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Developing citizen science
ecosystem: critical factors for
quadruple helix stakeholders
engagement
Aelita Skarzauskiene and Monika Maciuliene
Department of Creative Industries, Vilnius Gediminas Technical University,
Vilnius, Lithuania
Sabine Wildevuur and Maya van den Berg
DesignLab, University of Twente, Enschede, The Netherlands
Thomas Bakratsas and Artemis Psaltoglou
White Research, Brussels, Belgium, and
Efstratios Stylianidis,Ioannis Tavantzis and Kostas Karatzas
School of Spatial Planning and Development, Aristotle University of Thessaloniki,
Thessaloniki, Greece
Abstract
Purpose –The purpose of this paper is to provide answers regarding the factors that motivate or
discourage the quadruple helix (QH) stakeholders and the wider public in citizen science (CS)
activities. The research reveals a current overview of the perceptions, attitudes, concerns and
motivation with regard to development of CS ecosystem in four countries: Greece, Lithuania, the
Netherlands and Spain.
Design/methodology/approach –The researchers deploy a mixed methodology, entailing an in-
depth literature review and a large-scale quantitative survey (approximately 2,000 citizens) targeting QH
stakeholders and general public from the local national ecosystems. The results contain both descriptive
statistics and statistical analysis per country. After the comprehensive overview of drivers and barriers
regardingtheparticipationinCSactivitiesingeneral,thefocusisnarroweddownontheengagement
motivation of different QH stakeholders and the differences in enabling/hindering factors at the local
ecosystems.
Findings –Depending on the country and the pre-existing level of CS maturity, the results provide a
complicated network of factors that unlock or block participation in CS activities. These factors include, to
name a few, political maturity, civic engagement, technological infrastructures, economic growth, culture
of stakeholder collaboration, psychological stimulus and surplus of resources. The implications of the
findings necessitate the alignment of the envisioned CS ecosystem with the local dynamics in each
country.
Research limitations/implications –The quantitative nature of the survey method, limited sample
size and only four countries context are noted as limitations of the study and offer future research potential for
longitudinal settings and mixed-methods studies.
Originality/value –The results contribute to the wider literature on CS that focuses on perspectives,
possibilities and differences in local contexts with respect to the public engagement by developing CS ecosystem.
Funding: This research was funded by the European Union’s Horizon 2020 research and innovation
program funded this research and innovation under Grant Agreement No. 101005330 (INCENTIVE).
Citizen science
ecosystem
Received 18 August2022
Revised 22 September2022
25 October 2022
Accepted 26 November2022
Journal of Enterprising
Communities: People and Places in
the Global Economy
© Emerald Publishing Limited
1750-6204
DOI 10.1108/JEC-08-2022-0116
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1750-6204.htm
At the same time, its added value lies in the overall practical proposition, and how the latter can effectively and
efficiently attract and retain different stakeholder groups and citizens, under a collaborative approach.
Keywords Citizen science hub, Quadruple helix stakeholders, Engagement, Co-creation
Paper type Research paper
Introduction
Scientific knowledge is crucial for improving decision-making, providing communities with
greater capacity and safety to tackle existing societal problems. The opening of research data has
been indicated in many scientific sources and policy documents as a tool to increase the speed,
quantity and quality of scientific research results. However, the “debate on open science has
gained prominence, mainly in two of its axes: open research data and citizen science”(Albagli and
Iwama, 2022). Participatory- and community-based approaches and methodologies have shown
promising results for influencing the resilience of communities (Ensor et al., 2016;Kondo et al.,
2019) and making room for citizen innovations (Sauermann et al.,2020). Scientists usually create
citizen science (CS) programs to capture crowdsourced data that would be hard to obtain
otherwise due to time, geographic or resource constraints, or to make use of the collective
intelligence of humans that outperform automatic procedures in identifying and classifying
information in data collected via various ways. Participation and motivation of the most affected
and vulnerable social groups, policymakers and private actors play in these actions important
role to address complex problems. Bringing their own unique perspective to scientificproblems
and thinking “outside the box”may result in new scientificdiscoveries(Dickinson, 2011). In
addition, being part of CS community appears to be an important factor for creativity to take
place and provides motivation for solving project problems (Jennett et al.,2016). CS consist mainly
of voluntary non-scientist collaboration in collecting and interpreting data useful for research,
such as images, sounds and other types of records, improving research results and lowering
costs. CS projects may cover various scientificfields, such as biology, astronomy, medicine,
computer science, statistics, psychology and engineering. They may also range from large
international projects, which involve professional scientists and research institutions, to small
projects by groups with a common interest. It can span from being better informed about science
to contributing to the scientific process itself by developing the research question, designing the
method, gathering and analysing data and communicating the results (European Commission,
2020;European Citizen Science Association, 2022).
Today, the number and scope of CS projects have expanded due to the development of
smartphones that have built-in GPS receivers and other sensors. Besides, worldwide
participation in CS is growing because the percentage of highly educated, well informed and
active citizens interested in science is also increasing; and due to growing concerns about
various issues, such as environmental sustainability (Grey et al., 2016). In this frame, the
politicians throughout Europe progressively see CS as an opportunity for greater public
engagement and science democratisation. European Commission (EC) supports CS in its
research funding programs (e.g. Citizens‘Observatories, Responsible Research and Innovation).
Besides, the funding agencies promote CS with tailored programs, such as the European
Horizon 2020 “Science with and for Society”program (European Commission, 2021). CS
ecosystems thrive because of complex interdependencies and dynamic relationships between
and among its participants. Understanding the diversity and complexity of factors that either
enable or hinder the participation of general public and stakeholder groups is of critical
importance for any successful CS initiative. While citizens and universities have often been
highlighted as the key players in the CS projects, the role of other stakeholders, despite its
strategically influential position in stimulating innovation and entrepreneurship, has not
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received sufficient attention in scholarship (Pittz and Hertz, 2017;Carayannis and Campbell,
2012). The authors attempt to address this gap in scientific literature by exploring the role and
motivation factors of different quadruple helix (QH) stakeholders in the larger CS ecosystem.
These factors have been extracted from the relevant literature and identified through case
studies that cover different countries and contexts during this research project; thus, the results
aspire to provide a holistic picture that is able to accommodate –at a conceptual level –as
many dimensions as possible. Thus, the research questions to be answered through this study,
while examining the CS ecosystem in the local contexts, are as follows:
RQ1. What are current trends in citizen science?
RQ2. What are the main driving and hindering factors for QH stakeholders that usually
affect citizen science participation?
RQ3. What are the current patterns and similarities/differences in citizen science in the
four participating countries?
The remainder of the study is structured as follows: after a general introduction, the next
section provides a review of the literature underlying the concept and most recent trends of CS.
The third section outlines the research methodology, followed by the results of the research in
the fourth section, which are discussed in more detail in the final section. The research activities
were commenced using an exploratory research approach, whose objective was to identify a
range of cross-national driving factors and barriers with regard to CS participation from the
citizens on the one hand, while on the other hand presenting the current landscape of CS
maturity level in the four participating countries: Greece, Lithuania, the Netherlands and Spain.
Literature review
Citizen science, also known as “Community science”,“Amateur science”,“Crowdsourced science”
and “Volunteer monitoring”(Scistarter, 2021), is defined as the participation of non-scientific
stakeholders in the scientific process. CS can be interpreted as a part of “the wider open science
movement, along with open access publishing, open data standards and alternative metrics for
impact assessment”(Grey et al., 2016). It can promote the efficient and transparent use of science
and research funding, lead to better engagement in research, governance and accountability and
contribute to economic growth and social change through open innovation. In recent years, a
small number of world-leading Research Performing and Funding Organisations (RPFOs) have
established transdisciplinary hubs for stimulating and supporting excellent CS. Essentially, the
hubs aim to bring different stakeholders together and bridge society with science in an
institutionalized way by developing CS ecosystems. A defining aspect of most ecosystems is
interaction between multiple stakeholders (Velt et al., 2017). CS ecosystem entails a collaboration
between all QH stakeholders: the public and researchers/institutes, also governments and
funding agencies (Haklay et al., 2021). The development of crowdsourcing platforms and
networks that enable volunteers to contribute to different research projects, the use of machine
learning technologies and artificial intelligence may extend CS ecosystem capabilities, especially
those depending on human intelligence. As a result, citizens’roles in CS projects may expand
further and become more demanding (Grey et al., 2016).
Since CS is de facto based on creating and sustaining a critical mass of participation, it is
vital to identify what are the conditions that may foster or discourage the level of citizen
involvement (Larson et al., 2020). It is worth noting that citizen scientists come from all
walks of life, including retirees, online gamers, educators and students (Scistarter, 2021). In
principle, drivers can be distinguished between those stemming from subjective perceptions,
desires, motivations and personal goals, and to drivers that refer to objective circumstances
Citizen science
ecosystem
of the macro environment, such as the overall environmental conditions (e.g. weather),
cultural, social and economic conditions.
Citizen scientists, as the main stakeholders’groupinCS,cangainpersonalenjoyment,social
benefits and satisfaction by contributing to scientific evidence and influencing the policy (Rotman
et al., 2014a;Butteriss, 2019;Geoghegan et al.,2016;Jennett et al., 2016;Larson et al., 2020;Kragh,
2016). Psychosocial parameters such as the ability of citizen scientists to express their personal
value and creativity (Scotland Counts, 2016), as well as fulfilment of curiosity and personal desires
for discovery (Larson et al., 2020;Kragh, 2016) are also important. Self-prestige and personal growth
matter too: fostering personal reputation among community members, increasing scientificliteracy
and acquiring new skills and information are typical examples of internal motivations (Butteriss,
2019;Larson et al., 2020;Asingizwe et al., 2020). Accomplishment of such goals pertain to learning
benefits through participation in CS (Rotman et al., 2014b;Martin et al., 2016); fostering personal CV
and career track, especially for young persons (Gordienko, 2013;Geoghegan et al., 2016)aswellas
creating new professional contact points (Scotland Counts, 2016); monetary incentives as
compensation for the efforts and time devoted (Gordienko, 2013); incentives for recreation and
nature-based activities (Geoghegan et al., 2016;Kragh, 2016).
There are however, objective barriers that can seriously inflict the individual’s decision
to enter a CS initiative. Age is an objective factor: the desire to voluntarily participate in CS
increases until the 20s and then again between 40 and 45; consequently, demographic
dynamics is an important factor (Wilson Center: Commons Lab, 2014;Geoghegan et al.,
2016). Low scientific literary and poor skills from participants can indeed occur (Pocock
et al., 2018), low levels of self-confidence regard also the false inability to collect adequate
data volume (Mitchell et al., 2017), which can cause various observer and sampling biases
(Burgess et al.,2017). Language barriers (Pocock et al., 2018) and high level of difficulty in
the assigned tasks (Kleinke et al.,2018) can function as burden for participants and
discourage them. Still, one of the gravest obstacles can be time. When there are time
constraints from the side of citizen scientists, and when CS activities require excessive time,
then, this time-consuming process may simply exhaust participants and lead to drop out,
eventually blocking the scientific venture (Collins et al.,2015;Kleinke et al.,2018;Butteriss,
2019;Rotman et al., 2014b;Martin et al., 2016;Asingizwe et al., 2020). Finally, a very
common barrier is inertia and lack of general interest from participants (Pocock et al., 2018).
Individual motivational factors are important, but the significance of collective motivational
factors cannot be neglected. Collective motivational factors that are perceived to be critical are the
existence of previous good practices and “success stories”of CS that will inspire other QH
stakeholders (Wilson Center: Commons Lab,2014), the professional recognition of citizen
scientists’efforts and encouragement for further work (Garcia-Soto et al.,2017;Science
Communication Unit, University of the West of England, Bristol, 2013;Butteriss, 2019), and the
public acknowledgement of impact and contribution of citizen scientists by the scientific
community (Geoghegan et al.,2016;Jennett et al.,2016). The existence of scientists with previous
engagement in CS activities, who are more “outward-facing”and show higher level of trust in the
regional ecosystem is another factor (Burgess et al.,2017) that may facilitate the creation of team
bonds and sense of sharing common goals (Jennett et al.,2016). Values related to altruism and
collectivism may turn out particularly powerful collective drivers for NGOs and governmental
institutions. A sense of socio-ecological responsibility and commitment to a common cause can
push different QH stakeholders towards actions (Rotman et al., 2014a;Rotman et al., 2014b;
Butteriss, 2019;Geoghegan et al.,2016;Larson et al.,2020;Kragh, 2016;Asingizwe et al.,2020).
To a large extent, the barriers and factors that hinder the participation of citizens and
stakeholders in CS activities and projects can be understood as the reversal of the
facilitating factors outlined above (Martin et al., 2016). Misperceptions at a collective level
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and from other stakeholder groups can often prevail and burden CS’s success. The false
perception from citizens of being unable –as inadequately educated and thus amateur –to
deliver high-quality, reliable and rigorous scientific pushes traditional scientist to believe
that the results and data will be of poor quality (Garcia-Soto et al., 2017;Wilson Center:
Commons Lab,2014;Mitchell et al.,2017;Pocock et al.,2018;Science Communication Unit,
University of the West of England, Bristol, 2013). Some scientists believe that ethics
mismanagement and misuse of public data is hard to avoid in CS (Parthenos project, 2019;
Hecker et al.,2018). At the same time, citizen scientists may also be uncertain regarding the
honesty of the exploitation of the data they have collected from scientists, potentially
leading to harmful effects. This is linked to the fear of potentially inappropriate policy
decisions, such as block to public access to natural sites (Martin et al., 2016). Policymakers
can share analogous, yet often unfounded, fears about the quality of CS efforts and
outcomes. Fears of privacy violation and issues related to ownership of results and
intellectual property rights are often recorded (Wilson Center: Commons Lab,2014;Hecker
et al., 2018;Gordienko, 2013). Often, there is limited awareness from all groups of QH
stakeholders on the benefits and potentials of CS, whereas bias and scepticism pervade the
scientific community members regarding the acceptance of data sources from CS. The latter
is justified on lack of rigorous research design, data collection inconsistencies and a general
tendency to opt for data traditionally collected from peer scientists (Burgess et al.,2017).
Unsurprisingly, this cultivates mistrust between the scientific community, policymakers
and citizen scientists, leading to low levels of created social capital (Parthenos project, 2019;
Hecker et al.,2018;Pocock et al.,2018). It can be concluded, that the role and motivational
drivers of different QH stakeholders in the larger CS ecosystem are subjects that have been
largely unaddressed by current scholarship.
A last dimension to consider is those enabling factors that transcend the individual and
collective motivational level and stem directly from the wider macro-context. The dynamics
within the regional ecosystem define to a large extend the level and quality of citizens’
involvement. The growing research field neglects the issue of Open Science applicability in
the countries where the collaboration between science and society has limited traditions and
civic participation in public activities is not mature. Disparities in research and innovation
performance among member states are a well-recognized issue, and the European Union
(EU) aims to address it through increased investments. Robust funding is necessary for a CS
activity to flourish, so when there is lack of funding channels or opportunities, and when
budgetary constraints arise due to absence of national funding schemes, CS becomes a
luxury (Wilson Center: Commons Lab,2014;Parthenos project, 2019;Collins et al., 2015;
Pocock et al., 2018). Partnerships with universities, other federal agencies, contractors,
museums, philanthropists and schools (Wilson Center: Commons Lab,2014), technological
resources that support social networking and development are essential. Another aspect
deserving attention that applies to all the aforementioned factors is that they can change
over time and at different stages of stakeholders’participation (Land-Zandstra et al.,2016;
Vohland et al., 2021). Adequate communication mechanisms between scientists and citizens
as well as constant and good communication on project’s concept, objectives and progress
through channels such as website, social media, mails and newsletters is a precondition to
create a critical mass for participation (Rotman et al., 2014a;Butteriss, 2019).
The literature review, as a form of secondary data analysis offered a series of advantages
from a research point of view: it offered high-quality data as a starting point for theoretical
reasoning and questionnaire structure; and it opened new possibilities for re-interpretation
of collected primary data (Bryman, 2012). Generalizing theoretical framework, the drivers
and obstacles have been categorised into those that relate to individual motivations
Citizen science
ecosystem
(subjective perceptions, desires and personal goals), those who impact QH stakeholders and
those that derive from objective circumstances of the macro-environment (cultural, social
and economic conditions). On this basis, complementary information will be retrieved from
empirical research by narrowing focus on the engagement motivation of different QH
stakeholders and on the differences in enabling/hindering factors at the local ecosystems.
Methodology: quantitative survey in four countries (Greece, Lithuania, the
Netherlands and Spain)
The aim of the large-scale survey was to quantitatively capture the factors shaping the
willingness of the QH stakeholders and of the general public at large, to actively develop CS
ecosystem.
Sample size
The survey uses a quota sample of 1,936 responses in total, from the general public in four
countries across Europe: Greece, Lithuania, the Netherlands and finally Spain. The research
was initiated as part of the implementation of the H2020 project INCENTIVE (“Establishing
Citizen Science Hubs in European Research Performing and Funding Organisations to drive
institutional change and ground Responsible Research and Innovation in society”). Data
collection took place from April 2021 to June 2021. The four countries represent the four pilot
RPFOs of the INCENTIVE project. The project’s RPFOs have a nationwide scope in terms of
students, visiting personnel and networking channels and therefore having evidencefrom the
overall national population will help them co-create CS hubs that reflect national patterns.
However, the selected sample size is random sample and not representative for population.
Random sampling ensures that results obtained from sample should approximate what
would have been obtained if the entire population had been measured (Shadish et al.,2002).
The simplest random sample allows all the units in the population to have an equal chance of
being selected. Initially, a quota sample of 500 responses was set as target for each country
(resulting to 2,000 responses roughly). However, the demographic dynamics and uneven sizes
of the selected countries necessitated a different internal division of the sample, so as to
achieve representation in terms of actual population sizes. Therefore, following an open
deliberation between the pilot partners, it was decided that 350 responses should be collected
approximately for the cases of Greece and Lithuania, while 600 responses should be collected
approximately for the cases of the Netherlands and Spain. In the case of Spain, due to the
overall larger national population, an external platform “Clickworker”was used to collect the
number of responses needed through crowdsourcing. In the case of the Netherlands,
the external agency “Markeffect”was commissioned to disseminate the survey and collect
the answers. The selection was justified on the basis of the considerable population size of the
country, similar to the case of Spain.
For the creation of the survey, the EC survey platform was used, the survey was
translated into the national languages and then distributed through a survey link. As such,
pilot countries took over responsibility for collecting the required number of responses and
meet the internal quotas. The dissemination channels included:
the internal community of the pilot universities (e.g. staff, researchers and students);
the networks of various stakeholders and organizations;
local associations and networks where representatives from the pilot universities’
team are active members; and
stakeholders from the local media who shared the survey in their social media
accounts.
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Questionnaire structure
The survey questionnaire was structured to get additional insights and quantitatively
confirm the findings from the thematic analysis and literature review. The survey questions
were clustered in 6 main sections, each of which corresponds to a research question:
(1) Introduction to the topic: level of familiarity with concepts such as CS and
responsible research and innovation, previous experience with CS, if any;
(2) Perceptions towards CS: opinion about what are the most significant benefits of CS,
what should be the role of citizens in CS activities and how the envisioned CS
ecosystem should respond to current needs;
(3) Barriers: barriers to participation in CS activities, potential drawbacks of and
obstacles to CS;
(4) Drivers: most important driving motivation factors and enablers of CS activities;
(5) Willingness to join: the preferred scientific topics and stages of CS activities were
captured, with the aim to understand what fields attract most people and in which
phases they would like to be involved; and
(6) General information: basic demographic information such as sex, age, country,
educational background, occupational status and others.
Another critical element was the classification of responses according to the QH groups:
private sector/industry (e.g. SMEs owners, entrepreneurs and CEOs), public administration
(e.g. policymakers, civil servants and elected officials), academia (e.g. professors and
researchers) and civil society (e.g. CSOs representatives and NGOs representatives). For
participants that were reluctant to choose one of the previous categories, the category of
“general public”, could be selected.
All demographic information was collected in compliance with the General Data
Protection Regulation of the EU and used solely for research and statistical reasons. In
addition, to participate in the survey, all research subjects had to fill in a consent form that
was included in the introductory session of the questionnaire.
Stakeholder distribution is of equal importance to understand better the national
samples. For the case of Lithuania, the distribution of stakeholders is interestingly even,
with almost all categories having a share of roughly 20%, except from participants from the
general public and civil society (43.28%). On the other hand, both in the cases of the
Netherlands and Spain, the majority of respondents come from the general public/civil
society (60.00% and 43.05%, respectively). However, the two countries differ regarding the
distribution among the other stakeholder groups. In Spain, academic stakeholders come
second (25.80%), while in the Netherlands the second highest stakeholder group is industry
(21.04%). Finally, in Greece, stakeholders from academia form the majority of participants,
with a noticeable share of 63.04%.
Results
Familiarity and previous experience with citizen science. To ensure that all participants
approach the concept in an identical way, a description of the term was provided in the
survey questionnaire as follows:
Citizen Science refers to the active engagement of the general public in scientific research tasks of
several disciplines (from natural sciences to social sciences and humanities) and the collaborative
production of new knowledge.
Citizen science
ecosystem
This definition was selected as the most inclusive one, so people from different countries and
therefore socio-economic and cultural settings could easily understand the term and provide
valid answers.
Interestingly enough, results reveal that the lowest levels of familiarity are detected in
the Netherlands (57.60%) and Lithuania (57.60%) (Figure 1). Spain presents a more balanced
distribution of scales of familiarity, even though people who declare that they are
“somewhat familiar”form the majority (26.97%). Nevertheless, more than one out of four
participants in Spain state that they are completely unfamiliar with the term. Greece shows
some common patterns with Spain. On the one hand, the largest proportion of participants
(30.12%) fall under the category “not at all familiar”. On the other hand, people who state
that they are “somewhat familiar”form the second highest category, with 23.60%. At the
same time, across all countries, Spain and Greece have the highest shares of people who say
that they are extremely familiar with the term (7.54% and 8.07%, respectively). As general
conclusion, it can be inferred that the maturity level of CS varies across countries, and even
if two countries show commonalities, there are still differences with respect to the actual
type of experience of the general public with CS activities. This necessitates different
governance structures and possibly dissemination and engagement channels for the
development of proper CS hubs that respond to local social realities.
Nevertheless, it must be stressed that the national variations on the awareness of CS can
be partly explained by the age groups and educational level of participants. For instance, the
higher levels of awareness in Spain and Greece are influenced by the fact that CS is a quite
new concept and in those countries the percentage of 20–29 years old is higher with respect
to the Netherlands and Lithuania. Moreover, in Greece, half of participants hold either a
master’s degree or PhD, and there may be a positive correlation between educational level
and level of CS awareness. At the same time, more than 4 out of 10 participants in the
Netherlands hold only a high school diploma –a fact that may affect how knowledgeable are
about CS. Background variables must be taken into account when unpacking the reasons of
awareness levels.
Table 1 deals with the previous experience, if any, with CS from participants from all
pilot countries. In this case, all national countries show exactly the same trends: more than 8
out of 10 respondents have not any previous experience whatsoever with the CS activities.
On the other hand, it should be mentioned that Greece and Lithuania present the highest
shares of people who have some previous experience (18.32% and 18.42%, respectively).
Figure 1.
Level of familiarity
with citizen science
30.12%
57.60% 54.52%
25.29%
21.43%
19.59% 20.15%
22.11%
23.60%
10.82% 12.59%
26.97%
16.77%
7.02% 6.81%
18.09%
8.07% 4.97% 5.93% 7.54%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Greece Lithuania Netherlands Spain
Extremely Familiar
Moderately familiar
Somewhat familiar
Slightly familiar
Not at all familiar
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The participants who answered “Yes”in the survey questionnaire were re-directed to
another question that captures the precise type of previous experience with CS. Strong
differences can be observed between pilot countries. In Lithuania (63.39%) and Spain
(38.27%), most people responded that their type of experience is “I have heard the term
Citizen Science”. However, in the Netherlands, more than half of the sample (52.17%) stated
that they initiative and/or designed themselves a CS project –this finding strongly contrasts
the findings from the other countries. Finally, in Greece, almost half of participants (45.76%)
selected the category “I have participated myself in a Citizen Science project”.
Barriers and drivers, willingness to join. Significant benefits, drivers and barriers for CS
projects were analysed related to the different type of stakeholders to get some specific
insights about each type separately. All the identified factors that encourage or hinder
participation with respect to CS activities, were statistically analysed and then selected as
most important. This is valid for the hindering/enabling factors that were examined both for
each stakeholder group and for the local ecosystems. The factors that were found to be
statistically significant (i.e. they have an important impact on the perception or involvement
of participants) aremarked with a tick sign in all respective tables.
Starting from the academics and the researchers that participated in the survey, they have
highlighted all identified benefits –apart from considering CS as a hobby –as significant for
their perceptions and involvement in CS projects (Appendix 1). In terms of drivers, their
involvement seems to be motivated by continuous feedback, acknowledgements, inspiring
coordination teams and clear guidelines and instructions. Barriers related to their perceptions
include concerns whether they belong to a socio-demographic group that is underrepresented
in the scientific community, lack of receiving the necessary guidelines and feedback,
scepticism about cooperating with other stakeholder groups and fear that the project will not
be properly organized and managed.
When taking a closer look at the responses received from policymakers (Appendix 2), it is
obvious that fewer factors can be captured referring to these aspects. More specifically,
benefits related to society and/or natural environment are the only significant factor in the
first group, alongside with continuous feedback, inspiring coordinators and clear guidance
that can work as significant drivers for policymakers and stakeholders coming from the
public sector. Moreover, barriers related to their involvement in CS projects include lack of
receiving the necessary guidelines and feedback.
Almost all factors included in the benefits’list can significantly improve perceptions and
involvement of the participants from the business and the private sector (Appendix 3)inCS
projects. Drivers in this case include –once more –continuous feedback and updates about
the progress of the project, together with good organization and management. At the same
time, barriers referring to perceptions highlight the importance of the lack of the
technological equipment that might be required, receiving the necessary guidelines and
feedback, the feeling of belonging to a socio-demographic group that is underrepresented in
the scientific community, as well as concerns about the ability to effectively cooperate with
other stakeholder groups. When it comes to actual involvement in CS project, the identified
Table 1.
Previous experience
with citizen science
Previous experience Greece (%) Lithuania (%) Netherlands (%) Spain (%)
No 81.68 81.58 85.63 86.10
Yes 18.32 18.42 14.37 13.90
Total 100.00 100.00 100.00 100.00
Citizen science
ecosystem
barriers include low participation rates and lack of time to participate in such activities from
persons in the private sector.
The final stakeholder group under investigation refers to citizens and the civil society. All
identified benefits seem to motivate general public towards participating and having an
increased perception of CS initiatives. In terms of drivers, even though being acknowledged
for your contribution and provided with specific guidelines and instructions for the tasks
can boost positive perceptions, only receiving continuous feedback and updates about the
progress of the project seems to effectively improve participation in CS projects by general
public. At the same time, it is interesting to notice that there are more barriers that affect
both perceptions and involvement when compared to the other stakeholder groups.
Common barriers between these two attitudes include: lack of the technological equipment,
fear that the contribution will be exploited by scientists/policymakers, fear of leading to
wrong or harmful scientific or policy decisions and scepticism about cooperating with other
stakeholder groups. Barriers that are solely related to overall perception about CS refer to
the lack of the necessary skills and knowledge to be involved in such activities, the feeling of
belong to a socio-demographic group that is underrepresented in the scientific community,
the lack of receiving the necessary guidelines and feedback and fear that the project will not
be properly organized and managed. Finally, additional barriers that significantly affect
citizens’involvement in these initiatives include low participation rates, lack of time and fear
of not finding an interesting research topic.
The next section presents a more thorough descriptive analysis on the trends and
patterns in each of the four pilot countries, with the aim to provide a more detailed picture of
attitudes, perceptions and propensities at national level.
Results from Greece. To begin with, it is interesting to notice that even though the overall
perception of CS is positive among citizens in Greece (77% replied either “Agree or Strongly
Agree”), one out of five respondents state their uncertainty about the positive dimensions of
the CS concept. This scepticism is complemented by the fact that to the statement “In my
opinion, citizens should have an active role in the design and execution of scientific
projects”, 34% of the respondents –which equals one out of three –adopted a neutral
position (“neither agree, nor disagree”), while 13% said that they disagree. This is
important, because the neutral 34% is exactly the same as the positive 34% of respondents
who selected “I agree”.
With respect to the citizens’abilities to participate in CS activities, the vast majority
agreed (33%) or strongly agreed (47%) with the necessity to first provide adequate training
to citizens, with only 4% disagreeing. However, opinions are divided regarding whether
citizens have the required skills and knowledge for actual participation. Even though most
participants agreed that citizens lack the necessary skills and know-how, 20% disagreed
with the statement and 6% strongly disagreed. This dichotomy becomes more complex by
adding a 33% of neither agreeing nor disagreeing. The patterns in willingness to join CS
activities, are even between the different stakeholder groups. In all stakeholder groups,
almost half, or more than half of participants agreed that they would join a CS activity, even
though it is interesting to highlight that among the groups, the lowest percentage comes
from academia (47.78%) and the largest from public administration (57.14%). However,
regarding the trends on the participants who strongly agreed about participating in CS, the
highest percentage comes from of scientific stakeholders (27.09%), and the lowest (19.76%)
from the general public and civil society.
Figure 2 shows the main topics of interest per stakeholder group for the Greek case. As
we can see, there are some common topics between the different types of stakeholders, such
as energy and sustainability, educational and environmental sciences. However, we can see
JEC
that there are some specific topics that are of interest only in some of the QH groups.
More specifically, we can see that topics related to public administration and policy are
of high interest in the case of stakeholders coming from the public sector (as expected),
circular economy, human media interaction and communication science are in the
centre of attention when it comes to stakeholders from the business sector, whilst
health and educational services lie in the heart of interest when it comes to citizens and
academics.
Figure 3 presents the various stages in which the different stakeholder groups are
interested in participating.
The findings show that participants from academia are mostly interested in designing
studies and analysing data, policymakers want to be involved in the interpretation of
results, persons from the business sector like identifying research questions and exploiting
results, whilst citizens focus on data collection.
Figure 2.
Topics of interest in
Greece (per
stakeholder group)
15.00%
10.31%
16.67%
15.12%
8.04%
12.37%
18.18%
6.98%
4.46%
5.15%
3.03%
7.56%
13.57%
11.34%
7.58%
13.37%
17.32%
14.43%
10.61%
14.53%
6.96%
7.22%
10.61%
6.40%
6.43%
19.59%
4.55%
5.81%
4.11%
6.19%
10.61%
4.07%
14.82%
7.22%
13.64%
18.02%
9.29%
6.19%
4.55%
8.14%
ACADEMIA /
RESEARCH
PUBLIC
ADMINISTRATION
PRIVATE SECTOR/
INDUSTRY
GENERAL PUBLIC /
CIVIL SOCIETY
Energy & Sustainability Circular Economy Transport & Mobility Health
Educational sciences Human media interaction Public administration and policy Communication sciences
Environmental Sciences Information Technologies
Figure 3.
Stage of the research
process that you like
to be involved (per
stakeholder group)
13.43%
12.77%
18.03%
12.93%
17.35%
9.57%
18.03%
13.61%
17.35%
15.96%
16.39%
26.53%
18.66%
18.09%
13.11%
19.05%
16.04%
27.66%
18.03%
16.33%
17.16%
15.96%
16.39%
11.56%
ACADEMIA /
RESEARCH
PUBLIC
ADMINISTRATION
PRIVATE SECTOR/
INDUSTRY
CITIZENS
Identify a research question Design a study Collect data Analyse data Interpret results and write reports Exploit research outcomes
Citizen science
ecosystem
Results from Lithuania. Compared to the results from Greece, the findings from Lithuania
indicate even sharper divisions regarding both the value of CS. Starting with the statement
“My overall perception of Citizen Science is positive”, even though 4 out of 10 participants
(41%) either agree or strongly agree, it is noticeable that almost 30% of respondents selected
to either disagree (16%) or strongly disagree (13%). Another 30% had a neutral position,
complementing the complicated landscape about the society’s perspective on CS.
The overall perception is more positive when it comes to the role and capacities of
citizens in CS activities. 45% of participants either agreed (32%) or strongly agreed (13%)
that citizens should have an active role in designing and implementing scientific projects.
On the other hand, almost one out of three respondents neither agreed nor disagreed, and
18% opposed the idea. Regarding on whether citizens lack the necessary skills and on the
subsequent necessity to train citizens in order to be involved in scientific activities, the
public opinion is relatively more ambivalent. In both questionnaire items, the level of
agreement and the level of neither agreement nor disagreement is almost the same, with
those agreeing slightly being the majority. This shows a general consistency between
results, since it follows naturally that respondents who advocate that those citizens who lack
the scientific expertise must first be adequately trained. The latter point is explicitly
supported by the fact that 22% strongly agreed that citizens need to be trained.
In Figure 4, the willingness to join CS activities in Lithuania is presented and broken
down per QH stakeholder group. In general, in most stakeholder groups, the majority of
participants agree, thus showing willingness to participate. The highest levels of agreement
come from academic stakeholders, with 43.84% agreeing and one out of four respondents
(24.66%) strongly agreeing, contrary to previous findings that highlight the biases from
academic community towards CS. Public administration is also positively positioned, with
more than half of participants (60.61%) agreeing to be willing to join CS activities. General
public/civil society participants are also eager to support the participation in CS activities,
since more than 41% agreed and a further 10.81% strongly agreed. On the other hand,
interestingly enough, the highest levels of rejection come from both the private sectors and
the general public. In the case of the private sector, the majority of participants either
disagreed (21.82%) or strongly disagreed (16.36%). Once these two figures are aggregated,
they result to 38.18%, surpassing the 34.55% of neither agreeing nor disagreeing. Finally,
regarding the general public/civil society, when combining “disagree”and “strongly
Figure 4.
Willingness to join
citizen science
activities in Lithuania
(per stakeholder
group)
2.74% 1.52%
16.36%
6.76%
1.37% 1.52%
21.82%
16.22%
27.40% 28.79%
34.55%
25.00%
43.84%
60.61%
20.00%
41.22%
24.66%
7.58% 7.27% 10.81%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Academia Public administration Private sector General public/civil society
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
JEC
disagree”, the result is 22.98%, which however does not surpass the sum of “agree”and
“strongly agree”(52.03% in total). 25% neither agreed nor disagreed.
In the case of Lithuania, the benefits related to knowledge and skills, alongside aspects
related to society and environment can boost perceptions around CS, whereas benefits
related to new career opportunities can effectively promote involvement in these projects.
Good organization and management, together with clear guidelines and instructions on the
tasks are key elements for positive perceptions, whilst continuous feedback and updates
about the progress of the projects can act a significant driver for involvement. Barriers for
increased perceptions refer to the lack of the technological equipment that might be required
(e.g. personal computer, smartphone and internet access), low participation rates and lack of
time to participate in such activities. Lack of the technological equipment and time are also
significant barriers for being involved in these activities, complemented by individual
concerns of leading to wrong or harmful scientific or policy decisions and not being able to
find an interesting research topic, as well as lack of financial incentives and scepticism
about cooperating with other stakeholder groups.
The main topics of interest per stakeholder group for Lithuania are different form Greece.
Topics related to educational and communication sciences, as well as information technologies
are essential for stakeholders being part of the academia. Moreover, participants coming from
public administration are interested to a large extent in public administration and policy
issues, alongside human media interactions and communication sciences. Private sector
stakeholders mostly focus on circular economy and information technologies, whereas citizens
are interested in health, human media interactions and environmental sciences.
Results from the Netherlands. The third country of the statistical analysis is the
Netherlands, a where in which, as already discussed, the overall maturity levels of CS are
higher compared to Greece and Lithuania. Figure 5 indicates that energy and sustainability,
health and environmental sciences are the three most interesting topics for academics and
researchers in the case of the Netherlands. As we can see, the first two are common topics
between all different types of stakeholders. Policymakers are also interested in transport
and mobility issues and public administration and policy topics. Participants from industry
seem to be also interested in circular economy, transport and mobility issues, whereas
Figure 5.
Topics of interest in
the Netherlands (per
stakeholder group)
20.62%
21.70%
20.00%
20.51%
11.86%
10.38%
12.88%
6.24%
5.67%
16.04%
12.88%
10.57%
18.04%
16.98%
16.27%
29.55%
8.25%
6.60%
2.37%
4.84%
7.22%
6.60%
9.15%
6.50%
5.67%
11.32%
6.44%
5.35%
3.09%
3.77%
5.08%
5.35%
14.43%
4.72%
6.44%
6.75%
5.15%
1.89%
8.47%
4.33%
ACADEMIA /
RESEARCH
PUBLIC
ADMINISTRATION
PRIVATE SECTOR/
INDUSTRY
GENERAL PUBLIC /
CIVIL SOCIETY
Energy & Sustainability Circular Economy Transport & Mobility Health
Educational sciences Human media interaction Public administration and policy Communication sciences
Environmental Sciences Information Technologies
Citizen science
ecosystem
citizens do not have any other particular interest apart from the areas of energy,
sustainability and health.
When it comes to specific stages throughout the research process, Figure 6 shows an
increased interest of academics and researchers in the early stages of these processes,
including identifying research questions, designing studies and collecting data.
Data collection is a high priority for policymakers, businesses and citizens, together with
data analysis and exploitation. A common pattern exists between these three stakeholder
groups.
Results from Spain. Similar to the case of the Netherlands, results at first glance reveal
an overall positive mind-set from the Spanish society towards CS. In total, 39% agreed that
their perception is positive, and 36% strongly agreed, whereas only 4% disagreed, and 20%
neither agreed nor disagreed. Moving on to whether citizens should have an active role in
the design and execution of scientific projects, the share of those who do not agree or
disagree is higher (31%). Nevertheless, supporters of the statement form the majority, with
more than half of the sample (57%) either agreeing or strongly agreeing. The distribution
changes and becomes more conflicting when it comes to the skills and know-how of citizens.
With the statement “In my opinion, citizens lack the knowledge and skills to be part of the
scientific process”, 31% agree that citizens do not possess the know-how, but 32% remain
indecisive and 20% disagree or strongly disagree. There is consensus, however, regarding
the need to train citizens and prepare them for CS activities: 38% agreed that citizens need to
be trained in order to participate in science, and 36% strongly agreed.
In Figure 7, the levels of willingness to join CS activities are presented for each of QH. In
general, the willingness levels are quite high across all stakeholder groups, and naturally,
those who show disagreement form the minority in all cases. However, regarding the level of
disagreement, it should be stressed that the biggest shares are recorded from stakeholders
from public administration (8.16%) and private sector (6.57%). On the other hand, academic
stakeholders have a very high share of people who agree to join CS (40.26%), but also of
people who are strongly willing to participate (31.17%). Despite this last figure, it is the
private sector in which the highest percentage of people who strongly agree is recorded
(32.12%), indicating that private sector stakeholders are to certain extent divided regarding
their willingness to join. Finally, stakeholders from civil society and the general public have
the highest level of agreement (46.60%), while more than one out of four (25.27%) strongly
agree.
Figure 6.
Stage of the research
process that you like
to be involved (per
stakeholder group)
21.43%
17.98%
14.04%
12.54%
18.37%
8.99%
11.91%
11.50%
18.37%
24.72%
26.38%
25.09%
13.78%
15.73%
15.74%
17.42%
16.84%
14.61%
11.91%
10.63%
11.22%
17.98%
20.00%
22.82%
ACADEMIA /
RESEARCH
PUBLIC
ADMINISTRATION
PRIVATE SECTOR/
INDUSTRY
CITIZENS
Identify a research question Design a study Collect data Analyse data Interpret results and write reports Exploit research outcomes
JEC
There are some common topics of interest between three out of four types of stakeholders
(academia, business and citizens), such as energy and sustainability, health and environmental
sciences in Spanish case. However, the policymakers indicate a diversified pattern regarding
the topics they seem to be interested in, as they mostly highlight the areas of circular economy,
public administration and policy and educational sciences. It is interesting to notice that
communication sciences have received less attention in almost all cases. At the same time, the
findings show that survey participants from all stakeholder groups are mostly interested in
participating in data collection and analysis processes. Survey design and interpretation of
results are of increased interest in the case of academia and policymakers, whereas exploitation
of research outcomes is an interesting stage mostly for stakeholders coming from the private
sector.
Limitations and future lines of research
The quantitative nature of the survey method, limited sample size and only four countries
context are noted as limitations of the study and offer future research potential for
longitudinal settings and for mixed method studies. As new societal challenges continue to
emerge, and new democratic models of scientific progress become ever more a necessity, this
document at the end aspires to contribute to the wider literature of CS, and how the latter
evolve into the new scientific norm, by overcoming current barriers and strengthening its
enablers. Given the complexity of the landscape, understanding the specificities of each
country and the priorities of QH stakeholder groups is of outmost importance to effectively
fulfil local needs and urgent problems.
Discussion
This paper has presented the findings of five-month research on CS ecosystems the in four
EU countries –Greece, Lithuania, the Netherlands and Spain. The results reveal a wide
range of current trends in CS, both for at the EU (aggregated) level and for each national
setting of pilot (individual level). The researcher realized that citizens and communities
remain cautious and hesitant regarding new initiatives. All national countries show exactly
the same tendencies: more than 8 out of 10 respondents have not any previous experience
Figure 7.
Willingness to join
citizen science
activities in Spain
(per stakeholder
group)
4.55% 4.08% 5.11% 1.43%
5.84% 8.16% 6.57% 4.51%
18.18%
24.49% 21.90%
22.19%
40.26%
34.69% 34.31% 46.60%
31.17% 28.57% 32.12% 25.27%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Academia Public administration Private sector General public/civil society
Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree
Citizen science
ecosystem
whatsoever with the CS activities. However, it can be inferred that the maturity level of CS
varies across countries, and even if the countries show commonalities, there are still
differences with respect to the actual type of experience with CS activities (in the
Netherlands, more than half of experience is stated as initiating or designing a CS project, in
contrast with experience type in Greece and Lithuania –participating in events or project
related to CS). This necessitates different governance structures and possibly dissemination
and engagement channels for the development of proper CS hubs that respond to local social
realities.
Overall, the research conducted confirmed the relevance of some in literature review
mentioned factors. Communication and overall planning are aspects that can function as
either determinants or barriers, depending on how they are treated. Other aspects that
influence individual motivations refer to the provision of incentives that can stimulate
participation (e.g. monetary rewards) and the organisation of the project, in terms of
transparency and structure, which lead to reciprocal trust. If we look at such evidence
considering the specific national contexts, drives that appear relate to the structure of the
project (its organisation and management) and the provision of incentives, although with
different degrees of intensity. Focusing on the hindering factors, the scenario is slightly
more dispersed in that there are no specific barriers that are perceived as such in all four
contexts, which has been justified in light of the different socio-economic, cultural and
political frameworks.
Conclusions and recommendations
One of the main purposes of this paper was to identify the main driving and hindering
factors for QH stakeholders that usually affect CS participation. To reach this task, the
collected data from four pilot countries based on the type of stakeholder categorization were
analysed and therefore, it was possible to get some specific insights about each type
separately. The major concerns from the research/science stakeholder group are about how
the scientific and technological considerations can be introduced as part of the exchanges
between academia and society through a participatory process that facilitates this
interaction and strengthens the role of citizens as the key actors in the research “process”.In
terms of drivers, their involvement seems to be motivated by continuous feedback,
acknowledgements, inspiring coordination teams and clear guidelines and instructions.
Barriers related to their perceptions include scepticism about cooperating with other
stakeholder groups and fear that the project will not be properly organized and managed.
From public authorities’perspective, CS can effectively serve policymaking initiatives and
processes by providing evidence and useful insights to support regulatory compliance with
a transparent and participatory way. Indeed, considering citizens as “data collectors”and
simple sources for the policymaking perspective, eliminates the potential for the citizens to
provide valuable evidence to support policies, and this is directly linked to the fact that the
policymakers often have different interest, motivations, expectations and understanding
towards the achievements and outcomes of the citizens’science activities and efforts. Lack
of cooperation has been spotted as a barrier by public sector participants with low levels of
positive attitude towards CSs, whereas lack of guidance and lack of trust are two aspects
that policymakers with positive attitudes think should be improved. More specifically,
benefits related to society and/or natural environment are the only significant factor,
alongside with continuous feedback, inspiring coordinators and clear guidance that can
work as motivational drivers for policymakers and stakeholders coming from the public
sector. Participants from the industry sector feel cautious due to potential lack of the
technological equipment that might be required (e.g. personal computer, smartphone and
JEC
internet access) and guidance that might be missing from these initiatives, alongside lack of
time and cooperation. When it comes to citizens, evidence suggest that almost all factors
included in our model are statistically significant for their perceptions and willingness to
join CS projects. Lack of skills, inclusion, trust, technological equipment and low
participation rates mostly affect persons with positive perceptions on CS, whilst lack of
technological equipment, guidance, recognition, cooperation, organization and time are the
factors affecting mostly persons with low levels of CS acceptance. The results show that
receiving continuous feedback and updates about the progress of the projects is essential for
improving overall perceptions about CS across all types of stakeholders. At the same time,
having clear guidelines and instructions on the tasks is a significant driver for participants
coming from academia, public sector and general public. Business-oriented participants also
seem to be motivated from CS initiatives that are well-organized and properly managed.
On top of these, a further important implication is the acknowledgment that financial and
organisational aspects are expected to influence the establishment and operation of CS
projects. As shown from the literature, the thematic analysis and the statistical results,
funding and financial support in general is a critical ingredient. The low level of CS
penetration is exemplified several times throughout the paper. For instance, the literature
review highlights the insufficient scientific models and the statistical analysis proves that a
non-negligible percentage of academic stakeholders remain at least indecisive about their
willingness to join CS activities.
Another research task, which was challenged by this research, was to understand the
current patterns and similarities/differences in CS in the four participating countries. The
most important conclusion that can be drawn from the findings is that despite their
commonalities in many patterns, the four countries differ considerably in many aspects. As
shown through the exploratory literature review, civic engagement and active retainment in
CS activities are impacted by a plethora of factors, in which institutional structures,
historical pathways, political maturity and economic development play profound role.
Because the four countries are located in different angles of Europe, they have unique
characteristics and historical backgrounds, which in turn affect how CS is understood and
experienced. Technological and scientific maturity vary across the four countries, as do the
level of civic participation, the economic resources, the political environment and the social
norms. In sum, CS could not be possibly practiced and endorsed in the same way in so
different national contexts. It has been shown that not all factors have the same effect in all
countries and that the level of perception and readiness fluctuates in many sensitive issues,
such as the involvement of vulnerable groups, the support to citizens to initiate their own
scientific project and the topics of scientific interest. Depending on the country and the pre-
existing level of CS maturity, the results provide a complicated network of factors that
unlock or block participation in CS activities. These factors include, to name a few, political
maturity, civic engagement, technological infrastructures, economic growth, culture of
stakeholder collaboration, psychological stimulus and surplus of resources.
The implications of the findings necessitate the alignment of the envisioned CS
ecosystem with the local dynamics in each country. Transparent communication activities
performed by the different partners and effective management are quite important to ensure
effective science–policy–society–industry interaction under win-win conditions for all QH
stakeholders. Engaging public society is a great challenge, considering that the motivation
is not necessarily shared among theparticipants. Even more, there is a significant number of
people among these groups with low digital or project management skills. To lower this
barrier a life-long learning approach has to be implemented such that citizens adapt their
skills sets to today’s fast-paced technology developments.
Citizen science
ecosystem
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JEC
Appendix 1
Table A1.
Identified benefits,
drivers and barriers
related to perceptions
and the involvement
in citizen science
projects for
academics and
researchers
Benefits Perception Involvement
Benefits related to the individual (economic, social, educational)
Benefits related to knowledge and skills
Benefits derived when considering citizen science as a hobby
Benefits related to new professional/career opportunities
Benefits related to society and/or natural environment
Drivers
Continuous feedback and updates about the progress of the project
Acknowledgement of the contribution
The project is well-organized and managed
Inspiring coordination team
Clear guidelines and instructions on the tasks
Barriers
Lack of the necessary skills and knowledge to be involved in such activities
Lack of the technological equipment that might be required
Belong to a socio-demographic group that is underrepresented in the scientific
community
Lack of receiving the necessary guidelines and feedback
Low participation rate
Fear that the contribution will be exploited by scientists/policymakers
Fear of leading to wrong or harmful scientific or policy decisions
Lack of time to participate in such activities
Lack of financial incentives to participate
Fear of not finding an interesting research topic
Scepticism about cooperating with other stakeholder groups
Fear that the project will not be properly organized and managed
Citizen science
ecosystem
Appendix 2
Table A2.
Identified benefits,
drivers and barriers
related to perceptions
and the involvement
in citizen science
projects for
policymakers
Benefits Perception Involvement
Benefits related to the individual (economic, social and educational)
Benefits related to knowledge and skills
Benefits derived when considering citizen science as a hobby
Benefits related to new professional/career opportunities
Benefits related to society and/or natural environment
Drivers
Continuous feedback and updates about the progress of the project
Acknowledgement of the contribution
The project is well-organized and managed
Inspiring coordination team
Clear guidelines and instructions on the tasks
Barriers
Lack of the necessary skills and knowledge to be involved in such activities
Lack of the technological equipment that might be required
Belong to a socio-demographic group that is underrepresented in the scientific
community
Lack of receiving the necessary guidelines and feedback
Low participation rate
Fear that the contribution will be exploited by scientists/policymakers
Fear of leading to wrong or harmful scientific or policy decisions
Lack of time to participate in such activities
Lack of financial incentives to participate
Fear of not finding an interesting research topic
Scepticism about cooperating with other stakeholder groups
Fear that the project will not be properly organized and managed
JEC
Appendix 3
Corresponding author
Aelita Skarzauskiene can be contacted at: aelita@mruni.eu
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Table A3.
Identified benefits,
drivers and barriers
related to perceptions
and the involvement
in citizen science
projects for business
and private sector
Benefits Perception Involvement
Benefits related to the individual (economic, social and educational)
Benefits related to knowledge and skills
Benefits derived when considering citizen science as a hobby
Benefits related to new professional/career opportunities
Benefits related to society and/or natural environment
Drivers
Continuous feedback and updates about the progress of the project
Acknowledgement of the contribution
The project is well-organized and managed
Inspiring coordination team
Clear guidelines and instructions on the tasks
Barriers
Lack of the necessary skills and knowledge to be involved in such activities
Lack of the technological equipment that might be required
Belong to a socio-demographic group that is underrepresented in the scientific
community
Lack of receiving the necessary guidelines and feedback
Low participation rate
Fear that the contribution will be exploited by scientists/policymakers
Fear of leading to wrong or harmful scientific or policy decisions
Lack of time to participate in such activities
Lack of financial incentives to participate
Fear of not finding an interesting research topic
Scepticism about cooperating with other stakeholder groups
Fear that the project will not be properly organized and managed
Citizen science
ecosystem
