Social Influence, Risk and Benefit Perceptions, and the Acceptability of Risky Energy Technologies: An Explanatory Model of Nuclear Power Versus Shale Gas

Abstract

Risky energy technologies are often controversial and debates around them are polarized; in such debates public acceptability is key. Research on public acceptability has emphasized the importance of intrapersonal factors but has largely neglected the influence of interpersonal factors. In an online survey (N = 948) with a representative sample of the United Kingdom, we therefore integrate interpersonal factors (i.e., social influence as measured by social networks) with two risky energy technologies that differ in familiarity (nuclear power vs. shale gas) to examine how these factors explain risk and benefit perceptions and public acceptability. Findings show that benefit perceptions are key in explaining acceptability judgments. However, risk perceptions are more important when people are less familiar with the energy technology. Social network factors affect perceived risks and benefits associated with risky energy technology, hereby indirectly helping to form one's acceptability judgment toward the technology. This effect seems to be present regardless of the perceived familiarity with the energy technology. By integrating interpersonal with intrapersonal factors in an explanatory model, we show how the current “risk–benefit acceptability” model used in risk research can be further developed to advance the current understanding of acceptability formation.

Full text

Risk Analysis, Vol. 40, No. 6, 2020 DOI: 10.1111/risa.13457
Social Influence, Risk and Benefit Perceptions, and the
Acceptability of Risky Energy Technologies: An Explanatory
Model of Nuclear Power Versus Shale Gas
Judith I. M. de Groot,1,∗Elisa Schweiger,2and Iljana Schubert3
Risky energy technologies are often controversial and debates around them are polarized; in
such debates public acceptability is key. Research on public acceptability has emphasized the
importance of intrapersonal factors but has largely neglected the influence of interpersonal
factors. In an online survey (N=948) with a representative sample of the United Kingdom,
we therefore integrate interpersonal factors (i.e., social influence as measured by social net-
works) with two risky energy technologies that differ in familiarity (nuclear power vs. shale
gas) to examine how these factors explain risk and benefit perceptions and public accept-
ability. Findings show that benefit perceptions are key in explaining acceptability judgments.
However, risk perceptions are more important when people are less familiar with the energy
technology. Social network factors affect perceived risks and benefits associated with risky
energy technology, hereby indirectly helping to form one’s acceptability judgment toward
the technology. This effect seems to be present regardless of the perceived familiarity with
the energy technology. By integrating interpersonal with intrapersonal factors in an explana-
tory model, we show how the current “risk–benefit acceptability” model used in risk research
can be further developed to advance the current understanding of acceptability formation.
KEY WORDS: Acceptability; energy technologies; risks perception; social influence; social networks
1. INTRODUCTION
The U.K. energy market is facing an energy
trilemma of secure energy supply, affordable en-
ergy, and sustainable technologies (World Energy
Council, 2018). With the increase of oil prices, re-
duced fossil fuel reserves, the need of independent
energy extraction, and climate change, the U.K. gov-
ernment has reassessed the need to invest in nuclear
power (NP) and alternative methods of securing en-
1Faculty of Economics and Business, Department of Marketing,
University of Groningen, Groningen, The Netherlands.
2King’s Business School, London, UK.
3University of Basel, Basel, Switzerland.
∗Address correspondence to Judith I. M. de Groot, Faculty of Eco-
nomics and Business, Department of Marketing, PO Box 72, 9700
AB Groningen, the Netherlands; J.I.M.de.Groot@rug.nl.
ergy supply with limited CO2emissions, such as shale
gas (SG) extraction (BBC News, 2013, 2014).
NP and SG have caused great controversy world-
wide, and specifically in the United Kingdom (De-
partment for Business, Energy and Industrial Strat-
egy, 2017). Despite growing public acceptability for
NP stations over the last few decades, public accept-
ability of NP in the United Kingdom and other Euro-
pean countries remains ambivalent. Proponents view
NP as a very clean energy source with few green-
house gas emissions (International Atomic Agency,
2018), while opponents point to the problem of nu-
clear waste disposal and the fear of power plant acci-
dents (Li, Fuhrmann, Early, & Vedlitz, 2012). Data
from nationally representative British surveys over
the last years have shown that NP retains one of the
lowest acceptance rates among different sources of
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provided the original work is properly cited.

Social Influence, Risk and Benefit Perceptions and Acceptability 1227
energy generation (Department for Business, Energy
and Industrial Strategy, 2017).
More recently, SG (or “fracking”) has caused
great controversy in the United Kingdom (Depart-
ment for Business, Energy and Industrial Strategy,
2018; The Guardian, 2018). Proponents point to the
United States where the technique is common and
has strengthened their economy while securing an
independent energy supply (Stevens, 2012). They
also emphasize the economic advantage of obtain-
ing cheap energy (O’Hara, Humphrey, Andersson-
Hudson, & Knight, 2016). Opponents stress the neg-
ative environmental impact, including the increased
likelihood of earthquakes, the contamination and de-
pletion of ground and fresh water due to chemicals
used in the fracking fluid, and the impact on air
quality (O’Hara et al., 2016; Williams, Macnaghten,
Davies, & Curtis, 2017). A national U.K. survey on
public opinion in relation to SG showed that of the
people who felt knowledgeable enough to form an
opinion about SG, 33% opposed and 16% supported
fracking (Department for Business, Energy and
Industrial Strategy, 2017).
The energy trilemma requires urgent deci-
sions by policy makers. Such decisions are highly
influenced by public acceptability as opposition
toward energy technologies has repeatedly shown
to impact political decisions. For example, rejection
of NP plants peaked after the Fukushima accident
(Visschers & Siegrist, 2013), causing German na-
tional policy responses to favor renewable energy
technologies in the lead up to regional elections
(Wittneben, 2012). Similarly, fracking was delayed
until 2018 in the United Kingdom because it was
linked to earthquakes in 2011; and the first fracking
site in Lancashire has faced large protests since the
beginning (BBC News, 2018). Hence, understanding
public acceptability is vital in establishing energy se-
curity policies, as they drive decisions regarding the
future of the U.K. energy mix (Poortinga, Aoyagi, &
Pidgeon, 2013).
Public acceptability of risky technologies can
be regarded as an attitude (De Groot, Steg, &
Poortinga, 2013). Attitudes are psychological ten-
dencies to evaluate an attitude object (i.e., energy
technology) through weighting the costs (or “risks’)
and benefits of a specific object or behavior (Ajzen,
1985). The higher the perceived risks and the lower
the perceived benefits of an energy technology,
the less likely people are to evaluate that specific
technology positively, and vice versa (Siegrist &
Cvetkovich, 2000; Siegrist & S ¨
utterlin, 2014). The
affect heuristic provides an explanation for the
strong intercorrelation between risks and benefits:
people base their risk assessment on an initial overall
evaluation (“affect”) and adjust their specific beliefs
about the risks and benefits to fit into their precon-
ceived view (Finucane, Alhakami, Slovic, & Johnson,
2000). Even though risk and benefit perceptions are
strongly correlated, most research includes both risk
and benefit perceptions in relation to the acceptabil-
ity of risky attitude objects (Bearth & Siegrist, 2016;
Bearth, Cousin, & Siegrist, 2014; Bearth, Miesler, &
Siegrist, 2017; Dreyer, Polis, & Jenkins, 2017; Ho &
Watanabe, 2018; Hubert, Blut, Brock, Backhaus, &
Eberhardt, 2017; Poortvliet, Sanders, Weijma, & De
Vries, 2018; Siegrist, Stampfli, Kastenholz, & Keller,
2008), including energy technologies (Ho et al., 2018;
Lienert, S ¨
utterlin, & Siegrist, 2015; Visschers, Keller,
& Siegrist, 2011; Whitfield, Rosa, Dan, & Dietz,
2009).
Research on risk and benefit perceptions and
public acceptability of risky energy technologies has
extensively focused on the cognitive and attitudinal
processes at an intrapersonal level (De Groot &
Steg, 2010; Slimak & Dietz, 2006; Slovic, Fischhoff,
& Lichenstein, 1982), such as, values (De Groot
et al., 2013; Whitfield et al., 2009), and trust and
uncertainty (Knoblauch, Stauffacher, & Trutnevyte,
2018; Siegrist & Cvetkovich, 2000; Terwel, Harinck,
Ellemers, & Daamen, 2011). However, far less at-
tention has been given to the impact of interpersonal
influences, including social influence (Bickerstaff,
2004; Helgeson, van der Linden, & Chabay, 2012;
Howell et al., 2017). This is surprising seeing as social
influence is known to reduce conflict and uncertainty
within the individual through the development of
shared attitudes (Friedkin, 2001).
The present study examines an explanatory
model of the acceptability of NP and SG in a U.K.
context by integrating social influence in the ex-
isting “risk–benefit acceptability” model. Further-
more, social influence might impact risk and bene-
fit perceptions and public acceptability differently,
depending on how familiar individuals are with
an energy technology. Therefore, we compare the
slightly more familiar risky energy technology of
NP to SG, which people seem to be somewhat
less familiar with (Department for Business, En-
ergy and Industrial Strategy, 2017, 2018). Exam-
ining how intra- and interpersonal processes ex-
plain public acceptability will help to further develop
the “risk–benefit acceptability’ model used in the
field.

1228 de Groot, Schweiger, and Schubert
1.1. Risk and Benefit Perceptions and Public
Acceptability
Previous research shows that both risk and ben-
efit perceptions are relevant in explaining the accept-
ability of risky energy technologies (Dreyer et al.,
2017; Howell et al., 2017; Visschers et al., 2011). This
assumption has especially been validated in the field
of NP (De Groot et al., 2013; Greenberg & Truelove,
2011; Keller, Visschers, & Siegrist, 2012; see Ho
et al., 2018 for an overview). For example, a recent
meta-analysis including 34 studies examining public
perceptions toward NP showed that both benefit and,
although to a lesser extent, risk perceptions were
important predictors for the acceptability of NP (Ho
et al., 2018). Research investigating the processes of
how risk and benefit perceptions influence accept-
ability of SG is slowly growing as well (Christenson,
Goldfarb, & Douglas, 2017; Howell et al., 2017;
O’Connor & Fredericks, 2018; Pollard & Rose, 2018;
Thomas, Partridge, Harthorn, & Pidgeon, 2017).
For example, a multilevel analysis including both
intrapersonal- and state-level factors found both risk
and benefit perceptions are important intrapersonal
factors influencing the acceptability of fracking in the
United States (Howell et al., 2017). The few studies
that have focused on the processes underlying risk
and benefit perceptions and the acceptability of SG
imply that both risk and benefit perceptions are
important when explaining the acceptability of the
less familiar energy technology of SG. Like with NP,
benefit perceptions seem to be a stronger predictor
for the acceptability of SG, although with SG the
advantages seem to be easily forgotten when people
are confronted with the risks as well (Thomas et al.,
2017). However, the sparse amount of studies testing
these relationships makes conclusions tentative only.
This study will further validate the “risk–benefit
acceptability” model as proposed in risk research.
We put forward the following hypotheses:
Hypothesis 1: Risk and benefit perceptions toward
risky energy technologies (NP and
SG) will explain the public acceptabil-
ity of the technology.
That is, higher risk perceptions decrease the
acceptability of the respective energy technologies
(Hypothesis 1a); lower benefit perceptions decrease
the acceptability of the respective energy technolo-
gies (Hypothesis 1b).
Hypothesis 2: Benefit perceptions will relate more
strongly to the acceptability of
risky energy technologies than risk
perceptions.
1.2. Social Influence, Risk Perception, and Public
Acceptability
Interpersonal influences are known to reduce
conflict and uncertainty within the individual through
the development of shared attitudes (Friedkin, 2001).
Interpersonal relationships link social actors that
share beliefs and influence one another in attitude
formation (Helgeson et al., 2012). In the late 1980s,
scholars already acknowledged that social influences
among friends, family members, or coworkers, influ-
ence the process of shaping attitudes toward risky is-
sues (Kasperson, Renn, Slovic, Brown, & Emel, 1988;
Slovic, 1987). Given the remarkably large scope of
social phenomena that are shaped by social influence
(Latane, 1981), it is surprising that interpersonal de-
terminants, such as social influence, have been less
focused on in risk research. Moreover, research that
has included a social dimension, has done so from an
intrapersonal perspective only, in the form of social
norms (e.g., Featherman & Hajli, 2016; Hilverda &
Kuttschreuter, 2018; Silva, Jenkins-Smith, & Barke,
2007; Trumbo, 2018). As social norms have been con-
ceptualized as “personal beliefs” in relation to what
is commonly accepted or commonly done in a specific
social context (Cialdini, Reno, & Kallgren, 1990), it
therefore still treats social influence as a typical in-
trapersonal rather than interpersonal factor.
Investigating social networks has been one way
to examine the effects of social influence on attitude
formation from an interpersonal perspective. Social
networks are dyadic ties (relationships) between ac-
tors (individuals or organizations) that are character-
ized by resource exchange (Haythornthwaite, 1996).
These resources may include social support, infor-
mation exchange, or influence. Social network the-
ory conceptualizes actors and social structures as re-
lational in nature and investigates the outcomes of
inter- and intragroup processes (Borgatti & Halgin,
2011). Social networks occur in many different set-
tings, such as different stakeholder groups (Brooks,
Hogan, Ellison, Lampe, & Vitak, 2014), with peo-
ple belonging to a variety of different networks at
the time. Within these networks, individuals are in-
terconnected to different degrees and the number of
network ties varies.

Social Influence, Risk and Benefit Perceptions and Acceptability 1229
Research on social networks shows that merely
talking about a risky attitude object, such as energy
technologies, with others in your network, and in-
creasing your knowledge about this attitude topic
and other’s belief system, can play an important role
in influencing your own beliefs (i.e., risk and bene-
fit perceptions) and attitudes (i.e., acceptability) (cf.
Scott, 2017). However, only few studies have exam-
ined the relationship between social network char-
acteristics and risk and benefit perceptions (Kohler,
Behrman, & Watkins, 2007; Muter, Gore, & Riley,
2013; Scherer & Cho, 2003). Scherer and Cho (2003)
examined a social network contagion theory of risk
perception to account more adequately for social or
social–structural variables in environmental conflicts,
such as hazardous waste side cleanups. Their find-
ings showed that people who are in frequent con-
tact with one another (in their network) are also
more likely to share similar attitudes and beliefs re-
garding an environmental conflict over a hazardous
waste site cleanup. The social network contagion
theory of risk perception was further supported by
Kohler et al. (2007) who showed, in a longitudinal
study, that the risk perception in one’s social net-
work in relation to catching AIDS influences the ex-
tent to which someone believes that they are at risk
themselves.
Although these studies seem to suggest that so-
cial influence, as measured with social network char-
acteristics, likely relates to beliefs and attitudes of
risky attitude objects, they need to be expanded in
three ways to further develop the field of social influ-
ence and risk research. First, acknowledging that so-
cial influences partially constitute the process behind
shaping attitudes toward risky issues (Kasperson
et al., 1988; Slovic, 1987) raises the question: To what
extent and under what conditions does social influ-
ence impact the acceptability of highly controver-
sial and debated energy technologies, such as NP
and SG? Previous studies incorporating social in-
fluence to examine risk perception and evaluation
have focused on risk issues that are more observable
and identifiable, such as health risks (Kohler et al.,
2007). In contrast to most health-related risks, risks
associated with energy technologies, especially the
risks that are associated with climate change, can-
not be easily observed and identified (Helgeson et al.,
2012), which makes it difficult for lay people to esti-
mate the risks associated with it (Kasperson & Ram,
2013). This study focuses on energy technologies that
have been typically associated with climate change
(Poortinga et al., 2013), hereby extending our knowl-
edge on the extent to which social influence is rele-
vant in a different context.
Second, the few studies focusing on social influ-
ence in relation to risk perception have largely ne-
glected the previously well-established “risk–benefit
acceptability” model in risk research (Ho et al.,
2018; Lienert et al., 2015; Siegrist & S ¨
utterlin, 2014;
Visschers et al., 2011). That is, they have focused
on risk perception rather than on how risk percep-
tion influences evaluations (i.e., acceptability). Con-
sequently, we are still left in the dark on how social
influence fits into the model.
Third, the studies on social influence in rela-
tion to risk perceptions have only focused on gen-
eral social network ties within one’s network. How-
ever, not only the presence of social influence but
also the number of close network partners and their
perception of risks and benefits can influence people
(Haythornthwaite, 1996). For example, a person who
has a lot of close network partners supporting NP or
SG, may be more likely to evaluate that type of en-
ergy technology positively and deem it as less risky
than someone who has close network partners that
oppose or have no opinion about NP or SG. Simi-
larly, if you have never talked to your close friends
about NP or SG, they may have less influence on
how you form your opinion of the energy technolo-
gies. However, if you speak to your close friends
about risky energy technologies, they can influence
your risk and benefit perceptions toward these tech-
nologies in different ways, depending on how they
talk about it (i.e., emphasizing all the risks or all the
benefits). This study integrates these two social net-
work characteristics to provide a more comprehen-
sive view of how social influence affects risk and ben-
efit perceptions and acceptability.
This research determines the extent to which so-
cial influence is important in explaining one’s risk
and benefit perceptions toward the high-risk energy
technologies of NP and SG. By incorporating social
network analysis (SNA) to measure social influence,
this study will be the first to provide empirical in-
sights about the extent to which social influence is
relevant for explaining the acceptability of risky tech-
nologies. Based on previous research (Kohler et al.,
2007; Muter et al., 2013; Scherer & Cho, 2003), we
assume:
Hypothesis 3: One’s social network affects risk and
benefit perceptions of risky energy
technologies (NP and SG).

1230 de Groot, Schweiger, and Schubert
That is, the perceived risks toward a risky energy
technology will be higher the less the individual per-
ceives peer support from their social network for the
technology (Hypothesis 3a). Furthermore, the per-
ceived risks toward a risky technology will depend
on the extent to which the individual talks with their
peers about the technologies (Hypothesis 3b). The
perceived benefits toward a risky energy technology
will be higher the more the individual perceives peer
support from their social network for the technol-
ogy (Hypothesis 3c). Finally, the perceived benefits
toward a risky technology depend on the extent to
which the individual talks with their peers about the
technologies (Hypothesis 3d).
More specifically, as previous research suggests
that most determinants of risky energy technologies’
acceptability exert their influence via risk and bene-
fit perceptions (Siegrist & Cvetkovich, 2000), we hy-
pothesize a mediation effect:
Hypothesis 4: Social influence affects the acceptabil-
ity of the risky energy technology (NP
and SG) mainly indirectly, via risk
(Hypothesis 4a) and benefit (Hypoth-
esis 4b) perceptions.
1.3. The Impact of the Familiarity of NP and SG
on Risk Perception and Acceptability
People may differ slightly in how familiar they
are with NP and SG. Being less familiar with a risky
technology results in an increased uncertainty asso-
ciated with them (Department for Business, Energy
and Industrial Strategy, 2017, 2018; O’Hara et al.,
2016). Although NP and its associated risks and ben-
efits have been known for decades (Ho et al., 2018),
SG has only more recently attracted public atten-
tion (Department for Business, Energy and Indus-
trial Strategy, 2017). Although the awareness toward
SG has grown over the last years (Department for
Business, Energy and Industrial Strategy, 2018), most
people still perceive themselves to be slightly less
knowledgeable about the topic than about NP (De-
partment for Business, Energy and Industrial Strat-
egy, 2017; Williams et al., 2017).
Differences in the perceived familiarity between
NP and SG may also result in dissimilarities in
the importance of risk and benefit perceptions on
the acceptability of these risky energy technologies.
Heuristics, such as the negativity bias (Ahluwalia,
2002; Baumeister, Bratslavsky, Finkenauer, & Vohs,
2001), suggest that individuals tend to weigh in-
formation regarding the presence of risks more
strongly than when presented with neutral or pos-
itive information. Hence, even though in an abso-
lute sense benefit perceptions are more strongly re-
lated to the acceptability of risky technologies than
risk perceptions (Ho et al., 2018), the negativity
bias suggests that risk perceptions are easier “to
make salient” when people are confronted with in-
formation. Indeed, research into risk perceptions
shows that emphasizing risks, rather than benefits
or neutral information associated with unfamiliar
risky attitude objects, such as nanotechnology (Cobb,
2005), vaccination risks (Betsch, Haase, Renkewitz,
& Schmid, 2015), or general health dangers (Siegrist
& Cvetkovich, 2001), influences evaluations of them
more strongly. Van Giesen, Fischer, and Van Trijp
(2018) provide a possible explanation of why the neg-
ativity bias works differently depending on the famil-
iarity of an attitude object. In a longitudinal study
toward an emerging risky technology (nanotechnol-
ogy), they found that acceptability levels were less re-
liant on the affective than the cognitive components
of attitude formation over time. This effect occurred
because fewer knowledge structures were in place to
rationalize negative information (Van Giesen et al.,
2018). Over time and with the increase of knowledge,
more structures were available causing attitudes to
be formed as a combination of affect and cognitions.
Based on this, we hypothesize:
Hypothesis 5: Risk and benefit perceptions will re-
late differently to the acceptability of
NP as compared to SG.
That is, risk perceptions will affect the accept-
ability of the more familiar technology of NP less
strongly than of the less familiar technology of SG
(Hypothesis 5a); benefit perceptions will affect the
acceptability of the more familiar technology of NP
more strongly than of the less familiar technology of
SG (Hypothesis 5b).
1.4. Exploring the Effect of Social Influence on
Risk and Benefit Perceptions for Technologies
Differing in Familiarity
In addition to our argument that social influence
factors are important to understand risk and benefits
perceptions and acceptability of risky energy tech-
nologies, we also argue that differences in familiarity
between NP and SG impact the nature of these rela-
tionships. Social interactions are the key mechanism
through which individuals validate their attitudes

Social Influence, Risk and Benefit Perceptions and Acceptability 1231
Fig. 1. Model of the acceptability of risky energy technologies.
under conditions of uncertainty and conflict
(Moussa¨
ıd, K¨
ammer, Analytis, & Neth, 2013).
Although both NP and SG are risky technologies
associated with ambivalence, the difference in famil-
iarity may result in more perceived uncertainty with
the less familiar technology of SG.
As argued above, an important distinction
between NP and SG is the extent to which people
are familiar with these energy technologies. Social
influence, as conceptualized through social network
characteristics, could impact risk and acceptability
judgments of technologies that differ in how familiar
people are with them. For example, Christenson
et al. (2017) have shown how malleable the accept-
ability of SG is when citizens are less familiar with
the technology. These results imply that beliefs about
risks and benefits, and consequently acceptability-
judgments, are more easily influenced by the opinion
of close network members. Therefore, it could
be argued that people’s risk and benefit beliefs in
relation to SG (and their acceptability judgment) will
be more strongly influenced by the attitude of their
close peers than their perceptions in relation to NP
as they are likely more familiar with this technology.
This research includes an explorative research
question as a first step to understand whether so-
cial networks differently influence: (1) a person’s risk
perceptions directly; and (2) acceptability indirectly,
depending on the energy technology (NP and SG;
RQ1):
How do social network factors affect risk and
benefit perceptions, and acceptability differently for
risky technologies that differ in familiarity (NP vs.
SG)?
1.5. The Present Study
To test the presented hypotheses and RQ1,we
will present an explanatory model to investigate the
importance of social influence on the formation of
risk and benefit perceptions directly and indirectly
the acceptability of NP and SG. The baseline model
for both energy technologies is presented in Fig. 1.
As social influence might be differently related to
risk and benefit perceptions and public acceptability,
depending on how familiar individuals are with an
energy technology, we compare the more familiar
energy technology of NP with the less familiar
technology of SG.
2. METHODS
2.1. Sampling and Participants
A survey company distributed an online ques-
tionnaire to selected panelists from the British
population of over 18 years of age. We collected
1,000 responses of which 52 participants were re-
moved because they failed to follow the instructions
of the quality-fail question. The quality-fail question
asked participants to choose a specific answer. If they
failed to do this it indicated that they did not read
the survey questions properly. Therefore, a total
of 948 participants were included in the remaining
analysis. The sample’s mean age was 49.90 years old
(SD =14.08), and 52% were female. Distributions
of age and gender (UK Statistics, 2011), education
(Census, 2011), and income (Belfield, Cribb, Hood,
& Joyce, 2014) were compared with data of the U.K.

1232 de Groot, Schweiger, and Schubert
population. Comparisons of our data on these so-
ciodemographic variables indicated that our sample
of panelists reflected a reasonable representation of
the adult U.K. population.
2.2. Questionnaire and Measures
An online questionnaire was designed using a
survey programming tool. Respondents answered
questions regarding their familiarity with NP and
SG, followed by measuring risk and benefit percep-
tions of NP and SG, and an acceptability judgment
of these two energy technologies. Next, participants
were asked to provide information of their social net-
work and their social network’s perceptions of NP
and SG. The measures of the main variables are de-
scribed in more detail (Table I).4
2.2.1. Acceptability
Five items for each energy technology measured
the acceptability of NP and SG in the United King-
dom. All items were measured on a five-point Likert
scale ranging from 1 “strongly agree” to 5 “strongly
disagree.” Initial correlations between the constructs
and items showed that one item of acceptability (“We
can give up NP without any problem.”) showed low
cross correlations with the other acceptability items
and with the construct for both NP and SG. There-
fore, this item was deleted prior to the final eval-
uation of the measurement models. The use of SG
(M=3.00, SD =0.80, Cronbach’s α=0.89) was
evaluated as somewhat less acceptable than NP
(M=3.36, SD =0.79, Cronbach’s α=0.86).
2.2.2. Risk and Benefit Perceptions
Four items measured participants’ perceived
risks for NP and for SG, including beliefs in relation
to accident risks, safety concerns, environmental
degradation, and general risk to society. Four items
measured participants’ beliefs related to the per-
ceived benefits of NP and SG, such as climate change
mitigation, secure energy supply, affordable energy,
4The reviewers voiced concerns about the measure of the depen-
dent variable “acceptability” and the independent variable “fa-
miliarity.” To address these concerns, we decided to collect some
more data. Our additional data collection with amended items
showed no obvious differences from our main analyses as re-
ported in this article. Hence, our operationalizations of constructs
have not influenced the main conclusions of our reported find-
ings. For a summary of this additional study, please see the Sup-
porting Information.
and their general benefit to society. Risk and benefit
items were measured on a five-point Likert scale
ranging from 1 “strongly agree” to 5 “strongly dis-
agree” (with and exception of the two general items:
“How risky do you consider the use of NP to be to
society as a whole?” and “How beneficial do you
consider the use of NP to be to society as a whole?”;
see Table I). The means and standard deviations
for risk and benefit perceptions were relatively
similar for both SG and NP. The means indicated
that participants generally neither agreed, nor dis-
agreed that SG/NP was risky (SG risks M=3.17,
SD =0.82, Cronbach’s α=0.83; NP risks M=2.85,
SD =0.81, Cronbach’s α=0.82), or beneficial (SG
benefits M=2.95, SD =0.83, Cronbach’s α=0.85;
NP benefits M=3.24; SD =0.80, Cronbach’s
α=0.81).
2.2.3. Social Influence
One of the ways in which social influence has
been examined in the past is through SNA. SNA
helps to analyze the content, patterns, and dynamics
within social groups by statistically analyzing connec-
tions between different interdependent actors within
a group (Wasserman & Faust, 1994). There are two
main types of SNA. One of them investigates whole
or complete social networks by mapping the inter-
connections between all social network actors of
a specific network group to understand group dy-
namics, how social capital is achieved (Nahapiet &
Sumantra, 1998), and, how weak ties are being used
(Granovetter, 1973). The other type of SNA is the so-
called egocentric analysis, which focuses on the social
network of an individual (ego) and how his or her so-
cial network affects an individual also referred to as
the ego. An ego’s social network may include peer
groups such as friends, family, and colleagues, who
are called alters. Thus, in SNA, “the individual” is re-
ferred to as “the ego” and “the individual’s peers”
are referred to as “their alters.”
During an ego SNA, beliefs and attitudes of al-
ters can be assessed through questioning the indi-
vidual about their perceived attitudes and behavior.
Such an analysis is more appropriate and relevant for
the proposed study than a complete SNA, because an
individual needs to perceive his/her alters’ attitudes
in order to be influenced by them (Ajzen, 1985).
Consequently, the perceived alters’ acceptability to-
ward NP and SG is superior to the alters’ actual eval-
uation of it (Visser & Mirabile, 2004). Egocentric
analysis enables the collection of a larger sample of

Social Influence, Risk and Benefit Perceptions and Acceptability 1233
Table I. Constructs of the Questionnaire, Respective Items, and the Sources They Were Adapted From
Intrapersonal Variables Source
Acceptability The United Kingdom needs a lot of electricity; people should therefore accept
nuclear power.
Visschers et al. (2011)
We can give up nuclear power without any problem. Visschers et al. (2011)
I reluctantly accept that we will need nuclear power to help combat climate
change.
Corber et al. (2011)
I am in favor of nuclear power to be part of the of the United Kingdom’s
energy mix in 2025.
O’Hara et al. (2014)
I reluctantly accept that we will need nuclear power to help improve energy
security in the United Kingdom.
Corber et al. (2011)
Risk perception The risk of accidents in the U.K. nuclear power industry is minimal. Visschers et al. (2011)
U.K. nuclear power stations are safe. Visschers et al. (2011)
Nuclear power degrades animals, plants, land, and water. Greenberg (2009)
In general, how risky do you consider the use of nuclear power to be to the
society as a whole?*
Finucane, Alhakami,
Slovic, & Johnson (2000)
Benefit perception Nuclear power has a positive impact on climate mitigation. Visschers et al. (2011)
Nuclear power provides secure energy supply. Visschers et al. (2011)
Nuclear power results in cheap energy. O’Hara et al. (2014)
In general, how beneficial do you consider the use of nuclear power to be to
society as a whole?*
Finucane, Alhakami,
Slovic, & Johnson (2000)
Interpersonal Variables
Name generator:
Affect approach Who belongs to your closest circle of people you interact with and spend a lot
of time with? These may include people from your family, circle of friends, or
people from your professional life (i.e., university, school, work, sport clubs)
with whom you discuss personal matters and have spent a substantial amount
of time with within the past six months.
Marsden (2005)
Exchange approach With whom, out of the people that you have already listed, have you talked
about nuclear power or shale gas/fracking? You can click on multiple people.
Name interpreter: Is the following person a proponent of nuclear power? Carrington et al. (2005)
How close are you to each of the above-mentioned people?
How long have you known these people in years?
How risky does the following person consider the use of nuclear power to be to
the society as a whole?
Who influences your perspective of nuclear power? Please check all that apply.
Familiarity How familiar are you with the risks and benefits of nuclear power? Boudet et al. (2014)
How much have you ever heard or read about nuclear power? Boudet et al. (2014)
Note: The items are shown for nuclear power. The questions assessing shale gas used the same wording only replacing “nuclear power”
with “shale gas.” Intrapersonal variables were all measured on a Likert scale ranging from 1 “strongly agree” to 5 “strongly disagree.” The
item in italics has been deleted in the final measurement model because of low cross-loadings (<0.05) with the other construct items. Items
including an asterisk symbol were all measured on a different Likert scale, that is, risk items were measured on a scale ranging from 1 “not
at all risky” to 5 “extremely risky”; benefit items were measured on a scale ranging from 1 “not at all beneficial” to 5 “extremely beneficial.”
different ego networks in comparison to complete
SNA, thus resulting in a more coherent analysis
of how alters influence an individual’s perceptions.
Therefore, we applied an ego-network analysis by
asking participants to rate the attitudes of their net-
work peers toward energy technologies.
Participants’ social network alters and their char-
acteristics were retrieved in two stages: (1) generat-
ing names, followed by (2) interpretation questions.
This two-step approach enabled us to assess a per-
son’s perception of the attitudes prevalent in his or
her social network (Table I).
Name generator questions were used to obtain
a participant’s list of social network peers (i.e., al-
ters). Multiple name generator questions were em-
ployed in this study to increase the reliability of the
social network data (Marin & Wellman, 2011). The
first name generator question we used was based on
the affect approach, which meant we asked about al-
ters that were high in affective value to the partici-
pant (Marsden, 2005). This approach enabled us to
collect alters from a wide variety of social groups
such as family, friends, or colleagues. To further nar-
row down one’s social network to alters with whom

1234 de Groot, Schweiger, and Schubert
participants exchanged some kind of information
over the topic of NP or SG, we used the exchange
approach (Carrington, Scott, & Wasserman, 2005;
Marsden, 2005). Both name generator approaches al-
lowed for the collection of information regarding so-
cial network’s partners that (1) were close to the ego,
or (2) communicated with the ego about NP or SG.
In the second step of collecting social network
data, so called “name interpreter questions” were
used. These questions gathered additional informa-
tion from the network alters and the alters’ relation-
ship with the participant (Marin & Hampton, 2007).
We regarded the following two specific social influ-
ence factors as important for the aim of the present
study:
(1) The number of people you have talked to
about NP and SG in one’s social network. Via the
name generator (Marsden, 2005), participants were
asked to either impart first names or initials of the
alters to make them feel more confident about shar-
ing personal data. Participants could name between
two and 15 social network partners. On average, par-
ticipants named six social network members (M=
6.07, SD =3.06). After participants indicated, via the
name generator, their closest circle of people, they
were asked to state with whom they have had a dis-
cussion about NP or SG. On average, participants
talked to 1.60 people about NP or SG (SD =1.97).
(2) Social Network Index (SNI). SNI measured
the extent to which the individual (the ‘ego’) per-
ceived support for NP/SG in one’s social network.
Participants were asked to indicate for each person of
their social network whether that person was a pro-
ponent (Pro) or opponent (Con) of NP or SG. They
could also indicate if they did not know (DNK) the
person’s attitude toward the energy technologies of
NP and SG. Based on this data, we created an in-
dex for the overall support for NP and SG partici-
pants’ perceived in their social network. The index
ranged from 0 to 1. A zero indicated that all mem-
bers of the participant’s network opposed the energy
technology, while a one indicated that everyone in
the network supported the technology. A value of
0.50 indicated that the number of positive and nega-
tive social influence opinions equaled each other out
or that people in the network did not express their
attitude toward the energy technologies. The index
was calculated using the following formula: ((Con ×
0) +(DNK ×0.5) +(Pro ×1)): (Con +DNK +
Pro). Thus, we calculated a weighted average of the
opinions prevalent in a participant’s immediate social
network. The mean of the perceived opinions within
one’s social network were almost neutral for NP
(M=0.49, SD =0.23) and neutral to slightly oppos-
ing for SG (M=0.44, SD =0.23).
2.2.4. Familiarity
Familiarity with the energy technologies was
measured to check our assumption that people are
in general more familiar with NP than with SG. Par-
ticipants rated their subjective perception of knowl-
edge and the amount they had heard or read about
NP and SG on a Likert scale ranging from 1 “not at
all familiar” to 5 “extremely familiar” (Boudet et al.,
2014). As expected, participants indicated they were
slightly more familiar with NP (M=2.86, SD =1.00,
α=0.88) than with SG (M=2.53, SD =1.03, α=
0.89. The difference was significant, with a medium
to strong effect size (t(947) =12.99, p<0.001; Co-
hen’s d=0.33, 95% confidence intervals [CIs]: 0.45–
0.20). For the remaining analyses, we will therefore
report and compare both models of NP and SG sepa-
rately. This can show us whether social influence ex-
plains risk and benefit perceptions and acceptability
differently depending on the familiarity of the energy
technology.
2.3. Analyses
Partial least squares-structural equation model-
ing (PLS-SEM) was used to estimate both mod-
els using StataSE15. PLS-SEM is a composite-based
approach to SEM that combines principal compo-
nent analysis and regression to explain the vari-
ance of the target constructs in a structural model
(Chin, 2010). All path coefficients and specific item
loadings are simultaneously measured in the con-
text of the specified model. As regression analysis
inflates measurement errors, PLS-SEM is an effec-
tive tool to test the proposed relationships among
the constructs by reducing Type II errors (Hair, Hult,
Ringle, & Sarstedt, 2017). Furthermore, our study in-
cluded both reflective (i.e., risk perceptions; Mode
A), formative (i.e., benefit perceptions, acceptability;
Mode B), and single-item constructs (i.e., social in-
fluence factors). PLS-SEM seemed more appropriate
than covariance-based SEM, as it allows researchers
to eliminate biases and inconsistent parameter esti-
mates when dealing with these more complex models
(Hair et al., 2017).
We conducted a two-step procedure. The first
step included evaluating the measurement models of
NP and SG. Indicator reliability was assessed with

Social Influence, Risk and Benefit Perceptions and Acceptability 1235
Fig. 2. Estimated model with standardized regression weights for nuclear power (NP), N=948. *p<0.01; **p<0.001; n.s. =not significant.
item loadings of the four risk perception items, all
loadings were deemed acceptable (>0.50). We as-
sessed construct reliability with Cronbach’s alpha
and Dillon-Goldstein’s rho. Risk perception showed
a high internal consistency in both models, with
Cronbach alpha’s (αNP =0.82; αSG =0.84) and
Diller-Goldstein ƿvalues (ƿNP =0.88; ƿSG =0.89)
above the acceptable 0.70 criterion (Hair et al., 2017).
Convergent validity was tested with the average vari-
ance extracted (AVE) and by checking the standard-
ized loadings of the construct. The AVE for risk per-
ception was well above the recommended 0.50 for
both models (0.64 for NP and 0.67 for SG) (Chin,
2010). Standardized loadings all exceeded the rec-
ommended 0.70 (Chin, 2010). Finally, discriminant
validity was confirmed for the formative constructs,
with VIF scores lower than 4 (Hair et al., 2017).
Discriminant validity was checked with the Fornell-
Larcker Criterion (i.e., comparing the square root of
the AVE of the construct risk perception to its cor-
relations with other constructs (Fornell & Larcker,
1981) for the reflective construct “risk perception.”
The AVE of risk perception was higher than the cor-
relations with all other constructs for both models,
except for one. The AVE of the construct risk per-
ception for SG was 0.67 while the correlation be-
tween risk perception and acceptability was 0.68, in-
dicating a potential issue with discriminant validity.
We decided that the discriminant validity was accept-
able for the purpose of the present study, because
(1) the difference between the AVE and correlation
was negligible, with less than 0.01 difference; and,
(2) theoretically, scholars have measured risk, ben-
efit and acceptability in a similar way and regarded
them as distinct concepts (e.g., Finucane et al., 2000;
Visschers et al., 2011). As there were no serious is-
sues related to reliability and validity of the measure-
ment models of NP and SG, we will only report the
structural model in our results (Step 2).
Step 2 showed the structural models for NP and
SG, which enabled us to test our hypotheses and
RQ1. Convergence was achieved after 10 iterations
for both models. PLS-SEM does not assume a spe-
cific data distribution; therefore, no formal fit indices
are used. We used bootstrapping with 200 replica-
tions as a resampling technique to derive the param-
eters’ standard errors (Ali, Rasoolimanesh, Sarstedt,
Ringle, & Ryu, 2018). We reported the R2of the en-
dogenous constructs and included the effect sizes (ƒ2)
for the R2. Threshold values of 0.02, 0.15, and 0.35
indicate weak, moderate and strong effects respec-
tively (Cohen, 1988). We also reported the standard-
ized path coefficient estimates including significance
levels, and 95% CI where appropriate. We consid-
ered the path coefficients models to be significantly
different when the CIs of these weights overlapped
no more than half of the distance of one side on a
CI (Masson & Loftus, 2003). We conducted a medi-
ation analysis to test mediation effects and a multi-
group analysis to compare the model of NP with SG
(Venturini & Mehmetoglu, 2019).
3. RESULTS
3.1. Social Influence, Risk and Benefit Perceptions,
and Acceptability of Energy Technologies
Figs. 2 and 3 show the results of the structural
model estimation and evaluation for the relation-
ships between the two social influence factors, risk

1236 de Groot, Schweiger, and Schubert
Fig. 3. Estimated model with standardized regression weights for shale gas (SG), N=948. *p<0.01; **p<0.001; n.s. =not significant.
and benefit perceptions and acceptability for NP and
SG. Both of the proposed models’ showed a strong
effect in predicting acceptability (NP: R2=0.79, p<
0.001; ƒ2=3.76; SG: R2=0.81, p<0 001; ƒ2=4.26).
In line with Hypothesis 1, the less risks (βNP
=–0.30, p<0.001; βSG =–0.39, p<0.001;
Hypothesis 1a), and the more benefits respondents
perceived of the respective energy technologies
(βNP =0.64, p<0.001; βSG =0.56, p<0.001; Hy-
pothesis 1b), the more they evaluated NP and SG
as an acceptable technology. In line with Hypothe-
sis 2, perceived benefits of both NP (95% CI: 0.68–
0.60) and SG (95% CI: 0.80–0.52) more strongly con-
tributed to explaining the acceptability of the respec-
tive energy technology than perceived risks (95%
CINP: –0.3––0.25; 95% CISG : –0.4––0.35), as shown by
the 95% CIs that did not overlap at all.
Together with benefit perceptions, social net-
work characteristics explained 61% of risk percep-
tions toward NP (p<0.001; ƒ2=1.78), and 63% of
risk perceptions toward SG (p<0.001; ƒ2=1.70),
both representing a strong effect (Hypothesis 3). Re-
sults showed that the more respondents perceived
support toward the energy technology in a person’s
social network, the less risky they perceived the tech-
nology (NP: β=–0.14, p<0.001; SG: β=–0.18,
p<0.001), hereby supporting Hypothesis 3a. In line
with Hypothesis 3b, the more people a person talked
about risky energy technologies in their network, the
more risks they perceived toward NP (β=0.05, p<
0.01) and SG (β=0.08, p<0.001).
Social influence also contributed to the explana-
tion of benefit perceptions of NP (R2=0.25, p<
0.001; ƒ2=0.33) and SG (R2=0.23, p<0.001; ƒ2=
0.30), representing a strong effect. The more people
perceived support of NP (β=0.50, p<0.001) or SG
(β=0.48, p<0.001) in their social network, the more
benefits they assigned to NP and SG, hereby support-
ing Hypothesis 3c. Talking about risky energy tech-
nologies in one’s social network did not positively af-
fect benefit perceptions of NP (β=–0.00, n.s.) and
SG (β=–0.04, n.s.), hereby rejecting Hypothesis 3d.
With regard to Hypothesis 4, we tested the in-
direct effects of the social network factors on the
acceptability of the two energy technologies with
risk and benefit perceptions as mediator constructs.
Table II presents the estimates for the PLS path
model for both technologies. The mediator effect of
risk perceptions between perceived support in one’s
social network toward the energy technology and the
acceptability of the technology was supported (NP:
β=0.04, p<0.001, 95% CI: 0.03–0.06; SG: β=0.07,
p<0.001; 95% CI: 0.05–0.10). The mediator effect
of risk perceptions between the extent to which peo-
ple talk about risky energy technologies in one’s so-
cial network and acceptability was supported as well
(NP: β=0.02, p<0.05, 95% CI: –.05–0.01; SG: β=
–0.03, p<0.01; 95% CI: –0.05–0.01). Hence, risk per-
ceptions mediated the two social network factors for
both technologies, hereby supporting Hypothesis 4a.
The relationship between perceived support in
one’s social network toward the energy technologies
and their acceptability was mediated by benefit per-
ceptions (NP: β=0.32, p<0.001, 95% CI: 0.27–0.37;
SG: β=0.27; p<0.001; 95% CI: 0.22–0.31). How-
ever, the relationship between the extent to which
people talk about risky energy technologies in one’s
social network and acceptability was not significantly
mediated by benefit perceptions (NP: β=–0.00, p=
n.s., 95% CI: –0.04–0.03; SG: β=–0.02, n.s.; 95% CI:

Social Influence, Risk and Benefit Perceptions and Acceptability 1237
Table II. Mediating Effect of Risk and Benefit Perceptions of Nuclear Power (NP) and Shale Gas (SG): Indirect Effects of Social
Influence on Acceptability
Support→RP→AC Talk→RP→AC Support→BP→AC Talk→BP→AC
NP SG NP SG NP SG NP SG
Indirect effect (SE) 0.04 (0.01) 0.07 (0.01) −0.02 (0.01) −0.03 (0.01) 0.32 (0.03) 0.27 (0.02) −0.00 (0.02) −0.02 (0.02)
p-Value <0.001 <0.001 0.02 0.001 <0.001 <0.001 0.88 0.26
95% CI (0.03–0.06) (0.05–0.10) (−0.03–0.00) (−0.05–0.01) (0.27–0.37) (0.22–0.31) (−0.04–0.03) (−0.06–0.02)
Support =Perceived support toward nuclear power/shale gas in one’s network; Talk =talking about risky technologies in one’s social
network; RP =risk perception; BP =benefit perception; AC =acceptability of nuclear power/shale gas; NP =nuclear power; SG =shale
gas.
Table III. Differences of Path Coefficients for a Familiar
(Nuclear Power) and an Unfamiliar (Shale Gas) Risky Energy
Technology
Structural Effect
Nuclear
Power
Shale
Gas Difference
t(p-
Value)
RP →Acceptability −0.30 −0.39 0.10 2.11
(0.035)
BP →Acceptability 0.64 0.56 0.08 1.89
(0.059)
Support →RP −0.14 −0.18 0.04 1.18
(0.240)
Talk to →RP 0.05 0.08 0.02 0.73
(0.463)
Support →BP 0.50 0.48 0.02 0.51
(0.611)
Talk to →BP −0.00 −0.04 0.03 0.73
(0.465)
Support =Perceived support toward energy technology in one’s
network; Talk =talking about risky technologies in one’s social
network; RP =risk perception; BP =benefit perception.
–0.06–0.02). Therefore, these results provided partial
support for Hypothesis 4b.
3.2. Difference in Familiarity: Comparing Models
of NP Versus SG
To test Hypothesis 5 and RQ1, we conducted
a Multigroup Analysis via a bootstrap procedure.
Table III shows that most path coefficients were not
significantly different from one another, with two ex-
ceptions. Our findings showed that risk perceptions
affect the acceptability of the more familiar risk tech-
nology of NP (β=–0.30) less strongly than of the
less familiar technology of SG (β=–0.39; t(947) =
2.11, p<0.05), hereby supporting Hypothesis 5a.
Furthermore, benefit perceptions more strongly af-
fected the acceptability of NP (β=0.64) than SG
(β=0.56; t(947) =1.89, p=0.059), hereby sup-
porting Hypothesis 5b. Social influence factors did
not affect risk and benefit perceptions differently for
more (NP) or less (SG) familiar technologies, hereby
answering RQ1.
4. DISCUSSION AND CONCLUSION
Risky energy technologies are often controver-
sial and debates around them are polarized. In de-
bates surrounding risky energy technologies, public
acceptability is a key issue. Previous research shows
that risk and benefit perceptions influence public
acceptability of energy technologies (Visschers et al.,
2011). Within this research, there has been a strong
emphasis on intrapersonal factors, such as values
(De Groot et al., 2013) and knowledge (Helgeson
et al., 2012) influencing these relationships. This
research integrates interpersonal factors (i.e., social
influence measured through social network charac-
teristics) with two energy technologies that differ in
familiarity, NP versus SG, to examine how these fac-
tors explain risk and benefit perceptions and public
acceptability. By integrating interpersonal with in-
trapersonal factors in an explanatory model, we show
how social influence explains acceptability judgments
and how they could extend the current “risk–benefit
acceptability” model used in the field of risk research.
In line with Hypothesis 1, our findings show that
higher risk perceptions of NP and SG lead to a de-
crease in the acceptability of the respective energy
technology. Inversely, higher benefit perceptions are
related to an increase in the acceptability of NP and
SG. This finding supports previous studies related to
the acceptability of risky energy technologies (e.g.,
Bearth et al., 2014, 2017; Siegrist et al., 2008). The
findings extend current literature in replicating this
relationship for the lesser familiar energy technology
of SG.

1238 de Groot, Schweiger, and Schubert
The perceived benefits of NP and SG are more
important in informing acceptability judgments than
the perceived risks associated with them, hereby sup-
porting Hypothesis 2. Previous studies have pointed
to the superior role benefit perceptions play over
risk perceptions in people’s acceptability of hazards
(Siegrist et al., 2008). These findings seem to be
robust, as they hold up regardless of participants‘ fa-
miliarity with the specific energy technology. Gaskell
et al. (2004) have argued that the strong reliance on
benefit perceptions can be attributed to the lexico-
graphic heuristic in which people base their attitude
on the single most important attribute of the attitude
object. The benefits of NP and SG alike are that
both have been argued to be cheap and independent
energy technologies for households and the economy
(BBC, 2013; Boudet et al., 2014). These benefits may
have a more direct influence on the individual than
the risks associated with NP and SG, which could
be detrimental to the environmental and human
health on a short and long-term basis (Howarth,
Ingraffea, & Engelder, 2011; Siegrist & Cvetkovich,
2000). Thus, the formation of acceptability may
be most strongly influenced by benefit perceptions
because the benefits of these energy technologies
impact people’s lives more directly than do the risks
associated with these energy technologies.
Our findings support the assumption that so-
cial influence affects risk and benefit perceptions
of NP and SG (Hypothesis 3). The present study
operationalized social influence in two ways. First,
it is operationalized as the percentage of the total
number of close network peers that were perceived
to support NP and SG. Second, the study assessed
the amount of people an individual talk to about
risky energy technologies such as NP and SG in
one’s close network. Our results show that the more
individuals perceive support in their social network
toward NP or SG, and, the more they talk about
energy technologies in their network, the fewer risks
they perceive toward the technology. Similarly, the
more individuals perceive support in their social
network toward NP or SG, the more benefits they
perceive toward the technology. However, talking
about energy technologies in one’s social network
did not explain benefit perceptions for NP or SG.
Overall, the results support Scherer and Cho’s (2003)
social network contagion theory of risks. This theory
argues that relational aspects of an individual’s net-
work influence and form an individual’s perception.
The strong relationships between social influence
and risk and benefit perceptions are also coherent
with previous findings showing that risk perceptions
of individuals are heightened when their social
network peers display concern about the attitude
object (Kohler et al., 2007).
Our findings show that perceived support in
one’s social network toward the risky energy tech-
nology was especially relevant for explaining risk,
and, even more so, for benefit perceptions. Hence,
social network peers that support an energy tech-
nology are most likely to pass on their belief system
to their network members. This is coherent with the
finding that benefit perceptions are the strongest pre-
dictor of acceptability Finucane et al., 2000; Siegrist
& Cvetkovich, 2000). Furthermore, these results sug-
gest that attitudes are most strongly affected by
knowing and sharing actual beliefs about the posi-
tive, and, to a lesser extent, negative views of the en-
ergy technology. This occurs through the exchange
of opinions and sharing perceptions regarding the
energy technologies, rather than only talking about
risky energy technologies in general. This finding em-
phasizes that it is more the beliefs than the exchange
in information itself between peers that influence
one’s own attitude.
Previous research suggests that most de-
terminants of the acceptability of risky energy
technologies exert their influence via risk and benefit
perceptions (Siegrist & Cvetkovich, 2000). This
is indeed the case for intrapersonal factors, such
as values (De Groot et al., 2013) and knowledge
(Helgeson et al., 2012). Our findings show that this
process works similarly for interpersonal factors: we
found that social influence, as measured by social
network characteristics, affects public acceptability
of energy technologies indirectly via risk and benefit
perceptions (Hypothesis 4). These results contribute
by demonstrating the extent to which, and under
which circumstances, social influence explains public
acceptability of highly controversial and debated
energy technologies, which are different in familiar-
ity. The findings provide a starting point to further
develop the previous well-established “risk–benefit
acceptability” model as proposed in risk research
(e.g., Ho et al., 2018; Lienert et al., 2015; Visschers
et al., 2011) by investigating its underlying processes.
Overall, the study demonstrates that risk and
benefit perceptions are differently related to the ac-
ceptability of the familiar risky energy technology
of NP compared to the unfamiliar technology of SG
(Hypothesis 5). Risk perceptions are relatively more
important for explaining the acceptability of SG than
NP, while benefit perceptions more strongly affect

Social Influence, Risk and Benefit Perceptions and Acceptability 1239
the acceptability of NP than SG. These results are
in line with Van Giesen et al. (2018), which suggests
that when people are relatively unfamiliar with the
risky technology, they tend to rely more on biases,
such as the negativity bias (Baumeister et al., 2001),
as there are less knowledge structures in place to ra-
tionalize negative information. However, over time,
when knowledge structures are in place, the negativ-
ity bias might play less of a role. Therefore, future
studies could take a longitudinal approach to exam-
ine whether the differences in the relative contribu-
tion of risk and benefit perceptions in NP and SG will
diminish over time, as the knowledge structures, and,
therefore, the perceived familiarity for SG grows.
Finally, our findings do not provide evidence
that social influence works significantly different de-
pending on the familiarity with the context (RQ1).
It therefore rejects the idea that people’s attitudes
are more easily influenced by others when they are
less familiar or more uncertain with an attitude ob-
ject (Christenson et al., 2017). These results support
the robustness of our conceptual model as the famil-
iarity with the energy technologies does not seem to
change the importance of social influence factors, as
measured with social network characteristics, on risk
and benefit perceptions and acceptability. Therefore,
it seems that including social dimensions by integrat-
ing interpersonal factors (e.g., social network charac-
teristics) in more established risk–benefit acceptabil-
ity models is a fruitful way to understand the process
of how the acceptability of risky attitude objects is
established.
4.1. Limitations, Future Research, and
Implications
Our results show that including a broader social
context in the form of social network characteristics,
rather than focusing on the social context from an
intrapersonal perspective (e.g., social norms), can be
a fruitful way to understand the formation of beliefs,
attitudes, and behavior. However, two potential
limitations should be addressed in future studies.
First, the exploratory and correlational nature of
the present study prevents us from understanding
through which mechanisms social networks influence
these beliefs, attitudes, and behavior. Thus, limiting
the conclusions we can draw from our findings.
Future research could take our findings as a point
of departure to examine these mechanisms in more
in-depth. One way forward would be to integrate
important intrapersonal (social) factors with (inter-
personal) social network characteristics to examine
their interactions. For example, certain social net-
work characteristics might “trigger” social norms
in favor (perception that most people in your close
network are supporting the technology) or against
(perception that most people in your close network
are opposing the technology) risky energy technolo-
gies, hereby strengthening the relationship between
social norms, beliefs, and behavior (see, e.g., the
Theory of Normative Conduct; Cialdini et al., 1990).
In line with this, we would urge future research to
further examine the causal direction under which
opposition and support in one’s social network is
most relevant by taking a more experimental or
longitudinal approach.
Second, in the present study, we argue that social
network factors could be regarded as interpersonal
factors, while social norms have typically been con-
sidered to be an intrapersonal factor in past research.
Although conceptually social network factors are
on an interpersonal level, our present study has
measured social networks on an egocentric level,
which, like social norms, is based on the individual’s
perception of the views of their peers. Still, there
is an essential difference in the operationalization
between social networks and social norms. Social
norms are most often measured as the summation
of normative beliefs from several salient others (cf.
Ajzen, 1985). However, research in social norms has
found that the nature of the social norm depends on
whom the norm is derived from (Keizer & Schultz,
2019). That is, the nature of the influence from others
depends on who those relevant others are, which are
often not further investigated in research (see, e.g.,
Farrow, Grolleau, & Ibanez, 2017). Using egocentric
social networks can be regarded as a way to further
conceptualize and operationalize these “relevant
others.” Specifically, in the egocentric approach we
applied, individuals are asked to report the percep-
tion of NP and SGfor each one of their close network
members. Hence, researchers get a more detailed
picture following this approach than when they
assess the social norm as an entire network. From a
more practical point of view, it has been suggested
that complying with norms is most likely to emerge
when people interact in small homogeneous commu-
nities or in networks that can create these conditions
(Kinzig et al., 2013). When research only focuses
on measuring social norms rather than the detailed
networks, interventions based on social norms are

1240 de Groot, Schweiger, and Schubert
less likely to succeed. Thus, although the present
social network characteristics are conceptualized
on an interpersonal level but operationalized on an
intrapersonal level, it includes detail and systematic
analysis about an individual’s network. Hence, it may
help our understanding of conceptualizing and op-
erationalizing social influence. However, to further
align the conceptualization and operationalization of
social network factors, future studies should include
alter social network analyses as well.
Individuals’ benefit perceptions have a larger
impact on the acceptability of risky energy tech-
nologies, such as NP and SG, than risk percep-
tions. This influence is particularly pronounced for
forming ones’ acsceptability of more familiar energy
technologies. Communication strategies of busi-
nesses and policy makers, wishing to increase the ac-
ceptability of relatively familiar technologies, should
therefore focus on emphasizing their benefits. How-
ever, for less familiar risky technologies, our findings
suggest that emphasizing the perceived risks of (not)
implementing the energy technology will influence its
acceptability as well. Addressing perceived risks may
also further reduce the danger of public polariza-
tion of opinions. The strong correlation between risk
and benefit perceptions as found in our study sug-
gests that reducing perceived risks may also heighten
benefit perceptions, which in turn strengthens public
acceptability as well. Consequently, addressing risk
and benefit perceptions likely affects how individu-
als perceive the support toward risky technologies
in their social network and how the narrative will
be shaped in their social influence context (i.e., how
they perceive their network members to think about
these technologies). Thus, addressing risk and ben-
efits at an individual level will not only change the
opinion of a single person, but instead may have so-
cial influence effects on others that form their atti-
tudes based on opinions present within their social
network.
Public acceptability of risky energy technologies
is multifaceted and depends on different inter- and
intrapersonal factors. Present research shows that
social influence plays an essential role in evaluating
risks and benefits of NP and SG, and indirectly, pub-
lic acceptability. However, risk perceptions are more
important for unfamiliar energy technologies such
as SG, while benefit perceptions are more important
when explaining the acceptability of more familiar
risky energy technologies. Our findings help future
research to develop more comprehensive models
of acceptability formation of energy technologies
through the integration of social influence and
different energy technologies, hereby contributing
to the fields of risk research and social influence.
ACKNOWLEDGMENT
We acknowledge the support of the University
of Bath.
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SUPPORTING INFORMATION
Additional supporting information may be found on-
line in the Supporting Information section at the end
of the article.
Table SI. Review of the Revised Measures.
Table SII. Standardized Path Coefficients for Old
and New Acceptability Construct with Perceptions of
Risk and Benefits (N=153).