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

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-021-00781-1
1Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, UK. 2Institute for Transport Studies, University of Leeds,
Leeds, UK. 3Grantham Institute, Imperial College London, London, UK. 4Energy Futures Lab, Faculty of Engineering, Imperial College London, London, UK.
5Chemical Engineering Department, Imperial College London, London, UK. 6, Waikato, New Zealand. ✉e-mail: s.hall@leeds.ac.uk
D
omestic consumers’ choice of energy companies in com-
petitive markets affects energy transitions1,2. Energy utilities
have adopted a volume-sale business model to reduce costs
and attract consumers3–7. This incumbent business model is threat-
ened by low-carbon transitions8,9, because large renewables reduce
the profitability of legacy fossil plants10; prosumers and microgen-
eration reduce predictability and volume sales6,11; more suppliers
are entering the market7,12; and more flexible demand competes
with traditional plants for flexibility services13. These pressures are
forcing the volume-sale model to change and adapt, to survive the
energy transition14.
The changes utilities make to survive in low-carbon transitions
can also change the consumer energy contract15. New consumer con-
tracts are emerging that integrate decentralized renewables9,16, move
to energy-as-a-service as opposed to pay-per-kilowatt tariffs17,18,
and reward consumer behaviour change19–21. The energy transition
in liberalized markets is shaped by these trends22. However, little
has been done to understand consumer preferences for these new
business models and how these consumer preferences could affect
the transition.
Most research on consumer preferences in energy retail markets
relates to switching suppliers23. This work shows that consumers do
not respond well to price signals, and the number of households
that enter the market to find a better deal is lower than regulators
would hope or economists predicted24–27. Recent research has turned
to behavioural economics28 to explain these trends. Behavioural
economics recognizes that consumers are not perfectly rational;
they operate in dynamic markets, where options are too complex to
fully process29.
This complexity leads consumers to adopt satisficing over opti-
mizing behaviour. Satisficing behaviour aims at satisfactory levels of
performance given existing resources and imperfect information30.
There is strong evidence that consumers are satisficing in their
choice of energy supplier29,31,32. Consumers are likely to make deci-
sions based on simplification strategies that reduce the complex-
ity of decision-making and fall back on heuristics, such as trust in
institutions, to distinguish between options30,33. Consumers also
exhibit status-quo bias and loss aversion behaviour in making
energy choices34. However, this satisficing behaviour comes at a
price, as even consumers on the poorest energy contracts report
high confidence that they are on the best deal for them, and report
high trust in their current supplier23.
Consumer satisficing is problematic enough when consumers
are choosing between contracts competing only on price. This prob-
lem may be compounded if consumers are being asked to choose
between diverse contracts created by utility business model inno-
vation. Yet, there has been little work to explore the types of util-
ity energy contracts consumers are likely to opt for when presented
with a range of possible offers. This is the problem we address in
this study. We explicitly assume that utility companies and their
consumers are operating under uncertainty, searching for satisfac-
tory strategies in the market, and that these decisions will evolve
with each other to affect the direction and outcomes of energy
transitions. To explore this relationship, we define the business
models that utilities are exploring to respond to the pressures of a
low-carbon transition and which contractual attributes they might
offer consumers, and test how consumers respond to these new
business models, and which consumers prefer which new contracts.
We then use the results to explore the implications of these data on
energy transitions and market regulation.
We found that utilities are developing new ways of electrify-
ing heat and transport, servitising the energy contract, bundling
energy services with other infrastructure services and facilitating
peer-to-peer (P2P) platforms. We also identified four consumer
segments with varying appetites for new utility contracts. The seg-
ment with the highest appetite for new models is also the smallest
(16% of respondents), suggesting that new utility business models
might only have a limited niche to expand into. The other three
segments face barriers to participation (based on tenancy type
or income levels) or barriers to acceptance (based on social trust
or market engagement). Based on our segmentation, we define
three challenges for the energy transition. First, there is potential
Matching consumer segments to innovative utility
business models
Stephen Hall   1 ✉, Jillian Anable2, Jeffrey Hardy   3, Mark Workman4, Christoph Mazur5 and
Yvonne Matthews6
Energy as a service, smart home opportunities and electrification of heat and transport can lead to new ways of switching sup-
plier or choosing new energy contracts. Here, we used business model collaboration workshops to create archetypes of new
utility business models, which were then tested with a representative sample of British energy consumers to explore their
attractiveness to different segments of society. We show that some of these segments have a substantial appetite for new busi-
ness models. However, the segments that choose these models are more likely to be affluent, educated homeowners. Without
intervention, innovation in utility business models risks exacerbating existing social inequalities, as lower incomes, lower home
ownership and low education result in lower preferences for, or no ability to engage with, new utility business models. We also
find that institutional trust beyond the energy sector is a key driver of consumer segmentation.
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for market innovation to stall due to the most receptive segments
being relatively small and/or most likely to rent and the most disen-
gaged segments being most likely to own homes and have the abil-
ity to include building fabric alteration in their energy contracting.
Second, there is a social trust barrier to overcome that leads to low
confidence in new utility business models but also applies across
other societal institutions. Finally, the culmination of these issues
could lock in existing social inequalities and lock out some sectors
of society from participating in low-carbon transitions.
Business model generation
We adopted a collaborative business model innovation process35 to
explore how utility business models can evolve to meet the chal-
lenges of a low-carbon transition. We followed Rohrbeck et al.36,
who used this approach to support collaborative business model
generation in the German utilities sector. The workshop was under-
taken on 15 June 2016 with 38 industry, academic and government
stakeholders. We identified 11 future utility business model arche-
types that responded to a hierarchy of threats to the current util-
ity business model. We use the term archetype to describe a new
utility business model. Rohrbeck et al.36 suggested that collabora-
tive business model generation is done in three stages: (1) idea gen-
eration; (2) prioritization; and (3) validation. As this research was
time constrained to a one-day workshop, we used a project steering
group, comprising three utility executives, two infrastructure con-
sultants and two energy financiers drawn from the Energy Research
Partnership, for initial idea generation and took these ideas on util-
ity business models for further development in the workshop (see
Supplementary Methods for details of the workshop process).
Stage 1 prioritized the systemic challenges to the incumbent utility
business model (see Supplementary Fig. 6). The six highest-priority
threats to the incumbent volume-sale utility model were: (1) pol-
icy uncertainty; (2) large penetration of intermittent renewables;
(3) demand-side management; (4) diversifying the supply mar-
ket; (5) cost of capital; and (6) increasing micro-decentralized
optimization.
In stage 2, groups were asked to explore the implications of six
proposed innovative utility business models designed to address
these systemic challenges. These business models were visualized in
component diagrams37. The six initial business model concepts and
component diagrams were generated by the project steering group
and were refined and expanded by workshop participants. Figure 1
shows the resultant utility business models and Supplementary
Figs. 7–17 show all of the business models considered in the workshop.
The five business model archetypes shown in Fig. 1 were derived
from the workshop in June 2016 and set the business models to be
researched to the middle of 2019. During this time, some of these
propositions have been tested by utilities in the United Kingdom and
elsewhere and none have been rendered obsolete or unlikely since
their conception. Indeed, new electric vehicle tariffs are beginning
to enter the market38, P2P trials are ongoing39, automated-switching
models that show the early stages of the third-party-control arche-
type are emerging40, and bundled retrofit and energy service models
are being piloted41.
The attributes of the business models that could be presented
to a consumer in a switching situation were then developed by the
research team and are shown in Table 1, along with a control busi-
ness model: same but smart (SBS).
Conceptual model development
The attributes of each business model were presented to 2,024
British residential utility bill payers in a questionnaire survey. The
theoretical basis of the questionnaire design was the technology
acceptance model (TAM)42. The value of the TAM, or adapted vari-
ants of it, has been demonstrated in several technology contexts,
including the uptake of IT services, e-commerce and smart grids43.
Using an adapted version of the TAM in the technology-driven con-
text of innovative utility business models is justified given that each
business model involves different engagement with technologically
mediated, energy-smart behaviour44.
The original TAM model explains willingness to adopt a tech-
nology by two factors: (1) perceived ease of use; and (2) perceived
usefulness. Expanded versions of the TAM have found additional
constructs to add explanatory power and these capture individual
experience as well as beliefs about how the innovation under study
would perform in relation to multiple societal factors. We ensured
that our measure of perceived usefulness includes specific measures
of expected value for adopters of energy contracts as they involve
P2P
ESC
3PC
P2P customers directly buy,
sell or swap electricity with
each other
An ESC delivers energy services to
customers, such as comfort and
illumination, rather than units of
energy like a traditional supplier
A third party, such as a price
comparison website, takes
decisions on consumers’ behalf,
such as automatically switching
energy suppliers
Pure low-carbon generator
Producing low-carbon
power and selling directly to
large customers or
wholesale market
New electrifier
Traditional utility that is
helping consumers to switch to
electric heat and mobility,
including installing equipment
and automating DSR
Fig. 1 | Utility business model archetypes. Overview of the business models synthesized from the business model collaboration workshop. The pure
low-carbon generator was not analysed in the consumer-facing experiment as it sells only to wholesale markets. DSR, demand side response. Figure
reproduced from ref. 77, Elsevier.
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both technology and service components45. The model draws on
diffusion of innovations theory by capturing notions of perceived
ease of use and complexity46 and the theories of reasoned action
or planned behaviour47. These social-psychological theories have
informed behavioural economic approaches, which recognize that
consumers are not perfectly economically rational; they operate in
dynamic markets, where choice is expanding and options are too
complex to process fully29.
Through a combination of our domain-specific knowledge of
energy efficiency and tariff-switching behaviours within current
energy markets, together with the components of trust48 and per-
sonal innovativeness49, both of which have been proven to enhance
the TAM, we developed a conceptual framework on which to base
the questionnaire design and analysis (see Methods). The key con-
structs consist of: experience (current knowledge, engagement and
status-quo bias); salient beliefs (concern about the future, and green
Table 1 | Consumer-facing attributes of each new business model archetype
New utility
business model
Consumer-facing attributes
SBSa
You are free to switch companies as and when you want to. You will have a smart meter with live information at home and on your
phone. Your supplier can see how and when you use electricity. You can change your behaviour (not use the washing machine and so
on) when you see that electricity is cheaper.
New electrifier
You have a 2-year contract. You receive a discount for switching your home from gas to electric heat. It will cost about the same as
now. You might have some new features installed, such as electric radiators or a heat pump. Your supplier can pause your heating
occasionally for up to 15 min at a time or take control of when to charge your electric car to help you avoid paying the highest prices,
although you can opt out of this.
ESC
You have a 10-year contract. Your energy bills are guaranteed to be lower than you are currently paying for the duration of the
contract. You receive one bill for all of your light, heat and any electric car needs. You might have some new features installed, such
as insulation and a home energy management system. Your supplier can pause your heating and appliances (such as your fridge)
occasionally for up to 15 min at a time or take control of when to charge your electric car to help you avoid paying the highest prices,
although you can opt out of this.
P2P
You have no contract with any energy supplier. You use an app on your phone to choose, based on price, type or location of energy,
who to buy energy from. For example, you might want local green energy, even though it might not be the cheapest. You can change
who you receive your energy from as often as you like. If you have a solar panel on your roof, you can make money by selling the
energy from it through the app.
3PC
You have a multi-year contract. You tell the company how you want to live your life and it makes decisions on your behalf to deliver this.
You receive one bill for all of your energy, broadband, TV, mobile phone, electric vehicle and water services. Your company may offer
to install equipment, such as insulation and a home energy management system, to make your home more efficient and smarter. Your
company can pause your heating and appliances (such as your fridge) occasionally for up to 15 min at a time or take control of when to
charge your electric car to help you avoid paying the highest prices, although you can opt out of this.
aControl archetype used to reflect the contracts available to consumers today.
0
10
20
30
40
50
60
70
80
90
100
SBS
Intention to adopt (%)
P2P
3PC
Likely
Neutral
Unlikeley
Utility business model name
New electrifier
ESC
Fig. 2 | Likelihood of adopting each archetype. Graph of the percentage of participants (n = 2,024) who were likely, unlikely or neutral in their intention
to adopt each archetype. Responses to the question “If this option were available today, what is the likelihood you would sign up to it?” were scored on a
sliding scale from 0–1 (very unlikely to very likely), then coded as unlikely (0–0.4), neutral (0.5) or likely (0.6–1).
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beliefs); personal innovativeness (openness to new technology);
trust (trust in operators and perceived risk of system use); perceived
ease of use; perceived usefulness (including the value perceived
from adopting the contracts); and intention (stated likelihood
to adopt).
Archetype preference
Survey participants were presented with the information in Table 1 on
each archetype and were asked to score it on several semantic differen-
tial attribute scales. This exercise provided for some reflection on, and
assimilation of, each option. Intention to adopt was measured using a
Engaged but cautious
(n = 706; 35%)
Aspiring opt-outs
(n = 537; 27%)
Unconvinced and
unmotivated (n = 449;
22%)
Pragmatic innovators
(n = 330; 16%)
I really want to save the
planet but I already shop
around to get the best deals
and I am not sure these new
deals will be any better.
I don’t trust any of the
existing companies and I am
worried about future price
rises. I am quite attracted to
the idea of opting out and
going it alone if I could.
Science and technology are
the answers to
environmental problems
and I am open to change so
long as it leads to more
choice and freedom.
There is not much you could
do to interest me in my
energy use. These new
companies will peddle a
load of green nonsense
while exploiting your data.
Fig. 3 | Consumer segments. Label, size and defining statement for each consumer segment.
0
10
20
30
40
50
60
70
80
90
Engaged but cautious
Aspiring opt-outs
Unconvinced and
unmotivated
Pragmatic innovators
Probability of adoption (%)
SBS
New electrifier
ESC
P2P
3PC
Consumer segment title
Fig. 4 | Probability of adoption of each archetype in each consumer segment. Probability of adoption was measured as the proportion of times an
archetype was chosen (out of four eligible times for each archetype for each person). Using ANOVA, the segments differ on all five archetypes at
P > 0.0001, with the exception of new electrifier, with P = 0.21 (F = 95.138, d.f. = 3 and P > 0.0001 for SBS; F = 1.509, d.f. = 3 and P = 0.21 for new electrifier;
F = 36.898, d.f. = 3 and P > 0.0001 for ESC; F = 22.477, d.f. = 3 and P > 0.0001 for P2P; and F = 37.408, d.f. = 3 and P > 0.0001 for 3PC).
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Table 2 | Profile statistics for each segment on demographic characteristics and clustering variables
(A) Engaged but
cautiousa
(B) Aspiring
opt-outsa
(C) Unconvinced
and unmotivateda
(D) Pragmatic
innovatorsa
X2/F valueb
d.f.
P value
Demographics
 Male (%)
45.9
42.5c
55.9d
52.7
24.424
6
P < 0.001
 Owner occupier (%)
66.1
53.1c
71.7d
58.8
43.459
6
P < 0.001
 With children (%)
23.8
28.7
12.9c
49.4d
134.081
3
P < 0.001
 Degree education or
above (%)
38.7
35.4
28.3c
43.6d
24.399
9
P < 0.001
 In work (%)
51.1
60.0
44.8c
75.5d
83.908
4
P < 0.001
 >£60,000 per annum
income (%)
15.5
10.2c
11.1
18.7d
43.039
12
P < 0.001
 Age (mean (s.d.) years)
49.6 (15.8)B,C,D
44.2 (14.9)A,C,D
55.1c (14.0)A,B,D
36.6d (13.0)A,B,C
113.413
3
P < 0.001
Experience/engagement
 Never switched supplier (%)
27.2c
49.2d
35.9
31.5
24.034
6
P < 0.001
 Satisfaction with supply
(mean (s.d.))#
4.1d (0.5)B,C
3.1c (0.8)A,C,D
3.5 (0.7)B
4.0 (0.6)B
262.091
3
P < 0.001
 Number of energy actions
(mean (s.d.))3
3.2 (0.5)C,D
3.2 (0.5) C,D
3.1c (0.5)A,B,D
3.6d (0.7)A,B,D
58.788
3
P < 0.001
 Think about electricity
(mean (s.d.))
2.7 (0.8)B,C,D
3.4 (0.9)A,C,D
2.3c (0.8)A,B,D
3.8d (0.9)A,B,C
254.647
3
P < 0.001
 Willingness to think more
about electricity use
(mean (s.d.))
2.6 (0.8)B,C,D
3.1 (0.9)A,C,D
2.1c (0.8)A,B,D
3.7d (0.9)A,B,C
286.575
3
P < 0.001
 Energy engagement
(mean (s.d.))
3.6 (0.9)B,C,D
3.3 (0.9)A,C,D
3.1c (1.0)A,B,D
3.8d (0.7)A,B,C
5.794
3
P < 0.001
Salient beliefs
 Worried about price now
(mean (s.d.))
1.6c (0.8)B,D
3.1d (1.2)A,C,D
1.7 (1.1)B,D
2.6 (1.3)A,B,C
275.571
3
P < 0.001
 Worried for future
(mean (s.d.))
2.5c (0.9)B,D
3.8d (0.9)A,C,D
2.6 (1.1)B,D
3.4 (1.1)A,B,C
223.035
3
P < 0.001
 Environmentally responsible
(mean (s.d.))
3.8 (0.6)C
3.8 (0.6)C
3.2c (0.7)A,B,D
3.9d (0.6)C
101.669
3
P < 0.001
 Environmentally sceptical
(mean (s.d.))
2.4c (0.6)B,C,D
2.8 (0.6)A,D
2.7 (0.6)A,D
3.4d (0.8)A,B,C
191.267
3
P < 0.001
 Descriptive norm
(mean (s.d.))
3.3 (0.9)D
3.2c (0.9)D
3.3 (0.9)D
3.6d (0.9)A,B,C
16.928
3
P < 0.001
Personal innovativeness
 Adopts latest technology
(mean (s.d.))
2.7 (1.0)C,D
2.7 (1.1)C,D
2.1c (0.9)A,B,D
3.8d (1.0)A,B,C
176.039
3
P < 0.001
Trust
 Trusts own energy company
(mean (s.d.))
3.8 (0.7)B,C,D
2.8c (0.9)A,C,D
3.1 (1.0)A,B,D
4.1d (0.8)A,B,C
233.642
3
P < 0.001
 Trusts other energy
companies (mean (s.d.))
3.2 (0.7)B,C,D
2.6c (0.8)A,D
2.6c (0.8)A,D
3.7d (0.8)A,B,C
225.853
3
P < 0.001
 Willing to share info
(mean (s.d.))
3.3 (0.8)B,C,D
3.1 (0.8)A,C,D
2.3c (0.9)A,B,D
3.9d (0.7)A,B,C
249.425
3
P < 0.001
Perceived ease of use
 SBS (mean (s.d.))
0.7d (0.2)B,C,D
0.6 (0.2)A,C
0.4c (0.2)A,B,D
0.6 (0.2)A,C
8.906
3
P <0.001
 New electrifier (mean
(s.d.))
0.5 (0.2)B,C,D
0.5 (0.2)A,C,D
0.3c (0.2)A,B,D
0.6d (0.2)A,B,C
165.999
3
P < 0.001
 ESC (mean (s.d.))
0.4 (0.2)B,C,D
0.4 (0.2)A,C,D
0.3c (0.2)A,B,D
0.6d (0.2)A,B,C
136.260
3
P < 0.001
 P2P (mean (s.d.))
0.4 (0.2)C,D
0.4 (0.2)C,D
0.2c (0.2)A,B,D
0.6d (0.2)A,B,C
135.804
3
P < 0.001
 3PC (mean (s.d.))
0.5 (0.2)B,C,D
0.4 (0.2)A,C,D
0.3c (0.2)A,B,D
0.6d (0.2)A,B,C
115.045
3
P < 0.001
Continued
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likelihood-to-adopt scale. Participants were asked: “If this option was
available today, what is the likelihood that you would sign up for it?”.
Responses were made using a sliding scale from 0 (not at all likely) to
1 (very likely), which was then banded to create a three-point scale
(likely, neutral or unlikely). Figure 2 shows that the control case SBS
(see Table 1) performed best, followed by P2P, with energy service
company (ESC) showing the lowest overall attractiveness.
Consumer segments
We then identified energy consumer segments based on consum-
ers’ likelihood of adopting the new energy contracts. Segmentation
studies have been used to identify different types of consumer in
relation to household electricity storage50, acceptance of demand
control51, energy conservation behaviours52 or acceptance of smart
grids. Some studies have used measured or metred consumption
data combined with only basic household and building character-
istics to identify groups of energy-consuming households53. Others
have used a richer suite of variables, based on qualitative surveys
and constructs from behavioural psychology, to capture the psycho-
social antecedents of likely market acceptance and targets for behav-
iour change52. The bases used to cluster the sample populations in
these studies were multifarious, ranging among general values, life-
styles, general patterns of energy-consuming behaviours, attitudes
to environmental issues and specific energy-related behaviours.
Some focused the clustering on the antecedents of behaviour only43,
whereas others mixed both the antecedents and the intentions or
behaviours themselves52, as we have done here.
The cluster analysis identified four groups individually repre-
senting between 16 and 35% of the sample. The groups were profiled
using the variables used to create them as well as other factors, such
as demographic characteristics and current energy use. Profiling
consisted of characterizing each segment individually using descrip-
tive statistics as well as comparisons with the other segments using
measures of variance and association. Each of these segments was
given a short name and a representative narrative statement (Fig. 3).
Segment archetype preferences
In a paired comparison task, respondents were forced to choose
one or the other archetype in each of the ten paired cases (order
randomized). Each archetype was presented using the short para-
graph in Table 1, with two paragraphs displayed side by side. While
this method means that the key attributes are less directly compa-
rable than can be the case with some forms of conjoint analysis, it
was designed to promote engagement with the task by requiring
identification and consideration of salient characteristics of each
business model.
Figure 4 shows the number of times each business model was
chosen as a proportion of the number of times it was available to be
chosen (out of four eligible times for each archetype for each per-
son). This unweighted probability shows that there was a statistically
(A) Engaged but
cautiousa
(B) Aspiring
opt-outsa
(C) Unconvinced
and unmotivateda
(D) Pragmatic
innovatorsa
X2/F valueb
d.f.
P value
Perceived usefulness
 Perceived savings (mean
(s.d.))
2.4c (0.9)B,C,D
3.5d (0.8)A,C
3.1 (1.0)A,B,D
3.5d (0.9)A,C
158.602
3
P < 0.001
 Less energy to save money
(mean (s.d.))
3.9 (0.7)B,C
4.2 (0.7)A,C
3.5c (0.8)A,B,D
4.1d (0.8)C
93.795
3
P < 0.001
 Less energy for environment
(mean (s.d.))
3.6 (0.9)C,D
3.7 (0.9)C
2.7c (1.0)A,B,D
3.9d (0.9)A,C
132.611
3
P < 0.001
 Smart control benefits
(mean (s.d.))
3.2 (1.0)C,D
3.0 (1.0)C,D
2.0c (1.1)A,B,D
4.1d (0.8)A,B,C
280.458
3
P < 0.001
Intention
 Weighted probabilitye: SBS
(mean (s.d.))
0.56d (0.27)B,C,D
0.45 (0.26)A,C,D
0.27c (0.24)A,B
0.32 (0.23)A,B
158.955
3
P < 0.001
 Weighted probabilitye: new
electrifier (mean (s.d.))
0.20 (0.18)C,D
0.22 (0.19)C,D
0.10c (0.14)A,B,D
0.27d (0.20)A,B,C
160.901
3
P < 0.001
 Weighted probabilitye: ESC
(mean (s.d.))
0.15 (0.20)C,D
0.12 (0.17)C,D
0.07c (0.13)A,B,D
0.31d (0.25)A,B,C
179.495
3
P < 0.001
 Weighted probabilitye: P2P
(mean (s.d.))
0.22 (0.24)B,C
0.32d (0.27)A,C,D
0.14c (0.19)A,B,D
0.25 (0.23)B,C
129.876
3
P < 0.001
 Weighted probabilitye: 3PC
(mean (s.d.))
0.13 (0.17)C,D
0.14 (0.17)C,D
0.06c (0.11)A,B,D
0.29d (0.24)A,B,D
170.559
3
P < 0.001
 Spread of scores (mean
(s.d.))
0.55d (0.26)B,C,D
0.49 (0.26)A,C,D
0.30 (0.24)A,B
0.29c (0.21)A,B
145.771
3
P < 0.001
 Consistency of scoring
(mean (s.d.))
0.25c (0.64)D
0.34 (0.80)D
0.31 (0.72)D
1.27d (1.45)A,B,C
114.550
3
P < 0.001
Cluster size (n)
706
537
449
330
Cluster share (%)
35
27
22
16
aThe homogeneity-of-variance test was met in each case to apply Tukey’s post-hoc test alongside the ANOVA test. Different upper-case letters indicate statistically significant differences between specific
segments (as labelled A–D in the header) using this test (P < 0.05). bOne-way ANOVA, producing an F statistic or X2 value, was conducted to evaluate the relationship between cluster membership and
each variable. cValue of the segment with the lowest score for each segmentation variable. In all cases, the lower the mean score, the lower the agreement on each statement. For an explanation of each
variable name and the measurement scale used, see Supplementary Table 3. dValue of the segment with the highest score for each segmentation variable. In all cases, the higher the mean score, the greater
the agreement or value on this construct or issue. eWeighted probability refers to the probability score (proportion of times chosen (0–1)) multiplied by the stated likelihood-of-adoption score (0–1).
Table 2 | Profile statistics for each segment on demographic characteristics and clustering variables (Continued)
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significant difference between at least two of the segments for all
but the new electrifier archetype. Table 2 provides these prob-
ability variables weighted by the stated likelihood-to-adopt score,
to account for the fact that the paired comparison exercise forces
people to make a decision of some kind. Here, there are differ-
ences between at least two of the segments on all of the archetypes.
This shows that some segments are more discriminating across the
archetypes than others.
The status-quo option (SBS) is the standout preference of three
of the segments, although engaged but cautious people are still sub-
stantially more likely than all other groups to choose this option.
This segment demonstrates the joint-highest tendency to switch
utility providers now and achieve high levels of satisfaction and
engagement by doing so. They therefore represent an engaged and
informed group of consumers, but do not perceive the current sys-
tem to be problematic enough to need to change.
P2P stands out as being the second most favourable option for
three out of four of these groups. The aspiring opt-outs are the most
enthusiastic about this archetype (and the least in favour of the
ESC), motivated by a chance to break free from large utility compa-
nies and achieve greater cost savings.
Although the differences in preference for the new electrifier
archetype are less strong and more dependent on which measure-
ment of likelihood to adopt that we use, this option is neverthe-
less consistently perceived more favourably than ESC for all but
the pragmatic innovators, for whom it is no more or less acceptable
than any other. The long contract term involved in the ESC seems
to be off-putting to both those who are less trusting of large energy
suppliers (unconvinced and unmotivated or aspiring opt-outs) or
those who are more trusting but still want to take some control and
look for the best deals when they can (engaged but cautious or prag-
matic innovators).
The pragmatic innovators have the highest appetite for new types
of contract overall, although they have a lower tendency than the
other three groups for the P2P solution. The members of this seg-
ment are already markedly more likely to have adopted new tech-
nology, such as solar photovoltaics or electric vehicles, and express
great faith in scientific and technological solutions. However,
they are shown to have greater levels of trust in larger companies
than all of the other segments, and feel less convinced that there
are large financial savings to be made from choices about energy
suppliers. This might explain why the greater level of hands-on
decision-making involved in P2P is not regarded by pragmatic
innovators as so necessary to overcome trust and financial issues as
it is by the aspiring opt-outs or even the unconvinced and unmoti-
vated. While the unconvinced and unmotivated appear marginally
more receptive to this option than both the engaged but cautious
and pragmatic innovators, when the scores are weighted by the
stated likelihood to adopt (Table 2), their tendency to say they are
unlikely to adopt any of the models means they are the least enthu-
siastic for all of the options.
Demographic, experiential and attitudinal characteristics
Table 2 displays characteristic variables for each segment, orga-
nized by the constructs of the conceptual model in addition to
some demographic variables that were not used in the segmenta-
tion. Figure 5 focuses on one construct that proved to be one of the
strongest predictors of segment membership. Out of the 23 predic-
tor variables of the segments identified using discriminant analysis,
trust in one’s existing supplier is ranked fifth and trust in other sup-
pliers is ranked sixth. Figure 5 shows further differences between
the segments on trust of additional societal institutions using a
question that has been used to understand comparative switch-
ing behaviour in a number of different markets and to set this in
context of a consumer’s general trust tendency54. In Table 3, we
bring together the analysis of all of the characteristics to provide
a brief summary profile of each segment and the implications for
energy market transitions.
0
1
2
3
4
5
Own energy company
Other energy companies
Mobile phone providers
Insurance companies
Banks
Broadband providers
Charities
Car manufacturers
Train companies
Police
Engaged but cautious
Aspiring opt-outs
Unconvinced and unmotivated
Pragmatic innovators
Fig. 5 | Trust across societal institutions by consumer segment. For each institution, we asked the question: “To what extent do you trust or distrust
the following types of organization to treat you in a fair and honest way?”. This was measured on a five-point scale from 1 (distrust strongly) to 5 (trust
strongly). Using ANOVA, the segments differ significantly on all of the ten institutions at P > 0.0001. Supplementary Table 5 shows the mean values, s.d.,
ANOVA and Tukey’s post-hoc tests for these ten comparisons.
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Table 3 | Summary profiles of each segment and implications for energy market transitions
Summary profile
Implications for energy market transitions
Engaged but
cautious
(35%)
• These consumers are engaged in their energy and are the most likely to shop around
and switch supplier, finding it relatively straightforward to understand tariff information
and calculate potential savings. They have high trust in their own and other suppliers and
high willingness to share information with them. However, because they feel satisfied that
they can get what they want out of the current market and that they are paying the least
for their energy, they have the highest satisfaction levels and are not enthusiastic about
alternative business archetypes.
• They appear more motivated to get the best price for their energy than to save money
through demand reduction or by being early adopters of alternative energy sources. Their
motivations for being price conscious do not appear to be driven strongly by affordability
or fuel poverty.
• They are the most environmentally conscious and most consistently minded towards
green energy, although they are cautious about paying more. Although they are
environmentally motivated, they do not want to work very hard for this. For instance, they
are the most likely to admit that they could reduce their energy demand, despite being
highly likely to say that reducing energy would make them feel good. However, they are
clear about not being overly willing to spend time thinking about the energy they use,
and this is reflected in them being unconvinced by smart meters and not standing out for
undertaking many other energy-saving behaviours.
• Tendency to be older, female, childless, educated, in employment and owner occupiers
with an average income.
• This group stands out as the highest scorer
on SBS, with only moderate rankings of all
of the other archetypes. Thus, although they
are motivated to shop around and inform
themselves about tariffs, their satisfaction
with the status quo appears to lead to
resistance towards new business models
• Importantly, the two segments with
above-average owner occupation status
(engaged but cautious and unconvinced and
unmotivated) are the least likely to choose
the two archetypes that require alterations
to building fabric (that is, ESC and 3PC).
Aspiring
opt-outs
(27%)
• These consumers are the least likely to have switched supplier, despite the lowest
satisfaction and the highest tendency to say they have thought about switching. Those
who have switched have the greatest tendency to say that they found it difficult and
believe tariffs to be deliberately confusing, with little financial gain from one company
or tariff to another. They are the most likely to say that they find their bills difficult to
understand. This is also reflected in them showing the lowest levels of trust in their own
and other suppliers and the greatest resistance to suppliers controlling appliances.
• They do have some tendency to think about their energy use and do show the highest
engagement in energy saving, as they are very motivated to save money. They are paying
above average for their energy bills, yet are by far the least likely to say they are able to
keep warm in winter, with a high proportion citing cost as the reason. Despite being the
most worried about keeping warm in winter, they are by far the most likely of all of the
segments to say they would cut down on the amount of energy used if prices were to rise
by 20%.
• They are only moderately environmentally concerned and have the lowest faith in
science and technology, not considering themselves to be early adopters of technology.
They are reluctant to pay more for environmental gain.
• Tendency to be younger, female, with children and less educated and to have average
employment, low income and high rent.
• Highest scorer for P2P compared with
other segments, although this segment as a
whole scores highest for SBS and lowest for
ESC and 3PC.
• It is interesting that those who currently
have the greatest difficulty navigating
the energy market would want to take
responsibility for a more interactive market
experience. However, it makes sense in that
they do not trust the larger suppliers and
this would enable them to opt out of their
control.
• Their low trust is likely to have led to ESC
being their least favoured archetype, as this
would involve being tied in for a long period.
New electrifier is their third most preferred
option, perhaps based on their expressed
desire for improvements to their home
heating systems.
Unconvinced
and
unmotivated
(22%)
• This group are indifferent about their energy use and seem to have paid very little
attention to it in terms of their supply or their own behaviour.
• Although this group have switched suppliers in the past at an average frequency for
this sample and they are not particularly likely to say that it is too hard to switch, they
have low satisfaction with their current supplier and are the least likely to know details
such as the tariff they are on or the insulation level of their home, or to believe that they
understand their energy.
• They are not motivated, by money savings or climate change, to change their behaviour,
and are the least likely to be paying attention to any deals or their own energy use. They
are the least likely to say they are thinking about electricity use now and want to think
more about it in the future or that they want to change anything about their home energy
system. They are the least likely to have a smart meter or to say they want one, with the
highest likelihood to say that this kind of information is not at all helpful and the lowest
likelihood (8%) of saying they want one. They have the lowest willingness to share their
energy data with others or to have suppliers control their appliances.
• They consider themselves to be late adopters of technology.
• This group are the oldest and have a tendency to be out of work or retired, male and
childless, with low education and slightly below-average income, although they are more
likely to be owner occupiers.
• Although P2P is the second most popular
archetype after SNS for this group, their
tendency to say they are unlikely to actually
adopt any of them gives them the lowest
likelihood of choosing any of the business
models. They consistently rate all of the
archetypes as complicated, with the most
extreme scores on this measurement of
all the segments. SBS is rated as the least
complicated and P2P as the most.
• This group of consumers appear to be
difficult to hook into any alternative models
as they are not engaged in their energy
use or particularly motivated by cost or
environmental benefits. Even though it
would seem that they do not want to put
the effort into anything that involves them
having to think much about their supply,
they are also very negative towards the idea
of their data being shared or appliances
being controlled.
Continued
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Discussion
These data show that there is some consumer demand for innovative
retail energy contracts. Although it is clear that the business archetype
that represents only an incremental change from the status quo (SBS)
has proven the most popular, there are clear indications that different
consumers would be attracted to some other types of contractual rela-
tionship. Even the least innovative segment in our sample (engaged
but cautious) rejects the SBS model 15% of the time, and this increases
to 45% for the most innovative segment (pragmatic innovators).
These data show some desire for tying in new services, appliances and
even building works into an energy bill. This goes well beyond what
energy retail market regulation was initially designed to achieve55.
As flexible tariffs, home retrofit/appliance bundles and potential
P2P trading are all now technically possible56, and these data show
they each can find a compatible consumer segment, there is sub-
stantial potential for market disruption.
Given that the UK retail market already experiences regulatory
challenges from expanding conventional competition55, this latent
demand for innovative business models complicates the regulatory
task further and invites a policy and regulatory response. From
these data, we extract three challenges, as outlined below.
Pragmatic innovators show a high preference for two utility
business model archetypes that are radical departures from the cur-
rent utility model: ESC and third-party control (3PC). Importantly,
the two segments with above-average owner occupation status
(engaged but cautious (66% owned outright or mortgaged) and
unconvinced and unmotivated (72%)) are the least likely to choose
the two archetypes that require alterations to building fabric (that
is, ESC and 3PC). This is a problem when these business models
rely on changes to the building fabric that are paid for by long-term
energy bill savings. UK housing tenure trends show a reduction in
owner occupation and in increase in private rented tenure, espe-
cially for younger demographics57. Since both pragmatic innovators
and aspiring opt-outs are younger than the sample average, and of
below-average or decreasing likelihood to own their own home, this
means there is a disconnect between the segments that find such
business models attractive and their ability to sign up to them when
in rented tenure.
For aspiring opt-outs, who are younger, have lower income and
are probably renting, the opportunity to benefit from their preferred
archetype of P2P may be limited as they are the least likely to have their
own microgeneration to trade. With an average of 36% of the whole
study sample in rented tenure, a large proportion of consumers are
unable to access many of the benefits of new utility energy contracts.
Trust in one’s own and other suppliers is a significant driver of
segment membership. However, it may not be trust in the utilities
alone that drives segments to prefer different business models, but
also trust across societal institutions. Other recent work has pointed
to the role of trust and legitimacy between consumers and the
evolving retail energy market58. However, the data reported here
show remarkably consistent trust scores across societal institutions
by segment, in sectors as diverse as broadband provision, insurance,
banks and car manufacturers.
While societal legitimacy and institutional trust is important
for energy transitions59, this work shows that the sector may make
more progress on business model innovation by targeting segments
that already trust the system, as opposed to working to change the
perceptions of aspiring opt-outs or unconvinced and unmotivated
consumers. The low trust levels displayed by these segments may
be relatively fixed across institutions, resistant to trust messages
from the energy community, and not entirely unfounded, given
the Competition and Markets Authority found that UK utilities
were serially overcharging less engaged consumers by hundreds of
pounds per year7.
We may expect utilities to focus new tariffs and services towards
pragmatic innovators and aspiring opt-outs, who make up 43% of
the sample and are much more likely to choose a new type of utility
business model in a future switching decision. Further investigation
of the pragmatic innovators shows they are also amenable to direct
load control of appliances and most likely to already have (or to pur-
chase in the next 10 years) a plug-in electric car. This segment also
reports the highest income; segments with higher income find this
innovation most attractive and are likely to have the highest ability
and desire to purchase smart, flexible appliances and vehicles first.
Therefore, to mitigate the risk that the monetary benefits of flexible
electricity tariffs are most likely to be captured by higher-income
Summary profile
Implications for energy market transitions
Pragmatic
innovators
(16%)
• This group of consumers are engaged and potentially active but discerning.
• They have the joint-highest experience of switching suppliers so far, have very high
awareness and understanding of their energy tariff and are the most likely to be actively
undertaking energy-saving activities now and spending time thinking about their electricity
use. They are the most likely to say their homes are already insulated and to have a smart
meter, and they are by far the most likely to have a source of heat or electricity individual
to the dwelling, such as solar panels. They are also the most likely to own alternatively
fuelled cars. Thus, they are early adopters and perceive themselves as such.
• Interestingly, they have high trust in energy suppliers and do not believe that switching
suppliers brings much benefit because they are too similar. This means they have the
greatest preparedness to share information with these suppliers and for suppliers to
control appliances.
• They have a complex view about the environment. They say they are motivated to do
something about it and believe that issues are urgent, but do not believe people’s freedoms
should be curtailed and that the problems may be slightly exaggerated.
• They do believe that reducing energy use can save money and be environmentally
beneficial, and they are the most likely to say that reducing energy consumption would
make them feel good. However, they are also the most likely to say they could not reduce
their energy use any further.
• This group are the youngest, with few above 55 years. They are both men and women,
with a tendency to have children. They have the highest education, highest income and
highest employment and tend to be owner occupiers.
• Fairly equal choices among the options.
Highest of all of the groups on 3PC, which
may have appeal across the variety of
lifestyle services involved in a working family
home with some innovation tendencies.
• Largest number of reversals, showing some
ambivalence in choice. Thus, this groups
appears the least wedded to the status quo
and can see merit in a number of different
solutions.
See Supplementary Table 4 for a longer version and the Supplementary Data for background response scores.
Table 3 | Summary profiles of each segment and implications for energy market transitions (Continued)
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groups, groups such as the aspiring opt-outs, who also desire flex-
ibility and autonomy but more specifically the ability to manage
their more limited income more freely, need to be targeted directly
and given the means to act on their preferences. This segment
expresses the greatest preference for the P2P archetype, yet has the
least understanding of current bills, as well as the lowest trust scores
and lowest incomes. This highlights a particular risk as P2P offers
may only be beneficial to flexible, active consumers or those with
microgeneration to trade. While the current energy market suffers
from inequitable outcomes for unengaged consumers, the types
of utility business model innovation explored here may only risk
entrenching poor distributional outcomes.
Here, we have shown that some consumers do want the types
of business model being developed by utilities in response to the
pressures of existing low-carbon energy policies. While the entry
of these new types of contractual relation into the energy market
may be disruptive by itself, it also poses at least three specific chal-
lenges to the UK sector, which under similar demographics and
market conditions may be present in other liberalized, decarbon-
izing power sectors. First, there is the potential for market innova-
tion to stall due to the most receptive segments being most likely
to rent and the most disengaged segments being most likely to own
homes and have the power to opt for contracts that include build-
ing alteration. Second, there is a social trust barrier to overcome
in the energy sector, with low confidence across societal institu-
tions for some segments. Finally, there is a real risk that the cul-
mination of these issues could lock out some sectors of society,
including low-income, low-information and renting demograph-
ics, from participating in low-carbon transitions. As the market
diversifies and contracts become more complex, consumers may
rely even more on heuristics to make decisions, introducing more
complex consumer risks. The challenge for regulatory institutions
is to recognize these risks and evolve the regulatory model of the
retail market.
Methods
Survey design. The research aim for the questionnaire was to elicit the preferences
of domestic energy consumers across a set of business archetypes and to identify
a rich set of possible explanatory factors for these choices. The explanatory factors
included in the study were based on the technology assessment model combined
with a literature review, and detailed multiple situational, demographic and
attitudinal antecedents of engagement with electricity and energy usage in the
home, including energy efficiency and tariff-switching behaviours43,60–62. These
findings were used to inform the design of the survey, including some batteries
of questions already tried and tested in the UK context in the examination of
domestic energy usage61,63–65. The novelty in the present study is the profiling
of potential future consumer groupings based on a theoretically underpinned
conceptual framework of individual motivations involved in opting for different
ways of engaging with energy supply arrangements.
The questionnaire contained nine sections consisting of: (1) domestic
arrangements, including housing type and occupancy; (2) supply of energy to
the home, including billing, tariff and expenditure, and comprehension of these
elements; (3) heating methods and fuel, including perceived levels of insulation
and satisfaction; (4) perceived comfort and affordability, concern for and likely
response to future energy prices, experience with smart metering and demand
control; (5) reasons for choice of supplier, switching behaviour, satisfaction and
trust in energy suppliers and other types of service provider; (6) business archetype
attribute rating, paired choice experiment and overall likelihood to adopt each
model; (7) current car ownership and usage, willingness to adopt electric vehicles
and shared mobility; (8) attitudes towards different forms of energy generation,
energy supplier regulation and environmental beliefs/concerns; and (9) individual
and household sociodemographic characteristics.
Addressing limitations of hypothetical choices. Eliciting beliefs about and
preferences towards a set of products that are not currently available and also
represent an area of consumption (domestic energy use) in which consumers tend
to be minimally engaged is a serious research challenge. Three principal strategies
were used to mitigate this:
	1.
Sampling of householders at least partially responsible for (and there-
fore most likely to be engaged with) energy supply in the household (see
‘Sampling’)
	2.
Building up engagement with the issues successively through the question-
naire by starting with factual questions about current arrangements for en-
ergy supply against which people can mentally benchmark future archetypes
	3.
Inclusion of topic areas that have been found by other studies to result in a di-
versity of opinion relating to trust in organizations, environmental behaviour
and cost of energy
Altogether, the questionnaire took a minimum of 15 min to complete, with a
median duration of 30 min.
Data collection. The study employed a market research company (Accent)
to programme the online survey and organize the data collection. Accent
specialize in online stated-preference surveys with randomization and have
access to the Survey Sampling International (SSI) global market research panel of
demographically diverse adults (>18 years of age) who have voluntarily subscribed
to undertake such research. People register by providing varying amounts of
personal and demographic information that is later used to select participants for
specific surveys. Thus, as is typical with such panels, the SSI panel does not use
probability-based recruitment. Nevertheless, the panel from which this study’s
participants were selected was large enough to enable the selection of a nationally
representative sample or sample representative of sub-groups that reflect the actual
breakdown of their key demographics (for example, age, gender, region, social
grade, ethnicity, disability and so on).
Participants were sent an electronic communication (email or through a
phone app) asking them to participate, and were rewarded with a small incentive
(approximately £0.85) for completion of the survey. The survey underwent a pilot
test in March 2017, during which 66 participants completed this version. This led
to some removal of attitudinal questions eliciting a disproportionate number of
neutral responses. The final questionnaire was in the field between 30 March and
13 April 2017.
Sampling. The aim was to obtain a representative sample of electricity bill payers
in England, Wales and Scotland. To screen for such people, we used the question
“Which one of the following describes your level of involvement in decisions about
which company your household uses to supply gas and/or electricity?”, to which
participants could respond (1) “It is my responsibility entirely”; (2) “I have equal
responsibility with someone else in the household”; (3) “I have some involvement
in the decision”; or (4) “I have no involvement at all”. If a participant answered with
the fourth option, the survey was terminated.
As there are no reliable statistics against which to benchmark our achieved
demographic profile of bill payers to achieve representativeness among them,
we used a proportionate stratified sampling approach to mirror the general
population66, with quotas based on age (six groups), gender and residential location
(11 government office regions). Thus, the sample itself was made up of bill payers
only, but unlikely to be entirely representative of them. For example, the number of
18- to 24-year-old respondents had to be specifically boosted as a disproportionate
number of this age group were otherwise consistently screened out due to being
less likely to fall into this group. However, given that the focus of this research
requires future-facing preferences, over-representing this group was justified.
SSI sent a total of 41,579 invitations, from which 3,552 surveys were started and
2,090 were completed (that is, a 5% response rate and a 59% completion rate). While
this appears low as a response rate, it was not possible to calculate one accurately
due to the need to consider the incidence rate (the proportion of respondents
contacted who qualified for the survey) and the fact that the survey closed as soon
as the required sample size had been achieved. Once full quotas were considered,
the incidence rate was 57%. The final achieved sample was 2,024 respondents,
since some were removed (n = 66) who were deemed to have provided incomplete
or invalid data based on straight liner variables detected to indicate whether
participants had given the same response for all sub-questions in a relevant block.
Participant characteristics. As discussed, our sample comprised only domestic
energy bill payers, but with their proportions set through quota sampling to match
the characteristics of the general population. Comparisons with national data
for England, Wales and Scotland67–69 suggested that, compared with the general
population, participants: were somewhat better educated; were more likely to be on
home duties; included higher shares of people among the higher earners (>£40,000
per household per year); and had slightly higher car accessibility. In addition, more
owned outright (no mortgage), but also, more rented their households. Otherwise,
the study population was largely representative in its demographic, socioeconomic
and geographic characteristics. Therefore, no post-stratification weighting was
applied. See Supplementary Table 1 for details.
The sample was segmented using hierarchical cluster analysis (HCA) followed
by non-HCA. The clustering variables were those representing the constructs in
the conceptual model, which are a compilation of beliefs, along with experiential
and preference factors. Note that we chose to include a measure of likelihood to
adopt among the clustering variables. The mixing of generic and specific attitudinal
variables in this way has a solid conceptual justification. If based only on general
antecedent constructs to intention (that is, not including the likelihood variable),
the segmentation model could fail to identify that there is more than one group
exhibiting similar core beliefs or psychological processes but expressing different
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intentions or behaviours due to important contextual or experiential antecedents.
The converse may also be present, whereby groups with similar intentions may
be present, but with markedly different beliefs or experience. If only one or the
other type of variable were put into the segmentation, there would be a risk that
such multifaceted attitude–behaviour linkages could be hidden among generic
groupings. It is important to realize that we have not developed a segmentation
model here that we claim will stand the test of time. The purpose is to understand
now what the appetite for change is, for what change and by whom. We therefore
believe that our model can serve as a practical tool that offers a robust building
block for the development of strategy in the initial market evolution of new utility
business models.
Identification of preference factors. The first stage in identifying variables
to use as the bases for the cluster analysis was the development of measures of
preference for the five business archetypes. Five measures were derived from the
various questions on the survey. (1) Likelihood to adopt each archetype, using a
single question asked after each archetype, was scored on a number of sematic
differential attribute scales. (2) Adoption spread was the highest value of likely
minus the lowest for each individual to generate a signal of preference certainty. If
a person scored one or more options very highly in terms of likelihood to adopt,
and another option very low, they would have a larger spread than someone who
scored all options similarly. (3) Probability of adoption was the number of times
chosen during the paired comparison experiments divided by the number of times
available to be chosen. (4) Weighted probability of adoption was calculated by
multiplying the probability of adoption score by the likelihood score. (5) Preference
stability was a test of internal consistency calculated by using an excel macro to
detect reversals between each combination of three options and then adding up
the number per person. The majority of people were perfectly internally consistent
(that is, 76% did not reverse their ranking of options across the three possible
combinations with each option).
Data reduction. The attitudinal variables on the survey were subjected to data
reduction in order to reduce the variables to a smaller set of underlying dimensions
to be used in the subsequent segmentation. In factor analysis, variables that showed
similar patterns of variation across respondents were assumed to be associated with
the same underlying construct. Principal axis factoring was used in SPSS Statistics
version 22.0 (IBM), chosen to account for some non-normal distribution in the
data. Rotation of the final solution was necessary to clarify the underlying structure
and produce a set of arbitrary factors that provide the clearest conceptual picture
of the relationships among the items70. A direct oblimin rotation was used as this
maximizes the variance of the loadings within factors across variables so that each
of the original items loads on only one factor. Oblimin rotation also allows the
factors to correlate, which avoids unnecessary loss of information in orthogonal
methods and thus leads to more reproducible solutions71.
The three batteries of questions, each subject to the factor analysis
(amounting to 30 statements in total, each measured on a five-point scale
(usually, “strongly agree” to “strongly disagree”)), were: (1) attitudes towards
the environment and renewable energy; (2) general approaches to purchase
decision-making; and (3) process of choosing energy suppliers. Each of these was
subject to a series of analyses that were run iteratively, each time excluding items
with low communalities (h < 0.5). Communalities identify the items’ variance
and thus the ones that form highly consistent scales that discriminate well in the
clustering procedure72. Each component was subject to a reliability test using
Cronbach’s α, which measures internal consistency based on item correlation.
Alpha coefficients range from 0–1 and may be used to describe the reliability
of each factor. A value of 0.5 is generally regarded as an acceptable reliability
coefficient73 and was used here.
The three batteries of questions were reduced to six latent constructs:
(1) green urgency; (2) green scepticism; (3) information seeking when purchasing;
(4) inspiration seeking; (5) perceived savings; and (6) engagement with energy
usage discerning trustworthiness and the quality of service suppliers. Six questions
had been discarded from the analysis as inspection of the correlation matrix
revealed that they were not significantly related to any other items in this set or
were found to be responsible for a lower α value. These factors are a very valuable
set of internally consistent constructs to be used in further analyses to understand
consumer perceptions and motivations. Factor scores, as opposed to summated
scale scores, were computed for each respondent in the dataset to be used in the
cluster analysis.
Supplementary Table 2 itemizes the constructs, with the interpretive label,
factor loadings, Cronbach’s α score and percentage of variance explained in each of
the three batches of questions.
Cluster analysis. A two-stage cluster analysis common in market research74 was
performed to identify segments of potential consumers of the business archetypes.
Segmentation market research begins with the assumption that there is little value
in targeting the average customer and more value in treating different people in
different ways because they are motivated by varying rationales75. HCA is used first
in an exploratory structure-seeking phase, followed by the iterative partitioning
method (k-means) to fine-tune the analysis73.
HCA was performed on a set of 31 standardized variables comprising those
detailed in Supplementary Table 3 and chosen to represent the constructs of
the conceptual model. The variables were standardized and subjected to HCA,
applying Ward’s method. The squared Euclidean distance was used as the
proximity measure in the clustering procedure. This gave an indication of how the
sample population was partitioning and hinted at the optimal number of clusters to
be used in the second stage. Following visual inspection of the HCA dendrogram
(Supplementary Fig. 4), a range of cluster solutions were chosen (from three to six
segments) to be carried to the next step.
The k-means clustering took the cluster centres of the HCA cluster solution as
input and re-clustered the sample according to the squared Euclidean distance from
the centres. Since HCA does not correct cluster assignments, k-means can generate
more homogeneous groups and hence improved solutions whereby variability
within clusters is minimized while maximizing the variability between them73.
The number of clusters was specified at three, four, five or six. Selection criteria
suggested in the literature ranged from highly subjective to complex mathematical
procedures. The agglomeration schedule (Supplementary Fig. 5)76 showed a jump
in which the value of the error coefficient nearly doubled when four clusters were
reduced to three, giving a strong indication that a four-cluster solution made
the most sense. This was confirmed by inspecting one-way analysis of variance
(ANOVA) tables generated for the different solutions using the 31 variables used to
create them to ensure high variability between groups compared with within them
and the presence of discrete and concentrated clusters. This procedure indicated
that the four-cluster solution may be marginally better than both the three- and
five-cluster solutions, although significant and meaningful differences were found
across all 30 segmentation variables for all solutions. Since the four-cluster solution
separated clusters with particularly distinctive views on adoption likelihood, this
solution was selected. Two participants were not clustered due to some missing data
on key variables. Hence, the sample size for the cluster profiling was 2,022.
Characterizing the segments. The cluster analysis concluded that four relatively
stable groups could be identified ranging from 16–35% of the sample. The groups
were profiled on the variables used to create them, as well as other factors such
as demographic characteristics. ANOVA with Tukey’s post-hoc tests were used
for means comparisons, and two-tailed chi-squared tests were used where we
compared relative frequencies of categorical variables across clusters.
We also undertook a discriminant analysis to test whether it was possible
to define a set of variables that could predict group membership with adequate
reliability without including the specific willingness-to-adopt (likelihood) variables
in the analysis. The discriminant analysis confirmed that segment membership
could be predicted by only using the antecedent model constructs used to form
the clusters, as well as some demographic variables we had found to strongly
discriminate across segments. As explained in Supplementary Note 1, a satisfactory
solution that would predict group membership with 80% accuracy was found using
23 of the original 31 variables.
Ethics. The field-work proposal was reviewed and approved by the University of
Leeds AREA Faculty Research Ethics Committee (AREA 16-155). The research
was undertaken in line with the requirements of the international quality standard
for market, opinion and social research (ISO 20252:2012), to which Accent is
registered and audited annually. Accent is registered under the Data Protection
Act 1998.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The relevant survey data, including all raw data, generated or analysed during
this study are included in the Supplementary Data file. Data generated in the
construction of business model archetypes are summarized in the Supplementary
Information. The data that support the plots within this paper and other findings of
this study are available from the corresponding author upon reasonable request.
Received: 18 April 2019; Accepted: 18 January 2021;
Published online: 1 March 2021
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Acknowledgements
This research was partly funded by the Engineering and Physical Sciences Research
Council (grant EP/N029488/1), Economic and Social Research Council (grant ES/
M500562/1) and UK Research Councils (grants EPSRC EP/L024756/1 and NERC NE/
G007748/1) as part of the UK Energy Research Centre (UKERC).
Author contributions
S.H. led the development of the Utility 2050 research process and led the literature
review discussion and conclusions. J.A. led the survey design with input on business
model characteristics and question generation from J.H., S.H., C.M., and M.W.; J.A. led
the statistical analysis and segmentation exercise. M.W., C.M., J.A. and J.H. contributed
substantive analysis, redrafting and editing. Y.M. aided early analysis and drafting.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41560-021-00781-1.
Correspondence and requests for materials should be addressed to S.H.
Peer review information Nature Energy thanks Rachel Bray, Laura Olkkonen and the
other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
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 2021, corrected
publication 2021
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Behavioural & social sciences study design
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Study description
The study aimed to create archetypes of utility business models that are more compatible with a low carbon future than the current
utility business model. The study then used a survey method to explore how a representative sample of the domestic British energy retail
market (domestic consumers) responded to these models, what preferences they had, what consumer segments emerged and what this
could mean for the regulation of the British energy retail market in a low carbon future.
Research sample
The aim was for a representative sample of electricity bill payers in England, Wales and Scotland. To screen for such people, we used the
following question:
Sample screening question
“Which one of the following describes your level of involvement in decisions about which company your household uses to supply gas
and/or electricity?
1. It is my responsibility entirely
2. I have equal responsibility with someone else in the household
3. I have some involvement in the decision
I have no involvement at all  THANK & CLOSE
Sampling strategy
As there are no reliable statistics against which to benchmark our achieved demographic profile of bill payers to achieve
representativeness among them, we used a proportionate stratified sampling approach to mirror the general population with quotas
based on age (six groups), gender and residential location (eleven Government Office Regions). Thus the sample itself is made up only of
bill payers but unlikely to be entirely representative of them. For example, the number of 18-24-year-old respondents had to be
specifically boosted as a disproportionate number of this age group were otherwise consistently screened out due to being less likely to
fall in to this group. However, given the focus of this research requires future facing preferences, over-representing this group was
justified.
SSI sent a total of 41,579 invites, of whom 3,552 started and 2090 completed the survey (i.e. a 5% response rate and a 59% completion
rate). Whilst this appears as a low response rate, it is not possible to calculate one with on-line samples due to the need to consider the
incidence rate (the proportion of respondents contacted who qualify for the survey) and the fact the survey closes as soon as the
required sample size has been achieved. Once full quotas are considered, the incidence rate was 57%. The final achieved sample was
2,024 respondents after some were removed (N=66) who were deemed to have provided incomplete or invalid data based on ‘straight
liner variables’ detected to indicate whether participants had given the same response for all sub questions in a relevant block.

Data collection
The study employed a market research company (Accent) to programme the online survey and organise the data collection. Accent
specialise in online stated preference surveys with randomisation and have access to the SSI’s global market research panel of
demographically diverse adults (>18 years of age) who have voluntarily subscribed to undertake such research. People register by
providing varying amounts of personal and demographic information that is later used to select participants for specific surveys. Thus, as
is typical with such panels, the SSI panel does not use probability-based recruitment. Nevertheless, the panel from which this study’s
participants were selected is large enough to enable the selection of a nationally representative sample or sample representative of sub-
groups that reflect the actual breakdown of their key demographics (e.g. age, gender, region, social grade, ethnicity, disability etc.).
Timing
Participants were sent an electronic communication (email or through a phone app) to be asked to participate and were rewarded a
small incentive (approximately 0.85 GBP) for completion of the survey. The survey underwent a pilot test in March 2017 during which 66
participants completed this version. This led to some removal of attitudinal questions eliciting a disproportionate number of neutral
responses. The final questionnaire was in the field between 30th March and 13th April 2017.
Data exclusions
N=66 participants were excluded having been deemed to have provided incomplete or invalid data based on ‘straight liner variables’
detected to indicate whether participants had given the same response for all sub questions in a relevant block.
Non-participation
N/A
Randomization
In a paired comparison task, respondents were forced to choose one or the other archetype in each of the ten paired cases (order
randomized).
Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material,
system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

3
nature research  |  reporting summary
October 2018
Materials & experimental systems
n/a Involved in the study
Antibodies
Eukaryotic cell lines
Palaeontology
Animals and other organisms
Human research participants
Clinical data
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging
Human research participants
Policy information about studies involving human research participants
Population characteristics
As there are no reliable statistics against which to benchmark our achieved demographic profile of bill payers to achieve
representativeness among them, we used a proportionate stratified sampling approach to mirror the general population with
quotas based on age (six groups), gender and residential location (eleven Government Office Regions). Thus the sample itself is
made up only of bill payers but unlikely to be entirely representative of them. For example, the number of 18-24-year-old
respondents had to be specifically boosted as a disproportionate number of this age group were otherwise consistently screened
out due to being less likely to fall in to this group. However, given the focus of this research requires future facing preferences,
over-representing this group was justified.
Recruitment
Undertaken by a panel provider (Accent)
Ethics oversight
University of Leeds
Note that full information on the approval of the study protocol must also be provided in the manuscript.

    Scientific Research Study Methods:

Answer: This study recruited 38 industry, academic and government stakeholders in the UK energy system in June 2016. We used a business model collaboration process to identify four new utility business models that may become widely available to retail energy consumers. We then undertook a representative survey between 30 March and 13 April 2017 of 2,024 domestic consumers in the UK with some responsibility for choosing their energy contract. We presented the consumer‑facing attributes of the business models in a paired comparison exercise to identify preference factors alongside socio‑economic and attitudinal data collection. We performed a cluster analysis and identified segment characteristics. These findings should be read as how consumers state they would participate in a future energy market with new options, not how the current retail energy market is segmented.