Factors that influence consumers’ acceptance of future energy systems: the effects of adjustment type, production level, and price

Leijten, Fenna; Bolderdijk, Jan Willem; Keizer, Kees; Gorsira, Madelijne; Werff, Ellen van der; Steg, Linda

doi:10.1007/s12053-014-9271-9

Cited by 31 publications

(38 citation statements)

References 38 publications

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“…In essence, such settings remove the need for people to think about adjustments, making sustainability the "default" setting. Although these "green defaults" have tremendous potential for reducing energy consumption, it is also important to respond to consumers' desire to be, or at least to feel, in control of the devices in their homes by permitting adjustments and opt-outs [6,11]. We argue that perception is what matters here, as people may not repeatedly adjust settings once they are comfortable.…”

Section: Contributionsmentioning

confidence: 99%

“…Research on smart homes and smart energy systems has found that consumers prioritize autonomy even more than convenience: they prefer the ability to manually adjust smart energy technologies versus allowing them to operate without human intervention [3,4,6]. Because convenience is often seen an important added value of smart home technologies, this is an important finding.…”

Section: Perceived Controlmentioning

confidence: 99%

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Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Sintov

Schultz

2017

Sustainability

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Smart home technologies offer exciting opportunities to promote more efficient uses of energy. For instance, programmable thermostats, centralized lighting controls, and rooftop solar panels all have potential for energy conservation and efficiency. However, these technologies alone will not guarantee energy savings. Whereas previous research on smart homes has focused on the technologies themselves, relatively little work has addressed the factors that shape the human-technology interface. In this review paper, we argue that in order to ensure any savings, smart home technologies must first be adopted by end-users, and once adopted, they must be used in ways that promote energy efficiency. We focus on three areas of behavioral research with implications for smart home technologies: (1) defaults; (2) perceived adjustability or control; and (3) trust in automation. Linking these areas, we propose a new concept for improving the efficiency gains of smart homes. First, although smart device controls can help save energy, considerably larger energy efficiency gains can be realized through smart automation. But importantly, the default settings of systems should be "green", to maximize energy savings. Second, many people have concerns around relinquishing decision-making to technologies, which can reduce the likelihood of adoption. People want to be, or at least to feel, in control of their homes, even if they do not adjust settings post-installation. Further, consumer trust in technologies encourages adoption in the first place; trust also impacts consumer interactions with installed devices and can impact default acceptance. Combining these concepts, we recommend that smart home technologies build consumer trust and come pre-programmed with adjustable green defaults, which permit consumers to change initial green settings.

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Section: Contributionsmentioning

confidence: 99%

Section: Perceived Controlmentioning

confidence: 99%

Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Sintov

Schultz

2017

Sustainability

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“…Moral considerations affect policy support: acceptability of energy policies is higher when people are highly aware of energy problems and feel morally obliged to reduce these problems . Furthermore, energy policies and energy system changes are evaluated as more acceptable when they do not seriously threaten people's freedom of choice Steg et al, 2006;Schuitema et al, 2010;Leijten et al, 2014). More generally, people evaluate energy policies and changes in energy systems as more acceptable when these policies and changes are expected to have more positive and less negative individual and/or collective consequences (Dietz et al, 2007;Shwom et al, 2010;see Schuitema and Jakobsson Bergstad, 2012, for a review).…”

Section: Acceptability Of Energy Policies and Changes In Energy Systemsmentioning

confidence: 99%

Understanding the Human Factor of the Energy Transition: Mechanisms Underlying Energy-Relevant Decisions and Behaviors

Sander¹,

Brosch²,

Patel³

2016

Frontiers Research Topics

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“…On the one hand, according to the ISTAT (Italian National Institute of Statistics) [9], the age groups more willing to an active participation in public and/or social issues are [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44], and 45-54 years old, with an education level equal or higher than a high school diploma, and without a significant difference between males and females. The next step was to investigate, within these "boundaries", people's behaviours and needs on both gamification and co-design issues.…”

Section: A Survey On Gamification and Co-design Issues: The Followedmentioning

confidence: 99%

“…Finally, in line with the Theory of Planned Behavior [34], studies suggest that energy technology acceptance is partially explained by perceived control over the technology [35]. Direct control DR programs may achieve reliable reductions, but participation rates are estimated at 10 % [8].…”

Section: Behavioral Science Foundationsmentioning

confidence: 99%

Distributed, Ambient, and Pervasive Interactions

Streitz¹,

Markopoulos²

2015

Lecture Notes in Computer Science

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Abstract. Festivals and big scale events are becoming more and more popular, they can attract thousands of spectators. Ensuring the safety of the crowd has become a top priority to many organisers after the multitude of dramatic accidents that resulted in losses in human lives. Monitoring the crowd via smartphones is a relatively new technique that emerged recently with the capabilities of mobile phones to transmit their GPS location data. We present a novel approach, based on the local crowd pressure, combined with the detection of groups in a crowd, to detect critical situations and propose evacuation plans that does not separate groups of people that are together. Groups were detected using DBSCAN clustering algorithm with 80 % accuracy. Location acquisition was tested during the Campus Fever event, and 87 % of the collected data had an accuracy lower than 10 m while 29 % of the total data had 5 m of accuracy. During 2 h of monitoring, activity of the application, reduced the battery of 20 %.

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Factors that influence consumers’ acceptance of future energy systems: the effects of adjustment type, production level, and price

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Cited by 31 publications

References 38 publications

Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Adjustable Green Defaults Can Help Make Smart Homes More Sustainable

Understanding the Human Factor of the Energy Transition: Mechanisms Underlying Energy-Relevant Decisions and Behaviors

Distributed, Ambient, and Pervasive Interactions

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