Knowledge Sharing in Crowdsourcing – it is More Than
, Miia Kosonen1
, and Kirsimarja Blomqvist1
Technology Business Research Center, School of Business, Lappeenranta University of Technology,
Department of Information Management, Central China Normal University, Wuhan, P.R. China
Abstract: In recent years, companies have opened up their innovation activities to users through the
establishment of online crowdsourcing communities – firm-hosted customer co-innovation and value
co-creation platforms; which stands for an important strategy, known as open innovation. However,
we identify two research gaps exist: first, knowledge sharing literature emphasizes macro-level
(organizational level), and pays relatively less attention to micro-level (individual level); secondly,
online community research focuses mainly on the importance of motivation as a driver of behavior.
Therefore in this paper we develop a conceptual framework that draws on the micro-foundations
perspective to consider an integrated set of motivation, opportunity and ability (MOA) that drive users
to share knowledge in the crowdsourcing community. Based on theoretical background of knowledge
sharing in online communities, we put forward a theoretical framework to explain the relationship
between MOA and knowledge sharing for further empirical research.
Keywords: knowledge sharing; motivation; opportunity; ability; crowdsourcing; online community
Today, an increasing number of organizations are hosting online communities (OCs) for
commercial purposes. OCs are open collectives of dispersed individuals, consisting of members who
are not necessarily identifiable to each other but who share common interests (Faraj et al, 2011).
They are best characterized by voluntary input and interaction by users who together form a powerful
collective. One certain type of OCs is crowdsourcing communities, which afford firms opportunities to
involve their users in value creation and innovation activities.
Crowdsourcing community refers to the on-going use of online communication technologies and
online groups of individual contributors in implementing crowdsourcing strategy. Users are considered
a valuable source of innovation (von Hippel, 1988) in the community as co-creators (Nambisan,
2002), partners (Nambisan and Baron, 2010), or part of the organization as co-producers (Wind and
Rangaswamy, 2001). There are two main types of crowdsourcing plaftorms: firm-hosted (e.g., Nokia’s
IdeasProject, IBM’s Global Innovation Jam) and third-party providers (e.g., InnoCentive, Taskcn). In
this study, we focus on firm-hosted communities, where users freely ideate and share knowledge
about products/services and their features with other users and the hosting firm. Considering user-
driven innovation as one of its distinct characteristics, a deep understanding of the factors that shape
users’ sharing behavior in crowdsourcing communities have risen in importance on both research and
Due to the newness of the phenomenon, academic research on crowdsourcing is yet scarce. And
prior OC research has a general understanding regarding the factors determining knowledge sharing
behavior, including social and psychological needs, characteristics of interaction, and different user
types (Porter et al, 2011; Füller, 2010; Nambisan and Baron, 2009). However, two research gaps exist
in extant research on knowledge sharing and OCs. Firstly, knowledge sharing literature emphasizes
macro-level (organizational level), and pays relatively less attention to micro-level constructs and
mechanisms (Foss et al, 2010). Secondly, research on OCs focuses mainly on the importance of
motivation as a driver of knowledge-sharing behavior, which is not sufficient to explore factors that
affect user behavior. With respect to these gaps, we apply the motivation, opportunity and ability
(MOA) into the crowdsourcing context, and address the question: what are the specific roles of MOA
in driving knowledge sharing on online crowdsourcing communities?
The paper is structured as follows. Theoretical background on knowledge sharing in OCs is
introduced in Section 2. Then, MOA perspective on individual-level knowledge sharing behavior is put
forward in Section 3. Furthermore, a theoretical framework to explain the relationship between MOA
factors and knowledge sharing behavior is developed in Section 4. Finally, the discussion, limitations
and implications are presented in Section 5.
2. Knowledge sharing in online communities (OCs)
In this study, we view knowledge as embedded in emergent communities. According to this view,
knowledge is a public good that is socially generated, maintained and exchanged (Brown and Duguid,
1991; Lave and Wenger, 1991). A public good can be provided only if the members of the collective
contribute towards its provision (Wasko and Faraj, 2000). Such knowledge can be extracted from
individuals and made available to the collective, if the conditions are favorable. This brings us to the
individual-level knowledge sharing behavior and its focal role in eventually determining community
Knowledge sharing presumes an individual willing to disseminate knowledge to other members of
the collective (Hsu et al, 2007). Such perspective of knowledge “supply” is typical for OC literature
(see Kosonen, 2009 for a review). A more two-way approach on knowledge sharing would underline
the ability of the other party to interpret the knowledge expressed. Interpretation is dependent on the
social context and the individual backgrounds as well as experiences (Tsoukas, 1994). Hereby, the
norms and practices of the OC may provide a cognitive frame enabling community members to
engage in knowledge sharing, and to combine knowledge gained through community participation
(Chiu et al, 2006; Kosonen, 2009). We thus approach knowledge sharing as a form of generalized
exchange among a specific online group of people, involving indirect reciprocal dependence around a
shared interest, and a communication platform serving as an intermediary between contributors and
seekers of knowledge. Knowledge sharing is enabled through mechanisms that support posting and
responding to questions, sharing personal experiences in the form of stories, and discussing issues
that are relevant to the community (Wasko and Faraj, 2000). In a crowdsourcing community, users’
knowledge sharing behavior refers to e.g. putting forward ideas, sharing usage experience and
general know-how about products/services, following discussions and making comments.
Social capital theory has been applied to explain individual knowledge sharing within an online
collective. In the context of OCs, structural social capital has been found as the most significant driver
of knowledge sharing (Wasko and Faraj, 2005). Users who are central to the network are more likely
to provide important contributions to benefit the collective. Another significant predictor of knowledge
contributions seems to be the level of cognitive social capital, referring to the individual’s experience
on the field and thus ability to share knowledge. It is important to note that the study by Wasko and
Faraj (2005) was conducted within an online network of people engaged in a shared profession,
representing a loose collective of individuals who do not necessarily know each other, as it is the case
in crowdsourcing communities. Therefore, we believe that their work provides important insight for OC
researchers focusing on crowdsourcing.
3. MOA perspective on individual-level knowledge sharing behavior
In this section, we consider knowledge sharing behavior in the crowdsourcing community from the
perspective of knowledge governance micro-foundations (here, MOA). The micro-foundation is
needed because the crowdsourcing community relies significantly on users’ contributions (Lengnick-
Hall, 1996; von Hippel, 1988). As shown in Figure 1 (italic part), which builds on the work of Coleman
(1990), it displays two levels (macro and micro) and four links (arrow 1, 2, 3, and 4) (Foss, 2007). In
the present context, we focus on the micro – level (arrow 2: individual, micro-micro relationship),
which describes that “individual action” (the explanandum) could be influenced by “conditions of
individual action” (the explanans). Furthermore, we concentrate our attention on “MOA” and elaborate
on how these explain variation in “users’ knowledge sharing behavior” (see the boldface part in Figure
Figure 1: MOA as an antecedent to users’ knowledge sharing behavior (modified from Coleman , Foss
, Foss et al ).
MOA was originally used in information processing (MacInnis and Jaworski, 1989; MacInnis et al,
1991). Broadly speaking, motivation (‘will do’) refers to an individual’s willingness to perform some
tasks; opportunity represents the context that supports the individual’s actions; and ability (‘can do’)
denotes the individual’s potential (knowledge, skills or confidence) for the actions (Dunette, 1976;
Rothschild, 1999; Siemsen et al, 2008). Within the research on knowledge sharing in organizations,
special attention has been paid to the MOA attributes in determining knowledge sharing behavior.
However, this perspective has not yet been explicitly linked to OCs especially crowdsourcing
communities. Foss and Minbaeva (2009) indicated, “the MOA approach seem[ed] particularly
appropriate for examining the individual-level antecedents of knowledge transfer as it move[d] away
from the structure/content assumption characterizing previous research and adopt[ed] a more
process-driven view (Lane et al, 2006)”. And the MOA framework is warranted because its three
dimensions – motivation, opportunity and ability – are related constructs (Blumberg and Pringle, 1982)
and should play complementary roles in influencing behavior (Cummings and Schwab, 1973). Users
are likely to participate and contribute in crowdsourcing communities when they have motivation,
opportunity, and ability to do so (Foss and Minbaeva, 2009; Minbaeva et al, 2010). Thus, we consider
MOA framework to be more systematical and stronger for explaining individuals’ knowledge-sharing
Previous research has applied MOA to explain various types of knowledge-related behavior. For
example, Argote et al (2003) identified MOA to explain how and why contextual properties affect
knowledge management outcomes. Foss and Minbaeva (2009), in turn, noted the effect of MOA on
individual knowledge-related behavior in strategic human resource management field. The study by
Minbaeva et al (2010) considered how MOA affected knowledge acquisition and use. Building on
these studies, we apply the MOA framework into the context of crowdsourcing communities,
considering which types of attributes or factors precede the development of MOA and how they affect
users’ knowledge sharing behavior. Next, we will build a MOA framework considering both its
antecedents and outcomes in the context of crowdsourcing communities.
4. Factors of knowledge sharing in crowdsourcing community: MOA
As described above, the MOA framework could be used to better explain users’ knowledge sharing
behavior from the individual level. This section focuses on the proposed framework, the aim being to
identify factors of MOA that affect users’ sharing behavior in the crowdsourcing community. Figure 2
illustrates the factors, which are discussed in more detail in the following sub-sections.
Figure 2: MOA framework of knowledge sharing in the crowdsourcing community
Of the MOA variables, motivation has evoked the most debate and discussion as an antecedent in
affecting behavior. Motivation refers to the dynamic, personal energy with which an action is
performed (Cummings and Schwab, 1973). Crowdsourcing communities cannot develop without the
contributions of highly motivated users who are willing to donate their time and effort to it. Different
motivations to affect knowledge sharing behavior have been generally classified as either intrinsic or
extrinsic. Intrinsic motivation occurs when the activity itself or the corresponding end goal satisfies a
direct need in its own right; while extrinsic motivation serves to satisfy indirect or instrumental needs
(Frey and Osterloh, 2002). Based on the work of Nambisan and Baron (2007, 2009), we consider
these four benefits as users’ intrinsic motivation for engaging in crowdsourcing communities. In
addition, tangible rewards and career opportunities are seen to extrinsically motivate users.
Learning benefit. Learning benefit is related to learning knowledge about products/services.
Crowdsourcing communities hold valuable collective product-related knowledge that is rooted in the
specific context where it is practiced (Brown and Duguid, 1991). When users engage in a community,
they could easily get access to this knowledge through reading the ongoing conversations. Also,
participating in tasks/projects or discussions with others provide opportunities to better understand the
specific product/service. Nambisan and Baron (2007) showed that learning benefit has a quite strong
impact on actual customer participation in virtual customer environments.
Social integrative benefit. Social integrative benefit reflects social and relational ties (Nambisan,
2002). Users share similar interests, perceive significant overlap between their personal identity and
that of other community members, as well as feel a general sense of belonging to the community
(Porter et al, 2011; Tsai and Ghoshal, 1998). Tsai & Ghoshal (1998) suggested that social interaction
ties are channels for information and resource flows. Chiu et al (2006) showed that social interaction
ties increased individuals’ quantity of knowledge sharing in virtual communities.
Personal integrative benefit. Personal integrative benefit indicates recognition, reputation, status or
image. Users are gratified by sharing their knowledge so as to make better products/services or see
their own ideas come true, which could bring them feeling of being valued. When users perceive that
more involvement will position them to enhance their image, peer recognition, firm recognition or
status in the community, they will then be more likely to being engaged (Nambisan and Baron, 2010).
Hedonic benefit. Hedonic benefit reflects user experiences of curiosity, pleasure or stimulation.
Research shows that when users experience flow – a psychological state of having fun as well as
feeling absorbed, gratified, and in control over one’s experience – they develop favorable attitudes
toward communities that provide such an experience (Porter et al, 2011). An enjoyable, relaxing and
interesting user-experience in the crowdsourcing community could attract more users to participate
Tangible rewards. Tangible rewards, such as money, prizes or branded gifts, are considered to be
a motivator for users’ active participation. Tangible rewards have positive reinforcement, which can
encourage repeated behavior in the future (Skinner, 1953). Brabham (2010) showed that the
opportunity to make money is important for participation on the crowdsourcing site. Roberts et al
(2006) empirically examined that monetary compensation is positively affecting contributors’ level of
participation. In addition, considering different personal characteristics, Füller (2010) stated that goal-
oriented users may engage in crowdsourcing communities because they are more interested in the
outcomes such as monetary compensation.
Career opportunities. Crowdsourcing communities also provide potential career opportunities for
users. On one hand, uses who contribute the best ideas may gain an opportunity for attending firm-
hosted conferences (e.g., Nokia World), which will create paths for displaying oneself to the potential
employers. On the other hand, for those active users, community managers would pay more attention
on them, or even contact with them for further communications. Lerner and Tirole (2002) suggested
that open source software communities offer an excellent setting in which a participant motivated by
career concerns can signal his or her abilities to the labour market.
Users could be more motivated when coupled with supportive and empowering contexts derived
from opportunity. Effective crowdsourcing communities should provide users with the opportunity to
generate, communicate and share knowledge. Blumberg and Pringle (1982) defined opportunity as
“consist[ed] of the particular configuration of the field of forces surrounding a person and his or her
task that enable[d] or constrain[ed] that person’s task performance and that [were] beyond the
person’s direct control”. In context of crowdsourcing communities, opportunity refers to resources
availability, environment surrounded or tasks/projects designed for users. Here, three factors are
identified that play a vital role in knowledge sharing.
Community support. Community support implies the necessary conditions provided by the
community for creating and sharing knowledge. In this study, we consider two types of support -
technology and knowledge. Powerful technology (tools) and rich knowledge are seen as essential due
to reasons that: tools contribute to users’ easily learning about how to create knowledge and reduce
their cognitive effort to achieve it, and knowledge contributes to users’ better understanding of the
related contexts and inspires them to come up with creative ideas (Füller, 2010). Specially,
technology–based support refers to design aspects of the communities, which could address
requirements such as inspire creativity community functionality and increase efficiency (Piller and
Walcher, 2006). Prior research has focused on the design of toolkits for user innovation (e.g., von
Hippel and Katz, 2002; Nambisan, 2002; Mahr and Lievens, 2011). Also, knowledge-based support
refers to task/project-related knowledge provided by the community. This kind of support could reduce
the ambiguity of the tasks/projects; bridge knowledge gap between users and the community; help
users form or integrate ideas from scattered initial thoughts; and mentally or intellectually stimulate
their minds for generating ideas.
Community culture. Community culture is the collective programming of the mind which
distinguishes the users of one community from another, and has direct or indirect impacts on users’
patterns of thinking, feeling and acting (Hofstede, 1991). Here, a trustful and interactive community
culture could sustain users’ participation. Trust can be understood as the actor’s willingness to be
vulnerable (Mayer et al, 1995). It matters because it is a predisposition for users’ knowledge sharing
behavior (Blomqvist, 2008 and 2009). Nahapiet and Ghoshal (1998) presented that when trust exists
between the parties, they are more willing to engage in cooperative interaction. Interaction implies
dialogue and conversation. Culture that encourages intense interaction among users not only evokes
an individual’s active participation, but also enriches his/her personal relationships. Communities with
social network functionality encourage users to freely express personal features and find those with
Task/project design. It is also important to take tasks/projects design (such as complexity and time
needed) into account. Different tasks/projects usually attract different users and produce diverse
effects on their participation due to heterogeneous motivations and various user types. And
tasks/projects pose necessary requirements on the participants. Howe (2008) argued that users could
choose a spectrum of tasks based on interest, commitment level and available time. Zheng et al
(2011) found out the positive effects of contest autonomy, variety and analyzability, as well as the
negative effects of contest tacitness on intrinsic motivation.
Effects of motivation and opportunity may be complemented by an individual’s ability to share
his/her knowledge in crowdsourcing communities. Ability to share knowledge refers to the human
attributes residing within and utilized by individuals to accomplish the tasks/projects. Ability is an
indispensable driver of individual level knowledge sharing (Minbaeva et al, 2010) and lack of it will
limit one’s willing to do so. In this study, we focus on two aspects of the ability: knowledge and skills,
Knowledge and skills. Knowledge and skills could be represented as the prior related knowledge
and experience (Kim, 2001), generic and task-specific aspects (Minbaeva et al, 2010). Users with a
high degree of lead user characteristics (Von Hippel, 1986) may possess the relevant knowledge and
skills, thus tend to enjoy sharing knowledge with others (Jeppesen and Laursen, 2009). And
possession of certain knowledge and skills could increase deeper understanding of the current
tasks/projects and facilitate the formation of variety knowledge.
Self-efficacy. Self-efficacy refers to one’s beliefs that they can exert control over their motivation
and behavior and over their social environment (Bandura, 1990). In the context of crowdsourcing,
self-efficacy is concerned with one’s belief of his or her ability to accomplish related tasks/projects.
The degree of self-efficacy has effects on whether users will participate in crowdsourcing
tasks/projects, how many efforts they will make, and how to handle with in the face of difficulties.
Bandura (1982) argued that individuals choose to avoid activities when they think of lacking self-
efficacy, and judgments of self-efficacy also determine their actions towards obstacles or aversive
To sum up, the MOA factors and the outcomes of knowledge sharing in the crowdsourcing
community are shown in Figure 3.
Figure 3: MOA factors and outcomes of knowledge sharing in the crowdsourcing community
In this paper, we have contributed by building local contextual theory of individual knowledge-
sharing behavior in online crowdsourcing communities, while simultaneously introducing the micro-
foundations perspective into the open innovation community research. While prior knowledge-sharing
research has highlighted the importance of MOA factors across different organizational, until now they
have not been applied in OCs or crowdsourcing.
One possible limitation of the study is that we did not focus on the contextual differences between
various types of crowdsourcing communities. In general, there is a wide variety in crowdsourcing
initiatives (see Aitamurto et al, 2011). The levels of knowledge required and the factors preceding
knowledge-sharing behavior thus vary depending on the community and its purpose. However, taking
into account the lack of studies combining the micro-foundations perspective and MOA with OCs and
crowdsourcing, we have attempted to build a more generic framework to provide a starting point for
further research. The framework should be elaborated and tested in further quantitative and
qualitative research. Another limitation is that our focus has been in individual users. In further
studies, it would thus be valuable to pay attention to the hosting firm’s actions within the community,
while investigating what is the actual value of the crowdsourcing community for its business. The
dynamics of knowledge contributions on community level also deserve attention by researchers (Faraj
et al, 2011).
From managerial perspective, user intrinsic motivation, and especially learning and creativity could
be inspired by establishing direct links with community experts and professionals. Best user ideas and
experiences should also gain more visibility. Social integration could be enhanced by linking the
community with popular social networking sites or task force groups. Personal integration could be
supported by providing both tools for peer recognition and company recognition. Another means
would be publishing stories where development of products or services from idea to innovation is
described, simultaneously recognizing users’ role in developing them. Hedonic needs could be better
fulfilled with aesthetical and emotionally appealing tools for “flow experience”. In line with the hard
facts and knowledge, the community could provide certain spaces for virtual coffee breaks, fun and
entertainment, and relaxation together with other users.
User extrinsic motivation can be easily enhanced by prizes related, e.g. to the product, service or
brand in question. Personal preferences could be met if users can choose among a variety of prizes
or gifts. Companies like Google and Facebook organize challenging competitions where user skills
and creativity are called for, and the best users are invited for work interviews.
Opportunity can be enhanced by investing both in technology-based support (e.g. platform usability
and real-time support) and knowledge-based support. Platform usability and functionality is important
also from trustworthiness point of view. A trusting community culture can be designed by explicitly
agreeing on crowdsourcing community norms and rules. Various types of tasks could be offered, and
many of the tasks should be designed so that they cannot be solved without collaboration of users
with various expertise and skills.
Ability can be diffused if the most knowledgeable users act as role models and source of
knowledge for newcomers or less knowledgeable but eager users. Users could be asked to indicate
and rate specific knowledge held by other users. Finally, the community could set up interactive,
informal Q&A sessions giving guidance on how to share knowledge in the community.
How to foster individuals share their knowledge in crowdsourcing communities is crucial. In the
paper we have suggested that those who have strong motivations and high ability with many
opportunities would be more likely to share. Different factors of the MOA will have different influences
on individual behavior, which should be paid attention by researchers and practitioners.
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