=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper40
|storemode=property
|title=The Role of Emotional Intelligence as an Underlying Factor Towards Social Acceptance of Green Investments
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper40.pdf
|volume=Vol-2030
|authors=Stamatios Ntanos,Garyfallos Arabatzis,Miltiadis Chalikias
|dblpUrl=https://dblp.org/rec/conf/haicta/NtanosAC17
}}
==The Role of Emotional Intelligence as an Underlying Factor Towards Social Acceptance of Green Investments==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper40.pdf
The role of emotional intelligence as an underlying factor
towards social acceptance of green investments
Stamatios Ntanos1, Garyfallos Arabatzis1, Chalikias Miltiadis2
1
Department of Forestry and Management of the Environment and Natural Resources,
School of Agricultural Sciences and Forestry, Democritus University of Thrace, Orestiada,
68200, Greece, sdanos@ath.forthnet.gr
1
Department of Forestry and Management of the Environment and Natural Resources, School
of Agricultural Sciences and Forestry, Democritus University of Thrace, Orestiada, 68200,
Greece, garamp@fmenr.duth.gr
2
Department of Business Administration, School of Business and Economics, Piraeus
University of Applied Sciences, Aigaleo, 12244, Greece, mchalikias@hotmail.com
Abstract. This study focuses on the relationship between public acceptance of
renewable energy investments and emotional intelligence. For the purpose of
this study, a questionnaire survey was conducted at the regional unit of Evia -
Greece, by selecting a sample of 366 residents. Statistical analysis revealed
the existence of a positive correlation between trait emotional intelligence and
willingness to invest in renewable energy sources. Furthermore, statistically
significant correlation was also found between emotional intelligence and
citizens views about the contribution of renewable energy sources to life
quality and environmental improvement.
Keywords: Renewable energy sources, public acceptance, emotional
intelligence, green investments, TEIQue
1 Introduction
The impacts of climate change are noticeable, and the need for the promotion of
green investments and a shift to renewable energy sources (RES) are considered to
be imperative (Kyriakopoulos et al., 2015; Papageorgiou et al., 2015). Social
acceptance of renewable energy systems is measured both at a national and local
level, since citizens’ attitude towards RES can vary not only between countries but
also between regions in the same country (Bertsch et al, 2016; Tabi and
Wüstenhagen, 2017; Enevoldsen and Sovacool, 2016). The term social acceptance is
used in many studies, incorporating various concepts. In several studies, the term
“social acceptance” is used to assess the degree of people’s readiness to accept a
particular form of renewable energy investment in their region (Liu et al; 2014;
Caporale and De Lucia, 2015; Hall et al., 2013; D׳Souza and Yiridoe; 2014). In other
studies the term “social acceptance” is used as an assessment measure of active or
passive citizens’ attitude towards various technologies / RES products (Rosso-Cerón
341
and Kafarov, 2015). Several studies concerning Greece reflect positive citizens’
attitudes towards various forms of green investments and social responsibility actions
(Zografakis et al, 2010; Arabatzis and Myronidis, 2011; Chalikias, 2013; Chalikias &
Kolovos 2013; Kaldellis et al, 2012; Skordoulis et al, 2013; Ntanos et al, 2016). A
common finding in most studies measuring social acceptance is the need for
providing additional information to stakeholders. Most environmental campaigns run
on the principle that people need more information to behave pro-environmentally.
However, the approach of increasing social acceptance only in terms of
“information” has been criticized as insufficient to promote behavioural change, as
personality characteristics are not taken into account (Schultz, 2002; Schultz et al,
2004; Chalikias et. al, 2011; Kellstedt, Zahran, & Vedlitz, 2008; Ockwell et al,
2009). By using tools of social psychology, numerous studies examine
environmental attitudes and behaviours of individuals, under the prism of personality
traits (Parant et al, 2017; Stigka et al, 2014; Yazdanpanah et al, 2015; Aaen et al,
2016). Dunlap and Van Lier have created an instrument known as the NEP scale, for
measuring environmental attitudes and ecological intention (Dunlap and Van Lier,
1978). An interesting approach to the characteristics of the human personality,
which also includes individuals’ sensitivity towards the social environment, is the
concept of emotional intelligence. There is considerable debate about the feasibility
of measuring emotional intelligence (Goleman, 1996, Steiner, 1997). Emotional
intelligence is defined as the ability to perceive, assess and generate emotions as well
as understanding emotions and their regulation, so as to better promote both feelings
and thinking (Salovey and Mayer, 1989). People with high emotional intelligence
tend to have the ability to persist and to motivate themselves and others in difficult
situations. They can also control their emotions and develop high levels of empathy
(Goleman, 1996). Furthermore, people with increased emotional intelligence display
more willingness to cooperate and adopt a moral attitude towards their work
(Tsaousis et al., 2005).
There are many different tools that can be used to measure emotional intelligence,
including the Trait Meta-Mood Scale (TMMS, Salovey, 1995), the Bar-On
Emotional Quotient Inventory EQ-I (Bar-On, 2002), the Schutte Emotional
Intelligence Scale SEIS ( Schutte et al, 1998), the Trait Emotional Intelligence
Questionnaire TEIQUe Short Form (Petrides and Furnham, 2001,2003, Petrides et
al., 2010).
In the light of approaching social acceptance on the basis of personality, the
interest of this research focuses on the investigation of the relation between
emotional intelligence and social acceptance of RES. Data collection is performed
through by using a two dimensional questionnaire, in order to estimate: a) the degree
of emotional intelligence and b) the level of social acceptance of RES investments.
Hypothesis tests were performed in order to examine the relation between those two
variables and investigate the possible existence of a statistically significant relation.
342
2 Methodology
Due to its high wind potential, southern Evia has already attracted investment
interest in the wind energy sector. Most wind parks in or country are located in
central Greece and Evia. According to Baltas & Dervos (2012), three wind priority
areas (WPA) are distinguished in Greece. WPA’s are continental areas with
comparative advantages for wind farms. The total usable capacity in the Greek wind
priority areas is estimated at 2537 typical wind turbines, or 5074 MWe. Evia has
been classified in the 2nd wind priority area, according to the previous distinction.
Southern Evia presents the highest wind power density per km2 compared to the
national average. There are running applications for 1578 MW wind power projects
that have been submitted to the Regulatory Authority for Energy (Kontogianni et al,
2014). The recent completion of a new 150kV submarine interconnection between
south Evia island and Attica region will enable the development of 380MW of new
wind power capacity in south Evia and the nearby Cycladic Islands of Andros and
Tinos. The development has an estimated investment value of €700 million which
will be sponsored by some of the prominent market players. Moreover, the scheduled
upgrade of existing grid infrastructure in the area to 400kV will enable the
development of additional 450MW– 550MW of wind power capacity in south Evia
in the future (Norton, 2017). Because of the renewable potential of the area, the
region of Evia was selected in the context of this study.
A questionnaire survey was conducted during the period of March 2016 to
September 2016 by using random stratified sampling. According to 2001 census,
conducted by the Hellenic Statistical Authority, the population of Evia regional unit
is 207,305 inhabitants (National Statistics, 2009). The stratification research was
done at a municipal level, using the electoral register per municipality, for all 27
constituencies of the county regions. With this method we achieve a sample unit
consisting of adults over 18 years old. The regional unit of Evia has 432 polling
stations with 204,938 registered at the parliamentary elections of September 2015.
The electoral lists of January 2015, broken down by polling station, were retrieved
from the Ministry of Interior (Ministry of Interior, 2016). For the calculation of the
sample size, given that the dispersion of the population for the variables of our
investigation was unknown, a pre-study was conducted in the area with a sample of
size n = 50 respondents. By using this pilot sample, the variance (s2) and standard
deviation (s) were calculated for each quantitative variable, and the ratio (p) for each
qualitative variable research. The appropriate sample size was estimated at 366
persons, by using the proportions equation, with an error e = 0.05. The questionnaire
is divided into two sections. The first section contains 30 questions that are used to
calculate the level of an individuals’ emotional intelligence. These questions are
known as the TEIQue-SF scale (Trait Emotional Intelligence Questionnaire - short
form) which is an internationally recognized emotional intelligence measurement
tool (Psychometric Lab, 2016a; Zampetakis, 2011). Answers to each item on a scale
of 1 (Strongly Disagree) to 7 (Strongly Agree), are summed to calculate the total
score; half the questions are reverse-scored. The higher the score, the more
343
emotionally intelligent the respondent perceives himself to be. The second section of
the questionnaire contains various questions on RES, concerning degree of
knowledge and acceptance of consumers for investments in the photovoltaic sector,
small hydro parks, wind energy and biomass. The questions were drawn from similar
surveys on social acceptance of RES (Arabatzis and Myronidis, 2011; Kyriakopoulos
et. al., 2010; Chalikias et. al., 2010; Kolovos et. al., 2011; Kyriakopoulos and
Chalikias, 2013; Arabatzis and Malesios, 2013; Kaldellis, 2005; Zografakis et al.
2010).
The main research aim is to examine if there is a correlation between willingness
to invest and emotional intelligence. Statistical analysis includes independent
samples t-test and the Spearman correlation coefficient.
3 Analysis
The sample consists of 366 respondents from the regional unit of Evia. Regarding
their gender, 53.3% are men and 46.7% women. The average age of the sample is
38.6 years and the predominant age group category 41-44 years, including 30.0% of
the respondents. The predominant level of education is high school (39.7%) with
second the TEI / University category (27.2%). A percentage of 47.4% of the sample
declared an individual annual income up to € 10.000, while it should be noted that
about 1/4 of the sample declares income up to € 5000. Only 15% of the sample
reported annual income above 20,000 euros. Concerning occupational status, the
employees in public and private sectors account for 51.4% of the sample, while a
percentage of 22.5% of the sample are students, unemployed and housekeepers. With
regard to the area of residence, the majority of the sample (38.6%) resides in
suburban areas, a 33,3% in urban areas and the remaining 28.1% in rural areas.
The average emotional intelligence according to TEIQue-SF scale, is estimated at
4.66 units to a maximum of 7. On a dichotomous question (yes / no) regarding the
RES investment desire, 72.8% of the sample said they would like to invest in
renewable energy technologies. On several questions about the degree of knowledge
on various forms of renewable energy, a percentage of 38.2% of respondents
reported a good or very good knowledge of solar energy, followed by wind energy
with 34.5%. Less known forms of renewable energy are small hydroelectric and
biomass by gathering a percentage of about 19%. Concerning the contribution of
renewables, about 51% responded that renewable energy sources contribute to the
improvement of living standards while 65.5% answered that they contribute to
environmental improvement. More than half of the respondents agreed or fully
agreed on the statement that renewable energy is an economically efficient and
socially acceptable investment area. When asked about factors contributing the most
towards the spread of renewables, a percentage of 71% of the respondents agreed or
strongly agreed on the increasing need for environmental protection. The second
factor is updated information while the third factor is economic reasons, with rates of
57.4% and 57.3% respectively.
For the purpose of the analysis, we tested the variable named “emotional
intelligence” for normality by using the “One sample K-S” command in SPSS. By
344
observing the statistical significance of the test (.sig>0.05) we accepted that sample
distribution comes from a normal distribution. A hypothesis test followed, between
the variables “willingness to invest” and “emotional intelligence”. The test
hypotheses are:
H0: There is no correlation between investment desire and emotional intelligence
H1: There is a correlation between investment desire and emotional intelligence
Table 1. Mean score on emotional intelligence for respondents who would invest in
renewable sources and respondents who would not invest in renewable sources
Group Statistics
Would you invest in Mean EQ Std.
N Std. Error Mean
RES? score Deviation
Emotional YES 238 4.73 0.66 0.04
Intelligence
NO 80 4.45 0.61 0.07
(EQ)
Table 2. Independent samples t-test depicting the statistical significance of the mean score
difference on emotional intelligence between potential investors and non-investors in RES
Levene
t-test for Equality of Means
test
Mean Std. Error
F Sig. t df Sig. Lower Upper
Difference Difference
.694 .405 3.344 316 .001 .28 .083 .115 .443
3.471 145.3 .001 .28 .080 .120 .437
We observe that participants who express desire for investment in RES (category
"YES") have an average emotional intelligence score of 4.73 out of 7, while those
who do not want investment in RES have an average emotional intelligence of 4.45.
We also observe/notice from the independent samples t-test that the difference
between the two groups is considered statistically significant at the 99.9% level.
Therefore, it is obvious that respondents who gave a positive answer on RES
investment have a higher emotional intelligence quotient. This connection between
EQ and investment desire is of particular interest and is subject to further
investigation.
Continuing the analysis, we associated emotional intelligence score with
respondents’ views on various statements concerning RES issues, through Spearman
correlation coefficient.
345
Table 3. Spearman correlation coefficient between emotional intelligence and respondents’
opinion on issues concerning renewable energy systems
1.
3.
Emoti RES 2. RES 4.
Renewab
onal contribut contribute RES
le energy
Intelligenc e to to invest
is cost-
e (mean improved environmen socially
effective
score) living t improve acceptble
invest
condition
Spearman
Emotiona 1 .155** .121* .077 .190**
Correlation
l Intelligence
Sig. (2-
(mean score) .006 .031 .172 .001
tailed)
1. RES Spearman
contribute to .155** 1 .567** .305** .216**
Correlation
improved
living Sig. (2-
.006 .000 .000 .000
conditions tailed)
2. RES Spearman
.121* .567** 1 .239** .312**
contribute to Correlation
environment Sig. (2-
.031 .000 .000 .000
improvement tailed)
3. Spearman
Renewable .077 .305** .239** 1 .362**
Correlation
energy is
cost-effective Sig. (2-
.172 .000 .000 .000
investment tailed)
4. RES Spearman
.190** .216** .312** .362** 1
invest Correlation
socially Sig. (2-
.001 .000 .000 .000
acceptable tailed)
Results confirm a relationship between emotional intelligence quotient and the
view that a) RES is a socially acceptable investment area; Spearman correlation
coefficient =0.190 and sig = 0,001, b) RES contribute to improved living conditions;
Spearman correlation coefficient =0.155 and sig = 0.006 and c) RES contribute to
environmental improvement; Spearman correlation coefficient =0.121 and sig =
0.031. Furthermore, no statistically significant correlation was detected between
emotional intelligence and the belief that renewable energy is an economically
efficient investment. As it can be seen from the means plot below (fig.1) and as
shown by the previous analysis, there is a positive relationship between emotional
intelligence and the view of respondents on the environmental and social
contribution of green investment.
346
Fig. 1. Mean plots between emotional intelligence mean score and public acceptance of
renewable energy systems, depicting a positive relation
4 Conclusions
This study focuses on the relation between emotional intelligence and citizens
attitudes towards renewable energy sources. Data collection was performed through
a questionnaire survey at Evia regional unit, Greece. The sample majority (72%),
exhibited willingness to invest in renewable energy sources (RES). Out of the
respondents who wish to invest in RES, the majority prefers solar photovoltaics
(62%), with wind energy (23%) as the second most desirable investment, whereas
biomass and small hydropower parks gather low rates of investment desire.
Investment preference is consistent with the degree of acquaintance with the various
forms of renewable energy, since a percentage of 38.2% of respondents declared to
be familiar or very familiar with solar energy, followed by wind energy with a
percentage of 34.5%. The least recognizable forms of renewable energy systems are
the small hydroelectric parks and the biomass energy systems, which gather a
percentage of good or very good knowledge at about 19%. Concerning emotional
intelligence of the respondents, the mean sample score is 4.66 / 7, which is consistent
with similar studies. In statistical tests examining the relation of emotional
intelligence with public acceptance of renewable energy systems, a weak positive
correlation was found between emotional intelligence and a) the desire to invest in
renewable energy, b) social acceptance c) public opinion concerning the contribution
of these investments to increased living standards. The results revealed that
respondents who are willing to invest in RES scored higher on the emotional
intelligence section of the questionnaire. To have a clearer view of the income status
of those who are willing to invest in RES, we performed a chi square test between
“income” variable and the binary variable “would you invest in RES”. We noticed
that out of those who were willing to invest in RES, which have exhibited a higher
EQ score as previously mentioned, the categories of annual income between 10,000 –
347
25,000 euros include more respondents that expected. It therefore appears that the
attitude of respondents’ towards green investments is more positive amongst those
who exhibit a higher emotional level and becomes even stronger at medium to high
income levels, although further research with different samples is required in order
for this finding to be validated. The increase of emotional intelligence amongst
individuals through intervention programs even from the primary and secondary
education level is a concept with prominent results in the context of environmental
awareness (Nelis et al, 2009; Viguer et al., 2017).
This study belongs to the area of similar studies reported in the literature review
and dealing with how personality characteristics can incite social acceptance of RES
investments.
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