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Crowdsourcing for integrative and innovative knowledge: knowledge diversity, network position, and semantic patterns of collective reflection

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Crowdsourcing for integrative and innovative knowledge: knowledge diversity, network position, and semantic patterns of collective reflection

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CROWDSOURCING FOR INTEGRATIVE AND INNOVATIVE KNOWLEDGE:
KNOWLEDGE DIVERSITY, NETWORK POSITION, AND SEMANTIC PATTERNS OF
COLLECTIVE REFLECTION

by

Yao Sun

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMMUNICATION)

August 2019

ii

Acknowledgements
The completion of this dissertation would not have been possible without the support of
many people in the Annenberg community. First and foremost, I would like to thank my advisor
and dissertation chair, Dr. Margaret McLaughlin, who has constantly been encouraging,
supportive, and patient. Peggy, thank you for your brilliant thoughts. I will treasure them forever.
I am also deeply grateful to my committee members, Dr. Ann Majchrzak and Dr. Aimei Yang. I
always enjoy working with Dr. Ann Majchrzak. Thank you very much for your wonderful
guidance, your time and effort in helping me to shape my research over the years. I am also
thankful to you, Dr. Aimei Yang, for providing very constructive feedback as well as sharing all
your invaluable ideas and experience.
I would like to extend my sincere gratitude to Dr. Peter Monge, Dr. Michael Cody, and
Dr. Janet Fulk, who have offered enormous support and intellectual guidance to me along this
journey. I learned a lot from each one of you. Thank you all for being excellent mentors and
generous friends to me. Much sincere appreciation also goes to all the distinguished professors
who have offered courses or seminars and taught me how to think and how to do research. I
appreciate Drs. Ken Sereno and Brad Shipley for the time working together on teaching. I also
thank Anne Marie Campian, Sarah Holterman and all the staff in Annenberg for patiently
answering questions and providing help.
I feel very fortunate to have met and worked with numerous excellent friends in my
Annenberg life: colleagues Yu Xu, Larry Zhiming Xu, Ignacio Cruz, Yue Yang, Hye Min Kim,
Jillian Kwong, Yusi Xu, Ruqin Ren, Yiqi Li, Jingyi Sun, Grace Wang; alumni Mina Park,
Nathan Walter, Rong Wang, Joe Phua, Wenlin Liu, Lik Sam Chan, Zheng An, Jinghui Hou, Lin
iii

Zhang, Li Lu, Selene Hu, Jieun Shin, Chi Zhang, Wei Wang, Bei Yan, Xin Wang, Jingbo Meng,
Nancy Chen; all the members of Annenberg Networks Network and Peggy Teams, and many
others. Thank you all for the precious thoughts, advice, encouragement as well as
companionship.
I also gratefully acknowledge the institutional support that I have received throughout
these years from Annenberg School, Graduate School, and University of Southern California for
supporting my doctoral study and research projects with fellowships and grants.
Last but definitely not least, I am greatly indebted to my beloved family. I thank my
parents for their unconditional love and support, as well as for being my very first role models as
scholars and educators. I feel blessed to have my husband, Yu Guo, who is my best friend and
sometimes knows me better than I know myself. Thank you for your incredible love,
understanding, patience, encouragement, and support.

iv

Table of Contents
Acknowledgments ii
List of Tables vi
List of Figures vii
Abstract viii
Chapter 1: Introduction 1
Overview 1
Computer-Mediated Communication and Wisdom of the Crowd 2
Open Innovation 4
Communicating Knowledge in Virtual Space 6
Chapter Summaries 9

Chapter 2: Focus of The Current Research 11
Idea Generation for Innovation 11
Knowledge Collaboration in Crowdsourcing for Innovation 13
Knowledge Integration in Crowdsourcing Innovation Challenges 17
Online Knowledge Collaboration from a Social Network Perspective 20

Chapter 3: Theoretical Framework 24
Managing Knowledge Collectively: A Dynamic Process 24
Dynamic Knowledge 24
Knowledge constantly evolves 24
Knowledge can be tacit 25
Knowledge is embedded in social entities and values 27
Knowledge Collaboration in Various Communication Contexts 28
The Traditional Organizational Context 28
The Computer-mediated Communication Context 30
Uniqueness of Dynamic Knowledge Collaboration in Online Communities 34
Knowledge Diversity in Knowledge Collaboration: Conceptualization and Mechanism 36
Knowledge Diversity 36
A Perspective Taking Framework 39
Role of Knowledge Diversity in Knowledge Collaboration 41
Social Network Position, Knowledge Integration and Innovation 46
Networked Communication in Online Knowledge Collaboration 46
Attentional Distraction, Creativity Blocking, and Innovation 48
Communication Issues, Network Position, and Knowledge Integration 51
Knowledge Diversity, Network Position, Innovation and Knowledge Integration 54
Collective Reflection, Semantic Networks, and Knowledge Sharing 59
Collective Reflection in Knowledge Collaboration 59
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Socio-Semantic Network and Knowledge Sharing 63

Chapter 4: Three Studies: Analyses and Findings 66
Study I 68
Method 68
Data Analysis 70
Results 71
Study II 74
Method 74
Data Analysis 75
Results 75
Study III 78
Method 78
Data Analysis 79
Results 80

Chapter 5: Discussion and Conclusion 90
Study I 90
Study II 92
Study III 94
General Discussion 96
Limitations and Future Directions 98
Conclusion 100

References 101

vi

List of Tables
Table 1: Description of Organizations, Crowdsourcing Cases and Activities 67

Table 2: Effects on Innovativeness Statistics (Hypotheses 1, 3, and 5) 71

Table 3: Descriptive Statistics and Zero-order Correlations (Innovativeness) 73

Table 4: Descriptive Statistics and Zero-order Correlations (Knowledge Integration) 76

Table 5: Effects on Emergence of Knowledge Integration (Hypotheses 2, 4, and 6) 77

Table 6: The top 30 most frequently-occurring concepts in Financial and Banking Service
Crowdsourcing Innovation Challenge 81

Table 7: The top 30 most frequently-occurring concepts in Pest Management Crowdsourcing
Innovation Challenge 85

vii

List of Figures
Figure 1: Description of Organizations, Crowdsourcing Cases and Activities 66

Figure 2: Interaction Effect between Knowledge Diversity and Network Centrality on
Innovativeness 72

Figure 3: Interaction Effect between Knowledge Diversity and Network Centrality on
Knowledge Integration 78

Figure 4: Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 1 83

Figure 5: Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 2 84

Figure 6: Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 3 85

Figure 7: Semantic Network from Pest Management Crowdsourcing Challenge, Phase 1 86

Figure 8: Semantic Network from Pest Management Crowdsourcing Challenge, Phase 2 87

Figure 9: Semantic Network from Pest Management Crowdsourcing Challenge, Phase 3 89

viii

Abstract
This dissertation advances the literature on computer-mediated communication and
crowdsourcing for innovation by examining how online crowds collectively generate integrative
and innovative knowledge, as well as demonstrating the semantic patterns of collective reflection
in crowdsourcing. In particular, it tests the differential main effects and the interaction effects of
knowledge diversity and centralized network position on the production of innovation and
knowledge integration. Study I and Study II employed generalized linear modeling to investigate
the mechanisms of how knowledge diversity and knowledge contributors’ centralized position in
the communication network jointly lead up to the generation of innovative knowledge and
integrative knowledge, respectively. Study III employed semantic network analysis to examine
the evolving patterns generated by the crowd members’ collective reflection when taking part in
crowdsourcing innovation challenges. Findings showed that when knowledge contributors
occupy a centralized position in the networked communication, they are less likely to generate
integrative and innovative knowledge; and the benefits obtained from the exposure to others’
shared diverse knowledge tends to be mitigated by being centralized in communication.
Meanwhile, findings from semantic network analysis demonstrate how crowd-generated
knowledge artifacts are networked and evolving with reflective thinking.
Theoretical implications of this dissertation research are threefold. The research extends
the theory of perspective taking by incorporating the impacts of network position into studying
crowdsourcing innovation challenges, demonstrates a crowd-based mechanism of generating
integrative knowledge, as well as adopts a network perspective in examining the semantic
patterns of the ever-evolving online knowledge collaboration. Practically, this research suggests
ix

that crowdsourcing practitioners should design and implement platforms that can promote the
diversity of crowd members’ explicitly shared knowledge without centralizing certain knowledge
contributors. Furthermore, the semantic attributes of crowdsourcing participants’ networked
communication are considered worthy of a closer examination.

Keywords: Crowdsourcing, Innovation, Knowledge, Integration, Knowledge diversity,
Centralization, Network, Semantic, Reflection

1
CHAPTER 1 INTRODUCTION
Overview
Recent years have witnessed a burgeoning scholarly interest on crowdsourcing and open
innovation (Boudreau, 2010; Boudreau, Lacetera & Lakhani, 2011; Chesbrough, 2006; Enkel,
Gassmann & Chesbrough, 2009; Howe, 2006; Majchrzak & Malhotra, 2013, 2016; Malhotra &
Majchrzak, 2014; Malhotra, Majchrzak & Niemiec, 2017; Surowiecki, 2004). Innovation
nowadays is increasingly generated by harnessing knowledge collaboration amongst members of
online crowd, supported by the rapid advancement of information technology (Bayus, 2013;
Malone, Laubacher & Dellarocas, 2010).
Integrating perspectives of computer-mediated communication, dynamic knowledge
management, and crowdsourcing for innovation, this dissertation research examines how the
exposure to others’ shared knowledge as well as individuals’ structural positions in networked
online communication jointly influence crowd-generated integrative and innovative knowledge.
Recent research on large-scale knowledge collaboration and online crowdsourcing for innovation
has suggested the critical role of adopting diverse perspectives (Majchrzak & Malhotra, 2016;
Malone, et al., 2010), however, the role played by crowdsourcing participants’ different network
positions remains underexplored. The studies presented in this dissertation, therefore, are among
the first line of research to advance the understanding of compounding factors’ effects on
innovation and knowledge integration. The first and second studies are devoted to examining
these mechanisms using inferential statistical analysis, and the third study aims at detecting the
ever-evolving semantic network patterns produced during crowd members’ collective reflection.
2
This dissertation research contributes to the literature in several distinct ways.
Theoretically, it extends the framework of perspective taking by considering the impacts of
centralized network position, as well as empirically presents semantic network patterns that
manifest collective reflection in crowdsourcing innovation challenges. Moreover, it offers
practical suggestions for business practitioners who seek to use crowdsourcing to harvest
integrative and innovative solutions, encouraging them to be attentive to promoting knowledge
diversity, avoiding centralizing any participant, and scrutinizing the semantic dimension of
participants’ knowledge collaboration and collective reflection.
Computer-Mediated Communication and Wisdom of the Crowd
With the fast development of computer-based technology, communication research has
started to focus on the effectiveness and social attributes of online interaction (Walther, 1996).
Integrating “media attributes, social phenomena and social-psychological process” (Walther,
1996, p.5), computer-mediated communication has offered a unique avenue for exploring and
revealing the richness of human communication.
Powered by computer-mediated communication, wisdom of the crowd is increasingly
manifested through online collective activities (Moon & Sproull, 2008; O' Mahony & Ferraro,
2007). Individuals interact on various virtual platforms, share diverse information and
knowledge, and engage in all kinds of online social groups to achieve collective goals (Wenger,
McDermott, & Snyder, 2002). Within the virtual space, the collective wisdom can be cost-
effectively aggregated by a group of individuals who share common interests or needs (Howe,
2006). According to Surowiecki (2004), decisions drawn from collective wisdom “are likely to
be good ones when they’re made by people with diverse opinions reaching independent
3
conclusions, relying primarily on their private information” (p.57). He identified several
conditions for generating wisdom from the crowd. First, the crowd should be composed of
members with diverse backgrounds and knowledge. Second, the members should have free will
to think and make decisions independently. Third, collective activities should be decentralized
and not dominated by any individual party within the crowd. In particular, Surowiecki (2004)
positioned crowd wisdom as opposite to group think and argued that utilizing wisdom of the
crowd in innovation can help avoid the pitfalls commonly seen in group think, such as group
members’ conformity to the dominant voice within the group; when group think occurs,
individuals become disinclined to speak out different opinions because they tend to worry about
their positions being jeopardized.
Crowdsourcing, therefore, builds upon the wisdom of the crowd and aims to harness the
collective innovativeness amongst its distributed members (Howe, 2006, 2008). The Internet
offers an ideal forum in which collaborators who are geographically distributed may generate
ideas collectively, because it is “not simply a specific medium but a kind of active
implementation of a design technique able to deal with the openness of systems” (Terranova,
2004, p. 25). Hence, Internet-based crowdsourcing has become widely adopted in recent years by
companies wishing to generate innovative solutions to various business problems. For example,
companies seek for artistic designs for clothes (Lakhani & Kanji, 2008), or creative photography
solutions (Brabham, 2008; Howe, 2008), innovative business research and development
proposals (Lakhani, Jeppesen, Lohse & Panetta, 2007; West & Lakhani, 2008), or innovative
ideas related to film production (Lietsala & Joutsen, 2007). When necessary, rewards are given
based on obvious contributions, although most activities are completed by volunteers (Lampel &
4
Bhalla, 2007; Wu, Gerlach & Young, 2007). Essentially, online crowdsourcing makes use of the
Internet to harvest crowd wisdom in order to generate solutions that a small group of experts may
not be able to generate (Brabham, 2013).
Wisdom of the crowd is closely related to the notion of collective intelligence (Lévy &
Bononno, 1997), which highlights the intellectual potential held by a crowd. Cain (2012) echoes
this theorization by noting the positive role played by a crowd’s electronic brainstorming, “where
large groups outperform individuals, and the larger the group the better. The protection of the
screen mitigates many problems of group work. This is why the Internet has yielded such
wondrous collective creations” (p.4). However, as participant engagement requires time and
attention, crowdsourcing practitioners need to make wise decisions on selecting tools and
platforms as well as the design of engaging strategies to manage information flow generated by a
large number of participants. From participants’ point of view, time is a scarce resource, and they
must decide where to allocate it because there is a marked difference between contributing ideas
to crowdsourcing challenges and socializing with friends. As such, experiencing a sense of
community will motivate participants to contribute to the collective thinking, so that the overall
participation is ultimately boosted (Mandarano, Meenar & Steins, 2010).
Open Innovation
Open innovation (Chesbrough, 2003) is increasingly gaining attention from scholars
working in a wide variety of fields, including economics, psychology, sociology, and cultural
anthropology (von Krogh & Spaeth, 2007). This revolution in business model and management
strategy reflects the evolution supported by information technology and the corresponding
computer-mediated communication. In fact, the growth of open innovation is deeply rooted in
5
both theoretical and practical developments that have taken place over the present decade and
earlier (Christensen, Olesen & Kjær, 2005; Dodgson, Gann & Salter, 2005) driven by such
objects as improving internal innovation processes or utilizing external resources to generate new
internal innovation.
Open innovation, by definition, is to open up the innovation process. Chesbrough (2006)
defined it as “the use of purposive inflows and outflows of knowledge to accelerate internal
innovation, and to expand the markets for external use of innovation, respectively” (p.1). As
Dahlander and Gann (2010) reviewed, open innovation is tightly linked to several theoretical
frameworks or concepts such as complementary assets, absorptive capacity, exploration and
exploitation (Cohen & Levinthal, 1990; March, 1991; Teece, 1986), as well as congruent with
the research on innovating with lead users and the Not Invented Here syndrome (Katz & Allen,
1982; von Hippel, 1986).
Open innovation is contrary to closed innovation through which companies develop
creative ideas internally (Chesbrough, 2003); it is catalyzed by socioeconomic changes such as
the rapid advancement of information technologies, increased labor division, as well as the ever-
expanding globalization (Dahlander & Gann, 2010). Going hand in hand are trends such as
outsourcing happening parallel in management area, which makes companies more agile and
flexible (Gassman, 2006). Instead of reflecting a dichotomy, open innovation is better to be
described as a continuum which includes various degrees and forms of innovation (Dahlander &
Gann, 2010). It is a multifaceted and multidimensional notion consisting of different activities
such as inbound innovation (acquiring and sourcing), outbound innovation (selling and
revealing), or a compound mix of various types of innovation, as well as interactions that are
6
pecuniary or non-pecuniary (Dahlander & Gann, 2010; Gassmann & Enkel, 2004). From the
perspective of organizational knowledge management, Lichtenthaler and Lichtenthaler (2009)
identified specific organizational knowledge-related activities involved in open innovation, such
as knowledge exploration, exploitation and retention. And more recently, the Internet of Things
(IoT) has started to lend itself to knowledge management in open innovation as well (Santoro,
Vrontis, Thrassou & Dezi, 2018).
Communicating Knowledge in Virtual Space
Social interaction is a natural human motivation (Baumeister & Leary, 1995). Individuals
communicate knowledge inside the virtual space for a variety of purposes. As suggested by
Nelson and Cooprider (1996), communicating knowledge can help individuals satisfy the needs
for interaction, build social connections, as well as develop mutual trust and shared visions. In
general, individuals engage in regular and frequent social interaction to fulfill the need to belong.
As Baumeister and Leary (1995) explained, individuals have “a need to form and maintain at
least a minimum quantity of interpersonal relationships, [which] is innately present (and hence
nearly universal) among human beings” (p. 499). According to the theory, individuals take part
in social interactions in order to build sustained social connections, which ultimately helps to
establish and maintain a feeling of belonging.
Such an innate need triggers goal-oriented actions to satisfy it. It is a natural tendency of
humans to build interpersonal contacts and cultivate social connections. Constructing social
bonds requires both cognitive and emotional investment, and it can in turn lead to both cognitive
and emotional outcomes. For example, displaying positive affect helps form social bonds, and
solid social bonds can produce positive emotion (Carver, 2003). Accordingly, individuals show a
7
natural tendency of forming groups and engage in social interactions with one another (Mann,
1980).
Forming social attachments is often motivated by a personal attribute “that reflects an
individual’s desire for social interaction and a sense of communion with others” (Bowlby, 1969);
losing attachments will induce feelings of loss, separation, anger, as well as anxiety (Bowlby,
1973). Individuals are motivated to actively seek supports from their social network because they
enjoy each other’s company and feel safe when being part of a bonded community (Reis &
Patrick, 1996). As Hill (1987) suggested, social interactions are motivated by a variety of needs
related to interpersonal closeness, attention, social comparison, or the need of reducing a
negative affect through communication.
The affordances of online knowledge collaboration community enable knowledge
contributors to satisfy their self-presentation needs while co-creating knowledge artifacts
(Majchrzak & Malhotra, 2013), so that knowledge contributors can play different roles in
knowledge creation (Kane, Majchrzak, Johnson & Chen, 2009a, b; Wagner & Majchrzak, 2006).
As suggested by Goffman (1959), individuals tend to present different selves at front and back
stages. The motives for individuals to engage in self-presentation stem from a fundamental desire
to be recognized, understood, and accepted. As Schlenker (1980) put it, “there is nothing
intrinsic to the concept of impression management that dictates that it must be directed toward
only a real audience. Imagined audiences, which range from the generalized other to specific
significant others…can be conjured. These are often played to with more gusto than the
immediate real audiences people encounter” (p.306). As the Internet gains in popularity, people
increasingly initiate and maintain relationships online, leading to an increasing concern about
8
online self-presentation (e.g. Ellison, Steinfield & Lampe, 2007; Tong, Van Der Heide,
Langwell, & Walther, 2008; Walther, Van Der Heide, Kim, Westerman, & Tong, 2008; Zhao,
Grasmuck, & Martin, 2008). Visual anonymity and the freedom afforded by online
communication offer alternative ways of managing their presentations in front of others (Chester
& Bretherton, 2007), and individuals enjoy the autonomy in customizing their own presentations
and experimenting with alternative self-image constructions. As Wallace (1999) noted, the
Internet serves “an identity laboratory, overflowing with props, audiences, and players for our
personal experiments” (p.48).
Communicating knowledge online is jointly determined by an individual’s cognition and
the social context in which he or she is embedded. As computer-mediated communication
enables an extension of existing offline social networks as well as a construction of new online
social networks, individuals rely ever more on virtual communities to communicate their
knowledge. According to social cognitive theory, activities in virtual space may be viewed as the
inherently “triadic, dynamic, and reciprocal interaction of personal factors, behavior, and the
social network”, as “virtual communities are online social networks in which people with
common interests, goals, or practices interact to share information and knowledge, and engage in
social interactions” (Chiu, Hsu & Wang, 2006, p.1873). In other words, a virtual community is
essentially sustained by the interrelated connections and interactions embedded within the
network. According to Bandura (1989), an individual’s behavior not only depends on his or her
own values or beliefs but is also influenced by the social network or system in which the
individual is embedded. When participating in online knowledge sharing activities, collaborators
are involved in virtual communities where their interactions are interrelated and networked, and
9
thus knowledge can be exchanged along the network ties (Wellman & Wortley, 1990).
Meanwhile, the expectation of reciprocal exchange or relationships has been found to be
positively associated with the intention to share knowledge or contribute to common knowledge
repositories (Bock, Zmud, Kim & Lee, 2005; Kankanhalli, Tan & Wei, 2005). Moreover,
participants’ satisfaction with the networked communication, as well as a sense of belonging or
collective identity can promote online knowledge sharing as well (Dholakia, Bagozzi & Pearo,
2004; Hars & Ou, 2002; Langerak, Verhoef, Verlegh & de Valck, 2004; Yoo, Suh & Lee, 2002).
Chapter Summaries
This dissertation is organized as follows. This chapter mainly presents the general
background knowledge of the research and integrates the frameworks of computer-mediated
communication, wisdom of the crowd and open innovation as well as the mechanisms of
communicating knowledge in virtual space, suggesting that the emergence and rapid
development of crowdsourcing and open innovation are rooted in the advancement of
communication technology as well as in the natural human tendency for communicating and
sharing. Additionally, contributions of the current research are discussed.
Positing knowledge collaboration in the framework of crowdsourcing for innovation,
Chapter 2 discusses the main focus of the current research. It demonstrates the three major topics
that were examined: knowledge integration, innovation, and networked knowledge collaboration
in crowdsourcing challenges. It elaborates on why these specific areas were chosen and how they
might be understood through different theoretical lenses.
Chapter 3 outlines the major theories applied in the research, including the key concepts
that are of particular interest. Several hypotheses and a research question were derived based on
10
a review of relevant literature. Specifically, knowledge diversity and a centralized network
position were proposed as study topics – due to both their individual and interactive effects on
innovation and knowledge integration – as well as the evolving semantic patterns of knowledge
collaboration produced in collective reflection.
Chapter 4 details the three studies that were conducted to investigate the effects of
knowledge diversity and a centralized network position on the innovativeness of crowd-
generated ideas as well as on the emergence of knowledge integration. The research design,
measurements, data analysis as well as results for three studies are then presented in that order.
Finally, Chapter 5 focuses on the conclusions drawn from the empirical findings and
discusses the theoretical and practical implications of this research, acknowledging its limitations
and indicating possible directions for future research.

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CHAPTER 2 FOCUS OF THE CURRENT RESEARCH
Idea Generation for Innovation
Idea generation was first employed by Cisco in 2007, when the company hosted a
competition on an online platform to harvest innovative ideas for IT network solutions (Keating,
Rhodes & Richards, 2013; Schweitzer, Buchinger, Gassmann & Obrist, 2012). In order to
achieve this, the company applied its own network-based collaboration technology, allowing
participants from around the world to work in virtual teams and exchange their thoughts.
Participants were asked to submit ideas as well as business plans in order to show that their
solutions were implementable. A total of 2,500 ideas were proposed by contributors from 104
countries within five weeks and the ideas were evaluated by this crowd-based community.
Subsequently, the 450 contributors whose ideas had received the highest number of votes were
selected to advance to the next round of pitching and, finally, the contributors of the twelve best
proposals were invited to present to Cisco’s senior management team. The overall winner – a
student majoring in informatics who had teamed up with her husband and brother – was awarded
a $250,000 prize along with a position affiliated to Cisco to work on implementing her
innovative idea for promoting energy efficiency.
The Cisco case demonstrates how online idea generation can help give exposure to new
minds as well as incorporate outside knowledge into a company’s operations, triggering much
interest from other companies in applying this tool to gain crowd feedback. Thus far, companies
using online competitions to generate ideas have mainly focused on how to design new products
to attract potential consumers (e.g. Stock, von Hippel & Gillert, 2016). A company that carries
out idea generation should have a thorough understanding of its customers so as to design a
12
sufficiently attractive challenge for provoking innovative thought. Idea generation can be either
closed or open (Chesbrough, 2003). With a closed approach, companies retrieve customer
feedback internally and work with product design departments to determine methods for
implementing their insights. When applying an open innovation approach, companies are able to
involve the external crowd in the process of designing new products or business models. Such an
approach emphasizes the core belief that most innovative solutions originate from product or
service users; hence, companies utilizing this approach view users and potential customers as co-
creators in generating solutions to innovation problems (von Hippel, 2005). Based on an open
innovation approach, online idea generation challenges have successfully facilitated the
development of a range of innovations, from the incremental to the radical (Love & Hirschheim,
2017). Depending on the types of innovation, idea generation may either be open to the general
public or available only to a limited number of parties.
Benefiting from the advantages of virtual communication mechanisms, information and
knowledge in online idea generation can spread rapidly and widely and potentially reach an
unlimited number of participants at a relatively low cost, allowing a large number of ideas to be
generated immediately and in a cost-efficient way. In some instances, online idea generation
communities are sourced from active crowds on other virtual platforms such as Facebook or
Twitter (Füller, 2010). Compared to traditional focus groups that may take a long time to create,
individuals around the globe participating in Internet-based idea generation can often congregate
within a very short period (Schweitzer, et al., 2012), although this openness sometimes
engenders shared learning, causing participants’ ideas overlap with one another (Ebner,
Leimeister & Krcmar, 2009). As such, idea generation in the virtual platform is considered
13
outperforming traditional approaches of knowledge transfer and creation, such as focus groups or
lead-user workshops (Hemetsberger & Godula, 2007).
With the help of technological development, online idea generation offers easy access to
a virtually unlimited number of creative individuals who can easily share their thoughts at any
time (Gassmann, Enkel & Chesbrough, 2010). It is thus imperative to understand how to better
motivate the participants, because the quality of idea generation depends on the extent to which
the innovation challenges can attract or incentivize creative contributors who share applicable
knowledge.
Knowledge Collaboration in Crowdsourcing for Innovation
One of the main interests of the present studies was to explore the determinants and
processes of knowledge collaboration leading up to crowdsourced innovation. The research drew
on dynamic knowledge management theories and integrated the networked communication
perspective to develop a framework that positions knowledge diversity and a centralized network
position as key determinants of the innovativeness of crowd-contributed ideas, as well as
demonstrating the effects of their interaction on crowd-generated innovation.
As suggested by Surowiecki (2004), “under the right circumstances, groups are
remarkably intelligent, and are often smarter than the smartest people in them” (p. xiii). This
means that when members of a diverse and decentralized crowd combine their thoughts,
innovation is likely to occur. Thanks to the widespread of information technology, companies
can now assemble crowds to help perform tasks related to innovation production (Howe, 2006).
Thus, crowdsourcing is defined as a type of online participative activity attended by a large
group of diverse individuals volunteering to complete a task or generate responses to a challenge
14
(Estellés-Arolas & González-Ladrón-De-Guevara2012; Majchrzak & Malhotra, 2013; Saxton,
Oh & Kishore, 2013); it is constructed based on the notion that when faced with a challenging
problem, a large group of diverse minds, compared to a small group of elite experts, is able to
generate smarter solutions (Surowiecki, 2004). In other words, crowdsourcing promotes
innovation through allowing the incorporation of diverse perspectives, knowledge, skills and
expertise (Majchrzak & Malhotra, 2013). Such a new pattern of innovation is essentially a
manifestation of ‘everyday people using their spare cycles to create content, solve problems,
even do corporate R&D’ (Howe, 2006, p.1). Upon completion of the tasks, companies reward
winning participants for their contributions, thus acquiring the ideas so that the companies can
own the right for further exploiting the ideas (Mortara, Ford & Jaeger, 2013).
Crowdsourcing can be viewed as a means for companies to generate novel organizational
solutions and ideas (Afuah & Tucci, 2012; Poetz & Schreier, 2012) relying on a self-
identification process among capable participants who are willing to contribute their knowledge
to help complete the task (Howe, 2008). Gradually, as the virtual space grows, participants must
decide to which topic they intend to devote their time and energy (Wang, Butler & Ren, 2013),
thus making these resources scarce in crowdsourcing. As such, understanding participants’
motivation as well as finding ways to sustain it is crucial to efficient and successful
crowdsourcing (Frey, Luthje & Haag, 2011; Kollock, 1999; Lakhani & Von Hippel, 2004).
Online crowdsourcing communities offer a positive space for the crowd to collectively
innovate. Such a social climate is configured by innovation enablers through such activities as
constructive challenges, informational and emotional social support, as well as inspiration and
encouragement (Amabile, Conti, Coon, Lazenby & Herron, 1996; Kanter, 1988). Motivated
15
engagement, in this sense, plays a critical role as it often accompanies an enthusiasm amongst
members for supporting one another in carrying out tasks (Gilson & Shalley, 2004; Rich, Lepine
& Crawford, 2010). This is because those who are fully engaged in and passionate about an
activity are often willing to strive to a higher level to generate innovation, attempting new things
cognitively and emotionally (Amabile et al., 1996; Kahn, 1990). As such, the choice of whether
or not to engage in creating knowledge is essentially dependent upon an individual’s decision-
making rationale (Kazanjian & Drazin, 2012).
The notion of crowdsourcing pertains to concepts such as co-creation and user
innovation. It “can be both economically and intellectually (providing long-term unquantifiable
benefits) fruitful activity, but firms may need to be realistic about what types of problems and
users they can feasibly engage, and what capabilities they have or need to manage the
community and its expectations” (Aitamurto, Leiponen & Tee, 2011, p.23). In other words,
successful crowdsourcing requires companies’ intention and attentiveness over a relatively long
period; companies should not view it as a one-time action. Aitamurto et al. (2011) also indicate
that crowdsourcing is most beneficial when companies need to harvest distant knowledge and
expertise to generate creative solutions. Their work also has offered recommendations for
different crowdsourcing approaches with respect to different innovation purposes. When
companies seek to take advantage of past developments, community-based crowdsourcing
should be employed as a convenient method for participants to retrieve previous knowledge;
when companies need to conduct parallel experimentation and harvest winning solutions, they
should launch competitions or tournaments.
16
Crowdsourcing is also viewed as a strategic business model that builds upon information
technology to gather creative solutions through an open call to the public (Brabham, 2008).
According to Brabham (2010), “the crowd’s strength lies in its composite or aggregate of ideas,
rather than in a collaboration of ideas… this ‘wisdom of crowds’ is derived not from averaging
solutions, but from aggregating them” (p. 1125). With the Internet enabling worldwide
interactions, crowdsourcing allows firms to make a faster move when faced with a rapidly
changing business environment. In a similar vein, Schenk and Guittard (2009) view
crowdsourcing as an aggregation of crowd activity and outsourcing. Although it centers around
firms, it still differs essentially from user innovation as the innovation in crowdsourcing is
gleaned from an entire crowd, whereas user innovation is largely designed for satisfying specific
the needs of users.
In line with these findings, Lakhani, Jeppesen, Lohse and Panetta (2007) reported, “Our
most counter-intuitive finding was the positive and significant impact of the self-assessed
distance between the problem and the solver’s field of expertise on the probability of creating a
winning solution…We reason that the significance of this effect may be due to the ability of
‘‘outsiders’’ from relatively distant fields to see problems with fresh eyes and apply solutions
that are novel to the problem domain but well known and understood by them…as our results
suggest, opening up the scientific problem solving process can yield innovative technical
solutions, increase the probability of success in science programs and ultimately boost research
productivity.” Likewise, a study by Poetz and Schreier (2012) demonstrated that crowd-
generated ideas can be complementary to the ideas generated by internal experts, especially with
regard to the implementability of the ideas.
17
Knowledge Integration in Crowdsourcing Innovation Challenges
A second major interest of this research was to examine the factors and mechanisms that
lead up to the emergence of knowledge integration in crowdsourcing innovation challenges. As
with research on innovativeness, research on knowledge integration also focuses on the profound
impacts of knowledge diversity and the centralized network position on the likelihood that crowd
members will integrate knowledge, as well as the effect of the interactive relationship between
knowledge diversity and the centralized network position on the occurrence of knowledge
integration in crowdsourced knowledge collaboration that responds to innovation challenges.
According to Alavi and Tiwana (2002), knowledge integration refers to “the synthesis of
individuals’ specialized knowledge into situation-specific systemic knowledge” (p.1030) and
plays a critical role in distributed collaborators’ knowledge application in innovation. Compared
to knowledge transfer which is time-consuming and inefficient (Ciborra, 1996; Darr, Argote, &
Epple, 1995), knowledge integration relies more on the transcend approach beyond knowledge
transfer and offers a synergistic synthesis of the “nature of the dialogue” (Majchrzak, More &
Faraj, 2012, p.14). As such, possible conflicts caused by members’ heterogeneous deep
knowledge can be avoided or negotiated (Bechky, 2003; Carlile, 2002, 2004; Tsoukas, 2009).
Knowledge integration occurs when there is “rich communication, collaboration and creative
conflict” (Alavi & Tiwana, 2002, p.1031) among involved participants, thus enhancing their
collective creativity (Boutellier, Gassmann, Macho, & Roux, 1998; Madhavan & Grover, 1998).
Grant (1996) provides a framework for distinguishing knowledge integration from knowledge
acquisition, arguing that knowledge integration outperforms knowledge acquisition in generating
radical innovation, because acquisition may require a common knowledge base and a great deal
18
of time. In organizational innovation studies, similarly, innovation has been found to closely
relate to an organization’s capability to integrate internal and external knowledge as well as to
introduce new resources and perspectives (Alter & Hage, 1993; Ebers & Jarillo, 1997; Koza &
Lewin, 1998; Powell, 1998; Tidd & Trewhella, 1997).
The importance of knowledge integration to innovation processes has been made explicit
in a wide range of literatures. First, knowledge integration can be viewed as resulting from the
co-evolution of technological system and organizational context in which the synthesis of
existing and new knowledge constitutes the fundamental practice of development (Clark &
Staunton, 1989; Hislop, Newell, Scarbrough & Swan, 1997; Robertson, Swan & Newell, 1996;
Scarbrough, 1996). Additionally, knowledge integration extends the innovation literature by
emphasizing the blending and combination of existing internal organizational knowledge and
new external knowledge (Cohen & Levinthal, 1990; Leonard-Barton, 1995; Powell, Koput &
Smith-Doerr, 1996). Furthermore, the conceptual development on knowledge integration has
given rise to contemporary knowledge research which suggests that new knowledge emerges
from continuous conversations in which various types of knowledge are synthesized (Nonaka,
1994).
To achieve knowledge integration, collaborators often need to be exposed to enough
ideas in order to generate a reasonable amount of output (Ghezzi, Gabelloni, Martini, &
Natalicchio, 2017). In other words, the idea exposure serves as a stimulus for comprehensive
understanding and integrative thinking. Research has found that the exposure to peer ideas is a
major component of interaction among crowd members (Luo & Toubia, 2015; Siangliulue,
Arnold, Gajos & Dow, 2015), and that reacting to heterogeneous ideas can mitigate the negative
19
effects of fixation on one’s own thinking (Bayus, 2013). Being attentive to the ideas generated
by peers generally facilitates synergistic thinking, which improves an ideator’s ability to produce
feasible ideas (Schemmann, Herrmann, Chappin & Heimeriks, 2016). Essentially, exposure to
others’ ideas is a priming process based on triggering functions of memory. As the literature on
priming demonstrates, being primed activates one’s existing knowledge and makes it accessible,
so that new knowledge can be generated via combination and application (Nijstad & Stroebe,
2006; Rietzschel, Nijstad, & Stroebe, 2007).
In crowdsourcing innovation challenges, knowledge integration can be applied to solving
both well-defined and broadly defined problems. Companies may pose a general question, asking
the crowd’s opinion about new product design or business model development, and crowd
members are allowed to interpret and understand the question in their own way. As suggested by
Malhotra and Majchrzak (2014), crowd members in innovation challenges mainly undertake
three steps to integrate their knowledge in order to innovate. The first step is that participants
share their knowledge by writing posts or comments, talking about their opinions and
experience, asking questions, or raising points of conflict. In this way, crowdsourcing
participants are able to see “the breadth of concerns and issues needing to be resolved for
generating solutions with competitive advantage potential” (p. 105). Upon sharing knowledge
successfully, crowdsourcing participants need to identify the most relevant knowledge in a large
amount of shared knowledge through voting or promoting, for appropriate use in producing
solutions. Without the actions of knowledge identification and highlighting, “highly relevant
posts can be buried in the disorganized collage; thus preventing the most relevant knowledge
from gaining attention by the crowd for inclusion in solutions” (p.105). It is worth noting that the
20
process of highlighting knowledge should revolve around the core themes of innovation
challenges, rather than crowd members’ personal preferences regarding others’ posts, because
only the knowledge relevant to the challenges will be helpful for knowledge integration. In the
last step, crowd members are encouraged to find a way to combine the appropriate knowledge
with their own understanding, generating creative knowledge integration to solve organizational
problems. Finally, with regard to harvesting knowledge integration effectively, Malhotra and
Majchrzak (2014) have put forward several suggestions, such as giving explicit knowledge
integration tips to crowdsourcing participants, offering specific instructions on the types of
knowledge to be shared, as well as fostering the diverse roles played by different members in the
discussion.
Online Knowledge Collaboration from a Social Network Perspective
Adopting a network perspective, knowledge collaboration can be viewed as pertaining to
both the knowledge network itself and to the networked relationships among collaborators. In
this research, the network of knowledge was examined by scrutinizing the various networks and
semantic attributes of the crowd-generated content, with the collaborators’ networked
relationships applied as a conceptual foundation for investigating how individuals’ positions in
the networked communication influenced their performance in generating innovation as well as
integrative knowledge.
Network is a broad concept that can be applied to describing the relations across a set of
interconnected entities in various contexts. The social network perspective highlights the
interdependent actions and depicts the dynamic and interactive patterns that emerge within a
collective. Networked communication among knowledge collaborators acts as a resource of
21
social capital, which in turn facilitates further cooperation (Coleman & Coleman, 1994; Putnam,
1995). Defined as “the sum of the actual and potential resources embedded within, available
through, and derived from the network of relationships possessed by an individual or social unit”
(Nahapiet & Ghoshal,1998), social capital has been found to assist information exchange and
value creation (Tsai & Ghoshal, 1998). Communication taking place on the Internet extends
offline interaction and supplements social capital obtained offline (Wellman, Quan-Haase, Witte
& Hampton, 2001). Wasko, Faraj and Teigland (2004), in particular, conceptualized computer-
mediated knowledge collaboration in the context of collective action, theorized the properties of
various social network structures, demonstrating that networked online knowledge collaboration
is characterized by several unique patterns. First, knowledge collaboration can be perceived as
social networked communication for practice-related tasks, during which all participations are
voluntary, and no formal controls dictate the communication, with participants free to determine
how much time and effort they devote to the knowledge collaboration. Second, such a practice-
based communication network may involve both face-to-face and technology-aided interactions
in sustaining the exchange of knowledge and other resources. The third feature of the social
network of knowledge collaboration regards the size of the network. Thanks to the support of
information technology, networked knowledge collaboration allows the emergence as well as
expansion of very broad networks maintained by a large number of participants constantly
sharing their thoughts. The fourth attribute of the structure of the computer-mediated knowledge
collaboration network focuses on its openness, which is contrary to conventional face-to-face co-
located collaboration in that any passionate individual around the world can engage in the
communication without being restricted by their physical location or affiliations. Finally, such a
22
social network is configured by the fact that individuals’ involvements are interdependent. In
other words, a passionate knowledge contributor will find it difficult to make his or her efforts
visible if he or she cannot build connections with others.
The proliferation of information technology has facilitated the emergence and
advancement of semantic networks of the knowledge content shared by collaborators. The
recognition of the value of examining word-to-word relationship can be traced back to the early
research of Collins and Quillian (1969), which suggested that such relationship is a manifestation
of collaborators’ shared meaning. According to their work, words associated with each other are
stored hierarchically in individuals’ minds, and therefore meanings are constructed by words that
point to each other. In a similar manner, knowledge is stored in human memory. When
individuals attempt to describe concepts or events, relevant hierarchically stored words are
activated, so that individuals can create sentences to express their shared meanings (Barnett &
Woelfel, 1988; Chang, 1986; Collins & Quillian, 1969).
Semantic network is similar to social network in that it exhibits the structure of a
networked relationship; however, it differs from social network because it is based on the
connections created by communicators’ shared meaning rather than social connections. In such a
network, connections are formed by the use of overlapping concepts instead of interaction
instances (Doerfel & Barnett, 1999; Doerfel, 1998). The goal of semantic network analysis is to
allow the meanings to emerge and thus to be identified as part of the network; therefore,
semantic network analysis does not employ a pre-defined scheme that is often seen in traditional
content analysis. Semantic network analysis mainly utilizes natural language processing rather
than human coding to decode the shared meaning, inherently ensuring a satisfactory level of
23
reliability and validity (Rice & Danowski, 1993). Therefore, semantic network analysis is
appropriate for studying knowledge contribution because such an approach will not suppress new
insights that emerge from the interaction.
In sum, semantic network analysis is a powerful method to unpack the communication
configured by interconnected words that present individuals’ shared meanings. Text is first
scanned to find the most frequently used words; then an adjacency matrix is built based on
calculating the frequency of meaningful words, such that word co-occurrence pairs can be
identified. The matrix can be further analyzed in order to detect clusters or other types of inter-
words correlations so as to better understand all the interlinked meanings (Corman, Kuhn,
McPhee & Dooley, 2002). Allowing researchers to capture the visible and quantifiable attributes
on which shared meanings are built, semantic network analysis acts as a way to help deeply
understand individuals’ common beliefs and values.

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CHAPTER 3 THEORETICAL FRAMEWORK
Managing Knowledge Collectively: A Dynamic Process
Dynamic Knowledge
Knowledge is defined as “facts, information, and skills acquired through experience or
education; the theoretical understanding of a subject” in the Oxford Dictionary. It includes belief,
understanding, opinion, and awareness. Knowledge is an intangible resource that originates and
resides in the mind (Davenport & Prusak, 1998; Sveiby, 1997). Knowledge is often acquired
through active and dynamic learning (Polanyi, 1958) and observation, and communicating
knowledge relies on human intelligence, energy, and the will to use knowledge in cooperative
endeavors. Knowledge is therefore associated with a process that involves cognitive structures
capable of assimilating information and hence allowing subsequent actions to be undertaken
based on it. Knowledge is a socially constructed notion shaped by shared learning and mutual
exchange (Berger & Luckmann, 1966). In order to be able to innovate or discover, one must use
existing knowledge as well as acquire new knowledge through constant learning.
Knowledge constantly evolves.
The creation and development of knowledge can be viewed as a path-dependent
evolutionary process (Nerkar, 2003). In general, three approaches have been applied to
understanding the ever-evolving knowledge.
The first view draws upon recombination, which is considered as the basis of knowledge
evolution and refers to the process in which different streams of knowledge aggregate to be
recombined (Fleming, 2001). The emergence of knowledge, therefore, results from the
recombination of existing elements of knowledge (Kogut & Zander, 1992). Knowledge
25
evolution, to some extent, is forced by a continuous recombination of knowledge pieces. For
organizations, particularly, the competitive advantage derived from knowledge created through
the recombination process is sustainable, as the underlying capabilities required are in many
cases tacit, systemic, complex, and unobservable even if the knowledge chosen for
recombination is codified and observable (Kogut & Zander 1992; Winter 1987). In this
perspective, knowledge evolution is a dynamic process of knowledge recombination production
and involves the searching and use of existing knowledge (Henderson & Clark, 1990; Kogut &
Zander, 1992; Schumpeter, 1934).
The second perspective draws on the theoretical framework of chaos, suggesting that
knowledge evolution should be viewed as following a stochastic path. Along this path, the
emergence of new knowledge is a random variation that involves little rationality from the actors
(Arthur, 1989; David, 1988).
From the third viewpoint, knowledge evolution is developed on the basis of inventors’
conscious variations over time (Nelson & Winter, 1982). Variation acts as the resource for
environmental selection and retention (Campbell, 1965, 1974). Knowledge contributors are
bounded rational individuals who tend to satisfice rather than seeking optimal method when
creating knowledge (March & Simon 1958; Winter, 2000). When creating knowledge through
recombination, contributors show a tendency of preferring locally available knowledge and
neglecting foreign knowledge that may in fact be constructive and fruitful (Levinthal, 1997).
Knowledge can be tacit.
The concept of tacit knowledge was introduced to describe a form of knowledge that is
deeply rooted in the context and cannot be directly communicated in a codified way (Nonaka,
26
1991, 1994; Nonaka & Takeuchi, 1995, 1996; Nonaka, Umemoto & Senoo, 1996; Polanyi,
1969). Tacit knowledge is tightly connected to the senses, intuitions and tactile experiences, and
is developed along with actions, procedures, routines, and values (Nonaka et al., 1996; Nonaka,
Toyama & Konno, 2000; Nonaka, Toyama & Nagata, 2000). Much knowledge is tacit because of
owners’ unawareness of its existence as well as an inability to articulate it; in some cases, tacit
knowledge can be a major source of competitive advantage and thus owners are often not
motivated to make it explicit (Leonard & Sensiper, 1998). Obtaining this type of knowledge
usually takes the path of “implicit learning” (Reber, 1989), and the knowledge can be applied to
three common scenarios, including problem finding and solving, prediction, and anticipation. An
individual may unconsciously specify a problem based on prior experience and generate the most
appropriate approach to dealing with the problem. Further, possessing tacit knowledge allows
individuals to precisely predict future incidents so that they are able to prepare better strategies in
response.
The interaction between tacit and explicit knowledge is configured by knowledge
conversion, which illustrates how knowledge is created, transformed as well as applied to social
and organizational practice (Nonaka & von Krogh, 2009). When converted to explicit
knowledge, tacit knowledge becomes “a basis for reflection and conscious action, and…[is] less
costly to share with others” (Nonaka & von Krogh, 2009, p. 642). Consequently, the explicitly
shared knowledge can act as the foundation for creating new knowledge and improving the
capacity to act (Sabherwal & Becerra-Fernandez 2003; Wathne, Roos & von Krogh, 1996). For
example, in teamwork, when knowledge is shared explicitly, the team’s collective decision
making and problem solving can be enhanced by members’ sharing of insights, skills, mental
27
models and specialized expertise (Grant, 1996). Individuals may also benefit from the
conversion, too, by further advancing the bases of both tacit and explicit forms of knowledge in
taking part in social practice.
The knowledge possessed by a community includes both explicit and tacit components.
For example, explicit knowledge can reside in routines, documented strategies, recorded
discussions, and written plans. The implicit aspect of knowledge, for instance, lies in members’
shared experiences and socializations, decoding and articulating of implicit knowledge,
systematizing and incorporating of various knowledge components, as well as their internalizing
of explicit knowledge and turning it into applicable tacit knowledge (Nonaka & Konno, 1998).
Tacit knowledge also lies in multidisciplinary problem-solving skills which are developed based
on the vision that balances specific techniques and general knowledge assets. Visionary
individuals are able to recognize the interconnections across various knowledge domains and
thus to visualize the optimal solutions (von Krogh, Ichijo & Nonaka, 2000). Through social ties
and further collaborative interactions, experts’ unique tacit knowledge can be gradually absorbed
by others (Dutta, 1997).
Knowledge is embedded in social entities and values.
Knowledge formation cannot take place unless interaction and exchange happen among
individuals. To study knowledge formation and sharing, the social values rooted in the processes
and practices involved in the creation and establishment of competing bodies of knowledge must
be considered (Marshall & Rollinson, 2004). From the constructivist perspective, knowledge is
built in an ever-evolving working environment, and reflects the social relationships involved
(Tsoukas & Mylonopoulos, 2004). The way that individuals deal with knowledge is, to a large
28
extent, shaped by contextual factors such as the communicating styles and relationships in the
work place (Drazin, Glynn & Kazanjian, 1999; Oldham & Cummings, 1996). Likewise,
cognitive psychologists have demonstrated that collective successful management of dynamic
knowledge often results from ongoing interactions during which ideas and thoughts are
exchanged and revisited (Amabile, 1988; Hargadon & Sutton, 1997; Van de Ven, 1986). An
organization’s knowledge can be embedded into individuals transferred to others (Engstrom,
Brown, Engstrom & Koistinen, 1990; Starbuck, 1992).
Knowledge elements can reside in tasks, social principles or routines. Tasks are
knowledge themselves, for they manifest collective goals that motivate further knowledge
formation (Locke & Latham, 2002). As Levitt and March (1988) suggested, “routines are forms,
rules, procedures, conventions, strategies, and soft technologies” (p.517), and all of them
configure the knowledge needed for individuals to take actions and make decisions. In business
settings, routines serve as a way of shaping the form of an organization (Hannan & Freeman,
1984; March, 1991; Tyre & Orlikowski, 1994), constituting power structure (Milliken & Lam,
1991; Starbuck, 1983), as well as improving organizational efficiency (Hage & Aiken, 1999). In
other words, knowledge is embedded in individuals’ “established patterns of working together”
(Teece, 2000, p.36).
Knowledge Collaboration in Various Communication Contexts
The Traditional Organizational Context
Managing knowledge collectively relies on collaborators’ ability to re-use and co-create
knowledge. Grant (1996) provides a framework for knowledge reuse, arguing that instead of the
direct acquisition of knowledge, a comprehensive recycling of knowledge can help individuals to
29
better understand a problem and thus generate corresponding solutions. Majchrzak, Cooper and
Neece (2004) delineated six stages of knowledge reuse whereby collaborators must communicate
their thoughts on how to reconceptualize a problem, followed by a collective decision on how to
search for reusable ideas. After harvesting possible ideas, knowledge collaborators collectively
evaluate and analyze these ideas, ultimately developing them into implementable solutions
(p.184). During the first stage, collaborators identify and distinguish between traditional and new
knowledge so as to clarify the boundary of a problem. In the second stage, a gap is identified
between the existing knowledge and the new demands, and a decision is consequently reached to
search the solution space for answers. The ensuing stage entails a broad exploration of usable
ideas and, once collaborators become aware of the existence of meta-knowledge and successfully
locate the nontraditional knowledge, they access the knowledge and conduct a thorough analysis
to select which knowledge is reusable. As a result, they develop the reused ideas and share them
with others.
Sharing knowledge comprehensively can be complex since it requires participants to
transfer their existing knowledge into new knowledge that can be used by others in a
collaborative process (Carlile, 2002, 2004; Carlile & Rebentisch, 2003; Hargadon & Bechky,
2006; Tsoukas, 2009). Such a transformation demands a high level of intellectual involvement
from participants particularly when it is iterated (Dougherty & Tolboom, 2008; Skilton &
Dooley, 2010). When completing collaborative work in an organizational setting, collaborators
who successfully transfer ideas across time and projects are more likely to produce creative ideas
to solve problems (Walsh & Ungson, 1991). A closer examination of organization members’
knowledge collaboration activities reveals four major steps. The first step is “help seeking”,
30
which describes the practices that enable members to recognize problems and seek assistance
from others. It involves such actions as raising questions or initiating a conversation during work
to create opportunities for further interactions among organization members. The second is “help
giving”, which occurs when a member invites other members to participate in solving problems
collaboratively with others. Providing help in a timely manner is often the key to reaching a
moment of collective creativity. Therefore, help seeking and help giving together constitute the
basic social interactions among organization members that produce collective outcomes. Thirdly,
“reflective reframing” is the phase during which members make new sense of their preexisting
knowledge, such that “the locus of creativity in the interaction moves to the collective level when
each individual’s contributions not only give shape to the subsequent contributions of others, but,
just as importantly, give new meaning to others’ past contributions” (Walsh & Ungson, 1991,
p.492). The last category is “reinforcing”, which refers to the process of internalizing those new
meanings that the organization members created during previous phases. By creating common
understandings and shared beliefs about the problem tasks, reinforcing serves as a dynamic
social interaction process that facilitates the next round of help seeking, help giving, and
reflective reframing.
The Computer-mediated Communication Context
With the development of computer-mediated communication tools, knowledge
collaboration evolves beyond face-to-face interactions and extends to virtual contexts (Hoisl,
Aigner & Miksch, 2007; Hsu, Ju, Yen, & Chang, 2006; Klamma, Cao, & Spaniol, 2007). It
refers to a process in which individuals involved in a virtual environment disseminate and
acquire knowledge from distributed others (Wasko & Faraj, 2005). Such kind of social behavior
31
is deeply rooted in a networked digital environment and is inextricably interwoven into the
social, relational and technological contexts (Kollock, 1999; Olivera, Goodman & Tan, 2008;
Wasko & Faraj, 2005). From a social exchange perspective, researchers view online knowledge-
sharing communities as offering participants a virtual space in which to build new social
connections and obtain social capital. According to collective action theories, participants
contribute knowledge in the pursuit of reputation, recognition, or psychological enjoyment
(Wasco & Faraj, 2005). In addition, potential reciprocity and a sense of belonging to a
community have been found to be major factors motivating online knowledge collaboration
(Haythornthwaite, Guziec, Robins, & Shoemaker, 2000; Kollock, 1999). Similar mechanisms
have been demonstrated in studies on various types of knowledge-sharing communities such as
Wikipedia, open-source software communities, and online travel forums (Hoisl, et al., 2007;
Moore & Serva, 2007; Wagner & Majchrzak, 2007; Wang & Fesenmaier, 2003; West &
O’Mahony, 2008).
Crowd-based online knowledge collaboration can be viewed as an information system
configured by participation architectures or “sociotechnical systems design elements that
encourage and integrate contributions made by participants to an open online forum focused on
developing innovative solutions” (Majchrzak & Malhotra, 2013, p.258). Thus, two salient
components of participation architectures can be identified. One component is production, which
focuses on the way in which participants collectively produce innovation. For example,
participants can be directed to generating solutions for business models or product lines, post
ideas or comment about others’ ideas, or vote in various ways. The other component is the
management of co-creation boundaries, which “is often manifested in incentive structures and
32
intellectual property protections” (Majchrzak & Malhotra, 2013, p.259). Specifically, in some
cases, intrinsic or extrinsic incentives are offered to motivate crowd members to contribute ideas
throughout the entire knowledge generation process; in other cases, incentives are designed
based on the outcome or process, recognizing crowd members who contribute winning ideas or
assist with developing ideas by integrating viewpoints from multiple participants (Majchrzak &
Malhotra, 2013).
Knowledge contributors share diverse knowledge and perspectives to fulfill their self-
presentation needs. Self-presentation can be selective, as argued by Walther (1996), and two key
features of computer-mediated communication can be used to accomplish selective presentation
management. One feature pertains to controllable verbal cues’ quantity and quality, with which
users’ online self-presentation can be “more selective, malleable, and subject to self-censorship
than in face-to-face interaction” (p.20). The other feature derives from the asynchronous nature
of computer-mediated communication, which provides individuals sufficient time to construct
communication content with greater consciousness.
As a way of presenting the self, contributing knowledge in both online and offline
communities serves a number of purposes, which are “maximizing one’s reward-cost ratio in
social relations, enhancing one’s self-esteem, and facilitating the development of desired
identities” (Leary & Kowalski, 1990, p.37), as well as obtaining a feeling of self-affirmation and
self-integrity. Early communication research has offered evidence that successful self-
presentation management can help individuals to harvest social and material increments such as
enhanced friendship, prestige, and salary (Schlenker, 1980; Tedeschi & Riess, 1981); it also
enhances self-esteem as well as sense of belonging by increasing the interactions with others.
33
Additionally, people become involved in self-presentation because they need to create and
maintain their identities (Baumeister, 1982; Gollwitzer, Wicklund & Hilton, 1982). Through
self-presentation management, individuals seek to obtain an understanding of their identity as
well as others’ evaluation of their behaviors (Wiesenfeld, Wurthmann & Hambrick, 2008).
Sharing valuable knowledge facilitates promoting the values of self-image and self-perception,
which are two major components of self-affirmation. Through initiating and taking part in online
communication, individuals convey a desirable image which gives them a sense of affirmation
by helping them to construct and maintain social connections that are supportive and beneficial
(Donath & boyd, 2004; Sas, Dix, Hart & Su, 2009; Walther & Parks, 2002).
Intrinsically motivated knowledge contributors are generally seeking long-term
recognition, professional opportunities or social connections (Boudreau & Lakhani, 2009).
Therefore, when selecting knowledge to share, intrinsically motivated contributors tend to
present themselves as realistically as possible, since deceptive presentations are detrimental to
trust (Whitty, 2007). As Leary (1995) demonstrated, individuals are motivated to manage
impressions due to two factors. One is publicity, which refers to being attentive to what others
think about them; the other is the likelihood of future interactions, meaning that individuals
strive to create a good impression because they anticipate further interactions. In essence, self-
presentation is a behavior oriented by such goals as obtaining and sustaining personal resources,
as well as managing relationship boundaries (Rosenberg & Egbert, 2011). For passionate
contributors, sharing knowledge acts as a way of self-promotion through revealing factual details
of their experience or skills (Gordon, 1996; Utz, 2015), as it helps them to “achieve power
34
resources that can eventually be used in future unplanned encounters with their targets” (Rind &
Benjamin, 1994, p.19).
Uniqueness of Dynamic Knowledge Collaboration in Online Communities
An inherently dynamic process, knowledge collaboration in online crowdsourcing for
innovation has a uniqueness that attracts scholarly attention. In general, such uniqueness revolves
around the behavioral aspect of knowledge collaboration and the architectural characteristic of
online community.
Regarding the behavioral aspect, knowledge collaboration in online communities is
configured by the profound impacts of time, crowd members’ passion and ambiguous identities,
as well as the social disembodiment of contributed ideas (Faraj, Jarvenpaa & Majchrzak, 2011;
Majchrzak & Malhotra, 2013). The first uniqueness results from the passion and enthusiasm that
motivates community members to invest their knowledge and time in building a community
(Wasko & Faraj, 2000). On the positive side, collaborative process enables the community to
generate new ideas based on the recombination and integration of each member’s perspective
(Baum, Locke & Smith, 2001; Chen, Yao & Kotha, 2009; Elsbach & Kramer, 2003), meaning
that passionate members tend to obtain more resources through active participation. On the
negative side, however, personality and knowledge differences may lead passionate members to
clash with one another (Hinds & Bailey, 2003; Jehn, Northcraft & Neale, 1999), inhibiting the
generation of efficient solutions. The second uniqueness focuses on time. Spending time on
knowledge sharing benefits the production of knowledge by propelling idea generation (Fleming
& Waguespack, 2007; Lakhani & von Hippel, 2004; Rafaeli & Ariel, 2008); nevertheless, the
fact that most members devote little time to the process whilst and a few zealous members invest
35
an excessive amount of time in it may harm the development of a healthy cycle of knowledge
sharing (Goel & Mousavidin, 2008). The third uniqueness pertains to the social identity of
members in an online knowledge collaboration community. On the one hand, a community
member’s anonymity and ambiguous identity can encourage participation by offering “a degree
of liberation from social evaluation” (Pinsonneault & Heppel 1997, p.103). On the other hand,
such an anonymity hampers interpersonal trust as well as the trustworthiness of knowledge,
which can lead participants to contribute less to online knowledge sharing (Scott & Laws, 2006).
Moreover, the disembodiment of ideas is unique to online knowledge collaboration (Hughes &
Lang, 2006). Disembodiment of ideas facilitates the possible recombination and integration of
knowledge, but it can also induce misunderstanding and misapplication (Woodman, Sawyer &
Griffin, 1993). Lastly, its dynamic knowledge collaboration is unique because of the temporary
convergence of ideas. Temporary convergence of ideas can catalyze the evolution and
integration of knowledge, and yet may also eliminate opportunities for future knowledge
collaboration due to a lack of agreed evaluation criteria.
With respect to the architectural aspect, an online knowledge collaboration community
should be designed to afford a balance between competition and cooperation, familiarity among
strangers, crowd members’ generative role-taking, network-informed associating, meta-voicing
as well as triggered attending (Majchrzak, Faraj, Kane & Azad, 2013; Majchrzak & Malhotra,
2013). The efficient architecture of an online knowledge community encourages meta-voicing,
which refers to the different approaches that participants contribute to the meta-knowledge, such
as checking each other’s profiles, initiating discussion threads, or commenting on others’ posts.
The architecture also affords triggered attending, which highlights the propensity of some
36
individuals to refuse to actively participate until stimulated by others’ ideas and opinions.
Network-informed associating, another affordance of online community, refers to members’
engagement in an online knowledge conversation based on structural and relational ties. Finally,
another unique attribute of an online community is that it facilitates knowledge conversations
and generative role-taking, which occurs when members enact identities that maintain these
knowledge conversations within the community. As strangers, crowd members involve in both
competition and cooperation in online communities and are socialized during the back-forth
interactions afforded by various web-based platforms, such that they are able to make their voice
heard and self-presentation visible in virtual space (Treem & Leonardi, 2013). They also play
various roles in online conversations, such as shapers, flitters, or defenders (Kane et al., 2009ab;
Wagner & Majchrzak, 2006) in contributing a wide variety of knowledge.
Last but not the least, dynamic knowledge collaboration in online communities is
featured by its dynamic boundaries. According to O’Mahony and Bechky (2008), dynamic
boundaries offer online communities the flexibility and capability to evolve in a way that ensures
resources are integrated and knowledge collaboration sustained. Therefore, building and
maintaining multilayered and multifaceted boundaries acts as an effective strategy for managing
online knowledge sharing (Faraj et al., 2011).
Knowledge Diversity in Knowledge Collaboration: Conceptualization and Mechanism
Knowledge Diversity
Knowledge diversity refers to the variation and heterogeneity of expertise, working
disciplines, skills, know-how, values and so on. It originates from a variety of sources and is
37
composed of several elements, ranging from individual cognition and perceptions to contextual
factors such as the society, organization, and culture.
Knowledge diversity results from cognition diversity, which is made up of difference in
beliefs, viewpoints, and preferences (Miller, Burke & Glick, 1998). Cognitive diversity leads to
an ability to create varied knowledge (Mitchell, Nicholas & Boyle, 2009). A group of cognitively
diverse individuals tends to be open-minded and to generate curiosity for various knowledge
domains, which further engenders information seeking and the development of different
viewpoints (Tjosvold & Sun, 2003). Cognitive diversity also triggers the generation of additional
knowledge, which benefits a comprehensive decision-making (Chen & Tjosvold, 2002; Tjosvold
& Poon, 1998). Through the cognitive encoding, retrieval, and processing stages of information
processing, individuals incorporate their own viewpoints into the perception of things, thus
creating their unique knowledge (Hinsz, Tindale & Vollrath, 1997).
Knowledge diversity is characterized by expertise diversity, which is tightly linked to the
task-relevant skills (Faraj & Sproull, 2000, p.1555) and problem-solving abilities of individuals.
As discussed by Williams and O’Reilly (1998), such information-related diversity indicates
individuals’ capabilities as knowing resources from information may be acquired. Moreover, this
expertise diversity often serves as a source of cognitive frameworks for decoding the meaning of
different types of information as well as for selecting specific information to incorporate into
particular decisions (Bunderson & Sutcliffe, 2002; Cohen & Levinthal, 1990). Reflecting various
dominant skillsets, expertise diversity concerns those functional areas of individuals that are
associated with concerns and goals, and in which individuals can complete a task. Such kind of
38
diversity often accompanies educational background, leading to different “thought worlds”
(Dougherty, 1992, p.191).
Knowledge diversity is also a consequence of various levels of working experience and
stores of specialized knowledge or information relevant to particular work (Sturman, 2003;
Tesluk & Jacobs, 1998). Such knowledge often results from being socialized in an organization,
embedded in its networked relations, and possessing an understanding of its system and history
(Joshi & Jackson, 2003; Rollag, 2004; Sturman, 2003). Groups that are diverse in terms of work
experience or tenure level often consist of members who are knowledgeable as well as
inexperienced members who need to be socialized regarding working procedures and other
work-related policies (Gilson et al., 2013).
In addition, knowledge diversity is rooted in the context, which can mean individuals’
socioeconomic or cultural backgrounds, or specific units that individuals are affiliated with. For
example, individuals from different nations or cultures tend to demonstrate different values
regarding as well as normative expectations of work (Erez & Earley, 1993). Context-based
knowledge diversity is also configured by different communication styles or patterns, perceived
social norms, and distinct mind-sets about how collective work should be proceed (Gibson,
1996; Gibson & Zellmer-Bruhn, 2001; Goodman, Ravlin, & Schminke, 1990). Moreover,
knowledge diversity can be associated with structural diversity, such as differences in
organizational positions, affiliations, or roles. For example, collaborators in cross-functional
teams or geographically dispersed groups tend to be exposed to different information sources,
receive different instructions or feedback, and therefore develop unique understandings of the
tasks and exhibit different working patterns (DeSanctis & Monge 1999; Jarvenpaa & Leidner
39
1999; Maznevski & Chudoba, 2000). Further, members in different organizational units may
access diverse professional networks, thus leading up to a heterogeneity of their knowledge on
organizational work (Ancona & Caldwell 1992; Bunderson & Sutcliffe, 2002).
A Perspective Taking Framework
As an altruistic behavior, knowledge sharing is commonly seen among a group of pro-
socially motivated individuals, who tend to 1) provide each other with effective and constructive
help, and 2) communicate accurately in order to identify problems and collaborate on solutions,
and 3) encourage and support each other to complete tasks, and 4) present positive attitudes to
and valuations of one another and the task (Tjosvold, 1984). When faced with a task, pro-socially
motivated individuals tend to cooperate rather than compete with others, and are more likely to
communicate their ideas, recognize others’ expertise, and constructively manage distinctive
opinions and perspectives (Tjosvold & Deemer, 1980). Successful collaboration depends on the
understanding of others’ points of view as well as critically taking others’ perspectives. The
foundation of perspective taking lies at valuing the diversity of knowledge through enabling
individuals with various types of expertise to cognitively recognize and psychologically accept
the different forms of knowledge representations (Boland, Jr. & Tenkasi, 1995).
Perspective taking refers to a cognitive process in which individuals are able to
understand others’ viewpoints (Bartunek, Gordon & Weathersby, 1983) and empathize with
others’ feelings (Sawyer, 1975). Perspective taking denotes the expectation that others’
viewpoints may be different; when individuals are anticipating a difference of opinion, they are
able to reach a more accurate understanding of others’ thoughts (Tjosvold & Jognson, 1977). In
addition, when individuals engage in active perspective taking, they are more likely to empathize
40
with and feel concern for others and thus identify others’ experiences with their own
(Betancourt, 1990; Egan, 1990; Aron, Aron, Tudor & Nelson, 1991). Along with adopting
others’ perspectives, individuals are more likely to attribute positive motives to their behaviors
and outcomes, and to better recognize others’ knowledge, work, and ability. As Parker and
Axtell (2001) showed, organizational members’ active perspective taking increased their level of
interaction with other members of staff and enriched the content of their jobs. Recent research
has further shown that thinking from others’ perspectives and engaging in pro-social behaviors
are beneficial for an individual in coping with stressful life events (Brown & Cialdini, 2015).
Taking others’ perspectives leads up to success in collective endeavors, such as team
work or organizational activities. Demonstrating pro-social concern for others in cooperative
endeavors can result in more positive joint outcomes (Thompson, 1990). Organizational
members’ deepest understanding of stakeholders’ needs can be achieved through the active
adoption of multiple viewpoints from others (Amabile, 1996). The competitive advantage of
knowledge-based organizations is a result of members’ ability to appreciate and
comprehensively understand and utilize others’ viewpoints (Brown 1991; Dougherty 1992;
Henderson, 1994; Nonaka, 1994; Purser, Pasmore & Tenkasi, 1992). Mohrman, Gibson and
Mohrman (2001) demonstrated that successful managers of organizations typically employ such
other-focused cognitive mechanism and consider a wide-range of different viewpoints from
leaders, colleagues, and customers when making decisions.
Taking the perspectives of others can facilitate knowledge sharing, integration, as well as
innovation at the collective level. Requiring both individual cognition and group communication,
perspective taking is a process in which collaborators critically consider, narrate, and rationally
41
analyze their own and others’ experiences and viewpoints. Perspective taking is a key to success,
especially for diverse teams trading in creativity because it facilitates a comprehensive synergy
of the members’ diverse knowledge (Hoever, Van Knippenberg, van Ginkel, & Barkema, 2012).
De Dreu, Weingart and Kwon (2000) found that employees who successfully adopt multiple
perspectives are more likely to merge all the distinctive opinions together and thereby generate
effective solutions to problems. Grant and Berry (2011) examined the relationship between
perspective-taking and the emergence of innovation, arguing that perspective-taking improves
the creation of knowledge collage. They found that when members pay attention to what others
are thinking, “they will be more likely to develop ideas that are ultimately useful to others” (p.
77).
Role of Knowledge Diversity in Knowledge Collaboration
Knowledge diversity is beneficial for individual and collective innovation. While other
forms of diversity (for example, demographic diversity) may yield mixed results with regard to
collective innovation, diversity in terms of knowledge and expertise has consistently been found
to produce positive effects on collective creativity (Williams & O’Reilly, 1998). From the
perspectives of information processing and decision making, groups in which the members
possess a broad range of skills and knowledge domains, task-oriented viewpoints, and have
access to a wide network generally exhibit satisfactory performances (Bantel & Jackson, 1989;
van Knippenberg, De Dreu & Homan, 2004). Creativity grows out of the interaction among
individuals; therefore, the interactions across multiple knowledge domains tend to provoke more
innovative thoughts (Csikszentmihalyi, 1997; Engestrom, 2001).
42
Knowledge diversity can stimulate the exploration and exploitation of new knowledge
and encourage the adoption of multiple perspectives. Working with diverse others, explaining
opinions to others, and exchanging viewpoints with others may help individuals to generate a
better understanding of the knowledge that they already possess. As individuals engage in
various knowledge domains, the range and depth of their knowledge increase, providing a basis
for their critical thinking in seeking for creative ways to improve their current situation (Zhang &
Bartol, 2010). Moreover, the exposure to diverse knowledge provides an opportunity for
individuals to diverge from existing ideas and explore alternative solution paths, because
multiple potential relations to optimal solutions reside in diverse knowledge (Quintana-García &
Benavides-Velasco, 2008). Members’ diverse knowledge and experience can help organizations
or groups increase the possibility of combining internal and external knowledge through learning
(Lundvall, 1992; van der Vegt & Janssen, 2003; Woodman, Sawyer & Griffin, 1993; Wenger,
2000). Individuals who are able to adopt multiple perspectives have access to a spectrum of
knowledge and are inclined to seek alternative ideas on their own as well as help others with the
refinement of their existing ideas. Attempts to explore alternative ideas and refining existing
ones substantially contribute to collective innovation.
Knowledge diversity plays a critical role in collaborative endeavors, in both traditional
organizational settings and online community settings, fundamentally impacting the way
individuals categorize themselves and perceive others’ behaviors. This, in turn, shapes the
collective performance and patterns of collective communication (van Knippenberg & Schippers,
2007). The differences in knowledge obtained through education can influence how collaborators
utilize information (Bantel & Jackson, 1989; Cohen & Levinthal, 1990; Pelled, Eisenhardt, &
43
Xin, 1999). In the context of traditional organizations or working groups, knowledge diversity is
found to be positively related to innovative outcomes, both at the collective level and individual
levels. For example, research on the performance of firms has revealed that those with a diverse
knowledge base and technical assets are more flexible when searching for solutions, more
capable of generating innovation and can survive longer (Breschi, Lissoni & Malerba, 2003;
Dosi, 1988; Garcia-Vega, 2006; Nelson & Winter, 1982; Suzuki & Kodama, 2004). Studies on
organizational members have also demonstrated that exposure to heterogeneous knowledge can
improve their ability to generate and implement creative ideas when executing complex tasks
(Rodan & Galunic, 2004). In context of the online knowledge collaboration community, the
positive influence of knowledge diversity is amplified via participants’ online communication as
well as contributions to the community-of-practice. For instance, in online communities where
developers collaborate on open-source software development, diverse knowledge is shared in
order to solve a common problem and to develop a collective identity (Brown & Duguid, 1991).
Through such a process, developers construct a shared knowledge repository which serves as a
collective memory to benefit future projects (Vujovic & Parm Ulhoi, 2008). Taken together, this
research proposes that:
H1: Knowledge diversity positively predicts the innovativeness of ideas generated
by the crowd.
Knowledge diversity acts as a catalyst for knowledge integration. Given the cumulative
nature of science and technology, the development of knowledge accordingly draws upon the
combination of knowledge from diverse disciplines. The foundation for integrating diverse
knowledge lies in cognitive proximity (Boschma, 2005), which denotes the cognitive
44
interconnections that individuals perceive within a spectrum of knowledge from different
domains. Absorbing heterogenous knowledge facilitates the identification of connections and
potential combinations in various knowledge elements. As such, individuals armed with a broad
pool of knowledge are generally able to find connecting points between the tasks and their own
knowledge and are thus more capable of analyzing task-related attributes and identifying the
optimal path for finding effective solutions (Novick, 1988).
Integrating diverse knowledge depends upon successful perspective taking, and the
breadth of perspective can assist the reception and transmission of knowledge (Cohen &
Levinthal, 1990). In traditional organizational and teamwork settings, diverse knowledge has
been found to stimulate the active decoding of various opinions. For example, when members of
an organization develop and share their unique knowledge competences with each other, they are
then naturally able to proceed to the synergistic utilization of the distinctive knowledge in order
to enhance the organization’s competitive advantage (Dougherty, 1992; Nonaka, 1994). Working
in diverse functional areas can facilitate the integration of expertise, reinforce the successful
implementation of integrative solutions, and accelerate the process of new product development
(Eisenhardt & Tabrizi, 1995; Griffin & Hauser, 1992). When faced with strategic challenges that
are unsolvable by any sole individual, collaborators need to build continual and comprehensive
insights from a variety of viewpoints as well as synthesize distinctive knowledge by interaction
with others (Duncan, 1979). In collaborative project teams, the exposure to diverse knowledge
provides an opportunity for collaborators to become aware of the knowledge others possess
before coordinating tasks (Bakhtin, 1981; Krauss & Fussell, 1991; Mead, 1934). Through
attempting to make sense of heterogeneous knowledge, collaborators adopt a cognitive process
45
to seek links between others’ knowledge and their own knowledge, providing the basis for
further synthesis. Moreover, knowing others’ expertise areas of expertise improves the likelihood
that hidden knowledge will find its way into the collaboration and thus contribute to the
collective integration (Littlepage, Robison & Reddington, 1997).
Being presented with diverse knowledge fosters knowledge exploration and exploitation,
which ultimately benefits synthesis of various knowledge. Through exploitation, cumulative and
incremental interpretations of diverse knowledge are generated; through exploration, diverse
knowledge is interpreted based on identifying its interrelation with new areas of knowledge.
Exploiting the meanings of existing knowledge and exploring new knowledge jointly give rise to
the emergence of new comprehensive understandings (Tenkasi & Boland Jr., 1996). In-depth
consideration of diverse knowledge provokes additional exploration of information, which leads
up to a comprehensive examination of all the shared information (Gibson, 2001).
Knowledge diversity contributes to the shared mental models among collaborators, which
further assist the integration of knowledge. According to Holyoak (1984), a shared mental model
refers to a common “psychological representation of the environment and its expected behavior”
(p.193) and is collectively constructed among a group of individuals. It essentially represents the
shared meta-knowledge required for the completion of a collective task (Tsoukas, 2005). Shared
mental models lay the foundations for collaborators to discover inherent links between diverse
opinions, beliefs, values, and cognitive structures (Klimoski & Mohammed, 1994). As team
members find convergence, similarity, or compatibility across various domains of knowledge,
their shared mental model can be reinforced, and the quality of teamwork improved (Converse,
Cannon-Bowers & Salas, 1993; Mathieu et al., 2000).
46
Taken together, this research hypothesizes the positive effects of knowledge diversity on
knowledge integration in crowdsourcing innovation challenges, thus:
H2: Knowledge diversity positively predicts the emergence of knowledge
integration in crowdsourcing.
Social Network Position, Knowledge Integration and Innovation
Networked Communication in Online Knowledge Collaboration
Knowledge collaboration in online communities can be viewed as a common practice that
participants undertake, which enables them to create social networks to support their
communication in knowledge seeking and receiving (Brown & Duguid, 2000). In particular, the
rapid advancement of CMC tools allows collaborators to leverage their knowledge via network
connections and construct an electronic network of practice, defined as “a special case of the
broader concept of networks of practice where the sharing of practice-related knowledge occurs
primarily through computer-based communication technologies” (Wasko & Faraj, 2005, p.37).
Such a network can be either independent from an organization or sponsored by an organization
or professional association, and it is configured according to two features: voluntariness and
openness (Wasko & Faraj, 2005). In the network, participants voluntarily self-organize as a
community to share knowledge, and any individual who is interested in such a common practice
may join at any time.
Individuals shape each other’s thinking as they are embedded and interact in the network.
Early research such as Erickson’s (1988) reviewed several notions of the network’s ability to
address attitudinal changes by employing social network measures, maintaining that individuals’
attitudes are reinforced when they share opinions with members of their communication
47
networks or comparison groups. Marsden and Friedkin (1993) addressed “structural cohesion” as
a network account of social influence, which refers to the observation that individuals who are
directly tied into a network tend to exert social influence over each other. Structural attributes
were employed in early studies examining general workplace attitudes and behaviors, and
research following this conceptualization has fruitfully identified the impacts of structural
attributes in a much broader sense. Friedkin (1984), for instance, showed that being embedded in
a cohesive social network significantly influences policymakers’ attitudes. Contractor and
DeChurch (2014) suggested that members in a networked community influence each other’s
opinions via peer-based motivated interactions. In recent studies of online networked collective
actions, the structural attributes of online actors have been gaining in popularity. For instance,
Gonzalez-Bailon and Wang (2016)’s study of Twitter-based collective action has demonstrated
that within a crowd, members who can facilitate cluster-to-cluster information flow are those
who bridge various sub-groups in the network.
As such, networked community benefits knowledge sharing and meaning making
(Wenger, 1998). Knowledge is exchanged through mutual engagement of participants who do
not necessarily know each other; hence, online conversations may be sustained, enriched and
developed into a large pool of information (Kim, Zheng, & Gupta, 2011). Such an established
knowledge repository can benefit further application of the knowledge by other individuals
(Cheshire & Antin, 2008, 2010). Contributing knowledge to a common networked community
also incites a sense of belonging and a shared identification of membership among the
contributors, because they feel connected to one another by discussing common topics (Flanagin,
48
Hocevar & Samahito, 2014), and these positive sentiments in turn encourage future sharing and
exchange of knowledge within the community.
Attentional Distraction, Creativity Blocking, and Innovation
Occupying a centralized position in a collaborative network can be distracting. In
psychology, this is termed the “social facilitation-inhibition effect”, denoting the presence of co-
actors who may not only facilitate but also inhibit individuals’ performance (Bond & Titus,
1983). Following this rationale, Baron (1986) proposed the distraction-conflict theoretical
framework, suggesting individuals’ performance can be disrupted by the presence of others
because such a presence may be distracting. Other early research such as Lovelace (1986)’s
study has demonstrated that social needs can be distracting for scientists and that in order to be
productive they must work alone. In these contexts, focal individuals experience attentional
conflict between tasks and the presence of and the interaction with others; therefore, both the
conflict itself and the distractors add a cognitive load that prevents attention from being focused
on tasks (Cohen, 1978; Geen, 1976). Such a cognitive overload further results in limited
attentional focusing, which engenders an increased use of heuristic and peripheral cues in
generating solutions (Shalley, 1995; Speier, Vessey & Valacich, 2003). Consequently,
individuals’ performance diminishes as they become less capable of making full use of their
cognitive ability, and hence the creativity of their thoughts is hampered.
One reason that the interaction with others can be distracting is that individuals engage in
social comparison and that there is a natural tendency to seek the feeling of being liked and
feeling superior to others in communication (Sanders, Baron & Moore, 1978). As such, signals
of changes in interpersonal relationships can affect the quality of knowledge transfer (Burkink,
49
2002), because collaborators often seek to maintain social connections while exchanging
knowledge. Particularly with regard to complex innovative tasks, social distractions are found to
waste time (Dahms, 1988; Watson, Rainier & Koh, 1991) and interrupt the knowledge workers’
working progress (O’Conaill & Frolich, 1995).
Another factor that impedes creativity in knowledge collaboration communication is the
effect of creativity blocking, which is reinforced by the computer-mediated communication.
First, intense communication among team collaborators “may carry teams along by the
momentum of their enthusiasm…rather than by a clear understanding of its real value”
(Leenders, van Engelen & Kratzer, 2003, p.73). Passionate members who easily dominate
conversations are also likely to diverge discussion themes and impede reciprocal sense making
(Puntambekar, 2004), ultimately leading to their own perspectives being less creative. Such
unbridled passion may cause collaborators to perform poorly on rational thinking and to cause
the group, as a whole, to make poor collective decisions (Nyström, 1979). This is because
collaborators, especially those who occupy the central positions in the communication networks,
may invest more energy in constructing and sustaining group identity and the social connections
with.
Knowledge sharing communication mediated by CMC tools can be distracted in the
Internet-based environments. Building upon social media platforms, online communities afford
connections to others and thus encourages personal links and sharing knowledge. As knowledge
collaborators must deal with differences in motivations, knowledge bases, assumptions, and
working patterns (Shapiro, Furst, Spreitzer & Von Glinow, 2002), to complete knowledge-
related tasks requires a greater degree of rational thinking from collaborators. However, in
50
virtual communities where collaborators enjoy a high level of interactivity (Jones, 1997) and
pleasure, rational thinking may be temporarily inhibited.
Furthermore, holding a centralized position in a network may lead to a high level of
embeddedness (Uzzi, 1997), which may limit the focal individual’s creativity. Embeddedness
refers to the fact that the communicative history among members of a network stabilizes the
members’ connections (Marsden, 1981). Being embedded in collaboration networks creates
redundant informational ties that create knowledge homogeneity, resulting in lower levels of
collective creativity. As collaborators tend to rely on the connections with familiar others
(Hedegaard & Tyran, 2011), they are prone to applying a common existing framework to
understand new knowledge, thus decreasing the likelihood that creativity will emerge (Cohen &
Levinthal, 1990). As networks help to transfer and filter knowledge, a high level of
embeddedness implies a preference for homogeneous and redundant information obtained from a
high proportion of closely linked ties in the network; hence, the knowledge they share with each
other is inclined to repeat existing knowledge rather than explore unknown but innovative
domains (Leenders et al., 2003). Knowledge is accumulated through intense interaction in which
collaborators share similar perspectives and opinions (Lin, 2001) and eventually are inclined to
reject contradictory viewpoints and accept knowledge that is consistent with the dominant
perspectives. Such a process of knowledge diffusion gives rise to a lack of variety in points of
view, which brings about isolation and closure of a network (Reagans & Zuckerman, 2001). As a
result, network-imposed blindness occurs (Kautonen, Zolin, Kuckertz & Viljamaa, 2010) and the
socialized network members are expected to contribute a low level of creativity.
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Therefore, in the context of the crowdsourcing innovation challenge, this research
hypothesizes that:
H3: Crowd members’ network centrality negatively predicts the innovativeness of
their knowledge contribution in crowdsourcing.
Communication Issues, Network Position, and Knowledge Integration
Despite that networked communication may facilitate information sharing, digital
networks have been found to pose challenges for collaborators who particularly seek knowledge
integration. Research has shown that digital networks are increasingly used in collaborative work
at the group level (Baltes et al. 2002; Burke & Chidambaram, 1999; Dennis & Wixom, 2002;
Galegher & Kraut, 1994; Graetz et al. 1998; Hiltz & Johnson, 1990; Mennecke & Valacich,
1998; Straus & McGrath, 1994). However, compared with face-to-face communication, a limited
level of knowledge integration can be achieved due to several issues occurring at both the
individual and collective levels.
On the one hand, at the individual level, interaction mediated by CMC tools reduces
collaborators’ cognitive capacity for information processing because the cognitive load increases
when they respond to others and simultaneously consider their own ideas (Graetz et al., 1998;
Robert & Dennis 2005; Straus, 1996). In the mediated communication, collaborators tend not to
process all information because their attention may be blocked by a specific piece of information
and thus cannot be allocated to processing the remaining information (Heninger, Dennis &
Hilmer, 2006; Straus, 1996). Essentially, the generation of integrative knowledge can be viewed
as memory cognition. In electronic brainstorming, the process by which individuals identify idea
52
attributes and retrieve them from memory can be limited; hence, interpretations can be
significantly affected (Nagasundaram & Dennis, 1993).
On the other hand, at the collective level, decreased shared understanding is caused by a
lack of social cues or feedback and interactions in this community are restricted by text-based
asynchronous communicating approaches (Dennis & Wixom 2002; Straus & McGrath, 1994).
Using lean digital networks makes it difficult for collaborators to signal and recognize emotional
or attitudinal information, because the transmission of verbal tones and nonverbal cues is fairly
limited (Daft & Lengel 1984, 1986; Kiesler, Siegel & McGuire, 1984; Sproull & Kiesler, 1986).
Meanwhile, there is also a barrier for collaborators to sending and receiving concurrent feedback
when communicating via lean digital networks (Daft & Lengel, 1984, 1986; Dennis & Wixom,
2002; Kiesler et al., 1984; Sproull & Kiesler, 1986). Moreover, coordination of the work and
control over the quality of performance become difficult when using digital tools, since
traditional monitoring mechanisms for team coordination may not be as efficient as in face-to-
face collaboration (Jarvenpaa, Knoll & Leidner, 1998; Piccoli & Ives, 2003). When knowledge
collaborators communicate via digital networks, they can initiate and manage several parallel
conversations simultaneously, as well as leave current discussions and move on to new topics at
any time, ultimately resulting in unsatisfactory knowledge integration (Strømsø, Grøttum &
Lycke, 2007). In parallel conversations, useful knowledge can easily be ignored and the
“window” for appropriate integration can be lost (Dennis, 1996; Strømsø et al., 2007).
When collaborators work together in generating problem solutions, dominance of the
communication by certain individuals can yield certain negative results. First, those who
dominate the communication are likely to experience information overload (Bawden &
53
Robinson, 2009; Schick, Gordon & Haka, 1990). Knowledge integration is generated via
numerous bases of expertise from multiple knowledge collaborators rather than any single
individual. As such, dominators in knowledge collaboration conversations may waste time with
excessive responses to potentially irrelevant or peripheral information, spend little time on in-
depth analysis of useful knowledge, and consequently become impaired (Leenders et al., 2003),
thus contributing little to the integration of knowledge. In communication networks for
generating new product ideas, collaborators assigned as central nodes often need to manage the
timely flow of information across sub-groups, and therefore do not have time to think thoroughly
about the content of various knowledge; as a result, those occupying central positions in the
network are rarely able to integrate information (Leenders et al., 2003).
Occupying a centralized position in the network can lead to a high level of embeddedness
(Uzzi, 1997), which may limit the focal individual’s scope for knowledge transfer and
integration. As Granovetter (1985) suggested, embedded individuals can be over-socialized in a
network and hence sensitively react to network insiders’ opinions rather than the outsiders’
thoughts. In accordance this line of reasoning, several empirical studies have demonstrated that a
high level of embeddedness tends to generate unsatisfactory performance (Bathelt, Malmberg &
Maskell, 2004; Boschma, 2005; Gargiulo & Benassi, 2000; Masciarelli, Laursen & Prencipe,
2010). Furthermore, over embeddedness tends to change the pattern of resource allocation by
offering opportunities to those who are mostly embedded rather than most capable (Andersen,
2013). Individuals evaluate the resources they could obtain from various collaborators and are
then inclined to avoid possible risks while maximizing potential gains (Alhakami & Slovic,
1994; Delmestri, Montanari & Usai, 2005; Dyer & Chu, 2003; Granovetter, 1977; Sorenson &
54
Waguespack, 2006). Consequently, as collaborators tend to follow existing collaboration paths
and work with former partners, less integrative knowledge can be generated due to the fact that
all involved collaborators stick to their existing perspectives and do not feel the need to absorb
and synthesize new knowledge. Therefore, it can be hypothesized that:
H4: Crowd members’ network centrality negatively predicts their production of
knowledge integration in crowdsourcing.
Knowledge Diversity, Network Position, Innovation and Knowledge Integration
Embedded members in an organization or a network need to manage multiple links,
which are “formal or informal connections between a person and institutions or other people”
(Mitchell, et al., 2001, p.1105), and as a consequence may experience an excessive amount of
psychological pressure or emotional exhaustion. This leads to a decrease in members’ intrinsic
motivation so that they often exhibit poor performance in their work (Hur, Moon & Jun, 2016;
Karatepe, 2012). Excessive stress also hampers creativity, especially when it arises from
“relationship with others and from organizational structure” (Talbot, Cooper & Barrow, 1992,
p.183). Likewise, in collaborative teamwork, stressful working conditions may decrease the
creativity of the team (Dayan & Benedetto, 2011). Indeed, as individuals become more and more
embedded in networked interactions, they must invest an increasing amount of effort in
maintaining and managing social connections, and hence may have less time to devote to
thoroughly considering the content of interactions.
In collaborative projects, participants strongly embedded in a network possess various
kinds of social capital (Holtom, Mitchell & Lee, 2006; Moran, 2005); however, it also means
that they are the nexus of the networked information flow. Those who are deeply embedded in a
55
network are exposed to an excessive amount of information, bridging multiple sources and
transferring knowledge across various domains (Uzzi, 1997). Ultimately, an increasing level of
embeddedness may result in information overload, which describes a situation in which “there is
so much information that it is no longer possible effectively to use it” (Feather, 1998, p.118). For
information perceivers, an excessive information load is difficult to process efficiently (Klapp,
1986).
Information overload impedes perspective taking, as it creates an enormous cognitive
burden. Individuals who experience overload tend to feel anxious and thus are incapable of
coping with the vast amount of information. Recalling prior information becomes more difficult
for the confused individuals and their ability of to set priorities is reduced (Schick, Gorden &
Haka, 1990). When faced with the information, individuals often attempt to quickly digest it
“with omission, greater tolerance of error, mis-cueing or mis-attributing the source of
information, filtering its message, abstracting its meaning, using multiple channels to decode and
transmit its content, and finally through seeking escape” (Sparrow, 1999, p.144). Information
overload also decreases the quality of cognitive judgment or evaluation (Abdel-Khalik,1973;
Chewning & Harrell,1990; Shields,1980; Snowball, 1980) and creates more confusion in
interpreting the information (Cohen, 1980; Jacoby, Speller, & Kohn, 1974). In overload
situations, individuals are more inclined to seek confirmatory stimuli in order to reach a
conclusion, making it very likely that unexpected but important informational cues are neglected
or missed (Kiesler & Sproull, 1982; Waller, Huber & Glick, 1995). This selective perception
may give rise to an incomplete and inaccurate understanding of others’ viewpoints. In
collaborative teamwork or organizations, information overload has been found to negatively
56
relate to team performance such as face-to-face decision making (Chewning & Harrell, 1990;
O’Reilly III, 1980) as well as computer-mediated collaborative projects (Ellwart, Happ, Gurtner
& Rack, 2015). Information overload also closely relates to communication overload (Sparrow,
1999): when communicators have to deal with overly complex communication inputs generated
via interactions with others, including information and knowledge (Farace, Monge & Russell,
1977). Accordingly, communicators ‘drown’ in a sea of communication and are not able to
generate high-quality cognitive responses in their interactions with others. In collaborative work,
a large volume of information forces individuals to devote much time to searching for useful
information rather than undertaking in-depth analyses of the information (Sparrow, 1999), which
makes it difficult for collaborators to understand others’ perspectives. Subsequently, individuals
are less liable to employ cognitive or rational thinking or to thoroughly examine the assumptions
and meanings of others’ perspectives. When these types of overload appear in social interactions,
they exert a smaller degree of comfort as well as a higher level of turnover intensions in
collaborative work (Podsakoff, LePine & LePine, 2007).
Particularly in the context of computer-mediated communication, information overload is
mainly manifest in conversational overload (Whittaker, Terveen, Hill & Cherny, 1998) and
information entropy (Hiltz & Turoff, 1985). Conversational overload refers to the fact that in
virtual environment, individuals are faced with too many messages, to which they may not be
able to respond. Information entropy refers to that a large volume of messages is generated in
online conversations and cannot be easily decoded because they are in an unorganized state. For
individuals who are active in the communication in virtual communities, several factors can
cause conversational overload and information entropy. First, when online communication takes
57
place among a group of individuals not sharing a common knowledge base, inadequate prior
knowledge leads to an inadequate understanding of what others are talking about, thus cognitive
overload increases (Chen, et al., 2011). Second, individuals involved in online interaction may
vary in terms of their interests. And not favor the same types of interactions. Cognitive overload
can thus be triggered by engagement in conversations that are of no interest to the user or an
undesired communication approach (Ljungberg & Sorensen, 1998, 2000). For example, the
communication pattern might be obtrusive or intrusive, ephemeral or persistent (Ljungberg &
Sorensen, 2000). If the content or pattern of the communication pattern induces
uncomfortableness, cognitive overload will occur. With regard to computer-supportive
collaboration, the issue of overload acts as the underlying mechanism for interaction problems
occurring among collaborators, hinders coordination and leads to confusions (Thompson &
Coovert, 2003) and disagreements (Fjermestad, 2004). In order to reduce the overload, as
research has suggested, collaborators will need to select the information that is most relevant
rather than rely on all of it when collectively generating solutions (Engelmann, Tergan & Hesse,
2009).
When experiencing information overload, individuals are likely to ignore complicated
information, respond simpler messages, or simply terminate the interaction (Jones, Ravid &
Rafaeli, 2004). The communication amongst individuals constitutes a community. Nevertheless,
maintaining conversations and connected relationships, as well as coping with the negative
products of group activity (such as noise) can waste collaborators’ much time and effort.
Communication overload results in information overload, as processing messages requires
individuals’ cognitive effort. The limited capability of individuals to effectively process various
58
patterns of virtual public interactions leads to an overload status. As a result, communication in
virtual space can be constrained (Jones & Rafaeli, 2000).
Information overload harms the benefits of knowledge diversity in knowledge
collaboration. In dealing with an enormous amount of information, individuals occupying a
central place in the communication network may experience a cognitive overload. This overload
decreases individuals’ knowledge integration as well as diminishes their creativity and
innovativeness because it hinders perspective taking and the impact of knowledge diversity. To
comprehensively decode knowledge and generate innovation requires a cognitive capacity for
comprehensive thinking and analyzing; nevertheless, when knowledge collaborators are in
overload situation, such a capacity tends to decline. Moreover, an increased cognitive load may
reduce collaborators’ ability to handle the uncertainty, complexity, as well as the ambiguity of
diverse knowledge, such that messages which generate smaller information loads are more likely
to prevail. Cognitive overload occurs when individuals experience information-related anxiety
engendered by an increased need for a decision on categorizing various types of information, as
well as by various types of distraction when communicating with others (Kirsh, 2000). For
project collaborators strongly embedded in networks, this implies an increasing need to
anticipate and accept easily understood homogeneous knowledge and decreased motivation to
digest diverse knowledge that is unfamiliar with and thus difficult to understand. On this
account, the positive effect of heterogeneous knowledge is expected to decrease with information
and cognitive overload incited by a central position in the network.
In light of the theoretical observations mentioned above, this research proposes that:
59
H5: There is a negative interaction effect between crowd members’ network
centrality and knowledge diversity. Specifically, network centrality decreases the
effect of knowledge diversity on the innovativeness of ideas generated by the
crowd.

H6: There is a negative interaction effect between crowd members’ network
centrality and knowledge diversity. Specifically, network centrality decreases the
effect of knowledge diversity on the emergence of knowledge integration in
crowdsourcing.
Collective Reflection, Semantic Networks, and Knowledge Sharing
Collective Reflection in Knowledge Collaboration
Knowledge collaborators rely on reflection to comprehensively understand the
knowledge shared by others. Reflection indicates the process in which individuals thoroughly
consider previous performance in order to identify deficiencies and make improvements in future
actions (Grant, Franklin & Langford, 2002). In collaborative teamwork, such a process is
especially characterized by task reflexivity, which refers to “the extent to which team members
overtly reflect upon the group’s objectives, strategies, and processes and adapt them to current or
anticipated endogenous or environmental circumstances (West, 1996, p.559). Reflexivity, either
of tasks or social interactions, mainly consists of cognitive reflection, planning, as well as action
and adaptation (West, 2000). As suggested by West (2000), reflection comprises behaviors such
as “questioning, planning, exploratory learning, analysis, diversive exploration, making use of
knowledge explicitly, planfulness, learning at a meta-level, reviewing past events with self-
60
awareness, and coming to terms over time with a new awareness” (p.4). Cognitively, reflection
fosters the awareness of knowledge gaps and dissonance, so that individuals are motivated to
seek additional knowledge to make plans for future improvements. Through critical evaluations
of strength and weaknesses, individuals can come to deeper realizations of their current status,
and hence obtain a vision of the future.
Individuals undertake self-reflection when attempting to make sense of new knowledge
and recognize dissonance so as to advance performance. According to Murray, Hourigan and
Jeanneau (2007), self-reflection facilitates analyzing and planning. Similarly, in discussions with
others, individuals have the opportunity to learn about feedback from peers or other discussants
(Liu & Tsai, 2005; Wiecha, Gramling, Joachim, & Vanderschmidt, 2003). When teams are
reflexive, they think thoroughly about long-term strategies and consequences, collective
performances, as well as environmental cues they may be able to take advantage of, so as to
make advancements in collaborative work (West, 2000). Based on members’ collective
reflection, team collaborators then make plans in order to adjust goals and decide next-step
actions. Such a process is usually intertwined with the execution of adaptations and the
implementation of preplanned goals, as the adjustment plans are often shaped by dynamic
collective interactions as well as task feedback (Weingart, 1992). With a blueprint agreed upon,
team collaborators then conduct goal-oriented actions so as to accomplish the desired changes
(Hacker, 2003; West, 2000).
Reflection is more likely to occur among diverse rather than homogeneous collaborators,
because it is more important for collaborators to reflect on their opinions in order to achieve
agreement (Schippers, Den Hartog, Koopman & Wienk, 2003). Collaborators that are diverse in
61
knowledge domains and skillsets need to reflect on a shared goal so as to maintain a clear path
towards the common goal. Through reflexive thinking, collaborators achieve a better and clearer
understanding of each other’s roles and contribution (Dyer, 1995). Reflexivity facilitates mutual
understanding among diverse collaborators, and thus produces positive collective outcomes
(Gibson & Vermeulen, 2003). High task reflexivity amongst collaborators may also improve the
collective effectiveness as well as creativity (De Dreu, 2002; Schippers, et al., 2003; Tjosvold,
Tang, & West, 2004). This is because members with heterogenous views are likely to raise
different issues, which further trigger consideration of hidden facts and potential alternatives,
ultimately benefiting collective reflection (González-Romá & Hernández, 2014; Williams &
O‘Reilly, 1998).
Reflection occurs when the communication environment is friendly and when knowledge
collaborators experience safety and trust in sharing opinions. When knowledge collaborators
expect cooperative and unchallenged feedback (Kramer & Tyler, 1995), they feel encouraged to
speak up without worrying about criticism. Edmondson (1999) demonstrated that in teamwork,
when collaborators feel psychologically safe, they are able to undertake reflexive activities
together, including “asking questions, seeking feedback, experimenting, reflecting on results, and
discussing errors or unexpected outcomes of actions” (p.353). In a similar vein, other research
has found that reflection appears in collaborative groups where members display interests in and
show comprehension of others’ contributions (Hoegl & Parboteeah, 2006; Schippers, et al.,
2003). In such an environment of interaction, collaborators can adequately deal with possible
conflicts, as a cooperative communication environment may aid calm reflection on previous
errors and inconsistencies, leading to solutions for refining their work (Tjosvold, Hui & Yu,
62
2003). An open and constructive communication atmosphere promotes reflection induced by
paradoxical opinions (Jehn, 1995). Meanwhile, when collaborators work on interdependent tasks
and rely on each other, they tend to reap benefits from conflicting viewpoints during collective
reflection (Gurtner, Tschan, & Bogenstätter, 2009). Task-related contradictory opinions inspire
the consideration of alternative paths, in which innovation often resides (West & Richter, 2008).
Reflection can be found in crowdsourcing innovation challenges, whereby a common
vision is often set for participants cooperating in solving a broadly defined problem by the
contribution and exchange of unique knowledge. Collaborators with a shared vision are inclined
to engage in collective reflection, as the shared vision fosters a commitment to the task and
encourages risk taking and exploration, thus further giving rise to collective innovation (Agrell &
Gustafson, 1994; Senge, 1990). When collaborators work towards a common goal, their
reflective behavior can assist shared understanding of a task and boost creativity (Schippers, et
al., 2003). Meanwhile, reflection occurs when there is a high level of cooperation, which induces
reflexivity by allowing constructive controversy among collaborators (Tjosvold, Wong, Nibler,
& Pounder, 2002; Tjosvold, et al., 2004). What is more, the communicative skills that
collaborators display in facilitating the cohesiveness of a team or community play an important
role in collective reflection because a proactive communication approach tends to shape the
interaction in a positive way (Hoegl & Parboteeah, 2006; Schippers, Den Hartog & Koopman,
2007).
Reflective communication spurs innovation (Schippers, West & Dawson, 2015). For
example, in idea generation, individuals conduct reflective thinking to help each other find ways
to work more effectively and creatively (Paulus & Yang, 2000). As reflection essentially builds
63
on deep processing of information and critical evaluation of information, creative ideas can be
expected when collaborators collectively reflect on their performance rather than working
without reflection (Carter & West, 1998; De Dreu, Nijstad, & van Knippenberg, 2008;
Hülsheger, Anderson & Salgado, 2009; Tjosvold, et al., 2004; Wong, Tjosvold, & Su, 2007).
When collaborating on a broadly defined task, team members demonstrating a high level of
reflexivity are more capable of producing innovation (De Dreu, 2002). Therefore, when
leveraging reflexivity to crowdsourcing innovation challenges in which diverse crowd members
constantly make sense of existing knowledge, actively produce new knowledge, sharing a
common vision, as well as cooperating on finding solutions, the dynamic patterns of crowd
members’ reflection are worthy of close examination.
Socio-Semantic Network and Knowledge Sharing
As an important manifestation of knowledge sharing and online collaborative knowledge
management, semantic networks have increasingly received scholarly attention. Identifying and
motivating expertise contributions and experience sharing, online communication sites have
introduced an advanced approach to social participation.
Employing a communicative structure similar to the blogosphere (Hookway, 2008),
online sites for knowledge collaboration, such as those for crowdsourcing innovation challenges,
are constructed as a blog-based networks in which participants share, exchange and produce
knowledge through posting to or commenting on short blogs. In such a sphere, communication
and collaboration are embodied by participants’ non-verbal referencing of each other.
The blogosphere-type of online community is essentially a socio-semantic network in
which each blog can be identified by both semantic and relational attributes. Relational attribute
64
refers to that individuals’ positions within the network are configured by blog post contributors’
back-forth communication; semantic attribute refers to the cognitive embodiments displayed in
each post (Roth & Cointet, 2010). Accordingly, the blogosphere community offers a unique
avenue for observing knowledge sharing and information flow. Compared to social relationship
networks, blog-based networks are topic-oriented and are more structured, enabling a closer
examination of the influential nodes, emerging topics, as well as the ever-evolving link
structures. For instance, research on the community structure of blogosphere has presented that
the community is maintained by inter-post linkages among blog contributors (Kumar, Raghavan,
Rajagopalan & Tomkins, 1999).
The patterns of knowledge distribution are grounded in network structural features in
blog-based virtual community. Prior research has investigated the trend of topic evolution
(Glance, Hurst & Tomokiyo, 2004), the crowd’s sentiment underlying their various opinions
(Mishne & de Rijke, 2006), as well as the co-existents and the cyclic pattern of chatters and
spikes in online conversations (Balog, Mishne & deRijke, 2006; Bansal & Koudas, 2007; Gruhl,
Guha, Liben-Nowell & Tomkins, 2004). Viewing blog-based virtual community as a socio-
semantic web in which members co-produce visible symbolic artifacts of their collective
knowledge, knowledge management can be investigated by observing these interconnected
cognitive configurations (Schoop, De Moore, & Dietz, 2006). Socio-semantic web highlights the
human interaction underlying the creation of blog posts, and intends to maintain a community in
which participants collectively elicit information and contribute knowledge to improve their
collaborative work (Cahier, Zaher, Leboeuf & Guittard).
65
Adopting the view of socio-semantic networks, the content and patterns of shared
knowledge in crowdsourcing innovation challenges can be examined specifically by focusing on
mapping the dynamic configurations that crowd members’ networked communication displays.
In view of the foundational theoretical frameworks reviewed above, this research poses
the research question:
RQ1: From the perspective of semantic network, what is the pattern of crowd
members’ collective reflection when they collaboratively share knowledge in
crowdsourcing innovation challenges?

66
CHAPTER 4 THREE STUDIES: ANALYSES AND FINDINGS
This research project mainly analyzed online crowds’ activities in organization-partnered
open innovation challenges. Data was obtained from crowdsourcing open challenges sponsored
by 21 publicly known organizations that had been identified via university-associated
partnerships. Each organization used a similar virtual platform and broadcasted an open
challenge call with a strategic problem about the organization’s innovation and development, and
the crowd responded to these challenges on corresponding listservs. Brief descriptions of all
these 21 open challenges are presented in Figure 1 and Table 1.

Figure 1. Description of Numbers of Posts and Participants in Crowdsourcing Open Challenges

The open innovation challenges ran for 7 to 10 days, and users proposing top solutions
were offered small incentives by sponsoring organizations that determined the winners. Each
open challenge employed a unique crowd, so as to avoid possible assimilation effects (Lord &
Taylor, 2009). When participants registered and logged into the system, they were presented a
home page that stated the theme of the challenge as well as a list of most recent posts and
comments. After logging in, participants could initiate posts, view and comment to others’ posts,
67
or vote to posts that they liked. All the online activities were anonymous, and participants could
also create usernames if they wanted.
Table 1. Description of Organizations, Crowdsourcing Cases and Activities
Organization Type Crowdsourcing Theme
Total Numbers Of
Top Threads Comments
1. Fashion Industry
Company
Maintenance of Customer
Relationship
39 31
2. US IT Company Plan for Future Revenue 19 42
3. Telecom Infrastructure
Company
New Product Design 42 50
4. Distribution Company New Business Model 36 10
5. Film Production
Company
New Product Design 40 73
6. Data Storage and
Analytics Provider
Development of Business
Strategy
10 9
7. European IT Company Development of Business Model 11 56
8. Finance Company New Business Model 115 253
9. Chinese IT Company New Marketing Plan 11 18
10. Industrial Service
Provider
Plan for Future Revenue 66 29
11. Toy Manufacturing
Company
Marketing Plan 15 40
12. Pets Care Provider
Company
Marketing Plan 86 121
13. Pest Management
Company
Business Model 143 605
14. Enterprise Software
Provider
Marketing Plan 29 27
15. Data Software Provider New Product Design 92 95
16. US Financial Service
Company
Business Model 83 53
17. Japanese Automotive
Manufacturer
Plan for Future Revenue 45 43
18. Security Service
Provider
New Business Model 33 112
19. Health Insurance
Company
Plan for Future Revenue 101 195
20. Accounting Service
Provider
New Business Model 82 182
21. Sports Association New Product Design 34 24

A total of 3,200 posts were harvested from 21 crowdsourcing open challenges, in which a
total of 486 participants were involved. All the posts were rated in terms of novelty, competitive
68
advantage, as well as implementability by the organizations’ managers. The posts were identified
by the organizations in the crowdsourcing platform as eight types of knowledge: fact (332),
tradeoff (199), example (208), wild idea (1182), question (418), agreement (433), integrative
solution (242), and other (186). In particular, fact refers to posts that were presenting objective
statements, data, or statistics; tradeoff refers to posts that describe conflicting situations; example
demonstrats particular cases related to real-life experiences; wild idea refers to random thoughts
that were loosely structured and might not be well connected to the core problem; question raised
possible concerns or issues related to the theme of the open challenge; agreement explicitly
provides supports, endorsements, and affirmative responses; and finally, other refers to all the
irrelevant posts.
Study I
Method
The focus of Study I is the innovativeness of the posts generated by online crowd. Its
main purpose is to examine the effects of knowledge diversity and network centrality on the
innovativeness of the posts. After excluding the posts classified as other, all the other seven
types of knowledge were considered in this study. Measures in this study were operationalized as
follows.
Innovativeness of Knowledge Contribution. The main dependent variable of interest in
this study was the level of innovativeness of crowd members’ posts from the perspective of the
organizations. It was calculated by combining managers’ ratings of competitive advantage,
novelty, as well as implementability, which is consistent with the innovativeness measure in the
literature (Amabile et al., 1996). Each of them was based on a scale of 0 (not at all) to 7 (to a
69
great extent) and they were combined as the innovativeness measure on a 0-21 scale (M=1.58,
SD=3.78).
Prior Knowledge Diversity. As one major independent variable, knowledge diversity was
operationalized by considering the occurrence of facts, tradeoffs, examples, wild ideas,
questions, agreements, and integrative solutions prior to each individual post. Prior knowledge
contributed by the focal participants were not considered. Specifically, this study employed
Blau’s (1977) diversity index, which is defined on the basis of information entropy and
mathematically captures population diversity and qualitative variation. Blau’s index is an
appropriate calculating approach for this study as the dataset contains a reasonable number of
qualitative knowledge categories. All six/seven types of knowledge posted prior to each
individual post were used to calculate corresponding knowledge diversity scores as:
!=1−%&
'
(
)
'*+

where pi refers to the proportion of individuals or objects in a category and N refers to the total
number of categories.
Network Centrality. This independent variable was measured by calculating participants’
eigenvector centrality in the network constructed within each crowdsourcing open innovation
challenge. Based on observing an affiliation network, Grewal, Lilien and Marllapragada (2006)
used network centrality to conceptualize embeddedness; in particular, positional embeddedness
was defined as the eigenvector centrality. This study follows this operationalization. In
particular, adjacency matrix was constructed by considering all participants who had appeared
within a same thread and thus formed a tie with one another; doing so can capture the diffusion
70
of social influence in the production of collective innovation, because such kind of influence in
blogsphere-like forums usually lies at the topical interlink (Awotunde & Jimoh, 2019; Wang,
Hsiao, Yang & Hajli, 2016). For instance, if participants A, B and C took part in the same
discussion thread, they are viewed as forming a tie between each pair of them, namely, A and B,
A and C, B and C. In this study, each crowd member’s eigenvector centrality was calculated
using igraph package in R.
Time-based Phase. In order to control for the effect of timing on innovation (e.g. Bowen,
Rostami & Steel, 2010), this study includes timing as a control variable. According to the
distances between posts’ timestamps recorded in the system, each innovation challenge was
divided into early, middle and late phases using k-means clustering analysis with Matlab
programming. The three phases were coded as 1, 2, and 3, respectively.
Several control variables were included when building statistical models. First, based on
a consideration of the influence of group think on innovation (Janis, 1972; Wenger et al., 2002),
for each innovation challenge, the total number of involved participants (M=45.93, SD=24.33)
and the average number of posts (M=5.02, SD=1.89) were calculated and included in the model.
Moreover, the average number of votes and views (M=15.52, SD=7.72) within each challenge
have been included in the model as well, in order to control for possible inflation effect caused
by qualitative uniqueness of the challenge (Wang & Fesenmaier, 2003). Finally, participants’
own online activities such as votes (M=34.77, SD=50.76), views (M=11.78, SD=14.74), and
posts initiated (M=6.01, SD=6.27) were also controlled for in the model so as to take
participants’ online activeness into account.
Data Analysis
71
To test hypotheses about the innovativeness of crowd members’ knowledge contribution,
this study utilized generalized linear modeling for inferential statistical analysis. In order to rule
out a potential issue of multi-collinearity, variance inflation factors were checked, and the results
indicated that multicollinearity was not an issue (Craney & Surles, 2002; Kutner, Nachtsheim &
Neter, 2004). Results with a p-value lower than .05 were considered statistically significant.
Results
Descriptive statistics and zero-order correlations, as well as statistical results of
hypotheses testing are presented in Table 2 and Table 3. The first hypothesis suggested that prior
knowledge diversity positively predicts the innovativeness of crowd members’ knowledge
contributions. The hypothesis was supported by the data (β = 0.20, p < .05), indicating that being
exposed to a wide variety of knowledge can enable crowd members to be more innovative when
generating thoughts during crowdsourcing. The second hypothesis proposed that network
centrality tends to decrease the likelihood that crowd members generate innovative thoughts, and
it has been supported by the data as well (β = -1.27, p < .001).
Table 2. Effects on Innovativeness (Hypotheses 1, 3, and 5)
DV: Innovativeness Baseline Model Full Model
Intercept 1.58 (.07)*** 1.54 (.07)***
Total Votes -0.30 (.10)** 0.05 (.10)
Total Views -0.33 (.08)*** -0.07 (.08)
Total Top Posts 0.01 (.11) 0.36 (.11)**
Time Cluster -0.29 (.07)*** -0.25 (.07)***
Average Votes and Views in Challenges -0.18 (.09)* -0.48 (.09)***
Average Posts in Challenge 0.03 (.09) 0.02 (.10)
Total Number of Participants 0.05 (.09) -0.75 (.11)***
Prior Knowledge Diversity 0.20 (.10)*
Network Centrality -1.27 (.10)***
Prior Knowledge Diversity * Network Centrality -0.22 (.07)**
Likelihood Ratio Chi-Square 110.52 (df=7) 270.48 (df=10)
Bayesian Information Criterion (BIC) 17450.26 17314.49
*p<.05, **p<.01, ***p<.001

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Moreover, as the third hypothesis proposed, the data demonstrates an interaction effect (β
= -.22, p < .01) between network centrality and prior knowledge diversity on the innovativeness
of crowd members’ knowledge contribution. Specifically, a higher level of network centrality
will diminish the positive effect of prior knowledge diversity, which results in a negative effect
on the innovativeness of crowd members’ contributed knowledge (see Figure 2).

Figure 2. Interaction Effect between Knowledge Diversity and Network Centrality on
Innovativeness

73
Table 3. Descriptive Statistics and Zero-order Correlations (Innovativeness)
Min Max
Mean
(S.D.)
VIF
(standar-
dized)
Total
Votes
Total
Views
Thread
Initiated
Average
Votes and
Views in
Challenges
Average
Posts in
Challenge
Number of
Participants
in
Challenge
Time
Cluster
Knowledge
Diversity
Network
Centrality
Innovation
Total Votes 0 340
34.77
(50.76)
2.49 -- -- -- -- -- -- -- -- -- --
Total Views 0 53
11.78
(14.74)
1.66 0.33** -- -- -- -- -- -- -- -- --
Threads
Initiated
0 23
6.01
(6.27)
2.72 0.61** 0.49** -- -- -- -- -- -- -- --
Average Votes
and Views in
Challenge
0 27.6
15.52
(7.72)
1.84 0.42** 0.43** 0.32** -- -- -- -- -- -- --
Average Posts
in Challenge
0 7.16
5.02
(1.89)
2.05 0.13** 0.03 0.24** 0.15** -- -- -- -- -- --
Number of
Participants in
Challenge
0 82
45.93
(24.33)
2.81 0.09** -0.17** 0.11** -0.25** 0.53** -- -- -- -- --
Time Cluster 1 3
2.18
(0.85)
1.17 0.03 -0.01 0.05** 0.02 -0.13** -0.16** -- -- -- --
Knowledge
Diversity
0 0.86
0.69
(0.18)
2.05 0.11** -0.05** 0.17** -0.02 0.60** 0.58** 0.12** -- -- --
Network
Centrality
0 0.28
0.09
(0.07)
2.06 0.38** 0.42** 0.44** 0.16** -0.16** -0.16** 0.15** -0.17** -- --
Innovativeness
(Dependent
Variable)
0 17
1.58
(3.78)
-- -0.13** -0.13** -0.11** -0.12** 0.01 0.01 -0.08** 0.01 -0.24** --
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Study II
Method
The focus of Study II is the production of knowledge integration by online crowd. The
main purpose of this study is to investigate how knowledge diversity and network centrality
influence the generation of knowledge integration. As with Study I, posts classified as other were
excluded from the study. The operationalization of measurements are as follows.
Knowledge Integration. In this study, the dependent variable of interest is whether or not
a post is integrative. Accordingly, all the 242 integrative solutions were coded as 1, and the
remaining posts were coded as 0.
Prior Knowledge Diversity. Same with Study I, the present study only considered the
knowledge contributed by others, excluding the focal participants’ own contributions. However,
different from Study I, prior knowledge diversity was measured by considering all non-
integrative posts (i.e. facts, tradeoffs, examples, wild ideas, questions, agreements) appearing
prior to each post. Consistent with Study I, Blau (1977)’s diversity index was employed to
calculate the prior knowledge diversity score of crowd members’ every post.
Network Centrality. Consistent with Study I, network centrality was operationalized as
eigenvector centrality (Grewal, et al., 2006). Different from Study I, in this study, crowd
members’ network eigenvector centrality was calculated based on their post-comment network.
Specifically, adjacency matrix was built by focusing on direct comments, which captures a
precise observation of ‘who speaks to whom’ (Haythornthwaite, 2005) so as to benefit a clear
mapping of participants’ knowledge integration trajectory. For instance, when participants B and
C both comment to participant A’s post, a tie is considered to be formed between A and B as
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well as between A and C, but not between B and C. Same with Study I, this independent variable
was calculated using igraph package in R.
Time-based Phase. As with Study I, three phases were coded as 1, 2 and 3, respectively,
so as to control for the effect of timing on knowledge integration (e.g. Bowen et al., 2010).
Several control variables were included in the statistical model. First, consistent with
Study I, participants’ own online activities such as votes (M=34.77, SD=50.76), views (M=11.78,
SD=14.74), and posts initiated (M=6.01, SD=6.27) were included to take care of the status of
participants’ activeness. Second, the average number of votes and views (M=15.52, SD=7.72)
was included in order to control for the community’s attitude on knowledge usefulness for
integration (Lee, Law & Murphy, 2011). Different from Study I, due to high multicollinearity,
this study did not include the average number of posts and the total number of involved
participants.
Data Analysis
This study employed binary logistic generalized linear modeling to test the hypotheses.
Variance inflation factors were checked in order to rule out the issue of multicollinearity (Craney
& Surles, 2002; Kutner et al., 2004). Results with a p-value lower than .05 were considered
statistically significant.
Results
Descriptive statistics and zero-order correlations are presented in Table 4. Results of
statistical hypotheses testing are summarized in Table 5.

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Table 4. Descriptive Statistics and Zero-order Correlations (Knowledge Integration)
Min Max
Mean
(S.D.)
VIF
(standardi
zed)
Total
Votes
Total
Views
Thread
Initiated
Average
Votes and
Views in
Challenges
Time
Cluster
Knowledge
Diversity
Network
Centrality
Knowledge
Integration
Total Votes 0 340
34.77
(50.76)
2.26 -- -- -- -- -- -- -- --
Total Views 0 53
11.78
(14.74)
1.67 0.33** -- -- -- -- -- -- --
Threads Initiated 0 23
6.01
(6.27)
2.68 0.61** 0.49** -- -- -- -- -- --
Average Votes
and Views in
Challenge
0
27.5
7
15.52
(7.72)
1.41 0.42** 0.43** 0.32** -- -- -- -- --
Time Cluster 1 3
2.18
(0.85)
1.05 0.03 -0.01 0.05** 0.02 -- -- -- --
Knowledge
Diversity
0 0.86
0.67
(0.18)
1.18 0.09** -0.05** 0.16** -0.04* 0.12** -- -- --
Network
Centrality
0 0.32
0.08
(0.07)
1.67 0.38** 0.47** 0.50** 0.17** 0.11** -0.18** -- --
Knowledge
Integration
(Dependent
Variable)
0 1
0.08
(0.26)
-- -0.05** -0.11** -0.03 -0.13** -0.07** 0.17** -0.14** --

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Table 5. Effects on Emergence of Knowledge Integration (Hypotheses 2, 4, and 6)
DV: Knowledge Integration Baseline Model Full Model
Intercept 0.08*** -3.12***
Total Votes 0.01 0.23
Total Views -0.01 -0.77**
Total Top Posts 0.01 0.40**
Time Cluster -0.02*** -0.21**
Average Votes and Views in Challenges -0.03*** -0.17
Prior Knowledge Diversity 0.04*** 0.74***
Network Centrality -0.02*** -0.34**
Prior Knowledge Diversity * Network Centrality -0.33***
Likelihood Ratio Chi-Square 112.45 218.90
Bayesian Information Criterion (BIC) 1373.49 1291.24
*p<.05, **p<.01, ***p<.001

Hypothesis 4 states that prior knowledge diversity positively predicts the production of
knowledge integration. The hypothesis was supported by the data (β = 0.74, p < .001), indicating
that others’ previously contributed diverse knowledge can lead crowd members to
comprehensively integrate the shared knowledge and generate solutions. Hypothesis 5, on the
contrary, predicts that network centrality will lower the probability that a member of the crowd
generates integrative knowledge. The data provides support for this hypothesis as well (β= -0.34,
p < .01).
Finally, Hypothesis 6 proposed that network centrality tends to diminish the positive
effect of prior knowledge diversity (β = -0.33, p < .001). This interaction effect suggests that an
increase on network centrality may reduce the likelihood that a crowd member proposes
knowledge integration even after being exposed to the diverse knowledge shared by others (see
Figure 3).
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Figure 3. Interaction Effect between Knowledge Diversity and Network Centrality on
Knowledge Integration

Study III
Method
The focus of this study is to examine the semantic representation of knowledge
contribution in crowdsourcing innovation challenges. While the number of posts in each
innovation challenge varies, most organizations have received around 200 posts. Two
outstanding organizations that have harvested more than 300 posts were selected as cases for
analysis in this study. Organization A is a finance company based in the United States, and it
crowdsourced for new ideas on the design of its business model; it has harvested a total of 368
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posts. Organization B is a pest management company based in the New Zealand, and it also
sought for new ideas about new business model; this company has harvested a total of 748 posts.
In order to capture the evolution of semantic representations in crowdsourcing, all posts were
clustered into three phases using k-means clustering analysis, which is consistent with Study I
and Study II.
Data Analysis
For each company, semantic network analysis was conducted separately for each phase.
A text analytics tool, Leximancer (https://info.leximancer.com/), was employed for semantic
network analysis. In pre-processing, typical stop words were filtered out so that the remaining
words that contributed to the meaning of the text can be analyzed. In general, stop words
removed from this study include articles, conjunctions, prepositions, and transitive verbs, such as
“an”, “as”, “between”, “just”, “then”, “you”, etc.
Based on natural language processing techniques, Leximancer first analyzes the
occurrence and frequency of each word and generates several concepts that represent a collection
of interrelated words. In accordance with Doerfel (1998), such concepts refer to a constellation
of words appearing together. For instance, the concept “pleased” may contain the words
“happy”, “glad”, and “delighted”. As such, words and terms are weighted by analyzing the
frequency that they appear in a sentence together with the concepts. After generating a list of
concepts, this analysis then produces a co-occurrence network matrix of all concepts, in which
the value in each cell refer to the frequencies that two concepts occur together in a single
sentence. On the basis of this co-occurrence network, clusters of connected concepts are
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developed and visualized, with each cluster characterized by a unique theme that reflects the
major interest of the crowd (Smith & Humphreys, 2006).
In this study, a list of frequency counts of most used concepts was generated, followed by
a semantic network analysis demonstrating the emergence and evolution of the content of
knowledge that crowd members have shared.
Results
The top 30 concepts generated by the crowd are presented in Table 6 and Table 7 for
selected organizations. Semantic network maps are then presented for visualizing connections
among terms as well as concepts. The size of the concept node in the maps indicates the count of
co-occurrence, meaning that compared to small nodes, larger nodes are more connected with
other concepts and are more central in the crowd-generated semantic knowledge network.
The Case of Organization A
In Organization A’s innovation challenge, concepts like “financial”, “services”, “banks”,
“idea” consistently ranked the top of the list across three phases, naturally because of the
business of the company as well as the theme of the company’s crowdsourcing innovation
challenge. When comparing the concepts across three phases, the early phase was characterized
by concepts related to financial literacy as well as basic knowledge of banking.
In particular, concepts like “game” and “workshops” only appeared in the first phase,
because both these two can serve as educational tools to improve individuals’ financial literacy.
In the middle phase, concepts related to “credit”, “generation”, “website”, “score” and “media”
emerged, suggesting that as discussion went on, crowd members started to think about in-depth
topics that are more relevant to the core theme of innovation challenge. Finally, the late phase
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was characterized by 1) the production of solutions to current problems, such as “app”,
“savings”, “account”, “college”, and 2) the emergence of the concept like “future” that
demonstrated a collective attention given to the long-term development of the company.
Table 6. The top 30 most frequently-occurring concepts in Financial and Banking Service
Crowdsourcing Innovation Challenge
Phase 1 Phase 2 Phase 3

Concepts

Count

Relevance

Concepts

Count

Relevance

Concepts

Count

Relevance
financial 46 100% financial 32 100% financial 46 100%
services 27 59% services 23 72% money 41 89%
money 26 57% companies 22 69% banks 41 89%
idea 24 52% idea 20 62% idea 37 80%
banks 21 46% banks 18 56% people 31 67%
savings 20 43% money 15 47% credit 27 59%
literacy 19 41% credit 14 44% app 23 50%
account 16 35% people 13 41% need 23 50%
users 15 33% generation 12 38% students 20 43%
app 15 33% use 10 31% savings 20 43%
game 12 26% website 9 28% account 19 41%
use 11 24% platform 8 25% users 17 37%
goals 10 22% need 8 25% time 14 30%
program 10 22% free 7 22% young 13 28%
young 10 22% take 7 22% different 11 24%
people 9 20% user 7 22% program 11 24%
product 9 20% based 6 19% use 11 24%
post 9 20% score 6 19% college 10 22%
feel 9 20% card 6 19% things 9 20%
time 9 20% app 6 19% create 9 20%
credit 8 17% education 6 19% used 9 20%
interest 8 17% youth 6 19% literacy 9 20%
seems 7 15% finances 6 19% services 9 20%
agree 7 15% things 5 16% education 9 20%
used 7 15% special 5 16% similar 9 20%
interested 7 15% media 5 16% take 8 17%
cards 6 13% students 5 16% future 8 17%
workshops 6 13% create 4 12% points 8 17%
instead 6 13% process 4 12% cause 8 17%
market 6 13% promote 4 12% charge 7 15%

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Additionally, this study conducted semantic network analysis to examine the
interconnections among concepts as well as the emergence of major themes (see Figures 4-6). In
these network maps, the bubbles represent unique themes and the individual dots inside the
bubbles represent major concepts that emerged from crowd’s knowledge sharing in the
innovation challenge. For each phase, different but overlapping themes were identified. When
comparing the knowledge shared across these three phases, it can be found that along with the
unfolding of the discussion, members of the crowd develop their thoughts from focusing on basic
financial and banking activities (such as “savings”) to highlighting the role of technology (such
as “apps”) in improving financial and banking business models.
Specifically, in the early phase (Figure 4), eight major themes emerged that indicated: 1)
ways to facilitate savings, such as developing smartphone applications or help individuals make
long-term goals (the bubble on the bottom left); 2) financial and banking services and products
(the bubble on the bottom center); 3) usage of financial services (the bubble at the very bottom);
4) the ideas for improving the public’s financial literacy such as using games (the bubble on the
bottom right); 5) people’s money that can be taken care of by financial programs and services
(the bubble in the very center); 6) time invested to the management of money (the bubble on the
top left); 7) young adults’ involvements in financial activities (the bubble on the top center); 8)
general information about financial market (the bubble on the top right).
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Figure 4. Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 1

Likewise, eight major themes have been identified in the middle phase (Figure 5),
demonstrating the content different from that in the early phase: 1) details about financial
services such as free service (the bubble at the very bottom); 2) general information about
current users of financial services (the bubble on the bottom left); 3) generation-related financial
and banking issues (the bubble on the bottom center); 4) information regarding people’s financial
life (the bubble on the bottom right); 5) banking activities related to credit or debit cards (the
bubble on the top left); 6) financial and banking services based on websites or e-platforms (the
bubble in the very center); 7) ideas to improve financial services (the bubble on the top center),
8) individuals’ financing activities (the bubble on the top right). Although the first theme
“financial” has appeared in both the early and the middle phase, the concepts it included has
been enriched in the middle phase.
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Figure 5. Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 2

Finally, in the late phase, the theme “financial” (the bubble at the very center) has been
further enriched by including new concepts such as “students”, “time”, “young”, and “college”.
In addition, crowd members in this phase generated seven other themes that demonstrate: 1)
need-based ideas for improving financial services (the bubble at the very bottom); 2) the
similarity in terms of characteristics of target customers (the bubble on the bottom left); 3)
possible smartphone applications that could be developed to provide better banking services (the
bubble on the bottom right); 4) banking-related issues such as credit and savings (the bubble on
the center right); 5) proposed programs that can facilitate people’s daily financial activities (the
bubble on the top left); 6) the uses of financial services (the bubble on the top center); 7)
millennials who are the major customers of future financial services.
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Figure 6. Semantic Network from Financial and Banking Service Crowdsourcing Challenge,
Phase 3

The Case of Organization B
The innovation challenge of Organization B was about pest control, and the evolution of
the content indicates that crowd members reflectively engaged in the discussion (see Figures 7-
9).
Table 7. The top 30 most frequently-occurring concepts in Pest Management Crowdsourcing
Innovation Challenge
Phase 1 Phase 2 Phase 3
Concepts Count Relevance Concepts Count Relevance Concepts Count Relevance
pest 150 100% pest 134 100% pest 104 100%
need 78 52% control 91 68% traps 55 53%
species 72 48% possums 88 66% need 55 53%
areas 70 47% traps 67 50% rats 54 52%
control 69 46% need 67 50% people 50 48%
possums 64 43% species 61 46% control 47 45%
eradication 58 39% areas 60 45% use 44 42%
cats 58 39% rats 58 43% possums 39 38%
native 55 37% cats 56 42% species 30 29%
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people 53 35% people 42 31% work 30 29%
use 52 35% native 40 30% time 26 25%
birds 46 31% animals 37 28% cost 25 24%
idea 46 31% time 37 28% free 21 20%
rats 45 30% work 36 27% problem 20 19%
traps 43 29% birds 31 23% agree 20 19%
predators 43 29% use 31 23% native 19 18%
work 41 27% predators 30 22% birds 19 18%
time 40 27% kill 29 22% public 19 18%
problem 37 25% food 26 19% food 18 17%
cost 36 24% idea 25 19% hunters 18 17%
numbers 34 23% free 24 18% areas 17 16%
stoats 33 22% eradication 24 18% effective 17 16%
large 26 17% used 23 17% land 17 16%
agree 26 17% problem 20 15% idea 17 16%
fences 24 16% research 20 15% stoats 16 15%
better 23 15% populations 19 14% funding 16 15%
mice 23 15% bait 19 14% ground 15 14%
money 23 15% public 19 14% kill 14 13%
public 22 15% rodents 18 13% sure 14 13%
wildlife 21 14% large 17 13% animal 14 13%

Figure 7. Semantic Network from Pest Management Crowdsourcing Challenge, Phase 1
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Throughout the early, middle and late phases, concepts such as “pest”, “control”, “need”,
“species”, and relevant animals like “possums”, “cats”, “birds” and “rats” remained frequently
used. The top-ranked theme “eradication” in the early phase had fallen in the middle phase, and
finally disappeared in the late phase. The concept “traps” which had not been mentioned very
frequently in the early phase, became popular in both middle and late phases. More interestingly,
the concept “research” only occurred in the middle phase rather than in the early or late phases.
Such a cross-phase evolution of concepts indicates that crowd members learn one another’s
thoughts during crowdsourcing and make attempts to refine their own ideas. For example, in the
early phase, eradication was considered as a useful pest management approach by most members
of the crowd, however, after a period of thought-provoking discussion, the crowd started to
realize that eradication was difficult and that using traps might be a more efficient approach.
Along with the discussion, crowd members also realized the need of more research because a
satisfactory pest management relies on scientific tools.

Figure 8. Semantic Network from Pest Management Crowdsourcing Challenge, Phase 2
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When comparing the semantic networks generated throughout early, middle and late
phase of crowdsourcing, several themes remained consistent whereas others have evolved. In the
early phase, unique themes were identified as 1) the general concern or life status of the public
(the bubble on the bottom right); 2) management of relevant native species and animals (the
bubble on the top left); 3) the possibility of eradication of pests (the bubble at the top right). In
the middle phase, several new themes emerged such as: 1) the focus of people’s need (the bubble
on the top right); 2) the balance among various requests raised by different people (the bubble at
the very top); 3) the best approach to manage a variety of species (the bubble on the top left).
Finally, in the late phase, four new themes emerged that indicated: 1) the appropriate time of
implementing pest control strategies (the bubble on the top left); 2) the appropriate tools for pest
control (the bubble on the center left); 3) the use of pest management strategies (the bubble on
the top left); 4) the agreement on balancing pest management and people’s needs (the bubble on
the bottom right). Taken together, the content variation throughout different phases indicates that
as the crowdsourcing unfolded, the crowds’ interests developed from calling for eradication
efforts to striking a balance between protecting human habitat and animal habitat, and ultimately
the crowd reached some agreements in terms of tools and timing of pest management.
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Figure 9. Semantic Network from Pest Management Crowdsourcing Challenge, Phase 3

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CHAPTER 5 DISCUSSION AND CONCLUSION
Study I
Examining the collaborative response of online crowd to open innovation challenges, this
study unpacked the effects of knowledge diversity and the centralized network position on
crowdsourced innovation. In general, this study demonstrated the impact of knowledge diversity
and the centralized network position on the innovativeness of crowd-generated ideas by
indicating that 1) participants in online crowdsourcing challenges can benefit from the diversity
of others’ knowledge when producing innovative ideas; 2) a participant’s ability to produce
creative ideas is hampered by his or her centralized network position that the participant hold;
and 3) the inspiration brought by the exposure to diverse knowledge can be harmed by the
centralized network position when participants attempt to generate innovative solutions.
In the first place, the findings of this study offered insights into the open innovation and
crowdsourced knowledge collaboration literature by demonstrating that others’ heterogeneous
knowledge can inspire creative thoughts in those who are exposed to it. Aligned with the
theoretical framework of perspective taking (Bartunek, Gordon & Weathersby, 1983; De Dreu,
Weingart & Kwon, 2000; Grant & Berry, 2011; Tjosvold & Johnson, 1977), the findings
suggested that when crowd members are framed by diverse knowledge and adopt various
perspectives, they tend to become more innovative. Meanwhile, the exposure to different
perspectives also encourages crowd members to explore new knowledge, which catalyzes
knowledge recombination and ultimately facilitates the production of innovative ideas (Lundvall,
1992; Quintana-García & Benavides-Velasco, 2008; van der Vegt & Janssen, 2003; Wenger,
2000; Woodman et al., 1993).
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Moreover, this study leverages the impacts of network position for research into
crowdsourced innovation. By extending the teamwork and organization science literature on
network embeddedness which suggests that individuals occupying central positions in the
communication network tend to prefer homogeneous knowledge as a way of maintaining their
status and existing connections (Hedegaard & Tyran, 2011; Marsden, 1981), this study
investigated the role played by crowdsourcing participants’ positions in online networked
communication. In line with the theoretical framework, the findings showed that holding a
centralized position in online networked crowdsourcing communication tends to hinder the
generation of innovative ideas amongst crowd members and that those seeking to be well
connected with others in the communication network may not be able to think originally or
creatively.
The centralized network position also plays a moderating role in the relationship between
knowledge diversity and innovation. Congruent with the literature suggesting that cognitive
overload may inhibit the efficiency of dealing with diverse knowledge that is complex and
ambiguous (Bawden, 2001; Bergstrom & Stoll, 1990; Hwang & Lin, 1999; Sparrow, 1999), this
study shows that due to the impact of centralized network position, the diversity of knowledge
will not be appreciated, and its inspirational influence cannot be manifested. As a result,
emerging creative thoughts engendered by diverse knowledge are prone to be reduced.
Finally, this study suggested the potential effects produced by other kinds of activities of
knowledge contributors. In particular, those who have initiated a larger number of threads are
more likely to generate innovative ideas. This positive influence may be attributed to the
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knowledge contributors’ activeness in the collaborative communication in the virtual space
(Livingstone, Bober & Helsper, 2005; Luther, Caine, Ziegler & Bruckman, 2010).
Study II
This study investigated the mechanism that underlies the emergence of knowledge
integration in crowd-based open challenges. The findings are threefold. First, given that
integrative knowledge plays a key role in crowdsourcing for innovation, it is important to
consider the feature of knowledge that successfully leads up to the occurrence of integrative
knowledge. Second, confounding factors that may impede the production of knowledge
integration are also worth investigating so that crowdsourcing practitioners can be better advised.
Third, the way in which various factors jointly determine the emergence of integrative
knowledge needs to be examined, so as to better understand the complicity of generating
knowledge integration in crowdsourcing innovation challenges.
The first finding of this study sheds lights on the innovation and knowledge sharing
literature by highlighting the important role played by knowledge diversity in producing
integrative knowledge in crowdsourced challenges. Despite obstacles such as members’
unfamiliarity with one another and membership fluidity in a crowd’s construction of shared
meanings (Bolisani & Scarso, 1999; Carlile, 2004; Hislop, 2002), this study found that as online
crowd members are exposed to a greater range of knowledge, they become more capable of
contributing integrative and comprehensive knowledge. Through communicating with others in
online crowdsourcing platform, crowd members obtain an opportunity to deeply understand
others’ heterogeneous knowledge as well as connect the problem to this shared knowledge, so as
to further generate integrative solutions for companies posting innovation challenges. Echoing
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the theory of perspective taking (Hoever et al., 2012; Krauss & Fussell, 1991), the study showed
that crowd members are able to synthesize different perspectives when others have made their
diverse knowledge explicit during communication.
With regard to the role of the network position in crowdsourcing, findings of this study
also demonstrated a negative effect of centralized position on the crowd’s production of
integrative knowledge. Advancing the literature on information and cognitive overload in
collaboration (Bawden & Robinson, 2009; Schick et al., 1990), this study provides evidence that
being embedded in a communication network in online crowdsourcing challenges can lead the
dominator to be over-socialized and overwhelmed by the information that needs to be processed.
Consequently, crowdsourcing participants located in dominant positions in communication
networks are inclined to ignore unfamiliar knowledge which might actually be useful and
constructive, and thus to conduct less comprehensive thinking and produce less integrative
knowledge.
These effects are further reinforced when the centralized network position acts as a
compounding factor moderating the influence of knowledge diversity on innovation. As
suggested by the literature on communication and information overload (Fjermestad, 2004; Jones
et al., 2004; Thompson & Coovert, 2003), crowdsourcing participants who hold a central
position in a network are inclined to reduce cognitive overload as well as information-related
anxiety when handling large amounts of information, preferring to utilize a communitive
approach to generate simpler and fast responses (Jones et al., 2004) rather than synergistically
integrating heterogenous knowledge when taking part in crowdsourcing challenges.
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In addition, consistent with the literature on activeness and online communication
(Livingstone et al., 2005; Luther et al., 2010), this study also reveals the positive influence of
individuals’ activeness in participating online interaction. Specifically, initiating a larger number
of discussion threads is positively associated with knowledge contributors’ production of
integrative knowledge in crowdsourcing innovation challenges.
Study III
This study mainly presents the dynamic patterns of crowd members’ knowledge sharing
as well as collective reflection in response to open innovation challenges. Two outstanding
challenges in which participants contributed more than 300 posts were selected as cases for
analysis. Overall, both crowdsourcing challenges have demonstrated the dynamic features of
crowd-based knowledge collaboration. First, the emerging themes, concepts, and the semantic
networks in which the concepts and themes were connected in online discussion together
indicated that knowledge contributors cognitively reference each other when engaging in
collective activity and pondering of problems. Second, a comparison of semantic networks
generated across different phases of discussion reveals that the changes and differences indicated
members of the crowd has collectively undertaken reflective thinking in the course of their
ongoing discussions.
Several patterns relating to themes and concepts emerged from the knowledge
collaboration in crowdsourcing. First, the results suggested that the frequency of which each
discussion theme was used differed across three phases, representing symbolic artifacts that
participants had collectively produced. These symbolic artifacts can be viewed as manifestations
of the collective knowledge shared by the crowd (Roth & Cointet, 2010). In both open
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innovation challenges, the crowds have generated symbolic artifacts that were commonly used
throughout the entire discussion (for example, “banks”, “account” in the financial service
innovation challenge; “traps”, “species”, “control” in the pest management innovation
challenge), as well as several symbolic artifacts uniquely used in each phase (for example,
“game”, “generation”, “college” in the financial service innovation challenge; “fences”,
“research”, “land” in the pest management innovation challenge). Meanwhile, as shown by the
semantic co-occurrence networks, all the symbolic artifacts were connected into a socio-semantic
network where all posts were connected to each other because they contributed to a common
theme, and the link structures constantly evolve as new topics emerge (Roth & Cointet, 2010;
Roth, 2013). Such a topically interlinked tendency (Dokoohaki & Matskin, 2008; Roth, 2013)
helps sustain online crowdsourcing discussions in generating solutions to problems. Furthermore,
for both crowdsourcing challenges, the evolving semantic networks across three phases revealed
that crowd members constantly engage in collective reflective thinking. In line with the literature
on reflection and collective reflexivity (West, 1996, 2000; West & Richter, 2008), this study
revealed that reflection takes place when diverse members of a crowd conduct collaborations
oriented by the collective goal to generate innovative solutions, as well as when the actions occur
in a friendly and encouraging environment that makes collaborators feel safe to share unique
opinions. In accordance with the theoretical framework (Carter & West, 1998; De Dreu, 2002;
De Dreu et al., 2008; Hülsheger et al., 2009; Tjosvold, et al., 2004; Wong et al., 2007), collective
reflection also facilitated the setting of a common vision for crowdsourcing participants. For
example, in the challenge where the theme was to seek innovative solutions for financial service
business models, a comparison of co-occurrence networks throughout the three phases
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demonstrated that, as the discussion unfolded, crowd members constantly adjusted their common
visions according to financial and banking apps, different categories of customers, and the wider
financial and banking product markets, respectively. Likewise, in the challenge where the theme
was to harvest innovative solutions for pest management, a comparison of co-occurrence
networks across the three phases revealed that the crowd’s common vision changed along with
the progress of the collective discussion. In the first phase, the common vision was set to be
relevant to eradication; in the second phase, the common vision started to connect with the issue
of funding; and, finally, in the third phase, the common vision related to other tools that could be
used for pest control.
General Discussion
This dissertation research makes several theoretical and practical contributions to the
innovation and knowledge collaboration literature. Theoretically, it first extends the theory of
perspective taking by incorporating the impacts of network position in studying crowdsourcing
innovation challenges. Echoing what theory of perspective taking (Bartunek, 1983; Brown 1991;
Dougherty 1992; Henderson 1994; Nonaka 1994; Parker & Axtell, 2001; Purser et al., 1992) has
suggested, when generating integrative and innovative knowledge, crowd members benefit from
being exposed to heterogenous knowledge. However, members occupying a centralized position
in the networked online communication cannot enjoy the benefit because they tend to experience
cognitive overload in processing large amount of information. Accordingly, their contribution to
knowledge integration and innovation will be constrained. One important implication of this
finding is that, when studying crowdsourcing and knowledge sharing, each individual member of
the crowd should not be considered as equally capable of generating innovation and integrative
97
knowledge; rather, they differ in their network positions when engaging in online knowledge
collaboration, and thus vary in their production of integrative and innovative knowledge.
Theories of knowledge sharing and perspective taking, therefore, should consider embracing the
behavioral mechanisms that can explain various factors such as individuals’ network structural
attributes that either prompt or impede innovation and knowledge integration.
Meanwhile, given the critical role played by knowledge integration in collective
innovation (De Boer, van Den Bosch & Volberda, 1999; Du Plessis, 2007; Grant, 1996), this
research offers a mechanism which empirically explains how integrative knowledge could be
produced in crowdsourcing innovation challenges. In particular, through a comprehensive
demonstration of the impacts of diverse knowledge and centralized network position, this
dissertation extends organizational knowledge integration literature to the crowd level, as well as
provides unique insights into better understanding the roles of knowledge diversity and
centralized network position in leading up to the emergence of integrative knowledge.
Another theoretical contribution is the adoption of social semantic network perspective in
examining the dynamic patterns of collective reflection in crowdsourcing for innovation.
Viewing communication happening in crowdsourcing knowledge collaboration as a socio-
semantic network, this dissertation research unpacks the dynamics of how knowledge artifacts
are networked and evolving along with contributors’ collective reflection. It highlights the
importance of incorporating the semantic dimension into the studies of crowd-level collective
reflexivity in crowdsourcing innovation challenges.
Finally, this research also has several practical implications. In the first place, as
suggested by the findings, crowdsourcing practitioners who seek for integrative and innovative
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solutions should endeavor to implement systems that encourage the crowd to make their
heterogeneous knowledge explicit. Concurrently, they should also make the effort to prevent the
centralization or over-embeddedness of certain passionate members in crowd’s collective
interaction. For example, crowdsourcing platform designers could consider employing artificial
intelligence techniques to detect knowledge contributors’ levels of activeness and then provoke
more reactions or voices from inactive members, such that the passionate members’
centralization or over-embeddedness can be avoided. Furthermore, findings of this research
indicate that crowdsourcing practitioners should be attentive to the semantic connections
emerged from knowledge contributors’ back-forth online conversations when reacting to
crowdsourcing innovation challenges, as the ever-evolving semantic networks manifest the
reflection that crowd members collectively undertake. Understanding how the crowd’s opinion
evolves will help managers and business practitioners to integrate wisdom of the crowd into
future organizational activities more effectively.
Limitations and Future Directions
While this research contributed theoretically and methodologically to crowdsourcing and
innovation research, it was limited in a number of ways, setting the stage for future research. One
limitation was that, although the participating organizations were chosen from different
industries, all the innovation challenges were selected to be similar with regard to the challenge
themes. Particularly, these challenges were themed as searching for innovative solutions for
complicated problems with regard to business strategies. As such, it might be expected that
studies focusing on other types of crowdsourcing tasks may demonstrate different collaboration
patterns. In addition, although the crowds employed in this research were larger than small the
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groups studied in previous research (Paulus & Nijstad, 2003), several of them were on the low
end of crowd size. Future studies could explore whether the results remain robust for massively
large-scale crowds.
Moreover, in this research, the matter of whether individual behavioral characteristics are
influential remains unexplored, partly due to the fact that the crowd members were anonymous,
making it difficult to trace their individual attributes, such as whether or not they had creative
personalities (Gough, 1979). Future studies should attempt to combine collective knowledge
collaboration and individual characteristics. In addition, the findings of this research did not
reveal participants’ knowledge background. In other words, the knowledge context in which
crowd members generate diverse viewpoints is worth studying. Another relevant issue pertains to
the practical context. In this research, the question remains unanswered relates to whether there
is a connection between knowledge contributors’ motivations and their industrial background.
For example, in future research, it would be interesting to further examine whether those who
contribute integrative knowledge possess rich experience in industry and whether those
participants with rich practical experience are more motivated than those who have little when
taking part in knowledge collaboration.
As this research employed a single platform instead of making comparisons across
several, further work should compare multiple platforms to see the possible effects of the
platform on crowdsourcing for innovation. Considering the differences in architectural variation
across different crowdsourcing platforms, whether this would produce different findings is worth
exploring. Additionally, although all the challenge themes in this study were about the
innovation and strategic development of organizations, the specific topics that each of the
100
organizations launched were not identical across challenges. Future research should pay more
attention to the variations across particular topics in order to control for its possible effects on
dealing with crowd-sourced open innovation. Finally, experimental research is also needed to
investigate the reflective mechanisms through which online crowds generate integrative as well
as innovative knowledge, especially when they experience transitions across different time
periods in crowdsourcing.
Conclusion
Recent research suggests that crowdsourcing is an influential and efficient approach to
generating innovation. This dissertation research advances the scholarship insofar as it is one of
the first studies to examine the interaction effects of knowledge diversity and centralized
network position on the production of integrative and innovative knowledge, as well as to
demonstrate the semantic patterns of online crowd’s collective reflection when taking part in
crowdsourcing innovation challenges. In conclusion, this dissertation raises a call for future
research on crowdsourcing for innovation to emphasize not only the diversity of knowledge
shared, but more importantly the contributors’ structural position in the networked
communication as well as the semantic attributes of their collaborative knowledge creation.

101
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Asset Metadata

Creator Sun, Yao (author)

Core Title Crowdsourcing for integrative and innovative knowledge: knowledge diversity, network position, and semantic patterns of collective reflection

School Annenberg School for Communication

Degree Doctor of Philosophy

Degree Program Communication

Publication Date 07/28/2019

Defense Date 06/06/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag centralization,crowdsourcing,innovation,integration,knowledge,knowledge diversity,network,OAI-PMH Harvest,reflection,semantic

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor McLaughlin, Margaret (committee chair), Majchrzak, Ann (committee member), Yang, Aimei (committee member)

Creator Email yaosun@usc.edu,yaosunusc@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-198156

Unique identifier UC11663628

Identifier etd-SunYao-7661.pdf (filename),usctheses-c89-198156 (legacy record id)

Legacy Identifier etd-SunYao-7661.pdf

Dmrecord 198156

Document Type Dissertation

Format application/pdf (imt)

Rights Sun, Yao

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA