THE INFLUENCE OF SOCIAL NETWORKS ON KNOWLEDGE

MANAGEMENT FOR INNOVATION IN LIFE-SCIENCE

DISCOVERY NETWORKS

Claire Gubbins and Lawrence Dooley

Enterprise Research Centre, ER-1030, University of Limerick, Limerick, Ireland

Keywords: Innovation, Social Networks, Knowledge Management.

Abstract: The competitiveness and sustainability of a modern organisation depends on its ability to innovate. It is

increasingly accepted that knowledge, skills and competencies are the key drivers of innovation. Access to

the latest information can provide critical competitive edge for organisations’ innovation efforts. Social

networks are found to promote organisational and collective learning and are a significant source of

knowledge which subsequently leads to innovation. The paper aims to introduce social network analysis as

a useful methodology for organisations and managers to use to analyse how collaboration for knowledge

management for innovation efforts is accomplished. Social network analysis (SNA) facilitates analysis of

relationships among actors in a network. It describes a number of social network factors that are useful in

analysing overall network structures, network content, the characteristics of interactions and identifying the

impact they have on knowledge management for innovation efforts. This will illuminate the mechanisms

through which collaboration for innovation is accomplished. Three case studies of a knowledge network

within the life sciences sector are utilised to conduct an exploration into how knowledge is managed

through social networks for innovation.

1 INTRODUCTION

One useful view of innovation is that it is the

combined activity of generating creative ideas (i.e.

new knowledge) and the subsequent successful

exploitation of these for benefit (Roberts, 1988; von

Stamm, 2003; O’Sullivan and Dooley, 2008).

Creativity results in the development of new

knowledge and learning. Creativity may be viewed

as the combination of existing knowledge into new

and useful concepts to satisfy a need (Farid-Foad et

al., 1993). The exploitation of value from the

realisation of these novel ideas is the output of the

innovation process.

The paper aims to introduce social network

analysis as a useful and effective methodology for

organisations and managers to use to analyse how

cooperation and collaboration for knowledge

management for innovation efforts is accomplished.

Social network analysis (SNA) facilitates analysis of

relationships among actors in a network. It

describes a number of social network factors that are

useful in analysing overall network structures,

network content, the characteristics of interactions

and identifying the impact they have on knowledge

management for innovation efforts. This will

illuminate the mechanisms through which

cooperation and collaboration for innovation is

accomplished. Three case studies of a knowledge

network within the life sciences sector is utilised to

conduct an initial exploration of how knowledge is

managed through social networks for innovation.

The paper will explore key stages of a knowledge

management for innovation process from the social

network perspective.

2 KNOWLEDGE MANAGEMENT

FOR INNOVATION

For an organisation to successfully innovate i.e. to

optimize the way new knowledge is developed and

existing knowledge is exploited, it needs to facilitate

the dynamic capabilities required for converting the

knowledge available from the insights and

competences of individual people (the source of new

knowledge) into appropriate structures, processes,

182

Gubbins C. and Dooley L..

THE INFLUENCE OF SOCIAL NETWORKS ON KNOWLEDGE MANAGEMENT FOR INNOVATION IN LIFE-SCIENCE DISCOVERY NETWORKS.

DOI: 10.5220/0003075001820188

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2010), pages 182-188

ISBN: 978-989-8425-30-0

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

products and systems that allow the value, in this

case innovation, to be exploited (McKenzie & van

Winkelen, 2004). Current perspectives of the

innovation process view it as an interactive and

networked system that spans organisational

boundaries to draw on knowledge, experience and

capabilities from diverse sources to achieve

development objectives (Rothwell, 1992; Tidd et al,

2005). Moves in this direction include organisations

moving from functionally based formal structures to

matrix, team-based and networked structures

(Morton et al., 2006). Such organizations are ‘highly

adaptive entities that transcend traditional

boundaries as they develop deep and collaborative

relationship internally as well as with customers,

suppliers, alliance partners and increasingly

competitors’ (Neilson et al., 2004). It is argued that

these relationship-driven organizations are more

successful than their non relationship-driven

counterparts (Morton et al., 2006).

The social network perspective is an appropriate

lens through which to examine the interactions

among employees (both within and outside the firm)

that enable collaborative work to be accomplished

(Cross & Parker, 2004), or in this case, that enable

learning, knowledge access, transfer, absorption and

accumulation for the purposes of innovation. It

enables exploration of how collaborative social

networks facilitate knowledge management for

innovation. A social network perspective permits

conceptualizing the whole, rather than the parts

(Storberg & Gubbins, 2007. A social network is a set

of people or groups, called ‘actors’, with some

pattern of interaction or ‘ties’ between them.

2.1 Understanding the Knowledge

Management Phases

Innovation is about creating new possibilities

through combining different knowledge sets. Such

knowledge may be from the insights and

competences of individual people (the source of new

knowledge), found in experience or could be from a

process of search- such as research into

technologies, markets, competitor actions etc. This

knowledge could be codified in such a way that

others can access it, discuss it, transfer it etc. or it

can be in tacit form, ‘known about’ but not actually

put into words or formulae. A key contribution to

our understanding of the kinds of knowledge

involved in different kinds of innovation is that

innovation rarely involves dealing with a single

technology or market but rather a bundle of

knowledge which is brought together into a

configuration. Successful innovation management is

about getting hold of and using knowledge about

components but also about how these can be put

together- the architecture of an innovation (Tidd et

al., 2005). Tranfield et al. (2006) outline the phases

of the innovation process and extrapolate the

knowledge routines necessary to support each of the

innovation phases- discovery, realisation and

nurture. Taking the network perspective of

innovation necessitates understanding where and

how knowledge management routines impact the

innovation process and what characteristics of social

networks influence knowledge management and

how. For example, the discovery phase of

innovation relates to searching and scanning the

environment to pick up and process signals about

potential innovations. Thus potential sources of

knowledge in the network are scanned for items of

interest. The larger the social network, the more

knowledge sources will be scanned and thus the

likelihood of finding valuable items of interest for

innovation is higher. The knowledge sources located

are then potential members of the network to enable

any collaborative efforts. Utilisation of the actors in

the network then enables access to, capture and

articulation of this knowledge in an explicit usable

format.

The first phase of any innovation process relates

to discovery and involves searching the external

environment to identify potential shifts and

unfulfilled needs that provide the opportunity for

potential innovations. The knowledge inputs for this

phase of the innovation process necessitate the

organisation spreading as wide a net as possible to

capture information from relevant knowledge

sources. The broadness of the domain makes it

impossible for any one individual (or even

organisation) to adequately search all potential

sources. The use of social networks to search for

and access knowledge regarding emergent shifts in

the external environment improves the organisations

searching ability to identify appropriate

opportunities for innovation. The social network

literatures inform practice on how best to search for

and access valuable knowledge through social

networks. For example, Granovetter (1973) proposes

through his weak tie theory that weak tie

relationships, defined as not emotionally intense,

infrequent, and restricted to one narrow type of

relationship enable a focal individual to contact

another who resides in a different social circle and

hence access non-redundant knowledge.

Burt (1992) proposes, through his structural hole

theory, that boundary spanners, defined as those

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LIFE-SCIENCE DISCOVERY NETWORKS

183

actors in a network who connect otherwise

unconnected actors, gain access to novel knowledge

in a timely fashion, as well as bargaining power.

Once the search process is complete, the more

effectively an organisation can capture and

articulate the knowledge from these networks, the

richer the opportunities they have to feed their

innovation efforts. In order for meaningful

knowledge transfer and learning to occur, the social

networking process requires direct and intense

interaction, collaboration and cooperation between

individuals with relevant knowledge and expertise,

within the structure of the network (Hansen, 1999)

so that knowledge can be internalised in the

organisation and given expression in a form

understood by those tasked with exploring its

innovative potential. The requirement for intense

interaction emphasises the importance of the

strength of the relationships and the requirement for

individuals with relevant competencies emphasises

the need to investigate the absorptive capacity of the

network.

The second phase of the innovation process

relates to realisation. This relates to how the

organization can successfully implement the

innovation and selecting from the range of available

innovations those which the organization will work

on. It involves firstly screening and selecting

appropriate actions to be progressed along the

innovation process. Selection decisions are based on

available knowledge and expertise so the adoption of

a cooperative team-based, consensus approach to

decisions is facilitated by having access to a greater

network of expertise, knowledge and diverse

perspectives to enlighten the selection process. It

requires that the knowledge and innovations are

articulated such that they can relate to each

organizations context and particular challenges.

Possessing a wide diverse network of actors and thus

drawing on multiple perspectives, knowledge and

expertise can facilitate this contextualization and

ensure effective selection decisions are made. An

organisation must strive to identify and access all

pertinent information and absorb this knowledge to

enhance their decisions. Better informed decisions

regarding the approval of concepts will enhance the

likely success of the innovative actions pursued.

Understanding how the actors interact and the

network structures can enable development of

strategies to further enhance collaboration for this

phase of the innovation process.

The third phase of the innovation process relates

to nurturing the innovative actions approved from

the realisation phase. The challenge of this phase is

to transform the concept into a reality and align it

with the needs of the market. The further along this

phase an action is then the more difficult it is to

change the design. Consequently organisations need

to access information to ensure the design and

subsequent development is correct. The use of

concurrent engineering and co-design teams are

common in this phase of the process to enhance the

knowledge flows and eventual output. Concurrent

engineering brings together all relevant stakeholders

(e.g. design, manufacturing, logistics, sales, etc.) to

collaborate on the development of the action; co-

design engages suppliers and other independent

organisations to work together on the design of the

future innovation. The opening-up of this phase of

the innovation process to input from knowledge

sources external to the organisation enhances the

expertise and knowledge available, increases the

creative capability to solve problems encountered

and ensure that relevant stakeholder requirements

are incorporated into the design and development

activity. Since potential errors are minimised by

collective knowledge sharing, such collaborative

routines have the potential not only to develop

superior innovations but also to reduce the cost and

time of development. Such leveraging and

integration of necessary resources from the social

networks facilitate successful exploitation of the

‘new’ knowledge opportunity.

The exploitation of value from the developed

actions is the primary objective of this phase of the

process. The ability to commercialise developed

actions is essential to the long term sustainability of

any organisation. Knowledge inputs for this phase of

the process relate to how an organisation can ensure

the market adopts the innovation and what

mechanisms can be used to protect intellectual

property from competitors. Organisations must be

careful when securing intellectual property that the

associated secrecy does not adversely affect the

necessary knowledge flows to the innovation

process or encourage behaviour by individuals

within the network that undermines knowledge

exchange for mutual benefit. Thus management of

collaborative efforts within these constraints requires

an understanding of collaboration mechanisms such

as trust.

3 METHODOLOGY

The case studies detailed below are of a number of

university-industry collaborations studied through

longitudinal research by the researchers. The

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methodology adopted consists of a series of semi-

structured interviews over the life of the network to

assess the networks evolution, understand the

structures, routines and practices of the network and

identify the factors influencing positive network

behaviour. The interviews are conducted with key

members of the collaborations management team

who can provide both strategic and operational level

insights into the network functions. Interviews were

conducted every eighteen months. These case

studies were chosen for analysis as they were

created by multiple organizations to advance their

scientific understanding and generate knowledge

that they could exploit for potential innovations.

The study of these networks began in 2004. The

interview transcripts were analysed using a number

of social network analysis determinants as themes.

This analysis provides insight into the mechanisms

of a social network and how these mechanisms and

network characteristics explain how social

cooperation and collaboration is initiated, enhanced

for knowledge sharing and innovation

. A full

description of the three cases used is provided in Dooley et

al (2010).

4 SUMMARY FINDINGS

All three network cases were established for the

purpose of generating and advancing the knowledge

base of their scientific discipline for development of

future medical treatments. The knowledge diversity

of the life sciences context makes it impractical for a

single organization to internally consolidate the span

of expertise required (Powell et al, 1996). By

participating in knowledge networks, organisations

gain privileged access to knowledge-producers

involved in discovery, translational and clinical

research activities that facilitate their innovation

process. In all three cases, the motivating factor for

partners to collaborate was to access ‘valuable’

knowledge areas which they lacked internally.

All three networks are focused on emerging

areas of their scientific field where a disruptive shift

has resulted in industry lacking the required

capability or scanning capacity.

4.1 Searching for Knowledge

In all three cases, the impetus for venturing into the

external environment to locate suitably interested

organisations came from the lead academic within

the university organisation. These key individuals

foresaw the significant opportunity for their own

organisation and potential partner organisations,

should collaboration occur and thus actively

promoted the virtues of collaboration to interested

parties. All three lead academics fit the mould of

“knowledge brokers” (Hargadon, 2002) or

“boundary spanners” (Donaldson and O’Toole,

2007) by providing the ‘weak’ ties (Granovetter,

1973) that nurture embryonic relations into a

collaborating network. Each of the lead-academics

had established a reservoir of influential contacts as

a legacy of their past endeavours and could exploit

these contacts to establish linkages with potential

organisational partners.

The attraction for partnering organisations was

that network participation enhanced the scientific

scanning abilities of each organisation, allowed

access to proprietary knowledge and compound

libraries and provided a cost-effective mechanism

for undertaking the research work. While initial

discussions regarding network formation took place

between lead academics and like-minded scientific

peers within industrial organisation, once interest

was established, the size of the network increased as

individuals from the organisations became involved

to formalise contractual terms of reference for the

interaction and protect their institutions position.

This increased the bank of sources of knowledge

available for the knowledge sharing and innovation

process.

A large diverse social network is most effective

where members of the network are not only

connected to each other but have an awareness of

each others expertise such that knowledge of value

can be accessed and/or combined appropriately. In

the cases investigated here this process of awareness

initially begun on a formal basis as all three

networks were established as closed networks,

where partner selection was based on alignment of

competencies, expertise and interest in the

knowledge generating activities of the network.

During the formative stage of the network, the

academic members had to ‘sell’ the network by

communicating the latent expertise and its value to

prospective partners.

In all three cases, there is an obvious bias

towards interaction by industry personnel with

university researchers (perhaps due to this being the

locus of the networks active research capability but

maybe also because of competitive fears). This

suggests the academic institutions are in central

positions in the network. However there is evidence

within certain networks of increased awareness of

competitor industry’s competencies and fledgling

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collaborations between synergistic industrial

partners. This is suggestive of strong ties between

university-industry partners and weaker but

developing ties between industry-industry partners.

Universities are therefore acting more as knowledge

brokers and enablers of network development rather

than the ultimate and only benefactors of the

network. Industry needs to obtain advantages from

the network or they will quit the network. Thus

indications of developing ties between industry and

industry within this network should serve to

strengthen the search capacity of the network and

ultimately the knowledge sharing and innovative

capability of the network.

4.2 Capturing & Articulating

Knowledge

Possessing connections to and awareness of these

knowledge sources is only valuable to the extent to

which this knowledge can be accessed, captured and

articulated in a way that makes the knowledge useful

to a party. Access to the knowledge sources in the

cases explored was facilitated through a number of

structured and informally emerging channels.

Structured channels included those formally agreed

as part of the network’s institutional agreement or

internal routines. These included scheduled on-site

visits at university laboratories, access to centralised

laboratory information systems and intranets and

formalised project and annual reports. These are

important channels in that they exchange explicit

(the ‘what’) knowledge that has been generated by

the networks scientific endeavour.

However, the ability of these channels to

exchange more tacit knowledge (the ‘how’ regarding

the newly created knowledge) is poor. More

informal channels of knowledge exchange evident in

all three networks included co-location of industry

staff in university for short periods, one-to-one

discussions between researchers following on-site

visits, during social gatherings following such

events, during conferences or during follow-up

communications via email and telephone.

The capture and articulation of discovered

scientific knowledge involves an engrained process

of conceptual thinking common to research

scientists. At a generic level, the scientists have an

encultured knowledge of language and expert-

knowledge associated with the discipline. As

research scientists from the partner organisations

interact at scheduled meetings of the network and

informal communications, trust and friendships

develop. This increased affinity also narrows the

cognitive distance between individuals, increases

absorptive capacity and provides a ready basis for

knowledge transfer.

4.3 Transferring

Knowledge-Contextualise/Apply

The realisation phase of innovation requires that the

knowledge is contextualised and applied to

particular organisational contexts. Given the highly

encultured language and expert knowledge

associated with the scientific disciplines, this is

potentially a significant barrier to interaction and

knowledge absorption. However, given that all

individuals engaged in the networks possess

scientific qualifications (majority being Ph.D.’s) and

all are motivated by similar discovery focus, then the

networks have actually become communities of like-

minded peers. Irrespective of their particular

organisational origins, the network members firstly

view themselves as research scientists, whose

purpose is to better understand their scientific

domain. Yet despite this common foundation, each

network member has their own particular area of

science and expertise that challenges others

assumptions and mental models and creates the

creative tension necessary for learning and scientific

discovery. While the initial network founders often

have a previous legacy of interaction that has

validated their scientific credentials and thus

facilitates trust and cognitive proximity, newer

members require time and interaction to achieve

similar contentment. One PI researcher within case

3 went so far as to identify the tipping point for

network interaction as that when everyone trusted

the science that the other was doing. This common

frame of reference and absorptive capacity is the

minimum requirement for contextualising and

applying knowledge to particular organisational

contexts.

A number of mechanisms are utilised to

contextualise and apply the knowledge to members’

contexts. The knowledge available within the

organisational nodes is applied to solve specific

scientific problems which have been agreed as

mutually beneficial to the network participants. This

occurs through specified research projects, where

relevant network members contribute knowledge,

compounds, staff and capacity to achieve objectives

and generate new knowledge through scientific

discovery. The new knowledge generated through

exploration provides inputs and leads for

exploitation within the innovation processes of the

network’s organisations. Dependent upon

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contractual conditions agreed during the network’s

formative phase, the newly discovered knowledge

will be exploited unilaterally or collectively and

result in patents, leads for new treatments, licensing

agreements, new operational processes or even the

creation of new joint ventures.

Once research discoveries are achieved within

the network laboratories, they are communicated

with network members as per channels defined in

the contractual agreements. While these channels

remain as the networks mature, additional

communication channels evolve organically. All

three cases have established centralised information

systems for sharing information and have regular on-

site meetings of the network members to discuss

operations and nurture the exchange of both explicit

and tacit knowledge between members. These

communication channels have been supplemented

by telephone and email communication between

peers which is driven by specific scientific

challenges, as well as informal meetings at

conferences and site visits.

Evidence of the transfer and contextualisation

can be found in the outputs achieved to date from

the networks collaboration. Respondents highlight

that the early years of network operations were

occupied establishing the culture, routines and

project portfolios for the network. This period

demanded partner commitment for little immediate

value other than an enhanced scientific scanning

capability. However, the later years resulted in ‘real

value’ being transferred to the partner organisations

from the network generated knowledge.

4.4 Absorbing

Knowledge-Evaluate/Support/

Re-Innovate

All three cases under study have evolved and

expanded in terms of size and scope over the period

of study providing indications of the success of the

networks to date. While much of the detail

regarding direct support provided by network

members was specified in the consortium

agreements, certain partners surpassed their

indicated support by allocating additional

equipment, compounds and personnel to the

network. While member support of the network was

primarily in terms of financial funds, contribution of

staff in terms of full-time equivalents and

background IP in terms of patents and scientific

compounds, some of the most valuable contributions

occurred organically as research scientists interacted

together within the context of specific and

synergistic scientific problems. The partners to each

network not only transferred knowledge back to

their home organisation but also championed the

collaboration by developing linkages with

appropriate new researchers within their

organisation to enhance the networks value.

As the external environment is constantly

evolving, the networks themselves have recognised

the need to adapt to remain relevant and valuable to

the collaborating partners. A key challenge facing

the case consortia is that after prolonged interaction

and learning, the industrial partners no longer view

the university’s research expertise as internally

lacking within their own researchers. This reduces

the central position of the university and

consequently the knowledge and power gains the

university can obtain from the network, thus

isolating it to the periphery of the network. In light

of this, the lead-academic has incorporated an

emerging scientific area as a minor part of the third

cycle and this is likely to become a more significant

part of the next cycle in order to maintain scientific

and commercial relevance to partner organisations.

Similarly in case 2, the network evolution has

resulted in partner organisations within the network

establishing smaller, parallel consortia to pursue new

opportunities identified during interaction. Rather

than this being viewed as a threat to the original

network, it is seen as evidence of deepening

relations between organisational partners and added

value of participation.

5 CONCLUSIONS

With increasing environmental uncertainty,

organisations are collaborating more with external

parties, including other organisations and

educational institutions in order to access knowledge

to facilitate innovation. It is acknowledged that the

key to survival is to recognise that the locus of

innovation is found in networks of learning,

knowledge sharing and innovation. Thus, in order to

effectively manage the innovation process, one must

understand the structure and function of the network

contributing to the generation of innovations.

Previous research identifies the benefits of social

networks for the creation of new knowledge (Zander

and Kogut, 1995; Trott 2008) and the implications of

specific social network characteristics such as

density, cohesion, strength of relationships and

existence of relationships, on knowledge

management for innovation efforts. Thus,

understanding the implications and influence of

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collaboration within networks is key to facilitating

effective management of knowledge sharing and

innovation processes.

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