Influential factors to residential building Occupants’ acceptance and adoption of smart energy technologies in Denmark

Joy Dalmacio Billanes · Peter Enevoldsen

36 citations0/0 files storedEnergy and Buildings

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Technology Adoption and User Behaviour Environmental Education and Sustainability Social Acceptance of Renewable Energy

Abstract

Although Smart Energy Technologies (SETs) are proven effective in reducing energy consumption and improving energy efficiency in buildings, adoption of these technologies remains low. Scholars argue that lack of willingness to adopt SETs could hinder the transition to SETs. To address this challenge, this study integrates the substantial literature about technology acceptance, and applied the Technology Acceptance Model (TAM) as the theoretical framework. The research model proposed consists of TAM, and five external factors which have been selected based on literature review. A survey-based method was used, sampling more than 3,000 residents from various cities across Denmark. The validated model includes examining the effects of TAM factors and external factors (e.g., attitudes, trust, knowledge, policy, social influence, and awareness) on Behavioural Intention (BI). The majority of the results show statistically significant effects on BI at p= <0.05, except Perceived Usefulness (PU) at p= greater than 0.05. Specifically, Perceived Ease of Use (PEOU), attitudes, trust, knowledge, awareness, social influence, and policy have significant influence on BI. On the contrary, PU has no influence on BI at p = 0.904. The findings are relevant for various reasons. Firstly, it is important for ensuring residential buildings can participate in Danish energy goals. Secondly, it provides a valuable reference for relevant stakeholders such as scholars, technology providers, and policy makers to foster the adoption of SETs in residential buildings. Lastly, the findings will be added to the literature of technology acceptance.

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References

1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 1951Coefficient Alpha and the Internal Structure of Tests 2018Correlation Coefficients: Appropriate Use and Interpretation 1991Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior 2003Inexperience and experience with online stores: the importance of TAM and trust 2006Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet 2008Research Note —How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use 2020Developing a general extended UTAUT model for M-payment adoption

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