Redundancy and Knowledge Sharing:
Suggesting and Testing a New Empirical Construct
By:
Mette Præst Knudsen
Department of Marketing
University of Southern Denmark
Campusvej 55, DK-5230 Odense M, Denmark
E-mail: mpk@sam.sdu.dk
Revision: 08-03-2016 23:40
Paper for presentation at:
International Conference on Economics and Management of Networks
EMNet 2005 – Budapest, September 2005
Keywords: knowledge sharing, complementary and supplementary knowledge, inter-organizational
relationships, NPD
Abstract
This paper proposes a new measure of knowledge redundancy and tests it empirically using survey data.
The degree of redundancy in knowledge between two independent firms may be used to study the
knowledge sharing processes for instance in NPD-projects. The paper defines knowledge redundancy
as the degree of complementarity in knowledge leading to increased breadth in the knowledge base, and
the degree of supplementarity in knowledge leading to increased depth in the knowledge base. Once
the concepts are defined the paper discusses the effects of sharing supplementary and complementary
knowledge on NPD success.
The paper then tests the propositions on a survey on Danish manufacturing firms. The empirical
results support the theoretical expectations. The paper is concluded with a discussion on implications
for both theoretical research and managerial action.
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1. Motivation for the study
The literature on knowledge sharing between and within organizations has rested intensively on the
tacit-codified dichotomy for knowledge characterisation. This paper synthesises the scattered attempts
to develop other approaches for knowledge sharing between units (e.g. individuals, teams, or
organizations) by developing the distinction between supplementary and complementary knowledge
bases of firms. The degree of overlap in knowledge bases explicitly reveals the degree of redundancy in
knowledge; hence by verifying the relevance of supplementary and complementary knowledge to
knowledge sharing between units, we add an important new approach to the causes of e.g. new product
development success.
The role of knowledge as both input into the creation of new products and as foundation for new
product development activities stand undisputed in the literature (Cohen & Levinthal 1989;
Subramaniam & Venkatraman 2001; Hong, Doll, Nahm, & Li 2004). Knowledge is argued to lead to
shortened product development time, to ensure creativity sparks by combining existing knowledge, and
to close the gap between customer wants and product development offers.
Increasingly over the past years the role of external knowledge sources like inter-organizational
collaborations has gained interest in the research community (Hagedoorn, Link, & Vonortas 2000).
External partners offer both opportunities and threats in new product development processes. The
opportunities are related to the role of knowledge in general contributing to NPD success, but also to
the accelerated speed and quality of product development when knowledge comes from external
knowledge sources. However, these potential advantages are closely related to and potentially
neutralised by the threats from external partners, which are related to motivational problems, problems
with communication and cooperation and especially challenges from utilisation of knowledge received
from an external partner.
However, much of the literature has failed to investigate the knowledge sharing process and the object
of the knowledge sharing process, namely knowledge. Based on the following example1, this paper aims
at providing an analytical tool and a measurement proposition that allows for a direct test of degree of
the knowledge redundancy and NPD-success. The context is a NPD-collaboration between two
distinct firms. These firms collaborate on a given problem that must be solved to provide a new
product to the market. To solve the problem the engineers and technicians of the firms are forced to
work closely together and in doing so they are heavily involved in knowledge sharing. The key problem
facing these engineers is to identify, formulate and transfer the knowledge to the other partner with the
purpose of providing at least some elements to a solution. If the transfer is successful and the
knowledge transferred can be used to solve the problem, this particular process may assist in finalising
the product and bringing it to the market. In terms of the present paper, one important condition for
successful knowledge sharing is the overlap in the knowledge bases. The overlap in knowledge, which
we equalise to the degree of redundancy, is broken down into the degree of complementarity leading to
a broader knowledge base and the degree of supplementary knowledge leading to a deeper knowledge
base.
The paper first explores the arguments in the literature that underlines these aspects of the knowledge
base and the expected results for NPD-success. Based on the literature review, section 3 outlines the
analytical constructs and suggests an approach for empirical measurement. These suggestions are then
The example illustrates just one potential context to which the proposed tool may be applied. One could also think of
interactions between individuals in teams, individuals acting across functional areas etc.
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tested in an empirical survey on Danish manufacturing firms that tests both the concepts themselves
and the relevance for NPD-performance. Finally, the paper draws up the conclusions for both future
theoretical research and for managers of new product development projects.
2. Identifying the distinction between complementarity and supplementarity in
the literature
The literature on knowledge sharing, transfer or exchange has been growing rapidly in the last couple of
years. A prevailing problem is related to the definition of knowledge sharing. Becker and Knudsen
(2003: 14) outlined the problems based on a literature review of definitions of knowledge sharing and
pointed to one particularly important component in a definition, namely to explicitly include the
outcome of the knowledge sharing process. One such outcome-based definition was suggested by
Argote et. al. (2000: 151): knowledge transfer in organizations is the process through which one unit (e.g. group,
department, or division) is affected by the experience of another. Even though the definition is suggested within
the context of intra-organizational knowledge transfer, it is straight-forward to apply it to the context of
inter-organizational knowledge transfer. Furthermore, the definition explicitly points to the affect made
by the transfer on the receiving partner’s experience. If experience is equalised to knowledge, the affect
on experience equals learning i.e. upgrading of the knowledge base. Cohen and Levinthal (1990: 129)
discussed the potential for facilitation of learning based on the prior knowledge of the receiving firm.
They forcefully argued that prior knowledge forms the foundation for mutual understanding between
the partners and is therefore a pre-requisite for successful learning and knowledge absorption. Hence,
an important condition for successful knowledge sharing is the overlap in the knowledge bases, which
constitutes mutual understanding and prior knowledge.
The degree of overlap in knowledge is described in Rindfleisch and Moorman (2001: 3) as the degree of
similarity in the new product-related information, capabilities and skills among new product alliance participants.
Focusing on the degree of similarity in information allows us to search for a deeper understanding of
similar or supplementary knowledge and complementary knowledge.
The notion of complementary and supplementary activities was first discussed by Richardson (1972) in
his discussion of the organization of industries. An important limitation to the standard classical
discussion of markets is the observation that firms are incorporated in a dense network of cooperation
and affiliations (Richardson 1972: 883). Describing industries as carrying out an indefinitely large
number of activities like estimation of wants, research, development, design, marketing etc. allows
Richardson to introduce the notions of similar and complementary activities. Similar activities are
defined as activities which require the same capability for their undertaking, whereas complementary activities are
defined as representing different phases of a process of production and require in some ay or another to be coordinated
(Richardson 1972: 888-889). Based on Richardson’s notion of activities and production processes,
Dussauge et. al. (2000) apply the above notions to the context of inter-organisational relationships by
defining a scale alliance as an alliance where the partners contribute similar resources, pertaining to the same
stage or stages in the value chain. Similarly, link alliances are alliances, which aim at combining different and
complementary skills and resources that each partner contributes (Dussauge et al. 2000: 102). Even though
Dussauge et. al. provide a direct link between alliances and the associated exchanges between the
partners, the notion of similarities and complementarity as pertaining to particular stages in the value
chain fail to account for the fact that knowledge or resources within a stage in the value chain, like e.g.
R&D, may lead to exchange of highly complementary contributions. Sarkar et. al. (2001) develop their
notion of complementarity in resources based on Parkhe’s notion of interfirm diversity (1991). Sarkar
et. al. define resource complementarity as the extent to which each partner brings in unique strengths and resources
of value to the collaboration (2001: 360). Next to resolving the problem of similarities in the value chain this
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definition also extends the resource exchange to bring value to the receiving firm. Resource
compatibility is conversely defined as the congruence in organizational cultures and capabilities between partners
(Sarkar et al. 2001: 361). The compatibility on the other hand broadly defines similarities, not only in
terms of resources, but also cultures. Hence, a more precise definition must be found that both include
the degree of performance or the utilization of the resources and the degree of similarity in resources.
To that purpose, Das and Teng established the link by introducing the degree of performance directly
into resource alignment (Das & Teng 2000). Resource alignment may according to Das and Teng be
either similar or dissimilar. Similar resources that at the same time are utilized distinguish
supplementary (performing) from surplus resources, whereas dissimilar resources distinguish
complementary from wasteful resources (non-performing). Hence, Das and Teng combine the
exchange of resources in two dimensions, namely based on the degree of similarity and the extent of
performance in a two-by-two matrix (Das et al. 2000: 49). Since the interest in this paper by nature is
on the performing resources, hence those utilized for achieving goals, we can define supplementary and
complementary knowledge contributions based on the idea of performing resources.
3. Outlining and defining the concepts
The notion of supplementary ‘things’ is defined as ‘things that are added to something in order to improve it’
(Cobuild 1997). Supplementary knowledge may therefore be defined as high degrees of redundancy in the
form of similar product development knowledge and skills (Rindfleisch and Moorman, 2001: 2). Since
redundancy equals overlap in knowledge, supplementary knowledge is easier to transfer given prior
knowledge and mutual understanding between the receiving and the transmitting firm. When
knowledge is added to an existing knowledge base as illustrated by the overlap in the left hand side of
figure 1, we may deduct that the knowledge overlap leads to a deepening of the knowledge base.
Figure 1: Dimensions of redundancy
Suppl. know.
Compl. know.
Complementary knowledge
Supplementary knowledge
The notion of complementarity is defined as ‘things that are different from each other but make a good
combination’ (Cobuild 1997). Complementary knowledge may therefore be defined as low degrees of
redundancy in the form of dissimilar product development knowledge and skills. Based on the extreme illustration in
the right hand side of figure 1 with almost no redundancy, we may deduct that sharing of
complementary knowledge may increase the breadth of knowledge in the receiving firm. Moreover,
sharing of complementary knowledge is harder than for supplementary knowledge, because the basis
for mutual understanding is limited and the prior knowledge does not contribute to establish mutual
understanding.
3.1 Effects of sharing supplementary and complementary knowledge on NPD success
4
Exchange of supplementary knowledge allows the partners to achieve scale economies and to reduce
excess capacity (Dussauge et al. 2000: 102). Although redundancy enhances the chances of successful
knowledge sharing and ultimate utilization to solve problems, it also limits the opportunities for
learning, since most of the knowledge is known already. Redundant knowledge bases may ultimately
lead the partners to become competitors since they possess very similar resources and knowledge and
may therefore relatively easy satisfy the same market demands. Exchange of supplementary knowledge
is therefore associated with the dangers of double jeopardy by on the one hand, underscoring the lack
of learning potential, and on the other hand, by decreasing the knowledge gaps between the partners
leading to potential competition.
Since exchange of complementary knowledge results in a larger potential for learning through the
combination of dissimilar skills and resources (Sarkar et al. 2001; Dussauge et. al., 2000: 102;
Rothaermel, 2001), the relationship between the partners may potentially be longer-lived. The low
degree of redundancy allows the partners to explore the differences and create new projects or develop
new ideas over longer time spans.
The potential outcomes of knowledge sharing can be divided into 3 groups:
1. Completion of present project: ultimately knowledge sharing is intended to solve a particular
problem, as illustrated by the example above. If the exchanged knowledge serves the intended
purpose, knowledge sharing may lead to a faster completion of the present NPD project than if
the knowledge sharing process had not been initiated.
2. Creation of new projects: the process of sharing knowledge can spark new ideas once they are
applied into the existing knowledge base of the recipient firm (Cohen et al. 1989). These new
ideas may ultimately lead to the creation of new NPD projects for which new knowledge may
be needed.
3. Utilisation for other purposes: besides explicit creative ideas the knowledge received from an
external partner may be utilised for other purposes of a non-project specific nature. Hence,
knowledge may be received and also used to solve problems in other projects; both smaller
problems and more extensive problems.
Since supplementary knowledge deepens the existing knowledge base, we expect it to provide quicker
and more successful product development in the short term. Hence in terms of the three categories
above we hypothesise that:
H1: Supplementary knowledge is positively associated with (a) the completion of present projects and (b) with
sparking new projects, but may not necessarily (c) be utilised for other purposes.
As complementary knowledge may take longer to utilise depending on the level of absorptive capacity,
but carries a higher learning potential, we hypothesise that:
H2: Complementary knowledge is positively associated with (a) completion of present project, (b) creation of new
projects and (c) utilisation for other purposes.
4. Data and methodology
The data are drawn from a survey of 2527 business firms in the manufacturing industries with more
than 10 employees (as of Feb. 2004)2. With the permission of the CEO’s in the firms 105 product
development managers were approached, which resulted in 74 completed questionnaires (response rate
of 70.5%). Due to the low number of total responses, another 381 firms were approached of which 202
The survey and the methodology is described in more detail in:
Madsen, T. K. (2004). Market Strategy of Firms in Global Environments: Research Methodology, Working Papers in
Marketing: 30. Odense.
2
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agreed to participate. From these only 39 (response rate of 20%) returned the questionnaire. In total,
the following analyses are based on the total of 113 responses. The context for researching the above
proposition is ‘the most important completed NPD project within the last 3 years’ (the survey was
carried out in 2004). Within the boundaries of the project, the respondent was asked whether external
partners had been actively involved in the project and if so, which type of partner that was most
important. The suggested categories were supplier, customer, university or other research institution,
consultant or competitor. On the basis of this particular cooperation partnership, the respondent was
then asked to evaluate the following questions on a Likert-based scale rating each question on a scale
from 1 to 7:
 To what degree did this collaboration partner contribute to the most important product
development project with knowledge that deepened and nuanced the existing expertise of your
company?
 To what degree did this collaboration partner contribute to the most important product
development project with knowledge that was new and unknown compared to the existing
expertise of your company?
The first question aims at measuring the extent to which the company received supplementary
knowledge, and the second question at measuring reception of complementary knowledge. An
important notice must be made, namely that we do not survey the process of knowledge sharing, but
simply the overall evaluation of the received knowledge by the firm.
Methodologically, we first test the expected correlations as discussed in section 3.1 and if these are
identified as expected then we conclude that the measures are valid and reliable. Second, the measures
are tested against NPD-performance to establish whether the types of knowledge are important for
NPD-success as argued above. NPD performance may obviously be measured in a variety of ways. In
the following, we test the following measures:
1. NPD project speed and costs: based on the scale from Rindfleisch and Moorman (2001:
appendix) on product development speed and similar measures on product development cost.
We expect supplementary knowledge to contribute positively to speed and low costs, whereas
complementary knowledge should impose higher cost due to the lack of mutual understanding.
2. R&D project cost: compared to the total R&D expenditures, how much has the company
spend on the particular project. We expect a higher share of total expenditures to be an
indicator of commitment and therefore attention to ensuring successful knowledge sharing. We
do not, however, expect this to be associated particularly to any of the two knowledge types.
3. NPD project performance: we expect a higher share of turnover that comes from the particular
NPD-project to be an indicator of NPD-success. A better project results in higher turnover.
4. A composite measure of knowledge sharing performance is developed and tested. We expect
exchange of supplementary knowledge to contribute positively to knowledge sharing
performance, whereas complementary knowledge is not expected to make a significant
contribution.
5. Partner redundancy: based on the scale from Rindfleisch and Moorman (2001: appendix) on
what they label knowledge redundancy, the degree of similarity between the partners on their
products, skills, engineering knowledge and resources. We expect the measure to be positively
associated with supplementary knowledge since high numbers (rating 4-7) equals similarities.
This measure is seen as a further confirmation of our measures in case the signs are as
expected.
5. Verifying the nature of the concepts
6
The first step in verifying the measures includes a simple descriptive analysis. In the following, we first
present the descriptives to each measure and then present the bivariate correlations.
(N=77)
Mean
St. dev.
Mode
Supplementary knowledge
Complementary knowledge
4,32
1,58
5
3,46
1,80
3
From the descriptives, we see that the firms tend to exchange supplementary knowledge to a higher
extent than complementary knowledge. This result is likely, since supplementary knowledge is likely to
lead to immediate short-term effects. To test hypothesis 1 and 2, we use correlation analysis. If the
correlations are positive and significant we accept the hypotheses. Table 1 illustrates the relationship
between supplementary knowledge and the three outcome based measures.
Table 1: Correlations between extent of supplementary knowledge and project outcome
Supplementary
Completion of
Creation of new
knowledge
NPD project
projects
Supplementary
1
0,750
0,530
knowledge
(0,000)
(0,000)
Completion of
1
0,532
NPD project
(0,000)
Creation of new
1
projects
Utilisation for
other purposes


Utilisation for
other purposes
0,130
(0,279)
0,077
(0,521)
0,374
(0,001)
1
A test of the distribution for each variable shows that skewness is below the level of twice the standard deviation
and therefore the Pearson correlation coefficient can be used.
Numbers in parentheses represent the level of significance.
The correlations are positive and significant for the first two project outcome variables; completion of
NPD project and creation of new projects, but not significant for utilisation for other purposes (see
table 1), which lead us to accept hypotheses 1a, 1b, and 1c.
Similarly for the exchange of complementary knowledge we test the relationships using correlation
analysis. Table 2 highlights the results, which are as hypothesised, namely positive and significant
correlations for all three outcome variables.
In general, our operationalisation of the reception of supplementary and complementary knowledge
was verified using the project related outcomes, because we were able to find the expected results.
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Table 2: Correlations between extent of complementary knowledge and project outcome
Complementary
Completion of
Creation of new
knowledge
NPD project
projects
Complementary
1
0,738
0,388
knowledge
(0,000)
(0,001)
Completion of
1
0,407
NPD project
(0,000)
Creation of new
1
projects
Utilisation for
other purposes
Utilisation for
other purposes
0,392
(0,001)
0,401
(0,001)
0,625
(0,000)
1
6. Verifying the relevance for NPD-performance
Having verified the relevance and appropriateness of the suggested measures of redundancy, we now
wish to test the relevance of knowledge types, i.e. redundancy, for product development performance
in general. Table 3 checks the relationship between the knowledge types and 4 performance variables
using bi-variate Pearson correlations.
The project cost and speed variable is a semantic scale where the respondent assesses the performance
of the own project compared to the industry as a whole. Project cost and project speed are each
assessed using 3 items indicating that the scale as a whole is comprised of 6 items. Following upon the
aggregated analysis, project cost and speed are tested separately. Second, the R&D expenditures
associated with the particular project is measured up against the overall R&D expenditures in the firm.
The expectation is that a higher share indicates that the firm has more focus on the particular project
and therefore will make efforts to limit the experienced problems in knowledge sharing. Third, we
expect a higher share of turnover coming from this particular project to be positively related to
knowledge sharing. If a product contributes positively to turnover, we expect knowledge sharing efforts
to be successful. To test our measure of redundancy, we match it up against a measure of redundancy
coming from Rindfleish and Moorman (2001: appendix). We expect our redundancy measure to be
positively associated with the more general measure of redundancy suggested by Rindfleish and
Moorman. Finally, a composite variable reflecting knowledge sharing performance is tested. The
measure is developed exploratively using factor analysis followed by a test of reliability using Cronbach
Alpha. The composite measure includes the following 6 items:
1. The collaboration partner contributed to an achievement of a cost reduction
2. The collaboration partner contributed to a reduction of technical defects and an increase in the
overall quality of our product
3. We shared confidential information with the collaboration partner
4. There was a positive overlap between the goals of the company and the collaboration partner
for development of the new product
5. The product development process became much faster because we included this collaboration
partner
6. The product development process became much cheaper because we included this
collaboration partner
In total, the reliability is 0,773, which is above the level of 0,6 that is recommended in the literature.
The composite variable is calculated as the sum of the items divided by the number of items. The
composite variable for knowledge sharing performance is therefore accepted for further testing.
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Table 3: Correlations between knowledge types and performance
Supplementary knowledge
Redundancy
0,226
3
measure
(0,077)
Project cost and
0,169
speed4
(0,165)
Project cost5
-0,089
(0,463)
Project speed6
0,207
(0,088)
R&D project cost
0,351
(0,004)
Turnover share
0,142
(0,345)
Knowledge
sharing
0,424
performance
(0,001)
Complementary knowledge
0,143
(0,268)
0,162
(0,184)
0,003
(0,978)
0,180
(0,140)
0,279
(0,025)
0,092
(0,544)
0,190
(0,142)
The check variable for partner redundancy has the expected positive result for supplementary
knowledge, but is only marginally significant. However, since the measures are quite different in terms
of the focus, where the check variable includes very diverse factors, we also contribute part of the lack
of significance to variable content.
For the aggregated project cost and speed measure, the correlation has the expected sign, but is
insignificant. To investigate the role of speed and cost separately we split the items into two new
variables. The results of the two new variables show a negative and very small relationship between cost
and supplementary knowledge and a positive but almost indifferent relationship with complementary
knowledge. For project speed, we see as expected that speed is positively associated with the exchange
of supplementary knowledge and this relationship is tentatively significant. No significant effect can be
seen for complementary knowledge, which is as expected.
The R&D project cost yielded the expected correlation and is significant for both supplementary and
complementary knowledge. Hence our expectation; higher R&D project cost compared to the total
expenditures are followed by appropriate measures to stimulate knowledge sharing processes.
The turnover share yielded the expected positive sign, but a very low correlation resulting in nonsignificant results. For complementary knowledge, the relationship is almost neutral.
The Cronbach alpha for the measure is 0,728, with no possibilities of raising the level by excluding items. The alphas
coefficient is high enough to be considered reliable and is therefore included in the analysis. In the original article the
Cronbach alpha = 0,85.
4 The Cronbach alpha for the measure is 0,774, with no possibilities of raising the level by excluding items. The alphas
coefficient is high enough to be considered reliable and is therefore included in the analysis.
5 The Cronbach alpha for the measure is 0,777, when item 4 is excluded. Cronbach alpha before we removed the item is
0,713. The scale is used exclusive of item 4. The alphas coefficient is high enough to be considered reliable and is therefore
included in the analysis.
6 The Cronbach alpha for the measure is 0,790, with no possibilities of raising the level by excluding items. The alphas
coefficient is high enough to be considered reliable and is therefore included in the analysis.
3
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Finally, for the composite variable of knowledge sharing performance the correlations are positive for
both knowledge types, but highly significant for supplementary knowledge, whereas for complementary
knowledge the correlation is only tentatively significant.
Conclusions and Managerial Implications
The paper has argued for a new conceptual and empirical measure of knowledge redundancy as defined
by the degree of complementarity and supplementarity in the knowledge bases between the transferor
and the recipient of knowledge in knowledge sharing processes. The paper used the existing literature
to define supplementary knowledge as high degrees of redundancy in the form of similar product development
knowledge and skills, whereas complementary knowledge is defined as low degrees of redundancy in the form of
dissimilar product development knowledge and skills. These measures were expected to contribute to the
completion of the NPD-project, the creation of new projects and for complementary knowledge to the
utilisation for other purposes.
The test on empirical data on Danish manufacturing firms yielded the expected positive and significant
correlations. Having established these results empirically, we can conclude that these measures may
contribute significantly to our understanding of knowledge sharing processes between firms and
organizations in future research.
The second aim of the paper was to establish a relationship between knowledge transfer and NPDsuccess. For the measures that were not directly reflected knowledge sharing processes the results were
only tentatively significant. However, the results indicated that commitment to the NPD project in
terms of financial investments was significant and positive for both knowledge types, indicating that
more investments are indicative of commitment and related to successful transfer of knowledge. Third,
project cost and speed in a combined variable was close to significant and with the expected positive
correlation. Once the variable was split in two, we saw that the positive relationship was attached to
project speed. Finally, knowledge sharing performance was highly correlated with supplementary
knowledge, also positively related to complementary knowledge but less significant as was also expected
at the outset.
The study has important implications for both theoretical research and managerial action. For
theoretical research, the measures of redundancy need to be tested further based on a more
comprehensive literature review. However, the idea seems potentially to provide a lifting pole for
studies of knowledge development. It is obvious from the literature that knowledge is created through
use and application, but so far we have been unable to address questions like, what are the implications
of increased depth (i.e. reception of supplementary knowledge) for future NPD-activities? How is
learning in one period based on reception of supplementary knowledge related to future learning and
knowledge sharing processes? These questions may now be studied using the above propositions and
deducted implications for NPD-success as discussed in section 3.
For managers of NPD projects the study illustrates the potential dangers associated with partner choice
and knowledge redundancy. If a partner is chosen, who is closely related to the company in terms of
knowledge overlap, the process of sharing knowledge is not expected to give problems in terms of
mutual understanding, motivational problems and utilisation challenges. On the other hand, these
seemingly unproblematic processes may easily be followed by an intensified competitive pressure at the
product market, as the exchange of supplementary knowledge may lead to increasing similarities in
product features. Hence, the problem or challenge posed by double jeopardy should be carefully
managed and monitored by the management.
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On the other hand, too much complementarity has potential large learning effects through the
dissimilar knowledge bases. However, before these learning effects may be utilised the firm needs to
overcome the problems of the dissimilarities. The problems may relate to low mutual understanding,
motivational problems and insurance of incentive schemes. Hence, the well-known problems of
knowing exactly what the partner will contribute, become even more important to solve to identify the
degree of redundancy in the contribution such that appropriate measures may be taken to secure the
success of the knowledge sharing processes. It is therefore vital that managers try to establish the right
degree of redundancy such that appropriate learning effects may be harvested, while not becoming to
exposed to the problems of double jeopardy.
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