Discussion Dynamics of Cooperation and CompetitionBeing successfu

Discussion: Dynamics of Cooperation and Competition
Being successful in today’s business environment requires more
nuanced thinking than just stressing competition. Consider
General Electric, which found that a highly effective way to
improve its KPIs in the aircraft engine market was to actually
partner with a competitor. It seems counter-intuitive, but it
worked. When General Electric and Snecma created an alliance
to build aircraft engines, General Electric shielded certain
sections of the production process to protect against the excess
transfer of technology (“Snecma, GE Renew CFM Agreement,”
2008).
Consider the dynamics of cooperation and competition in the
future business environment. For organizations that are in an
environment of increasing cooperation/competition, consider the
proactive role the HR department can serve in helping the C-
suite think about balancing competition and cooperation. As
part of the Discussion, give specific examples.
To prepare for this Discussion,
Review this week’s Learning Resources, especially:
· Resource fit in inter‐ firm– See pdf
· Interpretive schemes – See pdf
· Knowledge transfer to partners – See pdf
· Two Favors of Open Innovation - See pdf
Assignment:
Post a cohesive and scholarly response based on your readings
and research this week that addresses the following:
Tommy McMillian request letters from you that can show the
parole board that he has a support system waiting outside.

Conduct additional research to analyze the dynamics of
cooperation and competition in future business environments.
· From your research, discuss specific ideas or concepts
regarding what proactive role can the HR department serve in
helping the C-suite think about balancing competition and
cooperation?
· Does cooperation/competition require equal resources from all
partners?
· How are the decisions made about the levels of resources
committed by each partner?
· If there is a wide disparity in net worth or market share of the
partners, is it reasonable to expect each to commit the same
percentage of resources?
· How are conflicts around cooperation and competition
anticipated, planned for, and resolved by the HR department?
· No Plagiarism
· APA citing
FROM THE EDITOR
James A. Euchner
TWO FLAVORS OF OPFNINN01MFI0N
Since Henry Chesbrough published Open Innovation
(2003), the paradigm he described has been a subject of
great interest and experimentation in corporations. Ches-
brough defined open innovation as breaking down the
boundaries of the corporation so that "valuable ideas can
come from inside or outside the company and can go to
market from inside or outside the company, as well." He
contrasted this open paradigm with the more-traditional
closed innovation paradigm based on the captive R&D
laboratory.

Chesbrough's work encouraged companies to create
porous innovation pipelines and to become more aggres-
sive about licensing, working with start-up companies,
spinning out concepts that don't fit with the core business,
and partnering with other organizations to produce inno-
vations. These approaches have created increased value
for firms as diverse as P&G and GE, but they may be only
the start of the redefinition of innovation. The emergence
of open-source intellectual property (IP) and online com-
munities for innovation and customer input is forcing
continued rethinking.
Open innovation approaches are designed to source new
technology and concepts broadly, seeking the seeds of
the next innovation both within and outside of the corpo-
rate firewall (see, for example, Slowinski et al. 2009).
Such initiatives are often supported by companies like In-
nocentive or Gen3 Partners, which help to frame the
problem, connect the finn with external sources of exper-
tise, and manage resulting IP. Control of the IP is a criti -
cal part of the management model. Similarly, control of
the innovation process itself remains with the firm,
which defines priorities, chooses how to source them,
selects providers, and integrates them into its product
roadmap. Open innovation stretches the role of R&D in
Jim Euchner is editor-in-chief of Research-Technology
Management and a visiting scientist at the Massachu-
setts Institute of Technology Sloan School of Manage-
ment. He previously held senior management positions
in the leadership of innovation at Pitney Bowes and Bell
Atlantic. He holds an MS in mechanical and aerospace
engineering from Princeton University and an MBA from
Southern Methodist University, [email protected]
important ways, but it operates within the current man-
agement paradigm. Open-source innovation, on the

other hand, redefines the corporation itself. Two critical
factors distinguish the approaches: the treatment of
intellectual property and control of the direction of
innovation.
Open-source innovation is a more radical model that is
increasingly important in the development of everything
from software to sports equipment. Economic research
indicates that it may soon dominate corporate innovation
in a steadily increasing number of fields. It is best known
today in software development, where open-source soft-
ware projects such as Linux and Apache are both commu-
nities and platforms that enable users to develop and share
code that they need. In the open-source software model,
there is no owned IP. Anyone can access, use, and modify
the code. A large, and largely anonymous, crowd contrib-
utes to the development of the software. Although there
are governance structures for deciding which code is in-
corporated into which release of the software, it is users,
acting both individually and as a community, that decide
what gets worked on. The users, therefore, dictate the di -
rection of the product. Open innovation in this context
means open governance, open IP, open direction.
Open-source innovation requires three large changes in
corporate innovation thinking, each of which is difficult.
First, it requires that firms take a modified view of IP,
trading patent control for other sources of competitive
advantage (speed, customer intimacy, voluntary contri-
butions to the product). This can be threatening to the
corporate R&D structure: creating, managing, leverag-
ing, and controlling IP has long been a central function
of R&D, and it continues to be under both open and
closed innovation models. Opening IP is countercultural,
more countercultural even than "open borders" innova-
tion, but it has the potential to open doors to even great-

er customer engagement and value.
Second, an open-source mindset requires shifting the
locus of control of new product directions closer to
the user community. This is also challenging. Even in
open innovation models, it is a central role of product
management and marketing to make these decisions.
But the world is changing. Online networks greatly
I July—August 2010
()K')5-(i.ï()K 1().S5.OÜ I 20111 liiiJi]>,iriiil R e s e a r c h
Institute. Inc.
increase the potential for engaging customers in real
time, shifting the locus of control of innovation away
from producers and toward user communities. At
times, as Eric von Hippel describes in Democrati zing
Innovation (2005), networks have enabled users to
radically redefine the role of the firms that supply
them.
Finally, open-source approaches to innovation require
business models that can survive in a more open world.
These models are only now emerging. They start with
a true understanding of the ways in which community
contributions can add value. Astute businesses use this
understanding to create platforms that allow their user
community to innovate—whether through technology
platforms (like the Android smartphone platform), cus-
tomer platforms (like open-source software), or plat-
forms for fulfilling designs created elsewhere. Often,
an open business model will also include a heavy dose
of support services to supplement freely available
products.

As online communities continue to emerge, and as the
pace of change fundamentally reshapes the power of IP,
the role of R&D and approaches to innovation within
corporate structures will continue to evolve. Changes
that simply open up corporate borders to innovations
developed elsewhere will not be enough to keep up.
Corporations increasingly need to consider open-source
innovation, which involves much deeper changes to cor-
porate culture and innovation practices than have been
embraced to date.
References
Chesbrough, H. 2003. Open Innovation: The New Imperative for
Creating and Profiting from Technology. Cambridge. MA:
Harvard
Business School Press.
Slowinski, G.. Hummel, E., Gupta, A., and Gilmont, E. R. 2009.
Effective practices for sourcing innovatio n. Research-
Technology
Management 52( I ): 27-34.
von Hippel, E. 2005. Democratizing Innovation. Cambridge,
MA: MIT Press. Available online at
http://mit.edu/cvhippel/www/
democl .htm (accessed May 25, 2010).
RTM Article Awarded Emerald Citation of Excellence
Emerald Management Reviews has awarded a Citation of
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Resource fit in inter-firm
partnership: intellectual capital

perspective
Tzu-Ju Ann Peng
Business Administration Department, College of Commerce,
National Chang-Chi University, Taipei, Taiwan, Republic of
China, and
Centre for Business Performance, School of Management,
Cranfield University,
Cranfield, UK
Abstract
Purpose – Previous studies on strategic alliance and network
have not paid sufficient attention to
resource fit based on intellectual capital perspective. This study
aims at understanding the input
resources and transformation in a dyadic inter-firm partnership,
given different types of value logics.
Design/methodology/approach – This study adopts a multiple
case study approach by in-depth
interviews in three inter-firm cooperative cases, which represent
three different types of value-creating
logics – value chain, value shop, and value network. This study
applies the intellectual capital
navigator (ICN) to analyze the resource transformation among
human capital, organizational capital,
relational capital, physical capital, and monetary capital that
was produced by two sides in three
inter-firm partnerships.
Findings – The results show that: given value chain logic, while
the inter-firm partnership
emphasizes standardization, efficiency and economy of scale,

resource fit in physical, monetary, and
organizational capital forms the basis of value creation; given
value shop logic, while the inter-firm
partnership emphasizes problem solution and economy of scope,
resource fit in human and
organizational capital forms the basis of value creation; and,
given value network logic, while the
inter-firm partnership emphasizes network economic behavior,
resource fit in human, organizational,
and relational capital forms the basis of value creation.
Research limitations/implications – Taking the unit of analysis
at dyad level, this study
demonstrates the detailed resources contributed by the focal
company and its partners based on
different value logics.
Practical implications – This study extends the use of the
intellectual capital approach for
analyzing the resource fit in the inter-firm context.
Originality/value – Theoretically, this study contributes as a
starting-point for analyzing the
resource input and transformation in the inter-organizational
context by using an intellectual capital
approach. Practically, this study contributes to more practical
references so as to reveal, given
different types of value-creating logic, how two partnering
companies can manage and deploy their
intellectual capital and traditional resources in order to fit in
the inter-firm cooperation.
Keywords Intellectual capital, Organizations, Partnership,
Resource management, Value analysis
Paper type Case study

The current issue and full text archive of this journal is
available at
www.emeraldinsight.com/1469-1930.htm
The author would like to acknowledge the financial support of
the National Science Council in
Taiwan (NSC-96-2416-H-126-011-MY2). This paper is an
extended version presented at the 2008
Annual International Conference of the Strategic Management
Society. The author is grateful to
the Conference participants for their valuable comments and
especially thanks Dr Catharina
Lemmer for her comprehensive proofreading.
JIC
12,1
20
Journal of Intellectual Capital
Vol. 12 No. 1, 2011
pp. 20-42
q Emerald Group Publishing Limited
1469-1930
DOI 10.1108/14691931111097908
Introduction
In the field of strategic management, strategic fit is a core
concept in normative models
of strategy formulation (Zajac et al., 2000), and the pursuit of
strategic fit has
traditionally been viewed as having desirable performance

implications. Scholars have
proposed conceptual frameworks to explore the strategic fit.
Two concerns of
mainstream strategy research are to explain what determines
firm performance and
what affect firm strategy (Farjoun, 2002). From the internal
aspect, strategy
coordinates goals and means, internal resources and
administrative infrastructure,
which constitute internal strategic fit. Scholars such as Yin and
Zajac (2004), Wright
and Snell (1998), Parthasarthy and Sethi (1992) have put their
efforts on internal fit.
They have studied strategic fit focusing on how firms reach
strategic fit between
strategy and internal factors such as structure and resource.
However, as inter-firm
cooperation has become one of the dominant strategies, none of
them emphasizes the
strategic resource fit and resource transformation in the context
of inter-organizational
cooperation based on an intellectual capital (IC) perspective.
Scholars interested in alliances and networks have recognized
the knowledge and
resources from partners and their links with competitive
success. Although there is a
well-established body of literature underscoring important
correlation between
resource and inter-firm cooperation (Inkpen and Tsang, 2005),
little attention has been
paid to understanding the resource fit between partners, in terms
of ICs. Although
various variables that affect network resource exchange and
transfer have been
posited, such as firm intent, absorptive capacity, and control

system (Inkpen and
Tsang, 2005), prior studies failed to examine how a pair of
cooperative firms
individually contribute their ICs and create resource fit for
cooperative relationship.
Barney (1991) presented a comprehensive framework to identify
the needed
characteristics of firm resources to generate sustainable
competitive advantages.
According to Hoskisson et al.’s (1999) analysis, one of the
criticisms of Barney’s
framework is that the framework does not account for boundless
resources. To remedy
this, some scholars, for example, Grant (1991), Black and Boal
(1994), propose that
resources are nested that have specific interrelationships and
that there is a need to
examine the dynamic interrelationships among resources. In this
study, we argue that
partners’ commitment of resources does not guarantee that they
both benefit from the
partnership. Particularly in the context of a cooperative
relationship, resources do not
create value unless they were deployed, transformed, and
combined appropriately and
effectively. In order to benefit from inter-firm cooperation, not
only the resource
commitment between partners is essential, but also the resource
transformation for
collective value creation.
From the intellectual capital management (ICM) perspective,
firms should deploy
and manage their IC resources in order to maximize the value
creation. The process of

resource transformation may vary with different value-creating
logics of firms. This is
true for IC resource management not only within the boundary
of the firm, but also in
the inter-firm partnership. Therefore, this study focuses on the
resource transformation
at the inter-organizational level rather than at intra-
organizational level to understand
how inter-firm partners individually deploy their ICs and how
the contributed
resources can transform between partners so as to meet with
value creating logics.
This study tries to link the concept of strategic resource fit and
IC perspective. The
following research questions were raised:
Resource fit in
inter-firm
partnership
21
(1) Given different types of value logics, what are the IC and
traditional resources
contributed by two partners in a dyadic inter-firm partnership?
What is the
relative importance of each input resource?
(2) Given different types of value logics, what is the
representation of resource
transformation in the dyadic inter-firm partnership?

By applying an analytical approach, intellectual capital
navigator (ICN), this study
investigated three inter-firm cooperative cases representing
three different types of
value-creating logics. The next section addresses theoretical
backgrounds. Research
setting and data collection are then described in the third
section, which is followed by
the detailed IC transformation in three cases of the results and
discussion section. The
last section summarizes research findings and contribution.
Theoretical backgrounds
IC perspective
In the field of strategic management, the resource-based view
(RBV) has emerged as a
widespread application and important research approach (Acedo
et al., 2006). A
fundamental question for strategy researchers is the utilization
of the RBV in
developing meaningful management tools in the form of
actionable prescriptions for
practitioners (Nohria, 1992; Mosakowski, 1998, Priem and
Butler, 2001). From the RBV,
Eisenhardt and Schoonhoven (1996) view alliances as
“cooperative relationships driven
by a logic of strategic resource needs and social resource
opportunities.” A firm may
acquire its essential resources from inside and outside the
boundary of the firm. Not
only building internally on its own but also obtaining externally
from alliances or
networks can a firm extend its resource base (Peng et al., 2006).
Barney (1991, p. 101) defines firm resources as firm attributes
that may enable firms

to conceive of and implement value-creating strategies. Of all
different kinds of
resources, intangible assets are considered the most important
resources for value
creation. In line with the RBV, IC represents as valuable,
intangible and inimitable
resources for facilitating productive activities and value
creation of a firm (e.g., Roos
et al., 2005; Nahapiet and Ghoshal, 1998; Bontis, 1998; Roos
and Roos, 1997). Grounded
in an RBV logic, IC-based view (ICV) represents one specific
aspect of the more general
RBV, in that it more narrowly considers intangible resources
that have been
theoretically linked to a firm’s competitive advantage (Reed et
al., 2006). ICV focuses on
the stocks and flows of intangible resources embedded in an
organization, and is
posited to have direct associations with financial performance
(Youndt et al., 2004).
Scholars have proposed various categorizations to classify IC
(e.g., Johannessen
et al., 2005; Edvinsson and Sullivan, 1996). Despite
considerable apparent differences
and overlaps among those categorizations, the broad domain
coverage is basically the
same. In accordance with Roos et al. (2005) definition, IC can
be classified into human
capital (HC), organizational capital (OC) and relational capital
(RC). HC relates to all the
resources embodied in the individual employed by or linked to
the organization in a
way that makes it possible for the organization to deploy these
resources (Roos et al.,
2005, p. 76). OC relates to all the resources that the

organization has developed or
produced and that the organization legally owns that are not
physical in nature, e.g.
brands, image, reputation, processes, routines, systems,
structures and information in
databases or on paper (Roos et al., 2005, p. 30). RC resources
encompass all those
JIC
12,1
22
relationships the organization has with entities outside the
organization and that
influence the organization’s ability to create value (Roos et al.,
2005, p. 74). Reed et al.
(2006) adopted the similar classification, proposing that IC
consists of three basic
components: human, organizational and social capital.
Value-creating logics
In order to understand the economic behaviors of firms for
value creation, it is
necessary to analyze the value-creating logics, which describe
the economic behavior of
the resources deployed by the firms. Based on Thompson’s
(1967) typology of
long-linked, intensive, and mediating technologies, Stabell and
Fjeldstad’s (1998)
typology of value chain, value shop, and value network are
three distinct generic value
configuration models for understanding and analyzing firm-
level value creation logic,

as follows:
(1) Value chain. A value chain model is a long-linked
technology where value is
created by transforming inputs into products. This type of value
creation relies
on standardized process and repetition (economies of learning)
and mass
production (economies of scale). The activities executed in the
value chain logic
are sequential and linear. The major driver of cost is scale,
capacity utilization
and economics of both internal and external scope. For firms
with value chain
logic, relationships between scale, capacity utilization, market
scope, and
uncertainty in input and output markets are the critical generic
determinants of
the strategic position.
(2) Value shop. A value shop model relies on an intensive
technology to solve a
customer or client problem. Value information asymmetry is the
important
attribute of an intensive technology. Clients problem often
involve more or less
standardized solutions, but the value creation process is
organized to deal with
unique cases. The professional often has standard information
acquisition
procedure to make sure that the problem has been correctly
framed. Therefore,
this type of value creation relies on the ability to continuously
reconfigure a
given resource portfolio to address economies of scope. The
activities executed

in this logic are cyclical and sequential, and reciprocal.
(3) Value network. A value network model relies on a mediating
technology to link
clients or customers who are or wish to be interdependent. The
mediating
technology facilitates exchange relationships among customers
distributed in
space and time. That is, linking and value creation in value
networks is the
organization and facilitation of exchange between customers.
Thus, the basis
for value creation lies in connecting people or organizations.
This type of value
creation relies on balancing network economic resources. The
activities
executed in this logic are parallel and non-linear. For firms with
value network
logic, value is derived from service, service capacity, and
service opportunity.
Therefore, scale and capacity utilization is a potential driver of
both cost and
value. Mediation activities are performed simultaneously.
Standardization
enables the mediator to match compatible customers and to
effectively maintain
and monitor the interaction between them.
Based on the IC perspective, the process of managing IC
emphasizes on the value
creation of the organizations. Differences in value creation
reflect different economics.
Resource fit in
inter-firm

partnership
23
While the chain has a cost orientation, the shop is oriented
towards value. The value
network needs to balance cost and value as scale and value as
scale and capacity
utilization are drivers of both (Stabell and Fjeldstad, 1998, pp.
433-4). Therefore, it is
important to identify what value-creating logics mean for the
organization. For each of
the activities in the different logics, the requirements for
resource deployment and
transformation are different (Roos et al., 2005, pp. 43-7).
Resource transformation in inter-firm partnership
All resources, including knowledge, are created through
combination and exchange.
Allee (2008) proposed the concept of value conversion, which is
the act of converting or
transforming financial to non-financial value or transforming
and intangible input or
asset into a financial value or asset. Whenever one type of value
has been created or
realized from another type of value, a value conversion has been
executed. In the
cooperative relationship, the anticipation of or receptivity to
learning and new
knowledge creation has been shown to be an important factor
affecting the success of
strategic alliance (Hamel, 1991). Das and Teng (2000) assert
that the overall rationale
for entering into a strategic alliance is to aggregate, share, or

exchange valuable
resources with other firms when these resources cannot be
efficiently obtained through
market exchanges. This outcome is often referred to as synergy,
which is driven by
factors such as sharing resources. In order to reali ze the
benefits, production has to be
rationalized, systems have to be developed to share information
or move people, and
marketing efforts have to be coordinated (Shaver, 2006).
Particularly in a cooperative
network in that participants create value collectively,
participants utilize their tangible
and intangible asset base by assuming or creating roles that
convert those assets into
more negotiable forms of value that can be delivered to other
roles through the
execution of a transaction (Allee, 2008). Therefore, IC fit and
resource transformation is
critical for cooperative success.
Given a cooperative strategy, on one hand, the interactions with
and learning from
alliance partners enable firms to improve their capabilities and
to expand their
resource endowments that will further enhance their competitive
advantages (Hitt et al.,
2000). As Dyer and Singh (1998) suggest that the competitive
advantages of
partnerships are generated from substantial knowledge exchange
and the combining
of complementary but scarce resources or capabilities. On the
other hand,
knowledge-sharing routines and relational mechanisms that
enhance collaboration
and mitigate appropriation hazards in alliances are primarily

partner-specific (Gulati
et al., 2003). Because partners with either homogeneous or
heterogeneous resources
linked together, resource exchange, sharing and transformation
between partners is
especially critical to achieve a collective goal.
Based on the IC perspective, resource fit largely depends on a
match between types
of IC resources (Roos et al., 2005). The concern about how to
coordinate diverse
production skills and integrate several technology streams has
been the complex ways
in which exchange contribute to the creation of IC (Nahapiet
and Ghoshal, 1998).
Therefore, IC management has become an even more essential
and challenging issue in
the inter-firm cooperation.
Despite scholars emphasize the importance of resource fit and
transformation in
strategic alliances, only very few of them adopted IC
perspective in the
inter-organizational context. For example, according to
knowledge-based view,
JIC
12,1
24
Grant and Baden-Fuller (2004) proposed the efficiency of
knowledge integration and
knowledge utilization in alliances. They argue that efficiency of

integration is
maximized through separate firms specializing in different areas
of knowledge and
linked by strategic alliances. If different types of knowledge
have different product
domains, the problem of fits arises between the firm’s
knowledge domain and its
product domain. The greater the uncertainty as to the future
knowledge
requirements of a firm’s product range, the greater its
propensity to engage in
inter-firm collaborations as a means of accessing and
integrating additional
knowledge.
Huang and Chang (2008) examined the innovation process in
the supplier-
manufacturer relationship, in which they focused on joint
problem-solving capability,
trust, and innovation. However, they did not incorporate more
IC resources in the
study. Another example is a study done by Joia and Malheiros
(2009), who examined
the impact of strategic alliances in the formation of firms’ IC
resources, in terms of HC,
internal process, innovation capacity, and relationship capacity.
Both of these studies were conducted by a survey method,
however, they took the
perspective from one-side aspect but not both-side aspect to
examine the resource
transformation. The Schotter and Bontis’ (2009) study
emphasized the capability
transfer in intra-organizational context. Conducting by case
study, they identified
antecedents and barriers for reverse capability-transfer in

multinational corporations.
They focused on the autonomy, the mandate, the development
process of new
capabilities, and the capability exchange within the company
network, but not on
detailed capability transferring. Therefore, this study extends
the scope of those prior
researches by linking IC and strategic alliance and adopting IC
approach to reveal the
detailed resource transformation at the dyadic partnership from
both sides.
ICN
The ICM is defined as the deployment and management of IC
resources and their
transformations (into other IC resources or into traditional
economic resources) to
maximize the present value of the organization’s value creation
in the eyes of its
stakeholders (Roos et al., 2005, p. 42). According to the ICV,
one component of
intellectual can leverage the value of resources in the other
components (Reed et al.,
2006). The presence of resource is not sufficient to create value.
Going beyond the mere
presence of a resource, IC considers the organization’s ability
to transform one resource
into another (Roos et al., 2001). Therefore, the primary concept
of ICM is to identify and
evaluate the organization’s resource transformation structure.
Roos and Roos (1997) propose the ICN, which then further
refined by Roos et al.
(2005). The ICN is a numeric and visual representation of how
management views
resource deployment to create value in the organization and

about identifying
transformations from one resource into another. By mapping
how resources influence
each other, the ICN provides an overall map of the logic used
by management when it
comes to resource deployment in a given organization.
Moreover, In order to
understand the value conversion and utilization of intangible
assets, Allee (2008)
proposed the technique of “value network analysis” to map out
the value exchanges in
that three elements are depicted: roles, transactions, and
deliverables.
In the value network analysis, roles are played by participants
in the network
who provide contributions and carry out functions; transactions
originate with one
Resource fit in
inter-firm
partnership
25
participant and end with another; whereas deliverables are the
actual things that
move from on role to another. On the map of value network
analysis, the nodes
depict the roles and the arrows indicate the transactions through
which deliverables
are conveyed from one role to another. In this study, we follow
the technique of ICN

and value network analysis to represent the resource
transformation in inter-firm
partnership.
The study
According to Hoskisson et al. (1999) analysis, the unobservable
poses a substantial
measurement challenge to RBV researchers. Because intangible
resources are more
difficult to measure, RBV researchers have used proxies as
measures of intangible
construct. However, proxies may not be valid measures for
underlying constructs.
Therefore, the method of research using large data samples and
secondary data
sources appear to be inadequate, particularly when used to
examine intangible
resources, such as tacit knowledge (Zander and Kogut, 1995).
Given that we do not
know very much about the contents of IC resource
transformation at the dyadic level,
our intention is to provide new exploratory evidence grounded
in an in-depth case
study to give an insight into what is occurring. As Hoskisson et
al. (1999) indicate, calls
for the use of qualitative methods to identify a firm’s resources
are increasing as each
firm is considered to have a distinctive bundle of resources. The
case study
methodology may be appropriate for the RBV research because
it can provide much
richer information about the firms’ idiosyncrasies.
Research setting
This study focuses on the resource transformation between
cooperative partners

particularly from the IC perspective. The unit of analysis in this
study is at the dyad
level. In this study, we investigated three inter-firm cooperative
projects that were
executed by the focal company – ACE geo-synthetics Co., Ltd.
ACE was established in
1996 and equipped with the first automatic production line in
Taiwan for geo-grid
manufacturing. The geo-grid provides high resistance to soil
micro-organisms and
chemicals, UV radiation and mechanical damage. Because of the
installation flexibility,
using geo-grid withstands earthquakes better than traditional
methods. The annual
production of ACE has exceeded 8 million square meters, which
makes ACE become
the leading company in the Taiwanese domestic market.
Recently, ACE has dedicated itself to develop the application of
construction. ACE
serves not only domestic market in Taiwan, but also
international markets over 40
more nations in the USA, South America, Europe, and Asia. In
this study, we explore
the resource transformation in inter-firm partnerships by three
inter-firm cooperative
cases:
(1) A dyadic cooperation between ACE and a turnkey machinery
supplier offering
services in automatic production line (Partner A), representing
as a value-chain
case.
(2) A dyadic cooperation between ACE and an engineering
consulting company

(Partner B), representing as a value-shop case.
(3) A dyadic cooperation between ACE and a labor agent
company (Partner C),
representing as a value-network case.
JIC
12,1
26
Research approach
Resource transformation. Based on IC perspective, resources
were classified into five
categories. Three of them are IC resources: HC, OC, and RC.
Two of them are traditional
resources: physical capital (PC) and monetary capital (MC). The
presence of resource is not
sufficient to create value. IC considers the organization’s
ability to transform one resource
into another (Roos et al., 2001). To create value, resources need
to be deployed effectively
and efficiently, to be put into a structure where one type of
resource is transformed
into another type of resource (Roos et al., 2005). In this study,
the ICN approach is used for
analyzing the resource transformations between cooperative
partners.
The resource transformation analysis was done in four steps.
First, in the
partnership, the informants from both focal company side and
partner side have
identified the resource elements in five categories that were

contributed to the
partnerships. Second, the informants measured the relative
importance of resources,
which gives the weight of each resource element. Third, the
informants evaluated
resource transformation from each party. For example, the
informants in the focal
company were asked to identify the resources transformation
from focal company to
partners. On the other side, the informants in partnering
companies were asked to
identify the resource transformation from partner side to focal
company side. Fourth,
the ICN was then analyzed in accordance with the resource
importance and
transformation that were evaluated from both parties.
Data collection
We collected data by in-depth face-to-face interviews. In order
to control the respondent
bias, we interviewed with eight informants including both focal
company side and partner
side. All the informants are the key persons in charge of those
cooperative partnerships.
Most of them are senior managers with eight to 30 years of
experiences in their expertise.
Table I shows the backgrounds of the informants and the times
of interviews.
Results and discussion
Value chain: Case I
Inter-firm partnership with value chain logic. The type of value
chain logic relies on a
standardized process and mass production. This logic has an
inherent drive toward

Experience Time
Case Informants Expert areas (years) Interview (hours)
I ACE – vice president Textile technician
management
26 Face to face 10
Partner A – vice president Mechanical design 20 plus Face to
face 8
II ACE – sales manager Civil consultant/sales
management
15 Face to face 6
Design engineer Civil engineering design 3 Face to face 10.5
Partner B – general
manager
Civil consultant 30 plus Face to face 6
Manager Civil engineering design 8 Phone 6
III ACE – HR manager HR relations 3 Face to face 8
Partner C – manager Foreign-labor service 10 Face to face 8
Table I.
The backgrounds of
informants
Resource fit in
inter-firm
partnership

27
efficiency, meaning that the resources form the basis for a
competitive advantage of
economies of scale (Roos et al., 2005). This study investigated
Case I as an example of
value chain logic. Case I refers to an inter-firm partnership
between ACE and a turnkey
machinery supplier offering services in the automatic
production line (Partner A). In
the early years when ACE was a traditional family business
supplying agricultural
grid, in order to diversify into the industrial-used geo-grid area,
ACE had been
struggling with a try-and-error process for survival. The lack of
know-how and
experience led to the unfavorable consequences such as unstable
quality, inefficiency,
and large waste. In 1999, ACE was equipped with fabric coating
facilities for
improving quality control in tension stability, heating
temperature, and natural
curling. With experience in design and assembly of an
automatic production line,
Partner A and ACE collaborated to develop coating and heating
equipment that were
designed to integrate originally separated production processes
into an integrated
sequential process. Benefiting from the cooperation, ACE was
enabled to achieve
higher production efficiency and quality stability whereas
Partner A improved its
technical applications in different business areas.

Contributed resources and transformation in the partnership. In
this partnership,
ACE contributed more MC (38 percent) and HC (23 percent)
than PC (20 percent) and
OC (16 percent). ACE also input a little RC (10 percent). In the
category of MC, ACE
invested its money in equipment such as an electric oven,
transmission rollers, and
fabric coating equipment. As for HC, senior engineers and the
vice-president, also an
expert engineer, all worked together on adjustments of
production automation and
optimization of weaving machines. Some R&D data were also
provided to partner A as
advice and references for customization. ACE also contributed
its RC for research and
development.
On the other side, Partner A contributed PC (50 percent), HC
(30 percent), and OC (20
percent). In order to join some co-development projects with
ACE, Partner A assigned
engineers (15 percent) and the vice president (15 percent) to
actively participate in
conference meetings. Based on their well-experienced
automation design, the engineers
from Partner A are able to propose useful approaches. In
addition, Partner A input in
the OC included mechanical design experience, know-how of
the electronic or
mechanical supervision, and knowledge of operation control.
Figure 1 shows the
resources and relative importance contributed to the partnership
in Case I.

Tables II and III show the transformation matrices from ACE to
Partner A as well
as from Partner A to ACE. According to the results in Tables II
and III, Figure 2 depicts
the resource transformation of ICN in this partnership. How did
ACE benefit from
Partner A’s resource transformation? As can been seen, the PC
contributed from
Partner A has transformed into ACE’s PC and HC, which
strengthened the capabilities
of ACE’s engineers. The HC and PC contributed by Partner A
was transformed into
ACE’s OC and PC, which enhanced the automation technology
and reduced the defect
rate in ACE. On the other side, how did Partner A benefit from
ACE’s resource
transformation? The HC such as senior engineers and
investment in physical assets
from ACE was transformed into Partner A’s HC and physical
assets, which allowed
Partner A to improve know-how and experience in production
automation and then
apply the knowledge to other similar industries.
JIC
12,1
28
Figure 1.
The resources contributed
to the partnership in Case I
Resource fit in

inter-firm
partnership
29
Value shop: Case II
Inter-firm partnership with value shop logic. The type of value
shop logic focuses on
solving a problem for the client. The value in this type resides
not only in the solution
itself, but also in the individuals who came up with the
solutions and the way they
reached it, implying that HC and OC are sources of competitive
advantages. A firm
with this value shop logic should enhance its ability to
continuously reconfigure a
Case I – chain
To Partner A
HC OC RC PC MC Sum(out)
From ACE (%) (%) (%) (%) (%) (%)
HC 6.9 6.9 9.2 23
OC 6.6 4.2 5.2 16
RC 2.7 0.3 3
PC 2.0 10.0 8.0 20
MC 38.0 38
Sum(in) 18.2 21.1 22.7 38.0 100
Table II.
Resource transformation
matrix in Case I – value

chain
Case I – chain
To ACE
From Partner A (%) (%) (%) (%) (%) (%)
HC 9.0 10.5 10.5 30.0
OC 7.0 6.0 7.0 20.0
RC
PC 15.0 10.0 25.0 50.0
MC
Sum(in) 31 26.5 42.5 100
Table III.
matrix in Case I – value
chain
Figure 2.
The resource
transformation between
partners in Case I
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12,1
30
given resource portfolio to address completely new problems,
meaning that the
resources form the basis for a competitive advantage of
economies of scope. Therefore,

monetary or physical resources can never be the basis for a
competitive advantage.
This study investigated Case II as an example of value shop
logic.
Case II refers to an inter-firm partnership between the focal
company and an
engineering consulting company (Partner B). In the construction
industry the
engineering consultant company technically starts the design
work based on the
clients’ (users) demands and requests, and then the construction
company follows up
with the construction design and purchases needed materials.
Before 2000, the
reinforced construction application had not been adopted
popularly in Taiwan. In such
a geo-technical engineering industry, material suppliers play a
fundamental role of
promoting the application for this ecosystem construction
method. The material
suppliers also integrate textile weave technology into civil
engineering, which has
become a competitive advantage in the construction and geo-
technical engineering
industry. Since 2002, ACE had organized a professional
construction team with the
intention to create and stimulate new market demands. In 2004,
Partner B was invited
by ACE to be an external consultant. The purposes of this inter-
firm cooperation are:
. As a mentor and a well-known expert, Partner B instructs ACE
to deal with
complicated and difficult construction projects.

. By linking with Partner B’s industrial relational connecti ons,
ACE is able to
promote the geo-technical materials and to increase its
reputation in the
construction industry.
Contributed resources and transformation in the partnership. In
this partnership, ACE
contributed more HC (30 percent), PC (25 percent) and MC (20
percent) and Partner B
contributed more OC (35 percent), HC (30 percent), and RC (25
percent). On the ACE
side, the professional engineers and customer service staff were
assigned to learn
knowledge from the partner consulting company. ACE also
input its facilities to work
together with partner B for more kinds of geo-grid material
research and development.
On the partner side, Partner B dedicated its know-how of design
methods, experience
and capability in promoting the geo-grid materials, and
knowledge of new material
application. Partner B also contributed its RC such as
connections with other
consultant firms and academic institutions. Figure 3 shows the
resources and relative
importance contributed to the partnership in Case II.
How did the resources transform between ACE and Partner B?
Tables IV and V
demonstrate the transformation matrices from ACE to Partner B
as well as from
Partner B to ACE. Figure 4 shows the ICN in accordance with
the results in Tables IV
and V. As shown in Figure 4, the HC, OC, and RC contributed
by Partner B

transformed into ACE’s HC, which strengthens the engineers’
capabilities. Figure 4
also shows that Partner B’s HC transformed into ACE’s OC.
Benefiting from the senior
consultant’s mentoring and instruction (HC) from Partner B,
ACE has elevated its
experience in design and practical construction (OC). On the
partner side, the PC, HC,
and OC contributed from ACE transformed into Partner B’s OC.
Benefiting from ACE
who contributed its testing laboratory facilities (PC), engineers
(HC), and production
experiences (OC), Partner B is able to enhance its OC in design
and application.
Resource fit in
inter-firm
partnership
31
Figure 3.
to the partnership in
Case II
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12,1
32

Value network: Case III
Inter-firm partnership with value network logic. The type of
value network logic relies
on connecting people or organizations. This type of value
creation relies on balancing
network economic resources. The resources that form the basis
for competitive
advantage must show network economic behavior, meaning that
the organizational
and relational resources are the base of competitive advantage.
This study investigated
Case III as an example of value network logic.
Case II – shop
To Partner B
From ACE (%) (%) (%) (%) (%) (%)
HC 5.5 19.5 5.0 30
OC 5.63 7.88 1.5 15
RC 5.20 3.55 1.25 10
PC 5.0 15.25 3.0 1.75 25
MC 20.0 20
Sum(in) 21.33 16.18 10.75 1.75 20.0 100
Table IV.
matrix in Case II –
value shop
Case II – shop
To ACE

From Partner B (%) (%) (%) (%) (%) (%)
HC 15.5 10.75 2.5 1.25 30
OC 24.0 10.5 0.5 35
RC 10.0 15.0 25
PC 7.5 2.5 10
MC
Sum(in) 57.0 38.75 2.5 1.75 100
Table V.
matrix in Case II –
value shop
Figure 4.
The resource
partners in Case II
Resource fit in
inter-firm
partnership
33
Case III refers to an inter-firm partnership between ACE and a
labor agent company
(Partner C). Due to the complex and tedious procedures of
importing laborers from
less-developing countries, Taiwanese companies have
encountered difficulties of

dealing with governmental regulations to recruit foreign
laborers. Therefore, the labor
agents have become the bridge for connecting foreign laborers
(supply side) and
employers (demand side). The Partner C was established in
1992 and has been one of
few leading agents who serve as a foreign labor broker. In order
to enlarge its network
economic resources and refine its professional services, Partner
C has standardized the
labor importation procedures. Having the professional services
offered by Partner C,
ACE is able to reduce the labor costs and to recruit foreign
laborers more efficiently.
Contributed resources and transformation in the partnership.
Figure 5 shows the
resources and relative importance contributed to the partnership
in Case III. On the
focal company side, ACE contributed more MC (35 percent) and
HC (35 percent). An
amount of money was paid to Partner C for expenses on foreign
labor recruitment. In
addition, ACE’s personnel administrator and production
managers are responsible for
training and managing foreign workers. On the partner side,
Partner C contributed
more HC (30 percent) and OC (25 percent). OC includes foreign
labor training and
regulatory consultation. Partner C dedicated its customer
service and translation staff
to the process of recruitment and training. Partner C also
contributed its RC with
governmental institutions.
Tables VI and VII indicate the transformation matrices from

ACE to Partner C as
well as from Partner C to ACE. In this partnership, ACE
transferred its MC to Partner
C. ACE transformed its HC into Partner C’s organizational and
human resources.
Benefiting from ACE’s OC, Partner C is able to enhance its
organizational capability of
customer services. On the other side, benefiting from Partner
C’s OC for labor
recruitment consultation, HC for input translators and customer
service staff, and RC
of connection with government, ACE is able to fulfill the need
of foreign labor
recruitment. Figure 6 shows the resource transformation in this
partnership.
Discussion
Overall, Table VIII summarizes the contributed, needed and
transformed-in resources
for both sides in the three cases, revealing how the resour ce fit
between partners match
with different types of value creating logic. As can be seen,
Case I focuses on the
efficiency of automation production facilities, representing the
type of value chain
logic. In this partnership, ACE needed more physical, human
and OC and did
transform-in PC (42.5 percent), HC (31 percent), and OC (26.5
percent) from Partner
A. Partner A needed more monetary, physical, and OC and did
get MC (38 percent), PC
(22.7 percent), and OC (21 percent) from the focal company.
The resource contribution
and transformation in Case I show that PC, MC, and OC form
the basis of competitive
advantage in the partnership with value chain logic.

In Case II, the cooperation relies on the value of solving
problems and providing
solutions in the construction material application, representing
the type of value shop
logic. In this partnership, ACE needed more organizational and
HC and did acquire OC
(57 percent) and HC (38.75 percent) from partner B. On the
other side, Partner B
acquired OC (46.18 percent), HC (21.33 percent), and RC (20
percent) from the focal
company, just as the resources that Partner B needed. The
resource contribution and
JIC
12,1
34
Figure 5.
to the partnership in
Case III
Resource fit in
inter-firm
partnership
35

transformation in Case II show that HC and OC form the basis
of competitive
advantage in the partnership with value shop logic.
Case III referred to a partnership between ACE and a labor
agent company,
representing the type of value network logic. In this
partnership, ACE needed more HC
and did benefit from the recruitment of foreign laborers in HC
(72.75 percent) by
Figure 6.
The resource
partners in Case III
Case III
To ACE
From Partner C (%) (%) (%) (%) (%) (%)
HC 17.25 8.25 3.0 1.5 30
OC 16.5 3.5 2.5 2.5 25
RC 10.0 10
PC 15.0 15
MC 14.0 6.0 20
Sum(in) 72.75 11.75 5.5 10 100
Table VII.
matrix in Case III –
value network
Case III
To Partner C

From ACE (%) (%) (%) (%) (%) (%)
HC 18.5 13.0 3.5 35
OC 8.0 7.0 2.0 3.0 20
RC
PC 6.0 4.0 10
MC 35 35
Sum(in) 32.5 24.0 5.5 3.0 35 100
Table VI.
matrix in Case III –
value network
JIC
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36
collaborating with Partner C. On the partner side, Partner C
needed monetary, human,
and organizational resources and did benefit from ACE’s MC
(35 percent), HC (32.5
percent), and OC (24 percent). The results of resource
contribution and transformation
between the two sides in each of these three cases indicate the
resource fit in the
inter-firm partnerships.
The results of resource transformation raise an important
question as to the
distinction between resource transformation and resource

transfer. The resource
transfer refers to “like-to-like” resources converting between
two partners such as
HC-to-HC; whereas the resource transformation indicates the
“like-to-unlike” resources
conversion, such as HC-to-OC.
Resource transform-in
Resource contributed HC OC RC PC MC
Resource (%) Resource needed (%) (%) (%) (%) (%)
Case I – value chain
ACE PC, HC, OC 31.00 26.50 42.50
HC 23
OC 16
RC 3
PC 20
MC 38
Partner A MC, PC, OC 18.20 21.10 22.70 38.00
HC 30
OC 20
PC 50
Case II – value shop
ACE HC, OC 57.00 38.75 2.50 1.75
HC 30
OC 15
RC 10
PC 25
MC 20
Partner B OC, HC, RC 21.33 46.18 10.75 1.75 20.00
HC 30

OC 35
RC 25
PC 10
Case III – value network
ACE HC 72.75 11.75 5.50 10.00
HC 35
OC 20
PC 10
MC 35
Partner C MC, OC, HC 32.50 24.00 5.50 3.00 35.00
HC 30
OC 25
RC 10
PC 15
MC 20
Table VIII.
The results of resource fit
in different types of
value logic
Resource fit in
inter-firm
partnership
37
As in Tables II and III, we observed that in the value chain
logic, IC transfer

(including HC-to-HC, OC-to-OC, PC-to-PC, MC-to-MC) is 57.1
percent and IC
transformation is 42.9 percent from ACE to Partner A; whereas
IC transfer is 40
percent and IC transformation is 60 percent from Partner A to
ACE. This implies that
when the major driver of cost is scale and capacity utilization in
the value chain logic,
relatively larger portion of IC transfer is needed to create value,
particularly the
resource transfer of physical and MC, because the “like-to-like”
resources converting
between partners enlarges the use of homogeneous resources,
therefore, increasing the
economy of scale and capacity utilization.
In contrast, with the value shop logic in Case II, Tables IV and
V show that IC
transfer is 36.38 percent but IC transformation is 63.62 percent
from ACE to Partner B;
whereas IC transfer is 26 percent and IC transformation is 74
percent from Partner B to
ACE. The results demonstrate that when information asymmetry
exists in the value
shop model, relatively larger portion of IC transformation is
needed to solve customer’s
problem, therefore, the IC transformation such as OC-to-HC,
RC-to-HC, RC-to-OC
enhances firm’s capability of problem solving.
In the case of value network model, Tables VI and VII shows
that IC transfer is 60.5
percent but IC transformation is 39.5 percent from ACE to
Partner C; whereas IC
transfer is 26.75 percent and IC transformation is 73.25 percent
from Partner C to ACE.

To take a closer look at this case, ACE transferred its MC to
Partner C (38 percent) in
order to exchange for HC, therefore, Partner C transformed its
resources (OC, RC, PC
and MC) into ACE’s HC (72.5 percent). The resource exchange
between two sides is
based on the contractual relationship where partner was being
paid on a service
contract to build human competence for the focal company. This
indicates that in the
value network logic, Partner C is able to increase its service
capacity by offering
mediating technology, and ACE is able to gain the needed
resources from partner’s
network.
Whether or not transformation of like-to-unlike resources shows
value creation
capacity more than straightforward transfer of like-to-like
resources? The findings of
this study imply that the portion between transformation and
transfer may vary with
types of different value-creating logic. In the inter-firm
partnership, what resource is
needed and what resource is contributed from each side can also
influence the decision
of transfer opposed to transformation when managers are about
to invest IC resources
for alliance building.
Another critical issue is raised. In the inter-firm partnership,
how to evaluate the IC
investment in terms of return on investment? For example from
the focal company’s
aspect, we calculated the ratio of transform-in (gained from
partner) to transform-out

(contributed to partner) for each IC component, which is similar
to the concept of return
on investment at the firm level. The results indicate that the
ACE’s in/out ratio of HC is
1.03, OC is 1.77, and PC is 1.7 in value chain model; ACE’s
in/out ratio of HC is 1.9, OC is
2.53 in the value shop model; whereas ACE’s in/out ratio of HC
is 2.08, OC is 0.59 in the
value network model. The findings imply that from the focal
company side, the
investments of OC and PC in the value chain model, HC and OC
in the value shop
model, and HC in the value network model are worthwhile,
since the in/out ratio is
larger than 1, meaning that for those IC components, the focal
company gained much
more from its partners than invested in the partnership.
JIC
12,1
38
Conclusion
Inter-firm partnership is considered the source of competitive
advantage. Resource fit
between partners is the key for successful inter-organizational
cooperation,
particularly the fit of IC. Roos et al. (2005) propose the ICN to
analyze the resource
transformation within the organizational cooperation. This study
contributes to extend
the use of the IC approach for analyzing the resource fit in the
inter-firm context. The

cooperation between two firms is to create collective value
logic. This study
investigated three cases representing three types of value
creation logic – value chain,
value shop, and value network – demonstrating how the
resource contribution and
transformation reveal resource fit between partners with
different value creating
logics. The results of this study demonstrate that:
(1) Given value chain logic, while the inter-firm partnership
emphasizes on
standardization, efficiency and economy of scale, resource fit
between partners
in traditional resources of physical and MC, and IC of OC can
form the basis of
value creation.
(2) Given value shop logic, while the inter-firm partnership
emphasizes on problem
solution and economy of scope, resource fit between partners in
IC of human
and OC can form the basis of value creation.
(3) Given value network logic, while the inter-firm partnership
emphasizes on
network economic behavior, resource fit between partners in IC
of human,
organizational, and RC can form the basis of value creation.
Except for the above-mentioned findings, this study also found
an interesting issue as
to the distinction from resource transfer to resource
transformation. Previous literature
pertaining to knowledge transfer has not been clarifying
whether IC resource

straightforwardly transfer from one partner to the other opposed
to IC resource
transformed from one side into another type of resource in the
other side. This study
examines how resources were transferred and/or transformed
between two partners.
The results imply that the portion between transformation of
“like-to-unlike” and
transfer of “like-to-like” may vary with types of different value-
creating logic in the
inter-firm partnership. For example, we found IC transfer makes
the larger portion of
value conversion than IC transformation in the value chain
model but IC
transformation constitutes the larger portion of value conversion
than IC transfer in
the value shop model.
However, the results did not answer the question as to what are
the performance of
resource transformation and transfer in the inter-firm
partnership. How the IC
investment (contributed resources) and benefit (gained
resources) affect the financial
performance and operational performance for each company in
the inter-firm alliance.
For example, when assessing IC investments in terms of ROI,
some approaches such as
EVATM (Stewart, 1997) can be used for IC evaluation at the
firm level. In the inter-firm
partnership, despite we calculated the ratio of transform-in
(gained from partner) to
transform-out (contributed to partner) to realize the return on
investment of each IC
component, more performance measurements related to
assessing IC investment at the

inter-firm level is needed in the future research.
This study is exploratory. Of course, our sample was limited to
three inter-firm
partnership cases representing three different types of value
configuration, thereby,
raising for questions of generalizability. However, the intention
of this study is not to
Resource fit in
inter-firm
partnership
39
propose generalized findings but to demonstrate an alternative
way for managers
investing IC resources in the inter-firm partnerships. This is
particularly critical when
previous studies on strategic alliance and network have paid
less attention to resource
fit based on IC perspective.
Theoretically, this study contributes to be a starting poi nt for
analyzing the resource
input and transformation in the inter-organizational context by
using the IC approach.
Practically, this study contributes to more practical references
as to reveal given different
types of value creating logic, how two partnering companies can
manage and deploy
their IC and traditional resources in order to fit in the inter -firm
cooperation.

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About the author
Tzu-Ju Ann Peng (PhD, The National Chengchi University) is
an Associate Professor in the
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JIC
12,1
42
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Knowledge transfer to partners: a firm level
perspective
Thomas Hutzschenreuter and Julian Horstkotte
Abstract

Purpose – Firms at the center of an organizational network may
benefit from educating and building up
competencies of their partners. For that reason, centers often
seek to transfer knowledge from the center
to partner firms. They even set up systems of inter-
organizational knowledge transfer to plan, to
coordinate, and to control such transfers on a firm level instead
of managing single knowledge transfer
projects individually. However, little systematic attention has
yet been paid to such systems on a firm
level. This paper seeks to analyze the managerial mechanism to
decide what knowledge to transfer to
what partners.
Design/methodology/approach – To address this gap, data were
gathered on nine leading multinational
center firms. An explorative approach was adopted using case
study research to look at the
characteristics of network centers, network partners, knowledge,
transfer channels, and programmes.
Findings – It was found that center firms offered knowledge
transfer products to partners and set up
portfolios of knowledge transfer programmes targeted at
specific partner groups. There is further
elaboration on fundamental decisions on the programmes’
design, communication, access, and
pricing.
Originality/value – The research contributes to shed light on
how center firms manage knowledge

transfer activities from the center to partners on the firm level
and how they structure it in the form of
programmes. Therefore, the paper does not focus on the
management of knowledge transfer in
particular partnerships or networks, but also considers
interdependencies between individual
knowledge transfer initiatives.
Keywords Knowledge management, Knowledge transfer,
Multinational companies, Partnerhip
Paper type Research paper
1. Introduction
Building and strengthening relationships to partner firms
strongly influences a firm’s
performance (Dyer and Singh, 1998). Particularly firms with a
central position in an
organizational network may profit (Spencer, 2003). They can
reinforce their position and
achieve their business objectives by creating value for partners
and thus attract and
strengthen partners to compete with firms in rival networks. As
knowledge can be
considered the dominant resource to create competitive
advantage (Grant and
Baden-Fuller, 1995), network centers create value by actively
transferring knowledge from

the center firm to partner firms. As a result, they attract new
partners and enable them to
collaborate as well as strengthen existing partners by
developing their skills and
competencies (Lorenzoni and Baden-Fuller, 1995).
However, partners are heterogeneous. Partnerships may cover
vertical and horizontal
collaborations along the entire value chain. Their importance
for achieving center firms’
business objectives and their demand concerning knowledge
provided by centers may vary
widely. Characteristics of partners differ, as can their
motivations and the roles they play
within a network (Inkpen, 1998). However, as knowledge is of
little value if not supplied to the
PAGE 428 j JOURNAL OF KNOWLEDGE MANAGEMENT j
VOL. 14 NO. 3 2010, pp. 428-448, Q Emerald Group Publishing
Limited, ISSN 1367-3270 DOI 10.1108/13673271011050148
Thomas Hutzschenreuter is
Professor of Corporate
Strategy and Julian
Horstkotte is a PhD

Candidate, both at WHU –
Otto Beisheim School of
Management, Vallendar,
Germany.
Received 3 November 2009
Accepted 26 January 2010
right partners (Teece, 2000), a fit between knowledge, transfer
channel, and partner
characteristics is required. With many diverse partners, this is
very complex. Thus,
managers of network centers need to establish a mechanism that
supplies the right
knowledge to the right partner via the right channel. However,
knowledge transfer is not free
of costs. Thus it should be carefully planned and controlled
considering both its benefits and
its costs.
Managing and integrating inter-organizational knowledge
transfer on a firm level has
advantages compared to managing knowledge transfer projects
independently from each
other. Knowledge can be transferred at lower costs or higher
quality compared to transfer in
independent projects. Advantages result from systematically
managing knowledge transfer

in the relationship to partners across functional or
organizational barriers of a center.
Network centers use facilities, e.g. classrooms, specifically
designed to carry out
inter-organizational knowledge transfer that are shared across
knowledge transfer
projects. Common technologies are used across transfer
projects, e.g. online learning
platforms. Furthermore, skills in integrating knowledge into
transferrable products, e.g.
lectures, can be leveraged. Experience gained in one knowledge
transfer project may be
used in subsequent transfers. For these reasons a network center
needs to design and
implement a system of inter-organizational knowledge transfer
that integrates, coordinates,
and structures knowledge transfer initiatives on a firm level.
Prior empirical research on inter-organizational knowledge
transfer has mainly investigated
two-way knowledge exchanges between firms or the way how
one focal firm may learn from
its partners (Van Wijk et al., 2008). For example, Mowery et al.
showed how the participation
in an alliance affects capabilities of focal firms (Mowery et al.,
1996). Yet, only few empirical
studies have focused on focal firms that deliberately transfer
knowledge to partner firms. For

example, Dyer and Hatch demonstrated that firms can achieve
competitive advantage as a
result of knowledge transfer to their suppliers (Dyer and Hatch,
2006). While prior studies
have focused on management of knowledge transfer in single
projects or in particular
alliances or networks, they did not look at the management of
interdependencies between
knowledge transfer initiatives of a firm. Consequently, little is
known about the phenomenon
of network centers setting up knowledge transfer systems. These
systems serve to integrate
and coordinate planned knowledge transfer to partners on a firm
level and help to structure
knowledge transfer in programmes that are targeted towards
specific partner groups.
The approach to structure knowledge transfer in programmes
can be compared to business
schools. These have set up multiple programmes for different
groups of students, among
others MBA, PhD, or executive education programmes. Each
programme needs to be
planned and controlled and varies in the knowledge content and
teaching approach, i.e. the
knowledge transfer products. They further comprise rules that
specify admission
requirements or whether courses are required or elective, among
others. While

participation in programmes of business schools is limited in
time, this is not necessarily
the case for partner firms enrolled in programmes of centers
firms where participation may
be indefinite.
Since systems of knowledge transfer to partners on a firm level
and programmes with
predefined rules to configure such knowledge transfer have, to
the authors’ knowledge, not
been investigated before, the purpose of this paper is to describe
and structure the
phenomenon. To do so, the authors investigate inter-
organizational knowledge transfer
systems of nine case firms by studying center, knowledge,
transfer, partner, and programme
characteristics. They explore approaches to the configuration of
transfers and to the
structure of programmes. Further, they identify context factors
that influence decisions on
knowledge transfer systems.
2. Theoretical background
It has been shown that the structure of inter-organizational
partnerships influences
knowledge transfer. In a network structure, central firms have a
positive impact on
knowledge transfer. Network centers are well connected with

other network members and
have considerable effect on the overall network (Iyer et al.,
2006). Most importantly, centers
VOL. 14 NO. 3 2010 jJOURNAL OF KNOWLEDGE
MANAGEMENTj PAGE 429
enjoy a knowledge advantage, for example in terms of
knowledge about network partners
and the design and management of the networks themselves.
This puts centers in a unique
position that allows them to further strengthen the network
through the transfer of knowledge
from the center to partners, and to eventually benefit from the
ability of network partners to
compete against firms in other networks. Therefore, center
firms that are actively planning
and fostering the transfer of knowledge with a predefined
offering to all partners, in contrast
to unplanned knowledge transfer that emerges in the course of a
partnership, are in the
focus of the authors’ research.
Knowledge transfer can be described with numerous
characteristics. Based on elements of
a knowledge transfer system, the authors structure these along
five categories. In this study,
the characteristics of the center as the sender, the body of
knowledge itself, transfer
channels, and partners as receivers are detailed. Moreover, a
sender’s managerial

mechanism to configure and coordinate knowledge transfer can
also be described by
characteristics. They constitute an additional category of
characteristics and are a particular
focus of this research (see Figure 1).
Center characteristics
When considering network center characteristics, managers
should keep uppermost in their
minds the business objectives of the center itself. Firms have
many different objectives with
varying time horizons when entering and participating in
networks (Koza and Lewin, 1998).
One elemental objective is to align firm business strategy with
network strategy (Koza and
Lewin, 2000). Likewise, knowledge transfer strategy must also
be aligned with network
strategy. Thus, centers are characterised by their business and
network objectives and their
efforts to plan and control transfer from the center to network
partners. The part that
knowledge transfer can play in achieving these objectives
differs according to the
knowledge intensity of the industry that the center is active in.
Apart from these
characteristics, number and diversity of partners that the center
transfers knowledge to, i.e.
receivers, is an important characteristic as it influences which
learning technologies the
center should use (Bates, 2005) or how to best communicate
with partners. Though
declining in importance in today’s wired world, the geographic
distance between the center

and partners still has an impact, notably in that it limits
teachability of knowledge and
increases the time needed for the transfer (Zander and Kogut,
1995).
Knowledge characteristics
Knowledge is tacit or explicit. The degree of tacitness, and how
quickly the body
of knowledge is changing, determine which modes of
collaboration are most suitable,
knowledge transfer effectiveness (Khamseh and Jolly, 2008) as
well as the speed at
which knowledge transfer can be carried out (Chen, 2004a). For
example, it may be well
worthwhile spending time and effort on developing a
sophisticated e-learning module if the
Figure 1 Characteristics of knowledge transfer in large networks
Knowledge
characteristics
Transfer
characteristics
Programme
characteristics
Managerial
mechanism
of sender
Knowledge transfer system

Centre
characteristics
Sender
Partner
characteristics
Receiver
Characteristics influencing knowledge transfer to partner firms
Knowledge
Channel
PAGE 430jJOURNAL OF KNOWLEDGE MANAGEMENTj
VOL. 14 NO. 3 2010
knowledge is stable over time. As highly personal tacit
knowledge is hard to formalize, it is
best transferred via long term visits, personnel transfers, or
personal communications
(Inkpen and Dinur, 1998). This poses specific challenges to
transfers in an
inter-organizational context. Moreover, the content that is to be
transferred, i.e. whether
knowledge differs in that it relates to products, markets, or
processes, influences decisions
on knowledge transfer.
Transfer characteristics

With regard to knowledge transfer from the center to its
partners, managers must also
consider the design of the channels over which knowledge is to
be provided. When is
knowledge best provided over a computer network and when is
it preferable to transfer it in a
traditional way, i.e. not via a computer network (Changchit,
2003)? Particularly, transfer
channels vary in the possibility to interact with participants
during knowledge transfer.
Further, the ease to individualize knowledge transfer, i.e.
customization of knowledge
transfer to partners, and to track the learning progress differs
across channels. However,
when centers simultaneously provide multiple transfer channels,
managers need to decide
on the degree of integration between these channels.
Partner characteristics
While primarily trying to meet the objectives of their own
firms, center managers also attempt
to address the needs of network partner firms (Chen et al.,
2006). The resources center
managers devote for doing so may depend on the relative size
and strength of partner firms
and the scope of value chain activities involved in the
collaboration with the center.
Managerial decisions are also strongly influenced by the
duration and stage of development
of a partnership. Research has shown that more knowledge is
transferred in partnerships
where the partner is perceived to be trustworthy (Dirks and
Ferrin, 2001). Firms are more
willing to transfer valuable and sensitive knowledge to partners
they trust (Andrews and

Delahaye, 2000). Particularly, the transfer of tacit knowledge
requires a high trust context
(Inkpen and Dinur, 1998; Becerra et al. 2008). Managers also
consider the level of a partner’s
related expertise, degree of motivation, and flexibility. For
instance, scheduling transfer
times and meeting deadlines is important to the partner
(Markus, 2001). In addition to
characteristics of the partner firms, center managers look at the
particular job functions of
individuals or positions they hold within a partner firm.
Programme characteristics
Based on its situation, the center firm needs to decide what
knowledge should be
transferred over what channels to what partners. To do so, a
managerial mechanism needs
to be in place to configure inter-organizational knowledge
transfer. Prior research has shown
that the configuration of these characteristics should fit the
context in which knowledge is
transferred (Hutzschenreuter and Listner, 2007). For centers
with a number of different
network partners, those contexts can be highly diverse. In order
to achieve the crucial fit
between configuration and context, the center might carry out
individual transfer projects
designed specifically for each partner contingent on their
particular characteristics and
needs. Since the knowledge transfer is designed specifically to
fulfil the exact requirements
of each partner, thus to contribute to the partner’s business
processes, the benefits are
usually greater (Chen, 2004b).

In contrast, the center might choose to standardise knowledge
transfer to a certain degree,
i.e. to configure certain knowledge transfer characteristics in
advance. A knowledge transfer
product is an offering to transfer certain knowledge over a
predefined channel. In the case of
standardised knowledge transfer products, such as new product
information that is taught in
classroom training, text books, course material, or cases,
particular knowledge and transfer
characteristics are fixed without knowing the exact transfer
context. Standardised delivery
can be carried out at lower cost as the center can make savings
both in planning and in
knowledge transfer efficiency (Levin and Cross, 2004). The
downside is that standardised
knowledge transfer neglects differences in the context, e.g. in
the situation of partners and in
the value they create for the center. The challenge for center
managers in large networks is to
strike a balance between potential savings on the side of
standardisation against the value
knowledge transfer can create for the center, value that could be
augmented by
individualized training.
Managing the configuration of knowledge transfer can be done
in the form of a programme.

A knowledge transfer programme predetermines a set of rules
according to which
knowledge transfer is configured for all partners enrolled in that
programme. Based on
specific characteristics, like partner size, these rules may
specify how knowledge transfer is
configured, in an entirely individualized or partly standardised
way. If these rules are known
to partners, they also know what knowledge transfer they will
receive before joining the
programme. Programme rules also may define dependencies
between knowledge transfer
characteristics. For example, whether particular knowledge
content is offered to partners
may be dependent on certain partner characteristics, like the
present duration of the
partnership.
3. Method and data
As the authors’ research question is explanatory in nature and
focuses on contemporary
events, and as managerial decision-making, the object of their
investigation, cannot be
manipulated, case study research is highly appropriate (Yin,
2003). Furthermore, as far as

the authors know, their research question represents a topic
previously untouched in the
literature. Therefore, they decided to employ case study
research, following the process
outlined by Eisenhardt (1989), to investigate what approaches
network center managers
follow when deciding on what knowledge to transfer, to whom,
and how, for the benefit of
researchers and practitioners alike.
Sample
To select the case firms and not to be overwhelmed by firms
that potentially might be worth
investigating, the authors first defined their research question
and derived criteria for the
selection of case firms (Mintzberg, 1979). First and foremost,
they considered only firms
positioned in the center of a network and that consider
management of network partners as a
key to achieving competitive advantage. The authors
concentrated on firms that had been
particularly successful in developing their networks as indicated
by their market leadership
and their large partner network which often comprises several

hundreds of partner firms.
These partners are usually smaller and asymmetric to the focal
firm. In particular the authors
included focal firms in knowledge-intensive industries to
provide cases of firms for which
knowledge transfer is particularly important. Firms were
selected from various industries in
order to control for environmental factors and to incorporate a
range of industry practices.
Partners of the case firms may be very diverse and areas of
collaboration may cover
research, joint development, operations, sales, marketing,
service, and development of
complementary products. To control for size differences the
authors limited the target
population to large multinational corporations and to firms
headquartered in Europe or the
USA. Further details about the authors’ approach to selecting
case firms are presented in the
Appendix.
Analysing too many cases can become unwieldy because it
increases complexity. The
number of cases of this study also had to be limited given its
rich research setting and the

number of characteristics under investigation. As random
sampling of cases is rather
uncommon, the authors deliberately chose cases representing
‘‘extreme situations’’ and
‘‘polar types’’ (Pettigrew, 1990). The selected firms represent
polar types as they have a
particularly large number of partner firms and considered
knowledge transfer programmes
to be a key factor in their network strategy. The authors
conducted a preliminary review of the
knowledge transfer programmes of selected firms using the
information given on their
websites. At the end of this process, the authors contacted the
firms that seemed particularly
promising. Nine agreed to participate, to make a considerable
time commitment, promised
access to documentation, named a firm contact, and to provide
ready access to a
designated interviewee.
VOL. 14 NO. 3 2010
Data gathering

When conducting interviews and analysing documents, the
authors used multiple sources to
increase the validity and reliability of the data. First the authors
collected definite data on the
knowledge transfer of the firms by reading the information that
these provided their partners
with. As programmes must be explained to partners in detail,
the authors were able to gather
extensive material on the knowledge transfer of centers. A
contact person in each firm
provided the internal documentation they needed. In case of a
firm having more than one
knowledge transfer programme, they focused particularly on
that programme with the
largest number of registered partners.
The authors analysed knowledge transfer products, like
trainings, knowledge certifications,
or learning communities, which are offered to partners for sale
or for free and the rules of the
programmes, including requirements and incentives. They
studied these in terms of center,
knowledge, transfer, partner, and programme characteristics.
The authors complemented their analysis of documents with two

interviews per case,
totalling 18 semi-structured interviews each of which lasted an
hour or more. Following a key
informant approach, they interviewed nine managers, one from
each case firm, who were
responsible for knowledge transfer activities of the firm. They
were especially interested in
eliciting from the interviewees the purpose and rationale behind
their knowledge transfer
programmes. They also used the interviews in part to confirm
the early findings from the
analysis of secondary material and sources. In addition to these
nine interviews, in order to
have a better understanding of knowledge transfer programmes
from the perspective of
network partners, the authors also conducted interviews with the
managing directors of
three network partner firms and, in the six partner firms where
this was not possible, with the
center firm managers responsible for partner support. The goal
in these interviews was to
identify and clarify the requirements, expectations, and pay-offs
to partner firms of
participating in knowledge transfer programmes. Finally, in

order to ensure the correctness
of the data, the authors asked the interviewees to review the
data and findings of the single
case studies of their respective firms. Further information about
the data gathering is
presented in the Appendix.
4. Analysis of cases
While the two phases were not totally separate, in general, the
authors first gathered the data
and then analysed the cases. In a similar vein, they looked at the
inter-organizational
knowledge transfer of each firm individually, gaining
familiarity with the data and developing
an understanding of the relationships between the
characteristics under investigation. This
within-case analysis comprised a detailed narrative description
for each case firm. This was
central to the generation of insights (Pettigrew, 1990) as it
helped to structure the extensive
data that were gathered.
Based on the collected data, the researchers first evaluated the
cases individually on the
knowledge transfer characteristics. It was not until they had a

thorough understanding of
each firm’s transfer that they further condensed the core data
into a common format for each
case. This was required for data of knowledge transfer
characteristics that could not easily
be compared across cases. For example, a high, medium, and
low classification was used
to code the data on the diversity of knowledge content of the
firms’ programmes.
Programmes were classified as ‘‘Low’’ when the knowledge
transfer products focused on
one type of knowledge content only. This was the case for the
programme of case firm
Euro-3 that focuses on transferring product knowledge only.
‘‘Medium’’ means a focus on
two types of knowledge content as it is the case for the
programme of firm US-4. ‘‘High’’
cases are those in which the programme focuses on 3 types of
knowledge content as does
the programme of case firm US-1.
Subsequently, the authors created a series of matrices to
structure the data. This allowed for
a case-oriented as well as variable-oriented analysis (Miles and
Huberman, 1994). In these

data displays, the researchers searched for patterns across firms
and variables to identify
relationships and common structures, issues, and approaches to
the management of
knowledge transfer. To increase the validity of the findings and
to avoid coming to premature
conclusions, the researchers put great emphasis on case-to-case
comparisons and
identifying and explaining similarities and dissimilarities across
cases and on thinking about
rival explanations (Yin, 2003). The authors describe in the
following sections their cross-case
analysis findings, regularly referring to single cases and quoting
interviewees when
appropriate.
Knowledge transfer systems of all center firms that participated
in this study are structured in
the form of programmes. Centers have a management system in
place to define a portfolio of
programmes and plan and control each programme. Such

programmes are targeted to
meet the particular demands of specific groups of partners
concerning knowledge transfer.
For each programme, a set of knowledge transfer products is
defined that is offered to the
enlisted partners. A programme further comprises rules that
specify conditions under which
knowledge is transferred to partners. The basic structure of the
knowledge transfer systems
of the case firms is shown in Figure 2.
4.1 Management system of knowledge transfer to partners
Defining a portfolio of programmes. When defining a portfolio
of programmes centers need
to determine how many programmes to provide, what criteria to
use to distinguish
programmes, how to manage programme overlaps, and which
partners to admit to the
programme. The authors detail each of these characteristics in
Table I.
A fundamental decision center managers make is on the number
of knowledge transfer
programmes to offer to partners. Centers may collaborate with a
set of highly diverse

partners. The partners in the networks differ with respect to
their characteristics, their
knowledge requirements, and the benefits they expect from
knowledge transfer. For this
reason, it is essential that center managers understand the needs
and expectations of
partners in the network if they are to determine if, and when,
there should be a separate
knowledge transfer programme. Just as customer segmentation
is common practice in
marketing, center firms can thus segment partners based on
their characteristics. Multiple
knowledge transfer programmes can be set up to address
particular segments, be it in
terms of the number and type of partners for which they are
designed, or the number and
type of standardised knowledge transfer products offered.
Specific programmes can also
Figure 2 Structure of knowledge transfer systems
Firm
KT
programme
i

KT
programme
…
KT
programme
n
KT
productjn
KT
productmn
…
KT
productji
KT
productmi
…
Defining a portfolio of
programmesManagement
system of KT
to partners Planning and controlling
programmes
…

…
…
Note: KT: Knowledge Transfer
VOL. 14 NO. 3 2010
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