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Technology-Mediated Citizen Science Participation: A Motivational Model.



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Nov, O., Arazy, O., and Anderson, D. (2011). Technology-Mediated Citizen Science Participation: A Motivational Model.
Proceedings of the AAAI International Conference on Weblogs and Social Media (ICWSM 2011). Barcelona, Spain, July
Technology-Mediated Citizen Science Participation:
A Motivational Model
Oded Nov1, Ofer Arazy2, David Anderson3
1Polytechnic Institute of New York University
2School of Business, University of Alberta
3 Space Sciences Laboratory, University of California, Berkeley
We propose and test a framework of the antecedents of
contribution in two technology-mediated citizen science
projects, with different degrees of task granularity.
Comparing earlier findings on the motivations of volunteers
in a web-based image analysis project (high granularity),
with new findings on the motivations of volunteers in a
volunteer computing project (low granularity), we found
that participation task granularity is correlated with
motivation levels. Further, we found that collective and
intrinsic motives are the most salient motivational factors,
whereas reward motives are less important for volunteers.
Intrinsic, norm-oriented and reputation-seeking motives
were most strongly associated with participation intentions,
which were, in turn, associated with participation. Finally,
comparing the relationship between motives and
participation among the two volunteer populations, we
found that active-participation volunteers are characterized
by significantly stronger association between collective
motives and contribution intention, whereas passive-
participation volunteers are characterized by significantly
stronger association between identification with the
community and contribution intention. Implications for
research and practice are discussed.
Many aspects of scientific research, such as observation,
classification and analysis, are labor-intensive, time-
consuming, and as a result, costly. Citizen science offers a
participatory approach for conducting scientific research.
In online citizen science projects (Hand 2010) participation
takes place primarily online. Some examples include
volunteer computing projects and web-based image
analysis projects. Recent reports in Science and Nature
about the discovery of a pulsar by Einstein@home
volunteers (Knispel et al. 2010), and the success of Foldit,
a multiplayer online game in which citizen scientists
compete by folding proteins into chemically stable
Copyright © 2011, Association for the Advancement of Artificial
Intelligence ( All rights reserved.
configurations (Cooper et al. 2010), illustrate the potential
of the participatory approach as a viable part of the
scientific research process.
Thus, technology-mediated citizen science represents a
low-cost way to strengthen the infrastructure for science
and at the same time engage members of the public in
science. Online citizen science is based on two pillars: a
technological pillar - computer systems to manage large
amounts of distributed resources, and a motivational pillar,
which involves attracting and retaining volunteers who
contribute their skills, time, and effort to a scientific cause.
While the technological dimension has been extensively
studied (Anderson 2007; Anderson et al. 2002; Luther et
al. 2009), the motivational dimension received relatively
little attention to date. In the present work we compare
earlier reported findings on the motivations of volunteers
in a web-based image analysis project (high granularity),
with new findings on the motivations of volunteers in a
volunteer computing project (low granularity).
Understanding the motivational aspect is crucial for the
design and management of technology-mediated citizen
science projects, especially given the low retention rate
many projects experience.
Background and related work
Citizen science projects differ substantially in the “task
granularity” required of volunteers. Task granularity is
defined as the smallest individual investment necessary in
order to make a contribution (Benkler 2006). Low
granularity contribution is represented by passive
participation such as in volunteer computing, which is
based on the division of a large computational task into
many small tasks that are then distributed over the internet
and completed on computers of volunteers who contribute
to the project. Two of the best known volunteer computing
projects are SETI@home
( and Folding@home
( More recently, this approach
has been extended through the creation of BOINC
(Berkeley Open Infrastructure for Network Computing; an open-source platform that enables
running large scale volunteer computing projects.
Currently, the BOINC platform serves over thirty projects
in various scientific fields, including astronomy, climate
modeling, mathematics, biology and medicine. BOINC
contributors can determine their level of contribution by
setting up a number of contribution parameters, including
the allocation of disk space, CPU time, and others. Higher
task-granularity contribution is represented by more active
volunteer tasks such as in image analysis. In such projects,
volunteers engage not only in monitoring, but also in
analysis, in a variety of scientific areas. Examples include
projects such as Galaxy Zoo (, or U.C.
Berkeley’s Stardust@Home
(, in which volunteers
analyze images of interstellar dust particles.
In non citizen science settings, mass collaboration of
large numbers of individuals distributed across time and
space represents a new and productive trend in the creation
and dissemination of information (Benkler 2006). This
phenomenon is characterized by distributed groups of
volunteers, who split up work into modular tasks, and are
supported by information systems that facilitate collective
action and social interaction online. Sustained contribution
by individual volunteers is critical for the viability of such
communities (Butler 2001). Reflecting this, understanding
the motivations of contributors has been viewed as critical
for successfully managing and sustaining web-based
information sharing communities, as well as for designing
the systems contributors use for contribution (Cheshire and
Antin 2008; Ling et al. 2005). Thus, in recent years much
research has been done to identify contributors’ incentives
for contribution to a wide range of information sharing
communities such as Flickr, Delicious, Twitter, YouTube
and Wikipedia in which contribution is made by
volunteering amateurs (Hertel, Niedner and Herrmann
2003), (Nov 2007; Peddibhotla and Subramani 2007;
Rashid et al. 2006). Extrinsic motivations for contribution
in such communities include improvement of skills and
enhancement of status (Lakhani and Wolf 2005). Intrinsic
motivations, on the other hand, include altruism, fun,
reciprocity, intellectual stimulation and a sense of
obligation to contribute. In addition, researchers have
examined other factors associated with participation in
information sharing communities, at the individual or
group level, including social network properties (Sohn and
Leckenby 2007), group membership size (Butler 2001),
and feedback (Joyce and Kraut 2006).
There are important differences between contributions
made to technology-mediated citizen science and those
made to other types of community-based projects, which
stress the need to investigate motivations for participation
in the specific context of citizen science. First, in online
citizen science there is a clear distinction between the
volunteers making the contribution and those benefiting
from the aggregate effort (i.e. the scientists who run the
project). This asymmetric structure differs from most other
community-based projects (e.g. Wikipedia, YouTube),
where the distinction is blurred. Second, it often takes a
long time for the output of the scientific project to be made
public, in contrast to community-based projects where the
contributions are viewable immediately, which may
provide instant gratification to contributors. Third, a single
contribution to an online citizen science project is
sometimes too small to be attributed to a specific
individual, whereas in other communities the deliverables
(e.g. text, code, photos) can stand on their own and are
usually attributable to their contributor.
Citizen science participation
Research on the factors driving online citizen science
participation has been scarce (Raddick et al. 2009; Raddick
et al. 2010). Examples include (Holohan and Garg 2005), a
descriptive study of volunteer computing motivations at
SETI@home, which exposed the desire to help scientific
research, competition, and gaining technical knowledge as
the top motivations of contributors. In that study,
respondents stated that being part of a team and having
social ties with other contributors were important for
maintaining and increasing contribution. Results from an
internal survey administered to contributors to BOINC
(Boinc 2009) suggests that contributors with different
longevity levels have different motivations. Both these
studies, however, did not link motivations to contribution
activity, and in particular, did not present a causal model
that explains how to increase contribution. A study of
Galaxy Zoo contributors (Raddick et al. 2010) classified
ten motivational categories, including excitement, learning,
desire to discover, social interaction, use the project as a
resource for teaching, the beauty of the images, fun,
amazement by vast scale of the universe, desire to help,
interest in the project, interest in astronomy, and interest in
science in general. Here, too, no link was made between
motivations and actual contribution.
Recent attempts to examine the relationships between
volunteers’ motivations and their participation levels
include (Nov, Anderson and Arazy 2010) and (Nov, Arazy
and Anderson 2011). In the present study, we extend
earlier work by comparing the results reported by (Nov,
Arazy and Anderson 2011) about the relationship between
motivations and participation among volunteers in active-
participation projects, with data on the same relationships
among volunteers in passive- participation projects.
Research Model
For the empirical study of citizen scientists’ motivations,
we follow the extended Klandermans model, a theoretical
framework used for explaining voluntary participation in
social movements (Klandermans 1997), (Simon et al.
1998). This framework includes four classes of volunteers’
motivations for participation: collective motives (the
importance attributed to the project’s objectives); norm-
oriented motives (expectations regarding the reactions of
important others, such as friends, family or colleagues);
reward motives (benefits such as gaining reputation, or
making new friends); and identification (identification with
the group, and following its norms). This conceptualization
has been recently extended to include a fifth factor, a
hedonistic or intrinsic motivation, operationalized as the
enjoyment associated with participation in the project in
studies of participation in open-source software
development (Hertel, Niedner and Herrmann 2003) and
Wikipedia editing (Schroer and Hertel 2009). Given the
broad range of possible ‘reward motives’ (Hertel, Niedner
and Herrmann 2003), we divided this factor to two specific
motives, which were identified in studies of previous
online communities: community reputation benefits and
social interaction benefits (Butler et al. 2002), (Roberts, Il-
Horn and Slaughter 2006).
The modeling approach used in the present study
follows two influential theories – the theory of reasoned
action (TRA) (Fishbein and Ajzen 1975), and its
application in the technology adoption literature
(Venkatesh et al. 2003). According to these theories, an
intention to perform a certain behavior links the actual
behavior to upstream antecedents. We used the intention to
increase participation and (Bhattacherjee 2001), as the
constructs linking motivations to behavior, and in our case,
to citizen science participation (see Figure 1).
This model was used in an early work on the
motivations of volunteers in a high-granularity citizen
science project (Nov, Arazy and Anderson 2011). In the
present study, we compare the results found with new
findings on the motivations on volunteer computing (low
granularity) volunteers.
Following the approach developed in the extended
Klandermans model (Hertel, Niedner and Herrmann 2003),
(Klandermans 1997), we expect all motivations to be
positively correlated with participation intention, which is,
in turn, expected to be related to contribution by the
projects’ volunteers. Furthermore, assuming that greater
effort and time investment required of volunteers carrying
out high-granularity tasks is associated with greater
commitment, we expect task granularity to be positively
correlated with motivation levels.
Figure 1. Research model
We examined two citizen science projects, representing
different task granularity levels. The first project, in which
participation is active, is Stardust@home, an online citizen
science project in which volunteers (also known as
“Dusters”) classify online images from NASA's Stardust
spacecraft. Using a virtual microscope developed by the
Stardust@home research team, dusters classify images
using their home computers and search for tracks left by
very small interstellar dust particles impacting Stardust’s
aerogel tiles. The second project, in which participation is
largely passive, is SETI@home, one of the best known
volunteer computing projects, which is hosted at U.C.
Berkeley. To contribute, a participant needs to download a
client application which is then used for managing the
volunteered computer’s allocated tasks. After the initial
download and installation, contribution is made without
any human intervention, and without a need for the
contributor to interact with the system. A participant’s
contribution level is determined by the setting of her
profile. The participant can set (and later change) her level
of contribution in a number of ways, for example, by
determining the disc space, memory and CPU time to be
used by the project, by contributing constantly or only
when the computer is idle, or determining whether or to
contribute when the computer operates on batteries. The
BOINC system grants computation credit denominated in
“Cobblestones” to participating computers – a measure of
how much work the contributing computer did.
Cobblestone credit is calculated as the CPU time
contributed multiplied by the CPU benchmarks as
measured by the BOINC system.
The survey was developed based on the extended
Klandermans model (Hertel, Niedner and Herrmann 2003;
Klandermans 1997), with additional sources (Butler et al.
2002; Hertel, Niedner and Herrmann 2003; Roberts, Il-
Horn and Slaughter 2006; Schroer and Hertel 2009) for
specific questionnaire items reflecting the model. Survey
items adapted from previous studies were adjusted to the
citizen science context (see Figure 2).
We used intention to increase participation
(Bhattacherjee 2001), as the construct linking motivations
to behavior, and in our case, to citizen science
participation. Contribution, our dependent variable, was
measured in the case of Stardust@home as the number of
weekly hours spent in active contribution, following the
operationalization used in previous studies of online
voluntary participation
(Hertel, Niedner and Herrmann 2003; Lakhani and Wolf
2005; Nov 2007). For SETI@home, participation was
measured using volunteers’ Recent Average Credit (RAC),
used by the BOINC system as a measure for users’
contribution of computing resources.
Collective motives: “advancing
the goals of Stardust@home is
important to me.”
Identification: “I identify with
the Stardust@home community.”
Norm-oriented motives: “My
friends think positively about my
contribution to Stardust@home.”
Intrinsic motives: “Participating
in Stardust@home is fun.”
Reputation: “Gaining reputation
in the Stardust@home community is
important to me.”
Social interaction: “Developing
personal exchange with others in
the Stardust@home community is
important to me.”
Figure 2. Sample questionnaire items
The survey was administered to volunteers in the project
who were active in the 30 days prior to the survey date,
and respondents were asked to rate the importance of the
different motives on a 1-7 Likert scale. 139
Stardust@home volunteers and 1843 SETI@home
volunteers, participated in the survey, representing
response rates of 27.1%, and 22.1% respectively, which are
relatively high compared to similar studies.
Structural equation modeling (SEM) was used to
analyze the survey results and estimate the relationships
between the constructs. Partial Least Squares (PLS) was
applied (Chin, Marcolin and Newsted 2003) using
SmartPLS 2.0 (Ringle and Wende) for the measurement
validation and structural model testing. PLS is used
extensively in information systems research as it offers a
number of advantages that are pertinent to the present
study: In addition to the verification of a complex model,
PLS enables testing of individual hypotheses and provides
amount of variance explained for each endogenous
variable. Compared to covariance-based SEM and
regression, it is better suited to dealing with data
nonnormality and small sample size (Chin, Marcolin and
Newsted 2003). Similar to other structural equation
modeling techniques, PLS allows measurement validation
and model verification to be performed in a single step.
To confirm the reliability of survey items, we conducted a
factor analysis. Seven factors emerged, corresponding to
our framework of six motivational factors and one
intention factor. All items’ factor loadings on the intended
construct were higher than their cross-loadings, as
expected. Furthermore, to confirm convergent and
discriminant validity, we calculated the average variance
extracted (AVE) for each construct. For each construct,
AVE exceeded 0.5, and the square root of AVE (RAVE)
exceeded the correlation with other constructs - thus
displaying convergent and discriminant validity. In
addition, all constructs exhibited Cronbach’s alpha values
above the generally accepted level of 0.70, indicating
measures reliability.
Figure 3(a). Distribution of Stardust@home volunteer
participation levels (in hours/week).
Figure 3(b). Distribution of SETI@home volunteer
participation levels (in RAC).
The analysis of the results reveals a diverse set of
volunteers (see Figure 3a and 3b for the distribution of
participation in terms of time and RAC gained; the Y axis
represents the number of volunteers), with the majority of
volunteers spending less than two hours per week, and a
minority of volunteers spending more.
Motivation levels rated by the respondents are presented
in Figure 4. Collective motives were rated highest (6.44
out of 7 in the case of active-participation, and 6.25 in the
case of active-participation), followed by intrinsic motives
(5.98 and 5.56); identification and norm-oriented motives
were found to be of secondary importance, and the reward
motives of reputation and social interaction did not seem to
play an important role. A comparison between the active
and passive participation projects, using t-test, revealed
that all motivation levels of active-participation volunteers
were significantly higher (p<0.05).
Figure 4. Motivation levels in both projects
The PLS results of testing the model are presented in
Figure 5. We used the log-transformed participation data in
the analysis because of the highly skewed distribution of
the participation variable (see Figures 3a and 3b). In both
models, intention was significantly related to participation,
and intrinsic motives were most strongly related to
participation intentions (the path coefficients were 0.294 in
the active-participation project, and 0.241 in the passive
participation project).
Figure 5. PLS analysis results (values on the lines represent
path coefficients of active/passive participation respectively)
Finally, to compare the differences in the strengths of the
relationships between motivations and intentions in the two
task-granularity cases, we performed a Chow test
(Dougherty 2007). We found that in among active-
participation volunteers, the correlation between collective
motives and intentions was significantly higher than
among passive-participation volunteers, whereas the
correlation between identification and intentions was
significantly lower than among passive-participation
Discussion and Conclusion
Online citizen science is emerging as a powerful way to
conduct scientific research by drawing on large numbers of
geographically distributed volunteers. Online citizen
science is founded on two pillars, technological and
motivational. While the technological aspects of online
citizen science have been investigated extensively in recent
years, the motivational aspects remain largely unexplored.
In the present study we proposed and tested a framework
of the antecedents of contribution in two online citizen
science projects with different degrees of task granularity.
We tested our proposed framework and used structural
equation modeling to examine the influences of the
antecedents identified on volunteers’ participation
intentions and contribution.
The findings demonstrate that task granularity is positively
correlated with motivation levels; however, additional
research, however, is needed to determine the direction of
this relation. Furthermore, with the exception of
identification (among active-participation volunteers), and
social interaction (among passive-participation volunteers),
all motivations were found to be positively and
significantly correlated with contribution intentions, which
were, in turn, found to correlate positively and significantly
with contribution levels. Thus, the findings demonstrate
the applicability of the motivation-intention-contribution
framework to the modeling of volunteers’ participation in
online citizen science projects.
The findings have important implications for the design
and management of online citizen science projects; we
recommend that designers and leaders of such projects
focus their recruiting and retention efforts on motivational
factors that are more salient and have a positive relation
with intention and participation.
While both collective and intrinsic motives were found
to be the two highest rated motivations, it is important to
note that only intrinsic motives were found to be both
highly rated and highly correlated with contribution
intentions. Thus, the high level of the intrinsic motives, as
well as the positive correlation with contribution level
found among both active and passive –participation
volunteers, stresses the need to develop enjoyable
contribution mechanisms, such as the one used in Foldit
(Cooper et al. 2010). Collective motives were not found to
be significantly related to intentions among passive-
participation volunteers, and only moderately related to
intentions among active-participation volunteers. This
finding is consistent with results from previous studies on
community-based projects such as Wikipedia. The finding
suggests that sharing the attributing importance to the
project’s objectives is a characteristic that helps explain
why people join the project in the first place, however once
active contributors, attributing importance to the project’s
objectives is not linked to intentions and contribution
levels. Thus, especially among new volunteers, citizen
science designers and leaders should strive to increase
volunteers’ commitment to the project and its goals. This
could be done by communicating the project’s mission,
achievements and its scientific contribution to the
The moderate levels observed for identification and
norm-oriented motives suggest that - although of
secondary importance – project designers and leaders
should not neglect the necessity to establish a community
of volunteers who share beliefs, interact regularly,
(possibly using existing channels such as social media),
and work collectively towards a common goal. The
significantly weaker relationship between identification
and participation intentions observed among active-
participation volunteers seems surprising – we would have
expected that more active participation would be related to
greater emotional involvement in the project (which Figure
4 shows – the reported identification level is higher for in
active-participation volunteers), and that this increased
involvement will be associated with greater intention to
increase participation. However, we believe that the
findings can be explained by the option of belonging to a
team (an artificial social structure whose accumulated
credit is the sum of of credit accumulated by the team
members); this available to SETI@home volunteers but not
to Stardust@home volunteers. Thus, we believe that being
in a team, or the possibility of it, strengthen the
relationship between volunteers’ identification and their
participation intention.
The significantly stronger relationship observed between
collective motives and participation intentions in the
active-participation project (compared to the passive-
participation project), may suggest that the combination of
active participation and a project whose objectives
volunteers strongly support (collective motives were rated
highest among the motivations – see Figure 4) strengthen
the translation of motivations into participation intentions
and actual contribution. The relation between task
granularity and motivations raises the need to create
dynamic contribution environments that allow volunteers
to start contributing at lower-level granularity tasks, and
gradually progress to more demanding tasks and
responsibilities. Many community-based projects, such as
open source software development and Wikipedia, have
long realized this, and they allow interested contributors to
progress in the tasks they perform and the responsibilities
they assume. This mechanism is currently absent in online
citizen science projects, where volunteers’ tasks are usually
restricted in their scope, and the governance and decision
making is left in the hands of the scientists managing the
projects. Adopting a more symmetric governance structure,
closer to the one in community-based projects such as
Wikipedia, represents a major paradigm shift, even for
those scientists who appreciate the potential benefits of
citizen science. However, as online citizen science
develops, and competition for volunteers’ resource
increase, such a trend toward greater empowerment of
volunteers may be inevitable.
The present study has a number of limitations that can
be addressed in future research. The study was conducted
in two specific citizen science projects. Studies of other
citizen science projects, in different fields, with different
goals, and covering more task granularity levels could help
verify the generalizability of the findings. Further, in this
study we applied a cross-sectional research design, which
allows establishing correlations between constructs, and
thus arguments regarding causal relationships should be
taken with caution. In addition, the study focused solely on
volunteers, and did not consider the motivational effects of
the interaction between volunteers and professional
A number of questions warrant future research; some of
the future research directions we identify are (a) examining
the mechanisms by which participants increase or decrease
their participation levels over time, possibly through a
longitudinal study (b) identifying additional factors that
may have an effect on contribution, such as personality
traits, other motivations not studied here, and (c) exploring
how changes in the design of online citizen science
systems could possibly help increase volunteer
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... A user must be motivated to participate in cocreation activities (Lorenzo-Romero et al., 2014), which entails many benefits; for example, they are more curious and creative and are much less likely to drop out of longitudinal innovation processes (Nov et al., 2011;Jørgensen et al., 2018;Mirkovic et al., 2018). There are no fundamental differences in terms of motivation between virtual and real-world communities (Lampel and Bhalla, 2007). ...
... There are no fundamental differences in terms of motivation between virtual and real-world communities (Lampel and Bhalla, 2007). Researchers have investigated the nature of users' participation in innovation communities and highlighted the factors motivating them to contribute, including learning new things, stimulating curiosity, testing innovative solutions, feeling useful, improving their reputations, and being entertained (Nov et al., 2011;Ståhlbröst and Bergvall-Kåreborn, 2011). ...
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This article discusses collaborative innovation during the initial stages of firms' innovation processes via virtual spaces, focusing on a specific group: elderly users. These users represent a large and growing consumer market, which entails opportunities for companies developing products and services for elderly individuals. Firms that intend to meet the real needs of elders must involve those individuals in collaborative innovation processes. However, firms face challenges in the technical and interpersonal spheres when basing their early‐stage innovation activities on the virtual inclusion of elderly individuals, which has received little attention. Focusing on these challenges, this article presents an exploratory case study employing a participatory action research approach, in which the authors were part of a project aimed at the development of a method of including elderly users via virtual spaces. Pilot implementations helped the innovation intermediary develop an improved method to better capture elderly individuals' inputs. We found that special efforts must be made prior to the virtual activity to familiarize elderly individuals with the technology. Additionally, virtual activity demands a more active role from intermediaries for two reasons: first, representatives from client organizations do not feel confident in leading virtual discussions and second, social hints, emotions and feelings are more difficult to grasp in a virtual space than in real‐life interactions, which necessitates more focused and prepared intermediation. Elderly individuals' involvement is driven by their curiosity and desire to learn something new; therefore, the participation of elderly users must be valuable both to the organization's innovation process and to the elderly individuals themselves.
... In non-profit crowdsourcing platforms, such as citizen science projects and Wikipedia, registration is optional and volunteers can engage anonymously. Unlike studies on commercial crowdsourcing, task-related studies in non-profit crowdsourcing contexts have mainly focused on task design (Sprinks et al., 2017), virtual taskrewarding (Cappa et al., 2018), task complexity and granularity (Nov et al., 2011), and task significance (Schroer & Hertel, 2009) to encourage volunteer engagement (Tinati et al., 2017), improving the quality of outcomes (Kittur & Kraut, 2008) and enriching scientific outputs (Phillips et al., 2018). Most relevant empirical studies are based on surveys, interviews, and quasi-experiments from a behavioral science perspective. ...
... Our results provide evidence of direct causes between task content characteristics (category, words, lists, and illustration) and volunteer engagement. The results add to previous studies concerning factors affecting volunteers' behavior in CHC (Nov et al., 2011;Zhang et al., 2020), which may be further used in developing theoretical models. Furthermore, situating the investigation of factors for task content characteristics under non-profit crowdsourcing makes solid progress in advancing the investigation in other specific crowdsourcing contexts such as citizen science. ...
As the crowdsourcing approach is increasingly being used for digitizing cultural heritage artifacts, there is a rising need for volunteer engagement in such collaborative digital humanities projects. This study focuses on the less explored topic of imbalanced volunteer engagement (IVE); it refers to the fact that most volunteers tend to focus only on a small portion of tasks, making it challenging to sustain cultural heritage crowdsourcing (CHC) projects. Using a public dataset containing 145,168,535 items captured from the Australian Newspaper Digitisation Project, we utilized a machine learning-based causal inference approach to investigate the IVE problem by examining the causal relationships between task content characteristics and volunteer engagements. We used the directed acyclic graph (DAG) to represent the structure, such that a causal relationship consisting of 11 nodes and 16 edges was obtained. Specifically, four causes, including task category, word count, number of task lists, and whether the task was illustrated, directly affect IVE. We further discuss these findings from a theoretical perspective and suggest three propositions: a) nudge-like intervention of a task list, b) subjective (perceived) low task complexity, and c) attraction of task presentation, alleviating the IVE problem. This study contributes to the literature on volunteer engagement in the CHC context and sheds new light on the design and implementation of collaborative digital humanities projects.
... Furthermore, Paul and Palfinger (2020) explored patterns in the human papillomavirus vaccination discourse and asked volunteers to code press releases. Second, existing studies on predictors and outcomes of citizen science participation are predominantly conducted with adults (e.g., Crall et al., 2013;Land-Zandstra et al., 2016;Price & Lee, 2013) and already active citizen scientists (e.g., Nov et al., 2011;Raddick et al., 2010;Rotman et al., 2012). Finally, research on youth participation in school contexts is scarce and research on the predictors and outcomes-related non-participation is absent. ...
... All existing studies on the predictors focus on already active citizen scientists and explore their motivations. For example, past research has shown that learning motivations, altruism, collective motivations, and social interaction are important motivators for existing citizen science volunteers (Land-Zandstra et al., 2016;Nov et al., 2011;Price & Lee, 2013;Rotman et al., 2012). However, results on the hierarchy of these motivators are inconsistent. ...
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Citizen science research has been rapidly expanding in the past years and has become a popular approach in youth education. We investigated key drivers of youth participation in a citizen social science school project and the effects of participation on scientific and topic-related (i.e., political) interest and efficacy. Findings suggest that females, more politically and scientifically interested and more scientifically efficacious adolescents were more motivated to learn from the project. Science efficacy was also positively related to external reward motivation (i.e., winning an award). Both learning and external reward motivation increased the likelihood of participation. Pre- and post-measurement further indicated that participation in the project slightly increased science interest, but not science efficacy. However, it did increase both political interest and efficacy. Furthermore, our data revealed a decrease in science efficacy and interest in those who did not participate in the project, indicating an increasing gap in adolescents’ scientific involvement.
... The free market model did not work as intended. Most volunteers "locked in" to a few projects and did not seek out new ones [12]. Additionally, in spite of the prospect of cheap computing power, relatively few scientists created BOINC projects. ...
... Instead selecting projects, volunteers now select the science areas they want to support. This aligns with the motivations of most volunteers: support of science goals has been shown to be the major motivation for participation in VC [12]. ...
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Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects use volunteer computing in areas ranging from biomedicine to cosmology. Each project has application programs with particular hardware and software requirements (memory, GPUs, VM support, and so on). Each volunteered device has specific hardware and software capabilities, and each device owner has preferences for which science areas they want to support. This leads to a scheduling problem: how to dynamically assign devices to projects in a way that satisfies various constraints and that balances various goals. We describe the scheduling policy used in Science United, a global manager for volunteer computing.
... However, for researchers to effectively attract and retain participants, knowledge of these benefits is not sufficient; rather, CS researchers need to understand the motivational goals that drive volunteers to choose to contribute their time and energy to CS projects in the first place (Clary and Snyder 1999;Rotman et al. 2012;Reed et al. 2013;Wright et al. 2015). Indeed, an extensive stream of research has sought to shed light on the motivations of CS volunteers (e.g., Bonney et al. 2009, Bonney et al. 2014Nov, Arazy, and Anderson 2011;Rotman et al. 2012;Maund et al. 2020;West et al. 2021). For instance, a recent study that surveyed environmental citizen scientists in Great Britain has identified six different types of participants groups based on motivations to participate (West et al. 2021). ...
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Understanding volunteers’ motivations to participate in Citizen Science (CS) projects is essential for these projects’ effective management and success. Many studies have investigated citizen scientists’ motivations, but only a few have used a theory-based approach to provide a standardized methodology to measure CS motivations. The current research aims to take the literature a step further by developing and applying a general, standardized, theory-based framework of CS motivation and a CS motivation scale (CSMS) that can be used to assess volunteers’ motivations across diverse CS projects. The CSMS comprises 58 items corresponding to 15 motivational categories. It is grounded in Schwartz’s theory of basic human values, while incorporating the wealth of empirical knowledge on citizen scientists’ motivations. We administered the scale to three separate samples of either Dutch or Hebrew-speaking participants who volunteered for three CS projects. Analysis of participants’ ratings of their motivations supported our theoretical framework, showing that 13 of the scale’s 15 motivational categories fell into 4 higher-order motivations, which correspond to Schwartz’s theory of values: openness to change, self-enhancement, continuity (conservation), and self-transcendence. Results further provide concrete insights into CS participation behavior, showing that certain motivations (including help with research, benevolence, and self-direction) were consistently among the most important motivators for participation across CS projects. Finally, we found that prioritizing certain motivations can also predict participation behavior (e.g., duration of participation and willingness to participate in additional volunteering activities). The CSMS is a new tool that can be applied across projects spanning diverse domains and populations, advancing and standardizing the growing literature on CS motivations.
... While citizen science research is often discussed as a singular method or science-engagement approach, it comprises multiple forms of engagement with, and for, the citizen-scientist. Haklay (2013) describes these approaches as covering 'crowdsourcing' that requires minimal cognitive engagement, such as volunteered computing (Nov et al., 2011); to 'distributed intelligence,' which utilises cognitive ability of participants and provides basic training before they collect or interpret data (e.g. Hand, 2010). ...
DOI: 10.1016/j.envsci.2022.03.015 The Australian citizen science research programs, VegeSafe and DustSafe, are novel and wide-reaching. Together, they capture the largest number of community-generated domestic garden soil and indoor house dust samples and associated trace metal analysis of any similar programme globally, totalling 26,500 samples from 7,200 homes in Australia alone. All citizen science research programs need to balance often conflicting expectations and imperatives of the researchers and the participants. This paper assesses VegeSafe and DustSafe participant and researcher outcomes against common goals of citizen science programs, including participant engagement, accessibility, motivations and learning in order to evaluate the programs’ impact and usefulness. Questionnaire data from 522 questionnaires were analysed which showed that VegeSafe and DustSafe have: enhanced participants’ involvement in science (76%), understanding of science (62%); addressed specific community concerns (91%); and were considered useful (93%). The success of the VegeSafe and DustSafe programs can be measured by the number of samples received, households engaged and its geographic footprint across Australia's most populated cities. The participant questionnaire provided deeper insight into positive participant outcomes, including participant autonomy in the scientific process and changes in attitudes and behaviours towards science. Many participants adopted interventions to mitigate potential toxic trace metal exposure in their domestic spaces after receiving their results. The VegeSafe and DustSafe programs provide valuable examples of how to establish programs to meet community needs effectively, educate the community and bring about positive change to ultimately improve community health.
... While citizen science research is often discussed as a singular method or science-engagement approach, it comprises multiple forms of engagement with, and for, the citizen-scientist. Haklay (2013) describes these approaches as covering 'crowdsourcing' that requires minimal cognitive engagement, such as volunteered computing (Nov et al., 2011); to 'distributed intelligence,' which utilises cognitive ability of participants and provides basic training before they collect or interpret data (e.g. Hand, 2010). ...
The Australian citizen science research programs, VegeSafe and DustSafe, are novel and wide-reaching. Together, they capture the largest number of community-generated domestic garden soil and indoor house dust samples and associated trace metal analysis of any similar programme globally, totalling 26,500 samples from 7,200 homes in Australia alone. All citizen science research programs need to balance often conflicting expectations and imperatives of the researchers and the participants. This paper assesses VegeSafe and DustSafe participant and researcher outcomes against common goals of citizen science programs, including participant engagement, accessibility, motivations and learning in order to evaluate the programs’ impact and usefulness. Questionnaire data from 522 questionnaires were analysed which showed that VegeSafe and DustSafe have: enhanced participants’ involvement in science (76%), understanding of science (62%); addressed specific community concerns (91%); and were considered useful (93%). The success of the VegeSafe and DustSafe programs can be measured by the number of samples received, households engaged and its geographic footprint across Australia's most populated cities. The participant questionnaire provided deeper insight into positive participant outcomes, including participant autonomy in the scientific process and changes in attitudes and behaviours towards science. Many participants adopted interventions to mitigate potential toxic trace metal exposure in their domestic spaces after receiving their results. The VegeSafe and DustSafe programs provide valuable examples of how to establish programs to meet community needs effectively, educate the community and bring about positive change to ultimately improve community health. Note author order: Cynthia Isley, Kara Fry, Emma L Sharp, Mark Patrick Taylor
... Ant Forest continuance Previous literature has identified that individuals have intrinsic motivational factors related to joint social activities such as self-promotion (Klandermans, 2013). For example, individuals who participate in the Galaxy Zoo Project refer to themselves as "Zooites" (Nov et al., 2011). Clery (2011) suggested that online projects can significantly develop one's identity. ...
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Purpose Technology has emerged as a leading tool to address concerns regarding climate change in the recent era. As a result, the green mobile application – Ant Forest – was developed, and it has considerable potential to reduce negative environmental impacts by encouraging its users to become involved in eco-friendly activities. Ant Forest is a novel unexplored green mobile gaming phenomenon. To address this gap, this study explores the influence of user experience (cognitive experience and affective experience), personal attributes (affection and altruism) and motivational factors in game play (reward for activities and self-promotion) on the continuation intention toward Ant Forest. Design/methodology/approach The authors assessed the data using partial least squares structural equation modeling (PLS-SEM) for understanding users' continuation intention toward Ant Forest. Findings Through a survey of 337 Ant Forest users, the results reveal that cognitive and affective experiences substantially affect Ant Forest continuation intention. Personal attributes and motivational factors also stimulate users to continue using Ant Forest. Originality/value The authors build and confirm a conceptual framework to understand users' continuation intention toward a novel unexplored Ant Forest phenomenon.
... Intrinsic motivation is a crucial mediator between achievement goal structure and achievement behavior (Elliot & Harackiewicz, 1996;Wolters, 2004). In citizen science, prior research found that among various types of motivation, intrinsic motivation is the most salient factor associated with participation (Nov, Arazy, & Anderson, 2011), as volunteers consider citizen science activities interesting or meaningful. Therefore, we proposed a research model ( Fig. 1) that depicts the interrelationships among feedback information (achievement goal structure), intrinsic motivation, and knowledge contribution (achievement behavior). ...
Citizen science involves non-expert volunteers collaborating to investigate scientific problems. Volunteers' knowledge contribution is a key premise to the success of citizen science projects. Despite previous research on the type and valence of feedback information, little is known about the main and interactive effects of these two facets of feedback information on volunteers' knowledge contribution. This study draws on achievement goal theory and hypothesizes how the type and valence of feedback information influence volunteers' motivations and knowledge contribution. We conducted a three (information type: task, self, and social) by two (information valence: positive and negative) between-subjects experiment with 290 participants to test the proposed research hypotheses. The results suggest task, self, and social feedback had varying impacts on volunteers' intrinsic motivation (i.e., perceived enjoyment, perceived meaning, and self-expansion). The impact of feedback information type differed by feedback information valence. Structural equation modeling (SEM) analysis showed perceived enjoyment, perceived meaning, and self-expansion positively influenced volunteers' knowledge contribution (perception and performance). The analysis results demonstrated knowledge contribution perception was a mediator between intrinsic motivation and knowledge contribution performance in the domain of citizen science. Our research contributes to the theoretical advancement of applying feedback information in citizen science and provides practical guidelines for human-information interaction design in future citizen science projects.
Citizen science has become a crucial approach to scientific research and a means of monitoring the environment, removing invasive species, and educating the public on conservation and the environment. However, maintaining a citizen science community is a challenge for moderators and core members. Thus, understanding the motivations for being involved in such communities is imperative for ensuring the ongoing contributions of citizen scientists. The Taiwan Roadkill Observation Network (TaiRON) is the most popular citizen science project in Taiwan, with approximately 19,000 members and at least 5,500 individuals who have uploaded data to TaiRON database. In this study, we used quantitative (n = 538) and qualitative (n = 12) approaches to explore participants’ motivations for joining TaiRON. The results revealed that the primary motivation of participants was learning, followed by acquiring a sense of self-achievement. This indicates that participants had intrinsic motivations for joining TaiRON. The qualitative results indicated the same trend of initial motivation. The motivation to continue participation among participants in TaiRON community also reflected their desire to learn and acquire a sense of self-achievement. These results indicate that participants in TaiRON are given the opportunity to continuously learn about conservation, thus satisfying their need for a sense of self-fulfillment. Moreover, the findings of this study can be applied for the management of citizen science projects and for promoting participants’ ongoing contributions to biological conservation.
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The motives of 141 contributors to a large Open Source Software (OSS) project (the Linux kernel) was explored with an Internet-based questionnaire study. Measured factors were both derived from discussions within the Linux community as well as from models from social sciences. Participants' engagement was particularly determined by their identification as a Linux developer, by pragmatic motives to improve own software, and by their tolerance of time investments. Moreover, some of the software development was accomplished by teams. Activities in these teams were particularly determined by participants' evaluation of the team goals as well as by their perceived indispensability and self-efficacy.
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Understanding what motivates participation is a central theme in the research on open source software (OSS) development. Our study contributes by revealing how the different motivations of OSS developers are interrelated, how these motivations influence participation leading to performance, and how past performance influences subsequent motivations. Drawing on theories of intrinsic and extrinsic motivation, we develop a theoretical model relating the motivations, participation, and performance of OSS developers. We evaluate our model using survey and archival data collected from a longitudinal field study of software developers in the Apache projects. Our results reveal several important findings. First, we find that developers' motivations are not independent but rather are related in complex ways. Being paid to contribute to Apache projects is positively related to developers' status motivations but negatively related to their use-value motivations. Perhaps surprisingly, we find no evidence of diminished intrinsic motivation in the presence of extrinsic motivations; rather, status motivations enhance intrinsic motivations. Second, we find that different motivations have an impact on participation in different ways. Developers' paid participation and status motivations lead to above-average contribution levels, but use-value motivations lead to below-average contribution levels, and intrinsic motivations do not significantly impact average contribution levels. Third, we find that developers' contribution levels positively impact their performance rankings. Finally, our results suggest that past-performance rankings enhance developers' subsequent status motivations.
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Determinants of collective behavior, as suggested by the social identity or self-categorization approach and social movement research, were examined in 2 field studies. Study 1 was conducted in the context of the older people's movement in Germany and Study 2 in the context of the gay movement in the United States. Both studies yielded similar results pointing to 2 independent pathways to willingness to participate in collective action; one is based on cost-benefit calculations (including normative considerations), and the other is based on collective identification as an activist. Study 2 included an experimental manipulation and provided evidence for the causal role of collective identification as an activist. Directions for future research on the proposed dual-pathway model are suggested. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Computers continue to get faster exponentially, but the computational demands of science are growing even faster. Extreme requirements arise in at least three areas.