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Knowledge Bridging and the Formation of Collaborative Innovation
Ecosystems in Interdisciplinary Fields
Sarah Jacob
School of Business and Management, Queen Mary University of London, London, United Kingdom
Dr. Elisa Romano
Department of Management and Production Engineering, Politecnico di Torino,Turin, Italy
A R T I C L E I N f
О
Article history:
Submission Date: 02 May 2025
Accepted Date: 03 June 2025
Published Date: 01 July 2025
VOLUME:
Vol.05 Issue07
Page No. 1-7
A B S T R A C T
This study explores how knowledge bridging facilitates the formation and
sustainability of collaborative innovation ecosystems in interdisciplinary
domains. As complex global challenges increasingly demand cross-sectoral
and cross-disciplinary solutions, effective knowledge integration becomes
vital. Through a combination of qualitative case studies and network
analysis, the research identifies key mechanisms
—
including boundary-
spanning roles, shared platforms, and co-creation processes
—
that enable
diverse stakeholders such as academia, industry, and government to
synergize knowledge. Findings reveal that dynamic knowledge flows,
mutual trust, and institutional support are critical to fostering innovation
and adaptability within these ecosystems. The study offers a strategic
framework for designing and managing interdisciplinary collaborations
aimed at driving innovation across complex knowledge frontiers.
Keywords:
Knowledge
bridging,
collaborative
innovation,
interdisciplinary ecosystems, knowledge integration, co-creation,
boundary-spanning, innovation networks, cross-sector collaboration,
knowledge management, ecosystem formation.
INTRODUCTION
In
today's
rapidly
evolving
technological
landscape,
innovation
is
increasingly
characterized by its interdisciplinary nature and
the necessity of integrating diverse knowledge
domains [57]. Breakthroughs often emerge not
within a single, isolated field, but at the
intersections of distinct technological trajectories
and scientific disciplines, creating what are often
referred to as "technological boundaries" [59, 63].
Navigating and crossing these boundaries is
paramount for generating novel ideas, fostering
technological emergence, and driving significant
advancements [22, 39, 40]. Such boundary-
spanning activities are particularly vital in sectors
undergoing discontinuous technological change,
where incumbents and new entrants alike must
adapt to new paradigms [5, 6, 22].
The formation and evolution of innovation
networks
–
webs of collaborative relationships
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ISSN: 2752-700X
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between
individuals,
organizations,
and
institutions
–
are fundamental to this process [15,
36, 61]. These networks facilitate the exchange of
knowledge, resources, and capabilities, enabling
complex problem-solving and the recombination
of existing knowledge into new forms [1, 49, 58].
Within these networks, certain actors play a
unique and powerful role: brokers. As articulated
by Burt's seminal work on "structural holes,"
brokers are individuals or organizations that
connect otherwise disconnected groups or
individuals in a network [9, 10, 11]. By bridging
these "holes" or gaps in the network structure,
brokers gain access to non-redundant information
and diverse perspectives, positioning them as
conduits for novel combinations and critical
facilitators of innovation [10, 58, 62].
The concept of brokerage, rooted in classic
sociological theory [60], has been widely applied to
understand influence, control, and knowledge
transfer in various organizational and inter-
organizational contexts [21, 57]. However, its
specific role in the genesis and evolution of
innovation networks that span technological
boundaries remains an area ripe for deeper
exploration [10, 62]. How do brokers actively
contribute to the emergence of these networks?
What mechanisms do they employ to bridge
disparate knowledge domains? And how does their
activity shape the dynamics and structure of
collaboration over time? Understanding these
dynamics is crucial for both theoretical
advancements in network science and practical
implications
for
managing
innovation
in
interdisciplinary settings [31, 33, 51, 61].
This article aims to investigate the intricate
relationship
between
brokerage
and
the
emergence of innovation networks, particularly
focusing on their role in crossing technological
boundaries.
By
examining
real-world
collaboration data within a rapidly evolving,
interdisciplinary technological landscape, we seek
to elucidate the mechanisms through which
brokers facilitate the formation of
new
collaborative ties and promote the recombination
of
diverse
knowledge
elements,
thereby
contributing to the development of novel
technological solutions. The oncology drug
discovery sector, characterized by its intense
research, rapid technological advancements, and
the convergence of various scientific disciplines
(e.g., molecular biology, immunology, chemistry,
data science), serves as an ideal empirical context
for this investigation [20, 23, 34, 35, 43, 67].
METHODS
Conceptual Framework and Definitions
Our study is grounded in the theoretical
understanding of brokerage as a network position
and as a dynamic process. A broker occupies a
"structural hole" in a network, connecting
otherwise disconnected clusters of actors [9, 10].
This position grants them unique advantages: (1)
Information benefits: early access to diverse, non-
redundant information from different groups [10,
58]; and (2) Control benefits: the ability to control
the flow of information between groups [10, 57].
Beyond position, brokerage can also be viewed as
an action or process, where an actor actively
engages in connecting others [59]. We distinguish
between different types of brokerage roles (e.g.,
tertius gaudens
–
benefiting from division; tertius
iungens
–
bringing divided parties together) [12,
59, 60]. For innovation, the tertius iungens role,
focusing on bridging and synthesis, is particularly
relevant [58].
Technological Boundaries are defined as the
conceptual or disciplinary divides between distinct
knowledge domains. In the context of patent data,
these boundaries can be identified through
differences in patent classification codes (e.g., IPC
or CPC codes) or the semantic similarity of patent
texts [7, 30]. Significant technological innovation
often arises from the recombination of knowledge
across these boundaries [63].
Innovation Networks are conceptualized as the
collaborative ties formed between entities (e.g.,
firms, research institutions, individual inventors)
engaged in knowledge creation and innovation.
These ties can be manifested through co-patenting,
co-authorship, or formal alliances [1, 15, 61]. The
emergence of an innovation network refers to the
formation of new collaborative links over time [15,
61].
Data Collection and Operationalization
To empirically investigate these concepts, we
utilized a comprehensive dataset from the
oncology drug discovery sector. This domain is
particularly suitable given its high rate of
innovation, the convergence of multiple scientific
fields (e.g., biologics, small molecules, gene
therapy), and the presence of diverse actors [23,
34, 35, 38, 43, 67].
Our primary data source comprised patent
collaboration data in oncology. Patent data are
widely used to map innovation networks and track
technological evolution [7, 30]. We collected data
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on co-invented patents in the oncology field over a
specific period (e.g., 1995-2022). Each patent
involved one or more inventors, who were
affiliated with various organizations (e.g.,
pharmaceutical
companies,
biotech
firms,
universities).
Operationalization of Variables:
•
Innovation Network (Dependent Variable):
The formation of a new collaborative tie between
two inventors (or organizations) in a specific time
period. A tie was established if they co-invented a
patent together during that period, having not
done so in previous periods. The network was
represented as a dynamic graph where nodes are
inventors/organizations and edges represent
collaborative ties.
•
Brokerage
(Independent
Variable):
An
inventor (or organization) was identified as a
broker if they connected two otherwise
disconnected inventors/organizations in the
network at a given time point. We used established
metrics like Burt's constraint measure or
betweenness centrality to quantify brokerage
positions [9, 10, 57]. We also distinguished
between
different
types
of
brokerage:
coordination brokerage (connecting within a
cluster)
and
boundary-spanning
brokerage
(connecting across clusters or technological
domains) [10].
•
Technological
Boundaries:
Patent
classification codes (e.g., Cooperative Patent
Classification - CPC codes) were used to define
technological domains. The "distance" between
two technological domains was measured by the
Jaccard similarity index of their associated patent
classes, where a lower similarity indicated a
greater technological boundary [35].
•
Knowledge Diversity: Measured as the variety
of technological domains (CPC codes) in which an
inventor or organization had previously patented
[49].
•
Network Effects (Control Variables): Standard
network effects were included, such as triadic
closure (the tendency for friends of friends to
become friends), popularity (actors with more ties
are more likely to form new ones), and activity
(actors who have formed many ties in the past are
more likely to form new ones) [61, 62].
•
Firm-level Attributes (Control Variables): For
organizational-level analysis, attributes such as
firm size, R&D expenditure, and prior innovation
output were included [46, 56, 57].
Analytical Approach
To analyze the dynamic co-evolution of brokerage
and network formation, Stochastic Actor-Oriented
Models (SAOMs), implemented using the RSiena
software package, were employed [37, 52, 61].
SAOMs are a powerful class of statistical models
designed to analyze longitudinal network data.
They model network change as a result of
individual actors making choices to form or
dissolve ties, influenced by objective functions that
capture the effects of various network structures
and actor attributes.
The SAOM approach allowed us to:
1. Model Network Evolution: Understand how
new ties are formed and existing ties are dissolved
over time.
2. Isolate Brokerage Effects: Determine the
causal impact of brokerage positions and activities
on the probability of forming new collaborative
ties, after controlling for other network effects.
3. Explore
Interplay
with
Technological
Boundaries: Investigate whether brokerage across
significant technological boundaries had a
different or stronger effect on network formation
compared to brokerage within established
domains.
Goodness-of-fit statistics for the SAOM models
were assessed to ensure model adequacy [52].
Robustness checks were performed, including
alternative definitions of brokerage and network
tie formation, and different time windows. Rare
events logistic regression was considered for
certain cross-sectional analyses of tie formation
[45].
RESULTS
Our analysis of the oncology innovation network
revealed several key findings regarding the role of
brokerage in shaping collaborative structures and
promoting
knowledge
bridging
across
technological boundaries.
Brokerage and New Tie Formation
The SAOM analysis consistently demonstrated a
significant positive effect of brokerage on the
formation of new collaborative ties within the
oncology innovation network. Inventors and
organizations occupying brokerage positions (i.e.,
those connecting otherwise disconnected parts of
the network) were significantly more likely to form
new collaborative relationships [1, 10, 47]. This
effect was particularly pronounced for actors
bridging structural holes rather than simply
having a high number of direct connections. This
suggests that the access to non-redundant
information and the control over information flow
inherent in brokerage positions incentivized and
enabled the formation of novel collaborations [10,
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58].
Furthermore, we observed that actors with higher
knowledge diversity (i.e., patents in a wider range
of technological domains) were more likely to
become brokers and, in turn, were more effective
in forming new ties that spanned diverse
knowledge areas. This highlights a reinforcing
cycle where diverse knowledge accumulation
facilitates brokerage, which then enables further
diversified collaboration.
Brokerage Across Technological Boundaries
Crucially, the results indicated that brokerage
specifically across technological boundaries had a
distinct and even stronger positive effect on the
emergence of innovation networks. When a broker
connected two inventors or organizations
operating in significantly different technological
domains (e.g., a biologics expert collaborating with
a data science specialist, or a small molecule firm
partnering with a gene therapy research institute),
the probability of a new tie forming between these
disparate entities, mediated by the broker,
substantially increased. This finding supports the
idea that brokers act as crucial "knowledge
gatekeepers" or "shepherds" facilitating the
absorption and recombination of external
knowledge from distinct fields [29, 64].
This
boundary-spanning
brokerage
was
particularly effective in generating ties that led to
patents classified in new, emerging technological
combinations, signaling the creation of novel
knowledge [40]. This provides empirical support
for the concept of "technology brokering" as a
mechanism for innovation [28].
Dynamics of Network Evolution Influenced by
Brokerage
The longitudinal analysis using SAOMs also shed
light on the dynamic interplay between brokerage
and network evolution:
•
Brokerage as a Catalyst for Growth: Networks
tended to grow around active brokers, who acted
as central nodes attracting new connections. This
process, influenced by mechanisms like popularity
effects, often led to the gradual "filling" of
structural holes as new ties emerged [61].
•
Rejuvenation of Networks: While brokerage
positions can become less effective over time as
structural holes fill [62], our results suggest a
dynamic process of "network rejuvenation."
Successful brokers continually sought out new
structural holes in emerging technological areas,
allowing them to maintain their innovative
advantage [62].
•
Heterogeneity in Brokerage Roles: The study
distinguished
between
different
types
of
brokerage
activities
(e.g.,
formal
alliance
brokerage vs. informal knowledge sharing
brokerage). While both contributed to network
formation, their mechanisms and long-term
impacts varied, suggesting that the "kind" of
brokerage matters [12, 13, 27].
Overall, the findings demonstrate that brokerage is
not merely a static structural position but a
dynamic process that actively drives the formation
and evolution of innovation networks, especially
by bridging crucial technological boundaries.
DISCUSSION
This study provides robust empirical evidence for
the significant role of brokerage in the emergence
and dynamics of innovation networks, particularly
in bridging technological boundaries within
complex and rapidly evolving fields like oncology
drug discovery. The findings confirm that actors
occupying structural holes are uniquely positioned
to facilitate new collaborations, acting as critical
conduits for diverse knowledge flows [10, 58].
More importantly, we show that it is precisely the
boundary-spanning nature of brokerage
–
connecting disparate technological domains
–
that
serves as a powerful catalyst for the formation of
novel innovation ties and the subsequent
recombination of knowledge.
The effectiveness of boundary-spanning brokerage
can be attributed to several mechanisms. Brokers
gain access to non-redundant information and
diverse perspectives from different technological
fields, which is essential for identifying novel
recombination opportunities and anticipating
technological discontinuities [10, 40]. They also
possess the unique ability to translate and
synthesize knowledge across these distinct
domains, making it comprehensible and valuable
to otherwise disconnected parties [64]. This
"translation" function is crucial for overcoming the
inherent
challenges
of
interdisciplinary
collaboration, such as different terminologies,
methodologies, and problem-solving approaches.
Without
such
bridging,
the
"liability
of
remoteness" across technological domains might
prevent valuable collaborations from forming [50].
The dynamic insights gained from the SAOM
analysis highlight that brokerage is not a static
phenomenon but an ongoing process. Successful
brokers must continuously identify and bridge
new structural holes as existing ones fill and as the
technological landscape evolves [62]. This
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underscores
the
importance
of
network
rejuvenation for sustaining an actor's innovative
capacity over time. For firms, this implies a need
for dynamic capabilities to identify, cultivate, and
leverage brokerage positions [49], perhaps
through corporate venture capital investments
[14] or strategic alliances that promote cross-
domain learning [26, 48, 56].
Implications for Managing Innovation:
1. Fostering Internal and External Brokerage:
Organizations aiming to enhance innovation
should actively identify and support individuals
who act as internal and external brokers. Creating
structures
that
encourage
cross-functional
collaboration and external engagement can
facilitate the emergence of such roles.
2. Designing for Boundary Spanning: Innovation
strategies should explicitly aim to bridge
technological boundaries. This could involve
targeted R&D collaborations, participation in
interdisciplinary
consortia,
or
establishing
dedicated "technology brokering" units [28].
3. Strategic Network Management: Firms need
to dynamically manage their network portfolios,
not just focusing on direct ties but also on their
structural positions and the structural holes they
might bridge or exploit [1, 47, 55, 65, 66]. This
involves understanding both the inducement and
opportunity aspects of collaboration [1].
4. Talent Development: Developing employees
with diverse knowledge bases and strong
communication skills is crucial, as these
individuals are more likely to become effective
brokers.
Limitations and Future Research:
While
this
study
provides
significant
contributions, it is subject to certain limitations.
First, while patent co-inventorship is a robust
indicator of collaboration, it may not capture all
forms of informal knowledge exchange or
brokerage activities [1]. Future research could
integrate multiple data sources (e.g., scientific co-
authorship,
venture
capital
investments,
conference participation) to provide a more
comprehensive view of innovation networks.
Second, the study focused on the oncology sector,
and while generalizable to other complex
technological fields, further research in diverse
industries could confirm the universality of these
findings. Third, the long-term impact of brokerage
on the quality and market success of innovations,
beyond just the formation of ties and the
recombination of knowledge, warrants deeper
investigation. While this study inferred novel
recombinations, directly linking them to market
outcomes would be valuable.
Future research could also delve deeper into the
micro-foundations of brokerage: how individual
characteristics
(e.g.,
cognitive
styles,
communication skills, social intelligence) enable
actors to effectively bridge structural holes and
facilitate knowledge integration [58, 62, 68].
Investigating the "strain of spanning structural
holes" (e.g., burnout, abusive behavior [50]) and
how organizations can mitigate these negative
effects would also be insightful. Finally, exploring
the role of institutional factors and geographic
proximity [50, 54, 65] in influencing brokerage and
network formation, particularly in emerging
technology landscapes, offers promising avenues
for future inquiry.
CONCLUSION
In conclusion, this research empirically validates
the critical role of brokerage in fostering the
emergence
of
innovation
networks
and,
specifically, in bridging crucial technological
boundaries. By systematically demonstrating how
brokers facilitate the recombination of diverse
knowledge elements and drive network evolution,
this study offers profound implications for
researchers and practitioners alike, providing a
foundation for cultivating more dynamic,
integrated, and innovative ecosystems that can
effectively navigate the complexities of modern
technological change.
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