Social networks and communication behaviour underlying smart home adoption in the UK

“…As our findings on communication behaviour and individuals' social contexts interrelate with these additional aspects, it is essential to situate these findings alongside other models which focus on other individualistic elements such as attributes and adopter characteristics (e.g. Whittle et al, 2020), followed by the consideration of wider contextual models (e.g. Grubler et al, 2018) to build a holistic theoretical framing.…”

“…Examples include research on: i) the adoption-decision process (e.g. Ford et al, 2016;Sanguinetti et al, 2018); ii) consumer intention to adopt (using variants of TAM) (Hubert et al, 2019;Park et al, 2018a;Shin et al, 2018;Whittle et al, 2020;Yang et al, 2018); iii) a specific group of users such as older adults (Demiris et al, 2004) or technology enthusiasts (Mennicken and Huang, 2012); and iv) a variety of combinations of the above i.e. a specific group of potential users' intention to adopt (Baudier et al, 2018) or consumer intentions to adopt based on environmental concerns, beliefs and perceived usefulness (Schill et al, 2019).…”

Privacy Design Strategies for Home Energy Management Systems (HEMS)

“…However, unlike [24], we specifically examine users' perceptions and attitudes towards collecting and sharing data, as users have varying attitudes towards collecting and sharing their data. Also, prior studies [46,47] argue that the interplay between energy consumption data and personal habits is the key to stimulating energy-efficient behavior. This notion suggests that adoption or acceptance would depend mainly on users' attitudes, but such insights are missing concerning HEMS.…”

Behavioural Theories and Purchase Intention of Renewable Energy Technologies—A Meta-Analysis

“…Since the time the model was conceptualised, it has been tested and has been the most widely applied model of user acceptance (Ma and Liu 2004). Whittle, Jones, and While (2020) in their study on identifying predictors of intentions to use home energy management system applied TAM. Bandara and Amarasena (2020) in their study on the intention to use solar technology proved that PEOU has a significant impact on solar technology adoption.…”