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Can Facebook likes predict the purchase probability of electricity storage systems?

December 2021
Social Network Analysis and Mining 11(1)

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This study among owners of photovoltaic systems investigates whether users' Big Five personality traits derived from their Facebook likes contribute to whether or not they adopt an electricity storage. It is based on the finding that the digital footprint, especially the Facebook likes, can in part predict the personality of users better than friends and family. The survey was conducted among 159 Facebook users in Germany who owned a photovoltaic system. For comparison, a control sample with data from the German Socio-Economic Panel with 425 photovoltaic owners among 7286 individuals was used. The results show that, for extraversion, agreeableness, and neuroticism, the mean scores could be sufficiently predicted. However, a positive correlation could only be detected for extraversion. The comparison of the user groups could not provide satisfying results. None of the Big Five personality traits could be used to distinguish the two user groups from each other. Although the results did not support the hypotheses, this study offers insights into the possibilities of combining data mining, personality psychology, and consumer research.

Login to the likeexchange app. Note: Figure presents the login screen to the Facebook app. The user must consent in advance to his likes being accessed. After proceeding, the user's likes were transferred to the AMS-API and processed. The resulting Big Five traits were re-transferred as percentiles

…

Big Five personality traits according to the NEO-FFI. Source: McCrae and Costa (1999)

…

presents descriptive statistics of the Big Five personality traits where all items were completed in the 2017 SOEP study; n, number of individuals; SD, standard deviation; α, Cronbach's alpha

…

Mean levels of battery adopters and non-adopters

…

Used facebook groups and member count

…

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Social Network Analysis and Mining (2021) 11:79

https://doi.org/10.1007/s13278-021-00789-1

ORIGINAL ARTICLE

Can Facebook likes predict thepurchase probability ofelectricity

storage systems?

StefanPoier1

Received: 25 June 2020 / Revised: 9 August 2021 / Accepted: 11 August 2021 / Published online: 23 August 2021

Abstract

This study among owners of photovoltaic systems investigates whether users' Big Five personality traits derived from their

Facebook likes contribute to whether or not they adopt an electricity storage. It is based on the ﬁnding that the digital foot-

print, especially the Facebook likes, can in part predict the personality of users better than friends and family. The survey

was conducted among 159 Facebook users in Germany who owned a photovoltaic system. For comparison, a control sample

with data from the German Socio-Economic Panel with 425 photovoltaic owners among 7286 individuals was used. The

results show that, for extraversion, agreeableness, and neuroticism, the mean scores could be suﬃciently predicted. However,

a positive correlation could only be detected for extraversion. The comparison of the user groups could not provide satisfying

results. None of the Big Five personality traits could be used to distinguish the two user groups from each other. Although the

results did not support the hypotheses, this study oﬀers insights into the possibilities of combining data mining, personality

psychology, and consumer research.

Keywords Big Five· Renewable energy· Consumer behavior· Social networks· Online marketing

1 Introduction

Since people have been on the planet, they have demonstrated

a tendency to attempt to classify their fellow human beings.

For example, the temperament theory, which has its roots in

antiquity, was developed to diﬀerentiate individuals according

to their diﬀerent temperaments (Merenda 1987). In modern

times, anthropological racial theories as well as personality

models can be found (e.g., Banks 1996). In the ﬁeld of eco-

nomics, the diﬀerentiation of products, markets, and market

actors serves to simplify processes and predict certain out-

comes. In the ﬁeld of marketing, it is useful to know whether

or not a consumer is likely to purchase a particular product—

without having to ask the consumer. There are several ways to

build correlations between user groups and user behavior. For

example, collaborative ﬁltering is used by online commerce

websites such as Amazon (“People who bought books about

statistics were also interested in econometrics”). This method

attempts to identify the future behavior of a consumer from his

or her past behavior (Das etal. 2007). Another way is to pre-

dict a certain attitude or behavior based on an individual’s per-

sonality traits. These could be, for example, personal human

values or the Big Five personality traits of openness, consci-

entiousness, extraversion, agreeableness, and neuroticism (Bil-

sky and Schwartz 1994; Cieciuch and Schwartz 2017; McCrae

and Costa 1997, 1999; Schwartz 2017). This approach posits

that, if we know an individual’s personality traits, we can pre-

dict his or her behavior to a certain extent (Aral and Walker

2012). The diﬃcult aspect of this is acquiring valid informa-

tion about a consumer’s personality traits. Since the US presi-

dential election in 2017, there has been growing interest and

controversial discussion about whether the US election or the

UK’s Brexit decision may have been inﬂuenced by personality-

driven advertising—so-called micro-targeting. The responsible

company, Cambridge Analytica, claimed to have derived the

personality proﬁles of US citizens from their digital footprints,

especially their Facebook likes. Although this was a fantastic

media headline, it is unclear whether it is true and actually

possible to determine the Big Five traits with enough accuracy

using likes alone or whether the contribution of likes to users’

proﬁles is suﬃcient. In marketing and especially in election

advertising, it has been a common practice for many years to

aggregate and use commercial demographic data to achieve

* Stefan Poier

s.poier.125@studms.ug.edu.pl

1 Faculty ofEconomics, University ofGdansk, Armii

Krajowej 119/121, 81-824Sopot, Poland

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Social Network Analysis and Mining (2021) 11:79

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79 Page 2 of 20

a targeted address of the individual. Today, it should be clear

that, even in the infamous cases of targeting and using psycho-

metric data in recent years, which have created scandals in the

media—due in part to a lack of understanding—not only have

the Big Five played a decisive role and not only were Facebook

likes used for their calculation (González 2017). However, if

individuals in a narrow target group are very similar in their

socio-economic variables, it could well be that their individual

personal dispositions play the decisive role. This narrow tar-

get group exists, for example, among owners of photovoltaic

(PV) systems who have to decide whether or not to buy an

electricity storage system. Jacksohn etal. (2019) found that

e.g., age, income, household size, and education level were

signiﬁcantly diﬀerent between adopters and non-adopters of

PV systems. Therefore, the target group of the present study is

very similar in these parameters, and it can be suspected that

individual personal dispositions play a greater role in diﬀeren-

tiating within this group. Although there exists some literature

examining the inﬂuence of personality on energy eﬃciency

investments (Busic-Sontic and Brick 2018; Poier 2021), it has

never been investigated whether the digital footprint of users—

and inferred from this their personality traits—also reveal a

contribution about the adoption of electricity storage among

owners of PV systems.

The aim of the present article is to narrow this research

gap and to investigate if the personality traits of PV users,

predicted by their Facebook likes, are suitable for distin-

guishing between adopters and non-adopters of electric-

ity storage in this target group. This contributes not only

to the understanding of consumer behavior but also to the

usefulness of data mining in social networks for consumer

research. In a ﬁrst step, it is tested whether the predic-

tions match the users’ self-assessments. A second round of

research will examine whether PV system owners can be dif-

ferentiated into adopters and non-adopters of electricity stor-

age based on their personality traits derived from Facebook

likes. The remainder of this article is organized as follows: a

review of the literature is provided in chapter2, followed by

an explanation of the methodology, as well as the hypothesis

formulation in chapter3. Data collection and preparation is

described in chapter4. After that, the results are presented in

chapter5. This is followed by a discussion of the results and

the limitations of the study in chapter6. After an outlook

on further research, the article ends with the conclusions.

2 Research background

Personality traits are a psychological construct used to

describe individuals. Assuming a certain stability, this could

be useful for describing or even predicting human behavior

and, for marketers in particular, purchase behavior. In the

scientiﬁc literature, a number of deﬁnitions of personality

traits can be found. DeYoung (2015), for example, described

them as “probabilistic descriptions of relatively stable pat-

terns of emotion, motivation, cognition, and behavior, in

response to classes of stimuli that have been present in

human cultures over evolutionary time.” Following John

etal. (2010) and Valchev etal. (2013), they are habitual

patterns of behavior, thought, and emotion that are stable

over time and in comparable situations. What all deﬁnitions

of personality traits share is “the emphasis on the relative

consistency of behavioral predispositions to behave in a par-

ticular manner across situations” (Fischer 2018).

Over the last three decades, researchers have developed

several frameworks to describe the personalities of individ-

uals using descriptive terms for patterns of behavior with

diﬀerent numbers of dimensions. Eysenck, for example,

introduced his PEN model consisting of three elements:

psychoticism, extraversion, and neuroticism; this later

formed the basis for Costa’s and McCrae’s NEO personal-

ity inventory (Barrett etal. 1998; Parish etal. 1965). In the

early 2000s, Ashton and Lee built on the research of Costa

and McCrae (2008) and Goldberg (1993) and introduced

Honesty-Humility as an additional factor to the ﬁve existing

traits (Ashton etal. 2004; Ashton and Lee 2007). This six-

factor model is known as the HEXACO model, derived from

the initial letters of the factors Honesty-Humility, Emotion-

ality, Extraversion, Agreeableness, Conscientiousness, and

Openness to Experience (Ashton and Lee 2009). Although

these models exist with more or less than ﬁve items, there is

a broad consensus in the scientiﬁc literature that ﬁve-factor

models make the greatest explanatory contribution. Thus,

the most-often used and best-known models in contempo-

rary research comprise ﬁve personality traits or personal-

ity factors. They are known as ﬁve-factor models (FFM)

or the Big Five (Goldberg etal. 2006; McCrae and Costa

1999; McCrae and John 1992). Costa and McCrae identi-

ﬁed neuroticism, extraversion, and openness to experience as

three factors of 16 in a ﬁrst step (Costa and McCrae 1976).

Some years later, they added agreeableness and conscien-

tiousness to the model, which later became known as the

NEO-Personality Inventory Revised (NEO PI-R) after sev-

eral improvements (Costa and McCrae 2008). The ﬁve traits

can be measured with a number of inventories such as the

original 44-item Big Five Inventory (BFI) (Benet-Martínez

and John 1998), the revised 60-item version BFI-2 (Soto

and John 2017), the 60-item NEO-FFI (McCrae and Costa

2004), and the 240-item NEO-PI-R (Costa 1996; Costa and

McCrae 2008). Table1 shows the ﬁve factors, each one com-

prising six facets.

Numerous studies demonstrate the contribution of per-

sonality traits to behavior (Busic-Sontic and Brick 2018;

Danielsbacka etal. 2019; Poier 2021; Rozgonjuk etal.

2021; Zhang etal. 2021). A recent study of Chinese students

found that their information-seeking behavior depended

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Social Network Analysis and Mining (2021) 11:79

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signiﬁcantly on their personality traits. Among other things,

information seeking can reduce perceived risk in purchas-

ing—a core construct of buyer behavior (Zhang etal. 2021).

Thus, there is evidence of a contribution of personality traits

on consumer behavior. Busic-Sontic and Brick (2018) and

Poier (2021) investigated the direct and indirect eﬀects of

the Big Five on energy eﬃciency installations and photo-

voltaic adoption, respectively. In both studies, the eﬀects

were weak, but the Big Five were also opposed to very het-

erogeneous socio-demographic signiﬁcant control variables.

The possibility of drawing conclusions about the per-

sonality traits of users of social media platforms, espe-

cially Facebook, from their proﬁles has been studied and

conﬁrmed in several studies (Kosinski etal. 2016; Kosinski

2021; Marengo etal. 2020; Marouf etal. 2020b; Segalin

etal. 2017; Youyou etal. 2015). One of the most popular

articles in recent years has been that of Youyou etal. (2015).

In their study, they looked at inferring the personality of

users from their Facebook proﬁles and found that a user with

more than 10 likes can be better described by his Facebook

proﬁle than by the work colleagues and that more than 300

likes can describe the user better than his or her spouse.

Segalin etal. (2017) were able to draw conclusions about the

personality traits of Facebook users from their proﬁle pho-

tographs. In contrast, Marengo etal. (2020) examined dif-

ferences in personality traits between users of social media

platforms and between users and non-users. They found that

above all extraversion of social media users was signiﬁcantly

higher than that of non-users.

3 Methodology andhypotheses

The aim of this study is to explore whether consumers’

digital footprints—their Facebook likes, in particular—are

suitable for predicting their purchase probability of a solar

electricity storage system in Germany. Based on the litera-

ture introduced in chapter2, the research question derived

from this is as follows:

Is it possible to make a prediction about an owner of

a photovoltaic system’s adoption of an electricity storage

system using only the predicted Big Five personality proﬁle

derived from Facebook likes?

To answer this question, two hypotheses were tested:

• H1: The predicted Big Five personality traits resulting

from Facebook likes are signiﬁcantly equivalent to the

Big Five personality traits that emerge from self-reports.

• H2: The Big Five personality traits between adopters and

non-adopters of electricity storage systems are signiﬁ-

cantly diﬀerent.

These considerations are based on the assumption that

the sum of the users' activities reﬂects their online behavior,

from which in turn their personality traits can be derived

(Kosinski etal. 2016; Marouf etal. 2020a; Youyou etal.

2015). In this study, an online prediction application pro-

gramming interface (API) provided by the Psychometrics

Centre of the University of Cambridge is used as the basis

for data processing (Popov etal. 2015). The developers of

the API collected data about the participants’ personality

traits and their Facebook likes and calculated correlations

between their Facebook usage behaviors and personalities

that could also be used the other way around—that is, to

predict behavior based on personality (Kosinski etal. 2013).

For the ﬁrst hypothesis, predictions provided by Apply

Magic Sauce (AMS) (www. apply magic sauce. com) will be

used as the source of comparison. AMS is an online predic-

tion service provided free of charge for academic purposes

by the Psychometrics Centre of the University of Cambridge

(Kosinski etal. 2019). It uses data from the myPersonality

project (www. myPer sonal ity. org), a Facebook app that was

active from 2007 until 2012 and used by approximately 6

million users. About 30–40% of the participants donated

their Facebook data voluntarily. To draw relations between

a psychological assessment and Facebook pages that were

liked by the participants, personality predictions are based

on opt-in data from 260,000 participants who completed

the 100-item International Personality Item Pool (IPIP)

questionnaire in English (Popov etal. 2015; Stillwell and

Kosinski 2019). The app was banned by Facebook in 2019,

although it hasn’t been active since 2012. Unfortunately, the

availability of the myPersonality dataset has since been dis-

continued following several concerns regarding data protec-

tion. Thus, it is no longer possible to derive raw scores from

the AMS results. AMS provides results for the estimated

Table 1 Big Five personality traits according to the NEO-FFI. Source: McCrae and Costa (1999)

Personality traits Personality trait facets

Openness to experience Fantasy, aesthetics, feelings, actions, ideas, values

Conscientiousness Competence, order, dutifulness, achievement striving, self-discipline, deliberation

Extraversion Warmth, gregariousness, assertiveness, activity, excitement seeking, positive emotions

Agreeableness Trust, straightforwardness, altruism, compliance, modesty, tender mindedness

Neuroticism Anxiety, hostility, depression, self-consciousness, impulsiveness, vulnerability

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Social Network Analysis and Mining (2021) 11:79

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79 Page 4 of 20

results not as absolute scores but as percentiles. Both self-

reports and predictions will be converted into z-scores in

advance in order to achieve a common base for t tests.

4 Data

Data were collected through an online survey between April

and June 2019. The questionnaire contained items regarding

household and personal demographics, technical features of

the PV system, and information about a possibly existing

battery. In addition, it included two question batteries about

psychological traits. The ﬁrst block, concerning the Big Five

personality traits, was mandatory and comprised 16 items

that were taken directly from the SOEP questionnaire (Goe-

bel etal. 2019). Prior to the online questionnaire, partici-

pants declared themselves to be of legal age and to be taking

part voluntarily. After being provided with detailed informa-

tion about data protection and the scientiﬁc use of personal

data, all participants gave their written consent to the use of

their data and information about the privacy policy associ-

ated with the survey. Facebook carefully reviewed the app

and, ﬁnally, after a few months of coordination and negotia-

tion, allowed its use for scientiﬁc purposes and activated the

app. This Facebook app is the key element of the present

study (Fig.1). It enabled the data exchange between Face-

book and AMS for data processing and the calculation of

the predictions.

Because the target group comprised Facebook users who

were also owners of a PV system, the study was advertised

directly on Facebook and addressed individuals who were

interested in solar power, photovoltaics, renewable energies,

and related topics. In addition, a call to participate in the

study was posted in relevant groups with a total of about

26,566 members. During the period, in which the survey

was conducted, it was not only the Europe-wide introduction

of the General Data Protection Regulation that was omni-

present. At the same time, several data-related scandals in

connection with the Facebook platform became public. The

result was an unexpectedly low participation rate since it

was actually to be assumed that a technology-savvy target

group within the platform, of which they are users, would

show more activity. The reactions to the advertising or post-

ings to the study were largely characterized by hostile rejec-

tion, including insults and insinuations. These, too, were

unexpected because the target group should have comprised

higher-earning and better-educated people, and a higher

share of married couples (Table12 in the Appendix). It was

Fig. 1 Login to the like-

exchange app. Note: Figure

presents the login screen to the

Facebook app. The user must

consent in advance to his likes

being accessed. After pro-

ceeding, the user’s likes were

transferred to the AMS-API and

processed. The resulting Big

Five traits were re-transferred as

percentiles

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Social Network Analysis and Mining (2021) 11:79

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Page 5 of 20 79

also unclear where the individuals who reacted hostilely to

the postings came from since some of them did not belong to

the target group. At the end of the questionnaire, the partici-

pants could compare their predicted personality proﬁle with

their self-assessment using a graphical compilation. This

should have created an incentive to provide honest answers

in order to receive a reasonable self-assessment. In addi-

tion, vouchers for an online department store were raﬄed

among all participants. The ads reached about 55,448 Face-

book users but with a manageable level of success. All in

all, 3509 individuals visited the starting page of the survey,

213 (6.1%) of whom claimed to own a PV system, which

was the basic criterion for participation in the study. Of the

66 cases where participants were able to connect their Face-

book account with the app (339 likes, on average), 43 could

be used for a prediction because they had enough likes (453

likes, on average).

The German Socio-Economic Panel (SOEP) is a repre-

sentative, nationwide survey across nearly 15,000 private

households (Goebel etal. 2019; Liebig etal. 2019). In this

wide-range longitudinal study, more than 25,000 respondents

are interviewed year by year. The survey started in 1984, and

the most recent data represent wave 35 from 2018 (Liebig

etal. 2019). In addition to the questions that are components

of every wave of the survey, there are also special topics

that ﬂow into the investigation. Among many other topics,

the SOEP includes variables about the Big Five personal-

ity traits and other psychological items. The data can be

retrieved from the German Institute for Economic Research

(Deutsches Institut für Wirtschaftsforschung, DIW) at no

cost and are reserved exclusively for academic use and

for registered researchers. In the years 2005, 2009, 2013,

and 2017, a self-completion questionnaire on the Big Five

personality traits was part of the SOEP study (DIW Berlin

2007). A short version of the Big Five Inventory was used,

called Big Five Inventory Short (BFI-S), with 16 questions.

Before the BFI-S was added to the SOEP panel, its external

validity was tested and it was considered to be suﬃcient for

capturing users’ personality traits (Dehne and Schupp 2007).

The internal consistency of the scales was determined by

the reliability coeﬃcient Cronbach's alpha (α). Although all

values were below the recommended measure of 0.7, Dehne

and Schupp argue that the low values are caused by the small

number of items and that the mean inter-item correlation

of the scales provides good results. Crobach's alpha thus

indicates how well the individual items are represented by

the scale. The more items are used (the longer the measur-

ing instrument), the better the α-values. However, for many

participants, the inclination to answer decreases if too many

questions are asked. Thus, some researchers note the low

reliability of such short scales as in the SOEP or the British

Household Panel (Smith etal. 2021). While most studies

concerning personality traits investigate student samples,

which result in a bias toward young adults with a higher

level of education, the great advantage of nationwide studies

is their representativeness.

4.1 Construction oftheworking sample

After deletion of all cases where the requirement of a PV

system was not answered positively, 159 cases remained.

Of these, 16 cases were excluded from the survey because

of obviously incorrect answers. Thus, the working sample

consists of 143 PV users (mean age 44.3, 18.0% female),

of whom there are 74 owners and 69 non-owners of a bat-

tery storage system. Of the 61 participants who managed

to connect their Facebook proﬁle to the app, there were 39

who had enough likes for a prediction of personality traits;

among them, there were 20 owners and 19 non-owners of

a battery storage system. Table2 gives an overview of the

self-assessments.

4.2 Construction ofthecontrol sample

For comparison, data from the German Socio-Economic

Panel (SOEP) were used. In 2015 and 2016, the question-

naire included an item that asked if the household owned

a PV system. A total of 13,083 individuals answered this

question “yes” or “no.” For individuals who took part in both

years, only the results of the second administration were

left in the dataset (number (n) = 7286, 59.7% male, mean

age 56.24). In survey year 2017 (n = 32,485, 51.4% female,

mean age 45.98), a 16-item question battery was used to

investigate the Big Five personality traits of the participants.

For every trait, a score was calculated when at least one trait-

related question was answered (Table3).

The Big Five scores were added to the PV dataset. The

deviation of the total standardized scores from zero could

be due to the fact that the majority of participants who

answered the question regarding PV ownership were home-

owners and were neither very young nor very old people. It

is noteworthy, then, that all traits, except for conscientious-

ness, score below the sample mean of the SOEP study 2017.

One reason could be the higher proportion of males and the

signiﬁcantly higher mean age.

5 Econometric analysis

For the prediction of personality traits through Facebook

likes, a t test with repeated measures was used because the

same individuals were assessed ﬁrst by the AMS app and

second through a self-report questionnaire. To evaluate

whether battery owners and non-owners could be distin-

guished, an independent t test was conducted. The independ-

ence of the measurements was fulﬁlled because two diﬀerent

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Social Network Analysis and Mining (2021) 11:79

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79 Page 6 of 20

groups of individuals were assessed concurrently. The same

applies to the comparison between self-reported scores and

the data from the 2017 SOEP study.

5.1 Predictability ofself‑assessments

throughfacebook likes

The self-reported personality traits were supposed to be

predicted through the Facebook like estimates. Thus, self-

reported scores and Facebook predictions should be signiﬁ-

cantly similar for a participant’s Big Five personality traits

and, in addition, both values should correlate positively.

Because the AMS app provides percentiles while self-

reports are given as absolute scores, both were computed

into z-scores for comparison (Table4). Z-scores (z) are a

standardized measure to compare scores in terms of stand-

ard deviations. Regarding the self-assessments, the z-scores

were calculated from raw values (x) by subtracting the mean

(μ) from each raw value and then dividing by the standard

deviation (σ):

In this study, mean and standard deviation of the 2017

SOEP data were used. To derive the z-scores from the

percentiles of the normal distribution, the SPSS function

IDF.normal was used. Here, the mean of 0 and a stand-

ard deviation of 1 were taken for the normal distribution.

After these calculations, their distributions has not been

z=(

−𝜇)∕𝜎

altered. The z-scores were compared via paired t tests

for each Big Five trait. For extraversion (p = 0.081) and

neuroticism (p = 0.530), the null hypothesis of mean level

equality could not be rejected, while the correlation was

positive only for extraversion (r = 0.318) and agreeable-

ness (r = 0.420), which is a necessary assumption for pre-

dictability (Table5). Lambiotte and Kosinski (2014) noted

that a typical correlation was between r = 0.2 and r = 0.4.

As an intermediate result, it could be stated that the results

of AMS prediction and self-reports were signiﬁcantly

equal and correlated only for extraversion. Or, in other

words, the Facebook likes predicted the self-assessments

of users to a signiﬁcance level of 95% in a suﬃcient way

only for this trait. Figure2 provides three important com-

parisons: (1) A comparison between self-assessments from

the SOEP study (blue) and all self-assessments from the

present investigation (green) revealed the greatest mean

deviations in agreeableness and conscientiousness. (2)

When all self-assessments from the present investigation

(green) and only the self-assessments from individuals

whose Facebook proﬁles could be evaluated (orange) were

compared the largest mean deviations were found in open-

ness and extraversion. (3) A comparison of self-assess-

ments of individuals whose Facebook proﬁles could be

evaluated (orange) and their predictions (yellow) showed

that users rate themselves as considerably more open and

extroverted but less agreeable and conscientious than their

Facebook likes predicted.

Table 2 Big Five personality

traits of the 2019 self-reports

Table2 presents descriptive statistics of the Big Five personality traits of the 2019 self-reports; n, number

of cases; SD, standard deviation; α, Cronbach’s alpha

nMissing Min Max Mean SD α

Openness 143 0 1.00 7.00 5.035 1.243 .769

Conscientiousness 143 0 2.00 7.00 5.173 1.226 .650

Extraversion 143 0 1.00 7.00 4.455 1.305 .707

Agreeableness 143 0 2.33 7.00 4.888 1.022 .449

Neuroticism 143 0 1.00 6.67 3.578 1.286 .643

Valid N (listwise) 143

Table 3 Big Five personality

traits in the 2017 SOEP study

Table3 presents descriptive statistics of the Big Five personality traits where all items were completed in

the 2017 SOEP study; n, number of individuals; SD, standard deviation; α, Cronbach’s alpha

nMissing Min Max Mean SD α

Openness 28,990 3495 1.00 7.00 4.969 1.082 .660

Conscientiousness 29,280 3205 1.00 7.00 5.797 .955 .614

Extraversion 29,318 3167 1.00 7.00 4.950 1.147 .662

Agreeableness 29,376 3109 1.33 7.00 5.493 .995 .508

Neuroticism 29,402 3083 1.00 7.00 3.783 1.241 .588

Valid N (listwise) 28,628

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Social Network Analysis and Mining (2021) 11:79

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5.2 Distinguishability betweenuser groups

Following the comparison of self-assessment and prediction,

whether owners and non-owners of batteries diﬀer signiﬁ-

cantly should be investigated. For this purpose, a t test for

independent samples should be conducted in the ﬁrst step,

which tested the group mean values of predicted scores for

diﬀerences. None of the p-values was signiﬁcant, and thus,

the null hypothesis of equality of means cannot be rejected

(Table6). As a result, it can be assumed that the groups can-

not be distinguished by their mean values. In order to verify

whether the failed distinctness aﬀected only the predicted

values, the self-reported scores were also checked. A second

t test was conducted, and again, the p-values revealed no

signiﬁcant diﬀerences between the two groups. Owners and

non-owners of batteries could not be distinguished either by

the AMS predictions or the self-assessments of the Big Five

personality traits. Thus, the Big Five alone were clearly not

suitable for determining group membership.

A linear discriminant analysis (LDA) should show

whether the Big Five personality traits have a discriminant

property on the two user groups and whether an enrich-

ment with further variables can enable diﬀerentiation.

This proceeding originates from the ﬁnance and insurance

industry, where it is used to assess whether a consumer

is creditworthy or not, depending on several predictor

variables. The discriminant analysis was ﬁrst conducted

with only the predicted Big Five traits; in a second step,

demographic variables were added; and in a third step,

risk preferences and risk perceptions completed the set of

independent variables. The ﬁrst analysis gave an eigen-

value of only 0.090 with a canonical correlation of 0.288

and Wilks’ lambda of 0.917 (p = 0.703). Thus, the whole

model was not significant. Neuroticism and openness

could be determinants of battery ownership, and consci-

entiousness, extraversion, and agreeableness were possi-

ble predictors of non-ownership. The model was able to

classify 51.3% of the cases correctly, which was margin-

ally more than chance. When demographic variables like

age, gender, education, and family status were added, the

eigenvalue increased to 0.256 with a correlation of 0.452.

Wilks’ lambda is 0.796 (p = 0.642). The model could

Table 4 Statistics of Z-scores

Table4 presents AMS predictions of the Big Five personality traits and self-reported measures converted

to z-scores; n, number of cases; SD, standard deviation

Battery non-adopters Battery adopters

Mean nMedian Min Max SD Mean nMedian Min Max SD

z-scores for predicted percentiles of Big Five traits

Openness − .16 19 − .11 − 1.03 .23 .26 − .01 20 − .12 − .61 1.49 .44

Conscientiousness − .06 19 − .10 − .51 .81 .31 − .23 20 − .13 − 2.30 .66 .54

Extraversion − .31 19 − .36 − .56 .15 .19 − .35 20 − .33 − .82 .02 .19

Agreeableness − .27 19 − .26 − .80 .09 .24 − .27 20 − .26 − 1.26 .46 .33

Neuroticism − .38 19 − .33 − 1.04 − .06 .23 − .28 20 − .34 − .61 .37 .24

z-scores for self-reported scores for Big Five traits

Openness − .03 74 .03 − 3.67 1.88 1.16 .16 69 .26 − 3.67 1.88 1.14

Conscientiousness − .78 74 − .66 − 3.98 1.26 1.34 − .52 69 − .14 − 3.63 1.26 1.21

Extraversion − .51 74 − .25 − 3.15 1.79 1.09 − .34 69 − .25 − 3.44 1.79 1.19

Agreeableness − .59 74 − .83 − 2.50 1.51 1.03 − .63 69 − .83 − 3.17 1.51 1.03

Neuroticism − .11 74 − .09 − 2.24 2.32 1.03 − .23 69 − .09 − 2.24 2.32 1.04

Table 5 Mean level comparison

of self-reports and predictions

Reported are paired t tests (n = 39) for Big Five personality traits for PV owners (prediction—self-reports).

SD, standard deviation; t, value of t test; df, degrees of freedom; p, p-value; r, Pearson’s correlation; p(r),

p-value of correlation

Paired diﬀerences Correlations

Mean SD Std. Error Mean t df p r p(r)

Openness − .649 .847 .136 − 4.789 38 .000 − .055 .738

Conscientiousness .487 1.326 .212 2.292 38 .028 − .121 .464

Extraversion − .284 .992 .159 − 1.790 38 .081 .318 .048

Agreeableness .344 .987 .158 2.179 38 .036 .420 .008

Neuroticism − .119 1.169 .187 − .633 38 .530 − .029 .861

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Social Network Analysis and Mining (2021) 11:79

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79 Page 8 of 20

classify 67.5% correctly but was still not signiﬁcant. When

the number of persons in the household and the household

income and expenses were added, the model could clas-

sify 73.0% correctly but was still insigniﬁcant. When risk

propensity and risk perceptions in several domains were

added, the eigenvalue increased to 2.787 with a correlation

of 0.858 and Wilks’ lambda is 0.264 (p = 0.022). Neu-

roticism and openness were still determinants of battery

ownership. The model was able to classify 91.4% of the

cases correctly. As a result, the Big Five personality traits

contributed only to a small degree to the diﬀerentiation

between the user groups. Instead of using latent variables

Fig. 2 Self-assessments and

predictions compared to SOEP

data. Note: Figure presents

mean scores of the Big Five

personality trait z-scores for

photovoltaic adopters from

the SOEP study (n = 425,

blue), all self-reports from the

present study (n = 139, green),

self-reports from the present

study with predictions (n = 39,

orange), predictions from AMS

(n = 39, yellow); n = number of

individuals

-1.60

-1.20

-0.80

-0.40

0.00

0.40

0.80

Openness

Conscienousness

ExtraversionAgreeableness

Neuro

cism

Solar users 2016, based on SOEP 2017 Self-reports total (n=139)

Self-reports (n=39)Predicons (n=39)

Table 6 Mean levels of battery

adopters and non-adopters

Reported are independent t tests for Big Five personality trait predictions (n = 39) and self-assessments

(n = 143) for both battery adopters and non-adopters

n, number of individuals; F, F-value (Levene test); Sig., p-value for Levene test; t, t-statistic; p, p-value

Levene statistic t test for Equality of Means

n F Sig tMean diﬀerence Std. error

diﬀerence p

AMS predictions

Openness 39 1.894 .177 − 1.299 − .151 .116 .202

Conscientiousness 39 .171 .681 1.161 .164 .141 .253

Extraversion 39 .102 .751 .685 .042 .061 .497

Agreeableness 39 .149 .701 .010 .000 .092 .992

Neuroticism 39 .413 .524 − 1.421 − .107 .076 .164

Self-assessments

Openness 143 .002 .966 − .988 − .189 .191 .325

Conscientiousness 143 1.314 .254 − 1.245 − .266 .213 .215

Extraversion 143 .591 .443 − .893 − .170 .190 .373

Agreeableness 143 .190 .664 .263 .045 .172 .793

Neuroticism 143 .167 .683 .678 .178 .174 .499

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as predictor variables, i.e., Big Five personality traits, the

like-IDs of the Facebook pages could be used as a source

for the discriminant analysis. A user-like matrix was cre-

ated from a total of 19,335 diﬀerent pages related to 61

individuals (mean number of likes = 335, SD = 542.4,

min = 2, max = 3495), 29 of whom were battery owners

and 32 were not. Every time an individual liked a single

page, this was represented by 1 or otherwise by 0. Both

were equally weighted. To reduce complexity, the matrix

was trimmed to all cases with at least 20 liked pages per

user and at least 2 users per page (see also Kosinski etal.

(2016)). The result was a matrix consisting of 1846 pages

by 43 users (4 users had to be deleted because of incorrect

answers) with 79,378 cells. Although discriminant analy-

sis actually requires continuous variables, it can also be

conducted with 1/0 coded binary independent variables.

According to the central limit theorem, a normal distribu-

tion of the independent variables could be assumed for a

sample larger than 30. A step-wise LDA was conducted

to explore the contribution of the likes to the ownership

of a solar battery. Since ultimately only 16 variables were

included in the equation, the condition that more cases

should be considered as parameters was also fulﬁlled. For

both user groups, it was striking that, among the top 20

most-liked pages, for battery owners and non-owners, 5

and 9, respectively, were for comedy or satirical enter-

tainment. The most popular fan page for PV owners was

“Der Postillon,” a satirical news website. Pages with the

most discriminant properties are listed in Table7. The

most important page for non-owners was “DFB Frauen,” a

fan page for the German women’s soccer association. The

page with the highest selectivity for owners was “Sonnen

Batterie,” a manufacturer of solar batteries.

The Facebook likes could classify 100.0% of all 43 cases

correctly with an eigenvalue of the model of 2,178.473

(canonical correlation r = 1.000) and a Wilks’ lambda of

0.000 (p = 0.000). Even the cross-validated result revealed

a 93% correct classiﬁcation. Thus, LDA was suitable to pre-

dict the correct group of PV owners, according to the present

data. In contrast, it was not possible to derive a prediction

from the Big Five personality traits alone, nor could the Big

Five be determined by single likes.

The results of the discriminant analysis could not be sub-

stantiated by a logistic regression. For a total of 61 users—

29 owners and 32 non-owners of a storage battery—19,335

diﬀerent pages were regressed on the dependent variable,

and none of them provided even the slightest signiﬁcant

results.

6 Discussion

The t tests revealed that the means of the Big Five z-scores

were only predicted suﬃciently for neuroticism and extra-

version. Extraversion and agreeableness had signiﬁcant

positive Pearson’s correlations between self-assessments

and predictions. Thus, only extraversion was suﬃciently

correctly predicted by the Apply Magic Sauce API for all

Table 7 Discriminant

coeﬃcients of facebook likes

Table8 presents standardized canonical discriminant function coeﬃcients (SCDFC) from a step-wise lin-

ear discriminant analysis of Facebook likes for battery owners and non-owners

Like-ID Name SCDFC Category

Battery owners (n = 20)

188688131822001 Sonnen Batterie 31.55 Solar Battery Manufacturer

31649251356 CSI Miami 25.94 TV Series

129773947075202 Pitztaler Gletscher 24.15 Pitztal Glacierski area

1453306071441090 ### private ### 24.15

202102663791918 Aufstehen 23.05 Leftwing political Organization

166200743435821 ÖDP Bayern 21.33 Oecological Political Party

26101560328 Depeche Mode 19.29 Music Group, Band

111938328822261 Das A-Team 13.96 TV-Series

553944091603407 Energiewende—Rocken 3.74 Energy Transition Activism

1454797278158590 ### private ### 2.42

513607188730244 Harry Gueber 2.12 Regional Comedian

Battery non-owners (n = 23)

102362899824580 DFB-Frauen − 8.16 German Women’s Soccer

118598124966673 Katharina Schulze − 6.61 Political MPs (Green Party)

69755621604 Monaco Franze − 5.70 Fictional TV-Series Character

204139474516 abgeordnetenwatch.de − 3.26 Political activism

108110462544365 Fußball − .76 About Football

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Social Network Analysis and Mining (2021) 11:79

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79 Page 10 of 20

photovoltaic users. Although the alpha reliability of the

traits measured was generally low, it was in the accept-

able range for extraversion at 0.707. In the data of the

SOEP study, good measurement characteristics could be

demonstrated for extraversion for the same scale (Smith

etal. 2021). The mean deviation for agreeableness with

simultaneous positive correlation could indicate, on the

one hand, that the predictions do not apply. On the other

hand, it could also indicate that Facebook users regularly

rate their own agreeableness lower than is actually the case

(Table5). This question should be investigated further.

The results for extraversion and conscientiousness sup-

port the ﬁndings of Marengo etal. (2020). Among other

things, they found that the self-assessments of users versus

non-users of Facebook did not diﬀer for conscientiousness,

while there were signiﬁcant diﬀerences for extraversion.

Although extraversion could be predicted correctly by the

Facebook likes, the hypothesis that the Big Five personal-

ity traits are signiﬁcantly diﬀerent between adopters and

non-adopters of electricity storage systems failed. Between

the two groups, signiﬁcant diﬀerences could not be found

for the self-assessments or the predictions (Table6). Fur-

ther, it was not possible to distinguish between battery

adopters and non-adopters because the variances overlap

in large parts for all Big Five traits. This can also be seen

in Table4, where the standard deviations of the users’

self-reports are up to ﬁve times higher than the standard

deviations of the AMS predictions. The most likely cause

for this may be the rather small sample size, as this leads

to strongly varying standard deviations. Apart from per-

sonality traits, however, there was a possibility to use the

digital footprint in the form of the liked pages to diﬀerenti-

ate between user groups. A sole consideration of the Big

Five enabled a prediction of group membership, which

was not much higher than chance. The additional inclusion

of demographic characteristics increased the proportion

of correct classiﬁcations to 93.8%. Regarding the useful-

ness of Facebook likes as a distinguishing characteristic

between adopters and non-adopters, a linear discriminant

analysis uncovered 16 pages that determined adopters and

non-adopters of battery storage. One could say that if you

are an owner of a PV system and you like CSI: Miami,

then you are likely to own a battery system, and if you

like football—especially the German women’s team—it

is likely that you do not. However, the single likes could

not be clearly assigned to the Big Five.

There exist, of course, several other limitations. The

applicability of linear discriminant analysis should be

tested with a much larger sample. Although the normal-

ity assumption is fulﬁlled according to the central limit

theorem, the suggestions of Feldesman (2002) could

be taken up. He recommends classification trees as a

non-parametric tool for classifying user groups when the

assumptions for LDA are not met.

The database of the Apply Magic Sauce API is from

2012. This means Facebook pages created later could not be

used to estimate the Big Five personality traits. Furthermore,

the API users come from all around the world, mostly from

the US, with a large proportion of younger people. While

this does not necessarily mean the predictions are not correct

for German users, the pages that are suitable for a prediction

relate mostly to the interests of American users. This results

in a lower share of possible predictions among users of the

AMS-API outside the US.

The sample’s personality traits are biased toward higher

scores of openness and higher extraversion and lower val-

ues of conscientiousness and agreeableness. This is likely

because only individuals who are very open-minded toward

new technologies and experiences are (a) members of a

social network and (b) willing to take part in a survey that

analyzes their personality, while in Europe at that time, eve-

ryone was talking about data protection issues and the dan-

ger associated with using American online services.

More research with larger sample sizes is needed to draw

conclusions from the users’ digital footprints, e.g., liked

pages, to self-report Big Five scores and, thus, to build a

study’s own database or to prove whether there is really no

signiﬁcant diﬀerence between the personality traits of bat-

tery adopters and non-adopters. Unfortunately, increased

consumer awareness of data protection issues has severely

limited the acceptance of empirical research in the online

sector. Furthermore, hardly any company would risk making

personal user data available for scientiﬁc purposes.

7 Conclusions

This research aimed to investigate whether the Facebook

likes of owners of PV systems were suitable for assessing

whether they own an electricity storage system. Although—

according to Youyou etal. (2015)—analysis of the digital

footprint is well-suited to making a prediction about the Big

Five personality traits, a satisfactory prediction about the

mean value could be found only for extraversion. Agreeable-

ness showed a positive correlation, but predictions diﬀered

from self-assessments. The second hypothesis, that signiﬁ-

cant diﬀerences exist between adopters and non-adopters of

battery storage, could not be conﬁrmed.

Although the results did not correspond to the hypoth-

eses, this study provides suggestions for further research

in this area. Reliable results require, above all, larger sam-

ples and comparable data without having to take a detour

via z-scores. For example, a suitable data source is the

German core energy market data register (Marktstam-

mdatenregister), which stores all solar power generators

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Social Network Analysis and Mining (2021) 11:79

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Page 11 of 20 79

in Germany. However, the European general data protec-

tion regulation (GDPR) sets high hurdles for the usability

of the data for scientiﬁc studies, especially in connection

with social network analysis. Additionally, further research

should be based on detailed scales rather than ultra-short

scales. This would also allow an in-depth investigation

using structural equation modeling.

Appendix

See Tables8, 9, 10, 11, 12, and 13.

Appendix2: Questionnaire

Page 1

Q01 Do you own a solar power generation system

(photovoltaic)?

• yes

• no

Page 2

Q02 Who in your household had the idea to purchase a PV

system?

• Myself

• My partner

• Both together

• Someone else

Q03 What is the approximate power of your PV system (in

kWp)? [please select]

Table 8 Used facebook groups and member count

Group name Number of

group mem-

bers

Photovoltaik 6522

sonnenBatterie-Besitzer 703

EUROPÄISCHE ENERGIEWENDE 12,202

E3/DC Speicherfreunde S10 Etc 1073

Photovoltaik/Solarforum—Info—Service und Verkauf 513

Photovoltaik und Stromspeicher 210

Dezentrale Energiewende 319

Das Netzwerk der Energiewende 1852

Photovoltaik-Gruppe 305

SolarPeople—Ein Forum für Solarenergie 55

Photovoltaik Fotovoltaik Windkraft Windkraftanlagen

BHKW Biogas Biomasse

1549

Solarenergie—Fragen und Antworten ! 97

Grüne Ökonomie: nachhaltiges Wirtschaften und

erneuerbare Energie

1166

Total 26,566

Table 9 Statistics of the Big

Five, original measures

Table presents AMS predictions of the Big Five personality traits as percentiles, self-reported measures as

raw scores

O, Openness; C, Conscientiousness; E, Extraversion; A, Agreeableness; N, Neuroticism; SD, standard

deviation; α, Cronbach’s alpha

Battery non-owners Battery owners

Mean NMedian Min Max SD Mean NMedian Min Max SD

Predicted percentiles of Big Five traits

O .440 19 .456 .153 .593 .092 .488 20 .453 .271 .932 .144

C .475 19 .461 .305 .790 .115 .431 20 .447 .011 .744 .131

E .379 19 .359 .288 .559 .073 .364 20 .370 .207 .510 .068

A .397 19 .398 .211 .534 .087 .399 20 .397 .105 .679 .113

N .355 19 .372 .149 .476 .076 .393 20 .367 .273 .645 .094

Self-reported original raw scores for Big Five traits

O 4.94 74 5.00 1.00 7.00 1.26 5.14 69 5.25 1.00 7.00 1.23

C 5.05 74 5.17 2.00 7.00 1.28 5.30 69 5.67 2.33 7.00 1.16

E 4.36 74 4.67 1.33 7.00 1.25 4.56 69 4.67 1.00 7.00 1.36

A 4.91 74 4.67 3.00 7.00 1.02 4.86 69 4.67 2.33 7.00 1.03

N 3.65 74 3.67 1.00 6.67 1.28 3.50 69 3.67 1.00 6.67 1.30

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79 Page 12 of 20

• under 1 kWp

• 1—under 2 kWp

• 2—under 3 kWp

• 3—under 4 kWp

• 4—under 5 kWp

• 5—under 6 kWp

• 6—under 7 kWp

• 7 -under 8 kWp

• 8—under 9 kWp

• 9—under 10 kWp

• more than 10 kWp

Q04 In which year was your PV system installed? [Please

select]

Q05 How expensive was your PV system approximately

(incl. VAT)? [Please select]

• less than 2500 Euro

• 2500—under 4000 Euro

• 4000—under 6000 Euro

• 6000—under 8000 Euro

• 8000—under 10,000 Euro

• 10,000—under 12,000 euros

• 12,000—under 14,000 Euro

• 14,000—under 16,000 Euro

• 16,000—under 18,000 Euro

• 18,000—under 20,000 Euro

• more than 20,000 Euro

Table 10 Big Five personality traits in detail. Source: McCrae and Costa (1999)

Personality traits Personality trait facets

Openness to experience: the active seeking and appreciation of experi-

ences for their own sake Fantasy: receptivity to the inner world of imagination

Aesthetics: appreciation of art and beauty

Feelings: openness to inner feelings and emotions

Actions: openness to new experiences on a practical level

Ideas: intellectual curiosity

Values: readiness to re-examine own values and those of authority

ﬁgures

Conscientiousness: degree of organization, persistence, control, and

motivation in goal-directed behavior Competence: belief in own self-eﬃcacy

Order: personal organization

Dutifulness: emphasis placed on importance of fulﬁlling moral obliga-

tions

Achievement Striving: need for personal achievement and sense of direc-

tion

Self-Discipline: capacity to begin tasks and follow through to comple-

tion despite boredom or distractions

Deliberation: tendency to think things through before acting or speak-

ing

Extraversion: quantity and intensity of energy directed outward into

the social world Warmth: interest in and friendliness toward others

Gregariousness: preference for the company of others

Assertiveness: social ascendancy and forcefulness of expression

Activity: pace of living

Excitement Seeking: need for environmental stimulation

Positive Emotions: tendency to experience positive emotions

Agreeableness: the kinds of interactions an individual prefers, from

compassion to tough mindedness Trust: belief in the sincerity and good intentions of others

Straightforwardness: frankness in expression

Altruism: active concern for the welfare of others

Compliance: response to interpersonal conﬂict

Modesty: tendency to play down own achievements and be humble

Tender-Mindedness: attitude of sympathy for others

Neuroticism: identiﬁes individuals who are prone to psychological

distress Anxiety: level of free-ﬂoating anxiety

Angry Hostility: tendency to experience anger and related states such as

frustration and bitterness

Depression: tendency to experience feelings of guilt, sadness, despond-

ency, and loneliness

Self-Consciousness: shyness or social anxiety

Impulsiveness: tendency to act on cravings and urges rather than reining

them in and delaying gratiﬁcation

Vulnerability: general susceptibility to stress

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Page 13 of 20 79

Q06 What was the total generation of your PV system in

the last 12months approximately (in kWh)? [Please select]

• under 2500 kWh

• 2500—under 4000 kWh

• 4000—under 5000 kWh

• 5000—under 6000 kWh

• 6000—under 7000 kWh

• 7000—under 8000 kWh

• 8000—under 10,000 kWh

• 10,000—under 12,000 kWh

• 12,000—under 14,000 kWh

• 14,000—under 20,000 kWh

• more than 20,000 kWh

Table 11 Descriptive statistics of present study

Table shows descriptive statistics (n = 143)

Adopters Non-Adopters Total

NMean/% SDaNMean/% SDaValid Mean/% SDa

Age of participant 60 43.53 15.03 67 45.00 18.72 127 44.31 17.02

Monthly income of the household in Euro

0 = Less than 250 0 0 0 0 0 0

1 = 250–499 0 0 2 2.7 2 1.4

2 = 500–999 1 1.4 2 2.7 3 2.1

3 = 1000–1499 1 1.4 4 5.4 5 3.5

4 = 1500–1999 2 2.9 7 9.5 9 6.3

5 = 2000–2999 6 8.7 12 16.2 18 12.6

6 = 3000–3999 14 20.3 13 17.6 27 18.9

7 = 4000–4999 7 10.1 11 14.9 18 12.6

8 = More than 25 36.2 12 16.2 37 25.9

5000 missing 6 8.7 8 10.8 14 9.8

Number of individuals in the household 62 3.13 1.41 71 3.08 1.28 133 3.11 1.34

Gender 69 100 74 100 133 100

0 = male 52 75.4 54 73 106 74.1

1 = female 9 13 15 20.3 24 16.8

2 = diverse 1 1.4 2 2.7 3 2.1

Missing 7 10.1 3 4.1 10 7

Marital status

single 7 10.1 5 6.8 12 8.4

with partner 22 31.9 27 36.5 49 34.3

married 32 46.4 39 52.7 71 49.7

divorced 1 1.4 0 0 1 .7

missing 7 10.1 3 4.1 10 7

Level of education

0 = no degree 2 2.9 1 1.4 3 2.1

2 = lower-secondary education

5 = upper-secondary education

7 = other missing 17 24.6 19 26.8 36 25.2

42 60.9 51 71.8 93 65

1 1.4 0 0 1 .7

7 10.1 3 4.1 10 7

Vocational education

0 = unskilled 2 2.9 8 10.8 10 7

worker 25 36.2 22 29.7 47 32.9

2 = professional skills 30 43.5 40 54.1 70

5 = technician, college, university degree 2 2.9 0 0 2 1.4

11 = other missing 10 14.4 4 5.5 14 9.8

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79 Page 14 of 20

Q07 Approximately how many kWh of this have you fed into

the grid in the last 12months? [Please select]

• less than 2500 kWh

• 2500—under 4000 kWh

• 4000—under 5000 kWh

• 5000—under 6000 kWh

• 6000—under 7000 kWh

• 7000—under 8000 kWh

• 8000—under 10,000 kWh

• 10,000—under 12,000 kWh

• 12,000—under 14,000 kWh

• 14,000—under 20,000 kWh

• more than 20,000 kWh

Q08 How high is your feed-in tariﬀ approximately (in ct/

kWh)?

Q09 Do you own a solar power storage system (battery)?

• yes

• not yet, but I am seriously thinking about it

• I have considered it, but discarded it

• No

Table 12 Descriptive statistics of control group from SOEP study

Table shows descriptive statistics of the SOEP study 2015–2017 (n = 7286)

PV owners PV non-owners Total

NMean/% SDaNMean/% SDaValid Mean/% SDa

Age of participant 517 53.75 12.53 6769 56.43 15.11 7286 56.24 14.96

Monthly income of the household in Euro

0 = Less than 250 0 0 2 0 2 0

1 = 250–499 2 0.4 20 0.3 22 0.3

2 = 500–999 2 0.4 122 1.8 124 1.7

3 = 1000–1499 16 3.1 418 6.2 434 6

4 = 1500–1999 16 3.1 590 8.7 606 14.3

5 = 2000–2999 97 18.8 1532 22.6 1629 22.4

6 = 3000–3999 113 21.9 1470 21.7 1583 21.7

7 = 4000–4999 95 18.4 949 14 1044 14.3

8 = More than 151 29.2 1264 18.7 1415 19.4

5000 Missing 25 4.8 402 6 427 5.9

Average 517 3938 2007 6769 3393 2346 7286 3432 2327

Gender 517 100 6769 100 7286 100

1 = male 334 64.6 4014 59.3 4348 59.7

2 = female 183 35.4 2755 40.7 2938 40.3

Marital status

Single 41 7.9 1230 18.2 1271 17.4

With partner 0 0 0 0 0 0

Married 449 86.8 4918 72.7 5367 73.7

Divorced 26 5.0 592 8.7 618 8.5

missing 1 .2 29 .4 30 .4

Level of school education

0 = no degree 1 .2 44 .7 43 .6

2 = lower secondar y education 32 6.2 481 7.1 513 7.0

3 = upper secondary education 202 39.1 3053 45.1 3255 44.7

4 = post-secondary education 91 17.6 996 14.7 1087 14.9

6 = Bachelor 120 23.2 1337 19.8 1457 20

7 = Master 63 12.2 738 10.9 801 11

8 = Doctorate degree 6 1.2 78 1.2 84 1.2

Missing 2 .4 44 .7 46 .6

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Social Network Analysis and Mining (2021) 11:79

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Page 15 of 20 79

Page 3—Personality Traits

Q10 Here are diﬀerent characteristics that a person can have.

Probably some characteristics will fully apply to you per-

sonally and others not at all. For still others, you may be

undecided

Please answer using the following scale. A value of 1

means: not at all true.

The value 7 means: fully applies. You can use the values

between 1 and 7 to grade your opinion.

I am someone who …

1) works thoroughly

2) is communicative, talkative

3) is sometimes a little rough with others

4) is original, introduces new ideas

5) often worries

6) can forgive

7) is rather lazy

8) can be outgoing, sociable

9) appreciates artistic, aesthetic experiences

10) gets nervous easily

11) performs tasks eﬀectively and eﬃciently

12) is reserved

13) is considerate and friendly with others

14) has a vivid imagination, ideas

15) is relaxed, can handle stress well

16) is inquisitive

Page 4—Human Values (optional)

Q11 In the following, we describe some people to you.

Please mark how similar or dissimilar the person described

is to you. Please answer using the following scale. The value

1 means: does not apply at all. The value 6 means: fully

applies. You can use the values between 1 and 6 to grade

your opinion. If you are not sure, please answer with "don't

know" in the last column.

o It is important for him/her to develop new ideas and be

creative. He/she likes to do things in his/her own origi-

nal way.

o It is important to her/him to be rich. She/he wants to

have a lot of money and own expensive things.

Table 13 Top 15 fan pages of PV users

Table presents the top 20 Facebook fan pages of battery owners and non-adopters. Most-discriminant pages (at least 20 pages per user, 2 users

per page) are highlighted in bold

Battery owners Battery non-owners

Rank Name Like-ID nRank Name Like-ID n

1 Postillon 268611646525 14 1 Heute Show 264820405985 12

2 Extra 3 37621248917 8 1 Postillon 268611646525 12

2 Sonnen.de 188688131822001 82 Extra 3 37621248917 11

3 Campact 82734241364 5 3 ruthe.de 289955244416 8

3 Quer 103687920727 5 3 Tagesschau 193081554406 8

3 Helge Schneider 149234265133802 5 4 Amazon.de 141727802539968 7

3 Tatortreiniger 174071539353019 5 5 Jan Boehmermann 110495738982958 6

3 Pfusch am Bau 268646656590270 5 5 Zeit online 37816894428 6

4 ZDF heute-show 264820405985 4 5 Campact 82734241364 6

4 Senator Sanders 9124187907 4 6 Dieter Nuhr 113781618677139 5

4 National Geographic 23497828950 4 6 LAD Bible 199098633470668 5

4 CSIMiami 31649251356 4 6 ### private ### 1669506439965710 5

4 Tatort 33214866692 4 7 Dr. House 7608631709 4

4 Der Spiegel 38246844868 4 7 How I Met Your Mother 7807422276 4

4 Sportfreunde Stiller 55085570811 4 7 The Big Bang Theory 2293468477 4

4 Amazon.de 141727802539968 4 7 Der Spiegel 38844868 4

4 Miniatur Wunderland 71726614987 4 7 quer 103687920727 4

4 Süddeutsche Zeitung 215982125159841 4 7 Miniatur Wunderland 71726614987 4

4 ZDF heute 112784955679 4 7 Unnützes Wissen 97848298204 4

4 Bob Marley 117533210756 4 7 Joscha Sauer 131582549993 4

10 DFB-Frauen 102362899824580 1

10 Katharina Schulze 118598124966673 1

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Social Network Analysis and Mining (2021) 11:79

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79 Page 16 of 20

o He/she thinks it is important that all people in the world

should be treated equally. He/she believes that everyone

should have equal opportunities in life.

o It is important for her/him to show his abilities. She/he

wants people to admire what she/he does.

o It is important to him/her to live in a safe environment.

He/she avoids anything that could jeopardize his/her

safety.

o She/he likes surprises and is always on the lookout for

new activities. She/he thinks that variety is important in

life.

o He/she thinks that people should do what they are told.

He/she thinks that people should always follow rules,

even when no one sees it.

o It is important to her/him to listen to people who are

diﬀerent from her/him. Even if she/he disagrees with

others, she/he still wants to understand them.

o It is important to him/her to be reserved and humble. He/

she tries not to draw attention to himself/herself.

o It is important to her/him to have fun. She/he likes to

treat herself/himself.

o It is important to him/her to decide for himself/herself

what he/she does. He/she likes to be free and independ-

ent of others.

o It is very important to her/him to help the people around

her/him. She/he wants to take care of their well-being.

o It is important to him/her to be very successful. He/she

hopes that people will recognize his/her achievements.

o It is important to her/him that the state ensures her/his

personal safety from all threats.She/he wants a strong

state that defends its citizens.

o He/she seeks adventure and likes to take risks. He/she

wants to have an exciting life.

o It is important to her/him to behave correctly at all times.

She/he avoids doing things that other people might think

are wrong.

o It is important to him/her that others respect him/her. He/

she wants people to do what he/she says.

o It is important to her/him to be loyal to her/his friends.

She/he wants to stand up for people who are close to her/

him.

o He/she strongly believes that people should take care of

nature. Environmental protection is important to him/

her.

o Tradition is important toher/him. She/he tries to adhere

to the customs and traditions handed down to her/him

by her/his religion or family.

o He/she never misses an opportunity to have fun. It is

important to him/her to do things that give him/her

pleasure.

Page 5—(For owners of an electricity storage)

Q12 Who in your household had the idea to purchase a solar

power storage system?

• Myself

• My partner

• Both together

• Child

• Someone else

• None of us

Q13 Who in your household ultimately made the decision

to buy a solar power storage system?

• Myself

• My partner

• Both together

• Child

• Someone else

• None of us

Q14 Why did you decide on a solar storage? (Multiple

response).

• The topic of the environment interests me.

• I ﬁnd that a solar power storage system pays oﬀ ﬁnan-

cially.

• A solar power storage system gives me the feeling of

being self-suﬃcient.

• I am interested in the technology.

Q15 What is the approximate capacity of your solar battery

(in kWh)? [Please select]

• under 1 kWh

• 1—2 kWh

• 2—3 kWh

• 3—4 kWh

• 4—5 kWh

• 5—6 kWh

• 6—7 kWh

• 7—8 kWh

• 8—9 kWh

• 9—10 kWh

• more than 10 kWh

Q16 In which year was your solar storage tank installed?

[Please select]

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Social Network Analysis and Mining (2021) 11:79

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Page 17 of 20 79

Q17 How expensive was your solar power storage approxi-

mately (incl. installation and VAT)? [Please select]

• less than 2000 Euro

• 2000—under 3000 Euro

• 3000—under 4000 Euro

• 4000—under 5000 Euro

• 5000—under 6000 Euro

• 6000—under 7000 Euro

• 7000—under 8000 Euro

• 8000—under 9000 Euro

• 9000—under 10,000 Euro

• more than 10,000 Euro

Q18 When do you think the investment in the electricity

storage system will have paid for itself (in years)? [Please

select]

• in less than 5years

• in 5—6years

• in 6—7years

• in 7—8years

• in 8—9years

• in 9—10years

• in 10—11years

• in 11—12years

• in 12—15years

• in 15—20years

• in more than 20years

Q19 How do you personally rate yourself: Are you generally

a risk-taker or do you try to avoid risks?

Please answer using the following scale. The value 0

means: not at all willing to take risks. The value 10 means:

very willing to take risks.

Q20 Did you have any sense of risk in the following

areas because of the purchase of the solar power storage

system? Please answer in each case using the following

scale. The value 0 means: no feeling of risk. The value 10

means: very strong feeling of risk

• general risk

• ﬁnancial risk for investments

• risk for my/our health

• risk in trusting other people

Page 6—(for non-owners of an electricity storage)

Q21 Why did you decide against a solar storage tank? (mul-

tiple responses)

• I'm not interested in the environmental issue.

• A solar power storage system is too expensive.

• A solar power storage system is too high a risk for my

household.

• The topic was too complicated to make a decision.

Q22 How do you personally rate yourself: Are you generally

a risk-taker or do you try to avoid risks? Please answer using

the following scale. The value 0 means: not at all willing to

take risks. The value 10 means: very willing to take risks.

Q23 Did you have any sense of risk in the following areas

because of the purchase of the solar power storage system?

Please answer in each case using the following scale. The

value 0 means: no feeling of risk. The value 10 means: very

strong feeling of risk.

• general risk

• ﬁnancial risk

• risk to my/our health

• risk in trusting other people

Page 7—Household statistics

Q23 How many people currently live in your household?

[Please select]

• 1

• 2

• 3

• 4

• 5

• 6

• more than 6

Q24 What are the ﬁrst two digits of your postal code? If you

are not from Germany, please enter the license plate number

for your country. My postal code starts with the digits ….

Q25 Who writes the shopping list at your house?

• Myself

• My partner

• Both together

• Someone else

• We do not write a shopping list.

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Social Network Analysis and Mining (2021) 11:79

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79 Page 18 of 20

Q26 Who usually goes shopping in your household?

• Myself

• My partner

• Both equally

• Someone else

Q27 Who ultimately decides on major investments (such

as consumer electronics, cars, renovations) in your home?

• Myself

• My partner

• Both together

• Someone else

Q28 What is approximately your net monthly household

income? This refers to the amount that is made up of the

income of all members of the household and that remains

after deduction of taxes and social security. [Please select]

• less than 250 €

• 250 € to under 500 €

• 500 € to under 1000 €

• 1000 € to under 1500 €

• 1500 € to under 2000 €

• 2000 € to under 3000 €

• 3000 € to under 4000 €

• 4000 € until under 5000 €

• 5000 € and more

• I do not want to answer

Q29 What are the total household costs (approximately)?

This means all costs for, e.g., rent, loans, electricity, water,

utilities. [Please select]

• less than 200 €

• 200 € to under 400 €

• 400 € to under 600 €

• 600 € to under 800 €

• 800 € to under 1.000 €

• € to under 1.200 €

• 1.200 € up to under 1.400 €

• 1.400 € to under 1.600 €

• 1.600 € and more

• I do not want to answer

Q30 In which year were you born?

Q31 What is your gender?

• female

• male

• diverse

Q32 What is your current relationship status?

• Single

• in a committed relationship

• married

• divorced

Q33 What is the highest educational qualiﬁcation you have?

• Still a student

• Finished school without graduation

• Secondary school diploma (Hauptschulabschluss)

• Secondary school diploma (Mittlere Reife)

• Completion of polytechnic secondary school 10th grade

(before 1965: 8th grade)

• Fachhochschulreife (completion of a specialized second-

ary school)

• Abitur, general or subject-linked higher education

entrance qualiﬁcation (Gymnasium or EOS)

• Other school-leaving qualiﬁcation:

Q34 Which vocational education degree do you have?

Please select the highest educational qualiﬁcation you have

achieved to date

• No vocational training qualiﬁcation

• Vocational training period with ﬁnal certiﬁcate, but no

apprenticeship

• Partial skilled worker qualiﬁcation

• Completed industrial or agricultural apprenticeship

• Completed commercial apprenticeship

• Professional internship, traineeship

• Vocational school diploma

• Technical school diploma

• Master craftsman, technician or equivalent technical col-

lege degree

• University of applied sciences degree

• University degree

• Other degree, namely:

Q35 Are you currently employed?

• Yes, I am employed.

• No, I am unemployed.

• No, I am a pensioner.

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Social Network Analysis and Mining (2021) 11:79

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Page 19 of 20 79

• No, I am a housewife or househusband.

• No, I am none of the above.

Q36 What do you do for a living?

• Student

• In training

• Student

• Salaried employee

• Civil servant

• Self-employed

• Unemployed/looking for work

• Other:

Author’s contributions SP analyzed and interpreted the data and was

primarily responsible for the drafting of the manuscript. He conducted

the statistical analyses and interpreted the ﬁndings.

Funding This research did not receive any speciﬁc grant from funding

agencies in the public, commercial, or not-for-proﬁt sectors.

Data availability Due to privacy restrictions on the part of Facebook,

it was not permitted to provide user data. In exceptional cases, the

anonymized data can be requested from the corresponding author. Due

to data protection restrictions, access to the SOEP data can only be pro-

vided to registered users of the SOEP study, available at the Deutsches

Institut für Wirtschaftsforschung, Berlin (DIW). Scientiﬁc use is free

of charge and can be requested at: https:// www. diw. de/ en/ diw_ 02.c.

222829. en/ access_ and_ order ing. html.

Declarations

Conflict of interest The author has no competing interests to declare.

Open Access This article is licensed under a Creative Commons Attri-

bution 4.0 International License, which permits use, sharing, adapta-

tion, distribution and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s) and the source,

provide a link to the Creative Commons licence, and indicate if changes

were made. The images or other third party material in this article are

included in the article's Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not included in

the article's Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will

need to obtain permission directly from the copyright holder. To view a

copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.

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