=Paper=
{{Paper
|id=Vol-3328/paper2
|storemode=property
|title=Thinking about Eco-feedback and Smart Plugs via a Survey and Thematic Analysis
|pdfUrl=https://ceur-ws.org/Vol-3328/paper2.pdf
|volume=Vol-3328
|authors=Diego Casado-Mansilla,Filipe Quintal,Mary Barreto,Augusto Esteves
|dblpUrl=https://dblp.org/rec/conf/etis/Casado-Mansilla22
}}
==Thinking about Eco-feedback and Smart Plugs via a Survey and Thematic Analysis==
https://ceur-ws.org/Vol-3328/paper2.pdf
Thinking about Eco-feedback and Smart Plugs via a
Survey and Thematic Analysis
Diego Casado-Mansilla1 , Filipe Quintal2 , Mary Barreto2 and Augusto Esteves3
1
DeustoTech, University of Deusto, Bilbao, Spain
2
ITI / LARSyS, University of Madeira, Madeira, Portugal
3
ITI / LARSyS, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal
Abstract
This paper continues a trend that looks at smart plugs as a means to promote more sustainable and
pro-environmental behaviors via, for example, just-in-time feedback information delivered at the source
of consumption (i.e., the plug). We showed a video of one of these eco-feedback smart plugs to 50
participants via an online survey and asked them to design plausible scenarios that draw from and
improve upon the interactivity, functionality, and ultimately the sustainability effect of these devices.
Results were analyzed through a thematic analysis and resulted in seven overarching themes: (1) what
are participants’ needs and (2) which energy-centered objectives would they like to achieve; (3) what
energy-related information and/or functionality would participants wish to access via a smart plug; (4)
what strategies would be most effective in supporting participants attain their energy-related objectives
over time; (5) what would be the most effective ways to control smart plugs and other connected devices;
(6) which would be the most effective triggers to cue energy-related user actions; and (7) how can the
previous themes come together to form new behaviors around energy use.
Keywords
Eco-feedback, sustainability, smart plug, IoT, thematic analysis
1. Introduction and Related Work
The majority of ICT-based proposals to address global warming partly rely on the Internet-of-
Things (IoT). Examples include demand response with smart-grids [1, 2] or occupancy-driven
energy management systems [3]; and, in general, are well perceived and often adopted by
end-users [4]. However, while these technologies can be part of the solution, often they are
also part of the problem – for example, when artifacts aimed at energy efficiency never recoup
the equivalent energy spent in their manufacturing. Moreover, energy efficiency improvements
have been found to generate rebound effects that lead to increased energy use [5, 6]. In the same
vein, over-reliance on automation may lead to reduced personal responsibility for action [7]. A
solution to relies on assistive and interactive cues to complement automation by presenting
Proceedings of ETIS 2022, November 7–10, 2022, Toulouse, France
Envelope-Open dcasado@deusto.es (D. Casado-Mansilla); filipe.quintal@staff.uma.pt (F. Quintal); mary.barreto@staff.uma.pt
(M. Barreto); augusto.esteves@tecnico.ulisboa.pt (A. Esteves)
GLOBE https://morelab.deusto.es/people/members/diego-casado-mansilla/ (D. Casado-Mansilla);
https://web.tecnico.ulisboa.pt/augusto.esteves/ (A. Esteves)
Orcid 0000-0002-1070-7494 (D. Casado-Mansilla); 0000-0002-3525-142X (F. Quintal); 0000-0002-9619-4254 (M. Barreto);
0000-0003-1621-5375 (A. Esteves)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
contextual information that helps users associate energy-related information to their everyday
practices [8, 9]. This research field revolving around feedback, nudging, and persuasion was
first described by Froehlich et at. as eco-feedback [10].
Eco-feedback has the potential to transform users’ decision-making process from habit-
driven to conscious and deliberate [11]. Further, not only has the effectiveness of eco-feedback
information been validated already, but countless prototypes have been devised to explore
different ways in which to present this information – from tangibles to smartphone applications,
social media [12] to software agents [2], to chat bots [13], etc. These interfaces are relevant
proof-of-concepts, but they often lack the properties of other systems developed via more
holistic and user-centered approaches. More importantly, we argue that there is a research gap
on what effective eco-feedback information to display over longer time; particularly information
that is bespoke to different users and dwellings.
The contribution of our work describes a information gathering approach based on scenarios
and plausible ideas from participants to personalize eco-feedback centered around the Wattom
smart plug. Essentially, we designed an online survey asking participants to complete three
scenarios in which they had to describe: i) what energy-related information and/or functionality
they would wish to access and why; ii) how should smart plugs or other connected devices
present this information along the time; iii) which appliances or systems does this informa-
tion/functionality relates to depending the context of use; iv) where would the ideal location for
this smart plug be; and v) when should this information be conveyed to them. Finally, we frame
our results around a series of insights for future research on smart plugs and smart devices
around sustainable HCI and new energy flexibility markets that start to flourish [14].
2. Study Design
There are many complex, socially-based phenomena that cannot be easily quantified or ex-
perimentally manipulated. Therefore, identifying users’ social drives and perspectives; their
motivations, expectations, trust, identity, social norms and so on; are paramount for creating
more than “just appealing” designs such as those reviewed in the previous section [15]. To
address this challenge, and taking into account that our study is related to contemporary energy
display systems, this study follows a user-center approach that asks participants for their input
on scenarios created by us and inspired by the ideas of Dunne and Raby [16]: “assuming it
is possible to create more socially constructive imaginary futures, could design help people
participate more actively as citizen-consumers? And if so, how?”. Furthermore, scenarios are a
simple way to ask for participant input without having to implement a novel system, and to
gather diverse responses from participants spread across the globe. Our survey, participant
responses, and analysis results can be found at [17].
2.1. Method
In order to inform the design of energy feedback systems (e.g., home energy displays such
as Wattom) we created an online survey to help end-users evaluate three scenarios related
to energy efficiency or the use of energy from renewable sources and how it related to load
shifting. As pointed out by Braun et al. [18], surveys allows to access data that range from
Figure 1: The figures provided in our online survey when asking participants to report on when they
would wish to receive energy-related information. From the left to right: at any point before the
interaction (priming); immediately before the interaction (about-to); during the interaction (just-in-time);
immediately after the interaction (advise/praise); and after the interaction (reflection).
people’s views, experiences, or material practices, through representation or meaning-making
practices, which in this specific case, meant energy consumption practices. For each scenario,
participants were asked the following four questions:
1. Description and rationale of an eco-feedback information idea (what), via free-form text.
2. Description of how a smart plug would display this information (how), via free-form text.
3. Description of which location would this information be most useful in (where). This was
provided via three sub-questions: (i) four multiple choice options (home, work, shared
home, other); (ii) a free-from text with more specific location information; and (iii) 23
multiple choice options with color-coded icons of various appliances and devices.
4. Description of when should this information be displayed throughout time (when). This
was provided via five multiple choice options with the images illustrated in Figure 1.
2.2. Design artifact: Wattom
As part of the introduction to the survey, participants were presented to the concept of smart
plugs via the latest version of Wattom [19] – a highly interactive smart plug first introduced
at ACM TEI ’19 [20]. For that, participants were invited to watch a three minute video that
illustrated its capabilities as both an energy-aware display and an interactive device1 . Regarding
the former, information can be obtained: (i) via moving lights of different colors, speeds, and
directions; (ii) via background colors; and (iii) via a smartwatch display. The moving lights
facilitate interactivity in the form of direct input to the plug via what is known as motion
matching [21]: an interaction paradigm where interface elements move in continuous and
distinct trajectories, and where users engage with these by tracking their movement with their
bodies. In Wattom, this user input is captured using the IMU on users’ smart watches [22]. The
lack of a rich graphical display in the smart plug means these moving targets are displayed with
a ring of LEDs. The video also illustrated four features that were easily generalizable:
• Turning a connected appliance on or off. This is done through a blue target if the appliance
is on, or white if not. This target’s speed and the plug’s background color change according
to the amount of renewables present in the grid at the moment of interaction [23, 24].
1
https://youtu.be/xM6ynzK1VCw
• Using the smartwatch as a second display for richer data visualizations. This relies
on Wattom’s non-intrusive load monitoring (NILM) capabilities to distinguish between
multiple concurrent appliances connected to a single plug. Wattom displays a colored
target per appliance (up to 12 appliances), which upon selection displays the latest energy
consumption of that device as a colored plot on the user’s smart watch. This data can
also represent the user’s personal consumption of a connected appliance, as Wattom logs
the ID of the smart watch used as input to power or disable the appliance.
• Creating operational schedules for connected appliances. The goal is to provide users
with a finer control over their electrical consumption, for example, planning certain
activities when the electrical costs are excepted to be lower (e.g., night plans), or when
the user expects more electrical availability from photo-voltaic installations on-site.
• Using the smart plug as an ambient display via its LED ring.
2.3. Results
In this section we report on the seven themes emerging from a thematic analysis approach
[25], a qualitative method in which codes are generated from the data rather than relying
on pre-existing categories. These were analyzed by three researchers, across the four main
questions asked in each scenario. Inter-rater reliability was promoted by having unique pairs of
researchers individually generate codes for each answer of the three scenarios [26]. For each of
these answers, the researcher outside of the pair was responsible for consolidating the available
codes. Finally, one of the researchers assessed the consolidated codes across the three scenarios
and proposed a final classification. All the content provided in this section is supported by the
tables in the annex which provide more insights into each individual answer.
79 participants completed our online survey using Prolific [27] (with a $3.5 incentive for
participation), and 50 participants were selected for analysis (35 females, 15 males). Our selection
process started by flagging participants taking less than 15 minutes to complete the survey, and
also by assessing their final answers. Working together, three researchers excluded participants
that did not understand or read the instructions, either by their own admission or by the answer
itself. These 50 participants were aged between 18 and 69 (M = 32.58, SD = 12.01); 48% were
employed, 20% were unemployed, 16% were students, 10% were self employed, and 6% were
retired. Using a 5-point Likert scale, participants knowledge on renewable energy was normally
distributed (M = 2.84, SD = 0.93, higher is better). These participants generated a total of 150
scenarios, and completed the survey in approximately 24 minutes (SD = 12).
Description and rationale of eco-feedback information (what): our analysis of the first
question resulted in 25 codes, which were observed 371 times in the 150 answers analyzed (see
Table 1). Using an online whiteboard2 and mimicking the axial coding phase from Grounded
Theory [28], we worked together to identify relationships and cluster these 25 codes together.
Figure 2 depicts the relationship between the themes, as well as the quantity of codes associated
with each theme.
How to display this eco-feedback information: these answers were again coded using
thematic analysis, but this time by a single researcher due to unambiguous nature of the answers
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Figure 2: The seven themes that emerged from a thematic analysis approach on participants’ descrip-
tions of their eco-feedback information ideas. The diagram was inspired by nascent theories to illustrate
the relative power, scope, and direction of the themes, as well as the connections between them for
linear narrative purposes – it should not be understood as a predictive or causal model [29]. Finally, the
numbers on the top-right corners indicate the frequency of codes associated with the theme (out of 371).
– e.g. “(...) background colors to show how long the appliance has been on, for example green
for one hour or less, orange for two hours (...)” (P20). This approach wielded 43 codes from
the 150 answers analyzed, which were observed 205 times in total. These 43 codes were then
consolidated under seven broader clusters such as “Feedback control in the watch” or “Color
coding information with lights” (the most recurrent clusters). The specific frequency for each
cluster and relation with the seven identified themes can be seen in Table 2. The results from
the additional multiple choice question describing which of the four Wattom functions better
relates to this eco-feedback information, in relation of the seven themes, can be seen in Table 3.
Where would this eco-feedback information be most useful: a single researcher coded
the free-form answers related to where the smart plug would be placed during these interactions;
having identified 20 unique locations (e.g., the kitchen). The frequency and relation of these
locations to our main seven themes can be seen in Table 4. Likewise, the frequency and relation
of various appliance and devices to the same seven themes can be seen in Table 5. Popular
pairings included a need for “Information” in the kitchen, or for “Control” in the bedroom.
When should this information be displayed: the last question on each scenario related to
when should participants’ eco-feedback ideas be displayed. The relation between the five options
available and our seven main themes can be seen in Table 6. Examples included participants’
matches between “Priming” and “Control”, or “Reflection” and “Information”.
3. Discussion
Our results provide broad insights into a wide range of topics relating to eco-feedback infor-
mation. This comes at a time where potential users are increasing aware of such topics, as
illustrated by the self-assessment of our participants on their knowledge of renewable energy.
When modeling what information participants would appreciate in this domain (see Figure
2), it is hard not to notice its similarity to existing behavioral models: from codes relating to
establish objectives (i.e., goal-setting); to what would enable such goals (e.g., energy literacy); to
defining personalized strategies to achieve them; to smart plug interface properties that support
these strategies; to ultimately looking to enduring and long-lasting behavior changes.
Not surprisingly, 65.5% of all codes were related to defining user needs (including information
and energy literacy), as these are arguably the easiest to articulate. Of these, participants
were quite interested in monitoring their energy use (23.04% of all “Needs and Enablers” and
“Information and Literacy” codes), in planning such use (22.22%), and assessing the source of the
energy they consume (22.22%); the latter two exemplified by P17: “I don’t think that it would be
all that useful to go to use an appliance, and find out then that it’s not a great time to use it
[referring to Wattom’s function of providing real-time feedback on the source of energy in the
grid]. I think it would be much more useful for the device to create a sustainability graph of
when the best times during the day are to use appliances so that I can plan around that”. While
this participant is not aware that the percentage of renewables in the grid can vary greatly
[30], this opens up interesting future work in modeling and predicting such data using – e.g.,
weather forecast information – and highlight the importance of work such as the FORE-Watch
that nudge people to use their appliances at more sustainable times via a wearable device [31].
Data visualization: when asked for further Wattom functions, seven participants asked for
these smart plugs to be paired to richer displays, while four others were interested in more
varied data visualizations (than the ones presented on the smartwatch): “I think that the (LEDs
do not provide) enough information and can be confusing” (P17). This matches Castelly et al.’s
assessment of advanced information visualization as a gap in sustainable HCI research [32].
Buying into smart devices: participants reported conflicting views on having smart plugs
interact with other smart devices. On one hand, they describe pairing Wattom with richer
displays and having eco-feedback information shared across smart devices (9.11% of all how-
related information): “a way for it [smart plug] to get and use temperature data so it can be
better used with AC / heating appliances” (P38). P47 described a version of these devices for
showering and water sustainability. On the other hand, two participants shared some concerns
about the cost of all these devices. P31 was concerned if a smart plug was needed per appliance,
despite this not being case with Wattom. Further, P23 reported wanting less functionality and
dependencies, for a simpler UX. This conflict has been highlighted before in He et al.’s work [33],
and might explain the lack of mainstream adoption of more general home energy management
systems (HEMS) systems such as Powerly. In sum, defining the target audience and marketing
efforts for an eco-system of such devices is another necessary future work in this domain.
Smart plug controls: automation was referred to 48.84% of the time in the “Control” theme:
“I would want to (...) set the plugs (to turn on) the kettle before I wake (up) and once I get in from
work” (P41). Three participants were critical of the mid-air gesture approach, citing accessibility
(P15) and older demographics (P26). Voice commands were referred to once in the “Control”
theme; a more popular input proposition referred to by six participants would use some form of
remote access (via, e.g., a web browser, smartphone app): “phone app customization – (i.e. the)
ability to program it to suit your own needs” (P16). This type of dashboard approach is well
known in sustainable HCI [34], and still seems to resonate with users.
4. Conclusion
Eco-feedback is hardly a novel research topic. What it is still a relevant research question is
how to think of eco-feedback information when we consider it as a very particular and personal
need. To that end, our paper conducted an online survey with 50 participants that required
them to produce three speculative scenarios around what, how, where, and when to display
this type of information. A smart plug was chosen as a design artifact to support participants
in this exercise, mostly due to its familiar form factor and ubiquitous nature.
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Table 1
Frequency of each of the 25 codes that emerged from participants’ descriptions of their eco-feedback
information ideas, clustered around seven themes.
Codes Frequency Themes
Energy literacy 22 Objectives
Energy efficiency 5 Objectives
Energy monitoring 56 Needs and Enablers
Consumption planning 54 Needs and Enablers
Forecast 9 Needs and Enablers
Energy source information 54 Information and Literacy
Energy consumption information 27 Information and Literacy
Disaggregated consumption information 24 Information and Literacy
Energy/tariff cost information 8 Information and Literacy
Grid state 5 Information and Literacy
Energy storage state 5 Information and Literacy
Standby information 1 Information and Literacy
Real-time information 11 Strategies
Historical information 10 Strategies
Disaggregated consumption comparisons 6 Strategies
Rankings 4 Strategies
Social comparisons 3 Strategies
Automation 21 Control
Appliances/devices control 11 Control
Consumption scheduling 10 Control
Voice input 1 Control
Alerts 8 Triggers
Tips 3 Triggers
Request/response 1 Triggers
Change routine/habits 12 Behavior
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Table 2
Frequency of each of the seven clusters that emerged from analyzing the second question of each
scenario (how). The frequency of these are matched to the seven themes identified.
Obj. Needs Inform. Strat. Control Triggers Behavior
Feedback/control in the watch 3 26 25 10 21 4 1
Color coding inform. w/ lights 17 79 89 24 13 7 13
Animations via the plug’s lights 4 17 17 6 10 5 1
Information as a graph 2 17 21 5 4 0 1
Using the plug’s lights as chart 0 11 8 3 0 0 0
Numerals on the smart plug 3 16 19 4 2 0 1
Feedback on other devices 8 11 15 4 6 5 2
Table 3
Frequency of each of the four Wattom functions, matched to our main seven themes.
Obj. Needs Inform. Strategies Control Triggers Behavior
Powering appliances 5 21 24 6 19 0 2
Smartwatch as 2nd display 10 36 44 18 5 5 3
Operational schedules 7 31 25 3 15 3 5
Ambient display 5 31 31 7 4 4 2
Table 4
Frequency of each location matched to our main seven themes. The top of the table illustrates multiple
choice answers about broad locations (e.g. home, work); the bottom the written answers about specific
places within those locations. While 20 individual locations were described, six were clustered under
”Anywhere” (e.g., ”any room”, ”any socket”) and three under ”Central location” (e.g., ”hub of the house”).
Home 23 101 107 27 39 8 11
Work 1 10 7 2 2 2 1
Shared home 2 5 7 3 0 0 0
Other 0 1 2 1 4 0 0
Obj. Needs Information Strategies Control Triggers Behavior
Kitchen 12 55 49 13 8 6 4
Living room 5 31 31 10 9 4 2
Hallway 6 20 24 9 9 0 4
Bedroom 3 8 4 0 10 0 1
Office 1 3 3 1 4 0 1
Meeting room 0 2 3 1 1 1 1
Desk 0 1 1 0 0 1 0
Entrance 0 0 1 0 0 0 0
Garage 0 0 0 0 0 0 0
Laundry room 1 0 1 0 0 0 0
Central location 0 6 5 2 0 0 1
Anywhere 1 9 11 1 8 0 1
Table 5
Frequency of each appliance or device matched to our main seven themes. These were presented to the
user in color-coded multiple choice options grouped by general locations, the kitchen, or as devices to
be charged. Finally, the option ’None’ was chosen to represent information that does not relate to a
specific appliance but to, for example, grid information.
Obj. Needs/Enablers Inform. Strategies Control Triggers Behavior
General 2 24 19 5 15 3 2
A/C 0 4 4 0 2 1 1
Game Console 0 2 1 1 0 0 0
PC 0 4 5 2 1 0 1
Personal Lamp 1 1 0 0 0 0 0
Lighting 1 2 0 0 5 1 0
Radio / Stereo 0 1 0 0 2 0 0
Fan 0 0 0 0 0 0 0
Heater 0 7 4 0 2 0 0
Alarm clock 0 0 0 0 1 0 0
TV 0 3 5 2 2 1 0
Kitchen 7 33 26 7 3 3 4
Washer 2 10 9 4 2 1 1
Fridge 0 0 0 0 0 0 0
Kettle 4 7 7 1 1 2 2
Dishwasher 0 6 1 1 0 0 0
Coffee Mach. 0 2 2 0 0 0 1
Cooker 1 7 5 0 0 0 0
Elec. Stove 0 1 2 1 0 0 0
Microwave 0 0 0 0 0 0 0
Chargers 4 7 4 0 8 3 0
Laptop 0 1 1 0 0 1 0
Smart Phone 3 3 2 0 5 2 0
Electric Car 1 3 1 0 3 0 0
Other 0 3 3 1 1 0 0
All / most 14 55 66 20 16 3 8
None 1 5 9 1 0 0 0
Table 6
Frequency of each of the five options on when to interact with the eco-feedback ideas proposed,
matched to our main seven themes. These represent different times of interaction: at any point before
the interaction (priming); immediately before the interaction (about-to); during the interaction (just-in-
time); immediately after the interaction (advise/praise); and after the interaction (reflection).
Obj. Needs/Enablers Information Strategies Control Triggers Behavior
Priming 12 43 35 8 16 5 4
About-to 0 0 1 1 1 1 1
Just-in-time 0 0 0 0 0 0 0
Advise/praise 3 7 1 1 1 1 0
Reflection 3 13 21 8 4 0 2