THE WORLD BANK OPERATIONS EVALUATION DEPARTMENT




                                                                                    29385




                        Knowledge Sharing:

                        A Review of the Literature



                         Jeffrey Cummings




  Director-General, Operations Evaluation: Gregory K. Ingram
  Director: Ajay Chhibber
  Manager: Victoria Elliott
  Task Manager: Catherine Gwin, Lead Evaluation Officer, OEDCM


                                                                             2003
                                                                  The World Bank
  This paper is available upon request from OED.
                                                                 Washington, D.C.

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ENHANCING DEVELOPMENT EFFECTIVENESS THROUGH EXCELLENCE
AND INDEPENDENCE IN EVALUATION

The Operations Evaluation Department (OED) is an independent unit within the World Bank; it reports directly to
the Bank’s Board of Executive Directors. OED assesses what works, and what does not; how a borrower plans to
run and maintain a project; and the lasting contribution of the Bank to a country’s overall development. The goals
of evaluation are to learn from experience, to provide an objective basis for assessing the results of the Bank’s
work, and to provide accountability in the achievement of its objectives. It also improves Bank work by identifying
and disseminating the lessons learned from experience and by framing recommendations drawn from evaluation
findings.




OED Working Papers are an informal series to disseminate the findings of work in progress to encourage the
exchange of ideas about development effectiveness through evaluation.

The findings, interpretations, and conclusions expressed here are those of the author(s) and do not necessarily reflect
the views of the Board of Executive Directors of the World Bank or the governments they represent.

The World Bank cannot guarantee the accuracy of the data included in this work. The boundaries, colors,
denominations, and other information shown on any map in this work do not imply on the part of the World Bank
any judgment of the legal status of any territory or the endorsement or acceptance of such boundaries.




Contact:
Operations Evaluation Department
Partnerships & Knowledge Programs (OEDPK)
e-mail: eline@worldbank.org
Telephone: 202-458-4497
Facsimile: 202-522-3125
http:/www.worldbank.org/oed




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                            Knowledge Sharing: A Review of the Literature

       Since 1996, when the Bank made a commitment to become a global knowledge bank, it
has taken numerous steps to improve its information systems, strengthen internally and
externally focused knowledge-sharing activities, and foster broader global knowledge-sharing
initiatives, all in support of enhancing the Bank’s and its partners’ and clients’ access to and
sharing of ideas (Wolfensohn, 1996). As background to an assessment of the Bank’s knowledge-
sharing activities, this paper presents an exploration of the literature on the factors that can affect
knowledge sharing success.

       Knowledge management involves the panoply of procedures and techniques used to get
the most from an organization’s tacit and codified know-how (Teece, 2000). While defined in
many different ways, knowledge management generally refers to how organizations create,
retain, and share knowledge (Argote, 1999; Huber 1991). The study of knowledge sharing,
which is the means by which an organization obtains access to its own and other organizations’
knowledge, has emerged as a key research area from a broad and deep field of study on
technology transfer and innovation, and more recently from the field of strategic management.
Increasingly, knowledge-sharing research has moved to an organizational learning perspective.
Indeed, experience and research suggest that successful knowledge sharing involves extended
learning processes rather than simple communication processes, as ideas related to development
and innovation need to be made locally applicable with the adaptation being done by the
‘incumbent firms’ (Nelson & Rosenberg, 1993) or ‘the local doers of development’ (Stiglitz,
1999) for the ideas to be successfully implemented.

       The literature identifies five primary contexts that can affect such successful knowledge-
sharing implementations, including the relationship between the source and the recipient, the
form and location of the knowledge, the recipient’s learning predisposition, the source’s
knowledge-sharing capability, and the broader environment in which the sharing occurs. A
synthesis of this research suggests three types of knowledge-sharing activities to be evaluated.
First, analyses of the form and the location of the knowledge are important because each can
affect the types of sharing processes that will be necessary as well as how challenging these



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processes might be. Second, the types of agreements, rules of engagement and managerial
practices adopted by the parties are important to evaluate in that they can shape both the flows of
resources and knowledge between the parties and the actions taken to overcome and
accommodate significant relational differences between the parties. Third, the specific
knowledge-sharing activities used are important in that they are the means through which the
parties seek to facilitate knowledge sharing.

         The paper begins with a discussion of why knowledge is important. Following this
section, the paper provides a brief overview of knowledge-sharing research from the technology
transfer & innovation and strategic management fields, and provides a definition of knowledge-
sharing success. Based on these research streams, a framework is developed that identifies the
five primary contexts affecting knowledge-sharing success. After exploring each of these
contexts in some detail, the research is synthesized in a final section to identify key areas for
evaluation of knowledge-sharing efforts.

Importance of ideas

         “Ideas are…the critical input in the production of more valuable human and nonhuman
capital,” (Romer, 1993, p. 71). While investments in machinery, technological infrastructures
and human capital are correlated with economic growth (e.g., DeLong & Summers, 1991), it is
the ideas of what to put those investments to use on – ideas developed through education,
research, and experimentation – that both drives the investments and provides the mechanisms
through which economic growth occurs (Freeman, 1982). That factor accumulation alone is
insufficient to support development is amply illustrated by the failures of East European
countries to succeed as the Asian newly industrializing economies (NIEs) thrived over the last
several decades (Nelson & Pack, 1999). Unlike most other countries, which also developed high
education levels and many research institutes, the distinguishing features of the NIEs have been
their openness to foreign knowledge, their superior capacity to use and improve upon transferred
knowledge, and the competitiveness of the markets into which they sold their outputs (Pack,
2000).

         It is this type of evidence that led Arrow (1999, p.19) to conclude that “countries and
firms must be open to new ideas, have multiple sources of new ideas, and see that ideas are


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diffused” if they are to achieve economic development and growth. Acceptance of and
competition among new ideas is what allows organizations and their nations to remain on the
creating rather than on the destructing end of Schumpeter’s (1942) ‘perennial gale of creative
destruction’, and the widespread diffusion of these ideas is what fosters the development of what
Quinn, Anderson & Finkelstein (1996) call know why (system understanding and trained
intuition) instead of only know what (cognitive knowledge) and know how (advanced skills). At
the same time, however, pursuit of new ideas does not come without costs, as organizations
encounter knowledge search (Stigler, 1961) and knowledge exchange (Hansen, 1999) costs and
limitations, as well as run risks of being distracted from using or progressing local knowledge
that could benefit them in the longer run (Atkinson & Stiglitz, 1969). Thus, those charged with
overseeing an enterprise’s knowledge management functions must balance the costs and benefits
inherent in knowledge sharing activities.

The study of knowledge sharing

       The study of knowledge sharing has its roots within the technology transfer and
innovation literature. The research in this area has focused on explanations for different nations’
successes or failures in fostering economic growth through technological development. While
some theorists argue that high investment rates in physical and human capital drive national
innovation and growth rates (Young, 1993; Kim & Lau, 1994; Krugman, 1994), ‘assimilation
theorists’ instead argue that entrepreneurship, effective learning, and innovation are separate, but
equally important variables affecting development (OECD, 1971; Freeman, 1982; Kim &
Nelson, 2000). Central to both approaches, nonetheless, is an understanding of the importance of
the sharing of ideas.

       In this literature, successful knowledge sharing results in firms mastering and getting into
practice product designs, manufacturing processes, and organizational designs that are new to
them (Nelson, 1993). As evidenced by the title of Richard Nelson’s recent volume on technology
transfer, Technology, Learning, & Innovation (Kim & Nelson, 2000), knowledge sharing is seen
as occurring through a dynamic learning process where organizations continually interact with
customers and suppliers to innovate or creatively imitate. Consider the case of technology
transfer as articulated by Lall (2000, p.15):



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       Developing     countries    obtain   industrial   technologies    mainly    from   the
       industrialized world, and their main technological problem, at least initially, is to
       master, adapt, and improve on the imported knowledge and equipment… Unlike
       the sale of a good, where the transaction is complete when physical delivery has
       taken place, the successful transfer of technology can be a prolonged process,
       involving local learning to complete the transaction. The embodied elements can
       be used at best practice levels only if they are complemented by a number of tacit
       elements that have to be developed locally.

       These conclusions have led development experts to recommend that activities focused on
facilitating knowledge sharing rather than on transmitting Northern knowledge to the South are
likely to prove more successful (Ellerman, Denning & Hanna, 2001; Knowledge for
Development, 1998; Social Development Group, 2002; Prusak, 1999). In other words, while
communication of knowledge is important, it is the processes through which knowledge is shared
that determine whether organizational learning occurs and, therefore, whether a knowledge-
sharing process was a success.

       Consistent with this literature, since the resulting designs, structures or strategies will
often be adapted and modified to a significant degree through learning by doing in interaction
with local interested parties, the technological heritage of what ultimately gets put into practice
may not be possible to identify. Moreover, while sometimes the underlying knowledge that has
been transferred may be embodied in easily identifiable offerings (Hedlund & Nonaka, 1993), it
just as often may take the form of a competence embedded in multiple repositories (Walsh &
Ungson, 1991) or routines, processes, procedures or structures (Teece, 2000). As Lall & Streeten
(1977) point out, and as Contractor (1981) confirmed empirically, technology transfers are
usually comprised of a mix of patented and unpatented knowledge. As a result, with limited
exceptions (e.g., Griliches’ work on the diffusion of hybrid corn), obtaining clear indicators of
transfer success, particularly on a micro, project level, is not within the domain of this literature.
Instead, this literature remains focused on identifying the patterns of institutional, industrial and
national factors that best support knowledge transfer in general – or what is termed knowledge
diffusion – among firms and between science and technology (Mansfield, 1961; 1977).




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       Sagafi-nejad (1990) synthesized this literature to identify four clusters of variables
affecting knowledge transfers. These clusters include the characteristics of the technology being
transferred, the activities and modes through which the transfers occur, organizational profiles of
the parties involved in the transfers, and broad environmental factors such as the level of
development and technological absorptive capacity of the host country. This literature provides
significant guidance on national capacity building, as well as many insights into how broad
environmental factors may affect knowledge-sharing activities (Rousseau, 1985).

       Other studies from this literature have focused on micro-level issues related to how
organizations can best accomplish international technology transfers. Early research found that
greater knowledge-sharing experience was associated with lower transfer costs (Mansfield,
Romeo & Wagner, 1979; Teece, 1976, 1977). Another topic was concerned with the speed
through which multinational organizations are able to transfer innovations to subsidiaries
(Mansfield & Romeo, 1980; Davidson, 1980; 1983). Other researchers examined the influences
of the mode of association between the parties (Mason, 1980; Balasubramanyam, 1973), the
level of technological development of the host country (Baranson, 1970), and the appropriateness
of the technology with respect to its capital- or labor-intensiveness (Schumacher, 1973). Gupta
and Govindarajan (1991) integrated these and other studies to develop a model of the
organization that categorized subsidiaries on the basis of the knowledge flows to and from the
rest of the organization. They posited that the key variables affecting organizational knowledge
flows were the broad task environments in which the flows occur, organizational structural
characteristics that can affect the relationship between the parties, and organizational cultural
norms with respect to a willingness to keep knowledge proprietary or accept outside knowledge.

       Knowledge sharing has also become an important focus in the strategic management
field, where knowledge is seen as “the most strategically-important resource which
[organizations] possess,” (Grant, 1996, p. 376) and a principal source of value creation, (Nonaka,
1991; Spender & Grant, 1996; Teece, Pisano & Shuen, 1997). Indeed, “in many industries, the
importance of developing abilities to better utilize the knowledge contained in the firm’s network
has become apparent...Benchmarking has demonstrated the potentially great benefits of best
practices transfer. Instances of failure in downsizing, on the other hand, have revealed the costs
of losing knowledge. Empowerment and globalization have created local knowledge with


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potential for utilization elsewhere, and information technology has given individuals increasingly
differentiated knowledge, unknown to [the] head office,” (Bresman, Birkinshaw & Nobel, 1999,
p. 441). Moreover, the very basis for some organizational activities is the sharing of knowledge
both between units and with outside partners and clients.

       Knowledge sharing has been viewed from two theoretical perspectives in this literature.
Beginning with Roger’s (1983) investigations of early and late adopters of technological
innovations, and more recently with Szulanski’s (1996) study of best practices transfers within
organizations, many researchers have used communications theory (Shannon & Weaver, 1949)
to examine in particular the factors that make knowledge transfers difficult. According to this
theory, “a transfer of knowledge is likened to the transmission of a message from a source to a
recipient in a given context. Characteristics of the message or the situation that limit the amount
of knowledge that can be transferred render the transfer stickier” (Szulanski, 1996, p. 438). More
recently, organizational learning theories have become a central focus in this field, as successful
knowledge transfers are increasingly seen as requiring an ongoing process of learning
interactions, rather than just a series of communications (Szulanski, 2000).

       As with the technology transfer and innovation research, strategic management scholars
have also identified a number of variables that can affect knowledge sharing, notably the nature
of the knowledge being shared in terms of its tacitness and embeddedness (Zander, 1991;
Szulanski, 1996, Dinur, Inkpen & Hamilton, 1998; Dixon, 2000), the strength of relationship ties
between the parties (Hansen, 1999), the learning mind-set and capability of the recipient (Yeung,
Ulrich, Nason & von Glinow, 1999), and the transfer activities undertaken (Dinur, et al., 1998;
Davenport & Prusak, 1998). In combination, the research and findings of this and the technology
transfer and innovation field, as explored in some detail below, provide a rich set of literature
from which to identify the critical factors affecting knowledge-sharing success. Before turning to
these variables, however, a discussion of knowledge-sharing success is first in order to establish
an appropriate focus for an organization’s knowledge-sharing efforts.

Knowledge-sharing success

       At its most basic level, knowledge sharing involves the processes through which
knowledge is channeled between a source and a recipient. The Bank may be, alternatively, a


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knowledge source, a knowledge recipient, or a facilitator or broker of knowledge between a
source and a recipient. Regardless of the Bank’s role, the objective of any knowledge-sharing
process is to transfer source knowledge successfully to a recipient.1

         One approach to defining knowledge-sharing success focuses on the degree to which the
knowledge is re-created in the recipient. Consistent with the innovation literature but on more
micro basis, knowledge can be seen as knowledge packages embedded in different structural
elements of an organization, such as in the people and their skills, the technical tools, and the
routines and systems used by the organization, as well as in the networks formed between and
among these elements (Argote & Ingram, 2000; Leonard-Barton, 1992). From this perspective,
knowledge transfer involves the re-creation of a source’s knowledge-related elements – its
knowledge package – in the recipient (Winter, 1995). Thus, knowledge-transfer success is
defined as the degree to which the underlying knowledge elements have been re-created in the
recipient to conform to those of the source. At a macro level, this is what a good deal of the work
on national innovation systems focuses on, as researchers seek to identify the key capacity-
building elements within industries and nations that best support the innovations that drive
economic development. At the micro level, however, this definition is problematic in that it is
often difficult to know which elements (e.g., people, tools and routines) comprise a source’s
knowledge package. Consider the case of EL Products, a manufacturer of electro-luminescent
lamps (Leonard-Barton, 1995).

         In the early 1990s, EL Products acquired another lamp-making company, Grimes, in
order to gain access to its dust-free lamp-making knowledge. However, while the company
indeed acquired the assets, equipment and related documents of Grimes, EL Products’ employees
were unable to duplicate the routines used by Grimes’ employees to reduce dust in the
production process. For EL Products, awareness of the knowledge that allowed Grimes’
employees to operate their equipment dust-free (their undocumented knowledge about the
appropriate routines to follow) only came about after EL employees experienced operation of the
equipment first-hand. Prior to having this first-hand experience, EL employees did not know that

1
  In most knowledge-sharing situations, reciprocal knowledge exchanges, rather than one-way knowledge transfers,
are either sought or occur. Nonetheless, even in reciprocal exchanges, each party is at times either a source or a
recipient with respect to what they are sending or receiving. A one-way perspective is adopted in the discussions that
follow for clarity purposes.


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the knowledge package to be transferred should have also included the uncodified, routine-based
knowledge that Grimes’ employees possessed.

       In addition to the fact that it is often difficult to know what aspects of knowledge are
important (Sowell, 1980), or which elements need to be transferred (Spender & Grant, 1996),
there is significant evidence that effective re-creation also requires that the knowledge package is
made accessible to the recipient so that ‘the local doers of development’ can convert it, adapt it
or reconfigure it to their localized needs (Dixon, 1994; Nonaka, 1994; Leonard-Barton, 1988;
Moreland, Argote & Krishnan, 1996; Devadas & Argote, 1995; Wegner, Erber & Raymond,
1991; Argote & Ingram, 2000; Epple, Argote & Murphy, 1996). Thus, even if the elements of
the knowledge package can be clearly identified, they may be hard to discern in their adapted
forms within the recipient. As a result, rather than using some notion of knowledge re-creation to
gauge sharing success, Kostova (1999) argues that a recipient’s internalization of knowledge is
more appropriate.

       Knowledge internalization refers to the degree to which a recipient obtains ownership of,
commitment to, and satisfaction with the transferred knowledge. With respect to ownership,
when knowledge is fully internalized by a recipient, it becomes theirs. “Control of an object
appears to be a key characteristic of the phenomena of ownership,” and the higher the discretion
exercised by individuals, the more likely that they “will invest more of their own ideas, unique
knowledge, and personal style” in the knowledge, thereby making it theirs (Pierce, Kostova &
Dirks, 2001, pp. 301, 302). In addition, development of a thorough understanding of the
knowledge is also related to ownership, and can be influenced by the intensity of the association
(i.e., the number of interactions involving the knowledge). A last aspect of ownership relates to
the degree that an individual invests energy, time, effort, and attention in the knowledge, as such
investments tend to cause individuals to develop ownership of the knowledge (Csikszentmihalyi
& Rochberg-Halton, 1981).

       Commitment is the second aspect of knowledge internalization. Since the relative
strength of an individual’s identification and ongoing involvement with the knowledge can also
affect the degree to which the recipient puts the knowledge into use (Mowday, Steers & Porter,
1979), obtaining knowledge commitment is another important element in knowledge



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internalization. Individuals develop knowledge commitment to the extent that they see the value
of the knowledge, develop competence in using the knowledge (Leonard-Barton, 1990), maintain
a working relationship or interaction with the knowledge, and are willing to put in extra effort to
work with the knowledge (Mowday, et al., 1979).

       The third aspect of knowledge internalization is satisfaction. Recipient satisfaction with
the knowledge is important because it can reduce the recipient’s stress (Ettlie, 1986) and
resistance levels in adapting and using the knowledge (Leonard-Barton & Deschamps, 1988) as
well as reduce the likelihood of the not-invented-here syndrome (Katz & Allen, 1982) occurring.

       Only when a recipient internalizes knowledge can it be sufficiently understood and
adapted by the recipient to allow for its effective re-creation and, ultimately, its use. In order to
foster knowledge internalization, researchers suggest that an organization needs to adopt an
active learning perspective through which it fosters situations where the knowledge sharing
parties catalyze the recipient’s learning experiences so that the recipient can actively
reappropriate, adapt, or reconfigure the knowledge to its needs (Dixon, 1994; Nonaka, 1994;
Leonard-Barton, 1988; Moreland, Argote & Krishnan, 1996; Devadas & Argote, 1995; Wegner,
Erber & Raymond, 1991; Argote & Ingram, 2000; Epple, Argote & Murphy, 1996). Such
‘reappropriation’ requires the clients to have the discretion to localize the knowledge, see the
value in doing so, invest in doing so, etc. (i.e. internalize the knowledge). Hence, the factors that
matter most to any given knowledge-sharing processes are those that support or inhibit the
recipient’s ability to internalize knowledge. For example, Cohen & Levinthal (1990) have found
that a recipient with limited absorptive capacity – with a limited stock of prior related knowledge
– is less likely to see the value of new knowledge. Thereby, the recipient is also less likely to see
the value in actively participating in knowledge sharing and, ultimately, is less likely to
internalize the knowledge.

       In the following sections, similar additional factors identified as affecting knowledge
internalization are explored. The literature identifies five primary contexts that can affect
knowledge internalization, including the relationship between the source and the recipient, the
form of the knowledge, the recipient’s learning predisposition, the source’s knowledge-sharing
capability, and the broader environment in which the sharing occurs. Collectively, as depicted in



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Figure 1, these five contexts define the overall setting in which knowledge sharing occurs. Each
context is discussed in the following sections.

                                     **************************
                                        Insert Figure 1 about here
                                     **************************

Relational context

       The relational context includes those factors that create different types of distances
between the parties. Five key relational factors identified in the literature include 1) the
organizational distance between the units, based on the governance modes through which the
transfer is conducted; and the distance between the source and recipient in terms of 2) physical
location 3) institutional settings, 4) knowledge competence, and 5) their relationship.

Organizational distance

       Research has found that parties embedded in superordinate relationships, such as
franchises (Darr, Argote & Epple, 1995), chains (Baum & Ingram, 1998), federations (Ingram &
Simons, 1997), strategic alliances (Powell, Koput & Smith-Doerr, 1996), and networks (McEvily
& Zaheer, 1998; Uzzi, 1996; Burns & Wholey, 1993), are able to share knowledge more
effectively among members than with outsiders. In an empirical study of 227 small
manufacturers participating in training, equipment demonstration, and certification activities
through specialized regional institutions, McEvily & Zaheer (1998) found that participation
enhanced firm capabilities. In another study, Uzzi (1996) found that more tacit knowledge
flowed across firms within a network than across independent firms.

       A key argument underlying much of this literature is that being embedded within a
network enhances the denseness of social ties (Tichy, Tushman & Frombrun, 1979; Tushman,
1977), and this, in turn, creates more opportunities to share knowledge and experiences, and
develop trust (Granovetter, 1985). Incentives for cooperation and communication are also greater
within franchises, chains, and networks, as “competition is usually minimized” among the
organizations, and “the organizations generally trust each other to a greater degree than those not



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[so] embedded,” (Argote, 1999:168). In addition, the structural arrangement between the parties
can serve “to shape (a) the flow of assets, (b) the depth and breadth of interaction between the
two [units], and (c) the incentives for collaboration” (Baughn, Denekamp, Stevens & Osborn,
1997, p. 109). These findings and conclusions about knowledge sharing across organizations are
similar to those related to knowledge sharing within organizations. For example, Birkinshaw &
Morrison (1995) found that firms that use organizational structures that support combining
activities and sharing resources across subsidiary boundaries are more innovative than firms
lacking such organizational linkages. Others have highlighted how different administrative
controls, such as the extent of decision-making authority delegated to a subsidiary manager, can
affect both knowledge flows (Gupta & Govindarajan, 1991) and the types of activities that might
be undertaken to support knowledge sharing (Stopford & Wells, 1972; Brooke & Remmers,
1978; Anderson & Gatignon, 1986; Geringer & Hebert, 1989; Killing, 1982; Kim & Hwang,
1992; Schaan, 1993). Administrative controls refer to the organizational systems and procedures
by which one entity uses power, authority (Etzioni, 1965) and bureaucratic, cultural, and
informal mechanisms (Baliga & Jaeger, 1984) to influence the behavior and output of another
entity (Ouchi, 1977). Where administrative controls are established that provide a subsidiary
manager with limited autonomy in decision-making and little communication with headquarters,
for example, knowledge flows and related activities that might support effective knowledge
sharing can similarly be limited. Moreover, in the context of an organizational acquisition,
Bresman, Birkinshaw & Nobel (1999: 442) argued that, consistent with the concept of a social
community (Durkheim, 1933; Etzioni, 1961; Selznick, 1966; Ouchi, 1980), individuals within
the source and recipient units “will only participate willingly in knowledge exchange once they
share a sense of identity or belonging with their colleagues.”

       In sum, the research on organizational distance suggests that the strength of social ties,
free-flow of communication, consistency in administrative controls, and levels of trust between
the source and recipient will be greater to the degree that the units interact through defined,
structured organizational arrangements, rather than through ad hoc processes. In turn, such
aspects of organizational distance can thereby have an effect on the ability of the recipient to
internalize the desired knowledge.




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Physical distance

       The physical distance between the parties can affect the difficulties, time requirements
and expenses of meeting face-to-face and communicating. Early work on agglomeration
economies, for example, argued that organizations concentrated their economic activities in
urban areas to obtain positive externalities, such as knowledge spillovers from other firms
(Marshall, 1916; Jacobs, 1969). As Marshall (1920) noted in his comparative analysis of nations,
economic activity is often drawn to regions rich in knowledge. More recently, consistent with
Wheeler’s (2001) contention that cities offer significant returns in the form of lower search costs,
Almeida (1996) found that patent citations frequently cluster in certain regions, and also are
often of a firm-to-firm nature. For example, SGS-Thomson in Texas commonly extends Texas
Instrument patents, as does Siemens-New Jersey with respect to nearby Bell Labs and AT&T.
On a more general basis, Galbraith (1990) found that the transfer of technology-embedded
knowledge was slower when the organizations transferring the knowledge were farther apart.
Lester & McCabe (1993) found that the transfer of knowledge related to nuclear reactor
operations was greatest when the reactors were located at the same, versus at geographically
distant, sites. In addition, Athanassiou & Nigh (2000) found in a study of top management team
knowledge transfer that strategically important issues facing executives were more likely to be
addressed in face-to-face meetings. Dutton & Starbuck (1979) found that face-to-face meetings
and conferences were more effective in transferring computer simulation technology than
exchanges of documents, manuals, and correspondences. Lastly, as Davenport & Prusak (1998,
p. 99) noted, “sometimes knowledge transfer can only work if the various parties are brought
together physically.”

       In each of these cases, the underlying logic is that the parties draw upon social capital
embedded within the regional or group relationships to facilitate knowledge transfer. For
example, a large contracting firm responsible for the tunneling for the Boston Harbor tunnel
project had overseen a similar project in New Zealand, and wanted the Boston tunnelers to adopt
some of their innovations. However, despite sending memos, detailed descriptions, diagrams and
manuals, and hiring consultants to speak with the Boston crew, it was only after the New
Zealanders joined the Boston crew “over rounds of Foster’s lager” that the Bostonians were able
to internalize and apply these innovations (Davenport & Prusak, 1998, p. 99). Cohendet, Kern,


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Mehmanpazir & Munier (1999: 232) suggest that such socialization contributes to the creation of
a “common knowledge base that becomes part of the organizational memory” that helps to
eliminate the spatial distances between the parties. Nonetheless, the physical distance separating
knowledge-sharing parties could have an impact on the ability and/or willingness of the parties to
develop such social relationships. This is because of the time, emotional and financial resources
associated with traveling to and from different locations, particularly the farther the distance of
the trips.

        There is also evidence that sharing mechanisms that involve people interactions can be
superior to those involving only document exchanges (Allen, 1977; Berry & Broadbent, 1984,
1987; Galbraith, 1990), since knowledge often needs to be adapted to the new context in order
for it to be effectively utilized (Leonard-Barton, 1988). In an empirical study of technology
transfer, Hakanson & Nobel (1998) found that even poorly articulated knowledge could be
transferred through personal contact, instruction, and apprenticeship. Others have argued that
successful knowledge sharing often requires the establishment of a sense of identity and
belonging between the parties (Bresman, et al., 1999). As with the transfer of technology-related
knowledge, the transfer of complex and causally ambiguous routines also typically requires
reconstruction and adaptation at the receiving end (Attewell, 1992; Kogut & Zander, 1992). This
is because the incompatibilities and incongruence of the routines, and their associated
organizational inefficiencies, may only come to light as they are put to use in the recipient (von
Hippel & Tyre, 1995; Argote, 1982; Leavitt, 1965; Nadler & Tushman, 1980). The subsequent
changes to the knowledge necessarily involve some degree of continual knowledge sharing as
comparisons between the templates and benchmarks and the replicas are made (Nelson &
Winter, 1982). The transfer of people facilitates such a process, as they can know who is good at
what activities, and it is this knowledge that the recipients need to figure out how to reconfigure
and adapt the original knowledge. Indeed, Saxenian (1990) cited the interaction of employees as
the key mechanism that supported the emergence of Silicon Valley and Boston’s Route 128 as
major innovation clusters.

        In addition to creating barriers to getting people together physically, large physical
distances can also create communication difficulties. Indeed, Allan’s (1977) empirical study
demonstrated that communication between R&D employees decreased markedly with increased


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physical distance. While personnel can conduct many knowledge exchanges through telephone
and other communication mechanisms, to the extent that the other party is in a different time
zone, the cost and arduousness of such communications can increase significantly. One
exception to this conclusion was found in Darr’s (1994) study of knowledge transfer in pizza
franchises, where strategic similarity mediated the relationship between geographic distance and
transfer success. Even with great physical distances between them, since all of the franchises
faced similar operating dynamics, they shared a common sense of purpose that allowed them to
exchange strategic knowledge without difficulty. Thus, the research suggests that physical
distance can have an impact on knowledge-sharing success; although where the parties share a
common sense of purpose, the impact may be less.

Institutional distance

       Following Kostova (1999), institutional distance refers to the degree of congruity
between the institutional environments facing the two parties. Empirical studies have supported
the notion that managers’ strategic orientations can change the way that they categorize
competitors, opportunities, threats, etc., whether such orientations are spawned from the
managers’ different educational backgrounds or from the different institutional environments in
which their organizations operate (Tyler & Steensma, 1998; Hitt, Dacin, Tyler & Park, 1997). At
the international level, technology transfer researchers have investigated the appropriation and
spillovers of technologies between countries with a specific focus on the role of national
institutional structures on such processes. For example, Almeida (1996) found that organizations
use foreign plants to upgrade technological ability in fields that are weak in their home countries.
Mansfield (1988) examined the basis for Japan’s apparent superior ability as imitators of
technologies developed in other nations, particularly with respect to the costs and speeds of
innovating, finding that the Japanese firms’ closer collaboration between engineers and workers,
and lesser investments in pre-product introduction marketing are key contributors to their quick
and effective use of external technology. More generally, recent research on U.S. and Japanese
firms supported the importance of the regulatory environment on knowledge transfer with
respect to explicit scientific knowledge (Spencer, 2000). Indeed, this research found that
“differences in national institutional structures led to differences in communication among a
country’s firms,” (Spencer, 2000, p. 527). Similarly, Appleyard (1996) found that factors such as


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the intellectual property regimes and employment systems of an industry affected the
knowledge-transfer activities undertaken.

       According to Kostova (1999), since knowledge is often meaning and value based, the
success of knowledge transfer is determined by the transferability of meaning and value, in
addition to the transferability of knowledge. Kostova’s (1999) argument is consistent with an
extensive literature on the differences in organizational practices across countries (e.g., Lincoln,
Hanada & McBride, 1986; Graham, 1985; Hofstede, 1980). As she stated, “the main ideas here
are that (1) countries differ in their institutional characteristics; (2) organizational practices
reflect the institutional environment of the country where they have been developed and
established; and, therefore, (3) when practices are transferred across borders, they may not ‘fit’
with the institutional environment of the recipient country, which, in turn, may be an impediment
to transfer,” (Kostova, 1999, p. 314).

Knowledge distance

       Knowledge distance refers to how large a gap exists between the source and the recipient
in terms of their knowledge bases. Hamel (1991, p. 97) found that organizational learning was
enhanced when the knowledge gap between a source and a recipient was not so great to make the
recipient unable “to identify, if not retrace, the intermediate learning ‘steps’ between its present
competence level and that of its partner.” Lane & Lubatkin (1998) found that a recipient that has
a large knowledge gap between it and the source would be less likely to assimilate the source’s
knowledge. They developed the concept of ‘relative absorptive capacity’ to move the concept of
absorptive capacity from an organizational basis to a relational basis. In other words, an
organization’s absorptive capacity, although the focus of numerous knowledge-related studies
(see e.g., Szulanski, 1996; Dixon, 2000; Cohen & Levinthal, 1990; Lyles & Salk, 1996; Baughn
et al., 1997), is not the appropriate concept to address the issue of the ability of an organization
to absorb knowledge. Rather, it is the relative knowledge of the recipient with respect to the
source’s knowledge (i.e., the extent of the knowledge gap between the parties) that is important,
and this is a relational concept. This is also consistent with Dinur, et al.’s (1998) discussion with
respect to the need for the two parties to have some alignment in terms of their knowledge to
facilitate knowledge transfer. They argued that the greater the degree to which the new context is



                                                                                                  15
in alignment with the natural context of the knowledge in terms of environment, culture,
strategy, decision making/organizational structure, and technology, the less difficult the
internalization of the knowledge will be in the new unit. Nonaka & Takeuchi (1995) also
emphasize the need for knowledge redundancy or overlapping areas of expertise to facilitate
knowledge transfer. As Nelson & Winter (1982, p. 78) noted, “the same knowledge, apparently,
is more tacit for some people than for others” depending upon how much knowledge overlap
exits. Moreover, as Hamel (1991, p. 97) concluded, “if the skill gap between partners is too
great, learning becomes almost impossible.”

       On the other hand, at times a recipient could be burdened with unlearning some existing
knowledge to avoid developing a ‘core-rigidity’ (Leonard-Barton 1992; March 1991), as the
presence of some knowledge may constrain learning or even encourage ineffective learning if
such knowledge focuses the organization inappropriately (Dixon, 1992; Burgleman, 1983;
Hedberg, 1981; Nystrom & Starbuck, 1984). For example, when CT scanners were first
introduced in radiology departments, their initial implementation was somewhat ineffective
because new role structures first had to be negotiated between radiologists and technicians
(Barley, 1986). Thus, existing knowledge of the roles of the radiologists and the technicians had
to be unlearned to allow for the new technology and related knowledge to be accepted. This is
similar to the argument that in some organizations a not-invented-here syndrome can prevent
recipients from accepting outside knowledge (Hayes & Clark, 1985; Katz & Allen, 1982). As
Leonard-Barton (1995) noted, people who have developed what she termed ‘signature skills’ can
become very resistant to any knowledge that may require them to abandon the very abilities and
knowledge that so define them professionally. In such situations, the dominance of the
organization’s inward-looking absorptive capacity “impedes the incorporation of outside
knowledge and results in the pathology of the not-invented-here (NIH) syndrome,” (Cohen &
Levinthal, 1990: 133). As a result, as Szulanski (1996: 31), drawing on Zaltman, Duncan &
Holbek (1973) noted, this “may result in foot dragging, passivity, feigned acceptance, hidden
sabotage, or outright rejection in the implementation and use of new knowledge.”

       On the whole, whether it is too little a knowledge overlap between the parties, or too
extensive a knowledge base on the part of the recipient relative to the knowledge to be shared,




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the knowledge distance between the parties to a knowledge transfer can affect the ability of the
recipient to internalize shared knowledge.

Relationship distance

       Relationship distance refers to the duration and quality of the experience that the source
and recipient have working together. Research has identified several relationship-related factors
that can affect knowledge-sharing success. For example, in their examination of knowledge
transfer from a population-level perspective, Baum & Berta (1999) found that organizations seek
to imitate the routines of other organizations that have high status, are socially proximate, and
are strategically similar. These findings stem in large part from the fact that organizations tend to
maintain the direction and emphasis of prior actions (Amburgey & Miner, 1992; Miller &
Friesen, 1980). In a study of knowledge transfer in international joint ventures, Child &
Rodrigues (1996) found that knowledge transfer is facilitated when the parties hold similar social
identities. In their work on cognitive strategic groups, Peteraf & Shanley (1997) found that
managers identify with managers in competing firms who are socially similar. Thus, one aspect
of relationship distance is the degree of similarity of social identities of the members of the units
involved in the transfer. The research suggests that as the social similarity of the parties’
increases, so will their ease of communications, and this will allow for greater transfer success.

       In addition to social similarity, researchers have also found that strategic similarity
between the source and recipient can affect knowledge transfer. For example, in Darr’s (1994)
study of knowledge transfer in pizza franchises, transfer success was found to be greater among
firms with similar generic strategies (Porter, 1980) of cost competitiveness or product
differentiation. Porac & Thomas (1994), in a study of retail organizations, found that similar
organizations were more likely to monitor and imitate each other than dissimilar ones. Likewise,
Baum & Berta (1999), in a quasi experiment using student groups, found that groups in similar
market positions in a simulation were more likely to imitate each other, and such imitation can
be viewed as a form of knowledge transfer. Yeung, et al. (1999) also found that strategy
similarity affects knowledge transfer.




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       Sometimes, however, the relationship between two organizations is less than pleasant.
For example, in a hostile acquisition, the acquired unit’s members may be less than receptive to
passing along their knowledge and expertise for fear that they will then become expendable. On
the other hand, Schrader (1991) concluded that a strong positive relationship between recipient
and source facilitates the trading or the transfer of information. Indeed, in an empirical study of
knowledge transfer, Szulanski (1996) found that the arduousness of the relationship between the
source and recipient was associated with knowledge stickiness or knowledge transfer difficulty.
Thus, the level of arduousness in the relationship is a third aspect of relationship distance.

       Following Lei & Slocum (1992), lack of experience in transferring knowledge in a
collaborative relationship can also lead to difficulties in the transfer process. Simonin (1997)
found empirical support for the hypothesis that collaborative knowledge-sharing experience
supports the development of competence at working collaboratively. The argument is that
“familiarity with...transfer processes facilitates the task of knowledge absorption by eliminating
much of the disruptive noise of cooperation,” (Simonin, 1999, p. 474). In addition, since “the
exchange of tacit knowledge must rely on extended social contact...it is not readily disentangled
and transferred in codified form” (Baughn, et al., 1997, p. 107). Rather, disentanglement requires
the development of a sense of identity or belonging with colleagues (Bresman, et al., 1999) as
well as of a social community (Durkheim, 1933; Etzioni, 1961; Selznick, 1966; Ouchi, 1980).
The argument is that as parties to a knowledge sharing arrangement work together, they develop
social bonds that allow them to better access the tacit knowledge that may only become
accessible (Dixon, 1994) through the use of experiential interactions between the parties
(Hansen, 1999). In addition, over time, as the parties develop an appreciation of their partner’s
social context, they establish their own social norms and expectations of one another, “thereby
enabling the development of trust and with it the successful exchange of knowledge” (Roberts,
2000: 434). Thus, the depth of experience of the parties in transferring knowledge is critical to
knowledge-sharing success.

       In addition to the depth of experience between the parties, the motivation of a recipient
has also been found to be associated with transfer difficulty, in that overly motivated sources
could exhibit “impatient enthusiasm” that can lead to transfer difficulties, so that some parity
between source and recipient motivations seemed to be in order (Szulanski, 1996). Other


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researchers have identified the conceptually similar concept of ‘learning intent’ of the recipient
as an important factor in transfer success (Baughn, et al., 1997; Hamel, 1991). “When
circumstances place a great premium on effective articulation, remarkable things can sometimes
be accomplished. For example, it has been established in occasional emergency situations that it
is not impossible to convey by radioed verbal commands enough information on how to fly a
small plane so that a person who lacks a pilot’s skills can bring the plane in for landing,” (Nelson
& Winter, 1982, p. 78). Moreover, the perception of substantial opportunities related to the
knowledge would also motivate the learners (Doz & Hamel, 1998). Hamel (1991) also noted that
the newness of knowledge could also provide motivations for a transfer to occur successfully,
given that this newer knowledge may carry significant first-to-make premiums. The idea is that,
when a recipient sees the knowledge to be transferred as valuable or important, the recipient will
have greater motivation to support the transfer than if the knowledge is seen as less significant.

       Related to arduousness, depth of experience between the parties, and recipient
motivation, another relational factor affecting knowledge-sharing processes is the level of trust
between the parties. Trust, which is a “warranted belief that someone else will honour their
obligations” (Casson, 1997: 118), is needed in situations where the complexity of the
relationship, or the fact that it is marked by unanticipated contingencies, prevents the parties
from having the ability to find recourse if things should not go as planned (Lazaric & Lorenz,
1998). Since knowledge has at least some degree of tacitness (Polanyi, 1966b; also see e.g.,
MacKenzie & Spinardi’s (1995) study of codified computer aided design data in the
development of nuclear weapons), while its transfer is thereby subject to a high level of risk and
uncertainty, knowledge-sharing processes are not generally amenable to enforcement by
contract. As a result, what the parties ultimately must rely upon is their trust in the other party
throughout the exchange of knowledge. Trust is related to several of the other relational factors
because, as Granovetter (1985: 490) noted, “information from one’s own past dealings” with a
party helps a recipient to accept knowledge at face value, since “individuals with whom one has
a continuing relation have an economic motivation to be trustworthy, so as not to discourage
future transactions; and…continuing economic relations often become overlaid with social
content that carries strong expectations of trust and abstention from opportunism.” Thus, trust is
seen in as a key factor affecting knowledge-sharing efforts.



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       Finally, the consistency between the parties’ normal ways of ‘doing business’ can also
affect knowledge sharing outcomes. Research has found that “group interaction will unfold
immediately in a well-coordinated fashion if (a) group members’ scripts (Abelson, 1976) are
similar to one another’s and (b) members’ definition of the situation are also similar. In effect,
the norm is imported and the absence of disagreement and miscues implicitly affirms that all
members accept it” (Gersick & Hackman, 1990, p. 76). As Hackman (1969) noted, when the new
assignment facing members is similar to the members’ prior experience, the member will be
prompted to recall, and be predisposed to act in accord with, routines that have worked in the
similar situation; Louis & Sutton (1991) argued the same. Further, research on technology
transfers has shown that differences in work values and organizational cultures can significantly
impair knowledge transfers (Allen, 1977; Tushman, 1977; Dougherty, 1990). Thus, the degree of
similarity of business norms is also an aspect of relationship that can impact upon knowledge-
transfer success.

       On an overall basis, each of these relational factors can be seen as potentially affecting
knowledge-sharing processes. The evidence seems to point to the need to develop extensive,
deep, friendly relationships between the parties so as to bridge any distances between them.

Knowledge context

       The second context in Figure 1 relates to the knowledge that is transferred. Two aspects
of knowledge have been emphasized in the literature, including explicitness and embeddedness.

Knowledge explicitness

       Knowledge explicitness refers to the extent to which knowledge is verbalized, written,
drawn or otherwise articulated; highly tacit knowledge is hard to articulate, is acquired through
experience (Polanyi, 1966a), whereas explicit knowledge is transmittable in formal, systematic
language. As first stated by Polanyi (1966a), individuals know more than they can explain. This
is because individuals have knowledge that is non-verbalized, intuitive, and unarticulated.
Polanyi (1962) defined such knowledge as ‘tacit.’ Tacit knowledge is hard to communicate and
is deeply rooted in action, involvement and commitment within a specific context; it is “a
continuous activity of knowing,” (Nonaka, 1994: 16); it is “the way things are done around



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here,” (Spender, 1996). On the contrary, individuals also have knowledge that is verbalized,
written, drawn or otherwise articulated (e.g., patents, computer programs). Thus, a primary
distinction with respect to knowledge is between its explicitness and its tacitness.

       According to Polanyi (1962), the tacit dimension of knowledge defines and gives
meaning to its complementary explicit dimension. That is, the inarticulable tacit aspect of
knowledge is only known by an awareness of it through a sensing of its corresponding explicit
complement (Polanyi, 1966a, p. 10). At the same time, as Polanyi (1966b, pp.7, 12) goes on to
state, “while tacit knowledge can be possessed by itself, explicit knowledge must rely on being
tacitly understood and applied. Hence all knowledge is either tacit or rooted in tacit knowledge.
A wholly explicit knowledge is unthinkable.” Many researchers have developed knowledge
taxonomies and categorization schemes along the lines of the explicit-tacit divide (see e.g.,
Winter, 1987; Anderson, 1983; Ryle, 1949; Kogut & Zander, 1992; Hedlund, 1994; Nonaka &
Takeuchi, 1995). Inkpen & Dinur (1998) argued that the distinction between tacit and explicit
knowledge is not a dichotomy, but a spectrum or continuum with extremes of the two types at
either end. At the explicit end of the continuum, knowledge is codified in specific products and
processes; at the tacit end, knowledge resides in individual cognition and organizational routines
all developed through experience and use.

       The logic behind identifying the explicitness vs. tacitness of knowledge is that explicit
knowledge is seen as more easily transferable than tacit knowledge. Lippman & Rumelt (1982)
argued that the transfer of knowledge is more difficult to the extent that there is “causal
ambiguity”, which is ambiguity about what factors, skills, or knowledge elements interactively
define the function of interest. The greater the causal ambiguity, the more difficult it is to
identify the related knowledge elements and subnetworks (i.e., the people, tools, and routines in
Figure 1) supporting the functional activity. Causal ambiguity, therefore, is often singled out as
an important factor affecting knowledge transfer (Spender, 1996; Nonaka, 1994; Grant, 1996).
For example, research has shown that knowledge embedded in products, that is codifiable, and
that is explicit, and thereby exhibits little causal ambiguity, transfers between units more easily
than less explicit knowledge (Zander & Kogut, 1995). On the other hand, given that “poorly
articulated knowledge is difficult to teach and learn,” such knowledge is more difficult to




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transfer (Hakanson & Nobel, 1998, p. 13). In an empirical study of best practices transfers,
Szulanski (1996) found causal ambiguity to be an important barrier to knowledge transfer.

       However, organizational learning theories tell us that the sending of a fully explicit
development manual to a client does not necessarily result in the contents and meanings of that
manual being internalized by the client. On the contrary, such a complete codification of
knowledge as would be contained in a manual could instead effectively preclude a recipient from
localizing or taking ownership of the knowledge, since the knowledge could be so predefined to
limit its adaptability. For example, the work of Nonaka (1994), Dixon (1994) and Yeung, et al.
(1999) in general suggest that organizational learning occurs through a process along the lines of:
1) tacit knowledge – knowledge held in someone’s head – is accessed from internal and external
boundary crossing interactions; 2) accessible knowledge is translated and recategorized to allow
members to make sense of it, to see where it fits within their focused area and overall within the
organization; 3) tacit knowledge is made explicit through dialogues; 4) knowledge is put into
action to allow its conversion from explicit to tacit by ‘learning by doing.’ While these activities
do not necessarily proceed in this order, the extent to which each does occur is likely to have an
influence on knowledge-sharing success. This organizational learning process suggests that, by
participating in articulation processes, recipients might be able to have better opportunities to
translate and recategorize the given knowledge, which in turn could allow them to see how it fits
within their own area, organization or country, as well as participate in the dialogues through
which much of the meaning behind the tacit components of the knowledge can become evident
(Nonaka, 1994). In other words, a recipient’s early participation is akin to their helping to create
a presentation rather than only being in the audience that receives one; all that would be omitted
from the final presentation, as well as the rationales for everything included and excluded, can
only be learned if the recipient is involved in the presentation’s creation.

       Thus, explicitness is potentially a two-edged sword. On the one hand, the existence of
casual ambiguity with respect to a package of knowledge to be shared suggests that efforts to
codify or articulate the knowledge could enhance its transferability. Indeed, Intel’s “copy
exactly” policy for building semiconductor plants (Iansiti, 1998) follows such a philosophy. On
the other hand, too much reliance upon codification might limit a knowledge package’s
internalization, as a seemingly complete codification could ignore the reality that tacit elements


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still exist (Polanyi, 1966b). For example, research on the use of information and communication
technologies to bring internationally dispersed teams together found that, while the technologies
were effective at facilitating the transfer of codified knowledge, they could not transfer related
sensory information, feelings, intuition, and non-verbal communications that were important to
the knowledge’s ultimate implementation (Boutellier, Grassmann, Macho & Roux, 1998).
Moreover, since knowledge codified by a source may be incompatible with a recipient’s cultural
beliefs (Morosini, Shane & Singh, 1998) and idiosyncratic ways of conducting business (Luo,
2000), such knowledge could lack legitimacy in the recipient’s context (Scott, 2001), and the
recipient may be less motivated (Kirkman, Gibson & Shapiro, 2001) to take ownership of, and
become committed to this knowledge.

Knowledge embeddedness

       The second aspect of knowledge that has been emphasized in the literature is
embeddedness. The concept of knowledge embeddedness is consistent with the notion of
knowledge complexity (Dixon, 2000). The issue, as depicted in Figure 1 within the recipient
context, is how many knowledge elements and related sub-networks (e.g., people, tools, and
routines) will need to be transferred, absorbed, adapted and adopted by the recipient, and/or how
many other recipients will be required to do so to allow the knowledge to be applied by the
recipient. In many situations, a significant component of an organization’s knowledge is
embedded in people (Engstrom, Brown, Engstrom & Koistinen, 1990; Starbuck, 1992). At its
simplest, the sharing of people-embedded knowledge would require only the movement of
people between units, since they would carry the knowledge with them. Alternatively, people-
embedded knowledge can also be shared by extracting their tacit knowledge through some series
of knowledge transfer activities.

       Knowledge can also be embedded in products, tools or technology (Argote & Ingram,
2000). Research on the transfer of such technology-embedded knowledge is quite extensive, and
covers both intra-firm transfers (e.g., Teece, 1976, 1977; Mansfield, et al, 1979; Mansfield &
Romeo, 1980; Davidson, 1980, 1983; Zander, 1991), and inter-firm transfers (e.g., Mowery, et
al, 1996; Bresman, et al., 1999). Such transfers have been studied from multiple perspectives
(see e.g., Zhao & Reisman, 1992), including with respect to the roles of national institutional



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structures on such processes (Mowery & Rosenberg, 1989; Mansfield, 1988; Mowery & Teece,
1992; Almeida, 1996), the impact of evolutionary patterns or ‘technology life cycles’ on
knowledge development (Abernathy & Utterback, 1978; Utterback, 1994; Anderson & Tushman,
1990, 1991; Foster, 1986; Hamilton, 1990), and the effects of technological complexities on the
ease of transfers (Zander & Kogut, 1995; Galbraith, 1990). What this literature tells us about the
sharing of technological knowledge is that since the knowledge itself may be in different stages
of flux or transition, its transferability may be affected, as the source’s ability to share the
knowledge may be in part determined by the degree of specificity of the knowledge. When such
specificity is not yet available, the knowledge can be much harder to share, as identification of
the appropriate knowledge elements to be shared is difficult. For example, while many
companies have reverse-engineered Sony’s products to make their own similar products, most
have had great difficulty understanding how to miniaturize electronic components and apply
miniaturization processes to other developments (Prahalad, 1993). Sony’s knowledge is, at least
to some degree, related to, and embedded in, its specific sites, physical assets, dedicated assets,
human assets, and organizational routines (Williamson, 1985; Nelson & Winter, 1982).

       In addition to being embedded in people or tools and technology, knowledge may also be
embedded in tasks or routines. Routines are “the forms, rules, procedures, conventions,
strategies, and [soft] technologies around which organizations are constructed and through which
they operate,” (Levitt & March, 1988: 517). Simon (1945), drawing on Stene (1940), described
organizational routines as actions taken without conscious consideration of alternatives in
response to recurring questions. Whereas technological knowledge may be important because of
its contribution to the development of certain processes, services or products, routines are
important because their automatic launch can continue to occur even in inappropriate new
situations, and thus are both ungovernable (Barnard, 1945) and govern most organizational
behavior (March & Simon, 1978). While several researchers have argued that organizational
routines and their development form the basis for the evolution of the firm (Nelson & Winter,
1982; Fuller, 1988; Plotkin, 1994), most of the research on routines has focused on how they
form (Hannan & Freeman, 1984; March, 1991; Tyre & Orlikowski, 1994), how they have
staying power (Miliken & Lant, 1991; Starbuck, 1983; Tyre & Orlikowski, 1994), and how they
help to both improve organizational efficiency as recipes of success (Miller, 1999) and create so



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called ‘competency traps’ (see e.g., Levitt & March, 1988; Starbuck & Hedberg, 1977; Whetten,
1980).

         The transfer of routines is complex. According to Kostova (1999), some routines as
encompass more than the task sequences represented, to include also the underlying meanings as
well. Similarly, Leonard-Barton (1992) argues that an organization’s knowledge system
includes, among other components, the value assigned to the context and structure of knowledge.
Because routines are implicitly embedded with underlying meaning structures, this makes their
transfer difficult.

         The final place where knowledge can be embedded is in a complex mix of multiple
elements and subnetworks. The people-routines network contains knowledge about who is good
at what tasks (Argote & Ingram, 2000). It is this knowledge that the recipients of routine-
embedded knowledge will need in order to figure out how to reconfigure and adapt the original
knowledge. While a routine may be easy to transfer, knowledge about who is good at using that
routine may take time to develop. In response to this very issue, many organizations have
attempted to codify who-knows-what in their organizations through the development of
directories of expertise or knowledge yellow pages (Davenport & Prusak, 1998; Prusak, 1999;
Yeung, et al., 1999; Dixon, 2000) so that they may access the organization’s intellectual capital
(Stewart, 1997; Sveiby, 1997). Moreover, knowledge about which tools best support which
routines (held in the tools-routines network) is also important with respect to the effectiveness
and the efficiency of the reconfiguration and adaptation process. As Teece (2000:36) noted, since
organizational knowledge is embedded in processes, procedures, routines and structures, “such
knowledge cannot be moved into an organization without the transfer of clusters of individuals
with established patterns of working together.” Kogut & Zander (1992: 383) made a similar
argument: “because we know that hiring new workers is not the equivalent to changing the skills
of a firm, an analysis of what firms can do must understand knowledge as embedded in the
organizing principles by which people cooperate within the organizations... The capabilities of
the firm in general are argued to rest in the organizing principles by which relationships among
individuals, within and between groups, and among organizations are structured.”




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       Research on the transfer of such complex knowledge is quite limited. In one study,
Moreland, Argote & Krishnan (1996) found that when there were no personnel transfers
accompanying knowledge transfers, the recipients failed to learn who was good at what tools and
tasks. Even where personnel transfers accompanied knowledge transfers, however, Devadas &
Argote (1995) found that the transfers were less successful when new people lacked a fit with
existing people-routines and people-technology networks. Also, organizational performance was
found to suffer when a shuffling of personnel made identification of member’s expertise more
difficult (Wegner, Erber & Raymond, 1991). Consistent with these findings, Argote & Ingram
(2000) concluded that new organizations are more successful than organizations with longer
operating histories as knowledge recipients due to their lack of established long-standing people-
related subnetworks that might inhibit their capacity to assimilate and adapt new knowledge.

       In terms of the transfer of knowledge about which tasks are best performed using which
tools (knowledge embedded in a technology-routine network), Epple, Argote & Murphy’s (1996)
study stands alone. This study examined a second shift added to an existing assembly line. It
found that the second shift was able to achieve performance levels consistent with and even
superior to the first shift within weeks of beginning operations. According to Epple, et al. (1996),
this indicated that the knowledge that the first shift had about which tools to use for any given
task was readily transferred to the second shift.

       With respect to the transfer of knowledge embedded in other networks, research has
found that group performance increases when everyone in a group is informed of each other
member’s expertise (Stasser, Stewart & Wittenbaum, 1995; Wegner, 1987; Moreland, et al.,
1996). This is because, as Sutton & Hargadon (1996) posit, such knowledge allows groups to
engage in joint brainstorming sessions that allows group members to explore new ideas and
discuss difficult issues, and so doing can help groups to avoid sub-optimal solutions (Ancona &
Caldwell, 1992; Jehn, Northcraft & Neale, 1999). Moreover, groups can provide forums for
sharing information across functional and cultural boundaries (Lipnack & Stamps, 1993), and
can gather together the diversity of information, backgrounds, and values necessary to make
things happen (Jackson, 1992). Moreland’s (1999) research confirmed that group training about
member’s expertise produces better group performance, and disruptions to a group’s knowledge




                                                                                                 26
about member’s expertise (through the reassignment or turnover of people) hurts group
performance.

       In sum, knowledge can be of different degrees of explicitness vs. tacitness, and it can be
embedded in different organizational elements. Since explicitness can vary across different
combinations of embeddedness, although the two concepts are related (as more deeply embedded
knowledge also generally carries a higher degree of tacitness than knowledge embedded in only
one element), they are nonetheless distinct. As such, each is discussed in the literature as
affecting the ease of transfer and the degree of internalization achievable based on the particular
knowledge being transferred. The research suggests that the development of a deep
understanding about these two aspects of to-be-transferred-knowledge might allow the
knowledge-sharing parties to be in positions to devise an appropriate assortment of source-
recipient interactions to both expose any related tacit knowledge and incorporate all appropriate
knowledge elements in the transfer. For example, research has shown that the implementation of
knowledge new to an organization requires the development of new organizational and social
knowledge (Attewell, 1992; Orlikowski, 1993), and one way that this is accomplished is in-
group settings through story telling (Brown & Duguid, 1991). In addition, it has also posited that
failure to include the recipient in some pre-transfer knowledge-preparation processes may
effectively prevent them from being able to assimilate certain tacit knowledge (Dixon, 1994),
since such knowledge may be accessible only through the dialogues in which the recipient did
not participate (see Boisot (1998) and Dixon (2000) for nascent treatments of how the selection
of alternative transfer mechanisms might affect knowledge transfers for knowledge in different
forms and possessing different degrees of embeddedness). That knowledge sharing is more
difficult than expected and expectations are often not met is widely reported (Galbraith, 1990;
Gupta and Govindarajan, 2000). Two often cited examples include General Motor’s failures in
sharing what it learned from its Saturn division’s successes with other divisions (Kerwin &
Woodruff, 1992), and IBM’s limitations in sharing its reengineered hardware design processes
(Economist, 1993). While perhaps not the only potential culprits with respect to these company’s
difficulties in knowledge sharing, the literature suggests that knowledge explicitness and
knowledge embeddedness are considered to be important factors affecting knowledge-sharing
success.



                                                                                                27
Recipient context

       The third broad context in Figure 1 is termed ‘recipient context.’ Prior studies have
included several constructs within the recipient context, including the recipient’s motivation
(Szulanski, 1996), absorptive and learning capacities (Szulanski, 1996; Dixon, 2000; Cohen &
Levinthal, 1990; Lyles & Salk, 1996; Baughn, et al., 1997), intent (Baughn, et al., 1997; Hamel,
1991), knowledge experience (Hackman, 1969; Gersick & Hackman, 1990) collaborative
experience (Doz, 1996; Powell, Koput & Smith-Doerr; 1996; Simonin, 1997), retentive capacity
(Glaser, Abelson & Garrison, 1983; Druckman & Bjork, 1991) and learning culture (Davenport
& Prusak, 1998). Many of these constructs are relational in nature, as Lane & Lubatkin (1999)
demonstrated with respect to absorptive capacity. The one variable emphasized in the literature
as specifically within the recipient context is its learning culture (Davenport & Prusak, 1998),
learning capability (Yeung, et al., 1999) or fertileness (Szulanski, 1996).

       The need for a culture of learning in an organization to facilitate organizational learning
in general, and knowledge internalization in specific, has been emphasized by many researchers
(Aubrey & Cohen, 1995; Argyris, 1982, 1991; Delta Consulting Group, 1990; Fiol & Lyles,
1985; Huber, 1991; Wick & León, 1993; Davenport & Prusak, 1998). In an organization that
fosters delegating responsibility, tolerating creative mistakes, and providing slack time to work
on new ideas, the richness of the knowledge transferred is likely to be much higher (Davenport
& Prusak, 1998). On the other hand, if learning is not considered important, the slack required to
enable people to think and discuss, and for learning groups to emerge, may be sacrificed in the
name of efficiency (Stewart, 1996). Moreover, in some organizations, as discussed above, the
not-invented-here syndrome can prevent recipients from accepting outside knowledge (Hayes &
Clark, 1985; Katz & Allen, 1982). Moreover, even when knowledge is transferred to a willing
recipient, the transfer will only be effective when the knowledge is retained (Glaser, et al., 1983;
Druckman & Bjork, 1991). As Szulanski (1996) noted, retention cannot be taken for granted,
given the evidence from research on innovations (e.g., Rogers, 1983) and planned organizational
change (see e.g., Glaser, et al., 1983). In addition, even if retained, the knowledge may not be
nurtured and further developed. Szulanski (1996, p. 32) termed organizational environments as
‘fertile’ or ‘barren’ depending on the extent to which they facilitated the development of
transferred knowledge or hindered the “gestation and evolution” of this knowledge.



                                                                                                 28
       Taken together, the literature on learning culture posits that organizations with extensive
sets of routines and competencies designed to retain and nurture transferred knowledge are better
able to support knowledge internalization than less fertile organizations (Yeung, et al., 1999).
Lacking the ability to invest significant time or other resources in new knowledge due to a barren
organizational learning environment, a recipient may be simply incapable of developing the
necessary degree of commitment and ownership toward the new knowledge to allow for its full
internalization. Moreover, since a recipient’s ability to retain and nurture transferred knowledge
interacts with its motivation to do so (Vroom, 1964; Porter & Lawler, 1968; Campbell, 1976),
having a fertile organizational environment can provide an offset to mitigate any potential low
motivation on the part of the recipient. Thus, the literature concludes that a recipient’s capability
with respect to accepting, retaining and nurturing new knowledge are an important factor
affecting the success of knowledge-sharing efforts.

Source context

       Yeung, et al., (1999) suggest that a source’s learning culture is also an important factor
affecting knowledge-transfer success. This is because a capable source is able to manage
knowledge-sharing activities in a way that improves a recipient’s learning of the specific
knowledge, much as a university professor structures lectures, readings and assignments to best
facilitate their students’ learning. In addition, a capable source may also be able to help a
recipient overcome some of the many of what Yeung, et al. (1999) term ‘learning disabilities.’
For one, by engaging the recipient through an administrative structure that allows for a greater
degree of autonomy for the recipient than it might generally have, the recipient may become
more adaptive and flexible Weick (1979), and this, in turn, can allow it to pursue the types of
varied experience-based learning opportunities that can move it along its learning curve (Yelle,
1979; Westney, 1988; Epple, Argote & Devadas, 1991). A second way that a capable source can
assist a less capable recipient is to help remove some of the perceptual ‘blind spots’ that can lead
it to fail to consider the decisions of others in its own decisions (Zajac & Bazerman, 1991).
Similarly, as research has shown that an organization’s existing stocks of resources and
capabilities can limit and channel its ability to develop these and other resources, thereby also
affecting its decision-making (Teece, Pisano & Shuen, 1997), the source can introduce new
resources that can help the recipient avoid becoming too constrained or developing learning



                                                                                                  29
myopia (Levinthal & March, 1993). In addition, a related stream of research examines how
cognitive processes can cloud organizations’ views as to with whom they are really competing.
Since managers model the behaviors of others in order to learn about uncertain environments
(Bandura, 1986), “the types of observations and experiences that prove useful will be repeated
and refined, while those that prove less useful will be discontinued. In this manner, social
learning processes give direction to the basic categorization processes that managers use to
cognitively order their environment,” (Peteraf & Shanley, 1997: 169). In other words, a capable
source can have positive effects on a recipient’s organizational learning capabilities by
broadening the recipient’s dominant logic (Prahalad & Bettis, 1986), path dependencies, and
categorization schemes, and even altering its ‘tried and proven ways of doing things’ (Argyris,
1990).

         In addition, however, two other recipient variables can also affect transfer success. These
variables include the credibility of the source with the recipient (Arrow, 1971) and the strategic
intent of the source to complete the transfer (Hamel, 1991). As described, knowledge
internalization requires that a recipient see the value of the knowledge being shared. If the source
is seen as less than credible, then its knowledge may also lose value in the eyes of the recipient,
thereby affecting the outcomes of the sharing processes. One oversight in the literature related to
credibility seems to be that it does not consider the content relevance of the knowledge-to-be-
shared to the recipient’s context (Feinstein, 2002). That is, it does not take into consideration the
applicability of a given knowledge’s content (e.g., how to organize a banking system) to a certain
context (e.g., in Uruguay). Instead, the literature looks at factors and activities that can affect
knowledge-sharing processes in general regardless of the content of the knowledge being shared.
For an organization like the Bank, which operates in very different content-related contexts,
content relevance could be a crucial factor affecting the credibility of its knowledge and
knowledge-sharing efforts.

         At the same time, the source’s intent also has an impact on knowledge transfer to the
extent that it creates the organizational incentives to learn. For example, since many knowledge-
sharing situations are reciprocal rather than one way, when one party has obtained what it has
sought from the sharing arrangement, it may terminate or fail to continue to participate actively
in the sharing processes (Khanna, Gulati & Nohria, 1998). Moreover, recent research suggests


                                                                                                  30
that time, budget, and job insecurity pressures can create disincentives for sharing, as possession
of knowledge can provide a source with a form of power (Pfeffer & Sutton, 2000). Indeed, a
recent study within the Bank found that 58% of focus group participants viewed incentives for
knowledge-sharing as inadequate (Prusak, 1999). Thus, both the source’s credibility and its
intent are important factors that can affect the success of a knowledge-transfer effort.

Environmental context

       The entrepreneurial, learning and innovation environments in which knowledge sharing
takes place can affect the parties and knowledge-sharing processes in many ways (Kim &
Nelson, 2000). For example, organizations in rapidly changing technological environments have
been found to pursue fewer site visits, benchmarking studies, and direct forms of communication
than those in more stable industries (Appleyard, 1996; von Hippel, 1988). In addition, the stage
of knowledge within its life cycle has also been posited to affect knowledge-transfer success,
given that the newer the knowledge in general, and to the recipient specifically, the less that the
recipient will be burdened with unlearning old knowledge (Burgleman, 1983; Hedberg, 1981;
Nystrom & Starbuck, 1984). Moreover, research has also found that the mobility of personnel, a
key industry structure measure, contributes to the flow of technology-related knowledge within
certain regions (Almeida & Kogut, 1999).

       Importantly, while numerous additional salient variables have also been identified related
to economic, cultural, political, and institutional environments in which knowledge sharing
occurs (e.g., see Yeung, et al., 1999; Sagafi-nejad, 1990), the strategic management literature
subsumes these variables primarily within the relational context. For example, many aspects of
the economic and societal sub-contexts are incorporated within the relational factors related
differences in social identities, knowledge gaps and strategic intents. Likewise, this literature
addresses level-of-development type variables through the relational variable, institutional
distance (Kostova, 1997, 1999). Moreover, the discussion on the embeddedness of knowledge,
especially on technology-embedded knowledge, incorporates many aspects of the technological
environment. With respect to other environmental variables, a nation-by-nation analysis may be
in order to fully understand the dynamics at play. For example, research on Korea has attributed
much of its rapid economic growth to the close coordination that exists between government and
business, as well as to the chaebol strategy followed by most of its businesses (Kim, 2000). The


                                                                                                31
argument can be made that the use of chaebol strategies by Korean firms indicates that they have
cultural norms supportive of collaboration. Where a party to a knowledge transfer with a Korean
organization has a less collaborative culture, the two organizations’ ways of ‘doing business’
would likely differ considerably, and, as discussed in the relationship distance context, this can
significantly impair knowledge transfers (Allen, 1977; Tushman, 1977; Dougherty, 1990).

       In the strategic management literature, both organization-level and environment-level
variables are as seen as affecting organizational performance, and it is through the strategies
adopted by organizations that the two sets of variables are joined (Andrews, 1971; Barney,
1991). With respect to the knowledge-sharing arena, what this literature suggests is that the
broad economic, cultural, political, and institutional environmental variables need to be
examined to determine the extent to which they play a role in affecting the micro-context
variables. In other words, a complete examination of the factors that can create distances
between the parties (relational context), make knowledge assessment and analysis more
challenging (knowledge context), or have an effect on the motivations and intents of the parties
(source and recipient contexts), requires consideration of the broader environment in which the
two parties conduct their knowledge-sharing.

A synthesis for evaluation

       Since knowledge sharing is but part of the organizational learning that takes place within
and between organizations, the generalized assessment question for the Bank is to what extent
does it facilitate organizational learning between its knowledge-sharing parties? More
specifically, the Bank undertakes all sorts of activities in its attempts to facilitate the sharing and
receiving of knowledge. The key question, however, is does it use an appropriate mix of
activities to address the many factors affecting (and related barriers to) successful knowledge
transfers?

       A synthesis of the literature suggests that successful knowledge sharing requires the use
of three interdependent types of knowledge-sharing activities, including:

       •     Those focused on assessing the form and embeddedness of the knowledge,




                                                                                                    32
       •   Those focused on establishing and managing an administrative structure through
           which differences and issues between the parties can be accommodated and reduced,
           and
       •   Those focused on transferring the knowledge.

       These activities are interdependent in that knowledge assessments and administrative
requirements will change as differences and issues become apparent between the parties, all
while efforts to transfer the desired knowledge are being implemented. Each type of activity is
also important in that, while an organization may implement of an appropriate administrative
structure (e.g., by entering into a formal knowledge-sharing agreement that sets out reporting
relationships, resource commitments, etc.) and take a number of managerial initiatives designed
to overcome any differences between the organizations, a lack of an assessment as to the
embeddedness of the knowledge may result in a less than successful outcome. Recent findings
within the Bank, for example, found that while managerial initiatives were sufficient in one
aspect of its knowledge-sharing efforts, more administrative resources would likely to enhance
outcomes (Social Development Group, 2002; Prusak, 1999).

       What these examples and the literature point to is that successful knowledge-sharing
outcomes require attention to each of the three types of activities, suggesting, therefore, that an
evaluation of the Bank’s knowledge-sharing efforts cover each of these sets of activities. In the
following sections, a number of questions related to these different types of activities are
presented for evaluation. The “Bank” referred to in each question is the Bank unit responsible for
facilitating knowledge sharing, whether this unit is the source, the recipient, or a third party
facilitating knowledge sharing between a source and a recipient.

Knowledge assessment: The form and embeddedness of knowledge

       The first area of focus is on the knowledge that is to be shared. Understanding the
explicitness vs. tacitness and embeddedness of the knowledge to be shared could prove critical
both to the development of appropriate knowledge-sharing activities and to the analysis of the
other factors to be considered throughout the sharing processes. This suggests several questions
for evaluation.




                                                                                                33
1. To what extent does the Bank make comprehensive embeddedness assessments of who
   knows what (people-people network), who works best with what tools or technology
   (people-tool network), and who does which tasks most effectively (people-task network)?
   Knowing all of the knowledge elements (people, tools, routines, and networks) that need
   to be transferred can be important in achieving desired outcomes. The development of a
   “knowledge repository map” for the knowledge to be shared is a first step in making sure
   that all necessary elements are first identified and then shared. Such a scheme would also
   allow managers to assess to the relative match of the parties’ knowledge repositories, so
   as to allow for development of appropriate knowledge-transfer plans. Importantly, since
   the literature tells us that the sharing of knowledge may be affected by the degree to
   which the knowledge is specific to the source (i.e., the degree to which it is related to
   specific organizational assets, people, routines, etc.), such an embeddedness analysis
   needs to highlight any source-specific assets that might be related to the knowledge to be
   shared. With the benefit of this information, the Bank can tailor knowledge-sharing
   activities to ensure an appropriate level of interaction with, or exchange of such assets to
   obtain more complete knowledge sharing outcomes.

2. To what extent does the Bank ensure that everyone within the units is informed of each
   other member’s expertise? Given that transfer performance increases when group
   members are informed about who knows what, the Bank can ensure that any such
   assessments are shared with the parties. In addition, since who knows what depends on
   who remains within a unit, and disruptions to a group’s membership can hurt group
   performance, a related question is how stable is each group’s membership?

3. To what extent does the Bank assess the degree to which the knowledge is explicit vs.
   tacit? The literature indicates that explicit knowledge is easier to transfer, but may be
   more challenging to have internalized by the recipient. On the other hand, highly tacit
   knowledge, which is embedded with meanings and values, is more difficult to transfer,
   but the commitment and effort required to transfer it can lead to fuller internalization by
   the recipient. Thus, with explicit knowledge, a reverse-engineering approach might be in
   order to allow the recipient to create an opportunity to participate in the knowledge’s re-
   articulation; whereas with highly tacit knowledge, a series of activities might need to be


                                                                                            34
       undertaken to allow the knowledge to become portable. In either case, lack of an
       assessment of the knowledge’s form could lead to inappropriate sharing activities being
       undertaken.

Relationship management: Establishing rules and goals; identifying and accommodating
differences

       Organizational learning theory posits that learning is enhanced when it takes place in an
environment of established rules, goals and norms, and where participants understand and
appreciate the other’s differences. In addition, administrative controls are known to shape the
flows of knowledge. The following questions seek to address the extent to which the Bank unit
charged with facilitating knowledge transfers has sought to establish such settings and controls,
and undertakes the types of efforts necessary to identify and manage any important differences
between the parties.

   1. To what extent does the Bank develop, communicate and reinforce shared goals between
       the parties? The Bank’s facilitation of the development of shared missions and goals,
       and/or of the inclusion of parties sharing similar strategic outlooks within knowledge
       sharing situations, could prove helpful in bridging any strategic distances that might exist
       between the parties. As one Bank study noted, “there is a sense among regional SD staff
       that knowledge sharing has been supply driven from the anchor to the Regions”,
       suggesting the need for more “learning from the ground” to take place (Social
       Development Group, pp. 4, 5). At the same time, such a common sense of purpose can
       also help to mediate the challenges presented by the Bank’s vast spatial distances
       between knowledge-sharing parties. Moreover, since one party may terminate its
       participation in a sharing arrangement at any time, a related question is to what extent
       have the parties’ intentions with respect to participating in the knowledge-sharing
       arrangement been identified, and to what extent has the Bank sought to reinforce
       intentions consistent with the knowledge-sharing objectives of the parties?

   2. To what extent does the Bank support joint development of rules of conduct between the
       parties? The greater the national, cultural, and institutional differences that exist between
       the parties, the greater will be the need for the development of a shared agreement on



                                                                                                 35
   how a knowledge-sharing arrangement will operate. The Bank can facilitate
   establishment of high norm similarity – of shared ways of doing business – by
   encouraging the joint development of a common set of rules of conduct by the parties
   engaged in extended knowledge-sharing activities.

3. To what extent does the Bank put in place administrative structures to support desired
   knowledge flows? Whereas question 1 focuses on the goals of the knowledge-sharing
   arrangement, and question 2 focuses the rules of engagement between the parties, this
   question focuses on the organizational structure put in place to support the goals of the
   knowledge-sharing arrangement. The Bank’s creation of Thematic Groups seems
   consistent with the idea of creating organizational support structures to shape knowledge
   flows. However, the commitment of sufficient resources might also be of concern here.
   For example, one Bank unit found that “a committed and skilled social scientist in the SD
   anchor with responsibility for the coordination of knowledge-sharing activities would be
   desirable” (Social Development Group, p. 8). Moreover, Prusak (1999) suggested that
   Thematic Groups provide facilitators/coordinators to support knowledge-sharing
   activities. Thus, a related question is to what extent does the Bank provide necessary
   resources to support effective knowledge sharing?

4. To what extent does the Bank facilitate each party’s appreciation for the other’s
   operational and cultural situation? The Bank can help the parties bridge any such
   differences that exist by helping them develop appreciation for the others’ situations.
   Approaches identified in the literature include cultural training and job rotations, among
   others. A related question has to do with the parties’ credibility; it involves evaluation of
   the Bank’s efforts first to assess if any credibility issues exist, and then second, to
   develop remedies to any identified deficits in the credibility of either party. The question
   is to what extent has the parties’ credibility been assessed, and to what extent has the
   Bank developed remedies, if needed, with respect to either party’s credibility?

5. To what extent does the Bank assess the knowledge-sharing capacities of the parties and
   develop plans through which to help them achieve compatible capacities? Evidence from
   the literature suggests that significant differences in knowledge bases between parties can



                                                                                             36
   hinder knowledge sharing. An assessment of “relative absorptive capacities” is first in
   order to make sure that an appropriate combination of knowledge-sharing activities is
   undertaken. Second, any gaps identified can then be addressed through the design of an
   appropriate plan to guide learning. At the same time, given the difficulties in bridging
   significant knowledge gaps, the Bank might also seek to use knowledge-capacity
   assessments to make selections from among alternative sources or recipients to ensure
   close matches. This suggests the related question to what extent does the Bank guide its
   knowledge sharing partner selections based on its knowledge-capacity assessments?

6. To what extent does the Bank manage the combinations of personnel involved in the
   sharing arrangement? The literature suggests that the degree of arduousness is driven
   fundamentally by mismatches, whether such mismatches are due to differences in skills,
   abilities, cultures, or personality types. Since arduous relationships can make knowledge
   sharing more difficult, the Bank’s careful management of the personnel involved in any
   given knowledge-sharing situation could prove important to sharing outcomes. Whereas
   in question 4 the assessment focuses on the unit’s efforts to reduce existing mismatches,
   here the focus is on managing the staffing processes to avoid mismatches.

7. To what extent has the Bank analyzed the many potential relational differences that might
   exist between the parties, and how great are such differences? The idea here is to assess
   the degree to which the Bank has sought to identify relational differences as a first step to
   allow for the eventual development of plans and implementation of actions to remedy or
   address any differences. The greater the differences identified, the greater the challenges
   the knowledge sharing project is likely to face. Upon the identification of any such
   differences, the question then becomes to what extent has the Bank developed and
   implemented appropriate remedies?

1. To what extent does the Bank seek to evaluate the recipient’s perception of the relevance
   of the knowledge? Again, the activities undertaken to transfer knowledge need to
   consider both the form and location of the knowledge if the transfer is to occur
   successfully




                                                                                             37
Transfer activities: Facilitating organizational learning

       The final area of focus is on the repertoire of activities undertaken to share knowledge.
As described in the section on knowledge explicitness vs. tacitness, organizational learning
theories posit that different types of knowledge-sharing activities might be more effective with
some forms of knowledge than with others, although, in general, research on the effectiveness of
different types of activities is quite limited. The following questions seek to address the extent to
which the Bank facilitates an appropriate set of knowledge-sharing activities for the parties.

   1. To what extent does the Bank facilitate the use of knowledge-preparation processes
       involving both the source and recipient units? Research has found that knowledge-
       sharing success is enhanced when recipients are included in the processes through which
       knowledge is articulated and codified by the source, rather than only as recipients of
       prepackaged knowledge. On the other hand, the exclusion of the recipient from the
       knowledge-conversion processes through which the knowledge is prepared for transfer
       from the source may limit the recipient’s willingness or ability to internalize this
       knowledge.

   2. To what extent does the Bank facilitate the interactions of people from the different units
       where appropriate? As described in the discussion on physical distance, the direct
       interaction of people from the source and recipient units is often seen as necessary to
       allow for knowledge to be adapted to a new context. The transfer of people facilitates
       such a process, as they can know who is good at what activities, and it is this knowledge
       that the recipients will need to figure out how to reconfigure and adapt the original
       knowledge.

   3. To what extent does the Bank facilitate group-based training activities between the
       parties? Research has confirmed that group training (the development of a transactive
       memory system for a group) produces better group performance because, among other
       reasons, group members can often avoid sub-optimal solutions by bouncing their ideas
       off of others. As a result, knowledge transfer activities engaging groups from the
       recipient unit may be more effective in creating a fertile environment within the recipient
       than individual-based activities.



                                                                                                  38
   4. To what extent does the Bank use the “knowledge-repository map” (discussed in point 1
       of the knowledge assessment section) to devise plans that accommodate gaining access to
       or acquiring all of the components of the knowledge necessary to allow its successful
       sharing? Again, the activities undertaken to transfer knowledge need to consider both the
       form and location of the knowledge if the transfer is to occur successfully.

Conclusion

       A successful knowledge-sharing effort requires a focus on more than simply the transfer

of the specific knowledge. Instead, many of the activities to be undertaken need to focus on

structuring and implementing the arrangement in a way that bridges both existing and potential

relationship issues, and examining the form and location of the knowledge to ensure its complete

transfer. In other words, while the activities used to share knowledge, such as document

exchanges, presentations, job rotations, etc., are important, overcoming the factors that can

impede, complicate and even harm knowledge internalization are equally important in

determining the ultimate results of a knowledge-sharing effort. Accordingly, any evaluations of

the Bank’s knowledge-sharing efforts need to incorporate assessments of its use of activities

related to understanding the form and embeddedness of the knowledge, establishing and

managing appropriate administrative structures, and facilitating the transfer of the knowledge.




                                                                                                  39
Figure 1. Five Contexts of Knowledge Sharing




                                       Environmental Context



              Source Context             Relational Context    Recipient Context



                       Source                                      Recipient


            Knowledge Context

             Knowledge Package                                    Internalized
                                                               Knowledge Package
                       People
                                           Sharing
                                           Processes
               Tools        Routines




                                                                                   40
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