Transactive Memory System in Clusters

The Knowledge Management Platform Experience

Pascal Amandine

and Thomas Catherine

Aix Marseille Université, CNRS, LEST UMR 7317, 13626, Aix-en-Provence, France

Université Nice Sophia Antipolis, CNRS, GREDEG, UMR 7321,

250, rue Albert Einstein- Sophia Antipolis 06050 Valbonne, France

Keywords: Transactive Memory System, Cluster, Knowledge Creation, Information Technology.

Abstract: Clusters produce social and cognitive proximities that support knowledge flows and combination. As such,

clusters affect both members’ motivation to engage in collective knowledge creation processes and their

ability to identify actors’ expertise and then to exchange and combine distributed pieces of knowledge. In

other words, social and cognitive interactions within a cluster should affect the development of Transactive

Memory Systems (TMS). This paper proposes to extend the TMS concept at the cluster level. Based on an

integrative design science methodology, this paper makes theoretical contributions on how a TMS within a

cluster functions with the assistance of social interactions and information technologies. The study builds on

the design of a semantic web service of competencies within the Sophia Antipolis telecom cluster. This

study provides empirical support to the potential benefits of the TMS approach at the cluster level and

specifically the ability of an IT to support the development of an effective TMS in a cluster.

1 INTRODUCTION

A vast literature emphasises the crucial role of

cluster, defined as localized network, in building

learning and innovative capabilities (Kogut, 2000;

Maskell and Lorenzen, 2004; Nooteboom, 2005;

among others). From a Schumpeterian perspective,

Nelson and Winter (1982) treat innovation as a

search process that explores the space of possible

combinations of pieces of knowledge to create new

or better alternatives. Cluster and network favour

interactions among actors, which in turn produce

social and cognitive proximities supporting

knowledge exchange and combination (Kogut, 2000;

Maskell and Lorenzen, 2004). As such, clusters

affect both members’ motivation to engage in

collective knowledge creation processes and their

ability to identify actors’ expertise and then to

exchange and combine distributed peaces of

knowledge. In other words, social and cognitive

interactions within a cluster should affect the

development of Transactive Memory Systems

(TMS).

We propose to extend the notion of TMS at the

cluster level. TMS refers to a collective system that

individuals in closed relationships use to encode,

store, and retrieve knowledge about different

substantive domains (Wegner, 1987). A commonly

used of TMS is a shared system that provides hints

about “who knows what” (Ren and Argote, 2011).

Whereas TMS were originally observed in small

groups, some recent researches wondered if the

TMS concept would “scale up” to fit well within

organizational settings (Nevo and Wand, 2005;

Jackson and Klobas, 2008). If the TMS concept

shows promise for being generalized to the

organizational level, our understanding of how an

organizational TMS functions remains limited (Ren

and Argote, 2011). In addition, extending the TMS

at the organizational level introduces the question of

how IT can support the development of effective

TMS (Nevo and Wand, 2005).

Based on an integrative design science

methodology, this paper makes theoretical

contributions on how a TMS within a cluster

functions with the assistance of social interactions

and information technologies. The study is based on

the Knowledge Management Platform (KMP)

project within the Sophia Antipolis telecom cluster,

one of the main European centres of high tech. We

explore technological interventions to assist clusters’

members in building R&D collaborative projects

Amandine P. and Catherine T..

Transactive Memory System in Clusters - The Knowledge Management Platform Experience.

DOI: 10.5220/0005026200050014

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 5-14

ISBN: 978-989-758-050-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

through the design of a map of competencies. The

KMP project is an experimental IT infrastructure

based on a semantic web service of competencies.

The KMP experience provides empirical support

to the potential benefits of the TMS approach at the

cluster level. Our study provides specific guidelines

to create an IT to support the development of an

effective TMS in a cluster. Based on an appropriate

formalization of knowledge about “who knows

what” (the map of competencies), this IT creates an

artificial directory to link different organisations and

subgroups in order to facilitate knowledge exchange

and combination.

The argument in this paper is organized as

follows. First, we articulate the literature on

knowledge creation within a cluster with TMS

discourse. Second, we describe the methodology

adopted in this paper. The argument then turns to a

case study of designing and developing a map of

competencies for fostering knowledge creation in a

telecom cluster. Finally, the findings from this case

study are discussed and implications for future

research are explored.

2 THEORETICAL FRAMEWORK

2.1 Knowledge Creation inside a

Cluster

According to Kogut and Zander (1992), Nahapiet

and Ghoshal (1998) and Shawney and Prandelli

(2000), organizational knowledge creation is above

all a social process. Moran and Ghoshal (1996) and

Nahapiet and Ghoshal (1998) enhance in a

schumpeterian perspective that organizational

knowledge creation is based on two key

mechanisms: exchange and combination. Creating

new knowledge therefore requires combining

elements previously unconnected or developing

novel ways of combining elements previously

associated. When various agents hold resources,

exchange is a prerequisite for resources

combination. Nahapiet and Ghoshal (1998)

identified four conditions that would favour

knowledge exchange and combination: 1) the

opportunity to engage in exchange and / or

combination; 2) the capability to anticipate

knowledge combination value; 3) the motivation to

engage in exchanging and combining knowledge

and 4) the capability to combine knowledge.

This approach of social knowledge creation

emphasizes the necessity for organizations to open

themselves to the outside in order to reach new

knowledge (Van de Ven, 2005). The network then

represents a privileged source of knowledge

exchange, and provides structures and stability that

can be used for collective learning (Kogut, 2000).

Hence, the network appears like an organizational

configuration able to create, accumulate and transfer

collective knowledge (Baskerville and Dulipovici,

2006). Moreover, cluster capacity to formulate

collective entrepreneurial projects becomes

nowadays crucial (Crevoisier and Jeannerat, 2009).

This gives way to combinatorial territorial dynamics

that are mainly based on the anchoring of composite

fields of knowledge (Antonelli and Calderini, 2008).

Beyond that, the key factor is the capacity to act

collectively (Crevoisier and Jeannerat, 2009). These

capabilities emerging from interactions of actors

within networks are named “network capabilities”

and included two main aspects: the architecture and

the identity (Kogut, 2000). Network architecture

refers to the links structure, the types of actors and

the coordination mechanisms. Through identity,

individual anchors their perception of self and other

and attach meaning to membership, as well as in the

categories of skill and knowledge that define a

spatial and cognitive division of labour. In addition,

Dyer and Nobeoka (2000) emphasise that if agents

are able to represent a shared space, knowledge

combination can be easily generated. Architecture

and identity thus allow to coordinate specialized and

distributed knowledge (Kogut, 2000). As such, they

enable the flow and the combination of knowledge

across organizational boundaries (Mitchell and

Nicholas, 2006). In sum, network innovation

capabilities rely not only on the existence on a broad

range of knowledge (cognitive variety), but also on

the ability of cluster’s members to access and

combine this knowledge, knowing ‘who knows

what’ and sharing mental models (transactive

memory).

However, network capabilities, and more

specifically the capacity to act collectively, are

rarely formed by design but rather “arise from

inherent characteristics of technologies that populate

an industry, as well as social norms and institutional

factors that favour the operation of particular rules”

(Kogut, 2000: 410). Thus, the question of how to

build effective network capabilities in order to foster

innovation through knowledge exchange and

combination is still open. In other words, can a

technological system support the development of a

transactive memory within a cluster defined as a

localized network in cases where it does not develop

naturally?

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

2.2 Transactive Memory System at the

Cluster Level

A Transactive Memory System (TMS) is a shared

system that people in closed relationships develop

for encoding, storing, and retrieving information

about different substantive domains (Wegner, 1987;

Ren and Argote, 2011). The basic idea is that

individual knowledge in a group consists of internal

knowledge (held in his mind) and external

knowledge (which the individual can access using

the TMS) (Jackson and Klobas, 2008). As such,

TMS supposes that individuals play the role of

external memory for other individuals who in turn

encode meta-memories (i.e., the label or subject of

the knowledge as well as its location, but not the

knowledge itself) (Nevo and Wand, 2005).

TMS at the group level

Originally, researches on TMS were developed

at dyad or team group level of analysis. TMS

implies a cooperative division of learning,

remembering and communicating knowledge within

the group (Wegner, 1987). Over time, knowledge in

TMS becomes more specialized or differentiated

among members as a result of the division of

learning; at the same time, shared or integrated

knowledge increases as individuals develop a shared

cognitive representations of “who knows what”

(Brandon and Hollingshead, 2004; Ren and Argote,

2011).

As a result, three components are crucial to

TMS: cognitive interdependence, expertise related to

task and people, and shared mental models (Brandon

and Hollingshead, 2004). First, cognitive

interdependence describes the extent to which team

member’s work outcomes depend on a combination

of their own input and the input of others members.

As such, it motivates and sustains the development

of TMS. Task interdependence led thus to a higher

level of TMS, which in turn led to improved team

performance (Ren and Argote, 2011). Second,

Brandon and Hollingshead (2004) expand the basic

notion of labels and location (who knows what) into

a more explicit portrait of relations between Task,

Expertise and People (TEP). Third, shared mental

model concern not only a shared representation of

“who knows what” or “TPE” units, but also macro-

organizations of those “TPE” units. These shared

mental models have implications for the

effectiveness of the TMS. Brandon and

Hollingshead (2004) propose to evaluate their

development along three dimensions: accuracy,

sharedness and validation.

TMS at the organizational level

Only four studies have recently extended the

TMS concept to the organizational level (Ren and

Argote, 2011), including one case study (Jackson

and Klobas, 2008). Generalizing TMS to the

organizational level raises several challenges (Ren

and Argote, 2011). First, members might have more

trouble identifying who knows what in large

organizations than in small groups. Second,

organizations are composed by multiple subgroups

increasingly geographically distributed with less

communication and knowledge sharing across these

subgroups. Finally, when tacit knowledge is

available in a distal part of organization, retrieval

becomes difficult. Because organizations are larger

than work groups and geographically distributed,

Nevo and Wand (2005) shows that TMS might rely

upon advanced technology to locate and shared

information. They suggest that a general directory of

meta-memories should be formed, linking the

different communities and supporting knowledge

transfer between individuals in different

communities. In this case, knowledge transfer is not

provided through repositories but rather through

technology mediated connections.

Extending TMS to a large group requires the use

of artificial directories based on formalized meta-

knowledge integrating three main dimensions (Nevo

and Wand, 2005): conceptual, descriptive and

persuasive. In fact, a set of concepts is needed to

describe the subject of knowledge (ontology can be

used here). Descriptive knowledge can be

formalized to describe the author of knowledge

(location) and to characterize the knowledge (date,

format…). At the persuasive level, source of

credibility and perceptions of expertise should be

formalized. In this line, Jackson and Klobas (2008)

add two insights: in an organizational TMS people

access each other’s knowledge through a

combination of personal and codified directory

system; maintaining these directories which can be

activated for retrieving knowledge when it’s needed

(passive allocation) is more efficient than storing the

knowledge and sending content trough a system

network (repositories and active allocation).

Finally, TMS at the organizational level raises

two main questions (Nevo and Wand, 2005; Jackson

and Klobas, 2008; Ren and Argote, 2011). How an

organizational TMS can be developed with the

assistance of social network and information

technologies? How manage the ability to keep the

meta-knowledge directories updated? These two

questions that remains unanswered at the

organizational level become more crucial at the

TransactiveMemorySysteminClusters-TheKnowledgeManagementPlatformExperience

cluster level. Indeed, clusters are larger than

organization and composed by multiple entities

which both compete and cooperate.

3 METHOD

This section describes the KMP experience which

was conducted in the well-known technology park of

Sophia Antipolis (SA) in France (Castells and Hall,

1994). In this project, we applied an integrative

design science methodology (Pascal et al, 2013) to

create an interactive map of competencies to

enhance knowledge creation through partnerships

within the Telecom Valley cluster.

3.1 The Knowledge Management

Platform Project

Since the mid 1990s, the SA cluster has

progressively developed from a computer industry to

a telecom and IT industry cluster (Krafft, 2004). As

such, Telecom Valley, a non-profit organization,

was founded in 1991 by eight leading firms and

other organizations in order to facilitate

collaboration.

In 2000, the main characteristics of the Telecom

Valley (TV) cluster could be summarized as follows

(Lazaric et al, 2008). First, firms in this cluster were

evolving in a diverse technological context, covering

a wide range of industries (e.g. computing,

multimedia, space, information processing, on-line

services and networking, and microelectronics).

Given that most parent companies were located

elsewhere, the participants in the cluster had been

developing strong external links. The internal

dynamics of the cluster arose from the interactions

in several communities, associations, clubs, and so

forth, but also revealed a huge potential synergy

between agents in the cluster that was still largely

unexploited.

The lack of internal dynamics was the starting

point of the KMP project, launched in 2001 by TV.

Because they only have a partial view of the

different flows of knowledge developed by the

actors of the cluster, members of TV asked a map of

competencies to create strong local links with local

high-tech SMEs and research institutes. The

objective of the KMP project was thus to build an

interactive map of competencies which suggests a

lack of shared representation of who knows what

within the cluster.

3.2 An Integrative Design Science

Methodology

Design science research develops knowledge in the

service of action and problem solving in

organizational settings. To address the research

objectives, we thus rely on an integrative design

science methodology that connects two perspectives

on design: science-based design drawing on design

propositions grounded in research and human-

centred design emphasizing an active and systematic

participation by users and other stakeholders (for

more details on the methodology see Pascal et al,

2013). This methodology is relevant in the case of

designing an innovative solution, where there

generally is no or limited scientific and practical

knowledge that is closely tied to the design goals at

hand (Pascal et al, 2013). It is also pertinent because

it assumes that technology per se and therefore TMS

based on an IT cannot determine work practices and

thus incorporates an enlarging network of users at

different stages of the design project (Newell et al,

2009; Nevo and Wand, 2005).

This methodology involves six steps. These steps

typically need to be taken in many iterations,

acknowledging that each step overlaps and is highly

intertwined with other steps.

1. Problem awareness. Before one can identify

any knowledge relevant to address a particular

design challenge or assignment, a clear

understanding of the nature of this assignment is

needed.

2. Developing design propositions. The scientific

knowledge relevant to the key problem addressed is

identified and synthesized into design propositions

thanks to the CIMO logic. CIMO involves four

components: (1) a problematic Context, in terms of

the surrounding (external and internal environment)

factors and the nature of the human actors

influencing behavioural change, (2) which suggests

a certain Intervention type that managers have at

their disposal to influence behaviour, (3) to produce,

by way of particular generative Mechanisms, the

processes that in a certain context generate (4) the

intended Outcomes (Denyer et al, 2008).

3. Creating scenarios of use. Scenarios of use

serve to explore the organizational context where

work practices are meaningfully accomplished

(Pascal and Rouby, 2006) and serve to convert and

articulate tacit knowledge of practitioners, and as

such, provide input for enriching the design

propositions (Plsek et al, 2007). Scenarios of use

therefore prevent the IT to interfered with the

implicit encoding system of the actors (Ren and

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

Argote, 2011).

4. Designing and developing artefacts. Drawing

on input from the (initial set of) scenarios of use and

design propositions, design work on artefacts is

conducted. Artefacts are the tangible result from the

design process and arise from contextualizing and

applying design propositions to particular practices.

5. Experimenting with prototypes. For any

information technology (IT) artefact, the design

evaluation process can not be limited to IT

performance but has to involve an in-depth study of

the (intended) artefact in its business environment

(Hevner et al, 2004; Pandza and Thorpe, 2010). As

such, the experimentation process exploits the

potential role of prototypes, extending the similar

role of other artefacts (e.g. drawings) developed and

used in earlier stages of the design process.

6. Organizational transformation. Finally, the

collaborative learning process may progressively

change the organizational context (or fail to do so).

As a result, the initial managerial problem typically

evolves, leading to redesign efforts or an entirely

new design cycle. At the same time, these

transformational processes modify, and possibly

enlarge, the network of users that support and apply

the tool (Akrich et al, 2002; Tatnall and Gilding,

1999).

3.3 Main Actors, Data Collection and

Analysis

Researchers from different academic fields

composed the project team: economics and

management, computer science and ergonomics,

telecommunication sciences. The number of users

engaged in the project has gradually grown from two

TV working groups and several pilot users to

representatives of all TV’ actors. At the end of the

project, all other TV members, several clubs and

associations in the SA territory, and IT firms located

outside SA participated in the project but without a

direct involvement as pilot users.

We gathered data from three types of sources:

(1) interviews (26 open interviews with key

stakeholders, 52 semi-structured interviews with

pilot users, and 21 interviews with users as well as

other stakeholders to evaluate the prototypes); (2)

steering committee and working group meetings;

and (3) occasional meetings. Overall, we employed a

purposeful sampling strategy (Kumar et al., 1993)

towards all key stakeholders of the KMP project. In

particular, we drew on an iterative process of

simultaneously collecting data, analyzing data,

building conjectures (the design propositions) and

testing them through action (via artefacts). At the

same time, we were seeking new users to embed and

integrate the KMP portal in the users’ network in

and around TV.

Data analysis follows the different design cycles.

The first round of data analysis was guided by the

central research question on the dynamics of

knowledge creation inside a cluster. This initial

stage was based on the method of constant

comparison (Conrad, 1982; Glaser and Strauss,

1967). In a large number of iterations, data from

many informants are compared to identify

differences and anomalies and to identify and define

major categories, dimensions, themes, or processes

(Agar, 1986; Miles and Huberman, 1984; Spardley,

1980). At this stage, five researchers analyzed the

data. As such, by examining the congruency of data

patterns among informants, we obtained a clear

picture of the cluster, its dynamics of innovation,

and barriers and difficulties in knowledge sharing

and creation.

During the second and the third design cycles,

operating logics and practices have been described

within scenarios of use. These scenarios of use were

used first to build the tool and subsequently to

evaluate it. Two types of scenarios of use were

analyzed: the process of looking for a partner and

the process of co-evolution between the firms and

the cluster. The method of constant comparison

served to identify differences and anomalies.

Specifically, the analysis strategy was the synthetic

one (Langley, 1999) and was mainly based on the

different categories of the scenarios of use : (1) who

was the informant; (2) how the informant describes

his/her activities; (3) the information needed for

performing these activities; (4) the problem

encountered while performing these activities. Two

researchers coded each semi-structured interview to

develop the scenarios of use. Once developed, the

scenarios of use served to ask critical questions and

introduce alternative interpretations regarding

regularities, contrasts and/or anomalies in the data

(Nemeth et al., 2001). The data analyzed in

interaction with the literature served to create new

design propositions for developing and

implementing new functionalities of the portal.

These design propositions were evaluated by testing

and using the different prototypes. Data obtained

from the (evaluation-oriented) semi-structured

interviews with users as well as other stakeholders

(e.g. associations), steering committee meetings and

meetings with potential users were analyzed and

synthesized by examining the congruency of data

across informants, in order to inform the design team

TransactiveMemorySysteminClusters-TheKnowledgeManagementPlatformExperience

about the usefulness of the portal and potential

modifications that would enhance this usefulness in

a pragmatic view (Dewey, 1938; Rorty, 1999).

4 RESULTS

Based on the literature review and our first local

practices analysis, we rapidly defined a meta-design

proposition that ensures the development of the

KMP platform: in a multi-actor cluster with a broad

scope of technologies (C), an interactive map of

competencies (I) will serve to foster knowledge

creation through R&D collaboration (O) by

reinforcing the four potential mediators of

knowledge creation: opportunity, anticipation

ability, motivation, and combinative capability (M).

This proposition does not specify the intervention

modalities, in terms of what kind of solution is

needed to activate each of the generative

mechanisms, and how to develop it. Three new

design propositions have thus been developed

through three successive design cycles between

2002 and 2006 (see Pascal et al, 2013) aiming at

foster TMS in the TV cluster in order to enhance

innovation.

4.1 Mapping Competencies:

Highlighting “Who Knows What”

Following the first design proposition, we

investigate the development of a 'competencies map'

in order to create the computerized directories of

meta-memories of “who knows what” and thus to

foster TMS. We choose to describe competencies

instead of knowledge because competencies

combine knowledge in action for the output at hand.

As such, describing competencies enriches the

comprehension of “who knows what” by linking

task, expertise and person (Brandon and

Hollingshead, 2004).

However, mapping competencies within a

cluster raises many challenges. First, it is necessary

to identify the appropriate level of competencies

description (individual or collective). The second

challenge is to describe competencies across the

cluster in sufficient detail without disclosing

strategic know-how. Finally, using an IT mapping

raises the issue of data collection and updating.

Combining literature review and local practices, we

established the following design proposition.

DP1: In a multi-actor cluster with a broad

scope of technologies (C), an interactive map of

competencies (I) provides relevant information that

enhances opportunities (M) for finding the good

partner for R&D collaboration (O). To trigger the

opportunity mechanism, a competency is defined as

an action that mobilizes technical, scientific and

managerial resources to produce deliverables that

are likely to create value in a business activity.

Given the size of the cluster, we decided to

describe collective competencies at the team level

which is the appropriate level when looking for a

R&D project partner. We defined an abstract model

of competencies, based on the four abstract

categories: action / resources (including knowledge)

/ deliverables / business activity (Rouby and

Thomas, 2004). These abstract categories are the

first codes shared by the community and the first

bricks for building shared representations. They

constitute the roots of the elaboration of four

specific ontologies (action, resources, deliverable

and business activity). An ontology is “an object

capturing the expressions of intensions and the

theory accounting for the aspects of the reality

selected for their relevance in the envisage

applications scenarios” (Gandon, 2001). The model

of competences and its four constitutive ontologies

permit to locate the competencies and to compare

them depending on the interest and vision of the

actor which can choose in its queries its relevant and

appropriate combination of categories. Users

scenario points different kind of queries: simple

queries on, for example, a particular technology (e.g.

“which firms are working on J2ME?”), a delivery

(e.g. “who has successfully produced video

games?”) or a business activity (e.g. “which firms

are doing work for the 3G mobile sector?”) as well

as more complex queries which combined several

categories such as technology and business activity.

Once competencies are identified and located, an

accurate description is suggested including what is

the problem solved by the competency (for instance

the storage of data on electronic chips), how this

problem is solved (the know-how, skills, equipment

on the building of chips), and the patents,

publications, R&D collaboration, and industrial

partnerships involved. These additional details are

essential to a proper understanding and to reinforce

the credibility of a partner’s competencies. This

description is not based on formal categories

allowing firms to be more or less precise on this

strategic aspect and to use natural language in this

purpose.

Ontologies used in the KMP project are relevant

because:

 A semantic representation of information

allows for more precise research and increases

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

the degree of answer liability. Ontologies

improve the retrieval of knowledge because

they can focus the results on a specific subset

and then reduce the set of results (Nevo and

Wand, 2005) or conversely to enlarge it if

necessary.

 A semantic representation of information

allows for more precise research and increases

the degree of answer liability. Ontologies

improve the retrieval of knowledge because

they can focus the results on a specific subset

and then reduce the set of results (Nevo and

Wand, 2005) or conversely to enlarge it if

necessary.

 Ontologies allow to acknowledge different

points of view held by spatially distributed

and heterogeneous actors. For example, for

actors belonging to the commercial

professions, 3G (third generation in

telecommunication) and multimedia are

ontologically equivalent. Conversely, in the

technologically oriented professions, 3G and

multimedia are quite distinct because they

belong to separate technological trajectories.

This is the reason why the two terms will be

considered distant in the ontology of the

technological resources, while they will be

very close in the business activity’s ontology.

Finally, to face the size and the large scope of

technologies characterizing the TV cluster, data

collection and updating were highly decentralized

and manage by teams composing the different

organizations. Each team described its own

competencies (between 5 and 10) and added when

necessary new concepts in ontologies. Several expert

groups first agreed on the basic roots of these

ontologies. New concepts were then integrated as

competencies’ description increases and were

regularly validated by expert groups.

4.2 Common Space: Highlighting

Similarities and Complementarities

The second major issue in designing the

competencies map involved developing a shared

identity of the cluster. Members of TV’s board

raised two problems regarding this lack of identity:

(1) “It has always been ambiguous whether Sophia

is more telecom or software”; (2) “We never know

when we have to accept the entry of a consultancy

firm. Generally, the decision depends on the size of

the firm. Thus, we lean more on political aspects

than on industrial or innovation logics. We are not

satisfied by this way of thinking, but we don’t know

how to do it otherwise”.

Regarding the cluster identity issue, the literature

reveals that the representation of a common space

may help individual to develop a shared meaning of

membership and a shared representation of the

cognitive interdependences of labour (Kogut, 2000;

Dyer and Noboeka, 2000). After several design

loops, a design proposition on the cluster’s common

space representation was therefore stabilised.

DP2: In a multi-actor cluster with a broad scope

of technologies (C), building a common space

representation of the cluster (I) reinforces the

motivation of actors and their ability to anticipate

value created from knowledge exchange and

combination (M) to effectively engage in R&D

collaboration (O).

This common space has to exhibit the following

properties: (a) it represents all actors in terms of

their main competencies: scientific and technical

competencies (key stakeholders), managerial

competencies (support) and relational competencies

(facilitators); and (b) it positions the competencies of

stakeholders in technological poles (similarity

concept) as well as value chains (complementarity

concept).

To evaluate the degree of similarity and

complementarity, the map of competencies draws on

the following definitions: competences are similar

when they share the same resources, and

complementary when sharing the same business

activity.

Figure 1: the cluster common space representation.

As figure 1 shows, the common space

representation identified three kinds of actors:

1. The stakeholders who participated in

knowledge creation in the cluster; that is to

say those who had technical competencies

such as firms and public research

laboratories. The competencies of the

stakeholders are positioned on the

technological poles depending on the main

resources they mobilized. As such, a firm

TransactiveMemorySysteminClusters-TheKnowledgeManagementPlatformExperience

can be present on different technological

poles.

2. The facilitators, including all associations,

clubs or service providers, whose goal was

to help find partners (relational

competencies).

3. Support organizations in the area of law,

finance and management that would ensure

partnerships by providing managerial

competencies.

This representation identifies technological poles

with actors who shared similar competencies and

value chains which combined complementary

competencies. Value chains are business-driven and

composed with competencies which shared the same

business activity. These competencies are different

but complementary by producing interrelated

outputs for the same business activity. Value chains

are not given, but dynamically built from the

particular competencies described by the users in the

platform. In addition, the representation allows,

through ontologies, to define general overviews on

business activity sector (eg the telecom value chain)

or precise overviews on market segment (eg the

bluetooth or 3G market). It also provides a diagnosis

of the weaknesses and strengths of the cluster in

terms of the nature and number of competencies in

each technological domain for a specific value

chain.

The distinction between similar and

complementary competencies supports the

development of a shared understanding of the

cognitive division of labour. It resolves the

perceived ambiguity on the cluster identity by

showing that the cluster enjoys a lot of software

competencies (which contribute to a technological

pole) which mainly addressed the telecom market.

More generally, the interactive representations of the

common space have effects on motivation and

ability to anticipate. By increasing the actors’ self-

consciousness about the competencies distributed in

the cluster and their interrelations, it reveals actor’s

games of interests. For example, firms in the

software pole realized that they could gain more in

being partners than being fierce competitors and

began to develop partnerships about joints solutions,

aiming to reach more and bigger customers, within

and outside Sophia Antipolis.

Finally, the KMP project showed that the

progressive design of the common space

representation really mobilized all the TV’ actors

and consequently motivated them to engage more

actively in the project (Pascal et al, 2013).

5 DISCUSSION AND

CONCLUSION

This study provides empirical support to the

potential benefits of the TMS approach at the cluster

level and specifically the ability of an IT to support

the development of an effective TMS in a cluster. A

TMS cluster exhibits specific characteristics

different to those of groups or organizations. It needs

appropriate directory structures and shared models

to an organizational context including a wide range

of actors (and expertises) who both cooperate and

compete. In line with Nevo and Wand (2005), our

results demonstrate the ability of IT to extend the

notion of TMS to large groups including clusters.

Our study suggests that artificial directory of

meta-memories can be formalized in order to link

different organisations and sub-groups (eg teams)

and to facilitate knowledge exchange and

combination. It provides specific guidelines to create

an artificial directory based on an appropriate

formalization of knowledge about who knows what

in a cluster. This formalization integrates the three

main dimensions highlighted by Nevo and Wand

(2005):

- the competency model based on four abstract

categories (action / resources / deliverables /

business activity) and their ontologies constitute the

conceptual dimension of knowledge. It allows to

accurately identify and locate the expertise within

the cluster.

- this expertise described at the team level is then

completed by additional information (eg know-how,

skills, equipments, partnerships, patents…)

constituting the descriptive dimension.

- these additional details which are more or less

precise regarding the firms’ communication

strategies reinforce the credibility of a team’s

competence ie the persuasive dimension.

While using a simple model (based on only four

categories) enables sharedness, the building of

ontologies for each category and the combination of

these categories in the encoding and retrieval of

competencies allows accuracy. In addition,

ontologies allow knowledge to become more

specialized or differentiated among members even in

context where members in different groups not share

concepts to describe the contents of knowledge. The

formalization of competencies in the KMP project

thus achieved accuracy and sharedness, two

dimensions of TMS effectiveness (Brandon and

Hollingshead, 2004).

Our project also reveals that building an artificial

directory to identify and locate expertise is not

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

sufficient to support an effective TMS. The IT has to

create structures that highlight the coordination or

combination of distributed expertise. In the KMP

project, this structure is based on the common space

representation which creates a share cluster identity

and a mutual understanding of the division and

coordination of labour and expertises in the cluster.

This identity and shared understanding constitute

motivational factors that affect the development of

TMS. Indeed, according to Ren and Argote (2011:

204), “in groups where members identify with the

group, they are more likely to invest in developing

the specialized division of labour that is defining

characteristics of TMS”. In addition, identity and

mutual understanding affect members’ motivation to

engage in collective processes of communication

and knowledge exchange and combination (Kogut,

2000; Dyer and Nobeoka, 2000). As such, the

common space representation supports the

interpersonal side of the TMS development.

In sum, an IT that supports TMS at a cluster

level must rely on two main characteristics: to

combine both accurate descriptions of knowledge at

a micro level (team) and macro representations of

the cluster knowledge; to favour both interpersonal

and technological approaches of TMS.

Several paths for future research can be derived

from the work described in this paper. One strand of

research might be to study the dynamics of cluster

TMS development with attention to how the

technical and interpersonal approaches of TMS

evolved and enriched each other over time. Other

research should focus on clusters with a well-

developed identity and architecture in order to

analyse the existence of an effective TMS in its four

dimensions: accuracy, sharedness and also

validation and convergence. Finally, a promising

new direction is the inclusion of innovation as an

outcome of TMS. Whereas team performance is

traditionally the focus of analysis, our study

examines the effect of TMS on how knowledge is

combined and recombined. Further investigations

are needed to analyse the relationship between TMS

and innovation at the team, organisational or cluster

level.

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