Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-018-0282-1
1ETH Zurich, Zurich, Switzerland. 2University of Bonn, Bonn, Germany. 3University of Bamberg, Bamberg, Germany. 4University of St. Gallen, St. Gallen,
Switzerland. *e-mail: vtiefenbeck@ethz.ch
I
ndividuals’ choices and behaviour are a key lever influencing
energy consumption, along with the technical energy efficiency
of the products and infrastructure used1. To tackle environmen-
tal challenges, it is important to put people at the centre of energy
research, and to empirically validate how to promote sustainable
decision-making among individual consumers. Energy consump-
tion is a low-involvement topic for most people; many consumers
are unaware of the energy efficiency of their homes and devices2, or
underestimate the long-term benefits of potential investments into
energy efficiency upgrades3.
As digitization advances, it becomes increasingly feasible to
monitor the energy consumption of households, specific appliances
or activities in real time4. As a result, digitally enabled behavioural
interventions can be deployed at population scale and become more
powerful through personalization and context specificity. Beyond
that, it becomes increasingly possible to systematically evaluate the
impact of behavioural interventions with large and diverse samples
of participants. The availability of high-resolution consumption
data enables more and more personalized and flexible interven-
tions4. These developments may open up new avenues towards
more powerful digital strategies for behaviour change.
Yet, while early feedback intervention studies in which people
were provided with information about their energy consumption
reported large savings effects of 5–15%5,6 with small convenience
samples, spurring the large-scale roll-out of smart meters in many
countries, those savings have not materialized in larger field tri-
als7–9. The most widespread form of feedback intervention is ‘home
energy reports’: periodic mailings that compare the electricity use
of individual households with similar homes in the neighbour-
hood, thus tapping into social norms3. Deployed at population level
(households can opt out, but few do), those programmes typically
yield electricity savings of 2%3,10. Other programmes use digital
technologies, delivering feedback on electricity use via web portals
or in-home displays; studies with large opt-in samples report elec-
tricity savings in the range of 1–5%8,11–13—far less than the savings
reported by earlier studies with smaller samples and a higher degree
of involvement from study administrators6,14.
Early studies were subject to several methodological issues
that compromised the internal and external validity of the results,
overestimating the savings potential of these feedback interven-
tions7,8,13. For instance, a meta-analysis of 156 field trials on energy
conservation found substantially smaller savings effects of 1.99%
for high-quality studies with adequate controls, compared to stud-
ies without such controls (9.57%)13. Likewise, a meta-analysis of 33
field trials on in-home displays found weighted mean conservation
effects of 2.61% for high-quality (‘class A’) studies using representa-
tive sampling techniques, compared to 8.21% for ‘class C’ studies
characterized by small samples of volunteers and a high degree of
involvement from study administrators8.
Although randomized controlled trials eliminate most threats to
the internal validity of studies15–17, the external validity of the results
may still be compromised if the people who choose to participate in a
study differ from the study’s intended population7. The vast majority
of feedback programmes on energy consumption use opt-in recruit-
ment strategies, where participants actively register to take part in
those programmes7,9. There is increasing evidence that individuals
who sign up for energy efficiency studies or demand-side manage-
ment programmes are indeed different from the general population:
participation rates are higher among households with high levels of
education and income18,19, among more altruistic and environmen-
tally concerned individuals18 and among those with a higher interest
and expertise in energy topics than the general population9,20. Most
behavioural programmes do not even provide information about
the number of households initially contacted, and those that do,
report participation rates in the range of 4–8%6,20–23. These numbers
have raised concerns that the results of opt-in studies may be largely
biased by an already-motivated subgroup of the general population
(‘energy enthusiasts’ or ‘positive greens’), who represent only a small
fraction of the population9,24. The response of these volunteers to
the treatments may not be very indicative for the response of the
Real-time feedback promotes energy conservation
in the absence of volunteer selection bias and
monetary incentives
Verena Tiefenbeck   1,2*, Anselma Wörner1, Samuel Schöb3, Elgar Fleisch1,4 and Thorsten Staake3
Feedback interventions have proved to be effective at promoting energy conservation behaviour, and digital technologies have
the potential to make interventions more powerful and scalable. In particular, real-time feedback on a specific, energy-inten-
sive activity may induce considerable behaviour change and savings. Yet the majority of feedback studies that report large
effects are conducted with opt-in samples of individuals who volunteer to participate. Here we show that real-time feedback
on resource consumption during showering induces substantial energy conservation in an uninformed sample of guests at 6
hotels (265 rooms, N = 19,596 observations). The treatment effects are large (11.4% reduction in energy use), indicating that
the real-time feedback induced substantial energy conservation among participants who did not opt in, and in a context where
participants were not financially responsible for energy costs. We thus provide empirical evidence for real-time feedback as a
scalable and cost-efficient policy instrument for fostering resource conservation among the broader public.
Corrected: Publisher Correction; Publisher Correction
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general population (volunteer selection bias). On the one hand, it is
conceivable that those individuals are already more aware of effec-
tive energy conservation measures and have already taken action
before the intervention, making it more difficult for them to realize
additional savings in those studies25. On the other hand, it is likely
that they are particularly open and receptive to these interventions,
thus inflating estimates of intervention effectiveness26.
One commonality that the majority of larger energy feedback tri-
als share is that they provide aggregate consumption information
at the household level. This makes it difficult for the individual to
establish a link between the current action and its impact on energy
consumption27. The results of a recent randomized controlled field
trial25 suggest that real-time feedback on a specific, energy-inten-
sive activity may induce much larger savings. In a two-month study
with an opt-in sample of 697 Swiss households, the treatment group
received real-time feedback on the environmental impact of specific,
energy-intensive activity (showering), while they could directly take
action. The intervention yielded large and stable energy savings of
22% on the target behaviour over the duration of the study. At the
household level, this reduction led to much larger conservation
gains—also in absolute terms—than aggregate feedback on energy
use among the same pool of households. From a technology and
cost perspective, the large-scale roll-out of focused real-time inter-
ventions is increasingly feasible25. Yet, given the decline in the effect
size and the resulting wave of disillusionment once smart meter-
ing trials with aggregate feedback moved from small convenience
samples to a broader population, the key question is whether the
promising large savings effects of activity-specific real-time feed-
back will also materialize among individuals who do not self-select
into a research study.
Another controversial issue is that the communication strategies
of most energy-conservation programmes focus on the financial
benefits for the consumer as incentives for behaviour change28–31.
From a standard economics perspective, this approach makes sense,
as rational consumers should respond to monetary incentives in
their resource-consumption decisions32–34. Consequently, mon-
etary incentives play a key role in demand-side management35,36;
they have the potential to break established consumer patterns
and to initiate the development of new patterns of behaviour by
making an alternative behaviour more attractive37,38. However, in
many contexts, financial motives are not a viable strategy to pro-
mote energy conservation: employees, tenants whose rents include
utilities or hotel guests who do not pay the marginal cost of their
energy consumption. While the provision of large, persuasive
monetary benefits does not scale well to the wider population,
monetary incentives may also crowd out the intrinsic motivation
for pro-social behaviour28,39,40 and generate adverse effects41: as
individuals tend to internalize the logic of reward systems easily,
monetary incentives can lead to the deterioration of morals and
reduce intrinsic motivation40,42.
Here we evaluate whether the large savings effects from digi-
tal activity-specific feedback25 are also realistic in settings where a
volunteer selection bias can be ruled out, and where study subjects
have no financial incentives for resource conservation. We provide
activity-specific feedback on resource consumption to uninformed
hotel guests during a habitual resource-intensive activity: shower-
ing. We find that even in this setting, the digital behavioural inter-
vention creates large and significant conservation effects of 11.4% or
0.215 kWh per shower. Given that most people take a daily shower,
scaling up this kind of intervention could produce substantial
energy (and water) savings. More importantly, the results suggest
that activity-specific real-time feedback—and possibly other digital
interventions—has the potential to transform behavioural interven-
tions into a highly relevant policy instrument for fostering energy
conservation and behaviour change at the population level3,13,43.
Effect of feedback among uninformed hotel guests
We conducted a natural field experiment in the context of an
energy-intensive habitual activity: showering. In a randomized con-
trolled trial, guests at six Swiss hotels (see Table 1) encountered a
smart shower meter fitted to the shower in the bathroom of their
hotel room. The devices measured the energy and water consump-
tion of every shower taken, and displayed feedback on each ongoing
shower in real time. We equipped a total of 265 rooms with these
devices and collected a data set with 19,596 observations (water
extractions, after pre-processing) from February to April 2016.
The shower meters were installed between the shower hose and
the shower head, and included a small screen that activated to dis-
play feedback as soon as the water was turned on (Fig. 1). In the
treatment condition (60% of rooms, randomly assigned), smart
shower meters displayed real-time feedback on the resource con-
sumption of the ongoing shower: total water consumption in litres
(one decimal), total energy use in (kilo)watt hours, a dynamic rating
of the current energy-efficiency class (A–G) and a four-stage anima-
tion of a polar bear standing on a gradually melting ice floe with
stage transitions at predefined energy-use thresholds (see Methods
for details). This is the same intervention with the same device
and display elements as the treatment group of the opt-in household
sample in Tiefenbeck et al.25. The remaining 40% of rooms were ran-
domly assigned to the control condition: while guests in the treat-
ment rooms received real-time feedback on their resource use from
the beginning of the shower (along with the current water tempera-
ture), control-group devices displayed only water temperature.
Hotel guests exposed to real-time feedback consumed signifi-
cantly less energy per shower than the control group (Fig. 2b). The
treatment effect of our intervention is large and significant: guests
Table 1 | Overview of the participating hotels
Hotel
Category
No. of
participating
rooms
No. of
observations
Hotel 1
Business, four-star
96
7,923
Hotel 2
Business, four-star
67
6,123
Hotel 3
Business, four-star
43
2,789
Hotel 4
Business, three-star
11
1,494
Hotel 5
Tourism, four-star
42
814
Hotel 6
Tourism
10
453
Total
269
19,596
a
b
c
Fig. 1 | Smart shower meter. a, The smart shower meter for displaying
real-time feedback on resource consumption to hotel guests was installed
between the shower head and the shower hose. b, Two snapshots of the
treatment group’s display. c, The control group’s display. Credit: Amphiro
AG (b,c)
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in the treatment group used on average 0.215 kWh less energy per
shower than the control-group mean of 1.883 kWh (Table 2,
column 1). This represents a reduction of 11.4% (t(19,596) = −4.88,
P < 0.001). Controlling for flow rate (column 2), the effect
is still highly significant, with a reduction of 0.188 kWh
(t(8,882) = 3.59, P < 0.001), or 10.0%. To determine whether
subsampling for observations in which flow rate is available biases
this results, we included a third model specification for this sub-
sample but without controlling for flow rate (column 3). The
treatment effect is significant in all three models (see Methods
for details on the regression analyses) and large (ranging between
10.0% and 13.2%).
The results illustrate that activity-specific real-time feedback
can be an efficient measure to foster energy conservation, not only
among a volunteer sample, but also among a random, uninformed
sample of individuals.
Additional analyses
As an alternative functional form, we estimated a log-linear regres-
sion model. The results (reported in Table 3) are consistent with the
results of the non-transformed version reported above and show a
strong and significant treatment effect of the real-time feedback.
To further corroborate the reported results, we ran the same
models with varying filter thresholds, reducing the data pre-pro-
cessing to an absolute minimum, with very similar results: if we
remove only observations deviating over five standard deviations
from mean energy or water consumption, and mean average tem-
perature, we obtain a sample of 25,490 out of the initial 25,647
observations. Running model (1) on this sample yields a slightly
smaller, but still highly significant treatment effect of −0.182 kWh
(s.e.m. 0.044, P < 0.001).
To get an understanding of the effects of the six hotels with their
different infrastructure and setting, we also computed a fixed-
effects model with dummy variables for the individual hotels. The
results are presented in Table 4 and show that the treatment effect
is highly significant, albeit slightly smaller than in models (1)–(3).
Only in hotel 5, the energy use per shower differs significantly from
the other hotels, which may be due to different infrastructure (for
example, more low-flow shower heads) or guest characteristics.
Otherwise, the impact on energy use per shower is very similar
between the different hotels. Regardless of the model specification,
the treatment effect is large and significant; thus, non-self-selected
participants also respond to real-time feedback in the complete
absence of monetary incentives.
Furthermore, we conducted a cost–benefit analysis for installing
the metering device in the hotels’ showers based on the treatment
effect estimated in model (1). We assumed a retail price of 40 CHF
for the smart shower meter and fuel cost for water heating of 0.128
CHF kWh−1 and water cost of 3.8 CHF m−3, as in Tiefenbeck et al.25.
If we extrapolate from the treatment effect of 0.21 kWh and 3.56 l
per shower and assume on average 1.2 showers per day per room, as
observed during the period of our study, this results in an amortiza-
tion time of 2.2 years.
Comparisons with a volunteer-household sample
In line with the earlier findings on volunteer selection bias7–9, the
treatment effect in the previous study with a volunteer-household
sample18 (0.592 kWh, or 22%) was larger than the effect we observe
in the hotel setting (0.215 kWh, or 11.4%). However, it is impor-
tant to note that we do not seek to quantify the self-selection effect.
Our hotel setting and the study context in Tiefenbeck et al.25 dif-
fer in multiple aspects other than the two key variables of interest.
First, our study examines the behavioural response to feedback in
the short term (one or a few nights spent per guest at the hotel).
Consequently, any attempt to compare the two studies would need
to focus on the short-term behaviour of the household volunteer
0
1
2
3
4
5
Mean energy use per shower (kWh)
Control
Treatment
Study group
Fig. 2 | Effect of consumption feedback. Group-wise distribution of
energy use per shower in hotel rooms with energy consumption feedback
(treatment group) and the control group, shown as boxplots (N = 19,596).
The line in the middle of the box represents the median, and the diamond
represents the mean energy use. The box spans the first quartile to the
third quartile, and the whiskers extend up to 1.5 times the interquartile
range from the top or bottom of the box.
Table 2 | Main treatment effect
Energy use per shower (kWh)
Consumption feedback (treatment = 1, control = 0)
−0.215*** (0.044)
−0.188*** (0.050)
−0.252*** (0.056)
Flow rate (mean-centred, min−1)
–
0.098*** (0.010)
–
Constant
1.884*** (0.032)
1.881*** (0.036)
1.902*** (0.039)
Observations
19,596
8,824
8,824
R2
0.008
0.047
0.011
Standard errors are in parentheses, adjusted for clustering at the room level; *** indicates significance at the 0.1% level.
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sample. Indeed, an analysis of the first three showers only in the
volunteer sample also yielded a smaller treatment effect (17.8%
or 0.46 kWh) than in the full two-month evaluation. Robustness
checks using the first two, four or five (instead of three) showers
were conducted, with very similar results. Second, individuals may
react differently to feedback in their familiar environment at home
versus in a hotel room, or guests may perceive the mere presence of
the shower meter as a signal that the hotel management cares about
environmental issues and pays attention to how much energy and
water their guests are using.
Another remarkable difference relates to participants’ energy
use per shower in the absence of feedback, measured in the respec-
tive control groups. Hotel guests in the control group consumed
28% less energy per shower, namely M = 1.88 kWh, s.d. = 1.25 kWh,
than the control group in the household setting, with a mean of
M = 2.62 kWh, s.d. = 1.67 kWh (ref. 25), t(20,236) = −11.1, P < 0.001.
This difference is noteworthy for two reasons. First, according to
standard economic theory, one would expect individuals to take
longer showers at a hotel than at home, as they do pay a marginal
cost for every kilowatt hour of consumed energy. Yet, the results
suggest that the hotel guests did not exploit the zero marginal cost
of consumption. We attribute lower consumption in the control
group largely to differences in the technical infrastructure between
the hotels and households: we observed lower flow rates in the hotel
rooms, probably caused by a higher share of water-saving shower
heads installed in the hotel rooms. Second, this difference may
also partially explain the smaller treatment effect in the hotels. The
study in the household setting had revealed a strong positive inter-
action between the treatment effect and baseline consumption: in
the household study, a 1-kWh increase in baseline consumption led
to a 0.32 kWh increase in the savings effect25. To put it simply, it
is far easier to cut a 20-min shower short by a few minutes (and
kilowatt hours) than to realize substantial reductions on a 1-min
shower. The control-group mean of energy consumption per
shower in our hotel sample is 1.88 kWh, compared to 2.62 kWh in
the household sample (0.74 kWh difference). Interpreting the con-
trol-group mean as a proxy for baseline consumption, an increase
in baseline consumption by 0.74 kWh would increase the savings
effect by 0.24 kWh, which almost exactly matches the difference
in the observed savings effect. Thus, if we control for the lower
consumption at the hotels in the absence of feedback, the savings
effects among the hotel guests and among the volunteer household
sample are in fact comparably large.
Discussion
In the case of aggregate feedback, most energy efficiency studies
yielded much smaller savings effects once those interventions were
evaluated with large, non-self-selected samples7–9. In other words,
those interventions resonated much less with broader, non-self-
selected audiences than with those individuals who had opted to
participate. By contrast, with a highly significant treatment effect of
11.4% among uninformed hotel guests, this study provides empirical
evidence that activity-specific real-time feedback can induce sub-
stantial behaviour change among a broader population, even in a
setting without monetary incentives for resource conservation.
Thus, providing real-time feedback on a specific energy-intensive
activity may generate large and persistent savings effects25 not only
among the small percentage of the population who tend to opt into
energy efficiency studies11,20,21,23; the results indicate that the inter-
vention successfully induces substantial behaviour change and
resource conservation among broader audiences.
Regarding the cost-effectiveness of the intervention in the hotel
context studied, on the basis of the savings effects observed, the
device pays itself off in a hotel within 2.2 years on average, which
is a very low amortization time as compared to other energy effi-
ciency investments2,44. Thus, the results suggest that even in settings
where third parties pay for the marginal cost of resource consump-
tion, activity-specific feedback can be a cost-effective and scalable
strategy to foster energy conservation.
Despite our best efforts, there are limitations to our study. While
we were able to measure effects for 100% of the hotel guests, they
may not be representative of the general population. Although we
diversified our sample by including different types of hotel, with dif-
ferent comfort categories, room rates and primary target custom-
ers (business versus tourism), and in different locations, additional
studies in other settings and other countries would be valuable.
Furthermore, despite our efforts to limit differences in potential
Hawthorne effects by displaying real-time water temperature on the
control-group devices, it is conceivable that the treatment with real-
time feedback on resource consumption draws more attention to the
fact that the smart shower meter measures data than does real-time
information on water temperature, conveying a stronger feeling of
being monitored among its users. Moreover, due to the short dura-
tion of guests’ hotel stays, we are not able to examine effects over
Table 3 | Main treatment effect with log transformation of the dependent variable
Energy use per shower (kWh)
Consumption feedback (treatment = 1, control = 0)
−0.124*** (0.024)
−0.112*** (0.028)
−0.148*** (0.032)
Flow rate (mean-centred, l min−1)
–
0.056*** (0.006)
–
Constant
0.413*** (0.017)
0.410*** (0.020)
0.422*** (0.022)
Observations
19,596
8,824
8,824
R2
0.008
0.047
0.011
Standard errors are in parentheses, adjusted for clustering at the room level; *** indicates significance at the 0.1% level.
Table 4 | Treatment effect of consumption feedback and fixed
effects for different hotels
Energy use per shower (kWh)
Consumption feedback (treatment = 1,
control = 0)
−0.197*** (0.041)
Hotel 2
0.037 (0.051)
Hotel 3
−0.072 (0.091)
Hotel 4
0.117 (0.090)
Hotel 5
−0.361*** (0.058)
Hotel 6
−0.191 (0.127)
Constant (Hotel 1)
1.918*** (0.045)
Observations
19,596
R2
0.021
Standard errors are in parentheses, adjusted for clustering per room; *** indicates significance at
the 0.1% level.
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time. While several studies (lasting between 2 and 16 months25,45)
document the mid-term effect stability of activity-specific real-time
feedback with opt-in samples, further research needs to investigate
whether the large savings effects also persist over time among non-
self-selected participants.
Here, we provide robust empirical evidence that activity-spe-
cific real-time feedback can induce substantial behaviour change
and resource conservation—even for a sample of individuals who
neither volunteered to participate in an environmental study, nor
reaped financial benefits from energy conservation. Given the
debate on volunteer self-selection7 and the dwindling treatment
effects of other feedback interventions once they are deployed
among broader samples7–9,12, this empirical validation is critical
to provide solid recommendations for the design of future energy
conservation programmes46. Information technology increasingly
makes it possible to monitor behaviour in real time, to provide
individuals with feedback on their ongoing activities and to col-
lect granular data on the real-world impact of interventions from
millions of individuals in the field4 at rapidly declining costs. The
results of this study highlight the potential of digital interventions
to transform behaviour in energy-intensive activities, which can be
implemented and monitored at the population level.
Methods
Experimental set-up. We conducted a natural field experiment in which we
targeted showering as an example of a resource-intensive, low-involvement
activity. Participants were not recruited as individuals; instead, we collaborated
with six hotels that allowed us to conduct the experiments in their rooms without
informing the guests upfront about the experiment. Shower data were collected in
batches after several weeks and without a time stamp, which guaranteed complete
anonymity of the guests’ identity. The study was approved by the Internal Review
Board of the University of Bamberg. Similar natural field experiments in hotel
contexts have been conducted to investigate the impact of other behavioural
interventions such as commitment strategies47 or social comparisons48,49.
Overall, guests staying in 265 different hotel rooms took part in our
experiment. Hotel guests encountered smart shower meters as part of their rooms’
bathroom equipment. The guests who stayed in rooms assigned to the treatment
group received real-time feedback on how much energy and water they consumed
over the course of their shower (details below). This activity-specific consumption
feedback was displayed by a shower meter that had previously been used in framed
field experiments in private households25. Room assignment was randomized over
floor levels and room categories to minimize confounding factors from differences
in infrastructure (for example, water pressure) and did not change throughout
the study. Approximately 40% of the rooms were assigned to the control group.
They serve as the reference group to calculate the treatment effect. In those rooms,
the same device was installed, but it displayed only water temperature. While the
temperature reading does not convey information about the resource use and
remains relatively static over the course of a shower, it indicates that the device
measures data, thus reducing potential differences between the treatment and
control group for Hawthorne effects to occur50,51.
As the smart shower meters are powered by the water flow via a small internal
generator, the screen displaying the feedback switches on as soon as water flows
through the device and remains active for up to three minutes after the end of a
shower. Thus, short interruptions to the water flow (for instance while soaping)
still result in a single shower being recorded. Before the device switches off, it stores
the final data in its internal memory, which was read out at the end of the study.
The experiment took place in six different hotels in Switzerland, recruited on
the basis of existing contacts. Data were collected between February and April
2016. Four of the hotels focus on business customers and the other two on private
tourists. The categorization into business and tourism was defined on the basis of
information provided by the hotels’ management. Of course, we cannot rule out
that business hotels also had guests on private holidays, or that the tourism hotels
hosted some business guests during the course of the study. Depending on their
size, the participating hotels allowed us to install shower meters in 10 to 96 of their
guest rooms, respectively.
Display content in the treatment group. Most feedback devices display a bundle
of elements rather than a single numeric metric52 to put the measurement data into
context; frequently used elements include historic comparisons, peer comparisons,
analogies and energy savings tips. Likewise, the smart shower meters in rooms
assigned to the treatment condition displayed water consumption in litres (one
decimal), energy use in (kilo)watt hours, current water temperature, a dynamic
rating of the current energy-efficiency class (A–G) and a four-stage animation
of a polar bear standing on a melting ice floe with stage transitions at predefined
energy use thresholds. The energy consumption displayed on the screen represents
the lower bound of the energy used (without losses), and is calculated using the
standard engineering formula for heat energy (Q = m ×cp × ∆T, with heat energy
Q, mass of water m, heat capacity cp, and ∆T being the difference between the
measured water temperature of the ongoing shower and the average cold-water
temperature). In the analysis of energy savings, we take into account the same
average heating efficiency and losses as in Tiefenbeck et al.25. The energy-efficiency
class displayed was inspired by the (static) energy-efficiency class scale indicated
on household appliances in Europe. The smart shower meter dynamically indicates
the energy efficiency of the current shower based on the energy use in the ongoing
shower, starting in energy efficiency class A and progressing to B, C and so on at
predefined kilowatt hour thresholds; the thresholds were defined on the basis of
the distribution of energy use per shower in a pilot study. The four stages of the
polar bear animation are tied to the energy-efficiency class, and change with the
transitions from B to C, D to E and E to F, respectively. While the polar bear may
be an eye-catching and memorable display element, it does not seem to drive the
savings effects. A related study specifically examined the effect of variations of the
design choices of the feedback elements; the results indicate that if the polar bear
animation makes any difference, it reduces rather than increases the effectiveness
of the display52.
Data. For each water extraction, the smart shower meter recorded energy and
water consumption, average water temperature, interruptions and the duration. In
addition, in 168 of the rooms, the average flow rate per shower was also measured.
Using the data stored on the device, energy consumption can be converted to
water consumption and vice versa. Given the high correlation between water and
energy consumption per shower (0.989)25, the choice of the unit of analysis does
not change the results in any meaningful way. This article focuses on resource
consumption in units of energy in kilowatt hours.
The raw data set included observations of 25,647 measured showers from 269
hotel rooms at 6 different hotels (see Data availability). In a first pre-processing
step, the data were cleaned by removing outliers from malfunctioning devices; to
this end, observations that deviated by over 3 standard deviations from the mean
of the energy consumed or water volume per shower were removed from the
sample—that is, only observations in the interval [ ̄
̄
−× . .
+ × . .
x
x
3
s d ,
3
s d
] were retained. Furthermore, we removed data points that most likely did not
represent showers—for example water extractions of volumes below 6.5 litres
and observations deviating over 2 standard deviations from average temperature,
which probably represent cleaning or other procedures. A member of the research
team accompanied cleaning personnel at one hotel for several hours to gather
information on cleaning practices to identify water extractions for cleaning. The
specific choice of 6.5 litres was based on this assessment; we conducted robustness
checks in which we changed this threshold to other values (5 litres or 10 litres),
which generated very similar results.
After this pre-processing step, the final data set included 19,596 showers
from 265 hotel rooms (11,384 observations in the treatment group and 8,218 in
the control group). The average flow rate per shower could be measured only for
168 rooms and 8,824 observations, so only these data points are included in the
estimation of models (2) and (3) in Table 2.
Since the study is a natural field experiment with uninformed participants, we
were not able to collect socio-demographic data about the guests who stayed in the
rooms with the smart shower meters during the study.
Data analysis. We used the data points observed in the control group to quantify
the energy use per shower in the participating hotels without feedback. To
estimate the treatment effect of the feedback intervention, we used a simple linear
regression model and also computed its log-linear transformation:
β
β
ϵ
=
+
+
y
x
(1)
i
i
i
0
1
β
β
ϵ
=
+
+
y
x
ln
(2)
i
i
i
0
1
where the dependent variable yi is the energy consumption in shower i. The
variable xi is binary, indicating treatment (=1) or no treatment (=0), and thus
coefficient β1 estimates the treatment effect. The intercept β0 represents the control-
group mean in this model, as xi = 0 for observations in the control group. The
results of this analysis are reported in Table 2, column 1, and in Table 3, column 1,
with the natural logarithm of the dependent variable.
For rooms in which the smart shower meter also measured the flow rate, we
estimated an additional model that controls for the centred flow rate in litres fi:
β
β
β
ϵ
=
+
+
+
y
x
f
(3)
i
i
i
i
0
1
2
β
β
β
ϵ
=
+
+
+
y
x
f
ln
(4)
i
i
i
i
0
1
2
The results for model (3), which includes the flow rate in the regression, are
reported in Table 2, column 2, and for model (4) in Table 3, column 2, with log-linear
Nature Energy | VOL 4 | JANUARY 2019 | 35–41 | www.nature.com/natureenergy
39

Articles
Nature Energy
transformation. In both model specifications, standard errors were clustered at
the room level to account for infrastructural influences. Two-sided t-tests were
conducted to test whether the coefficients were significantly different from zero.
To get an understanding of the effects of the six hotels with their different
infrastructure and setting, we also computed a fixed-effects model with dummy
variables for the individual hotels. In this model, the constant represents the
estimates for the largest hotel (hotel 1) and dummy variables are included for the
other hotels. Results are reported in Table 4.
β
β
α
α
α
α
α
ϵ
=
+
+
+
+
+
+
+
y
x
h
h
h
h
h
(5)
i
i
i
i
i
i
i
i
0
1
2 2
3 3
4 4
5 5
6 6
Code availability. The Stata code used to generate results reported in this article is
available on request from the corresponding author.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The data that support the findings of this study are available at https://doi.
org/10.6084/m9.figshare.6984323.v1.
Received: 27 March 2018; Accepted: 9 October 2018;
Published online: 19 November 2018
References
	1.	 Sovacool, B. K. Diversity: energy studies need social science. Nature 511,
529–530 (2014).
	2.	 Allcott, H. & Greenstone, M. Is there an energy efficiency gap? J. Econ.
Perspect. 26, 3–28 (2012).
	3.	 Allcott, H. & Mullainathan, S. Behavior and energy policy. Science 327,
1204–1205 (2010).
	4.	 Tiefenbeck, V. Bring behaviour into the digital transformation. Nat. Energy 2,
17085 (2017).
	5.	 Darby, S. The Effectiveness of Feedback on Energy Consumption - A Review for
DEFRA of the Literature on Metering Billing and Direct Displays
(Environmental Change Institute, University of Oxford, Oxford, 2006).
	6.	 Ehrhardt-Martinez, K. et al. Advanced Metering Initiatives and Residential
Feedback Programs: A Meta-Review for Household Electricity-Saving
Opportunities (Washington, DC, American Council for an Energy-Efficient
Economy, 2010).
	7.	 Davis, A. L., Krishnamurti, T., Fischhoff, B. & Bruine de Bruin, W. Setting a
standard for electricity pilot studies. Energy Policy 62, 401–409 (2013).
	8.	 McKerracher, C. & Torriti, J. Energy consumption feedback in perspective:
integrating Australian data to meta-analyses on in-home displays. Energy
Effic. 6, 387–405 (2013).
	9.	 Kelly, J. & Knottenbelt, W. Does disaggregated electricity feedback reduce
domestic electricity consumption? A systematic review of the literature. 1605,
00962v2 (2016).
	10.	Allcott, H. & Mullainathan, S. External Validity and Partner Selection Bias
Working Paper No. 18373 (NBER, 2012).
	11.	Schleich, J., Klobasa, M., Götz, S. & Brunner, M. Effects of feedback on
residential electricity demand: findings from a field trial in Austria. Energy
Policy 61, 1097–1106 (2013).
	12.	Buchanan, K., Russo, R. & Anderson, B. The question of energy reduction:
The problem(s) with feedback. Energy Policy 77, 89–96 (2015).
	13.	Delmas, M. A., Fischlein, M. & Asensio, O. I. Information strategies and
energy conservation behavior: A meta-analysis of experimental studies from
1975 to 2012. Energy Policy 61, 729–739 (2013).
	14.	Abrahamse, W., Steg, L., Vlek, C. & Rothengatter, T. A review of intervention
studies aimed at household energy conservation. J. Environ. Psychol. 25,
273–291 (2005).
	15.	Haynes, L., Service, O., Goldacre, B. & Torgerson, D. Test, Learn, Adapt:
Developing Public Policy with Randomised Controlled Trials (London Cabinet
Office Behavioural Insights Team, 2012).
	16.	Vine, E., Sullivan, M., Lutzenhiser, L., Blumstein, C. & Miller, B.
Experimentation and the evaluation of energy efficiency programs. Energy
Effic. 7, 627–640 (2014).
	17.	Campbell, D. T. Reform as experiments. Am. Psychol. 24, 409–429 (1969).
	18.	Clark, C. F., Kotchen, M. J. & Moore, M. R. Internal and external influences
on pro-environmental behavior: Participation in a green electricity program.
J. Environ. Psychol. 23, 237–246 (2003).
	19.	Sulyma, I., Tiedemann, K., Pedersen, M., Rebman, M. & Yu, M. Experimental
evidence: a residential time of use pilot. In Proc. ACEEE Summer Study
Energy Effic. Build. 292–304 (ACEEE, 2008).
	20.	Baladi, S. M., Herriges, J. A. & Sweeney, T. J. Residential response to
voluntary time-of-use electricity rates. Resour. Energy Econ. 20,
225–244 (1998).
	21.	Herter, K., Wood, V. & Blozis, S. The effects of combining dynamic pricing,
AC load control, and real-time energy feedback: SMUD’S 2011 Residential
Summer Solutions Study. Energy Effic. 6, 641–653 (2013).
	22.	Schleich, J., Klobasa, M., Gölz, S. & Brunner, M. Effects of feedback on
residential electricity demand-findings from a field trial in Austria. Energy
Policy 61, 1097–1106 (2013).
	23.	Lossin, F., Kozlovskiy, I., Sodenkamp, M., & Staake, T. Incentives to go green:
an empirical investigation of monetary and symbolic rewards to motivate
energy savings. In Proc. Eur. Conf. Inf. Syst. 1–16 (2016).
	24.	A Framework for Pro-Environmental Behaviours (Department for
Environment Food and Rural Affairs, 2008).
	25.	Tiefenbeck, V. et al. Overcoming salience bias: how real-time feedback fosters
resource conservation. Manag. Sci. 64, 983–1476 (2016).
	26.	Frederiks, E. R., Stenner, K., Hobman, E. V. & Fischle, M. Evaluating energy
behavior change programs using randomized controlled trials: Best practice
guidelines for policymakers. Energy Res. Soc. Sci. 22, 147–164 (2016).
	27.	Faruqui, A., Sergici, S. & Sharif, A. The impact of informational feedback on
energy consumption-A survey of the experimental evidence. Energy 35,
1598–1608 (2010).
	28.	Schwartz, D., Bruine de Bruin, W., Fischhoff, B. & Lave, L. Advertising energy
saving programs: The potential environmental cost of emphasizing monetary
savings. J. Exp. Psychol. Appl. 21, 158–166 (2015).
	29.	Allcott, H. & Sweeney, R. L. The role of sales agents in information
disclosure: evidence from a field experiment. Manage. Sci. 63, 21–39 (2017).
	30.	Energy Efficiency Campaign: ‘Deutschland Macht’s Effizient’ (German Federal
Ministry for Economic Affairs and Energy, 2016); http://www.deutschland-
machts-effizient.de/
	31.	Customer Incentives for Energy Efficiency Through Program Offerings (US
Environmental Protection Agency, 2010).
	32.	Landry, C. E., Lange, A., List, J. A., Price, M. K. & Rupp, N. G. Toward an
understanding of the economics of charity: evidence from a field experiment.
Q. J. Econ. 121, 747–782 (2006).
	33.	Karlan, D. & List, J. A. Does price matter in charitable giving? Evidence from
a large-scale natural field experiment. Am. Econ. Rev. 97, 1774–1793 (2007).
	34.	Goette, L., Stutzer, A. & Frey, B. M. Prosocial motivation and blood
donations: a survey of the empirical literature. Transfus. Med. Hemotherapy
37, 149–154 (2010).
	35.	Olmstead, S. M. & Stavins, R. N. Comparing price and nonprice approaches
to urban water conservation. Water Resour. Res. 45, 1–10 (2009).
	36.	Borenstein, S. The long-run efficiency of real time electricity pricing. Energy J.
26, 93–116 (2005).
	37.	Maki, A., Burns, R. J., Ha, L. & Rothman, A. J. Paying people to protect the
environment: a meta-analysis of financial incentive interventions to promote
proenvironmental behaviors. J. Environ. Psychol. 47, 242–255 (2016).
	38.	Bamberg, S. Is a residential relocation a good opportunity to change people’s
travel behavior?: Results from a theory-driven intervention study. Environ.
Behav. 38, 820–840 (2006).
	39.	Frey, B. S. & Oberholzer-Gee, F. The cost of price incentives: an
empirical analysis of motivation crowding-out. Am. Econ. Rev. 87,
746–755 (1997).
	40.	Sandel, M. J. What Money Can’t Buy: The Moral Limits of Markets
(Macmillan, London, 2012).
	41.	Gneezy, U. & Rustichini, A. A fine is a price. J. Legal Stud. 29, 1–17 (2000).
	42.	Thøgersen, J. Monetary incentives and environmental concern. Effects of a
differentiated garbage fee. J. Consum. Policy 17, 407–442 (1994).
	43.	Karlin, B., Zinger, J. F. & Ford, R. The effects of feedback on energy
conservation: a meta-analysis. Psychol. Bull. 141, 1205–1227 (2015).
	44.	Schopfer, S., Tiefenbeck, V. & Staake, T. Economic assessment of
photovoltaic battery systems based on household load profiles. Appl. Energy
223, 229–248 (2018).
	45.	Tiefenbeck, V., Tasic, V., Schöb, S. & Staake, T. Long-lasting effects or
short-term spark? On the persistence of behaviour change induced by
real-time feedback on resource consumption. In Proc. Eur. Conf. Inf. Syst.
1–17 (2016).
	46.	I’m Not Surprised. Nat. Energy 2, 17101 (2017).
	47.	Baca-Motes, K., Brown, A., Gneezy, A., Keenan, E. A. & Nelson, L. D.
Commitment and behavior change: evidence from the field. J. Consum. Res.
39, 1070–1084 (2013).
	48.	Goldstein, N. J., Cialdini, R. B. & Griskevicius, V. A room with a viewpoint:
using social norms to motivate environmental conservation in hotels.
J. Consum. Res. 35, 472–482 (2008).
	49.	Schultz, P. W., Khazian, A. M. & Zaleski, A. C. Using normative social
influence to promote conservation among hotel guests. Soc. Influ. 3,
4–23 (2008).
	50.	Schwartz, D. et al. The Hawthorne effect and energy awareness. Proc. Natl
Acad. Sci. USA 110, 15242–15246 (2013).
	51.	Tiefenbeck, V. On the magnitude and persistence of the Hawthorne effect
- evidence from four field studies. In Proc. European Conference on Behaviour
and Energy Efficiency 1–6 (2016).
Nature Energy | VOL 4 | JANUARY 2019 | 35–41 | www.nature.com/natureenergy
40

Articles
Nature Energy
	52.	Ableitner, L., Schöb, S., Tiefenbeck, V. & Fridgen, G. Real-world impact of
information systems: the effect of seemingly small design choices. In Proc.
Work. Inf. Technol. Syst. 1–16 (2017).
Acknowledgements
We thank S. Häcki and T. Bachmann from Swiss Mobiliar insurance for their great
efforts in reaching out to hotels and managing the on-site study implementation and
data collection. We would also like to express our gratitude to the management of the six
Swiss hotels for the opportunity to run the study. Funding for this work was provided
by the MTEC foundation of ETH Zurich (data analysis) as well as by Swiss Mobiliar
insurance (hardware, deployment).
Author contributions
V.T. and T.S. designed the study. S.S. wrote the software of the study devices and enabled
the technical side of the data collection. V.T. oversaw the study implementation. A.W.
and V.T. analysed the data. V.T. and A.W. drafted the manuscript; T.S. and E.F. edited the
manuscript. V.T., T.S. and E.F. secured funding for the study.
Competing interests
V.T., A.W. and E.F. declare no competing financial interests. T.S. and S.S. are
co-founders of and hold shares in Amphiro AG, the SME that manufactures the smart
shower meters. T.S. and S.S. were not involved in the data analysis, hotel selection or
room assignment.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-018-0282-1.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to V.T.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2018
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Corresponding author(s):
Verena Tiefenbeck
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Data collection
Custom software for the shower meter Amphiro was used to measure resource consumption in the showers in 265 rooms in 6 different
hotels and for the data read-out
Data analysis
Stata SE 14.1 was used to preprocess the resource consumption data, to conduct regression analyses, and plot the presented graphs
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The data that support the findings of this study is available on figshare.com: https://doi.org/10.6084/m9.figshare.6984323.v1.

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Study design
All studies must disclose on these points even when the disclosure is negative.
Study description
Our study is a randomized controlled trial at six hotels, to examine resource consumption during showering in a natural field experiment
in a setting of complete absence of monetary incentives among a sample of uninformed participants who did not self-select into the
study. We collected quantitative data from 19,602 showers (energy and water consumption, temperature, flowrate).
Research sample
The study sample contains data on resource consumption in the shower from the guests of 265 rooms of 6 different tourist and business
hotels in Switzerland. Due to the the hotels' privacy regulations we were not able to collect any demographical data, but this sampling
strategy had the advantage that our sample did not self-select into the study. This point is discussed in the article.
Sampling strategy
We ran the study as a natural field experiment, thus guests encountered the measurement and feedback device as part of their
bathroom equipment. We conducted powertests based on the data of a similar previous study in households (Tiefenbeck et al. (2016),
Management Science).
Data collection
A smart shower meter was used to collect data on resource consumption from every water extraction during the study period. The
device called "Amphiro" is depicted in the article. The data is stored on the device and was read out by the researchers at the end of the
study.
Timing
Data collection took place from February to April 2016.
Data exclusions
The data was cleaned by removing outliers from malfunctioning devices; to this end, observations that deviated by over 3 standard
deviations from the mean of the energy consumed or water volume per shower were removed.  Furthermore, we excluded data points
that most likely did not represent showers – e.g. water extractions of volumes below 6.5 litres and observations deviating over 2
standard deviations from average temperature, which probably represent cleaning or other procedures .
Non-participation
We are not aware of any hotel guests that asked to have the device removed, so to the best of our knowledge, data on all guests of the
participating rooms during the period of the study are present in our raw dataset.
Randomization
At each hotel, we randomly allocated rooms to the two experimental conditions; the randomization was done by floor level to account
for heterogeneity in the flow rates in the different floors of the buildings.

    Scientific Research Findings:

Answer:
Hotel guests who received real-time feedback on the energy consumed while showering used 11.4% (0.21 kWh) less energy per shower than hotel guests in a control group. The results are important for two reasons. First, they indicate that real-time feedback on a specific activity can induce large behaviour change and resource savings among the broader population and not only among the kind of people who volunteer to participate in energy conservation studies. Second, participants had no financial motives for behaviour change (hotel guests pay a fixed room rate). Even in this setting, the digital behavioural intervention created large conservation effects.
Two potential caveats remain. First, we do not know how representative the behaviour of hotel guests is for the tested behaviour compared to the general population. Second, we cannot measure long-term behaviour in a hotel setting. However, in other experiments that we conducted with households over several months, conservation effects from real-time feedback were stable.